体重调整腰围指数对心血管疾病和全因死亡风险的影响

Association of weight-adjusted waist circumference index with cardiovascular disease and all-cause mortality

摘要

目的 本研究旨在综合美国国家健康与营养调查（NHANES）横断面研究、开滦前瞻性队列研究，系统探讨体重调整腰围指数（WWI）与心血管疾病（CVD）及全因死亡的关联，并运用孟德尔随机化（MR）分析探究构成WWI的两个组分（体重与腰围）与CVD及死亡的因果关系，为WWI作为复合指标的合理性提供遗传学层面的支持。方法 WWI的计算公式为腰围（cm）/$\sqrt{\text{体重(kg)}}$。NHANES横断面研究采用多因素logistic回归模型探讨WWI与CVD的关联。在基于开滦人群的队列研究中，将WWI四分位分组，采用Cox比例风险模型探讨WWI与CVD（包括其亚型）及全因死亡的关联，并按年龄、性别、教育水平、收入水平、高血压、糖尿病、吸烟和饮酒习惯进行分层分析。此外，采用单变量和多变量MR，主要运用逆方差加权（IVW）方法进一步探讨体重、腰围与CVD之间的因果关系。结果 NHANES横断面分析显示，WWI与CVD患病风险呈正向关联（趋势P＜0.001）。队列研究显示，随着WWI水平升高，CVD和全因死亡风险增高，校正所有协变量后，以第1四分位数分组为参照，最高四分位数分组CVD的HR为1.35（95%CI: 1.28～1.44）。亚组分析显示，在较年轻、受过高等教育和患有糖尿病的人群中，WWI与CVD风险的相关性更大。单变量MR分析显示体重与心房颤动、脑梗死、脑卒中、心肌梗死、心力衰竭存在因果关联，腰围与心房颤动、脑出血、脑梗死、脑卒中、心力衰竭存在因果关联；但在多变量MR分析中，排除腰围影响后，体重与脑梗死、心肌梗死的因果关联消失；排除体重影响后，腰围与脑出血、脑梗死的因果关联消失。结论 WWI与CVD及全因死亡风险呈正向关联。MR分析提示体重和腰围与CVD及死亡存在因果关联且非彼此独立，这为WWI作为复合指标的合理性提供了遗传学层面的支持。

Abstract

Objective To integrate the cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) and the prospective Kailuan cohort study to systematically investigate the association of weight-adjusted waist circumference index (WWI) with cardiovascular disease (CVD) and all-cause mortality. Furthermore, Mendelian randomization (MR) analysis was employed to explore the causal relationships between the two components of WWI (body weight and waist circumference) and CVD as well as mortality, thereby providing genetic-level support for the rationality of WWI as a composite indicator. Methods WWI was calculated as waist circumference(cm)/$\sqrt{\text{body weight (kg)}}. In the NHANES cross-sectional study, multivariable logistic regression models were employed to examine the association between WWI and CVD. In the cohort study based on the Kailuan population, participants were categorized into quartiles based on WWI. Cox proportional hazards models were used to assess the associations of WWI with CVD (including its subtypes) and all-cause mortality. Stratified analyses were conducted according to age, sex, education level, income level, hypertension, diabetes, smoking and drinking habits. In addition, univariate and multivariate MR analyses, primarily using the inverse variance weighted (IVW) method, were performed to further explore the causal relationships between body weight, waist circumference and CVD. Results NHANES cross-sectional analysis showed that WWI was positively associated with the risk of CVD prevalence (trend P<0.001). The cohort study showed that with the increase of WWI level, the risk of CVD and all-cause mortality increased. After adjusting for all covariates, taking the first quartile group as reference, the HR of CVD in the highest quartile group was 1.35 (95%CI: 1.28-1.44). Subgroup analysis showed that the correlation between WWI and CVD risk was greater in younger people, those with higher education and those with diabetes. Univariate MR analysis showed that body weight was causally associated with atrial fibrillation, cerebral infarction, stroke, myocardial infarction and heart failure, and waist circumference was causally associated with atrial fibrillation, cerebral hemorrhage, cerebral infarction, stroke and heart failure; however, in multivariate MR analysis, after excluding the influence of waist circumference, the causal association between body weight and cerebral infarction and myocardial infarction disappeared; after excluding the influence of body weight, the causal association between waist circumference and cerebral hemorrhage and cerebral infarction disappeared. Conclusion WWI is positively associated with the risk of CVD and all-cause mortality. MR analysis suggests that body weight and waist circumference are causally associated with CVD and mortality and are not independent of each other, which provides genetic-level support for the rationality of WWI as a composite indicator.

关键词

体重调整腰围指数 / 心血管疾病 / 全因死亡 / 横断面研究 / 前瞻性队列研究 / 孟德尔随机化分析

Key words

weight-adjusted waist circumference index / cardiovascular disease / all-cause mortality / cross-sectionalstudy / prospective cohort study / Mendelian randomization analysis

参考文献

[1] Yanovski JA . Obesity: trends in underweight and obesity: scale of the problem[J]. Nat Rev Endocrinol, 2018, 14(1): 5-6.

[2] NCD Risk Factor Collaboration (NCD Ris C) . Worldwide trends in body—mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population—based measurement studies in 128·9 million children, adolescents, and adults[J]. Lancet, 2017, 390(10113): 2627-2642.

[3] NCD Risk Factor Collaboration (NCD—RisC) . Trends in adult body—mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population—based measurement studies with 19.2 million participants[J]. Lancet, 2016, 387(10026): 1377-1396.

[4] Chooi YC, Ding C, Magkos F . The epidemiology of obesity[J]. Metabolism, 2019, 92: 6-10.

[5] Piché ME, Tchernof A, Després JP . Obesity phenotypes, diabetes, and cardiovascular diseases[J]. Circ Res, 2020, 126(11): 1477-1500.

[6] Avgerinos KI, Spyrou N, Mantzoros CS, et al. Obesity and cancer risk: emerging biological mechanisms and perspectives[J]. Metabolism, 2019, 92: 121-135.

[7] Kawai T, Autieri MV, Scalia R . Adipose tissue inflammation and metabolic dysfunction in obesity[J]. Am J Physiol Cell Physiol, 2021, 320(3): C375-C391.

[8] Kim MS, Kim WJ, Khera AV, et al. Association between adiposity and cardiovascular outcomes: an umbrella review and meta—analysis of observational and Mendelian randomization studies[J]. Eur Heart J, 2021, 42(34): 3388-3403.

[9] Lv Y, Zhang Y, Li X, et al. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study[J]. Eur Heart J, 2024, 45(24): 2145-2154.

[10] Antonopoulos AS, Oikonomou EK, Antoniades C, et al. From the BMI paradox to the obesity paradox: the obesity—mortality association in coronary heart disease[J]. Obes Rev, 2016, 17(10): 989-1000.

[11] Hainer V, Aldhoon—Hainerová I . Obesity paradox does exist[J]. Diabetes Care, 2013, 36 Suppl 2(Suppl 2): S276-281.

[12] Clark AL, Fonarow GC, Horwich TB . Waist circumference, body mass index, and survival in systolic heart failure: the obesity paradox revisited[J]. J Card Fail, 2011, 17(5): 374-380.

[13] Park Y, Kim NH, Kwon TY, et al. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality[J]. Sci Rep, 2018, 8(1): 16753.

[14] Kim NH, Park Y, Kim NH, et al. Weight—adjusted waist index reflects fat and muscle mass in the opposite direction in older adults[J]. Age Ageing, 2021, 50(3): 780-786.

[15] Tian X, Chen S, Wang P, et al. Insulin resistance mediates obesity—related risk of cardiovascular disease: a prospective cohort study[J]. Cardiovasc Diabetol, 2022, 21(1): 289.

[16] Fang H, Xie F, Li K, et al. Association between weight—adjusted—waist index and risk of cardiovascular diseases in United States adults: a crosssectional study[J]. BMC Cardiovasc Disord, 2023, 23(1): 435.

[17] Han Y, Shi J, Gao P, Zhang L, et al. The weight—adjusted—waist index predicts all—cause and cardiovascular mortality in general US adults[J]. Clinics (Sao Paulo), 2023, 78: 100248.

[18] Wu S, Huang Z, Yang X, et al. Prevalence of ideal cardiovascular health and its relationship with the 4—year cardiovascular events in a northern Chinese industrial city[J]. Circ Cardiovasc Qual Outcomes, 2012, 5(4): 487-493.

[19] 中国高血压防治指南修订委员会, 高血压联盟 (中国), 中国医疗保健国际交流促进会高血压病学分会 等 . 中国高血压防治指南 (2024年修订版)[J]. 中华高血压杂志 (中英文), 2024, 32(7): 603-700.

[20] Zhu D, Society C . Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition)[J]. Chin J Endocr Metab, 2021, 37: 311-398.

[21] 邓佳玲, 丁雄, 陈朔华 等 . 2006—2020年开滦18～44岁青年人群心血管疾病流行趋势[J]. 中华高血压杂志 (中英文), 2025, 33(3): 258-266.

[22] Yang L, Yue Q, Fang F, et al. Effect of dual residual risk of cholesterol and inflammation on all—cause mortality in patients with cardiovascular disease[J]. Cardiovasc Diabetol, 2023, 22(1): 96.

[23] Bowden J, Del Greco MF, Minelli C, et al. Improving the accuracy of two—sample summary—data Mendelian randomization: moving beyond the NOME assumption[J]. Int J Epidemiol, 2019, 48(3): 728-742.

[24] Burgess S, Butterworth A, Thompson SG . Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-665.

[25] Xu T, Wang S, Zhao L, et al. A two—sample Mendelian randomization study on the relationship of body weight, body mass index, and waist circumference with cardiac arrest[J]. World J Emerg Med, 2025, 16(2): 129-135.

[26] Bowden J, Davey Smith G, Burgess S . Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525.

[27] Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314.

[28] Silveira EA, Kliemann N, Noll M, et al. Visceral obesity and incident cancer and cardiovascular disease: an integrative review of the epidemiological evidence[J]. Obes Rev, 2021, 22(1): e13088.

[29] Koskinas KC, Van Craenenbroeck EM, Antoniades C, et al. Obesity and cardiovascular disease: an ESC clinical consensus statement[J]. Eur J Prev Cardiol, 2025, 32(3): 184-220.

[30] Wang L, Zhou B, Zhao Z, et al. Body—mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004—18[J]. Lancet, 2021, 398(10294): 53-63.

[31] Kilfoyle KA, Vitko M, O'Conor R, et al. Health literacy and women's reproductive health: a systematic review[J]. J Womens Health (Larchmt), 2016, 25(12): 1237-1255.

[32] Chen H, Li M, Zhang Y . Educational attainment and male—female health—survival paradox among older adults in China: a nationally representative longitudinal study[J]. BMC Geriatr, 2025, 25(1): 112.

[33] Ross CE, Masters RK, Hummer RA . Education and the gender gaps in health and mortality[J]. Demography, 2012, 49(4): 1157-1183.

[34] Kivimäki M, Pentti J, Ferrie JE, et al. Work stress and risk of death in men and women with and without cardiometabolic disease: a multicohort study[J]. Lancet Diabetes Endocrinol, 2018, 6(9): 705-713.

[35] Tomiyama AJ . Stress and obesity[J]. Annu Rev Psychol, 2019, 70: 703-718.

[36] Powell—Wiley TM, Poirier P, Burke LE, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association[J]. Circulation, 2021, 143(21): e984-e1010.

[37] Wildman RP, McGinn AP, Lin J, et al. Cardiovascular disease risk of abdominal obesity vs. metabolic abnormalities [J]. Obesity (Silver Spring), 2011, 19(4): 853-860.

[38] Song Z, Zhong X, Li M, et al. 1—MNA ameliorates high fat dietinduced heart injury by upregulating Nrf2 expression and inhibiting NF—κB in vivo and in vitro[J]. Front Cardiovasc Med, 2021, 8: 721814.

[39] Cybulska AM, Rachubińska K, Szkup M, et al. Serum levels of proinflammatory cytokines and selected bioelements in perimenopausal women with regard to body mass index[J]. Aging (Albany NY), 2021, 13(23): 25025-25037.

[40] Furukawa S, Fujita T, Shimabukuro M, et al. Increased oxidative stress in obesity and its impact on metabolic syndrome[J]. J Clin Invest, 2004, 114(12): 1752-1761.

[41] Heitzer T, Schlinzig T, Krohn K, et al. Endothelial dysfunction, oxidative stress, and risk of cardiovascular events in patients with coronary artery disease[J]. Circulation, 2001, 104(22): 2673-2678.

[42] Stocker R, Keaney JF, Jr. Role of oxidative modifications in atherosclerosis[J]. Physiol Rev, 2004, 84(4): 1381-1478.

[43] Alpert MA, Omran J, Bostick BP . Effects of obesity on cardiovascular hemodynamics, cardiac morphology, and ventricular function[J]. Curr Obes Rep, 2016, 5(4): 424-434.

[44] Spratlen MJ, Perera FP, Lederman SA, et al. The association between perfluoroalkyl substances and lipids in cord blood[J]. J Clin Endocrinol Metab, 2020, 105(1): 43-54.

[45] Wiklund P, Toss F, Weinehall L, et al. Abdominal and gynoid fat mass are associated with cardiovascular risk factors in men and women[J]. J Clin Endocrinol Metab, 2008, 93(11): 4360-4366.