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Xing XY, Wang XY, Fang X, Xu JQ, Chen YJ, Xu W, Wang HD, Liu ZR, Tao SS. Glycemic control and its influencing factors in type 2 diabetes patients in Anhui, China. Front Public Health 2022; 10:980966. [PMID: 36267995 PMCID: PMC9577366 DOI: 10.3389/fpubh.2022.980966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/20/2022] [Indexed: 01/25/2023] Open
Abstract
Objective To investigate the status of glycemic control and analyze its influencing factors in patients with type 2 diabetes (T2D) in Anhui, China. Methods 1,715 T2D patients aged 18-75 years old were selected from 4 counties or districts in Anhui Province in 2018, using a convenience sampling method. All patients have undergone a questionnaire survey, physical examination, and a glycosylated hemoglobin (HbA1c) test. According to the 2022 American Diabetes Association criteria, HbA1c was used to evaluate the glycemic control status of patients, and HbA1c < 7.0% was defined as good glycemic control. The influencing factors of glycemic control were analyzed by multivariate unconditional logistic regression. Results The prevalence of good glycemic control among people with T2D in the Anhui Province was low (22.97%). On univariate analysis, gender, education level, occupation, region, smoking, drinking, waist circumference and disease duration (all P < 0.05) were significantly associated with glycemic control. The factors associated with pool glycemic control were female gender [OR = 0.67, 95%CI (0.52, 0.86), P = 0.001], higher level of education [OR = 0.47, 95%CI (0.27, 0.83), P = 0.001], living in rural areas [OR = 1.77, 95%CI (1.39, 2.26), P < 0.001], central obesity [OR = 1.58, 95%CI (1.19, 2.09), P = 0.001] and longer duration of disease [OR = 2.66, 95%CI (1.91, 3.69), P < 0.001]. Conclusions The prevalence of good glycemic control in people with T2D in Anhui Province was relatively low, and gender, region, education level, central obesity and course of the disease were influencing factors. The publicity and education on the importance of glycemic control should be further strengthened in T2D patients, and targeted intervention measures should be carried out for risk groups.
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Affiliation(s)
- Xiu-Ya Xing
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China
| | - Xin-Yi Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China,First Clinical Medical College, Anhui Medical University, Hefei, China
| | - Xi Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Jing-Qiao Xu
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China
| | - Ye-Ji Chen
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China
| | - Wei Xu
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China
| | - Hua-Dong Wang
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China
| | - Zhi-Rong Liu
- Department of Chronic Non-communicable Disease Prevention and Control, Anhui Provincial Center for Disease Control and Prevention, Hefei, China,Zhi-Rong Liu
| | - Sha-Sha Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China,*Correspondence: Sha-Sha Tao
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Cross-sectional associations between central and general adiposity with albuminuria: observations from 400,000 people in UK Biobank. Int J Obes (Lond) 2020; 44:2256-2266. [PMID: 32678323 PMCID: PMC7577847 DOI: 10.1038/s41366-020-0642-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 06/28/2020] [Accepted: 07/06/2020] [Indexed: 01/19/2023]
Abstract
Background Whether measures of central adiposity are more or less strongly associated with risk of albuminuria than body mass index (BMI), and by how much diabetes/levels of glycosylated haemoglobin (HbA1c) explain or modify these associations, is uncertain. Methods Ordinal logistic regression was used to estimate associations between values of central adiposity (waist-to-hip ratio) and, separately, general adiposity (BMI) with categories of urinary albumin-to-creatinine ratio (uACR) in 408,527 UK Biobank participants. Separate central and general adiposity-based models were initially adjusted for potential confounders and measurement error, then sequentially, models were mutually adjusted (e.g. waist-to-hip ratio adjusted for BMI, and vice versa), and finally they were adjusted for potential mediators. Results Levels of albuminuria were generally low: 20,425 (5%) had a uACR ≥3 mg/mmol. After adjustment for confounders and measurement error, each 0.06 higher waist-to-hip ratio was associated with a 55% (95%CI 53–57%) increase in the odds of being in a higher uACR category. After adjustment for baseline BMI, this association was reduced to 32% (30–34%). Each 5 kg/m2 higher BMI was associated with a 47% (46–49%) increase in the odds of being in a higher uACR category. Adjustment for baseline waist-to-hip ratio reduced this association to 35% (33–37%). Those with higher HbA1c were at progressively higher odds of albuminuria, but positive associations between both waist-to-hip ratio and BMI were apparent irrespective of HbA1c. Altogether, about 40% of central adiposity associations appeared to be mediated by diabetes, vascular disease and blood pressure. Conclusions Conventional epidemiological approaches suggest that higher waist-to-hip ratio and BMI are independently positively associated with albuminuria. Adiposity–albuminuria associations appear strong among people with normal HbA1c, as well as people with pre-diabetes or diabetes.
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Man REK, Gan ATL, Fenwick EK, Gupta P, Wong MYZ, Wong TY, Tan GSW, Teo BW, Sabanayagam C, Lamoureux EL. The Relationship between Generalized and Abdominal Obesity with Diabetic Kidney Disease in Type 2 Diabetes: A Multiethnic Asian Study and Meta-Analysis. Nutrients 2018; 10:nu10111685. [PMID: 30400648 PMCID: PMC6266073 DOI: 10.3390/nu10111685] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 12/31/2022] Open
Abstract
This study examined the associations of body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR) and waist-height ratio (WHtR) with diabetic kidney disease (DKD) in a clinical sample of Asian patients with type 2 diabetes (T2DM); substantiated with a meta-analysis of the above associations. We recruited 405 patients with T2DM (mean (standard deviation (SD)) age: 58 (7.5) years; 277 (68.4%) male; 203 (50.1%) with DKD) from a tertiary care centre in Singapore. DKD was defined as urinary albumin-creatinine ratio >3.3 mg/mmoL and/or estimated glomerular filtration rate <60 mL/min/1.73 m2. All exposures were analysed continuously and categorically (World Health Organization cut-points for BMI and WC; median for WHR and WHtR) with DKD using stepwise logistic regression models adjusted for traditional risk factors. Additionally, we synthesized the pooled odds ratio of 18 studies (N = 19,755) in a meta-analysis of the above relationships in T2DM. We found that overweight and obese persons (categorized using BMI) were more likely to have DKD compared to under/normal weight individuals, while no associations were found for abdominal obesity exposures. In meta-analyses however, all obesity parameters were associated with increased odds of DKD. The discordance in our abdominal obesity findings compared to the pooled analyses warrants further validation via longitudinal cohorts.
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Affiliation(s)
- Ryan Eyn Kidd Man
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
- Duke-NUS Medical School, Singapore 169857, Singapore.
| | - Alfred Tau Liang Gan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
| | - Eva Katie Fenwick
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
- Duke-NUS Medical School, Singapore 169857, Singapore.
| | - Preeti Gupta
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
| | - Mark Yu Zheng Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
- Duke-NUS Medical School, Singapore 169857, Singapore.
- Singapore National Eye Centre, Singapore 168751, Singapore.
| | | | - Boon Wee Teo
- Department of Nephrology, University Medicine Cluster, National University Health System, Singapore 119074, Singapore.
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
- Duke-NUS Medical School, Singapore 169857, Singapore.
| | - Ecosse Luc Lamoureux
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 169856, Singapore.
- Duke-NUS Medical School, Singapore 169857, Singapore.
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Wang X, Chen J, Liu X, Gao F, Zhao H, Han D, Jing X, Liu Y, Cui Z, Li C, Ma J. Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis. Diabetes Ther 2017; 8:1379-1392. [PMID: 29094299 PMCID: PMC5688992 DOI: 10.1007/s13300-017-0327-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Lifestyle behaviors are essential elements of diabetes care. The aims of this study were to identify distinct subgroups of people with type 2 diabetes based on personal levels of lifestyle behaviors and explore the different characteristics across these subgroups. METHODS In 2015 and 2016, 1504 outpatients with a diagnosis of type 2 diabetes were selected via two-stage simple random sampling from 10 municipal district hospitals in Tianjin. Participants accepted an invitation by experienced physicians to complete a questionnaire containing demographic and lifestyle content. Clinical data were collected by reviewing medical records. Latent class analysis was applied to identify patterns of lifestyle behaviors. Multinomial logistic regression was used to investigate the characteristics of the subgroups. RESULTS The final model yielded a four-class solution: the healthy behavioral group, unhealthy diet and less activity group, smoking and drinking group, and sedentary and extremely inactive group. Further analysis found that variables, including age, sex, general/central obesity, treatment modalities, glycemic control, diabetes duration, and diabetes-related complications and comorbidities, were disproportionately distributed across the four latent classes (P < 0.05). Participants in the unhealthy diet and less activity group were more likely to have a longer duration of diabetes, poor glycemic control and more diabetes-related diseases relative to the other three latent classes. CONCLUSIONS Identification and characterization of subgroups based on lifestyle behaviors in individuals with type 2 diabetes can help health care providers to shift to targeted intervention strategies.
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Affiliation(s)
- Xuying Wang
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Jiageng Chen
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Xiaoqian Liu
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Fei Gao
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Haozuo Zhao
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Duolan Han
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Xiyue Jing
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Yuanyuan Liu
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Zhuang Cui
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China
| | - Changping Li
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
| | - Jun Ma
- Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
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Ma Y, Li X, Zhao D, Wu R, Sun H, Chen S, Wang L, Fang X, Huang J, Li X, Zhang Y, Jiang G, Zhang D, Pan Y, An T, Shi Y, Zuo J, Yu N, Gao S. Association between cognitive vulnerability to depression - dysfunctional attitudes and glycaemic control among in-patients with type 2 diabetes in a hospital in Beijing: a multivariate regression analysis. PSYCHOL HEALTH MED 2017. [DOI: 10.1080/13548506.2017.1339894] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Yue Ma
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Xun Li
- Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Dandan Zhao
- Diabetes Research Centre, Beijing University of Chinese Medicine, Beijing, China
| | - Rui Wu
- Department of Endocrinology, South Area of Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Hongfeng Sun
- Department of Endocrinology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Shibo Chen
- Department of Endocrinology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Linyun Wang
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Xue Fang
- The First Clinical Medical School, Beijing University of Chinese Medicine, Beijing, China
| | - Jin Huang
- The First Clinical Medical School, Beijing University of Chinese Medicine, Beijing, China
| | - Xia Li
- The First Clinical Medical School, Beijing University of Chinese Medicine, Beijing, China
| | - Ying Zhang
- Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Guangjian Jiang
- Diabetes Research Centre, Beijing University of Chinese Medicine, Beijing, China
| | - Dongwei Zhang
- Diabetes Research Centre, Beijing University of Chinese Medicine, Beijing, China
| | - Yanyun Pan
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Tian An
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Yue Shi
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Jiacheng Zuo
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Na Yu
- School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Sihua Gao
- Diabetes Research Centre, Beijing University of Chinese Medicine, Beijing, China
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