==== Front J Glob Health J Glob Health JGH Journal of Global Health 2047-2978 2047-2986 International Society of Global Health 37394907 jogh-13-04061 10.7189/jogh.13.04061 Articles Altitude and metabolic syndrome in China: Beneficial effects of healthy diet and physical activity Zhou et al. Altitude and metabolic syndrome in China Zhou Junmin 1 * He Ruifeng 2 * Shen Zhuozhi 3 Zhang Yan 4 Gao Xufang 5 Dejiquzong 6 Xiao Xiong 1 Zhang Tao 1 Yang Dan 1 Wang Yufei 1 Song Huan 7 8 Guo Yuming 9 Li Shanshan 9 Chen Gongbo 9 Yin Jianzhong 10 11 Zhao Xing 1 China Multi-Ethnic Cohort (CMEC) collaborative group 1 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China 2 Tibet Center for Disease Control and Prevention, Lhasa, China 3 Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China 4 School of Public Health, Guizhou Medical University, Guiyang, China 5 Chengdu Center for Disease Control & Prevention, Chengdu, China 6 Tibet University, Lhasa, China 7 West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China 8 Med-X Center for Informatics, Sichuan University, Chengdu, China 9 Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia 10 School of Public Health, Kunming Medical University, Kunming, China 11 Baoshan College of Traditional Chinese Medicine, Baoshan, China * Joint first authorship. Correspondence to: Jianzhong Yin School of Public Health, Kunming Medical University 1168# West Chunrong Road,Chenggong Zone, Kunming China yinjianzhong2005@sina.com Gongbo Chen School of Public Health and Preventive Medicine, Monash University 553 St Kilda Road, Melbourne VIC 3004 Australia gongbo.chen1@monash.edu 30 6 2023 2023 13 04061Copyright © 2023 by the Journal of Global Health. All rights reserved. 2023 https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. Background The correlation between altitude and metabolic syndrome has not been extensively studied, and the mediation effects of diet and physical activity remain unclear. We evaluated the cross-sectional correlations between altitude and metabolic syndrome and the possible mediation effects of diet and physical activity in China. Methods We included 89 485 participants from the China Multi-Ethnic Cohort. We extracted their altitude information from their residential addresses and determined if they had metabolic syndrome by the presence of three or more of the following components: abdominal obesity, reduced high-density lipoprotein cholesterol (HDL-C), elevated triglycerides, elevated glucose, and high blood pressure at recruitment. We conducted multivariable logistic regression and mediation analyses for all and separately for Han ethnic participants. Results The participants had a mean age of 51.67 years and 60.56% were female. The risk difference of metabolic syndrome was -3.54% (95% confidence interval (CI) = -4.24, -2.86) between middle and low altitudes, -1.53% (95%CI = -2.53, -0.46) between high and low altitudes, and 2.01% (95% CI = 0.92, 3.09) between high and middle altitudes. Of the total estimated effect between middle and low altitude, the effect mediated by increased physical activity was -0.94% (95% CI = -1.04, -0.86). Compared to low altitude, the effects mediated by a healthier diet were -0.40% (95% CI = -0.47, -0.32) for middle altitude and -0.72% (95% CI = -0.87, -0.58) for high altitude. Estimates were similar in the Han ethnic group. Conclusions Living at middle and high altitudes was significantly associated with lower risk of metabolic syndrome compared to low altitude, with middle altitude having the lowest risk. We found mediation effects of diet and physical activity. ==== Body pmcMetabolic syndrome is a cluster of risk factors for cardiovascular diseases, metabolic diseases, and overall mortality. It affects 20% to 30% of adults in most countries and poses a high disease burden globally [1]. In 2010, 842 million people (12% of the total global population) resided at 1500 m or more above sea level, with 36% of them concentrated in East and South Asia [2]. However, due to the rugged geographical environment and historical isolation, health issues in this population have been understudied [3,4]. Furthermore, most of current studies focusing on altitude and health are limited by their ecological design [5]. The few studies that used individual data were constricted by either small samples or relatively low variability of altitude, potentially impacting the robustness of their results. Several studies examining the effects of living at high altitudes on individual components of metabolic syndrome found that living at high altitude is inversely associated with obesity [6-8] and diabetes or glucose [9,10], but positively related to blood pressure [11] and dyslipidaemia [12]. However, these studies focused on one specific metabolic outcome as opposed to comprehensive indicators which may better reflect people’s cardiometabolic health profiles. Using data from a cross-sectional survey, we aimed to examine the correlation between altitude and metabolic syndrome in Southwest China, an area which covers a wide range of altitudes and peoples including Tibetans, one of the three major high-altitude communities in the world [13]. We hypothesised that altitude would have an inverse correlation with metabolic syndrome. The mechanisms underlying this correlation, if present, remain unclear, and might be affected by diet and physical activity (PA), which are key modifiable lifestyles that vary widely at different altitude levels [14]. Therefore, our secondary aim was to investigate the mediating effects of diet and PA on the correlation between altitude and metabolic syndrome. We hypothesised that both diet and PA would mediate the correlation between altitude and metabolic syndrome. METHODS Population The source of data was a baseline survey of the China Multi-Ethnic Cohort (CMEC) study [15]. The CMEC is a community-based cohort study which included 99 556 permanent residents from the Tibetan, Yi, Miao, Bai, Dong, Bouyei, and Han ethnicities in Southwest China. The study complied with the Declaration of Helsinki and received ethical approval from the Sichuan University Medical Ethical Review Board (K2016038, K2020022). All participants provided written informed consent prior to data collection. We used data from 89 485 participants aged 30-79 years (n = 98 513), with a valid home address (n = 97 728), and with no missing values for all included variables (n = 89 485). The participants were not involved in the design or conduct of research. Measurements Assessment of exposure We downloaded global Shuttle Radar Topography Mission (SRTM) 4 elevation data for China at a spatial resolution of three arc-seconds (approximately 90 m) from the Consultative Group on International Agricultural Research – Consortium for Spatial Information (http://srtm.csi.cgiar.org/). We extracted the participants’ altitude information from the SRTM data according to their residential addresses. They lived at altitudes ranging from 7 m to 5346 m (Figure 1), so we divided them into low altitude (0-1499 m), middle altitude (1500-2999 m), and high altitude (≥3000 m) groups [6,12]. Figure 1 Spatial distribution of study sites. Ascertainment of outcome We used the National Cholesterol Education Program – Adult Treatment Panel III criteria to assess metabolic syndrome [16]. We defined abdominal obesity as a waist circumference ≥90 cm for men and ≥85 cm for women [17], reduced HDL-C as a level <1.03 mmol/L (<40 mg/dL) in men or 1.29 mmol/L (<50 mg/dL) in women, or using drug treatment for reduced HDL-C [16]. We defined elevated triglycerides as fasting triglycerides level ≥1.7 mmol/L (150 mg/dL), or being on drug treatment for elevated triglycerides [16] and elevated glucose as a fasting plasma glucose level ≥5.6 mmol/L (100 mg/dL) or using drug treatment for elevated glucose [16]. We classified high blood pressure as ≥130/85 mm Hg or using antihypertensive drug treatment in a patient with a history of hypertension [16]. We considered the presence of three or more of these components as metabolic syndrome [16]. Trained technicians obtained waist circumference measurements by placing a soft non-stretchable tape placed midway between the participants’ iliac crest and the lowest rib, to the nearest 0.1 cm. The participants wore only light clothes and fasted overnight (at least eight hours). Venous blood samples were collected on site. We measured blood lipids (including HDL-C and triglycerides) and glucose using an AU5800 Automated Chemistry Analyzer (Beckman Coulter Commercial Enterprise, Shanghai, China). Trained technicians, following the American Heart Association’s standardised protocol [18], measured participants’ blood pressure using electronic sphygmomanometers, which were calibrated before measurement. We averaged three blood pressure readings to calculate systolic and diastolic blood pressures. Assessment of mediators We assessed dietary patterns using a food frequency questionnaire regarding participants’ food consumption in the past year. We used the Dietary Approaches to Stop Hypertension (DASH) guidelines to determine adherence to a healthy diet, modifying the DASH score by replacing non-fat and low-fat dairy by full-fat dairy products [19], which are universally consumed in China. According to a randomised controlled trial [19], the modified DASH diet was more likely to reduce cardiometabolic risks. We excluded the food group component of sweetened beverages since the regular consumption of sweetened beverages was as low as 7.2% in our sample. For each of the remaining seven food group components of DASH, we categorised food group consumption into quintiles and scored all participants from 1 to 5 based on their intake ranking. Thus, the DASH score for each participant ranged between 7 and 35 (minimal to maximal adherence). PA considered participants’ occupational, traffic, chores, and leisure time activities. We obtained both PA intensity (quantified as using the corresponding metabolic equivalent values (MET) [20,21]) and duration during the preceding year. We calculated the product of PA intensity (MET) and duration (hours) as the volume of activity (MET-h), grouping all participants into three categories based on terciles of the PA volume: low (0-14.9 MET-h/d), moderate (15.0-31.6 MET-h/d), and high (≥31.7 MET-h/d). We used both continuous PA and PA for data analysis. Assessment of covariates We collected information on covariates (i.e. age, sex, marital status, educational level, income, alcohol consumption, smoking, and passive smoking) at recruitment through a questionnaire. We did not include body mass index (BMI), since the metabolic syndrome definition included abdominal obesity. Statistical analysis To simultaneously compare the prevalence of metabolic syndrome between the three altitude groups, we used the likelihood ratio test to compare the deviance between two nested models, with or without the altitude variable. A logistic regression model was used to examine the correlation between altitude and metabolic syndrome. We presented the results as risk difference (RD) using average predictive comparison with corresponding 95% confidence intervals (CIs) in altitude [22]. RD estimates the expected difference in metabolic syndrome associated with different altitude levels. In linear models with no interactions, this is identical to the coefficient estimates, while in generalised linear models, it allows interpretation of the original scale of the response variable, which, in the case of logistic regression, is the probability scale rather than the odds. We performed stratified analysis by age (30-60 years, 60-70 years, 70-79 years), sex, marital status (did not co-habit, co-habited), educational level (illiteracy, primary school, junior high school, high school or above), income (<20 000, 20 000-59 999, ≥60 000, in RMB Yuan), smoking status (never, current, former), passive smoking (no, yes), and alcohol consumption (0 g/d, (0, 5)g/d, ≥5g/d). We performed mediation analysis of dietary patterns and PA. First, we examined the interaction by including a product term for altitude and diet in the model and performing a test for altitude and PA. We tested the mediators one at a time, using the mediation package to estimate the direct, indirect, and total effects and the proportion mediated [23]. To examine the robustness of the estimates for ethnicity, we conducted sensitivity analyses by repeating all the above analyses for the Han ethnic group. We adjusted all multivariable models for all covariates unless stated otherwise. We did not adjust for BMI, because it highly correlated with waist circumference in the metabolic syndrome (correlation coefficient (r) = 0.78; P < 0.0001) based on previous literature [24]. The correlation was statistically significant if the two-sided P-value was <0.05. We performed all statistical analyses using R (version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria). Patient and public involvement Patients or the public were not involved in the study. RESULTS Participants characteristics The mean age of the 89 485 participants was 51.67 years (range = 30-79), and 60.56% were female (Table 1); 8701 were from high, 21 395 from middle, and 59 389 from low altitude settings. Table 1 Baseline characteristics of the included participants according to different altitude groups* Characteristic All participants (n = 89 485) High altitude (n = 8701) Middle altitude (n = 21 395) Low altitude (n = 59 389) P value Age in years, mean (SD) 51.67 (11.48) 48.87 (11.19) 52.90 (10.42) 51.63 (11.81) <0.001 Sex Male 39.44 39.32 31.90 42.04 <0.001 Female 60.56 60.68 68.10 57.96 Marital status Did not cohabit 11.22 12.44 10.30 11.35 <0.001 Cohabited 88.78 87.56 89.70 88.65 Educational level Illiteracy 26.54 65.77 27.42 20.06 <0.001 Primary school 25.35 25.04 38.35 20.37 Junior high school 25.59 5.51 26.32 29.09 High school or above 22.52 3.68 7.91 30.47 Income in yuan† <20 000 35.70 50.75 43.26 30.52 <0.001 20 000-59 999 36.12 37.11 42.05 34.11 ≥60 000 28.18 12.14 14.69 35.37 Smoking status Never 75.13 83.71 77.21 73.32 <0.001 Current 19.84 11.39 19.82 20.90 Former 5.04 4.90 2.96 5.79 Passive smoking No 51.85 84.97 46.45 48.89 <0.001 Yes 48.15 15.03 53.55 51.11 Alcohol consumption in g/d 0 57.71 81.28 74.54 48.31 <0.001 (0, 5) 29.79 14.63 15.92 36.96 ≥5 12.50 4.08 9.55 14.73 Physical activity (MET-h/d), mean (SD)† 26.05 (18.25) 20.62 (16.95) 33.22 (19.04) 24.29 (16.55) <0.001 Dietary pattern (DASH score), mean (SD) 20.48 (4.41) 20.56 (3.55) 20.72 (3.89) 20.39 (4.65) <0.001 Metabolic syndrome No 74.77 76.43 78.18 73.30 <0.001 Yes 25.23 23.57 21.82 26.70 SD – standard deviation, DASH – Dietary Approaches to Stop Hypertension, MET – metabolic equivalent *Data are percentages unless otherwise indicated. Data were age adjusted where appropriate. One-way analysis of variance and χ2 tests were used to produce P-values. †As of January 2021, the exchange rate was approximately 6.46 Yuan per US$. Correlation between altitude and metabolic syndrome The prevalence of metabolic syndrome was higher in the low altitude group (26.70%) than in the high (23.57%) and middle altitude (21.82%) groups (Table 1). The RD of metabolic syndrome was -3.54% (95% CI = -4.24, -2.86) between the middle and the low altitude groups, and -1.53% (95% CI = -2.53, -0.46) between the high and the low altitude groups, while the RD between the high and the middle altitude groups was 2.01% (95% CI = 0.92, 3.09) after adjusting for potential confounders (Table 2). Table 2 Correlation between altitude and metabolic syndrome* Likelihood ratio test of altitude‡ Altitude Risk difference, % 95% CI, % P-value Deviance df P-value Middle to low -3.54 (-4.24, -2.86) <0.001† 102.79 2.00 <0.001 High to low -1.53 (-2.53, -0.46) 0.016† High to middle 2.01 (0.92, 3.09) <0.001† CI – confidence interval, df – degrees of freedom *Analyses were adjusted for age, sex, marital status, education, income, smoking status, passive smoking, alcohol consumption (continuous), physical activity, and dietary pattern. †The difference of multiple comparison was significant, which we corrected using the Bonferroni method, α = 0.05/3. ‡To simultaneously compare the prevalence of metabolic syndrome between three altitude groups, we conducted the likelihood ratio test to compare the deviance between two nested models, which were models with or without altitude variable. Mediation effects of diet and PA The interactions between altitude and diet and altitude and PA were not significant (P > 0.05). We detected mediation effects of diet and PA. Assuming underlying assumptions of the mediation analyses hold, the correlation between altitude and metabolic syndrome can be partly explained by increased levels of PA only when comparing middle altitude to low altitude. Of the total estimated effect, the effect and proportion mediated were -0.94% (95% CI = -1.04, -0.86) and 26.10% (95% CI = 22.05, 34.92), respectively (Figure 2 and Table S1 in the Online Supplementary Document). For dietary patterns, the correlation could have been partly explained by higher DASH scores when comparing middle and high altitudes to low altitude. The effect mediated and proportion mediated were -0.40% (95% CI = -0.47, -0.32) and 11.62% (95% CI = 8.30, 14.94) for middle altitude vs low altitude, respectively, and -0.72% (95% CI = -0.87, -0.58) and 52.19% (95% CI = 26.38, 100.00) for high altitude vs low altitude, respectively (the 100.00% was truncated from 167.14% (explanations for proportion mediated in the mediation analysis in the Online Supplementary Document)). Figure 2 Mediation analyses of altitude (binary) and mediators of physical activity and dietary pattern (continuous) on metabolic syndrome. Analyses were adjusted for age, sex, marital status, education, income, smoking status, passive smoking, and alcohol consumption (continuous). Results of stratified analyses are presented in the Figures S1A-S1c in the Online Supplementary Document. The prevalence of the five components of metabolic syndrome and their possible combinations (three out of five components) at different altitudes are presented in the Tables S2 and S3 Online Supplementary Document. The sensitivity analysis including only Han people from middle and low altitudes showed that middle altitude Han people had a lower risk of metabolic syndrome than their low altitude counterparts (RD = -6.58%; 95% CI = -7.48, -5.69). The effect and proportion of the total estimated effect mediated by PA were -1.28% (95% CI = -1.51, -1.02) and 19.22% (95% CI = 14.55, 24.30), respectively, while the effect and proportion mediated by dietary pattern were -0.31% (95% CI = -0.37, -0.24) and 4.75% (95% CI = 3.40, 5.85), respectively (Table S5-S8 and Figure S2 and S3 in the Online Supplementary Document). DISCUSSION In our study involving nearly 90 000 adults across areas with a wide range of altitudes in Southwest China, living at middle and high altitudes was significantly associated with lower risk of metabolic syndrome compared to low altitude, with middle altitude having the lowest risk. Notably, diet mediated such correlations in both middle and high altitudes when compared to low altitude, while PA mediated these correlations in middle altitude vs low altitude. If these findings are largely causal, they would not only help explain varied incidence of cardiometabolic diseases among individuals living at different altitudes, but also have public health implications for behavioural interventions at lower altitudes. Comparison with other studies There is limited evidence on the correlation between altitude and metabolic syndrome, as most studies focused on metabolic profiles among people living at high altitudes [25] without comparing metabolic syndrome at different altitudes. Only one cross-sectional study involving 260 Ecuadorians has shown an inverse correlation between metabolic syndrome and altitude [26]. A few epidemiological studies examined altitude and specific metabolic outcome and found that higher altitude is protective against obesity and diabetes [6-10], but harmful to blood pressure and blood lipids [11,12]. Such findings were primarily based on altitudes around 2000 m, which is comparable to our middle altitude and in line with our findings that this group was less likely to have metabolic syndrome, large waist circumference, reduced HDL-C, and elevated fasting glucose, while being more likely to have elevated triglycerides than the low altitude group. Although no studies have examined the correlation between high altitude and metabolic syndrome, two studies have shown that, high altitude (≥3000 m) had a similar but stronger inverse correlation with diabetes and central obesity compared to low altitude than middle altitude did [6,9]. A study on diabetes [9] was consistent with our findings of elevated fasting glucose, while a central obesity study [6] showed found the lowest prevalence of large waist circumference at high altitude, where we found it to be highest. This discrepancy could be due to the different ethnicities and different lifestyle behaviours between the two study populations (Tibetans vs Peruvians). Our findings that high altitude compared to middle altitude had similar but smaller effects on metabolic syndrome advance our current knowledge, but require further verification in future studies. Potential mechanism The potential mechanism underlying these correlations remains unclear. However, studies have proposed that diverse factors, such as ethnicity, temperature, chronic hypoxia, and lifestyle behaviours might play an important role [12,27]. First, people residing at high or middle altitudes tend to be ethnically distinct from those living at low altitudes [27], which might have confounded the correlations. Second, metabolic expenditures are increased to cope with extreme temperatures caused by elevated altitude [8,28], meaning exertions at higher altitudes may represent greater exercise and in turn promote heart health [29]. Third, every contained less oxygen in higher altitudes than in lower altitude because of the decreasing barometric pressure [30]. Exposure to chronic hypoxia may modulate plasma leptin levels through hypoxia-inducible factor 1, and thus produces negative feedback on appetite to prevent obesity [31]. Fourth, lifestyle behaviours differed across altitudes, and might have moderated the correlations, which we examined and confirmed through our mediation analyses. We found that dietary pattern tended to be healthier and PA levels higher with increasing altitudes (Table S4 in the Online Supplementary Document). For instance, coarse grain was consumed more at high altitude, while animal oil was more prevalent at low altitude [32]. Such different dietary patterns and PA levels may account for the observed differences in metabolic syndrome at different altitudes. One exception is the lower PA at high altitude than at middle altitude. This exception was consistent with and might explain our finding that risk of metabolic syndrome was higher at high altitude than at middle altitude. Limitations and strengths This study has several limitations. First, the cross-sectional design does not allow us to infer causality, so reverse causality could be problematic. For example, it could not be ruled out for diet or physical activity. However, all participants were permanent residents, so the reverse causality between altitude and metabolic syndrome would be unlikely. Second, some information was based on self-report, allowing for possible recall bias, especially for variables based on participants’ long-term memory such as history of hypertension or diabetes. Third, ethnicity might have confounded our findings, as we included participants from different ethnicities residing at different altitudes. Specifically, the high altitude group consisted of Tibetans, the middle altitude group included Han, Bai, and Yi, and the low altitude group consisted of Han, Dong, Miao, and Bouyei ethnicities. To minimise such confounding effects, we conducted a separate analysis including only Han people from middle and low altitudes (Tables S5-S8 and Figures S2 and S3 in the Online Supplementary Document). The findings showed that middle altitude Han people had a lower risk of metabolic syndrome than their low altitude counterparts, implying that the observed effects of altitude were unlikely to be confounded by ethnicity. Despite these limitations, our study is unique. Our data were based on a large sample with a wide range of altitudes, which allowed us to explore the valid effects of high altitude. Moreover, as opposed to focusing on specific cardiometabolic outcomes, we comprehensively investigated metabolic syndrome, which could have more comprehensive implications on cardiometabolic health. Finally, although the mechanism of the effects of altitude on health is complex, we examined the mediation effects of lifestyle on the correlations. Unlike temperature and hypoxia, lifestyle behaviours are modifiable, so our findings could not only help explain the correlations, but also have public health implications for behavioural interventions. However, the mediating effects should be interpreted cautiously considering their small effect size. Further studies should examine why dietary and PA patterns tend to be healthier and whether and how those patterns can be promoted elsewhere in China and beyond. CONCLUSIONS We found that living at middle and high altitudes was significantly associated with lower risks of metabolic syndrome compared to low altitude, with middle altitude having the lowest risk. We also observed mediation effects of diet and PA. Future studies may examine the promotable healthy parts of dietary and PA patterns at higher altitudes (especially middle altitude). Additional material Online Supplementary Document Acknowledgement Data used in the study come from the China Multi-Ethnic Cohort (CMEC). Ethics statement: The CMEC received ethical approval from the Sichuan University Medical Ethical Review Board (K2016038, K2020022). Funding: This work was supported by the National Key R&D Program of China (Grant no: 2017YFC0907302). The funders had no role in the design and conduct of the study, the analysis and interpretation of data, or in the preparation, review, or approval of the manuscript. Authorship contributions: Conceptualization, JZ and XZ; Data curation, XZ; Formal analysis, DY, YW, and XZ; Funding acquisition, JY and XZ; Investigation, RH, ZS, YZ, XG, DQ, XX, TZ, JY, and XZ; Methodology, JZ, HS, YG, SL, GC, and XZ; Project administration, JZ, RH, GC, JY, and ZX; Resources, RH, ZS, YZ, XG, DQ, YG, SL, GC, JY, and XZ; Software, YW and XZ; Supervision, JY and XZ; Validation, JZ and XZ; Visualization, JZ and YW; Writing – original draft, JZ; Writing – review & editing, RH, ZS, YZ, XG, DQ, XX, TZ, DY, YW, HS, YG, SL, GC, JY, and XZ. Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests. ==== Refs REFERENCES 1 Gu D Reynolds K Wu XG Chen F Duan XF Reynolds RF Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 2005;365 :1398-405. 10.1016/S0140-6736(05)66375-1 15836888 2 Center for International Earth Science Information Network. National Aggregates of Geospatial Data Collection: Population, Landscape, and Climate Estimates, Version 3 (PLACE III). Available: https://sedac.ciesin.columbia.edu/data/set/nagdc-population-landscape-climate-estimates-v3. Accessed: 19 June 2023. 3 Baye K Hirvonen K Evaluation of Linear Growth at Higher Altitudes. JAMA Pediatr. 2020;174 :977-84. 10.1001/jamapediatrics.2020.2386 32832998 4 Foggin PM Torrance ME Dorje D Xuri W Foggin JM Torrance JJSS Assessment of the health status and risk factors of Kham Tibetan pastoralists in the alpine grasslands of the Tibetan plateau. Soc Sci Med. 2006;63 :2512-32. 10.1016/j.socscimed.2006.06.018 16891047 5 Faeh D Gutzwiller F Bopp M Swiss National Cohort Study G. Lower mortality from coronary heart disease and stroke at higher altitudes in Switzerland. Circulation. 2009;120 :495-501. 10.1161/CIRCULATIONAHA.108.819250 19635973 6 Woolcott OO Gutierrez C Castillo OA Elashoff RM Stefanovski D Bergman RN Inverse association between altitude and obesity: A prevalence study among andean and low-altitude adult individuals of Peru. Obesity (Silver Spring). 2016;24 :929-37. 10.1002/oby.21401 26935008 7 Voss JD Masuoka P Webber BJ Scher AI Atkinson RL Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States. Int J Obes (Lond). 2013;37 :1407-12. 10.1038/ijo.2013.5 23357956 8 Sherpa LY Deji, Stigum H, Chongsuvivatwong V, Thelle DS, Bjertness E. Obesity in Tibetans aged 30-70 living at different altitudes under the north and south faces of Mt. Everest. Int J Environ Res Public Health. 2010;7 :1670-80. 10.3390/ijerph7041670 20617052 9 Woolcott OO Castillo OA Gutierrez C Elashoff RM Stefanovski D Bergman RN Inverse association between diabetes and altitude: a cross-sectional study in the adult population of the United States. Obesity (Silver Spring). 2014;22 :2080-90. 10.1002/oby.20800 24890677 10 Woolcott OO Marilyn A Bergman RN Glucose homeostasis during short-term and prolonged exposure to high altitudes. Endocr Rev. 2015;36 :149-73. 10.1210/er.2014-1063 25675133 11 Aryal N Weatherall M Bhatta YKD Mann S Blood pressure and hypertension in people living at high altitude in Nepal. Hypertens Res. 2019;42 :284-91. 10.1038/s41440-018-0138-x 30459461 12 Hirschler V Cardiometabolic risk factors in native populations living at high altitudes. Int J Clin Pract. 2016;70 :113-8. 10.1111/ijcp.12756 26820389 13 Cohen JE Small C Hypsographic demography: The distribution of human population by altitude. Proc Natl Acad Sci U S A. 1998;95 :14009-14. 10.1073/pnas.95.24.14009 9826643 14 Mohanna S Baracco R Seclen S Lipid profile, waist circumference, and body mass index in a high altitude population. High Alt Med Biol. 2006;7 :245-55. 10.1089/ham.2006.7.245 16978137 15 Zhao X Hong F Yin J Tang W Zhang G Liang X Cohort profile: the China Multi-Ethnic cohort (CMEC) study. Int J Epidemiol. 2021;50 :721-721l. 10.1093/ije/dyaa185 33232485 16 Grundy SM Cleeman JI Daniels SR Donato KA Eckel RH Franklin BA Diagnosis and management of the metabolic syndrome - An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112 :2735-52. 10.1161/CIRCULATIONAHA.105.169404 16157765 17 Task Force on Chinese Guidelines for the Prevention of Cardiovascular Diseases (2017)Editorial Board of Chinese Journal of Cardiology [Chinese guidelines for the prevention of cardiovascular diseases(2017)]. Zhonghua Xin Xue Guan Bing Za Zhi. 2018;46 :10-25. Chinese.29374933 18 Perloff D Grim CM Flack JM Frohlich ED Morgenstern BZ Human blood pressure determination by sphygmomanometry. Circulation. 1993;88 :2460-70. 10.1161/01.CIR.88.5.2460 8222141 19 Chiu S Bergeron N Williams PT Bray GA Sutherland B Krauss RM Comparison of the DASH (Dietary Approaches to Stop Hypertension) diet and a higher-fat DASH diet on blood pressure and lipids and lipoproteins: a randomized controlled trial. Am J Clin Nutr. 2016;103 :341-7. 10.3945/ajcn.115.123281 26718414 20 Ainsworth BE Haskell WL Leon AS Jacobs DR Montoye HJ Sallis JF Compendium of physical activities - classification of energy costs of human physical activitieS. Med Sci Sports Exerc. 1993;25 :71-80. 10.1249/00005768-199301000-00011 8292105 21 Ainsworth BE Haskell WL Whitt MC Irwin ML Swartz AM Strath SJ Compendium of Physical Activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32 :S498-S504. 10.1097/00005768-200009001-00009 10993420 22 Gelman A Pardoe I Average predictive comparisons for models with nonlinearity, interactions, and variance components. Sociol Methodol. 2007;37 :23-51. 10.1111/j.1467-9531.2007.00181.x 23 Imai K Keele L Tingley D A general approach to causal mediation analysis. Psychol Methods. 2010;15 :309-34. 10.1037/a0020761 20954780 24 Gujral UP Vittinghoff E Mongraw-Chaffin M Vaidya D Kandula NR Allison M Cardiometabolic Abnormalities Among Normal-Weight Persons From Five Racial/Ethnic Groups in the United States: A Cross-sectional Analysis of Two Cohort Studies. Ann Intern Med. 2017;166 :628-36. 10.7326/M16-1895 28384781 25 Villegas-Abrill CB Vidal-Espinoza R Gomez-Campos R Ibañez-Quispe V Mendoza-Mollocondo C Cuentas-Yupanqui SR Diagnostic Criteria for Metabolic Syndrome in High-Altitude Regions: A Systematic Review. Medicina (Kaunas). 2022;58 :3. 10.3390/medicina58030451 35334627 26 Lopez-Pascual A Arévalo J Martínez JA González-Muniesa P Inverse Association Between Metabolic Syndrome and Altitude: A Cross-Sectional Study in an Adult Population of Ecuador. Front Endocrinol (Lausanne). 2018;9 :658. 10.3389/fendo.2018.00658 30483215 27 Buechley RW Key CR Morris DL Morton WE Morgan MV Altitude and ischemic heart disease in tricultural New Mexico: an example of confounding. Am J Epidemiol. 1979;109 :663-6. 10.1093/oxfordjournals.aje.a112729 453186 28 Hansen JC Gilman AP Odland JO Is thermogenesis a significant causal factor in preventing the “globesity” epidemic? Med Hypotheses. 2010;75 :250-6. 10.1016/j.mehy.2010.02.033 20363565 29 Mortimer EA Jr Monson RR MacMahon B Reduction in mortality from coronary heart disease in men residing at high altitude. N Engl J Med. 1977;296 :581-5. 10.1056/NEJM197703172961101 840241 30 Beall CM High-altitude adaptations. Lancet. 2003;362 :s14-5. 10.1016/S0140-6736(03)15058-1 14698112 31 Yingzhong Y Droma Y Rili G Kubo K Regulation of body weight by leptin, with special reference to hypoxia-induced regulation. Intern Med. 2006;45 :941-6. 10.2169/internalmedicine.45.1733 16974055 32 Xiao X Qin Z Lv X Dai Y Ciren Z Yangla Y Dietary patterns and cardiometabolic risks in diverse less-developed ethnic minority regions: results from the China Multi-Ethnic Cohort (CMEC) Study. Lancet Reg Health West Pac. 2021;15 :100252. 10.1016/j.lanwpc.2021.100252 34528018