{"title":"泰国东北部人口瘦体重和脂肪体重的标准值和人体测量预测模型。","authors":"Chatlert Pongchaiyakul, Nipith Charoenngam, Thanitsara Rittiphairoj, Dueanchonnee Sribenjalak","doi":"10.1089/met.2024.0098","DOIUrl":null,"url":null,"abstract":"<p><p><b><i>Background:</i></b> Data on reference values for lean mass (LM) and fat mass (FM) in the Southeast Asian populations are currently lacking. Therefore, we aimed to estimate the normative values and generate anthropometric prediction models for LM and FM in the Thai population. <b><i>Methods:</i></b> Consecutive community-dwelling individuals aged 20-90 years were recruited from Srinagarind Hospital, Khon Kaen, Thailand, between 2010 and 2015. LM and FM were measured using dual energy X-ray absorptiometry. Age and sex stratified percentile of LM and FM were presented. Anthropometric prediction models for LM and FM were developed by using linear regression to generate competing models. <b><i>Results:</i></b> A total of 832 individuals (334 males and 498 females) were included in the study. The mean ± SD age, LM, and FM were 50.0 ± 16.2 years, 38.9 ± 8.0 kg, and 15.5 ± 7.7 kg, respectively. LM decreased with age from 49.4 kg in 20-29 years group to 42.3 kg in ≥70 years group in male and 34.6 kg in 30-39 years group to 30.8 kg in ≥70 years group in females. FM has an inverse U-shaped association with age, which peaked at 11.9 kg in 60-69 years group in males and 20.7 kg in 50-59 years group in females. Among the various anthropometric models, the models incorporating age, sex, weight, and height were considered the best fit for predicting both LM and FM. <b><i>Conclusion:</i></b> In the Thai population, peak LM was reached during early adulthood and decline with age, whereas FM showed an inverse U-shaped association with age. The prediction models incorporating age, sex, weight, and height were proposed as practical tools for assessing LM and FM in clinical practice.</p>","PeriodicalId":18405,"journal":{"name":"Metabolic syndrome and related disorders","volume":" ","pages":"695-702"},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Normative Values and Anthropometric Prediction Models for Lean Mass and Fat Mass in the Northeastern Thai Population.\",\"authors\":\"Chatlert Pongchaiyakul, Nipith Charoenngam, Thanitsara Rittiphairoj, Dueanchonnee Sribenjalak\",\"doi\":\"10.1089/met.2024.0098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b><i>Background:</i></b> Data on reference values for lean mass (LM) and fat mass (FM) in the Southeast Asian populations are currently lacking. Therefore, we aimed to estimate the normative values and generate anthropometric prediction models for LM and FM in the Thai population. <b><i>Methods:</i></b> Consecutive community-dwelling individuals aged 20-90 years were recruited from Srinagarind Hospital, Khon Kaen, Thailand, between 2010 and 2015. LM and FM were measured using dual energy X-ray absorptiometry. Age and sex stratified percentile of LM and FM were presented. Anthropometric prediction models for LM and FM were developed by using linear regression to generate competing models. <b><i>Results:</i></b> A total of 832 individuals (334 males and 498 females) were included in the study. The mean ± SD age, LM, and FM were 50.0 ± 16.2 years, 38.9 ± 8.0 kg, and 15.5 ± 7.7 kg, respectively. LM decreased with age from 49.4 kg in 20-29 years group to 42.3 kg in ≥70 years group in male and 34.6 kg in 30-39 years group to 30.8 kg in ≥70 years group in females. FM has an inverse U-shaped association with age, which peaked at 11.9 kg in 60-69 years group in males and 20.7 kg in 50-59 years group in females. Among the various anthropometric models, the models incorporating age, sex, weight, and height were considered the best fit for predicting both LM and FM. <b><i>Conclusion:</i></b> In the Thai population, peak LM was reached during early adulthood and decline with age, whereas FM showed an inverse U-shaped association with age. The prediction models incorporating age, sex, weight, and height were proposed as practical tools for assessing LM and FM in clinical practice.</p>\",\"PeriodicalId\":18405,\"journal\":{\"name\":\"Metabolic syndrome and related disorders\",\"volume\":\" \",\"pages\":\"695-702\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolic syndrome and related disorders\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1089/met.2024.0098\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolic syndrome and related disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1089/met.2024.0098","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/23 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
背景:目前缺乏东南亚人群瘦体重(LM)和脂肪量(FM)的参考值数据。因此,我们旨在估算泰国人口的标准值,并生成瘦体重和脂肪量的人体测量预测模型。研究方法2010 年至 2015 年间,我们在泰国孔敬市斯利那加林医院连续招募了 20-90 岁的社区居民。使用双能 X 射线吸收仪测量 LM 和 FM。结果显示了 LM 和 FM 的年龄和性别分层百分位数。通过线性回归生成竞争模型,建立了 LM 和 FM 的人体测量预测模型。结果:共有 832 人(男性 334 人,女性 498 人)参与了研究。平均(±SD)年龄、LM 和 FM 分别为 50.0 ± 16.2 岁、38.9 ± 8.0 千克和 15.5 ± 7.7 千克。随着年龄的增长,男性的 LM 从 20-29 岁组的 49.4 kg 下降到≥70 岁组的 42.3 kg,女性的 LM 从 30-39 岁组的 34.6 kg 下降到≥70 岁组的 30.8 kg。FM 与年龄呈反 U 型关系,男性在 60-69 岁组达到峰值 11.9 千克,女性在 50-59 岁组达到峰值 20.7 千克。在各种人体测量模型中,包含年龄、性别、体重和身高的模型被认为最适合预测 LM 和 FM。结论在泰国人群中,LM 在成年早期达到峰值,并随着年龄的增长而下降,而 FM 则与年龄呈反 U 型关系。建议将包含年龄、性别、体重和身高的预测模型作为临床实践中评估 LM 和 FM 的实用工具。
Normative Values and Anthropometric Prediction Models for Lean Mass and Fat Mass in the Northeastern Thai Population.
Background: Data on reference values for lean mass (LM) and fat mass (FM) in the Southeast Asian populations are currently lacking. Therefore, we aimed to estimate the normative values and generate anthropometric prediction models for LM and FM in the Thai population. Methods: Consecutive community-dwelling individuals aged 20-90 years were recruited from Srinagarind Hospital, Khon Kaen, Thailand, between 2010 and 2015. LM and FM were measured using dual energy X-ray absorptiometry. Age and sex stratified percentile of LM and FM were presented. Anthropometric prediction models for LM and FM were developed by using linear regression to generate competing models. Results: A total of 832 individuals (334 males and 498 females) were included in the study. The mean ± SD age, LM, and FM were 50.0 ± 16.2 years, 38.9 ± 8.0 kg, and 15.5 ± 7.7 kg, respectively. LM decreased with age from 49.4 kg in 20-29 years group to 42.3 kg in ≥70 years group in male and 34.6 kg in 30-39 years group to 30.8 kg in ≥70 years group in females. FM has an inverse U-shaped association with age, which peaked at 11.9 kg in 60-69 years group in males and 20.7 kg in 50-59 years group in females. Among the various anthropometric models, the models incorporating age, sex, weight, and height were considered the best fit for predicting both LM and FM. Conclusion: In the Thai population, peak LM was reached during early adulthood and decline with age, whereas FM showed an inverse U-shaped association with age. The prediction models incorporating age, sex, weight, and height were proposed as practical tools for assessing LM and FM in clinical practice.
期刊介绍:
Metabolic Syndrome and Related Disorders is the only peer-reviewed journal focusing solely on the pathophysiology, recognition, and treatment of this major health condition. The Journal meets the imperative for comprehensive research, data, and commentary on metabolic disorder as a suspected precursor to a wide range of diseases, including type 2 diabetes, cardiovascular disease, stroke, cancer, polycystic ovary syndrome, gout, and asthma.
Metabolic Syndrome and Related Disorders coverage includes:
-Insulin resistance-
Central obesity-
Glucose intolerance-
Dyslipidemia with elevated triglycerides-
Low HDL-cholesterol-
Microalbuminuria-
Predominance of small dense LDL-cholesterol particles-
Hypertension-
Endothelial dysfunction-
Oxidative stress-
Inflammation-
Related disorders of polycystic ovarian syndrome, fatty liver disease (NASH), and gout