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1. Combination of Waist Circumference and Circulating Levels of IGFBP-2 as a Simple Screening Tool for Early Detection of Metabolic Dysfunction-Associated Steatotic Liver Disease

Author

Rauzier C, Chartrand DJ, Alméras N, Lemieux I, Larose E, Mathieu P, Pibarot P, Lamarche B, Rhéaume C, Poirier P, Després JP, and Picard F

Subjects
humans, insulin-like growth factor binding protein-2, waist circumference, hepatic fat, metabolic dysfunction–associated steatotic liver disease, biomarker, plasma, non-invasive, Specialties of internal medicine, RC581-951
Abstract

Chloé Rauzier,1,2 Dominic J Chartrand,1,3 Natalie Alméras,1,3 Isabelle Lemieux,1 Eric Larose,1,4 Patrick Mathieu,1,5 Philippe Pibarot,1,4 Benoît Lamarche,6,7 Caroline Rhéaume,1,8,9 Paul Poirier,1,2 Jean-Pierre Després,1,3,9 Frédéric Picard1,2 1Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec (IUCPQ) – Université Laval, Québec, QC, Canada; 2Faculté de pharmacie, Université Laval, Québec, QC, Canada; 3Département de kinésiologie, Faculté de médecine, Université Laval, Québec, QC, Canada; 4Département de médecine, Faculté de médecine, Université Laval, Québec, QC, Canada; 5Département de chirurgie, Faculté de médecine, Université Laval, Québec, QC, Canada; 6Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec, QC, Canada; 7École de nutrition, Faculté des sciences de l’agriculture et de l’alimentation, Université Laval, Québec, QC, Canada; 8Département de médecine familiale et de médecine d’urgence, Faculté de médecine, Université Laval, Québec, QC, Canada; 9VITAM – Centre de recherche en santé durable, Québec, QC, CanadaCorrespondence: Frédéric Picard; Jean-Pierre Després, Email Frederic.Picard@criucpq.ulaval.ca; jean-pierre.despres.ciussscn@ssss.gouv.qc.caAbstract: Optimal non-invasive biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) remain elusive, especially in the detection of early stages. This study tested in an asymptomatic cohort of 171 men (49.2 ± 8.6 years) and 131 women (51.8 ± 8.5 years) whether waist circumference (WC) and circulating levels of insulin-like growth factor-binding protein 2 (IGFBP-2) could identify individuals with liver fat > 5% as assessed by magnetic resonance spectroscopy. Participants with high WC (> 85 or 90 cm for women and men, respectively) and low IGFBP-2 (< 260 or 230 ng/mL for women and men, respectively) were characterized by a higher risk of having MASLD (46.3%, p < 0.0001). Among the 68 individuals with MASLD, 73.5% fell into the subgroup with high WC and low IGFBP-2 concentrations (p < 0.0001). When combined, these markers reached a sensitivity of 73.5% and specificity of 75.2% for MASLD. Thus, WC and plasma IGFBP-2 levels might be useful as a novel, simple, and non-invasive index to support existing tools in the identification of individuals at risk of early-stage MASLD.Keywords: humans, insulin-like growth factor-binding protein 2, waist circumference, hepatic fat, metabolic dysfunction-associated steatotic liver disease, biomarker, plasma, non-invasive

Published
2024

2. Low Liver Density Is Linked to Cardiovascular Comorbidity in COPD: An ECLIPSE Cohort Analysis

Author

Viglino D, Martin M, Almeras N, Després JP, Coxson HO, Pépin JL, Vivodtzev I, and Maltais F

Subjects
chronic obstuctive pulmonary disease, liver density, steatosis, fatty liver disease, chest ct-scan, cardiovascular cormorbidity, eclipse cohort, Diseases of the respiratory system, RC705-779
Abstract

Damien Viglino,1,2,* Mickaël Martin,1,* Natalie Almeras,1 Jean-Pierre Després,1 Harvey O Coxson,3 Jean-Louis Pépin,2 Isabelle Vivodtzev,2 François Maltais1 On behalf of the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) investigators1Centre de Recherche, Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec, Canada; 2Hypoxia Pathophysiology Laboratory INSERM U1042, Grenoble Alpes University Hospital, Grenoble, France; 3Radiology, University of British Columbia, Vancouver, British Columbia, Canada*These authors contributed equally to this workCorrespondence: Damien ViglinoCentre de Recherche Institut Universitaire de Cardiologie et de Pneumologie de Québec, 2725 Chemin Sainte-Foy, Québec, QC G1V 4G5, CanadaTel +1 418 656 8711Email damien.viglino.1@ulaval.caPurpose: Fatty liver disease is associated with cardiometabolic disorders and represents a potential key comorbidity in Chronic Obstructive Pulmonary Disease (COPD). Some intermediary mechanisms of fatty liver disease (including its histological component steatosis) include tissue hypoxia, low-grade inflammation and oxidative stress that are key features of COPD. Despite these shared physiological pathways, the effect of COPD on the prevalence of hepatic steatosis, and the association between hepatic steatosis and comorbidities in this population remain unclear. Liver density measured by computed tomography (CT)-scan is a non-invasive surrogate of fat infiltration, with lower liver densities reflecting more fat infiltration and a liver density of 40 Hounsfield Units (HU) corresponding to a severe 30% fat infiltration.Patients and Methods: We took advantage of the international cohort ECLIPSE in which non-enhanced chest CT-scans were obtained in 1554 patients with COPD and 387 healthy controls to analyse the liver density at T12-L1.Results: The distribution of liver density was similar and the prevalence of severe steatosis (densitya=1.59, 95% CI 1.12 to 2.24) and stroke (ORa=2.20, 95% CI 1.07 to 4.50), in comparison with the highest liver density quartile.Conclusion: The present data indicate that a low liver density emerged as a predictor of cardiovascular comorbidities in the COPD population. However, the distribution of liver density and the prevalence of severe steatosis were similar in patients with COPD and control subjects.Keywords: chronic obstructive pulmonary disease, liver density, steatosis, fatty liver disease, chest CT-scan, cardiovascular comorbidity, ECLIPSE

Published
2020

3. Ectopic adiposity and cardiometabolic health in COPD

Author

Coats V, Després JP, Alméras N, Martin M, Sin DD, Rabasa-Lhoret R, Larose E, Tan WC, Bourbeau J, and Maltais F

Subjects
Pulmonary Disease, Chronic Obstructive, Obesity, Abdominal, Comorbidity, Diseases of the respiratory system, RC705-779
Abstract

Valérie Coats,1 Jean-Pierre Després,1 Natalie Alméras,1 Mickaël Martin,1 Don D Sin,2 Rémi Rabasa-Lhoret,3 Éric Larose,1 Wan C Tan,2 Jean Bourbeau,4 François Maltais1On behalf of the CanCOLD Collaborative Research Group and the Canadian Respiratory Research Network 1Centre de recherche, Institut universitaire de cardiologie et de pneumologie de Québec, Université, Laval, QC, Canada; 2University of British Columbia, Vancouver, BC, Canada; 3Institut de Recherches Cliniques de Montréal, Département de Nutrition et Service d’Endocrinologie, Centre Hospitalier de l’Université de Montréal, Montréal, QC, Canada; 4Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, Montreal, QC, CanadaRationale: Obesity/overweight is the most prevalent body composition abnormality in COPD. However, little is known about the impact of fat distribution on cardiometabolic health in COPD.Objective: To study the associations between ectopic adiposity, cardiometabolic health, and COPD.Methods: A total of 263 subjects (166 males; age=65±9 years) were randomly selected from the general population. Subjects were classified as non-COPD controls and COPD, according to the Global initiative for chronic Obstructive Lung Disease (GOLD) classification, and the presence of cardiometabolic comorbidities was recorded. Ectopic fat accumulation was documented from computed tomography measurements of visceral adipose tissue cross-sectional areas and muscle mean attenuation, assessed at L4–L5. Blood glucose, lipid, and adipokine profiles were also evaluated.Results: After correcting for age, sex, and tobacco exposure, visceral adipose tissue cross-sectional area was higher in GOLD 2+ compared to GOLD 1 individuals. Consistent with this, mean muscle tissue attenuation was lower in GOLD 2+ vs GOLD 1 and non-COPD controls (P

Published
2018

4. Ectopic fat accumulation in patients with COPD: an ECLIPSE substudy

Author

Martin M, Almeras N, Després JP, Coxson HO, Washko GR, Vivodtzev I, Wouters EFM, Rutten E, Williams MC, Murchison JT, MacNee W, Sin DD, and Maltais F

Subjects
metabolic syndrome, obesity, abdominal adiposity, muscle, Diseases of the respiratory system, RC705-779
Abstract

Mickaël Martin,1 Natalie Almeras,1 Jean-Pierre Després,1 Harvey O Coxson,2 George R Washko,3 Isabelle Vivodtzev,4 Emiel FM Wouters,5 Erica Rutten,6 Michelle C Williams,7 John T Murchison,8 William MacNee,7 Don D Sin,2 François Maltais1 On behalf of the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) Study Group 1Research Centre, Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, QC, 2Department of Radiology, University of British Columbia, Vancouver, BC, Canada; 3Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4Hypoxia Pathophysiology Laboratory, Grenoble University Hospital, Grenoble, France; 5Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, 6Research and Development, CIRO, Horn, the Netherlands; 7Department of Respiratory Medicine, University of Edinburgh, 8Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK Background: Obesity is increasingly associated with COPD, but little is known about the prevalence of ectopic fat accumulation in COPD and whether this can possibly be associated with poor clinical outcomes and comorbidities. The Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) substudy tested the hypothesis that COPD is associated with increased ectopic fat accumulation and that this would be associated with COPD-related outcomes and comorbidities.Methods: Computed tomography (CT) images of the thorax obtained in ECLIPSE were used to quantify ectopic fat accumulation at L2–L3 (eg, cross-sectional area [CSA] of visceral adipose tissue [VAT] and muscle tissue [MT] attenuation, a reflection of muscle fat infiltration) and CSA of MT. A dose–response relationship between CSA of VAT, MT attenuation and CSA of MT and COPD-related outcomes (6-minute walking distance [6MWD], exacerbation rate, quality of life, and forced expiratory volume in 1 second [FEV1] decline) was addressed with the Cochran–Armitage trend test. Regression models were used to investigate possible relationships between CT body composition indices and comorbidities.Results: From the entire ECLIPSE cohort, we identified 585 subjects with valid CT images at L2–L3 to assess body composition. CSA of VAT was increased (P

Published
2017

5. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation

Author

Kaptoge, S, Seshasai, SRK, Sun, L, Walker, M, Bolton, T, Spackman, S, Ataklte, F, Willeit, P, Bell, S, Burgess, S, Pennells, L, Altay, S, Assmann, G, Ben-Shlomo, Y, Best, LG, Björkelund, C, Blazer, DG, Brenner, H, Brunner, EJ, Dagenais, GR, Cooper, JA, Cooper, C, Crespo, CJ, Cushman, M, D'Agostino, RB, Sr, Daimon, M, Daniels, LB, Danker, R, Davidson, KW, de Jongh, RT, Donfrancesco, C, Ducimetiere, P, Elders, PJM, Engström, G, Ford, I, Gallacher, I, Bakker, SJL, Goldbourt, U, de La Cámara, G, Grimsgaard, S, Gudnason, V, Hansson, PO, Imano, H, Jukema, JW, Kabrhel, C, Kauhanen, J, Kavousi, M, Kiechl, S, Knuiman, MW, Kromhout, D, Krumholz, HM, Kuller, LH, Laatikainen, T, Lowler, DA, Meyer, HE, Mukamal, K, Nietert, PJ, Ninomiya, T, Nitsch, D, Nordestgaard, BG, Palmieri, L, Price, JF, Ridker, PM, Sun, Q, Rosengren, A, Roussel, R, Sakurai, M, Salomaa, V, Schöttker, B, Shaw, JE, Strandberg, TE, Sundström, J, Tolonen, H, Tverdal, A, Verschuren, WMM, Völzke, H, Wagenknecht, L, Wallace, RB, Wannamethee, SG, Wareham, NJ, Wassertheil-Smoller, S, Yamagishi, K, Yeap, BB, Harrison, S, Inouye, M, Griffin, S, Butterworth, AS, Wood, AM, Thompson, SG, Sattar, N, Danesh, J, Di Angelantonio, E, Tipping, RW, Russell, S, Johansen, M, Bancks, MP, Mongraw-Chaffin, M, Magliano, D, Barr, ELM, Zimmet, PZ, Whincup, PH, Willeit, J, Leitner, C, Lawlor, DA, Elwood, P, Sutherland, SE, Hunt, KJ, Selmer, RM, Haheim, LL, Ariansen, I, Tybjaer-Hansen, A, Frikkle-Schmidt, R, Langsted, A, Lo Noce, C, Balkau, B, Bonnet, F, Fumeron, F, Pablos, DL, Ferro, CR, Morales, TG, Mclachlan, S, Guralnik, J, Khaw, KT, Holleczek, B, Stocker, H, Nissinen, A, Vartiainen, E, Jousilahti, P, Harald, K, Massaro, JM, Pencina, M, Lyass, A, Susa, S, Oizumi, T, Kayama, T, Chetrit, A, Roth, J, Orenstein, L, Welin, L, Svärdsudd, K, Lissner, L, Hange, D, Mehlig, K, Tilvis, RS, Dennison, E, Westbury, L, Norman, PE, Almeida, OP, Hankey, GJ, Hata, J, Shibata, M, Furuta, Y, Bom, MT, Rutters, F, Muilwijk, M, Kraft, P, Lindstrom, S, Turman, C, Kiyama, M, Kitamura, A, Gerber, Y, Salonen, JT, van Schoor, LN, van Zutphen, EM, Melander, O, Psaty, BM, Blaha, M, de Boer, IH, Kronmal, RA, Grandits, G, Shin, H-C, Albertorio, JR, Gillum, RF, Hu, FB, Humphries, S, Hill- Briggs, F, Vrany, E, Butler, M, Schwartz, JE, Iso, H, Amouyel, P, Arveiler, D, Ferrieres, J, Gansevoort, RT, de Boer, R, Kieneker, L, Trompet, S, Kearney, P, Cantin, B, Després, JP, Lamarche, B, Laughlin, G, McEvoy, L, Aspelund, T, Thorsson, B, Sigurdsson, G, Tilly, M, Ikram, MA, Dorr, M, Schipf, S, Fretts, AM, Umans, JG, Ali, T, Shara, N, Davey-Smith, G, Can, G, Yüksel, H, Özkan, U, Nakagawa, H, Morikawa, Y, Ishizaki, M, Njølstad, I, Wilsgaard, T, Mathiesen, E, Buring, J, Cook, N, Arndt, V, Rothenbacher, D, Manson, J, Tinker, L, Shipley, M, Tabak, AG, Kivimaki, M, Packard, C, Robertson, M, Feskens, E, and Geleijnse, M

Published
2023
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6. 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

7. Minor Contribution of Endogenous GLP-1 and GLP-2 to Postprandial Lipemia in Obese Men

Author

Matikainen N, Björnson E, Söderlund S, Borén C, Eliasson B, Pietiläinen KH, Bogl LH, Hakkarainen A, Lundbom N, RIVELLESE, ANGELA ALBAROSA, RICCARDI, GABRIELE, Després JP, Alméras N, Holst JJ, Deacon CF, Borén J, Taskinen MR, Matikainen, N, Björnson, E, Söderlund, S, Borén, C, Eliasson, B, Pietiläinen, Kh, Bogl, Lh, Hakkarainen, A, Lundbom, N, Rivellese, ANGELA ALBAROSA, Riccardi, Gabriele, Després, Jp, Alméras, N, Holst, Jj, Deacon, Cf, Borén, J, and Taskinen, Mr

Subjects
postprandial lipemia, GLP-1, gastrointestinal hormones, postprandial glucose response
Abstract

CONTEXT: Glucose and lipids stimulate the gut-hormones glucagon-like peptide (GLP)-1, GLP-2 and glucose-dependent insulinotropic polypeptide (GIP) but the effect of these on human postprandial lipid metabolism is not fully clarified. OBJECTIVE: To explore the responses of GLP-1, GLP-2 and GIP after a fat-rich meal compared to the same responses after an oral glucose tolerance test (OGTT) and to investigate possible relationships between incretin response and triglyceride-rich lipoprotein (TRL) response to a fat-rich meal. DESIGN: Glucose, insulin, GLP-1, GLP-2 and GIP were measured after an OGTT and after a fat-rich meal in 65 healthy obese (BMI 26.5-40.2 kg/m(2)) male subjects. Triglycerides (TG), apoB48 and apoB100 in TG-rich lipoproteins (chylomicrons, VLDL1 and VLDL2) were measured after the fat-rich meal. MAIN OUTCOME MEASURES: Postprandial responses (area under the curve, AUC) for glucose, insulin, GLP-1, GLP-2, GIP in plasma, and TG, apoB48 and apoB100 in plasma and TG-rich lipoproteins. RESULTS: The GLP-1, GLP-2 and GIP responses after the fat-rich meal and after the OGTT correlated strongly (r = 0.73, p

Published
2016

8. CVD risk assessment: do we need the metabolic syndrome or better global cardiometabolic risk calculators?

Author

Després Jp

Subjects
Metabolic Syndrome, Cardiometabolic risk, medicine.medical_specialty, Nutrition and Dietetics, business.industry, Cvd risk, Endocrinology, Diabetes and Metabolism, Medicine (miscellaneous), Prognosis, medicine.disease, Risk Assessment, Cardiovascular Diseases, Risk Factors, medicine, Physical therapy, Humans, cardiovascular diseases, Metabolic syndrome, Intensive care medicine, business
Abstract

CVD risk assessment: do we need the metabolic syndrome or better global cardiometabolic risk calculators?

Published
2008

9. 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

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. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality

Author

Emerging Risk Factors Collaboration, Erqou S, Kaptoge S, Perry PL, Di Angelantonio E, Thompson A, White IR, Marcovina SM, Collins R, Thompson SG, Tipping RW, Ford CE, Simpson LM, Walldius G, Jungner I, Folsom AR, Chambless L, Panagiotakos D, Pitsavos C, Chrysohoou C, Stefanadis C, Goldbourt U, Benderly M, Tanne D, Whincup P, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Santer P, Mayr A, Wald N, Ebrahim S, Lawlor D, Yarnell J, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Cushman M, Psaty BM, Tracy R, Tybjaerg Hansen A, Nordestgaard BG, Frikke Schmidt R, Kamstrup PR, 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 A, Smith FB, Taylor J, Guralnik JM, Phillips CL, Wallace RB, 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, 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 J, 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, Hedblad B, Berglund G, Engström G, Verschuren WM, Blokstra A, Döring A, Koenig W, Meisinger C, Mraz W, Bueno de Mesquita HB, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum RF, Mussolino M, Hankinson S, Manson JE, Cooper JA, Bauer KA, 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, Shepherd J, Packard CJ, Sattar N, Ford I, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Daniels LB, Laughlin GA, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler MM, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe G, Ben Shlomo Y, Davey Smith G, Howard BV, Zhang Y, Best L, Umans J, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Sundström J, Lind L, Lannfelt L, Gaziano JM, Stampfer M, Ridker PM, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Marmot M, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Cobbe S, Robertson M, He Y, Marin Ibanñez A, Feskens E, Kromhout D, Walker M, Watson S, Lewington S, Orfei L, Pennells L, Ray KK, Sarwar N, Alexander M, Wensley F, Wood AM, Danesh J., PANICO, SALVATORE, Developmental Genetics, EMGO+ - Lifestyle, Overweight and Diabetes, Emerging Risk Factors, Collaboration, Erqou, S, Kaptoge, S, Perry, Pl, Di Angelantonio, E, Thompson, A, White, Ir, Marcovina, Sm, Collins, R, Thompson, Sg, Tipping, Rw, Ford, Ce, Simpson, Lm, Walldius, G, Jungner, I, Folsom, Ar, Chambless, L, Panagiotakos, D, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Goldbourt, U, Benderly, M, Tanne, D, Whincup, P, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, D, Yarnell, J, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Cushman, M, Psaty, Bm, Tracy, R, Tybjaerg Hansen, A, Nordestgaard, Bg, Frikke Schmidt, R, Kamstrup, Pr, 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, A, Smith, Fb, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, Rb, 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, 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, J, 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, Hedblad, B, Berglund, G, Engström, G, Verschuren, Wm, Blokstra, A, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Bueno de Mesquita, Hb, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, Rf, Mussolino, M, Hankinson, S, Manson, Je, Cooper, Ja, Bauer, Ka, 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, Shepherd, J, Packard, Cj, Sattar, N, Ford, I, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Daniels, Lb, Laughlin, Ga, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, Mm, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, G, Ben Shlomo, Y, Davey Smith, G, Howard, Bv, Zhang, Y, Best, L, Umans, J, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Sundström, J, Lind, L, Lannfelt, L, Gaziano, Jm, Stampfer, M, Ridker, Pm, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Marmot, M, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Cobbe, S, Robertson, M, He, Y, Marin Ibanñez, A, Feskens, E, Kromhout, D, Walker, M, Watson, S, Lewington, S, Orfei, L, Pennells, L, Ray, Kk, Sarwar, N, Alexander, M, Wensley, F, Wood, Am, and Danesh, J.

Subjects
medicine.medical_specialty, Context (language use), Coronary Disease, Article, Risk Factors, General & Internal Medicine, Internal medicine, Cause of Death, medicine, Humans, Myocardial infarction, Prospective cohort study, Stroke, biology, business.industry, 11 Medical And Health Sciences, General Medicine, Lipoprotein(a), medicine.disease, Surgery, Relative risk, Nested case-control study, biology.protein, business, Cohort study
Abstract

Udgivelsesdato: 2009-Jul-22 CONTEXT: Circulating concentration of lipoprotein(a) (Lp[a]), a large glycoprotein attached to a low-density lipoprotein-like particle, may be associated with risk of coronary heart disease (CHD) and stroke. OBJECTIVE: To assess the relationship of Lp(a) concentration with risk of major vascular and nonvascular outcomes. STUDY SELECTION: Long-term prospective studies that recorded Lp(a) concentration and subsequent major vascular morbidity and/or cause-specific mortality published between January 1970 and March 2009 were identified through electronic searches of MEDLINE and other databases, manual searches of reference lists, and discussion with collaborators. DATA EXTRACTION: Individual records were provided for each of 126,634 participants in 36 prospective studies. During 1.3 million person-years of follow-up, 22,076 first-ever fatal or nonfatal vascular disease outcomes or nonvascular deaths were recorded, including 9336 CHD outcomes, 1903 ischemic strokes, 338 hemorrhagic strokes, 751 unclassified strokes, 1091 other vascular deaths, 8114 nonvascular deaths, and 242 deaths of unknown cause. Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. Analyses excluded participants with known preexisting CHD or stroke at baseline. DATA SYNTHESIS: Lipoprotein(a) concentration was weakly correlated with several conventional vascular risk factors and it was highly consistent within individuals over several years. Associations of Lp(a) with CHD risk were broadly continuous in shape. In the 24 cohort studies, the rates of CHD in the top and bottom thirds of baseline Lp(a) distributions, respectively, were 5.6 (95% confidence interval [CI], 5.4-5.9) per 1000 person-years and 4.4 (95% CI, 4.2-4.6) per 1000 person-years. The risk ratio for CHD, adjusted for age and sex only, was 1.16 (95% CI, 1.11-1.22) per 3.5-fold higher usual Lp(a) concentration (ie, per 1 SD), and it was 1.13 (95% CI, 1.09-1.18) following further adjustment for lipids and other conventional risk factors. The corresponding adjusted risk ratios were 1.10 (95% CI, 1.02-1.18) for ischemic stroke, 1.01 (95% CI, 0.98-1.05) for the aggregate of nonvascular mortality, 1.00 (95% CI, 0.97-1.04) for cancer deaths, and 1.00 (95% CI, 0.95-1.06) for nonvascular deaths other than cancer. CONCLUSION: Under a wide range of circumstances, there are continuous, independent, and modest associations of Lp(a) concentration with risk of CHD and stroke that appear exclusive to vascular outcomes.

Published
2009

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. Relation of components of insulin resistance syndrome to coronary disease risk

Author

Marette A and Després Jp

Subjects
Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Coronary Disease, Hyperlipidemias, Coronary disease, Bioinformatics, Disease cluster, Insulin resistance, Risk Factors, Diabetes mellitus, Genetics, medicine, Humans, Insulin, Genetic Predisposition to Disease, Exercise, Molecular Biology, Nutrition and Dietetics, business.industry, Cell Biology, medicine.disease, Coronary heart disease, Diet, Review article, Adipose Tissue, Diabetes Mellitus, Type 2, Etiology, Insulin Resistance, Cardiology and Cardiovascular Medicine, business
Abstract

Insulin resistance, which is a prevalent condition, has been associated with a cluster of metabolic disturbances that increase the risk of non-insulin-dependent diabetes mellitus and cardiovascular diseases. In this review article, the complexity of the etiology of insulin resistance is emphasized as it results from the interaction of genetic and environmental factors. Potential cellular defects underlying insulin resistance are discussed as well as the relation of impaired insulin action to dyslipidaemia and coronary heart disease.

Published
1994

33. 10 Visceral Obesity, Insulin Resistance, and Dyslipidemia

Author

Després Jp

Subjects
medicine.medical_specialty, business.industry, Adipose tissue, Physical Therapy, Sports Therapy and Rehabilitation, Anthropometry, medicine.disease, Obesity, Insulin resistance, Endurance training, Internal medicine, Hyperlipidemia, Medicine, Orthopedics and Sports Medicine, business, Dyslipidemia, Visceral Obesity
Published
1997

36. Effect of rimonabant on carotid intima-media thickness (CIMT) progression in patients with abdominal obesity and metabolic syndrome: the AUDITOR Trial.

Author

O'Leary DH, Reuwer AQ, Nissen SE, Després JP, Deanfield JE, Brown MW, Zhou R, Zabbatino SM, Job B, Kastelein JJ, Visseren FL, AUDITOR investigators, O'Leary, Daniel H, Reuwer, Anne Q, Nissen, Steven E, Després, Jean-Pierre, Deanfield, John E, Brown, Michael W, Zhou, Rong, and Zabbatino, Salvatore M

Abstract

Objective: The aim of this trial was to determine whether obese patients benefit from treatment with rimonabant in terms of progression of carotid atherosclerosis. Rimonabant, a selective cannabinoid-1 receptor blocker, reduces body weight and improves cardiometabolic risk factors in patients who are obese. Design, Setting, Patients, Interventions and Results: A prospective, double-blind, placebo-controlled trial (Atherosclerosis Underlying Development assessed by Intima-media Thickness in patients On Rimonabant (AUDITOR)) randomised 661 patients with abdominal obesity and metabolic syndrome to rimonabant or placebo for 30 months of treatment. The absolute change in the average value for six segments of far wall carotid intima-media thickness from baseline to month 30 was 0.010 ± 0.095 mm in the rimonabant group and 0.012 ± 0.091 mm in the placebo group (p=0.67). The annualised change was an increase of 0.005 ± 0.042 mm for the rimonabant-treated group and 0.007 ± 0.043 mm for the placebo-treated group (p=0.45). Conclusions: There was no difference in atherosclerosis progression between patients receiving rimonabant for 30 months and those receiving placebo for the primary efficacy measure (absolute change in carotid intima-media thickness). These findings are consistent with a similar study using coronary intravascular ultrasound and another study evaluating the occurrence of cardiovascular events. Our findings suggest that a 5% loss of body weight over a 30-month period with rimonabant is insufficient to modify atherosclerosis progression in the carotid artery in obese patients with metabolic syndrome. Clinical trial registration information clinicaltrials.gov Identifier: NCT00228176. [ABSTRACT FROM AUTHOR]

Published
2011
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37. Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis.

Author

Malik VS, Popkin BM, Bray GA, Després JP, Willett WC, Hu FB, Malik, Vasanti S, Popkin, Barry M, Bray, George A, Després, Jean-Pierre, Willett, Walter C, and Hu, Frank B

Abstract

Objective: Consumption of sugar-sweetened beverages (SSBs), which include soft drinks, fruit drinks, iced tea, and energy and vitamin water drinks has risen across the globe. Regular consumption of SSBs has been associated with weight gain and risk of overweight and obesity, but the role of SSBs in the development of related chronic metabolic diseases, such as metabolic syndrome and type 2 diabetes, has not been quantitatively reviewed.Research Design and Methods: We searched the MEDLINE database up to May 2010 for prospective cohort studies of SSB intake and risk of metabolic syndrome and type 2 diabetes. We identified 11 studies (three for metabolic syndrome and eight for type 2 diabetes) for inclusion in a random-effects meta-analysis comparing SSB intake in the highest to lowest quantiles in relation to risk of metabolic syndrome and type 2 diabetes.Results: Based on data from these studies, including 310,819 participants and 15,043 cases of type 2 diabetes, individuals in the highest quantile of SSB intake (most often 1-2 servings/day) had a 26% greater risk of developing type 2 diabetes than those in the lowest quantile (none or <1 serving/month) (relative risk [RR] 1.26 [95% CI 1.12-1.41]). Among studies evaluating metabolic syndrome, including 19,431 participants and 5,803 cases, the pooled RR was 1.20 [1.02-1.42].Conclusions: In addition to weight gain, higher consumption of SSBs is associated with development of metabolic syndrome and type 2 diabetes. These data provide empirical evidence that intake of SSBs should be limited to reduce obesity-related risk of chronic metabolic diseases. [ABSTRACT FROM AUTHOR]

Published
2010
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39. The hypertriglyceridemic-waist phenotype and the risk of coronary artery disease: results from the EPIC-Norfolk prospective population study.

Author

Arsenault BJ, Lemieux I, Després JP, Wareham NJ, Kastelein JJ, Khaw KT, Boekholdt SM, Arsenault, Benoit J, Lemieux, Isabelle, Després, Jean-Pierre, Wareham, Nicholas J, Kastelein, John J P, Khaw, Kay-Tee, and Boekholdt, S Matthijs

Abstract

Background: Screening for increased waist circumference and hypertriglyceridemia (the hypertriglyceridemic-waist phenotype) has been proposed as an inexpensive approach to identify patients with excess intra-abdominal adiposity and associated metabolic abnormalities. We examined the relationship between the hypertriglyceridemic-waist phenotype to the risk of coronary artery disease in apparently healthy individuals.Methods: A total of 21,787 participants aged 45-79 years were followed for a mean of 9.8 (standard deviation 1.7) years. Coronary artery disease developed in 2109 of them during follow-up. The hypertriglyceridemic-waist phenotype was defined as a waist circumference of 90 cm or more and a triglyceride level of 2.0 mmol/L or more in men, and a waist circumference of 85 cm or more and a triglyceride level of 1.5 mmol/L or more in women.Results: Compared with participants who had a waist circumference and triglyceride level below the threshold, those with the hypertriglyceridemic-waist phenotype had higher blood pressure indices, higher levels of apolipoprotein B and C-reactive protein, lower levels of high-density lipoprotein cholesterol and apolipoprotein A-I, and smaller low-density lipoprotein particles. Among men, those with the hypertriglyceridemic-waist phenotype had an unadjusted hazard ratio for future coronary artery disease of 2.40 (95% confidence interval [CI] 2.02-2.87) compared with men who did not have the phenotype. Women with the phenotype had an unadjusted hazard ratio of 3.84 (95% CI 3.20-4.62) compared with women who did not have the phenotype.Interpretation: Among participants from a European cohort representative of a contemporary Western population, the hypertriglyceridemic-waist phenotype was associated with a deteriorated cardiometabolic risk profile and an increased risk for coronary artery disease. [ABSTRACT FROM AUTHOR]

Published
2010
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43. Comparison of plaque sealing with paclitaxel-eluting stents versus medical therapy for the treatment of moderate nonsignificant saphenous vein graft lesions: the moderate vein graft lesion stenting with the taxus stent and intravascular ultrasound (VELETI) pilot trial.

Author

Rodés-Cabau J, Bertrand OF, Larose E, Déry JP, Rinfret S, Bagur R, Proulx G, Nguyen CM, Côté M, Landcop MC, Boudreault JR, Rouleau J, Roy L, Gleeton O, Barbeau G, Noël B, Courtis J, Dagenais GR, Després JP, and DeLarochellière R

Published
2009
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44. Inflammatory biomarkers and the prediction of coronary events among people at intermediate risk: the EPIC-Norfolk prospective population study.

Author

Rana JS, Cote M, Després JP, Sandhu MS, Talmud PJ, Ninio E, Wareham NJ, Kastelein JJ, Zwinderman AH, Khaw KT, and Boekholdt SM

Abstract

OBJECTIVE: To evaluate the role of the inflammatory biomarkers C-reactive protein (CRP), myeloperoxidase, paraoxonase, secretory phospholipase A2 group IIA (sPLA2), lipoprotein-associated phospholipase A2, fibrinogen, macrophage chemoattractant protein-1 and adiponectin, in predicting the risk of coronary heart disease (CHD) among people estimated to be at intermediate risk according to the Framingham Risk Score (FRS). DESIGN: Prospective case-control study nested in EPIC-Norfolk cohort. SETTING: Norfolk, UK. PATIENTS: Apparently healthy men and women aged 45-79 years. MAIN OUTCOME MEASURES: Risk of future coronary artery disease. RESULTS: For participants predicted to be at intermediate risk by the FRS, the highest c statistics were observed for FRS plus CRP (0.61, 95% CI 0.57 to 0.65) and for FRS plus sPLA2 (0.56, 95% CI 0.52 to 0.6). Net correct reclassification of cases and controls for each marker was assessed for people across the entire risk spectrum and again for people at intermediate risk only. The largest differences were observed for CRP, 12.0% net reclassification improvement in the entire risk spectrum and 28.4% net reclassification improvement in the intermediate-risk group and for sPLA2, the net reclassification improvement was 6.4% in the entire risk spectrum and 16.3% in the intermediate-risk group. CONCLUSIONS: The discriminatory potential of inflammatory biomarkers was substantially different when analysed across the entire risk spectrum compared with the subgroup of people at intermediate risk. [ABSTRACT FROM AUTHOR]

Published
2009
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47. Effect of rimonabant on progression of atherosclerosis in patients with abdominal obesity and coronary artery disease: the STRADIVARIUS randomized controlled trial.

Author

Nissen SE, Nicholls SJ, Wolski K, Rodés-Cabau J, Cannon CP, Deanfield JE, Després JP, Kastelein JJ, Steinhubl SR, Kapadia S, Yasin M, Ruzyllo W, Gaudin C, Job B, Hu B, Bhatt DL, Lincoff AM, Tuzcu EM, STRADIVARIUS Investigators, and Nissen, Steven E

Abstract

Context: Abdominal obesity is associated with metabolic abnormalities and increased risk of atherosclerotic cardiovascular disease. However, no obesity management strategy has demonstrated the ability to slow progression of coronary disease.Objective: To determine whether weight loss and metabolic effects of the selective cannabinoid type 1 receptor antagonist rimonabant reduces progression of coronary disease in patients with abdominal obesity and the metabolic syndrome.Design, Setting, and Patients: Randomized, double-blinded, placebo-controlled, 2-group, parallel-group trial (enrollment December 2004-December 2005) comparing rimonabant with placebo in 839 patients at 112 centers in North America, Europe, and Australia.Interventions: Patients received dietary counseling, were randomized to receive rimonabant (20 mg daily) or matching placebo, and underwent coronary intravascular ultrasonography at baseline (n = 839) and study completion (n = 676).Main Outcome Measures: The primary efficacy parameter was change in percent atheroma volume (PAV); the secondary efficacy parameter was change in normalized total atheroma volume (TAV).Results: In the rimonabant vs placebo groups, PAV (95% confidence interval [CI]) increased 0.25% (-0.04% to 0.54%) vs 0.51% (0.22% to 0.80%) (P = .22), respectively, and TAV decreased 2.2 mm3 (-4.09 to -0.24) vs an increase of 0.88 mm3 (-1.03 to 2.79) (P = .03). In the rimonabant vs placebo groups, imputing results based on baseline characteristics for patients not completing the trial, PAV increased 0.25% (-0.04% to 0.55%) vs 0.57% (0.29% to 0.84%) (P = .13), and TAV decreased 1.95 mm3 (-3.8 to -0.10) vs an increase of 1.19 mm3 (-0.73 to 3.12) (P = .02). Rimonabant-treated patients had a larger reduction in body weight (4.3 kg [-5.1 to -3.5] vs 0.5 kg [-1.3 to 0.3]) and greater decrease in waist circumference (4.5 cm [-5.4 to -3.7] vs 1.0 cm [-1.9 to -0.2]) (P < .001 for both comparisons). In the rimonabant vs placebo groups, high-density lipoprotein cholesterol levels increased 5.8 mg/dL (4.9 to 6.8) (22.4%) vs 1.8 mg/dL (0.9 to 2.7) (6.9%) (P < .001), and median triglyceride levels decreased 24.8 mg/dL (-35.4 to -17.3) (20.5%) vs 8.9 mg/dL (-14.2 to -1.8) (6.2%) (P < .001). Rimonabant-treated patients had greater decreases in high-sensitivity C-reactive protein (1.3 mg/dL [-1.7 to -1.2] [50.3%] vs 0.9 mg/dL [-1.4 to -0.5] [30.9%]) and less increase in glycated hemoglobin levels (0.11% [0.02% to 0.20%] vs 0.40% [0.31% to 0.49%]) (P < .001 for both comparisons). Psychiatric adverse effects were more common in the rimonabant group (43.4% vs 28.4%, P < .001).Conclusions: After 18 months of treatment, the study failed to show an effect for rimonabant on disease progression for the primary end point (PAV) but showed a favorable effect on the secondary end point (TAV). Determining whether rimonabant is useful in management of coronary disease will require additional imaging and outcomes trials, which are currently under way.Trial Registration: clinicaltrials.gov Identifier: NCT00124332. [ABSTRACT FROM AUTHOR]

Published
2008
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48. International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168,000 primary care patients in 63 countries.

Author

Balkau B, Deanfield JE, Després JP, Bassand JP, Fox KA, Smith SC Jr, Barter P, Tan CE, Van Gaal L, Wittchen HU, Massien C, Haffner SM, Balkau, Beverley, Deanfield, John E, Després, Jean-Pierre, Bassand, Jean-Pierre, Fox, Keith A A, Smith, Sidney C Jr, Barter, Philip, and Tan, Chee-Eng

Published
2007

50. Effects of rimonabant on metabolic risk factors in overweight patients with dyslipidemia.

Author

Després JP, Golay A, Sjöström L, Rimonabant in Obesity-Lipids Study Group, Després, Jean-Pierre, Golay, Alain, and Sjöström, Lars

Abstract

Background: Rimonabant, a selective cannabinoid-1 receptor (CB1) blocker, has been shown to reduce body weight and improve cardiovascular risk factors in obese patients. The Rimonabant in Obesity-Lipids (RIO-Lipids) study examined the effects of rimonabant on metabolic risk factors, including adiponectin levels, in high-risk patients who are overweight or obese and have dyslipidemia.Methods: We randomly assigned 1036 overweight or obese patients (body-mass index [the weight in kilograms divided by the square of the height in meters], 27 to 40) with untreated dyslipidemia (triglyceride levels >1.69 to 7.90 mmol per liter, or a ratio of cholesterol to high-density lipoprotein [HDL] cholesterol of >4.5 among women and >5 among men) to double-blinded therapy with either placebo or rimonabant at a dose of 5 mg or 20 mg daily for 12 months in addition to a hypocaloric diet.Results: The rates of completion of the study were 62.6 percent, 60.3 percent, and 63.9 percent in the placebo group, the group receiving 5 mg of rimonabant, and the group receiving 20 mg of rimonabant, respectively. The most frequent adverse events resulting in discontinuation of the drug were depression, anxiety, and nausea. As compared with placebo, rimonabant at a dose of 20 mg was associated with a significant (P<0.001) mean weight loss (repeated-measures method, -6.7+/-0.5 kg, and last-observation-carried-forward analyses, -5.4+/-0.4 kg), reduction in waist circumference (repeated-measures method, -5.8+/-0.5 cm, and last-observation-carried-forward analyses, -4.7+/-0.5 cm), increase in HDL cholesterol (repeated-measures method, +10.0+/-1.6 percent, and last-observation-carried-forward analyses, +8.1+/-1.5 percent), and reduction in triglycerides (repeated-measures method, -13.0+/-3.5 percent, and last-observation-carried-forward analyses, -12.4+/-3.2 percent). Rimonabant at a dose of 20 mg also resulted in an increase in plasma adiponectin levels (repeated-measures method, 57.7 percent, and last-observation-carried-forward analyses, 46.2 percent; P<0.001), for a change that was partly independent of weight loss alone.Conclusions: Selective CB1-receptor blockade with rimonabant significantly reduces body weight and waist circumference and improves the profile of several metabolic risk factors in high-risk patients who are overweight or obese and have an atherogenic dyslipidemia. [ABSTRACT FROM AUTHOR]

Published
2005

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