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Association of adiposity indices with cardiometabolic multimorbidity among 101,973 chinese adults: a cross-sectional study

BACKGROUND: Cardiometabolic multimorbidity (CMM) and obesity represent two major health problems. The relationship between adiposity indices and CMM, however, remains understudied. This study aimed to investigate the associations of body mass index (BMI), waist circumference (WC), waist-to-height ra...

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Detalles Bibliográficos
Autores principales: Qin, Xiaoru, Chen, Chaolei, Wang, Jiabin, Cai, Anping, Feng, Xiaoxuan, Jiang, Xiaofei, Feng, Yingqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10590510/
https://www.ncbi.nlm.nih.gov/pubmed/37865773
http://dx.doi.org/10.1186/s12872-023-03543-x
Descripción
Sumario:BACKGROUND: Cardiometabolic multimorbidity (CMM) and obesity represent two major health problems. The relationship between adiposity indices and CMM, however, remains understudied. This study aimed to investigate the associations of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), a body shape index (ABSI), body roundness index (BRI), and conicity index (CI) with CMM among Chinese adults. METHODS: Data of 101,973 participants were collected from a population-based screening project in Southern China. CMM was defined as having two or more of the following diseases: coronary heart disease, stroke, hypertension, and diabetes. The relationship between the six adiposity indices and CMM was investigated by multivariate logistic regression and restricted cubic splines. Receiver operator characteristic curve, C-statistic and net reclassification index were used to estimate the discriminative and incremental values of adiposity indices on CMM. RESULTS: Logistic regression models showed the six adiposity indices were all significantly associated with the odds of CMM with non-linear relationships. For per SD increment, WC (Odds ratio [OR]: 1.66; 95% confidence interval (CI): 1.62–1.70) and WHtR (OR, 1.61; 95% CI, 1.58–1.65) were more significantly associated with a higher prevalence of CMM than BMI (OR, 1.55; 95% CI, 1.52–1.58) (all P < 0.05). In addition, WC, WHtR, and BRI displayed significantly better performance in detecting CMM compared with BMI (all P < 0.05). Their respective area under the curve (AUC) values were 0.675 (95% CI: 0.670–0.680), 0.679 (95% CI: 0.675–0.684), and 0.679 (95% CI: 0.675–0.684), while BMI yielded an AUC of 0.637 (95% CI: 0.632–0.643). These findings hold true across all subgroups based on sex and age. When Adding WC, WHtR, or BRI to a base model, they all provided larger incremental values for the discrimination of CMM compared with BMI (all P < 0.05). CONCLUSIONS: Adiposity indices were closely associated with the odds of CMM, with WC and WHtR demonstrating stronger associations than BMI. WC, WHtR, and BRI were superior to BMI in discriminative ability for CMM. Avoidance of obesity (especially abdominal obesity) may be the preferred primary prevention strategy for CMM while controlling for other major CMM risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12872-023-03543-x.