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Wang L, Ding H, Deng Y, Huang J, Lao X, Wong MCS. Associations of obesity indices change with cardiovascular outcomes: a dose-response meta-analysis. Int J Obes (Lond) 2024; 48:635-645. [PMID: 38336864 DOI: 10.1038/s41366-024-01485-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Little is known about the degrees and shapes of associations of changes in obesity indices with cardiovascular disease (CVD) and mortality risks. We aimed to conduct a dose-response meta-analysis for the associations of changes in weight, body mass index (BMI), waist circumference (WC), waist-to-hip ratio, and waist-to-height ratio with CVD events, CVD-specific deaths, and all-cause mortality. METHODS We searched MEDLINE via OvidSP, Embase via OvidSP, Web of Science, CINAHL, and Scopus for articles published before January 8th, 2023. Dose-response relationships were modeled using the one-stage mixed-effects meta-analysis. Random-effects models were used to pool the relative risk (RR) and 95% confidence interval (CI). RESULTS We included 122 articles. Weight change was negatively associated with deaths from CVD and any cause, while WC change elevated CVD-specific mortality. Non-linear relationships also confirmed the adverse effects of increased WC on CVD-specific mortality. Additionally, gains of 5 kg in weight and 1 kg/m2 in BMI or more were associated with elevated CVD events, especially among young adults and individuals without CVD. Conversely, reductions of 5 kg in weight and 1 kg/m2 in BMI or more were associated with higher CVD-specific and all-cause deaths than increased counterparts, particularly among old adults and individuals with CVD. Similar non-linear relationships between relative changes in weight and BMI and deaths from CVD and any cause were observed. CONCLUSIONS The effects of changes in weight and BMI on CVD outcomes were affected by age and cardiovascular health. Tailored weight management and avoidance of increased WC should be recommended.
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Affiliation(s)
- Lyu Wang
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hanyue Ding
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunyang Deng
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Junjie Huang
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China
- Centre for Health Education and Health Promotion, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xiangqian Lao
- Department of Biomedical Science, City University of Hong Kong, Hong Kong SAR, China
- School of Public Health, Zhengzhou University, Zhengzhou, China
| | - Martin C S Wong
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China.
- School of Public Health, Peking University, Beijing, China.
- Centre for Health Education and Health Promotion, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China.
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Man S, Deng Y, Ma Y, Yang X, Wang X, Fu J, Yu C, Lv J, Du J, Wang B, Li L. Association between weight change, waist circumference change, and the risk of nonalcoholic fatty liver disease in individuals with metabolically healthy overweight or obesity and metabolically unhealthy overweight or obesity. Obes Res Clin Pract 2024; 18:109-117. [PMID: 38443283 DOI: 10.1016/j.orcp.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND This study aimed to explore and compare the effect of weight change, and waist circumference (WC) change, on the risk of nonalcoholic fatty liver disease (NAFLD) in individuals with metabolically healthy overweight or obesity (MHOW/O) and metabolically unhealthy overweight or obesity (MUOW/O) in a health check-up cohort in China. METHODS 5625 adults with overweight or obesity, and free from NAFLD at baseline were included. Metabolically healthy was defined as not having any components of metabolic syndrome. Weight/WC changes were calculated as the relative difference between the first and second visits of check-up. NAFLD was assessed based on abdominal ultrasound. RESULTS During a median follow-up of 2.1 (IQR: 1.1-4.3) years, 1849 participants developed NAFLD. In MHOW/O participants, the multivariable adjusted HRs (95 % CIs) for NAFLD in weight change ≤ -5.0 %, and - 4.9-- 1.0 % were 0.36 (0.23-0.59), 0.59 (0.43-0.80), respectively, compared to the weight stable group (-0.9% to 0.9 %). The corresponding HRs (95 % CIs) for the association between WC change (≤ 6.0 %, - 5.9 to -3.0 %) and NAFLD in MHOW/O participants were 0.41 (0.27-0.62), and 0.74 (0.54-1.01), respectively, compared to the WC stable group (-2.9-2.9 %). Similar patterns were observed in MUOW/O participants. A more marked gradient of cumulative incidence of NAFLD across weight/WC change categories was observed in MHOW/O than in MUOW/O individuals. CONCLUSIONS A more evident association between weight/WC loss and risk of NAFLD was observed in MHOW/O than in MUOW/O individuals. Our findings indicate the practical significance of encouraging all individuals with overweight and obesity to achieve a clinically relevant level of weight/WC loss to prevent NAFLD, even among metabolic healthy groups.
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Affiliation(s)
- Sailimai Man
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Meinian Institute of Health, Beijing 100083, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Yuhan Deng
- Meinian Institute of Health, Beijing 100083, China; Chongqing Research Institute of Big Data, Peking University, Chongqing 400000, China
| | - Yuan Ma
- Meinian Institute of Health, Beijing 100083, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xiaochen Yang
- Department of Social Medicine and Health Education, School of Public Health, Peking University, Beijing 100191, China
| | - Xiaona Wang
- Beijing MJ Health Check-up Center, Beijing 100000, China
| | - Jingzhu Fu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Jing Du
- Beijing Centre for Disease Prevention and Control, Beijing 100013, China.
| | - Bo Wang
- Meinian Institute of Health, Beijing 100083, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Peking University Health Science Center Meinian Public Health Institute, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
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3
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He SY, Zhang WS, Jiang CQ, Jin YL, Lam TH, Cheng KK, Xu L. Association of adverse childhood experiences with anemia in older Chinese: Guangzhou Biobank Cohort Study. Sci Rep 2024; 14:4729. [PMID: 38413624 PMCID: PMC10899217 DOI: 10.1038/s41598-024-54378-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
To examine the association of adverse childhood experiences (ACEs) with anemia among older people. 24,116 participants aged 50 years or above were recruited. Multivariable linear and logistic regression was used to assess the associations of self-reported ACEs number with hemoglobin concentrations (g/dL) and presence of anemia. Older individuals with two or more ACEs, versus no ACEs, showed lower hemoglobin concentrations (β = - 0.08 g/dL, 95% confidence intervals (CI) - 0.12 to - 0.03) and higher odds of anemia (odds ratio = 1.26, 95% CI 1.01-1.59). A more pronounced association between ACEs and anemia in the lower education group was found, while the association became non-significant in those with higher education (P for ACEs-education interaction = 0.02). ACEs was associated with anemia in older people, and the association was stronger in those with lower education, highlighting the significance of early-life psychological stressors assessment and consideration of education background in geriatric care.
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Affiliation(s)
- Shao Yi He
- School of Public Health, Sun Yat-Sen University, No. 74, 2nd Zhongshan Road, Guangzhou, Guangdong, China
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China
| | - Wei Sen Zhang
- Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China.
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China.
| | - Chao Qiang Jiang
- Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China
| | - Ya Li Jin
- Guangzhou Twelfth People's Hospital, Guangzhou, 510620, China
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China
| | - Tai Hing Lam
- School of Public Health, The University of Hong Kong, Hong Kong, China
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China
| | - Kar Keung Cheng
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China
| | - Lin Xu
- School of Public Health, Sun Yat-Sen University, No. 74, 2nd Zhongshan Road, Guangzhou, Guangdong, China.
- School of Public Health, The University of Hong Kong, Hong Kong, China.
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
- Greater Bay Area Public Health Research Collaboration, Guangzhou, China.
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Lin M, Wu S, Deng X, Chen Y, Tan X. Visceral fat and its dynamic change are associated with renal damage: Evidence from two cohorts. Clin Exp Hypertens 2023; 45:2271187. [PMID: 37871163 DOI: 10.1080/10641963.2023.2271187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/10/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND AND AIMS To evaluate the association of Chinese visceral adiposity index (CVAI) and its dynamic trends with risk of renal damage, and to compare its prediction performance with that of other obesity indices. METHODS AND RESULTS A community-based population with 23 905 participants from Shantou city was included in the cross-sectional analysis. A total of 9,778 individuals from two separated cohort were included in the longitudinal portion. Five patterns of CVAI change were predefined (low-stable, decreasing, moderate, increasing, and persistent-high). Logistic and Cox regressions were used to evaluate the association between CVAI and renal damage. We explored potential mechanisms using the mediating effect method, and the prediction performance was determined by receiver operating characteristic curve analysis. Results from both cross-sectional and longitudinal data revealed a positive and linear association between CVAI and risk of renal damage. Pooled analysis of the two cohorts showed that per unit increase in Z score of CVAI induced 18% increased risk of renal damage (P = .008). Longitudinal trends of CVAI were also associated with renal damage, and the moderate, increasing, and persistent-high patterns showing a higher risk. Blood pressure and glucose had a mediating effect on renal damage induced by CVAI. Among several obesity indices, CVAI was the optimal for predicting renal damage. CONCLUSION A higher level of immediate CVAI and longitudinal increasing and persistent-high patterns of CVAI were independently associated with increased risk of renal damage. Monitoring immediate level and long-term trend of CVAI may contribute to the prevention of renal damage.
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Affiliation(s)
- Mengyue Lin
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shiwan Wu
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiulian Deng
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yequn Chen
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Clinical Research Center, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xuerui Tan
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
- Clinical Research Center, First Affiliated Hospital of Shantou University Medical College, Shantou, China
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Hussain SM, Newman AB, Beilin LJ, Tonkin AM, Woods RL, Neumann JT, Nelson M, Carr PR, Reid CM, Owen A, Ball J, Cicuttini FM, Tran C, Wang Y, Ernst ME, McNeil JJ. Associations of Change in Body Size With All-Cause and Cause-Specific Mortality Among Healthy Older Adults. JAMA Netw Open 2023; 6:e237482. [PMID: 37036703 PMCID: PMC10087052 DOI: 10.1001/jamanetworkopen.2023.7482] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/25/2023] [Indexed: 04/11/2023] Open
Abstract
Importance The association between weight change and subsequent cause-specific mortality among older adults is not well described. The significance of changes in waist circumference (WC) has also not been compared with weight change for this purpose. Objective To examine the associations of changes in body weight and WC with all-cause and cause-specific mortality. Design, Setting, and Participants This cohort study is a post hoc analysis of data from the Aspirin in Reducing Events in the Elderly (ASPREE) randomized clinical trial, which recruited participants between March 1, 2010, and December 31, 2014. The study included community-based older adults (16 703 Australian participants aged ≥70 years and 2411 US participants aged ≥65 years) without evident cardiovascular disease (CVD), dementia, physical disability, or life-limiting chronic illness. Data analysis was performed from April to September 2022. Exposures Body weight and WC were measured at baseline and at annual visit 2. Analysis models were adjusted for baseline body mass index because height and weight were measured at baseline, allowing for calculation of body mass index and other variables. Both body weight and WC changes were categorized as change within 5% (stable), decrease by 5% to 10%, decrease by more than 10%, increase by 5% to 10%, and increase by more than 10%. Main Outcomes and Measures All-cause, cancer-specific, CVD-specific, and noncancer non-CVD-specific mortality. Mortality events were adjudicated by an expert review panel. Cox proportional hazards regression and competing risk analyses were used to calculate hazard ratios (HRs) and 95% CIs. Results Among 16 523 participants (mean [SD] age, 75.0 [4.3] years; 9193 women [55.6%]), 1256 deaths were observed over a mean (SD) of 4.4 (1.7) years. Compared with men with stable weight, those with a 5% to 10% weight loss had a 33% higher (HR, 1.33; 95% CI, 1.07-1.66) risk of all-cause mortality, and those with more than a 10% decrease in body weight had a 289% higher (HR, 3.89; 95% CI, 2.93-5.18) risk. Compared with women with stable weight, those with a 5% to 10% weight loss had a 26% higher (HR, 1.26; 95% CI, 1.00-1.60) risk of all-cause mortality, and those with more than a 10% decrease in body weight had a 114% higher (HR, 2.14; 95% CI, 1.58-2.91) risk. Weight loss was associated with a higher cancer-specific mortality (>10% decrease among men: HR, 3.49; 95% CI, 2.26-5.40; 5%-10% decrease among women: HR, 1.44; 95% CI, 1.46-2.04; >10% decrease among women: HR, 2.78; 95% CI, 1.82-4.26), CVD-specific mortality (>10% decrease among men: HR, 3.14; 95% CI, 1.63-6.04; >10% decrease among women: HR, 1.92; 95% CI, 1.05-3.51), and noncancer non-CVD-specific mortality (>10% decrease among men: HR, 4.98; 95% CI, 3.14-7.91). A decrease in WC was also associated with mortality. Conclusions and Relevance This cohort study of healthy older adults suggests that weight loss was associated with an increase in all-cause and cause-specific mortality, including an increased risk of cancer, CVD, and other life-limiting conditions. Physicians should be aware of the significance of weight loss, especially among older men.
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Affiliation(s)
- Sultana Monira Hussain
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Medical Education, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anne B. Newman
- Center for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Lawrence J. Beilin
- Medical School, Royal Perth Hospital, University of Western Australia, Perth, Western Australia, Australia
| | - Andrew M. Tonkin
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Johannes T. Neumann
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Cardiology, University Heart & Vascular Center Hamburg, Hamburg, Germany
- German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Mark Nelson
- Discipline of General Practice, University of Tasmania, Hobart, Australia
| | - Prudence R. Carr
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Christopher M. Reid
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Alice Owen
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Jocasta Ball
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Flavia M. Cicuttini
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Cammie Tran
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Yuanyuan Wang
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Michael E. Ernst
- Department of Pharmacy Practice and Science, College of Pharmacy, The University of Iowa, Iowa City
- Department of Family Medicine, Carver College of Medicine, The University of Iowa, Iowa City
| | - John J. McNeil
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Liu R, Dang S, Zhao Y, Yan H, Han Y, Mi B. Long-term waist circumference trajectories and body mass index with all-cause mortality in older Chinese adults: a prospective nationwide cohort study. Arch Public Health 2022; 80:94. [PMID: 36088350 PMCID: PMC9463814 DOI: 10.1186/s13690-022-00861-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/18/2022] [Indexed: 11/11/2022] Open
Abstract
Backgrounds Abdominal obesity has been linked to risk of mortality, but whether and how trajectory of waist circumstance (WC) underpins this association remains unclear. The study aimed to identify long-term WC change trajectories and examine their association and joint effect with body mass index (BMI) on mortality among Chinese older adults. Methods This present study included participants 60 years of age or older from China Health and Nutrition Survey (CHNS) from 1991 to 2015. The duration of follow-up was defined as period from the first to latest visit date attended with information on mortality, end of follow-up, or loss to follow-up (censoring). Latent class trajectory analysis (LCTA) was used to assess the changes of WC trajectories overtime. Cox proportional hazard models were used to assess hazard ratios (HRs) and corresponding 95% confidence internal (CIs) for mortality. Results A total of 2601 participants with 8700 visits were included, and 562 mortality (21.6%) occurred during a median follow-up of 8.7 years. Using a group-based modeling approach, four distinct trajectories of WC change among Chinese older adults were identified as loss (13.5%), stable (46.8%), moderate gain (31.2%) and substantial gain (8.5%). With WC stable group as reference, the multivariable adjusted HRs for mortality were 1.34(95%CI:1.01-1.78) in loss group, 1.13(0.91-1.41) in moderate gain and 1.54(1.12-2.12) in substantial gain group. Compared with participants with normal BMI at baseline and maintained WC stable, the risk of mortality generally increased for all WC change group in initial overweight/obesity individuals, and the highest risk were observed for WC loss and stable pattern (HR:2.43, 95%CI: 1.41–4.19; HR:1.67 (1.07–2.60)). Conclusions In older Chinese, both long-term WC loss and substantial gain conferred excess risk for mortality. The baseline BMI might modify the effect as overweight individuals had a greater risk imposed by WC loss than those in normal weight. Maintaining stable WC and normal weight might be necessary to reduce the risk of mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s13690-022-00861-y.
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Yuan Y, Liu K, Zheng M, Chen S, Wang H, Jiang Q, Xiao Y, Zhou L, Liu X, Yu Y, Wu J, Ding X, Yang H, Li X, Min X, Zhang C, Zhang X, He M, Zheng Y, Sun D, Qi L, Hemler EC, Wu S, Wu T, Pan A. Analysis of Changes in Weight, Waist Circumference, or Both, and All-Cause Mortality in Chinese Adults. JAMA Netw Open 2022; 5:e2225876. [PMID: 35939299 PMCID: PMC9361078 DOI: 10.1001/jamanetworkopen.2022.25876] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
IMPORTANCE Although numerous studies have separately investigated the associations of changes in weight or waist circumference with mortality risk, few studies have examined the associations of concurrent changes in these 2 anthropometric parameters with all-cause mortality. OBJECTIVE To assess the associations of changes in body weight, waist circumference, or both, combined with all-cause mortality. DESIGN, SETTING, AND PARTICIPANTS This cohort study used data from 2 longitudinal cohort studies in Dongfeng-Tongji and Kailuan, China. Participants included 58 132 adults (aged 40 years and older) with measures of weight and waist circumference at baseline and follow-up visit. Statistical analysis was performed from June 2020 to September 2021. EXPOSURES Changes in weight and waist circumference between 2 visits (2008-2010 to 2013 in the Dongfeng-Tongji cohort, and 2006-2007 to 2010-2011 in the Kailuan study). Stable weight was defined as change in weight within 2.5 kg between the 2 visits and stable waist circumference was defined as changes within 3.0 cm. Changes were categorized as loss, stable, or gain for weight and waist circumference separately, and created a 9-category variable to represent the joint changes. MAIN OUTCOMES AND MEASURES All-cause mortality from follow-up visit (2013 in Dongfeng-Tongji cohort and 2010-2011 in Kailuan study) until December 31, 2018. Cox proportional hazard regression models were used to estimate the associations with adjustment for potential confounders. Results were obtained in the 2 cohorts separately and pooled via fixed-effect meta-analysis. RESULTS A total of 10 951 participants in the Dongfeng-Tongji cohort (median [IQR] age, 62 [56-66] years; 4203 [38.4%] men) and 47 181 participants in the Kailuan study (median [IQR] age, 51 [46-58] years; 36 663 [77.7%] men) were included in the analysis. During 426 072 person-years of follow-up, 4028 deaths (523 in the Dongfeng-Tongji cohort and 3505 in the Kailuan study) were documented. When changes in weight and waist circumference were examined separately, U-shape associations were found: both gain and loss in weight (weight loss: pooled hazard ratio [HR], 1.33; 95% CI, 1.23-1.43; weight gain: HR, 1.10; 95% CI, 1.02-1.19) or waist circumference (waist circumference loss: HR, 1.14; 95% CI, 1.05-1.24; waist circumference gain: HR, 1.11; 95% CI, 1.03-1.21) were associated with higher mortality risk compared with stable weight or waist group. When changes in weight and waist circumference were jointly assessed, compared with participants with stable weight and waist circumference (16.9% of the total population [9828 of 58 132] with 508 deaths), participants with different combinations of weight and waist circumference change all had higher mortality risks except for those with stable weight but significant loss in waist. Notably, those who lost weight but gained waist circumference (6.4% of the total population [3698 of 58 132] with 308 deaths) had the highest risk of all-cause mortality (HR, 1.69; 95% CI, 1.46-1.96; absolute rate difference per 100 000 person-years in the Dongfeng-Tongji cohort: 414; 95% CI, 116-819; and in the Kailuan study: 333; 95% CI, 195-492) among the joint subgroups. CONCLUSIONS AND RELEVANCE In this cohort study, weight loss with concurrent waist circumference gain was associated with a higher mortality risk in middle-aged and older Chinese adults. This study's findings suggest the importance of evaluating the changes in both body weight and waist circumference when assessing their associations with mortality.
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Affiliation(s)
- Yu Yuan
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kang Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Public Health, Guangzhou Medical University, Guangzhou, China
| | - Mengyi Zheng
- Graduate School of North China University of Science and Technology, Tangshan, China
| | - Shuohua Chen
- Health Department of Kailuan Group, Tangshan, China
| | - Hao Wang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qin Jiang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xiao
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lue Zhou
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuezhen Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanqiu Yu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiachen Wu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiong Ding
- Graduate School of North China University of Science and Technology, Tangshan, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Xiulou Li
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Xinwen Min
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Ce Zhang
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Meian He
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute and School of Life Sciences, Fudan University, Shanghai, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Elena C. Hemler
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Shouling Wu
- Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, Tangshan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - An Pan
- Department of Epidemiology and Biostatistics, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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