Results 41 to 50 of about 6,786,852 (260)

Anthropometric indicators and cardiovascular diseases risk in pre-diabetic and diabetic adults: NHANES 1999–2018 cross-sectional analysis

Experimental Gerontology
Background: Since cardiovascular disease (CVD) stands as the primary cause of death in those with diabetes, and given the substantial influence of obesity as a common risk factor for both diabetes and atherosclerotic conditions, this investigation sought
Yi He, Lingfeng Shi
doaj   +1 more source

Obesity, metabolic syndrome and risk of atrial fibrillation: a Swedish, prospective cohort study.

PLoS ONE, 2015
AimWe aimed to investigate whether different measures of obesity could similarly predict atrial fibrillation, and whether the atrial fibrillation risk associated with obesity is dependent on presence of metabolic syndrome.Material and methodsWe performed
Petter K Nyström, Axel C Carlsson, Karin Leander, Ulf de Faire, Mai-Lis Hellenius, Bruna Gigante   +5 more
doaj   +1 more source

Cut-off points for anthropometric indices of adiposity: differential classification in a large population of young women

, 2009
Anthropometric indices of adiposity include BMI, waist circumference and waist:height ratio. In the recruitment phase of a prospective cohort study carried out between 1998 and 2002 we studied a population sample of 11 786 white Caucasian non-pregnant ...
Jackson, Alan A., Duggleby, Sarah L., Robinson, Siân M., Godfrey, Keith M., Inskip, Hazel M.   +4 more
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Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies [PDF]

, 2011
BACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI),
Nordestgaard, Borge G., Stehouwer, Coen; id_orcid, Kaptoge, S., Danesh, J, Wood, Angela M., Thompson, Alex, Thompson, A, Wormser, D, Emerging Risk Factors Collaboration, Risérus, Ulf,, Salomma, V, Sarwar, N, Danesh, J., Wood, Angela M, Pennells, L, Ärnlöv, Johan,, Woodward, M., Salomaa, Veikko, Sundström, Johan,, Thompson, S.G., Kaptoge, S, Tybjærg-Hansen, Anne, Wormser, D., Ridker, P, Thompson, Simon G, null The Emerging Risk Factors Collaboration, Woodward, Mark, Kizer, Jorge R, Frikke-Schmidt, Ruth, Nordestgaard, BG, Kaptoge, Stephen, Lawlor, DA, Eriksson, Henry,, Sarwar, Nadeem, Wood, AM, Kizer, Jorge R., Pennells, Lisa, Stevens, June, Pennells, L., Whitlock, G, Salomaa, V., Nordestgaard, B.G., Collins, Rory, Ridker, Paul, Thompson, A., Lawlor, D.A., Woodward, M, Collins, R, Kizer, J.R., Sattar, N., Lawlor, Debbie A, Sarwar, N., Ingelsson, Erik,, Davey Smith, George, Danesh, John, Ridker, P., Di Angelantonio, Emanuele, Wood, A.M., Whitlock, G., Di Angelantonio, E., Sattar, Naveed, Lawlor, Debbie A., Collins, R., Di Angelantonio, E, Kizer, JR, Whitlock, Gary, Thompson, SG, Nordestgaard, Børge G, Stevens, J, Wormser, David, Stevens, J., Thompson, Simon G., Di Angeloantonio, Emanuele, Emerging Risk Factors Collaboration (Döring, A. ; Meisinger, C.), Tavendale, R., Nakamura, K., Bettencourt, R., Vartiainen, E., Noda, H., Tunstall-Pedoe, H., Ingelsson, E., Alexander, M., Björkelund, C., Lissner, L., Witteman, J., Jousilahti, P., Hillege, H.L., Tilvis, R.S., Umans, J., Blazer, D.G., Lowe, G.D.O., Chetrit, A., ørnlöv, J., Best, L., Pai, J.K., Franco, O.H., Nijpels, G., Koenig, W., Kitamura, A., Wingard, D.L., Ebrahim, S., Hofman, A., Vanuzzo, D., Yarnell, J., Poppelaars, J.L., Miettinen, T.A., Kiyohara, Y., Stehouwer, C.D.A., Giampaoli, S., Rosengren, A., Khaw, K.-T., Döring, A., Fox, C.S., Onat, A., Panico, S., Shaw, J.E., Dankner, H.R., Guralnik, J.M., Arima, H., Can, GÜNAY, Løchen, M.L., Welin, L., Yamagishi, K., Brunner, E., Kiechl, S., Wilsgaard, T., Bengtsson, C., Dekker, J.M., Phillips, C.L., Watson, S., Pencina, M.J., D’Agostino, R.B., Davidson, K.W., Deeg, D., Evans, A., Pilotto, L., Pitsavos, C., Shipley, M., Sato, S., Strandberg, T.E., Folsom, A.R., Barrett-Connor, E., Morris, R.W., Gobin, R., Mayr, A., Tiemeier, H., Gillum, R.F., Zhang, Y., Ninomiya, T., Gao, P., Palmieri, L., Iso, H., Kardys, I., Korin, M.R., Fletcher, A., De Boer, I.H., Chrysohoou, C., Barr, E.L.M., Verschuren, W.M.M., Frikke-Schmidt, R., Butterworth, A.S., Bakker, S.J.L., Whincup, P.H., Tybjærg-Hansen, A., Howard, B.V., Gallacher, J., Shea, S., Mathiesen, E.B., Arveiler, D., Atkins, R., Zimmet, P.Z., Bueno-De-Mesquita, H.B., Eriksson, H., Hankinson, S.E., Nakagawa, H., Njølstad, I., Buring, J., Mussolino, M., Kondapally Seshasai, S.R., Chambless, L.E., Nietert, P.J., Lubin, F., Panagiotakos, D.B., Mukamal, K.J., Keil, J.E., Wannamethee, S.G., Lewington, S., Rimm, E.B., Willeit, J., Walker, M., De La Cámara, A. Gómez, Wilhelmsen, L., Bachman, D.L., Psaty, B.M., Morikawa, Y., Blokstra, A., Sundström, J., Oberhollenzer, F., Harald, K., Wassertheil-Smoller, S., Sakurai, M., Stefanadis, C., Manson, J.E., Sutherland, S.E., Risérus, U., Svärdsudd, K., Ferrières, J., Amouyel, P., Meisinger, C., Gansevoort, R.T., Vasan, R.S., White, I.R., Rubio, M.A., Haycock, P., Lappas, G., Doi, Y., Shaffer, J.A., Hergenç, G., Kirkland, S.   +218 more
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Association between weight-adjusted waist index (WWI) and multimorbidity: Evidence from NHANES and prospective validation in CHARLS

Experimental Gerontology
Background: The burden of chronic diseases is known to be significantly increased by obesity, yet conventional measures like waist circumference (WC) and body mass index (BMI) do not accurately capture central adiposity.
Xiaorong Ye, Dingqi Shi, Ruixuan Li, Yuxin Li, Luwen Zhang, Lei Shi, Yao Xiao   +6 more
doaj   +1 more source

Association between new obesity index, weight adjusted waist circumference index, and type 2 diabetes

International Journal of Diabetes in Developing Countries
Abstract Background Obesity is a well-established risk factor for T2DM, and various obesity-related indices have been developed to assess the relationship between obesity and the disease. Traditional indices such as body mass index (BMI) have certain limitations in accurately predicting the ...
Yi-Min Chen, Hai-Dong Kuang, Yi-Kai Mi, Yu-Xiang Zheng, Su-Wen Wang   +4 more
openaire   +1 more source

Adiposity, adipokines, and risk of incident stroke in older men [PDF]

, 2013
<p>Background and Purpose—The association between adiposity and adipocytes and risk of stroke in older adults is uncertain. We have examined the association between adiposity measures and adipocytes (adiponectin and leptin) with incident stroke ...
Shaper, A.G., Whincup, P.H., Lennon, L., Sattar, N., Wannamethee, S.G.   +4 more
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Towards a Compositional Framework for Describing Human Phenotypes

Advanced Science, EarlyView.
The Phenotype Assembly Method (PhenoAM) decomposes phenotype variables into measurable Features and typed Qualifiers, enabling standardized, machine‐readable Phenome Data Elements (PhenoDEs) that preserve measurement context. Applied in the International Human Phenome Project (IHPP), the framework yields 58 371 PhenoDEs and supports component‐level ...
Wanting Hu, Yunchao Ling, Xinyue Hu, Wei Chen, Rong Zhou, Xinhao Zhuang, Yanfeng Jiang, Jialu Zhao, Xingdong Chen, Jingchun Luo, Li Jin, Guoqing Zhang   +11 more
wiley   +1 more source

Association between weight-adjusted-waist index and anxiety among adults in the National Health and Nutrition Examination Survey (NHANES), 2007–2012

Frontiers in Nutrition
Background and aimsAnxiety is a pervasive mental health concern and has evolved into a multifaceted and pressing global health concern. Despite this, the connection between the weight-adjusted waist index (WWI) and anxiety symptoms remains unexplored ...
Xiao Liang, Kai Chen, Yongcan Xia, Siyuan Ding, Hao Wu, Lijuan Huang, Zhenlin Chen, Yuqian Yan   +7 more
doaj   +1 more source

Evaluation of Bioelectrical Impedance Analysis for Identifying Overweight Individuals at Increased Cardiometabolic Risk:A Cross-Sectional Study [PDF]

, 2014
OBJECTIVE: To investigate whether bioelectrical impedance analysis could be used to identify overweight individuals at increased cardiometabolic risk, defined as the presence of metabolic syndrome and/or diabetes.DESIGN AND METHODS: Cross-sectional study
Lamb Maxine J. E., Christopher D Byrne, Wilson, J.F., Lamb, M.J.E., Wild, S.H., Lamb, Maxine J E, Byrne, Christopher D, Wild, Sarah H; id_orcid, Wilson James F., Byrne Christopher D., Sarah H Wild, James F Wilson, Wild Sarah H., Wilson, James F; id_orcid, Byrne, Christopher D., Maxine J E Lamb   +15 more
core   +1 more source