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Prevalence, associated factors, and gene polymorphisms of obesity in Tibetan adults in Qinghai, China

BMC Public Health volume 24, Article number: 305 (2024) Cite this article

Abstract

Objectives

To explore the prevalence and associated factors of obesity in Tibetan adults in Qinghai, China, and to determine the association between the FTO (rs1121980 and rs17817449) and MC4R gene (rs17782313 and rs12970134) polymorphisms with obesity.

Methods

A cross-sectional survey was conducted in 2015 in Qinghai to selected Tibetan adults aged 20 to 80 years. Prevalence of obesity (BMI ≥ 28 kg/m2) and overweight (BMI 24 ~ 27.9 kg/m2) were evaluated. Multivariable logistic models were used to determine the associated factors. Pair-matched subjects of obesity cases and normal-weight controls were selected for the gene polymorphism analyses. Conditional logistic models were used to assess the association between gene polymorphisms with obesity. Additive and multiplicative gene-environment interactions were tested.

Results

A total of 1741 Tibetan adults were enrolled. The age- and sex- standardized prevalence of obesity and overweight was 18.09% and 31.71%, respectively. Male sex, older age, heavy level of leisure-time exercise, current smoke, and heavy level of occupational physical activity were associated with both obesity and overweight. MC4R gene polymorphisms were associated with obesity in Tibetan adults. No significant gene-environment interaction was detected.

Conclusion

The prevalence of obesity and overweight in Tibetan adults was high. Both environmental and genetic factors contributed to the obesity prevalent.

Peer Review reports

Introduction

Obesity is one of the fast-growing health problems worldwide. The prevalence of obesity is rapidly rising in both developed and developing countries [1, 2]. Obesity is a widely-recognized risk factor for cardiovascular disease, diabetes mellitus, chronic kidney disease, and some types of cancer [3,4,5,6]. The high prevalence of obesity has led to a huge burden of obesity-related illnesses [7].

In China, the prevalence of obesity greatly increased over the past decades [8]. The up-to-date data reported that half of the adults in China were prevalent of overweight or obesity [9]. The obesity pandemic is greatly influenced by the combination of physiological and behavioral factors that are triggered by the changes in the food environment and the environmentally driven reductions in physical activity [10]. China is a multi-ethnic nation, with 56 distinct ethnicities living in the vast territory. The prevalence of obesity among different ethnicities differs due to the disparities in lifestyle, genetics, living environment, socioeconomics, etc [11, 12]. Previous publications have reported the differences in the prevalence of cardiovascular disease risk factors including obesity [13,14,15,16].

Tibetan is one of the largest ethnic minorities in China, mainly living in the Qinghai-Tibet Plateau with an average altitude higher than 3000 m. Many Tibetans live as pastoralists on the plateau and keep a relatively primitive lifestyle. The natural environment in the plateau, socioeconomic condition, unique lifestyles, and genetic features may significantly impact the obesity epidemic. However, few studies in China evaluate the prevalence and the determinants of obesity in Tibetan adults. The substantial contribution of genetic factors to obesity has been well-established. The FTO was the first GWAS-identified obesity gene in 2007 [17]. Before GWAS, MC4R deficiency was identified as the commonest monogenic form of obesity [18]. The FTO and MC4R gene are two of the verified obesity-related variants, but evidence from ethnic minorities in China is limited. Especially, in Tibetan adults, the role of the FTO and MC4R gene and their interaction with environmental factors is unclear. The China National Health Survey (CNHS) [19] provides the opportunity to compare not only the health status but also the genetic backgrounds of different ethnic groups. It is essential to determine the factors related to obesity of different ethnic groups in China.

In this study, we used cross-sectional data to assess the prevalence and associated factors of obesity in Tibetan adults living in Qinghai Province in China. Based on the surveyed population, a 1:1 pair-matched case-control study was designed to determine the association between FTO (rs1121980 and rs17817449) and MC4R gene (rs17782313 and rs12970134) polymorphisms with obesity in Tibetan adults. The gene-environment interaction was also tested.

Methods

Study population

This study was one part of the (CNHS), which was a nationwide multi-ethnic cross-sectional study conducted from 2012 to 2017 in eleven provinces in mainland China. The CNHS was designed to obtain the reference intervals of physiological constants and the determinants of noncommunicable diseases among different ethnic populations aged 20 to 80 years in China. Details of the sampling procedures have been described elsewhere [19]. The eligibility criteria were as follows: (1) aged 20–80 years, (2) living in their current residence for at least 1 year, and (3) the participant and his/her parents were all Tibetan. The exclusion criteria included: (1) individual with severe mental or physical illness, (2) pregnant females, and (3) military personnel on active service. In this present study, a stratified cluster sampling method was used to select Tibetan in Qinghai province in 2015. In the first stage, to account for socioeconomic development and distribution of ethnic residence, Xining City (the capital of Qinghai Province), Hainan Tibetan Autonomous Prefecture, and Haidong City were selected. In the second stage, Chengxi District in Xining, Gonghe County and Guide County in Hainan, and Hualong County in Haidong were selected. In the third stage, Qiabuqia Town and Jiangxigou Town in Gonghe, Heyin Town in Guide, and Bayan Town in Hualong were selected as the survey sites. In the final stage, adults aged 20 to 80 years and resident in the selected sites for at least 1 year before the survey were eligible to participate (n = 1741). To account for genetic ancestry, we identified Tibetan people as individuals whose parents were both of Tibetan ethnicity. All participants provided written informed consent before the survey. The study was approved by the bioethical committee of Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.

Based on the cross-sectional survey, a 1:1 pair-matched case-control study (matched for age (± 3 years) and sex) was then designed for the SNPs analyses. Those with BMI ≥ 28 kg/m2 were all included in the case group (n = 319), and their counterparts with BMI < 24 kg/m2 were eligible for the control group selection (n = 319). The flowchart for the study population enrollment is shown in Fig. 1.

Fig. 1
figure 1

The flowchart for selecting participants in this study

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Data collection

In the fieldwork, a standardized questionnaire was used to collect social-demographic characteristics (sex, age, ethnicity, education, income, etc.) and lifestyles (smoking, drinking, physical activity, etc.) through face-to-face interviews. The interviews were conducted by well-trained medical staff.

Education was categorized into three categories according to the last educational institution attended: primary school or below, junior or senior high school, and college or above. Personal annual income was categorized into ≤ 10,000 and > 10,000 CNY according to the median. Current smoking was defined as smoking at least one cigarette per day for at least 6 months. Current drinking was defined as drinking at least twice per month with an intake of more than 640 ml (one bottle) of beer or 100 ml of liquor for at least 6 months. Occupational physical activity was grouped into light, moderate, and heavy according to intensity. The leisure-time exercise was considered participation in moderate or vigorous activity for at least 20 min at leisure time, with three levels: light (< 1 day per week), moderate (1–4 days per week), and heavy (5–7 days per week).

The body weight was measured in light clothing using a body composition analyzer (BC-420, TANITA, Japan) with an accuracy of 0.1 kg. Height was measured with bare feet by a fixed stadiometer with an accuracy of 0.1 cm. The average of two height measurements was recorded. The body mass index (BMI) was calculated as the weight in kilograms divided by the square of the height in meters.

Genotyping

The genomic DNA was extracted from blood samples using the magnetic bead-based kit. The MC4R rs17782313 (T > C), rs12970134 (G > A), FTO rs1121980 (G > A), and rs17817449 (T > G) polymorphisms were genotyped using TaqMan assay. For the four SNPs, the PCR reaction was optimized in a 10 µL total volume containing 1 µL DNA template, 5 µL Master Mix, 3 µL primer, and 3 µL water.

Definition

According to the diagnostic criteria for the Chinese population by the Working Group on Obesity in China (WGOC) [20], obesity was defined as BMI ≥ 28 kg/m2, and overweight was defined as 24 ≤ BMI < 28 kg/m2. Participants with 18.5 ≤ BMI < 24 kg/m2 were defined as normal-weigh.

Statistical analysis

Descriptive results were stratified by BMI categories. Data were summarized as numbers (percentage) for categorical variables and mean ± SD for continuous variables. The differences between groups were compared using chi-square tests or analysis of variance.

The standardized prevalence of obesity and overweight was calculated based on the distributions of the 2010 China census population using the direct method. Multivariable logistic regression models that took account of the complexity of the sampling strategy were applied to determine the associated factors of obesity and overweight, using sociodemographic and lifestyle factors as the independent variables [21]. As a sensitive analysis, participants with BMI < 18.5 kg/m2 were deleted to determine the associated factors.

In the case-control analyses, the Hardy-Weinberg equilibrium (HWE) was tested to assess the deviation between observed and expected genotype frequencies using chi-square tests in the control group. Chi-square tests were used to compare genotypes and allele frequencies between the obesity cases and the controls.

To determine the association between MC4R and FTO gene polymorphisms with obesity, conditional logistic regression models were applied to test all four inheritance models for each variant (dominant, recessive, codominant, and additive). All models were adjusted by socioeconomic status and lifestyle factors. The unweighted genetic risk score (GRS) was then derived by summing the total number of risk alleles for each individual across the four SNPs [22]. To account for uncertainty in risk allele effect sizes, the weighted genetic risk score (wGRS) was calculated as the sum of the number of risk alleles multiplied by the corresponding log odds ratio of each risk allele [23]. To assess the association between GRS and wGRS with obesity, the sample was categorized into three groups according to tertiles.

Both multiplicative and additive interactions were evaluated to examine the interaction between SNPs and behavioral factors (smoking, alcohol drinking, occupational physical activity, and leisure-time exercise in this study) on obesity. The multiplicative interactions were evaluated by the product terms added in the regression models. The relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) were used to evaluate the additive interactions, which were calculated using the regression coefficients and covariance matrix. The 95% confidence intervals (CIs) of RERI, AP, and SI were calculated by the delta method [24]. If the 95% CIs of RERI and AP did not include 0 and the 95% CI of SI did not include 1, an interaction was present.

All analyses were conducted using SAS statistical software (version 9.4; SAS Institute Inc., Cary, NC, USA). A 2-tailed alpha with P < 0.05 was considered statistically significant.

Results

A total of 1,741 Tibetan adults were included in the cross-sectional survey, half of whom were male participants (870/1741). The characteristics of the participants are summarized in Table 1. The mean age of the study population was 43.96 ± 12.55 years. Compared to normal-weight, the subjects with overweight and obesity were at an older age and had higher proportions of male participants. Details of the comparison in demographic characteristics are displayed in Table 1.

Table 1 Basic characteristics of study sample by BMI categories
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The crude and standardized prevalence of obesity and overweight are illustrated in Fig. 2A and B. The overall prevalence of obesity and overweight in Tibetan adults was 19.47% and 33.66%. Males had higher prevalence than females (22.07% vs. 16.88%, P = 0.0062; 37.01% vs. 30.31%, P = 0.0031). With standardized by age and sex, the prevalence of obesity and overweight was 18.09% and 31.71%. The age-standardized prevalence in males remained significantly higher than in females (20.29% vs. 16.11%, P = 0.0416; 35.24% vs. 28.38%, P = 0.0119). The age-specific prevalence of obesity in females increased with age, while in males the prevalence peaked at 40–49 years, and decreased. The age-specific prevalence of overweight in both males and females increased with age and peaked at 50–59 years (see Fig. 2C and D).

Fig. 2
figure 2

Sex- and age-specific prevalence of obesity and overweight in Tibetan adults

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In multivariable analyses, male sex, older age, and heavy level of leisure-time exercise were found to be associated with higher odds of both obesity and overweight, while current smoke and heavy level of occupational physical activity were associated with lower odds. Relative to females, males were at 2.33-folds (95% CI: 1.71 to 3.16) and 1.82-folds (95% CI: 1.42 to 2.34) higher odds of obesity and overweight, respectively. Individuals aged 50–59 years were at the highest odds of obesity (12.22, 95% CI: 7.71 to 19.36) and overweight (5.01, 95% CI: 2.99 to 8.41) than the young adults (20–29 years). Those engaging in heavy leisure-time exercise were associated with 1.55-folds (95% CI: 1.13 to 2.14) and 1.53-folds (95% CI: 1.20 to 1.95) higher odds of obesity and overweight than those slightly engaging in. Current smoking was negatively associated with obesity (0.44, 95%CI: 0.32 ~ 0.60) and overweight (0.69, 95%CI: 0.50 ~ 0.95). Heavy level of occupational was found to be related to a lower risk of obesity (0.79, 95%CI: 0.67 ~ 0.93) and overweight (0.70, 95%CI: 0.59 ~ 0.84). (Fig. 3) The sensitive analysis with deleting participants with BMI < 18.5 kg/m2 showed consistent results (see supplementary Table S1).

Fig. 3
figure 3

Multivariable logistic analysis of obesity and overweight in Tibetan adults

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In the case-control study, a comparable control group (n = 319) was selected for the cases (n = 319). As Table 2 shows, there was no significant difference between the cases and controls except for smoke and leisure-time exercise. The genotype and allele frequencies of the four SNPs in the case and control groups were not significantly different (P > 0.05 for all). The HWE tests for all four SNPs show genetic equilibrium (P > 0.05 for all, see Table 3).

Table 2 Basic characteristics of obesity cases and normal-weight controls in the case-control sample
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Table 3 Comparison of genotype and allele frequencies and the HWE test between case and control groups in Tibetan adults
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Figure 4 illustrates the association between MC4R and FTO gene polymorphisms with obesity in Tibetan adults by conditional logistic regression models. With adjustment for socioeconomic status and lifestyle factors, MC4R gene polymorphisms were significantly associated with obesity under dominant, codominant, and additive inheritance models (Rs17782313 genotype T/C + C/C vs. T/T OR = 1.61, 95%CI: 1.12 ~ 2.33; genotype T/C vs. T/T OR = 1.59, 95%CI: 1.08 ~ 2.33; additive model OR = 1.45, 95%CI: 1.07 ~ 1.95. Rs12970134 genotype G/A + A/A vs. G/G OR = 1.53, 95%CI: 1.05 ~ 2.22; genotype G/A vs. G/G OR = 1.51, 95%CI: 1.03 ~ 2.23; additive model OR = 1.42, 95%CI: 1.03 ~ 1.96). The SNPs of FTO were not found to be significantly associated with obesity in Tibetan adults. As Fig. 5 shows, after adjustment, the highest tertile group of GRS (≥ 3) and wGRS (≥ 0.55) were significantly associated with obesity in Tibetan adults.

Fig. 4
figure 4

Association of FTO and MC4R gene polymorphisms with obesity in Tibetan adults

Models were adjusted for residence, education, income, smoking status, drinking status, occupational physical activity, and leisure-time exercise

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Fig. 5
figure 5

Association of genetic risk score (GRS) and weighted-genetic risk score (wGRS) with obesity in Tibetan adults

Models were adjusted for residence, education, income, smoking status, drinking status, occupational physical activity, and leisure-time exercise

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We assessed the additive and multiplicative interactions between the MC4R gene polymorphisms with the consistently significant associated environmental factors (smoking, occupational physical activity, and leisure-time exercise) based on the dominant inheritance model. As the Supplementary Table S2 and Table S3 show, no significant interaction was found.

Discussion

The Tibetan is a characteristic ethnic in western China, mainly settling in the Qinghai-Tibet Plateau at high altitudes. Up to now, the data on obesity in this population is limited. In this study, using a population-based cross-sectional survey, we reported the prevalence and associated factors of obesity and overweight in Tibetan adults in Qinghai, China. The results indicated that according to WGOC criteria, nearly half of the Tibetan adults were overweight or obesity. The age- and sex-standardized prevalence of obesity was about 18%. Besides the sex and age, the study showed that the modifiable factors (including smoking, alcohol consumption, physical activity, and exercise) were associated with obesity and overweight in this population. This study verified the contribution of single nucleotide polymorphisms of the MC4R gene to obesity, but a significant association was not found in FTO.

The prevalence of obesity in China is experiencing rapid growth in recent decades, rising from 7.1% to 2002 to 12% in 2010 [9]. According to the latest round of China National Nutrition Surveys (CNNSs) in 2015-19, 34.3% and 16.4% of adults ≥ 18 years in China were overweight and obesity [9]. Our study showed that the prevalence of overweight and obesity in Tibetan adults was near the average in China. The pattern of the obesity epidemic in China varies by geographic location and demographic clusters [25]. Obesity in different ethnicities differs because of the genetic ancestry, ecological environment, and importantly the diverse lifestyles. As a multi-ethnic cross-sectional study, the CNHS provides the opportunity of comparing the health status of ethnicities located in different regions in China. According to the published results of the CNHS, the obesity prevalence in Tibetan adults was higher than that in Bouyei in Guizhou [15], Yi in Sichuan [26], Uyghur in Xinjiang [13], and was slightly lower than that in Mongolian in Inner Mongolia [27] and Yugur in Gansu [28].

Obesity occurs with the combined contribution of genetic regulation and the obesogenic environment. While the fact is the worldwide epidemic of obesity is mostly attributed to the lifestyle transition as the human gene is unlikely to substantially change in the past few decades. It is the food environment and the environmentally driven reductions in physical activity that trigger the weight gain in genetically susceptible individuals [29]. We compared the obesity-related lifestyles in Tibetan and other ethnicities in China and found that Tibetan adults were more prevalent in light level physical activity than Bouyei [15] and Yi [26], which may partly explain the disparities in obesity. The leisure-time exercise in Tibetan was high but it was most likely the sequentially behavior change because of the presence of obesity [30]. We did not collect the dietary data in the study, previous studies identified the major dietary patterns in Tibetan adults - urban, western, and pastoral patterns [31]. Among these patterns, beef and mutton were the common sources of meat. The urban dietary pattern was characterized by frequent consumption of vegetables, tubers/roots, and refined carbohydrates. The western pattern was rich in sweetened drinks, snacks, and desserts, and the pastoral pattern featured tsamba (roasted Tibetan barley), Tibetan cheese, butter tea/milk tea, and whole-fat dairy foods. These dietary factors were found to be associated with an increased risk of metabolic syndrome, including obesity [31]. Besides, a proportion of Tibetan in China practice Tibetan Buddhism, which may affect the social practices and lifestyles, and may therefore have an impact on obesity. However, we did not collect information on religious beliefs. The interrelationship of religion, lifestyles, and health outcomes remain to be explored.

The contributions of genetic factors to body weight regulation have been proved. The heritability of BMI was estimated to range from 40 to 70% [32]. Association between SNPs in FTO and MC4R gene with obesity were widely replicated in different ethnic groups [33, 34]. Among Chinese, the associations have been demonstrated in Han people [35], but rarely in ethnic majorities. In this study, we designed the pair-matched case-control study based on the cross-sectional data and found that MC4R gene polymorphisms were determinants of obesity in Tibetan. Our previous work explored the same SNPs as this study in Yi people and showed similar results [36]. Notably, the minor allele frequencies (MAF) in Tibetan were higher than that in other ethnicities in China [36, 37], which provide a potential mechanism for the prevalence disparities. The gene-environment interaction is what we expect to discover, as it not only helps explain the underlying mechanism of disease occurrence but also provides a basis for eliminating environmental risk factors. However, in this study, neither the additive nor the multiplicative gene-environment interaction was found. It suggests that more genetic determinants and detailed environmental factors need to be further explored.

One of the strengths of this study lies in the collection of large-scale population-based data on the unique ethnic living at the high altitudes in China. It provides the diversity of the obesity epidemic in different ethnicities and geographic locations in China. Another strength is that this study comprehensively evaluated the environmental and genetic determinants of obesity in Tibetan.

One major limitation of this study is the lack of some important obesity-related variables, especially dietary data. Hence, unfortunately, we cannot analyze the impact of nutrition on obesity in Tibetan, nor adjust the confounding when analyzing other variables of interest. The future works are expected to explore the integrated factors associated with obesity in Tibetan. Secondly, we only used BMI to define obesity, which cannot distinguish fat and lean tissue or the body fat distribution. Further analysis is expected to define obesity by combining body muscle and body fat. Besides, obesity is a polygenic inheritable disease, while in this study we only detected four SNPs in two genes. The high throughput screening and verification are expected for a full understanding of the genetic mechanism of obesity in this population.

In conclusion, this study suggests that the prevalence of obesity and overweight in Tibetan adults was high. Both modifiable environmental factors and MC4R gene polymorphisms were associated with the obesity risk in this population. This study calls for the urgent need to improve education attainment especially health literacy. Healthy lifestyle adherence is also emphasized for obesity prevention.

Data availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, Mozaffarian D, Swinburn B, Ezzati M. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 2019;7(3):231–40.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Teufel F, Seiglie JA, Geldsetzer P, Theilmann M, Marcus ME, Ebert C, Arboleda WAL, Agoudavi K, Andall-Brereton G, Aryal KK, et al. Body-mass index and Diabetes risk in 57 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 685 616 adults. Lancet. 2021;398(10296):238–48.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Powell-Wiley TM, Poirier P, Burke LE, Després JP, Gordon-Larsen P, Lavie CJ, Lear SA, Ndumele CE, Neeland IJ, Sanders P, et al. Obesity and Cardiovascular Disease: A Scientific Statement from the American Heart Association. Circulation. 2021;143(21):e984–e1010.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kreiner FF, Schytz PA, Heerspink HJL, von Scholten BJ, Idorn T. Obesity-related Kidney Disease: current understanding and future perspectives. Biomedicines 2023, 11(9).

  5. Ye J. Mechanism of insulin resistance in obesity: a role of ATP. Front Med. 2021;15(3):372–82.

    Article  PubMed  Google Scholar 

  6. Lee-Rueckert M, Canyelles M, Tondo M, Rotllan N, Kovanen PT, Llorente-Cortes V, Escolà-Gil JC. Obesity-induced changes in cancer cells and their microenvironment: mechanisms and therapeutic perspectives to manage dysregulated lipid metabolism. Sem Cancer Biol. 2023;93:36–51.

    Article  CAS  Google Scholar 

  7. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309(1):71–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang L, Zhou B, Zhao Z, Yang L, Zhang M, Jiang Y, Li Y, Zhou M, Wang L, Huang Z, et al. Body-mass index and obesity in urban and rural China: findings from consecutive nationally representative surveys during 2004-18. Lancet (London England). 2021;398(10294):53–63.

    Article  PubMed  Google Scholar 

  9. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. The Lancet Diabetes & Endocrinology. 2021;9(6):373–92.

    Article  Google Scholar 

  10. Ravussin E, Ryan DH. Three New Perspectives on the Perfect Storm: What’s Behind the Obesity Epidemic? Obesity 2018, 26(1):9–10.

  11. Zhao P, Gu X, Qian D, Yang F. Socioeconomic disparities in abdominal obesity over the life course in China. Int J Equity Health. 2018;17(1):96.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hui-Fang L, Cai L, Wang XM, Golden AR. Ethnic disparities in prevalence and clustering of Cardiovascular Disease risk factors in rural Southwest China. BMC Cardiovasc Disord. 2019;19(1):200.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gong H, Pa L, Wang K, Mu H, Dong F, Ya S, Xu G, Tao N, Pan L, Wang B, et al. Prevalence of Diabetes and Associated Factors in the Uyghur and Han Population in Xinjiang, China. Int J Environ Res Public Health. 2015;12(10):12792–802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dong F, Wang D, Pan L, Yu Y, Wang K, Li L, Wang L, Liu T, Zeng X, Sun L, et al. Disparities in Hypertension prevalence, awareness, treatment and control between Bouyei and Han: results from a Bi-ethnic Health Survey in developing regions from South China. Int J Environ Res Public Health. 2016;13(2):233.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang K, Wang D, Pan L, Yu Y, Dong F, Li L, Wang L, Liu T, Zeng X, Sun L, et al. Prevalence of obesity and related factors among Bouyei and Han peoples in Guizhou Province, Southwest China. PLoS ONE. 2015;10(6):e0129230.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wu Y, Wang Y, Dang Z, Zhang Y, Wang Y, Tong L, Peng W. Ethnic differences in eating patterns and their associations with obesity among adults in West China. Br J Nutr 2023:1–12.

  17. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N Engl J Med. 2003;348(12):1085–95.

    Article  CAS  PubMed  Google Scholar 

  19. He H, Pan L, Pa L, Cui Z, Ren X, Wang D, Liu F, Wang X, Du J, Wang H, et al. Data Resource Profile: the China National Health Survey (CNHS). Int J Epidemiol. 2018;47(6):1734–1735 f.

    Article  PubMed  Google Scholar 

  20. Zhou BF. Predictive values of body mass index and waist circumference for risk factors of certain related Diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002;15(1):83–96.

    PubMed  Google Scholar 

  21. Cole SR. Analysis of complex survey data using SAS. Comput Methods Programs Biomed. 2001;64(1):65–9.

    Article  CAS  PubMed  Google Scholar 

  22. Tian Y, Li P. Genetic risk score to improve prediction and treatment in gestational Diabetes Mellitus. Front Endocrinol (Lausanne). 2022;13:955821.

    Article  PubMed  Google Scholar 

  23. Wang J, Conti DV, Bogumil D, Sheng X, Noureddin M, Wilkens LR, Le Marchand L, Rosen HR, Haiman CA, Setiawan VW. Association of Genetic Risk Score with NAFLD in an ethnically diverse cohort. Hepatol Commun. 2021;5(10):1689–703.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Andersson T, Alfredsson L, Källberg H, Zdravkovic S, Ahlbom A. Calculating measures of biological interaction. Eur J Epidemiol. 2005;20(7):575–9.

    Article  PubMed  Google Scholar 

  25. Zhao R, Zhao L, Gao X, Yang F, Yang Y, Fang H, Ju L, Xu X, Guo Q, Li S et al. Geographic Variations in Dietary Patterns and Their Associations with Overweight/Obesity and Hypertension in China: Findings from China Nutrition and Health Surveillance (2015–2017). Nutrients 2022, 14(19).

  26. Wang Y, Pan L, Wan S, Yi H, Yang F, He H, Li Z, Zhang J, Wang X, Yong Z, et al. Increasing prevalence of overweight and obesity in Yi farmers and migrants from 2007 to 2015 in China: the Yi migrant study. BMC Public Health. 2018;18(1):659.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Li Y, Wang H, Wang K, Wang W, Dong F, Qian Y, Gong H, Xu G, Li G, Pan L et al. Optimal body fat percentage cut-off values for identifying cardiovascular risk factors in Mongolian and Han adults: a population-based cross-sectional study in Inner Mongolia, China. BMJ Open 2017, 7(4).

  28. Yu C, Zhao H, Pan L, Zhang J, Wang X, Chang L, Tuo Y, Xi Je, Liu B, Wang Y, et al. The Additive Interaction between Body Mass Index and Hypertension Family History in Han and Yugur: the China National Health Survey (CNHS). Int J Hypertens. 2019;2019:8268573–3.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ravussin E, Ryan DH. Three New perspectives on the Perfect Storm: what’s behind the obesity epidemic? Obes (Silver Spring). 2018;26(1):9–10.

    Article  Google Scholar 

  30. Westerterp KR. Changes in physical activity over the lifespan: impact on body composition and sarcopenic obesity. Obes Reviews: Official J Int Association Study Obes. 2018;19(Suppl 1):8–13.

    Article  Google Scholar 

  31. Peng W, Liu Y, Malowany M, Chen H, Su X, Liu Y. Metabolic syndrome and its relation to dietary patterns among a selected urbanised and semi-urbanised tibetan population in transition from nomadic to settled living environment. Public Health Nutr. 2021;24(5):984–92.

    Article  PubMed  Google Scholar 

  32. Heymsfield SB, Wadden TA. Mechanisms, pathophysiology, and management of obesity. N Engl J Med. 2017;376(3):254–66.

    Article  CAS  PubMed  Google Scholar 

  33. Wen W, Cho YS, Zheng W, Dorajoo R, Kato N, Qi L, Chen CH, Delahanty RJ, Okada Y, Tabara Y, et al. Meta-analysis identifies common variants associated with body mass index in east asians. Nat Genet. 2012;44(3):307–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Loos RJ, Yeo GS. The bigger picture of FTO: the first GWAS-identified obesity gene. Nat Rev Endocrinol. 2014;10(1):51–61.

    Article  CAS  PubMed  Google Scholar 

  35. Huang W, Sun Y, Sun J. Combined effects of FTO rs9939609 and MC4R rs17782313 on obesity and BMI in Chinese Han populations. Endocrine. 2011;39(1):69–74.

    Article  CAS  PubMed  Google Scholar 

  36. Wang Y, Pan L, Wan S, Yihuo W, Yang F, He H, Li Z, Yong Z, Shan G. MC4R gene polymorphisms interact with the urbanized living environment on obesity: results from the Yi migrant study. Front Genet. 2022;13:849138.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wei BL, Yin RX, Liu CX, Deng GX, Guan YZ, Zheng PF. The MC4R SNPs, their haplotypes and gene-environment interactions on the risk of obesity. Mol Med. 2020;26(1):77.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors express their sincere appreciation to all the staff for performing the field work. They are also grateful to all the study participants.

Funding

This work was supported by grants from Key Basic Research Program of the Ministry of Science and Technology of China (Grant No. 2013FY114100).

Author information

Authors and Affiliations

  1. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Ye Wang & Guangliang Shan

  2. Department of Epidemiology and Statistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, 5 Dong Dan San Tiao, Dong Cheng District, Beijing, 100005, China

    Li Pan, Huijing He & Guangliang Shan

  3. Qinghai University Affiliated Hospital, Qinghai, China

    Zhanquan Li, Sen Cui, Airong Yang, Wenfang Li, Guoqiang Jia & Ximing Han

  4. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Peking Union Medical College, 236 Baidi Road, Nankai District, Tianjin, 300192, China

    Xianghua Wang

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  1. Ye Wang
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Contributions

GS designed the study and supervised data collection. YW analyzed the data, interpreted the results, and drafted the manuscript. LP, XW, HH, ZL, SC, AY, WL, GJ, and XH participated in data collection. YW, LP, and HH conceived and carried out the experiments. All authors have approved the submitted versions.

Corresponding authors

Correspondence to Xianghua Wang or Guangliang Shan.

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Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by bio Ethical Committee of Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. The patients/participants provided their written informed consent to participate in this study. All methods and experiments were performed in accordance with the relevant guidelines and regulations.

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Not applicable.

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The authors declare no competing interests.

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Wang, Y., Pan, L., He, H. et al. Prevalence, associated factors, and gene polymorphisms of obesity in Tibetan adults in Qinghai, China. BMC Public Health 24, 305 (2024). https://doi.org/10.1186/s12889-023-17181-7

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Keywords

BMC Public Health

ISSN: 1471-2458

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