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Liu Y, Wang H, Liang Y, Guo Z, Qu L, Wang Y, Zhang C, Sun G, Li Y. Dietary intakes of methionine, threonine, lysine, arginine and histidine increased risk of type 2 diabetes in Chinese population: does the mediation effect of obesity exist? BMC Public Health 2023; 23:1551. [PMID: 37582714 PMCID: PMC10428589 DOI: 10.1186/s12889-023-16468-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Published studies have shown positive associations of branched chain and aromatic amino acids with type 2 diabetes mellitus (T2DM), and the findings remain consistent. However, the associations of other essential and semi-essential amino acids, i.e., methionine (Met), threonine (Thr), lysine (Lys), arginine (Arg) and histidine (His), with T2DM remain unknown. Obesity is an important independent risk factor for T2DM, and excessive amino acids can convert into glucose and lipids, which might underlie the associations of amino acids with obesity. Therefore, we aimed to estimate the associations between dietary intakes of these 5 amino acids and T2DM risk, as well as the mediation effects of obesity on these associations, in a Chinese population. METHODS A total of 10,920 participants (57,293 person-years) were included, and dietary intakes of 5 amino acids were investigated using 24-h dietary recalls. Anthropometric obesity indices were measured at both baseline and the follow-up endpoints. Associations of amino acids with T2DM were estimated using COX regression models, hazard ratios (HRs) and 95% confidence intervals (95% CIs) were shown. The mediation effects of obesity indices were analyzed, and the proportion of the mediation effect was estimated. RESULTS Higher intakes of the 5 amino acids were associated with increasing T2DM risk, while significant HRs were only shown in men after adjustments. No interaction by gender was found. Regression analyses using quintiles of amino acids intakes showed that T2DM risk was positively associated with amino acids intakes only when comparing participants with the highest intake levels of amino acids to those with the lowest intake levels. Adjusted correlation coefficients between amino acid intakes and obesity indices measured at follow-up endpoints were significantly positive. Mediation analyses showed that mediation effects of obesity indices existed on associations between amino acids intakes and T2DM risk, and the mediation effect of waist circumference remained strongest for each amino acid. CONCLUSIONS We found positive associations of dietary intakes of Met, Thr, Lys, Arg and His with increasing T2DM risk in general Chinese residents, on which the mediation effect of obesity existed. These findings could be helpful for developing more constructive guidance in the primary prevention of T2DM based on dietary interventions.
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Affiliation(s)
- Yuyan Liu
- Department of Clinical Epidemiology, The Fourth Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Huan Wang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yuanhong Liang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Zijun Guo
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Litong Qu
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Ying Wang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Chengwen Zhang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Guifan Sun
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China
- School of Public Health, China Medical University, Shenyang, Liaoning, China
| | - Yongfang Li
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, China.
- School of Public Health, China Medical University, Shenyang, Liaoning, China.
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Nematollahi MA, Jahangiri S, Asadollahi A, Salimi M, Dehghan A, Mashayekh M, Roshanzamir M, Gholamabbas G, Alizadehsani R, Bazrafshan M, Bazrafshan H, Bazrafshan Drissi H, Shariful Islam SM. Body composition predicts hypertension using machine learning methods: a cohort study. Sci Rep 2023; 13:6885. [PMID: 37105977 PMCID: PMC10140285 DOI: 10.1038/s41598-023-34127-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.
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Affiliation(s)
| | - Soodeh Jahangiri
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arefeh Asadollahi
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Maryam Salimi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
- Bone and Joint Diseases Research Center, Department of Orthopedic Surgery, Shiraz University of Medical Science, Shiraz, Iran
| | - Azizallah Dehghan
- Non Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran
| | - Mina Mashayekh
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, 74617-81189, Iran
| | - Ghazal Gholamabbas
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
| | | | - Hanieh Bazrafshan
- Department of Neurology, Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamed Bazrafshan Drissi
- Cardiovascular Research Center, Shiraz University of Medical Sciences, PO Box: 71348-14336, Shiraz, Iran.
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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Liu Y, Sun G, Li Y. A prospective cohort study on the association of lean body mass estimated by mid-upper arm muscle circumference with hypertension risk in Chinese residents. J Clin Hypertens (Greenwich) 2022; 24:329-338. [PMID: 35174624 PMCID: PMC8925010 DOI: 10.1111/jch.14412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/15/2021] [Accepted: 11/25/2021] [Indexed: 02/02/2023]
Abstract
The associations of lean body mass (LBM) with elevated blood pressure (BP) and hypertension were controversial, and the causalities have never been shown. Mid-upper arm muscle circumference (MAMC), an easily obtained anthropometric measurement, could provide an accurate estimate for LBM. Therefore, a prospective cohort study in general Chinese residents aiming to find out the relationship between LBM estimated using MAMC and hypertension risk was performed. Eight thousand one hundred eighty-five eligible participants were included in the baseline analysis, among whom 3442 were subsequently selected into cohort analysis. MAMC was calculated using mid-upper arm circumference (MUAC) and triceps skinfold thickness (TST). Associations of MAMC with BP values and hypertension prevalence were estimated by linear and logistic regression models. Associations with hypertension incidence were estimated by COX regression models, hazard ratio (HR) and 95% confidence interval (CI) were given. Nonlinear relationship between MAMC and hypertension risk was estimated using restricted cubic spline method. Standardized coefficients of MUAC and TST were compared to estimate their strengths of associations with hypertension. Baseline analysis showed that after adjusted for confounders, the increase of systolic BP per standard deviation (SD) of MAMC were 1.97 mmHg (95%CI: 1.46, 2.48) and 1.63 mmHg (95%CI: 1.10, 2.16) respectively in men and women, and the increases of diastolic BP per SD were 1.58 mmHg (95%CI: 1.23, 1.92) and 1.08 mmHg (95%CI: 0.74, 1.42). Additionally, the association of MAMC with the prevalence of hypertension were also found in both men and women (OR = 1.36, 95%CI: 1.26, 1.47 in men; OR = 1.33, 95%CI: 1.22, 1.44 in women). Cohort analysis showed that MAMC increased the risk of hypertension (HR = 1.10, 95%CI: 1.01, 1.19 for men; HR = 1.15, 95%CI: 1.06, 1.26 for women), and a trend of J-shaped relationship was found. Additionally, the stronger associations of MUAC with both BP values and hypertension than that of TST were found in both baseline and cohort analyses. Findings in our study implied that we cannot neglect the capacity of LBM in predicting hypertension risk, and LBM estimates should be recommended in general health surveys or examinations.
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Affiliation(s)
- Yuyan Liu
- Department of Clinical EpidemiologyThe Fourth Affiliated Hospital of China Medical UniversityShenyangLiaoningChina
| | - Guifan Sun
- Research Center of Environmental and Non‐Communicable DiseaseSchool of Public HealthChina Medical UniversityShenyangLiaoningChina
| | - Yongfang Li
- Research Center of Environmental and Non‐Communicable DiseaseSchool of Public HealthChina Medical UniversityShenyangLiaoningChina
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Goto K, Yokokawa H, Fukuda H, Saita M, Hamada C, Hisaoka T, Naito T. An association between subcutaneous fat mass accumulation and hypertension. J Gen Fam Med 2021; 22:209-217. [PMID: 34221795 PMCID: PMC8245738 DOI: 10.1002/jgf2.427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 11/17/2022] Open
Abstract
Evidence to assess relationships between subcutaneous fat area (SFA) and lifestyle-related diseases, including hypertension, remains limited. The aim of this study was to investigate the relationship between SFA and hypertension. This study was a single-institution, cross-sectional study of 1,899 eligible Japanese participants who underwent health checkups between December 2016 and December 2018. All patients were measured for SFA and visceral fat area (VFA) by abdominal computed tomography (CT). SFA was divided into quartiles by gender, and multivariate logistic regression analysis was performed to estimate associations between SFA quartiles (Q) and hypertension. Mean age and SFA were 60.9 9 (standard devastation [SD]:12.0) years and 123.0 (56.9) cm2 in men, and 60.6 (12.8) years and 146.6 (79.0) cm2 in women, respectively. Risk of hypertension from multivariate regression modeling compared with the lowest quartile (Q) in both sexes was as follows: for men Q2 [odds ratio (OR), 1; 95% confidence interval (CI), 0.55-1.51 ], Q3 (OR, 1.73; 95%CI, 1.17-2.56), and Q4 (OR, 1.96; 95%CI, 1.31-2.94); for women Q2 (OR, 0.87; 95%CI, 0.48-1.58), Q3 (OR, 1.73; 95%CI, 1.02-2.95), and Q4 (OR, 2.54; 95%CI, 1.51-4.28). The optimal SFA cutoff value at risk of hypertension was 114.7 cm2 in men and 169.3 cm2 in women. The prevalence of hypertension was positively associated with SFA quartiles in both genders. The present results may indicate the necessity of considering not only VFA, but also SFA for the primary and secondary prevention of hypertension.
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Affiliation(s)
- Kento Goto
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Hirohide Yokokawa
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Hiroshi Fukuda
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Mizue Saita
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Chieko Hamada
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Teruhiko Hisaoka
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
| | - Toshio Naito
- Department of General MedicineJuntendo University School of MedicineTokyoJapan
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