1
|
Wu YF, Chien KL, Chen YC. Association between genetic risk score and tri-ponderal mass index growth trajectories among different dietary consumption adolescents in a prospective Taiwanese cohort. Nutr Metab (Lond) 2022; 19:83. [PMID: 36536439 PMCID: PMC9762089 DOI: 10.1186/s12986-022-00718-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Single-nucleotide polymorphisms (SNPs) in various genetic loci are associated with childhood obesity; however, their influence on adolescent growth patterns has rarely been explored. This study investigated whether genetic variants could predict tri-ponderal mass index (TMI)-derived growth trajectories and the interaction between genetic and dietary factors. METHODS We conducted Taiwan Puberty Longitudinal Study, a prospective cohort that recruited 1,135 children since 2018. Anthropometric measurements were recorded every three months, while dietary nutrition assessment and biological sampling for genotyping were collected during the first visit. TMI growth trajectory groups were identified using growth mixture modeling. A multinomial logistic regression model for different growth trajectories was used to examine the effect of candidate SNPs, and the most related SNPs were used to establish the genetic risk score. We then explored the effect of the genetic risk score in subgroup analysis according to dietary calories and different dietary consumption patterns. RESULTS Three TMI-based growth trajectory groups were identified among adolescents. The "increased weight" trajectory group accounted for approximately 9.7% of the participants. FTO/rs7206790 was associated with the increased weight growth trajectory after adjusting for the baseline TMI and other correlated covariates (OR: 2.13, 95% CI: 1.08-4.21). We generated the genetic risk score using 4 SNPs (FTO/rs7206790, ADCY9/rs2531995, TFAP2B/rs4715210, and TMEM18/rs6548238) and selected the threshold of 10 points to define risk categories. There were 11.66% and 3.24% of participants belonged to the increased weight trajectory in high- and low-risk groups, respectively; and the predictive ability of the genetic risk score was notable among low calories intake participants (OR: 1.90, 95% CI: 1.18-3.05 vs. OR: 1.17, 95% CI: 0.78-1.75 in high calories intake group). CONCLUSION Our results offer a new perspective on the genetic and dietary basis of changes in adolescent obesity status. Individualized interventions for obesity prevention may be considered among high-risk children.
Collapse
Affiliation(s)
- Yi-Fan Wu
- Department of Family Medicine, Renai Branch, Taipei City Hospital, Taipei, Taiwan ,grid.19188.390000 0004 0546 0241Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan ,grid.412042.10000 0001 2106 6277Department of Psychology, National Chengchi University, Taipei, Taiwan
| | - Kuo-Liong Chien
- grid.19188.390000 0004 0546 0241Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan ,grid.412094.a0000 0004 0572 7815Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yang-Ching Chen
- grid.412897.10000 0004 0639 0994Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan ,grid.412896.00000 0000 9337 0481Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan ,grid.412896.00000 0000 9337 0481School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan ,grid.412896.00000 0000 9337 0481Graduate Institute of Metabolism and Obesity Sciences, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|
2
|
Kan Y, Liu L, Li X, Pang J, Bi Y, Zhang L, Zhang N, Yuan Y, Gong W, Zhang Y. Association between distinct body mass index trajectories according to the group-based trajectory modeling and the risk of incident diabetes: A systematic review. Obes Rev 2022; 23:e13508. [PMID: 36269000 DOI: 10.1111/obr.13508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/16/2022] [Accepted: 09/27/2022] [Indexed: 11/29/2022]
Abstract
We aimed to determine the association between distinct body mass index (BMI) trajectories, using group-based trajectory modeling, and the subsequent risk of incident diabetes. Five databases were systematically searched. Fourteen population-based cohort studies that summarized the association between different BMI trajectories and subsequent diabetes, with the four most common BMI trajectories including the "stable," "increasing," "decreasing," and "turning" groups, were included. The rapid increase and stable high-level BMI groups showed the strongest association with the subsequent risk of diabetes compared with the stable normal BMI group. Increased baseline BMI levels resulted in a steeper slope and greater risk of subsequent diabetes. In the decreasing BMI group, one study reported that those aged >50 years showed the highest incidence of subsequent diabetes, whereas the other two studies reported no association between these two variables. In the turning group, an increase followed by a decrease in BMI levels from adolescence to late adulthood could reduce the risk of developing diabetes, although the residual risk remained. By contrast, the incidence of subsequent diabetes remained high in the middle-aged BMI-turning group. This study can provide further insights for identifying populations at high risk of diabetes and for developing targeted prevention strategies.
Collapse
Affiliation(s)
- Yinshi Kan
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Lin Liu
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Xiangning Li
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Juan Pang
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Yaxin Bi
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Lu Zhang
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Ning Zhang
- School of Nursing, Yangzhou University, Yangzhou, China
| | - Yuan Yuan
- Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Weijuan Gong
- Department of Basic Medicine, School of Medicine, Yangzhou University, Yangzhou, China
| | - Yu Zhang
- School of Nursing, Yangzhou University, Yangzhou, China.,Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou, China
| |
Collapse
|
3
|
Ma Y, Wu H, Shen J, Wang J, Wang J, Hou Y. Correlation between lifestyle patterns and overweight and obesity among Chinese adolescents. Front Public Health 2022; 10:1027565. [PMID: 36408045 PMCID: PMC9670141 DOI: 10.3389/fpubh.2022.1027565] [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: 08/25/2022] [Accepted: 10/19/2022] [Indexed: 11/25/2022] Open
Abstract
Lifestyles such as physical exercise, sedentary behavior, eating habits, and sleep duration are all associated with adolescent overweight and obesity. The purpose of this study was to investigate how Chinese adolescents' lifestyles clustered into different lifestyle patterns, and to analyze the correlation between these patterns and adolescent overweight and obesity. The investigated respondents included 13,670 adolescents aged 13-18 from various administrative regions in China. Latent class analysis was employed to cluster the lifestyles of adolescents, χ2 test and Logistic regression were used to explore the relationship between lifestyle patterns and overweight and obesity in adolescents. The results identified 6 types of Chinese adolescents' lifestyle patterns, as well as the significant differences in gender and age. The adolescents with high exercise-high calorie diet had the lowest risk of overweight and obesity, and the adolescents with low consciousness-low physical activity and low consciousness-unhealthy had the highest risk of overweight and obesity, which were 1.432 times and 1.346 times higher than those with high exercise-high calorie diet, respectively. The studied demonstrated that there was a coexistence of healthy behaviors and health-risk behaviors in the lifestyle clustering of Chinese adolescents. Low physical exercise and high intake of snacks and carbonated beverages were the most common. Physical exercise and health consciousness were the protective factors of overweight and obesity in adolescents.
Collapse
Affiliation(s)
- Yuanyuan Ma
- Research Center for Health Promotion of Children and Adolescents, Taiyuan Institute of Technology, Taiyuan, China
| | - Huipan Wu
- Research Center for Health Promotion of Children and Adolescents, Taiyuan Institute of Technology, Taiyuan, China,*Correspondence: Huipan Wu
| | - Jinbo Shen
- Research Center for Health Promotion of Children and Adolescents, Taiyuan Institute of Technology, Taiyuan, China
| | - Jian Wang
- Department of Physical Education, Shanxi University, Taiyuan, China
| | - Jinxian Wang
- Research Center for Health Promotion of Children and Adolescents, Taiyuan Institute of Technology, Taiyuan, China
| | - Yuxin Hou
- Department of Physical Education, Shanxi University, Taiyuan, China
| |
Collapse
|
4
|
Owora AH, Allison DB, Zhang X, Gletsu-Miller N, Gadde KM. Risk of Type 2 Diabetes Among Individuals with Excess Weight: Weight Trajectory Effects. Curr Diab Rep 2022; 22:471-479. [PMID: 35781782 PMCID: PMC10094425 DOI: 10.1007/s11892-022-01486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/01/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Increased risk of type 2 diabetes mellitus (T2D) among individuals with overweight or obesity is well-established; however, questions remain about the temporal dynamics of weight change (gain or loss) on the natural course of T2D in this at-risk population. Existing epidemiologic evidence is limited to studies that discretely sample and assess excess weight and T2D risk at different ages with limited follow-up, yet changes in weight may have time-varying and possibly non-linear effects on T2D risk. Predicting the impact of weight change on the risk of T2D is key to informing primary prevention. We critically review the relationship between weight change, trajectory groups (i.e., distinct weight change patterns), and T2D risk among individuals with excess weight in recently published T2D prevention randomized controlled trials (RCTs) and longitudinal cohort studies. RECENT FINDINGS Overall, weight trajectory groups have been shown to differ by age of onset, sex, and patterns of insulin resistance or beta-cell function biomarkers. Lifestyle (diet and physical activity), pharmacological, and surgical interventions can modify an individual's weight trajectory. Adolescence is a critical etiologically relevant window during which onset of excess weight may be associated with higher risk of T2D. Changes in insulin resistance and beta-cell function biomarkers are distinct but related correlates of weight trajectory groups that evolve contemporaneously over time. These multi-trajectory markers are differentially associated with T2D risk. T2D risk may differ by the age of onset and duration of excess body weight, and the type of weight loss intervention. A better understanding of the changes in weight, insulin sensitivity, and beta-cell function as distinct but related correlates of T2D risk that evolve contemporaneously over time has important implications for designing and targeting primary prevention efforts.
Collapse
Affiliation(s)
- Arthur H Owora
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA.
| | - David B Allison
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Xuan Zhang
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Nana Gletsu-Miller
- Indiana University School of Public Health, St, Bloomington, IN, 1025 E. 7th47405, USA
| | - Kishore M Gadde
- Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge, LA, USA
| |
Collapse
|
5
|
Zhu X, Yang Z, He Z, Hu J, Yin T, Bai H, Li R, Cai L, Guo H, Li M, Yan T, Li Y, Shen C, Sun K, Liu Y, Sun Z, Wang B. Factors correlated with targeted prevention for prediabetes classified by impaired fasting glucose, impaired glucose tolerance, and elevated HbA1c: A population-based longitudinal study. Front Endocrinol (Lausanne) 2022; 13:965890. [PMID: 36072930 PMCID: PMC9441664 DOI: 10.3389/fendo.2022.965890] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There is still controversy surrounding the precise characterization of prediabetic population. We aim to identify and examine factors of demographic, behavioral, clinical, and biochemical characteristics, and obesity indicators (anthropometric characteristics and anthropometric prediction equation) for prediabetes according to different definition criteria of the American Diabetes Association (ADA) in the Chinese population. METHODS A longitudinal study consisted of baseline survey and two follow-ups was conducted, and a pooled data were analyzed. Prediabetes was defined as either impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or elevated glycosylated hemoglobin (HbA1c) according to the ADA criteria. Robust generalized estimating equation models were used. RESULTS A total of 5,713 (58.42%) observations were prediabetes (IGT, 38.07%; IGT, 26.51%; elevated HbA1c, 23.45%); 9.66% prediabetes fulfilled all the three ADA criteria. Among demographic characteristics, higher age was more evident in elevated HbA1c [adjusted OR (aOR)=2.85]. Female individuals were less likely to have IFG (aOR=0.70) and more likely to suffer from IGT than male individuals (aOR=1.41). Several inconsistency correlations of biochemical characteristics and obesity indicators were detected by prediabetes criteria. Body adiposity estimator exhibited strong association with prediabetes (D10: aOR=4.05). For IFG and elevated HbA1c, the odds of predicted lean body mass exceed other indicators (D10: aOR=3.34; aOR=3.64). For IGT, predicted percent fat presented the highest odds (D10: aOR=6.58). CONCLUSION Some correlated factors of prediabetes under different criteria differed, and obesity indicators were easily measured for target identification. Our findings could be used for targeted intervention to optimize preventions to mitigate the obviously increased prevalence of diabetes.
Collapse
Affiliation(s)
- Xiaoyue Zhu
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Zhipeng Yang
- School of Software, Fudan University, Shanghai, China
| | - Zhiliang He
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Jingyao Hu
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Tianxiu Yin
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Hexiang Bai
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Ruoyu Li
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Le Cai
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Haijian Guo
- Integrated Business Management Office, Jiangsu Province Centre Disease Control and Prevention, Nanjing, China
| | - Mingma Li
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Tao Yan
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - You Li
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Chenye Shen
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Kaicheng Sun
- Yandu Centre for Disease Control and Prevention, Yancheng, China
| | - Yu Liu
- Jurong Centre for Disease Control and Prevention, Jurong, China
| | - Zilin Sun
- Department of Endocrinology, Institute of Diabetes, Medical School, Southeast University, Nanjing, China
| | - Bei Wang
- Key Laboratory of Environment Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
- *Correspondence: Bei Wang,
| |
Collapse
|
6
|
Sun J, Yang R, Zhao M, Bovet P, Xi B. Tri-Ponderal Mass Index as a Screening Tool for Identifying Body Fat and Cardiovascular Risk Factors in Children and Adolescents: A Systematic Review. Front Endocrinol (Lausanne) 2021; 12:694681. [PMID: 34744995 PMCID: PMC8566753 DOI: 10.3389/fendo.2021.694681] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 09/28/2021] [Indexed: 01/22/2023] Open
Abstract
UNLABELLED Because of the limitation of body mass index (BMI) in distinguishing adipose mass from muscle, the tri-ponderal mass index (TMI) has been proposed as a new indicator for better assessing adiposity in children and adolescents. However, it remains unclear whether TMI performs better than BMI or other adiposity indices in predicting obesity status in childhood and obesity-related cardiovascular risk factors (CVRFs) in childhood or adulthood. We searched PubMed, Cochrane Library, and Web of Science for eligible publications until June 15, 2021. A total of 32 eligible studies were included in this systematic review. We found that TMI had a similar or better ability to predict body fat among children and adolescents than BMI. However, most of the included studies suggested that TMI was similar to BMI in identifying metabolic syndrome although TMI was suggested to be a useful tool when used in combination with other indicators (e.g., BMI and waist circumference). In addition, limited evidence showed that TMI did not perform better than BMI for identifying specific CVRFs, including insulin resistance, high blood pressure, dyslipidemia, and inflammation in children and adolescents, as well as CVRFs in adults. SYSTEMATIC REVIEW REGISTRATION https://www.crd.york.ac.uk/prospero, CRD42021260356.
Collapse
Affiliation(s)
- Jiahong Sun
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Rong Yang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Min Zhao
- Department of Nutrition and Food Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Pascal Bovet
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | - Bo Xi
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Bo Xi,
| |
Collapse
|