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Keywords:

  • anxiety;
  • depression;
  • epidemiology;
  • metabolic syndrome

Abstract

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

Objective:  To examine the associations of depression and anxiety with the metabolic syndrome.

Method:  Cross-sectional study of 9571 participants aged 20–89 years in the Nord-Trøndelag Health Study (HUNT 2). We assessed anxiety and depression with the Hospital Anxiety and Depression Scale and the metabolic syndrome with the International Diabetes Federation criteria.

Results:  Despite generous statistical power and use of both continuous and categorical approaches, we found no association between anxiety or depression and the metabolic syndrome in models adjusted for age, gender, educational level, smoking, physical activity and pulse rate. When adjusted for age and gender only, we found a weak positive association for depression when a continuous measure was used, but not at the case level. The findings were similar across sexes, and robust for exclusion of cardiovascular disease and antidepressants.

Conclusion:  In this largest study to date we found no association of anxiety and depression with the metabolic syndrome.


Significant outcomes

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References
  • • 
    Despite generous power, and in contrast to most of the smaller studies in this field, we found no consistent associations of anxiety and depression with the metabolic syndrome.
  • • 
    An initial weakly positive association of depression (continuous measure only) with the syndrome was entirely explained by confounders, whereof physical activity and educational level were most important.
  • • 
    We found an inverse association of anxiety (categorical measure only) with the metabolic syndrome in the oldest age group.

Limitations

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References
  • • 
    Previous studies have found non-participants in HUNT 2 to be less healthy than participants.
  • • 
    The participants had fasted four or more hours before blood sampling rather than overnight fasting as recommended in diagnosing the metabolic syndrome. This was likely to affect levels of glucose and triglycerides, which was adjusted for by statistical procedures.
  • • 
    A single cross-sectional measure of symptoms of anxiety and depression is less valid than a clinical diagnosis based on longitudinal information.

Introduction

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

Several studies have indicated that depression and anxiety are risk factors for type 2 diabetes (1–3) and for cardiovascular disease and mortality (4–7). However, the mechanisms underlying these risk factors are unclear. Some reports have argued that psychological characteristics may be linked to cardiovascular disease and type 2 diabetes through their associations with the metabolic syndrome, in other words, that psychological factors are present earlier in the pathogenesis of cardiovascular and diabetes disorders (8, 9). As depression and anxiety are potentially modifiable targets in the prevention of cardiac and diabetes morbidity, exploration of the nature and strength of their association with metabolic syndrome becomes clinically relevant.

A recent review suggested a weak cross-sectional association between depression and the metabolic syndrome, while the association between anxiety and the syndrome was even less consistent (10). The review included studies of samples that were small or from highly selected hospital material (11, 12). Only a few have been population-based studies (8, 13–18) of which only three also included anxiety (15, 16, 18), reporting mostly no significant association between anxiety and the metabolic syndrome. Two prospective studies, conducted in the same sample of middle-aged women, only found that depression, but not anxiety, was associated with an increased prevalence of metabolic syndrome (15, 19).

Aims of the study

On the background of limited and inconsistent evidence, the aim of this study was to examine whether depression and anxiety were associated with the metabolic syndrome in a large general health survey of a population aged 20–89 years old and to explore whether the potential associations were mediated or confounded by other risk factors for cardiovascular disease and type 2 diabetes, such as smoking, low educational level, low physical activity and tachycardia.

Material and methods

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

Subjects

The Nord-Trøndelag Health Study (HUNT) is a large, population-based health study (20). All inhabitants aged 20 years and above in Nord-Trøndelag County of Norway were invited. For the present study, 10 206 attendees 20–89 years of age at the second examination cycle (HUNT 2, 1995–1997) had valid data on the five components of metabolic syndrome (21). These participants were representative of the entire HUNT 2 study population (21). Among them, 9571 participants also had valid scores on the Hospital Anxiety and Depression Scale (HADS) and made up the sample of the present study. Secondary analyses were performed in two sub-samples: first, we excluded 729 individuals with self-reported cardiovascular disease (angina pectoris, myocardial infarction or stroke) (= 8842) and, second, we excluded 295 individuals who reported the use of antidepressants (= 9276).

All participants in the HUNT 2 study gave written informed consent. The Norwegian Data Inspectorate and the Regional Committee for Medical Research Ethics approved the study.

Data

We obtained information on symptoms of anxiety and depression, use of antidepressants and antihypertensive and diabetes medication, cardiovascular disease, diabetes and relevant covariates from questionnaires. Blood pressure and waist circumference were measured at the screening site. In venous blood samples, plasma glucose was measured with an enzymatic hexokinase method, total cholesterol and high-density lipoprotein (HDL) cholesterol applying an enzymatic colorimetric cholesterolesterase method and triglycerides by enzymatic colorimetric method. Participants indicating a history of diabetes were diagnosed with type 2 diabetes based on an additional fasting blood test for glucose, C-peptide, antibodies to glutamic acid decarboxylase and information on start of insulin treatment (20).

To comply with a proposed model for understanding the association between psychological factors and metabolic syndrome (10), we selected potential behavioural mediators (smoking, physical activity) and level of education (as a measure of socio-economic status) for adjustment in the multivariate analyses. Physical activity was defined as self-reported leisure time activity per week (four categories) and smoking as never, previous or current cigarette smoking. As long-term tachycardia potentially could be a result of chronic stress and an increased sympathetic tone, we also included pulse rate in the multivariate analyses.

Anxiety and depression

Symptoms of anxiety and depression were measured using the HADS, which is a well-established screening instrument for epidemiological studies. The HADS is a self-report questionnaire comprising of 14 four-point Likert-scaled items, seven for anxiety (HADS-A) and seven for depression (HADS-D). A cut-off score of 8 on both subscales was found to give an optimal balance between sensitivity and specificity, both at about 0.80, for depression (major depressive disorder and dysthymia) and anxiety (generalized anxiety disorder, specific phobias and adjustment disorders) as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R and DSM-IV) classification systems (22). Using these cut-offs, we identified four groups: anxiety only (HADS-A ≥8, HADS-D <8) (= 967), depression only (HADS-D ≥8, HADS-A <8) (= 469), comorbid anxiety and depression (both HADS-A and HADS-D ≥8) (= 600) and a reference control group (both HADS-A and HADS-D <8) (= 7535).

Metabolic syndrome

We defined the metabolic syndrome according to the 2005 International Diabetes Federation (IDF) criteria (23) consisting of central obesity (for population of European origin: ≥94 cm in men and ≥80 cm in women) plus any two of the four components: serum triglycerides >1.7 mmol/l or specific treatment for this lipid abnormality; HDL cholesterol <1.03 mmol/l in men and <1.29 mmol/l in women or specific treatment for this lipid abnormality; systolic blood pressure ≥130 and/or diastolic blood pressure ≥85 or treatment for previously diagnosed hypertension; and fasting plasma glucose ≥5.6 mmol/l or previously diagnosed type 2 diabetes. We had no data on specific treatment for lipid abnormalities, as such treatment was infrequent in Norway at that time.

Each participant reported from four to nine or more hours fasting before blood sampling. We adjusted triglyceride and glucose levels for the participants who had been fasting 4–8 h, with those reporting ≥9 h fasting as reference (details described in a previous publication) (21).

Power calculation

The prevalence of metabolic syndrome in individuals with depression was 39.9%. Given 80% power (80% of studies would then give a significant finding) and α = 0.05 (equivalent to < 0.05 for a significant finding), the minimum group difference detectable would be 6.4%. That is equivalent to 33.5% prevalence of the syndrome in those without depression (group sizes for this calculation are given above). This is equivalent to an odds ratio of 1.32.

For the analysis applying depression as a continuous measure, the minimum detectable difference in symptom load of depression between individuals with and without the metabolic syndrome was 0.056 of a standard deviation. This power calculation assumed power = 0.80, α = 0.05 and total = 9571.

Data analysis

We compared characteristics of participants who had the metabolic syndrome or not. Partial correlation coefficients were used to examine the associations of HADS-defined anxiety and depression as continuous measures with components of the metabolic syndrome adjusted for age and gender. Binary logistic regression analyses adjusted for age (as continuous measure) and gender were used to assess the association of anxiety and depression (case level as defined above, and as continuous measures) with the metabolic syndrome and its individual components. In forced entry multivariate models, we further adjusted separately for smoking, pulse rate, level of education and level of physical activity (reported in text only) and finally for all variables. Secondly, age-adjusted analyses were performed stratified by gender (without gender in the model) and gender-adjusted analyses by the age groups 20–39, 40–59 and 60–89 years respectively. We tested for effect modification of age groups (without age as continuous measure in the model) and gender by incorporating interaction terms in the logistic regression models (measures of anxiety or depression, respectively, by age or gender respectively) for risk of metabolic syndrome. As we found no anxiety by gender or depression by gender interaction (all > 0.162), all results are given jointly for men and women.

In supplementary analyses, we repeated the fully adjusted logistic models by using the 2005 revised criteria for metabolic syndrome by the National Cholesterol Education Program, Adult Treatment Panel III (2005 NCEP) (24), which included higher cut-offs for waist circumference (>102 cm in men, >88 cm in women) than the IDF criteria. Again, we tested for effect modification by age.

A value of < 0.05 (two-sided) was considered statistically significant. All analyses were performed with spss software (version 14.0; SPSS Inc., Chicago, IL, USA).

Results

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

Table 1 shows the characteristics of participants having metabolic syndrome or not. Those with the syndrome were considerably older than those without the syndrome, reflecting the strong increase in prevalence of the syndrome with age (21).

Table 1.   Characteristics of participants in relation to the metabolic syndrome
VariableNo metabolic syndromeMetabolic syndrome
  1. Values are means ± SD unless otherwise stated.

  2. HADS, Hospital Anxiety and Depression Scale; HDL, high-density lipoprotein.

  3. *HADS-A ≥8, D <8.

  4. †HADS-D ≥8, A <8.

  5. ‡HADS-A & D ≥8.

  6. §Self-reported angina pectoris, myocardial infarction and/or stroke.

Number (%) 6855 (71.6)2716 (28.4)
Age (years)44.4 ± 15.856.2 ± 16.3
Female sex (%)49.549.9
Education (%)
 Low32.252.7
 Medium47.435.1
 High20.412.2
Mean HADS score
 Anxiety4.4 ± 3.54.1 ± 3.5
 Depression3.3 ± 3.03.9 ± 3.2
HADS case level (%)
 Anxiety*10.88.3
 Depression†4.16.9
 Comorbid anxiety and depression‡6.07.0
Waist circumference (cm)
 Women77.3 ± 9.792.9 ± 9.8
 Men88.2 ± 7.9101.7 ± 7.1
HDL cholesterol (mmol/l)
 Women1.59 ± 0.371.30 ± 0.36
 Men1.32 ± 0.331.10 ± 0.31
Systolic blood pressure (mmHg)132.8 ± 19.6150.3 ± 22.0
Diastolic blood pressure (mmHg)78.9 ± 11.587.6 ± 12.4
Triglycerides (mmol/l)1.14 ± 0.762.16 ± 1.30
Glucose (mmol/l)5.26 ± 0.755.91 ± 1.58
Type 2 diabetes (%) 4.5
Antihypertensive medication (%)5.423.5
Antidepressants (%)2.74.1
Pulse rate (per minute)70.0 ± 12.074.0 ± 13.9
Cardiovascular disease§4.914.5
Cigarette smoking, currently (%)34.927.1
Cigarette smoking, previously (%)21.430.9
Physical activity (%)
 None6.911.2
 <1 h per week14.416.0
 1–2 h per week34.732.1
 >2 h per week37.827.6
 data not available6.213.7

We first examined the relations of HADS-defined anxiety and depression as continuous measures with components of the metabolic syndrome, adjusted for age and gender (Table 2). The sizes of all correlation coefficients were small. Both anxiety and depression positively correlated with triglycerides and negatively correlated with systolic blood pressure. Depression was found positively or negatively correlated with some of the other components or sub-components of metabolic syndrome. There were no other statistically significant correlations for anxiety.

Table 2.   Partial correlations* of anxiety and depression as continuous measures with components of the metabolic syndrome
 HADS depressionHADS anxiety
CorrelationP-valueCorrelationP-value
  1. HADS, Hospital Anxiety and Depression Scale; HDL, high-density lipoprotein.

  2. *Adjusted for age and gender.

Waist0.043<0.0010.0110.262
Triglycerides0.050<0.0010.0230.024
HDL cholesterol−0.036<0.0010.0080.451
Glucose0.0110.2760.0080.447
Systolic blood pressure−0.037<0.001−0.044<0.001
Diastolic blood pressure0.0060.5290.0190.058
Antihypertensive drugs0.038<0.0010.0170.099
Type 2 diabetes0.0250.0140.0190.062

We applied five logistic regression models examining the associations of anxiety and depression to the metabolic syndrome. A positive association was found only in one of these models adjusted for age and gender, and this association was entirely confounded, mainly by physical activity and educational level. Anxiety was not found associated with the syndrome in any model (Table 3). Further, no significances were found in models applying case levels for depression and comorbid anxiety depression in association with the metabolic syndrome. The only positive association with the syndrome was for depression as a continuous measure (OR 1.07 per standard deviation increase in symptom load, 95% CI 1.02–1.12, = 0.007, Table 3) adjusted for age and gender only. In further separate adjustments, the association was not confounded by smoking (OR 1.07, CI 1.03–1.13, = 0.003), but weakened after adjustment for pulse rate (OR 1.06, CI 1.01–1.11, = 0.021) or educational level (OR 1.05, CI 1.01–1.11, = 0.027). The association was not significant after adjustment for physical activity (OR 1.04, CI 1.00–1.09, = 0.069) or in the fully adjusted model (OR 1.04, CI 0.99–1.09, = 0.123).

Table 3.   Association of anxiety and depression with metabolic syndrome and its components
 Metabolic syndromeCentral obesityHigh blood pressure or medication useHigh triglyceridesLow HDL cholesterolHigh plasma glucose or type 2 diabetes
  1. Significant associations (< 0.05) shown in bold. OR, odds ratio; CI, confidence interval; HADS, Hospital Anxiety and Depression Scale; SD, standard deviation.

  2. *HADS-A ≥8, D < 8.

  3. †HADS-D ≥8, A < 8.

  4. ‡HADS-A & D ≥ 8.

Model adjusted for age and gender
 Anxiety
  Case-level* (vs. not case-level anxiety)0.89 (0.76–1.05)0.95 (0.82–1.09)0.72 (0.62–0.84)1.02 (0.87–1.20)0.94 (0.80–1.10)0.98 (0.84–1.15)
  Continuous (per SD increase)0.98 (0.94–1.03)0.97 (0.93–1.01)0.90 (0.86–0.94)1.05 (1.01–1.10)0.99 (0.95–1.04)1.01 (0.96–1.06)
 Depression
  Case-level† (vs. not case-level depression)1.04 (0.85–1.27)1.11 (0.91–1.35)0.75 (0.59–0.96)1.02 (0.83–1.26)1.18 (0.96–1.45)1.07 (0.87–1.31)
  Continuous (per SD increase)1.07 (1.02–1.12)1.07 (1.02–1.11)0.91 (0.87–0.96)1.08 (1.03–1.13)1.05 (1.01–1.10)1.02 (0.98–1.07)
 Comorbid anxiety and depression
  Case-level‡ (vs. not case-level anxiety/depression)1.10 (0.92–1.33)1.02 (0.85–1.21)0.77 (0.64–0.93)1.20 (1.00–1.45)1.22 (1.02–1.46)1.07 (0.89–1.29)
Model adjusted for age, gender, education, physical activity, smoking and pulse rate
 Anxiety
  Case-level* (vs. not case-level anxiety)0.87 (0.73–1.02)0.94 (0.81–1.08)0.70 (0.60–0.82)0.98 (0.84–1.16)0.92 (0.78–1.07)0.96 (0.81–1.13)
  Continuous (per SD increase)0.97 (0.92–1.02)0.96 (0.92–1.00)0.89 (0.85–0.93)1.03 (0.98–1.08)0.98 (0.93–1.02)1.00 (0.95–1.05)
 Depression
  Case-level† (vs. not case-level depression)0.95 (0.77–1.18)1.04 (0.85–1.28)0.70 (0.55–0.90)0.95 (0.77–1.18)1.14 (0.92–1.40)1.00 (0.81–1.23)
  Continuous (per SD increase)1.04 (0.99–1.09)1.05 (1.00–1.09)0.90 (0.85–0.94)1.04 (1.00–1.09)1.03 (0.98–1.08)1.01 (0.96–1.05)
 Comorbid anxiety and depression
  Case-level‡ (vs. not case-level anxiety/depression)1.03 (0.85–1.26)0.98 (0.82–1.17)0.74 (0.61–0.90)1.10 (0.91–1.33)1.14 (0.95–1.37)1.02 (0.84–1.23)

Among the individual components of metabolic syndrome, high blood pressure was found inversely associated with anxiety and depression across all analyses. Depression and comorbid anxiety and depression were weakly associated with central obesity and with high triglycerides.

No significant gender × anxiety or gender × depression interaction was observed (all  0.162). Similarly, we found no age × depression or age × comorbid anxiety/depression interactions (Table 4). However, the age by anxiety interaction term was statistically significant when anxiety was coded categorically but not when included as a continuous variable; in persons aged 60–89 years, we found anxiety to be inversely associated with the syndrome, but no associations were found in the two youngest age groups (Table 4).

Table 4.   Age-stratified association of anxiety and depression with metabolic syndrome
 20–39 years (= 3417)40–59 years (= 3671)60–89 years (= 2483)P-value for age interaction*
OR (95% CI)OR (95% CI)OR (95% CI)
  1. Significant associations (< 0.05) shown in bold. OR, odds ratio; CI, confidence interval; HADS, Hospital Anxiety and Depression Scale; SD, standard deviation.

  2. OR adjusted for age (continuous measure), gender, education, physical activity, pulse rate and smoking.

  3. *P-value for interaction between measures of anxiety or depression, respectively, and age group (as change in step chi-squared value).

  4. †HADS-A ≥8, D < 8.

  5. ‡HADS-D ≥8, A < 8.

  6. §HADS-A & D ≥ 8.

Anxiety
 Case level†0.73 (0.52–1.03)1.09 (0.86–1.39)0.59 (0.42–0.83)0.032
 Continuous (per SD increase)0.94 (0.85–1.05)1.01 (0.93–1.08)0.90 (0.83–0.98)0.336
Depression
 Case level‡1.05 (0.53–2.07)1.16 (0.82–1.65)0.91 (0.69–1.21)0.280
 Continuous (per SD increase)1.02 (0.91–1.14)1.08 (1.00–1.16)1.01 (0.93–1.09)0.268
Comorbid anxiety and depression
 Case level§0.79 (0.49–1.29)1.18 (0.89–1.56)0.93 (0.67–1.28)0.356

When we repeated the fully adjusted logistic analyses by using the 2005 NCEP criteria for metabolic syndrome, we obtained similar results as by using the IDF criteria (data not shown). Finally, exclusion of the 729 participants that reported cardiovascular disease or the 295 individuals that reported use of antidepressants did not change the associations of anxiety and depression with IDF-defined metabolic syndrome (data not shown).

Discussion

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

In this largest to date population study, we found no association of anxiety and depression to the metabolic syndrome beyond confounding, mainly by physical activity and educational level. This result is consistent with one of the largest studies (16), but in contrast to the smaller studies we have identified (13–15, 17, 19). If effect size (or proportion of significant findings) is negatively associated with sample size, there might be a risk of publication bias in previous literature.

The inconclusive findings in previous population-based studies have included a weak association between depressive symptoms and the metabolic syndrome in elderly persons (13, 17), in elderly white but not in black people (18), in younger women but not in men (8), in middle-aged men but not in women (14), in middle-aged women (15, 19) and no association in younger persons (16). To our knowledge, the present study is the first on this topic analysing data of both men and women across the age range 20–89 years. This enabled us to examine interactions with age and gender, which is warranted from previous reports of gender-specific associations (8, 14, 18) and from varying results across studies of different age groups (13, 16). Strongly powered studies are well suited for analysis of statistical interaction. Despite this, we were unable to detect any gender interaction in the associations of interest, neither in the total sample nor in the age strata.

As to anxiety, our finding of no association with the metabolic syndrome is generally consistent with the few studies that have investigated this relationship (15, 16, 19). The only exception is a recent study that found a weak association in elderly men, but not in women (18). We found an inverse association in the age group 60–89 years, and a significant age × anxiety interaction. We had no a priori hypothesis for this, and we cannot exclude this to be a type 1 error.

Most previous studies (8, 14–18) defined metabolic syndrome according to the original (2001) NCEP criteria (25), but assessed depression and/or anxiety by various instruments. The IDF and 2005 NCEP criteria used in the present study give higher prevalence of metabolic syndrome than the 2001 NCEP criteria because of lower thresholds for glucose (IDF and 2005 NCEP) and central obesity (IDF). A recent study (11) of predominantly middle-aged outpatients at risk of cardiovascular disease found that 2005 NCEP-defined metabolic syndrome was associated with HADS-defined depression but not with anxiety, which is similar to our population-based findings. To our knowledge, only one previous study (19) on this topic has used the IDF criteria for metabolic syndrome.

Individual components of the metabolic syndrome

According to the IDF criteria, central obesity is an obligatory component of the metabolic syndrome. Our finding of a positive association between waist circumference and depression is consistent with some (11, 16, 17) but not all (8, 13, 14) of the previous studies on this association. Other studies have reported similar positive associations: one found a cross-sectional association between obesity (defined as body mass index ≥30) and major depression (26), whereas the other found a larger accumulation of visceral fat mass over time in elderly patients with major depression than in controls, despite similar weight gain (27). A new study indicates that central obesity, also when measured as waist-to-hip ratio, is associated with depression (28). The finding that blood pressure was inversely associated with anxiety and depression has recently been reported in detail in a cross-sectional study (29) and in a longitudinal (30) study based on the HUNT data. We have recently (30) discussed that neuropeptide Y, which has been linked both to anxiety and depression and to decrease in blood pressure, seems involved in the mechanism behind this inverse association. A similar finding was reported in another population (16), whereas other studies found no association (13, 14, 17) or a positive association between depression and blood pressure in women but not in men (8). Furthermore, we found positive associations of anxiety and depression with elevated triglycerides and low HDL cholesterol respectively. Although statistically significant, these associations were very small. Our results may differ slightly from that of other studies (8, 13, 14, 16) that found no significant associations, because of our large sample size. The lack of association between glucose and anxiety/depression in our study is consistent with previous reports (13, 14, 16, 17). Similarly, our finding of an association between depression and type 2 diabetes is consistent with a review indicating that depression is a risk factor for the onset of type 2 diabetes (1), but this review acknowledges several weaknesses of the nine studies included. Furthermore, a large cross-sectional study found that depression was associated with type 2 diabetes only in the presence of other chronic somatic diseases (31).

Explanatory models

Previous reports of positive associations between depression and the metabolic syndrome have stimulated speculations as to underlying mechanisms in terms of mediating factors. A proposed model includes behavioural and physiological mediating pathways as well as moderating effects of demographic characteristics such as sex and socio-economic status (10). Confounding is obviously also a major issue. Studies reporting a positive association between depression and metabolic syndrome have not, however, consistently found a confounding or mediating effect of health behaviours such as unhealthy diet, smoking and a sedentary lifestyle (8, 15, 18, 19). Of possible physiological mediating pathways, dysregulation of the autonomic nervous system and hyperactivity of the hypothalamic-pituitary-adrenal axis are proposed as plausible candidates (32, 33). Of interest is also that the metabolic syndrome has been associated with reduced central serotonergic function, a known marker of depression (34). Both depression (35) and the metabolic syndrome (21) increase strongly in prevalence with age. Age is an obvious confounding factor, which must be included in all studies of this association. Physical activity and educational level accounted for most of the association between depression and metabolic syndrome in our study. To the extent these factors are established before exposure and outcome, they are to be regarded as confounders rather than mediators. As we found no association beyond that from confounding by these factors, our data indicate no further need for explanatory models.

Strengths and limitations

The strength of the present study is that the HUNT 2 study aimed to include the whole adult population (ethnically homogenous, <3% non-Caucasian) of Nord-Trøndelag County, which is considered fairly representative of Norway. The county is mostly rural; the largest of six small towns has a population of 21 000. The main objectives of the HUNT study concerned large public health issues like diabetes, cardiovascular disease, obstructive lung disease, osteoporosis and mental health (20). The participation rate was age dependent, with the highest participation rate (85.6%) in the age group 60–69 years. A study of non-participants (20) showed that the main reasons for not attending were of practical nature in young people and poor health in elderly people. There is a potential selection bias from non-participation, although the participation rate in the HUNT study was similar to or higher than in comparable population studies (20). In addition, mortality rates among non-participants were higher than among participants (4), indicating that our sample represented a healthier sub-population.

The study has several limitations. First, it was impractical to request the whole population in the county to be fasting as the examinations were spread out during daytime. Consequently, we excluded participants who reported less than 4 h since their last meal. However, the participants who were fasting ≥4 h before blood sampling were representative of the entire HUNT 2 study population (21), and our adjustments of the glucose and triglycerides levels based on time since the last meal should diminish a potential bias in diagnosing the metabolic syndrome.

Second, a single cross-sectional measure of symptoms of anxiety and depression might be less valid than a clinical diagnosis based on longitudinal information. However, chronic cases are more prone to be identified in a cross-sectional health survey than cases with shorter episodes (36). Thus, we believe a ‘snap shot’ will give quite valid data on mental illnesses (that tends to be of a chronic nature). Problems related to ‘snap shots’ are reducing the reliability of the measure, and thus also the observed effect size.

Third, there is a possibility of confounding by not adjusting for antipsychotic medication, which may have metabolic side effects (37). Sales data, however, indicate that antipsychotics were prescribed to only 0.72% of the population at the time of the HUNT 2 study (38). In our sample, this is equivalent to 69 individuals being on antipsychotics, included seven individuals being on atypical antipsychotics. This was unlikely to have led to any significant bias.

Forth, we could not distinguish unipolar from bipolar disorder in this study. There are, to our knowledge, no population-based studies of metabolic syndrome in relation to mental disorder making this distinction, but that might be an interesting issue for the future. However, unipolar disorders were far more common than bipolar in other samples (39), and we have no reason to believe that the same did not apply to our sample.

In summary, we found no association of anxiety and depression to the metabolic syndrome beyond confounding. Indications of an inverse association between anxiety and the metabolic syndrome after the age of 60 years are in need of replication.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References

The HUNT study is a collaboration between the HUNT Research Centre at the Faculty of Medicine, Norwegian University of Science and Technology (NTNU), the Norwegian Institute of Public Health, and the Nord-Trøndelag County Council.

References

  1. Top of page
  2. Abstract
  3. Significant outcomes
  4. Limitations
  5. Introduction
  6. Material and methods
  7. Results
  8. Discussion
  9. Acknowledgements
  10. References
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