145 results on '"Farjam M."'
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2. A Mini Review on Discovery and Synthesis of Remdesivir as an Effective and Promising Drug against COVID-19
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Zarenezhad, E., Behrouz, S., Farjam, M., and Rad, M. N. Soltani
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- 2021
- Full Text
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3. The Association of Metabolic Syndrome and Its Components with Electrocardiogram Parameters and Abnormalities Among an Iranian Rural Population: The Fasa PERSIAN Cohort Study
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Yazdanpanah MH, Sayyadipoor S, Hojati SR, Nikmanesh A, Farjam M, and Homayounfar R
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electrocardiogram ,metabolic syndrome ,p wave ,qrs ,qtc interval ,Specialties of internal medicine ,RC581-951 - Abstract
Mohammad Hosein Yazdanpanah,1,2 Sepideh Sayyadipoor,2 Sayed Reza Hojati,2 Amirreza Nikmanesh,2 Mojtaba Farjam,1 Reza Homayounfar1,3 1Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran; 2Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran; 3National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, IranCorrespondence: Reza HomayounfarNoncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, IranTel +989125140840Email r_homayounfar@yahoo.comBackground: Metabolic syndrome (MetS) as a set of cardiac risk factors and its growing prevalence is one of the major concerns in different societies. In this study, we aimed to investigate the relationship between Mets and electrocardiogram (ECG) parameters and abnormalities as indicators for subclinical cardiovascular diseases (CVD).Methods: In this sub-analysis study, we used the data from Fasa PERSIAN Cohort Study which includes subjects age 35– 70 years. Subjects with available ECG data included in the study (n=7002) and subjects with missing data on MetS components and non-sinus rhythm ECG were excluded (n=44). The MetS definition based on the Adult Treatment Panel (ATP) III guidelines and also a 12-lead ECG was obtained from all participants.Results: Our study population (n=6958) showed a mean age of 48.60± 9.34 years and also 1656 (24.2%) subjects had MetS. Except for P duration, PR interval and S amplitude in men and P amplitude, S amplitude, Sokolow-Lyon Index, and QT interval in women, other ECG parameters differ significantly between subjects with and without Mets (P< 0.05). Also among ECG abnormalities, prolonged P duration (≥ 120ms), QRS duration (≥ 100ms), and QTc interval (> 450ms in male, > 470ms in female) had a significant association with MetS in the total population. Waist circumferences (WC) showed the most count of significant relationship with ECG parameters in both genders. In males, WC more than ATP cut-points had significant associations with prolonged P and QRS duration, and also blood pressure (BP) had significant associations with prolonged P and QRS durations and QTc interval. In females, the MetS component except triglyceride had at least a significant relationship with prolonged P and/or QRS duration.Conclusion: MetS and its component especially WC and BP were associated with ECG parameters and abnormalities. These associations with ECG as a marker of subclinical CVD showed the importance of MetS and each component in our population to monitor in the further longitudinal studies.Keywords: electrocardiogram, metabolic syndrome, P wave, QRS complex, QTc interval
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- 2020
4. Health Professionals’ Perception of Psychological Safety in Patients with Coronavirus (COVID-19)
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Mohammadi F, Farjam M, Gholampour Y, Tehranineshat B, Oshvandi K, and Bijani M
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clinical challenges ,medical personnel ,emerging diseases ,coronavirus ,qualitative research. ,Public aspects of medicine ,RA1-1270 - Abstract
Fateme Mohammadi,1 Mojtaba Farjam,2 Yousef Gholampour,2 Banafsheh Tehranineshat,3 Khodayar Oshvandi,4 Mostafa Bijani5 1Chronic Diseases (Home Care) Research Center and Autism Spectrum Disorders Research Center, School of Nursing and Midwifery, Department of Nursing, Hamadan University of Medical Sciences, Hamadan, Iran; 2Noncommunicable Diseases Research Center (NCDRC), Fasa University of Medical Sciences, Fasa, Iran; 3Community-Based Psychiatric Care Research Center, Department of Nursing, School of Nursing and Midwifery, Shiraz University of Medical Sciences, Shiraz, Iran; 4Mother and Child Care Research Center, School of Nursing and Midwifery, Hamadan University of Medical Sciences, Hamadan, Iran; 5Department of Medical Surgical Nursing, Fasa University of Medical Sciences, Fasa, IranCorrespondence: Mostafa BijaniDepartment of Medical Surgical Nursing, Fasa University of Medical Sciences, Fasa 81936-13119, IranTel +98 9173308451Email bizhani_mostafa@yahoo.comBackground: Medical personnel act as the protectors of people’s health by preventing, controlling, and treating emerging diseases, including the coronavirus infection. COVID-19 is a highly infectious and contagious disease which has presented the Iranian healthcare system with a variety of clinical challenges. There is a lack of research on clinical challenges in health crises especially those caused by emerging diseases, hence a need for more exploration of these clinical challenges and dilemmas. The present study aims to determine the different aspects of health professionals’ perception of the psychological safety of patients infected with the coronavirus (COVID-19).Methods: The present study is a qualitative work which uses conventional content analysis. The participants were 17 health professionals who were responsible for COVID-19 patients and met the inclusion criteria. They were selected via purposeful sampling. The study lasted from February to March 2020. Data were collected through semi-structured, individual interviews which were conducted via video call. The collection of data was kept up to the point of saturation. The collected data were analyzed using the conventional content analysis method.Results: Three themes such as “respect for dignity”, “comprehensive support” and “peaceful environment” with 11 categories were extracted from the findings of the study.Conclusion: According to the results of the present study, patients infected with the coronavirus should be given care in a peaceful environment where they receive comprehensive support and have their dignity respected. Such conditions can guarantee the psychological safety of these patients and produce positive behavioral results on their part. Thus, it is essential that the cultural, professional, and organizational prerequisites of protecting all the dimensions of the psychological safety of these patients be provided.Keywords: Patient Safety, Safety Management, medical personnel, emerging diseases, coronavirus, qualitative research
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- 2020
5. Physical Activity and Renal Function: Results of the Entry Phase of the Fasa Cohort Study.
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Chijan, M. Rostami, Dehghan, A., Kazemi, M., Naghizadeh, M. M., Emkani, M., Bijani, M., Farjam, M., Homayounfar, R., Samimi, N., Rahimabadi, M. Sedigh, and Ghaemi, A.
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EXERCISE physiology ,SLEEP duration ,GLOMERULAR filtration rate ,CHRONIC kidney failure ,BODY mass index - Abstract
Background and Objective: Chronic kidney disease is a major health problem that is associated with low quality of life and premature death. According to the contradictory results of some studies regarding the effect of exercise and physical activity on renal indicators, this study was conducted to determine the relationship between physical activity and glomerular filtration rate (GFR) in the population of Fasa County, Fars province, Iran. Methods: The current cross-sectional study is derived from the data of the Fasa Persian cohort study. In order to measure the level of physical activity, a standard questionnaire was used in three levels of low, moderate and intense physical activity. Also, renal function was evaluated in three levels: normal, mild insufficiency, and severe insufficiency based on milliliters per minute. In addition to this, the demographic characteristics of people including age, gender, etc. were also evaluated. Findings: The number of studied people was 5963. The mean physical activity and GFR level in the studied subjects were 2540.1±703.9 (MET-min/Week) and 77.7±11.2 ml/min, respectively. Analytical evaluation showed that there was a statistically significant relationship between GFR levels and physical activity in all three levels (p<0.001). Also, a statistically significant relationship was observed between GFR levels with the variables of age, gender, body mass index, waist circumference, blood sugar, blood pressure, sleep duration, smoking, diabetes and hypothyroidism (p<0.001). Conclusion: The results of this study showed that there is a direct relationship between the levels of glomerular filtration rate (GFR) and physical activity. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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6. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants
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Zhou, B, Carrillo-Larco, Rm, Danaei, G, Riley, Lm, Paciorek, Cj, Stevens, Ga, Gregg, Ew, Bennett, Je, Solomon, B, Singleton, Rk, Sophiea, Mk, Iurilli, Mlc, Lhoste, Vpf, Cowan, Mj, Savin, S, Woodward, M, Balanova, Y, Cifkova, R, Damasceno, A, Elliott, P, Farzadfar, F, He, J, Ikeda, N, Kengne, Ap, Khang, Yh, Kim, Hc, Laxmaiah, A, Lin, Hh, Maira, Pm, Miranda, Jj, Neuhauser, H, Sundstrom, J, Varghese, C, Widyahening, Is, Zdrojewski, T, Ezzati, M, Abarca-Gomez, L, Abdeen, Za, Rahim, Hfa, Abu-Rmeileh, Nm, Acosta-Cazares, B, Adams, Rj, Aekplakorn, W, Afsana, K, Afzal, S, Agdeppa, Ia, Aghazadeh-Attari, J, Aguilar-Salinas, Ca, Agyemang, C, Ahmad, Na, Ahmadi, A, Ahmadi, N, Ahmadizar, F, Ahmed, Sh, Ahrens, W, Ajlouni, K, Al-Raddadi, R, Alarouj, M, Albuhairan, F, Aldhukair, S, Ali, Mm, Alkandari, A, Alkerwi, A, Allin, K, Aly, E, Amarapurkar, Dn, Amougou, N, Amouyel, P, Andersen, Lb, Anderssen, Sa, Anjana, Rm, Ansari-Moghaddam, A, Ansong, D, Aounallah-Skhiri, H, Araujo, J, Ariansen, I, Aris, T, Arku, Re, Arlappa, N, Aryal, Kk, Aspelund, T, Assah, Fk, Assuncao, Mcf, Auvinen, J, Avdicova, M, Azevedo, A, Azimi-Nezhad, M, Azizi, F, Azmin, M, Babu, Bv, Bahijri, S, Balakrishna, N, Bamoshmoosh, M, Banach, M, Banadinovic, M, Bandosz, P, Banegas, Jr, Baran, J, Barbagallo, Cm, Barcelo, A, Barkat, A, Barreto, M, Barros, Ajd, Barros, Mvg, Bartosiewicz, A, Basit, A, Bastos, Jld, Bata, I, Batieha, Am, Batyrbek, A, Baur, La, Beaglehole, R, Belavendra, A, Ben Romdhane, H, Benet, M, Benson, Ls, Berkinbayev, S, Bernabe-Ortiz, A, Bettiol, H, Bezerra, J, Bhagyalaxmi, A, Bhargava, Sk, Bia, D, Biasch, K, Lele, Ecb, Bikbov, Mm, Bista, B, Bjerregaard, P, Bjertness, E, Bjertness, Mb, Bjorkelund, C, Bloch, Kv, Blokstra, A, Bo, S, Bobak, M, Boeing, H, Boggia, Jg, Boissonnet, Cp, Bojesen, Se, Bongard, V, Bonilla-Vargas, A, Bopp, M, Borghs, H, Bovet, P, Boyer, Cb, Braeckman, L, Brajkovich, I, Branca, F, Breckenkamp, J, Brenner, H, Brewster, Lm, Briceno, Y, Brito, M, Bruno, G, Bueno-de-Mesquita, Hb, Bueno, G, Bugge, A, Burns, C, Bursztyn, M, de Leon, Ac, Cacciottolo, J, Cameron, C, Can, G, Candido, Apc, Capanzana, Mv, Capkova, N, Capuano, E, Capuano, V, Cardoso, Vc, Carlsson, Ac, Carvalho, J, Casanueva, Ff, Censi, L, Cervantes-Loaiza, M, Chadjigeorgiou, Ca, Chamukuttan, S, Chan, Aw, Chan, Q, Chaturvedi, Hk, Chaturvedi, N, Chee, Ml, Chen, Cj, Chen, Ff, Chen, Hs, Chen, Sh, Chen, Zm, Cheng, Cy, Cheraghian, B, Dekkaki, Ic, Chetrit, A, Chien, Kl, Chiolero, A, Chiou, St, Chirita-Emandi, A, Chirlaque, Md, Cho, B, Christensen, K, Christofaro, Dg, Chudek, J, Cinteza, E, Claessens, F, Clarke, J, Clays, E, Cohen, E, Concin, H, Cooper, C, Coppinger, Tc, Costanzo, S, Cottel, D, Cowell, C, Craig, Cl, Crampin, Ac, Crujeiras, Ab, Cruz, Jj, Csilla, S, Cui, Lf, Cureau, Fv, Cuschieri, S, D'Arrigo, G, D'Orsi, E, Dallongeville, J, Dankner, R, Dantoft, Tm, Dauchet, L, Davletov, K, De Backer, G, De Bacquer, D, De Curtis, A, de Gaetano, G, De Henauw, S, de Oliveira, Pd, De Ridder, D, De Smedt, D, Deepa, M, Deev, Ad, Degennaro, V, Delisle, H, Demarest, S, Dennison, E, Deschamps, V, Dhimal, M, Di Castelnuovo, Af, Dias-da-Costa, Js, Diaz, A, Dickerson, Tt, Dika, Z, Djalalinia, S, Htp, Do, Dobson, Aj, Donfrancesco, C, Donoso, Sp, Doring, A, Dorobantu, M, Dorr, M, Doua, K, Dragano, N, Drygas, W, Duante, Ca, Duboz, P, Duda, Rb, Dulskiene, V, Dushpanova, A, Dzakula, A, Dzerve, V, Dziankowska-Zaborszczyk, E, Eddie, R, Eftekhar, E, Eggertsen, R, Eghtesad, S, Eiben, G, Ekelund, U, El-Khateeb, M, El Ati, J, Eldemire-Shearer, D, Eliasen, M, Elosua, R, Erasmus, Rt, Erbel, R, Erem, C, Eriksen, L, Eriksson, Jg, Escobedo-de la Pena, J, Eslami, S, Esmaeili, A, Evans, A, Faeh, D, Fakhretdinova, Aa, Fall, Ch, Faramarzi, E, Farjam, M, Fattahi, Mr, Fawwad, A, Felix-Redondo, Fj, Felix, Sb, Ferguson, Ts, Fernandes, Ra, Fernandez-Berges, D, Ferrante, D, Ferrao, T, Ferrari, M, Ferrario, Mm, Ferreccio, C, Ferreira, Hs, Ferrer, E, Ferrieres, J, Figueiro, Th, Fink, G, Fischer, K, Foo, Lh, Forsner, M, Fouad, Hm, Francis, Dk, Grego, Franco, Frikke-Schmidt, R, Frontera, G, Fuchs, Fd, Fuchs, Sc, Fujita, Y, Fumihiko, M, Furdela, V, Furer, A, Furusawa, T, Gaciong, Z, Galbarczyk, A, Galenkamp, H, Galvano, F, Gao, Jl, Gao, P, Garcia-de-la-Hera, M, Garcia, P, Gareta, D, Garnett, Sp, Gaspoz, Jm, Gasull, M, Gazzinelli, A, Gehring, U, Geleijnse, Jm, George, R, Ghanbari, A, Ghasemi, E, Gheorghe-Fronea, Of, Ghimire, A, Gialluisi, A, Giampaoli, S, Gieger, C, Gill, Tk, Giovannelli, J, Gironella, G, Giwercman, A, Gkiouras, K, Goldberg, M, Goldsmith, Ra, Gomez, Lf, Gomula, A, da Silva, Bgc, Goncalves, H, Goncalves, M, Gonzalez-Chica, Da, Gonzalez-Gross, M, Gonzalez-Rivas, Jp, Gonzalez-Villalpando, C, Gonzalez-Villalpando, Me, Gonzalez, Ar, Gorbea, Mb, Gottrand, F, Graff-Iversen, S, Grafnetter, D, Grajda, A, Grammatikopoulou, Mg, Gregor, Rd, Grodzicki, T, Grosso, G, Gruden, G, Df, Gu, Guan, Op, Gudmundsson, Ef, Gudnason, V, Guerrero, R, Guessous, I, Guimaraes, Al, Gulliford, Mc, Gunnlaugsdottir, J, Gunter, Mj, Gupta, Pc, Gupta, R, Gureje, O, Gurzkowska, B, Gutierrez, L, Gutzwiller, F, Ha, S, Hadaegh, F, Haghshenas, R, Hakimi, H, Halkjaer, J, Hambleton, Ir, Hamzeh, B, Hange, D, Hanif, Aam, Hantunen, S, Hao, J, Hardman, Cm, Kumar, Rh, Hashemi-Shahri, Sm, Hata, J, Haugsgjerd, T, Hayes, Aj, Yn, He, Heier, M, Hendriks, Me, Henrique, Rd, Henriques, A, Cadena, Lh, Herrala, S, Heshmat, R, Hill, Ag, Sy, Ho, Sc, Ho, Hobbs, M, Holdsworth, M, Homayounfar, R, Dinc, Gh, Horimoto, Arvr, Hormiga, Cm, Horta, Bl, Houti, L, Howitt, C, Htay, Tt, Htet, As, Htike, Mmt, Yh, Hu, Huerta, Jm, Huhtaniemi, It, Huiart, L, Huisman, M, Husseini, As, Huybrechts, I, Hwalla, N, Iacoviello, L, Iannone, Ag, Ibrahim, Mm, Wong, Ni, Ikram, Ma, Iotova, V, Irazola, Ve, Ishida, T, Isiguzo, Gc, Islam, M, Islam, Sms, Iwasaki, M, Jackson, Rt, Jacobs, Jm, Jaddou, Hy, Jafar, T, James, K, Jamrozik, K, Janszky, I, Janus, E, Jarvelin, Mr, Jasienska, G, Jelakovic, A, Jelakovic, B, Jennings, G, Jha, Ak, Jiang, Cq, Jimenez, Ro, Jockel, Kh, Joffres, M, Johansson, M, Jokelainen, Jj, Jonas, Jb, Jorgensen, T, Joshi, P, Joukar, F, Jozwiak, J, Juolevi, A, Jurak, G, Juresa, V, Kaaks, R, Kafatos, A, Kajantie, Eo, Kalmatayeva, Z, Kalpourtzi, N, Kalter-Leibovici, O, Kampmann, Fb, Kannan, S, Karaglani, E, Karhus, Ll, Karki, Kb, Katibeh, M, Katz, J, Kauhanen, J, Kaur, P, Kavousi, M, Kazakbaeva, Gm, Keil, U, Boker, Lk, Keinanen-Kiukaanniemi, S, Kelishadi, R, Kemper, Hcg, Keramati, M, Kerimkulova, A, Kersting, M, Key, T, Khader, Ys, Khalili, D, Khaw, Kt, Kheiri, B, Kheradmand, M, Khosravi, A, Kiechl-Kohlendorfer, U, Kiechl, S, Killewo, J, Kim, Dw, Kim, J, Klakk, H, Klimek, M, Klumbiene, J, Knoflach, M, Kolle, E, Kolsteren, P, Kontto, Jp, Korpelainen, R, Korrovits, P, Kos, J, Koskinen, S, Kouda, K, Kowlessur, S, Koziel, S, Kratenova, J, Kriaucioniene, V, Kristensen, Pl, Krokstad, S, Kromhout, D, Kruger, Hs, Kubinova, R, Kuciene, R, Kujala, Um, Kulaga, Z, Kumar, Rk, Kurjata, P, Kusuma, Ys, Kutsenko, V, Kuulasmaa, K, Kyobutungi, C, Laatikainen, T, Lachat, C, Laid, Y, Lam, Th, Landrove, O, Lanska, V, Lappas, G, Larijani, B, Latt, Ts, Le Coroller, G, Bao, Kln, Le, THUY DUNG, Lee, J, Lehmann, N, Lehtimaki, T, Lemogoum, D, Levitt, Ns, Yp, Li, Lilly, Cl, Lim, Wy, Lima-Costa, Mf, Lin, X, Lin, Yt, Lind, L, Lingam, V, Linneberg, A, Lissner, L, Litwin, M, Wc, Lo, Loit, Hm, Lopez-Garcia, E, Lopez, T, Lotufo, Pa, Lozano, Je, Lovrencic, Il, Lukrafka, Jl, Luksiene, D, Lundqvist, A, Lundqvist, R, Lunet, N, Lustigova, M, Luszczki, E, Gs, Ma, Ma, J, Machado-Coelho, Gll, Machado-Rodrigues, Am, Macia, E, Macieira, Lm, Madar, Aa, Maggi, S, Magliano, Dj, Magriplis, E, Mahasampath, G, Maire, B, Majer, M, Makdisse, M, Malekzadeh, F, Malekzadeh, R, Malhotra, R, Mallikharjuna, K, Malyutina, Sk, Maniego, Lv, Manios, Y, Mann, Ji, Mansour-Ghanaei, F, Manzato, E, Marcil, A, Margozzini, P, Marild, Sb, Glavic, Mm, Marques-Vidal, P, Marques, Lp, Marrugat, J, Martorell, R, Mascarenhas, Lp, Matasin, M, Mathiesen, Eb, Mathur, P, Matijasevich, A, Matlosz, P, Matsha, Te, Mavrogianni, C, Mbanya, Jcn, Posso, Ajm, Mcfarlane, Sr, Mcgarvey, St, Mclachlan, S, Mclean, Rm, Mclean, Sb, Mcnulty, Ba, Benchekor, Sm, Medzioniene, J, Mehdipour, P, Mehlig, K, Mehrparvar, Ah, Meirhaeghe, A, Meisinger, C, Montano, Cm, Menezes, Amb, Menon, Gr, Mereke, A, Meshram, Ii, Metspalu, A, Meyer, He, Mi, J, Michels, N, Mikkel, K, Milkowska, K, Miller, Jc, Minderico, Cs, Mini, Gk, Mirjalili, Mr, Mirrakhimov, E, Misigoj-Durakovic, M, Modesti, Pa, Moghaddam, Ss, Mohajer, B, Mohamed, Mk, Mohamed, Sf, Mohammad, K, Mohammadi, Mr, Mohammadi, Z, Mohammadifard, N, Mohammadpourhodki, R, Mohan, V, Mohanna, S, Yusoff, Mfm, Mohebbi, I, Mohebi, F, Moitry, M, Mollehave, Lt, Molnar, D, Momenan, A, Mondo, Ck, Monterrubio-Flores, E, Monyeki, Kdk, Moon, Js, Moosazadeh, M, Moreira, Lb, Morejon, A, Moreno, La, Morgan, K, Moschonis, G, Mossakowska, M, Mostafa, A, Mostafavi, Sa, Mota, J, Motlagh, Me, Motta, J, Moura-dos-Santos, Ma, Mridha, Mk, Msyamboza, Kp, Tt, Mu, Muhihi, Aj, Muiesan, Ml, Muller-Nurasyid, M, Murphy, N, Mursu, J, Musa, Ki, Milanovic, Sm, Musil, V, Mustafa, N, Nabipour, I, Naderimagham, S, Nagel, G, Naidu, Bm, Najafi, F, Nakamura, H, Namesna, J, Nang, Eek, Nangia, Vb, Narake, S, Ndiaye, Nc, Neal, Wa, Nejatizadeh, A, Nenko, I, Neovius, M, Neuhauser, Hk, Nguyen, Ct, Nguyen, Nd, Nguyen, Qv, Nguyen, Qn, Nieto-Martinez, Re, Niiranen, Tj, Nikitin, Yp, Ninomiya, T, Nishtar, S, Njelekela, Ma, Noale, M, Noboa, Oa, Noorbala, Aa, Norat, T, Nordendahl, M, Nordestgaard, Bg, Noto, D, Nowak-Szczepanska, N, Al Nsour, M, Nunes, B, O'Neill, Tw, O'Reilly, D, Ochimana, C, Oda, E, Odili, An, Oh, K, Ohara, K, Ohtsuka, R, Olie, V, Olinto, Mta, Oliveira, Io, Omar, Ma, Onat, A, Ong, Sk, Ono, Lm, Ordunez, P, Ornelas, R, Ortiz, Pj, Osmond, C, Ostojic, Sm, Ostovar, A, Otero, Ja, Overvad, K, Owusu-Dabo, E, Paccaud, Fm, Padez, C, Pahomova, E, de Paiva, Km, Pajak, A, Palli, D, Palmieri, L, Pan, Wh, Panda-Jonas, S, Panza, F, Paoli, M, Papandreou, D, Park, Sw, Park, S, Parnell, Wr, Parsaeian, M, Pasquet, P, Patel, Nd, Pavlyshyn, H, Pecin, I, Pednekar, Ms, Pedro, Jm, Peer, N, Peixoto, Sv, Peltonen, M, Pereira, Ac, Peres, Kgda, Peres, Ma, Peters, A, Petkeviciene, J, Peykari, N, Pham, St, Pichardo, Rn, Pigeot, I, Pikhart, H, Pilav, A, Pilotto, L, Pitakaka, F, Piwonska, A, Pizarro, An, Plans-Rubio, P, Polasek, O, Porta, M, Poudyal, A, Pourfarzi, F, Pourshams, A, Poustchi, H, Pradeepa, R, Price, Aj, Price, Jf, Providencia, R, Puhakka, Se, Puiu, M, Punab, M, Qasrawi, Rf, Qorbani, M, Queiroz, D, Bao, Tq, Radic, I, Radisauskas, R, Rahimikazerooni, S, Rahman, M, Raitakari, O, Raj, M, Rakhimova, Em, Rao, Sr, Ramachandran, A, Ramos, E, Rampal, L, Rampal, S, Reina, Dar, Rarra, V, Rech, Cr, Redon, J, Reganit, Pfm, Regecova, V, Revilla, L, Rezaianzadeh, A, Ribeiro, R, Riboli, E, Richter, A, Rigo, F, de Wit, Tfr, Ritti-Dias, Rm, Robitaille, C, Rodriguez-Artalejo, F, Rodriguez-Perez, Md, Rodriguez-Villamizar, La, Roggenbuck, U, Rojas-Martinez, R, Romaguera, D, Romeo, El, Rosengren, A, Roy, Jgr, Rubinstein, A, Ruidavets, Jb, Ruiz-Betancourt, Bs, Ruiz-Castell, M, Rusakova, Ia, Russo, P, Rutkowski, M, Sabanayagam, C, Sabbaghi, H, Sachdev, Hs, Sadjadi, A, Safarpour, Ar, Safi, S, Safiri, S, Saidi, O, Sakarya, S, Saki, N, Salanave, B, Martinez, Es, Salmeron, D, Salomaa, V, Salonen, Jt, Salvetti, M, Sanchez-Abanto, J, Sans, S, Santos, Da, Santos, Is, Santos, Lc, Santos, Mp, Santos, R, Saramies, Jl, Sardinha, Lb, Sarganas, G, Sarrafzadegan, N, Sathish, T, Saum, Ku, Savva, S, Sawada, N, Sbaraini, M, Scazufca, M, Schaan, Bd, Schargrodsky, H, Schipf, S, Schmidt, Co, Schnohr, P, Schottker, B, Schramm, S, Schultsz, C, Schutte, Ae, Sebert, S, Sein, Aa, Sen, A, Senbanjo, Io, Sepanlou, Sg, Servais, J, Shalnova, Sa, Shamah-Levy, T, Shamshirgaran, M, Shanthirani, Cs, Sharafkhah, M, Sharma, Sk, Shaw, Je, Shayanrad, A, Shayesteh, Aa, Shi, Zm, Shibuya, K, Shimizu-Furusawa, H, Shin, Dw, Shirani, M, Shiri, R, Shrestha, N, Si-Ramlee, K, Siani, A, Siantar, R, Sibai, 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Aluisio Jd Barros, Mauro Virgílio Gomes Barros, Anna Bartosiewicz, Abdul Basit, Joao Luiz D Bastos, Iqbal Bata, Anwar M Batieha, Assembekov Batyrbek, Louise A Baur, Robert Beaglehole, Antonisamy Belavendra, Habiba Ben Romdhane, Mikhail Benet, Lowell S Benson, Salim Berkinbayev, Antonio Bernabe-Ortiz, Gailute Bernotiene, Heloísa Bettiol, Jorge Bezerra, Aroor Bhagyalaxmi, Santosh K Bhargava, Daniel Bia, Katia Biasch, Elysée Claude Bika Lele, Mukharram M Bikbov, Bihungum Bista, Peter Bjerregaard, Espen Bjertness, Marius B Bjertness, Cecilia Björkelund, Katia V Bloch, Anneke Blokstra, Simona Bo, Martin Bobak, Heiner Boeing, Jose G Boggia, Carlos P Boissonnet, Stig E Bojesen, Vanina Bongard, Alice Bonilla-Vargas, Matthias Bopp, Herman Borghs, Pascal Bovet, Christopher B Boyer, Lutgart Braeckman, Imperia Brajkovich, Francesco Branca, Juergen Breckenkamp, Hermann Brenner, Lizzy M Brewster, Yajaira Briceño, Miguel Brito, Graziella Bruno, H Bas Bueno-de-Mesquita, Gloria Bueno, Anna Bugge, Con Burns, Michael Bursztyn, Antonio Cabrera de León, Joseph Cacciottolo, Christine Cameron, Günay Can, Ana Paula C Cândido, Mario V Capanzana, Naděžda Čapková, Eduardo Capuano, Vincenzo Capuano, Viviane C Cardoso, Axel C Carlsson, Joana Carvalho, Felipe F Casanueva, Laura Censi, Marvin Cervantes-Loaiza, Charalambos A Chadjigeorgiou, Snehalatha Chamukuttan, Angelique W Chan, Queenie Chan, Himanshu K Chaturvedi, Nish Chaturvedi, Miao Li Chee, Chien-Jen Chen, Fangfang Chen, Huashuai Chen, Shuohua Chen, Zhengming Chen, Ching-Yu Cheng, Bahman Cheraghian, Imane Cherkaoui Dekkaki, Angela Chetrit, Kuo-Liong Chien, Arnaud Chiolero, Shu-Ti Chiou, Adela Chirita-Emandi, María-Dolores Chirlaque, Belong Cho, Kaare Christensen, Diego G Christofaro, Jerzy Chudek, Eliza Cinteza, Frank Claessens, Janine Clarke, Els Clays, Emmanuel Cohen, Hans Concin, Cyrus Cooper, Tara C Coppinger, Simona Costanzo, Dominique Cottel, Chris Cowell, Cora L Craig, Amelia C Crampin, Ana B Crujeiras, Juan J Cruz, Semánová Csilla, Liufu Cui, Felipe V Cureau, Sarah Cuschieri, Graziella D'Arrigo, Eleonora d'Orsi, Jean Dallongeville, Rachel Dankner, Thomas M Dantoft, Luc Dauchet, Kairat Davletov, Guy De Backer, Dirk De Bacquer, Amalia De Curtis, Giovanni de Gaetano, Stefaan De Henauw, Paula Duarte de Oliveira, David De Ridder, Delphine De Smedt, Mohan Deepa, Alexander D Deev, Vincent Jr DeGennaro, Hélène Delisle, Stefaan Demarest, Elaine Dennison, Valérie Deschamps, Meghnath Dhimal, Augusto F Di Castelnuovo, Juvenal Soares Dias-da-Costa, Alejandro Diaz, Ty T Dickerson, Zivka Dika, Shirin Djalalinia, Ha Tp Do, Annette J Dobson, Chiara Donfrancesco, Silvana P Donoso, Angela Döring, Maria Dorobantu, Marcus Dörr, Kouamelan Doua, Nico Dragano, Wojciech Drygas, Charmaine A Duante, Priscilla Duboz, Rosemary B Duda, Virginija Dulskiene, Anar Dushpanova, Aleksandar Džakula, Vilnis Dzerve, Elzbieta Dziankowska-Zaborszczyk, Ricky Eddie, Ebrahim Eftekhar, Robert Eggertsen, Sareh Eghtesad, Gabriele Eiben, Ulf Ekelund, Mohammad El-Khateeb, Jalila El Ati, Denise Eldemire-Shearer, Marie Eliasen, Roberto Elosua, Rajiv T Erasmus, Raimund Erbel, Cihangir Erem, Louise Eriksen, Johan G Eriksson, Jorge Escobedo-de la Peña, Saeid Eslami, Ali Esmaeili, Alun Evans, David Faeh, Albina A Fakhretdinova, Caroline H Fall, Elnaz Faramarzi, Mojtaba Farjam, Mohammad Reza Fattahi, Asher Fawwad, Francisco J Felix-Redondo, Stephan B Felix, Trevor S Ferguson, Romulo A Fernandes, Daniel Fernández-Bergés, Daniel Ferrante, Thomas Ferrao, Marika Ferrari, Marco M Ferrario, Catterina Ferreccio, Haroldo S Ferreira, Eldridge Ferrer, Jean Ferrieres, Thamara Hubler Figueiró, Günther Fink, Krista Fischer, Leng Huat Foo, Maria Forsner, Heba M Fouad, Damian K Francis, Maria do Carmo Franco, Ruth Frikke-Schmidt, Guillermo Frontera, Flavio D Fuchs, Sandra C Fuchs, Yuki Fujita, Matsuda Fumihiko, Viktoriya Furdela, Ariel Furer, Takuro Furusawa, Zbigniew Gaciong, Andrzej Galbarczyk, Henrike Galenkamp, Fabio Galvano, Jingli Gao, Pei Gao, Manoli Garcia-de-la-Hera, Pablo Garcia, Dickman Gareta, Sarah P Garnett, Jean-Michel Gaspoz, Magda Gasull, Andrea Gazzinelli, Ulrike Gehring, Johanna M Geleijnse, Ronnie George, Ali Ghanbari, Erfan Ghasemi, Oana-Florentina Gheorghe-Fronea, Anup Ghimire, Alessandro Gialluisi, Simona Giampaoli, Christian Gieger, Tiffany K Gill, Jonathan Giovannelli, Glen Gironella, Aleksander Giwercman, Konstantinos Gkiouras, Marcel Goldberg, Rebecca A Goldsmith, Luis F Gomez, Aleksandra Gomula, Helen Gonçalves, Mauer Gonçalves, Bruna Gonçalves Cordeiro da Silva, David A Gonzalez-Chica, Marcela Gonzalez-Gross, Juan P González-Rivas, Clicerio González-Villalpando, María-Elena González-Villalpando, Angel R Gonzalez, Mariano Bonet Gorbea, Frederic Gottrand, Sidsel Graff-Iversen, Dušan Grafnetter, Aneta Grajda, Maria G Grammatikopoulou, Ronald D Gregor, Tomasz Grodzicki, Giuseppe Grosso, Gabriella Gruden, Dongfeng Gu, Ong Peng Guan, Elias F Gudmundsson, Vilmundur Gudnason, Ramiro Guerrero, Idris Guessous, Andre L Guimaraes, Martin C Gulliford, Johanna Gunnlaugsdottir, Marc J Gunter, Prakash C Gupta, Rajeev Gupta, Oye Gureje, Beata Gurzkowska, Laura Gutierrez, Felix Gutzwiller, Seongjun Ha, Farzad Hadaegh, Rosa Haghshenas, Hamid Hakimi, Jytte Halkjær, Ian R Hambleton, Behrooz Hamzeh, Dominique Hange, Abu Am Hanif, Sari Hantunen, Jie Hao, Carla Menêses Hardman, Rachakulla Hari Kumar, Seyed Mohammad Hashemi-Shahri, Jun Hata, Teresa Haugsgjerd, Alison J Hayes, Yuna He, Margit Heier, Marleen Elisabeth Hendriks, Rafael Dos Santos Henrique, Ana Henriques, Leticia Hernandez Cadena, Herqutanto, Sauli Herrala, Ramin Heshmat, Allan G Hill, Sai Yin Ho, Suzanne C Ho, Michael Hobbs, Michelle Holdsworth, Reza Homayounfar, Gonul Horasan Dinc, Andrea Rvr Horimoto, Claudia M Hormiga, Bernardo L Horta, Leila Houti, Christina Howitt, Thein Thein Htay, Aung Soe Htet, Maung Maung Than Htike, Yonghua Hu, José María Huerta, Ilpo Tapani Huhtaniemi, Laetitia Huiart, Martijn Huisman, Abdullatif S Husseini, Inge Huybrechts, Nahla Hwalla, Licia Iacoviello, Anna G Iannone, Mohsen M Ibrahim, Norazizah Ibrahim Wong, M Arfan Ikram, Violeta Iotova, Vilma E Irazola, Takafumi Ishida, Godsent C Isiguzo, Muhammad Islam, Sheikh Mohammed Shariful Islam, Masanori Iwasaki, Rod T Jackson, Jeremy M Jacobs, Hashem Y Jaddou, Tazeen Jafar, Kenneth James, Konrad Jamrozik, Imre Janszky, Edward Janus, Marjo-Riitta Jarvelin, Grazyna Jasienska, Ana Jelaković, Bojan Jelaković, Garry Jennings, Anjani Kumar Jha, Chao Qiang Jiang, Ramon O Jimenez, Karl-Heinz Jöckel, Michel Joffres, Mattias Johansson, Jari J Jokelainen, Jost B Jonas, Torben Jørgensen, Pradeep Joshi, Farahnaz Joukar, Jacek Jóżwiak, Anne Juolevi, Gregor Jurak, Vesna Jureša, Rudolf Kaaks, Anthony Kafatos, Eero O Kajantie, Zhanna Kalmatayeva, Natasa Kalpourtzi, Ofra Kalter-Leibovici, Freja B Kampmann, Srinivasan Kannan, Eva Karaglani, Line L Kårhus, Khem B Karki, Marzieh Katibeh, Joanne Katz, Jussi Kauhanen, Prabhdeep Kaur, Maryam Kavousi, Gyulli M Kazakbaeva, Ulrich Keil, Lital Keinan Boker, Sirkka Keinänen-Kiukaanniemi, Roya Kelishadi, Han Cg Kemper, Maryam Keramati, Alina Kerimkulova, Mathilde Kersting, Timothy Key, Yousef Saleh Khader, Davood Khalili, Kay-Tee Khaw, Bahareh Kheiri, Motahareh Kheradmand, Alireza Khosravi, Ursula Kiechl-Kohlendorfer, Stefan Kiechl, Japhet Killewo, Dong Wook Kim, Jeongseon Kim, Heidi Klakk, Magdalena Klimek, Jurate Klumbiene, Michael Knoflach, Elin Kolle, Patrick Kolsteren, Jukka P Kontto, Raija Korpelainen, Paul Korrovits, Jelena Kos, Seppo Koskinen, Katsuyasu Kouda, Sudhir Kowlessur, Slawomir Koziel, Jana Kratenova, Vilma Kriaucioniene, Peter Lund Kristensen, Steiner Krokstad, Daan Kromhout, Herculina S Kruger, Ruzena Kubinova, Renata Kuciene, Urho M Kujala, Zbigniew Kulaga, R Krishna Kumar, Pawel Kurjata, Yadlapalli S Kusuma, Vladimir Kutsenko, Kari Kuulasmaa, Catherine Kyobutungi, Tiina Laatikainen, Carl Lachat, Youcef Laid, Tai Hing Lam, Orlando Landrove, Vera Lanska, Georg Lappas, Bagher Larijani, Tint Swe Latt, Gwenaëlle Le Coroller, Khanh Le Nguyen Bao, Tuyen D Le, Jeannette Lee, Jeonghee Lee, Nils Lehmann, Terho Lehtimäki, Daniel Lemogoum, Naomi S Levitt, Yanping Li, Christa L Lilly, Wei-Yen Lim, M Fernanda Lima-Costa, Xu Lin, Yi-Ting Lin, Lars Lind, Vijaya Lingam, Allan Linneberg, Lauren Lissner, Mieczyslaw Litwin, Wei-Cheng Lo, Helle-Mai Loit, Esther Lopez-Garcia, Tania Lopez, Paulo A Lotufo, José Eugenio Lozano, Iva Lukačević Lovrenčić, Janice L Lukrafka, Dalia Luksiene, Annamari Lundqvist, Robert Lundqvist, Nuno Lunet, Michala Lustigová, Edyta Luszczki, Guansheng Ma, Jun Ma, George Ll Machado-Coelho, Aristides M Machado-Rodrigues, Enguerran Macia, Luisa M Macieira, Ahmed A Madar, Stefania Maggi, Dianna J Magliano, Emmanuella Magriplis, Gowri Mahasampath, Bernard Maire, Marjeta Majer, Marcia Makdisse, Fatemeh Malekzadeh, Reza Malekzadeh, Rahul Malhotra, Kodavanti Mallikharjuna Rao, Sofia K Malyutina, Lynell V Maniego, Yannis Manios, Jim I Mann, Fariborz Mansour-Ghanaei, Enzo Manzato, Anie Marcil, Staffan B Mårild, Mihalea Marinović Glavić, Pedro Marques-Vidal, Larissa Pruner Marques, Jaume Marrugat, Reynaldo Martorell, Luis P Mascarenhas, Marija Matasin, Ellisiv B Mathiesen, Prashant Mathur, Alicia Matijasevich, Piotr Matlosz, Tandi E Matsha, Christina Mavrogianni, Jean Claude N Mbanya, Anselmo J Mc Donald Posso, Shelly R McFarlane, Stephen T McGarvey, Stela McLachlan, Rachael M McLean, Scott B McLean, Breige A McNulty, Sounnia Mediene Benchekor, Jurate Medzioniene, Parinaz Mehdipour, Kirsten Mehlig, Amir Houshang Mehrparvar, Aline Meirhaeghe, Christa Meisinger, Carlos Mendoza Montano, Ana Maria B Menezes, Geetha R Menon, Alibek Mereke, Indrapal I Meshram, Andres Metspalu, Haakon E Meyer, Jie Mi, Nathalie Michels, Kairit Mikkel, Karolina Milkowska, Jody C Miller, Cláudia S Minderico, G K Mini, Mohammad Reza Mirjalili, Erkin Mirrakhimov, Marjeta Mišigoj-Duraković, Pietro A Modesti, Sahar Saeedi Moghaddam, Bahram Mohajer, Mostafa K 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William A Neal, Azim Nejatizadeh, Ilona Nenko, Martin Neovius, Chung T Nguyen, Nguyen D Nguyen, Quang V Nguyen, Quang Ngoc Nguyen, Ramfis E Nieto-Martínez, Teemu J Niiranen, Yury P Nikitin, Toshiharu Ninomiya, Sania Nishtar, Marina A Njelekela, Marianna Noale, Oscar A Noboa, Ahmad Ali Noorbala, Teresa Norat, Maria Nordendahl, Børge G Nordestgaard, Noto Davide, Natalia Nowak-Szczepanska, Mohannad Al Nsour, Baltazar Nunes, Terence W O'Neill, Dermot O'Reilly, Caleb Ochimana, Eiji Oda, Augustine N Odili, Kyungwon Oh, Kumiko Ohara, Ryutaro Ohtsuka, Valérie Olié, Maria Teresa A Olinto, Isabel O Oliveira, Mohd Azahadi Omar, Altan Onat, Sok King Ong, Lariane M Ono, Pedro Ordunez, Rui Ornelas, Pedro J Ortiz, Clive Osmond, Sergej M Ostojic, Afshin Ostovar, Johanna A Otero, Kim Overvad, Ellis Owusu-Dabo, Fred Michel Paccaud, Cristina Padez, Elena Pahomova, Karina Mary de Paiva, Andrzej Pająk, Domenico Palli, Luigi Palmieri, Wen-Harn Pan, Songhomitra Panda-Jonas, Francesco Panza, Mariela Paoli, 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Bas, Bueno, Gloria, Bugge, Anna, Burns, Con, Bursztyn, Michael, Cabrera de Leon, Antonio, Cacciottolo, Joseph, Cameron, Christine, Can, Gunay, Candido, Ana Paula C., Capanzana, Mario V., Capkova, Nadezda, Capuano, Eduardo, Capuano, Vincenzo, Cardoso, Viviane C., Carlsson, Axel C., Carvalho, Joana, Casanueva, Felipe F., Censi, Laura, Cervantes-Loaiza, Marvin, Chadjigeorgiou, Charalambos A., Chamukuttan, Snehalatha, Chan, Angelique W., Chan, Queenie, Chaturvedi, Himanshu K., Chaturvedi, Nish, Chee, Miao Li, Chen, Chien-Jen, Chen, Fangfang, Chen, Huashuai, Chen, Shuohua, Chen, Zhengming, Cheng, Ching-Yu, Cheraghian, Bahman, Dekkaki, Imane Cherkaoui, Chetrit, Angela, Chien, Kuo-Liong, Chiolero, Arnaud, Chiou, Shu-Ti, Chirita-Emandi, Adela, Chirlaque, Maria-Dolores, Cho, Belong, Christensen, Kaare, Christofaro, Diego G., Chudek, Jerzy, Cinteza, Eliza, Clarke, Janine, Clays, Els, Cohen, Emmanuel, Concin, Hans, Cooper, Cyrus, Coppinger, Tara C., Costanzo, Simona, Cottel, Dominique, Cowell, Chris, Craig, Cora L., Crampin, Amelia C., Crujeiras, Ana B., Cruz, Juan J., Csilla, Semanova, Cui, Liufu, Cureau, Felipe V., Cuschieri, Sarah, D'Arrigo, Graziella, d'Orsi, Eleonora, Dallongeville, Jean, Dankner, Rachel, Dantoft, Thomas M., Dauchet, Luc, Davletov, Kairat, De Backer, Guy, De Bacquer, Dirk, De Curtis, Amalia, de Gaetano, Giovanni, De Henauw, Stefaan, de Oliveira, Paula Duarte, De Ridder, David, De Smedt, Delphine, Deepa, Mohan, Deev, Alexander D., DeGennaro, Vincent Jr, Delisle, Helene, Demarest, Stefaan, Dennison, Elaine, Deschamps, Valerie, Dhimal, Meghnath, Di Castelnuovo, Augusto F., Dias-da-Costa, Juvenal Soares, Diaz, Alejandro, Dickerson, Ty T., Dika, Zivka, Djalalinia, Shirin, Do, Ha T. P., Dobson, Annette J., Donfrancesco, Chiara, Donoso, Silvana P., Doering, Angela, Dorobantu, Maria, Doerr, Marcus, Doua, Kouamelan, Dragano, Nico, Drygas, Wojciech, Duante, Charmaine A., Duboz, Priscilla, Duda, Rosemary B., Dulskiene, Virginija, Dushpanova, Anar, Dzakula, Aleksandar, Dzerve, Vilnis, Dziankowska-Zaborszczyk, Elzbieta, Eddie, Ricky, Eftekhar, Ebrahim, Eggertsen, Robert, Eghtesad, Sareh, Eiben, Gabriele, Ekelund, Ulf, El-Khateeb, Mohammad, El Ati, Jalila, Eldemire-Shearer, Denise, Eliasen, Marie, Elosua, Roberto, Erasmus, Rajiv T., Erbel, Raimund, Erem, Cihangir, Eriksen, Louise, Eriksson, Johan G., Escobedo-de la Pena, Jorge, Eslami, Saeid, Esmaeili, Ali, Evans, Alun, Faeh, David, Fakhretdinova, Albina A., Fall, Caroline H., Faramarzi, Elnaz, Farjam, Mojtaba, Fattahi, Mohammad Reza, Fawwad, Asher, Felix-Redondo, Francisco J., Felix, Stephan B., Ferguson, Trevor S., Fernandes, Romulo A., Fernandez-Berges, Daniel, Ferrante, Daniel, Ferrao, Thomas, Ferrari, Marika, Ferrario, Marco M., Ferreccio, Catterina, Ferreira, Haroldo S., Ferrer, Eldridge, Ferrieres, Jean, Figueiro, Thamara Hubler, Fink, Gunther, Fischer, Krista, Foo, Leng Huat, Forsner, Maria, Fouad, Heba M., Francis, Damian K., Franco, Maria do Carmo, Frikke-Schmidt, Ruth, Frontera, Guillermo, Fuchs, Flavio D., Fuchs, Sandra C., Fujita, Yuki, Fumihiko, Matsuda, Furdela, Viktoriya, Furer, Ariel, Furusawa, Takuro, Gaciong, Zbigniew, Galbarczyk, Andrzej, Galenkamp, Henrike, Galvano, Fabio, Gao, Jingli, Gao, Pei, Garcia-de-la-Hera, Manoli, Garcia, Pablo, Gareta, Dickman, Garnett, Sarah P., Gaspoz, Jean-Michel, Gasull, Magda, Gazzinelli, Andrea, Gehring, Ulrike, Geleijnse, Johanna M., George, Ronnie, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Ghimire, Anup, Gialluisi, Alessandro, Giampaoli, Simona, Gieger, Christian, Gill, Tiffany K., Giovannelli, Jonathan, Gironella, Glen, Giwercman, Aleksander, Gkiouras, Konstantinos, Goldberg, Marcel, Goldsmith, Rebecca A., Gomez, Luis F., Gomula, Aleksandra, Cordeiro da Silva, Bruna Goncalves, Goncalves, Helen, Goncalves, Mauer, Gonzalez-Chica, David A., Gonzalez-Gross, Marcela, Gonzalez-Rivas, Juan P., Gonzalez-Villalpando, Clicerio, Gonzalez-Villalpando, Maria-Elena, Gonzalez, Angel R., Bonet Gorbea, Mariano, Gottrand, Frederic, Graff-Iversen, Sidsel, Grafnetter, Dusan, Grajda, Aneta, Grammatikopoulou, Maria G., Gregor, Ronald D., Grodzicki, Tomasz, Grosso, Giuseppe, Gruden, Gabriella, Gu, Dongfeng, Guan, Ong Peng, Gudmundsson, Elias F., Gudnason, Vilmundur, Guerrero, Ramiro, Guessous, Idris, Guimaraes, Andre L., Gulliford, Martin C., Gunnlaugsdottir, Johanna, Gunter, Marc J., Gupta, Prakash C., Gupta, Rajeev, Gureje, Oye, Gurzkowska, Beata, Gutierrez, Laura, Gutzwiller, Felix, Ha, Seongjun, Hadaegh, Farzad, Haghshenas, Rosa, Hakimi, Hamid, Halkjaer, Jytte, Hambleton, Ian R., Hamzeh, Behrooz, Hange, Dominique, Hanif, Abu A. M., Hantunen, Sari, Hao, Jie, Hardman, Carla Meneses, Kumar, Rachakulla Hari, Hashemi-Shahri, Seyed Mohammad, Hata, Jun, Haugsgjerd, Teresa, Hayes, Alison J., He, Yuna, Heier, Margit, Hendriks, Marleen Elisabeth, Henrique, Rafael dos Santos, Henriques, Ana, Cadena, Leticia Hernandez, Herrala, Sauli, Heshmat, Ramin, Hill, Allan G., Ho, Sai Yin, Ho, Suzanne C., Hobbs, Michael, Holdsworth, Michelle, Homayounfar, Reza, Dinc, Gonul Horasan, Horimoto, Andrea R. V. R., Hormiga, Claudia M., Horta, Bernardo L., Houti, Leila, Howitt, Christina, Htay, Thein Thein, Htet, Aung Soe, Htike, Maung Maung Than, Hu, Yonghua, Huerta, Jose Maria, Huhtaniemi, Ilpo Tapani, Huiart, Laetitia, Huisman, Martijn, Husseini, Abdullatif S., Huybrechts, Inge, Hwalla, Nahla, Iacoviello, Licia, Iannone, Anna G., Ibrahim, Mohsen M., Wong, Norazizah Ibrahim, Ikram, M. Arfan, Iotova, Violeta, Irazola, Vilma E., Ishida, Takafumi, Isiguzo, Godsent C., Islam, Muhammad, Islam, Sheikh Mohammed Shariful, Iwasaki, Masanori, Jackson, Rod T., Jacobs, Jeremy M., Jaddou, Hashem Y., Jafar, Tazeen, James, Kenneth, Jamrozik, Konrad, Janszky, Imre, Janus, Edward, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jelakovic, Ana, Jelakovic, Bojan, Jennings, Garry, Jha, Anjani Kumar, Jiang, Chao Qiang, Jimenez, Ramon O., Joeckel, Karl-Heinz, Joffres, Michel, Johansson, Mattias, Jokelainen, Jari J., Jonas, Jost B., Jorgensen, Torben, Joshi, Pradeep, Joukar, Farahnaz, Jozwiak, Jacek, Juolevi, Anne, Jurak, Gregor, Juresa, Vesna, Kaaks, Rudolf, Kafatos, Anthony, Kajantie, Eero O., Kalmatayeva, Zhanna, Kalpourtzi, Natasa, Kalter-Leibovici, Ofra, Kampmann, Freja B., Kannan, Srinivasan, Karaglani, Eva, Karhus, Line L., Karki, Khem B., Katibeh, Marzieh, Katz, Joanne, Kauhanen, Jussi, Kaur, Prabhdeep, Kavousi, Maryam, Kazakbaeva, Gyulli M., Keil, Ulrich, Boker, Lital Keinan, Keinanen-Kiukaanniemi, Sirkka, Kelishadi, Roya, Kemper, Han C. 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L., Machado-Rodrigues, Aristides M., Macia, Enguerran, Macieira, Luisa M., Madar, Ahmed A., Maggi, Stefania, Magliano, Dianna J., Magriplis, Emmanuella, Mahasampath, Gowri, Maire, Bernard, Majer, Marjeta, Makdisse, Marcia, Malekzadeh, Fatemeh, Malekzadeh, Reza, Malhotra, Rahul, Mallikharjuna, Kodavanti, Malyutina, Sofia K., Maniego, Lynell V., Manios, Yannis, Mann, Jim I., Mansour-Ghanaei, Fariborz, Manzato, Enzo, Marcil, Anie, Margozzini, Paula, Marild, Staffan B., Glavic, Mihalea Marinovic, Marques-Vidal, Pedro, Marques, Larissa Pruner, Marrugat, Jaume, Martorell, Reynaldo, Mascarenhas, Luis P., Matasin, Marija, Mathiesen, Ellisiv B., Mathur, Prashant, Matijasevich, Alicia, Matlosz, Piotr, Matsha, Tandi E., Mavrogianni, Christina, Mbanya, Jean Claude N., Mc Donald Posso, Anselmo J., McFarlane, Shelly R., McGarvey, Stephen T., McLachlan, Stela, McLean, Rachael M., McLean, Scott B., McNulty, Breige A., Benchekor, Sounnia Mediene, Medzioniene, Jurate, Mehdipour, Parinaz, Mehlig, Kirsten, Mehrparvar, Amir Houshang, Meirhaeghe, Aline, Meisinger, Christa, Mendoza Montano, Carlos, Menezes, Ana Maria B., Menon, Geetha R., Mereke, Alibek, Meshram, Indrapal I., Metspalu, Andres, Meyer, Haakon E., Mi, Jie, Michels, Nathalie, Mikkel, Kairit, Milkowska, Karolina, Miller, Jody C., Minderico, Claudia S., Mini, G. K., Mirjalili, Mohammad Reza, Mirrakhimov, Erkin, Misigoj-Durakovic, Marjeta, Modesti, Pietro A., Moghaddam, Sahar Saeedi, Mohajer, Bahram, Mohamed, Mostafa K., Mohamed, Shukri F., Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohan, Viswanathan, Mohanna, Salim, Yusoff, Muhammad Fadhli Mohd, Mohebbi, Iraj, Mohebi, Farnam, Moitry, Marie, Mollehave, Line T., Molnar, Denes, Momenan, Amirabbas, Mondo, Charles K., Monterrubio-Flores, Eric, Monyeki, Kotsedi Daniel K., Moon, Jin Soo, Moosazadeh, Mahmood, Moreira, Leila B., Morejon, Alain, Moreno, Luis A., Morgan, Karen, Moschonis, George, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Mota, Jorge, Motlagh, Mohammad Esmaeel, Motta, Jorge, Andre Moura-dos-Santos, Marcos, Mridha, Malay K., Msyamboza, Kelias P., Mu, Thet Thet, Muhihi, Alfa J., Muiesan, Maria L., Muller-Nurasyid, Martina, Murphy, Neil, Mursu, Jaakko, Musa, Kamarul Imran, Milanovic, Sanja Music, Musil, Vera, Mustafa, Norlaila, Nabipour, Iraj, Naderimagham, Shohreh, Nagel, Gabriele, Naidu, Balkish M., Najafi, Farid, Nakamura, Harunobu, Namesna, Jana, Nang, Ei Ei K., Nangia, Vinay B., Narake, Sameer, Ndiaye, Ndeye Coumba, Neal, William A., Nejatizadeh, Azim, Nenko, Ilona, Neovius, Martin, Neuhauser, Hannelore K., Nguyen, Chung T., Nguyen, Nguyen D., Nguyen, Quang V., Quang Ngoc Nguyen, Nieto-Martinez, Ramfis E., Niiranen, Teemu J., Nikitin, Yury P., Ninomiya, Toshiharu, Nishtar, Sania, Njelekela, Marina A., Noale, Marianna, Noboa, Oscar A., Noorbala, Ahmad Ali, Norat, Teresa, Nordendahl, Maria, Nordestgaard, Borge G., Noto, Davide, Nowak-Szczepanska, Natalia, Al Nsour, Mohannad, Nunes, Baltazar, O'Neill, Terence W., O'Reilly, Dermot, Ochimana, Caleb, Oda, Eiji, Odili, Augustine N., Oh, Kyungwon, Ohara, Kumiko, Ohtsuka, Ryutaro, Olie, Valerie, Olinto, Maria Teresa A., Oliveira, Isabel O., Omar, Mohd Azahadi, Onat, Altan, Ong, Sok King, Ono, Lariane M., Ordunez, Pedro, Ornelas, Rui, Ortiz, Pedro J., Osmond, Clive, Ostojic, Sergej M., Ostovar, Afshin, Otero, Johanna A., Overvad, Kim, Owusu-Dabo, Ellis, Paccaud, Fred Michel, Padez, Cristina, Pahomova, Elena, de Paiva, Karina Mary, Pajak, Andrzej, Palli, Domenico, Palmieri, Luigi, Pan, Wen-Harn, Panda-Jonas, Songhomitra, Panza, Francesco, Paoli, Mariela, Papandreou, Dimitrios, Park, Soon-Woo, Park, Suyeon, Parnell, Winsome R., Parsaeian, Mahboubeh, Pasquet, Patrick, Patel, Nikhil D., Pavlyshyn, Halyna, Pecin, Ivan, Pednekar, Mangesh S., Pedro, Joao M., Peer, Nasheeta, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C., Peres, Karen G. D. A., Peres, Marco A., Peters, Annette, Petkeviciene, Janina, Peykari, Niloofar, Son Thai Pham, Pichardo, Rafael N., Pigeot, Iris, Pikhart, Hynek, Pilav, Aida, Pilotto, Lorenza, Pitakaka, Freda, Piwonska, Aleksandra, Pizarro, Andreia N., Plans-Rubio, Pedro, Polasek, Ozren, Porta, Miquel, Poudyal, Anil, Pourfarzi, Farhad, Pourshams, Akram, Poustchi, Hossein, Pradeepa, Rajendra, Price, Alison J., Price, Jacqueline F., Providencia, Rui, Puhakka, Soile E., Puiu, Maria, Punab, Margus, Qasrawi, Radwan F., Qorbani, Mostafa, Queiroz, Daniel, Tran Quoc Bao, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Raitakari, Olli, Raj, Manu, Rakhimova, Ellina M., Rao, Sudha Ramachandra, Ramachandran, Ambady, Ramos, Elisabete, Rampal, Lekhraj, Rampal, Sanjay, Rangel Reina, Daniel A., Rarra, Vayia, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M., Regecova, Valeria, Revilla, Luis, Rezaianzadeh, Abbas, Ribeiro, Robespierre, Riboli, Elio, Richter, Adrian, Rigo, Fernando, de Wit, Tobias F. 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R., Theobald, Holger, Theodoridis, Xenophon, Thinggaard, Mikael, Thomas, Nihal, Thorand, Barbara, Thuesen, Betina H., Timmermans, Erik J., Tjandrarini, Dwi H., Tjonneland, Anne, Toft, Ulla, Tolonen, Hanna K., Tolstrup, Janne S., Topbas, Murat, Topor-Madry, Roman, Jose Tormo, Maria, Tornaritis, Michael J., Torrent, Maties, Torres-Collado, Laura, Touloumi, Giota, Traissac, Pierre, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh T. H., Trivedi, Atul, Tshepo, Lechaba, Tsugane, Shoichiro, Tuliakova, Azaliia M., Tulloch-Reid, Marshall K., Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Turley, Maria L., Twig, Gilad, Tynelius, Per, Tzourio, Christophe, Ueda, Peter, Ugel, Eunice, Ulmer, Hanno, Uusitalo, Hannu M. T., Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M., van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T., Van Herck, Koen, Hoang Van Minh, Van Schoor, Natasja M., van Valkengoed, Irene G. M., van Zutphen, Elisabeth M., Vanuzzo, Diego, Varbo, Anette, Vasan, Senthil K., Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Veronesi, Giovanni, Verschuren, W. M. Monique, Verstraeten, Roosmarijn, Victora, Cesar G., Viet, Lucie, Villalpando, Salvador, Vineis, Paolo, Vioque, Jesus, Virtanen, Jyrki K., Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vlasoff, Tiina, Vollenweider, Peter, Voutilainen, Ari, Wade, Alisha N., Walton, Janette, Wambiya, Elvis O. A., Bebakar, Wan Mohamad Wan, Mohamud, Wan Nazaimoon Wan, Wanderley Junior, Rildo de Souza, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S. Goya, Wareham, Nicholas, Wei, Wenbin, Weres, Aneta, Werner, Bo, Whincup, Peter H., Widhalm, Kurt, Wiecek, Andrzej, Wilks, Rainford J., Willeit, Johann, Willeit, Peter, Williams, Emmanuel A., Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A., Wong, Andrew, Wong, Tien Yin, Woo, Jean, Wu, Frederick C., Wu, Shouling, Wyszynska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yasuharu, Tabara, Ye, Xingwang, Yiallouros, Panayiotis K., Yoosefi, Moein, Yoshihara, Akihiro, You, San-Lin, Younger-Coleman, Novie O., Yusoff, Ahmad Faudzi, Zainuddin, Ahmad A., Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Elisa Zapata, Maria, Zaw, Ko Ko, Zejglicova, Kristyna, Vrkic, Tajana Zeljkovic, Zeng, Yi, Zhang, Luxia, Zhao, Dong, Zhao, Ming-Hui, Zhen, Shiqi, Zheng, Yingfeng, Zholdin, Bekbolat, Zhu, Dan, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuniga Cisneros, Julio., School of Medicine, ACS - Diabetes & metabolism, APH - Global Health, Pulmonology, Medical Informatics, Adult Psychiatry, Global Health, APH - Quality of Care, APH - Methodology, Vascular Medicine, ACS - Atherosclerosis & ischemic syndromes, Anesthesiology, Graduate School, and ACS - Heart failure & arrhythmias
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Male ,Latin Americans ,Nutrition and Disease ,Epidemiology ,[SDV]Life Sciences [q-bio] ,Medizin ,BLOOD-PRESSURE ,030204 cardiovascular system & hematology ,Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants ,Hypertension ,Prevalence ,Control ,Tretament ,GUIDELINES ,Global Health ,Worldwide trends ,0302 clinical medicine ,Hypertension prevalence ,Voeding en Ziekte ,Medicine and Health Sciences ,kohonnut verenpaine ,Medicine ,030212 general & internal medicine ,Prevention and Control ,11 Medical and Health Sciences ,ComputingMilieux_MISCELLANEOUS ,education.field_of_study ,food and beverages ,Public Health, Global Health, Social Medicine and Epidemiology ,General Medicine ,Noncommunicable diseases ,Period prevalence ,Middle Aged ,kansainvälinen vertailu ,3142 Public health care science, environmental and occupational health ,3. Good health ,MIDDLE-INCOME ,Pooled analysis ,SYSTEMATIC ANALYSIS ,INCOME COUNTRIES ,ADULTS ,PREVENTION ,MANAGEMENT ,ADHERENCE ,DIAGNOSIS ,Western europe ,[SDE]Environmental Sciences ,Hypertension/diagnosis ,NCD Risk Factor Collaboration (NCD-RisC) ,Female ,B990 Subjects Allied to Medicine not elsewhere classified ,Life Sciences & Biomedicine ,Adult ,health-care ,esiintyvyys ,Central asia ,Population ,Nursing ,3121 Internal medicine ,03 medical and health sciences ,Medicine, General & Internal ,Drug Therapy ,General & Internal Medicine ,Life Science ,Humans ,ddc:610 ,education ,Antihypertensive Agents ,VLAG ,Aged ,Science & Technology ,Antihypertensive Agents/therapeutic use ,business.industry ,Omvårdnad ,fungi ,General and internal medicine ,Estados de Saúde e de Doença ,Taking medication ,Treatment ,Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi ,Blood pressure ,Faculdade de Ciências Sociais ,3121 General medicine, internal medicine and other clinical medicine ,lääkehoito ,1182 Biochemistry, cell and molecular biology ,business ,Demography - Abstract
Background: hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods: we used data from 1990 to 2019 on people aged 30-79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings: the number of people aged 30-79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306-359) million women and 317 (292-344) million men in 1990 to 626 (584-668) million women and 652 (604-698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55-62) of women and 49% (46-52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43-51) of women and 38% (35-41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20-27) for women and 18% (16-21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings., British Heart Foundation Centre of Research Excellence Grant; World Health Organization (WHO); Abdul Latif Jameel Institute for Disease and Emergency Analytics Fellowship
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- 2021
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7. Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight
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Iurilli, M.L.C. Zhou, B. Bennett, J.E. Carrillo-Larco, R.M. Sophiea, M.K. Rodriguez-Martinez, A. Bixby, H. Solomon, B.D. Taddei, C. Danaei, G. Di Cesare, M. Stevens, G.A. Riley, L.M. Savin, S. Cowan, M.J. Bovet, P. Damasceno, A. Chirita-Emandi, A. Hayes, A.J. Ikeda, N. Jackson, R.T. Khang, Y.-H. Laxmaiah, A. Liu, J. Miranda, J.J. Saidi, O. Sebert, S. Sorić, M. Starc, G. Gregg, E.W. Abarca-Gómez, L. Abdeen, Z.A. Abdrakhmanova, S. Ghaffar, S.A. Rahim, H.F.A. Abu-Rmeileh, N.M. Garba, J.A. Acosta-Cazares, B. Adams, R.J. Aekplakorn, W. Afsana, K. Afzal, S. Agdeppa, I.A. Aghazadeh-Attari, J. Aguilar-Salinas, C.A. Agyemang, C. Ahmad, M.H. Ahmad, N.A. Ahmadi, A. Ahmadi, N. Ahmed, S.H. Ahrens, W. Aitmurzaeva, G. Ajlouni, K. Al-Hazzaa, H.M. Al-Lahou, B. Al-Raddadi, R. Alarouj, M. AlBuhairan, F. AlDhukair, S. Ali, M.M. Alkandari, A. Alkerwi, A. Allin, K. Alvarez-Pedrerol, M. Aly, E. Amarapurkar, D.N. Amiri, P. Amougou, N. Amouyel, P. Andersen, L.B. Anderssen, S.A. Ängquist, L. Anjana, R.M. 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Milanovic, S.M. Musil, V. Mustafa, N. Nabipour, I. Naderimagham, S. Nagel, G. Naidu, B.M. Najafi, F. Nakamura, H. Námešná, J. Nang, E.E.K. Nangia, V.B. Nankap, M. Narake, S. Nardone, P. Nauck, M. Neal, W.A. Nejatizadeh, A. Nekkantti, C. Nelis, K. Nelis, L. Nenko, I. Neovius, M. Nervi, F. Nguyen, C.T. Nguyen, N.D. Nguyen, Q.N. Nieto-Martínez, R.E. Nikitin, Y.P. Ning, G. Ninomiya, T. Nishtar, S. Noale, M. Noboa, O.A. Nogueira, H. Norat, T. Nordendahl, M. Nordestgaard, B.G. Noto, D. Nowak-Szczepanska, N. Al Nsour, M. Nuhoglu, I. Nurk, E. O’Neill, T.W. O’Reilly, D. Obreja, G. Ochimana, C. Ochoa-Avilés, A.M. Oda, E. Oh, K. Ohara, K. Ohlsson, C. Ohtsuka, R. Olafsson, O. Olinto, M.T.A. Oliveira, I.O. Omar, M.A. Onat, A. Ong, S.K. Ono, L.M. Ordunez, P. Ornelas, R. Ortiz, A.P. Ortiz, P.J. Osler, M. Osmond, C. Ostojic, S.M. Ostovar, A. Otero, J.A. Overvad, K. Owusu-Dabo, E. Paccaud, F.M. Padez, C. Pagkalos, I. Pahomova, E. de Paiva, K.M. Pajak, A. Palli, D. Palloni, A. Palmieri, L. Pan, W.-H. Panda-Jonas, S. Pandey, A. Panza, F. Papandreou, D. Park, S.-W. Park, S. Parnell, W.R. Parsaeian, M. Pascanu, I.M. Pasquet, P. Patel, N.D. Pecin, I. Pednekar, M.S. Peer, N. Pei, G. Peixoto, S.V. Peltonen, M. Pereira, A.C. Peres, M.A. Pérez-Farinós, N. Pérez, C.M. Peterkova, V. Peters, A. Petersmann, A. Petkeviciene, J. Petrauskiene, A. Pettenuzzo, E. Peykari, N. Pham, S.T. Pichardo, R.N. Pierannunzio, D. Pigeot, I. Pikhart, H. Pilav, A. Pilotto, L. Pistelli, F. Pitakaka, F. Piwonska, A. Pizarro, A.N. Plans-Rubió, P. Poh, B.K. Pohlabeln, H. Pop, R.M. Popovic, S.R. Porta, M. Posch, G. Poudyal, A. Poulimeneas, D. Pouraram, H. Pourfarzi, F. Pourshams, A. Poustchi, H. Pradeepa, R. Price, A.J. Price, J.F. Providencia, R. Puder, J.J. Pudule, I. Puhakka, S.E. Puiu, M. Punab, M. Qasrawi, R.F. Qorbani, M. Bao, T.Q. Radic, I. Radisauskas, R. Rahimikazerooni, S. Rahman, M. Rahman, M. Raitakari, O. Raj, M. Rakhimova, E. Rakhmatulloev, S. Rakovac, I. Rao, S.R. Ramachandran, A. Ramke, J. Ramos, E. Ramos, R. Rampal, L. Rampal, S. Rarra, V. Rascon-Pacheco, R.A. Rasmussen, M. Rech, C.R. Redon, J. Reganit, P.F.M. Regecová, V. Revilla, L. Rezaianzadeh, A. Ribas-Barba, L. Ribeiro, R. Riboli, E. Richter, A. Rigo, F. Rinaldo, N. de Wit, T.F.R. Rito, A. Ritti-Dias, R.M. Rivera, J.A. Robitaille, C. Roccaldo, R. Rodrigues, D. Rodríguez-Artalejo, F. del Cristo Rodriguez-Perez, M. Rodríguez-Villamizar, L.A. Roggenbuck, U. Rojas-Martinez, R. Rojroongwasinkul, N. Romaguera, D. Romeo, E.L. Rosario, R.V. Rosengren, A. Rouse, I. Roy, J.G.R. Rubinstein, A. Rühli, F.J. Ruidavets, J.-B. Ruiz-Betancourt, B.S. Ruiz-Castell, M. Moreno, E.R. Rusakova, I.A. Jonsson, K.R. Russo, P. Rust, P. Rutkowski, M. Sabanayagam, C. Sacchini, E. Sachdev, H.S. Sadjadi, A. Safarpour, A.R. Safiri, S. Saki, N. Salanave, B. Martinez, E.S. Salmerón, D. Salomaa, V. Salonen, J.T. Salvetti, M. Samoutian, M. Sánchez-Abanto, J. Sans, S. Marina, L.S. Santos, D.A. Santos, I.S. Santos, L.C. Santos, M.P. Santos, O. Santos, R. Sanz, S.S. Saramies, J.L. Sardinha, L.B. Sarrafzadegan, N. Sathish, T. Saum, K.-U. Savva, S. Savy, M. Sawada, N. Sbaraini, M. Scazufca, M. Schaan, B.D. Rosario, A.S. Schargrodsky, H. Schienkiewitz, A. Schipf, S. Schmidt, C.O. Schmidt, I.M. Schnohr, P. Schöttker, B. Schramm, S. Schramm, S. Schröder, H. Schultsz, C. Schutte, A.E. Sein, A.A. Selamat, R. Sember, V. Sen, A. Senbanjo, I.O. Sepanlou, S.G. Sequera, V. Serra-Majem, L. Servais, J. Ševcíková, L. Shalnova, S.A. Shamah-Levy, T. Shamshirgaran, M. Shanthirani, C.S. Sharafkhah, M. Sharma, S.K. Shaw, J.E. Shayanrad, A. Shayesteh, A.A. Shengelia, L. Shi, Z. Shibuya, K. Shimizu-Furusawa, H. Shin, D.W. Shirani, M. Shiri, R. Shrestha, N. Si-Ramlee, K. Siani, A. Siantar, R. Sibai, A.M. Silva, A.M. Silva, D.A.S. Simon, M. Simons, J. Simons, L.A. Sjöberg, A. Sjöström, M. Skodje, G. Slowikowska-Hilczer, J. Slusarczyk, P. Smeeth, L. So, H.-K. Soares, F.C. Sobek, G. Sobngwi, E. Sodemann, M. Söderberg, S. Soekatri, M.Y.E. Soemantri, A. Sofat, R. Solfrizzi, V. Somi, M.H. Sonestedt, E. Song, Y. Sørensen, T.I.A. Sørgjerd, E.P. Jérome, C.S. Soto-Rojas, V.E. Soumaré, A. Sovic, S. Sparboe-Nilsen, B. Sparrenberger, K. Spinelli, A. Spiroski, I. Staessen, J.A. Stamm, H. Stathopoulou, M.G. Staub, K. Stavreski, B. Steene-Johannessen, J. Stehle, P. Stein, A.D. Stergiou, G.S. Stessman, J. Stevanovic, R. Stieber, J. Stöckl, D. Stocks, T. Stokwiszewski, J. Stoyanova, E. Stratton, G. Stronks, K. Strufaldi, M.W. Sturua, L. Suárez-Medina, S. Suka, M. Sun, C.-A. Sundström, J. Sung, Y.-T. Sunyer, J. Suriyawongpaisal, P. Swinburn, B.A. Sy, R.G. Syddall, H.E. Sylva, R.C. Szklo, M. Szponar, L. Tai, E.S. Tammesoo, M.-L. Tamosiunas, A. Tan, E.J. Tang, X. Tanrygulyyeva, M. Tanser, F. Tao, Y. Tarawneh, M.R. Tarp, J. Tarqui-Mamani, C.B. Braunerová, R.T. Taylor, A. Taylor, J. Tchibindat, F. Tebar, W.R. Tell, G.S. Tello, T. Tham, Y.C. Thankappan, K.R. Theobald, H. Theodoridis, X. Thijs, L. Thomas, N. Thuesen, B.H. Tichá, L. Timmermans, E.J. Tjonneland, A. Tolonen, H.K. Tolstrup, J.S. Topbas, M. Topór-Madry, R. Torheim, L.E. Tormo, M.J. Tornaritis, M.J. Torrent, M. Torres-Collado, L. Toselli, S. Touloumi, G. Traissac, P. Tran, T.T.-H. Trichopoulos, D. Trichopoulou, A. Trinh, D.T.H. Trivedi, A. Tshepo, L. Tsigga, M. Tsugane, S. Tuliakova, A.M. Tulloch-Reid, M.K. Tullu, F. Tuomainen, T.-P. Tuomilehto, J. Turley, M.L. Twig, G. Tynelius, P. Tzotzas, T. Tzourio, C. Ueda, P. Ugel, E. Ukoli, F.A.M. Ulmer, H. Unal, B. Usupova, Z. Uusitalo, H.M.T. Uysal, N. Vaitkeviciute, J. Valdivia, G. Vale, S. Valvi, D. van Dam, R.M. Van der Heyden, J. van der Schouw, Y.T. Van Herck, K. Van Minh, H. Van Schoor, N.M. van Valkengoed, I.G.M. Vanderschueren, D. Vanuzzo, D. Varbo, A. Varela-Moreiras, G. Varona-Pérez, P. Vasan, S.K. Vega, T. Veidebaum, T. Velasquez-Melendez, G. Velika, B. Veronesi, G. Verschuren, W.M.M. Victora, C.G. Viegi, G. Viet, L. Villalpando, S. Vineis, P. Vioque, J. Virtanen, J.K. Visser, M. Visvikis-Siest, S. Viswanathan, B. Vladulescu, M. Vlasoff, T. Vocanec, D. Vollenweider, P. Völzke, H. Voutilainen, A. Voutilainen, S. Vrijheid, M. Vrijkotte, T.G.M. Wade, A.N. Wagner, A. Waldhör, T. Walton, J. Wambiya, E.O.A. Bebakar, A.M.W. Mohamud, W.N.W. de Souza Wanderley Júnior, R. Wang, M.-D. Wang, N. Wang, Q. Wang, X. Wang, Y.X. Wang, Y.-W. Wannamethee, S.G. Wareham, N. Weber, A. Wedderkopp, N. Weerasekera, D. Weghuber, D. Wei, W. Weres, A. Werner, B. Whincup, P.H. Widhalm, K. Widyahening, I.S. Wiecek, A. Wilks, R.J. Willeit, J. Willeit, P. Williams, J. Wilsgaard, T. Wojtyniak, B. Wong-McClure, R.A. Wong, A. Wong, J.E. Wong, T.Y. Woo, J. Woodward, M. Wu, F.C. Wu, J. Wu, L.J. Wu, S. Xu, H. Xu, L. Yaacob, N.A. Yamborisut, U. Yan, W. Yang, L. Yang, X. Yang, Y. Yardim, N. Yaseri, M. Yasuharu, T. Ye, X. Yiallouros, P.K. Yoosefi, M. Yoshihara, A. You, Q.S. You, S.-L. Younger-Coleman, N.O. Yusof, S.M. Yusoff, A.F. Zaccagni, L. Zafiropulos, V. Zainuddin, A.A. Zakavi, S.R. Zamani, F. Zambon, S. Zampelas, A. Zamrazilová, H. Zapata, M.E. Zargar, A.H. Zaw, K.K. Zdrojewski, T. Zejglicova, K. Vrkic, T.Z. Zeng, Y. Zhang, L. Zhang, Z.-Y. Zhao, D. Zhao, M.-H. Zhao, W. Zhen, S. Zheng, W. Zheng, Y. Zholdin, B. Zhou, M. Zhu, D. Zins, M. Zitt, E. Zocalo, Y. Cisneros, J.Z. Zuziak, M. Ezzati, M. Filippi, S. NCD Risk Factor Collaboration (NCD-RisC)
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nutritional and metabolic diseases ,sense organs ,skin and connective tissue diseases - Abstract
From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions. © Copyright.
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- 2021
8. Energy of step formation on metal surfaces from the stabilized-jellium model
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Farjam, M.
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- 2005
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9. Projection operator approach to unfolding supercell band structures
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Farjam, M.
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Condensed Matter - Materials Science ,Materials Science (cond-mat.mtrl-sci) ,FOS: Physical sciences ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,Condensed Matter - Disordered Systems and Neural Networks - Abstract
While the methodology of band structure unfolding has appeared in several publications, the original derivations of the unfolding formulas can be considerably simplified by using the $k$-projection method. In this work, more transparent derivations of unfolded spectral weights are given by using the projection operator approach. A range of illustrative examples are also presented which include finite and random one-dimensional chains, and Kekul\'e-textured honeycomb lattice., Comment: 7 pages, 5 figures, minor corrections and improvements made
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- 2015
10. Effect of hydrogen adsorption on the quasiparticle spectra of graphene.
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Farjam, M., Haberer, D., and Grüneis, A.
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HYDROGEN spectra , *HEAT of adsorption , *GRAPHENE , *QUASIPARTICLES , *GREEN'S functions , *SCANNING tunneling microscopy - Abstract
We use the noninteracting tight-binding model to study the effect of isolated hydrogen adsorbates on the quasiparticle spectra of single-layer graphene. Using the Green's function approach, we obtain analytic expressions for the local density of states and the spectral function of hydrogen-doped graphene, which are also numerically evaluated and plotted. Our results are relevant for the interpretation of scanning tunneling microscopy and angle-resolved photoemission spectroscopy data of functionalized graphene. [ABSTRACT FROM AUTHOR]
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- 2011
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11. Uniaxial strain on gapped graphene
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Farjam, M. and Rafii-Tabar, H.
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STRAINS & stresses (Mechanics) , *GRAPHENE , *ELECTRONIC structure , *SYMMETRY (Physics) , *LATTICE theory , *HONEYCOMB structures , *BAND gaps , *SOLID state chemistry - Abstract
Abstract: We study the effect of uniaxial strain on the electronic band structure of gapped graphene. We consider two types of gapped graphene, one which breaks the symmetry between the two triangular sublattices (staggered model), and another which alternates the bonds on the honeycomb lattice (Kekulé model). In the staggered model, the effect of strains below a critical value is only a shift of the band gap location. In the Kekulé model, as strain is increased, band gap location is initially pinned to a corner of the Brillouin zone while its width diminishes, and after gap closure the location of the contact point begins to shift. Analytic and numerical results are obtained for both the tight-binding and Dirac fermion descriptions of gapped graphene. [Copyright &y& Elsevier]
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- 2010
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12. Giardia lamblia infection and the sera level of trace elements, Fe, Zn and Cu
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Davami, Hassan and Farjam, M. Reza
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- 2011
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13. Direct observation of a dispersionless impurity band in hydrogenated graphene.
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Haberer, D., Petaccia, L., Farjam, M., Taioli, S., Jafari, S. A., Nefedov, A., Zhang, W., Calliari, L., Scarduelli, G., Dora, B., Vyalikh, D. V., Pichler, T., Wöll, Ch., Alfè, D., Simonucci, S., Dresselhaus, M. S., Knupfer, M., Büchner, B., and Grüneis, A.
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PHOTOEMISSION , *SPECTRUM analysis , *ELECTRONS , *MONOMOLECULAR films , *GRAPHENE - Abstract
We show with angle-resolved photoemission spectroscopy that a new energy band appears in the electronic structure of electron-doped hydrogenated monolayer graphene (H-graphene). Its occupation can be controlled with the hydrogen amount and allows for tuning of graphene's doping level. Our calculations of the electronic structure of H-graphene suggest that this state is largely composed of hydrogen 1s orbitals and remains extended for low H coverages despite the random chemisorption of H. Further evidence for the existence of a hydrogen state is provided by x-ray absorption studies of undoped H-graphene which are clearly showing the emergence of an additional state in the vicinity of the π* resonance. [ABSTRACT FROM AUTHOR]
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- 2011
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14. Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants
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Rodriguez-Martinez, Andrea, Zhou, Bin, Sophiea, Marisa K, Bentham, James, Paciorek, Christopher J, Iurilli, Maria LC, Carrillo-Larco, Rodrigo M, Bennett, James E, Di Cesare, Mariachiara, Taddei, Cristina, Bixby, Honor, Stevens, Gretchen A, Riley, Leanne M, Cowan, Melanie J, Savin, Stefan, Danaei, Goodarz, Chirita-Emandi, Adela, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Malekzadeh, Reza, Miranda, J Jaime, Moon, Jin Soo, Popovic, Stevo R, Sorensen, Thorkild IA, Soric, Maroje, Starc, Gregor, Zainuddin, Ahmad A, Gregg, Edward W, Bhutta, Zulfiqar A, Black, Robert, Ezzati, Majid, Abarca-Gomez, Leandra, Abdeen, Ziad A, Abdrakhmanova, Shynar, Ghaffar, Suhaila Abdul, Rahim, Hanan F Abdul, Abu-Rmeileh, Niveen M, Garba, Jamila Abubakar, Acosta-Cazares, Benjamin, Adams, Robert J, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Aguilar-Salinas, Carlos A, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Othman, Amani Rashed, Al-Raddadi, Rajaa, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alkandari, Abdullah, Alkerwi, Ala'a, Allin, Kristine, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiri, Parisa, Amougou, Norbert, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A, Angquist, Lars, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Aounallah-Skhiri, Hajer, Araujo, Joana, Ariansen, Inger, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Aspelund, Thor, Assah, Felix K, Assuncao, Maria Cecilia F, Aung, May Soe, Auvinen, Juha, Avdicova, Maria, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Azmin, Mehrdad, Babu, Bontha V, Jorgensen, Maja Baeksgaard, Baharudin, Azli, Bahijri, Suhad, Baker, Jennifer L, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, Jose R, Baran, Joanna, Barbagallo, 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Zhamyila, Uusitalo, Hannu MT, Uysal, Nalan, Vaitkeviciute, Justina, Valdivia, Gonzalo, Vale, Susana, Valvi, Damaskini, van Dam, Rob M, Van der Heyden, Johan, van der Schouw, Yvonne T, Van Herck, Koen, Hoang, Van Minh, van Valkengoed, Irene GM, Vanderschueren, Dirk, Vanuzzo, Diego, Varbo, Anette, Varela-Moreiras, Gregorio, Varona-Perez, Patricia, Vasan, Senthil K, Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Velika, Biruta, Veronesi, Giovanni, Verschuren, WM Monique, Victora, Cesar G, Viegi, Giovanni, Viet, Lucie, Villalpando, Salvador, Vineis, Paolo, Vioque, Jesus, Virtanen, Jyrki K, Visser, Marjolein, Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vladulescu, Mihaela, Vlasoff, Tiina, Vocanec, Dorja, Volzke, Henry, Voutilainen, Ari, Voutilainen, Sari, Vrijheid, Martine, Vrijkotte, Tanja GM, Wade, Alisha N, Wagner, Aline, Waldhor, Thomas, Walton, Janette, Wambiya, Elvis OA, Bebakar, Wan Mohamad Wan, Mohamud, Wan Nazaimoon Wan, de Souza, Rildo, Junior, Wanderley, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S Goya, Wareham, Nicholas, Weber, Adelheid, Wedderkopp, Niels, Weerasekera, Deepa, Weghuber, Daniel, Wei, Wenbin, Weres, Aneta, Werner, Bo, Whincup, Peter H, Widhalm, Kurt, Widyahening, Indah S, Wiecek, Andrzej, Wilks, Rainford J, Willeit, Johann, Willeit, Peter, Williams, Julianne, Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A, Wong, Andrew, Wong, Jyh Eiin, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Wu, Frederick C, Wu, Jianfeng, Wu, Li Juan, Wu, Shouling, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yamborisut, Uruwan, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yardim, Nazan, Yaseri, Mehdi, Yasuharu, Tabara, Ye, Xingwang, Yiallouros, Panayiotis K, Yoosefi, Moein, Yoshihara, Akihiro, You, Qi Sheng, You, San-Lin, Younger-Coleman, Novie O, Yusof, Safiah Md, Yusoff, Ahmad Faudzi, Zaccagni, Luciana, Zafiropulos, Vassilis, Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Zamrazilova, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zdrojewski, Tomasz, Vrkic, Tajana Zeljkovic, Zeng, Yi, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zocalo, Yanina, Cisneros, Julio Zuniga, Zuziak, Monika, Faculdade de Ciências da Nutrição e Alimentação, Instituto de Saúde Pública da Universidade do Porto, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Environnement, Santé, Sociétés (ESS), Centre National de la Recherche Scientifique (CNRS), European Project: 774548, Reproductive Origins of Adult Health and Disease (ROAHD), Rodriguez-Martinez A, Zhou B, Sophiea MK, Bentham J, Paciorek CJ, Iurilli ML, Carrillo-Larco RM, Bennett JE, Di Cesare M, Taddei C, Bixby H, Stevens GA, Riley LM, Cowan MJ, Savin S, 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Ferrario MM, Ferreccio C, Ferrer E, Ferrieres J, Figueiró TH, Fijalkowska A, Fink G, Fischer K, Föger B, Foo LH, Forsner M, Fouad HM, Francis DK, Franco MDC, Franco OH, Frikke-Schmidt R, Frontera G, Fuchs FD, Fuchs SC, Fujiati II, Fujita Y, Fumihiko M, Furusawa T, Gaciong Z, Gafencu M, Galbarczyk A, Galenkamp H, Galeone D, Galfo M, Galvano F, Gao J, Garcia-de-la-Hera M, García-Solano M, Gareta D, Garnett SP, Gaspoz J-M, Gasull M, Gaya ACA, Gaya AR, Gazzinelli A, Gehring U, Geiger H, Geleijnse JM, Ghanbari A, Ghasemi E, Gheorghe-Fronea O-F, Giampaoli S, Gianfagna F, Gill TK, Giovannelli J, Gironella G, Giwercman A, Gkiouras K, Godos J, Gogen S, Goldsmith RA, Goltzman D, Gómez SF, Gomula A, Goncalves Cordeiro da Silva B, Gonçalves H, Gonzalez-Chica DA, Gonzalez-Gross M, González-Leon M, González-Rivas JP, González-Villalpando C, González-Villalpando M-E, Gonzalez AR, Gottrand F, Graça AP, Graff-Iversen S, Grafnetter D, Grajda A, Grammatikopoulou MG, Gregor RD, Grodzicki T, Grøholt EK, 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Klimont J, Klumbiene J, Knoflach M, Koirala B, Kolle E, Kolsteren P, König J, Korpelainen R, Korrovits P, Korzycka M, Kos J, Koskinen S, Kouda K, Kovacs VA, Kowlessur S, Koziel S, Kratzer W, Kriemler S, Kristensen PL, Krokstad S, Kromhout D, Krtalic B, Kruger HS, Kubinova R, Kuciene R, Kujala UM, Kujundzic E, Kulaga Z, Kumar RK, Kunešová M, Kurjata P, Kusuma YS, Kuulasmaa K, Kyobutungi C, La QN, Laamiri FZ, Laatikainen T, Lachat C, Laid Y, Lam TH, Lambrinou C-P, Landais E, Lanska V, Lappas G, Larijani B, Latt TS, Lauria L, Lazo-Porras M, Le Nguyen Bao K, Le Port A, Le TD, Lee J, Lee J, Lee PH, Lehmann N, Lehtimäki T, Lemogoum D, Levitt NS, Li Y, Liivak M, Lilly CL, Lim W-Y, Lima-Costa MF, Lin H-H, Lin X, Lin Y-T, Lind L, Linneberg A, Lissner L, Litwin M, Liu J, Liu L, Lo W-C, Loit H-M, Long KQ, Lopes L, Lopes O, Lopez-Garcia E, Lopez T, Lotufo PA, Lozano JE, Lukrafka JL, Luksiene D, Lundqvist A, Lundqvist R, Lunet N, Lunogelo C, Lustigová M, Luszczki E, Ma G, Ma J, Ma X, Machado-Coelho 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Nordestgaard B.G., Noto D., Nowak-Szczepanska N., Nsour M.A., Nuhoglu I., Nurk E., O'Neill T.W., O'Reilly D., Obreja G., Ochimana C., Ochoa-Aviles A.M., Oda E., Oh K., Ohara K., Ohlsson C., Ohtsuka R., Olafsson O., Olinto M.T.A., Oliveira I.O., Omar M.A., Onat A., Ong S.K., Ono L.M., Ordunez P., Ornelas R., Ortiz A.P., Ortiz P.J., Osler M., Osmond C., Ostojic S.M., Ostovar A., Otero J.A., Overvad K., Owusu-Dabo E., Paccaud F.M., Padez C., Pagkalos I., Pahomova E., Paiva K.M.D., Pajak A., Palli D., Palloni A., Palmieri L., Pan W.-H., Panda-Jonas S., Pandey A., Panza F., Papandreou D., Park S.-W., Park S., Parnell W.R., Parsaeian M., Pascanu I.M., Pasquet P., Patel N.D., Pednekar M.S., Peer N., Peixoto S.V., Peltonen M., Pereira A.C., Peres M.A., Perez-Farinos N., Perez C.M., Peterkova V., Peters A., Petersmann A., Petkeviciene J., Petrauskiene A., Pettenuzzo E., Peykari N., Pham S.T., Pichardo R.N., Pierannunzio D., Pigeot I., Pikhart H., Pilav A., Pilotto L., Pistelli F., Pitakaka F., Piwonska A., Pizarro A.N., Plans-Rubio P., Poh B.K., Pohlabeln H., Pop R.M., Porta M., Posch G., Poudyal A., Poulimeneas D., Pouraram H., Pourfarzi F., Pourshams A., Poustchi H., Pradeepa R., Price A.J., Price J.F., Providencia R., Puder J.J., Pudule I., Puhakka S.E., Puiu M., Punab M., Qasrawi R.F., Qorbani M., Quoc Bao T., Radic I., Radisauskas R., Rahimikazerooni S., Rahman M., Raitakari O., Raj M., Rakhimova E., Rakhmatulloev S., Rakovac I., Ramachandra Rao S., Ramachandran A., Ramke J., Ramos E., Ramos R., Rampal L., Rampal S., Rarra V., Rascon-Pacheco R.A., Rasmussen M., Rech C.R., Redon J., Reganit P.F.M., Regecova V., Revilla L., Rezaianzadeh A., Ribas-Barba L., Ribeiro R., Riboli E., Richter A., Rigo F., Rinaldo N., Rinke de Wit T.F., Rito A., Ritti-Dias R.M., Rivera J.A., Robitaille C., Roccaldo R., Rodrigues D., Rodriguez-Artalejo F., Rodriguez-Perez M.D.C., Rodriguez-Villamizar L.A., Roggenbuck U., Rojas-Martinez R., Rojroongwasinkul N., Romaguera D., Romeo E.L., Rosario R.V., Rosengren A., Rouse I., Roy J.G., Rubinstein A., Ruhli F.J., Ruidavets J.-B., Ruiz-Betancourt B.S., Ruiz Moreno E., Rusakova I.A., Russell Jonsson K., Russo P., Rust P., Rutkowski M., Sabanayagam C., Sacchini E., Sachdev H.S., Sadjadi A., Safarpour A.R., Safi S., Safiri S., Saidi O., Saki N., Salanave B., Salazar Martinez E., Salmeron D., Salomaa V., Salonen J.T., Salvetti M., Samoutian M., Sanchez-Abanto J., Sandjaja, Sans S., Santa Marina L., Santos D.A., Santos I.S., Santos L.C., Santos M.P., Santos O., Santos R., Santos Sanz S., Saramies J.L., Sardinha L.B., Sarrafzadegan N., Sathish T., Saum K.-U., Savva S., Savy M., Sawada N., Sbaraini M., Scazufca M., Schaan B.D., Schaffrath Rosario A., Schargrodsky H., Schienkiewitz A., Schindler K., Schipf S., Schmidt C.O., Schmidt I.M., Schnohr P., Schottker B., Schramm S., Schroder H., Schultsz C., Schutte A.E., Sebert S., Sein A.A., Selamat R., Sember V., Sen A., Senbanjo I.O., Sepanlou S.G., Sequera V., Serra-Majem L., Servais J., Sevcikova L., Shalnova S.A., Shamah-Levy T., Shamshirgaran M., Shanthirani C.S., Sharafkhah M., Sharma S.K., Shaw J.E., Shayanrad A., Shayesteh A.A., Shengelia L., Shi Z., Shibuya K., Shimizu-Furusawa H., Shin D.W., Shin Y., Shirani M., Shiri R., Shrestha N., Si-Ramlee K., Siani A., Siantar R., Sibai A.M., Silva A.M., Silva D.A.S., Simon M., Simons J., Simons L.A., Sjoberg A., Sjostrom M., Skodje G., Slowikowska-Hilczer J., Slusarczyk P., Smeeth L., So H.-K., Soares F.C., Sobek G., Sobngwi E., Sodemann M., Soderberg S., Soekatri M.Y., Soemantri A., Sofat R., Solfrizzi V., Somi M.H., Sonestedt E., Song Y., Sorgjerd E.P., Sossa Jerome C., Soto-Rojas V.E., Soumare A., Sovic S., Sparboe-Nilsen B., Sparrenberger K., Spinelli A., Spiroski I., Staessen J.A., Stamm H., Stathopoulou M.G., Staub K., Stavreski B., Steene-Johannessen J., Stehle P., Stein A.D., Stergiou G.S., Stessman J., Stevanovic R., Stieber J., Stockl D., Stocks T., Stokwiszewski J., Stoyanova E., Stratton G., Stronks K., Strufaldi M.W., Sturua L., Suarez-Medina R., Suka M., Sun C.-A., Sundstrom J., Sung Y.-T., Sunyer J., Suriyawongpaisal P., Swinburn B.A., Sy R.G., Syddall H.E., Sylva R.C., Szklo M., Szponar L., Tai E.S., Tammesoo M.-L., Tamosiunas A., Tan E.J., Tang X., Tanser F., Tao Y., Tarawneh M.R., Tarp J., Tarqui-Mamani C.B., Taxova Braunerova R., Taylor A., Taylor J., Tchibindat F., Tebar W.R., Tell G.S., Tello T., Thankappan K.R., Theobald H., Theodoridis X., Thijs L., Thomas N., Thuesen B.H., Ticha L., Timmermans E.J., Tjonneland A., Tolonen H.K., Tolstrup J.S., Topbas M., Topor-Madry R., Torheim L.E., Tormo M.J., Tornaritis M.J., Torrent M., Torres-Collado L., Toselli S., Traissac P., Tran T.T.-H., Trichopoulos D., Trichopoulou A., Trinh O.T., Trivedi A., Tshepo L., Tsigga M., Tsugane S., Tuliakova A.M., Tulloch-Reid M.K., Tullu F., Tuomainen T.-P., Tuomilehto J., Turley M.L., Tynelius P., Tzotzas T., Tzourio C., Ueda P., Ugel E., Ukoli F.A., Ulmer H., Unal B., Usupova Z., Uusitalo H.M., Uysal N., Vaitkeviciute J., Valdivia G., Vale S., Valvi D., van Dam R.M., Van der Heyden J., van der Schouw Y.T., Van Herck K., Van Minh H., van Valkengoed I.G., Vanderschueren D., Vanuzzo D., Varbo A., Varela-Moreiras G., Varona-Perez P., Vasan S.K., Vega T., Veidebaum T., Velasquez-Melendez G., Velika B., Veronesi G., Verschuren W.M., Victora C.G., Viegi G., Viet L., Villalpando S., Vineis P., Vioque J., Virtanen J.K., Visser M., Visvikis-Siest S., Viswanathan B., Vladulescu M., Vlasoff T., Vocanec D., Volzke H., Voutilainen A., Voutilainen S., Vrijheid M., Vrijkotte T.G., Wade A.N., Wagner A., Waldhor T., Walton J., Wambiya E.O., Wan Bebakar W.M., Wan Mohamud W.N., Wanderley Junior R.D.S., Wang M.-D., Wang N., Wang Q., Wang X., Wang Y.X., Wang Y.-W., Wannamethee S.G., Wareham N., Weber A., Wedderkopp N., Weerasekera D., Weghuber D., Wei W., Weres A., Werner B., Whincup P.H., Widhalm K., Widyahening I.S., Wiecek A., Wilks R.J., Willeit J., Willeit P., Williams J., Wilsgaard T., Wojtyniak B., Wong-McClure R.A., Wong A., Wong J.E., Wong T.Y., Woo J., Woodward M., Wu F.C., Wu J., Wu L.J., Wu S., Xu H., Xu L., Yaacob N.A., Yamborisut U., Yan W., Yang L., Yang X., Yang Y., Yardim N., Yaseri M., Yasuharu T., Ye X., Yiallouros P.K., Yoosefi M., Yoshihara A., You Q.S., You S.-L., Younger-Coleman N.O., Yusof S.M., Yusoff A.F., Zaccagni L., Zafiropulos V., Zakavi S.R., Zamani F., Zambon S., Zampelas A., Zamrazilova H., Zapata M.E., Zargar A.H., Zaw K.K., Zdrojewski T., Zeljkovic Vrkic T., Zeng Y., Zhang L., Zhang Z.-Y., Zhao D., Zhao M.-H., Zhao W., Zhen S., Zheng W., Zheng Y., Zholdin B., Zhou M., Zhu D., Zocalo Y., Zuniga Cisneros J., Zuziak M., and Ezzati M.
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Male ,body-mass index ,ADULTHOOD ,Adolescents ,pituuskasvu ,Pediatrics ,Body Mass Index ,0302 clinical medicine ,Child Development ,nuoret ,Public health surveillance ,Medicine ,Health Status Indicators ,10. No inequality ,Child ,11 Medical and Health Sciences ,Body mass index ,ComputingMilieux_MISCELLANEOUS ,education.field_of_study ,VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801 ,General Medicine ,Body mass indexes ,kansainvälinen vertailu ,3. Good health ,Geography ,Health ,030220 oncology & carcinogenesis ,Child, Preschool ,Medical and Health sciences ,purl.org/becyt/ford/3 [https] ,medicine.medical_specialty ,School-aged adolescents ,Socio-culturale ,lapset (ikäryhmät) ,Nursing ,territories ,ravinto ,purl.org/becyt/ford/3.3 [https] ,03 medical and health sciences ,School Children ,SDG 3 - Good Health and Well-being ,SYSTEMATIC ANALYSIS ,Humans ,school-aged children and adolescents ,Montenegro ,education ,Science & Technology ,Omvårdnad ,Health sciences, Medical and Health sciences ,Ciências médicas e da saúde ,Bayes Theorem ,Anthropometry ,Adolescent Development ,medicine.disease ,TRENDS ,Height and Body-mass Index ,Faculdade de Ciências Sociais ,UNDERNUTRITION ,Height index trajectories ,Height, body mass index, children , epidemiology ,risk factors, growth ,Stature ,Demography ,Settore MED/09 - Medicina Interna ,Internationality ,[SDV]Life Sciences [q-bio] ,030204 cardiovascular system & hematology ,Body-mass index trajectories ,Epidemiology ,Medicine and Health Sciences ,risk factors ,countries ,EPIDEMIOLOGY ,height ,children ,adolescents ,BMI ,030212 general & internal medicine ,painoindeksi ,Child development ,2. Zero hunger ,Medicine(all) ,School age child ,obestity children cardiovascular ,Population Health ,1. No poverty ,Pediatrik ,Public Health, Global Health, Social Medicine and Epidemiology ,3142 Public health care science, environmental and occupational health ,Pooled analysis ,NUTRITION ,Female ,medicine.symptom ,pooled analysis ,Life Sciences & Biomedicine ,terveys ,height, BMI, nutrition, health, children, adolescents ,Adolescent ,growth ,Population ,body-mass ,Population based ,Body-mass index ,Young Adult ,Medicine, General & Internal ,Meta-Analysis as Topic ,General & Internal Medicine ,parasitic diseases ,Weight gain ,School-aged childrens ,Age trajectories ,business.industry ,Height ,Weight ,Body Height ,Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi ,Malnutrition ,ONSET ,Ciências da Saúde, Ciências médicas e da saúde ,School-aged children ,VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801 ,business ,terveysriskit ,Estilos de Vida e Impacto na Saúde - Abstract
BACKGROUND: Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents., METHODS: For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence., FINDINGS: We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls., INTERPRETATION: The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks., Wellcome Trust, AstraZeneca Young Health Programme, EU., peer-reviewed
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- 2020
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15. The association between insulin resistance and QT interval: A systematic review and Meta-Analysis.
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Mobasheri-Shiri M, Bazmi S, Soleimani-Meigoli MS, Karimimoghadam Z, Tabrizi R, and Farjam M
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- Humans, Risk Assessment, Middle Aged, Female, Male, Adult, Electrocardiography, Aged, Risk Factors, Action Potentials, Long QT Syndrome physiopathology, Long QT Syndrome diagnosis, Biomarkers blood, Blood Glucose metabolism, Arrhythmias, Cardiac physiopathology, Arrhythmias, Cardiac diagnosis, Arrhythmias, Cardiac etiology, Heart Conduction System physiopathology, Prognosis, Insulin blood, Time Factors, Insulin Resistance, Heart Rate
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Background: Insulin resistance (IR) is a major health concern associated with various diseases, and prolonged QT interval can potentially lead to life-threatening arrhythmias and death. There are conflicting views on the relationship between IR and QT interval. This meta-analysis aims to comprehensively investigate the association between IR and QT interval., Methods: An extensive search in databases PubMed, Scopus, Cochrane Library, Embase, and Web of Science up to October 2024 was conducted. Cohort studies which reported means and standard deviations for the QTc interval across the case and control groups with and without insulin resistance based on HOMA-IR were eligible for inclusion. Research with partial or inaccessible primary data, those involving participants with pre-existing cardiac conditions, and those with ambiguous results were excluded. The evaluation of study quality utilized the Newcastle-Ottawa Scale. A random-effects model was applied for the meta-analysis, and Egger's test was used to assess publication bias. GRADEproGDT was used to evaluate the certainty of the evidence., Results: Five studies, encompassing 603 participants, met the inclusion criteria. A significant positive association was observed between IR and QT interval (Weighted Mean Difference [WMD] = 12.38, 95% Confidence Interval [CI]: 5.51, 19.25). All included studies demonstrated high methodological quality. Assessment for publication bias revealed no significant findings (p-value for Egger's test = 0.39). The quality of evidence for the main outcome was moderate. Subgroup analyses revealed a significant link between IR and QT interval in studies from Turkey and India, with samples over fifty, and involving adults., Conclusions: This meta-analysis highlights that IR is linked to an elevated risk of QT prolongation. Early identification of IR is crucial to mitigate the risk of QT prolongation and subsequent arrhythmias, thus emphasizing the importance of early intervention to prevent adverse cardiac outcomes and sudden cardiac death. Caution is needed when interpreting our results due to study heterogeneity, certainty of evidence, and sensitivity analysis findings. More rigorous research on this subject is required., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)
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- 2025
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16. Comparison of 10-year atherosclerotic cardiovascular disease (ASCVD) risk in metropolitan and rural areas of South of Iran.
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Zibaeenezhad MJ, Sayadi M, Pourmontaseri H, Khalili D, Farjam M, Bahramail E, Parsa N, Dehghan A, Mohammadi SS, Razeghian-Jahromi I, Bazrafshan Drissi H, and Sepehrinia M
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- Humans, Iran epidemiology, Male, Female, Middle Aged, Adult, Risk Factors, Aged, Incidence, Cardiovascular Diseases epidemiology, Cohort Studies, Rural Population statistics & numerical data, Atherosclerosis epidemiology, Urban Population statistics & numerical data
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The first step to reducing the growing burden of cardiovascular disease (CVD) is to find modifiable risk factors with the highest burden in each population. Urban and rural citizens may have different priorities in this regard. This study aimed to compare the 10-year incidence probability of CVD events and its associated risk factors between rural and urban areas in Iran. Data was extracted from two big cohorts, Fasa Adults Cohort Study (FACS) and Shiraz Heart Study (SHS), with participation of over 12,000 general population. Linear regression models were used to test the difference in CVD risk between two populations. Totally, 6,258 FACS and 6,101 SHS participants entered the study. Urban participants had a significantly higher mean ASCVD score (4.43% vs. 5.51%, p-value < 0.001). Also, they significantly showed higher body mass index, waist circumference, cholesterol level, fasting blood glucose level, systolic blood pressure, educational attainment, and occupational status. However, the prevalence of smoking was higher in rural areas. Notably, socioeconomic parameters including marital, occupational, and educational statuses seem to have strong impact on cardiovascular risk factors. After adjustment for all confounders, living in the urban areas seemed to be associated with higher atherosclerotic CVD risk (β = 0.78, 95%CI: [0.69-1.05]), which was consistent across both sexes. Given the higher risk of cardiovascular events in urban areas and different profiles of risk factors between these two regions, preventive strategies should be precisely and separately designed for each population by the health authorities and policymakers in order to reduce the CVD toll efficiently., Competing Interests: Declarations. Ethics: The present study was conducted considering the Helsinki Declaration and approved by the Ethical Committee of Shiraz University of Medical Sciences (ethical code: IR.SUMS.REC.1400.413). All of the participants were aware of the aims, benefits, and risks of the study by a written informed consent. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)
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- 2025
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17. Systematic Review and Meta-Analysis of OCT measurements in patients with chronic kidney disease.
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Bineshfar N, Changizi F, Farjam M, Sharafi F, and Williams BK Jr
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Purpose: To assess neurodegeneration and chorioretinal thickness in subjects with and without chronic kidney disease (CKD)., Methods: PubMed, Web of Science, Scopus and Embase were searched using proper keywords for articles published in the English language from their inception until January 2024. Publications were included if they reported optical coherence tomography (OCT) measurements of retinal or choroidal layers in patients with CKD compared to healthy or non-CKD controls. We used a random-effects model to calculate pooled standardized mean difference (SMD) estimates and 95% confidence intervals (CIs)., Results: Twelve studies, with 29,340 patients, were included in quantitative synthesis. In comparison to controls, patients with CKD had a significantly lower value for average retinal thickness (SMD [CI]: -0.35 [-0.58; -0.12], P = 0.028), choroidal thickness (SMD [CI]: -1.84 [-4.17; 0.49], P = 0.122), macular ganglion cell-inner plexiform layer (GC-IPL) (SMD [CI]: -0.58 [-0.78; -0.38]], P < 0.001), and peripapillary retinal nerve fiber layer (RNFL) thickness (SMD [CI]: -0.32 [-0.44; -0.20], P < 0.001). Significant RNFL thinning was observed in both diabetic CKD excluded and not excluded subgroups., Conclusion: Compared to controls, the eyes of patients with CKD have significantly thinner retina, GC-IPL, and RNFL., Competing Interests: Conflict of Interest: The authors have no conflict of interest to disclose.
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- 2024
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18. A rare case of recurrent spinal hydatid cyst in a 17-year-old man with neurological deficits and balance impairment.
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Farjam M, Changizi F, Ebrahimzadeh K, Sabeti S, Bidari Zerehpoush F, Javandoust Gharehbagh F, and Alavi Darazam I
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- Humans, Male, Adolescent, Recurrence, Albendazole therapeutic use, Animals, Iran, Echinococcus granulosus isolation & purification, Afghanistan, Anthelmintics therapeutic use, Spinal Diseases parasitology, Spinal Diseases surgery, Spinal Diseases drug therapy, Magnetic Resonance Imaging, Echinococcosis surgery, Echinococcosis drug therapy, Echinococcosis complications
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Background: Hydatid cysts are caused by the larval stage of the tapeworm parasite Echinococcus granulosus, leading to a rare but significant zoonotic infection. This disease is primarily observed in regions with prevalent agricultural and livestock practices. While the liver, lungs, and brain are most affected, spinal involvement is notably rare. Hydatid cyst disease develops gradually and is usually asymptomatic in the early stages. Symptoms usually emerge when the cysts enlarge sufficiently to cause compression. Effective treatment typically combines surgical intervention with antiparasitic medication to reduce the risk of recurrence, highlighting the necessity of a comprehensive approach to treatment and follow-up., Case Presentation: A 17-year-old male from Afghanistan, now residing in Iran, presented with muscle weakness in his lower extremities, primarily in the right leg, back pain, and impaired balance. The patient had a history of previous surgical resection for a primary spinal hydatidosis in Afghanistan. Upon examination, he exhibited reduced muscle strength, sensory loss in the right lower limb, paresthesia in the left medial forearm, hyperactive deep tendon reflexes in both legs and a positive Babinski sign on the right. Imaging revealed signs of previous laminectomy at T2 and T3 and a well-defined cystic mass at the T2 level. Surgical intervention was performed to decompress and resect the cyst, and pathological examination confirmed it as a hydatid cyst. Post-surgery, the patient was prescribed chewable albendazole (400 mg twice daily) for six months to prevent recurrence. Routine follow-ups and physiotherapy sessions were recommended after., Conclusion: Patients with spinal hydatidosis often exhibit spinal cord compression symptoms. When imaging modalities reveal multiple cysts and there is a history of residency in an endemic area, spinal hydatid cyst should be considered. Although rare, spinal hydatid cysts are difficult to treat completely. This case highlights the critical need for precise surgical removal, adequate postoperative medication, and long-term follow-up in managing of spinal hydatid cysts., Competing Interests: Declarations. Ethics approval and consent to participate: Our study underwent review and approval by the ethical committee of Shahid Beheshti University of Medical Sciences (ethical number IR.SBMU.RETECH.REC.1403.054). Consent for publication: The participant in this study was briefed about the survey and consented to share his past medical records and disease history. Additionally, written informed consent was acquired. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)
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- 2024
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19. Determining the sensitivity and specificity of the calculated fatty liver index in comparison with ultrasound.
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Saberian A, Dehghan A, Homayounfar R, Kaffashan S, Zarei F, Niknejad S, and Farjam M
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- Humans, Male, Female, Middle Aged, Adult, Iran, Waist Circumference, Triglycerides blood, Liver diagnostic imaging, Aged, Non-alcoholic Fatty Liver Disease diagnostic imaging, Non-alcoholic Fatty Liver Disease diagnosis, Ultrasonography methods, Sensitivity and Specificity, Body Mass Index, ROC Curve, gamma-Glutamyltransferase blood
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Background: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease in human history and it is expected to surpass other causes of liver disease mortality by 2030. Therefore, finding an alternative way to diagnose steatosis in the early stage when imaging modalities are not available is crucial. This study decided to validate the optimal cut-off points and the sensitivity and specificity of the Fatty Liver Index (FLI) based on the Iranian population compared to ultrasonography., Methods: The data of 367 individuals, 108 males and 259 females over 35, were analyzed. Hepatic steatosis was identified by ultrasound. FLI was determined from waist circumference, gamma-glutamyl transferase, triglyceride, and body mass index data. The receiver operating characteristic curve (ROC) was used to determine the best FLI index cut point for diagnosing nonalcoholic fatty liver. The sensitivity and specificity indices were calculated for the determined cut point., Results: The AUC of the FLI index in diagnosing NAFLD in the total population was 0.733 (95% CI: 0.68-0.77, specificity = 0.6705, sensitivity = 0.7320) with the optimal COP of 40.6. There was a statistically significant association between non-alcoholic liver disease and FLI-based ultrasound (p < 0.0001). Furthermore, the sex-specific optimal COPs of FLI was 33.4, specificity = 0.6071, sensitivity = 0.8462 in men vs. 27.8, sensitivity = 0.8233, specificity = 0.7655 in women., Conclusion: FLI is a reliable tool for identifying individuals with NAFLD. It has the potential to aid in detecting and managing this condition in large-scale populations while other methods are not available. We also determine an optimal COP of 40.6 with sensitivity and specificity of 73.20% and 67.05% in the general population, respectively., Competing Interests: Declarations. Declarations: The results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration by another publisher. While preparing this work, the author used ChatGPT to paraphrase some parts of the article. After using this tool, the author reviewed and edited the content as needed and took full responsibility for the publication’s content. Ethics approval and consent to participate: The study protocol was registered and approved by the Ethics Committee of Fasa University of Medical Sciences (FUMS) by No: IR.FUMS.REC.1400.095 Furthermore, the study was performed following the Declaration of Helsinki. Informed consent was obtained from all subjects and/or their legal guardian(s). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)
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- 2024
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20. Association between diet protein score comprising plants to animal protein ratio and body composition in an Iranian population.
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Amlashi MA, Jafarpour A, Eirdmousa MH, Homayounfar R, Farjam M, and Askari A
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- Male, Humans, Female, Iran, Adult, Middle Aged, Animal Proteins, Dietary, Dietary Proteins, Obesity metabolism, Plant Proteins metabolism, Plant Proteins, Dietary analysis, Body Composition
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Obesity is increasingly recognized as a leading cause of death and is associated with various comorbidities. This study evaluates the relationship between protein score, characterized by the plant-to-animal protein ratio (PAR) and total protein per calorie (Pro%), and body composition: fat percentage (FATP), fat mass (FATM), and fat-free mass (FFM). We categorized 4512 individuals (55.2% female) into tertiles based on their protein score and its components. Male participants in the highest and middle protein score tertiles exhibited significantly greater FFM in both adjusted and crude models, and lower FATP and FATM in adjusted model 2. FFM was elevated in the top (P < 0.001) and middle (P = 0.002) Pro% tertiles in males in both adjusted models and only in the top tertile of all models in females (P = 0.003). The analysis of male participants revealed significantly lower FATP and FATM in the highest tertiles of Pro% in adjusted models. Among female participants, only the highest PAR tertile was associated with significantly lower FATM in adjusted model 1 (P = 0.042). Our findings indicate that protein score and its components are associated with favorable body composition differences. Health administrators may leverage these insights to refine dietary guidelines., Competing Interests: Declarations Ethics approval The study followed the Declaration of Helsinki guidelines and received approval from the IRB (Institutional Review Board) of Fasa University of Medical Sciences (Code: IR.FUMS.REC.1395.177). Competing interests The authors declare no competing interests., (© 2024. The Author(s).)
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- 2024
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21. The effects of aerobic and resistant exercises on the lipid profile in healthy women: a systematic review and meta-analysis.
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Pourmontaseri H, Farjam M, Dehghan A, Karimi A, Akbari M, Shahabi S, Nowrouzi-Sohrabi P, Estakhr M, Tabrizi R, and Ahmadizar F
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- Female, Humans, Cholesterol, HDL blood, Randomized Controlled Trials as Topic, Triglycerides blood, Exercise, Lipids blood, Resistance Training
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Exercise can have a wide range of health benefits, including improving blood lipid profiles. For women to achieve optimal cardiovascular health, it is vital to determine the effect of exercise on their health and whether different exercise intensities can affect their blood lipid profile. A systematic review and meta-analysis were conducted to examine the effects of exercise on improving the lipid profile of healthy women. A database search was conducted using PubMed, Google Scholar, Embase, Scopus, and Web of Science from inception until July 2, 2021, for randomized controlled trials (RCTs) investigating exercise's effects on healthy women's blood lipid profiles. A total of 10 eligible articles (or 17 trials) with 576 participants were identified as eligible for the study. Overall, the meta-analysis shows that physical activity significantly improved total cholesterol (TC), triglycerides (TG), and high-density lipoprotein (HDL-C) levels: TC [WMD = -5.77 mg/dL, 95% CI: -10.41, -1.13, P < 0.01]; TG [WMD = -5.60 mg/dL, 95% CI: -8.96, -2.23, P < 0.01]; HDL [WMD = 4.49 mg/dL, 95% CI: 0.33, 8.65, P = 0.03]. Additionally, sub-group analyses indicated that combined exercise training improved TG and TC (p 0.05), and aerobic exercise significantly increased HDL. In this study, physical activity appears to be one of the most effective non-pharmacological means for improving HDL, TG, and TC in healthy women. In terms of TG and TC, CT was the most effective., Competing Interests: Declarations. Compliance with ethical standards: The study protocol was recorded in PROSPERO (CRD42021265496) and reviewed by the ethics committee at Fasa University of Medical Sciences (IR.FUMS.REC.1400.062). Conflict of interest: The authors have no competing interests to declare relevant to this article’s content., (© 2024. The Author(s) under exclusive licence to University of Navarra.)
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- 2024
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22. Immune Regulatory Circular RNAs, circRasGEF1B and circHIPK3, are Upregulated in Peripheral Blood Mononuclear Cells of COVID-19 Patients.
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Mirzaei E, Shahi A, Daraei A, Movahedi B, Karimi J, Farjam M, Gholampoor Y, Meshkibaf MH, Ansari A, Firoozi Z, and Mansoori Y
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- Humans, Female, Male, Middle Aged, Adult, Up-Regulation, ROC Curve, Aged, Intracellular Signaling Peptides and Proteins genetics, Case-Control Studies, RNA, Circular genetics, COVID-19 genetics, COVID-19 diagnosis, Protein Serine-Threonine Kinases genetics, SARS-CoV-2 genetics, Leukocytes, Mononuclear metabolism
- Abstract
Background: COVID-19 is one of the worst pandemics worldwide, and its diagnosis and treatment are of great importance. Recent evidence has shown that circular RNA (circRNA deregulation is involved in different infectious diseases. In the present study, we tried to investigate the expression of cirRNAs RasGEF1B (hsa_circ_0127052), HIPK3 (hsa_circ_100783), and GATAD2A (hsa_circ_0050236) in COVID-19 patients. Methods: Using quantitative real-time polymerase chain reaction, the expression profiles of candidate circRNAs were detected in 57 COVID-19 patients and 51 healthy controls. As part of the process of identifying a candidate circRNA that is sensitive and specific, receiver operating characteristic (ROC) curves were also utilized. Results: Our results showed higher expression levels of circRasGEF1B and circHIPK3 in COVID-19 patients, however, circGATAD2A showed no statistical difference between patients and controls. ROC curves showed that circRasGEF1B (hsa_circ_0127052), and HIPK3 (hsa_circ_100783) had favorable specificity and sensitivity, whereas GATAD2A (hsa_circ_0050236) did not. Conclusion: In summary, our study highlights the potential of CircRasGEF1B (hsa_circ_0127052) and HIPK3 (hsa_circ_100783) as biomarkers for COVID-19 diagnosis due to their high expression levels and demonstrated diagnostic accuracy. These findings suggest that circRNAs could play a crucial role in the development of diagnostic tools for COVID-19, providing a new avenue for early detection and management of the disease.
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- 2024
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23. A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study.
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Taheri Ghaleno SM, Safari A, Homayounfar R, Farjam M, Rezaeian M, Asadi F, Masaebi F, Salehi M, Heydarpour Meymeh M, and Zayeri F
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Background: In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the predictors of this disease can help health policymakers to reduce its burden. This study aimed to identify some of the most important influential factors of hypertension and present a model to predict this disease in the data from a large sample cohort study., Methods: The data set included 10,138 people from the baseline phase of the Fasa cohort study during 2014 and 2016. The main outcome under study was having hypertension in the baseline phase of the study according to self-reports or medical examinations. To identify the related factors of hypertension, logistic regression, classification tree, and random forest models were utilized. Statistical analyses were performed in R., Results: Among the 10,138 people examined, 2819 (27.8%) had hypertension. In the initial screening, 39 variables were regarded as potential indicators of hypertension. After preliminary analysis, 11 variables were recognized as important predictors based on the importance index: history of cardiovascular disease, cardiac disease, waist circumference to height ratio, body mass index, sex, hypertension in a first-degree relative, weight, fatty liver, cardiac disease in a first-degree relative, diabetes in a first-degree relative, and energy intake. The area under the receiving operating characteristic (ROC) curve for predicting hypertension using logistic regression, classification tree, and random forest models was about 72.8%, 73%, and 87.6%, respectively. Also, the accuracy of these models was 65.2%, 67.4% and 77.8%, respectively., Conclusion: In general, our findings showed that machine learning-based approaches, such as random forest models, outperformed classical methods, such as logistic regression in predicting hypertension. Regarding the rather high prevalence of hypertension in the population under study, there is an urgent need to pay more attention to its indicators for early diagnosis of the patients and reducing the burden of this silent disease in our country., Competing Interests: The authors declare that they have no competing interests., (© 2024 Iran University of Medical Sciences.)
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- 2024
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24. Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.
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Masaebi F, Azizmohammad Looha M, Mohammadzadeh M, Pahlevani V, Farjam M, Zayeri F, and Homayounfar R
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- Humans, Female, Male, Iran, Middle Aged, Adult, Prospective Studies, ROC Curve, Body Composition, Logistic Models, Risk Factors, Predictive Value of Tests, Bayes Theorem, Algorithms, Non-alcoholic Fatty Liver Disease diagnostic imaging, Area Under Curve, Body Mass Index, Machine Learning
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Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population., Methods: Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy., Results: A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (±standard deviation [SD]) of 48.12 (±9.61) and 49.47 (±9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure., Conclusion: Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)
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- 2024
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25. Aggressive low-density lipoprotein (LDL) lowering for primary prevention: still an elusive goal.
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Sepehrinia M, Homayounfar R, and Farjam M
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- Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors therapeutic use, Cost-Benefit Analysis, Cardiovascular Diseases prevention & control, Primary Prevention methods, Lipoproteins, LDL blood, Cholesterol, LDL blood
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Cardiovascular disease (CVD) is the leading cause of mortality globally. Low-density lipoprotein (LDL) plays an important role in CVD pathophysiology. Research has shown the safety and efficacy of keeping LDL at very low levels for CVD prevention. Therefore, experts recommend intense LDL-lowering approaches starting at young ages, promoting the mantras "the lower, the better" and "the earlier, the better." This commentary discusses the challenges regarding applying aggressive LDL-lowering approaches in the general population, including pharmacological efficacy and side effects, the cost-effectiveness of interventions, and patient adherence to treatment regimens., (© 2024. The Author(s).)
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- 2024
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26. Androgenic alopecia is associated with higher dietary inflammatory index and lower antioxidant index scores.
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Bazmi S, Sepehrinia M, Pourmontaseri H, Bazyar H, Vahid F, Farjam M, Dehghan A, Hébert JR, Homayounfar R, and Shakouri N
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Background: Androgenic alopecia (AGA), the most prevalent hair loss type, causes major psychological distress and reduced quality of life. A definite and safe cure/prevention for this condition is still lacking. The role of oxidative stress and inflammation in AGA pathogenesis prompted us to investigate the association between dietary antioxidant index (DAI) and energy-adjusted dietary inflammatory index (E-DII) with AGA., Methods: The investigation was designed based on data from 10,138 participants from the Fasa Adult Cohort Study (FACS). DAI and energy-adjusted DII (E-DII) were calculated utilizing a validated 125-item food frequency questionnaire (FFQ). A physician diagnosed AGA. Logistic regression models were utilized to evaluate the association of DAI and E-DII with AGA., Results: After exclusion, 9,647 participants (44.0% men, mean age: 48.6 ± 9.5 years) consisting of 7,348 participants with AGA entered the analyses. Higher DAI was associated with 10% lower AGA odds, while higher E-DII showed 4% higher AGA odds after adjusting for various confounding variables. However, significant associations were found only among women, and adjusting for metabolic syndrome (MetS) made the E-DII-AGA association insignificant., Conclusion: Antioxidant-rich diets protect against AGA, while pro-inflammatory diets increase the risk, likely through developing MetS. Patient nutrition is frequently overlooked in clinical practice, yet it plays a crucial role, especially for women genetically predisposed to androgenetic alopecia. Dietary changes, such as reducing pro-inflammatory foods (like trans and saturated fats) and increasing anti-inflammatory options (fruits and vegetables), can help prevent hair loss and mitigate its psychological impacts, ultimately lowering future treatment costs., Competing Interests: JH owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the right to his invention of the dietary inflammatory index (DII®) from the University of South Carolina in order to develop computer and smart phone applications for patient counseling and dietary intervention in clinical settings. CHI owns exclusive rights to the energy-adjusted DII (E-DIITM). The subject matter of this paper has no direct bearing on that work, nor has any CHI-related activity exerted any influence on this project. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bazmi, Sepehrinia, Pourmontaseri, Bazyar, Vahid, Farjam, Dehghan, Hébert, Homayounfar and Shakouri.)
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- 2024
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27. The association between energy-adjusted dietary inflammatory index and metabolic syndrome and its mediatory role for cardiometabolic diseases: a prospective cohort study.
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Pourmontaseri H, Sepehrinia M, Kuchay MS, Farjam M, Vahid F, Dehghan A, Homayounfar R, Naghizadeh MM, and Hebert JR
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Background: Metabolic syndrome (MetS) is a collection of medical conditions that elevate the chance of cardiovascular disease. An unhealthy diet is a major risk factors for MetS through different mechanisms, especially systemic chronic inflammation., Objective: This study aimed to investigate the effect of dietary inflammatory potential on MetS incidence and the role of MetS in the association between Energy-adjusted dietary inflammatory index (E-DII) and cardiometabolic diseases., Methods: In this prospective cohort study, 10,138 participants were recruited. All participants were divided into MetS or non-MetS groups based on the Adult Treatment Panel III criteria. The E-DII was used to assess the inflammatory potential of diet. After excluding the participants with MetS at baseline, 2252 individuals were followed for 5 years (longitudinal phase), and the effect of E-DII on MetS incidence was investigated using logistic regression models ( p -value <0.05)., Results: The cohort's mean age (45.1% men) was 48.6 ± 10.0 years. E-DII ranged from -6.5 to 5.6 (mean: -0.278 ± 2.07). Higher E-DII score had a 29% (95%CI: 1.22-1.36) increased risk for incidence of MetS and its components during five-year follow-up. Also, E-DII was significantly associated with the prevalence of MetS (OR = 1.55, 95%CI: 1.51-1.59). Among MetS components, E-DII had the strongest association with waist circumference in the cross-sectional study (OR = 2.17, 95%CI: 2.08-2.25) and triglyceride in the longitudinal study (OR = 1.19, 95%CI: 1.13-1.25). The association between E-DII and MetS was consistent in both obese (OR = 1.13, 95%CI:1.05-1.21) and non-obese (OR = 1.42, 95%CI: 1.27-1.60) individuals and stronger among non-obese participants. Additionally, MetS mediated the association between E-DII and hypertension, diabetes, and myocardial infarction., Conclusion: In conclusion, a pro-inflammatory diet consumption is associated with a higher risk of MetS and its components. Furthermore, a pro-inflammatory diet increases the risk of cardiometabolic diseases. The higher E-DII had a stronger association with MetS, even among normal-weight individuals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Pourmontaseri, Sepehrinia, Kuchay, Farjam, Vahid, Dehghan, Homayounfar, Naghizadeh and Hebert.)
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- 2024
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28. Reply to Ghobadi and Jafari: diet quality in relation to the risk of hypertension among Iranian adults: cross-sectional analysis of Fasa PERSIAN cohort study.
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Motamedi A, Ekramzadeh M, Bahramali E, Farjam M, and Homayounfar R
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- Humans, Iran epidemiology, Cross-Sectional Studies, Adult, Risk Factors, Cohort Studies, Male, Hypertension epidemiology, Diet methods, Diet statistics & numerical data
- Abstract
Ghobadi and Jafari have mentioned some points about our article titled "Diet quality in relation to the risk of hypertension among Iranian adults: cross-sectional analysis of Fasa PERSIAN cohort study" which was published in the Nutrition Journal. Thanks for their consideration, the following is provided as a response to their comments., (© 2024. The Author(s).)
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- 2024
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29. The High-Dietary Insulin Load Score Is Associated With Elevated Level of Fasting Blood Sugar in Iranian Adult Men: Results From Fasa PERSIAN Cohort Study.
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Rajaie SH, Khayyatzadeh SS, Faghih S, Mansoori Y, Naghizadeh MM, Farjam M, Homayounfar R, and Mozaffari-Khosravi H
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- Humans, Male, Middle Aged, Adult, Iran epidemiology, Aged, Cohort Studies, Cross-Sectional Studies, Female, Body Mass Index, Insulin blood, Blood Glucose metabolism, Fasting blood, Metabolic Syndrome blood, Metabolic Syndrome epidemiology, Diet
- Abstract
Aim: The potential of different foods to induce postprandial hyperinsulinemia may be involved in the development of metabolic syndrome (MetS). We aimed to evaluate the association between dietary insulin indices and MetS in a large population of adults in Iran. Methods: A total of 6356 adults aged 35-70 years were included in the present cross-sectional study. A validated block-format 125-item semiquantitative food frequency questionnaire (FFQ) was used to obtain usual food intakes, and MetS was defined according to the International Diabetes Federation (IDF) and American Heart Association (AHA)/National Heart, Lung, and Blood Institute (NHLBI) criteria. Results: MetS was prevalent in 13.8% of participants. Mean age of the study participants was 46.58 ± 8.82 years, and mean body mass index (BMI) was 25.02 ± 4.60 kg/m
2 . Mean dietary insulin index (DII) and dietary insulin load (DIL) were 63.15 ± 7.57 and 168.253 ± 52.09, respectively. In the crude model, men in the highest DIL quartile were more likely to have hyperglycemia than those in the lowest quartile (OR: 1.75, 95% CI: 1.12-2.73, p trend = 0.04). This association remained significant and was even stronger after adjusting for potential confounders in model I (OR: 3.64, 95% CI: 1.57-8.47, p trend = 0.005) and further adjustment for BMI in model II (OR: 3.61, 95% CI: 1.55-8.44, p trend = 0.006). Conclusions: In healthy men, adherence to a high-DIL diet may be associated with a greater likelihood of having hyperglycemia. No statistically significant association was observed between insulin indices and the odds of having MetS., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Seyede Hamide Rajaie et al.)- Published
- 2024
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30. Improving Fertility in Non-obstructive Azoospermia: Results from an Autologous Bone Mar-row-Derived Mesenchymal Stromal/Stem Cell Phase I Clinical Trial.
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Zhankina R, Zhanbyrbekuly U, Askarov M, Zare A, Jafari N, Saipiyeva D, Sherkhanov R, Akhmetov D, Hashemi A, Farjam M, Tanideh N, Aflatoonian B, Mussin NM, Kaliyev AA, Sultangereyev Y, Baneshi H, Shirazi R, Mahdipour M, Bakhshalizadeh S, Rahmanifar F, and Tamadon A
- Abstract
Background: In this phase I clinical trial, our primary objective was to develop an innovative therapeutic approach utilizing autologous bone marrow-derived mesenchymal stromal/stem cells (BM-MSCs) for the treatment of nonobstructive azoospermia (NOA). Additionally, we aimed to assess the feasibility and safety of this approach., Materials and Methods: We recruited 80 participants in this non-randomized, open-label clinical trial, including patients undergoing NOA treatment using autologous BM-MSCs (n=40) and those receiving hormone therapy as a control group (n=40). Detailed participant characteristics, such as age, baseline hormonal profiles, etiology of NOA, and medical history, were thoroughly documented. Autotransplantation of BM-MSCs into the testicular network was achieved using microsurgical testicular sperm extraction (microTESE). Semen analysis and hormonal assessments were performed both before and six months after treatment. Additionally, we conducted an in-silico analysis to explore potential protein-protein interactions between exosomes secreted from BM-MSCs and receptors present in human seminiferous tubule cells., Results: Our results revealed significant improvements following treatment, including increased testosterone and inhibin B levels, elevated sperm concentration, and reduced levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and prolactin. Notably, in nine patients (22.5%) previously diagnosed with secondary infertility and exhibiting azoospermia before treatment, the proposed approach yielded successful outcomes, as indicated by hormonal profile changes over six months. Importantly, these improvements were achieved without complications. Additionally, our in-silico analysis identified potential binding interactions between the protein content of BM-MSC-derived exosomes and receptors integral to spermatogenesis., Conclusion: Autotransplantation of BM-MSCs into the testicular network using microTESE in NOA patients led to the regeneration of seminiferous tubules and the regulation of hormonal profiles governing spermatogenesis. Our findings support the safety and effectiveness of autologous BM-MSCs as a promising treatment modality for NOA, with a particular focus on the achieved outcomes in patients with secondary infertility (registration number: IRCT20190519043634N1).
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- 2024
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31. The risk of premature cardiac contractions (PAC/PVC) related to caffeine consumption among healthcare workers: A comprehensive review.
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Abdolmaleki M, Ohadi L, Changizi F, Seyed S, and Farjam M
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Background and Aims: Premature atrial contractions (PACs) and premature ventricular contractions (PVCs) are ectopic heart rhythm disorders with implications for cardiovascular health. This study explores the relationship between caffeine consumption and the risk of PACs and PVCs, with a focus on healthcare workers, such as doctors, nurses, pharmacists, and midwives, who often rely on caffeine to combat fatigue, especially during night shifts., Methods: A thorough review was conducted through PubMed, Scopus, Google Scholar, and Web of Science, utilizing a combination of MeSH terms and keywords. Studies examining the link between caffeine consumption and PACs and PVCs, particularly in healthcare workers, were included., Results: We found that caffeine shows various effects based on dosage and can impact arrhythmia risk. Individuals working long shifts, including healthcare professionals, are prone to increased caffeine intake, leading to higher cardiovascular risk. To mitigate these risks, tailored guidelines for caffeine consumption, flexible shift scheduling, and mental health support services are recommended. Promoting caffeine alternatives within healthcare institutions can be beneficial., Conclusion: Although caffeine may have potential benefits, its drawbacks, particularly concerning cardiovascular health, may surpass its advantages, especially when consumed in high doses. A multidisciplinary approach is crucial for healthcare workers' well-being and quality of patient care. Further research is required to refine and support these recommendations., Competing Interests: The authors declare no conflicts of interest., (© 2024 The Author(s). Health Science Reports published by Wiley Periodicals LLC.)
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- 2024
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32. Comparison of atherosclerotic cardiovascular disease (ASCVD) and Framingham risk scores (FRS) in an Iranian population.
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Sepehrinia M, Pourmontaseri H, Sayadi M, Naghizadeh MM, Homayounfar R, Farjam M, Dehghan A, and Alkamel A
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Background: Framingham risk score (FRS) and Atherosclerotic Cardiovascular Disease risk score (ASCVDrs) are widely used tools developed based on the American population. This study aimed to compare the ASCVDrs and FRS in an Iranian population., Method: The participants of the Fasa Adult Cohort Study and the patients of the cardiovascular database of Vali-Asr Hospital of Fasa, aged 40-80 years, were involved in the present cross-sectional study. After excluding non-eligible participants, the individuals with a history of myocardial infarction or admission to the cardiology ward due to heart failure were considered high-risk, and the others were considered low-risk. The discriminative ability of FRS and ASCVDrs was evaluated and compared using receiver operating characteristic curve analysis. The correlation and agreement of ASCVDrs and FRS were tested using Cohen Kappa and Spearman., Results: Finally, 8983 individuals (mean age:53.9 ± 9.5 y, 49.2 % male), including 1827 high-risk participants, entered the study. ASCVDrs detected a greater portion of participants as high-risk in comparison with FRS (28.7 % vs. 15.7 %). ASVD (AUC:0.794) had a higher discriminative ability than FRS (AUC:0.746), and both showed better discrimination in women. Optimal cut-off points for both ASCVDrs (4.36 %) and FRS (9.05 %) were lower than the original ones and in men. Compared to FRS, ASCVDrs had a higher sensitivity (79.3 % vs. 71.6 %) and lower specificity (64.5 % vs. 65.1 %). FRS and ASCVDrs had a moderate agreement (kappa:0.593, p -value<0.001) and were significantly correlated (Spearman:0.772, p -value<0.001)., Conclusions: ASCVDrs had a more accurate prediction of cardiovascular events and identified a larger number of people as high-risk in the Iranian population., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)
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- 2024
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33. Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.
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Asadi F, Homayounfar R, Farjam M, Mehrali Y, Masaebi F, and Zayeri F
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- Humans, Middle Aged, Male, Female, Adult, Aged, Iran epidemiology, Linear Models, Logistic Models, Cohort Studies, Risk Assessment methods, Risk Factors, Heart Disease Risk Factors, Area Under Curve, Cardiovascular Diseases epidemiology
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Background: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators., Methods: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software., Results: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD ( P <0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models., Conclusion: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)
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- 2024
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34. Do you really believe that? The effect of economic incentives on the acceptance of real-world data in a polarized context.
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Farjam M and Bravo G
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Attitudes and expectations towards others are major drivers of political polarization. However, there is limited understanding of their relevance when decisions with high stakes are taken. In this study, we compare self-reported attitudes against economically incentivized estimates of data coming from official sources and offer participants financial rewards for accuracy. Our methodology yields three principal findings. (i) Extreme attitudes from a small partisan subgroup primarily account for the observed partisan divide; this subgroup diminishes when incentivized estimates are considered. (ii) There is a weak correlation between economically incentivized and unincentivized measures within individual respondents. (iii) We introduce a novel metric for assessing perceived polarization. This metric allows participants to estimate data points for those with opposing political views, rewarding accurate predictions financially. Interestingly, this measure of perceived polarization correlates with attitudes but not with incentivized data estimates. This is in line with the concept of 'false polarization', attributing polarization more to expectations towards others than to genuine differences. These findings challenge the reliability of standard attitude surveys and suggest avenues for mitigating perceived polarization in contentious issues., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)
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- 2024
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35. Reference Interval for Non-HDL-Cholesterol, Remnant Cholesterol and Other Lipid Parameters in the Southern Iranian Population; Findings From Bandare Kong and Fasa Cohort Studies.
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Farjam M, Kheirandish M, Ghanbarnejad A, Nikpoor AR, Nejatizadeh A, Aghamolaei T, Shahmoradi M, Alizade H, Homayounfar R, Zarei H, Ghavidel S, Jamshidi V, and Eftekhar E
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- Male, Humans, Female, Iran epidemiology, Triglycerides, Cohort Studies, Cholesterol, Health Status
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Background: Growing evidence shows the undisputable role of non-HDL-C and remnant cholesterol (remnant-C) in cardiovascular disease (CVD) risk assessment and treatment. However, the reference interval (RI) for these lipid parameters is not readily available. The aim of the present investigation was to determine the age and sex-specific RIs for non-HDL-C and remnant-C as well as other lipid parameters among a healthy population in southern Iran. We also report the RI of lipid parameters in rural and urban residents, smokers and post-menopausal women., Methods: Among 14063 participants of Bandare Kong and Fasa cohort studies, 792 healthy subjects (205 men and 578 women) aged 35-70 years were selected. Fasting blood samples were used for determination of total cholesterol (TC), triglycerides (TG) and HDL-C using colorimetric methods. Non-HDL-C and remnant-C were calculated using the valid formula. The 2.5th and 97.5th percentiles were calculated and considered as RI., Results: In the total population (n=792, age 35-70), RIs for non-HDL-C and remnant-C was 74.0-206.8 and 8.0-52.7 mg/dL, respectively. Age (35-44 and≥45 years) and gender-specific RIs for serum non-HDL-C and remnant-C were determined. Remnant-C and non-HDL-C level were different between sex and age categories. The mean value of all lipid parameters except HDL-C was higher in men, urban residents, subject with age≥45 years and smokers., Conclusion: This is the first study in which the RIs for non-HDL-C and remnant-C in southern Iran are reported. This may help physicians to conveniently use these lipid parameters for patient care and better cardiovascular risk assessment., (© 2024 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.)
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- 2024
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36. The Effects of Selective Serotonin Reuptake Inhibitors on Neurological and Depressive Symptoms in Multiple Sclerosis: A Systematic Review and Meta-analysis of Randomized Controlled Trials.
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Yousefi F, Kamyab P, Fakhraei B, Farjam M, Rezaei S, Mahmoudi SS, Karimimoghadam Z, Tabrizi R, and Jaafari N
- Abstract
Background: Multiple sclerosis (MS) affects the central nervous system and creates plaques by demyelination of neurons. Several studies have investigated the effect of selective serotonin reuptake inhibitors (SSRIs) on MS clinical courses. The current meta-analysis was conducted to determine the effect of SSRIs on neurological and depressive symptoms of MS disease based on a systematic review and meta-analysis of randomized controlled trials., Materials and Methods: We searched the PubMed/Medline, Scopus, EMBASE, Google scholar, Web of Science, and Cochrane Library until June 2023. The effects of SSRI were assessed through indictors such as symbol digit modalities test (SDMT), expanded disability status scale (EDSS), modified fatigue impact scale (MFIS), and Beck's depression inventory/psychiatric (BDI)., Results: Considering the inclusion criteria, seven articles (including eight trials) were included in this review. The meta-analysis results demonstrated that SSRIs treatments did not have significant effects on indicators of neurological and depressive symptoms, such as SDMT (Weighted Mean Difference (WMD)=-0.87; 95% CI, -7.74, 5.99, P=0.35; I2=0.0%), EDSS (WMD=-0.05; 95% CI, -0.24, 0.14, P=0.62; I2=0.0%), MFIS (WMD=5.29; 95% CI, -18.10, 28.68, P=0.21; I2=0.0%), and BDI (WMD=-0.47; 95% CI, -2.61, 1.67, P=0.67; I2=32.05%) in patients with MS compared with controls., Conclusion: This study shows that the consumption of SSRIs in MS patients compared to the control group does not bring about a significant change in the indices related to neurological and depressive symptoms. Further meta-analyses are required in order to provide stronger evidence in the future., Competing Interests: The authors declare no conflict of interest., (Copyright© 2023, Galen Medical Journal.)
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- 2023
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37. Association of Dietary Acid Load With Metabolic Syndrome and Its Components in Iranian Adults: A Cross-Sectional Study.
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Rajaie SH, Homayounfar R, Khayyatzadeh SS, Faghih S, Mansoori Y, Naghizadeh MM, Farjam M, and Mozaffari-Khosravi H
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Introduction Metabolic syndrome (MetS) remains one of the leading health challenges worldwide. A combination of genetic and environmental factors has been implicated in the etiology of MetS. Diet is a changeable environmental risk factor, and dietary modifications could significantly reduce the incidence and mortality of numerous diseases, including MetS. Certain dietary factors may contribute to MetS by affecting the acid-base balance within the body. This study examined the association of dietary acid load (DAL) with MetS and its components in Iranian adults. Materials and methods This cross-sectional study was conducted in 2022 on 6356 Iranian adults aged 35-70 years. Potential renal acid load (PRAL) and net endogenous acid production (NEAP) as two indicators of DAL were calculated based on nutrient intake data from validated food frequency questionnaires. MetS and its components were defined according to the Adult Treatment Panel III criteria. Logistic regression analysis was used to explore the associations between DAL and MetS and its components. Age, energy intake, physical activity, education, marital status, home ownership, socioeconomic status, history of obesity-related disease, and calcium supplements were included in model I. Further adjustment in model II was made for body mass index. Results Higher NEAP scores were associated with increased odds of low high-density lipoprotein cholesterol (HDL-C) in the crude model (OR: 1.26, 95% CI: 1.01-2.56, p trend = 0.06) in women, which was confirmed in the adjusted models. In model I, women in the last quintile of NEAP had 54% greater odds of having hypertriglyceridemia compared to the first quintile (OR: 1.54, 95% CI: 1.007-2.36, p trend = 0.02). This association was still significant and even stronger after further adjustment for BMI (OR: 1.55, 95% CI: 1.01-2.40, p trend = 0.01). In addition, in model I, men in the fourth quintile of NEAP had 5.68-fold greater odds of hyperglycemia compared to the first quintile (OR: 5.68, 95% CI: 1.18-27.25, p trend = 0.11). Similar results were found in the fully adjusted model (OR: 5.89, 95% CI: 1.19-28.99, p trend = 0.54). Conclusion There was no significant association between DAL and MetS. DAL was positively associated with the odds of low HDL-C and hypertriglyceridemia in women. Moreover, moderate DAL (NEAP) was associated with an increased odds of hyperglycemia in men., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Rajaie et al.)
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- 2023
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38. A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.
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Nematollahi MA, Askarinejad A, Asadollahi A, Bazrafshan M, Sarejloo S, Moghadami M, Sasannia S, Farjam M, Homayounfar R, Pezeshki B, Amini M, Roshanzamir M, Alizadehsani R, Bazrafshan H, Bazrafshan Drissi H, Tan RS, Acharya UR, and Islam MSS
- Abstract
Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms., Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively., Results: Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively., Conclusions: Our machine learning model showed that regional body fat compositions were predictive of diabetes status., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s), under exclusive licence to Tehran University of Medical Sciences 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
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- 2023
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39. Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling.
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Nouri-Keshtkar M, Shojaei Shahrokhabadi M, Ghaheri A, Hosseini R, Ketabi H, Farjam M, Chen DG, Rezaeian M, Homayounfar R, Tahamtani Y, and Totonchi M
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- Humans, Female, Male, Iran epidemiology, Rural Population, Latent Class Analysis, Cohort Studies, Risk Factors, Triglycerides, Metabolic Syndrome epidemiology
- Abstract
Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population., (© 2023. Springer Nature Limited.)
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- 2023
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40. Correction: Evaluation of the topical gel and oral administration of Punica Granatum Var Pleniflora on oral mucositis induced by 5-Fluorouracil in golden hamsters.
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Hamidi SP, Koohi-Hosseinabadi O, Khaksar S, Ghanbariasad A, Dehghanian AR, Dehghan A, Haddadi Z, Gorgin R, Farjam M, and Alipanah H
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- 2023
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41. The relationship between pregnancy count and duration of breast-feeding with metabolic syndrome (Fasa Persian cohort study).
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Zareei S, Behrasi F, Naghizadeh MM, Talebzadeh F, Kharmandar A, Farjam M, and Homayounfar R
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- Adult, Humans, Female, Pregnancy, Cohort Studies, Cross-Sectional Studies, Breast Feeding, Risk Factors, Prevalence, Adenosine Triphosphate, Metabolic Syndrome epidemiology, Metabolic Syndrome diagnosis
- Abstract
Background: Changes that occur during pregnancy and after that during breastfeeding induce some symptoms similar to metabolic syndrome (MetS) risk factors. This study aims to determine the relationship between pregnancy, as well as the duration of breastfeeding and MetS controlling the effect of other risk factors like hypertension, glucose intolerance, triglyceride, central obesity, and reduction of high-density lipoprotein in women of Fasa Persian Cohort Study., Materials and Methods: In this cross-sectional study, 5015 women aged 35-70 years were investigated in the Sheshdeh region from 2016 to 2021, and the information related to the disease symptoms was collected through questionnaires, examinations, and laboratory tests. MetS was calculated based on two guidelines according to adult treatment panel III (ATP III) and international diabetes federation (IDF) methods. For reporting the data, the odds ratio with its 95% confidence interval was used. In order to eliminate the effect of confounders, logistic regression was used., Results: Prevalence of MetS showed a descending trend in women with up to two pregnancies and it reached 22.6% and 22.4% using ATPIII and IDF methods respectively, while with an increase in the number of pregnancies of more than two, MetS prevalence was ascending. The prevalence of MetS did not have any specific trend across various breastfeeding duration groups. Multivariate analysis approved that the odds ratio of developing MetS in comparison with women who had two pregnancies was significantly increasing trend when the pregnancy counts increased., Conclusion: The chance of developing MetS based on both IDF and ATP III methods after adjustment for confounding effects would grow with an increase in the number of pregnancies to more than two and breast-feeding of more than seven years. It is recommended that women with more than two pregnancies or the long duration of breast-feeding women undergo a specialized examination to investigate and control MetS problems so that future diseases could be prevented., (© 2023. The Author(s).)
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- 2023
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42. Dietary inflammatory index (DII) is correlated with the incidence of non-alcoholic fatty liver disease (NAFLD): Fasa PERSIAN cohort study.
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Valibeygi A, Davoodi A, Dehghan A, Vahid F, Hébert JR, Farjam M, and Homayounfar R
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Background: Non-alcoholic fatty liver disease (NAFLD) is a prevalent liver disease predisposing patients to life-threatening conditions, including cirrhosis. There is evidence that the incidence of NAFLD is related to the individuals' dietary patterns; however, it is still remaining unknown whether the inflammatory potential of various foods/dietary patterns can directly predict a higher incidence of NAFLD., Methods: In this cross-sectional cohort study, we investigated the relationship between the inflammatory potential of various food items and the incidence/odds of NAFLD. We used data from Fasa PERSIAN Cohort Study comprising 10,035 individuals. To measure the inflammatory potential of diet, we used the dietary inflammatory index (DII®). Fatty liver index (FLI) was also calculated for each individual to identify the presence of NAFLD (cut-off = 60)., Results: Our findings showed that higher DII is significantly associated with increased incidence/odds of NAFLD (OR = 1.254, 95% CI: 1.178-1.334). Additionally, we found out that higher age, female gender, diabetes mellitus, hypertriglyceridemia, hypercholesterolemia, and hypertension are other predictors of developing NAFLD., Conclusions: It can be concluded that consuming foods with a higher inflammatory potential is associated with a greater risk of developing NAFLD. Additionally, metabolic diseases, including dyslipidemia, diabetes mellitus, and hypertension, can also predict the incidence of NAFLD., (© 2023. The Author(s).)
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- 2023
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43. Evaluation of the topical gel and oral administration of Punica Granatum Var Pleniflora on oral mucositis induced by 5-Fluorouracil in golden hamsters.
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Hamidi SP, Koohi-Hosseinabadi O, Khaksar S, Ghanbariasad A, Dehghanian AR, Dehghan A, Haddadi Z, Gorgin R, Farjam M, and Alipanah H
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- Cricetinae, Male, Animals, Mesocricetus, Fluorouracil toxicity, Administration, Oral, Pomegranate, Stomatitis chemically induced, Stomatitis drug therapy
- Abstract
Background: Oral mucositis (OM), an acute inflammation of the oral cavity, is a common complication in patients undergoing invasive myeloblastic chemotherapy or radiation therapy. 5-fluorouracil (5-FU) is one of the most effective therapeutic drugs, but one of the common side effects of 5-FU administration is OM. Unfortunately, no suitable treatment has been found, so far to control its side effects. Studies showed that herbal medicine like Punica granatum var pleniflora (PGP) has medicinal properties such as anti-inflammatory and antibacterial and can be an alternative for the treatment of fungal infection. Accordingly, we decided to investigate the therapeutic effect of PGP in the treatment of OM caused by 5-FU in golden hamsters., Methods: Sixty male golden hamsters were divided into six main group. Chemotherapy with 5-FU at dose of 60 mg/kg was performed at a ten-day duration. Then, cheek pouches of the hamsters were scratched with an 18-gauge sterile needle to induce oral mucositis in animals. On the twelfth day, as a day of intensification of OM, treatment with PGP including topical gel with concentrations of 5% and 10% and oral administration of hydro-alcoholic extract with doses of 125 mg/kg and 250 mg/kg for three- and five-day therapeutic duration were separately started. Finally, samples of cheek pouches in hamsters were collected on 14th and 17th days and histopathologic score (HPS), malondialdehyde (MDA), and myeloperoxidase (MPO) levels were assayed., Results: A significant (p < 0.05) decrease in histopathologic score was observed in G
10%-, P125 -treated groups in comparison to the Ctrl group. Our data showed that treatment with G10% is more potent than P125 -treated group. In contrast, histopathologic score in G10%, P125 , and P250 treated groups demonstrated almost similar values On the 17th day. However, the levels of MDA and MPO in the treatment groups were enhanced compared with control group (p < 0.05)., Conclusions: It is possible that PGP can play protective role in the healing of tissue damage caused by chemotherapy with 5-FU due to the presence of its natural compounds and antioxidant properties., (© 2023. The Author(s).)- Published
- 2023
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44. Cohort Profile: The Fasa Adults Cohort Study (FACS): a prospective study of non-communicable diseases risks.
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Homayounfar R, Farjam M, Bahramali E, Sharafi M, Poustchi H, Malekzadeh R, Mansoori Y, Naghizadeh MM, Vakil MK, and Dehghan A
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- Humans, Adult, Prospective Studies, Cohort Studies, Risk Factors, Noncommunicable Diseases epidemiology
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- 2023
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45. The association between nonalcoholic fatty liver disease and corrected QT interval prolongation among generally healthy Iranian population: Fasa Cohort Study (FACS).
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Naderi A, Farjam M, Mojarrad Sani M, Abdollahi A, Alkamel A, Keshavarzian O, and Tabrizi R
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- Humans, Male, Female, Cohort Studies, Iran epidemiology, Risk Factors, Cholesterol, Non-alcoholic Fatty Liver Disease diagnosis, Non-alcoholic Fatty Liver Disease epidemiology
- Abstract
Background: There are limited studies about the association between nonalcoholic fatty liver disease (NAFLD) and corrected QT interval (QTc) prolongation worldwide., Hypothesis: Therefore, we designed the current study to determine this association in a large cohort of a generally healthy population., Methods: We analyzed the data of 4603 individuals aged 35-70 who participated in the Fasa Cohort Study (FACS). Based on 12-lead electrocardiograms, QT intervals were calculated and corrected by Bazzet's formula. A QTc interval of more than 430 ms in men and 450 ms in women was considered prolonged. The Fatty Liver Index was used to identify the participants with NAFLD., Results: Of all participants, 1550 (33.6%) met the NAFLD criteria. In subjects of both genders with NAFLD, the mean values of the QTc interval were considerably higher than in those without NAFLD (p < .001). After adjusting for a wide range of confounders, including age, gender, smoking status, physical activity, total cholesterol, high-density lipoprotein-cholesterol levels, diabetes, and hypertension status, in linear regression analysis, the standardized β coefficient of QTc interval among participants with NAFLD was 2.56 ms (95% confidence interval [CI]: 0.49-4.64). After controlling the same confounders, the odds ratio of NAFLD for a prolonged QTc interval in men was 1.47 (95% CI: 1.18-1.84; p < .001) and in women was 1.39 (95% CI: 1.15-1.68; p < .001) using logistic regression analysis., Conclusions: NAFLD was a risk factor for QTc interval prolongation. Awareness about the risk of NAFLD in increasing the potential cardiac arrhythmias should be raised to lower cardiac mortality., (© 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.)
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- 2023
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46. Translation and psychometric assessment of a Persian version of medication safety competence scale (MSCS) for clinical nurses.
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Mohammadi F, Kouhpayeh SA, Bijani M, Farjam M, Faghihi A, and Badiyepeymaiejahromi Z
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- Humans, Psychometrics, Reproducibility of Results, Cross-Sectional Studies, Surveys and Questionnaires, Nurses
- Abstract
Nurses play a key role in medication safety and, by extension, patient safety. Evaluation of medication safety competence in nurses requires valid, specific, and comprehensive instruments. The present study was conducted to translate and psychometric assessment a Persian version of medication safety competence scale (MSCS) for clinical nurses in Iran. This is a cross-sectional and multi-centric work of research with a methodological design. A total of 1080 clinical nurses were selected from 5 cities located in Iran. The original version of the MSCS was translated into Persian and the psychometric properties of MSCS were assessed using COSMIN criteria. The exploratory factor analysis (EFA) showed that the factor loading of the 36 items was between 0.72-0.87, all of which were significant. The confirmatory factor analysis (CFA) fitted the data well (χ
2 /df = 7, RMSEA = 0.01, CFI = 0.96, NFI = 0.95, and TLI = 0.97). The reliability of the instrument was assessed in terms of its internal homogeneity where the Cronbach's alpha of the whole instrument was found to be 0.96. The Persian version of MSCS for nurses possesses satisfactory validity and reliability. Thus, nurse managers can use this instrument to measure medication safety competence in nurses., (© 2023. The Author(s).)- Published
- 2023
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47. Nano-scaled emulsion and nanogel containing Mentha pulegium essential oil: cytotoxicity on human melanoma cells and effects on apoptosis regulator genes.
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Azadi S, Osanloo M, Zarenezhad E, Farjam M, Jalali A, and Ghanbariasad A
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- Humans, Nanogels, Emulsions, bcl-2-Associated X Protein, Genes, Regulator, Apoptosis, Oils, Volatile pharmacology, Oils, Volatile chemistry, Mentha pulegium chemistry, Melanoma drug therapy
- Abstract
Background: Topical drug delivery using nanoemulsions and nanogels is a promising approach to treating skin disorders such as melanoma., Methods: In this study, the chemical composition of Mentha pulegium essential oil with five major compounds, including pulegone (68.11%), l-menthone (8.83%), limonene (2.90%), iso-pulegone (2.69%), and iso-menthone (1.48%) was first identified using GC-MS (Gas chromatography-Mass Spectrometry) analysis. Afterward, a nano-scaled emulsion containing the essential oil with a droplet size of 7.70 ± 1 nm was prepared. Nanogel containing the essential oil was then prepared by adding (2% w/v) carboxymethyl cellulose to the nano-scaled emulsion. Moreover, the successful loading of M. pulegium essential oil in the nano-scaled emulsion and nanogel was confirmed using ATR-FTIR (Attenuated total reflectance-Fourier Transform InfraRed) analysis. Then, human A375 melanoma cells were treated with different concentrations of samples, the MTT assay evaluated cell viability, and cell apoptosis was confirmed by flow cytometry. In addition, the expression of apoptotic and anti-apoptotic genes, including Bax and Bcl-2, was evaluated using the qPCR (quantitative Polymerase Chain Reaction) technique., Results: The results showed that cell viability was reduced by 90 and 45% after treatment with 300 μg/mL of the nanogel and nano-scaled emulsion. As confirmed by flow cytometry, this effect was mediated by apoptosis. Furthermore, gene expression analysis showed up-regulation of Bax and down-regulation of Bcl-2 genes. Therefore, the prepared nanogel, with high efficacy, could be considered a potent anticancer agent for supplementary medicine and in vivo research., (© 2023. The Author(s).)
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- 2023
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48. Diet quality and dietary acid load in relation to cardiovascular disease mortality: Results from Fasa PERSIAN cohort study.
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Fereidouni S, Hejazi N, Homayounfar R, and Farjam M
- Abstract
Dietary intake is a determining factor in the morbidity and mortality of chronic disorders. However, not many documents have investigated this relationship. The aim of this study was to evaluate the associations of the Mediterranean dietary score (MDS), Alternative Healthy Eating Index (AHEI), Dietary Inflammatory Index (DII), DASH score, and dietary acid load with cardiovascular disease (CVD) mortality. A total of 2158 CVD patients (mean age of 54.73 ± 8.62 years) from the Fasa cohort study, Iran, participated in the current study. Diet quality indices including DII, AHEI, MDS, DASH, and dietary acid load (NEAP score) were computed using a validated 125-item Food Frequency Questionnaire (FFQ). Cox regression analyses were used to determine HRs and 95% CIs. During a follow-up of 3 years, we documented 59 CVD deaths. After adjusting for relevant confounders (age, gender, family history of CVD, smoking, physical activity, alcohol intake, and HTN) in the final model, we found that higher DII scores and dietary acid load were significantly related to increased mortality due to CVD (HR = 1.11; 95% CI = 1.01-1.24; and HR = 1.02; 95% CI = 1.01-1.03). However, the DASH score was insignificantly associated with decreased CVD mortality by 20.4% (HR = 0.79; 95% CI = 0.57-1.09). There was no significant relationship among AHEI score, MDS, and CVD mortality. This study showed that increasing dietary acidity and the use of inflammatory food compounds could contribute to CVD mortality. Also, adherence to the DASH diet may be associated with reduced CVD mortality., Competing Interests: None declare., (© 2022 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)
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- 2022
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49. Correction: Laboratory-based and office-based Globorisk scores to predict 10-year risk of cardiovascular diseases among Iranians: results from the Fasa PERSIAN cohort.
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Jahangiry L, Dehghan A, Farjam M, Aune D, and Rezaei F
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- 2022
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50. Laboratory-based and office-based Globorisk scores to predict 10-year risk of cardiovascular diseases among Iranians: results from the Fasa PERSIAN cohort.
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Jahangiry L, Dehghan A, Farjam M, Aune D, and Rezaei F
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- Male, Humans, Female, Middle Aged, Cohort Studies, Iran epidemiology, Risk Factors, Body Mass Index, Cardiovascular Diseases diagnosis, Cardiovascular Diseases epidemiology
- Abstract
Background: Globorisk is a novel risk prediction model for predicting cardiovascular disease (CVD). Globorisk is a country-specific risk prediction model that determines CVD risk for all countries. This model has two versions; laboratory-based and office-based. This study aimed to determine the agreement between laboratory-based and office-based models in a large sample of the general population., Methods: Baseline data from the Fasa cohort study was used for the current study. In total, 6810 participants ≥ 40 years without any history of cardiovascular disease or stroke were included in the study. To determine the laboratory-based risk model, factors include age, sex, current smoking status, history of diabetes, systolic blood pressure (SBP), and total cholesterol. To estimate the office-based risk model, factors were age, sex, current smoking status, SBP, and body mass index (BMI). Kappa statistics was used to distinguish the agreement between grouped scores in these two models. Additionally, correlation coefficients and scatter plots were used to determine the linear correlation between the two models., Results: In this study 46.53% of the participants were men. The mean age (SD) of participants was 51.08 (7.88) years. Agreements between the two models were moderate and substantial in all women and all men, respectively. The agreement between the two CVD risk groups was 90.15% (kappa = 0.717) in all men, 92.94% (kappa = 0.571) among men aged < 60 years and 77.60% (kappa = 0.645) in men aged ≥ 60 years. The agreement between the two CVD risk groups was 86.68% (kappa = 0.572) among all women, 93.96% (kappa = 0.274) among women aged < 60 years and 62.46% (kappa = 0.422) among women aged ≥ 60 years. A very strong positive correlation (r = 0.94) was found between the two risk scores in all men, and it was similar among men aged < 60 years (r = 0.84) and men aged > 60 years (r = 0.94). Among all women, there was a very strong positive correlation (r = 0.87), and the strong positive correlation remained among < 60 years old (r = 0.76) and women > 60 years old (r = 0.76)., Conclusion: The Globorisk office-based model which is easier to use as it does not require blood testing can determine the risk groups in this population. The Globorisk office-based model may be used for CVD risk screening in low-middle income countries where resources are limited., (© 2022. The Author(s).)
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- 2022
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