Different adiposity indices and their associations with hypertension among Chinese population from Jiangxi province - PubMed (original) (raw)

Comparative Study

doi: 10.1186/s12872-020-01388-2.

Guiping Hu 2, Xiao Huang 1, Wei Zhou 3, Chunjiao You 1, Juxiang Li 1, Ping Li 1, Yanqing Wu 1, Qinghua Wu 1, Zengwu Wang 4, Runlin Gao 5, Huihui Bao 6, Xiaoshu Cheng 7

Affiliations

Comparative Study

Different adiposity indices and their associations with hypertension among Chinese population from Jiangxi province

Lihua Hu et al. BMC Cardiovasc Disord. 2020.

Abstract

Background: To date, the best adiposity index that predicts or associates strongly with hypertension remains controversial. Therefore, we aimed to compare the performance of different adiposity indices [BMI (body mass index), WC (waist circumference), WHtR (waist-to-height ratio), ABSI (a body shape index), VAI (visceral adipose index), BFP (body fat percentage)] as associates and potential predictors of risk of hypertension among Chinese population.

Methods: A cross-sectional survey was conducted in Jiangxi province, China from 2013 to 2014. A total of 14,573 participants were included in the study. The physical measurements included body height, weight, WC, BFP and VAI. Multivariate logistic regression analysis was performed to assess the associations between different adiposity indices and the prevalence of hypertension. Receiver operating characteristic (ROC) analysis was also performed.

Results: All adiposity indices were independently and positively associated with the prevalence of hypertension in a dose response fashion. The area under the curves (AUCs) for WHtR, BFP and VAI were significantly larger than those for other adiposity indices in both males and females (all P < 0.01). For males, no statistically significant difference was found in AUCs among WHtR and BFP (0.653 vs. 0.647, P = 0.4774). The AUC of WHtR was significantly higher than VAI (0.653 vs. 0.636, P < 0.01). For females, the AUCs demonstrated that WHtR was significantly more powerful than BFP and VAI (both P < 0.05) for predicting hypertension [WHtR, 0.689 (0.677-0.702); BFP, 0.677 (0.664-0.690); VAI, 0.668 (0.655-0.680)]. Whereas no significant differences were found in AUCs for hypertension among BFP and VAI in both sexes (all P > 0.1). The AUCs for hypertension associated with each adiposity index declined with age in both males and females. For subjects aged < 65 years, WHtR still had the largest AUC. However, for participants aged ≥65 years, BMI had the largest AUC.

Conclusion: The findings indicated that WHtR was the best for predicting hypertension, followed by BFP and VAI, especially in younger population.

Keywords: Body fat percentage; Hypertension; Receiver operating characteristic curve; Visceral adipose index; Waist-to-height ratio.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1

Fig. 1

Multivariable-adjusted ORs (95%CI) of hypertension according to quartiles of BMI, WC, WHtR, ABSI, BFP and VAI. Adjusted for sex, age, area, smoking, drinking, education status, occupation, family history of hypertension, antihypertensive medications, sleep duration (workdays and non-workdays), BMR and RHR. Cut-points of quartiles:BMI (kg/m2) 20.30, 22.50, 25.00; WC (cm) 72.00, 78.00, 85.00; WHtR 0.46, 0.50, 0.54; ABSI (m11/6 kg-2/3) 0.0748, 0.0786, 0.0823; BFP 22.00, 27.00, 32.40; VAI 4.00, 7.00, 9.00

Fig. 2

Fig. 2

ROC curves of adiposity indices and the combination model including BMI and WC for identifying hypertension according to sex. a and b ROC curves for the relationships between adiposity indices and hypertension in males and in females, respectively. cP values for pairwise comparison of ROC curves for different adiposity indices in males and in females

References

    1. GBD 2016 Causes of Death Collaborators Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1151–1210. doi: 10.1016/S0140-6736(17)32152-9. - DOI - PMC - PubMed
    1. Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1.7 million adults in a population-based screening study (China PEACE Million Persons Project) Lancet. 2017;390:2549–2558. doi: 10.1016/S0140-6736(17)32478-9. - DOI - PubMed
    1. Wang ZW, Chen Z, Zhang LF, Wang X, Hao G, Zhang ZG, et al. Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation. 2018;137:2344–2356. doi: 10.1161/CIRCULATIONAHA.117.032380. - DOI - PubMed
    1. Hu Lihua, Huang Xiao, You Chunjiao, Bao Huihui, Zhou Wei, Li Juxiang, Li Ping, Wu Yanqing, Wu Qinghua, Wang Zengwu, Gao Runlin, Liang Qian, Cheng Xiaoshu. Relationship of sleep duration on workdays and non-workdays with blood pressure components in Chinese hypertensive patients. Clinical and Experimental Hypertension. 2018;41(7):627–636. doi: 10.1080/10641963.2018.1529777. - DOI - PubMed
    1. Hu L, Huang X, You C, Li JX, Hong K, Li P, et al. Prevalence and risk factors of prehypertension and hypertension in southern China. PLoS One. 2017;12:e170238. doi: 10.1371/journal.pone.0170238. - DOI - PMC - PubMed

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