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Geng T, Lu Q, Jiang L, Guo K, Yang K, Liao YF, He M, Liu G, Tang H, Pan A. Circulating concentrations of bile acids and prevalent chronic kidney disease among newly diagnosed type 2 diabetes: a cross-sectional study. Nutr J 2024; 23:28. [PMID: 38429722 PMCID: PMC10908139 DOI: 10.1186/s12937-024-00928-2] [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: 10/25/2023] [Accepted: 02/23/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND The relationship between circulating bile acids (BAs) and kidney function among patients with type 2 diabetes is unclear. We aimed to investigate the associations of circulating concentrations of BAs, particularly individual BA subtypes, with chronic kidney disease (CKD) in patients of newly diagnosed type 2 diabetes. METHODS In this cross-sectional study, we included 1234 newly diagnosed type 2 diabetes who participated in an ongoing prospective study, the Dongfeng-Tongji cohort. Circulating primary and secondary unconjugated BAs and their taurine- or glycine-conjugates were measured using ultraperformance liquid chromatography-tandem mass spectrometry. CKD was defined as eGFR < 60 ml/min per 1.73 m2. Logistic regression model was used to compute odds ratio (OR) and 95% confidence interval (CI). RESULTS After adjusting for multiple testing, higher levels of total primary BAs (OR per standard deviation [SD] increment: 0.78; 95% CI: 0.65-0.92), cholate (OR per SD: 0.78; 95% CI: 0.66-0.92), chenodeoxycholate (OR per SD: 0.81; 95% CI: 0.69-0.96), glycocholate (OR per SD: 0.81; 95% CI: 0.68-0.96), and glycochenodeoxycholate (OR per SD: 0.82; 95% CI: 0.69-0.97) were associated with a lower likelihood of having CKD in patients with newly diagnosed type 2 diabetes. No significant relationships between secondary BAs and odds of CKD were observed. CONCLUSIONS Our findings showed that higher concentrations of circulating unconjugated primary BAs and their glycine-conjugates, but not taurine-conjugates or secondary BAs, were associated with lower odds of having CKD in patients with type 2 diabetes.
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Affiliation(s)
- Tingting Geng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
- Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China
| | - Qi Lu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Limiao Jiang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Kunquan Guo
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Kun Yang
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Yun-Fei Liao
- Department of Endocrinology, Union Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Meian He
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Laboratory of Metabonomics and Systems Biology, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - An Pan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
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Li H, Cao Z, Li J, King L, Zhang Z, Zhao Y, Zhang S, Song Y, Zhang Q, Chen L, Tang Y, Dai L, Yao P. Associations of Combined Lifestyle Factors with MAFLD and the Specific Subtypes in Middle-Aged and Elderly Adults: The Dongfeng-Tongji Cohort Study. Nutrients 2023; 15:4588. [PMID: 37960242 PMCID: PMC10650607 DOI: 10.3390/nu15214588] [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: 09/05/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) is the crucial pathogenesis for intra-hepatic and extra-hepatic diseases, especially in elderly adults. Lifestyle management may be a modifiable cost-effective measure for MAFLD prevention, but the evidence is limited. A total of 23,408 middle-aged and elderly individuals were included in a longitudinal study from 2008 to 2018. Combined lifestyle scores (range 0-6) were evaluated by BMI, smoking, drinking, diet, physical activity, and sleep. Logistic regression models were used to calculate ORs for the risks of MAFLD and specific subtypes. The mean age of participants was 61.7 years, and 44.5% were men. Compared with poor lifestyle (scores 0-2), ORs (95% CIs) of the ideal lifestyle (scores 5-6) were 0.62 (0.57-0.68) for MAFLD, 0.31 (0.28-0.34) for MAFLD with excess weight and obesity, 0.97 (0.75-1.26) for MAFLD with diabetes, and 0.56 (0.51-0.62) for MAFLD with metabolic dysregulation. Additionally, lifestyle improvement was associated with lower risks of MAFLD (OR, 0.76; 95% CI, 0.68-0.86), MAFLD with excess weight and obesity (OR, 0.72; 95% CI, 0.63-0.81), MAFLD with diabetes (OR, 0.74; 95% CI, 0.54-1.02) and MAFLD with metabolic dysregulation (OR, 0.49; 95% CI, 0.43-0.55), respectively. Our findings suggest that adherence to a combined healthy lifestyle was associated with lower risks of MAFLD, particularly in excess weight/obese individuals or those with metabolic dysregulation.
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Affiliation(s)
- Hongxia Li
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Zhiqiang Cao
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Jingxi Li
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Lei King
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Zhuangyu Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Ying Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Siyi Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Yajing Song
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Qian Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Yuhan Tang
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Lingling Dai
- Experimental Teaching Center of Preventive Medicine, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
| | - Ping Yao
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China; (H.L.); (Z.C.); (J.L.); (L.K.); (Z.Z.); (Y.Z.); (S.Z.); (Y.S.); (Q.Z.); (L.C.); (Y.T.)
- Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan 430030, China
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Yehia NA, Isai L, Semnani-Azad Z, Lai KZH, Retnakaran R, Harris SB, Beaudry JL, Bazinet RP, Hanley AJ. Association of circulating branched chain fatty acids with insulin sensitivity and beta cell function in the PROMISE cohort. Lipids 2023; 58:171-183. [PMID: 37165723 DOI: 10.1002/lipd.12373] [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: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 05/12/2023]
Abstract
Branched chain fatty acids (BCFAs) are mainly saturated fatty acids with a methyl branch on the penultimate or antepenultimate carbon atom. While BCFAs are endogenously produced via the catabolism of branched chain amino acids, the primary exogenous source of BCFAs in the human body is via the diet, including dairy products. Recently, BCFAs have been identified as having a potentially protective role in the etiology of cardiometabolic disorders although current literature is limited. We aimed to investigate the longitudinal associations of circulating BCFAs across four serum pools with insulin sensitivity, beta cell function, and glucose concentrations in the PROMISE Cohort. Estimates of insulin sensitivity were assessed using Matsuda's insulin sensitivity index (ISI) and the homeostasis model assessment of insulin sensitivity (HOMA2). Estimates of beta cell function were determined using the insulinogenic index divided by HOMA insulin resistance and the insulin secretion-sensitivity index-2 (ISSI-2). Baseline serum samples were analyzed for BCFAs using gas-chromatography flame ionization detection. Longitudinal associations were determined using generalized estimating equations. In the free fatty acid (FFA) pool, iso15:0 and anteiso15:0 were positively associated with logHOMA2 (iso15:0 logHOMA2-%S: β = 6.86, 95% CI: [1.64, 12.36], p < 0.05, anteiso15:0 logHOMA2-%S: β = 6.36, 95% CI: [0.63, 12.42], p < 0.05) while anteiso14:0 was inversely associated with measures of insulin sensitivity (iso14:0 logHOMA2-%S: β = -2.35, 95% CI: [-4.26, -0.40], p < 0.05, logISI: β = -2.30, 95% CI: [-4.32, -0.23], p < 0.05, anteiso14:0 logHOMA2-%S: β = -4.72, 95% CI: [-7.81, -1.52], p < 0.05, logISI: β = -6.13, 95% CI: [-9.49, -2.66], p < 0.01). Associations in other pools were less consistent. We identified the potential importance of specific BCFAs, specifically iso14:0, anteiso14:0, iso15:0, anteiso15:0, in cardiometabolic phenotypes underlying type 2 diabetes.
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Affiliation(s)
- Nagam A Yehia
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Liridona Isai
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kira Zhi Hua Lai
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ravi Retnakaran
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Stewart B Harris
- Department of Family Medicine, Western University, London, Canada
| | - Jacqueline L Beaudry
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Richard P Bazinet
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anthony J Hanley
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Family Medicine, Western University, London, Canada
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Zhang L, Wang W, Chen Y, Abudoula A, Wang X, Yuan X, Luo Y, Wu M, Ma L. Adverse childhood experiences, unhealthy lifestyle, and nonsuicidal self-injury: findings from six universities in Shaanxi province, China. Front Public Health 2023; 11:1199882. [PMID: 37397740 PMCID: PMC10308309 DOI: 10.3389/fpubh.2023.1199882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 05/18/2023] [Indexed: 07/04/2023] Open
Abstract
Background Nonsuicidal self-injury (NSSI) is a serious public health problem. The role of adverse childhood experiences (ACEs) and lifestyle on the risk for NSSI is still underexplored, especially among college students. We aimed to investigate the association of ACEs with the risk of NSSI, and effect modifications by lifestyle among college students. Methods A total of 18,723 college students from six universities were recruited through a multistage, random cluster sampling method in Shaanxi province, China. The Adverse Childhood Experiences International Questionnaire was used to assess ACEs for each participant, and the Chinese version of the Ottawa Self-injury Inventory was used to assess the presence or absence of NSSI behaviors. Information about lifestyle was collected by a self-designed questionnaire. The associations of NSSI with ACEs and lifestyle were analyzed using logistic regression models. Furthermore, we constructed a combination score of multiple lifestyles and evaluated whether lifestyle modified the effect of ACEs on the risk of NSSI. Results The prevalence of NSSI for the past 1 month, 6 months, and 12 months was 3.8, 5.3, and 6.5%, respectively. 82.6% of participants have reported experiencing at least one type of ACEs, and participants with higher levels of ACEs (≥4) were more likely to have higher odds of developing NSSI during the past 1 month (OR, 4.10; 95%CI, 3.38-4.97), 6 months (OR, 4.76; 95%CI, 4.03-5.62), and 12 months (OR, 5.62; 95%CI, 4.83-6.55), as compared with participants with low levels of ACEs (0-1). There were additive interactions between ACEs and lifestyle. Compared with participants with low levels of ACEs and healthy lifestyle, participants with high levels of ACEs and unhealthy lifestyle had the highest odds of NSSI during the past 1 month (OR, 5.56; 95%CI, 3.80-8.31), 6 months (OR, 6.62; 95%CI, 4.73-9.42), and 12 months (OR, 7.62; 95%CI, 5.59-10.52). Conclusion These results suggest that ACEs play an important role in the occurrence of NSSI among college students, especially in those with unhealthy lifestyle. Our findings may help develop targeted intervention strategies for the prevention of NSSI.
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Affiliation(s)
- Lei Zhang
- School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China
- Shaanxi Medical Association, Xi’an, China
- Shaanxi Provincial Health Industry Association Service Center, Xi’an, China
| | - Wenhua Wang
- Shaanxi Provincial Health Industry Association Service Center, Xi’an, China
| | - Yan Chen
- Changjun Kaifu Middle School, Changsha, China
| | - Aisimila Abudoula
- Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xue Wang
- Shaanxi Medical Association, Xi’an, China
| | - Xiaoxiao Yuan
- Shaanxi Provincial Health Industry Association Service Center, Xi’an, China
| | - Yi Luo
- Shaanxi Provincial Health Industry Association Service Center, Xi’an, China
| | - Mingyang Wu
- Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, China
| | - Le Ma
- School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China
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Liu J, Wang L, Cui X, Shen Q, Wu D, Yang M, Dong Y, Liu Y, Chen H, Yang Z, Liu Y, Zhu M, Ma H, Jin G, Qian Y. Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study. Nutrients 2023; 15:2144. [PMID: 37432247 DOI: 10.3390/nu15092144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 07/12/2023] Open
Abstract
The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.
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Affiliation(s)
- Jia Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Xuan Cui
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qian Shen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Dun Wu
- College of Arts and Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Man Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yunqiu Dong
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yongchao Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Hai Chen
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Zhijie Yang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Yaqi Liu
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yun Qian
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University (Wuxi Center for Disease Control and Prevention), Wuxi 214023, China
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Ling Z, Zhang C, He J, Ouyang F, Qiu D, Li L, Li Y, Li X, Duan Y, Luo D, Xiao S, Shen M. Association of Healthy Lifestyles with Non-Alcoholic Fatty Liver Disease: A Prospective Cohort Study in Chinese Government Employees. Nutrients 2023; 15:nu15030604. [PMID: 36771311 PMCID: PMC9921275 DOI: 10.3390/nu15030604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Evidence indicates that certain healthy lifestyle factors are associated with non-alcoholic fatty liver disease (NAFLD). However, little is known about the effect of combined healthy lifestyle factors. OBJECTIVE To assess the association of combined healthy lifestyle factors with the incidence of NAFLD. METHODS This cohort study was conducted in Changsha, Hunan Province, China. The healthy lifestyles factors studied were not being a current smoker, having a healthy diet, engaging in physical activity, having a normal body mass index (BMI) and engaging in non-sedentary behavior. NAFLD was diagnosed based on abdominal ultrasonography. Logistic regression models were conducted to investigate the associations being studied. RESULTS Of the 5411 participants, 1280 participants had NAFLD, with a prevalence of 23.7% at baseline. The incidence of NAFLD among participants without NAFLD at baseline was found to be 7.2% over a mean follow-up of 1.1 years. Compared with participants with 0-1 low-risk factors, the OR of NAFLD was 0.50 (95% CI: 0.29-0.82, p = 0.008) for those with at least 4 low-risk factors. Similar associations were observed in subgroup analyses and sensitivity analyses. CONCLUSION This study suggests that a combined healthy lifestyle pattern may considerably decrease the risk of NAFLD in Chinese government employees.
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Affiliation(s)
- Zhen Ling
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Chengcheng Zhang
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Jun He
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Feiyun Ouyang
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Dan Qiu
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Ling Li
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Yilu Li
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Xuping Li
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Yanying Duan
- Department of Occupational and Environmental Health, Xiangya School of Public Health, Central South University, Changsha 410078, China
| | - Dan Luo
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
| | - Shuiyuan Xiao
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410008, China
- Correspondence: (S.X.); (M.S.)
| | - Minxue Shen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha 430013, China
- Furong Laboratory, Changsha 410008, China
- Correspondence: (S.X.); (M.S.)
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7
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Zhang YB, Pan XF, Lu Q, Wang YX, Geng TT, Zhou YF, Liao LM, Tu ZZ, Chen JX, Xia PF, Wang Y, Wan ZZ, Guo KQ, Yang K, Yang HD, Chen SH, Wang GD, Han X, Wang YX, Yu D, He MA, Zhang XM, Liu LG, Wu T, Wu SL, Liu G, Pan A. Association of Combined Healthy Lifestyles With Cardiovascular Disease and Mortality of Patients With Diabetes: An International Multicohort Study. Mayo Clin Proc 2023; 98:60-74. [PMID: 36603958 PMCID: PMC9830550 DOI: 10.1016/j.mayocp.2022.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 07/29/2022] [Accepted: 08/12/2022] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To prospectively examine the associations of combined lifestyle factors with incident cardiovascular disease (CVD) and mortality in patients with diabetes. PATIENTS AND METHODS Patients with prevalent diabetes were included from 5 prospective, population-based cohorts in China (Dongfeng-Tongji cohort and Kailuan study), the United Kingdom (UK Biobank study), and the United States (National Health and Nutrition Examination Survey and National Institutes of Health-AARP Diet and Health Study). Healthy lifestyle scores were constructed according to non-current smoking, low to moderate alcohol drinking, regular physical activity, healthy diet, and optimal body weight; the healthy level of each lifestyle factor was assigned 1 point, or 0 for otherwise, and the range of the score was 0 to 5. Cox proportional hazards models were used to estimate hazard ratios for incident CVD, CVD mortality, and all-cause mortality adjusting for sociodemographic, medical, and diabetes-related factors, and outcomes were obtained by linkage to medical records and death registries. Data were collected from October 18, 1988, to September 30, 2020. RESULTS A total of 6945 incident CVD cases were documented in 41,350 participants without CVD at baseline from the 2 Chinese cohorts and the UK Biobank during 389,330 person-years of follow-up, and 40,353 deaths were documented in 101,219 participants from all 5 cohorts during 1,238,391 person-years of follow-up. Adjusted hazard ratios (95% CIs) comparing patients with 4 or 5 vs 0 or 1 healthy lifestyle factors were 0.67 (0.60 to 0.74) for incident CVD, 0.58 (0.50 to 0.68) for CVD mortality, and 0.60 (0.53 to 0.68) for all-cause mortality. Findings remained consistent across different cohorts, subgroups, and sensitivity analyses. CONCLUSION The international analyses document that adherence to multicomponent healthy lifestyles is associated with lower risk of CVD and premature death of patients with diabetes.
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Affiliation(s)
- Yan-Bo Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiong-Fei Pan
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China; Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN
| | - Qi Lu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Xiu Wang
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Ting-Ting Geng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Feng Zhou
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linda M Liao
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Zhou-Zheng Tu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun-Xiang Chen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng-Fei Xia
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen-Zhen Wan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun-Quan Guo
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Kun Yang
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Han-Dong Yang
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Shuo-Hua Chen
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Guo-Dong Wang
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Xu Han
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Yi-Xin Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN
| | - Mei-An He
- Department of Occupational and Environmental Health, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Min Zhang
- Department of Occupational and Environmental Health, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lie-Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shou-Ling Wu
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China.
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - An Pan
- Department of Epidemiology and Biostatistics, 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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8
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Zhang YB, Pan XF, Lu Q, Wang YX, Geng TT, Zhou YF, Liao LM, Tu ZZ, Chen JX, Xia PF, Wang Y, Wan ZZ, Guo KQ, Yang K, Yang HD, Chen SH, Wang GD, Han X, Wang YX, Yu D, He MA, Zhang XM, Liu LG, Wu T, Wu SL, Liu G, Pan A. Associations of combined healthy lifestyles with cancer morbidity and mortality among individuals with diabetes: results from five cohort studies in the USA, the UK and China. Diabetologia 2022; 65:2044-2055. [PMID: 36102938 PMCID: PMC9633429 DOI: 10.1007/s00125-022-05754-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/30/2022] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS Cancer has contributed to an increasing proportion of diabetes-related deaths, while lifestyle management is the cornerstone of both diabetes care and cancer prevention. We aimed to evaluate the associations of combined healthy lifestyles with total and site-specific cancer risks among individuals with diabetes. METHODS We included 92,239 individuals with diabetes but without cancer at baseline from five population-based cohorts in the USA (National Health and Nutrition Examination Survey and National Institutes of Health [NIH]-AARP Diet and Health Study), the UK (UK Biobank study) and China (Dongfeng-Tongji cohort and Kailuan study). Healthy lifestyle scores (range 0-5) were constructed based on current nonsmoking, low-to-moderate alcohol drinking, adequate physical activity, healthy diet and optimal bodyweight. Cox regressions were used to calculate HRs for cancer morbidity and mortality, adjusting for sociodemographic, medical and diabetes-related factors. RESULTS During 376,354 person-years of follow-up from UK Biobank and the two Chinese cohorts, 3229 incident cancer cases were documented, and 6682 cancer deaths were documented during 1,089,987 person-years of follow-up in the five cohorts. The pooled multivariable-adjusted HRs (95% CIs) comparing participants with 4-5 vs 0-1 healthy lifestyle factors were 0.73 (0.61, 0.88) for incident cancer and 0.55 (0.46, 0.67) for cancer mortality, and ranged between 0.41 and 0.63 for oesophagus, lung, liver, colorectum, breast and kidney cancers. Findings remained consistent across different cohorts and subgroups. CONCLUSIONS/INTERPRETATION This international cohort study found that adherence to combined healthy lifestyles was associated with lower risks of total cancer morbidity and mortality as well as several subtypes (oesophagus, lung, liver, colorectum, breast and kidney cancers) among individuals with diabetes.
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Affiliation(s)
- Yan-Bo Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiong-Fei Pan
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Centre, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Qi Lu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Xiu Wang
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Ting-Ting Geng
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Feng Zhou
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linda M Liao
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhou-Zheng Tu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun-Xiang Chen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng-Fei Xia
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen-Zhen Wan
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun-Quan Guo
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Kun Yang
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Han-Dong Yang
- Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Shuo-Hua Chen
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Guo-Dong Wang
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Xu Han
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China
| | - Yi-Xin Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Centre, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Mei-An He
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Min Zhang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lie-Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shou-Ling Wu
- Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, Tangshan, China.
| | - Gang Liu
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - An Pan
- Department of Epidemiology and Biostatistics, 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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9
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Qin Y, Wu J, Xiao W, Wang K, Huang A, Liu B, Yu J, Li C, Yu F, Ren Z. Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215027. [PMID: 36429751 PMCID: PMC9690067 DOI: 10.3390/ijerph192215027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/04/2022] [Accepted: 11/10/2022] [Indexed: 06/01/2023]
Abstract
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning models for diabetes prediction using lifestyle data from the National Health and Nutrition Examination Survey (NHANES) database. The 1999-2020 NHANES database yielded data on 17,833 individuals data based on demographic characteristics and lifestyle-related variables. To screen training data for machine models, the Akaike Information Criterion (AIC) forward propagation algorithm was utilized. For predicting diabetes, five machine-learning models (CATBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM)) were developed. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic (ROC) curve. Among the five machine-learning models, the dietary intake levels of energy, carbohydrate, and fat, contributed the most to the prediction of diabetes patients. In terms of model performance, CATBoost ranks higher than RF, LG, XGBoost, and SVM. The best-performing machine-learning model among the five is CATBoost, which achieves an accuracy of 82.1% and an AUC of 0.83. Machine-learning models based on NHANES data can assist medical institutions in identifying diabetes patients.
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Affiliation(s)
- Yifan Qin
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Jinlong Wu
- College of Physical Education, Southwest University, Chongqing 400715, China
| | - Wen Xiao
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Kun Wang
- Physical Education College, Yanching Institute of Technology, Langfang 065201, China
| | - Anbing Huang
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Bowen Liu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Jingxuan Yu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Chuhao Li
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Fengyu Yu
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
| | - Zhanbing Ren
- College of Physical Education, Shenzhen University, Shenzhen 518000, China
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10
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Yun JS, Jung SH, Shivakumar M, Xiao B, Khera AV, Park WY, Won HH, Kim D. Associations between polygenic risk of coronary artery disease and type 2 diabetes, lifestyle, and cardiovascular mortality: A prospective UK Biobank study. Front Cardiovasc Med 2022; 9:919374. [PMID: 36061534 PMCID: PMC9428483 DOI: 10.3389/fcvm.2022.919374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/11/2022] [Indexed: 11/29/2022] Open
Abstract
Background Previous studies primarily targeted the ability of polygenic risk scores (PRSs) to predict a specific disease, and only a few studies have investigated the association between genetic risk scores and cardiovascular (CV) mortality. We assessed PRSs for coronary artery disease (CAD) and type 2 diabetes (T2DM) as the predictive factors for CV mortality, independent of traditional risk factors, and further investigated the additive effect between lifestyle behavior and PRS on CV mortality. Methods We used genetic and phenotypic data from UK Biobank participants aged 40-69 years at baseline, collected with standardized procedures. Genome-wide PRSs were constructed using >6 million genetic variants. Cox proportional hazard models were used to analyze the relationship between PRS and CV mortality with stratification by age, sex, disease status, and lifestyle behavior. Results Of 377,909 UK Biobank participants having European ancestry, 3,210 (0.8%) died due to CV disease during a median follow-up of 8.9 years. CV mortality risk was significantly associated with CAD PRS [low vs. very high genetic risk groups, CAD PRS hazard ratio (HR) 2.61 (2.02-3.36)] and T2DM PRS [HR 2.08 (1.58-2.73)], respectively. These relationships remained significant even after an adjustment for a comprehensive range of demographic and clinical factors. In the very high genetic risk group, adherence to an unfavorable lifestyle was further associated with a substantially increased risk of CV mortality [favorable vs. unfavorable lifestyle with very high genetic risk for CAD PRS, HR 8.31 (5.12-13.49); T2DM PRS, HR 5.84 (3.39-10.04)]. Across all genetic risk groups, 32.1% of CV mortality was attributable to lifestyle behavior [population attributable fraction (PAF) 32.1% (95% CI 28.8-35.3%)] and 14.1% was attributable to smoking [PAF 14.1% (95% CI 12.4-15.7%)]. There was no evidence of significant interaction between PRSs and age, sex, or lifestyle behavior in predicting the risk of CV mortality. Conclusion PRSs for CAD or T2DM and lifestyle behaviors are the independent predictive factors for future CV mortality in the white, middle-aged population. PRS-based risk assessment could be useful to identify the individuals who need intensive behavioral or therapeutic interventions to reduce the risk of CV mortality.
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Affiliation(s)
- Jae-Seung Yun
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Brenda Xiao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Amit V. Khera
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, South Korea
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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11
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Wang F, Zhang Y, Zhang S, Han X, Wei Y, Guo H, Zhang X, Yang H, Wu T, He M. Combined effects of bisphenol A and diabetes genetic risk score on incident type 2 diabetes: A nested case-control study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119581. [PMID: 35680067 DOI: 10.1016/j.envpol.2022.119581] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
Observational studies reported inconsistent results on the association between bisphenol A (BPA) and type 2 diabetes (T2D) risk. Whether genetic factors modified the association remains unclear. The present nested case-control study prospectively investigated the association of BPA with T2D risk, and the interaction and combined effects of diabetes genetic risk score (GRS) and serum BPA on T2D risk. Based on the Dongfeng-Tongji cohort study, 995 incident diabetes cases and 1:1 age- and gender-matched controls were included. T2D was diagnosed based on the American Diabetes Association criteria. Serum BPA concentration was measured at baseline. Diabetes GRS was constructed by 88 diabetes-related SNPs selected from large-scale GWASs. A U-shaped association was observed between serum BPA levels and T2D risk, with the lowest odds of T2D at the serum BPA levels of 1.00 ng/mL (P = 0.001 for nonlinearity). Compared with the middle group, the multivariate-adjusted ORs of T2D in the lowest group and the highest group of serum BPA were 1.52 (95% CI: 1.04, 2.22) and 1.40 (95% CI: 1.08, 1.81), respectively. Both serum BPA levels (β = 0.107, P = 0.001) and weighted-GRS (w-GRS) (β = 0.072, P = 0.02) were significantly associated with baseline FPG levels. Participants with both highest w-GRS and serum BPA levels had highest risk of T2D (OR = 2.53, 95%CI: 1.49, 4.31, P = 0.001) and higher baseline FPG levels (β = 0.218, P = 0.01), compared with those with both lowest w-GRS and serum BPA levels. Non modified effects of serum BPA levels and w-GRS on T2D, baseline FPG levels, and 5-y changes of FPG levels were detected (All Pinteraction > 0.05). Our results suggested a U-shaped association between serum BPA levels and T2D risk. Participants with higher serum BPA levels and diabetes genetic risk had higher FPG levels and higher risk of T2D.
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Affiliation(s)
- Fei Wang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China; Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, 530021, PR China
| | - Ying Zhang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Shiyang Zhang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Xu Han
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Yue Wei
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Huan Guo
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, 442008, PR China
| | - Tangchun Wu
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China
| | - Meian He
- Department of Occupational and Environmental Health and Key Laboratory of Environmental and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
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12
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Zhuang P, Liu X, Li Y, Wan X, Wu Y, Wu F, Zhang Y, Jiao J. Effect of Diet Quality and Genetic Predisposition on Hemoglobin A 1c and Type 2 Diabetes Risk: Gene-Diet Interaction Analysis of 357,419 Individuals. Diabetes Care 2021; 44:2470-2479. [PMID: 34433621 DOI: 10.2337/dc21-1051] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/29/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess the interactions between diet quality and genetic predisposition to incident type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS Between 2006 and 2010, 357,419 participants with genetic and complete dietary data from the UK Biobank were enrolled and prospectively followed up to 2017. The genetic risk score (GRS) was calculated on the basis of 424 variants associated with T2D risk, and a higher GRS indicates a higher genetic predisposition to T2D. The adherence to a healthy diet was assessed by a diet quality score comprising 10 important dietary components, with a higher score representing a higher overall diet quality. RESULTS There were 5,663 incident T2D cases documented during an average of 8.1 years of follow-up. A significant negative interaction was observed between the GRS and the diet quality score. After adjusting for major risk factors, per SD increment in the GRS and the diet quality score was associated with a 54% higher and a 9% lower risk of T2D, respectively. A simultaneous increment of 1 SD in both the diet quality score and GRS was additionally associated with a 3% lower T2D risk due to the antagonistic interaction. In categorical analyses, a sharp reduction of 23% in T2D risk associated with a 1-SD increment in the diet quality score was detected among participants in the extremely high GRS group (GRS >95%). We also observed a strong negative interaction between the GRS and the diet quality score on the blood HbA1c level at baseline (P < 0.001). CONCLUSIONS The adherence to a healthy diet was associated with more reductions in blood HbA1c levels and subsequent T2D risk among individuals with a higher genetic risk. Our findings support tailoring dietary recommendations to an individual's genetic makeup for T2D prevention.
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Affiliation(s)
- Pan Zhuang
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaohui Liu
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yin Li
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xuzhi Wan
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuqi Wu
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Fei Wu
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingjing Jiao
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Jia X, Xuan L, Dai H, Zhu W, Deng C, Wang T, Li M, Zhao Z, Xu Y, Lu J, Bi Y, Wang W, Chen Y, Xu M, Ning G. Fruit intake, genetic risk and type 2 diabetes: a population-based gene-diet interaction analysis. Eur J Nutr 2021; 60:2769-2779. [PMID: 33399975 PMCID: PMC8275558 DOI: 10.1007/s00394-020-02449-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE Whether the association between fruit and type 2 diabetes (T2D) is modified by the genetic predisposition of T2D was yet elucidated. The current study is meant to examine the gene-dietary fruit intake interactions in the risk of T2D and related glycemic traits. METHODS We performed a cross-sectional study in 11,657 participants aged ≥ 40 years from a community-based population in Shanghai, China. Fruit intake information was collected by a validated food frequency questionnaire by asking the frequency of consumption of typical food items over the previous 12 months. T2D-genetic risk score (GRS) was constructed by 34 well established T2D common variants in East Asians. The risk of T2D, fasting, 2 h-postprandial plasma glucose, and glycated hemoglobin A1c associated with T2D-GRS and each individual single nucleotide polymorphisms (SNPs) were tested. RESULTS The risk of T2D associated with each 1-point of T2D-GRS was gradually decreased from the lower fruit intake level (< 1 times/week) [the odds ratio (OR) and 95% confidence interval (CI) was 1.10 (1.07-1.13)], to higher levels (1-3 and > 3 times/week) [the corresponding ORs and 95% CIs were 1.08 (1.05-1.10) and 1.07 (1.05-1.08); P for interaction = 0.04]. Analyses for associations with fasting, 2 h-postprandial plasma glucose and glycated hemoglobin A1c demonstrated consistent tendencies (all P for interaction ≤ 0.03). The inverse associations of fruit intake with risk of T2D and glucose traits were more prominent in the higher T2D-GRS tertile. CONCLUSIONS Fruit intakes interact with the genetic predisposition of T2D on the risk of diabetes and related glucose metabolic traits. Fruit intake alleviates the association between genetic predisposition of T2D and the risk of diabetes; the association of fruit intake with a lower risk of diabetes was more prominent in population with a stronger genetic predisposition of T2D.
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Affiliation(s)
- Xu Jia
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liping Xuan
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huajie Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Zhu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chanjuan Deng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Polygenic risk score, healthy lifestyles, and risk of incident depression. Transl Psychiatry 2021; 11:189. [PMID: 33782378 PMCID: PMC8007584 DOI: 10.1038/s41398-021-01306-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/16/2021] [Accepted: 02/24/2021] [Indexed: 02/01/2023] Open
Abstract
Genetic factors increase the risk of depression, but the extent to which this can be offset by modifiable lifestyle factors is unknown. We investigated whether a combination of healthy lifestyles is associated with lower risk of depression regardless of genetic risk. Data were obtained from the UK Biobank and consisted of 339,767 participants (37-73 years old) without depression between 2006 and 2010. Genetic risk was categorized as low, intermediate, or high according to polygenic risk score for depression. A combination of healthy lifestyles factors-including no current smoking, regular physical activity, a healthy diet, moderate alcohol intake and a body mass index <30 kg/m2-was categorized into favorable, intermediate, and unfavorable lifestyles. The risk of depression was 22% higher among those at high genetic risk compared with those at low genetic risk (HR = 1.22, 95% CI: 1.14-1.30). Participants with high genetic risk and unfavorable lifestyle had a more than two-fold risk of incident depression compared with low genetic risk and favorable lifestyle (HR = 2.18, 95% CI: 1.84-2.58). There was no significant interaction between genetic risk and lifestyle factors (P for interaction = 0.69). Among participants at high genetic risk, a favorable lifestyle was associated with nearly 50% lower relative risk of depression than an unfavorable lifestyle (HR = 0.51, 95% CI: 0.43-0.60). We concluded that genetic and lifestyle factors were independently associated with risk of incident depression. Adherence to healthy lifestyles may lower the risk of depression regardless of genetic risk.
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