The authors have declared that no competing interests exist.
To examine whether having metabolic syndrome (MS) among seniors is associated with using anti-depression medication.
A total of 1366 (617 men and 749 women) individuals aged 60+ years from the NHANES 2007/08 survey who had no reported heart disease and/or cancers but had information on prescribed medications in previous month were included in this analysis. All subjects were categorized into three prescribed drug use status, ie, none (group 1); no anti-depressants (group 2); and with anti-depressants (group 3). MS was defined with the criteria of the ATP III.
Over 80% of individuals reported taking prescribed medications with 6% of men and 16% of women respectively having used anti-depressants. About 36% of men and 40% of women respectively were considered to have MS. Results from multiple logistic regression analyses indicated that in comparing to group 1, the odds ratios (95% CI) of MS was 2.73 (1.96, 3.82) for group2 and 2.25 (1.07, 4.69) for group 3, respectively. Both group 2 and 3 had a similar metabolic risk profile, in comparing to group 1, they had higher odds of having diabetes and high level of blood pressures.
Seniors with medications are more likely to be with MS, diabetes, and high level blood pressures. However, the observed the cardio-metabolic risk association seems similar between seniors using anti-depressant drugs and using other prescribed medications.
Available epidemiological evidence suggests that depression among the elderly is a significant public health problem both for its effect on quality of life and for its impact on expected lifespan
There is no doubt that medication treatment for seniors with serious depression is important and necessary. However, seniors are often with large degree of medical co-morbidity, especially vascular diseases, or with much unfavorable profile of inflammatory and metabolic dysregulations, which may contribute to the development of depression in late life
Our study was based on public domain data abstracted from the 2007-2008 National Health and Nutritional Examination Survey (NHANES). A detailed description of the database may be found online (http://wwwn.cdc.gov/nchs/nhanes/search hanes07_08.aspx). The original NHANES database included self-reports on 2145 persons aged 60 and older. Within this age bracket we excluded from analysis those who provided incomplete information on prescription drug use in the preceding month and those who reported a history of either CVD (i.e., yes with either coronary heart disease, congestive heart failure, heart attack, stroke, or angina) or cancer (i.e., any type cancer or malignancy), leaving a sample size of 1366 subjects (617 men and 749 women).
When survey participants responded ‘yes’ to the question on prescription medication use during the past month, further information would be gathered regarding each generic drug name and its duration of use. In 86% of cases a check of medication containers made verification of details possible. In this study, we defined three drug use categories: (1) no prescription drug use, (2) no anti-depressants among prescription drugs, and (3) anti-depressants among prescription drugs. We have further categorized two subgroups from Group 3 according to relative duration of drug exposure: (3a), duration of taking non-antidepressant drugs is longer than that of taking anti-depressant drugs, and (3b), duration of taking anti-depressant drugs alone or is longer than that of taking non-antidepressant drugs.
The 2007-2008 NHANES questionnaire included a depression screening tool, the Patient Health Questionnaire (PHQ-9) used to assess mood over the previous two weeks. The PHQ-9 has been extensively validated with performance characteristics reported in peer-reviewed publications
Classification criteria for metabolic syndrome (MS) and diabetes: Survey respondents were identified as having MS based on criteria adopted by theThird Adult Treatment Panel Report of the National Cholesterol Education Program (ATP III)
Covariates included in the analysis were age (years), sex (male vs. female), highest education (< high school: yes vs. no), marital status (married or living with a partner: yes vs. no), home income (<$25k: yes vs. no), cigarette smoking (never, ex-smoker, current smoker), alcohol consumption (drinks/day), and physical activity (10+ min vigorous recreation activity/day: yes vs. no).
All analyses were conducted using survey procedures in SAS 9.2 (SAS Institute Inc., Cary, NC, USA), taking into account the weighted and clustered sampling design of NHANES. Logistic regression models were used to estimate odds ratios for the association between anti-depressant drug use and the corresponding risk of MS. Our analysis was built around two primary regression constructs each modeled three ways by adjusting for different sets of covariates. In the first construct, we used those who reported having taken no prescribed medications during the past month as the reference group and created two indicators, one for those without antidepressants in their prescribed medications, and another for those with antidepressants in their prescribed medications. In the second construct we included for analysis only those who reported prescription drug use. Those without antidepressants in their prescribed drugs were the referent group. We then specified two indicator variables from among those on anti-depressants, i.e., one for the duration of taking non-anti-depressant medications is longer than that of taking anti-depressant medications for those in group 3a, and one for the duration of taking antidepressants alone or is longer than that of taking non-anti-depressant medications for those in group 3b. Duration of taking anti-depressant and non-anti-depressant drugs respectively was calculated as the longest period of use, in years, reported for any of the drugs in each category. To examine whether depression as detected by the PHQ-9 screening tool may be associated with a risk of MS, we specified a binary indicator to classify summary scores as either screen-positive for mild to severe depression (PHQ-9≥5) or screen-negative (PHQ-9 <5). Analysis was conducted only among persons in group 1 and group 2. The level for statistical significance was set up as a 2-tailed type I error of 0.05.
Overall, approximately 80% of males and 86% of females reported taking prescribed medications during the prior month, with 6% of males and 16% of females respectively having used anti-depressants. Among those who reported taking anti-depressant drugs, approximately 82% of them were on selective serotonin reuptake inhibitors (SSRIs). Characteristics of the sample selected for analysis are shown in
Men | Women | |
n=617 | n=749 | |
Age (yrs, mean (SE) | 68.2 (0.4) | 69.9 (0.3) |
Less than high school (%) | 24.9 | 26.7 |
Non-Hispanic White (%) | 77 | 77.9 |
Married or live with a partner (%) | 75.4 | 51.2 |
Home income <$25K (%) | 26.5 | 34.4 |
Cigarette smoking (%) | ||
None | 65.6 | 61.6 |
Ex-smoker | 16 | 21.6 |
Current smoker | 18.4 | 16.8 |
10+ min vigorous re-creativities (%) | 26.2 | 26 |
Medications use (%) | ||
None | 20.4 | 13.5 |
Group1 | 73.8 | 70.3 |
Group2 | 5.7 | 16.2 |
Metabolic syndrome (%) | 36 | 40.5 |
Group1 –without antidepressants in the prescribed drugs
Group2 – with antidepressants in the prescribed drugs
p < 0.05 between genders
None | Without antidepressants in prescribed drugs | With antidepressants in prescribed drugs | ||||
|
||||||
n (unweighted) | 140 | 439 | 38 | |||
Demographic Characteristics | ||||||
Age (yrs, mean (SE)) | 66.2 (0.7) | 68.6 (0.5) | 70.5 (0.7) |
|||
Less than high school (%) | 19.7 | 26.3 | 25.6 | |||
Non-Hispanic White (%) | 69 | 78.9 | 81.1 | |||
Married or live with a partner (%) | 62.4 | 78.9 | 75.5 | |||
Home income <$25K (%) | 30.3 | 25.1 | 30.4 | |||
Cigarette smoking (%) | ||||||
None | 68.3 | 64.7 | 67.1 | |||
Ex-smoker | 13.8 | 16.8 | 13.5 | |||
Current smoker | 17.9 | 18.5 | 19.5 | |||
10+ min vigorous re-creativities (%) | 29.2 | 25.3 | 27.8 | |||
Metabolic syndrome components (%) | ||||||
Fasting glucose > 100 mg/dL | 74.5 | 77.6 | 67.2 | |||
HDL cholesterol <40 mg/dL | 16.8 | 29 | 17.5 | |||
SBP>130 or DBP >85 mmHg or taking medicine | 52.8 | 78.9 |
71.7 |
|||
Triglyceride >150 mg/dL | 18.2 | 41.9 |
52.7 |
|||
Waist circumference>102 cm | 41.2 | 61.4 | 44.3 | |||
Metabolic syndrome (%)# | 10.8 | 43.0 |
35.2 |
|||
Diabetes (%) | 7.1 | 28.3 |
26.6 |
|||
Years of taking mediations (yrs, mean (SE)) | n/a | 8.2 (0.5) | 10.5 (1.9) | |||
|
||||||
n (unweighted) | 134 | 526 | 89 | |||
Demographic Characteristics | ||||||
Age (yrs, mean (SE)) | 67.8 (0.8) | 70.8 (0.4) |
67.6(0.7) | |||
Less than high school (%) | 22.1 | 28 | 24.6 | |||
Non-Hispanic White (%) | 68.4 | 76.8 | 90.3 |
|||
Married or live with a partner (%) | 51.1 | 49.3 | 61.6 | |||
Home income <$25K (%) | 26.3 | 36.6 | 31.2 | |||
Cigarette smoking (%) | ||||||
None | 65.4 | 60.2 | 64.6 | |||
Ex-smoker | 18.7 | 23.7 | 15 | |||
Current smoker | 15.9 | 16.1 | 20.4 | |||
10+ min vigorous re-creativities (%) | 27.2 | 25.3 | 27.9 | |||
Metabolic syndrome components (%) | ||||||
Fasting glucose > 100 mg/dL | 58.7 | 59.4 | 67.9 | |||
HDL cholesterol <50 mg/dL | 34.9 | 27.6 | 27.7 | |||
SBP>130 or DBP >85 mmHg or taking medicine | 66.8 | 81.7 |
76.9 | |||
Triglyceride >150 mg/dL | 35.7 | 30.6 | 35.9 | |||
Waist circumference>88 cm | 67.8 | 72.3 | 78.3 | |||
Metabolic syndrome (%)# | 33.8 | 41.7 | 40.7 | |||
Diabetes (%) | 6.3 | 19.4 |
12.4 | |||
Years of taking mediations (yrs, mean SE) | n/a | 12.0 (0.6) | 12.2 (1.6) |
three or more metabolic syndrome components
p <0.05 compared to non-drug users
p <0.05 compared to without anti-depressants in prescription.
None | Without antidepressant in prescribed drugs | With antidepressants in prescribed drugs | ||||
OR | 95% CI | OR | 95% CI | |||
Model 1 | n=1366 | 1 | 2.73 | 1.96 -3.82 | 2.25 | 1.07 - 4.69 |
Model 2 | n=1365 | 1 | 2.65 | 1.88 -3.74 | 2.16 | 1.07 - 4.37 |
Model 3 | n=1237 | 1 | 2.89 | 1.80 - 4.63 | 2.65 | 1.18 - 5.95 |
Model 1 adjusting for age, gender and ethnicity
Model 2 further adjusting for family income, education, and marriage status
Model 3 further adjusting for smoking and physical activity
Drug user |
Drug user |
Drug user |
||||
OR | 95% CI | OR | 95% CI | |||
Model 1 | n=1020 | 1 | 0.79 | 0.39 -1.61 | 1.05 | 0.40 -2.72 |
Model 2 | n=1020 | 1 | 0.78 | 0.40 - 1.54 | 1.05 | .041 -2.70 |
Model 3 | n=934 | 1 | 0.84 | 0.46 -1.55 | 1.36 | 0.47 -3.91 |
without antidepressants in prescribed drugs
duration of taking non-antidepressant drugs is longer than that of taking antidepressants
duration of taking antidepressants alone or is longer than that of taking non-antidepressant drugs.
Model 1 adjusting for age, gender, ethnicity and yrs of taking medications
Model 2 further adjusting for family income, education, and marriage status
Model 3 further adjusting for smoking and physical activity.
To generate the results in
The prevalence of depression detected by the PHQ-9 screening tool (PHQ-9≥5) was 8.6% for males and 11.4% for females among group 1 (non- users) and 14.1% for males 16.7% for females among group 2 (non-anti-depressant drugs in the prescribed medications). Compared to a group who screened negative for depression (PHQ-9<5), the OR (95%CI) of MS was 1.16 (0.90, 1.49) for PHQ-9 ≥5 (mild or more severe depression) and 1.06 (0.44, 2.56) for PHQ-9≥10 (moderate or more severe depression).
Using the NHANES 2007-08 data we examined the cardio-metabolic risk association with anti-depressants drug use among US seniors and found that in comparing to no drug users, seniors with prescribed anti-depressants were in more than two times higher odds of having MS. The high odds of MS association with using anti-depressant drugs have been observed by several different studies
Although results from some other studies suggested that the increased cardio-metabolic risk may be associated with the severity of mental health status
However, results from some studies indicate an alternative direction that depression, in particular seniors’ depression, may be contributed by their cardio-metabolic abnormality. The term "vascular depression" has been proposed to describe a subset of depressive disorders that occurs in old age as a consequence of cerebrovascular disease
Several limitations should be aware when interpreting the results from this study. First, like any cross-sectional study, the observed relationship cannot be explained as a causal relationship. Second, the depression measured with PHQ-9 screening tool score among those without taking anti-depressant drugs may not be so sensitive. However, the questionnaire has been validated and applied in many larger population studies; using it as a screening tool to identify the possible depression cases is still useful. Third, the prescribed medications and their duration were self-reported. Although the majority of these medications were verified with the medication containers it might still have some misclassification. The durations particularly for those seniors who took multiple medications might be less accurate. Furthermore, it was difficulty to examine the truly impact of antidepressants on MS due to the small size in that group, which was mixed with people who took the medications for depression at the first place and people who took it after other health problems. Nevertheless, the strengths of the study include the exclusion of those with serious disease history, such as CVD and cancer, the standardized measurements of MS components, detail information on demographic and potential confounding variables, and the data quality since it was from a well-organized and national representative survey.
In conclusion, seniors with prescribed medications, regardless whether taking anti-depressants or not, are more likely to have MS, diabetes, and high level blood pressure. However, the observed the cardio-metabolic risk association seems similar between seniors taking prescribed medications either with or without antidepressants. Thus, more research may be needed to explore the relationship between depression and cardio-metabolic risk among the elderly.