341 related articles for article (PubMed ID: 36011884)
1. The Effect of Obesity on the Waist Circumference Cut-Point Used for the Diagnosis of the Metabolic Syndrome in African Women: Results from the SWEET Study.
Gradidge PJ; Norris SA; Crowther NJ
Int J Environ Res Public Health; 2022 Aug; 19(16):. PubMed ID: 36011884
[TBL] [Abstract][Full Text] [Related]
2. Predicting value of five anthropometric measures in metabolic syndrome among Jiangsu Province, China.
Tian T; Zhang J; Zhu Q; Xie W; Wang Y; Dai Y
BMC Public Health; 2020 Aug; 20(1):1317. PubMed ID: 32867710
[TBL] [Abstract][Full Text] [Related]
3. Determining the best method for evaluating obesity and the risk for non-communicable diseases in women of childbearing age by measuring the body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio, A Body Shape Index, and hip index.
Hewage N; Wijesekara U; Perera R
Nutrition; 2023 Oct; 114():112135. PubMed ID: 37453224
[TBL] [Abstract][Full Text] [Related]
4. Obesity diagnosis and mortality risk based on a body shape index (ABSI) and other indices and anthropometric parameters in university students.
Gažarová M; Galšneiderová M; Mečiarová L
Rocz Panstw Zakl Hig; 2019; 70(3):267-275. PubMed ID: 31515986
[TBL] [Abstract][Full Text] [Related]
5. The most appropriate cut-off point of anthropometric indices in predicting the incidence of metabolic syndrome and its components.
Farhangiyan Z; Latifi SM; Rashidi H; Shahbazian H
Diabetes Metab Syndr; 2019; 13(4):2739-2745. PubMed ID: 31405702
[TBL] [Abstract][Full Text] [Related]
6. Predicting metabolic syndrome by anthropometric measures among adults 35-65 years in the west of Iran; a cross sectional study from an Iranian RaNCD cohort data.
Hamzeh B; Bagheri A; Pasdar Y; Darbandi M; Rezaeian S; Najafi F; Moradinazar M
Diabetes Metab Syndr; 2020; 14(5):1293-1298. PubMed ID: 32755824
[TBL] [Abstract][Full Text] [Related]
7. Anthropometric cut-points for discriminating diabetes and the metabolic syndrome among Arabs and Asians: the Kuwait Diabetes Epidemiology Program.
Oguoma VM; Coffee NT; Alsharrah S; Abu-Farha M; Al-Refaei FH; Alkandari A; Al-Mulla F; Daniel M
Br J Nutr; 2022 Jan; 127(1):92-102. PubMed ID: 33658089
[TBL] [Abstract][Full Text] [Related]
8. Waist circumference shows the highest predictive value for metabolic syndrome, and waist-to-hip ratio for its components, in Spanish adolescents.
Perona JS; Schmidt-RioValle J; Rueda-Medina B; Correa-Rodríguez M; González-Jiménez E
Nutr Res; 2017 Sep; 45():38-45. PubMed ID: 29037330
[TBL] [Abstract][Full Text] [Related]
9. Anthropometric parameter that best predict metabolic syndrome in South west Nigeria.
Adejumo EN; Adejumo AO; Azenabor A; Ekun AO; Enitan SS; Adebola OK; Ogundahunsi OA
Diabetes Metab Syndr; 2019; 13(1):48-54. PubMed ID: 30641748
[TBL] [Abstract][Full Text] [Related]
10. Waist circumference, body mass index and waist to hip ratio for prediction of the metabolic syndrome in Chinese.
Wang F; Wu S; Song Y; Tang X; Marshall R; Liang M; Wu Y; Qin X; Chen D; Hu Y
Nutr Metab Cardiovasc Dis; 2009 Oct; 19(8):542-7. PubMed ID: 19188050
[TBL] [Abstract][Full Text] [Related]
11. DXA-Derived vs Standard Anthropometric Measures for Predicting Cardiometabolic Risk in Middle-Aged Australian Men and Women.
Zhu K; Walsh JP; Murray K; Hunter M; Hui J; Hung J
J Clin Densitom; 2022; 25(3):299-307. PubMed ID: 35177350
[TBL] [Abstract][Full Text] [Related]
12. The discriminative ability of waist circumference, body mass index and waist-to-hip ratio in identifying metabolic syndrome: Variations by age, sex and race.
Cheong KC; Ghazali SM; Hock LK; Subenthiran S; Huey TC; Kuay LK; Mustapha FI; Yusoff AF; Mustafa AN
Diabetes Metab Syndr; 2015; 9(2):74-8. PubMed ID: 25819369
[TBL] [Abstract][Full Text] [Related]
13. Relationship between the optimal cut-off values of anthropometric indices for predicting metabolic syndrome and carotid intima-medial thickness in a Korean population.
Yang YJ; Park HJ; Won KB; Chang HJ; Park GM; Kim YG; Ann SH; Park EJ; Kim SJ; Lee SG
Medicine (Baltimore); 2019 Oct; 98(42):e17620. PubMed ID: 31626142
[TBL] [Abstract][Full Text] [Related]
14. The six obesity indices, which one is more compatible with metabolic syndrome? A population based study.
Motamed N; Sohrabi M; Poustchi H; Maadi M; Malek M; Keyvani H; Amoli MS; Zamani F
Diabetes Metab Syndr; 2017; 11(3):173-177. PubMed ID: 27658893
[TBL] [Abstract][Full Text] [Related]
15. The discriminatory performance of body mass index, waist circumference, waist-to-hip ratio and waist-to-height ratio for detection of metabolic syndrome and their optimal cutoffs among Iranian adults.
Hajian-Tilaki K; Heidari B; Hajian-Tilaki A; Firouzjahi A; Bagherzadeh M
J Res Health Sci; 2014; 14(4):276-81. PubMed ID: 25503283
[TBL] [Abstract][Full Text] [Related]
16. Differential obesity indices identify the metabolic syndrome in Black men and women in Cape Town: the CRIBSA study.
Peer N; Steyn K; Levitt N
J Public Health (Oxf); 2016 Mar; 38(1):175-82. PubMed ID: 25618906
[TBL] [Abstract][Full Text] [Related]
17. The discriminatory power of visceral adipose tissue area vs anthropometric measures as a diagnostic marker for metabolic syndrome in South African women.
Davidson FE; Matsha TE; Erasmus RT; Kengne AP; Goedecke JH
Diabetol Metab Syndr; 2019; 11():93. PubMed ID: 31719847
[TBL] [Abstract][Full Text] [Related]
18. A waist-to-height ratio of 0.54 is a good predictor of metabolic syndrome in 16-year-old male and female adolescents.
Vasquez F; Correa-Burrows P; Blanco E; Gahagan S; Burrows R
Pediatr Res; 2019 Feb; 85(3):269-274. PubMed ID: 30631139
[TBL] [Abstract][Full Text] [Related]
19. Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio.
Bener A; Yousafzai MT; Darwish S; Al-Hamaq AO; Nasralla EA; Abdul-Ghani M
J Obes; 2013; 2013():269038. PubMed ID: 24000310
[TBL] [Abstract][Full Text] [Related]
20. Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective, longitudinal study.
Wang H; Liu A; Zhao T; Gong X; Pang T; Zhou Y; Xiao Y; Yan Y; Fan C; Teng W; Lai Y; Shan Z
BMJ Open; 2017 Sep; 7(9):e016062. PubMed ID: 28928179
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
