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1. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants

Author

Zhou, B, Lu, Y, Hajifathalian, K, Bentham, J, Di Cesare, M, Danaei, G, Bixby, H, Cowan, MJ, Ali, MK, Taddei, C, Lo, WC, Reis-Santos, B, Stevens, GA, Riley, LM, Miranda, JJ, Bjerregaard, P, Rivera, JA, Fouad, HM, Ma, G, Mbanya, JC, McGarvey, ST, Mohan, V, Onat, A, Pilav, A, Ramachandran, A, Romdhane, HB, Paciorek, CJ, Bennett, JE, Ezzati, M, Abdeen, ZA, Abdul Kadir, K, Abu-Rmeileh, NM, Acosta-Cazares, B, Adams, R, Aekplakorn, W, Aguilar-Salinas, CA, Agyemang, C, Ahmadvand, A, Al-Othman, AR, Alkerwi, A, Amouyel, P, Amuzu, A, Andersen, LB, Anderssen, SA, Anjana, RM, Aounallah-Skhiri, H, Aris, T, Arlappa, N, Arveiler, D, Assah, FK, Avdicová, M, Azizi, F, Balakrishna, N, Bandosz, P, Barbagallo, CM, Barceló, A, Batieha, AM, Baur, LA, Benet, M, Bernabe-Ortiz, A, Bharadwaj, S, Bhargava, SK, Bi, Y, Bjertness, E, Bjertness, MB, Björkelund, C, Blokstra, A, Bo, S, Boehm, BO, Boissonnet, CP, Bovet, P, Brajkovich, I, Breckenkamp, J, Brenner, H, Brewster, LM, Brian, GR, Bruno, G, Bugge, A, Cabrera de León, A, Can, G, Cândido, AP, Capuano, V, Carlsson, AC, Carvalho, MJ, Casanueva, FF, Casas, JP, Caserta, CA, Castetbon, K, Chamukuttan, S, Chaturvedi, N, Chen, CJ, Chen, F, Chen, S, Cheng, CY, Chetrit, A, Chiou, ST, Cho, Y, Chudek, J, Cifkova, R, Claessens, F, Concin, H, Cooper, C, Cooper, R, Costanzo, S, Cottel, D, Cowell, C, Crujeiras, AB, D'Arrigo, G, Dallongeville, J, Dankner, R, Dauchet, L, de Gaetano, G, De Henauw, S, Deepa, M, Dehghan, A, Deschamps, V, Dhana, K, Di Castelnuovo, AF, Djalalinia, S, Doua, K, Drygas, W, Du, Y, Dzerve, V, Egbagbe, EE, Eggertsen, R, El Ati, J, Elosua, R, Erasmus, RT, Erem, C, Ergor, G, Eriksen, L, Escobedo-de la Peña, J, Fall, CH, Farzadfar, F, Felix-Redondo, FJ, Ferguson, TS, Fernández-Bergés, D, Ferrari, M, Ferreccio, C, Feskens, EJ, Finn, JD, Föger, B, Foo, LH, Forslund, AS, Francis, DK, Franco Mdo, C, Franco, OH, Frontera, G, Furusawa, T, Gaciong, Z, Garnett, SP, Gaspoz, JM, Gasull, M, Gates, L, Geleijnse, JM, Ghasemian, A, Ghimire, A, Giampaoli, S, Gianfagna, F, Giovannelli, J, Giwercman, A, Gross, MG, González Rivas, JP, Gorbea, MB, Gottrand, F, Grafnetter, D, Grodzicki, T, Grøntved, A, Gruden, G, Gu, D, Guan, OP, Guerrero, R, Guessous, I, Guimaraes, AL, Gutierrez, L, Hambleton, IR, Hardy, R, Hari Kumar, R, Hata, J, He, J, Heidemann, C, Herrala, S, Hihtaniemi, IT, Ho, SY, Ho, SC, Hofman, A, Hormiga, CM, Horta, BL, Houti, L, Howitt, C, Htay, TT, Htet, AS, Htike, MM, Hu, Y, Hussieni, AS, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, AG, Ibrahim, MM, Ikeda, N, Ikram, MA, Irazola, VE, Islam, M, Iwasaki, M, Jacobs, JM, Jafar, T, Jamil, KM, Jasienska, G, Jiang, CQ, Jonas, JB, Joshi, P, Kafatos, A, Kalter-Leibovici, O, Kasaeian, A, Katz, J, Kaur, P, Kavousi, M, Keinänen-Kiukaanniemi, S, Kelishadi, R, Kengne, AP, Kersting, M, Khader, YS, Khalili, D, Khang, YH, Kiechl, S, Kim, J, Kolsteren, P, Korrovits, P, Kratzer, W, Kromhout, D, Kujala, UM, Kula, K, Kyobutungi, C, Laatikainen, T, Lachat, C, Laid, Y, Lam, TH, Landrove, O, Lanska, V, Lappas, G, Laxmaiah, A, Leclercq, C, Lee, J, Lehtimäki, T, Lekhraj, R, León-Muñoz, LM, Li, Y, Lim, WY, Lima-Costa, MF, Lin, HH, Lin, X, Lissner, L, Lorbeer, R, Lozano, JE, Luksiene, D, Lundqvist, A, Lytsy, P, Machado-Coelho, GL, Machi, S, Maggi, S, Magliano, DJ, Makdisse, M, Mallikharjuna Rao, K, Manios, Y, Manzato, E, Margozzini, P, Marques-Vidal, P, Martorell, R, Masoodi, SR, Mathiesen, EB, Matsha, TE, McFarlane, SR, McLachlan, S, McNulty, BA, Mediene-Benchekor, S, Meirhaeghe, A, Menezes, AM, Merat, S, Meshram, II, Mi, J, Miquel, JF, Mohamed, MK, Mohammad, K, Mohammadifard, N, Mohd Yusoff, MF, Møller, NC, Molnár, D, Mondo, CK, Morejon, A, Moreno, LA, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mota, J, Motta, J, Mu, TT, Muiesan, ML, Müller-Nurasyid, M, Mursu, J, Nagel, G, Námešná, J, Nang, EE, NangThetia, VB, Navarrete-Muñoz, EM, Ndiaye, NC, Nenko, I, Nervi, F, Nguyen, ND, Nguyen, QN, Nieto-Martínez, RE, Ning, G, Ninomiya, T, Noale, M, Noto, D, Nsour, MA, Ochoa-Avilés, AM, Oh, K, Ordunez, P, Osmond, C, Otero, JA, Owusu-Dabo, E, Pahomova, E, Palmieri, L, Panda-Jonas, S, Panza, F, Parsaeian, M, Peixoto, SV, Pelletier, C, Peltonen, M, Peters, A, Peykari, N, Pham, ST, Pitakaka, F, Piwonska, A, Piwonski, J, Plans-Rubió, P, Porta, M, Portegies, ML, Poustchi, H, Pradeepa, R, Price, JF, Punab, M, Qasrawi, RF, Qorbani, M, Radisauskas, R, Rahman, M, Raitakari, O, Rao, SR, Ramke, J, Ramos, R, Rampal, S, Rathmann, W, Redon, J, Reganit, PF, Rigo, F, Robinson, SM, Robitaille, C, Rodríguez-Artalejo, F, Rodriguez-Perez Mdel, C, Rodríguez-Villamizar, LA, Rojas-Martinez, R, Ronkainen, K, Rosengren, A, Rubinstein, A, Rui, O, Ruiz-Betancourt, BS, Russo Horimoto, RV, Rutkowski, M, Sabanayagam, C, Sachdev, HS, Saidi, O, Sakarya, S, Salanave, B, Salonen, JT, Salvetti, M, Sánchez-Abanto, J, Santos, D, dos Santos, RN, Santos, R, Saramies, JL, Sardinha, LB, Sarrafzadegan, N, Saum, KU, Scazufca, M, Schargrodsky, H, Scheidt-Nave, C, Sein, AA, Sharma, SK, Shaw, JE, Shibuya, K, Shin, Y, Shiri, R, Siantar, R, Sibai, AM, Simon, M, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Smeeth, L, Snijder, MB, So, HK, Sobngwi, E, Söderberg, S, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Stathopoulou, MG, Steene-Johannessen, J, Stehle, P, Stein, AD, Stessman, J, Stöckl, D, Stokwiszewski, J, Stronks, K, Strufaldi, MW, Sun, CA, Sundström, J, Sung, YT, Suriyawongpaisal, P, Sy, RG, Tai, ES, Tamosiunas, A, Tang, L, Tarawneh, M, Tarqui-Mamani, CB, Taylor, A, Theobald, H, Thijs, L, Thuesen, BH, Tolonen, HK, Tolstrup, JS, Topbas, M, Torrent, M, Traissac, P, Trinh, OT, Tulloch-Reid, MK, Tuomainen, TP, Turley, ML, Tzourio, C, Ueda, P, Ukoli, FA, Ulmer, H, Uusitalo, HM, Valdivia, G, Valvi, D, van Rossem, L, van Valkengoed, IG, Vanderschueren, D, Vanuzzo, D, Vega, T, Velasquez-Melendez, G, Veronesi, G, Verschuren, WM, Verstraeten, R, Viet, L, Vioque, J, Virtanen, JK, Visvikis-Siest, S, Viswanathan, B, Vollenweider, P, Voutilainen, S, Vrijheid, M, Wade, AN, Wagner, A, Walton, J, Wan Mohamud, WN, Wang, F, Wang, MD, Wang, Q, Wang, YX, Wannamethee, SG, Weerasekera, D, Whincup, PH, Widhalm, K, Wiecek, A, Wijga, AH, Wilks, RJ, Willeit, J, Wilsgaard, T, Wojtyniak, B, Wong, TY, Woo, J, Woodward, M, Wu, FC, Wu, SL, Xu, H, Yan, W, Yang, X, Ye, X, Yoshihara, A, Younger-Coleman, NO, Zambon, S, Zargar, AH, Zdrojewski, T, Zhao, W, Zheng, Y, and Zuñiga Cisneros, J

Published
2016
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2. Major lipids, apolipoproteins, and risk of vascular disease

Author

Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, Packard CJ, Collins R, Thompson SG, Tipping RW, Ford CE, Pressel SL, Walldius G, Jungner I, Folsom AR, Chambless LE, Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman M, Goldbourt U, Benderly M, Tanne D, Whincup PH, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Santer P, Mayr A, Wald N, Ebrahim S, Lawlor DA, Yarnell JW, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Psaty BM, Tracy RP, Tybjaerg Hansen A, Nordestgaard BG, Benn M, Frikke Schmidt R, Giampaoli S, Palmieri L, Vanuzzo D, Pilotto L, Gómez de la Cámara A, Gómez Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee AJ, Smith FB, Taylor J, Guralnik JM, Phillips CL, Wallace R, Guralnik J, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Wolf PA, Vasan RS, Pencina MJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Rodriguez B, Dekker JM, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Nyyssönen K, Tuomainen TP, Deeg DJ, Poppelaars JL, Meade TW, Cooper JA, Hedblad B, Berglund G, Engstrom G, Verschuren WM, Blokstra A, Döring A, Koenig W, Meisinger C, Mraz W, Verschure WM, Bas Bueno de Mesquita H, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum R, Mussolino M, Hankinson S, Manson J, Knottenbelt C, Bauer KA, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Shepherd J, Ford I, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt R, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler M, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe GD, Howard BV, Zhang Y, Best L, Umans J, Ben Shlomo Y, Davey Smith G, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Lind L, Giedraitis V, Lannfelt L, Gaziano JM, Stampfer M, Ridker P, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Marmot M, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Cobbe SM, Robertson M, He Y, Marin Ibañez A, Feskens EJ, Kromhout D, Walker M, Watson S, Erqou S, Orfei L, Pennells L, Perry PL, Alexander M, Wensley F, White IR, Danesh J., PANICO, SALVATORE, Developmental Genetics, EMGO+ - Lifestyle, Overweight and Diabetes, Epidemiology and Data Science, General practice, Psychiatry, EMGO - Lifestyle, overweight and diabetes, Emerging Risk Factors, Collaboration, Di Angelantonio, E, Sarwar, N, Perry, P, Kaptoge, S, Ray, Kk, Thompson, A, Wood, Am, Lewington, S, Sattar, N, Packard, Cj, Collins, R, Thompson, Sg, Tipping, Rw, Ford, Ce, Pressel, Sl, Walldius, G, Jungner, I, Folsom, Ar, Chambless, Le, Panagiotakos, Db, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, M, Goldbourt, U, Benderly, M, Tanne, D, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, Da, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, Gómez de la Cámara, A, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Smith, Fb, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Guralnik, J, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Deeg, Dj, Poppelaars, Jl, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engstrom, G, Verschuren, Wm, Blokstra, A, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Verschure, Wm, Bas Bueno de Mesquita, H, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, J, Knottenbelt, C, Bauer, Ka, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Ford, I, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Lannfelt, L, Gaziano, Jm, Stampfer, M, Ridker, P, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Marmot, M, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Cobbe, Sm, Robertson, M, He, Y, Marin Ibañez, A, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Erqou, S, Orfei, L, Pennells, L, Perry, Pl, Alexander, M, Wensley, F, White, Ir, and Danesh, J.

Subjects
medicine.medical_specialty, Apolipoprotein B, biology, Triglyceride, business.industry, Vascular disease, Cholesterol, Proportional hazards model, Hazard ratio, Context (language use), General Medicine, 11 Medical And Health Sciences, medicine.disease, Gastroenterology, Article, chemistry.chemical_compound, Endocrinology, chemistry, Internal medicine, General & Internal Medicine, biology.protein, Medicine, lipids (amino acids, peptides, and proteins), Myocardial infarction, business
Abstract

Udgivelsesdato: 2009-Nov-11 CONTEXT: Associations of major lipids and apolipoproteins with the risk of vascular disease have not been reliably quantified. OBJECTIVE: To assess major lipids and apolipoproteins in vascular risk. DESIGN, SETTING, AND PARTICIPANTS: Individual records were supplied on 302,430 people without initial vascular disease from 68 long-term prospective studies, mostly in Europe and North America. During 2.79 million person-years of follow-up, there were 8857 nonfatal myocardial infarctions, 3928 coronary heart disease [CHD] deaths, 2534 ischemic strokes, 513 hemorrhagic strokes, and 2536 unclassified strokes. MAIN OUTCOME MEASURES: Hazard ratios (HRs), adjusted for several conventional factors, were calculated for 1-SD higher values: 0.52 log(e) triglyceride, 15 mg/dL high-density lipoprotein cholesterol (HDL-C), 43 mg/dL non-HDL-C, 29 mg/dL apolipoprotein AI, 29 mg/dL apolipoprotein B, and 33 mg/dL directly measured low-density lipoprotein cholesterol (LDL-C). Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. RESULTS: The rates of CHD per 1000 person-years in the bottom and top thirds of baseline lipid distributions, respectively, were 2.6 and 6.2 with triglyceride, 6.4 and 2.4 with HDL-C, and 2.3 and 6.7 with non-HDL-C. Adjusted HRs for CHD were 0.99 (95% CI, 0.94-1.05) with triglyceride, 0.78 (95% CI, 0.74-0.82) with HDL-C, and 1.50 (95% CI, 1.39-1.61) with non-HDL-C. Hazard ratios were at least as strong in participants who did not fast as in those who did. The HR for CHD was 0.35 (95% CI, 0.30-0.42) with a combination of 80 mg/dL lower non-HDL-C and 15 mg/dL higher HDL-C. For the subset with apolipoproteins or directly measured LDL-C, HRs were 1.50 (95% CI, 1.38-1.62) with the ratio non-HDL-C/HDL-C, 1.49 (95% CI, 1.39-1.60) with the ratio apo B/apo AI, 1.42 (95% CI, 1.06-1.91) with non-HDL-C, and 1.38 (95% CI, 1.09-1.73) with directly measured LDL-C. Hazard ratios for ischemic stroke were 1.02 (95% CI, 0.94-1.11) with triglyceride, 0.93 (95% CI, 0.84-1.02) with HDL-C, and 1.12 (95% CI, 1.04-1.20) with non-HDL-C. CONCLUSION: Lipid assessment in vascular disease can be simplified by measurement of either total and HDL cholesterol levels or apolipoproteins without the need to fast and without regard to triglyceride.

Published
2016

3. Glycated hemoglobin measurement and prediction of cardiovascular disease

Author

Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Khan H, Butterworth AS, Wormser D, Kaptoge S, Kondapally Seshasai SR, Thompson A, Sarwar N, Willeit P, Ridker PM, Barr EL, Khaw KT, Psaty BM, Brenner H, Balkau B, Dekker JM, Lawlor DA, Daimon M, Willeit J, Njølstad I, Nissinen A, Brunner EJ, Kuller LH, Price JF, Sundström J, Knuiman MW, Feskens EJ, Verschuren WM, Wald N, Bakker SJ, Whincup PH, Ford I, Goldbourt U, Gómez de la Cámara A, Gallacher J, Simons LA, Rosengren A, Sutherland SE, Björkelund C, Blazer DG, Wassertheil Smoller S, Onat A, Marín Ibañez A, Casiglia E, Jukema JW, Simpson LM, Giampaoli S, Nordestgaard BG, Selmer R, Wennberg P, Kauhanen J, Salonen JT, Dankner R, Barrett Connor E, Kavousi M, Gudnason V, Evans D, Wallace RB, Cushman M, D'Agostino RB Sr, Umans JG, Kiyohara Y, Nakagawa H, Sato S, Gillum RF, Folsom AR, van der Schouw YT, Moons KG, Griffin SJ, Sattar N, Wareham NJ, Selvin E, Thompson SG, Danesh J. Collaborators Simpson LM, Coresh J, Wagenknecht L, Shaw JE, Zimmet PZ, Magliano D, Wannamethee SG, Morris RW, Kiechl S, Santer P, Bonora E, Casas JP, Ebrahim S, Ben Shlomo Y, Yarnell JW, Elwood P, Bachman DL, Nietert PJ, Håheim LL, Søgaard AJ, Tybjaerg Hansen A, Frikke Schmidt R, Benn M, Palmieri L, Vanuzzo D, Bonnet F, Copin N, Roussel R, Gómez Gerique JA, Rubio Herrera MA, Gutiérrez Fuentes JA, Friedlander Y, McCallum J, Simons J, Lee AJ, McLachlan S, Taylor JO, Guralnik JM, Phillips CL, Evans DA, Kohout F, Cohen H, George L, Fillenbaum G, McGloin JM, Khaw K., Schöttker B, Müller H, Rothenbacher D, Jansson J., Hallmans G, Tuomilehto J, Donfrancesco C, Woodward M, Oizumi T, Kayama T, Kato T, Danker R, Chetrit A, Wilhelmsen L, Eriksson H, Lappas G, Bengtsson C, Lissner L, Skoog I, Cremer P, Arima H, Ninomiya T, Hata J, Nijpels G, Stehouwer CD, Tuomainen T., Voutilainen S, Kurl S, de Boer IH, Bertoni AG, Veschuren WM, Dullaart RP, Lambers Heerspink HJ, Hilege HL, Trompet S, Stott DJ, Dagenais GR, Cantin B, Dehghan D, Hofman A, Franco OH, Tunstall Pedoe H, Lee E, Best L, Howard BV, Can G, Ademoğlu E, Sakurai M, Nakamura K, Morikawa Y, Løchen M., Mathiesen EB, Wilsgaard T, Byberg L, Cederholm T, Olsson E, Pradhan AD, Cook NR, Kromhout D, Walker M, Watson S, Burgess S, Gregson J, Harshfield E, Pennells L, Spackman S, Warnakula S, Wood AM, Danesh J., PANICO, SALVATORE, Emerging Risk Factors, Collaboration, Di Angelantonio, E, Gao, P, Khan, H, Butterworth, A, Wormser, D, Kaptoge, S, Kondapally Seshasai, Sr, Thompson, A, Sarwar, N, Willeit, P, Ridker, Pm, Barr, El, Khaw, Kt, Psaty, Bm, Brenner, H, Balkau, B, Dekker, Jm, Lawlor, Da, Daimon, M, Willeit, J, Njølstad, I, Nissinen, A, Brunner, Ej, Kuller, Lh, Price, Jf, Sundström, J, Knuiman, Mw, Feskens, Ej, Verschuren, Wm, Wald, N, Bakker, Sj, Whincup, Ph, Ford, I, Goldbourt, U, Gómez de la Cámara, A, Gallacher, J, Simons, La, Rosengren, A, Sutherland, Se, Björkelund, C, Blazer, Dg, Wassertheil Smoller, S, Onat, A, Marín Ibañez, A, Casiglia, E, Jukema, Jw, Simpson, Lm, Giampaoli, S, Nordestgaard, Bg, Selmer, R, Wennberg, P, Kauhanen, J, Salonen, Jt, Dankner, R, Barrett Connor, E, Kavousi, M, Gudnason, V, Evans, D, Wallace, Rb, Cushman, M, D'Agostino RB, Sr, Umans, Jg, Kiyohara, Y, Nakagawa, H, Sato, S, Gillum, Rf, Folsom, Ar, van der Schouw, Yt, Moons, Kg, Griffin, Sj, Sattar, N, Wareham, Nj, Selvin, E, Thompson, Sg, Danesh J., Collaborators Simpson LM, Coresh, J, Wagenknecht, L, Shaw, Je, Zimmet, Pz, Magliano, D, Wannamethee, Sg, Morris, Rw, Kiechl, S, Santer, P, Bonora, E, Casas, Jp, Ebrahim, S, Ben Shlomo, Y, Yarnell, Jw, Elwood, P, Bachman, Dl, Nietert, Pj, Håheim, Ll, Søgaard, Aj, Tybjaerg Hansen, A, Frikke Schmidt, R, Benn, M, Palmieri, L, Vanuzzo, D, Panico, Salvatore, Bonnet, F, Copin, N, Roussel, R, Gómez Gerique, Ja, Rubio Herrera, Ma, Gutiérrez Fuentes, Ja, Friedlander, Y, Mccallum, J, Simons, J, Lee, Aj, Mclachlan, S, Taylor, Jo, Guralnik, Jm, Phillips, Cl, Evans, Da, Kohout, F, Cohen, H, George, L, Fillenbaum, G, Mcgloin, Jm, Khaw, K., Schöttker, B, Müller, H, Rothenbacher, D, Jansson, J., Hallmans, G, Tuomilehto, J, Donfrancesco, C, Woodward, M, Oizumi, T, Kayama, T, Kato, T, Danker, R, Chetrit, A, Wilhelmsen, L, Eriksson, H, Lappas, G, Bengtsson, C, Lissner, L, Skoog, I, Cremer, P, Arima, H, Ninomiya, T, Hata, J, Nijpels, G, Stehouwer, Cd, Tuomainen, T., Voutilainen, S, Kurl, S, de Boer, Ih, Bertoni, Ag, Veschuren, Wm, Dullaart, Rp, Lambers Heerspink, Hj, Hilege, Hl, Trompet, S, Stott, Dj, Dagenais, Gr, Cantin, B, Dehghan, D, Hofman, A, Franco, Oh, Tunstall Pedoe, H, Lee, E, Best, L, Howard, Bv, Can, G, Ademoğlu, E, Sakurai, M, Nakamura, K, Morikawa, Y, Løchen, M., Mathiesen, Eb, Wilsgaard, T, Byberg, L, Cederholm, T, Olsson, E, Pradhan, Ad, Cook, Nr, Kromhout, D, Walker, M, Watson, S, Burgess, S, Gregson, J, Harshfield, E, Pennells, L, Spackman, S, Warnakula, S, Wood, Am, and Danesh, J.

Published
2014

4. Association between dietary meat consumption and incident type 2 diabetes: the EPIC-InterAct study

Author

Benedinelli, B, Palli, D, Masala, G, Sharp, Sj, Schulz, Mb, Guevara, M, van der AD, Sera, F, Amiano, P, Balkau, B, Barricarte, A, Boeing, H, Crowe, Fl, Dahm, Cc, Dalmeijer, G, de Lauzon Guillain, B, Egeberg, R, Fagherazzi, G, Franks, Pw, Krogh, V, Huerta, Jm, Jakszyn, P, Khaw, Kt, Li, K, Mattiello, A, Nilsson, Pm, Overvad, K, Ricceri, Fulvio, Rolandsson, O, Sánchez, Mj, Slimani, N, Sluijs, I, Spijkerman, Am, Teucher, B, Tjonneland, A, Tumino, R, van den Berg SW, Forouhi, Ng, Langeberg, C, Feskens, Ej, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Adult, Male, Risk, Meat, Nutrition and Disease, Cross-sectional study, Endocrinology, Diabetes and Metabolism, Type 2 diabetes, Cohort Studies, Young Adult, Voeding en Ziekte, Diabetes mellitus, Internal Medicine, Humans, Medicine, Prospective Studies, Prospective cohort study, Aged, Aged, 80 and over, Sex Characteristics, business.industry, Incidence, Case-control study, Middle Aged, medicine.disease, Diet, European Prospective Investigation into Cancer and Nutrition, Europe, Meat Products, Cross-Sectional Studies, Case-cohort study, Diabetes Mellitus, Type 2, Case-Control Studies, Red meat, Female, business, Iron, Dietary, Follow-Up Studies, Demography, Cohort study
Abstract

Aims/hypothesis: A diet rich in meat has been reported to contribute to the risk of type 2 diabetes. The present study aims to investigate the association between meat consumption and incident type 2 diabetes in the EPIC-InterAct study, a large prospective case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Methods: During 11.7 years of follow-up, 12,403 incident cases of type 2 diabetes were identified among 340,234 adults from eight European countries. A centre-stratified random subsample of 16,835 individuals was selected in order to perform a case-cohort design. Prentice-weighted Cox regression analyses were used to estimate HR and 95% CI for incident diabetes according to meat consumption. Results: Overall, multivariate analyses showed significant positive associations with incident type 2 diabetes for increasing consumption of total meat (50 g increments: HR 1.08; 95% CI 1.05, 1.12), red meat (HR 1.08; 95% CI 1.03, 1.13) and processed meat (HR 1.12; 95% CI 1.05, 1.19), and a borderline positive association with meat iron intake. Effect modifications by sex and class of BMI were observed. In men, the results of the overall analyses were confirmed. In women, the association with total and red meat persisted, although attenuated, while an association with poultry consumption also emerged (HR 1.20; 95% CI 1.07, 1.34). These associations were not evident among obese participants. Conclusions/interpretation: This prospective study confirms a positive association between high consumption of total and red meat and incident type 2 diabetes in a large cohort of European adults.

Published
2012

5. Age at menopause, reproductive life span, and type 2 diabetes risk: results from the EPIC-InterAct study

Author

Brand JS, van der Schouw YT, Onland Moret NC, Sharp SJ, Ong KK, Khaw KT, Ardanaz E, Amiano P, Boeing H, Chirlaque MD, Clavel Chapelon F, Crowe FL, de Lauzon Guillain B, Duell EJ, Fagherazzi G, Franks PW, Grioni S, Groop LC, Kaaks R, Key TJ, Nilsson PM, Overvad K, Palli D, Quir?s JR, Rolandsson O, Sacerdote C, S?nchez MJ, Slimani N, Teucher B, Tjonneland A, Tumino R, van der A. DL, Feskens EJ, Langenberg C, Forouhi NG, Riboli E, Wareham NJ, InterAct Consortium, PANICO, SALVATORE, Brand, J, van der Schouw, Yt, Onland Moret, Nc, Sharp, Sj, Ong, Kk, Khaw, Kt, Ardanaz, E, Amiano, P, Boeing, H, Chirlaque, Md, Clavel Chapelon, F, Crowe, Fl, de Lauzon Guillain, B, Duell, Ej, Fagherazzi, G, Franks, Pw, Grioni, S, Groop, Lc, Kaaks, R, Key, Tj, Nilsson, Pm, Overvad, K, Palli, D, Panico, Salvatore, Quir?s, Jr, Rolandsson, O, Sacerdote, C, S?nchez, Mj, Slimani, N, Teucher, B, Tjonneland, A, Tumino, R, van der A., Dl, Feskens, Ej, Langenberg, C, Forouhi, Ng, Riboli, E, Wareham, Nj, and Interact, Consortium

Published
2013

6. Dietary glycemic index, glycemic load, and digestible carbohydrate intake are not associated with risk op type 2 diabetes in eight European countries

Author

Sluijs I, Beulens JW, van der Schouw YT, van der A. DL, Buckland G, Kuijsten A, Schulze MB, Amiano P, Ardanaz E, Balkau B, Boeing H, Gavrila D, Grote VA, Key TJ, Li K, Nilsson P, Overvad K, Palli D, Quir?s JR, Rolandsson O, Roswall N, Sacerdote C, S?nchez MJ, Sieri S, Slimani N, Spijkerman AM, Tj?nneland A, Tumino R, Sharp SJ, Langenberg C, Feskens EJ, Forouhi NG, Riboli E, Wareham NJ, InterAct consortium, PANICO, SALVATORE, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, Sluijs, I, Beulens, Jw, van der Schouw, Yt, van der A., Dl, Buckland, G, Kuijsten, A, Schulze, Mb, Amiano, P, Ardanaz, E, Balkau, B, Boeing, H, Gavrila, D, Grote, Va, Key, Tj, Li, K, Nilsson, P, Overvad, K, Palli, D, Panico, Salvatore, Quir?s, Jr, Rolandsson, O, Roswall, N, Sacerdote, C, S?nchez, Mj, Sieri, S, Slimani, N, Spijkerman, Am, Tj?nneland, A, Tumino, R, Sharp, Sj, Langenberg, C, Feskens, Ej, Forouhi, Ng, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Male, Nutrition and Disease, Medicine (miscellaneous), Type 2 diabetes, Gastroenterology, Cohort Studies, prevention, Risk Factors, Surveys and Questionnaires, Voeding en Ziekte, Medicine, Prospective Studies, Nutrition and Dietetics, cohort, Middle Aged, European Prospective Investigation into Cancer and Nutrition, Europe, Glycemic index, nutrition, Population study, Digestion, Female, women, Adult, medicine.medical_specialty, Diabetes risk, life-style, Diabetes mellitus, Internal medicine, Glycemic load, Dietary Carbohydrates, Humans, cancer, Aged, VLAG, Global Nutrition, Wereldvoeding, disease, business.industry, Case-control study, Reproducibility of Results, fiber intake, medicine.disease, Diet, Endocrinology, Diabetes Mellitus, Type 2, Food, Glycemic Index, Case-Control Studies, business, energy-intake, human activities, Follow-Up Studies, mellitus
Abstract

The association of glycemic index (GI) and glycemic load (GL) with the risk of type 2 diabetes remains unclear. We investigated associations of dietary GI, GL, and digestible carbohydrate with incident type 2 diabetes. We performed a case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition Study, including a random subcohort (n = 16,835) and incident type 2 diabetes cases (n = 12,403). The median follow-up time was 12 y. Baseline dietary intakes were assessed using country-specific dietary questionnaires. Country-specific HR were calculated and pooled using random effects meta-analysis. Dietary GI, GL, and digestible carbohydrate in the subcohort were (mean +/- SD) 56 +/- 4, 127 +/- 23, and 226 +/- 36 g/d, respectively. After adjustment for confounders, GI and GL were not associated with incident diabetes [HR highest vs. lowest quartile (HRQ4) for GI: 1.05 (95% CI = 0.96, 1.16); HRQ4 for GL: 1.07 (95% CI = 0.95, 1.20)]. Digestible carbohydrate intake was not associated with incident diabetes [HRQ4: 0.98(95% CI = 0.86, 1.10)]. In additional analyses, we found that discrepancies in the GI value assignment to foods possibly explain differences in GI associations with diabetes within the same study population. In conclusion, an expansion of the GI tables and systematic GI value assignment to foods may be needed to improve the validity of GI values derived in such studies, after which GI associations may need reevaluation. Our study shows that digestible carbohydrate intake is not associated with diabetes risk and suggests that diabetes risk with high-GI and -GL diets may be more modest than initial studies suggested. J. Nutr. 143: 93-99, 2013.

Published
2013

7. Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality : an individual participant Metaanalysis

Author

Danesh, J, Lewington, S, Thompson, SG, Lowe, GD, Collins, R, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, R, Vahtera, E, Jousilahti, P, Pekkanen, J, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Arocha-Piñango, CL, Rodriguez-Larralde, A, Nagy, E, Mijares, M, Espinosa, R, Rodriquez-Roa, E, Ryder, E, Diez-Ewald, MP, Campos, G, Fernandez, V, Torres, E, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, JT, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Lind, P, Loewel, H, Koenig, W, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Després, JP, Dagenais, GR, Tunstall-Pedoe, H, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Kromhout, D, Dickinson, A, Ireland, B, Juzwishin, K, Kaptoge, S, Memon, A, Sarwar, N, Walker, M, Wheeler, J, White, I, and Wood, A

Subjects
Adult, Risk, medicine.medical_specialty, base-line findings, blood-viscosity, Nutrition and Disease, Regression dilution, Blood viscosity, Myocardial Infarction, Context (language use), Coronary Disease, c-reactive protein, edinburgh artery, Internal medicine, Cause of Death, Voeding en Ziekte, medicine, follow-up, Humans, Vascular Diseases, Stroke, Aged, Proportional Hazards Models, VLAG, hemostatic factors, business.industry, Proportional hazards model, Confounding, Hazard ratio, Fibrinogen, General Medicine, Middle Aged, medicine.disease, myocardial-infarction, Surgery, relative risk, Relative risk, factor-vii, business, coronary-heart-disease
Abstract

CONTEXT: Plasma fibrinogen levels may be associated with the risk of coronary heart disease (CHD) and stroke. OBJECTIVE: To assess the relationships of fibrinogen levels with risk of major vascular and with risk of nonvascular outcomes based on individual participant data. DATA SOURCES: Relevant studies were identified by computer-assisted searches, hand searches of reference lists, and personal communication with relevant investigators. STUDY SELECTION: All identified prospective studies were included with information available on baseline fibrinogen levels and details of subsequent major vascular morbidity and/or cause-specific mortality during at least 1 year of follow-up. Studies were excluded if they recruited participants on the basis of having had a previous history of cardiovascular disease; participants with known preexisting CHD or stroke were excluded. DATA EXTRACTION: Individual records were provided on each of 154,211 participants in 31 prospective studies. During 1.38 million person-years of follow-up, there were 6944 first nonfatal myocardial infarctions or stroke events and 13,210 deaths. Cause-specific mortality was generally available. Analyses involved proportional hazards modeling with adjustment for confounding by known cardiovascular risk factors and for regression dilution bias. DATA SYNTHESIS: Within each age group considered (40-59, 60-69, and > or =70 years), there was an approximately log-linear association with usual fibrinogen level for the risk of any CHD, any stroke, other vascular (eg, non-CHD, nonstroke) mortality, and nonvascular mortality. There was no evidence of a threshold within the range of usual fibrinogen level studied at any age. The age- and sex- adjusted hazard ratio per 1-g/L increase in usual fibrinogen level for CHD was 2.42 (95% confidence interval [CI], 2.24-2.60); stroke, 2.06 (95% CI, 1.83-2.33); other vascular mortality, 2.76 (95% CI, 2.28-3.35); and nonvascular mortality, 2.03 (95% CI, 1.90-2.18). The hazard ratios for CHD and stroke were reduced to about 1.8 after further adjustment for measured values of several established vascular risk factors. In a subset of 7011 participants with available C-reactive protein values, the findings for CHD were essentially unchanged following additional adjustment for C-reactive protein. The associations of fibrinogen level with CHD or stroke did not differ substantially according to sex, smoking, blood pressure, blood lipid levels, or several features of study design. CONCLUSIONS: In this large individual participant meta-analysis, moderately strong associations were found between usual plasma fibrinogen level and the risks of CHD, stroke, other vascular mortality, and nonvascular mortality in a wide range of circumstances in healthy middle-aged adults. Assessment of any causal relevance of elevated fibrinogen levels to disease requires additional research.

Published
2005

8. Alcohol consumption and risk of type 2 diabetes in European men and women: influence of beverage type and body size The EPIC-InterAct study

Author

Beulens JW, van der Schouw YT, Bergmann MM, Rohrmann S, Schulze MB, Buijsse B, Grobbee DE, Arriola L, Cauchi S, Tormo MJ, Allen NE, van der A. DL, Balkau B, Boeing H, Clavel Chapelon F, de Lauzon Guillan B, Franks P, Froguel P, Gonzales C, Halkjaer J, Huerta JM, Kaaks R, Key TJ, Khaw KT, Krogh V, Molina Montes E, Nilsson P, Overvad K, Palli D, Ram?n Quir?s J, Rolandsson O, Romieu I, Romaguera D, Sacerdote C, S?nchez MJ, Spijkerman AM, Teucher B, Tjonneland A, Tumino R, Sharp S, Forouhi NG, Langenberg C, Feskens EJ, Riboli E, Wareham NJ, InterAct Consortium, PANICO, SALVATORE, Beulens, Jw, van der Schouw, Yt, Bergmann, Mm, Rohrmann, S, Schulze, Mb, Buijsse, B, Grobbee, De, Arriola, L, Cauchi, S, Tormo, Mj, Allen, Ne, van der A., Dl, Balkau, B, Boeing, H, Clavel Chapelon, F, de Lauzon Guillan, B, Franks, P, Froguel, P, Gonzales, C, Halkjaer, J, Huerta, Jm, Kaaks, R, Key, Tj, Khaw, Kt, Krogh, V, Molina Montes, E, Nilsson, P, Overvad, K, Palli, D, Panico, Salvatore, Ram?n Quir?s, J, Rolandsson, O, Romieu, I, Romaguera, D, Sacerdote, C, S?nchez, Mj, Spijkerman, Am, Teucher, B, Tjonneland, A, Tumino, R, Sharp, S, Forouhi, Ng, Langenberg, C, Feskens, Ej, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Cohort Studies, Europe, Male, Sex Factors, Alcohol Drinking, Diabetes Mellitus, Type 2, Risk Factors, Alcoholic Beverages, Body Size, Humans, Female, Prospective Studies, Middle Aged
Abstract

OBJECTIVE: To investigate the association between alcohol consumption and type 2 diabetes, and determine whether this is modified by sex, body mass index (BMI) and beverage type. DESIGN: Multicentre prospective case-cohort study. SETTING: Eight countries from the European Prospective Investigation into Cancer and Nutrition cohort. SUBJECTS: A representative baseline sample of 16 154 participants and 12 403 incident cases of type 2 diabetes. INTERVENTIONS: Alcohol consumption assessed using validated dietary questionnaires. MAIN OUTCOME MEASURES: Occurrence of type 2 diabetes based on multiple sources (mainly self-reports), verified against medical information. RESULTS: Amongst men, moderate alcohol consumption was nonsignificantly associated with a lower incidence of diabetes with a hazard ratio (HR) of 0.90 (95% CI: 0.78-1.05) for 6.1-12.0 versus 0.1-6.0 g day(-1) , adjusted for dietary and diabetes risk factors. However, the lowest risk was observed at higher intakes of 24.1-96.0 g day(-1) with an HR of 0.86 (95% CI: 0.75-0.98). Amongst women, moderate alcohol consumption was associated with a lower incidence of diabetes with a hazard ratio of 0.82 (95% CI: 0.72-0.92) for 6.1-12.0 g day(-1) (P interaction gender

Published
2012

9. The prospective association between total and type of fish intake and type 2 diabetes in 8 European countries: EPIC-InterAct Study

Author

Patel PS, Forouhi NG, Kuijsten A, Schulze MB, van Woudenbergh GJ, Ardanaz E, Amiano P, Arriola L, Balkau B, Barricarte A, Beulens JW, Boeing H, Buijsse B, Crowe FL, de Lauzon Guillan B, Fagherazzi G, Franks PW, Gonzalez C, Grioni S, Halkjaer J, Huerta JM, Key TJ, K?hn T, Masala G, Nilsson P, Overvad K, Quir?s JR, Rolandsson O, Sacerdote C, S?nchez MJ, Schmidt EB, Slimani N, Spijkerman AM, Teucher B, Tjonneland A, Tormo MJ, Tumino R, van der A. DL, van der Schouw YT, Sharp SJ, Langenberg C, Feskens EJ, Riboli E, Wareham NJ, InterAct Consortium, PANICO, SALVATORE, Patel, P, Forouhi, Ng, Kuijsten, A, Schulze, Mb, van Woudenbergh, Gj, Ardanaz, E, Amiano, P, Arriola, L, Balkau, B, Barricarte, A, Beulens, Jw, Boeing, H, Buijsse, B, Crowe, Fl, de Lauzon Guillan, B, Fagherazzi, G, Franks, Pw, Gonzalez, C, Grioni, S, Halkjaer, J, Huerta, Jm, Key, Tj, K?hn, T, Masala, G, Nilsson, P, Overvad, K, Panico, Salvatore, Quir?s, Jr, Rolandsson, O, Sacerdote, C, S?nchez, Mj, Schmidt, Eb, Slimani, N, Spijkerman, Am, Teucher, B, Tjonneland, A, Tormo, Mj, Tumino, R, van der A., Dl, van der Schouw, Yt, Sharp, Sj, Langenberg, C, Feskens, Ej, Riboli, E, Wareham, Nj, Interact, Consortium, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, and APH - Health Behaviors & Chronic Diseases

Subjects
Adult, Male, validity, Nutrition and Disease, design, Medicine (miscellaneous), Type 2 diabetes, project, Diabetes mellitus, Environmental health, Voeding en Ziekte, Fatty Acids, Omega-3, Animals, Humans, Medicine, cancer, Prospective Studies, Food science, consumption, Prospective cohort study, omega-3-fatty-acids, Shellfish, Proportional Hazards Models, VLAG, risk, Nutrition and Dietetics, Proportional hazards model, business.industry, Incidence, Incidence (epidemiology), Fishes, Middle Aged, medicine.disease, calibration, Dietary Fats, Diet, European Prospective Investigation into Cancer and Nutrition, Europe, nutrition, Diabetes Mellitus, Type 2, Seafood, Quartile, Female, Energy Intake, business, Follow-Up Studies, mellitus
Abstract

Background: Epidemiologic evidence of an association between fish intake and type 2 diabetes (T2D) is inconsistent and unresolved. Objective: The objective was to examine the association between total and type of fish intake and T2D in 8 European countries. Design: This was a case-cohort study, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study, with 3.99 million person-years of follow-up, 12,403 incident diabetes cases, and a random subcohort of 16,835 individuals from 8 European countries. Habitual fish intake (lean fish, fatty fish, total fish, shellfish, and combined fish and shellfish) was assessed by country-specific dietary questionnaires. HRs were estimated in each country by using Prentice-weighted Cox regression models and pooled by using a random-effects meta-analysis. Results: No overall association was found between combined fish and shellfish intake and incident T2D per quartile (adjusted HR: 1.00; 95% Cl: 0.94, 1.06; P-trend = 0.99). Total fish, lean fish, and shellfish intakes separately were also not associated with T2D, but fatty fish intake was weakly inversely associated with T2D: adjusted HR per quartile 0.97 (0.94, 1.00), with an HR of 0.84 (0.70, 1.01), 0.85 (0.76, 0.95), and 0.87 (0.78, 0.97) for a comparison of the second, third, and fourth quartiles with the lowest quartile of intake, respectively (P-trend = 0.06). Conclusions: These findings suggest that lean fish, total fish, and shellfish intakes are not associated with incident diabetes but that fatty fish intake may be weakly inversely associated. Replication of these findings in other populations and investigation of the mechanisms underlying these associations are warranted. Meanwhile, current public health recommendations on fish intake should remain unchanged. Am J Clin Nutr 2012;95:1445-53

Published
2012

10. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis

Author

Emerging Risk Factors Collaboration, Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, Danesh JTipping RW, Ford CE, Pressel SL, Walldius G, Jungner I, Folsom AR, Chambless L, Ballantyne CM, Panagiotakos D, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman MW, Goldbourt U, Benderly M, Tanne D, Whincup P, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Mayr A, Schett G, Wald N, Ebrahim S, Lawlor D, Yarnell J, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Psaty BM, Tracy R, Tybjaerg Hansen A, Nordestgaard BG, Zacho J, Frikke Schmidt R, Giampaoli S, Palmieri L, Vanuzzo D, Pilotto L, de la Cámara AG, Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee A, Taylor J, Guralnik JM, Phillips CL, Wallace RB, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Wolf PA, Vasan RS, Benjamin EJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Kiyohara Y, Arima H, Doi Y, Ninomiya T, Rodriguez B, Dekker J, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Nyyssönen K, Tuomainen TP, Laukkanen JA, Deeg DJ, Bremmer MA, Meade TW, Cooper JA, Hedblad B, Berglund G, Engström G, Verschuren WM, Blokstra A, Shea S, Döring A, Koenig W, Meisinger C, Bueno de Mesquita HB, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum RF, Mussolino M, Hankinson S, Manson JE, Knottenbelt C, Bauer KA, Davidson K, Kirkland S, Shaffer J, Korin MR, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Luc G, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Packard CJ, Sattar N, Westendorp RG, Buckley BM, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt RR, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler MM, Hofman A, Tunstall Pedoe H, Tavendale R, Howard BV, Zhang Y, Best L, Umans J, Ben Shlomo Y, Davey Smith G, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Basu S, Cederholm T, Byberg L, Gaziano JM, Stampfer M, Ridker PM, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Wassertheil Smoller S, Marmot IM, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Shepherd J, Cobbe S, Ford I, Robertson M, He Y, Ibañez AM, Feskens EJ, Walker M, Watson S, Erqou S, Lewington S, Pennells L, Perry PL, Ray KK, Sarwar N, Alexander M, Thompson A, White IR, Wood AM, Danesh J., PANICO, SALVATORE, Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: CARIM School for Cardiovascular Diseases, General practice, EMGO - Lifestyle, overweight and diabetes, Psychiatry, EMGO - Mental health, Emerging Risk Factors, Collaboration, Kaptoge, S, Di Angelantonio, E, Lowe, G, Pepys, Mb, Thompson, Sg, Collins, R, Danesh JTipping, Rw, Ford, Ce, Pressel, Sl, Walldius, G, Jungner, I, Folsom, Ar, Chambless, L, Ballantyne, Cm, Panagiotakos, D, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, Mw, Goldbourt, U, Benderly, M, Tanne, D, Whincup, P, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Mayr, A, Schett, G, Wald, N, Ebrahim, S, Lawlor, D, Yarnell, J, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, R, Tybjaerg Hansen, A, Nordestgaard, Bg, Zacho, J, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, de la Cámara, Ag, Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, A, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, Rb, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Benjamin, Ej, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Kiyohara, Y, Arima, H, Doi, Y, Ninomiya, T, Rodriguez, B, Dekker, J, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Laukkanen, Ja, Deeg, Dj, Bremmer, Ma, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engström, G, Verschuren, Wm, Blokstra, A, Shea, S, Döring, A, Koenig, W, Meisinger, C, Bueno de Mesquita, Hb, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, Rf, Mussolino, M, Hankinson, S, Manson, Je, Knottenbelt, C, Bauer, Ka, Davidson, K, Kirkland, S, Shaffer, J, Korin, Mr, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Luc, G, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, Rr, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, Mm, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Basu, S, Cederholm, T, Byberg, L, Gaziano, Jm, Stampfer, M, Ridker, Pm, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Wassertheil Smoller, S, Marmot, Im, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Shepherd, J, Cobbe, S, Ford, I, Robertson, M, He, Y, Ibañez, Am, Feskens, Ej, Walker, M, Watson, S, Erqou, S, Lewington, S, Pennells, L, Perry, Pl, Ray, Kk, Sarwar, N, Alexander, M, Thompson, A, White, Ir, Wood, Am, and Danesh, J.

Subjects
Lung Diseases, Male, Databases, Factual, Plasma-fibrinogen, Blood Pressure, Coronary Disease, 030204 cardiovascular system & hematology, Associations, Body Mass Index, Low-density-lipoprotein, Leukocyte Count, 0302 clinical medicine, Risk Factors, Neoplasms, 030212 general & internal medicine, Stroke, Framingham Risk Score, biology, Smoking, 11 Medical And Health Sciences, General Medicine, Articles, Middle Aged, 3. Good health, C-Reactive Protein, Cholesterol, Regression Analysis, low-density lipoprotein cardiovascular-disease nonvascular mortality regression dilution plasma-fibrinogen mendelian randomization independent predictor prospective cohorts vascular-disease inflammation, Female, Risk assessment, medicine.medical_specialty, Alcohol Drinking, Regression dilution, Motor Activity, Risk Assessment, 03 medical and health sciences, Sex Factors, Cardiovascular-disease, General & Internal Medicine, Internal medicine, Diabetes Mellitus, medicine, Humans, Women, Risk factor, Serum Albumin, Triglycerides, Inflammation, Markers, Independent predictor, Interleukin-6, business.industry, Vascular disease, C-reactive protein, Fibrinogen, medicine.disease, Surgery, Relative risk, biology.protein, Nonvascular mortality, business, Biomarkers
Abstract

Udgivelsesdato: 2010-Jan-9 BACKGROUND: Associations of C-reactive protein (CRP) concentration with risk of major diseases can best be assessed by long-term prospective follow-up of large numbers of people. We assessed the associations of CRP concentration with risk of vascular and non-vascular outcomes under different circumstances. METHODS: We meta-analysed individual records of 160 309 people without a history of vascular disease (ie, 1.31 million person-years at risk, 27 769 fatal or non-fatal disease outcomes) from 54 long-term prospective studies. Within-study regression analyses were adjusted for within-person variation in risk factor levels. RESULTS: Log(e) CRP concentration was linearly associated with several conventional risk factors and inflammatory markers, and nearly log-linearly with the risk of ischaemic vascular disease and non-vascular mortality. Risk ratios (RRs) for coronary heart disease per 1-SD higher log(e) CRP concentration (three-fold higher) were 1.63 (95% CI 1.51-1.76) when initially adjusted for age and sex only, and 1.37 (1.27-1.48) when adjusted further for conventional risk factors; 1.44 (1.32-1.57) and 1.27 (1.15-1.40) for ischaemic stroke; 1.71 (1.53-1.91) and 1.55 (1.37-1.76) for vascular mortality; and 1.55 (1.41-1.69) and 1.54 (1.40-1.68) for non-vascular mortality. RRs were largely unchanged after exclusion of smokers or initial follow-up. After further adjustment for fibrinogen, the corresponding RRs were 1.23 (1.07-1.42) for coronary heart disease; 1.32 (1.18-1.49) for ischaemic stroke; 1.34 (1.18-1.52) for vascular mortality; and 1.34 (1.20-1.50) for non-vascular mortality. INTERPRETATION: CRP concentration has continuous associations with the risk of coronary heart disease, ischaemic stroke, vascular mortality, and death from several cancers and lung disease that are each of broadly similar size. The relevance of CRP to such a range of disorders is unclear. Associations with ischaemic vascular disease depend considerably on conventional risk factors and other markers of inflammation. FUNDING: British Heart Foundation, UK Medical Research Council, BUPA Foundation, and GlaxoSmithKline.

Published
2010

11. Emerging Risk Factors Collaboration. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

Author

hompson S, Kaptoge S, White I, Wood A, Thompson SG, White IR, Wood AM, Perry PL, Danesh J, Tipping RW, Ford CE, Simpson LM, Walldius G, Jungner I, Chambless LE, Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman M, Goldbourt U, Benderly M, Tanne D, Whincup PH, Wannamethee SG, Morris RW, Willeit J, Kiechl S, Santer P, Mayr A, Lawlor DA, Yarnell JW, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Tracy RP, Tybjærg Hansen A, Nordestgaard BG, Benn M, Frikke Schmidt R, Giampaoli S, Palmieri L, Vanuzzo D, Gómez de la Cámara A, Gómez Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee AJ, Taylor J, Guralnik JM, Wallace R, Guralnik J, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Vasan RS, Pencina MJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Lappas G, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Kiyohara Y, Arima H, Doi Y, Ninomiya T, Rodriguez B, Dekker JM, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Tuomainen TP, Deeg DJ, Poppelaars JL, Meade TW, Cooper JA, Hedblad B, Berglund G, Engstrom G, Verschuren WM, Blokstra A, Shea S, Döring A, Koenig W, Meisinger C, Mraz W, Bas Bueno de Mesquita H, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum R, Mussolino M, Hankinson S, Manson JE, Knottenbelt C, Bauer KA, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Crespo CJ, Garcia MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Shepherd J, Packard CJ, Sattar N, Ford I, Cantin B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt R, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler M, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe GD, Ben Shlomo Y, Davey Smith G, Howard BV, Zhang Y, Umans J, Onat A, Wilsgaard T, Ingelsson E, Lind L, Giedraitis V, Lannfelt L, Gaziano JM, Ridker P, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Wassertheil Smoller S, Marmot M, Clarke R, Collins R, Brunner E, Shipley M, Buring J, Cobbe SM, Robertson M, He Y, Marín Ibañez A, Feskens EJ, Kromhout D, Di Angelantonio E, Erqou S, Lewington S, Orfei L, Pennells L, Ray KK, Sarwar N, Alexander M, Thompson A, Walker M, Watson S, Wensley F, Perry P, Danesh J., PANICO, SALVATORE, Hompson, S, Kaptoge, S, White, I, Wood, A, Thompson, Sg, White, Ir, Wood, Am, Perry, Pl, Danesh, J, Tipping, Rw, Ford, Ce, Simpson, Lm, Walldius, G, Jungner, I, Chambless, Le, Panagiotakos, Db, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, M, Goldbourt, U, Benderly, M, Tanne, D, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Willeit, J, Kiechl, S, Santer, P, Mayr, A, Lawlor, Da, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Tracy, Rp, Tybjærg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Gómez de la Cámara, A, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Wallace, R, Guralnik, J, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Lappas, G, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Kiyohara, Y, Arima, H, Doi, Y, Ninomiya, T, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Tuomainen, Tp, Deeg, Dj, Poppelaars, Jl, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engstrom, G, Verschuren, Wm, Blokstra, A, Shea, S, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Bas Bueno de Mesquita, H, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, Je, Knottenbelt, C, Bauer, Ka, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Crespo, Cj, Garcia, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Packard, Cj, Sattar, N, Ford, I, Cantin, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Ben Shlomo, Y, Davey Smith, G, Howard, Bv, Zhang, Y, Umans, J, Onat, A, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Lannfelt, L, Gaziano, Jm, Ridker, P, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Wassertheil Smoller, S, Marmot, M, Clarke, R, Collins, R, Brunner, E, Shipley, M, Buring, J, Cobbe, Sm, Robertson, M, He, Y, Marín Ibañez, A, Feskens, Ej, Kromhout, D, Di Angelantonio, E, Erqou, S, Lewington, S, Orfei, L, Pennells, L, Ray, Kk, Sarwar, N, Alexander, M, Thompson, A, Walker, M, Watson, S, Wensley, F, Perry, P, and Danesh, J.

Published
2010

12. Dietary intakes of individual flavanols and flavonols are inversely associated with incident type 2 diabetes in European populations

Author

Zamora Ros, R, Forouhi, Ng, Sharp, Sj, González, Ca, Buijsse, B, Guevara, M, van der Schouw YT, Amiano, P, Boeing, H, Bredsdorff, L, Fagherazzi, G, Feskens, Ej, Franks, Pw, Grioni, S, Katzke, V, Key, Tj, Khaw, Kt, Kühn, T, Masala, G, Mattiello, A, Molina Montes, E, Nilsson, Pm, Overvad, K, Perquier, F, Redondo, Ml, Ricceri, Fulvio, Rolandsson, O, Romieu, I, Roswall, N, Scalbert, A, Schulze, M, Slimani, N, Spijkerman, Am, Tjonneland, A, Tormo, Mj, Touillaud, M, Tumino, R, van der A., Dl, van Woudenbergh GJ, Langenberg, C, Riboli, E, Wareham, Nj, Forouhi, Nita [0000-0002-5041-248X], Sharp, Stephen [0000-0003-2375-1440], Khaw, Kay-Tee [0000-0002-8802-2903], Langenberg, Claudia [0000-0002-5017-7344], Wareham, Nicholas [0000-0003-1422-2993], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Flavonols, Nutritional Status, Motor Activity, White People, Risk Factors, Surveys and Questionnaires, Humans, Nutritional Epidemiology, Proanthocyanidins, Prospective Studies, Life Style, Proportional Hazards Models, Flavonoids, Nutrition and Dietetics, Incidence, Middle Aged, Diet, Europe, Näringslära, Diabetes Mellitus, Type 2, Socioeconomic Factors, Multivariate Analysis, Female, Follow-Up Studies
Abstract

Dietary flavanols and flavonols, flavonoid subclasses, have been recently associated with a lower risk of type 2 diabetes (T2D) in Europe. Even within the same subclass, flavonoids may differ considerably in bioavailability and bioactivity. We aimed to examine the association between individual flavanol and flavonol intakes and risk of developing T2D across European countries. The European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study was conducted in 8 European countries across 26 study centers with 340,234 participants contributing 3.99 million person-years of follow-up, among whom 12,403 incident T2D cases were ascertained and a center-stratified subcohort of 16,154 individuals was defined. We estimated flavonoid intake at baseline from validated dietary questionnaires using a database developed from Phenol-Explorer and USDA databases. We used country-specific Prentice-weighted Cox regression models and random-effects meta-analysis methods to estimate HRs. Among the flavanol subclass, we observed significant inverse trends between intakes of all individual flavan-3-ol monomers and risk of T2D in multivariable models (all P-trend < 0.05). We also observed significant trends for the intakes of proanthocyanidin dimers (HR for the highest vs. the lowest quintile. 0.81; 95% Cl: 0.71, 0.92; P-trend = 0.003) and trimers (HR: 0.91; 95% Cl: 0.80, 1.04; P-trend = 0.07) but not for proanthocyanidins with a greater polymerization degree. Among the flavonol subclass, myricetin (HR: 0.77; 95% Cl: 0.64, 0.93; P-trend = 0.001) was associated with a lower incidence of T2D. This large and heterogeneous European study showed inverse associations between all individual flavan-3-ol monomers, proanthocyanidins with a low polymerization degree, and the flavonol myricetin and incident T2D. These results suggest that individual flavonoids have different roles in the etiology of T2D.

Published
2014

13. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases

Author

Danesh, J, Erqou, S, Walker, M, Thompson, Sg, Tipping, R, Ford, C, Pressel, S, Walldius, G, Jungner, I, Folsom, Ar, Chambless, Le, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Willeit, J, Kiechl, S, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, Da, Yarnell, Jw, Gallacher, J, Casiglia, Edoardo, Tikhonoff, Valerie, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, S, Vanuzzo, D, Pilotto, L, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Smith, Fb, Taylor, J, Guralnik, J, Phillips, C, Wallace, R, Blazer, D, Khaw, Kt, Jansson, Jh, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, Rs, Pencina, Mj, Bladbjerg, Em, Jorgensen, T, Moller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Bjorkelund, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, T, Rodriguez, B, Bouter, Lm, Heine, Rj, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, J, Sato, S, Iso, H, Kitamura, A, Noda, H, Goldbourt, U, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Deeg, D, Poppelaars, Jl, Meade, T, Cooper, J, Hedblad, B, Berglund, G, Engstrom, G, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Kuller, L, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, J, De Stavola, B, Knottenbelt, C, Cooper, Ja, Bauer, Ka, Rosenberg, Rd, Naito, Y, Holme, I, Nakagawa, H, Miura, H, Ducimetiere, P, Jouven, X, Crespo, C, Garcia Palmieri, M, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Packard, C, Sattar, N, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, D, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Ben Shlomo, Y, Howard, Bv, Zhang, Y, Best, L, Umans, J, Onat, A, Meade, Tw, Njolstad, I, Mathiesen, E, Lochen, Ml, Wilsgaard, T, Gaziano, Jm, Stampfer, M, Ridker, P, Ulmer, H, Diem, G, Concin, H, Rodeghiero, F, Tosetto, A, Brunner, E, Shipley, M, Buring, J, Cobbe, Sm, Ford, I, Robertson, M, He, Y, Ibanez, Am, Feskens, Ej, Kromhout, D, Collins, R, Di Angelantonio, E, Kaptoge, S, Lewington, S, Orfei, L, Pennells, L, Perry, P, Ray, K, Sarwar, N, Scherman, M, Thompson, A, Watson, S, Wensley, F, White, Ir, Wood, Am, Emerging Risk Factors Collaboration, Interne Geneeskunde, RS: NUTRIM School of Nutrition and Translational Research in Metabolism, RS: CARIM School for Cardiovascular Diseases, Movement Behavior, Executive board Vrije Universiteit, Clinical Child and Family Studies, Sociology and Social Gerontology, Earth and Climate, Environmental Policy Analysis, Developmental Genetics, Danesh, J, Erqou, S, Walker, M, Thompson, Sg, Tipping, R, Ford, C, Pressel, S, Walldius, G, Jungner, I, Folsom, Ar, Chambless, Le, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Willeit, J, Kiechl, S, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, Da, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Smith, Fb, Taylor, J, Guralnik, J, Phillips, C, Wallace, R, Blazer, D, Khaw, Kt, Jansson, Jh, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jorgensen, T, Moller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Bjorkelund, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, T, Rodriguez, B, Bouter, Lm, Heine, Rj, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, J, Sato, S, Iso, H, Kitamura, A, Noda, H, Goldbourt, U, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Deeg, D, Poppelaars, Jl, Meade, T, Cooper, J, Hedblad, B, Berglund, G, Engstrom, G, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Kuller, L, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, J, De Stavola, B, Knottenbelt, C, Cooper, Ja, Bauer, Ka, Rosenberg, Rd, Naito, Y, Holme, I, Nakagawa, H, Miura, H, Ducimetiere, P, Jouven, X, Crespo, C, Garcia Palmieri, M, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Packard, C, Sattar, N, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, D, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Ben Shlomo, Y, Howard, Bv, Zhang, Y, Best, L, Umans, J, Onat, A, Meade, Tw, Njolstad, I, Mathiesen, E, Lochen, Ml, Wilsgaard, T, Gaziano, Jm, Stampfer, M, Ridker, P, Ulmer, H, Diem, G, Concin, H, Rodeghiero, F, Tosetto, A, Brunner, E, Shipley, M, Buring, J, Cobbe, Sm, Ford, I, Robertson, M, He, Y, Ibanez, Am, Feskens, Ej, Kromhout, D, Collins, R, Di Angelantonio, E, Kaptoge, S, Lewington, S, Orfei, L, Pennells, L, Perry, P, Ray, K, Sarwar, N, Scherman, M, Thompson, A, Watson, S, Wensley, F, White, Ir, and Wood, A. M.

Subjects
Databases, Factual, Nutrition and Disease, Epidemiology, Inflammatory markers, middle-aged men, Disease, Leukocyte Count, c-reactive protein, Risk Factors, Voeding en Ziekte, adult-treatment-panel, Prospective Studies, Myocardial infarction, Prospective cohort study, education.field_of_study, biology, plasma-fibrinogen, Asia, Eastern, Confounding, density-lipoprotein cholesterol, Cardiovascular disease, Lipids, myocardial-infarction, Coronary heart disease, Cardiovascular Diseases, Meta-analysis, Lipoproteins, HDL, high blood cholesterol, medicine.medical_specialty, low-dose aspirin, Population, 10-year follow-up, SDG 3 - Good Health and Well-being, Albumins, medicine, Humans, education, Triglycerides, VLAG, Inflammation, Estimation, business.industry, C-reactive protein, medicine.disease, Surgery, Emergency medicine, biology.protein, business, coronary-heart-disease, Biomarkers
Abstract

Many long-term prospective studies have reported on associations of cardiovascular diseases with circulating lipid markers and/or inflammatory markers. Studies have not, however, generally been designed to provide reliable estimates under different circumstances and to correct for within-person variability. The Emerging Risk Factors Collaboration has established a central database on over 1.1 million participants from 104 prospective population-based studies, in which subsets have information on lipid and inflammatory markers, other characteristics, as well as major cardiovascular morbidity and cause-specific mortality. Information on repeat measurements on relevant characteristics has been collected in approximately 340,000 participants to enable estimation of and correction for within-person variability. Re-analysis of individual data will yield up to approximately 69,000 incident fatal or nonfatal first ever major cardiovascular outcomes recorded during about 11.7 million person years at risk. The primary analyses will involve age-specific regression models in people without known baseline cardiovascular disease in relation to fatal or nonfatal first ever coronary heart disease outcomes. This initiative will characterize more precisely and in greater detail than has previously been possible the shape and strength of the age-and sex-specific associations of several lipid and inflammatory markers with incident coronary heart disease outcomes (and, secondarily,with other incident cardiovascular outcomes) under a wide range of circumstances. It will, therefore, help to determine to what extent such associations are independent from possible confounding factors and to what extent such markers (separately and in combination) provide incremental predictive value.

Published
2007

14. The association between dietary flavonoid and lignan intakes and incident type 2 diabetes in European populations: the EPIC-InterAct study

Author

Zamora-Ros, R, Forouhi, NG, Sharp, SJ, González, CA, Buijsse, B, Guevara, M, van der Schouw, YT, Amiano, P, Boeing, H, Bredsdorff, L, Clavel-Chapelon, F, Fagherazzi, G, Feskens, EJ, Franks, PW, Grioni, S, Katzke, V, Key, TJ, Khaw, KT, Kühn, T, Masala, G, Mattiello, A, Molina-Montes, E, Nilsson, PM, Overvad, K, Perquier, F, Quirós, JR, Romieu, I, Sacerdote, C, Scalbert, A, Schulze, M, Slimani, N, Spijkerman, AM, Tjonneland, A, Tormo, MJ, Tumino, R, van der A, DL, Langenberg, C, Riboli, E, and Wareham, NJ

Subjects
Flavonoids, Male, Incidence, Nutritional Status, Feeding Behavior, Middle Aged, Lignans, Europe, Diabetes Mellitus, Type 2, Population Surveillance, Humans, Female, Prospective Studies, Epidemiology/Health Services Research, Follow-Up Studies, Original Research
Abstract

OBJECTIVE: To study the association between dietary flavonoid and lignan intakes, and the risk of development of type 2 diabetes among European populations. RESEARCH DESIGN AND METHODS: The European Prospective Investigation into Cancer and Nutrition-InterAct case-cohort study included 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 participants from among 340,234 participants with 3.99 million person-years of follow-up in eight European countries. At baseline, country-specific validated dietary questionnaires were used. A flavonoid and lignan food composition database was developed from the Phenol-Explorer, the U.K. Food Standards Agency, and the U.S. Department of Agriculture databases. Hazard ratios (HRs) from country-specific Prentice-weighted Cox regression models were pooled using random-effects meta-analysis. RESULTS: In multivariable models, a trend for an inverse association between total flavonoid intake and type 2 diabetes was observed (HR for the highest vs. the lowest quintile, 0.90 [95% CI 0.77-1.04]; P value trend = 0.040), but not with lignans (HR 0.88 [95% CI 0.72-1.07]; P value trend = 0.119). Among flavonoid subclasses, flavonols (HR 0.81 [95% CI 0.69-0.95]; P value trend = 0.020) and flavanols (HR 0.82 [95% CI 0.68-0.99]; P value trend = 0.012), including flavan-3-ol monomers (HR 0.73 [95% CI 0.57-0.93]; P value trend = 0.029), were associated with a significantly reduced hazard of diabetes. CONCLUSIONS: Prospective findings in this large European cohort demonstrate inverse associations between flavonoids, particularly flavanols and flavonols, and incident type 2 diabetes. This suggests a potential protective role of eating a diet rich in flavonoids, a dietary pattern based on plant-based foods, in the prevention of type 2 diabetes.

Published
2013

15. Systematically missing confounders in individual participant data meta-analysis of observational cohort studies

Author

Fibrinogen Studies Collaboration, Jackson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Thompson, SG, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, V, Vahtera, E, Jousilahti, P, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Levy, D, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, J, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Engström, G, Berglund, G, Loewel, H, Koenig, W, Hense, HW, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Despres, JP, Dagenais, GR, Tunstall-Pedoe, H, Lowe, GD, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Di Angelantonio, E, Kaptoge, S, Lewington, S, Sarwar, N, Walker, M, Watson, S, White, IR, Wood, AM, and Danesh, J

Abstract

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts

Published
2009

16. Systematically missing confounders in individual participant data meta-analysis of observational cohort studies

Author

Jackson, D, White, I, Kostis, JB, Wilson, AC, Folsom, AR, Wu, K, Chambless, L, Benderly, M, Goldbourt, U, Willeit, J, Kiechl, S, Yarnell, JW, Sweetnam, PM, Elwood, PC, Cushman, M, Psaty, BM, Tracy, RP, Tybjaerg-Hansen, A, Haverkate, F, de Maat, MP, Thompson, SG, Fowkes, FG, Lee, AJ, Smith, FB, Salomaa, V, Harald, K, Rasi, V, Vahtera, E, Jousilahti, P, D'Agostino, R, Kannel, WB, Wilson, PW, Tofler, G, Levy, D, Marchioli, R, Valagussa, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Cremer, P, Nagel, D, Curb, JD, Rodriguez, B, Yano, K, Salonen, J, Nyyssönen, K, Tuomainen, TP, Hedblad, B, Engström, G, Berglund, G, Loewel, H, Koenig, W, Hense, HW, Meade, TW, Cooper, JA, De Stavola, B, Knottenbelt, C, Miller, GJ, Bauer, KA, Rosenberg, RD, Sato, S, Kitamura, A, Naito, Y, Iso, H, Palosuo, T, Ducimetiere, P, Amouyel, P, Arveiler, D, Evans, AE, Ferrieres, J, Juhan-Vague, I, Bingham, A, Schulte, H, Assmann, G, Cantin, B, Lamarche, B, Despres, JP, Dagenais, GR, Tunstall-Pedoe, H, Lowe, GD, Woodward, M, Ben-Shlomo, Y, Davey Smith, G, Palmieri, V, Yeh, JL, Rudnicka, A, Brennan, P, Ridker, P, Rodeghiero, F, Tosetto, A, Shepherd, J, Ford, I, Robertson, M, Brunner, E, Shipley, M, Feskens, EJ, Di Angelantonio, E, Kaptoge, S, Lewington, S, Sarwar, N, Walker, M, Watson, S, White, IR, Wood, AM, and Danesh, J

Subjects
Statistics and Probability, Male, Nutrition and Disease, Epidemiology, Coronary Disease, Bivariate analysis, Logistic regression, 01 natural sciences, survival analysis, Cohort Studies, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Meta-Analysis as Topic, Voeding en Ziekte, Statistics, Medicine, Humans, Computer Simulation, 030212 general & internal medicine, 0101 mathematics, observational studies, patient data, Survival analysis, time, VLAG, Models, Statistical, aggregate data, model, business.industry, Incidence (epidemiology), Confounding, Fibrinogen, confounders, event outcomes, meta-analysis, Meta-analysis, Data Interpretation, Statistical, missing covariates, logistic-regression analysis, Observational study, Female, heterogeneity, business, Demography, Cohort study, Research Article
Abstract

One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154 012 participants in 31 cohorts.† Copyright © 2009 John Wiley & Sons, Ltd.

Published
2009

18. Iron metabolism is associated with adipocyte insulin resistance and plasma adiponectin: the Cohort on Diabetes and Atherosclerosis Maastricht (CODAM) study.

Author

Wlazlo N, van Greevenbroek MM, Ferreira I, Jansen EH, Feskens EJ, van der Kallen CJ, Schalkwijk CG, Bravenboer B, Stehouwer CD, Wlazlo, Nick, van Greevenbroek, Marleen M J, Ferreira, Isabel, Jansen, Eugene H J M, Feskens, Edith J M, van der Kallen, Carla J H, Schalkwijk, Casper G, Bravenboer, Bert, and Stehouwer, Coen D A

Abstract

Objective: Adipocyte insulin resistance (IR) is a key feature early in the pathogenesis of type 2 diabetes mellitus (T2DM), and although scarce, data in the literature suggest a direct role for iron and iron metabolism-related factors in adipose tissue function and metabolism. Serum ferritin and transferrin were shown to be associated with muscle insulin resistance (IR) and T2DM, but little is known about the role of iron metabolism on adipose tissue. We therefore investigated whether markers of iron metabolism were associated with adipocyte IR and plasma adiponectin.Research Design and Methods: Serum ferritin, transferrin, total iron, non-transferrin-bound iron (NTBI), transferrin saturation, and plasma adiponectin were determined in 492 individuals. Adipocyte IR was defined by the product of fasting insulin and nonesterified fatty acids (NEFAs). Using linear regression analyses, we investigated the difference in adipocyte IR or adiponectin (in %) according to differences in iron metabolism markers.Results: Serum ferritin (β = 1.00% increase in adipocyte IR per 10 μg/L [95% CI 0.66-1.34]), transferrin (4.18% per 0.1 g/L [2.88-5.50]), total iron (1.36% per μmol/L [0.61-2.12]), and NTBI (5.14% per μmol/L [1.88-8.52]) were associated with adipocyte IR after adjustment for several covariates, including inflammatory markers. All markers of iron metabolism were also associated with NEFAs (all P < 0.01). In addition, ferritin and transferrin were inversely associated with adiponectin (both P < 0.01).Conclusions: The observed associations of several markers of iron metabolism with adipocyte IR and adiponectin suggest that factors related to iron and iron metabolism may contribute to adipocyte IR early in the pathogenesis of T2DM. [ABSTRACT FROM AUTHOR]

Published
2013
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19. Association between High Fat-low Carbohydrate Diet Score and Newly Diagnosed Type 2 Diabetes in Chinese Population.

Author

HE, Yu Na, FESKENS, EJ, LI, Yan Ping, ZHANG, Jian, FU, Ping, MA, Guan Sheng, and YANG, Xiao Guang

Subjects
HIGH-fat diet, LOW-carbohydrate diet, PEOPLE with diabetes, CROSS-sectional method, HEALTH surveys, MULTIVARIATE analysis, HYPERGLYCEMIA, DIAGNOSIS
Abstract

Abstract: Objective: To study the association between high fat-low carbohydrate diet score and newly diagnosed type 2 diabetes in Chinese population. Methods: Data about 20 717 subjects aged 45–59 years from the cross-sectional 2002 China National Nutrition and Health Survey were analyzed. High fat-low carbohydrate diet was scored according to the energy of carbohydrate, fat, and protein. Results: Of the 20 717 subjects, 1 332 were diagnosed with hyperglycemia and 662 were newly diagnosed with type 2 diabetes. Multivariate adjusted analysis showed that the highest score of type 2 diabetes patients was 2.75 (95% CI: 2.09–3.61). The score of type 2 diabetes patients was 1.87 (95% CI: 1.35–2.58) after further adjustment for their socioeconomic status and physical activity. No significant difference was found in the odds ratio after further adjustment for BMI, blood pressure, lipid level, and energy intake. No evidence was observed for the relation between high fat-low carbohydrate-diet score in type 2 diabetes patients due to high family income, less education, physical activity, overweight, hypertension, high TG, or low HDL level. Conclusion: High fat-low carbohydrate diets, far different from traditional Chinese diets, are associated with the high incidence of type 2 diabetes in Chinese population. [Copyright &y& Elsevier]

Published
2012
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21. Meat consumption and its association with C-reactive protein and incident type 2 diabetes: the Rotterdam Study.

Author

van Woudenbergh GJ, Kuijsten A, Tigcheler B, Sijbrands EJ, van Rooij FJ, Hofman A, Witteman JC, Feskens EJ, van Woudenbergh, Geertruida J, Kuijsten, Anneleen, Tigcheler, Basia, Sijbrands, Eric J G, van Rooij, Frank J A, Hofman, Albert, Witteman, Jacqueline C M, and Feskens, Edith J M

Abstract

Objective: To investigate whether intake of different types of meat is associated with circulating C-reactive protein (CRP) and risk of type 2 diabetes in a prospective cohort study.Research Design and Methods: Our analysis included 4,366 Dutch participants who did not have diabetes at baseline. During a median follow-up period of 12.4 years, 456 diabetes cases were confirmed. Intake of red meat, processed meat, and poultry was derived from a food frequency questionnaire, and their association with serum high-sensitivity CRP was examined cross-sectionally using linear regression models. Their association with risk of type 2 diabetes was examined using multivariate Cox proportional hazards model, including age, sex, family history of diabetes, and lifestyle and dietary factors.Results: An increment of 50 g of processed meat was associated with increased CRP concentration (β(processed meat) = 0.12; P = 0.01), whereas intake of red meat and poultry was not. When comparing the highest to the lowest category of meat intake with respect to diabetes incidence, the adjusted relative risks were as follows: for red meat (1.42 [95% CI 1.06-1.91]), for processed meat (1.87 [1.26-2.78]), and for poultry (0.95 [0.74-1.22]). Additional analysis showed that the associations were not affected appreciably after inclusion of CRP into the model. After adjustment for BMI, however, the association for red meat attenuated to 1.18 (0.88-1.59).Conclusions: Intake of processed meat is associated with higher risk of type 2 diabetes. It appears unlikely that CRP mediates this association. [ABSTRACT FROM AUTHOR]

Published
2012
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24. Eating fish and risk of type 2 diabetes: A population-based, prospective follow-up study.

Author

van Woudenbergh GJ, van Ballegooijen AJ, Kuijsten A, Sijbrands EJ, van Rooij FJ, Geleijnse JM, Hofman A, Witteman JC, Feskens EJ, van Woudenbergh, Geertruida J, van Ballegooijen, Adriana J, Kuijsten, Anneleen, Sijbrands, Eric J G, van Rooij, Frank J A, Geleijnse, Johanna M, Hofman, Albert, Witteman, Jacqueline C M, and Feskens, Edith J M

Abstract

Objective: To investigate the relation between total fish, type of fish (lean and fatty), and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) intake and risk of type 2 diabetes in a population-based cohort.Research Design and Methods: The analysis included 4,472 Dutch participants aged >or=55 years without diabetes at baseline. Dietary intake was assessed with a semiquantitative food frequency questionnaire. Hazard ratios (relative risk [RR]) with 95% CIs were used to examine risk associations adjusted for age, sex, lifestyle, and nutritional factors.Results: After 15 years of follow-up, 463 participants developed type 2 diabetes. Median fish intake, mainly lean fish (81%), was 10 g/day. Total fish intake was associated positively with risk of type 2 diabetes; the RR was 1.32 (95% CI 1.02-1.70) in the highest total fish group (>or=28 g/day) compared with that for non-fish eaters (P(trend) = 0.04). Correspondingly, lean fish intake tended to be associated positively with type 2 diabetes (RR highest group [>or=23 g/day] 1.30 [95% CI 1.01-1.68]; P(trend) = 0.06), but fatty fish was not. No association was observed between EPA and DHA intake and type 2 diabetes (RR highest group [>or=149.4 mg/day] 1.22 [0.97-1.53]). With additional adjustment for intake of selenium, cholesterol, and vitamin D, this RR decreased to 1.05 (0.80-1.38; P(trend) = 0.77).Conclusions: The findings do not support a beneficial effect of total fish, type of fish, or EPA and DHA intake on the risk of type 2 diabetes. Alternatively, other dietary components, such as selenium, and unmeasured contaminants present in fish might explain our results. [ABSTRACT FROM AUTHOR]

Published
2009
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26. Fruit and vegetable intakes and subsequent changes in body weight in European populations: results from the project on Diet, Obesity, and Genes (DiOGenes)

Author

Buijsse B, Feskens EJ, Schulze MB, Forouhi NG, Wareham NJ, Sharp S, Palli D, Tognon G, Halkjaer J, Tjønneland A, Jakobsen MU, Overvad K, van der A DL, Du H, Sørensen TI, and Boeing H

Abstract

BACKGROUND: High fruit and vegetable intakes may limit weight gain, particularly in susceptible persons, such as those who stop smoking. OBJECTIVE: The objective was to assess the association of fruit and vegetable intake with subsequent weight change in a large-scale prospective study. DESIGN: The data used were from 89,432 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC). The association between fruit and vegetable intake and weight change after a mean follow-up of 6.5 y was assessed by linear regression. Polytomous logistic regression was used to evaluate whether fruit and vegetable intake relates to weight gain, weight loss, or both. RESULTS: Per 100-g intake of fruit and vegetables, weight change was -14 g/y (95% CI: -19, -9 g/y). In those who stopped smoking during follow-up, this value was -37 g/y (95% CI: -58, -15 g/y; P for interaction < 0.0001). When weight gain and loss were analyzed separately per 100-g intake of fruit and vegetables in a combined model, the odds ratios (95% CIs) were 0.97 (0.95, 0.98) for weight gain > or =0.5 and <1 kg/y, 0.94 (0.92, 0.96) for weight gain > or =1 kg/y, and 0.97 (0.95, 0.99) for weight loss > or =0.5 kg/y. In those who stopped smoking during follow-up, the odds ratios (95% CIs) were 0.93 (0.88, 0.99), 0.87 (0.81, 0.92), and 0.97 (0.88, 1.07), respectively (P for interaction < 0.0001). CONCLUSIONS: Fruit and vegetable intake relates significantly, albeit weakly inversely, to weight change. For persons who stop smoking, high fruit and vegetable intakes may be recommended to reduce the risk of weight gain. © 2009 American Society for Nutrition [ABSTRACT FROM AUTHOR]

Published
2009
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29. Subscapular skinfold thickness distinguishes between transient and persistent impaired glucose tolerance: Study on Lifestyle-Intervention and Impaired Glucose Tolerance Maastricht (SLIM)

Author

Mensink M, Feskens EJ, Kruijshoop M, de Bruin TW, Saris WH, and Blaak EE

Abstract

AIMS: To assess whether adding anthropometric measurements to an oral glucose tolerance test (OGTT) can help to distinguish between transient and persistent impaired glucose tolerance (IGT). METHODS: From the SLIM project (Study on Lifestyle-Intervention and IGT Maastricht), a study designed to evaluate whether diet and physical activity intervention can improve glucose tolerance in subjects at risk for diabetes, 108 subjects with IGT underwent a repeated OGTT 2-4 months after the initial OGTT. Following the second test, subjects were classified as transient IGT, or persistent IGT. Anthropometric measurements, including body mass index, waist and hip circumference, sagittal and transverse abdominal diameters and skinfold thickness measurements, were done during the second OGTT. RESULTS: Persistent IGT was diagnosed in 47 subjects (44%), transient IGT in 40 (37%), impaired fasting glucose in eight subjects (7%), and diabetes in 13 cases (12%). Two-hour blood glucose levels at the initial OGTT and subscapular skinfold thickness were significantly higher in subjects with persistent IGT (2-h blood glucose 9.8+/-0.1 mmol/l vs. 10.2+/-0.1 mmol/l for transient IGT and persistent IGT, respectively; subscapular skinfold thickness 25.4+/-1.4 mm vs. 29.8+/-1.2 mm for transient IGT and persistent IGT, respectively). After adjustment for age, sex and family history of diabetes mellitus, logistic regression indicated that 2-h blood glucose level during the initial OGTT represented the strongest predictor of persistent IGT (P<0.02), followed by subscapular skinfold thickness (P<0.05). After adjustment for 2-h blood glucose levels during the first OGTT, subscapular skinfold thickness remained significantly associated with persistent IGT (odds ratio 1.84; P<0.05). CONCLUSIONS: In addition to the 2-h blood glucose level, subscapular skinfold thickness was the best predictor of persistent IGT, suggesting that adding simple anthropometric measures to oral glucose tolerance testing may improve the distinction between persistent and transient glucose intolerance. [ABSTRACT FROM AUTHOR]

Published
2003
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30. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies

Author

Triglyceride Coronary Disease Genetics Consortium, Emerging Risk Factors Collaboration, Sarwar, N, Sandhu, Ms, Ricketts, Sl, Butterworth, As, Di Angelantonio, E, Boekholdt, Sm, Ouwehand, W, Watkins, H, Samani, Nj, Saleheen, D, Lawlor, D, Reilly, Mp, Hingorani, Ad, Talmud, Pj, Collaborators: Braund PS, Danesh J., Hall, As, Thompson, J, März, W, Sivapalaratnam, S, Soranzo, N, Trip, M, Lawlor, Da, Casas, Jp, Ebrahim, S, Arsenault, Bj, Khaw, Kt, Wareham, Nj, Grallert, H, Illig, T, Humphries, Se, Talmud, T, Rader, Dj, He, J, Clarke, R, Hamsten, A, Hopewell, Jc, Frossard, P, Deloukas, P, Danesh, J, Ye, S, Simpson, Ia, Onat, A, Kömürcü Bayrak, E, Martinelli, Nicola, Olivieri, Oliviero, Girelli, Domenico, Kivimäki, M, Kumari, M, Aouizerat, Be, Baum, L, Campos, H, Chaaba, R, Chen, Bs, Cho, Ey, Evans, D, Hill, J, Hsu, La, Hubacek, Ja, Lai, Cq, Lee, Jh, Klos, K, Liu, H, Masana, L, Melegh, B, Nabika, T, Ribalta, J, Ruiz Narvaez, E, Thomas, Gn, Tomlinson, B, Szalai, C, Vaverkova, H, Yamada, Y, Yang, Y, Tipping, Rw, Ford, Ce, Pressel, Sl, Ballantyne, C, Brautbar, A, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, S, Vanuzzo, D, Pilotto, L, de la Cámara AG, Gómez Gerique JA, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Blazer, Dg, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, D'Agostino, Rb, Vasan, Rs, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Voutilainen, S, Meade, Tw, Cooper, Ja, Kuller, Lh, Grandits, G, Gillum, R, Mussolino, M, Rimm, E, Hankinson, S, Manson, Ja, Pai, Jk, Bauer, Ka, Naito, Y, Amouyel, P, Arveiler, D, Evans, A, Ferrières, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Woodward, M, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Michaëlsson, K, Brunner, E, Shipley, M, Ridker, P, Buring, J, Shepherd, J, Cobbe, Sm, Ford, I, Robertson, M, Ibañez, Am, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Collins, R, Kaptoge, S, Perry, Pl, Thompson, A, Thompson, Sg, White, Ir, Wood, Am, Danesh, J., ACS - Amsterdam Cardiovascular Sciences, Cardiology, Vascular Medicine, Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: CARIM School for Cardiovascular Diseases, Sarwar, N, Sandhu, M, Ricketts, Sl, Butterworth, A, Braund, P, Hall, A, Samani, Nj, Thompson, J, März, W, Ouwehand, W, Sivapalaratnam, S, Soranzo, N, Trip, M, Lawlor, Da, Casas, Jp, Ebrahim, S, Arsenault, Bj, Boekholdt, Sm, Khaw, Kt, Wareham, Nj, Grallert, H, Illig, T, Humphries, Se, Talmud, T, Rader, Dj, He, J, Reilly, Mp, Clarke, R, Hamsten, A, Hopewell, Jc, Watkins, H, Saleheen, D, Frossard, P, Deloukas, P, Danesh, J, Ye, S, Simpson, Ia, Onat, A, Kömürcü Bayrak, E, Martinelli, N, Olivieri, O, Girelli, D, Hingorani, Ad, Kivimäki, M, Kumari, M, Aouizerat, Be, Baum, L, Campos, H, Chaaba, R, Chen, B, Cho, Ey, Evans, D, Hill, J, Hsu, La, Hubacek, Ja, Lai, Cq, Lee, Jh, Klos, K, Liu, H, Masana, L, Melegh, B, Nabika, T, Ribalta, J, Ruiz Narvaez, E, Thomas, Gn, Tomlinson, B, Szalai, C, Vaverkova, H, Yamada, Y, Yang, Y, Kastelein, Jj, Tipping, Rw, Ford, Ce, Pressel, Sl, Ballantyne, C, Brautbar, A, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, de la Cámara, Ag, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Blazer, Dg, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, D'Agostino, Rb, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Voutilainen, S, Meade, Tw, Cooper, Ja, Kuller, Lh, Grandits, G, Gillum, R, Mussolino, M, Rimm, E, Hankinson, S, Manson, Ja, Pai, Jk, Bauer, Ka, Naito, Y, Amouyel, P, Arveiler, D, Evans, A, Ferrières, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Woodward, M, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Michaëlsson, K, Brunner, E, Shipley, M, Ridker, P, Buring, J, Shepherd, J, Cobbe, Sm, Ford, I, Robertson, M, Ibañez, Am, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Collins, R, Di Angelantonio, E, Kaptoge, S, Perry, Pl, Thompson, A, Thompson, Sg, White, Ir, Wood, Am, Lawlor, D, Talmud, Pj, Danesh, J., Epidemiology and Data Science, General practice, and EMGO - Lifestyle, overweight and diabetes

Subjects
Very low-density lipoprotein, Nutrition and Disease, Heart disease, Coronary Disease, Lipoproteins, VLDL, 030204 cardiovascular system & hematology, low-density-lipoprotein apolipoprotein-a-v transfer protein heart-disease myocardial-infarction metabolic syndrome rich lipoproteins risk dyslipidemia association, Bioinformatics, chemistry.chemical_compound, 0302 clinical medicine, triglyceride, APOA5 gene polymorphysm, coronary heart disease, Gene Frequency, Risk Factors, Voeding en Ziekte, Medicine, Myocardial infarction, Promoter Regions, Genetic, risk, 0303 health sciences, Men, Mendelian Randomization Analysis, Articles, General Medicine, Lipids, myocardial-infarction, 3. Good health, Lipoproteins, LDL, Low-density lipoprotein, Lipoproteins, HDL, apolipoprotein-a-v, Receptor, medicine.medical_specialty, Genotype, transfer protein, Snp, Polymorphism, Single Nucleotide, metabolic syndrome, 03 medical and health sciences, Internal medicine, Humans, Particle Size, Apolipoproteins A, Triglycerides, VLAG, 030304 developmental biology, low-density-lipoprotein, Triglyceride, business.industry, dyslipidemia, association, rich lipoproteins, medicine.disease, heart-disease, Apolipoproteins, Endocrinology, chemistry, Apolipoprotein A-V, Metabolic syndrome, business, Dyslipidemia
Abstract

Udgivelsesdato: May-8 BACKGROUND: Whether triglyceride-mediated pathways are causally relevant to coronary heart disease is uncertain. We studied a genetic variant that regulates triglyceride concentration to help judge likelihood of causality. METHODS: We assessed the -1131T>C (rs662799) promoter polymorphism of the apolipoprotein A5 (APOA5) gene in relation to triglyceride concentration, several other risk factors, and risk of coronary heart disease. We compared disease risk for genetically-raised triglyceride concentration (20,842 patients with coronary heart disease, 35,206 controls) with that recorded for equivalent differences in circulating triglyceride concentration in prospective studies (302 430 participants with no history of cardiovascular disease; 12,785 incident cases of coronary heart disease during 2.79 million person-years at risk). We analysed -1131T>C in 1795 people without a history of cardiovascular disease who had information about lipoprotein concentration and diameter obtained by nuclear magnetic resonance spectroscopy. FINDINGS: The minor allele frequency of -1131T>C was 8% (95% CI 7-9). -1131T>C was not significantly associated with several non-lipid risk factors or LDL cholesterol, and it was modestly associated with lower HDL cholesterol (mean difference per C allele 3.5% [95% CI 2.6-4.6]; 0.053 mmol/L [0.039-0.068]), lower apolipoprotein AI (1.3% [0.3-2.3]; 0.023 g/L [0.005-0.041]), and higher apolipoprotein B (3.2% [1.3-5.1]; 0.027 g/L [0.011-0.043]). By contrast, for every C allele inherited, mean triglyceride concentration was 16.0% (95% CI 12.9-18.7), or 0.25 mmol/L (0.20-0.29), higher (p=4.4x10(-24)). The odds ratio for coronary heart disease was 1.18 (95% CI 1.11-1.26; p=2.6x10(-7)) per C allele, which was concordant with the hazard ratio of 1.10 (95% CI 1.08-1.12) per 16% higher triglyceride concentration recorded in prospective studies. -1131T>C was significantly associated with higher VLDL particle concentration (mean difference per C allele 12.2 nmol/L [95% CI 7.7-16.7]; p=9.3x10(-8)) and smaller HDL particle size (0.14 nm [0.08-0.20]; p=7.0x10(-5)), factors that could mediate the effects of triglyceride. INTERPRETATION: These data are consistent with a causal association between triglyceride-mediated pathways and coronary heart disease. FUNDING: British Heart Foundation, UK Medical Research Council, Novartis.

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34. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies

Author

Emerging Risk Factors Collaboration, Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, Stehouwer CD, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray KK, Danesh J. Tipping RW, Ford CE, Pressel SL, Folsom AR, Chambless LE, Wagenknecht LE, Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman M, Whincup PH, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Oberhollenzer F, Mayr A, Wald N, Ebrahim S, Yarnell JW, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, de Boer IH, Kizer JR, Mukamal KJ, Tybjaerg Hansen A, Nordestgaard BG, Benn M, Frikke Schmidt R, Palmieri L, Vanuzzo D, Pilotto L, de la Cámara AG, Rubio MA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee AJ, Taylor J, Guralnik JM, Phillips CL, Wallace R, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Giampaoli S, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Vasan RS, Fox CS, Pencina MJ, Bladbjerg E, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Wilhelmsen L, Eriksson H, Svärdsudd K, Welin L, Rosengren A, Lappas G, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Strandberg TE, Tilvis RS, Miettinen TA, Kiyohara Y, Arima H, Doi Y, Ninomiya T, Rodriguez B, Dekker JM, Nijpels G, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Goldbourt U, Nyyssönen K, Tuomainen TP, Salonen JT, Deeg D, Poppelaars JL, Meade TW, Cooper JA, Hedblad B, Berglund G, Engström G, Verschuren WM, Blokstra A, Cushman M, Psaty BM, Shea S, Döring A, Koenig W, Meisinger C, Mraz W, Bueno de Mesquita HB, Fletcher A, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum R, Mussolino M, Hankinson S, Manson JE, Bauer KA, Davidson KW, Kirkland S, Shaffer J, Korin MR, Holme I, Ducimetiere P, Jouven X, Bakker SJ, Gansevoort RT, Hillege HL, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrières J, Schulte H, Assmann G, Westendorp RG, Buckley BM, Packard CJ, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt R, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler M, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe GD, Howard BV, Zhang Y, Best L, Umans J, Ben Shlomo Y, Davey Smith G, Onat A, Hergenç G, Can G, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Zethelius B, Risérus U, Berne C, Gaziano JM, Ridker P, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Tinker L, Liu S, Marmot IM, Clarke R, Collins R, Brunner E, Shipley M, Buring J, Shepherd J, Cobbe SM, Ford I, Robertson M, Ibañez AM, Feskens EJ, Kromhout D, Walker M, Watson S, Alexander M, Erqou S, Haycock P, Perry PL, Thompson SG, Wood AM, Wormser D, Danesh J., PANICO, SALVATORE, Interne Geneeskunde, RS: NUTRIM - R1 - Metabolic Syndrome, RS: CARIM School for Cardiovascular Diseases, Emerging Risk Factors, Collaboration, Sarwar, N, Gao, P, Seshasai, Sr, Gobin, R, Kaptoge, S, Di Angelantonio, E, Ingelsson, E, Lawlor, Da, Selvin, E, Stampfer, M, Stehouwer, Cd, Lewington, S, Pennells, L, Thompson, A, Sattar, N, White, Ir, Ray, Kk, Danesh J., Tipping RW, Ford, Ce, Pressel, Sl, Folsom, Ar, Chambless, Le, Wagenknecht, Le, Panagiotakos, Db, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Oberhollenzer, F, Mayr, A, Wald, N, Ebrahim, S, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, de Boer, Ih, Kizer, Jr, Mukamal, Kj, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, de la Cámara, Ag, Rubio, Ma, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Giampaoli, S, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Vasan, R, Fox, C, Pencina, Mj, Bladbjerg, E, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Wilhelmsen, L, Eriksson, H, Svärdsudd, K, Welin, L, Rosengren, A, Lappas, G, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Strandberg, Te, Tilvis, R, Miettinen, Ta, Kiyohara, Y, Arima, H, Doi, Y, Ninomiya, T, Rodriguez, B, Dekker, Jm, Nijpels, G, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Goldbourt, U, Nyyssönen, K, Tuomainen, Tp, Salonen, Jt, Deeg, D, Poppelaars, Jl, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engström, G, Verschuren, Wm, Blokstra, A, Cushman, M, Psaty, Bm, Shea, S, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Bueno de Mesquita, Hb, Fletcher, A, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, Je, Bauer, Ka, Davidson, Kw, Kirkland, S, Shaffer, J, Korin, Mr, Holme, I, Ducimetiere, P, Jouven, X, Bakker, Sj, Gansevoort, Rt, Hillege, Hl, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrières, J, Schulte, H, Assmann, G, Westendorp, Rg, Buckley, Bm, Packard, Cj, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Onat, A, Hergenç, G, Can, G, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Zethelius, B, Risérus, U, Berne, C, Gaziano, Jm, Ridker, P, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Tinker, L, Liu, S, Marmot, Im, Clarke, R, Collins, R, Brunner, E, Shipley, M, Buring, J, Shepherd, J, Cobbe, Sm, Ford, I, Robertson, M, Ibañez, Am, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Alexander, M, Erqou, S, Haycock, P, Perry, Pl, Thompson, Sg, Wood, Am, Wormser, D, Danesh, J., and University of Groningen

Subjects
Adult, Blood Glucose, Male, medicine.medical_specialty, PATHOPHYSIOLOGY, 030209 endocrinology & metabolism, Coronary Disease, Disease, 030204 cardiovascular system & hematology, THERAPY, Diabetes Complications, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Diabetes mellitus, Internal medicine, General & Internal Medicine, medicine, Diabetes Mellitus, Humans, CORONARY-HEART-DISEASE, Prospective cohort study, Stroke, Aged, Glucose Metabolism Disorders, business.industry, Vascular disease, MORTALITY, Absolute risk reduction, ASIA-PACIFIC REGION, WOMEN, General Medicine, Fasting, 11 Medical And Health Sciences, Articles, Middle Aged, medicine.disease, Impaired fasting glucose, 3. Good health, Endocrinology, Blood pressure, ATHEROSCLEROSIS, Cardiovascular Diseases, CARDIOVASCULAR-DISEASES, Female, coronary-heart-disease asia-pacific region cardiovascular-diseases task-force pathophysiology atherosclerosis mortality therapy women, business, TASK-FORCE
Abstract

Summary Background Uncertainties persist about the magnitude of associations of diabetes mellitus and fasting glucose concentration with risk of coronary heart disease and major stroke subtypes. We aimed to quantify these associations for a wide range of circumstances. Methods We undertook a meta-analysis of individual records of diabetes, fasting blood glucose concentration, and other risk factors in people without initial vascular disease from studies in the Emerging Risk Factors Collaboration. We combined within-study regressions that were adjusted for age, sex, smoking, systolic blood pressure, and body-mass index to calculate hazard ratios (HRs) for vascular disease. Findings Analyses included data for 698 782 people (52 765 non-fatal or fatal vascular outcomes; 8·49 million person-years at risk) from 102 prospective studies. Adjusted HRs with diabetes were: 2·00 (95% CI 1·83–2·19) for coronary heart disease; 2·27 (1·95–2·65) for ischaemic stroke; 1·56 (1·19–2·05) for haemorrhagic stroke; 1·84 (1·59–2·13) for unclassified stroke; and 1·73 (1·51–1·98) for the aggregate of other vascular deaths. HRs did not change appreciably after further adjustment for lipid, inflammatory, or renal markers. HRs for coronary heart disease were higher in women than in men, at 40–59 years than at 70 years and older, and with fatal than with non-fatal disease. At an adult population-wide prevalence of 10%, diabetes was estimated to account for 11% (10–12%) of vascular deaths. Fasting blood glucose concentration was non-linearly related to vascular risk, with no significant associations between 3·90 mmol/L and 5·59 mmol/L. Compared with fasting blood glucose concentrations of 3·90–5·59 mmol/L, HRs for coronary heart disease were: 1·07 (0·97–1·18) for lower than 3·90 mmol/L; 1·11 (1·04–1·18) for 5·60–6·09 mmol/L; and 1·17 (1·08–1·26) for 6·10–6·99 mmol/L. In people without a history of diabetes, information about fasting blood glucose concentration or impaired fasting glucose status did not significantly improve metrics of vascular disease prediction when added to information about several conventional risk factors. Interpretation Diabetes confers about a two-fold excess risk for a wide range of vascular diseases, independently from other conventional risk factors. In people without diabetes, fasting blood glucose concentration is modestly and non-linearly associated with risk of vascular disease. Funding British Heart Foundation, UK Medical Research Council, and Pfizer.

35. The contribution of dairy products to micronutrient intake in the Netherlands.

Author

Vissers PA, Streppel MT, Feskens EJ, de Groot LC, Vissers, Pauline A J, Streppel, Martinette T, Feskens, Edith J M, and de Groot, Lisette C P G M

Abstract

Objective: To assess the contribution of dairy products to the intake of various vitamins and minerals in several life stages in the Dutch population.Method: Data from 3 Dutch Food Consumption Surveys and the Leiden Longevity Study were used to estimate the contribution of dairy products--as percentage of total intake--to the intake of iron, copper, selenium, zinc, calcium, folic acid, vitamin D, vitamin C, and vitamin B(12).Results: In young children, dairy products contributed substantially to the intake of calcium (73%), selenium (21%), iron (8%), zinc (39%), copper (12%), folic acid (24%), vitamin C (18%), vitamin D (16%), and vitamin B(12) (58%). Of all dairy products, milk contributed the most to the intake of these nutrients. In adults and elderly subjects, the contribution of dairy products to total micronutrient intake was 65%-68% for calcium, 18%-19% for selenium, 3%-4% for iron, 28%-31% for zinc, 6%-7% for copper, 17%-19% for folic acid, 10%-14% for vitamin C, 11%-16% for vitamin D, and 44%-46% for vitamin B(12). Milk as well as cheese contributed the most to the intake of these nutrients.Conclusions: Dairy products are an important source of vitamins and minerals in the Dutch population. Dairy products, especially milk and cheese, contribute substantially to the intake of calcium, selenium, zinc, and vitamin B(12). [ABSTRACT FROM AUTHOR]

Published
2011

36. Consumption of plant foods and stomach cancer mortality in the seven countries study. is grain...

Author

Jansen MC, Bueno-De-Mesquita HB, Rasanen L, Fidanza F, Menotti A, Nissinen A, Feskens EJ, Kok FJ, and Kromhout D

Abstract

Plant foods are generally considered to be beneficial for health. A higher consumption of fruits, and to a lesser extent vegetables, is consistently associated with a lower risk of stomach cancer. Results on the association between stomach cancer and grain consumption are less clear. We associated plant food consumption with 25-year stomach cancer mortality at population level in the Seven Countries Study. Around 1960, > 12,000 men aged 40-59 years from 7 countries and 16 cohorts were enrolled. In each cohort, dietary information was collected in small random samples. Crude and adjusted associations were calculated for a change of 10% of mean intake. Results differed for the plant foods studied: an inverse association was observed for fruits (adjusted rate ratio = 0.96, 95% confidence interval = 0.91-0.99), a positive relation for refined grains (adjusted rate ratio = 1.07, 95% confidence interval = 1.03-1.12), and no association for total plant foods, vegetables, whole grains, and potatoes. A high intake of refined grains was correlated with a low consumption of fruits. In conclusion, high intake of refined grains may increase stomach cancer risk. However, because adjustment could only be limited in this study, high intake of refined grains may just reflect the deleterious effect of a diet low in fruits or other characteristics associated with low fruit consumption. [ABSTRACT FROM AUTHOR]

Published
1999
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37. Dietary protein intake and incidence of type 2 diabetes in Europe: the EPIC-InterAct Case-Cohort Study

Author

Matthias B. Schulze, Peter M. Nilsson, Pilar Amiano, Marco Mensink, Kay-Tee Khaw, José María Huerta, Nita G. Forouhi, Françoise Clavel-Chapelon, Rosario Tumino, Joline W.J. Beulens, Kim Overvad, María José Sánchez, Olov Rolandsson, Daphne L. van der A, Elio Riboli, Ivonne Sluijs, Vittorio Krogh, Tilman Kühn, Isabelle Romieu, Carlotta Sacerdote, Claudia Langenberg, Anne Tjønneland, Edith J. M. Feskens, Monique van Nielen, Jytte Halkjær, Anne Mette Lund Würtz, B. Balkau, Virginia Menéndez, Nicholas J. Wareham, Esther Molina, Stephen J. Sharp, Salvatore Panico, Annemieke M.W. Spijkerman, Guy Fagherazzi, Domenico Palli, Raul Zamora-Ros, Verena Katzke, Timothy J. Key, Paul W. Franks, Eva Ardanaz, Heiner Boeing, van Nielen, M, Feskens, Ej, Mensink, M, Sluijs, I, Molina, E, Amiano, P, Ardanaz, E, Balkau, B, Beulens, Jw, Boeing, H, Clavel Chapelon, F, Fagherazzi, G, Franks, Pw, Halkjaer, J, Huerta, Jm, Katzke, V, Key, Tj, Khaw, Kt, Krogh, V, K?hn, T, Men?ndez, Vv, Nilsson, P, Overvad, K, Palli, D, Panico, Salvatore, Rolandsson, O, Romieu, I, Sacerdote, C, S?nchez, Mj, Schulze, Mb, Spijkerman, Am, Tjonneland, A, Tumino, R, van der A., Dl, W?rtz, Am, Zamora Ros, R, Langenberg, C, Sharp, Sj, Forouhi, Ng, Riboli, E, Wareham, Nj, Interact, Consortium, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, and APH - Health Behaviors & Chronic Diseases

Subjects
medicine.medical_specialty, Nutrition and Disease, dairy consumption, Endocrinology, Diabetes and Metabolism, Physiology, Type 2 diabetes, metabolic syndrome, insulin-resistance, Insulin resistance, Diabetes mellitus, Internal medicine, Voeding en Ziekte, Internal Medicine, medicine, cancer, Prospective cohort study, humans, VLAG, risk, Advanced and Specialized Nursing, Global Nutrition, Wereldvoeding, business.industry, Incidence (epidemiology), association, medicine.disease, womens health, fish intake, Endocrinology, nutrition, Plant protein, Metabolic syndrome, business, Cohort study
Abstract

OBJECTIVE The long-term association between dietary protein and type 2 diabetes incidence is uncertain. We aimed to investigate the association between total, animal, and plant protein intake and the incidence of type 2 diabetes. RESEARCH DESIGN AND METHODS The prospective European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study consists of 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 individuals from eight European countries, with an average follow-up time of 12.0 years. Pooled country-specific hazard ratios (HRs) and 95% CI of prentice-weighted Cox regression analyses were used to estimate type 2 diabetes incidence according to protein intake. RESULTS After adjustment for important diabetes risk factors and dietary factors, the incidence of type 2 diabetes was higher in those with high intake of total protein (per 10 g: HR 1.06 [95% CI 1.02–1.09], Ptrend < 0.001) and animal protein (per 10 g: 1.05 [1.02–1.08], Ptrend = 0.001). Effect modification by sex (P < 0.001) and BMI among women (P < 0.001) was observed. Compared with the overall analyses, associations were stronger in women, more specifically obese women with a BMI >30 kg/m2 (per 10 g animal protein: 1.19 [1.09–1.32]), and nonsignificant in men. Plant protein intake was not associated with type 2 diabetes (per 10 g: 1.04 [0.93–1.16], Ptrend = 0.098). CONCLUSIONS High total and animal protein intake was associated with a modest elevated risk of type 2 diabetes in a large cohort of European adults. In view of the rapidly increasing prevalence of type 2 diabetes, limiting iso-energetic diets high in dietary proteins, particularly from animal sources, should be considered.

Published
2014

38. Fruit and vegetable intake and type 2 diabetes: EPIC-InterAct prospective study and meta-analysis

Author

Birgit Teucher, B. Balkau, Nita G. Forouhi, Kim Overvad, Giovanna Masala, Annemieke M.W. Spijkerman, Andrew J. M. Cooper, Elio Riboli, M-J Sanchez, Guy Fagherazzi, Timothy J. Key, C. Navarro, Anne Tjønneland, Larraitz Arriola, C A González, Olov Rolandsson, Edith J. M. Feskens, Brian Buijsse, Rudolf Kaaks, Zheng Ye, Joline W.J. Beulens, Stephen J. Sharp, Salvatore Panico, Peter M. Nilsson, Sara Grioni, Ivonne Sluijs, Rosario Tumino, Claudia Langenberg, N. Roswall, J. Ramón Quirós, B. de Lauzon-Guillain, Nadia Slimani, Paul W. Franks, Aurelio Barricarte, Heiner Boeing, Christina C. Dahm, Nicholas J. Wareham, Frederike L. Büchner, Carlotta Sacerdote, Cooper, Aj, Forouhi, Ng, Ye, Z, Buijsse, B, Arriola, L, Balkau, B, Barricarte, A, Beulens, Jw, Boeing, H, B?chner, Fl, Dahm, Cc, de Lauzon Guillain, B, Fagherazzi, G, Franks, Pw, Gonzalez, C, Grioni, S, Kaaks, R, Key, Tj, Masala, G, Navarro, C, Nilsson, P, Overvad, K, Panico, Salvatore, Ram?n Quir?s, J, Rolandsson, O, Roswall, N, Sacerdote, C, S?nchez, Mj, Slimani, N, Sluijs, I, Spijkerman, Am, Teucher, B, Tjonneland, A, Tumino, R, Sharp, Sj, Langenberg, C, Feskens, Ej, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Risk, life-style, Nutrition and Disease, food frequency questionnaire, dietary patterns, Medicine (miscellaneous), Type 2 diabetes, Plant Roots, Article, Voeding en Ziekte, Diabetes mellitus, Environmental health, Vegetables, Prevalence, Humans, cancer, Medicine, consumption, Food science, Prospective cohort study, VLAG, Global Nutrition, Wereldvoeding, Evidence-Based Medicine, Nutrition and Dietetics, business.industry, Incidence, Hazard ratio, medicine.disease, 10 european countries, Confidence interval, Diet, chronic disease risk, Europe, Plant Leaves, beta-carotene, magnesium intake, Diabetes Mellitus, Type 2, Quartile, Fruit, Meta-analysis, Relative risk, randomized controlled-trial, business
Abstract

Fruit and vegetable intake (FVI) may reduce the risk of type 2 diabetes (T2D), but the epidemiological evidence is inconclusive. The aim of this study is to examine the prospective association of FVI with T2D and conduct an updated meta-analysis. In the European Prospective Investigation into Cancer-InterAct (EPIC-InterAct) prospective case-cohort study nested within eight European countries, a representative sample of 16 154 participants and 12 403 incident cases of T2D were identified from 340 234 individuals with 3.99 million person-years of follow-up. For the meta-analysis we identified prospective studies on FVI and T2D risk by systematic searches of MEDLINE and EMBASE until April 2011. In EPIC-InterAct, estimated FVI by dietary questionnaires varied more than twofold between countries. In adjusted analyses the hazard ratio (95% confidence interval) comparing the highest with lowest quartile of reported intake was 0.90 (0.80-1.01) for FVI; 0.89 (0.76-1.04) for fruit and 0.94 (0.84-1.05) for vegetables. Among FV subtypes, only root vegetables were inversely associated with diabetes 0.87 (0.77-0.99). In meta-analysis using pooled data from five studies including EPIC-InterAct, comparing the highest with lowest category for FVI was associated with a lower relative risk of diabetes (0.93 (0.87-1.00)). Fruit or vegetables separately were not associated with diabetes. Among FV subtypes, only green leafy vegetable (GLV) intake (relative risk: 0.84 (0.74-0.94)) was inversely associated with diabetes. Subtypes of vegetables, such as root vegetables or GLVs may be beneficial for the prevention of diabetes, while total FVI may exert a weaker overall effect.European Journal of Clinical Nutrition advance online publication, 1 August 2012; doi:10.1038/ejcn.2012.85.

Published
2012

39. Consumption of fatty foods and incident type 2 diabetes in populations from eight European countries

Author

Nita G. Forouhi, Kim Overvad, Stephen J. Sharp, Salvatore Panico, Giovanna Masala, M-J Sanchez, Peter M. Nilsson, Annemieke M.W. Spijkerman, Pilar Amiano, Kay-Tee Khaw, Dagmar Drogan, B. de Lauzon-Guillain, Paul W. Franks, Guy Fagherazzi, José María Huerta, Matthias B. Schulze, Valeria Pala, Olov Rolandsson, Elio Riboli, Edith J. M. Feskens, Heiner Boeing, Brian Buijsse, Ivonne Sluijs, Timothy J. Key, Anne Tjønneland, Aurelie Moskal, Claudia Langenberg, Y. T. van der Schouw, Françoise Clavel-Chapelon, Ana Fonseca-Nunes, Marianne Uhre Jakobsen, Fulvio Ricceri, Aurelio Barricarte, María-Luisa Redondo, Rudolf Kaaks, R. Tumino, Nicholas J. Wareham, Buijsse, B, Boeing, H, Drogan, D, Schulze, Mb, Feskens, Ej, Amiano, P, Barricarte, A, Clavel Chapelon, F, de Lauzon Guillain, B, Fagherazzi, G, Fonseca Nunes, A, Franks, Pw, Huerta, Jm, Jakobsen, Mu, Kaaks, R, Key, Tj, Khaw, Kt, Masala, G, Moskal, A, Nilsson, Pm, Overvad, K, Pala, V, Panico, Salvatore, Redondo, Ml, Ricceri, F, Rolandsson, O, Sánchez, Mj, Sluijs, I, Spijkerman, Am, Tjonneland, A, Tumino, R, van der A., Dl, van der Schouw, Yt, Langenberg, C, Sharp, Sj, Forouhi, Ng, Riboli, E, Wareham, Nj, and Interact, C. o. n. s. o. r. t. i. u. m.

Subjects
Male, saturated fat, Nutrition and Disease, Saturated fat, nut consumption, Medicine (miscellaneous), Type 2 diabetes, MEAT CONSUMPTION, Body Mass Index, chemistry.chemical_compound, SATURATED FAT, Risk Factors, Voeding en Ziekte, Surveys and Questionnaires, Nuts, Food science, Prospective Studies, Non-U.S. Gov't, physical-activity, METABOLIC SYNDROME, Nutrition and Dietetics, INSULIN SENSITIVITY, Research Support, Non-U.S. Gov't, Incidence, POSTPRANDIAL GLYCEMIA, Europe, epic-interact, Female, Adult, Trans fat, Research Support, DIETARY-FAT, metabolic syndrome, Diabetes mellitus, medicine, Journal Article, insulin sensitivity, Humans, Plant Oils, CORONARY-HEART-DISEASE, Life Style, meat consumption, VLAG, Proportional Hazards Models, dietary-fat, business.industry, Unsaturated fat, Case-control study, medicine.disease, postprandial glycemia, Dietary Fats, Margarine, Diet, PHYSICAL-ACTIVITY, Nutrition Assessment, chemistry, Diabetes Mellitus, Type 2, Case-Control Studies, Mental Recall, Butter, NUT CONSUMPTION, EPIC-INTERACT, Metabolic syndrome, business, coronary-heart-disease, Energy Intake, Energy Metabolism, Body mass index, Follow-Up Studies
Abstract

Background/Objectives:Diets high in saturated and trans fat and low in unsaturated fat may increase type 2 diabetes (T2D) risk, but studies on foods high in fat per unit weight are sparse. We assessed whether the intake of vegetable oil, butter, margarine, nuts and seeds and cakes and cookies is related to incident T2D.Subjects/Methods:A case-cohort study was conducted, nested within eight countries of the European Prospective Investigation into Cancer (EPIC), with 12 403 incident T2D cases and a subcohort of 16 835 people, identified from a cohort of 340 234 people. Diet was assessed at baseline (1991-1999) by country-specific questionnaires. Country-specific hazard ratios (HRs) across four categories of fatty foods (nonconsumers and tertiles among consumers) were combined with random-effects meta-analysis.Results:After adjustment not including body mass index (BMI), nonconsumers of butter, nuts and seeds and cakes and cookies were at higher T2D risk compared with the middle tertile of consumption. Among consumers, cakes and cookies were inversely related to T2D (HRs across increasing tertiles 1.14, 1.00 and 0.92, respectively; P-trend

Published
2014

40. Different in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study

Author

Matthias B. Schulze, Nita G. Forouhi, Carlos González, Kim Overvad, María José Tormo, Fumiaki Imamura, Domenico Palli, J. Ramón Quirós, Yvonne T. van der Schouw, José María Huerta, Francesca L. Crowe, Joline W.J. Beulens, Geertruida J. van Woudenbergh, Valeria Pala, Françoise Clavel-Chapelon, Stephen J. Sharp, Daphne L. van der A, Claudia Langenberg, Edith J. M. Feskens, Marcela Guevara, Keith Summerhill, Pilar Amiano, Tilman Kühn, Kay-Tee Khaw, Guy Fagherazzi, Elio Riboli, Rosario Tumino, María José Sánchez, Nicholas J. Wareham, Nina Roswall, Rudolf Kaaks, Amalia Mattiello, Marianne Uhre Jakobsen, Timothy J. Key, Julian L. Griffin, L. Wang, Janine Kröger, Olov Rolandsson, Laureen Dartois, Annemieke M.W. Spijkerman, Carlotta Sacerdote, Heiner Boeing, Anne Tjønneland, Albert Koulman, Nadia Slimani, Paul W. Franks, Peter M. Nilsson, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, [Forouhi,NG] MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. [Koulman,A, Wang,L, Summerhill,K, Griffin,JL] MRC Human Nutrition Research, Cambridge, UK. [Sharp,SJ, Imamura,F, Langenberg,C, Wareham,NJ] MRC Epidemiology Unit, University of Cambridge, Cambridge, UK. [Kröger,J, Schulze,MB, Boeing,H] German Institute of Human Nutrition Potsdam-Rehbruecke, Potsdam, Germany. [Crowe,FL, Key,TJ] Nuffield Department of Medicine, University of Oxford, Oxford, UK. [Huerta,JM] Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain. [Guevara,M] CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain, Navarre Public Health Institute (ISPN), Pamplona, Spain. [Beulens,JW, van der Schouw,YT] University Medical Center Utrecht, Utrecht, Netherlands. [van Woudenbergh,GJ, Feskens,EJ] Wageningen University, Wageningen, Netherlands. [Amiano,P] CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain, Public Health Division of Gipuzkoa, San Sebastian, Spain, Instituto BIO-Donostia, Basque Government, San Sebastian, Spain. [Clavel-Chapelon,F, Dartois L, Fagherazzi,G] Inserm, CESP, Villejuif, France, Univ Paris-Sud, Villejuif, France, Gustave Roussy Institute, Villejuif, France. [Franks,PW] Lund University, Malmö, Sweden, Umeå University, Umeå, Sweden. [Gonzalez,C] Catalan Institute of Oncology (ICO), Barcelona, Spain. [Jakobsen,MU] Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark. [Kaaks,R, Kühn,T] German Cancer Research Centre (DKFZ), Heidelberg, Germany. [Khaw,KT] Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. [Mattiello,A] Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy. [Nilsson,PM] Lund University, Malmö, Sweden. [Overvad,K] Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark, Aalborg University Hospital, Aalborg, Denmark. [Pala,V] Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. [Palli,D] Cancer Research and Prevention Institute (ISPO), Florence, Italy. [Quirós,JR] Public Health Directorate, Asturias, Spain. [Rolandsson,O] Umeå University, Umeå, Sweden. [Roswall,N] Danish Cancer Society, Copenhagen, Denmark. [Sacerdote,C] Unit of Cancer Epidemiology, Citta' della Salute e della Scienza Hospital-University of Turin and Centre for Cancer Prevention (CPO), Turin, Italy, Human Genetics Foundation (HuGeF), Turin, Italy. [Sánchez,MJ] CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain, Andalusian School of Public Health, Granada, Spain, Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Granada, Spain. [Slimani,N] International Agency for Research on Cancer, Lyon, France. [Spijkerman,AM, van der A,DL] National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands. [Tjonneland,A] Danish Cancer Society Research Center, Copenhagen, Denmark. [Tormo,MJ] Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Murcia, Spain, Department of Health and Social Sciences, Universidad de Murcia, Spain. [Tumino,R] Associazione Italiana Registri Tumori, Dipartimento di Prevenzione Medica, Azienda Sanitaria Provinciale, Ragusa, Italy, Aire Onlus, Ragusa, Italy. [Riboli,E] School of Public Health, Imperial College London, London, UK., and EU FP6 programme, Medical Research Council Epidemiology Unit, Medical Research Council Human Nutrition Research, and Cambridge Lipidomics Biomarker Research Initiative.

Subjects
Male, Nutrition and Disease, 030309 nutrition & dietetics, Endocrinology, Diabetes and Metabolism, Type 2 diabetes, Named Groups::Persons::Age Groups::Adult::Middle Aged [Medical Subject Headings], Pentadecanoic acid, Palmitic acid, Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings], chemistry.chemical_compound, 0302 clinical medicine, Endocrinology, project, Voeding en Ziekte, Estudios prospectivos, Fosfolípidos, Prospective Studies, humans, risk-factors, Ácidos grasos, Phospholipids, 2. Zero hunger, 0303 health sciences, Estudios de casos y controles, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Case-Control Studies [Medical Subject Headings], Fatty Acids, de-novo lipogenesis, Articles, Chemicals and Drugs::Lipids::Fatty Acids [Medical Subject Headings], Middle Aged, 3. Good health, Endokrinologi och diabetes, Diseases::Nutritional and Metabolic Diseases::Metabolic Diseases::Glucose Metabolism Disorders::Diabetes Mellitus::Diabetes Mellitus, Type 2 [Medical Subject Headings], biomarker, dietary, Heptadecanoic acid, Female, Stearic acid, medicine.medical_specialty, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies::Prospective Studies [Medical Subject Headings], Diabetes Mellitus tipo 2, Lignoceric acid, Check Tags::Male [Medical Subject Headings], 030209 endocrinology & metabolism, Endocrinology and Diabetes, 03 medical and health sciences, Internal medicine, Diabetes mellitus, Internal Medicine, medicine, Humans, cancer, Chemicals and Drugs::Lipids::Phospholipids [Medical Subject Headings], VLAG, Mediana edad, disease, business.industry, medicine.disease, dairy product intake, chemistry, Check Tags::Female [Medical Subject Headings], Diabetes Mellitus, Type 2, Case-Control Studies, Odd-chain fatty acid, business, mellitus
Abstract

Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't; BACKGROUND Conflicting evidence exists regarding the association between saturated fatty acids (SFAs) and type 2 diabetes. In this longitudinal case-cohort study, we aimed to investigate the prospective associations between objectively measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants. METHODS The EPIC-InterAct case-cohort study includes 12,403 people with incident type 2 diabetes and a representative subcohort of 16,154 individuals who were selected from a cohort of 340.234 European participants with 3·99 million person-years of follow-up (the EPIC study). Incident type 2 diabetes was ascertained until Dec 31, 2007, by a review of several sources of evidence. Gas chromatography was used to measure the distribution of fatty acids in plasma phospholipids (mol%); samples from people with type 2 diabetes and subcohort participants were processed in a random order by centre, and laboratory staff were masked to participant characteristics. We estimated country-specific hazard ratios (HRs) for associations per SD of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression, which is weighted for case-cohort sampling, and pooled our findings using random-effects meta-analysis. FINDINGS SFAs accounted for 46% of total plasma phospholipid fatty acids. In adjusted analyses, different individual SFAs were associated with incident type 2 diabetes in opposing directions. Even-chain SFAs that were measured (14:0 [myristic acid], 16:0 [palmitic acid], and 18:0 [stearic acid]) were positively associated with incident type 2 diabetes (HR [95% CI] per SD difference: myristic acid 1·15 [95% CI 1·09-1·22], palmitic acid 1·26 [1·15-1·37], and stearic acid 1·06 [1·00-1·13]). By contrast, measured odd-chain SFAs (15:0 [pentadecanoic acid] and 17:0 [heptadecanoic acid]) were inversely associated with incident type 2 diabetes (HR [95% CI] per 1 SD difference: 0·79 [0·73-0·85] for pentadecanoic acid and 0·67 [0·63-0·71] for heptadecanoic acid), as were measured longer-chain SFAs (20:0 [arachidic acid], 22:0 [behenic acid], 23:0 [tricosanoic acid], and 24:0 [lignoceric acid]), with HRs ranging from 0·72 to 0·81 (95% CIs ranging between 0·61 and 0·92). Our findings were robust to a range of sensitivity analyses. INTERPRETATION Different individual plasma phospholipid SFAs were associated with incident type 2 diabetes in opposite directions, which suggests that SFAs are not homogeneous in their effects. Our findings emphasise the importance of the recognition of subtypes of these fatty acids. An improved understanding of differences in sources of individual SFAs from dietary intake versus endogenous metabolism is needed. FUNDING EU FP6 programme, Medical Research Council Epidemiology Unit, Medical Research Council Human Nutrition Research, and Cambridge Lipidomics Biomarker Research Initiative. Yes

Published
2014

41. The Association between Dietary Energy Density and Type 2 Diabetes in Europe: Results from the EPIC-InterAct Study

Author

InterAct Consortium, Saskia W van den Berg, Daphne L van der A, Annemieke M W Spijkerman, Geertruida J van Woudenbergh, Mariken J Tijhuis, Pilar Amiano, Eva Ardanaz, Joline W J Beulens, Heiner Boeing, Françoise Clavel-Chapelon, Francesca L Crowe, Blandine de Lauzon-Guillain, Guy Fagherazzi, Paul W Franks, Heinz Freisling, Carlos Gonzalez, Sara Grioni, Jytte Halkjaer, José María Huerta, Inge Huybrechts, Rudolf Kaaks, Kay Tee Khaw, Giovanna Masala, Peter M Nilsson, Kim Overvad, Salvatore Panico, J Ramón Quirós, Olov Rolandsson, Carlotta Sacerdote, María-José Sánchez, Matthias B Schulze, Nadia Slimani, Ellen A Struijk, Anne Tjonneland, Rosario Tumino, Stephen J Sharp, Claudia Langenberg, Nita G Forouhi, Edith J M Feskens, Elio Riboli, Nicholas J Wareham, The InterAct Consortium, [van den Berg,SW, van der A,DL, Spijkerman,AMW, Tijhuis,MJ] Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. [van Woudenbergh,GJ, Feskens,EJM] Division of Human Nutrition - Section Nutrition and Epidemiology, Wageningen University, Wageningen, The Netherlands. [Amiano,P] Instituto BIO-Donostia, Basque Government, Donostia, Spain. [Amiano,P, Ardanaz,E, Huerta,JM, Sánchez,M] Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública), Madrid, Spain. [Amiano,P] Public Health Division of Gipuzkoa, San Sebastian, Spain. [Ardanaz,E] Navarre Public Health Institute (ISPN), Pamplona, Spain. [Beulens,JWJ, Struijk,EA] Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands. [Boeing,H] Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany. [Clavel-Chapelon,F, Fagherazzi,G] Nutrition, Hormones and Women’s Health, Institut National de la Santé et de la Recherche Médicale (INSERM) and Center for Research in Epidemiology and Population Health (CESP), Villejuif, France. [Clavel-Chapelon,F, de Lauzon-Guillain,B, Fagherazzi,G] University Paris Sud, Villejuif, France. [Crowe,FL] Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom. [de Lauzon- Guillain,B] Institut National de la Santé et de la Recherche Médicale (INSERM) and Center for Research in Epidemiology and Population Health (CESP), Villejuif, France. [Franks,PW] Department of Clinical Sciences, Skane University Hospital, Lund University, Malmö, Sweden. [Rolandsson,O] Department of Public Health and Clinical Medicine, Umea University, Umea, Sweden. [Freisling,H, Huybrechts,I] International Agency for Research on Cancer, Lyon, France. [Gonzalez,C] Unit Nutrition, Environment and Cancer, Department of Epidemiology, Catalan Institute of Oncology, Barcelona, Spain. [Grioni,S] Nutritional Epidemiology Unit, Milan, Italy. [Halkjaer,J, Tjonneland,A] Danish Cancer Society Research Center, Copenhagen, Denmark. [Huerta,JM] Department of Epidemiology, Murcia Regional Health Authority, Murcia, Spain. [Kaaks,R] Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany. [Khaw,KT] Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. [Masala,G] Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute, Florence, Italy. [Nilsson,PM] Department of Clinical Sciences Medicine, University Hospital Scania, Lund University, Malmö, Sweden. [Overvad,K] Department of Cardiology, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark. [Overvad,K] Department of Epidemiology, School of Public Health, Aarhus, Denmark. [Panico,S] Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy. [Quirós,JR] Asturias Health & Health Care Council, Oviedo, Spain. [Sacerdote,C] Human Genetic Foundation (HuGeF), Torino, Italy. Center for Cancer Prevention, Torino, Italy. [Sánchez,MJ] Andalusian School of Public Health, Granada, Spain. [Schulze,MB] Department of Molecular Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, Germany. [Slimani,N] Dietary Exposure Assessment Group (DEX), International Agency for Research on Cancer, Lyon, France. [Tumino,R] Cancer Registry and Histopathology Unit, Ragusa, Italy. Aire Onlus, Ragusa, Spain. [Sharp,SJ, Langenberg,C, Forouhi,NG, Wareham,NJ] MRC Epidemiology Unit, Cambridge, United Kingdom. [Riboli,E] Department of Epidemiology and Biostatistics, Imperial College London, London, UK., The EPIC-InterAct Study received funding from the European Union (Integrated project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). DLvdA: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands, AMWS: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands, EA: Health Research Fund (FIS) of the Spanish Ministry of Health, Navarre Regional Government and CIBER Epidemiologı´a y Salud Pu´ blica (CIBERESP)., JWJB: Verification of diabetes cases in EPIC-NL was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht., FLC: Cancer Research UK, PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart- Lung Foundation, JH: Danish Cancer Society, JMH: Health Research Fund of the Spanish Ministry of Health, Murcia Regional Government (Nu 6236), RK: German Cancer Aid, German Ministry of Research (BMBF), KTK: Medical Research Council UK, Cancer Research UK, PN: Swedish Research Council, KO: Danish Cancer Society, SP: Compagnia di San Paolo, JRQ: Asturias Regional Government, OR: The Vasterboten County Council, AT: Danish Cancer Society, RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government, ER: ER was supported in this work by the Imperial College Biomedical Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, van den Berg, Sw, van der A., Dl, Spijkerman, Am, van Woudenbergh, Gj, Tijhuis, Mj, Amiano, P, Ardanaz, E, Beulens, Jw, Boeing, H, Clavel Chapelon, F, Crowe, Fl, de Lauzon Guillain, B, Fagherazzi, G, Franks, Pw, Freisling, H, Gonzalez, C, Grioni, S, Halkjaer, J, Huerta, Jm, Huybrechts, I, Kaaks, R, Khaw, Kt, Masala, G, Nilsson, Pm, Overvad, K, Panico, Salvatore, Quir?s, Jr, Rolandsson, O, Sacerdote, C, S?nchez, Mj, Schulze, Mb, Slimani, N, Struijk, Ea, Tjonneland, A, Tumino, R, Sharp, Sj, Langenberg, C, Forouhi, Ng, Feskens, Ej, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Gerontology, Anatomy and Physiology, endocrine system diseases, Nutrition and Disease, Epidemiology, lcsh:Medicine, Type 2 diabetes, Diabetes Mellitus Tipo 2, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::Risk::Risk Factors [Medical Subject Headings], Cohort Studies, Impaired glucose tolerance, Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings], MELLITUS, Endocrinology, Risk Factors, Voeding en Ziekte, Medicine and Health Sciences, Longitudinal Studies, lcsh:Science, Prospective cohort study, risk, METABOLIC SYNDROME, RISK, Multidisciplinary, Estudios de casos y controles, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Case-Control Studies [Medical Subject Headings], weight change, Ingestión de energía, WOMEN, Geographicals::Geographic Locations::Europe [Medical Subject Headings], Phenomena and Processes::Physiological Phenomena::Nutritional Physiological Phenomena::Diet::Energy Intake [Medical Subject Headings], Humanos, Europe, us adults, Endokrinologi och diabetes, Observational Studies, Medicine, Science & Technology - Other Topics, Diseases::Nutritional and Metabolic Diseases::Metabolic Diseases::Glucose Metabolism Disorders::Diabetes Mellitus::Diabetes Mellitus, Type 2 [Medical Subject Headings], impaired glucose-tolerance, LIFE-STYLE, Dieta, women, Europa, Research Article, WEIGHT CHANGE, Factores de riesgo, life-style, Clinical Research Design, united-states, UNITED-STATES, Endocrine System, Phenomena and Processes::Physiological Phenomena::Nutritional Physiological Phenomena::Diet [Medical Subject Headings], Endocrinology and Diabetes, metabolic syndrome, IMPAIRED GLUCOSE-TOLERANCE, US ADULTS, Environmental health, Glycemic load, glycemic load, medicine, GLYCEMIC LOAD, Humans, Obesity, Biology, Nutrition, VLAG, Diabetic Endocrinology, Science & Technology, Population Biology, business.industry, MULTIDISCIPLINARY SCIENCES, lcsh:R, Weight change, Case-control study, Diabetes Mellitus Type 2, medicine.disease, Diet, Diabetes Mellitus, Type 2, Case-Control Studies, Other Clinical Medicine, lcsh:Q, Metabolic syndrome, Energy Intake, business, mellitus
Abstract

Journal Article; Research Support, Non-U.S. Gov't; BACKGROUND Observational studies implicate higher dietary energy density (DED) as a potential risk factor for weight gain and obesity. It has been hypothesized that DED may also be associated with risk of type 2 diabetes (T2D), but limited evidence exists. Therefore, we investigated the association between DED and risk of T2D in a large prospective study with heterogeneity of dietary intake. METHODOLOGY/PRINCIPAL FINDINGS A case-cohort study was nested within the European Prospective Investigation into Cancer (EPIC) study of 340,234 participants contributing 3.99 million person years of follow-up, identifying 12,403 incident diabetes cases and a random subcohort of 16,835 individuals from 8 European countries. DED was calculated as energy (kcal) from foods (except beverages) divided by the weight (gram) of foods estimated from dietary questionnaires. Prentice-weighted Cox proportional hazard regression models were fitted by country. Risk estimates were pooled by random effects meta-analysis and heterogeneity was evaluated. Estimated mean (sd) DED was 1.5 (0.3) kcal/g among cases and subcohort members, varying across countries (range 1.4-1.7 kcal/g). After adjustment for age, sex, smoking, physical activity, alcohol intake, energy intake from beverages and misreporting of dietary intake, no association was observed between DED and T2D (HR 1.02 (95% CI: 0.93-1.13), which was consistent across countries (I(2) = 2.9%). CONCLUSIONS/SIGNIFICANCE In this large European case-cohort study no association between DED of solid and semi-solid foods and risk of T2D was observed. However, despite the fact that there currently is no conclusive evidence for an association between DED and T2DM risk, choosing low energy dense foods should be promoted as they support current WHO recommendations to prevent chronic diseases. Yes

Published
2013

42. The amount and type of dairy product intake and incident type 2 diabetes:results from the EPIC-InterAct Study

Author

Peter M. Nilsson, Rudolf Kaaks, Jytte Halkjær, Anne Tjønneland, Nita G. Forouhi, Domenico Palli, Kim Overvad, María José Sánchez, Blandine de Lauzon-Guillain, Aurelio Barricarte, Françoise Clavel-Chapelon, Claudia Langenberg, Yvonne T. van der Schouw, H. Bas Bueno-de-Mesquita, Edith J. M. Feskens, Elio Riboli, Annemieke M.W. Spijkerman, María José Tormo, Nadia Slimani, Larraitz Arriola, Paul W. Franks, Olov Rolandsson, Daphne L. van der A, Beverley Balkau, Carlos González, Heiner Boeing, José Ramón Quirós, Nicholas J. Wareham, Francesca L. Crowe, Carlotta Sacerdote, Claudia Agnoli, Fulvio Ricceri, Joline W.J. Beulens, Ivonne Sluijs, Stephen J. Sharp, Salvatore Panico, Sabina Rinaldi, Dagmar Drogan, Aurelie Moskal, Birgit Teucher, Diana Gavrila, Rosario Tumino, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, Sluijs, I, Forouhi, Ng, Beulens, Jw, van der Schouw, Yt, Agnoli, C, Arriola, L, Balkau, B, Barricarte, A, Boeing, H, Bueno de Mesquita, Hb, Clavel Chapelon, F, Crowe, Fl, de Lauzon Guillain, B, Drogan, D, Franks, Pw, Gavrila, D, Gonzalez, C, Halkjær, J, Kaaks, R, Moskal, A, Nilsson, P, Overvad, K, Palli, D, Panico, Salvatore, Quirós, Jr, Ricceri, F, Rinaldi, S, Rolandsson, O, Sacerdote, C, Sánchez, Mj, Slimani, N, Spijkerman, Am, Teucher, B, Tjonneland, A, Tormo, Mj, Tumino, R, van der A., Dl, Sharp, Sj, Langenberg, C, Feskens, Ej, Riboli, E, Wareham, Nj, on behalf of The InterAct, C. o. n. s. o. r. t. i. u. m., Halkjaer, J, Quir?s, Jr, S?nchez, Mj, and Interact, Consortium

Subjects
Adult, Male, medicine.medical_specialty, Nutrition and Disease, vitamin-d, Medicine (miscellaneous), metabolic risk-factors, Type 2 diabetes, EPIC, Cohort Studies, Risk Factors, Voeding en Ziekte, Environmental health, Internal medicine, Diabetes mellitus, Surveys and Questionnaires, medicine, Humans, consumption, Product (category theory), Prospective Studies, dietary assessment methods, VLAG, Proportional Hazards Models, Nutrition and Dietetics, business.industry, Incidence, Follow up studies, cohort, Middle Aged, medicine.disease, myocardial-infarction, Diet, Europe, nutrition, Endocrinology, Diabetes Mellitus, Type 2, milk-fat, Milk fat, insulin-resistance syndrome, Female, Dairy Products, energy-intake, business, Energy Intake, Follow-Up Studies
Abstract

Background: Dairy product intake may be inversely associated with risk of type 2 diabetes, but the evidence is inconclusive for total dairy products and sparse for types of dairy products. Objective: The objective was to investigate the prospective association of total dairy products and different dairy subtypes with incidence of diabetes in populations with marked variation of intake of these food groups. Design: A nested case-cohort within 8 European countries of the European Prospective Investigation into Cancer and Nutrition Study (n = 340,234; 3.99 million person-years of follow-up) included a random subcohort (n = 16,835) and incident diabetes cases (n = 12,403). Baseline dairy product intake was assessed by using dietary questionnaires. Country-specific Prentice-weighted Cox regression HRs were calculated and pooled by using a random-effects meta-analysis. Results: Intake of total dairy products was not associated with diabetes (HR for the comparison of the highest with the lowest quintile of total dairy products: 1.01; 95% CI: 0.83, 1.34; P-trend = 0.92) in an analysis adjusted for age, sex, BMI, diabetes risk factors, education, and dietary factors. Of the dairy subtypes, cheese intake tended to have an inverse association with diabetes (HR: 0.88; 95% CI: 0.76, 1.02; P-trend = 0.01), and a higher combined intake of fermented dairy products (cheese, yogurt, and thick fermented milk) was inversely associated with diabetes (HR: 0.88; 95% CI: 0.78, 0.99; P-trend = 0.02) in adjusted analyses that compared extreme quintiles. Conclusions: This large prospective study found no association between total dairy product intake and diabetes risk. An inverse association of cheese intake and combined fermented dairy product intake with diabetes is suggested, which merits further study.

Published
2012

43. Long-term risk of incident type 2 Diabetes and measures of overall and regional obesity: The EPIC-InterAct Case-Cohort Study

Author

Nicola D. Kerrison, Olov Rolandsson, Joachim Spranger, Nicholas J. Wareham, Kay-Tee Khaw, Luigi Palla, Carlos González, Heiner Boeing, Nita G. Forouhi, Ivonne Sluijs, María José Tormo, Joline W.J. Beulens, Kim Overvad, Matthias B. Schulze, Isabelle Romieu, Beverley Balkau, Francesca L. Crowe, Domenico Palli, María José Sánchez, J. Ramón Quirós, Claudia Langenberg, Yvonne T. van der Schouw, Dora Romaguera, Nadia Slimani, Paul W. Franks, Rosario Tumino, Eva Ardanaz, Göran Hallmans, Stephen J. Sharp, José María Huerta, Anne Tjønneland, Birgit Teucher, H. Bas Bueno-de-Mesquita, Salvatore Panico, Edith J. M. Feskens, Teresa Norat, L. van der A. Daphne, Carlotta Sacerdote, Elio Riboli, Annemieke M.W. Spijkerman, Blandine de Lauzon-Guillan, Peter M. Nilsson, Dagmar Drogan, Timothy J. Key, Sara Grioni, Rudolf Kaaks, Jytte Halkjær, Larraitz Arriola, Amalia Mattiello, Françoise Clavel-Chapelon, The InterAct Consortium, [Langenberg,C, Sharp,SJ, Forouhi,NG, Kerrison,ND, Palla,L, Wareham,NJ] MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom. [Schulze,MB] Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Arthur-Scheunert-Allee, Nuthetal, Germany. [Rolandsson,O, Hallmans,G] Department of Public Health and Clinical Medicine, Umea University, Norrlands Universitetsjukhus, Umea, Sweden. [Overvad,K] Department of Epidemiology, School of Public Health, Aarhus C, Denmark. [Spranger.J] Charité-Universitatsmedizin Berlin, Department of Endocrinology, Diabetes and Nutrition, Berlin, Germany. [Drogan,D, Boeing,H] Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.[Arriola,L, Tormo,MJ] Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain. [Huerta,JM, Arriola,L, Tormo,MJ, Ardanaz,E, Sánchez,MJ] Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pu´ blica), Madrid, Spain . [Arriola,L] Public Health Division of Gipuzkoa, Basque Government, San Sebastian, Spain. Instituto BIO-Donostia, Hospital Donostia, Donostia, Basque Country. [Lauzon-Guillan,B, Balkau,B] INSERM, CESP Centre for Research in Epidemiology and Population Health, U1018: Epidemiology of Diabetes, Obesity and Chronic Kidney Disease over the Lifecourse, Villejuif Cedex, France. University Paris, Villejuif Cedex, France. [Ardanaz,E] Navarre Public Health Institute, Pamplona, Navarra, Spain. [Beulens,JWJ, Sluijs,I, Schouw,T] Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands. [Bueno-de-Mesquita,HB] National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. [Clavel-Chapelon,F] INSERM, CESP Centre for Research in Epidemiology and Population Health,U1018: Nutrition, Hormones and Women’s Health, IGR, Villejuif Cedex, France. [Crowe,FL, Key,TJ] Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford,Richard Doll Building, Roosevelt Drive, Oxford, United Kingdom. [Grioni,S] Fondazione IRCCS Istituto Nazionale Tumori Milan, Milan, Italy. [Halkjaer,J, Tjonneland,A] Danish Cancer Society Research Center, Copenhagen, Denmark. [Kerrison,ND, Teucher,B] German Cancer Research Centre, Heidelberg, Germany. [Khaw,KT] Department of Public Health and Primary Care, University of Cambridge Addenbrooke’s Hospital, Cambridge, United Kingdom. [Mattiello,A] Department of Clinical and Experimental Medicine Federico II University, Naples, Italy. [Nilsson,P, Panico,S] University of Lund, Department of Clinical Sciences Medicine, University Hospital Scania, Malmo, Sweden. [Norat,T, Romaguera,D, Riboli,E] Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. [Palli,D] Molecular and Nutritional Epidemiology Unit, ISPO Ponte Nuovo, Florence, Italy. [Quirós, JR] Public Health Directorate, Oviedo, Spain. [Romieu,I, Slimani,N] International Agency for Research on Cancer, Lyon Cedex, France. [Sacerdote,C] Center for Cancer Prevention (CPO-Piemonte), Torino, Italy. Human Genetics Foundation (HuGeF), Torino, Italy. [Sánchez,MJ] Andalusian School of Public Health, Granada, Spain. [Spijkerman,AMW, A Daphne,L] National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. [Tumino,R] Cancer Registry and Histopathology Unit, ‘‘Civile - M.P. Arezzo’’ Hospital, Dante Ragusa, Italy. [Feskens,EJM] Division of Human Nutrition - Section Nutrition and Epidemiology, University of Wageningen, Wageningen, The Netherlands. [Franks,PW] Department of Clinical Sciences, Clinical Research Center, Ska°ne University Hospital, Lund University, Malmö , Sweden. [Bueno-de-Mesquita,HB] Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands., Funding: EU Integrated Project LSHM-CT-2006-037197. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. InterAct investigators acknowledge funding from the following agencies: KO: Danish Cancer Society, JS: ‘Heisenberg-Professorship (SP716/2-1), clinical research group (KFO218/1), and a research group (Molecular Nutrition to JS) support of the Bundesministerium fu¨ r Bildung und Forschung (BMBF), MJT: Health Research Fund (FIS) of the Spanish Ministry of Health, the CIBER en Epidemiologı´a y Salud Pu´ blica (CIBERESP), Spain, Murcia Regional Government (Nu 6236), JWJB, HBBdM, IS, AMWS, DLvdA, YTvdS: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands, verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht, FLC: Cancer Research UK, PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation, GH: The county of Va¨sterbotten, RK: German Cancer Aid, Federal Ministry of Education and Research, TJK: Cancer Research UK, KK: Medical Research Council UK, Cancer Research UK, PN: Swedish Research Council, JRQ: Asturias Regional Government, BT: German Cancer Aid, Federal Ministry of Education and Research, AT: Danish Cancer Society, RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government., Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, Langenberg, C, Sharp, Sj, Schulze, Mb, Rolandsson, O, Overvad, K, Forouhi, Ng, Spranger, J, Drogan, D, Huerta, Jm, Arriola, L, de Lauzon Guillan, B, Tormo, Mj, Ardanaz, E, Balkau, B, Beulens, Jw, Boeing, H, Bueno de Mesquita, Hb, Clavel Chapelon, F, Crowe, Fl, Franks, Pw, Gonzalez, Ca, Grioni, S, Halkjaer, J, Hallmans, G, Kaaks, R, Kerrison, Nd, Key, Tj, Khaw, Kt, Mattiello, A, Nilsson, P, Norat, T, Palla, L, Palli, D, Panico, Salvatore, Quir?s, Jr, Romaguera, D, Romieu, I, Sacerdote, C, S?nchez, Mj, Slimani, N, Sluijs, I, Spijkerman, Am, Teucher, B, Tjonneland, A, Tumino, R, van der A., Dl, van der Schouw, Yt, Feskens, Ej, Riboli, E, Wareham, Nj, and Interact, Consortium

Subjects
Male, Gerontology, Índice de Masa Corporal, Time Factors, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Models, Statistical::Proportional Hazards Models [Medical Subject Headings], Nutrition and Disease, Epidemiology, Phenomena and Processes::Physical Phenomena::Time::Time Factors [Medical Subject Headings], Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Physical Examination::Anthropometry [Medical Subject Headings], body-mass index, Diabetes Mellitus Tipo 2, Named Groups::Persons::Age Groups::Adult::Middle Aged [Medical Subject Headings], Overweight, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::Risk::Risk Factors [Medical Subject Headings], Body Mass Index, Cohort Studies, Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings], Endocrinology, prevention, Risk Factors, Voeding en Ziekte, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Physical Examination::Body Constitution::Body Weights and Measures::Body Size::Waist Circumference [Medical Subject Headings], Cumulative incidence, Longitudinal Studies, Prospective Studies, Nutrition and Dietetics, Anthropometry, Incidence, primary-care, Hazard ratio, Absolute risk reduction, Geographicals::Geographic Locations::Europe [Medical Subject Headings], General Medicine, Middle Aged, waist circumference, Europe, Näringslära, nutrition, Cohort, Medicine, Diseases::Nutritional and Metabolic Diseases::Metabolic Diseases::Glucose Metabolism Disorders::Diabetes Mellitus::Diabetes Mellitus, Type 2 [Medical Subject Headings], Female, medicine.symptom, Life Sciences & Biomedicine, Research Article, Cohort study, Clinical Research Design, abdominal adiposity, Check Tags::Male [Medical Subject Headings], Analytical, Diagnostic and Therapeutic Techniques and Equipment::Diagnosis::Diagnostic Techniques and Procedures::Physical Examination::Body Constitution::Body Weights and Measures::Body Mass Index [Medical Subject Headings], Endocrinology and Diabetes, Medicine, General & Internal, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Data Collection::Vital Statistics::Morbidity::Incidence [Medical Subject Headings], General & Internal Medicine, medicine, Humans, cancer, Obesity, Risk factor, Proportional Hazards Models, VLAG, Diabetic Endocrinology, Science & Technology, business.industry, association, nutritional and metabolic diseases, Diabetes Mellitus Type 2, Diseases::Nutritional and Metabolic Diseases::Nutrition Disorders::Overnutrition::Obesity [Medical Subject Headings], Diabetes Mellitus, Type 2, Antropometría, Check Tags::Female [Medical Subject Headings], Other Clinical Medicine, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies [Medical Subject Headings], Meta-Analyses, participants, business, Estudios de Cohortes, Body mass index, Demography, mellitus
Abstract

A collaborative re-analysis of data from the InterAct case-control study conducted by Claudia Langenberg and colleagues has established that waist circumference is associated with risk of type 2 diabetes, independently of body mass index., Background Waist circumference (WC) is a simple and reliable measure of fat distribution that may add to the prediction of type 2 diabetes (T2D), but previous studies have been too small to reliably quantify the relative and absolute risk of future diabetes by WC at different levels of body mass index (BMI). Methods and Findings The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. We used Prentice-weighted Cox regression and random effects meta-analysis methods to estimate hazard ratios for T2D. Kaplan-Meier estimates of the cumulative incidence of T2D were calculated. BMI and WC were each independently associated with T2D, with WC being a stronger risk factor in women than in men. Risk increased across groups defined by BMI and WC; compared to low normal weight individuals (BMI 18.5–22.4 kg/m2) with a low WC (102/88 cm). Among the large group of overweight individuals, WC measurement was highly informative and facilitated the identification of a subgroup of overweight people with high WC whose 10-y T2D cumulative incidence (men, 70 per 1,000 person-years; women, 44 per 1,000 person-years) was comparable to that of the obese group (50–103 per 1,000 person-years in men and 28–74 per 1,000 person-years in women). Conclusions WC is independently and strongly associated with T2D, particularly in women, and should be more widely measured for risk stratification. If targeted measurement is necessary for reasons of resource scarcity, measuring WC in overweight individuals may be an effective strategy, since it identifies a high-risk subgroup of individuals who could benefit from individualised preventive action. Please see later in the article for the Editors' Summary, Editors' Summary Background Worldwide, more than 350 million people have diabetes, and this number is increasing rapidly. Diabetes is characterized by dangerous levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone that the pancreas releases after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. The long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes. Why Was This Study Done? A high body mass index (BMI, a measure of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) is a strong predictor of type 2 diabetes. Although the risk of diabetes is greatest in obese people (who have a BMI of greater than 30 kg/m2), many of the people who develop diabetes are overweight—they have a BMI of 25–30 kg/m2. Healthy eating and exercise reduce the incidence of diabetes in high-risk individuals, but it is difficult and expensive to provide all overweight and obese people with individual lifestyle advice. Ideally, a way is needed to distinguish between people with high and low risk of developing diabetes at different levels of BMI. Waist circumference is a measure of fat distribution that has the potential to quantify diabetes risk among people with different BMIs because it estimates the amount of fat around the abdominal organs, which also predicts diabetes development. In this case-cohort study, the researchers use data from the InterAct study (which is investigating how genetics and lifestyle interact to affect diabetes risk) to estimate the long-term risk of type 2 diabetes associated with BMI and waist circumference. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease and in a randomly chosen subcohort. What Did the Researchers Do and Find? The researchers estimated the association of BMI and waist circumference with type 2 diabetes from baseline measurements of the weight, height, and waist circumference of 12,403 people who subsequently developed type 2 diabetes and a subcohort of 16,154 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC). Both risk factors were independently associated with type 2 diabetes risk, but waist circumference was a stronger risk factor in women than in men. Obese men (BMI greater than 35 kg/m2) with a high waist circumference (greater than 102 cm) were 22 times more likely to develop diabetes than men with a low normal weight (BMI 18.5–22.4 kg/m2) and a low waist circumference (less than 94 cm); obese women with a waist circumference of more than 88 cm were 31.8 times more likely to develop type 2 diabetes than women with a low normal weight and waist circumference (less than 80 cm). Importantly, among overweight people, waist circumference measurements identified a subgroup of overweight people (those with a high waist circumference) whose 10-year cumulative incidence of type 2 diabetes was similar to that of obese people. What Do These Findings Mean? These findings indicate that, among people of European descent, waist circumference is independently and strongly associated with type 2 diabetes, particularly among women. Additional studies are needed to confirm this association in other ethnic groups. Targeted measurement of waist circumference in overweight individuals (who now account for a third of the US and UK adult population) could be an effective strategy for the prevention of diabetes because it would allow the identification of a high-risk subgroup of people who might benefit from individualized lifestyle advice. Additional Information Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001230. The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health care professionals, and the general public, including detailed information on diabetes prevention (in English and Spanish) The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (including some information in Spanish) The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes, about the prevention of type 2 diabetes, and about obesity; it also includes peoples stories about diabetes and about obesity The charity Diabetes UK also provides detailed information for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes More information on the InterAct study is available MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention and about obesity (in English and Spanish)

Published
2012

44. Dietary glycaemic index and glycaemic load in the European Prospective Investigation into Cancer and Nutrition

Author

K. Avloniti, Furio Brighenti, Françoise Clavel-Chapelon, H. B. Bueno-de-Mesquita, Emily Sonestedt, Nadia Slimani, Eva Ardanaz, Sabine Rohrmann, Ingegerd Johansson, Guri Skeie, S. Rinaldi, Anne Tjønneland, Anne E. Cust, Theodora Psaltopoulou, Edith J. M. Feskens, Sheila Bingham, Carine Biessy, Genevieve Buckland, Antonia Trichopoulou, Valeria Pala, M. J. Tormo, Ailsa A Welch, Elio Riboli, Joline W.J. Beulens, Salvatore Panico, Kim Overvad, E. A. Spencer, P. H. M. Peeters, Giovanna Masala, M. M. E. van Bakel, M.A.H. Lentjes, Matthias B. Schulze, Y. T. van der Schouw, Huaidong Du, Rudolf Kaaks, Jytte Halkjær, Mathilde Touvier, P. Ferrari, van Bakel, Mm, Kaaks, R, Feskens, Ej, Rohrmann, S, Welch, Aa, Pala, V, Avloniti, K, van der Schouw, Yt, van der A., Dl, Du, H, Halkjaer, J, Tormo, Mj, Cust, Ae, Brighenti, F, Beulens, Jw, Ferrari, P, Biessy, C, Lentjes, M, Spencer, Ea, Panico, Salvatore, Masala, G, Bueno de Mesquita, Hb, Peeters, Ph, Trichopoulou, A, Psaltopoulou, T, Clavel Chapelon, F, Touvier, M, Skeie, G, Rinaldi, S, Sonestedt, E, Johansson, I, Schulze, M, Ardanaz, E, Buckland, G, Tjønneland, A, Overvad, K, Bingham, S, Riboli, E, Slimani, N., University of Groningen, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, and an Bakel, Mm

Subjects
Male, Health Knowledge, Attitudes, Practice, Nutrition and Disease, 030309 nutrition & dietetics, epic project, Medicine (miscellaneous), Body Mass Index, Food group, Glycaemic index, 0302 clinical medicine, Nutrient, Reference Values, dependent diabetes-mellitus, Voeding en Ziekte, Medicine, Prospective Studies, Food science, risk, 2. Zero hunger, RISK, CALIBRATION, 0303 health sciences, education.field_of_study, Nutrition and Dietetics, Starch, health, Bread, Middle Aged, Diet Records, 3. Good health, European Prospective Investigation into Cancer and Nutrition, Europe, Cohort, Female, HEALTH, fiber, metaanalysis, glycaemic load, Adult, 24-h dietary recall, ENDB, Population, 030209 endocrinology & metabolism, FIBER, Clinical nutrition, DEPENDENT DIABETES-MELLITUS, Diet Surveys, middle-aged women, 03 medical and health sciences, Sex Factors, Animal science, CARBOHYDRATE, Dietary Carbohydrates, Humans, BREAST-CANCER, education, MIDDLE-AGED WOMEN, breast-cancer, METAANALYSIS, Aged, VLAG, standardization, EPIC PROJECT, business.industry, calibration, Diet, Glycemic Index, carbohydrate, glycaemic index, Energy Intake, business, EPIC, Body mass index
Abstract

Objectives: To describe dietary glycaemic index (GI) and glycaemic load (GL) values in the population participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study according to food groups, nutrients and lifestyle characteristics. Methods: Single 24-h dietary recalls (24-HDRs) from 33 566 subjects were used to calculate dietary GI and GL, and an ad hoc database was created as the main reference source. Mean GI and GL intakes were adjusted for age, total energy intake, height and weight, and were weighted by season and day of recall. Results: GI was the lowest in Spain and Germany, and highest in the Netherlands, United Kingdom and Denmark for both genders. In men, GL was the lowest in Spain and Germany and highest in Italy, whereas in women, it was the lowest in Spain and Greece and highest in the UK health-conscious cohort. Bread was the largest contributor to GL in all centres (15-45%), but it also showed the largest inter-individual variation. GL, but not GI, tended to be lower in the highest body mass index category in both genders. GI was positively correlated with starch and intakes of bread and potatoes, whereas it was correlated negatively with intakes of sugar, fruit and dairy products. GL was positively correlated with all carbohydrate components and intakes of cereals, whereas it was negatively correlated with fat and alcohol and with intakes of wine, with large variations across countries. Conclusions: GI means varied modestly across countries and genders, whereas GL means varied more, but it may possibly act as a surrogate of carbohydrate intake. European Journal of Clinical Nutrition (2009) 63, S188-S205; doi: 10.1038/ejcn.2009.81

Published
2009

45. Associations between Variability in Between- and Within-Day Dietary Intake with Adiposity and Glucose Homeostasis in Adults: A Systematic Review.

Author

Suyoto PS, Pamungkas NP, de Vries JH, and Feskens EJ

Subjects
Humans, Adult, Blood Glucose metabolism, Energy Intake, Glucose metabolism, Male, Female, Eating physiology, Feeding Behavior, Middle Aged, Meals, Adiposity, Homeostasis, Diabetes Mellitus, Type 2, Diet
Abstract

This systematic review aims to comprehensively evaluate the literature regarding the impact of variations in dietary intake, both between- and within-day, on adiposity and glucose metabolism. We included observational and experimental articles obtained from PubMed, Scopus, Cochrane Library, and gray literature until 9 October, 2023, evaluating the impact of between- or within-day variations in meal, energy, or macronutrient intake on these outcomes. Our focus was on adults aged ≥18 y, spanning both healthy individuals and those with type 2 diabetes mellitus (T2DM). Given the diverse range of exposures, treatments, and outcomes among the selected articles, we chose a qualitative synthesis approach to effectively analyze the data. Eighty articles from 43 observational and 37 experimental studies were included, involving 89,178 participants. Patterns of dietary intake variation were identified and systematically organized into distinct categories based on similarities. Between-day variations in dietary intake consisted of between-day variations in both the quantity consumed and meal timing. Meanwhile, within-day variations encompassed factors such as eating window, meal omission, within-day meal timing, within-day variation in dietary intake quantity, and temporal distribution. Despite mixed results, time-restricted eating was generally associated with lower adiposity. However, limited control for total daily energy intake (TDEI) suggests that the contribution of lower energy intake cannot be conclusively excluded. Conversely, the adverse effect of meal omission on glucose parameters was consistently supported by randomized trials. Interestingly, the results showed that consuming a substantial portion of TDEI in the morning may increase the likelihood of observing improvements in adiposity. Furthermore, inconsistencies in outcomes across articles examining the effects in healthy compared with T2DM populations, or in energy-sufficient compared with deficient individuals, indicate potential condition-specific effects. These findings support the need for further investigation into the effects of between- and within-day variations in dietary intake to better understand their impact on adiposity and glucose homeostasis. This review was registered in PROSPERO as CRD42020214307., Competing Interests: Conflict of interest The authors report no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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46. The Effect of Meal Frequency and Glycemic Index During the Night Shift on Alertness, Hunger, and Gastrointestinal Complaints in Female Health Care Workers-A Two-Armed Randomized Crossover Trial.

Author

de Rijk MG, Boesveldt S, Feskens EJ, and de Vries JH

Abstract

Background: Nutrition strategies for night-shift workers could optimize alertness and minimize hunger and reduce gastrointestinal complaints, enhancing safety and well-being., Objectives: This study aimed to investigate the effects of 1 or 3 small meals, with either low or high glycemic index (GI), compared with no meal, on alertness, hunger, and gastrointestinal complaints during the night shift., Methods: Fifty-one female health care workers, aged 18 to 61 y, participated in a 2-armed randomized crossover design. In 1 study arm, participants received 1 yogurt meal during the night shift, AND in the other, they received 3. Each study arm involved 3 intervention periods during night shifts, with participants consuming yogurt with low GI (1LGI or 3LGI) OR high GI (1HGI or 3HGI) carbohydrates, or no meal (0NGI). Objective alertness was assessed using a validated brief psychomotor vigilance task (PVT-B), subjective alertness with the Samn-Perelli scale, and hunger and gastrointestinal complaints through questionnaires., Results: Participants in the 1LGI (β: -4.6; 95% CI: 0.0, 9.3) and 3LGI (β: -3.4; 95% CI: 0.0, 6.8) conditions had fewer lapses during the PVT-B than those in the 3HGI condition. No differences were found between meal conditions for median and reciprocal reaction time or subjective alertness. All 4 conditions reported less hunger (β: from -0.6 to -1.2) compared with no meal. The 3LGI condition resulted in more rumbling intestines than the 3HGI (β: 1.1; 95% CI: 0.4, 1.7) and 0NGI (β: 0.74; 95% CI: 0.11, 1.37) conditions., Conclusions: Our study suggests that consuming 3 small low GI meals during the night shift helps maintain alertness and reduces lapses compared with 3 high GI meals. It also minimizes hunger but may cause mild gastrointestinal complaints. This trial was registered at International Clinical Trial Registry (https://trialsearch.who.int/Trial2.aspx?TrialID%3dNL-OMON25574)., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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47. Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial.

Author

Gijbels A, Jardon KM, Trouwborst I, Manusama KC, Goossens GH, Blaak EE, Feskens EJ, and Afman LA

Subjects
Humans, Male, Middle Aged, Female, Aged, Adult, Blood Glucose metabolism, Liver metabolism, Diet, High-Protein, Muscle, Skeletal metabolism, Insulin Resistance, Postprandial Period, Fasting
Abstract

Background: We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet., Objectives: To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile., Methods: Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m 2 ) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy., Results: A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041)., Conclusions: Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo&#95;search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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48. A Systematized Review of the Relationship Between Obesity and Vitamin C Requirements.

Author

Bird JK, Feskens EJ, and Melse-Boonstra A

Abstract

Obesity rates have increased globally in recent decades. Body weight is used as a modifiable factor in determining vitamin requirements. Accordingly, vitamin C requirements are volumetrically scaled from data for healthy weight males to other age- and sex-based categories. Likewise, it is possible that increases in body weight due to obesity may affect vitamin C needs. A systematized literature review was performed to summarize evidence on whether obesity affects vitamin C intake or status. The literature was also scanned for potential mechanisms for the relationship. Many observational studies showed that vitamin C status is lower in overweight and obese children and adults; this may be explained by lower vitamin C intakes. Nevertheless, a reanalysis of carefully conducted intervention studies has demonstrated a lower vitamin C status in participants who were overweight or obese when given the same dose of vitamin C as subjects of normal weight. Several mechanisms have been proposed to potentially explain why vitamin C status is lower in people with obesity: changes in vitamin C partitioning between lean and adipose tissue, volumetric dilution, metabolic alterations due to obesity, and gut microbial dysbiosis. Depletion-repletion or pharmacokinetic studies that include individuals of diverse body weights and ages would be helpful to further investigate whether obesity increases requirements for vitamin C. The current evidence base supports a lower vitamin C status in people who are overweight or obese; however, the association may be attenuated by lower vitamin C intakes., (© 2024 The Authors.)

Published
2024
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49. Relative validity of habitual sugar and low/no-calorie sweetener consumption assessed by food frequency questionnaire, multiple 24-h dietary recalls and urinary biomarkers: an observational study within the SWEET project.

Author

Buso ME, Boshuizen HC, Naomi ND, Maho W, Diepeveen-de Bruin M, Balvers MG, de Vries JH, Harrold JA, Halford JC, Raben A, Feskens EJ, and Brouwer-Brolsma EM

Subjects
Humans, Adult, Middle Aged, Aged, Beverages, Sucrose urine, Fructose, Surveys and Questionnaires, Biomarkers urine, Sweetening Agents, Sugars
Abstract

Background: Studies investigating associations between sweeteners and health yield inconsistent results, possibly due to subjective self-report dietary assessment methods., Objectives: We compared the performance of a food frequency questionnaire (FFQ), multiple 24-h dietary recalls (24hRs), and urinary biomarkers to estimate intake of sugars and low/no-calorie sweeteners (LNCSs)., Methods: Participants (n = 848, age 54 ± 12 y) from a 2-y observational study completed 1 semiquantitative FFQ and ≥ 3 nonconsecutive 24hRs. Both methods assessed intake of sugars (mono- and disaccharides, sucrose, fructose, free and added sugars) and sweetened foods and beverages (sugary foods, fruit juice, and sugar or LNCS-containing beverages [sugar-sweetened beverages and low/no-calorie sweetened beverages (LNCSBs)]); 24hRs also included LNCS-containing foods and tabletop sweeteners (low/no-calorie sweetened foods [LNCSFs]). Urinary excretion of sugars (fructose+sucrose) and LNCSs (acesulfame K+sucralose+steviol glucuronide+cyclamate+saccharin) were simultaneously assessed using ultrapressure liquid chromatography coupled to tandem mass spectrometry in 288 participants with 3 annual 24-h urine samples. Methods were compared using, amongst others, validity coefficients (correlations corrected for measurement error)., Results: Median (interquartile range) FFQ intakes ranged from 0 (0-7) g/d for LNCSBs to 94 (73-117) g/d for mono- and disaccharides. LNCSB use was reported by 32% of participants. Median LNCSB+LNCSF intake using 24hRs was 1 (0-50) g/d and reported by 58%. Total sugar excretions were detected in 100% of samples [56 (37-85) mg/d] and LNCSs in 99% of urine samples [3 (1-10) mg/d]. Comparing FFQ against 24hRs showed VCs ranging from 0.38 (fruit juice) to 0.74 (LNCSB). VCs for comparing FFQ with urinary excretions were 0.25 to 0.29 for sugars and 0.39 for LNCSBs; for 24hR they amounted to 0.31-0.38 for sugars, 0.37 for LNCSBs, and 0.45 for LNCSFs., Conclusions: The validity of the FFQ against 24hRs for the assessment of sugars and LNCSBs ranged from moderate to good. Comparing self-reports and urine excretions showed moderate agreement but highlighted an important underestimation of LNCS exposure using self-reports., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2024
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50. The Effect of Meal Glycemic Index and Meal Frequency on Glycemic Control and Variability in Female Nurses Working Night Shifts: A Two-Arm Randomized Cross-Over Trial.

Author

Suyoto PS, de Rijk MG, de Vries JH, and Feskens EJ

Subjects
Humans, Female, Cross-Over Studies, Blood Glucose Self-Monitoring, Glycemic Control, Insulin, Glucose, Meals, Postprandial Period, Glycemic Index, Blood Glucose
Abstract

Background: Night shift workers are exposed to circadian disruption, which contributes to impaired glucose tolerance. Although fasting during the night shift improves glucose homeostasis, adhering to this dietary strategy may be challenging., Objectives: This study evaluated the effect of fasting compared with the consumption of meals with different combinations of glycemic index (GI, low or high) and frequency (1 or 3 times) during the night shift on continuous glucose monitoring metrics., Methods: A 2-arm randomized cross-over trial was conducted on female nurses working night shifts. In each of those arms, the participants were either provided with no meal (fasted), low GI, or high-GI meal during the night shift with a meal frequency according to which arm they were randomly allocated to, either 1-MEAL or 3-MEAL. Outcome variables were glycemic control and variability (GC and GV) metrics during the night shift (21:30-7:00), in the morning after the night shift (07:00-13:00), and in the 24 h period (18:00-18:00)., Results: Compared to no meal, the consumption of 1 high-GI meal increased all GV metrics not only during the night shifts but also in the morning, for instance, as observed in the coefficient of variation (β = 0.03 mmol/L; 95% CI: 0.01, 0.05), and GV percentage (β = 4.13; 95% CI: 2.07, 6.18). The consumption of 1 or 3 low GI meals did not raise GC or GV metrics except for continuous overall net glycemic action during the night shifts after consuming 3 low GI meals. When controlling for GI, night shift meal frequency did not affect any metrics in any timeframe., Conclusions: High meal GI but not higher meal frequency during the night shift increased GC and GV in female night shift workers. Results for 1 low-GI meal during the night shift were not different from a glucose profile after no meal. This trial was registered at trialsearch.who.int as NL8715., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2024
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