IJE Advance Access originally published online on January 19, 2005
International Journal of Epidemiology 2005 34(2):327-334; doi:10.1093/ije/dyi007
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Article |
Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women
1 Department of Public Health, University of Helsinki, PO Box 41, Mannerheimintie 172, FIN-00014, Finland
2 Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 S. Second Street, Scrite 300, MN 55454-1015, USA
2 National Public Health Institute, Department of Epidemiology and Health Promotion, Mannerheimintie 166, FIN-00300 Helsinki, Finland
* Corresponding author. Department of Public Health, University of Helsinki, PO Box 41, Mannerheimintie 172, FIN-00014, Finland. E-mail: karri.silventoinen{at}helsinki.fi
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Abstract |
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Background Previous studies have shown socioeconomic inequalities in the metabolic syndrome and coronary heart disease (CHD), but it is not known whether educational disparities in the metabolic syndrome explain educational inequalities in CHD. We investigated this question in a prospective study of middle-aged men and women.
Methods Baseline data were collected in 1992 in Finland from 864 men and 1045 women aged 45–64 years without history of CHD. A total of 113 new CHD cases were identified by the end of 2001. Logistic and Cox regression models were used in data analysis.
Results The metabolic syndrome defined by NCEP criteria was less prevalent in subjects with university education (21% in men and 14% in women) compared with basic level education (41% and 27%, respectively). Adjusting for health behavioural factors had only a slight effect on the educational gradient in the metabolic syndrome. An educational gradient in CHD incidence was clear [hazard ratio (HR) = 0.67 95% confidence interval (CI) 0.48–0.94, men and women combined]. Adjustment for the metabolic syndrome attenuated this gradient only slightly, but when individual components of the metabolic syndrome were included as covariates the attenuation was more substantial (HR = 0.73 95% CI 0.52–1.04).
Conclusions Educational differences in the metabolic syndrome and CHD incidence are clear. Metabolic risk factors explain the gradient in CHD incidence partly, but only when they are treated as independent risk factors. Screening for the metabolic syndrome alone is not sufficient to account for socioeconomic inequalities in cardiovascular disease.
Keywords Metabolic syndrome, coronary heart disease, education
Accepted 29 November 2004
Educational inequalities in cardiovascular disease are evident in many countries, especially those in northern Europe.1 Cardiovascular disease also strongly contributes to overall health inequalities in these countries due to its relatively high prevalence.2 However, much less is known about biological mechanisms accounting for these inequalities. The metabolic syndrome is one potential factor behind educational and other socioeconomic inequalities in cardiovascular disease.3 The metabolic syndrome is a metabolic state characterized by many classical risk factors of cardiovascular disease, i.e. abdominal obesity, low high-density lipoprotein (HDL) cholesterol, elevated triglycerides, hyperinsulinaemia, and hyperglycaemia.4 The causes of the metabolic syndrome are not yet well understood. In addition to behavioural factors, such as diet and physical activity, previous research indicates a strong genetic influence.5 It has also been suggested that undernutrition during fetal life and early childhood may cause permanent changes in human metabolism and thus affect the development of the metabolic syndrome in later life.6 Thus, the metabolic syndrome may mediate the effect of early material resources on later cardiovascular disease risk.
Inequalities in the prevalence of the metabolic syndrome by occupational status or education have been examined by three previous studies. In the Whitehall II Study with a large sample of British civil servants, a clear negative association was found between occupational status and the prevalence of the metabolic syndrome.7 Among men, the prevalence of the metabolic syndrome decreased across the six categories of the occupational scale, but among women a higher prevalence was found only in the three lowest categories. In a follow-up study in the UK,8 negative, but statistically insignificant, associations were found between the metabolic syndrome and socioeconomic class in childhood or in adulthood. However, the sample size was smaller than in the Whitehall II study, which may explain the statistically insignificant results. In a study of Swedish women, an inverse gradient in the prevalence of the metabolic syndrome was found across categories of education.9 In this study, the age-adjusted prevalence of the metabolic syndrome was 2.6 times higher among women with basic education compared with women who had college or university level education. Adjustment for other risk factors only slightly decreased the occupational gradient in the Whitehall II study and the educational gradient in the Swedish study.
The social gradient in the metabolic syndrome could help explain socioeconomic inequalities in coronary heart disease (CHD). If so, then factors that cause the metabolic syndrome may also be important in the formation of social inequalities in CHD risk. Further, the metabolic syndrome may offer a simple screening tool to find sub-groups and individuals at high risk for CHD.3 If educational variation is found in the metabolic syndrome, then interventions to prevent and treat metabolic abnormalities, especially in people with low social position, may help to narrow socioeconomic inequalities in CHD. In this study, we examined educational disparities in the metabolic syndrome in a cohort of Finnish middle-aged men and women. Education is a good indicator of social position in epidemiological studies because it precedes other indicators, such as occupational based social position or income, is comparable between men and women, does not usually change in adulthood, and shapes health behaviours through attitudes, values, and knowledge. First, we investigated whether there were educational differences in the prevalence of the metabolic syndrome and whether adjusting for other risk factors attenuated these differences. Second, we investigated whether the educational differences in the prevalence of the metabolic syndrome at baseline explained educational inequalities in CHD incidence.
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Data and methods |
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The baseline data were derived from a survey carried out in 1992 in four geographical areas in eastern (Kuopio and North Karelia provinces), south-western (Turku–Loimaa area), and southern (Helsinki–Vantaa area) parts of Finland. The sample included subjects aged 25–64 years, and it was stratified so that at least 250 men and women in each ten-year age group were chosen in all geographical areas. Less than 1% of the invitation letters were returned because of wrong or incomplete contact information, reflecting the high quality of the Finnish population registry. The response rates were 73% for men and 81% for women.10 The study was conducted according to the national data protection legislation and the ethical rules of the National Public Health Institute, Finland. The protocol of the international WHO MONICA (MONItoring trends and determinants in CArdiovascular disease) Study was followed.11 Before a clinical examination, a self-administrated questionnaire was sent to the participants including questions about alcohol use, smoking, physical activity, education, and marital status. Participants were classified as current smokers, former smokers, or never smokers. Based on average alcohol consumption, they were classified as non-drinkers, low moderate drinkers (<35 g ethanol per week), high moderate drinkers (from 35 to 100 g ethanol per week), or heavy drinkers (>100 g ethanol per week). Nutrition was classified by asking whether a participant eats fresh fruits, vegetables, or berries daily. The participants reported their physical exercise by the number of leisure time physical activities with the duration of
20 min per week. Marital status was dichotomized as living with (married or co-habiting) or without (unmarried, widow/widower, divorced) a spouse.
Education was classified into three categories: basic education, middle level education, and university education. Basic education is mandatory for all citizens in Finland and lasts 9 years. Middle level education includes a minimum of 2 years of theoretical and/or vocational education after basic education. University education lasts 4 years and follows 3 years of theoretical middle level education. Together with mandatory middle level education, it requires a minimum of 7 years of study after basic education.
At each study site, specially trained nurses measured height, weight, waist circumference (WC), hip circumference, and blood pressure and took a venous blood specimen. Height was rounded to the nearest centimetre, WC and hip circumference to the nearest half centimetre, and weight to the nearest 100 g. Systolic (SBP) and diastolic (DBP) blood pressures were measured twice and the mean of these measures was used in this study. Cholesterol, HDL cholesterol and triglycerides were determined from fresh serum samples by using an enzymatic method (CHOD-PAP and CPO-PAP, Boehringer MANNHEIM, Mannheim, Germany) in the same central laboratory at the Finnish National Public Health Institute.
Of the 6062 people who took part in the original survey, 2642 participants aged 45–64 years were invited for additional glucose and insulin tests in selected municipal health care centres. These participants were not selected based on blood glucose level or any variable except age. Blood was collected after an overnight fast within a few weeks after the first visit. Glucose concentration was determined by the hexokinase method. There were 26 participants who reported that they were using diabetes medication. Two participants who were using diabetes medication had a normal glucose level (<110 mg/dl), and we removed them from analyses since diabetes medication may have affected their blood glucose level. We have information on all metabolic traits from 1909 people, and they are included in this study.
We classified participants according to modified definitions of the metabolic syndrome from the National Cholesterol Education Program 2001 (NCEP)12 and World Health Organization (WHO).3,13,14 The NCEP definition classified a participant as having the metabolic syndrome if he or she has at least three of the following five metabolic abnormalities: high serum triglycerides (150 mg/dl); impaired fasting glucose (IFG) or diabetes (fasting plasma glucose 110 mg/dl); low HDL cholesterol (<40 mg/dl in men and <50 mg/dl in women); high blood pressure (DBP 85 mm Hg or SBP 130 mm Hg or self-reported hypertension medication); and abdominal obesity (WC >102 cm in men and >80 cm in women). Modified WHO definition classified a participant as having the metabolic syndrome if he or she had, first, hyperinsulinaemia (upper quartile of the non-diabetic population, 9.3 uU/ml in this population) or IFG or diabetes (fasting plasma glucose 110 mg/dl) and, second, at least two of the following three metabolic abnormalities: obesity [body mass index (BMI) kg/m2] 30 or waist-to-hip ratio (WHR) >0.90 in men and >0.85 in women), dyslipidaemia (serum triglycerides 150 mg/dl or HDL cholesterol 
35 mg/dl in men and 39 in women) and high blood pressure (DBP 85 mm Hg or SBP 130 mm Hg or self-reported hypertension medication). We did not have data on microalbuminuria, included in the original WHO definition of the metabolic syndrome. It is possible that because of this omission, a few cases of the metabolic syndrome were not identified. However, it is not likely that this omission would have strongly affected educational disparities in the metabolic syndrome.
The main difference between these two definitions of the metabolic syndrome is that fasting insulin has an important role in the WHO definition whereas it is not included in the NCEP definition. Also hyperglycaemia is somewhat more important in the WHO than in the NCEP definition. There is no common agreement yet as to which of the two definitions is recommended. However, previous studies have suggested that the WHO definition is a better predictor of CHD risk3 and Type 2 diabetes15 compared with the NCEP definition.
Participants were followed-up until the end of 2001. We used CHD death or hospitalization for CHD as the outcome variable (ICD 9 codes 410-414 and ICD 10 codes I20-I25). Non-fatal CHD events were derived from the National Hospital Discharge Register, which has shown good validity in Finland.16 CHD mortality data were obtained from the Central Statistical Office of Finland based on death certificates. Because of the universal and free health care system in Finland, both registers are likely to cover the entire population. These data were linked to the baseline survey by the social security number, which is assigned to every Finnish citizen. Those who died during the follow-up period from other causes of death than CHD were censored. Participants with CHD at baseline identified from the National Hospital Discharge Register (65 men and 27 women) were excluded from these analyses. Mean age was higher among those who were excluded because of baseline CHD compared with those without CHD (59 years vs 54 years, respectively). Age-adjusted prevalence of CHD at baseline was somewhat lower in participants with university level education (4% in men and 2% in women) than in participants with middle level (8% and 3%, respectively) or basic education (7% and 3%, respectively), but the differences were not statistically significant. During the follow-up, there were 113 new CHD cases (73 men and 40 women), including 16 fatal cases of CHD.
In the statistical analyses, we first calculated means or prevalences of metabolic variables adjusting for age and study centre. Because of the skewed distributions of plasma glucose and insulin, serum triglycerides, and HDL cholesterol, we used geometric means instead of arithmetic means for these variables. We also conducted multiple logistic regression analyses with the metabolic syndrome as the dependent variable. We first calculated prevalence odds ratios (ORs) for each educational category with basic education as the reference category (Model 1). Secondly, we added leisure time physical activity, alcohol use, daily consumption of fresh vegetables, berries or fruits, smoking, and living without a spouse as indicators of health behaviour (Model 2). Thirdly, we added height (Model 3) as a proxy measure of childhood living conditions. Previous studies have shown that height is positively associated with education,17 CHD risk,18 and several metabolic disorders,19,20 which may reflect the effect of material deprivation in childhood on these traits. If material deprivation in childhood is the cause of the association between education and the metabolic syndrome, we assume that adjusting for height would weaken this association.
We analysed associations between education, the metabolic syndrome, and incident CHD using a Cox proportional hazards model. Follow-up time was measured in days from the baseline exam to CHD death or hospitalization (cases), to death from other causes of death (censored), or December 31, 2001 (censored). Since the number of new CHD cases was small and the interaction between sex and education statistically insignificant (P = 0.62), we combined men and women in these analyses and adjusted the models by sex. We first calculated hazard ratios (HRs) for education categories adjusting for age and study centre (Model 1). Next we evaluated whether further adjustment for the metabolic syndrome and metabolic abnormalities affected the educational differences in CHD risk (Models 2–5). Finally, we adjusted the model for health behavioural factors (Model 6). All analyses were conducted using the SAS statistical package.21 Because of different CHD risks in men and women, the STRATA option of the SAS statistical package was used for sex.
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Results |
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The educational distribution was similar among men and women (Table 1); 9% of men and 10% of women had university level education, whereas 56% of men and 58% of women had basic education only. Mean levels of the metabolic variables were more favourable among women than among men (Table 1). Among men, an educational gradient was found for SBP, serum triglycerides, fasting plasma glucose, WC, and WHR whereas for plasma insulin, DBP, BMI, and HDL cholesterol, only university level education differed from other categories. Among women an educational gradient was found for all characteristics except for DBP.
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Using the modified NCEP definition, the metabolic syndrome was more prevalent among men than among women (Table 2). Among men, 37% (95% confidence interval (CI) 34–41) were classified as having the metabolic syndrome whereas among women the prevalence was 24% (95% CI 21–27). The prevalence of the metabolic syndrome was lowest in those with university education among both men (21%; 95% CI 10–31) and women (14%; 95% CI 5–22). The prevalence of the metabolic syndrome using the modified WHO definition was about the same (37%; 95% CI 34–40 in men and 20%; 95% CI 17–22 in women) than when the NCEP definition was used. Educational differences in the prevalence of the metabolic syndrome were of similar magnitude using the modified NCEP and WHO definitions.
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In the logistic model adjusted only for age and study centre (Model 1), the OR for the metabolic syndrome (NCEP definition) was 0.39 (95% CI 0.22–0.68) for men and 0.40 (95% CI 0.21–0.76) for women comparing university level with basic education (Table 3). When the WHO definition was used, these associations were similar in magnitude, with an OR of 0.41 (95% CI 0.23–0.71) in men and 0.36 (95% CI 0.18–0.75) in women. Adjusting for the behavioural risk factors (Model 2) attenuated these associations only slightly. Further adjustment for height had virtually no effect on the associations (Model 3) regardless of the definition of the metabolic syndrome. We also tested whether the age- and center-adjusted association between education and the metabolic syndrome was similar in men and women; the formal test for interaction between education and sex was statistically insignificant both for the NCEP definition (P = 0.62) and WHO definition (P = 0.52).
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Finally, we analysed whether the metabolic syndrome explained educational differences in CHD incidence (Table 4). The OR for CHD for each category increase in education was 0.67 (95% CI 0.48–0.94) after adjustment for age, study centre, and sex (Model 1). This educational gradient was only slightly attenuated when the metabolic syndrome was included as a covariate regardless of the definition (Models 2 and 3). When we adjusted for all metabolic abnormalities individually as dichotomized variables (Model 4), the educational gradient was further attenuated (HR = 0.70; 95% CI 0.50–0.99). When metabolic variables were included into the model as continuous covariates (Model 5), the educational gradient was attenuated to a greater extent (HR = 0.73; 95% CI 0.52–1.04). Adjusting for behavioural risk factors (Model 6) attenuated the educational gradient further (HR = 0.77; 95% CI 0.54–1.10).
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Discussion |
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TopAbstractData and methodsResultsDiscussionReferences |
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Our results show that there are clear educational differences in the prevalence of the metabolic syndrome in the Finnish middle-aged population, and the adjusting for behavioural risk factors narrowed these differences only slightly. The educational gradient was roughly similar when modified NCEP and WHO definitions of the metabolic syndrome were used. Our finding of an educational gradient for the metabolic variables is in accordance with many previous studies which have also found clear socioeconomic differences in metabolic abnormalities.22–27
The prevalence of the metabolic syndrome was relatively high in our study population: 37% of men and 20–24% of women had the metabolic syndrome depending on the definition. This is a higher proportion, especially among men, than reported in representative samples of the US population.28 This high prevalence of the metabolic syndrome in our study was partly due to the high prevalence of obesity (78% in men and 32% in women according to the WHO definition) and especially high blood pressure (84% and 73%, respectively), which are both serious public health problems in the Finnish population.
We found that the educational differences in the metabolic syndrome were very similar in magnitude among men and women regardless of the definition, even though the metabolic syndrome was more prevalent among men. We are not aware of any previous study, which has examined educational differences in the metabolic syndrome both in men and women. In the Whitehall II Study, the difference in the prevalence of the metabolic syndrome between the lowest and the highest occupational grade was found to be larger among women than among men.7 This may suggest that education and occupation based social scales give somewhat different results regarding socioeconomic disparities in the metabolic syndrome, especially when comparing men and women. It is noteworthy that the distribution of occupational categories was very different between men and women in the Whitehall II study, which may have affected the results. By contrast, the distribution of educational categories was similar in men and women in our study.
When we analysed the associations between education and CHD incidence we found a clear gradient. The results are in accordance with previous results which have found that socioeconomic differences in CHD mortality are relatively large in Finland compared with other Western countries.29 Adjusting for the metabolic syndrome had only a slight effect on this association, but when we included each of the metabolic abnormalities as independent risk factors the educational differences in CHD incidence narrowed. Thus, metabolic status at baseline seems to partially explain socioeconomic differences in CHD incidence, but the metabolic syndrome as a single dichotomized measure is too crude to account for educational differences in CHD risk. However, it is noteworthy that even when metabolic factors were included as continuous covariates the educational gradient did not disappear completely. It is likely that single measurements of the metabolic variables at baseline only partially capture habitual or cumulative effects of these variables. Thus, it is probable that the effect of metabolic abnormalities on the risk of CHD, and their possible effect on educational CHD inequalities is underestimated in this study. However, the relatively modest reduction in the educational inequalities in CHD incidence after the adjustment of metabolic abnormalities suggests that they are only a partial explanation for educational inequalities in CHD risk.
More research is needed to identify factors that mediate educational, and more generally socioeconomic, differences in the metabolic syndrome and CHD. Genetic factors probably have a crucial effect on the development of the metabolic syndrome,5 but it is not likely that they would be a major factor behind socioeconomic differences. If undernutrition has an effect on the development of the metabolic syndrome, then previous material deprivation may increase the prevalence of the metabolic syndrome among the least educated adults whose childhood living conditions are also likely to be poorest. There is evidence that poor childhood living conditions have affected height in the Finnish population.17 However, we did not find that adjustment for height attenuated the educational differences in the metabolic syndrome even when it was inversely associated with the metabolic syndrome among women. Many behavioural factors, such as low consumption of fresh fruits and vegetables30 and physical inactivity,31 are associated with increased prevalence of the metabolic syndrome and increased risk of CHD.32,33 It is also possible that there are differences in these behavioural factors between educational categories. We did not find that adjusting for behavioural factors attenuated the association between educational disparities and the metabolic syndrome but they did partially account for the educational gradient in the CHD risk after adjustment for metabolic disorders. Thus, behavioural factors may contribute to educational disparities in CHD risk independently of metabolic risk factors.
In conclusion, our results show an inverse association between education and the metabolic syndrome as well as CHD incidence in the Finnish population. Metabolic risk factors partially but not completely explained the educational gradient in CHD incidence. The metabolic syndrome alone is not sufficient to account for socioeconomic inequalities in cardiovascular disease. A more detailed assessment of the metabolic risk profile appears to be warranted and more research is needed to elucidate the reasons for educational inequalities in CHD.
KEY MESSAGES
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Acknowledgments |
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This study was supported by the Academy of Finland (grants #46558, #53585, #204274, and #205657).
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References |
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