Chronic Intermittent High Altitude Exposure,Occupation,and Body Mass Index in Workers of Mining Industry

Abstract

Esenamanova, Marina K, Kochkorova, Firuza A, Tsivinskaya, Tatyana A, Vinnikov, Denis, and Aikimbaev, Kairgeldy. Chronic intermittent high altitude exposure, occupation, and body mass index in workers of mining industry. High Alt Med Biol 15: 412–417, 2014.—The obesity and overweight rates in population exposed to chronic intermittent exposure to high altitudes are not well studied. The aim of the retrospective study was to evaluate whether there are differences in body mass index in different occupation groups working in intermittent shifts at mining industry at high altitude: 3800–4500 meters above sea level. Our study demonstrated that obesity and overweight are common in workers of high altitude mining industry exposed to chronic intermittent hypoxia. The obesity rate was lowest among miners as compared to blue- and white-collar employees (9.5% vs. 15.6% and 14.7%, p=0.013). Obesity and overweight were associated with older age, higher rates of increased blood pressure (8.79% and 5.72% vs. 1.92%), cholesterol (45.8% and 45.6% vs. 32.8%) and glucose (4.3% and 1.26% vs. 0.57%) levels as compared to normal body mass index category (p<0.0001 for all). There were differences in patterns of cholesterol and glucose levels in men and women employees according to occupation type. In conclusion, obesity and overweight rates are prevalent and associated with increase in blood pressure, cholesterol, and glucose levels in workers of mining industry exposed to intermittent high-altitude hypoxia. Therefore, assessment and monitoring of body mass index seems to be essential in those who live and work at high altitudes to supply the correct nutrition, modify risk factors, and prevent related disorders.

Introduction

Low atmospheric pressure and tissue hypoxia at high altitudes exert unfavorable effects on human physiology, including the reduction in work capacity of cardiovascular and respiratory systems, progressive reduction of exercise capacity, poor sleep quality, and malnutrition (Friedlander et al, 2008; Kayser and Verges, 2013). It is obvious that physical activity at high altitudes, such as work at mining industry and mountaineering, requires proper nutrition, prevention of loss in exercise capacity, and prevention of mountain sickness and pulmonary edema development (Vearrier and Greenberg, 2011). Weight loss at high altitudes is a result of increase in basal metabolic rate, increased exertion, and suppression of appetite by hypoxia (Simon-Schnass, 1992; Friedlander et al, 0000; Kayser and Verges, 2013). In a population-based study, it was shown that obesity risk was five times higher for those living at altitudes <500 m above sea level as compared to residents of >3000 m altitude (Voss et al., 2013). Recent studies demonstrated that high-altitude environment could be used in treatment of obesity (Schobersberger et al., 2010; Urdampilleta et al, 2012).

On the other hand, obesity in high altitudes can aggravate sleep apnea and trigger the development of mountain sickness (Ri-Li et al., 2003; Negi et al., 2013). Several studies described increasing prevalence of overweight and obesity along with other cardiovascular risk factors in native highlanders. It has been shown that prevalence of overweight and obesity ranges between 13.5%—14.4% and 1.8%—2.5%, respectively, in permanent residents of high altitudes and is associated with business/skilled worker occupation and hypertension (Shah et al., 2004). In another population, residing at 3700 meters above sea level, prevalence of metabolic syndrome was reported to be 8.2%, the low prevalence of metabolic syndrome was associated with male gender, higher education, and physical activity >2000 kcal/week (Sherpa et al., 2013). In a study of high altitude native residents of 4000 m above sea level, the higher prevalence of hyperlipidemia (34.3%) and obesity (14.1%) was found specific for women (Mohanna et al., 2006).

However, the current knowledge on prevalence of overweight and obesity and associated factors in those who works at high-altitude industries is limited (Vearrier and Greenberg, 2011). Previous studies addressed the issues of acclimatization and adaptation to intermittent high-altitude exposure in miners and military personnel (Jalil et al., 1994; Gunga et al., 1996; Richalet et al., 2002; Heinicke et al., 2003; Farias et al., 2013). It was demonstrated that 7-day shift work at high altitudes still poses the risk for acute mountain sickness despite acclimatization (Richalet et al. 2002; Farias et al., 2013). In another study of cardiovascular response to exercise in miners working at altitudes over 4500 meters above sea level in 4-day shifts of rest and ascent, exercise duration as well as oxygen saturation were found to be reduced on the first and fourth day of ascent as compared to sea levels (Jalil et al., 1994; Gunga et al., 1996). Heinicke et al. (2003) described that long-term intermittent hypoxia exposure (about 22 years) in army officers resulted in similar increased hemoglobin and hematocrit levels as those in permanent residents of high-altitudes (3550 meters above the sea level). Remarkably, the soluble transferrin receptor levels were not different from those at sea level, which means absence of tissue hypoxia in military personnel due to adaptation to long-term intermittent hypoxia. On the other hand, erythropoietin levels have been shown to react to each ascent with further reduction to the sea level on the seventh day of stay at altitude (Gunga et al., 1996; Heinicke et al., 2003). Nevertheless, how the work in the intermittent hypoxia environment of high-altitudes affects metabolic syndrome components and body composition remains unclear.

The aim of our study was to evaluate whether there are differences in body mass index (BMI) in different occupation groups working in intermittent shifts at mining industry at high altitude—3800–4500 m above sea level.

Methods

Study design

Retrospective cohort study on the effects of type of occupation on body mass index, blood pressure, heart rate, and cholesterol and glucose levels in employees of high altitude gold-mining industry.

Study population and setting

Overall, 1714 employees of gold-mining industry who underwent examinations between 2009 and 2010 were included in this study. Among 1714 employees, 11.1% (191) were females and 88.9% (1523) were males; 19% (326) were white-collar employees, 57.7% (989) were blue-collar employees, and 23.3% (399) were miners.

The assessment of energy and nutritive value of food rations and energy expenditures were performed at a high-altitude production facility situated at 3800–4500 m above the sea level in the Issyk-Kul region of Kyrgyz Republic. The work was characterized by intermittent shifts of 15-day work at high altitude facility and 15-day descent to 720 meters above sea level and rest.

All participants were divided into three groups according to work type:

1) White-collar employees: geologists-engineers, administration staff, accountants, software engineers, translators

2) Blue-collar employees: drivers, technicians, cafeteria workers

3) Miners: mechanics, miners, locksmiths

In addition, the participants were grouped according to gender and BMI categories by ACC/AHA/TOS criteria (Jensen et al., 2014). All participants gave informed consent to undergo examinations. The current study was performed at high-altitude facility in frame of gold-mining company project on optimization of nutrition and prevention risk factors among employees.

Examinations

All participants underwent physical examinations with determination of age, gender, work experience, and BMI, heart rate (HR), blood pressure (BP) measurements, and collection of blood samples for determination of total cholesterol and glucose levels at the medical department situated 720 m above sea level before ascent to production facility situated at 3800–4500 m above sea level.

Blood pressure and HR were measured using Propaq monitor (Walch Allyn Inc., Skaneateles, NY). The BP measurements were taken from both arms in a sitting position with inclusion in analysis of the average value. Cholesterol and glucose levels were determined using biochemistry autoanalyzer Hospitex EOS BRAVO (Hospitex Diagnostics, Fiorentino, Italy).

Definitions

Obesity and overweight were defined as: 1) Obesity, BMI>30 kg/m²; 2) Overweight, BMI 25–29.9 kg/m², and 3) Normal, BMI <25 kg/m² (Jensen et al., 2014). Other risk factors were defined according to criteria of healthy population published in ESC guidelines on cardiovascular prevention for general population (Perk et al., 2012): BP <140/90 mmHg, total cholesterol <5 mmol/L, glucose level <7 mmol/L.

Statistical analysis was performed using Medcalc Software version 12.5.0.0 (Medcalc Software bvba, Belgium). Data are presented as proportions and mean SD. The categorical variables were compared using Chi-square test and continuous variables, using one-way ANOVA with Student-Newman-Keuls posthoc tests and t-test for independent samples. In presence of abnormal distribution, comparison was performed after logarithmic transformation of continuous variables. The significance level was accepted as p<0.05 value.

Results

Obesity was detected in 14.1% (241); overweight in 43.9% (752), and normal BMI values were recorded in 42.1% (721) of employees. Among women, 16.7% were obese, and among men, 13.7% were obese, while 40.1% and 44.2% of women and men, respectively, were overweight.

Increased BP levels were recorded in 4.3% of 752 employees, cholesterol levels >5 mmol/L in 40.3% of 1644 employees, and glucose levels >7 mmol/L in 1.4% of 1639 employees.

Analysis of the BMI and risk factors according to the type of occupation demonstrated (Table 1) that blue-collar workers were older as compared to white-collar workers and miners and had higher BMI as compared to miners (p<0.05 for all). On the other hand, frequency of obesity was the lowest in the miners group as compared to white-collar and blue-collar workers (9.5% vs. 15.6% and 14.7%, p=0.013). SBP levels were lower in white-collar workers as compared to blue-collar workers and miners (p<0.05), while there were no differences in HR or DBP levels between groups. There was a tendency to higher glucose and cholesterol levels in white-collar workers (p>0.05). The work experience was significantly higher for white-collar workers as compared to blue-collar workers and miners (p<0.05).

Table 1.

Anthropometric, Biochemical, and Hemodynamic Characteristics of Workers of High-Altitude Mining Industry According to the Type of Occupation

Variables	White-collar (n=326)	Blue-collar (n=989)	Miners (n=399)	F/Chi-square	p
Age, years	37.76±11.04	40.37±9.35^a,c	38.02±9.35	13.7	<0.0001
Gender, n (%)
Male	87.4 (285)	84.8 (839)	100 (399)	66.9	<0.0001
Female	14.3 (41)	15.1 (150)	0
BMI, kg/m²	25.61±3.97	25.99±3.76^c	25.40±3.47	3.8	0.021
Overweight, (n)	39.7 (130)	44.6 (442)	45.1 (180)	12.5	0.013
Obesity, (%n)	14.7 (48)	15.6 (155)	9.5 (38)
Work experience, years	8.78±5.21^{^, ^*}	7.57±5.11	7.19±4.63	8.9	<0.0001
HR, beats/min	64.74±5.56	65.19±6.19	65.73±6.27	1.1	0.346
SBP, mm Hg	113.39±12.15^{^,^*}	116.13±11.89	117.28±11.71	5.0	0.007
DBP, mm Hg	75.64±8.55	76.02±8.23	75.46±7.54	0.4	0.701
Cholesterol, mmol/L	4.86±1.08	4.72±1.05	4.80±1.12	2.1	0.127
Glucose, mmol/L	5.37±0.81	5.31±0.67	5.28±0.82	1.2	0.289

Data are presented as mean±SD; ANOVA test.

Posthoc Student-Newman-Keuls test: ^ap<0.05 as compared to white-collar group, ^bp<0.05 as compared to blue-collar, ^cp<0.05 as compared to miners group.

BMI, body mass index, DBP, diastolic blood pressure, HR, heart rate, SBP, systolic blood pressure.

Analysis of characteristics according to gender and occupation showed that women and men had different patterns in cholesterol, glucose, and blood pressure levels. Blue-collar female employees (Table 2) were older, had longer work experience, significantly higher BMI and higher BP levels (p<0.05 for all) and tendency to higher cholesterol levels (p=0.096) compared to white-collar women workers. Among males (Table 3) the blue-collar employees were of older age; white-collar employees had longer work experience (p<0.05 for both). Miners had the lowest BMI as compared to white-collar and blue-collar workers (p<0.05). White-collar males had significantly higher cholesterol levels (p<0.05) and trend to higher glucose levels (p=0.08) and lower SBP (p<0.05) as compared to blue-collar males and miners.

Table 2.

Characteristics of Female Workers of Mining Industry According to the Type of Occupation

Variables	White-collar (n=41)	Blue-collar (n=150)	p
Age, years	34.43±8.72	42.36±8.09	<0.001
Height, cm	162.78±6.18	158.23±5.88	<0.001
Weight, kg	62.39±10.79	66.42±10.59	0.037
BMI, kg/m²	23.63±4.11	26.16±3.74	0.001
Work experience, years	7.42±6.73	10.94±5.19	0.008
HR, beats/min	64.62±4.35	64.59±5.08	0.986
SBP, mm Hg	104.71±13.63	112.84±12.40	0.033
DBP, mm Hg	69.53±7.37	73.94±9.03	0.041
Cholesterol, mmol/L	4.52±0.90	4.79±0.87	0.096
Glucose, mmol/L	5.05±0.48	5.16±0.63	0.233

Data are presented as mean±SD; t-test for independent samples.

Abbreviations as in Table 1.

Table 3.

Characteristics of Male Workers of Mining Industry According to the Type of Occupation

Variables	White-collar (n=285)	Blue-collar (n=839)	Miners (n=399)	F	p
Age, years	38.22±11.25	40.02±9.51^a,c	38.02±9.35	7.2	0.001
BMI, kg/m²	25.88±3.87	25.95±3.76^c	25.40±3.46	3.1	0.047
Work experience, years	8.94±4.97^b,c	6.92±4.83	7.18±4.62	17.4	<0.001
HR, beats/min	64.75±5.68	65.33±6.42	65.73±6.27	0.9	0.4
SBP, mm Hg	114.39±11.60	116.90±11.65	117.28±11.71	3.0	0.049
DBP, mm Hg	76.35±8.40	76.50±7.95	75.46±7.53	1.1	0.322
Cholesterol, mmol/L	4.90±1.08^b	4.70±1.07	4.80±1.12	3.4	0.033
Glucose, mmol/L	5.41±0.83	5.32±0.66	5.28±0.81	2.5	0.081

Data are presented as mean±SD; ANOVA test

Posthoc Student-Newman-Keuls test: ^ap<0.05 as compared to white-collar group, ^bp<0.05 as compared to blue-collar, ^c p<0.05 as compared to miners group.

Abbreviations as in Table 1.

Analysis of characteristics according to BMI categories demonstrated (Table 4) that overweight and obese employees were of older age and with longer work experience as compared to normal BMI group (p<0.05 for both). Mean BMI, SBP, DBP, HR, and cholesterol and glucose levels were significantly higher in obese and overweight groups as compared to the normal BMI group (p<0.05 for all). There was a significantly higher percentage of employees with high BP among obese and overweight as compared to normal BMI group (8.79% and 5.72% vs. 1.92%, p<0.05). About half of overweight and obese groups had increased cholesterol level, while only one-third of normal BMI group had cholesterol levels >5 mmol/L (45.6% and 45.8% vs. 32.8%, p<0.05). The number of employees with glucose levels >7 mmol/L was higher in obese group as compared with overweight and normal BMI groups (4.3% vs. 1.26% and 0.57%, p=0.0001).

Table 4.

Characteristics of Workers of Mining Industry According to BMI Categories

Variables	BMI normal (n=721)	BMI overweight (n=752)	BMI obese (n=241)	F/Chi-square	p
Age, years	35.71±9.85	41.30±8.78^a	44.01±8.67^a	11.9	<0.0001
Gender, n (%)
Male	88.8 (640)	89.7 (674)	86.8 (209)	1.6	0.45
Female	11.2 ( 81)	10.3 (78)	13.2 (32)
BMI, kg/m²	22.37±1.84	27.25±1.36^a	32.12±2.28^a,b	3296.7	<0.001
Work experience, years	6.36±4.65	8.37±5.134^a	9.35±4.91^a,b	43.8	<0.001
HR, beats/min	65.06±6.30	64.969±5.55	67.115±6.83	4.1	0.016
SBP, mm Hg	113.51±11.54	116.53±12.05^a	122.53±10.65^a,b	22.8	<0.001
DBP, mm Hg	74.10±7.36	76.73±8.46^a	79.06±8.35^a,b	18.2	<0.001
BP>140/90 mm Hg, (%n)	1.92 (7 of 363)	5.72 (19/332)	8.79 (8/91)	10.9	0.0009
Cholesterol, mmol/L	4.54±1.06	4.89±1.07^a	4.99±1.02^a	26.4	<0.001
Cholesterol>5mmol/L, (%n)	32.8 (229/697)	45.6 (329/720)	45.8 (104/227)	27.6	<0.0001
Glucose, mmol/L	5.19±0.65	5.38±0.70^a	5.48±0.96^a	18.8	<0.001
Glucose>7mmol/L, (%n)	0.57 (4/697)	1.26 (9/714)	4.3 (10/228)	18.2	0.0001

Data are presented as mean±SD and number (percentage); ANOVA test.

Posthoc Student-Newman-Keuls test: ^ap<0.05 as compared to normal BMI group, ^bp<0.05 as compared to overweight BMI group

Abbreviations as in Table 1.

Discussion

Our study demonstrated that prevalence of obesity was 14.1% in overall population of employees of the high-altitude mining industry, with the lowest rate among the miners group as compared to blue- and white-collar workers. Analysis according to gender showed the blue-collar women had higher BMI and higher BP and tendency to higher cholesterol levels, while in males the BMI was the lowest for miners. White-collar males more often had higher cholesterol and a tendency to higher glucose levels. Analysis of participants according to BMI categories demonstrated significant associations of obesity with high BP, and elevated cholesterol and glucose levels. Obese and overweight employees were characterized by higher frequency of elevated BP, about half of these employees had increase of cholesterol levels above normal limits for healthy population and high glucose levels were specific for obese patients.

In our study, obesity and overweight prevalence rates were found close to those reported for the general population residing at low lands (Jensen et al., 2014), being higher than in permanent residents of high altitudes (Shah et al., 2004; Mohanna et al., 2006). However, the obesity rate for women in our study resembled the rates in permanent women residents of high altitudes, who had higher obesity prevalence as compared to men (Mohanna et al., 2006). The findings that obesity rates were lowest among miners as compared to white and blue-collar workers are in agreement with previous studies in permanent residents of high altitudes, which demonstrated that male gender and physical activity were associated with reduced obesity prevalence (Mohanna et al., 2006), while office work was linked to higher frequency of obesity (Shah et al., 2004). The lower obesity rate in miners therefore can be attributed to the higher physical activity inherent for this type of labor as compared to white- or blue-collar workers. We found similar associations of obesity and overweight with increased BP and glucose levels and association of BP with type of work as in previous studies (Shah et al., 2004; Mohanna et al., 2006; Sherpa et al., 2013).

Our study demonstrated different patterns in cholesterol and glucose levels in men and women. The blue-collar women had a tendency to higher cholesterol and glucose levels, while in men high cholesterol and glucose levels were characteristic for white-collar employees as compared to blue-collar men and miners. The trends of high cholesterol and glucose level in blue-collar women may be explained by older age, higher BMI, and longer experience of work in intermittent hypoxia as compared to white-collar women (Zamboni et al., 1997; Jousilahti et al., 1999). Experimental and human studies have demonstrated that intermittent hypoxia might cause insulin resistance and hyperlipidemia (Braun et al., 2001; Li et al., 2007; Iiyori et al., 2007). However, in men, BMI, cholesterol, and glucose levels were lowest in miners and highest in white-collar men. This in part may be explained by physical activity and high energy expenditures in miners and blue-collar males, and sedentary lifestyles in white-collar men (Rennie et al., 2003; Earnest et al., 2013). The inverse relationship between parameters of metabolic syndrome and physical activity or cardiovascular fitness has already been demonstrated (Earnest et al., 2013). Though blue-collar women presumably are involved in work requiring less energy expenditures than blue-collar men and miners, gender differences need to be elucidated in further studies.

Although the overall rates of high BP and increased glucose levels were low, the increased cholesterol levels were found in about 40% of population, which requires the modification of risk factors. We did not observe weight loss due to hypoxia effects in our population (Simon-Schnass, 1992). Previous studies demonstrated that intermittent hypoxia through insulin resistance and metabolic dysfunction can increase obesity rates (Polotsky et al., 2003; Iiyori et al., 2007; Drager et al., 2011). On the other hand, this can be explained by acclimatization during long-term chronic intermittent hypoxia, which has been shown in Chilean miners working in 7-day shifts, with average duration of exposure to intermittent hypoxia of 3–8 years and military personnel with up to 22 years of exposure to intermittent hypoxia (Jalil et al., 1994; Gunga et al., 1996; Heinicke et al., 2003; Farias et al., 2013). Our population`s mean work experience at high altitudes varied between 7.18–8.77 years, which may explain their acclimatization.

The main limitations of this preliminary study are: retrospective design, BP, HR and biochemistry analysis records were not available for all participants, the recordings were performed before the ascent to high altitudes and the results are not generalizable for entire population of those working at high altitudes. Another limitation is that we could not include in the analysis the smoking and alcohol consumption habits of participants, which might influence the parameters of metabolic syndrome and body composition in adults. Further prospective follow-up studies are required to identify the association of BMI with cardiovascular risk factors and its prognostic role in workers of high altitudes.

Conclusion

Our study demonstrated that obesity and overweight are common in workers of high altitude mining industry exposed to chronic intermittent hypoxia. Obesity is significantly seen less in miners as compared to other occupations. Obesity and overweight are associated with increased rates of high BP, and cholesterol and glucose levels. Therefore, assessment and monitoring of BMI seems to be essential in those who live and work at high-altitudes to supply the correct nutrition, modify risk factors, and prevent related disorders.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

References

Braun

, Prock

, Zamudio

, et al. Women at altitude: Short-term exposure to hypoxia and/or a1-adrenergic blockade reduces insulin sensitivity. J Appl Physiol, 2001; 91:623–631.

Drager

, Li

, Reinke

, Bevans-Fonti

, Jun

, and Polotsky

. Intermittent hypoxia exacerbates metabolic effects of diet-induced obesity. Obesity, 2011; 19:2167–2174.

Earnest

, Artero

, Sui

, Lee

, Church

, and Blair

. Maximal estimated cardiorespiratory fitness, cardiometabolic risk factors, and metabolic syndrome in the aerobics center longitudinal study. Mayo Clin Proc, 2013; 88:259–270.

Farias

, Jimenez

, Osorio

, Zepeda

, Figueroa

, and Pulgar

. Acclimatization to chronic intermittent hypoxia in mine workers: A challenge to mountain medicine in Chile. Biol Res, 2013; 46:59–67.

Friedlander

, Braun

, and Marquez

. Making molehills out of the mountains: Maintaining performance at high altitude. ACSM Health Fitness J, 2008; 12:15–21.

Gunga

, Rocker

, Behn

, et al. Shift working in Chilean Andes (>3,600 m) and its influence on erythropoietin and the low pressure system. J Appl Physiol, 1996; 81:846–852.

Heinicke

, Prommer

, Cajigal

, Viola

, Behn

, and Schmidt

. Long-term exposure to intermittent hypoxia results in increased hemoglobin mass, reduced plasma volume, and elevated erythropoietin plasma levels in man. Eur J Appl Physiol, 2003; 88:535–543.

Iiyori

, Alonso

, Li

, et al. Intermittent hypoxia causes insulin resistance in lean mice independent of autonomic activity. Am J Respir Crit Care Med, 2007; 175:851–857.

Jalil

, Braun

, Chamorro

, et al. Cardiovascular response to exercise at high-altitude in workers chronically exposed to intermittent hypobaric hypoxia. Revista Med Chile, 1994; 122:1120–1125.

10.

Jensen

, Ryan

, Apovian

, et al. 2013 AHA/ACC/TOS Guideline for the Management of Overweight and Obesity in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation, 2014; 129(25 Suppl 2):S102–S138.

11.

Jousilahti

, Vartiainen

, Tuomilehto

, and Puska

. Sex, age, cardiovascular risk factors, and coronary heart disease: A prospective follow-up study of 14 786 middle-aged men and women in Finland. Circulation, 1999; 99:1165–1172.

12.

Kayser

, and Verges

. Hypoxia, energy balance and obesity: From pathophysiological mechanisms to new treatment strategies. Obes Rev, 2013; 14:579–592.

13.

, Savransky

, Nanayakkara

, Smith

, O'Donnell

, and Polotsky

. Hyperlipidemia and lipid peroxidation are dependent on the severity of chronic intermittent hypoxia. J Appl Physiol, 2007; 102:557–563.

14.

Mohanna

, Baracco

, and Seclen

. Lipid profile, waist circumference, and body mass index in a high altitude population. High Alt Med Biol, 2006; 7:245–255.

15.

Negi

, Asotra

, Kumar

, et al. Epidemiological study of chronic mountain sickness in natives of Spiti valley in the Greater Himalayas. High Alt Med Biol, 2013; 14:220–229.

16.

Perk

, Guy De Backer

, Gohlke

, et al. European Guidelines on cardiovascular disease prevention in clinical practice (version 2012. The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J, 2012; 33:1635–1701.

17.

Polotsky

, Li

, Punjabi

, Rubin

, Smith

, Schwartz

, and O'Donnell

. Intermittent hypoxia increases insulin resistance in genetically obese mice. J Physiol, 2003; 552:253–264.

18.

Rennie

, McCarthy

, Yazdgerdi

, Marmot

, and Brunner

. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol, 2003; 32:600–606.

19.

Ri-Li

, Chase

, Witkowski

, et al. Obesity: Associations with acute mountain sickness. Ann Intern Med, 2003; 139:253–257.

20.

Richalet

, Donoso

, Jiménez

, et al. Chilean miners commuting from sea level to 4500 m: A prospective study. High Alt Med Biol, 2002; 3:159–166.

21.

Schobersberger

, Leichtfried

, Mueck-Weymann

, and Humpeler

. Austrian Moderate Altitude Studies (AMAS): Benefits of exposure to moderate altitudes (1,500–2,500 m). Sleep Breathing, 2010; 14:201–207.

22.

Shah

, Nanan

, Rahbar

, Rahim

, and Nowshad

. Assessing obesity and overweight in a high mountain Pakistani population. Trop Med Int Health, 2004; 9:526–532.

23.

Sherpa

; Deji Stigum

, Chongsuvivatwong

, Nafstad

, and Bjertness

. Prevalence of metabolic syndrome and common metabolic components in high altitude farmers and herdsmen at 3700 m in Tibet. High Alt Med Biol, 2013; 14:37–44.

24.

Simon-Schnass

. Nutrition at high altitude. J Nutr, 1992; 122:778–781.

25.

Urdampilleta

, Gonzalez-Muniesa

, Portillo

, and Martinez

. Usefulness of combining intermittent hypoxia and physical exercise in the treatment of obesity. J Physiol Biochem, 2012; 68:289–304.

26.

Vearrier

, and Greenberg

. Occupational health of miners at altitude: Adverse health effects, toxic exposures, pre-placement screening, acclimatization, and worker surveillance. Clin Toxicol, 2011; 49:629–640.

27.

Voss

, Masuoka

, Webber

, Scher

, and Atkinson

. Association of elevation, urbanization and ambient temperature with obesity prevalence in the United States. Int J Obes, 2013; 37:1407–1412.

28.

Zamboni

, Armellini

, Harris

, et al. Effect of age on body weight distribution and cardiovascular risk factors in women. Am J Nutr, 1997; 66:111–115.