Prevalence of Metabolic Syndrome and Common Metabolic Components in High Altitude Farmers and Herdsmen at 3700 m in Tibet

Abstract

Sherpa, Lhamo Y., Deji, Hein Stigum, Virasakdi Chongsuvivatwong, Per Nafstad, and Espen Bjertness. Prevalence of metabolic syndrome and common metabolic components in high altitude farmers and herdsmen at 3700 m in Tibet. High Alt Med Biol 14:37–44, 2013.— Objectives: To estimate the prevalence of metabolic syndrome, its associated factors and components in 30–80-year-old Tibetans living at high altitude. Method: Multistage sampling of 692 participants. We used IDF criteria for estimation of the metabolic syndrome, and a questionnaire based on the WHO MONICA protocol. Results: The prevalence of metabolic syndrome was 8.2% (Confidence interval (CI):6.1–10.2) while the common components were: fasting hyperglycemia 57.5% (53.8–61.1); abdominal obesity 46% (42.2–49.7); and high blood pressure 37% (33.4–40.5). Metabolic syndrome was significantly lower for males, those with higher education and physical activity >2000 Kcal/week. Self awareness, treatment and control were low for both diabetes and lipid abnormality. Conclusion: The overall prevalence of metabolic syndrome in high altitude farmers and herdsmen in Tibet was lower compared to other high altitude natives, while its components (hyperglycemia, obesity, and high blood pressure) were higher than in other high altitude communities. Implications of the findings of high prevalence of smoking (among men), obesity, and hypertension and low rates of awareness, treatment, and control of the components of the metabolic syndrome among rural highlanders propels the need for health programs targeting risk factors.

Introduction

Metabolic syndrome is an important cluster of risk factors contributing to cardiovascular diseases (CVD) and diabetes. Meta-analysis of large studies has shown that metabolic syndrome increases the risk for CVD by 2-fold (Gami et al., 2007; Mottillo et al., 2010). A higher prevalence of metabolic syndrome at high altitudes (Baracco et al., 2007; Khalid, 2008, and that hypoxia contributes to the syndrome in native populations has been reported (Ptitsyn, 2006).

Tibetans are known as one of the largest and oldest high-altitude natives in the world. Tibet Autonomous Region (TAR), situated in the Himalayan plateau in the Western part of China is home to approximately 3 million Tibetans. The majority of the population in TAR live in rural areas with subsistence farming as the predominant occupation. However, rapid socioeconomic transition in China resulted in an increase in motorized transport, access to water and electricity; conversely it also increased the consumption of various processed foods, high in saturated fats and sugar. Likewise, with the advances in agricultural software, traditional farming methods have partly been replaced by high powered tractors and threshers requiring less energy expenditure to get the work done (Tibet- Its ownership and Human Rights Situation, 1992). This combination of development policies, market economy, and rural reforms have driven the Tibetan population from a lifestyle of nomadic towards more sedentary lifestyles with lower levels of physical activity. There is also growing evidence on association between sedentary behavior and the risk of metabolic syndrome (Chang et al., 2008; Ma et al., 2008; Chen et al., 2009). However, metabolic syndrome and its components from Tibet have not been addressed yet. Moreover, prominent metabolic features such as high blood pressure in Tibetans have increased substantially for all age groups over the past few decades (Sehgal et al., 1968; Zhao et al., 2012). Additionally, low levels of awareness, treatment, and control of hypertension among Tibetan population in TAR has also been reported (Zhao et al., 2012; Zheng et al., 2012). As the economic conditions within TAR are not uniform, the unfair burden of metabolic syndrome is particularly harsh on people with low income who face lifelong major illnesses and find themselves caught in a poverty trap. Moreover, high altitude itself constitutes a multi stress environment that may account for shorter survival at high altitudes (Virues-Ortega et al., 2009). This study was undertaken to estimate the prevalence of metabolic syndrome, associated factors and its metabolic components among 30–80-year-old males and females in the rural communities residing throughout their life at a high altitude of 3700 meters. Furthermore, consistent with a previous report of metabolic syndrome and socioeconomic status (Dallongeville et al., 2005), we expected that the metabolic syndrome would be high in these low income population.

Methods

Study population

A cross-sectional study of the metabolic syndrome and associated risk factors was undertaken among the adult Tibetan population in rural communities in 2010. For this purpose we randomly selected two counties of Lhasa municipality representing the Northeast (Lhunzhub) and Southwest (Qushu). Lhunzhub County is located 65 kilometers away from Lhasa. The county has jurisdiction over nine villages and one town, whereas Qushu County is located 48 kilometers from Lhasa, it has jurisdiction over five villages and one town. Both counties are without public transportation, neither to the nearest township nor to Lhasa. The counties are agricultural and the crops grown are mainly highland barley, winter wheat, spring wheat, peas, and rapeseed. Animal husbandry is common, with the main animals farmed including yak, goats, sheep, and horses.

Study sampling

All the native Tibetan residents between the ages of 30 to 80 years were selected for this study using a stratified, multistage sampling method. After selecting Lhunzhub and Qushu Counties in the initial stage, two villages from each of these counties were randomly selected in the second stage. Finally, 766 Tibetan males and females living and registered in the village committees and who met the inclusion criteria were invited. A simple random sampling method was used to include the eligible participants. Invitations were sent prior to data collection, each participant was given full explanation of the research purpose, invited to participate and sign informed consent. Participation was voluntary and the participants could withdraw from the study for any reason, at any time without any consequences. A total number of 692 (82%) out of 766 invitees completed the survey.

Data collection and measurement

Data collection included an interview questionnaire adapted from the WHO MONICA project (MONICA Manual, 1998–1999). Some of the questions were modified to suit the local conditions. The individual questionnaires for data collection were translated in Tibetan language and back translated by experts from the Tibet Academy of Social Sciences. Ethics approval for this study was obtained from the Ethical Committee in Norway. Data collectors included a team of trained teachers, physician, and laboratory personnel from Tibet University Medical College. The participants were requested to come to the data collection site after night fasting for at least 8 h. After explaining the research purpose and signing the consent form, a clinical examination, including blood pressure, and anthropometric measurements were performed. BP was measured three times on the right arm using a digital blood pressure monitoring device (Omron), each reading separated by at least 30 seconds. Waist circumference was measured at the midpoint between the lower costal margin and superior iliac crest after exhaling. Fasting venous blood was drawn for the measurement of blood lipids. Blood samples were transferred into glass tubes without anticoagulants and clotted at room temperature. They were sent to a clinical laboratory in Lhasa and centrifuged at 2000 rpm for 15 min the same morning. The serum thus obtained was analyzed on the same day for triglycerides (TG) and high density lipoprotein cholesterol (HDL-C) using an automatic biochemistry analyzer (Hitachi, 7060, Tokyo, Japan). TG concentrations were determined enzymatically using a GPO-PAP method, and concentrations of HDL-C were measured by direct method. The reagents were obtained from Sichuan Maker Biotechnology Co. Ltd., Chengdu, China. Capillary blood for fasting blood glucose concentration was measured using Accu-Chek Aviva Plus (Roche Diagnostics, Switzerland). The instrument measured blood glucose using glucose dehydrogenase (GDH) (Tang et al., 2001). The machine was calibrated with each new vial of test strips using Accu-Chek Aviva Control Solution. The average temperatures during the data collection period in Linzhou and Chushul were approximately 15° and 20°C, respectively.

Variables

Metabolic Syndrome was defined using the International Diabetes Federation (IDF) criteria (Eapen et al., 2009), that is, the presence of a high waist circumference (WC) (>90 cm in men, and >80 cm in women), plus any two of the four additional risk factors: elevated blood pressure ≥130/≥85 mmHg or antihypertensive therapy, fasting plasma glucose ≥100 mg/dL (5.6 mmol/L) or history of diabetes mellitus, triglycerides ≥150 mg/dL (1.7 mmol/L) or treatment, or low HDL cholesterol (in men<40 mg/dL (1.03 mmol/L), and in women<50 mg/dL (1.29 mmol/L) or treatment. In the present study we defined common metabolic components as a combination of high WC, elevated blood pressure, and high fasting plasma glucose, excluding TG and HDL-C.

Awareness of hypertension, diabetes, and abnormal lipids were defined as self report of any prior diagnosis of hypertension, diabetes, and abnormal lipids, respectively, by a health care professional. Treatment of hypertension, diabetes, and abnormal lipids was defined as self-reported use of a prescribed medication for the management of hypertension or diabetes or abnormal blood lipids at the time of the interview. Control of hypertension, diabetes, and abnormal blood lipids were defined as medical treatment of hypertension, diabetes, and abnormal blood lipids, respectively, associated with systolic blood pressure<140 mm Hg and diastolic blood pressure<90 mm Hg; fasting blood sugar<126 mg/dL (<7 mmol/l), and lipid levels of HDL-C>50 mg/dL (>1.30 mmol/L) or TG<150 mg/dL (<1.7 mmol/L).

Questionnaires on socioeconomic and lifestyle factors were administered verbally. For physical activity, metabolic equivalent of tasks (MET) scores were calculated using the International Physical Activity Questionnaire (IPAQ) (Craig et al., 2003), which had been translated and back translated to ensure accuracy. Physical activity data from the IPAQ were computed for metabolic equivalent MET.minutes per week. It was assumed that 1 MET was equivalent to 1 kcal/kg/hr for all participants (Ainsworth et al., 1996). MET values were converted into kcal/min by multiplying by the participant's body weight (kg) and then dividing by 60 minutes. A dietary questionnaire was developed on the basis of food habits practiced each day over a period of at least one month. Subjects were also classified into having a “healthy diet” if the diet included fruits, fresh vegetables, and white meat each day; “unhealthy” if the diet included red meat, full-fat milk, and no fruits and vegetables each day; and “moderately healthy” if the diet alternated between healthy and unhealthy. Alcohol consumption was defined as “yes” if a person drank alcohol regularly for at least 5 days a week; otherwise, “no.” Smoking was classified as “yes” if a person smoked daily or occasionally over the past year; otherwise, “no.” Average monthly income were classified into either<300 yuan a month (300 Yuan is equivalent to 44.11 $US) (World Bank, 2009), ≥300 yuan a month, or no income, based on a question with predefined categories: “no income”, “100–300 yuan/month”, “301–600 yuan/month”, “601–900 yuan/month”, “901–1100 yuan/month”, “1101–2000 yuan/month”, “2001–3000 yuan/month”, and “>3000 yuan/month”. The poverty line in China was defined below 2300 yuan/year in 2011 (China's poverty line, 2011).

Statistical methods

Data were analyzed using R 2.14.0 (R development Core Team, 2011). Sociodemographic and basic characteristics of subjects were stratified by gender and displayed in percentages. The prevalence of metabolic syndrome and its components were age-standardized directly using the WHO world standard population (Ahmad et al., 2000). Logistic regression analysis was used to examine the independent contribution of age, gender, diet, physical activity, smoking, alcohol consumption, income, and education to metabolic syndrome and common metabolic components. Two different models were applied, one with all components of the metabolic syndrome included (Model 1) and another with high triglycerides and low HDL-C excluded (Model 2). We also checked for interactions between age, gender, and education with the outcome. However, we did not find any evidence of interaction. Sample size was calculated using estimated prevalence of hypertension of 37.6% from a study in urban Tibet (unpublished data). Using 5% type 1 error rate and 80% power, the required sample size was estimated to be 638. After taking non-response problems into account, the final sample size was determined to be 766. Level of statistical significance was set to p≤0.05 or 95% confidence interval.

Results

Of the 766 invitees, 692 completed the survey, giving a response rate of 82%. Selected characteristics of the study populations are compared in Table 1, indicating significant differences in income and smoking between the two sexes. Smoking was practically nil among females (Table 1).

Table 1.

Socioeconomic and Lifestyle Characteristics of 30–80-Year-Old Tibetans Living in Rural Areas of Lhasa Governance at 3700 Meters

	Male, n (%)	Female, n (%)	Total, n (%)
Variables	N=317	N=375	N=692	P value
Age				0.147
30–39	53 (16.8)	88 (23.5)	141 (20.4)
40–49	114 (35.9)	141(37.6)	255 (36.8)
50–59	88 (27.9)	85 (22.7)	173 (25)
60–69	51 (16)	50 (13.3)	101(14.7)
<70	11 (3.4)	11 (2.9)	22(3.1)
Educational level				0.739
No education	151 (47.6)	178 (47.5)	329 (47.5)
Primary school or less	150 (47.6)	179 (47.5)	329 (47.9)
Secondary school	12 (3.8)	16 (4.3)	28 (4.04)
Intermediate	2 (0.6)	2 (0.5)	4 (0.54)
University or college	2 (0.6)	0	2 (0.02)
Income				<0.001
No income	57 (18)	113 (30.1)	170 (24.6)
Income<300 yuan/month	99 (31.2)	150 (40)	249 (35.9)
Income<300 yuan/month	161 (50.8)	112 (29.9)	273 (39.5)
Occupation				0.162
No job	18 (5.7)	11 (2.9)	29 (4.3)
Farmer and herdsman	279 (88)	344 (91.7)	623 (90)
Other jobs	20 (6.3)	20 (5.3)	40 (5.7)
Diet				0.895
Unhealthy	54 (17)	67 (17.9)	121(17.5)
Healthy	53 (16.7)	66 (17.6)	119 (17.2)
Moderately healthy	210 (66.2)	242 (64.5)	452 (65.3)
Physical activity				0.387
<2000 Kcal/week	232 (73.2)	286 (76.3)	518 (74.9)
<2000 Kcal/week	85 (26.8)	89 (23.7)	174 (25.1)
Smoking				<0.001
Yes	105 (33.1)	1 (0.3)	106 (15.3)
No	212 (66.9)	374 (99.7)	586 (84.7)
Alcohol consumption				0.751
Yes	151 (47.6)	173 (46.1)	324 (46.8)
No	166 (52.4)	202 (53.9)	368 (53.2)
County				0.156
Qushu	182 (57.4)	194 (51.7)	376 (54.3)
Lhunzhub	135 (42.6)	181 (48.3)	316 (45.7)

Table 2 illustrates sex-specific prevalence of components of the metabolic syndrome, using IDF criteria. The overall prevalence of metabolic syndrome was 8.2%, while the age-adjusted prevalence was 7.1%. Fasting hyperglycemia, obesity, and hypertension were the most common components of metabolic syndrome in both sexes. Central obesity was more prevalent among females than males.

Table 2.

Prevalence of Metabolic Syndrome and its Components Based on IDF Criteria (2005) (Eapen et al., 2009) in 30–80-Year-Old Tibetans Living in Rural Areas of Lhasa Governance at 3700 Meters

IDF (2005)	Males=317n% (95% CI)	Females=375n% (95% CI)	Total=692n% (95% CI)	Total, adjusted to World Standard Population % (95% CI)
HDL cholesterol (<1.03 mmol/L in males &<1.29 in females, or treatment	2	25	27	27
	0.6 (0.6, 0.7)	6 (3.5, 8.4)	3.9 (2.4, 5.3)	4.2 (2.7, 5.6)
Triglycerides (<1.7 mmol/L or treatment)	31	22	53	53
	9.8 (6.4, 12)	5.8 (3.4, 8.1)	7.6 (5.6, 9.5)	7.5 (5.5, 9.4)
Fasting glucose (<5.6 mmol/L or T2DM diagnosis)	181	217	398	398
	57 (51.5, 62.4)	57.8 (52.8, 62.7)	57.5 (53.8, 61.1)	56 (52.3, 59.6)
Blood pressure (<130/<85 mm Hg or treatment)	112	144	256	256
	35.3 (30, 40)	38.4 (33.4, 43.3)	37 (33.4, 40.5)	36 (32.4, 39.5)
Obesity (waist circumference<90 cm in males or<80 cm in females)	78	241	290	290
	25 (20, 29)	64.3 (59.4, 69.1)	46 (42.2, 49.7)	40.5 (36.8, 44.1)
Metabolic syndrome	18	39	57	57
	5.6 (3, 8.1)	10.4 (7.3, 13.4)	8.2 (6.1, 10.2)	7.1 (5.2, 9.04)

Table 3 shows the proportion of awareness diabetes, treatment, and control of fasting capillary blood glucose, high blood pressure, and lipid abnormality. Awareness was higher for hypertension and lower for diabetes and lipid abnormality. Among treated patients with hypertension, only 33% were at target BP levels (i.e., systolic (and diastolic) BP<140 (90) mm Hg). Fewer people were treated and controlled for diabetes and lipid abnormality.

Table 3.

Prevalence of Awareness, Treatment, and Control for Common Metabolic Disorders (Hypertension, Diabetes, and Lipid Abnormality) in 30–80-Year-Old Tibetans Living in Rural Areas of Lhasa Governance at 3700 meters

	Total abnormality^†	Already aware^€	Treatment^£	Situation being controlled^®
	n (%)	n (%)	n (%)	n (%)
Hypertension^±
Male	104 (32.8)	68 (65.3)	57 (54.8)	24(23)
Female	121 (32.2)	91 (75.2)	76 (62.8)	20 (16.5)
Total	225 (32.5)	159 (69.4)	133 (59.1)	44 (19.5)
Diabetes^‡
Male	8 (2.5)	2 (25)	2 (25)	0 (0)
Female	19 (5)	6 (31.5)	4 (21)	2 (10.5)
Total	27 (3.9)	8 (29.6)	6 (22.2)	2 (7.4)
Lipid abnormality^¥
Male	33 (10.4)	8 (24.2)	5 (15.1)	3 (9)
Female	47 (12.5)	7 (14.8)	2 (4.2)	0 (0)
Total	80 (11.5)	15 (18.7)	7 (8.7)	3 (3.7)

†

{^±BP<140 and/or<90 or taking antihypertensive medication; ^‡Fasting capillary blood sugar=126 mg/dL (<7 mmol/L) or on treatment for diabetes; ^¥Lipid abnormality HDL cholesterol 40 mg/dL in males and <50 mg/dL (<1.03 mmol/L in males and <1.29 in females) or Triglycerides=150 mg/dL (<1.7 mmol/L) or on treatment for either.}

€

Already aware was defined as self report of any prior diagnosis of hypertension, diabetes, or abnormal lipids by a health care professional.

Treatment was defined as self reported use of a prescribed medication for the management of hypertension or diabetes or abnormal blood lipids at the time of the interview.

Situation being controlled were defined as medical treatment of hypertension with systolic blood pressure<140 mm Hg and diastolic blood pressure<90 mm Hg; treatment of diabetes with fasting blood sugar<126 mg/dL (<7 mmol/L) and abnormal blood lipids associated with lipid levels of HDL-C>50 mg/dL (>1.30 mmol/L) or TG<150 mg/dL (<1.7 mmol/L).

The adjusted odds ratio of socioeconomic and lifestyle variables with metabolic syndrome is shown in Table 4. In Model 1, female gender, low physical activity, and a low education level were associated with metabolic syndrome. In Model 2, in which we removed lipids as components of the metabolic syndrome, only the association of gender and education on metabolic syndrome remained statistically significant. The positive association with age not statistically significant in Model 1, became significant in Model 2 (p<0.001). Data were also analyzed by removing other components of metabolic syndrome one at a time. The effect of age, gender, and education remained significant when either HDL-C or TG were removed from the full model (data not shown).

Table 4.

Crude and Adjusted Odds Ratio of Socioeconomic and Lifestyle Variables with Metabolic Syndrome Compared for Obesity, Fasting Blood Glucose, High Blood Pressure and Lipids (Model 1) and Compared for the Same Components except Lipids (Model 2) using Logistic Regression

	Model 1 ^±		Model 2 ^‡
Variables (95 % CI)	Crude OR (95 % CI)	Adjusted OR (95 % CI)	Crude OR (95 % CI)	Adjusted OR (95 % CI)
Age	1.01 (0.99, 1.03)	1.01 (0.99, 1.03)	1.05 (1.02,1.08)^***	1.06 (1.03,1.1)^***
Gender
Male	Ref	Ref	Ref	Ref
Female	2.07 (1.35, 3.16)^**	2.2 (1.3, 3.71)^**	1.84 (0.96, 3.53)^**	3.11 (1.37,7.08)^**
Total. PA. MET
<2000 Kcal/week	Ref	Ref	Ref	Ref
<2000 Kcal/week	0.35 (0.2, 0.6)^**	0.43 (0.24, 0.77)^**	0.7 (0.35,1.42)	0.72 (0.32,1.62)
Diet
Healthy	Ref	Ref	Ref	Ref
Mod. healthy	1.7 (0.89, 3.24)	1.48 (0.77, 2.85)	1.88 (0.65,5.46)	1.86 (0.63,5.53)
Unhealthy	2.75 (1.35, 5.63)	1.89 (0.83, 3.91)	3.13 (0.98,10.01)	2.69 (0.75,9.66)
Smoking
No	Ref	Ref	Ref	Ref
Yes	0.68 (0.37, 1.25)	1.29 (0.62, 2.71)	0.69 (0.27,1.8)	1.6 (0.51,5.04)
Alcohol
No	Ref	Ref	Ref	Ref
Yes	0.6 (0.4, 0.9)	0.71 (0.46, 1.1)	0.86 (0.46,1.59)	0.93 (0.48,1.8)
Income
No income	Ref	Ref	Ref	Ref
<300 Yuan/m	0.65 (0.4, 1.03)	0.84 (0.57, 1.41)	0.41 (0.18,0.92)	0.56 (0.23,1.36)
<301 Yuan/m	0.43 (0.26, 0.72)	0.91 (0.49, 1.67)	0.7 (0.35,1.42)	1.62 (0.65,4.02)
School education
No	Ref	Ref	Ref	Ref
Yes	0.77 (0.51, 1.14)	0.66 (0.43, 1)^*	0.71 (0.38,1.32)	0.45 (0.23,0.9)^*

Metabolic syndrome using IDF criteria (obesity, fasting blood glucose<5.6 mmol/L, high blood pressure, high total cholesterol and low high density lipoprotein cholesterol)

‡

Including only a combination of (obesity, fasting blood glucose<5.6 mmol/L and high blood pressure) as the outcome.

p<0.05; ^**p<0.01; ^***p<0.001.

Discussion

This study is the first to report the association of metabolic syndrome with socioeconomic and lifestyle factors among Tibetan highlanders from rural areas in TAR. Fasting hyperglycemia, obesity, and high blood pressure were the most common metabolic risk factors in the study population. The prevalence of metabolic syndrome was higher in women than in men. Moreover, the odds of having the metabolic syndrome was significantly higher for those with lower education as compared with high education, after controlling for several potential confounders. The significant association of low physical activity with metabolic syndrome diminished when dyslipidemia was not included as a component of the metabolic syndrome (i.e., the outcome). Self awareness, treatment, and control were very low for both diabetes and lipid abnormality.

The unstandardized, overall prevalence of metabolic syndrome 8.2% (95% CI 6.1–10.2) was similar to the prevalence among Tibetans in Qinghai Plateau (8%) (Matsubayashi et al., 2009), and overlapping the confidence interval of those residing in Lhasa city, 11.3% (95% CI: 9.5–12.9) (Chen et al., 2011). Compared to a study of other high altitude natives and sea level residents in Peru (Baracco et al., 2007), our estimate of metabolic syndrome was considerably lower (24.2% and 22.1% versus 8.2%). The prevalence of individual components such as obesity, high fasting glucose, and high blood pressure (46%, 57.5%, and 37%) was higher in the present study than in the high altitude (40.4%, 14.1%, and 18.2%) and low altitude (36.6%, 26.7%, and 30.8%) natives in Peru, respectively (Baracco et al., 2007). However, high altitude and low altitude Peru residents (Baracco et al., 2007) and urban residents of Lhasa (Sherpa et al., 2011) exhibited higher fasting hypertriglyceridemia (56.6% and 12.2%) and high low HDL-C (30.3% and 22.4%) compared to rural Tibetans in the present study.

Most studies (Baracco et al., 2007; Matsubayashi et al., 2009; Chen et al., 2011) do not present estimates age-standardized to the world population, which makes a comparison to less precise studies. It is, however, not likely that such estimates would have changed the main picture of differences. A higher prevalence of fasting hyperglycemia among rural Tibetans may be due to a rapid exposure to food variety such as soft drinks, processed snack foods and instant noodles (Dickerson et al., 2008) rather than simple traditional foods (i.e., roasted barley (tsamba), yak meat, milk, and butter). A study comparing Tibetan and Han elderly found higher blood glucose among Tibetans with higher food diversity than Tibetans with low food diversity (Kimura et al., 2009). A previous study among Sherpa residents also found higher fasting hyperglycemia among Sherpas in the city compared to Sherpas at high altitude with limited food variety (Lhamo et al., 2008). The present study also found a high prevalence of abdominal obesity, which can be ascribed to low levels of physical activity. Only a quarter of rural residents in the current study had energy expenditure ≥2000 kcal/week. A study among elderly highlanders in Tibet found basic activities of daily living among Tibetans being lower than Han and Mongolian highlanders and the lowest when compared to other elderly in eight villages in eight Asian countries (Singapore, Korea, Japan, Vietnam, Laos, Indonesia, Myanmar, and Thailand) (Matsubayashi et al., 2009). Further, other studies have found lower physical activity among men and women with lower socioeconomic status (Ford et al., 1991). Since the rural Tibetans have lower levels of education, it may be possible that general awareness of health among these people could be limited. The present study also found lower odds of metabolic syndrome with an energy expenditure ≥2000 kcal/week. However, the effect of energy expenditure was no longer significant when triglycerides and HDL-C were excluded as a component of the metabolic syndrome (Model 2). Studies have shown that exercise-mediated improvement in lipid profile (Kraus et al., 2002; Botezelli et al., 2011) acts through changes in total body fat or body fat distribution, and thus physical activity may well be of importance for the metabolic profile (Botezelli et al., 2011).

The high prevalence of high blood pressure of one-third of the population reported in this study could be due to high altitude residence, although the effect of altitude on blood pressure among long-term high altitude residents have been variable (Dasgupta et al, 1982; Arslan et al., 2003; Bachman et al. 2004; Baracco et al., 2007; Tripathy et al, 2007). In accordance with studies of Tibetans (Sehgal et al., 1968; Zhao et al., 2012), high rates of obesity may have contributed to the present high proportion of individuals with high blood pressure. In the present study, self awareness and treatment for hypertension was high, however, only one-fifth had their hypertension controlled. Close to 20% of hypertensive patients in this study took Tibetan and or Chinese medicine with unknown efficacy.

An inverse association of education with metabolic syndrome was also found in this study, consistent with the other studies (Aekplakorn et al., 2011; Yang et al., 2012). A study from another county in Tibet also found that rural Tibetan women with lower education had more unhealthy dietary habits than higher educated women (Wang et al., 2010). Since eating habits can also be influenced by purchasing power, advertising, and practicality in consumption, inappropriate food choices along with low physical activity may result in higher blood sugar and obesity (Korwanich et al., 2007; Kimura et al., 2009).

This study also found a positive association of age with the combined model of fasting hyperglycemia, abdominal obesity and high blood pressure. However, the association was no longer significant when dyslipidemia was included, suggesting that the principal effect of age is related to blood lipids.

Several limitations in this study are acknowledged. The cross-sectional nature of the study precludes the causal inferences between independent risk factors, and metabolic syndrome. We used capillary blood in this study with a blood glucometer. Accu-check aviva blood glucometers have been used in several large surveys (Tirimacco et al., 2010; Napoli et al., 2011), and is reported to give venous plasma equivalent results (Tirimacco et al., 2010). However, the meter is validated for use at altitudes only up to 10,150 feet (i.e., 3093.72 meters). Since the meter is oxygen independent (Tang et al., 2001) and temperature during the data collection period was not low, bias towards lower capillary blood glucose should have been minimal.

Another limitation of the study is that our regression models were adjusted for known risk factors derived from studies in low altitude populations. Thus, unmeasured confounding factors could bias our estimates. Further research with more detailed data could help us better understand the biologic processes underlying our findings.

The response rate was high at 82% which indicates that selection may not have affected prevalence estimates substantially. However, we cannot rule out if we have a selection towards more healthy participants (i.e., an underestimation of the prevalence of metabolic syndrome and its components). Under-estimation of disease prevalence and risk factors due to low response rate is a common finding from Western cross-sectional studies, while association measures are regarded as robust (Sogaard et al., 2004).

Conclusions

This study was an attempt to understand the factors that drive ethnic specific vulnerabilities in metabolic syndrome. The overall prevalence of metabolic syndrome in high altitude farmers and herdsmen in Tibet was lower compared to other high altitude natives. However, the prevalence of some individual metabolic components (fasting hyperglycemia, abdominal obesity, and high blood pressure) were higher than in other high altitude communities. Implications of the findings of high prevalence of smoking (among men), obesity, and hypertension and low rates of awareness, treatment, and control of the components of the metabolic syndrome among rural highlanders propels the need for health programs targeting risk factors.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

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