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Wang D, Chen Z, Wu Y, Ren J, Shen D, Hu G, Mao C. Association between two novel anthropometric measures and type 2 diabetes in a Chinese population. Diabetes Obes Metab 2024. [PMID: 38783824 DOI: 10.1111/dom.15651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/27/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024]
Abstract
AIMS To investigate the associations of conicity index (C-index) and relative fat mass (RFM) with incident type 2 diabetes mellitus (T2DM) among adults in China. MATERIALS AND METHODS A total of 10 813 participants aged over 18 years in Shenzhen Longhua district were enrolled in a follow-up study conducted from 2018 to 2022. The participants were categorized based on quartiles (Q) of C-index and RFM. The Cox proportional hazards model was performed to examine the relationships between C-index, RFM and the risk of T2DM. RESULTS After adjusting for potential confounding factors, including age, sex, occupation, marital status, education level, smoking status, alcohol consumption, physical exercise, hypertension status, fasting blood glucose (FBG) and total cholesterol (TC), both C-index and RFM showed positive and independent associations with risk of T2DM. The multivariable-adjusted hazard ratios (95% confidence intervals) for T2DM risk in participants in C-index Q3 and Q4 compared with those in C-index Q1 were 1.50 (1.12, 2.02) and 1.73 (1.29, 2.30), and 1.94 (1.44, 2.63), 3.18 (1.79, 5.64), 4.91 (2.68, 9.00) for participants in RFM Q2, Q3 and Q4 compared with RFM Q1. These differences were statistically significant (all p < 0.05). CONCLUSION C-index and RFM are strongly associated with new-onset T2DM and could be used to identify the risk of diabetes in large-scale epidemiological studies.
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Affiliation(s)
- Di Wang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ziting Chen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yinru Wu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiaojiao Ren
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Dong Shen
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Guifang Hu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chen Mao
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
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Bai X, Li Z, Cai Z, Yao M, Chen L, Wang Y. Gender differences in risk factors for ischemic stroke: a longitudinal cohort study in East China. BMC Neurol 2024; 24:171. [PMID: 38783249 PMCID: PMC11112765 DOI: 10.1186/s12883-024-03678-0] [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: 12/16/2023] [Accepted: 05/17/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVES Epidemiological studies of stroke and its risk factors can help develop strategies to prevent stroke. We aimed to explore the current gender-specific prevalence of stroke and associated risk factors. METHODS Data were collected using a structured precoded questionnaire designed by the Stroke Screening and Prevention Programme of the National Health and Wellness Commission Stroke Prevention and Control Project Committee, between June 2020 and November 2021. A total of 7394 residents took part in the study, 187 of whom had a stroke. The baseline information of each participant was obtained and included in this study. The chi-square test and Kruskal-Wallis tests were used to examine the relationship between these indicators and stroke, and then multivariate logistic regression was used to construct the prediction scale between different genders. RESULTS of 7394 participants,4571 (61.82%) were female. The overall prevalence of stroke patients in the study population was 2.53%, Multivariate analysis found that residence status (OR = 0.43, p = 0.002) 、HCY (OR = 0.962, p = 0.000)、Previous TIA (OR = 0.200, p = 0.002) 、Hypertension (OR = 0.33, p = 0.000) and Dyslipidemia (OR = 0.668, p = 0.028) were significant predictors of stroke. there are gender differences in the traditional risk factors for stroke, and women have more risk factors. ROC analysis confirmed the accuracy of the stroke risk model, and the AUC of the stroke risk model for the general population was 0.79 with p < 0.05. In the gender model, the female AUC was 0.796 (p < 0.05). and the male AUC was 0.786 with p < 0.05. CONCLUSION The prevalence of stroke in adults aged 40 years and above is high in eastern China were high. management of risk factors can effectively prevent the occurrence of most strokes. more attention should be paid to gender differences associated with stroke.
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Affiliation(s)
- Xinping Bai
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Zifeng Li
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Zhuo Cai
- Department of Neurology, Fuyang Hospital Affiliated to Bengbu Medical University, Anhui, 236000, People's Republic of China
| | - Mingren Yao
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Lin Chen
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China
| | - Youmeng Wang
- Department of Neurology, Fuyang People's Hospital, Anhui, 236000, People's Republic of China.
- Department of Neurology, Fuyang Hospital Affiliated to Bengbu Medical University, Anhui, 236000, People's Republic of China.
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Huan C, Wang M, Song Y, Jia Z, Wei D, Wang L, Xu Q, Wang J, Zhao M, Geng J, Shi J, Ma C, Mao Z, Wang C, Huo W. Inflammatory markers and androstenedione modify the effect of serum testosterone on obesity among men: Findings from a Chinese population. Andrology 2024; 12:850-861. [PMID: 37823215 DOI: 10.1111/andr.13544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/15/2023] [Accepted: 09/30/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Few studies are available on the relationship of androstenedione with inflammation and obesity and the effect of androstenedione and inflammation on the association between testosterone and obesity. This study intended to examine the mediation effect of inflammatory markers on the association of testosterone with obesity and the moderation effect of androstenedione on the association of testosterone with inflammation and obesity in Chinese rural men. MATERIALS AND METHODS This cross-sectional research enrolled 2536 male rural inhabitants from the Henan Rural Cohort study. The serum concentrations of testosterone and androstenedione were determined by liquid chromatography-tandem mass spectrometry. Linear and logistic regression were used to examine the relationships between testosterone, inflammatory markers, and obesity. Mediation and moderation analyses were carried out to evaluate the potential effects of inflammatory markers on the relationship between testosterone and obesity, as well as androstenedione on the relationships of testosterone with inflammation and obesity. RESULTS After adjusting for confounding factors, the results showed that testosterone and androstenedione were negatively related to obesity, and inflammatory markers were positively associated with obesity. Besides, testosterone and androstenedione were negatively associated with inflammatory markers. Mediation analysis showed that white blood cell, neutrophil, monocyte, and high-sensitivity C-reactive protein had mediating effects on the association between testosterone and obesity. The most vital mediator was high-sensitivity C-reactive protein, and its proportion of the effect was 11.02% (defined by waist circumference), 11.15% (defined by waist-to-hip ratio), 12.92% (defined by waist-to-height ratio), and full mediating effect (defined by body mass index). Moreover, androstenedione played negative moderation effects on the associations of testosterone with inflammation and obesity. CONCLUSION Inflammatory markers and androstenedione were first found to have modifying effects on the association of testosterone with obesity. Higher levels of testosterone and androstenedione could reduce the inflammation level and risk of obesity, indicating their potential roles in the prevention and treatment of chronic diseases.
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Affiliation(s)
- Changsheng Huan
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Mian Wang
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Yu Song
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Zexin Jia
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Dandan Wei
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Lulu Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Qingqing Xu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Juan Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Mengzhen Zhao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Jintian Geng
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Jiayu Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Cuicui Ma
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Zhenxing Mao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Wenqian Huo
- Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, Zhengzhou, Henan, P. R. China
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Zhao Y, Han M, Qie R, Zhang Y, Wu Y, Fu X, Zhang D, Kuang L, Qin P, Hu F, Li J, Lu X, Hu D, Zhang M. Associations of body mass index trajectory, waist circumference trajectory, or both with type 2 diabetes mellitus risk in Chinese adults: The China-PAR project. Diabetes Obes Metab 2024; 26:1919-1928. [PMID: 38418401 DOI: 10.1111/dom.15508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 03/01/2024]
Abstract
AIMS To identify the trajectories of body mass index (BMI) and waist circumference (WC), and assess the associations of BMI trajectory, WC trajectory, or the two combined, with type 2 diabetes mellitus (T2DM) risk in Chinese adults. MATERIALS AND METHODS This study was based on a prospective project-the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR). A total of 54 434 participants (39.21% men) who were measured on at least two occasions were included. Three slowly increasing trajectory patterns were identified for BMI, and four for WC, by latent mixed modelling. A nine-category variable was derived by combining the WC trajectory (low, moderate, moderate-high/high) and the BMI trajectory (low, moderate, high). Logistic regression models were applied to estimate the odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS The risk of developing T2DM increased with elevated BMI or WC trajectory levels (all ptrend <0.001). The risks were 2.85 (2.59-3.14) for high BMI trajectory and 4.34 (3.78-4.99) for high WC trajectory versus low trajectory groups, respectively. The association was more pronounced among younger individuals (pinteraction <0.001). In the joint analysis, compared to participants with low WC and BMI trajectory, those with moderate-high/high WC combined with high BMI trajectory had the highest risk of T2DM (OR 3.96, 95% CI 3.48-4.50); even those who maintained moderate-high/high WC but low BMI trajectory showed a higher T2DM risk (OR 3.00, 95% CI 2.31-3.91). CONCLUSIONS This study suggests that simultaneous dynamic and continuous monitoring of BMI and WC may contribute more than single measurements to predicting T2DM risk and determining preventive strategies.
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Affiliation(s)
- Yang Zhao
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
| | - Minghui Han
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ranran Qie
- Department of Cancer Prevention and Control, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanyan Zhang
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuying Wu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Xueru Fu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Dongdong Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
| | - Lei Kuang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
| | - Pei Qin
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
| | - Jianxin Li
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongsheng Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, China
- Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Shenzhen University Medical School, Shenzhen, China
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Liu M, Jia C, Hu Y, Liu J, Liu L, Sun S, Wang H, Liu Y. Prevalence and factors associated with overweight, obesity and central obesity among adults in Shenmu City, Shaanxi Province, China. Prev Med Rep 2024; 40:102673. [PMID: 38495769 PMCID: PMC10940174 DOI: 10.1016/j.pmedr.2024.102673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
Objectives This research aimed to explore the prevalence and determinants of overweight, obesity, and central obesity in Shenmu City, Shaanxi Province, China and to offer guidance for preventative health measures. Methods We conducted a multi-stage, stratified random sampling survey among 4,565 residents of Shenmu City. Data collection included questionnaires and anthropometric assessments to gather socio-demographic data and to identify cases of overweight, obesity, and central obesity. Multivariable logistic regression analysis was utilized to assess the association between various factors and these conditions. Results The observed prevalence rates for overweight, obesity, central obesity, and the combination of overweight/obesity with central obesity were 39.9%, 18.2%, 48.0%, 32.8%, and 22.8%, respectively. Notably, the incidence of these conditions was significantly higher in men compared to women. The prevalence of overweight and obesity initially increased and then decreased with age, whereas the prevalence of central obesity consistently rose. Furthermore, a higher educational level correlated with lower prevalence rates. Additionally, our analysis indicated that hypertension, dyslipidemia, and hyperuricemia are risk factors for these conditions. Conclusions The findings of this study offer crucial insights for formulating effective strategies to prevent and manage obesity in Shenmu City.
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Affiliation(s)
- Mingxia Liu
- Department of Prevention and Health Care, Shenmu Hospital, The Affifiliated Shenmu Hospital of Northwest University, Shenmu, China
| | - Chunjiao Jia
- Medical Department, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Yaoda Hu
- Department of Epidemiology and Statistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Juan Liu
- Department of Prevention and Health Care, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Lizhen Liu
- Ultrasound Medicine Department, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Shengli Sun
- Department of Neurology, Shenmu Hospital, The Affifiliated Shenmu Hospital of Northwest University, Shenmu, China
| | - Haiying Wang
- Science and Education Department, Shenmu Hospital, The Affifiliated Shenmu Hospital of Northwest University, Shenmu, China
| | - Yonglin Liu
- Science and Education Department, Shenmu Hospital, The Affifiliated Shenmu Hospital of Northwest University, Shenmu, China
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Yao P, Iona A, Kartsonaki C, Said S, Wright N, Lin K, Pozarickij A, Millwood I, Fry H, Mazidi M, Chen Y, Du H, Bennett D, Avery D, Schmidt D, Pei P, Lv J, Yu C, Hill M, Chen J, Peto R, Walters R, Collins R, Li L, Clarke R, Chen Z. Conventional and genetic associations of adiposity with 1463 proteins in relatively lean Chinese adults. Eur J Epidemiol 2023; 38:1089-1103. [PMID: 37676424 PMCID: PMC10570181 DOI: 10.1007/s10654-023-01038-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/28/2023] [Indexed: 09/08/2023]
Abstract
Adiposity is associated with multiple diseases and traits, but little is known about the causal relevance and mechanisms underlying these associations. Large-scale proteomic profiling, especially when integrated with genetic data, can clarify mechanisms linking adiposity with disease outcomes. We examined the associations of adiposity with plasma levels of 1463 proteins in 3977 Chinese adults, using measured and genetically-instrumented BMI. We further used two-sample bi-directional MR analyses to assess if certain proteins influenced adiposity, along with other (e.g. enrichment) analyses to clarify possible mechanisms underlying the observed associations. Overall, the mean (SD) baseline BMI was 23.9 (3.3) kg/m2, with only 6% being obese (i.e. BMI ≥ 30 kg/m2). Measured and genetically-instrumented BMI was significantly associated at FDR < 0.05 with levels of 1096 (positive/inverse: 826/270) and 307 (positive/inverse: 270/37) proteins, respectively, with FABP4, LEP, IL1RN, LSP1, GOLM2, TNFRSF6B, and ADAMTS15 showing the strongest positive and PON3, NCAN, LEPR, IGFBP2 and MOG showing the strongest inverse genetic associations. These associations were largely linear, in adiposity-to-protein direction, and replicated (> 90%) in Europeans of UKB (mean BMI 27.4 kg/m2). Enrichment analyses of the top > 50 BMI-associated proteins demonstrated their involvement in atherosclerosis, lipid metabolism, tumour progression and inflammation. Two-sample bi-directional MR analyses using cis-pQTLs identified in CKB GWAS found eight proteins (ITIH3, LRP11, SCAMP3, NUDT5, OGN, EFEMP1, TXNDC15, PRDX6) significantly affect levels of BMI, with NUDT5 also showing bi-directional association. The findings among relatively lean Chinese adults identified novel pathways by which adiposity may increase disease risks and novel potential targets for treatment of obesity and obesity-related diseases.
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Affiliation(s)
- Pang Yao
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Andri Iona
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Saredo Said
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Neil Wright
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Alfred Pozarickij
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Iona Millwood
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dan Schmidt
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Richard Peto
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Robin Walters
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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Mouchti S, Orliacq J, Reeves G, Chen Z. Assessment of correlation between conventional anthropometric and imaging-derived measures of body fat composition: a systematic literature review and meta-analysis of observational studies. BMC Med Imaging 2023; 23:127. [PMID: 37710156 PMCID: PMC10503139 DOI: 10.1186/s12880-023-01063-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/24/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND In studies of the association of adiposity with disease risk, widely used anthropometric measures of adiposity (e.g. body-mass-index [BMI], waist circumference [WC], waist-hip ratio [WHR]) are simple and inexpensive to implement at scale. In contrast, imaging-based techniques (e.g. magnetic resonance imaging [MRI] and dual x-ray absorptiometry [DXA]) are expensive and labour intensive, but can provide more accurate quantification of body fat composition. There is, however, limited evidence about the relationship between conventional and imaging-derived measures of adiposity. METHODS We searched Scopus and Web of Science for published reports in English of conventional versus imaging-derived measurements of adiposity. We identified 42 articles (MRI = 22; DXA = 20) that met selection criteria, involving 42,556 (MRI = 15,130; DXA = 27,426) individuals recruited from community or hospital settings. Study-specific correlation coefficients (r) were transformed using Fisher's Z transformation, and meta-analysed to yield weighted average correlations, both overall and by ancestry, sex and age, where feasible. Publication bias was investigated using funnel plots and Egger's test. RESULTS Overall, 98% of participants were 18 + years old, 85% male and 95% White. BMI and WC were most strongly correlated with imaging-derived total abdominal (MRI-derived: r = 0.88-; DXA-derived: 0.50-0.86) and subcutaneous abdominal fat (MRI-derived: 0.83-0.85), but were less strongly correlated with visceral abdominal fat (MRI-derived: 0.76-0.79; DXA-derived: 0.80) and with DXA-derived %body fat (0.76). WHR was, at best, strongly correlated with imaging-derived total abdominal (MRI-derived: 0.60; DXA-derived: 0.13), and visceral abdominal fat (MRI-derived: 0.67; DXA-derived: 0.65), and moderately with subcutaneous abdominal (MRI-derived: 0.54), and with DXA-derived %body fat (0.58). All conventional adiposity measures were at best moderately correlated with hepatic fat (MRI-derived: 0.36-0.43). In general, correlations were stronger in women than in men, in Whites than in non-Whites, and in those aged 18 + years. CONCLUSIONS In this meta-analysis, BMI and WC, but not WHR, were very strongly correlated with imaging-derived total and subcutaneous abdominal fat. By comparison, all three measures were moderately or strongly correlated with imaging-based visceral abdominal fat, with WC showing the greatest correlation. No anthropometric measure was substantially correlated with hepatic fat. Further larger studies are needed to compare these measures within the same study population, and to assess their relevance for disease risks in diverse populations.
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Affiliation(s)
- Sofia Mouchti
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK
| | - Josefina Orliacq
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Gillian Reeves
- Cancer Epidemiology Unit, Richard Doll Building, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Zhengming Chen
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford, OX3 7LF, UK.
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8
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Yu HJ, Ho M, Liu X, Yang J, Chau PH, Fong DYT. Incidence and temporal trends in type 2 diabetes by weight status: A systematic review and meta-analysis of prospective cohort studies. J Glob Health 2023; 13:04088. [PMID: 37651631 PMCID: PMC10471153 DOI: 10.7189/jogh.13.04088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/02/2023] Open
Abstract
Background Diabetes is more prevalent among overweight/obese individuals, but has become a significant public health challenge among normal weight populations. In this meta-analysis, we aimed to estimate diabetes/prediabetes incidence and its temporal trends by weight status. Methods PubMed, Embase, Web of Science, and Cochrane Library were searched until 8 December 2021. Prospective cohort studies reporting diabetes incidence by baseline body mass index (BMI) categories in adults were included. The median year of data collection was used to assess the temporal trends. Subgroup analyses and meta-regression were also performed. Results We included 94 studies involving 3.4 million adults from 22 countries. The pooled diabetes incidence in underweight, normal-weight, and overweight/obese adults was 4.5 (95% confidence interval (CI) = 2.8-7.3), 2.7 (95% CI = 2.2-3.3), and 10.5 (95% CI = 9.3-11.8) per 1000 person-years, respectively. The diabetes incidence in low- and middle-income countries (LMICs) was higher than in high-income countries among normal-weight (5.8 vs 2.0 per 1000 person-years) or overweight/obese (15.9 vs 8.9 per 1000 person-years) adults. European and American regions had a higher diabetes incidence than the non-Western areas, regardless of weight status. Underweight diabetes incidence decreased significantly from 1995-2000 to 2005-2010. Diabetes incidence in normal-weight populations has increased continuously since 1985 by an estimated 36% every five years. In overweight/obese adults, diabetes incidence increased between 1985-1990 and 1995-2000, stabilised between 2000 and 2010, and spiked suddenly after 2010. Conclusions Diabetes incidence and its temporal trends differed by weight status. The continuous upward trend of diabetes incidence among overweight/obese individuals requires urgent attention, particularly in LMICs. Furthermore, diabetes among normal-weight individuals is becoming a significant public health problem. Registration PROSPERO (CRD42020215957).
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Affiliation(s)
- Hong-jie Yu
- School of Nursing, University of Hong Kong, Hong Kong SAR, China
| | - Mandy Ho
- School of Nursing, University of Hong Kong, Hong Kong SAR, China
| | - Xiangxiang Liu
- National Clinical Research Center for Infectious Diseases, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Jundi Yang
- School of Nursing, University of Hong Kong, Hong Kong SAR, China
| | - Pui Hing Chau
- School of Nursing, University of Hong Kong, Hong Kong SAR, China
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9
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Yang R, Kuang M, Qiu J, Yu C, Sheng G, Zou Y. Assessing the usefulness of a newly proposed metabolic score for visceral fat in predicting future diabetes: results from the NAGALA cohort study. Front Endocrinol (Lausanne) 2023; 14:1172323. [PMID: 37538796 PMCID: PMC10395081 DOI: 10.3389/fendo.2023.1172323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/29/2023] [Indexed: 08/05/2023] Open
Abstract
Objective Visceral adipose tissue assessment holds significant importance in diabetes prevention. This study aimed to explore the association between the newly proposed Metabolic Score for Visceral Fat (METS-VF) and diabetes risk and to further assess the predictive power of the baseline METS-VF for the occurrence of diabetes in different future periods. Methods This longitudinal cohort study included 15,464 subjects who underwent health screenings. The METS-VF, calculated using the formula developed by Bello-Chavolla et al., served as a surrogate marker for visceral fat obesity. The primary outcome of interest was the occurrence of diabetes during the follow-up period. Established multivariate Cox regression models and restricted cubic spline (RCS) regression models to assess the association between METS-VF and diabetes risk and its shape. Receiver operating characteristic (ROC) curves were used to compare the predictive power of METS-VF with body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and visceral adiposity index (VAI) for diabetes, and time-dependent ROC analysis was conducted to assess the predictive capability of METS-VF for the occurrence of diabetes in various future periods. Results During a maximum follow-up period of 13 years, with a mean of 6.13 years, we observed that the cumulative risk of developing diabetes increased with increasing METS-VF quintiles. Multivariable-adjusted Cox regression analysis showed that each unit increase in METS-VF would increase the risk of diabetes by 68% (HR 1.68, 95% CI 1.13, 2.50), and further RCS regression analysis revealed a possible non-linear association between METS-VF and diabetes risk (P for non-linearity=0.002). In addition, after comparison by ROC analysis, we found that METS-VF had significantly higher predictive power for diabetes than other general/visceral adiposity indicators, and in time-dependent ROC analysis, we further considered the time-dependence of diabetes status and METS-VF and found that METS-VF had the highest predictive value for predicting medium- and long-term (6-10 years) diabetes risk. Conclusion METS-VF, a novel indicator for assessing visceral adiposity, showed a significantly positive correlation with diabetes risk. It proved to be a superior risk marker in predicting the future onset of diabetes compared to other general/visceral adiposity indicators, particularly in forecasting medium- and long-term diabetes risk.
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Affiliation(s)
- Ruijuan Yang
- Department of Endocrinology, Jiangxi Provincial People’s Hospital, Medical College of Nanchang University, Nanchang, Jiangxi, China
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Maobin Kuang
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Department of Cardiology, Jiangxi Provincial People’s Hospital, Medical College of Nanchang University, Nanchang, Jiangxi, China
| | - Jiajun Qiu
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Department of Cardiology, Jiangxi Provincial People’s Hospital, Medical College of Nanchang University, Nanchang, Jiangxi, China
| | - Changhui Yu
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
- Department of Cardiology, Jiangxi Provincial People’s Hospital, Medical College of Nanchang University, Nanchang, Jiangxi, China
| | - Guotai Sheng
- Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
| | - Yang Zou
- Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China
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10
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Zhang X, Li G, Shi C, Tian Y, Zhang L, Zhang H, Sun Y. Comparison of conventional and unconventional obesity indices associated with new-onset hypertension in different sex and age populations. Sci Rep 2023; 13:7776. [PMID: 37179428 PMCID: PMC10182979 DOI: 10.1038/s41598-023-34969-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 05/10/2023] [Indexed: 05/15/2023] Open
Abstract
We aimed to compare the relationship between hypertension and obesity-related anthropometric indices (waist circumference [WC], waist-height ratio, waist-hip ratio [WHR], and body mass index; unconventional: new body shape index [ABSI] and body roundness index [BRI]) to identify best predictors of new-onset hypertension. The study included 4123 adult participants (2377 women). Hazard ratios (HRs) and 95% confidence intervals (CIs) were determined using a Cox regression model to estimate the risk of new-onset hypertension with respect to each obesity index. In addition, we assessed the predictive value of each obesity index for new-onset hypertension using area under the receiver operating characteristic curve (AUC) after adjusting for common risk factors. During the median follow-up of 2.59 years, 818 (19.8%) new hypertension cases were diagnosed. The non-traditional obesity indices BRI and ABSI had predictive value for new-onset hypertension; however, they were not better than the traditional indexes. WHR was the best predictor of new-onset hypertension in women aged ≤ 60 and > 60 years, with HRs of 2.38 and 2.51 and AUCs of 0.793 and 0.716. However, WHR (HR 2.28, AUC = 0.759) and WC (HR 3.24, AUC = 0.788) were the best indexes for predicting new-onset hypertension in men aged ≤ 60 and > 60 years, respectively.
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Affiliation(s)
- Xueyao Zhang
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Guangxiao Li
- Department of Medical Record Management, First Hospital of China Medical University, Shenyang, China
| | - Chuning Shi
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Yichen Tian
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Linlin Zhang
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Hongyu Zhang
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China
| | - Yingxian Sun
- Department of Cardiology, First Hospital of China Medical University, 155 Nanjing North Street, Heping District, Shenyang, 110001, China.
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Wang Y, Tan H, Zheng H, Ma Z, Zhan Y, Hu K, Yang Z, Yao Y, Zhang Y. Exposure to air pollution and gains in body weight and waist circumference among middle-aged and older adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 869:161895. [PMID: 36709892 DOI: 10.1016/j.scitotenv.2023.161895] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Emerging research suggested a nexus between air pollution exposure and risks of overweight and obesity, while existing longitudinal evidence was extensively sparse, particularly in densely populated regions. This study aimed to quantify concentration-response associations of changes in weight and waist circumference (WC) related to air pollution in Chinese adults. METHODS We conceived a nationally representative longitudinal study from 2011 to 2015, by collecting 34,854 observations from 13,757 middle-aged and older adults in 28 provincial regions of China. Participants' height, weight and WC were measured by interviewers using standardized devices. Concentrations of major air pollutants including fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) predicted by well-validated spatiotemporal models were assigned to participants according to their residential cities. Possible exposure biases were checked through 1000 random simulated exposure at individual level, using a Monte Carlo simulation approach. Linear mixed-effects models were applied to estimate the relationships of air pollution with weight and WC changes, and restricted cubic spline functions were adopted to smooth concentration-response (C-R) curves. RESULTS Each 10-μg/m3 rise in PM2.5, NO2 and O3 was associated with an increase of 0.825 (95% confidence interval: 0.740, 0.910), 0.921 (0.811, 1.032) and 1.379 (1.141, 1.616) kg in weight, respectively, corresponding to WC gains of 0.688 (0.592, 0.784), 1.189 (1.040, 1.337) and 0.740 (0.478, 1.002) cm. Non-significant violation for linear C-R relationships was observed with exception of NO2-weight and PM2.5/NO2-WC associations. Sex-stratified analyses revealed elevated vulnerability in women to gain of weight in exposure to PM2.5 and NO2. Sensitive analyses largely supported our primary findings via assessing exposure estimates from 1000 random simulations, and performing reanalysis based on non-imputed covariates and non-obese participants, as well as alternative indicators (i.e., body mass index and waist-to-height ratio). CONCLUSIONS We found positively robust associations of later-life exposure to air pollutants with gains in weight and WC based on a national sample of Chinese adult men and women. Our findings suggested that mitigation of air pollution may be an efficient intervention to relieve obesity burden.
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Affiliation(s)
- Yaqi Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Huiyue Tan
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China; Healthcare Associated Infection Control Department, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, Enshi 445000, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
| | - Zongwei Ma
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Kejia Hu
- Institute of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Zhiming Yang
- School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
| | - Yao Yao
- China Center for Health Development Studies, Peking University, Beijing 100871, China
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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Prognostic Implications of OSA in Acute Coronary Syndrome by Obesity Status. Chest 2023:S0012-3692(23)00173-3. [PMID: 36764513 DOI: 10.1016/j.chest.2023.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 12/20/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND A close relationship exists between OSA and obesity. The impact of obesity on the prognostic significance of OSA in patients with acute coronary syndrome (ACS) remains unclear. RESEARCH QUESTION Do the effects of OSA on subsequent cardiovascular events in patients with ACS vary with obesity status? STUDY DESIGN AND METHODS This is a prospective cohort study. Patients 18 to 85 years of age and hospitalized for ACS were consecutively enrolled and underwent portable sleep monitoring after clinical stabilization. OSA was defined as an apnea hypopnea index ≥ 15 events/h. The primary end point was major adverse cardiovascular and cerebrovascular event (MACCE), including cardiovascular death, hospitalization for ACS, stroke, ischemia-driven revascularization, or hospitalization for heart failure. RESULTS Among 1,920 patients enrolled (84.5% men; mean age ± SD, 56.4 ± 10.5 years), 1,013 (52.8%) had OSA, and 718 (37.4%) were obese (BMI ≥ 28 kg/m2). During 2.9 years (1.5, 3.6) follow up, the incidence of MACCE was significantly higher in patients with obesity than in patients without obesity (hazard ratio [HR], 1.29; 95% CI, 1.06-1.58; P = .013). Although the prevalence of OSA was lower in patients without obesity than in those with obesity (43.9% vs 67.5%, P < .001), OSA independently predicted the incidence of MACCE only in patients without obesity (adjusted HR, 1.34; 95% CI, 1.03-1.75; P = .03), but not in patients with obesity (adjusted HR, 1.10; 95% CI, 0.78-1.55; P = .58). No significant interaction between obesity and OSA was noted (P for interaction = .35). The incremental risk associated with OSA in patients without obesity might be explained by more hospitalization for ACS and ischemia-driven revascularization. INTERPRETATION For patients with ACS, OSA was independently associated with an increased risk of subsequent events, particularly among patients without obesity. These findings highlight the importance of identifying OSA in nonobese patients with ACS. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov; No.: NCT03362385; URL: www. CLINICALTRIALS gov.
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13
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Wang Q, Zhang L, Li Y, Tang X, Yao Y, Fang Q. Development of stroke predictive model in community-dwelling population: A longitudinal cohort study in Southeast China. Front Aging Neurosci 2022; 14:1036215. [PMID: 36620776 PMCID: PMC9813513 DOI: 10.3389/fnagi.2022.1036215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Background Stroke has been the leading cause of death and disability in the world. Early recognition and treatment of stroke could effectively limit brain damage and vastly improve outcomes. This study aims to develop a highly accurate prediction model of stroke with a list of lifestyle behaviors and clinical characteristics to distinguish high-risk groups in the community-dwelling population. Methods Participants in this longitudinal cohort study came from the community-dwelling population in Suzhou between November 2018 and June 2019. A total of 4,503 residents participated in the study, while stroke happened to 22 participants in the 2-year follow-up period. Baseline information of each participant was acquired and enrolled in this study. T-test, Chi-square test, and Fisher's exact test were used to examine the relationship of these indexes with stroke, and a prediction scale was constructed by multivariate logistic regression afterward. Receiver operating characteristic analysis was applied to testify to the prediction accuracy. Results A highly accurate prediction model of stroke was constructed by age, gender, exercise, meat and vegetarian diet, BMI, waist circumference, systolic blood pressure, Chinese visceral adiposity index, and waist-height ratio. Two additional prediction models for overweight and non-overweight individuals were formulated based on crucial risk factors, respectively. The stroke risk prediction models for community-dwelling and overweight populations had accuracies of 0.79 and 0.82, severally. Gender and exercise were significant predictors (χ2 > 4.57, p < 0.05) in the community-dwelling population model, while homocysteine (χ2 = 4.95, p < 0.05) was significant in the overweight population model. Conclusion The predictive models could predict 2-year stroke with high accuracy. The models provided an effective tool for identifying high-risk groups and supplied guidance for improving prevention and treatment strategies in community-dwelling population.
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Affiliation(s)
- Qi Wang
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Lulu Zhang
- Department of Neurology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yidan Li
- Department of Neurology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiang Tang
- Department of Neurology, First Affiliated Hospital of Soochow University, Suzhou, China,*Correspondence: Xiang Tang,
| | - Ye Yao
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China,National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, China,Ye Yao,
| | - Qi Fang
- Department of Neurology, First Affiliated Hospital of Soochow University, Suzhou, China
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Staple Food Preference and Obesity Phenotypes: The Regional Ethnic Cohort Study in Northwest China. Nutrients 2022; 14:nu14245243. [PMID: 36558402 PMCID: PMC9784345 DOI: 10.3390/nu14245243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Staple food preference vary in populations, but evidence of its associations with obesity phenotypes are limited. Using baseline data (n = 105,840) of the Regional Ethnic Cohort Study in Northwest China, staple food preference was defined according to the intake frequency of rice and wheat. Overall and specifically abdominal fat accumulation were determined by excessive body fat percentage and waist circumference. Logistic regression and equal frequency substitution methods were used to evaluate the associations. We observed rice preference (consuming rice more frequently than wheat; 7.84% for men and 8.28% for women) was associated with a lower risk of excessive body fat (OR, 0.743; 95%CI, 0.669-0.826) and central obesity (OR, 0.886; 95%CI, 0.807-0.971) in men; and with lower risk of central obesity (OR, 0.898; 95%CI, 0.836-0.964) in women, compared with their wheat preference counterparties. Furthermore, similar but stronger inverse associations were observed in participants with normal body mass index. Wheat-to-rice (5 times/week) reallocations were associated with a 36.5% lower risk of normal-weight obesity in men and a 20.5% lower risk of normal-weight central obesity in women. Our data suggest that, compared with wheat, rice preference could be associated with lower odds ratios of certain obesity phenotypes in the Northwest Chinese population.
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15
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Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods. Twin Res Hum Genet 2022; 25:234-244. [PMID: 36606461 DOI: 10.1017/thg.2022.39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
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Fan G, Liu Q, Wu M, Bi J, Qin X, Fang Q, Wan Z, Lv Y, Wang Y, Song L. Exposure to Metal Mixtures and Overweight or Obesity Among Chinese Adults. Biol Trace Elem Res 2022:10.1007/s12011-022-03484-0. [PMID: 36383287 DOI: 10.1007/s12011-022-03484-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022]
Abstract
Previous research has investigated the association between individual metal exposure and overweight/obesity (OW/OB). However, there is limited data about metal mixture exposure and OW/OB. This study aimed to explore the individual and joint effects of 21 metals on OW/OB and its metabolic phenotypes. A total of 4042 participants were enrolled in our study, and 51.0% of them were overweight/obese. We quantified 21 metal levels in the urine sample. OW/OB was defined as BMI ≥ 24 kg/m2, while the metabolic phenotypes, including metabolic unhealthy overweight/obesity (MUOW/OB) and metabolic health overweight/obesity (MHOW/OB), were determined by BMI and metabolic state. We used logistic regression to analyze the effect of individual metal exposure on OW/OB and its metabolic phenotypes. Quantile g-computation was applied to evaluate the joint effect of metal exposure on OW/OB and its metabolic phenotypes. In logistic regression, zinc (Zn) was positively associated with OW/OB, with the odds ratio (OR) in the highest quartiles of 2.19 (95% confidence interval (CI), 1.74, 2.77; P trend < 0.001), while arsenic (As) and cadmium (Cd) were negatively associated with OW/OB (OR = 0.70 (0.56, 0.87) and 0.61 (0.48, 0.78), respectively). After adjustment for age, gender, education, cigarette smoking, alcohol drinking, physical activity, meat intake, and vegetable intake, Zn was positively associated with MUOW/OB, while As, Cd, nickel (Ni), and strontium (Sr) were negatively associated with MUOW/OB (all P trend < 0.05). Quantile g-computation showed a significantly negative association between metal mixture exposure and MUOW/OB. Our study suggested that metal mixture exposure might be negatively associated with OW/OB, particularly with MUOW/OB. Zn, As and Cd contributed most to the effect of the mixture. More prospective studies are warranted to confirm these findings and reveal the underlying mechanisms.
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Affiliation(s)
- Gaojie Fan
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Liu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mingyang Wu
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jianing Bi
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiya Qin
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qing Fang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhengce Wan
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongman Lv
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Youjie Wang
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Lulu Song
- Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, 430030, Hubei, China.
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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17
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Spieler G, Westfall AO, Long DM, Cherrington A, Burkholder GA, Funderburg N, Raper JL, Overton ET, Willig AL. Trends in diabetes incidence and associated risk factors among people with HIV in the current treatment era. AIDS 2022; 36:1811-1818. [PMID: 35950938 PMCID: PMC9529800 DOI: 10.1097/qad.0000000000003348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To examine type 2 diabetes mellitus incidence and associated risk factors among people with HIV (PWH). DESIGN A retrospective clinical cohort study of PWH at a Southeastern US academic HIV clinic between 2008 and 2018. METHODS PWH who attended at least two clinic visits were evaluated with demographic and clinical data extracted from the electronic medical record (EMR). Diabetes was defined as: hemoglobin A1C ≥6.5% and/or 2 glucose results >200 mg/dl (at least 30 days apart), diagnosis of diabetes in the EMR, or exposure to diabetes medication. Time to diabetes incidence was computed from the entire clinic population for each year. Multivariable Cox proportional hazard regression models with time-dependent covariates were created to evaluate the independent association between covariates and time to incident diabetes. RESULTS Among 4113 PWH, we identified 252 incident cases of diabetes. Incidence increased from 1.04 incidents per 1000 person years (PY) in 2008, to 1.55 incidents per 1000 PY in 2018. Body mass index (hazard ratio [HR] 10.5 (6.2, 17.7)), liver disease (HR 1.9 (1.2, 3.1)), steroid exposure (HR 1.5 (1.1, 1.9)), and use of integrase inhibitors (HR 1.5 (1.1, 2.0)) were associated with incident diabetes. Additional associated factors included lower CD4 + cell counts, duration of HIV infection, exposure to nonstatin lipid-lowering therapy, and dyslipidemia. CONCLUSIONS Rapidly increasing incident diabetes rates among PWH were associated with both traditional and HIV-related associated risk factors, particularly body weight, steroid exposure, and use of Integrase Inhibitors. Notably, several of the risk factors identified are modifiable and can be targeted for intervention.
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Affiliation(s)
| | | | | | - Andrea Cherrington
- Department of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | | | - Nicholas Funderburg
- School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, Ohio, USA
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18
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Pan XF, Chen ZZ, Wang TJ, Shu X, Cai H, Cai Q, Clish CB, Shi X, Zheng W, Gerszten RE, Shu XO, Yu D. Plasma metabolomic signatures of obesity and risk of type 2 diabetes. Obesity (Silver Spring) 2022; 30:2294-2306. [PMID: 36161775 PMCID: PMC9633360 DOI: 10.1002/oby.23549] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 06/12/2022] [Accepted: 07/14/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D. METHODS In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk. RESULTS A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001). CONCLUSIONS Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.
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Affiliation(s)
- Xiong-Fei Pan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Thomas J. Wang
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Xiang Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clary B. Clish
- Metabolomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Xu Shi
- Broad Institute of Massachusetts Institute of Technology and Harvard & Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert E. Gerszten
- Broad Institute of Massachusetts Institute of Technology and Harvard & Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
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19
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Zhen J, Liu S, Zhao G, Peng H, Samaranayake N, Xu A, Li C, Wu J, Cheung BMY. Association of waist circumference with haemoglobin A1c and its optimal cutoff for identifying prediabetes and diabetes risk in the Chinese population. Intern Emerg Med 2022; 17:2039-2044. [PMID: 36002618 PMCID: PMC9522717 DOI: 10.1007/s11739-022-03072-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 07/29/2022] [Indexed: 12/19/2022]
Abstract
Haemoglobin A1c (HbA1c) is a marker of glycaemic control in type 2 diabetes mellitus (T2DM). Increased waist circumference (WC) is known to be associated with T2DM. Therefore, we investigated the relationship of WC with HbA1c and explored its optimal cutoff for identifying prediabetes and diabetes risk. This study included 2339 participants between 18 and 84 years of age [mean (SD) age, 43.5 (11.9) years] with valid data on WC, HbA1c and related variables in the Shenzhen-Hong Kong United Network on Cardiovascular Disease study. Participants on anti-diabetic medications were excluded. Multiple linear regression was used to investigate the relationship between HbA1c and WC. Cutoff values of WC indicating an HbA1c level of 5.7% and 6.5% were also assessed using optimal binning. There was a significant linear relationship between WC and HbA1c in the overall population (B = 0.261, P < 0.001), men (B = 0.206, P < 0.001) and women (B = 0.311, P < 0.001). After adjustment for smoking, alcohol consumption, physical activity, hypertension, hypercholesterolaemia and age, the association remained significant in the overall population (B = 0.201, P < 0.001), men (B = 0.186, P < 0.001) and women (B = 0.182, P < 0.001). The optimal cutoff values of WC indicating an HbA1c level of 5.7% and 6.5% was 83 cm (entropy = 0.943) and 85 cm (entropy = 0.365) in men, and 78 cm (entropy = 0.922) and 86 cm (entropy = 0.256) in women. The linear relationship between WC and HbA1c in this study suggests that addressing central obesity issue is beneficial to people with T2DM or at risk of T2DM. WC cutoff values of 85 cm for men and 86 cm for women are appropriate for recommendation to undergo diabetes screening.
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Affiliation(s)
- Juanying Zhen
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong SAR, China
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Shuyun Liu
- Department of Neurology, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Guoru Zhao
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hao Peng
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, China
| | - Nithushi Samaranayake
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Allied Health Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Aimin Xu
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong SAR, China
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Chao Li
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong SAR, China.
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China.
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
| | - Jun Wu
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China.
| | - Bernard Man Yung Cheung
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Pokfulam, Hong Kong SAR, China.
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
- Institute of Cardiovascular Science and Medicine, The University of Hong Kong, Hong Kong SAR, China.
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20
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Owora AH, Allison DB, Zhang X, Gletsu-Miller N, Gadde KM. Risk of Type 2 Diabetes Among Individuals with Excess Weight: Weight Trajectory Effects. Curr Diab Rep 2022; 22:471-479. [PMID: 35781782 PMCID: PMC10094425 DOI: 10.1007/s11892-022-01486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/01/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Increased risk of type 2 diabetes mellitus (T2D) among individuals with overweight or obesity is well-established; however, questions remain about the temporal dynamics of weight change (gain or loss) on the natural course of T2D in this at-risk population. Existing epidemiologic evidence is limited to studies that discretely sample and assess excess weight and T2D risk at different ages with limited follow-up, yet changes in weight may have time-varying and possibly non-linear effects on T2D risk. Predicting the impact of weight change on the risk of T2D is key to informing primary prevention. We critically review the relationship between weight change, trajectory groups (i.e., distinct weight change patterns), and T2D risk among individuals with excess weight in recently published T2D prevention randomized controlled trials (RCTs) and longitudinal cohort studies. RECENT FINDINGS Overall, weight trajectory groups have been shown to differ by age of onset, sex, and patterns of insulin resistance or beta-cell function biomarkers. Lifestyle (diet and physical activity), pharmacological, and surgical interventions can modify an individual's weight trajectory. Adolescence is a critical etiologically relevant window during which onset of excess weight may be associated with higher risk of T2D. Changes in insulin resistance and beta-cell function biomarkers are distinct but related correlates of weight trajectory groups that evolve contemporaneously over time. These multi-trajectory markers are differentially associated with T2D risk. T2D risk may differ by the age of onset and duration of excess body weight, and the type of weight loss intervention. A better understanding of the changes in weight, insulin sensitivity, and beta-cell function as distinct but related correlates of T2D risk that evolve contemporaneously over time has important implications for designing and targeting primary prevention efforts.
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Affiliation(s)
- Arthur H Owora
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA.
| | - David B Allison
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Xuan Zhang
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Nana Gletsu-Miller
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Kishore M Gadde
- Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge, LA, USA
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21
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Sun X, Yan AF, Shi Z, Zhao B, Yan N, Li K, Gao L, Xue H, Peng W, Cheskin LJ, Wang Y. Health consequences of obesity and projected future obesity health burden in China. Obesity (Silver Spring) 2022; 30:1724-1751. [PMID: 36000246 DOI: 10.1002/oby.23472] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 03/20/2022] [Accepted: 03/22/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE This study examined the effects of overweight/obesity on mortality and morbidity outcomes and the disparities, time trends, and projected future obesity health burden in China. METHODS Cohort studies that were conducted in China and published in English or Chinese between January 1, 1995, and July 31, 2021, were systematically searched. This study focused on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension, cardiovascular diseases, metabolic syndrome, cancers, and chronic kidney disease. RESULTS A total of 31 cohorts and 50 cohort studies reporting on mortality (n = 20) and morbidities (n = 30) associated with obesity met study inclusion criteria. Overall, BMI was nonlinearly (U-shaped) associated with all-cause mortality and linearly associated with risks of T2DM, cardiovascular diseases, hypertension, cancer, metabolic syndrome, and chronic kidney disease. In 2018, among adults, the prevalence of overweight/obesity, hypertension, and T2DM was 51.2%, 27.5%, and 12.4%, respectively. Their future projected prevalence would be 70.5%, 35.4%, and 18.5% in 2030, respectively. The projected number of adults having these conditions would be 810.65 million, 416.47 million, and 217.64 million, respectively. The urban-rural disparity in overweight/obesity prevalence was projected to shrink and then reverse over time. CONCLUSIONS The current health burden of obesity in China is high and it will sharply increase in coming years and affect population groups differently. China needs to implement vigorous interventions for obesity prevention and treatment.
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Affiliation(s)
- Xiaomin Sun
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Alice Fang Yan
- Center for Advancing Population Science, Division of Internal Medicine, Department of Medicine, Medical College of Wisconsin, Wauwatosa, Wisconsin, USA
| | - Zumin Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Bingtong Zhao
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Na Yan
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Ke Li
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Liwang Gao
- Center for Non-communicable Disease Management, Beijing Children's Hospital, National Center for Children's Health, Capital Medical University, Beijing, China
| | - Hong Xue
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, Virginia, USA
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University and Global Health Institute, Xi'an Jiaotong University, Xi'an, China
| | - Lawrence J Cheskin
- Department of Nutrition and Food Studies, College of Health and Human Services, George Mason University, Fairfax, Virginia, USA
- Department of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Youfa Wang
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
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22
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Zhang M, Yang BY, Sun Y, Qian Z, Xaverius PK, Aaron HE, Zhao X, Zhang Z, Liu R, Dong GH, Yin C, Yue W. Non-linear Relationship of Maternal Age With Risk of Spontaneous Abortion: A Case-Control Study in the China Birth Cohort. Front Public Health 2022; 10:933654. [PMID: 35910867 PMCID: PMC9330030 DOI: 10.3389/fpubh.2022.933654] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Background Spontaneous abortion is one of the prevalent adverse reproductive outcomes, which seriously threatens maternal health around the world. Objective The current study is aimed to evaluate the association between maternal age and risk for spontaneous abortion among pregnant women in China. Methods This was a case-control study based on the China Birth Cohort, we compared 338 cases ending in spontaneous abortion with 1,352 controls resulting in normal live births. The main exposure indicator and outcome indicator were maternal age and spontaneous abortion, respectively. We used both a generalized additive model and a two-piece-wise linear model to determine the association. We further performed stratified analyses to test the robustness of the association between maternal age and spontaneous abortion in different subgroups. Results We observed a J-shaped relationship between maternal age and spontaneous abortion risk, after adjusting for multiple covariates. Further, we found that the optimal threshold age was 29.68 years old. The adjusted odds ratio (95% confidence interval) of spontaneous abortion per 1 year increase in maternal age were 0.97 (0.90–1.06) on the left side of the turning point and 1.25 (1.28–1.31) on the right side. Additionally, none of the covariates studied modified the association between maternal age and spontaneous abortion (P > 0.05). Conclusions Advanced maternal age (>30 years old) was significantly associated with increased prevalence of spontaneous abortion, supporting a J-shaped association between maternal age and spontaneous abortion.
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Affiliation(s)
- Man Zhang
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yongqing Sun
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Zhengmin Qian
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO, United States
| | - Pamela K. Xaverius
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO, United States
| | - Hannah E. Aaron
- Department of Epidemiology and Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, MO, United States
| | - Xiaoting Zhao
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Zheng Zhang
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Ruixia Liu
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- *Correspondence: Ruixia Liu
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guang-Hui Dong
| | - Chenghong Yin
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Chenghong Yin
| | - Wentao Yue
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Wentao Yue
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23
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Ji Y, Shi Y, Zhou J, Li X, Qin R, Zhu Q. Analysis on the Change of College Students' Life Pattern and its Impact during the COVID-19 Outbreak in China. Am J Health Behav 2022; 46:218-230. [PMID: 35794758 DOI: 10.5993/ajhb.46.3.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Objectives: Our objective was to analyze changes in lifestyle patterns of Chinese college students at home during the COVID-19 outbreak. Methods: According to a structured online questionnaire covering socio- demographic information, anthropometric data, and changes in food intake, physical activity, and sleep during the COVID-19 outbreak, the relationships between the above data before and during the outbreak were analyzed. Results: Among the 781 participants, 38.5% had significantly increased their total food intake and 29.1% had significantly decreased their physical activity. Overall, 44% of participants reported weight gain. The average weight gain was 0.7±2.5 kg. The main causes of weight gain were increased food intake (p < .001), decreased physical activity (p < .01), and an excessive increase or reduction in sleep duration (p < .024). Conclusion: During the COVID-19 outbreak, college students' food intake was found to be increased and physical activity decreased; sleep duration was irregular, and all these factors influenced weight gain.
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Affiliation(s)
- Yu Ji
- Yu Ji, Department of Medicine, Xinglin College, Nantong University, Nantong China
| | - Youpeng Shi
- Youpeng Shi, Department of Medicine, Xinglin College, Nantong University, Nantong, China
| | - Jie Zhou
- Jie Zhou, Department of Medicine, Xinglin College, Nantong University, Nantong, China
| | - Xiyue Li
- Xiyue Li, Department of Medicine, Xinglin College, Nantong University, Nantong, China
| | - Ruoyu Qin
- Ruoyu Qin, Department of Medicine, Xinglin College, Nantong University, Nantong, China
| | - Qi Zhu
- Qi Zhu, Department of Medicine, Xinglin College, Nantong University, Nantong, China and Department of Preventive Medicine, School of Public Health, Nantong University, Nantong, China;,
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24
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Li JJ, Liu HH, Li S. Landscape of cardiometabolic risk factors in Chinese population: a narrative review. Cardiovasc Diabetol 2022; 21:113. [PMID: 35729555 PMCID: PMC9215083 DOI: 10.1186/s12933-022-01551-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/13/2022] [Indexed: 12/17/2022] Open
Abstract
With rapid economic growth and changes at all levels (including environmental, social, individual), China is facing a cardiovascular disease (CVD) crisis. In China, more than 40% of deaths are attributable to CVDs, and the number of CVD deaths has almost doubled in the past decades, in contrast to a decline in high-income countries. The increasing prevalence of cardiometabolic risk factors underlies the rise of CVDs, and thus curbing the rising cardiometabolic pandemic is imperative. Few articles have addressed this topic and provided an updated review of the epidemiology of cardiometabolic risk factors in China.In this narrative review, we describe the temporal changes in the prevalence of cardiometabolic risk factors in the past decades and their management in China, including both the well-recognized risk factors (general obesity, central obesity, diabetes, prediabetes, dyslipidemia, hypertension) and the less recognized ones (hyperhomocysteinemia, hyperuricemia, and high C-reactive protein). We also summarize findings from landmark clinical trials regarding effective interventions and treatments for cardiometabolic risk factors. Finally, we propose strategies and approaches to tackle the rising pandemic of cardiometabolic risk factors in China. We hope that this review will raise awareness of cardiometabolic risk factors not only in Chinese population but also global visibility, which may help to prevent cardiovascular risk.
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Affiliation(s)
- Jian-Jun Li
- Cardiometabolic Center, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 BeiLiShi Road, XiCheng District, Beijing, 100037, China.
| | - Hui-Hui Liu
- Cardiometabolic Center, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 BeiLiShi Road, XiCheng District, Beijing, 100037, China
| | - Sha Li
- Cardiometabolic Center, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.167 BeiLiShi Road, XiCheng District, Beijing, 100037, China
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25
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Qiu G, Wang H, Yan Q, Ma H, Niu R, Lei Y, Xiao Y, Zhou L, Yang H, Xu C, Zhang X, He M, Tang H, Hu Z, Pan A, Shen H, Wu T. A Lipid Signature with Perturbed Triacylglycerol Co-Regulation, Identified from Targeted Lipidomics, Predicts Risk for Type 2 Diabetes and Mediates the Risk from Adiposity in Two Prospective Cohorts of Chinese Adults. Clin Chem 2022; 68:1094-1107. [PMID: 35708664 DOI: 10.1093/clinchem/hvac090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/18/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND The roles of individual and co-regulated lipid molecular species in the development of type 2 diabetes (T2D) and mediation from metabolic risk factors remain unknown. METHODS We conducted profiling of 166 plasma lipid species in 2 nested case-control studies within 2 independent cohorts of Chinese adults, the Dongfeng-Tongji and the Jiangsu non-communicable disease cohorts. After 4.61 (0.15) and 7.57 (1.13) years' follow-up, 1039 and 520 eligible participants developed T2D in these 2 cohorts, respectively, and controls were 1:1 matched to cases by age and sex. RESULTS We found 27 lipid species, including 10 novel ones, consistently associated with T2D risk in the 2 cohorts. Differential correlation network analysis revealed significant correlations of triacylglycerol (TAG) 50:3, containing at least one oleyl chain, with 6 TAGs, at least 3 of which contain the palmitoyl chain, all downregulated within cases relative to controls among the 27 lipids in both cohorts, while the networks also both identified the oleyl chain-containing TAG 50:3 as the central hub. We further found that 13 of the 27 lipids consistently mediated the association between adiposity indicators (body mass index, waist circumference, and waist-to-height ratio) and diabetes risk in both cohorts (all P < 0.05; proportion mediated: 20.00%, 17.70%, and 17.71%, and 32.50%, 28.73%, and 33.86%, respectively). CONCLUSIONS Our findings suggested notable perturbed co-regulation, inferred from differential correlation networks, between oleyl chain- and palmitoyl chain-containing TAGs before diabetes onset, with the oleyl chain-containing TAG 50:3 at the center, and provided novel etiological insight regarding lipid dysregulation in the progression from adiposity to overt T2D.
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Affiliation(s)
- Gaokun Qiu
- 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 430030, China
| | - Hao Wang
- 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 430030, China
| | - Qi Yan
- 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 430030, China
| | - Hongxia Ma
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Rundong Niu
- 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 430030, China
| | - Yanshou Lei
- 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 430030, China
| | - Yang Xiao
- 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 430030, China
| | - Lue Zhou
- 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 430030, China
| | - Handong Yang
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan 442008, China
| | - Chengwei Xu
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan 442008, China
| | - Xiaomin Zhang
- 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 430030, China
| | - Meian He
- 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 430030, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China.,CAS Key Laboratory of Magnetic Resonance in Biological Systems, University of Chinese Academy of Sciences, Wuhan 430071, China
| | - Zhibin Hu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - An Pan
- 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 430030, China
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Tangchun Wu
- 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 430030, China
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Zhang M, Sun Y, Zhao X, Liu R, Yang BY, Chen G, Zhang W, Dong GH, Yin C, Yue W. How Parental Predictors Jointly Affect the Risk of Offspring Congenital Heart Disease: A Nationwide Multicenter Study Based on the China Birth Cohort. Front Cardiovasc Med 2022; 9:860600. [PMID: 35722125 PMCID: PMC9204142 DOI: 10.3389/fcvm.2022.860600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveCongenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD.MethodsThis was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD.ResultsDominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10–1.19], increased paternal age (1.05, 95% CI: 1.02–1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22–3.76), paternal drinking (1.41, 95% CI: 1.08–1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95–5.87), maternal fever (3.35, 95% CI: 2.49–4.50), assisted reproductive technology (2.89, 95% CI: 2.13–3.94), and environmental pollution (1.61, 95% CI: 1.18–2.20). A higher household annual income (100,000–400,000 CNY: 0.47, 95% CI: 0.34–0.63; > 400,000 CNY: 0.23, 95% CI: 0.15–0.36), higher maternal education level (13–16 years: 0.68, 95% CI: 0.50–0.93; ≥ 17 years: 0.87, 95% CI: 0.55–1.37), maternal folic acid (0.21, 95% CI: 0.16–0.27), and multivitamin supplementation (0.33, 95% CI: 0.26–0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD.ConclusionWe revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.
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Affiliation(s)
- Man Zhang
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Yongqing Sun
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, China
| | - Xiaoting Zhao
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Ruixia Liu
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Bo-Yi Yang
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Gongbo Chen
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wangjian Zhang,
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guang-Hui Dong,
| | - Chenghong Yin
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, China
- Chenghong Yin,
| | - Wentao Yue
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Wentao Yue,
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Effectiveness of a Worksite-Based Lifestyle Intervention on Employees' Obesity Control and Prevention in China: A Group Randomized Experimental Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116738. [PMID: 35682322 PMCID: PMC9180232 DOI: 10.3390/ijerph19116738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 02/05/2023]
Abstract
Background: This study was to culturally adapt a lifestyle intervention for employees’ obesity control and prevention using a participatory process, and evaluate the effectiveness of the project at worksites. Methods: A group randomized experimental study included four worksites (two intervention, two control) in the Yangtze River Delta in China was conducted. A total of 388 participants (216 in the intervention worksites and 172 in the control worksites) were finally recruited from 955 employees at the four worksites (464 in the intervention worksites and 491 in the control worksites). The final evaluation was completed by two hundred and seventy-eight employees (159 in the intervention worksites and 119 in the control worksites, respectively). Data of demographic information, weight, BMI, waist circumference, hip circumference and weight-related behaviors including diary behaviors and physical activities were collected before and after a 12-month intervention and analyzed using descriptive statistics, t-test, chi-square test, linear mixed regression and logistic mixed regression. Results: Although the intervention worksites had a reduction in body mass index (23.21 to 22.95, p < 0.01), hip circumference (95.97 to 95.28, p = 0.03) and waist-to-height ratio (0.49 to 0.48, p = 0.01), the differential changes compared to those of the control group were not statistically significant. The frequency of sweet beverages (−1.81, 95%CI: −0.52, −3.11), frequency of vegetable intake (5.66, 95%CI: 1.59, 9.74), daily servings of vegetables (0.53, 95%CI: 0.24, 0.82), frequency of fruit intake (3.68, 95%CI: 1.25, 6.12), daily servings of fruit (0.26, 95%CI: 0.44, 0.92), daily servings of vegetables and fruit (0.79, 95%CI: 0.43, 1.16), daily steps (863.19, 95%CI: 161.42, 1564.97) and self-efficacy to change physical activity (OR = 1.91, 95%CI: 1.02,3.60) were more improved in the intervention group than were those measures in the control group. Conclusions: The worksite-based lifestyle intervention project for obesity control and prevention improved several employees’ dietary behaviors and physical activities at worksites in China in a short time. Long-term intervention with larger samples in more worksites should be further examined.
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Hariri P, Clarke R, Bragg F, Chen Y, Guo Y, Yang L, Lv J, Yu C, Li L, Chen Z, Bennett DA. Frequency and types of clusters of major chronic diseases in 0.5 million adults in urban and rural China. JOURNAL OF MULTIMORBIDITY AND COMORBIDITY 2022; 12:26335565221098327. [PMID: 35615751 PMCID: PMC9125108 DOI: 10.1177/26335565221098327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Little is known about the frequency and types of disease clusters involving major chronic diseases that contribute to multimorbidity in China. We examined the frequency of disease clusters involving major chronic diseases and their relationship with age and socioeconomic status in 0.5 million Chinese adults. Methods Multimorbidity was defined as the presence of at least two or more of five major chronic diseases: stroke, ischaemic heart disease (IHD), diabetes, chronic obstructive pulmonary disease (COPD) and cancer. Multimorbid disease clusters were estimated using both self-reported doctor-diagnosed diseases at enrolment and incident cases during 10-year follow-up. Frequency of multimorbidity was assessed overall and by age, sex, region, education and income. Association rule mining (ARM) and latent class analysis (LCA) were used to assess clusters of the five major diseases. Results Overall, 11% of Chinese adults had two or more major chronic diseases, and the frequency increased with age (11%, 24% and 33% at age 50-59, 60-69 and 70-79 years, respectively). Multimorbidity was more common in men than women (12% vs 11%) and in those living in urban than in rural areas (12% vs 10%), and was inversely related to levels of education. Stroke and IHD were the most frequent combinations, followed by diabetes and stroke. The patterns of self-reported disease clusters at baseline were similar to those that were recorded during the first 10 years of follow-up. Conclusions Cardiometabolic and cardiorespiratory diseases were most common disease clusters. Understanding the nature of such clusters could have implications for future prevention strategies.
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Affiliation(s)
- Parisa Hariri
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- National Centre for Cardiovascular Diseases, Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick A Bennett
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Yu HJ, Ho M, Liu X, Yang J, Chau PH, Fong DYT. Association of weight status and the risks of diabetes in adults: a systematic review and meta-analysis of prospective cohort studies. Int J Obes (Lond) 2022; 46:1101-1113. [PMID: 35197569 DOI: 10.1038/s41366-022-01096-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/30/2022]
Abstract
Obesity is a known risk factor for type 2 diabetes mellitus (T2DM); however, the associations between underweight and T2DM and between weight status and prediabetes have not been systematically reviewed. We aimed to estimate the relative risks (RRs) of prediabetes/T2DM in underweight/overweight/obesity relative to normal weight. PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to December 8, 2021. Prospective cohort studies with a minimum 12-month follow-up period reporting the association between baseline body mass index (BMI) categories and risk of prediabetes/T2DM in adults were included. Study quality was assessed using the Newcastle-Ottawa Scale. The main analyses of T2DM risk were performed using the ethnic-specific (Asian/non-Asian) BMI classification and additional analyses of prediabetes/T2DM risk by including all eligible studies. Random-effects models with inverse variance weighting were used. Subgroup analyses and meta-regression were conducted to explore the potential effects of pre-specified modifiers. The study protocol was registered with PROSPERO (CRD42020215957). Eighty-four articles involving over 2.69 million participants from 20 countries were included. The pooled RR of prediabetes risk was 1.24 (95% CI: 1.19-1.28, I2 = 9.7%, n = 5 studies) for overweight/obesity vs. normal weight. The pooled RRs of T2DM based on the ethnic-specific BMI categories were 0.93 (95% CI: 0.75-1.15, I2 = 55.5%, n = 12) for underweight, 2.24 (95% CI: 1.95-2.56, I2 = 92.0%, n = 47) for overweight, 4.56 (95% CI: 3.69-5.64, I2 = 96%, n = 43) for obesity, and 22.97 (95% CI: 13.58-38.86, I2 = 92.1%, n = 6) for severe obesity vs. normal weight. Subgroup analyses indicated that underweight is a protective factor against T2DM in non-Asians (RR = 0.68, 95% CI: 0.40-0.99, I2 = 56.1%, n = 6). The magnitude of the RR of T2DM in overweight/obesity decreased with age and varied by region and the assessment methods for weight and T2DM. Overweight/obesity was associated with an increased prediabetes/T2DM risk. Further studies are required to confirm the association between underweight and prediabetes/T2DM, particularly in Asian populations.
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Affiliation(s)
- Hong-Jie Yu
- School of Nursing, the University of Hong Kong, Hong Kong, SAR, China
| | - Mandy Ho
- School of Nursing, the University of Hong Kong, Hong Kong, SAR, China.
| | | | - Jundi Yang
- School of Nursing, the University of Hong Kong, Hong Kong, SAR, China
| | - Pui Hing Chau
- School of Nursing, the University of Hong Kong, Hong Kong, SAR, China
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Ge Q, Li M, Xu Z, Qi Z, Zheng H, Cao Y, Huang H, Duan X, Zhuang X. Comparison of different obesity indices associated with type 2 diabetes mellitus among different sex and age groups in Nantong, China: a cross-section study. BMC Geriatr 2022; 22:20. [PMID: 34979974 PMCID: PMC8725504 DOI: 10.1186/s12877-021-02713-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/09/2021] [Indexed: 01/03/2023] Open
Abstract
Background Obesity is associated with type 2 diabetes mellitus (T2DM). However, the obesity index that is most closely related to type 2 diabetes remains controversial. Therefore, the aim of this study was to compare the associations of five anthropometric indices (body mass index [BMI], body adiposity index, waist circumference [WC], waist-to-hip ratio, and waist-to-height ratio [WHtR]) with T2DM among Chinese adults divided into four groups according to sex and age. Methods A total of 4007 adult participants (1669 men and 2338 women) were included in the study. Odds ratios (ORs) and 95% confidence intervals were used with binary logistic regression models to estimate the risk of T2DM for each obesity index. Furthermore, we compared the area under the receiver operating characteristic curve (AUC) of each obesity index for the criterion of T2DM under the influence of risk factors. Results WC had the highest OR (3.211 and 1.452) and AUC (0.783 and 0.614) in both age groups of men. However, WHtR (OR = 2.366, AUC = 0.771) and BMI (OR = 1.596, AUC = 0.647) were the optimal criteria for predicting T2DM among females in the 18–59 and ≥ 60 years age groups, respectively. Conclusions This study suggests that there is a positive association between obesity-related anthropometric indices and T2DM in different sex and age groups. WC appears to be the optimal anthropometric index for predicting T2DM in men. The optimal obesity indices related to T2DM were WHtR and BMI for women aged 18–59 and ≥ 60 years, respectively.
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Affiliation(s)
- Qiwei Ge
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Min Li
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Zhengcheng Xu
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Zhigang Qi
- Nantong Chongchuan District Center for Disease Control and Prevention, Nantong, Jiangsu, 226000, China
| | - Huiyan Zheng
- Nantong Chongchuan District Center for Disease Control and Prevention, Nantong, Jiangsu, 226000, China
| | - Yuxin Cao
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Hao Huang
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Xiaoyang Duan
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China
| | - Xun Zhuang
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No 9 Seyuan Road, Nantong, Jiangsu, 226019, China.
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Sun Y, Zhang M, Liu R, Wang J, Yang K, Wu Q, Yue W, Yin C. Protective Effect of Maternal First-Trimester Low Body Mass Index Against Macrosomia: A 10-Year Cross-Sectional Study. Front Endocrinol (Lausanne) 2022; 13:805636. [PMID: 35222271 PMCID: PMC8866317 DOI: 10.3389/fendo.2022.805636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 01/05/2022] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE We aimed to assess whether maternal first-trimester low body mass index (BMI) has a protective effect against macrosomia. METHODS This was a cross-sectional study from January 1, 2011, to June 30, 2021, and 84,900 participants were included. The predictive performance of maternal first-trimester and parental pre-pregnancy BMI for macrosomia was assessed using the area under the receiver-operating characteristics curve (AUC). Multivariate logistic regression analyses were performed to evaluate the independent effect of maternal first-trimester low BMI on macrosomia. Interactions were investigated to evaluate the potential variation of the effect of first-trimester low BMI across different groups. Furthermore, interactions were also examined across groups determined by multiple factors jointly: a) gestational diabetes mellitus (GDM)/GDM history status, parity, and maternal age; and b) GDM/GDM history status, fetal sex, and season of delivery. RESULTS The proportion of macrosomia was 6.14% (5,215 of 84,900). Maternal first-trimester BMI showed the best discrimination of macrosomia (all Delong tests: P < 0.001). The protective effect of maternal first-trimester low BMI against macrosomia remained significant after adjusting for all confounders of this study [adjusted odds ratios (aOR) = 0.37, 95% CI: 0.32-0.43]. Maternal first-trimester low BMI was inversely associated with macrosomia, irrespective of parity, fetal sex, season of delivery, maternal age, and GDM/GDM history status. The protective effect was most pronounced among pregnant women without GDM/GDM history aged 25 to 29 years old, irrespective of parity (multipara: aOR = 0.32, 95% CI: 0.22-0.47; nullipara: aOR = 0.32, 95% CI: 0.24-0.43). In multipara with GDM/GDM history, the protective effect of low BMI was only observed in the 30- to 34-year-old group (aOR = 0.12, 95% CI: 0.02-0.86). For pregnant women without GDM/GDM history, the protective effect of maternal first-trimester low BMI against macrosomia was the weakest in infants born in winter, irrespective of fetal sex (female: aOR = 0.45, 95% CI: 0.29-0.69; male: aOR = 0.39, 95% CI: 0.28-0.55). CONCLUSION Maternal first-trimester low BMI was inversely associated with macrosomia, and the protective effect was most pronounced among 25- to 29-year-old pregnant women without GDM/GDM history and was only found among 30- to 34-year-old multipara with GDM/GDM history. The protective effect of maternal first-trimester low BMI against macrosomia was the weakest in winter among mothers without GDM/GDM history.
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Affiliation(s)
- Yongqing Sun
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Man Zhang
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Ruixia Liu
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Jingjing Wang
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Kai Yang
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
| | - Qingqing Wu
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- *Correspondence: Chenghong Yin, ; Wentao Yue, ; Qingqing Wu,
| | - Wentao Yue
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- *Correspondence: Chenghong Yin, ; Wentao Yue, ; Qingqing Wu,
| | - Chenghong Yin
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
- Beijing Maternal and Child Health Care Hospital, Beijing, China
- *Correspondence: Chenghong Yin, ; Wentao Yue, ; Qingqing Wu,
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Tan C, Li B, Xiao L, Zhang Y, Su Y, Ding N. A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population. Diabetes Metab Syndr Obes 2022; 15:3555-3564. [PMID: 36411787 PMCID: PMC9675349 DOI: 10.2147/dmso.s386687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND This study aimed to distinguish the risk factors for type 2 diabetes mellitus (T2DM) and construct a predictive model of T2DM in Japanese adults with abdominal obesity. METHODS This study was a post hoc analysis. A total of 2012 individuals with abdominal obesity were included and randomly divided into training and validation groups at 70% (n = 1518) and 30% (n = 494), respectively. The LASSO method was used to screen for risk variables for T2DM, and to construct a nomogram incorporating the selected risk factors in the training group. We used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration and clinical significance of the nomogram. RESULTS In the training cohort, the C-index and receiver operating characteristic were 0.819 and the 95% CI was 0.776-0.858, with a specificity and sensitivity of 77% and 74.68%, respectively. In the validation cohort, the C-index was 0.853; sensitivity and specificity were 77.6% and 88.1%, respectively. The decision curve analysis showed that the model's prediction was effective and cumulative hazard analysis demonstrated that the high-risk score group was more likely to develop T2DM than the low-risk score group. CONCLUSION This nomogram may help clinicians screen abdominal obesity at a high risk for T2DM.
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Affiliation(s)
- Caixia Tan
- The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China
| | - Bo Li
- The Second Affiliated Hospital, Department of Critical Care Medicine, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China
| | - Lingzhi Xiao
- The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China
| | - Yun Zhang
- The Second Affiliated Hospital, Department of Emergency Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, People’s Republic of China
| | - Yingjie Su
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of China
| | - Ning Ding
- Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, People’s Republic of China
- Correspondence: Ning Ding, Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, No. 161 Shaoshan South Road, Changsha, People’s Republic of China, Email
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Pan L, Chen L, Lv J, Pang Y, Guo Y, Pei P, Du H, Yang L, Millwood IY, Walters RG, Chen Y, Hua Y, Sohoni R, Sansome S, Chen J, Yu C, Chen Z, Li L. Association of Red Meat Consumption, Metabolic Markers, and Risk of Cardiovascular Diseases. Front Nutr 2022; 9:833271. [PMID: 35495958 PMCID: PMC9051033 DOI: 10.3389/fnut.2022.833271] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The metabolic mechanism of harmful effects of red meat on the cardiovascular system is still unclear. The objective of the present study is to investigate the associations of self-reported red meat consumption with plasma metabolic markers, and of these markers with the risk of cardiovascular diseases (CVD). Methods Plasma samples of 4,778 participants (3,401 CVD cases and 1,377 controls) aged 30-79 selected from a nested case-control study based on the China Kadoorie Biobank were analyzed by using targeted nuclear magnetic resonance to quantify 225 metabolites or derived traits. Linear regression was conducted to evaluate the effects of self-reported red meat consumption on metabolic markers, which were further compared with the effects of these markers on CVD risk assessed by logistic regression. Results Out of 225 metabolites, 46 were associated with red meat consumption. Positive associations were observed for intermediate-density lipoprotein (IDL), small high-density lipoprotein (HDL), and all sizes of low-density lipoprotein (LDL). Cholesterols, phospholipids, and apolipoproteins within various lipoproteins, as well as fatty acids, total choline, and total phosphoglycerides, were also positively associated with red meat consumption. Meanwhile, 29 out of 46 markers were associated with CVD risk. In general, the associations of metabolic markers with red meat consumption and of metabolic markers with CVD risk showed consistent direction. Conclusions In the Chinese population, red meat consumption is associated with several metabolic markers, which may partially explain the harmful effect of red meat consumption on CVD.
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Affiliation(s)
- Lang Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Lu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, National Center for Cardiovascular Diseases, Beijing, China
| | - Pei Pei
- National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yujie Hua
- Noncommunicable Diseases Prevention and Control Department, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Rajani Sohoni
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Sam Sansome
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
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Guo Z, Liu L, Yu F, Cai Y, Wang J, Gao Y, Ping Z. The causal association between body mass index and type 2 diabetes mellitus-evidence based on regression discontinuity design. Diabetes Metab Res Rev 2021; 37:e3455. [PMID: 33860627 DOI: 10.1002/dmrr.3455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 02/23/2021] [Accepted: 03/06/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE This study aimed to investigate and determine the precise causal association between body mass index (BMI) and type 2 diabetes mellitus (T2DM) using a regression discontinuity design (RDD). METHODS The cross-sectional data of 8550 participants were from the China Health and Nutrition Survey (CHNS) in 2015. Influencing factors with statistically significant were selected with logistic regression analysis, and a risk prediction model was established to obtain the risk of individuals suffering from T2DM. RDD was performed with BMI as the grouping variable and the risk of individuals suffering from T2DM as the outcome variable. RESULTS The predictive factors in the T2DM risk prediction model were age, gender, BMI, habitation, education, physical activity level, preference for sugary beverages, walking, self-evaluation health status and history of hypertension. The AUC (area under receiver operating characteristic curve) of the T2DM risk prediction model was 0.849 (95% CI: 0.833, 0.866). BMI was an independent risk factor for T2DM (OR = 1.109, p < 0.001); at BMI = 31 kg/m2 , the risk of T2DM increased sharply by 5.03% (p = 0.006). CONCLUSIONS There was a positive causal association between BMI and T2DM; when BMI = 31 kg/m2 , the risk of individuals suffering from T2DM was sharply increased.
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Affiliation(s)
- Zhaoyan Guo
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Li Liu
- School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Fangfang Yu
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Yaning Cai
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Junyi Wang
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Yang Gao
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhiguang Ping
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
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Chen Y, Wang Y, Xu K, Zhou J, Yu L, Wang N, Liu T, Fu C. Adiposity and Long-Term Adiposity Change Are Associated with Incident Diabetes: A Prospective Cohort Study in Southwest China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111481. [PMID: 34769995 PMCID: PMC8582792 DOI: 10.3390/ijerph182111481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/25/2021] [Accepted: 10/29/2021] [Indexed: 02/08/2023]
Abstract
In order to estimate the associations of different adiposity indicators and long-term adiposity changes with risk of incident type 2 diabetes (T2DM), we conducted a 10-year prospective cohort study of 7441 adults in Guizhou, China, from 2010 to 2020. Adiposity was measured at baseline and follow-up. Cox proportional hazard models were used to estimated hazard ratios (HRs) and 95% confidence intervals (95% CIs). A total of 764 new diabetes cases were identified over an average follow-up of 7.06 years. Adiposity indicators, body mass index (BMI), waist circumference (WC), waist-height ratio (WHtR), and long-term adiposity changes (both weight change and WC change) were significantly associated with an increased risk of T2DM (adjusted HRs: 1.16–1.48). Significant non-linear relationships were found between weight/WC change and incident T2DM. Compared with subjects with stable WC from baseline to follow-up visit, the subjects with WC gain ≥9 cm had a 1.61-fold greater risk of T2DM; those with WC loss had a 30% lower risk. Furthermore, the associations were stronger among participants aged 40 years or older, women, and Han Chinese. Preventing weight or WC gain and promoting maintenance of normal body weight or WC are important approaches for diabetes prevention, especially for the elderly, women, and Han Chinese.
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Affiliation(s)
- Yun Chen
- School of Public Health, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China; (Y.C.); (K.X.); (N.W.)
| | - Yiying Wang
- Guizhou Center for Disease Control and Prevention, Guiyang 550004, China; (Y.W.); (J.Z.); (L.Y.)
| | - Kelin Xu
- School of Public Health, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China; (Y.C.); (K.X.); (N.W.)
| | - Jie Zhou
- Guizhou Center for Disease Control and Prevention, Guiyang 550004, China; (Y.W.); (J.Z.); (L.Y.)
| | - Lisha Yu
- Guizhou Center for Disease Control and Prevention, Guiyang 550004, China; (Y.W.); (J.Z.); (L.Y.)
| | - Na Wang
- School of Public Health, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China; (Y.C.); (K.X.); (N.W.)
| | - Tao Liu
- Guizhou Center for Disease Control and Prevention, Guiyang 550004, China; (Y.W.); (J.Z.); (L.Y.)
- Correspondence: (T.L.); (C.F.); Tel.: +86-21-3356-3933 (C.F.)
| | - Chaowei Fu
- School of Public Health, Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China; (Y.C.); (K.X.); (N.W.)
- Correspondence: (T.L.); (C.F.); Tel.: +86-21-3356-3933 (C.F.)
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Wang H, Zhao M, Magnussen CG, Xi B. Change in waist circumference over 2 years and the odds of left ventricular hypertrophy among Chinese children. Nutr Metab Cardiovasc Dis 2021; 31:2484-2489. [PMID: 34088584 DOI: 10.1016/j.numecd.2021.04.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/18/2021] [Accepted: 04/30/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND AND AIMS High waist circumference (WC) is associated with left ventricular mass index (LVMI) in childhood. However, no studies have assessed the association between WC change and left ventricular hypertrophy (LVH) in childhood. This study aimed to investigate the association between change in WC status over 2 years on LVH among Chinese children. METHODS AND RESULTS Data were from a population-based prospective cohort study in China. Children without LVH at baseline (n = 1067) were assigned to four WC status change groups (persistent normal WC, WC loss, WC gain, and persistent abdominal obesity). Over a 2-year follow-up, 103 (out of 1067) children had LVH. LVMI levels were the highest among the persistent abdominal obesity group (31.5 ± 3.8 g/m 2.7), lower in the WC gain group (31.0 ± 3.6 g/m 2.7) and the WC loss group (29.8 ± 3.7 g/m 2.7), and lowest in the persistent normal WC group (29.1 ± 3.7 g/m 2.7). Compared with children in the persistent normal WC group, the odds of LVH was highest in the persistent abdominal obesity group [odds ratio (OR) = 3.57, 95% confidence interval (CI): 2.18-5.83], followed by the WC gain group (OR = 2.85, 95% CI: 1.50-5.41). In contrast, the odds of LVH was not increased in the WC loss group (OR = 0.93, 95% CI: 0.21-4.07). CONCLUSION Although these findings highlight the importance of maintaining normal WC in childhood to reduce the odds of developing LVH, our data suggest the increased odds associated with abdominal obesity can be reversed by WC loss.
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Affiliation(s)
- Huan Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Min Zhao
- Department of Toxicology and Nutrition, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Costan G Magnussen
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Bo Xi
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
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Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol 2021; 9:446-461. [PMID: 34097869 DOI: 10.1016/s2213-8587(21)00118-2] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 12/16/2022]
Abstract
China has experienced many drastic social and economic changes and shifts in people's lifestyles since the 1990s, in parallel with the fast rising prevalence of obesity. About half of adults and a fifth of children have overweight or obesity according to the Chinese criteria, making China the country with the highest number of people with overweight or obesity in the world. Assuming that observed time trends would continue in the future, we projected the prevalence of and the number of people affected by overweight and obesity by 2030, and the associated medical costs. The rising incidence of obesity and number of people affected, as well as the related health and economic consequences, place a huge burden on China's health-care system. China has made many efforts to tackle obesity, including the implementation of relevant national policies and programmes. However, these measures are inadequate for controlling the obesity epidemic. In the past decade, China has attached great importance to public health, and the Healthy China 2030 national strategy initiated in 2016 provides a historical opportunity to establish comprehensive national strategies for tackling obesity. China is well positioned to explore an effective model to overcome the obesity epidemic; however, strong commitment and leadership from central and local governments are needed, as well as active participation of all related society sectors and individual citizens. TRANSLATION: For the Chinese translation of the paper see Supplementary Materials section.
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Affiliation(s)
- Youfa Wang
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China.
| | - Li Zhao
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Liwang Gao
- Global Health Institute, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - An Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Xue
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, USA
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38
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Wang Z, Liu Z, He S. Fasting plasma glucose and risk of type 2 diabetes mellitus in a group of Chinese people with normoglycemia and without obesity. J Diabetes 2021; 13:601-602. [PMID: 33728817 DOI: 10.1111/1753-0407.13180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 02/05/2023] Open
Affiliation(s)
- Ziqiong Wang
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
| | - Zheng Liu
- Nursing Department, West China School of Nursing, West China Hospital of Sichuan University, Chengdu, China
| | - Sen He
- Department of Cardiology, West China Hospital of Sichuan University, Chengdu, China
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39
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Fu W, Wang C, Zou L, Jiang H, Miller M, Gan Y, Cao S, Xu H, Mao J, Yan S, Yue W, Yan F, Tian Q, Lu Z. Association of adiposity with diabetes: A national research among Chinese adults. Diabetes Metab Res Rev 2021; 37:e3380. [PMID: 32596997 DOI: 10.1002/dmrr.3380] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Adiposity is an established risk factor for diabetes. The different measurements of adiposity for predicting diabetes have been compared in recent studies in Western countries. However, similar researches among Chinese adults are limited. METHODS Data were collected from a national survey conducted during September 2014 and May 2015 Among Chinese adults aged 40 years and older across 30 China's provinces. Multilevel model analysis was performed to examine the impacts of different obesity indices [body mass index (BMI), waist circumference (WC), lipid accumulation product index (LAP), visceral adiposity index (VAI), and body adiposity index (BAI)] on the risk of diabetes. RESULTS A total of 162 880 participants were included in this study. Of them, 54.47% were female. With an increase in BMI, WC, LAP, VAI, and BAI, the prevalence of diabetes significantly grew (P < 0.001). The multilevel model analysis showed that WC has the strongest impact on diabetes prevalence, while BAI was the weakest. For one SD increment in BMI, WC, LAP, VAI, and BAI, the prevalence of diabetes increased by 27.0% (Odds Ratio (OR) = 1.270, 95% Confidence interval (CI) = 1.251-1.289), 37.4% (OR = 1.374, 95% CI = 1.346-1.401), 28.1% (OR = 1.281, 95% CI = 1.266-1.297), 22.0% (OR = 1.220, 95% CI = 1.204-1.236), and 17.4% (OR = 1.174, 95% CI = 1.151-1.192), respectively. CONCLUSION Obesity indicators of BMI, WC, LAP, VAI, and BAI have significant positive relationships with the risk of diabetes. WC has the strongest impact on diabetes, while BAI has the weakest.
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Affiliation(s)
- Wenning Fu
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chao Wang
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Zou
- Department of Neurology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Heng Jiang
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Melbourne, Victoria, Australia
- Centre for Health Equity, School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Mia Miller
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Melbourne, Victoria, Australia
| | - Yong Gan
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiyi Cao
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongbin Xu
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Mao
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shijiao Yan
- School of Public Health, Hainan Medical University, Haikou, China
- Key Laboratory of Emergency and Trauma of Ministry of Education, Hainan Medical University, Haikou, China
| | - Wei Yue
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Feng Yan
- Department of Neurosurgery, Xuanwu Hospital, Capital medical University, Beijing, China
| | - Qingfeng Tian
- College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Zuxun Lu
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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40
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Mu L, Liu J, Zhou G, Wu C, Chen B, Lu Y, Lu J, Yan X, Zhu Z, Nasir K, Spatz ES, Krumholz HM, Zheng X. Obesity Prevalence and Risks Among Chinese Adults: Findings From the China PEACE Million Persons Project, 2014-2018. Circ Cardiovasc Qual Outcomes 2021; 14:e007292. [PMID: 34107739 PMCID: PMC8204767 DOI: 10.1161/circoutcomes.120.007292] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Supplemental Digital Content is available in the text. China has seen a burgeoning epidemic of obesity in recent decades, but few studies reported nationally on obesity across socio-demographic subgroups. We sought to assess the prevalence and socio-demographic associations of obesity nationwide.
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Affiliation(s)
- Lin Mu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.).,Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | | | - Guohai Zhou
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Chaoqun Wu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Bowang Chen
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Yuan Lu
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Jiapeng Lu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Xiaofang Yan
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Zhihong Zhu
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
| | - Khurram Nasir
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Erica S Spatz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.)
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital (L.M., G.Z., Y.L., K.N., E.S.S., H.M.K.).,Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT (H.M.K.).,Department of Health Policy and Management, Yale School of Public Health, New Haven, CT (H.M.K.)
| | - Xin Zheng
- National Clinical Research Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (J.L., C.W., B.C., J.L., X.Y., Z.Z., X.Z.)
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Zeng Q, Li N, Pan XF, Chen L, Pan A. Clinical management and treatment of obesity in China. Lancet Diabetes Endocrinol 2021; 9:393-405. [PMID: 34022157 DOI: 10.1016/s2213-8587(21)00047-4] [Citation(s) in RCA: 99] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 02/08/2023]
Abstract
China has one of the largest populations with obesity in the world, and obesity has become a major challenge for the country's health-care system. Current guidelines for obesity management are not adequately supported by evidence from clinical studies in Chinese populations. Effective lifestyle interventions suitable for Chinese populations are scarce, insufficient weight-loss medications have been approved by regulatory bodies, and there is low acceptance of non-lifestyle interventions (ie, medications and surgery) among both health-care providers and the general public. Large, well designed, and well implemented clinical trials are needed to strengthen the evidence base for the clinical management of obesity in China. Obesity management can be improved through use of a tiered system involving health management centres, integrated lifestyle interventions and medical treatments, strengthened obesity education and training, and use of advanced electronic health technologies. Resource mobilisation, support from major stakeholders for people with overweight or obesity, and education and changes to social norms among the wider public are also needed. National health policies should prioritise both obesity prevention and improvement of the treatment and management of obesity.
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Affiliation(s)
- Qiang Zeng
- Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Naishi Li
- Department of Endocrinology, Key Laboratory of Endocrinology of the National Health Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiong-Fei Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
| | - An Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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42
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Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol 2021; 9:373-392. [PMID: 34022156 DOI: 10.1016/s2213-8587(21)00045-0] [Citation(s) in RCA: 571] [Impact Index Per Article: 190.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 01/15/2021] [Accepted: 02/12/2021] [Indexed: 12/11/2022]
Abstract
Obesity has become a major public health issue in China. Overweight and obesity have increased rapidly in the past four decades, and the latest national prevalence estimates for 2015-19, based on Chinese criteria, were 6·8% for overweight and 3·6% for obesity in children younger than 6 years, 11·1% for overweight and 7·9% for obesity in children and adolescents aged 6-17 years, and 34·3% for overweight and 16·4% for obesity in adults (≥18 years). Prevalence differed by sex, age group, and geographical location, but was substantial in all subpopulations. Strong evidence from prospective cohort studies has linked overweight and obesity to increased risks of major non-communicable diseases and premature mortality in Chinese populations. The growing burden of overweight and obesity could be driven by economic developments, sociocultural norms, and policies that have shaped individual-level risk factors for obesity through urbanisation, urban planning and built environments, and food systems and environments. Substantial changes in dietary patterns have occurred in China, with increased consumption of animal-source foods, refined grains, and highly processed, high-sugar, and high-fat foods, while physical activity levels in all major domains have decreased with increasing sedentary behaviours. The effects of dietary factors and physical inactivity intersect with other individual-level risk factors such as genetic susceptibility, psychosocial factors, obesogens, and in-utero and early-life exposures. In view of the scarcity of research around the individual and collective roles of these upstream and downstream factors, multidisciplinary and transdisciplinary studies are urgently needed to identify systemic approaches that target both the population-level determinants and individual-level risk factors for obesity in China.
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Affiliation(s)
- Xiong-Fei Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Limin Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - An Pan
- Department of Epidemiology and Biostatistics and Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5527460. [PMID: 34095297 PMCID: PMC8140840 DOI: 10.1155/2021/5527460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/08/2021] [Indexed: 12/23/2022]
Abstract
Background The prevention of type 2 diabetes (T2D) and its associated complications has become a major priority of global public health. In addition, there is growing evidence that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of diabetes. Therefore, the purpose of this study was to develop and validate a nomogram based on independent predictors to better assess the 8-year risk of T2D in Japanese patients with NAFLD. Methods This is a historical cohort study from a collection of databases that included 2741 Japanese participants with NAFLD without T2D at baseline. All participants were randomized to a training cohort (n = 2058) and a validation cohort (n = 683). The data of the training cohort were analyzed using the least absolute shrinkage and selection operator method to screen the suitable and effective risk factors for Japanese patients with NAFLD. A cox regression analysis was applied to build a nomogram incorporating the selected features. The C-index, receiver operating characteristic curve (ROC), calibration plot, decision curve analysis, and Kaplan-Meier analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. The results were reevaluated by internal validation in the validation cohort. Results We developed a simple nomogram that predicts the risk of T2D for Japanese patients with NAFLD by using the parameters of smoking status, waist circumference, hemoglobin A1c, and fasting blood glucose. For the prediction model, the C-index of training cohort and validation cohort was 0.839 (95% confidence interval (CI), 0.804-0.874) and 0.822 (95% CI, 0.777-0.868), respectively. The pooled area under the ROC of 8-year T2D risk in the training cohort and validation cohort was 0.811 and 0.805, respectively. The calibration curve indicated a good agreement between the probability predicted by the nomogram and the actual probability. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions We developed and validated a nomogram for the 8-year risk of incident T2D among Japanese patients with NAFLD. Our nomogram can effectively predict the 8-year incidence of T2D in Japanese patients with NAFLD and helps to identify people at high risk of T2D early, thus contributing to effective prevention programs for T2D.
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Zhang W, He K, Zhao H, Hu X, Yin C, Zhao X, Shi S. Association of body mass index and waist circumference with high blood pressure in older adults. BMC Geriatr 2021; 21:260. [PMID: 33874889 PMCID: PMC8056549 DOI: 10.1186/s12877-021-02154-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 03/15/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The relationship between obesity and prevalent high blood pressure in older adults has predominantly been estimated using categorical measures of body mass index (BMI) and waist circumference (WC), masking the shape of the dose-response relationship. We aimed to examine the precise relationship of BMI, WC with high blood pressure and to assess the appropriate level of BMI and WC for high blood pressure. METHODS We examined data for 126,123 individuals in Xinzheng city aged ≥60 years from a population based study from January to December 2019. Logistic regression and restricted cubic spline models were applied to assess the relationship and the appropriate level of BMI and WC for high blood pressure. An additive interaction analysis was used to test synergistic effects between a higher BMI and WC for high blood pressure. RESULTS The full-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of an increase of 1 kg/m2 in BMI and 1 cm in WC for high blood pressure were 1.084 (1.080-1.087) and 1.026(1.024-1.027), respectively. Multivariable adjusted restricted cubic spline analyses showed the nonlinear relationships of BMI and WC with high blood pressure in both men and women (all P < 0.001). The risk of high blood pressure increased steeply with increasing BMI from ≥25 kg/m2 and WC ≥ 88 cm or 86 cm for males and females, respectively. And we observed a significant additive interaction between a higher BMI and WC such that the prevalence of high blood pressure was significantly enhanced. CONCLUSION These findings suggest increased high blood pressure prevalence in the older adults with increased BMI and WC. BMI ≤ 25 kg/m2 and WC ≤ 88 cm or 86 cm for males and females may be the best suggestion with regard to primary prevention of high blood pressure in older adults.
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Affiliation(s)
- Wenli Zhang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Songhe Shi, 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China
| | - Kun He
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Songhe Shi, 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China
| | - Hao Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Songhe Shi, 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China
| | - Xueqi Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Songhe Shi, 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China
| | - Chunyu Yin
- Department of Neurology, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China
| | - Xiaoyan Zhao
- Department of Neurology, Chinese People's Liberation Army General Hospital, Beijing, People's Republic of China
| | - Songhe Shi
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Songhe Shi, 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
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Purnell JQ, Dewey EN, Laferrère B, Selzer F, Flum DR, Mitchell JE, Pomp A, Pories WJ, Inge T, Courcoulas A, Wolfe BM. Diabetes Remission Status During Seven-year Follow-up of the Longitudinal Assessment of Bariatric Surgery Study. J Clin Endocrinol Metab 2021; 106:774-788. [PMID: 33270130 PMCID: PMC7947785 DOI: 10.1210/clinem/dgaa849] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Indexed: 12/13/2022]
Abstract
CONTEXT Few studies have examined the clinical characteristics that predict durable, long-term diabetes remission after bariatric surgery. OBJECTIVE To compare diabetes prevalence and remission rates during 7-year follow-up after Roux-en-Y gastric bypass (RYGB) and laparoscopic gastric banding (LAGB). DESIGN An observational cohort of adults with severe obesity recruited between 2006 and 2009 who completed annual research assessments for up to 7 years after RYGB or LAGB. SETTING Ten US hospitals. PARTICIPANTS A total sample of 2256 participants, 827 with known diabetes status at both baseline and at least 1 follow-up visit. INTERVENTIONS Roux-en-Y gastric bypass or LAGB. MAIN OUTCOME MEASURES Diabetes rates and associations of patient characteristics with remission status. RESULTS Diabetes remission occurred in 57% (46% complete, 11% partial) after RYGB and 22.5% (16.9% complete, 5.6% partial) after LAGB. Following both procedures, remission was greater in younger participants and those with shorter diabetes duration, higher C-peptide levels, higher homeostatic model assessment of β-cell function (HOMA %B), and lower insulin usage at baseline, and with greater postsurgical weight loss. After LAGB, reduced HOMA insulin resistance (IR) was associated with a greater likelihood of diabetes remission, whereas increased HOMA-%B predicted remission after RYGB. Controlling for weight lost, diabetes remission remained nearly 4-fold higher compared with LAGB. CONCLUSIONS Durable, long-term diabetes remission following bariatric surgery is more likely when performed soon after diagnosis when diabetes medication burden is low and beta-cell function is preserved. A greater weight-independent likelihood of diabetes remission after RYGB than LAGB suggests mechanisms beyond weight loss contribute to improved beta-cell function after RYGB.Trial Registration clinicaltrials.gov Identifier: NCT00465829.
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Affiliation(s)
- Jonathan Q Purnell
- Department of Medicine, Oregon Health & Science University, Portland, OR, USA
- Correspondence and Reprint Requests: Jonathan Q Purnell, MD, Knight Cardiovascular Institute, HRC5N, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239. E-mail:
| | - Elizabeth N Dewey
- Department of Surgery, Oregon Health & Science University, Portland, OR, USA
| | - Blandine Laferrère
- Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Faith Selzer
- Department of Orthopedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David R Flum
- Department of Surgery, University of Washington, Seattle, WA, USA
| | - James E Mitchell
- Department of Clinical Neuroscience, University of North Dakota, Fargo, ND, USA
| | - Alfons Pomp
- Department of Surgery, Weill Cornell Medical College, New York, NY, USA
| | - Walter J Pories
- Department of Surgery, East Carolina University, Greenville, NC, USA
| | - Thomas Inge
- Department of Surgery, University of Colorado, Denver, CO, USA
| | - Anita Courcoulas
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce M Wolfe
- Department of Surgery, Oregon Health & Science University, Portland, OR, USA
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Nie L, Wei D, Liu P, Zhang L, Fan K, Song Y, Wang M, Wang L, Xu Q, Wang J, Liu X, Li L, Mao Z, Huang H, Wang C, Huo W. C-Reactive Protein Mediates the Effect of Serum Progesterone on Obesity for Men and Postmenopausal Women in Henan Rural Cohort Study. J Inflamm Res 2021; 14:633-644. [PMID: 33692633 PMCID: PMC7939495 DOI: 10.2147/jir.s293882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/03/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose Studies of progesterone with obesity have been lacking, and no prior studies have investigated progesterone and C-reactive protein in rural natural populations. This study aimed to investigate the association of serum progesterone with obesity based on anthropometric parameters in Henan Rural Cohort, then further to explore the potential role of C-reactive protein in this association. Patients and Methods A total of 4687 participants (2474 men and 2213 postmenopausal women) from the Henan Rural Cohort study were included. Logistic regression analysis, linear regression analysis, and restricted cubic splines were performed to estimate the relationships between progesterone, C-reactive protein, obesity, and obesity-related parameters. Mediation analysis was carried out to assess the intermediary role of C-reactive protein played in the association between progesterone and obesity. Results After controlling for confounders, the odds ratios of obesity no matter how obesity was defined was associated with progesterone (P trend <0.05). Moreover, progesterone was related to obesity-related parameters. Also, C-reactive protein increased with the elevation of progesterone, and C-reactive protein was associated with obesity no matter how obesity was defined (P trend <0.001). Mediation analysis indicated that hsCRP had the mediating effects on the obesity, and the proportion of the effects were 15.35% and full mediating effect (defined by body mass index), 15.46% and 12.24% (defined by waist circumference), 19.14% and 12.12% (defined by waist-to-hip ratio), 23.81% and 15.09% (defined by waist-to-height ratio) for men and postmenopausal women, relatively. Conclusion In the Chinese rural men and postmenopausal women population, progesterone and C-reactive protein are related to obesity. We firstly found C-reactive protein partly mediates the effect of progesterone on obesity, indicating that systemic inflammation played a critical role in the association.
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Affiliation(s)
- Luting Nie
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dandan Wei
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Pengling Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Li Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Keliang Fan
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yu Song
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Mian Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Lulu Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Qingqing Xu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Juan Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xiaotian Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Linlin Li
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Zhenxing Mao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Hui Huang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Wenqian Huo
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
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Causal associations of waist circumference and waist-to-hip ratio with type II diabetes mellitus: new evidence from Mendelian randomization. Mol Genet Genomics 2021; 296:605-613. [PMID: 33629185 DOI: 10.1007/s00438-020-01752-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 12/13/2020] [Indexed: 10/22/2022]
Abstract
Type II diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. The associations among waist circumference (WC), waist-to-hip ratio (WHR), and T2DM were discovered in observational studies. However, the causality of these associations still remains unknown. The present study aims to apply two-sample Mendelian randomization (TSMR) using genetic variants as instrumental variables (IVs) to evaluate the causality among WC, WHR, and T2DM. The participants were from three independent studies in genome-wide association studies (GWAS) datasets, which included 127,997 Europeans for WC, 73,137 Europeans for WHR and 659,316 Europeans for T2DM. Furthermore, 16 were associated WC SNPs and eight were associated WHR SNPs as instrument variables were selected for TSMR using P < 5 × 10-8 standard. The pooled odd ratios (ORs) for the assessment of higher WC and WHR on the risk of T2DM for these SNPs were calculated using inverse variance weighted (IVW) method, and validated through extensively complementary analyses. The OR for T2DM per SD (cm) higher WC was 2.623 (95% CI 2.286-3.010, P = 5.000E-43), and the OR for T2DM per SD (cm) higher WHR was 1.751 (95% CI 1.122-2.733, P = 0.014). Consistent results for other methods were obtained. Furthermore, the range of OR fluctuation between WC and T2DM was from 2.623 to 2.986, while that between WHR and T2DM was from 0.990 to 2.931. Overall, these present results provide genetic support that suggests that the use of TSMR, and higher WC and WHR increased the T2DM risk among the European population.
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Han M, Qin P, Li Q, Qie R, Liu L, Zhao Y, Liu D, Zhang D, Guo C, Zhou Q, Tian G, Huang S, Wu X, Li Y, Yang X, Zhao Y, Feng Y, Liu Y, Li H, Sun X, Chen Q, Wang T, Chen X, Hu D, Zhang M. Chinese visceral adiposity index: A reliable indicator of visceral fat function associated with risk of type 2 diabetes. Diabetes Metab Res Rev 2021; 37:e3370. [PMID: 32562335 DOI: 10.1002/dmrr.3370] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/19/2020] [Accepted: 06/08/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The evidence of the association between Chinese visceral adiposity index (CVAI) and risk of type 2 diabetes mellitus (T2DM) is limited. We explored the association of CVAI with T2DM and directly compared with the predictive power of CVAI with other visceral obesity indices (visceral adiposity index, waist to height ratio, waist circumference and body mass index) based on a large prospective study. METHODS We conducted a population-based study of 12 237 Chinese participants. Cox proportional-hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between CVAI and T2DM. RESULTS During follow-up (median: 6.01 years), the incidence of T2DM was 3.29, 7.34, 12.37 and 23.72 per 1000 person-years for quartiles 1, 2, 3 and 4 of CVAI, respectively. The risk of T2DM was increased with quartiles 2, 3 and 4 vs quartile 1 of CVAI (HR 2.12 [95% CI 1.50-3.00], 2.94 [2.10-4.13] and 5.01 [3.57-7.04], Ptrend < 0.001). Per-SD increase in CVAI was associated with a 72% increased risk of T2DM (HR 1.72 [95% CI 1.56-1.88]). Sensitivity analyses did not alter the association. The area under receiver operating characteristic curve was significantly higher for CVAI than other visceral obesity indices (all P <.001). Similar results were observed in stratified analyses by sex. CONCLUSIONS Our findings show a positive association between CVAI and risk of T2DM. CVAI has the best performance in predicting incident T2DM, so the index might be a reliable and applicable indicator identifying people at high risk of T2DM.
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Affiliation(s)
- Minghui Han
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Pei Qin
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Quanman Li
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
- Community Health Management Center, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, PR China
| | - Ranran Qie
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Leilei Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yang Zhao
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Dechen Liu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Dongdong Zhang
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Chunmei Guo
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Qionggui Zhou
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Gang Tian
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Shengbing Huang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Xiaoyan Wu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Yang Li
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Xingjin Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yifei Feng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
| | - Yu Liu
- Community Health Management Center, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Honghui Li
- Community Health Management Center, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Xizhuo Sun
- Community Health Management Center, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
| | - Qing Chen
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, PR China
| | - Tieqiang Wang
- Key Lab of Epidemiology, Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, PR China
| | - Xiaoliang Chen
- Key Lab of Epidemiology, Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, PR China
| | - Dongsheng Hu
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China
- Community Health Management Center, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
- Department of Mental Health, Bao'an Chronic Diseases Prevent and Cure Hospital, Shenzhen, Guangdong, PR China
- Key Lab of Epidemiology, Department of Chronic Disease Control and Prevention, Shenzhen Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, PR China
| | - Ming Zhang
- Department of Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, PR China
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Yuan Y, Wang H, He J, Sun H, Zhu D, Bi Y. Peripheral Administration of NMU Promotes White Adipose Tissue Beiging and Improves Glucose Tolerance. Int J Endocrinol 2021; 2021:6142096. [PMID: 34422045 PMCID: PMC8373479 DOI: 10.1155/2021/6142096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/25/2021] [Accepted: 07/21/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Targeting white adipose tissue (WAT) beiging has been proposed as an effective way to increase thermogenesis and improve glucose metabolism. Neuromedin U (NMU) is a neuropeptide that could increase energy expenditure, while its effects on WAT beiging and glucose homeostasis remain to be investigated. METHODS Male C57BL/6 mice were fed with high fat diet (HFD) to induce obesity and hyperglycemia and then treated with chronic subcutaneous injection of NMU. Body weight and food intake were recorded daily. After 14 days of injection, intraperitoneal glucose tolerance tests and 18F-fluorodeoxyglucose micro-positron emission tomography/computed tomography (18F-FDG micro-PET/CT) scans were conducted. Subcutaneous WAT (sWAT) and interscapular brown adipose tissue were collected for the evaluation of adipocyte size, expression of uncoupling protein 1 (Ucp1), and other thermogenic-related genes. Stromal vascular fraction of subcutaneous WAT was extracted for the measurement of type 2 innate lymphocytes (ILC2s) proportions. RESULTS Glucose tolerance was markedly improved by peripherally administered NMU. Micro-PET/CT suggested that NMU promoted WAT beiging, which was further confirmed by haematoxylin and eosin (H&E) staining and immunohistochemistry. In diet-induced-obese (DIO) mice, NMU activated thermogenic-related genes in WAT. In addition, NMU stimulated ILC2s in the stromal vascular fraction of WAT. CONCLUSION Taken together, our study indicates that peripheral administration of NMU is a potential therapeutic strategy for the promotion of WAT beiging and the improvement of impaired glucose tolerance.
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Affiliation(s)
- Yue Yuan
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hongdong Wang
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jielei He
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Haixiang Sun
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Dalong Zhu
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yan Bi
- Department of Endocrinology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Wen J, Wang A, Liu G, Wang M, Zuo Y, Li W, Zhai Q, Mu Y, Gaisano HY, He Y, Dou J. Elevated triglyceride-glucose (TyG) index predicts incidence of Prediabetes: a prospective cohort study in China. Lipids Health Dis 2020; 19:226. [PMID: 33059672 PMCID: PMC7565371 DOI: 10.1186/s12944-020-01401-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/06/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Prediabetes has become a pandemic. This study aimed to identify a better predictor for the incidence of prediabetes, which we hypothesize to be the triglyceride-glucose (TyG) index, a simplified insulin resistance index. We compared its predictive value with the other common risk factors of prediabetes. METHODS The participants of this analysis were derived from the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 4543 participants without initial prediabetes or diabetes were followed up for 3.25 years. Using multivariate logistic regression model, the associations between baseline obesity, lipid profiles and non-insulin-based insulin resistance indices with the incidence of prediabetes were analyzed. To assess which is better predictor for the incidence of prediabetes, the area under curves (AUCs) calculated from the receiver operating characteristic curve analyses were used to evaluate and compare with the predictive value of the different indices. RESULTS During the 3.25 years, 1071 out of the 4543 participants developed prediabetes. Using the logistic regression analysis adjusted for some potential confounders, the risk of incidence of prediabetes increased 1.38 (1.28-1.48) fold for each 1-SD increment of TyG index. The predictive ability (assessed by AUCs) of TyG index for predicting prediabetes was 0.60 (0.58-0.62), which was superior to the indices of obesity, lipid profiles and other non-insulin-based insulin resistance indices. Although the predictive ability of the TyG index was overall similar to fasting plasma glucose (FPG) (P = 0.4340), TyG index trended higher than FPG in females (0.62 (0.59-0.64) vs. 0.59 (0.57-0.61), P = 0.0872) and obese subjects (0.59 (0.57-0.62) vs. 0.57 (0.54-0.59), P = 0.1313). TyG index had superior predictive ability for the prediabetic phenotype with isolated impaired glucose tolerance compared with FPG (P < 0.05) and other indices. Furthermore, TyG index significantly improved the C statistic (0.62 (0.60-0.64)), integrated discrimination improvement (1.89% (1.44-2.33%)) and net reclassification index (28.76% (21.84-35.67%)) of conventional model in predicting prediabetes than other indices. CONCLUSIONS TyG could be a potential predictor to identify the high risk individuals of prediabetes.
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Affiliation(s)
- Jing Wen
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Anping Wang
- Department of Endocrinology, the First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Guangxu Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Meiping Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Yingting Zuo
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Wei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Qi Zhai
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Yiming Mu
- Department of Endocrinology, the First Medical Center, Chinese PLA General Hospital, Beijing, China
| | | | - Yan He
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China. .,Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
| | - Jingtao Dou
- Department of Endocrinology, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
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