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Metabolite profiles of distinct obesity phenotypes integrating impacts of altitude and their association with diet and metabolic disorders in Tibetans.
Peng, Wen; Shi, Lin; Huang, Qingxia; Li, Tiemei; Jian, Wenxiu; Zhao, Lei; Xu, Ruijie; Liu, Tianqi; Zhang, Bin; Wang, Haijing; Tong, Li; Tang, Huiru; Wang, Youfa.
Afiliação
  • Peng W; Department of Public Health, Qinghai University Medical College, No. 16 Kunlun Rd, Xining, 810008, China; Nutrition and Health Promotion Center, Qinghai University Medical College, No. 16 Kunlun Rd, Xining 810008, China; Qinghai Provincial Key Laboratory of Prevention and Control of Glucolipid Metab
  • Shi L; School of Food Engineering and Nutritional Science, Shaanxi Normal University, No. 199 Chang'an South Rd, Xi'an, Shaanxi 710062, China.
  • Huang Q; State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, No. 825 Zhangheng Rd, Shanghai 200438, China.
  • Li T; Department of Public Health, Qinghai University Medical College, No. 16 Kunlun Rd, Xining, 810008, China; Nutrition and Health Promotion Center, Qinghai University Medical College, No. 16 Kunlun Rd, Xining 810008, China.
  • Jian W; Department of Public Health, Qinghai University Medical College, No. 16 Kunlun Rd, Xining, 810008, China; Nutrition and Health Promotion Center, Qinghai University Medical College, No. 16 Kunlun Rd, Xining 810008, China.
  • Zhao L; Department of Public Health, Qinghai University Medical College, No. 16 Kunlun Rd, Xining, 810008, China; Nutrition and Health Promotion Center, Qinghai University Medical College, No. 16 Kunlun Rd, Xining 810008, China.
  • Xu R; Global Health Institute, School of Public Health, Xi'an Jiaotong University, Room 3104, No. 21 Hongren Building, West China Science and Technology lnnovation Harbour (iHarbour), Xi'an 710061, China.
  • Liu T; School of Food Engineering and Nutritional Science, Shaanxi Normal University, No. 199 Chang'an South Rd, Xi'an, Shaanxi 710062, China.
  • Zhang B; School of Mathematics and Statistics, Qinghai Nationalities University, No. 3 Bayi Middle Rd, Xining 810007, China.
  • Wang H; Department of Public Health, Qinghai University Medical College, No. 16 Kunlun Rd, Xining, 810008, China; Nutrition and Health Promotion Center, Qinghai University Medical College, No. 16 Kunlun Rd, Xining 810008, China.
  • Tong L; Qinghai Provincial Key Laboratory of Prevention and Control of Glucolipid Metabolic Diseases with Traditional Chinese Medicine, Medical College, Qinghai University, No. 16 Kunlun Rd, Xining 810008, China.
  • Tang H; State Key Laboratory of Genetic Engineering, Zhongshan Hospital and School of Life Sciences, Human Phenome Institute, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Fudan University, No. 825 Zhangheng Rd, Shanghai 200438, China. Electronic addre
  • Wang Y; Global Health Institute, School of Public Health, Xi'an Jiaotong University, Room 3104, No. 21 Hongren Building, West China Science and Technology lnnovation Harbour (iHarbour), Xi'an 710061, China. Electronic address: youfawang@gmail.com.
Sci Total Environ ; 949: 174754, 2024 Jul 18.
Article em En | MEDLINE | ID: mdl-39032745
ABSTRACT

OBJECTIVE:

Improved understanding of metabolic obesity phenotypes holds great promise for personalized strategies to combat obesity and its co-morbidities. Such investigation is however lacking in Tibetans with unique living environments and lifestyle in the highlands. Effects of altitude on heterogeneous metabolic obesity phenotypes remain unexplored.

METHODS:

We defined metabolic obesity phenotypes i.e., metabolically healthy/unhealthy and obesity/normal weight in Tibetans (n = 1204) living at 2800 m in the suburb or over 4000 m in pastoral areas. 129 lipoprotein parameters and 25 low-molecular-weight metabolites were quantified and their associations with each phenotype were assessed using logistic regression models adjusting for potential confounders. The metabolic BMI (mBMI) was generated using a machine learning strategy and its relationship with prevalence of obesity co-morbidities and dietary exposures were investigated.

RESULTS:

Ultrahigh altitude positively associated with the metabolically healthy and non-obese phenotype and had a tendency towards a negative association with metabolically unhealthy phenotype. Phenotype-specific associations were found for 107 metabolites (e.g., lipoprotein subclasses, N-acetyl-glycoproteins, amino acids, fatty acids and lactate, p < 0.05), among which 55 were manipulated by altitude. The mBMI showed consistent yet more pronounced associations with cardiometabolic outcomes than BMI. The ORs for diabetes, prediabetes and hypertriglyceridemia were reduced in individuals residing at ultrahigh altitude compared to those residing at high altitude. The mBMI mediated the negative association between pastoral diet and prevalence of prediabetes, hypertension and hypertriglyceridemia, respectively.

CONCLUSIONS:

We found metabolite markers representing distinct obesity phenotypes associated with obesity co-morbidities and the modification effect of altitude, deciphering mechanisms underlying protective effect of ultrahigh altitude and the pastoral diet on metabolic health.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article