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Status and transition of normal-weight central obesity and the risk of cardiovascular diseases: A population-based cohort study in China

  • Author Footnotes
    1 Ziyang Ren and Weidi Sun contributed equally to this study.
    Ziyang Ren
    Footnotes
    1 Ziyang Ren and Weidi Sun contributed equally to this study.
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Institute of Reproductive and Child Health/Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Peking University, Beijing, China

    Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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  • Author Footnotes
    1 Ziyang Ren and Weidi Sun contributed equally to this study.
    Weidi Sun
    Footnotes
    1 Ziyang Ren and Weidi Sun contributed equally to this study.
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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  • Shuhui Wang
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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  • Jiayao Ying
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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  • Wen Liu
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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  • Lijun Fan
    Affiliations
    Department of Medical Insurance, School of Public Health, Southeast University, Nanjing, China
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  • Yang Zhao
    Affiliations
    The George Institute for Global Health, University of New South Wales, Sydney, Australia

    The George Institute for Global Health at Peking University Health Science Center, Beijing, China
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  • Chenkai Wu
    Affiliations
    Global Health Research Center, Duke Kunshan University, Kunshan, China
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  • Peige Song
    Correspondence
    Corresponding author. School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China.
    Affiliations
    School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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  • Author Footnotes
    1 Ziyang Ren and Weidi Sun contributed equally to this study.
Open AccessPublished:August 08, 2022DOI:https://doi.org/10.1016/j.numecd.2022.07.023

      Highlights

      • Normal weight central obesity was associated with incident cardiovascular diseases.
      • Progression to normal weight central obesity was associated with cardiovascular diseases risks.
      • Remission from normal weight central obesity reduces cardiovascular risks.

      Abstract

      Background and aims

      Cardiovascular disease (CVD) has become a growing public health concern. Normal weight central obesity (NWCO) has emerged as a potential risk factor for cardiometabolic dysregulation. To date, the association between NWCO and new-onset CVDs remains unclear. We aimed to evaluate the associations of NWCO and its longitudinal transitions with cardiovascular risks in middle-aged and older Chinese.

      Methods and results

      Data were from the China Health and Retirement Longitudinal Study 2011–2018. NWCO was defined as the combination of a body mass index (BMI) of <24.0 kg/m2 and a waist circumference (WC) of >85 cm in males or >80 cm in females. CVDs included heart diseases and stroke. Cause-specific hazard models and subdistribution hazard models with all-cause death as the competing event were applied. In 2011, 9856 participants without prior CVDs were included, of whom 1814 developed CVDs during a 7-year follow-up. Compared to normal weight and non-central obesity (NWNCO), NWCO was significantly associated with new-onset CVDs, with cause-specific hazard ratios (cHRs) and 95% confidence intervals (CIs) of 1.21 (1.04–1.41) for heart diseases and 1.40 (1.11–1.76) for stroke. From 2011 to 2013, 571 NWNCO participants developed NWCO who subsequently demonstrated a 45% higher risk of CVDs than those with maintained NWNCO.

      Conclusion

      NWCO and transition from NWNCO to NWCO are associated with higher risks of CVDs. Identification and prevention of NWCO may be useful in the management of CVDs.

      Keywords

      Abbreviations:

      CVD (cardiovascular disease), NWCO (normal weight central obesity), BMI (body mass index), WC (waist circumference), NWNCO (normal weight and non-central obesity), cHRs (cause-specific hazard ratios), CIs (confidence intervals), CHARLS (China Health and Retirement Longitudinal Study), WHtR (waist-to-height ratio), AWCO (abnormal weight with central obesity), AWNCO (abnormal weight with non-central obesity), ln[PCE] (logarithm of per capita expenditures), HbA1c (glycated hemoglobin), SBP (systolic blood pressure), DBP (diastolic blood pressure), IQRs (interquartile ranges), CIF (cumulative incidence function), sHR (subdistribution hazard ratio)

      1. Introduction

      Cardiovascular diseases (CVDs) have become a serious public health problem worldwide, with a rise in the number of deaths from 12.1 million to 18.6 million during the past three decades [
      • Roth G.A.
      • Mensah G.A.
      • Johnson C.O.
      • Addolorato G.
      • Ammirati E.
      • Baddour L.M.
      • et al.
      Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study.
      ]. According to the latest data reported by the China Death Surveillance Point System [
      • Liu J.
      • Qi J.
      • Yin P.
      • Liu Y.
      • You J.
      • Lin L.
      • et al.
      Cardiovascular disease mortality - China, 2019.
      ], CVDs, principally including coronary heart disease and stroke, are the leading causes of death in China, with a mortality rate of 364.5 per 100 000 population in 2019. The average monthly treatment expenditures for CVDs ranged between US$ 300 and US$ 1000, resulting in enormous socioeconomic and medical burdens [
      • Ma Q.
      • Li R.
      • Wang L.
      • Yin P.
      • Wang Y.
      • Yan C.
      • et al.
      Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019.
      ,
      • Gheorghe A.
      • Griffiths U.
      • Murphy A.
      • Legido-Quigley H.
      • Lamptey P.
      • Perel P.
      The economic burden of cardiovascular disease and hypertension in low- and middle-income countries: a systematic review.
      ].
      Prior research has shown that obesity is significantly related to diabetes, hypertension, and other metabolic abnormalities [
      • Piché M.E.
      • Tchernof A.
      • Després J.P.
      Obesity phenotypes, diabetes, and cardiovascular diseases.
      ,
      • Seravalle G.
      • Grassi G.
      Obesity and hypertension.
      ,
      • Moore K.J.
      • Shah R.
      Introduction to the obesity, metabolic syndrome, and CVD compendium.
      ], all of which have been proven to be risk factors for CVDs. Traditionally, body mass index (BMI) has been used as the primary indicator of adiposity. However, it cannot reflect regional body fat distribution and ectopic fat accumulation [
      • Piche M.E.
      • Poirier P.
      • Lemieux I.
      • Despres J.P.
      Overview of Epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update.
      ]. Waist circumference (WC) has been widely used as a cost-effective and reliable indicator to further identify central obesity, which is characterized by abdominal fat distribution [
      • Sun Y.B.
      • Liu B.Y.
      • Snetselaar L.G.
      • Wallace R.B.
      • Caan B.J.
      • Rohan T.E.
      • et al.
      Association of normal-weight central obesity with all-cause and cause-specific mortality among postmenopausal women.
      ]. Previous studies indicated that subgroups of elevated WC in a given BMI category were also significantly associated with cardiovascular mortality risk [
      • Cornier M.A.
      • Després J.P.
      • Davis N.
      • Grossniklaus D.A.
      • Klein S.
      • Lamarche B.
      • et al.
      Assessing adiposity: a scientific statement from the American Heart Association.
      ,
      • Sahakyan K.R.
      • Somers V.K.
      • Rodriguez-Escudero J.P.
      • Hodge D.O.
      • Carter R.E.
      • Sochor O.
      • et al.
      Normal-weight central obesity: implications for total and cardiovascular mortality.
      ]. Considering the limitations of BMI or WC as a single indicator in defining adiposity, simultaneous interpretation of those two anthropometric measurements is suggested to comprehensively capture the heterogeneity and phenotypes of obesity [
      • Kim S.H.
      • Despres J.P.
      • Koh K.K.
      Obesity and cardiovascular disease: friend or foe?.
      ,
      • Du T.
      • Sun X.
      • Yin P.
      • Huo R.
      • Ni C.
      • Yu X.
      Increasing trends in central obesity among Chinese adults with normal body mass index, 1993-2009.
      ].
      Recently, a distinct obesity phenotype named normal weight with central obesity (NWCO), characterized by normal BMI and abdominal obesity [
      • Coutinho T.
      • Goel K.
      • Corrêa de Sá D.
      • Carter R.E.
      • Hodge D.O.
      • Kragelund C.
      • et al.
      Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: role of “normal weight central obesity”.
      ,
      • Schlesinger S.
      • Neuenschwander M.
      • Schwedhelm C.
      • Hoffmann G.
      • Bechthold A.
      • Boeing H.
      • et al.
      Food groups and risk of overweight, obesity, and weight gain: a systematic review and dose-response meta-analysis of prospective studies.
      ,
      • Yu Y.
      Normal-weight central obesity and mortality risk.
      ], has become prevalent and received growing research attention [
      • Song P.
      • Li X.
      • Bu Y.
      • Ding S.
      • Zhai D.
      • Wang E.
      • et al.
      Temporal trends in normal weight central obesity and its associations with cardiometabolic risk among Chinese adults.
      ]. Previous studies have demonstrated that NWCO is related to multiple cardiovascular risk factors (i.e., hypertension, diabetes, and dyslipidemia) [
      • Ying-Xiu Z.
      • Da-Yong S.
      • Jing-Yang Z.
      • Jin-Shan Z.
      • Zun-Hua C.
      Blood pressure among children and adolescents with normal weight but large waist circumference in Shandong, China.
      ,
      • Mitsuhashi K.
      • Hashimoto Y.
      • Tanaka M.
      • Toda H.
      • Matsumoto S.
      • Ushigome E.
      • et al.
      Combined effect of body mass index and waist-height ratio on incident diabetes; a population based cohort study.
      ,
      • Shirasawa T.
      • Ochiai H.
      • Yoshimoto T.
      • Nagahama S.
      • Kobayashi M.
      • Ohtsu I.
      • et al.
      Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: a cross-sectional study.
      ] and an elevated risk of cardiovascular mortality [
      • Sahakyan K.R.
      • Somers V.K.
      • Rodriguez-Escudero J.P.
      • Hodge D.O.
      • Carter R.E.
      • Sochor O.
      • et al.
      Normal-weight central obesity: implications for total and cardiovascular mortality.
      ,
      • Sahakyan K.R.
      • Somers V.K.
      • Rodriguez-Escudero J.P.
      • Hodge D.O.
      • Carter R.E.
      • Sochor O.
      • et al.
      Normal-weight central obesity: implications for total and cardiovascular mortality.
      ]. People with NWCO, however, are frequently overlooked in clinical guidelines and risk-reduction strategies [
      • Sun Y.
      • Liu B.
      • Snetselaar L.G.
      • Wallace R.B.
      • Caan B.J.
      • Rohan T.E.
      • et al.
      Association of normal-weight central obesity with all-cause and cause-specific mortality among postmenopausal women.
      ]. For example, in the latest Chinese expert consensus on the procedure of bodyweight management among patients with overweight or obesity [
      Chinese Society of Health Management CNBoCNS
      Medical nutrition industry branch of the national association of health industry and enterprise management, the editorial board of Chinese Journal of health management. Expert consensus on the procedure of body weight management among patients with overweight or obesity (2021).
      ], preventive interventions were only recommended for people with BMI-defined overweight/obesity. Identification of suboptimal health status at the early stage of CVD is essential for the promotion of ideal cardiovascular health [
      • Wang W.
      Cardiovascular health in China: low level vs high diversity.
      ,
      • Lloyd-Jones D.M.
      • Hong Y.
      • Labarthe D.
      • Mozaffarian D.
      • Appel L.J.
      • Van Horn L.
      • et al.
      Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.
      ].
      To date, limited longitudinal studies have specifically explored the associations between NWCO and CVDs. To add evidence to this research area, we conducted this prospective longitudinal study among Chinese adults aged 45 years and older to investigate the risk of CVDs in people with NWCO and whether the transition from normal weight with non-central obesity (NWNCO) to NWCO may lead to an increased risk of new-onset CVDs.

      2. Methods

      2.1 Study population

      This study used data from the 2011–2018 waves of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey of residents aged 45 years and older in Mainland China. The detailed procedures of CHARLS have been reported elsewhere [
      • Zhao Y.
      • Hu Y.
      • Smith J.P.
      • Strauss J.
      • Yang G.
      Cohort profile: the China health and retirement longitudinal study (CHARLS).
      ]. In brief, a stratified multistage probability sampling strategy was adopted to recruit participants from 450 villages/urban communities in 28 provinces across China. Subsequently, random sampling was applied to select households from communities, and individuals aged 45 and above were chosen as the main respondents within each household. The baseline survey was conducted from 2011 to 2012. Participants were followed up every two years, with a small share of new respondents recruited simultaneously. In total, three follow-up surveys were conducted, one each in 2013, 2015, and 2018. Ethical approval for this study was granted by the Institutional Review Board at Peking University . Each respondent who agreed to participate in the survey has signed the written informed consent.

      2.2 Assessment and definition of obesity phenotypes

      In 2011 and 2013, physical measurements, including height, weight, and WC were collected by trained healthcare workers. BMI was calculated as weight (kg)/height squared (m2). Waist-to-height ratio (WHtR) was calculated as WC (cm)/height (cm). Abnormal weight was defined as a BMI of ≥24 kg/m2, and participants with a BMI of <24 kg/m2 were classified as normal weight [
      • Zhou B.F.
      Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults.
      ]. Central obesity was defined as a WC of >85 cm in males or >80 cm in females [
      • Zhou B.F.
      Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults.
      ].
      Four obesity phenotypes were classified according to the combination of abnormal weight and central obesity: NWCO, NWNCO, abnormal weight with central obesity (AWCO), and abnormal weight with non-central obesity (AWNCO). From 2011 to 2013, seven obesity transition patterns related to NWCO were defined to investigate the association between transition of NWCO and incident CVDs: NWNCO to NWCO (vs. maintained NWNCO); NWCO to NWNCO and NWCO to AWCO (vs. maintained NWCO); and AWCO to NWCO (vs. maintained AWCO).

      2.3 Assessment of outcomes

      CVDs were self-reported by respondents. At the baseline survey in 2011 and the subsequent follow-up surveys (2013, 2015, and 2018), participants were asked through a questionnaire whether and when they had been diagnosed to have heart diseases (including heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems) or stroke. Those who answered as having either heart diseases or stroke were considered to have CVDs. Furthermore, the time-to-CVDs was defined as 1–7 years (diagnosed between 2012 and 2018) after 2011.
      CHARLS only recorded the exact year of death for participants who died between 2011 and 2013. The specific year of death for participants who died after 2013 was not accurately recorded but was uniformly reported at the 2015 and 2018 follow-ups. For example, those who died in 2016 were recorded as dead in 2018. Hence, the time-to-death was defined as 1 (died in 2012), 2 (died in 2013), 4 (died between 2013 and 2015), and 7 (died between 2015 and 2018) years after 2011.

      2.4 Measurement of covariates

      Information on age, sex, residence, education, marital status, economic status, smoking history, and drinking history was collected through face-to-face interviews. Residence was classified as rural or urban. Education was categorized as illiterate, literate, primary school, or middle school or higher. Marital status was classified as single or married/cohabited. Economic status was evaluated by the natural logarithm of per capita expenditures (ln [PCE]) [
      • Song P.
      • Wang H.
      • Xia W.
      • Chang X.
      • Wang M.
      • An L.
      Prevalence and correlates of hyperuricemia in the middle-aged and older adults in China.
      ] and was divided into bottom, middle, and top tertiles, respectively, representing poor, middle, and rich groups. Smoking history was classified as never smoker or ever and current smoker. Drinking history was similarly classified as never drinker or ever and current drinker.
      Physicians’ diagnoses of hypertension, diabetes, and dyslipidemia were self-reported at baseline. Blood pressure was measured three times at 45 s intervals using a sphygmomanometer (Omron™ HEM-7200, Dalian, China) and was recorded to the nearest mmHg. Venous blood samples of each participant were collected using standard blood-taking materials to measure plasma glucose concentration and glycated hemoglobin (HbA1c). Hypertension was identified if any of the following criteria occurred: (1) having physician-diagnosed hypertension; (2) mean systolic blood pressure (SBP) ≥ 140 mmHg; (3) mean diastolic blood pressure (DBP) ≥ 90 mmHg; and (4) on antihypertensive drugs [
      • Chobanian A.V.
      • Bakris G.L.
      • Black H.R.
      • Cushman W.C.
      • Green L.A.
      • Izzo Jr., J.L.
      • et al.
      Seventh report of the Joint national committee on prevention, detection, evaluation, and treatment of high blood pressure.
      ]. Diabetes was defined as meeting any of the following criteria: (1) reported physician-diagnosed diabetes; (2) fasting plasma glucose ≥7.0 mmol/L; (3) random plasma glucose ≥11.1 mmol/L; (4) HbA1c ≥ 6.5%; and (5) on antidiabetic medication [
      Classification and diagnosis of diabetes: standards of medical care in diabetes-2021.
      ].

      2.5 Statistical analysis

      The characteristics of included participants by obesity phenotypes were described as medians with interquartile ranges (IQRs) for continuous variables and number and percent (%) for categorical variables. Wilcoxon rank sum tests for continuous variables and Chi-square tests for categorical variables were utilized to calculate P values.
      Competing-risks regression models were utilized in the current longitudinal analyses given the all-cause death serving as a competing event for CVDs. The cumulative incidence of CVDs (including heart diseases and stroke) and death was estimated using the cumulative incidence function (CIF). First, the cause-specific hazard regression models were conducted to investigate the associations (cause-specific hazard ratio [cHR] with 95% CI) of obesity phenotypes in 2011 with new-onset CVDs, including heart diseases and stroke between 2011 and 2018. The subdistribution hazard regression models were simultaneously performed to estimate the effects of NWCO on the relative incidence of CVDs with a subdistribution hazard ratio (sHR) and 95%CI. Models were adjusted for age, sex (except for sex-specific analysis), residence, education, economic status, marital status, smoking history, drinking history, hypertension, diabetes, and dyslipidemia.
      Second, the associations of NWCO transitions from 2011 to 2013 (including NWNCO to NWCO, NWCO to NWNCO, NWCO to AWCO, and AWCO to NWCO) with new-onset CVDs, heart diseases, and stroke between 2013 and 2018 were investigated utilizing the cause-specific hazard regression models (cHR with 95% CI) and subdistribution hazard regression models (sHR with 95% CI), with the maintained status as reference (including maintained NWNCO, maintained NWCO, and maintained AWCO).
      Third, sensitivity analyses were performed using a WHtR of >0.5 to define central obesity for the robustness of the associations of NWCO and its related transition patterns with incident CVD events [
      • Ashwell M.
      • Gunn P.
      • Gibson S.
      Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis.
      ,
      • Peng Y.
      • Li W.
      • Wang Y.
      • Bo J.
      • Chen H.
      The cut-off Point and boundary values of waist-to-height ratio as an indicator for cardiovascular risk factors in Chinese adults from the PURE study.
      ].
      Reporting of this study was done in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. All analyses were conducted using SAS statistical software (version 9.4; SAS Institute Inc., Cary, NC, USA). All analyses were two-sided, and a P value of <0.05 or a 95% CI that did not cross 1.00 was considered statistically significant.

      3. Results

      The study flow chart is presented in Fig. 1. A total of 17 708 adults were recruited at baseline in 2011. For the current investigation, those who were under the age of 45 years or with incomplete data on sex, education, or economic status (n = 3089), who had incomplete data on smoking history, drinking history, BMI, WC, marital status, hypertension, diabetes, or dyslipidemia (n = 3233), and who had prior CVDs (n = 1530) were excluded, leaving 9856 participants included for analysis, of whom 1914 (19.4%) had NWCO. From 2011 to 2018, 1814 participants developed new-onset CVDs, 1388 developed new-onset heart diseases, and 594 developed new-onset strokes. Table 1 shows the baseline characteristics of included participants. The cumulative incidence of CVDs (including heart diseases and stroke) and all-cause death is separately presented in Fig. 2.
      Fig. 1
      Figure 1Study flowchart of participant selection.
      Note: BMI, body mass index. CVDs are cardiovascular diseases and include heart diseases and stroke.
      Table 1Baseline characteristics of participants in longitudinal analysis from 2011 to 2018 (N = 9856).
      Baseline characteristicsNWNCONWCOAWNCOAWCOP value
      Participants4110 (41.7)1914 (19.4)268 (2.7)3564 (36.2)
      Age, year58.0 (52.0–66.0)59.0 (54.0–67.0)56.0 (49.0–62.0)56.0 (49.0–63.0)<0.001
      Sex<0.001
       Male2537 (61.7)710 (37.1)158 (59.0)1423 (39.9)
       Female1573 (38.3)1204 (62.9)110 (41.0)2141 (60.1)
      Residence<0.001
       Rural3031 (73.8)1233 (64.4)161 (60.1)2061 (57.8)
       Urban1079 (26.3)681 (35.6)107 (39.9)1503 (42.2)
      Education<0.001
       Illiterate2010 (48.9)989 (51.7)93 (34.7)1510 (42.4)
       Literate946 (23.0)403 (21.1)66 (24.6)767 (21.5)
       Primary education781 (19.0)344 (18.0)73 (27.2)821 (23.0)
       Middle school or higher373 (9.1)178 (9.3)36 (13.4)466 (13.1)
      Marital status<0.001
       Single553 (13.5)306 (16.0)23 (8.6)302 (8.5)
       Married or cohabited3557 (86.6)1608 (84.0)245 (91.4)3262 (91.5)
      Economic status<0.001
       Bottom tertile1533 (37.3)665 (34.7)93 (34.7)1030 (28.9)
       Middle tertile1414 (34.4)631 (33.0)80 (29.9)1165 (32.7)
       Top tertile1163 (28.3)618 (32.3)95 (35.5)1369 (38.4)
      Smoking history<0.001
       Ever or current smoker2132 (51.9)628 (32.8)110 (41.0)1119 (31.4)
       Never smoker1978 (48.1)1286 (67.2)158 (59.0)2445 (68.6)
      Drinking history<0.001
       Ever or current drinker1306 (31.8)426 (22.3)74 (27.6)796 (22.3)
       Never drinker2804 (68.2)1488 (77.7)194 (72.4)2768 (77.7)
      Hypertension<0.001
       Yes1129 (27.5)757 (39.6)104 (38.8)1741 (48.9)
       No2981 (72.5)1157 (60.5)164 (61.2)1823 (51.2)
      Diabetes<0.001
       Yes290 (7.1)243 (12.7)28 (10.5)561 (15.7)
       No3820 (92.9)1671 (87.3)240 (89.6)3003 (84.3)
      Dyslipidemia<0.001
       Yes571 (13.9)443 (23.2)61 (22.8)1127 (31.6)
       No3539 (86.1)1471 (76.9)207 (77.2)2437 (68.4)
      BMI, kg/m220.6 (19.2–21.9)22.7 (21.6–23.4)24.8 (24.3–26.0)26.5 (25.2–28.3)<0.001
      WC, cm76.6 (73.0–79.9)86.9 (84.0–89.8)80.0 (76.7–83.0)93.1 (89.0–98.2)<0.001
      New-onset CVDs<0.001
       Yes581 (14.1)374 (19.5)45 (16.8)814 (22.8)
       No3529 (85.9)1540 (80.5)223 (83.2)2750 (77.2)
      New-onset heart diseases<0.001
       Yes435 (10.6)287 (15.0)38 (14.2)628 (17.6)
       No3675 (89.4)1627 (85.0)230 (85.8)2936 (82.4)
      New-onset stroke<0.001
       Yes182 (4.4)129 (6.7)12 (4.5)271 (7.6)
       No3928 (95.6)1785 (93.3)256 (95.5)3293 (92.4)
      Notes: Values are presented as number (N) with percent (%) or medians with interquartile ranges (IQRs). NWCO, normal weight with central obesity. NWNCO, normal weight with non-central obesity. AWNCO, abnormal weight with non-central obesity. AWCO, abnormal weight with central obesity. BMI, body mass index. WC, waist circumference. CVDs, cardiovascular diseases.
      Fig. 2
      Figure 2Cumulative incidence function of CVDs, stroke and heart diseases with all-cause death as a competing risk event Notes: CIF, cumulative incidence function. CVD, cardiovascular disease.
      The associations between obesity phenotypes and CVDs are shown in Table 2. Those with NWCO had higher risks of developing incident CVDs, heart diseases, and stroke than those with NWNCO, with adjusted cHRs (95% CI) of 1.22 (1.06–1.39), 1.21 (1.04–1.41), and 1.40 (1.11–1.76), respectively (Table 2 and Table S1). Sex-stratified analyses demonstrated similar patterns only in males but not in females. Additionally, AWCO status was remarkably associated with an increased hazard of CVDs. No significant associations were found between AWNCO and CVDs in either sex. These results were in line with those depicted by sHRs (95%CI) (Table 2 and Table S1). In sensitivity analyses using a WHtR of >0.5 to define central obesity, significant associations between NWCO and new-onset CVD events were also found (Table S2).
      Table 2Associations of obesity phenotypes with incident CVDs.
      No. of casesCause-Specific Hazard ModelSubdistribution Hazard Model
      cHR (95%CI)sHR (95%CI)
      Total population
      NWNCO581ReferenceReference
      NWCO3741.22 (1.06–1.39)1.21 (1.06–1.37)
      AWNCO451.15 (0.85–1.56)1.15 (0.85–1.56)
      AWCO8141.43 (1.27–1.60)1.41 (1.26–1.57)
      Male
      NWNCO329ReferenceReference
      NWCO1261.29 (1.05–1.59)1.27 (1.04–1.56)
      AWNCO261.16 (0.77–1.73)1.16 (0.78–1.72)
      AWCO2971.47 (1.24–1.75)1.45 (1.23–1.71)
      Female
      NWNCO252ReferenceReference
      NWCO2481.16 (0.97–1.39)1.16 (0.98–1.37)
      AWNCO191.14 (0.71–1.82)1.14 (0.72–1.81)
      AWCO5171.39 (1.18–1.62)1.37 (1.18–1.59)
      Notes: cHR, cause-specific hazard ratio. sHR, subdistribution hazard ratio. CI, confidence interval. CVDs, cardiovascular diseases. NWCO, normal weight with central obesity. NWNCO, normal weight with non-central obesity. AWNCO, abnormal weight with non-central obesity. AWCO, abnormal weight with central obesity. Models were adjusted for age, sex (except for sex-specific analysis), residence, education, economic status, marital status, smoking history, drinking history, hypertension, diabetes, and dyslipidemia.
      Between 2011 and 2013, 3106 participants who were lost to follow-up or had incomplete data on BMI or WC in 2013, 412 who developed new-onset CVDs, and 497 who had obesity phenotype transitions unrelated to NWCO were excluded. Ultimately, 5841 eligible participants were included to explore the association between transitions of NWCO from 2011 to 2013 and incident CVDs during 2013 and 2018. The transition patterns of obesity phenotypes of the 5841 participants interviewed in both 2011 and 2013 are shown in a sex-stratified Sankey diagram (Fig. S1). Among these participants, 1928 (33.0%) maintained NWNCO, 571 (9.8%) transformed from NWNCO to NWCO, 626 (10.7%) maintained NWCO, 272 (4.7%) turned from NWCO to NWNCO, 286 (4.9%) transformed from NWCO to AWCO, 1977 (33.9%) maintained AWCO, and 181 (3.1%) transformed from AWCO to NWCO during 2011–2013. The characteristics of participants by transition patterns are described in Table S3.
      As shown in Fig. 3 and Table S4, the transition from NWNCO to NWCO was significantly associated with new-onset CVDs (cHR 1.45, 95% CI 1.13–1.87) and heart diseases (cHR 1.49, 95% CI 1.12–2.00) when compared to maintained NWNCO. The associations remained significant for CVDs in sex-stratified analyses. Furthermore, we found that the transition pattern of NWCO to NWNCO was significantly associated with decreased risks of CVDs in the total population (cHR 0.64, 95% CI 0.42–0.97) and stroke in females (cHR 0.22, 95% CI 0.05–0.92) when compared to maintained NWCO, as shown in Fig. 3. Associations estimated by sHRs were consistent with those described by cHRs (Fig. S2 and Table S4). We further conducted sensitivity analyses using a WHtR of >0.5 to define central obesity and derive similar results, as shown in Table S5.
      Fig. 3
      Figure 3Associations of transition of NWCO between 2011 and 2013 with incident CVDs (cause-specific hazard models).
      Notes: HR, hazard ratio. CI, confidence interval. NWCO, normal weight with central obesity. NWNCO, normal weight with non-central obesity. AWCO, abnormal weight with central obesity. CVD, cardiovascular disease. HRs for cause-specific models were adjusted for age, sex (except for sex-specific analysis), residence, education, economic status, marital status, smoking history, drinking history, hypertension, diabetes, and dyslipidemia. Maintained NWNCO is the reference of NWNCO to NWCO; Maintained NWCO is the reference of NWCO to NWNCO and NWCO to AWCO; Maintained AWCO is the reference of AWCO to NWCO.

      4. Discussion

      This study found that NWCO was significantly associated with a higher risk of CVDs, heart diseases, and stroke in comparison to NWNCO. Transition from NWNCO to NWCO was also identified to be associated with a higher risk of CVDs than maintained NWNCO in both sexes. Furthermore, participants with transition from NWCO to NWNCO had lower cause-specific risks and a relative incidence of CVDs, especially stroke, when compared with those who maintained NWCO.
      Our findings are in accordance with previous studies. Several cross-sectional studies have demonstrated significant associations between NWCO and CVDs [
      • Shirasawa T.
      • Ochiai H.
      • Yoshimoto T.
      • Nagahama S.
      • Kobayashi M.
      • Ohtsu I.
      • et al.
      Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: a cross-sectional study.
      ,
      • Thaikruea L.
      • Thammasarot J.
      Prevalence of normal weight central obesity among Thai healthcare providers and their association with CVD risk: a cross-sectional study.
      ]. In a Korean cohort study, Choi D et al. indicated that NWCO individuals aged 40 years or older had an elevated risk of developing major adverse cardiovascular events and stroke [
      • Choi D.
      • Choi S.
      • Son J.S.
      • Oh S.W.
      • Park S.M.
      Impact of discrepancies in general and abdominal obesity on major adverse cardiac events.
      ]. However, their study utilized multivariable Cox proportional hazard regression models to examine the effects of NWCO. Our study, comparatively, adopted competing-risks regression including cause-specific hazard models and subdistribution hazard models, which took competing outcomes into account and prevented biased results estimated by the traditional Cox regression function. Moreover, this study explored the transition of NWCO on the risk of CVDs, which greatly extends beyond the scope of previous research. A prior study of 9447 participants in Jilin, China demonstrated that 27.0% of NWCO individuals self-assessed themselves as abnormal status and only 12.7% took methods to reduce weight [
      • Zhang P.
      • Wang R.
      • Gao C.
      • Jiang L.
      • Lv X.
      • Song Y.
      • et al.
      Prevalence of central obesity among adults with normal BMI and its association with metabolic diseases in northeast China.
      ], showing a severe lack of public awareness of NWCO and its adverse impacts on health management. The results depicted in our study imply the significant health risk of NWCO status and highlight the need for public health attention and relevant clinical guidelines.
      Generally, people with NWCO possess more visceral adipose tissue than those with NWNCO [
      • Janssen I.
      • Heymsfield S.B.
      • Allison D.B.
      • Kotler D.P.
      • Ross R.
      Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat.
      ,
      • Després J.P.
      • Lemieux I.
      • Bergeron J.
      • Pibarot P.
      • Mathieu P.
      • Larose E.
      • et al.
      Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk.
      ,
      • Ibrahim M.M.
      Subcutaneous and visceral adipose tissue: structural and functional differences.
      ], which has been proven to be positively associated with an increased risks of multiple cardiovascular events and metabolic disorders [
      • Neeland I.J.
      • Ayers C.R.
      • Rohatgi A.K.
      • Turer A.T.
      • Berry J.D.
      • Das S.R.
      • et al.
      Associations of visceral and abdominal subcutaneous adipose tissue with markers of cardiac and metabolic risk in obese adults.
      ,
      • Ballin M.
      • Nordström P.
      • Niklasson J.
      • Nordström A.
      Associations of visceral adipose tissue and skeletal muscle density with incident stroke, myocardial infarction, and all-cause mortality in community-dwelling 70-year-old individuals: a prospective cohort study.
      ,
      • Le Jemtel T.H.
      • Samson R.
      • Milligan G.
      • Jaiswal A.
      • Oparil S.
      Visceral adipose tissue accumulation and residual cardiovascular risk.
      ]. The visceral adipose tissue can express multiple inflammatory cytokines including interleukin-6 (IL-6) and C-reactive protein (CRP) to cause low-grade inflammation and oxidative stress, contributing to the prothrombotic state of the cardio-cerebrovascular smooth muscle and endothelium. Higher levels of central fat are also positively related to increases in pro-inflammatory immunoglobulin G (IgG) N-glycans [
      • Russell A.C.
      • Kepka A.
      • Trbojević-Akmačić I.
      • Ugrina I.
      • Song M.
      • Hui J.
      • et al.
      Increased central adiposity is associated with pro-inflammatory immunoglobulin G N-glycans.
      ,
      • Wang W.
      Glycomedicine: the current state of the art.
      ], which appear to play an important role in the inflammatory cascade and acceleration of the aging process [
      • Yu Y.
      Normal-weight central obesity and mortality risk.
      ,
      • Yu X.
      • Wang Y.
      • Kristic J.
      • Dong J.
      • Chu X.
      • Ge S.
      • et al.
      Profiling IgG N-glycans as potential biomarker of chronological and biological ages: a community-based study in a Han Chinese population.
      ]. Together with platelet reactivity and impaired fibrinolysis, CVD events will eventually be triggered [
      • Marini S.
      • Merino J.
      • Montgomery B.E.
      • Malik R.
      • Sudlow C.L.
      • Dichgans M.
      • et al.
      Mendelian randomization study of obesity and cerebrovascular disease.
      ]. Through the activation of the renin-angiotensin-aldosterone system, normal BMI with abdominal obesity status may exert adverse effects on left ventricular systolic function and lead to heart diseases [
      • Russo C.
      • Sera F.
      • Jin Z.
      • Palmieri V.
      • Homma S.
      • Rundek T.
      • et al.
      Abdominal adiposity, general obesity, and subclinical systolic dysfunction in the elderly: a population-based cohort study.
      ]. Additionally, the subcutaneous fat deposits excessively on vital organs such as cardiac and cerebral arteries, contributing to arteriosclerosis and lipotoxicity [
      • Ibrahim M.M.
      Subcutaneous and visceral adipose tissue: structural and functional differences.
      ,
      • Koliaki C.
      • Liatis S.
      • Kokkinos A.
      Obesity and cardiovascular disease: revisiting an old relationship.
      ,
      • Kabootari M.
      • Asgari S.
      • Mansournia M.A.
      • Khalili D.
      • Valizadeh M.
      • Azizi F.
      • et al.
      Different weight histories and risk of incident coronary heart disease and stroke: tehran lipid and glucose study.
      ]. Previous findings have also suggested that NWCO is directly associated with cardiometabolic risk factors including hypertension, diabetes, insulin resistance, dyslipidemia, and circulating triglycerides [
      • Song P.
      • Li X.
      • Bu Y.
      • Ding S.
      • Zhai D.
      • Wang E.
      • et al.
      Temporal trends in normal weight central obesity and its associations with cardiometabolic risk among Chinese adults.
      ,
      • Shirasawa T.
      • Ochiai H.
      • Yoshimoto T.
      • Nagahama S.
      • Kobayashi M.
      • Ohtsu I.
      • et al.
      Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: a cross-sectional study.
      ,
      • Viitasalo A.
      • Schnurr T.M.
      • Pitkänen N.
      • Hollensted M.
      • Nielsen T.R.H.
      • Pahkala K.
      • et al.
      Abdominal adiposity and cardiometabolic risk factors in children and adolescents: a Mendelian randomization analysis.
      ].
      This study found a noticeable association between transition from NWNCO to NWCO and increased risks of CVDs. There is some evidence that fluctuation in body weight and WC serves as an independent risk factor for cardiovascular events [
      • Bangalore S.
      • Fayyad R.
      • Laskey R.
      • DeMicco D.A.
      • Messerli F.H.
      • Waters D.D.
      Body-weight fluctuations and outcomes in coronary disease.
      ,
      • Wang L.
      • Lee Y.
      • Wu Y.
      • Zhang X.
      • Jin C.
      • Huang Z.
      • et al.
      A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study.
      ]. Specially, females with transition from NWCO to NWNCO between 2011 and 2013 were found to have a decreased risk of stroke. As previously reported, around 85% of the casualties in stroke patients can be attributed to vascular occlusion [
      • Kuriakose D.
      • Xiao Z.
      Pathophysiology and treatment of stroke: present status and future perspectives.
      ]. The buildup of arterial thrombus goes through a series of processes, including excess lipid accumulation, immune cell recruitment, and migration and proliferation of smooth muscle cells, which take a relatively long time [
      • Maida C.D.
      • Norrito R.L.
      • Daidone M.
      • Tuttolomondo A.
      • Pinto A.
      Neuroinflammatory mechanisms in ischemic stroke: focus on cardioembolic stroke, background, and therapeutic approaches.
      ,
      • Corti R.
      • Hutter R.
      • Badimon J.J.
      • Fuster V.
      Evolving concepts in the triad of atherosclerosis, inflammation and thrombosis.
      ]. The finding suggests that reversing NWCO status during the lengthy process of thrombus formation could be effective in preventing stroke. However, eliminating NWCO was not noticeably associated with a reduced stroke risk in males. This might be explained by the sexual disparity in hormonal status and body fat distribution. Under hormonal estrogen influence, premenopausal women store more subcutaneous fat at the gluteofemoral level than men [
      • Trouwborst I.
      • Goossens G.H.
      • Astrup A.
      • Saris W.H.M.
      • Blaak E.E.
      Sexual dimorphism in body weight loss, improvements in cardiometabolic risk factors and maintenance of beneficial effects 6 Months after a low-calorie diet: results from the randomized controlled DiOGenes trial.
      ], which protects them against metabolic syndrome and various cardiovascular risk factors [
      • Piché M.E.
      • Tchernof A.
      • Després J.P.
      Obesity phenotypes, diabetes, and cardiovascular diseases.
      ,
      • Pucci G.
      • Alcidi R.
      • Tap L.
      • Battista F.
      • Mattace-Raso F.
      • Schillaci G.
      Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: a review of the literature.
      ]. These cardiometabolic advantages disappear in postmenopausal women due to hormonal homeostatic dysregulation and fat redistribution [
      • Goossens G.H.
      • Jocken J.W.E.
      • Blaak E.E.
      Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver.
      ]. Hence, postmenopausal women with central obesity may be more susceptible to CVD events [
      • Saeed A.
      • Kampangkaew J.
      • Nambi V.
      Prevention of cardiovascular disease in women.
      ]. Eliminating NWCO, on the other hand, appears to have a more significant impact on reducing CVD risks in females. The female participants in our study were of 45 years and older, most of whom were postmenopausal and more likely to benefit from central weight loss. Additionally, the protective effect of transition from NWCO to NWNCO on incident heart diseases was not significant. One possible explanation is that certain heart problems like angina are prone to be detected earlier, prompting people to modify their lifestyles including exercise and diets. Hence, the effect of obesity transitions might be diminished by these potential factors.
      To the best of our knowledge, this study is the first to evaluate the longitudinal associations of NWCO and its transitions with CVDs, including heart diseases and stroke, in China. We performed cause-specific hazard models together with subdistribution hazard models considering all-cause death as a competing risk for CVD outcomes. This enables us to obtain a more accurate and comprehensive understanding of the association between NWCO and CVDs with both cause-specific hazards and cumulative incidence [
      • Austin P.C.
      • Lee D.S.
      • Fine J.P.
      Introduction to the analysis of survival data in the presence of competing risks.
      ,
      • Latouche A.
      • Allignol A.
      • Beyersmann J.
      • Labopin M.
      • Fine J.P.
      A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions.
      ]. Besides, this study uniquely considered the effect of NWCO transitions on CVD events, rather than using merely obesity phenotypes at baseline, which could be more accurate when estimating long-term cardiovascular risks.
      Despite the above-mentioned strengths, this study also has some limitations. First, the information on our key outcomes (i.e., CVD events) was collected based on self-reported physician diagnoses, which may be subjected to retrospective biases. Second, CHARLS only covers middle-aged and older adults, thus the associations among the younger population remain unclear. Third, we excluded around 35% of participants at baseline due to missing data, which might influence the representativeness of our findings. Finally, the lack of dietary habits and physical activity as covariates in our analyses might have biased the associations we observed.
      In conclusion, our findings demonstrated that NWCO and transition from NWNCO to NWCO were both associated with cardiovascular risks, and this should not be overlooked. The combined use of BMI and measures of central obesity helps to identify targeted obesity phonotypes and serves as crucial identification parameters for preventive strategies of CVDs. Moreover, reducing abdominal adiposity contributes to a lower risk of new-onset CVDs, especially stroke, which might be a modifiable factor in preventive and therapeutic approaches. Individuals with NWCO, especially females, are suggested to be intervened using preventive, predictive, and personalized/precision medicine to reverse this suboptimal state and improve cardiovascular health [
      • Wang W.
      Cardiovascular health in China: low level vs high diversity.
      ]. To address this burden and attenuate the risk of CVDs, efforts should be made to educate the public, increase access to preventive measures, and develop policy and clinical practice aimed at identifying and reducing NWCO.

      Financial support

      This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

      Declaration of competing interest

      The authors declared no conflict of interest.

      Acknowledgments

      We are grateful to the China Center for Economic Research at Beijing University for providing us with the data, and we thank the CHARLS research and field team for collecting the data.

      Appendix A. Supplementary data

      The following is the Supplementary data to this article:

      References

        • Roth G.A.
        • Mensah G.A.
        • Johnson C.O.
        • Addolorato G.
        • Ammirati E.
        • Baddour L.M.
        • et al.
        Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study.
        J Am Coll Cardiol. 2020; 76: 2982-3021
        • Liu J.
        • Qi J.
        • Yin P.
        • Liu Y.
        • You J.
        • Lin L.
        • et al.
        Cardiovascular disease mortality - China, 2019.
        China CDC Weekly. 2021; 3: 323-326
        • Ma Q.
        • Li R.
        • Wang L.
        • Yin P.
        • Wang Y.
        • Yan C.
        • et al.
        Temporal trend and attributable risk factors of stroke burden in China, 1990-2019: an analysis for the Global Burden of Disease Study 2019.
        Lancet Public Health. 2021; 6: e897-e906
        • Gheorghe A.
        • Griffiths U.
        • Murphy A.
        • Legido-Quigley H.
        • Lamptey P.
        • Perel P.
        The economic burden of cardiovascular disease and hypertension in low- and middle-income countries: a systematic review.
        BMC Publ Health. 2018; 18: 975
        • Piché M.E.
        • Tchernof A.
        • Després J.P.
        Obesity phenotypes, diabetes, and cardiovascular diseases.
        Circ Res. 2020; 126: 1477-1500
        • Seravalle G.
        • Grassi G.
        Obesity and hypertension.
        Pharmacol Res. 2017; 122: 1-7
        • Moore K.J.
        • Shah R.
        Introduction to the obesity, metabolic syndrome, and CVD compendium.
        Circ Res. 2020; 126: 1475-1476
        • Piche M.E.
        • Poirier P.
        • Lemieux I.
        • Despres J.P.
        Overview of Epidemiology and contribution of obesity and body fat distribution to cardiovascular disease: an update.
        Prog Cardiovasc Dis. 2018; 61: 103-113
        • Sun Y.B.
        • Liu B.Y.
        • Snetselaar L.G.
        • Wallace R.B.
        • Caan B.J.
        • Rohan T.E.
        • et al.
        Association of normal-weight central obesity with all-cause and cause-specific mortality among postmenopausal women.
        JAMA Netw Open. 2019; 2e197337
        • Cornier M.A.
        • Després J.P.
        • Davis N.
        • Grossniklaus D.A.
        • Klein S.
        • Lamarche B.
        • et al.
        Assessing adiposity: a scientific statement from the American Heart Association.
        Circulation. 2011; 124: 1996-2019
        • Sahakyan K.R.
        • Somers V.K.
        • Rodriguez-Escudero J.P.
        • Hodge D.O.
        • Carter R.E.
        • Sochor O.
        • et al.
        Normal-weight central obesity: implications for total and cardiovascular mortality.
        Ann Intern Med. 2015; 163: 827-835
        • Kim S.H.
        • Despres J.P.
        • Koh K.K.
        Obesity and cardiovascular disease: friend or foe?.
        Eur Heart J. 2016; 37: 3560-3568
        • Du T.
        • Sun X.
        • Yin P.
        • Huo R.
        • Ni C.
        • Yu X.
        Increasing trends in central obesity among Chinese adults with normal body mass index, 1993-2009.
        BMC Publ Health. 2013; 13: 327
        • Coutinho T.
        • Goel K.
        • Corrêa de Sá D.
        • Carter R.E.
        • Hodge D.O.
        • Kragelund C.
        • et al.
        Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: role of “normal weight central obesity”.
        J Am Coll Cardiol. 2013; 61: 553-560
        • Schlesinger S.
        • Neuenschwander M.
        • Schwedhelm C.
        • Hoffmann G.
        • Bechthold A.
        • Boeing H.
        • et al.
        Food groups and risk of overweight, obesity, and weight gain: a systematic review and dose-response meta-analysis of prospective studies.
        Adv Nutr. 2019; 10: 205-218
        • Yu Y.
        Normal-weight central obesity and mortality risk.
        Ann Intern Med. 2016; 165: 298
        • Song P.
        • Li X.
        • Bu Y.
        • Ding S.
        • Zhai D.
        • Wang E.
        • et al.
        Temporal trends in normal weight central obesity and its associations with cardiometabolic risk among Chinese adults.
        Sci Rep. 2019; 9: 5411
        • Ying-Xiu Z.
        • Da-Yong S.
        • Jing-Yang Z.
        • Jin-Shan Z.
        • Zun-Hua C.
        Blood pressure among children and adolescents with normal weight but large waist circumference in Shandong, China.
        Eur J Pediatr. 2014; 173: 285-289
        • Mitsuhashi K.
        • Hashimoto Y.
        • Tanaka M.
        • Toda H.
        • Matsumoto S.
        • Ushigome E.
        • et al.
        Combined effect of body mass index and waist-height ratio on incident diabetes; a population based cohort study.
        J Clin Biochem Nutr. 2017; 61: 118-122
        • Shirasawa T.
        • Ochiai H.
        • Yoshimoto T.
        • Nagahama S.
        • Kobayashi M.
        • Ohtsu I.
        • et al.
        Associations between normal weight central obesity and cardiovascular disease risk factors in Japanese middle-aged adults: a cross-sectional study.
        J Health Popul Nutr. 2019; 38: 46
        • Sahakyan K.R.
        • Somers V.K.
        • Rodriguez-Escudero J.P.
        • Hodge D.O.
        • Carter R.E.
        • Sochor O.
        • et al.
        Normal-weight central obesity: implications for total and cardiovascular mortality.
        Ann Intern Med. 2015; 163: 827-835
        • Sun Y.
        • Liu B.
        • Snetselaar L.G.
        • Wallace R.B.
        • Caan B.J.
        • Rohan T.E.
        • et al.
        Association of normal-weight central obesity with all-cause and cause-specific mortality among postmenopausal women.
        JAMA Netw Open. 2019; 2e197337
        • Chinese Society of Health Management CNBoCNS
        Medical nutrition industry branch of the national association of health industry and enterprise management, the editorial board of Chinese Journal of health management. Expert consensus on the procedure of body weight management among patients with overweight or obesity (2021).
        Chin J Health Manage. 2021; 15: 317-322
        • Wang W.
        Cardiovascular health in China: low level vs high diversity.
        Lancet Reg Health West Pac. 2020; 3100038
        • Lloyd-Jones D.M.
        • Hong Y.
        • Labarthe D.
        • Mozaffarian D.
        • Appel L.J.
        • Van Horn L.
        • et al.
        Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.
        Circulation. 2010; 121: 586-613
        • Zhao Y.
        • Hu Y.
        • Smith J.P.
        • Strauss J.
        • Yang G.
        Cohort profile: the China health and retirement longitudinal study (CHARLS).
        Int J Epidemiol. 2014; 43: 61-68
        • Zhou B.F.
        Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults.
        Biomed Environ Sci : BES (Biomed Environ Sci). 2002; 15: 83-96
        • Song P.
        • Wang H.
        • Xia W.
        • Chang X.
        • Wang M.
        • An L.
        Prevalence and correlates of hyperuricemia in the middle-aged and older adults in China.
        Sci Rep. 2018; 8: 4314
        • Chobanian A.V.
        • Bakris G.L.
        • Black H.R.
        • Cushman W.C.
        • Green L.A.
        • Izzo Jr., J.L.
        • et al.
        Seventh report of the Joint national committee on prevention, detection, evaluation, and treatment of high blood pressure.
        Hypertension. 2003; 42: 1206-1252
      1. Classification and diagnosis of diabetes: standards of medical care in diabetes-2021.
        Diabetes Care. 2021; 44: S15-S33
        • Ashwell M.
        • Gunn P.
        • Gibson S.
        Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis.
        Obes Rev : Off J Int Assoc Study Obes. 2012; 13: 275-286
        • Peng Y.
        • Li W.
        • Wang Y.
        • Bo J.
        • Chen H.
        The cut-off Point and boundary values of waist-to-height ratio as an indicator for cardiovascular risk factors in Chinese adults from the PURE study.
        PLoS One. 2015; 10e0144539
        • Thaikruea L.
        • Thammasarot J.
        Prevalence of normal weight central obesity among Thai healthcare providers and their association with CVD risk: a cross-sectional study.
        Sci Rep. 2016; 637100
        • Choi D.
        • Choi S.
        • Son J.S.
        • Oh S.W.
        • Park S.M.
        Impact of discrepancies in general and abdominal obesity on major adverse cardiac events.
        J Am Heart Assoc. 2019; 8e013471
        • Zhang P.
        • Wang R.
        • Gao C.
        • Jiang L.
        • Lv X.
        • Song Y.
        • et al.
        Prevalence of central obesity among adults with normal BMI and its association with metabolic diseases in northeast China.
        PLoS One. 2016; 11e0160402
        • Janssen I.
        • Heymsfield S.B.
        • Allison D.B.
        • Kotler D.P.
        • Ross R.
        Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat.
        Am J Clin Nutr. 2002; 75: 683-688
        • Després J.P.
        • Lemieux I.
        • Bergeron J.
        • Pibarot P.
        • Mathieu P.
        • Larose E.
        • et al.
        Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk.
        Arterioscler Thromb Vasc Biol. 2008; 28: 1039-1049
        • Ibrahim M.M.
        Subcutaneous and visceral adipose tissue: structural and functional differences.
        Obes Rev : Off J Int Assoc Study Obes. 2010; 11: 11-18
        • Neeland I.J.
        • Ayers C.R.
        • Rohatgi A.K.
        • Turer A.T.
        • Berry J.D.
        • Das S.R.
        • et al.
        Associations of visceral and abdominal subcutaneous adipose tissue with markers of cardiac and metabolic risk in obese adults.
        Obesity. 2013; 21: E439-E447
        • Ballin M.
        • Nordström P.
        • Niklasson J.
        • Nordström A.
        Associations of visceral adipose tissue and skeletal muscle density with incident stroke, myocardial infarction, and all-cause mortality in community-dwelling 70-year-old individuals: a prospective cohort study.
        J Am Heart Assoc. 2021; 10e020065
        • Le Jemtel T.H.
        • Samson R.
        • Milligan G.
        • Jaiswal A.
        • Oparil S.
        Visceral adipose tissue accumulation and residual cardiovascular risk.
        Curr Hypertens Rep. 2018; 20: 77
        • Russell A.C.
        • Kepka A.
        • Trbojević-Akmačić I.
        • Ugrina I.
        • Song M.
        • Hui J.
        • et al.
        Increased central adiposity is associated with pro-inflammatory immunoglobulin G N-glycans.
        Immunobiology. 2019; 224: 110-115
        • Wang W.
        Glycomedicine: the current state of the art.
        Engineering. 2022;
        • Yu X.
        • Wang Y.
        • Kristic J.
        • Dong J.
        • Chu X.
        • Ge S.
        • et al.
        Profiling IgG N-glycans as potential biomarker of chronological and biological ages: a community-based study in a Han Chinese population.
        Medicine (Baltim). 2016; 95: e4112
        • Marini S.
        • Merino J.
        • Montgomery B.E.
        • Malik R.
        • Sudlow C.L.
        • Dichgans M.
        • et al.
        Mendelian randomization study of obesity and cerebrovascular disease.
        Ann Neurol. 2020; 87: 516-524
        • Russo C.
        • Sera F.
        • Jin Z.
        • Palmieri V.
        • Homma S.
        • Rundek T.
        • et al.
        Abdominal adiposity, general obesity, and subclinical systolic dysfunction in the elderly: a population-based cohort study.
        Eur J Heart Fail. 2016; 18: 537-544
        • Koliaki C.
        • Liatis S.
        • Kokkinos A.
        Obesity and cardiovascular disease: revisiting an old relationship.
        Metab Clin Exp. 2019; 92: 98-107
        • Kabootari M.
        • Asgari S.
        • Mansournia M.A.
        • Khalili D.
        • Valizadeh M.
        • Azizi F.
        • et al.
        Different weight histories and risk of incident coronary heart disease and stroke: tehran lipid and glucose study.
        J Am Heart Assoc. 2018; 7e006924
        • Viitasalo A.
        • Schnurr T.M.
        • Pitkänen N.
        • Hollensted M.
        • Nielsen T.R.H.
        • Pahkala K.
        • et al.
        Abdominal adiposity and cardiometabolic risk factors in children and adolescents: a Mendelian randomization analysis.
        Am J Clin Nutr. 2019; 110: 1079-1087
        • Bangalore S.
        • Fayyad R.
        • Laskey R.
        • DeMicco D.A.
        • Messerli F.H.
        • Waters D.D.
        Body-weight fluctuations and outcomes in coronary disease.
        N Engl J Med. 2017; 376: 1332-1340
        • Wang L.
        • Lee Y.
        • Wu Y.
        • Zhang X.
        • Jin C.
        • Huang Z.
        • et al.
        A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study.
        Am J Clin Nutr. 2021; 113: 338-347
        • Kuriakose D.
        • Xiao Z.
        Pathophysiology and treatment of stroke: present status and future perspectives.
        Int J Mol Sci. 2020; 21: 7609
        • Maida C.D.
        • Norrito R.L.
        • Daidone M.
        • Tuttolomondo A.
        • Pinto A.
        Neuroinflammatory mechanisms in ischemic stroke: focus on cardioembolic stroke, background, and therapeutic approaches.
        Int J Mol Sci. 2020; 21: 6454
        • Corti R.
        • Hutter R.
        • Badimon J.J.
        • Fuster V.
        Evolving concepts in the triad of atherosclerosis, inflammation and thrombosis.
        J Thromb Thrombolysis. 2004; 17: 35-44
        • Trouwborst I.
        • Goossens G.H.
        • Astrup A.
        • Saris W.H.M.
        • Blaak E.E.
        Sexual dimorphism in body weight loss, improvements in cardiometabolic risk factors and maintenance of beneficial effects 6 Months after a low-calorie diet: results from the randomized controlled DiOGenes trial.
        Nutrients. 2021; 13: 1588
        • Pucci G.
        • Alcidi R.
        • Tap L.
        • Battista F.
        • Mattace-Raso F.
        • Schillaci G.
        Sex- and gender-related prevalence, cardiovascular risk and therapeutic approach in metabolic syndrome: a review of the literature.
        Pharmacol Res. 2017; 120: 34-42
        • Goossens G.H.
        • Jocken J.W.E.
        • Blaak E.E.
        Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver.
        Nat Rev Endocrinol. 2021; 17: 47-66
        • Saeed A.
        • Kampangkaew J.
        • Nambi V.
        Prevention of cardiovascular disease in women.
        Methodist DeBakey Cardiovasc J. 2017; 13: 185-192
        • Austin P.C.
        • Lee D.S.
        • Fine J.P.
        Introduction to the analysis of survival data in the presence of competing risks.
        Circulation. 2016; 133: 601-609
        • Latouche A.
        • Allignol A.
        • Beyersmann J.
        • Labopin M.
        • Fine J.P.
        A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions.
        J Clin Epidemiol. 2013; 66: 648-653