a Unidad de Investigación del Hospital de la Candelaria y Atención Primaria, Santa Cruz de Tenerife, Spain.
b Universidad de La Laguna, Área de Medicina Preventiva y Salud Pública, Spain.
c Servicio de Cardiología, Hospital Universitario de Canarias, Santa Cruz Tenerife, Spain.
Reprint requests to: Dr Antonio Cabrera De León, Unidad de Investigación, Hospital de la Candelaria, 38010 Santa Cruz de Tenerife, Spain. E-mail: acabrera{at}hcan.rcanaria.es
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Abstract |
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Methods A cross-sectional study was carried out in the adult population (3064 years) of the Island of El Hierro (Canary Islands, Spain). In all, 594 individuals representative of the El Hierro population for gender, age, district and the altitude at which they lived were included. The factors measured included HDL, living altitude, body mass index (BMI), smoking habits, alcohol consumption, diabetes, menopause in women, and physical activity and dietary habits.
Results The HDL showed a correlation with living altitude (r = 0.14, P < 0.01) and with BMI (r = 0.19, P < 0.01). Smokers had lower HDL levels than ex-smokers and non-smokers (P < 0.05). Men who were moderate drinkers had higher HDL levels than heavy or mild drinkers and non-drinkers (P < 0.01). Physical activity was only related to HDL in men with levels >1.52 mmol/l, who walked on the average more than the rest (P < 0.05). Variables not showing the expected relationship with HDL were diabetes and the menopause in women (probably due to a low statistical power of their subsamples). Regression analysis, with HDL as dependent variable showed that the association between HDL and altitude persists when taking altitude as a categorical or a continuous variable.
Conclusions High density lipoprotein cholesterol levels are linearly and significantly increased when living at a higher altitude. This fact should be taken into account when comparing cardiovascular risk in populations living at different altitudes.
Keywords HDL-cholesterol, altitude, cardiovascular risk factors
Accepted 22 June 1999
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Introduction |
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Several studies46 have demonstrated a negative correlation between high density lipoprotein cholesterol serum levels (HDL) and the incidence of coronary heart disease. Body mass index (BMI), smoking, physical activity, and alcohol consumption are factors which have been most frequently related to variations in serum HDL, also women exhibit higher mean levels of HDL than men.4
A lower mortality from coronary heart disease has been observed in populations living in areas of high altitude.7,8 Moreover, higher levels of serum HDL have been detected in those who live at high altitudes,9,10 and increases in HDL have been observed in a population migrating from lower altitudes to high mountain regions.11 However, the effect of living at higher altitudes in itself is difficult to quantify, as there are other variables that must be taken into account such as genetic factors, diet and physical activity. The purpose of this research was to evaluate the distribution of HDL in the adult population of El Hierro island according to the altitude at which they normally live, and study its relationship with other factors that could affect this distribution.
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Subjects and Methods |
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Study design and sample selection
This cross-sectional study of the adult population (age range 3064 years) was carried out from October 1993 to October 1994. A stratified random sample was obtained on the basis of municipality, age, and gender. For a confidence interval of 95%, an estimated prevalence of coronary heart disease of 3%, and a mean error of ±1.4%, the estimated sample size was 570 subjects. However, the final sample was 594 due to the success of the enrolment campaign.
Subjects, procedures, and methods
The subjects, with the provision of an extra 25% of possible participants in case of absentees, were invited to enrol in the study through an explanatory letter sent by mail. In the letter subjects were asked to attend the primary health centre nearest to their homes after an overnight fast. Each individual was assigned the altitude at which he/she normally lived (census data). Subsequently, subjects were categorized into three altitude groups: a shore group (those living <351 m above sea level), an intermediate group (351799 m), and a mountain group (8001050 m). The following equipment was used in the present study: three Seca® scales incorporating measuring rods, graded at 100 g and 1 mm intervals respectively, an Hitachi 705 Autoanalyser (Boehringer Mannhein), test material of the same make for determinating serum levels of total cholesterol, triglycerides, HDL and glucose. The HDL levels were determined by the phosphotungstic acid and magnesium chloride precipitation method, while glucose, total cholesterol (TC) and triglycerides (TG) were determined by colorimetric enzymatic tests. Low density lipoprotein (LDL) cholesterol was defined at TG <4.52 mmol/l, as LDL = TC HDL TG/2.2.15 Quality control was carried out every 20 tests by calibration with Precinorm U® and Precipath U®.
Data collection
Initially a 5-ml fasting blood sample was obtained by venipuncture from all participants who attended at the health centre as requested. This sample was sent to the laboratory at Valverde Hospital for later testing. Individuals were also weighed, measured, and asked to answer a questionnaire regarding smoking habits, physical activity, alcohol consumption, diet, and medical history (including personal details, place of birth and menstrual history). Blood pressure was measured with the subjects in a seated position, employing a mercury sphygmomanometer. Cardiac health was assessed by electrocardiogram.
Data classification
The BMI was calculated as weight in kg divided by height in m2. Alcohol consumption, in g/day, was calculated by multiplying ml of consumed alcohol by proof of beverages by density of alcohol (0.8) and dividing the result by 100. Subjects were considered as: non-drinkers, mild (120 g alcohol/day), moderate (2159 g/day), and heavy drinkers (>59 g/day). The USA National Diabetes Data Group criteria13 were used for detection of diabetes; subjects were considered diabetics if: (a) they declared themselves as such during the enrolment questionnaire and were treated with insulin or antidiabetic drugs, (b) they had a fasting serum glucose of 11.1 mmol/l (
200 mg/dl), (c) a fasting serum glucose level of
7.8 mmol/l (
140 mg/dl) on at least two occasions, or (d) they were found to have serum glucose levels of
11.1 mmol/l 2 h after an oral loading dose of 75 g glucose. The criteria of Ford et al.14 were used to establish physical activity, by adding up activity at work and at home, time spent walking other than work and leisure, and that spent in leisure activities. The global physical activity was calculated by multiplying METS of each activity by the number of hours per week and by the individual's weight in kg, and it was expressed in kcal/week. Subjects were also classified as consumers (or not) of a list of main foodstuffs twice a week.
Statistical analysis
Data analysis was carried out with the SPSS program. Initially, we carried out a univariate descriptive analysis of the variables. For the bivariate analysis, the possible relationship between categorical variables was analysed by the Pearson 2 test. The association between categorical and continuous variables was analysed by t-Student test whenever the categorical variable was binary and by means of variance analysis, if not. The relationship between continuous variables was assessed by means of the Pearson correlation coefficient. For multivariate analysis, the linear regression model used HDL as the dependent variable, altitude as the independent variable, and as possible confounders, place of birth, age, gender, BMI, smoking, alcohol consumption, diabetes, menstrual status, and physical activity apart from the variables that resulted from interaction of these. All results with a value of P < 0.05 were deemed significant. Quantitative values are expressed as mean ± SD.
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Results |
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The only relationship found between HDL levels and physical activity was observed in the case of walking: men with HDL levels >59 mg/dl (69 subjects) walked on average more than the rest (2625 ± 3769 versus 1839 ± 2586 kcal/week; P < 0.05). Men whose total physical activity amounted to >2000 kcal/week showed lower heart frequencies than those who were less physically active (P < 0.05), while women showed lower heart frequencies if they carried out activity equivalent to 4900 kcal/week (P < 0.05).
No differences were observed in HDL between those who consumed the foodstuffs studied twice or more per week and those that did not.
For the purpose of discerning if the association between altitude and HDL levels could be caused by one of the known variables acting as a confounding factor, a multiple linear regression analysis was carried out in which all the mentioned factors were included. Two models were set out; in the first one altitude was considered a categorical variable (Table 3A), and in the second one altitude was taken as a continuous variable (Table 3B
). The residuals showed a normal distribution in both cases with mean values of 0 ± 0.36. Initially both regression models were tried including total physical activity but later physical expenditure during walking was substituted, however the final result did not vary in either case. Because of the bivariate association between altitude and glucaemia, this too was included in the models as an independent variable.
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Discussion |
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We have noted an association between living at higher altitudes and higher levels of HDL, which remains throughout the multivariant analysis, both when altitude is considered as a categorized variable and when it is considered a continuous quantitative variable. This association has also been described by Sharma9 and Aytbaev,10,11 but they did not take into account possible confounders. Perhaps genetic or other socio-ecological factors which were not considered in our study could explain the fact that those native to the island exhibited lower levels of HDL than those born elsewhere. All other variables which were associated with HDL levels had been previously described, i.e. female gender, BMI,15 smoking16 and alcohol consumption.17,18
Diabetic patients usually have decreased levels of HDL when compared to non-diabetics.19 This fact was not detected in this study probably due to the lowered statistical power of the sub-sample of diabetics. But it may be that the greater proportion of diabetics in the mountain group (Table 2), in conjunction with the increasing of HDL with altitude, attenuated the expected decrease of HDL in diabetics. The same problem of lack of power could explain the fact that though it has been reported that levels of HDL tend to be lower in menopausal women,20 this could not be proven in menopausal women of El Hierro. Although eating habits were not associated with HDL values, it could be because we did not study nutrients. However, no foodstuff has been previously clearly associated with HDL changes.
The association observed between HDL levels and physical activity in men only during walking could be explained by the fact that in this case there was an energy expenditure of >2000 kcal/week, which is considered the accepted cardioprotective threshold.21 The global physical activity was absent from the final regression models, and this could possibly be due to at least two reasons. First, it could be that the questionnaire carried out during the study was not sufficiently precise when measuring physical activity. However, we found that physical activity and cardiac frequency were consistently related, indicating that the questionnaire was not imprecise in its evaluations. Second, this could be explained by the type of activity; it seems that HDL levels are increased only with aerobic activity, and this has been proven in the case of sportsmen that practice anaerobic exercises and who do not show higher HDL serum levels than control groups.22 Thus, in El Hierro, physical activity carried out by women is mainly of doing house chores, an activity which is not necessarily aerobic. This could explain to some degree why cardiac frequency values in women decrease only after they have spent a higher amount of energy then men.
Another possible limitation of this study is that temperature was not taken into account. It is obvious that at higher altitudes the ambient temperature is lower. Although it is known that colder temperatures produce an increased breakdown of triglycerides in muscle and adipose tissues, it has not yet been proven that the same happens with plasma triglycerides in humans.23 In addition, we have not found any community study which establishes that there is an increase in serum levels of HDL at lower temperatures, although Elwood24 has described the opposite effect.
The presence of higher levels of HDL at higher altitudes could be originated by changes produced in hepatic tissue. It seems that there is a re-adaptation of the body to periodic altitude hypoxaemic conditions, which in some unknown way would alter lipidic oxidation at hepatic level.25 Ferezou et al.,26 proved that levels of HDL increased and levels of triglyceride-rich lipoproteins decreased in six subjects who were transported from low altitudes to high altitudes at 4350 m to an observatory in the Alps, thus suggesting that hypoxia induced lipolysis of plasma triglycerides and, in addition, an increase of serum HDL. In their study the effects of physical activity, cold exposure and dietary conditions were controlled, but the sample size was too small and altitude was extreme, rendering it of limited interest for large populations. The altitudes at which El Hierro inhabitants live do not exceed 1050 m above sea level, and as is shown in Table 3B, there is a linear relationship between altitude and HDL, metre by metre, which suggests that the effects of a mild sustained hypoxia could induce an increase of HDL levels.
Because of its cross-sectional design, our study does not allow us to establish a causal relationship. But it is the first one, to our knowledge, in which the relationship between high density lipoproteins and altitude has been analysed by means of a multivariant model, considering the possible influence of other variables which could act as confounders. We think that living altitude should be taken into account when comparing cardiovascular risk in populations living at different altitudes.
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Acknowledgments |
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