Depression and Type 2 Diabetes Over the Lifespan
OBJECTIVE— It has been argued that the relationship between depression and diabetes is
bi-directional, but this hypothesis has not been explicitly tested. This systematic review examines
the bi-directional prospective relationships between depression and type 2 diabetes.
RESEARCH DESIGN AND METHODS— A search was conducted using Medline for
publications from 1950 through 2007. Reviewers assessed the eligibility of each report by
exposure/outcome measurement and study design. Only comparative prospective studies of
depression and type 2 diabetes that excluded prevalent cases of depression (for diabetes predicting
depression) or diabetes (for depression predicting diabetes) were included. Two sets of
pooled risk estimates were calculated using random effects: depression predicting type 2 diabetes
and type 2 diabetes predicting depression.
RESULTS— Of 42 full-text publications reviewed, 13 met eligibility for depression predicting
onset of diabetes, representing 6,916 incident cases. Seven met criteria for diabetes predicting
onset of depression, representing 6,414 incident cases. The pooled relative risk (RR) for
incident depression associated with baseline diabetes was 1.15 (95% CI 1.02–1.30). The RR for
incident diabetes associated with baseline depression was 1.60 (1.37–1.88).
CONCLUSIONS— Depression is associated with a 60% increased risk of type 2 diabetes.
Type 2 diabetes is associated with only modest increased risk of depression. Future research
should focus on identifying mechanisms linking these conditions.
Depression and diabetes are highly prevalent in the U.S. Over 6.5% of the U.S. adult population has been diagnosed with diabetes (1), and the incidence of type 2 diabetes is increasing, in part due to the national increase in obesity. Approximately 16% of U.S. adults will suffer major depressive disorder at some point in their lives, and this proportion is greater when other forms of depressive disorder, such as dysthymia and
minor depression, are included (2). Thus, the hypothesis that depression and diabetes are causally related deserves attention from researchers and policy-makers alike. Depression is associated with poor health behaviors (i.e., smoking, physical inactivity, caloric intake) that increase
risk of type 2 diabetes (3). Depression is also related to central obesity and potentially
to impaired glucose tolerance (4). Depression is associated with physiological abnormalities, including activation of the hypothalamic-pituitary-adrenal axis, sympathoadrenal system, and proinflammatory
cytokines, which can induce insulin resistance and contribute to diabetes risk (5). Diabetes may increase
risk of depression because of the sense of threat and loss associated with receiving this diagnosis and the substantial lifestyle changes necessary to avoid developing debilitating complications. Also, studies
suggest that late-life depression is associated with a history of vascular disease, including
diabetes (6). In sum, evidence suggests that the exposure/outcome relationship between these conditions is bidirectional and may change over the life course.
Previous reviews have explored the relationship between depression and diabetes using retrospective (7) and prospective(8) studies, but none have assessed this relationship from a lifespan perspective by simultaneously examining its bidirectionality. Retrospective studies often use lifetime prevalence measures and thus they do not inform questions of temporality. The search criteria used in previous
reviews have been relatively conservative
and may have missed studies in which
depression or diabetes was not the primary
exposure of interest. While both depression
and diabetes are more common
among certain demographic groups (i.e.,
women and African Americans, respectively),
it is unclear whether this relationship
varies across such groups. We
therefore undertook a new review to synthesize
the current evidence of the prospective
relationships between
RESEARCH DESIGN AND
METHODS
Search strategy
We conducted literature searches using
MEDLINE with the three limits “publication
date from 1 January 1950 to 31 December
2007,” “English language,” and
“human subjects” and combinations of
the medical subject headings “Diabetes
Mellitus” or “Diabetes Mellitus, Type 2,”
“Depression” or “Depressive Disorder,”
“Risk Factors,” and “Prospective Studies”
or “Longitudinal Studies.” The reference
lists of previous meta-analyses and selected
articles were screened.
Selection criteria
To be included, a report had to 1) have a
prospective design, 2) include cases of
probable type 2 diabetes (i.e., studies that
examined only type 1 diabetes or diabetes
before age 30 years were excluded), 3)
provide enough data to generate a relative
risk estimate, and 4) exclude prevalent
cases of either depression (for diabetes
predicting depression onset) or diabetes
(for depression predicting diabetes onset).
In the event of multiple publications,
only the most recent manuscript for a particular
study population was included.
Data extraction
Two reviewers used a custom data abstraction
form to evaluate and summarize
selected articles. Abstracted information
included authors, year, location, source
of participants, sample composition, assessment
of diabetes/depression, and
matching and/or statistical adjustment for
potential confounders. If multiple risk estimates
(with error measurements) were
presented in a given manuscript (e.g.,
nested multivariable models), the estimate
that most closely adjusted for only
demographic characteristics (e.g., age,
sex, race, socioeconomic indicators, and
marital status/household composition)
was selected. We chose this approach because
some studies adjusted for prominent
effect modifiers (i.e., family history,
health behaviors, adiposity) while others
did not, and thus the interpretation of the pooled value using the most-adjusted estimates
from each study is misleading, for
it is neither an estimate of the direct nor
the total effect (9).
Statistical analysis
The estimate from each study was used to
generate a pooled relative risk using random
effects. Two separate analyses were
conducted: depression predicting type 2
diabetes and type 2 diabetes predicting depression.
Random-effects modeling explicitly
accounts for unmeasured variability
across the values using the DerSimonian
and Laird method, resulting in a pooled
estimate with a wider confidence interval
relative to the fixed-effects models (10). We
evaluated heterogeneity in the estimates using
the Cochrane Q statistic. If a study only
presented stratified risk estimates (i.e., by
sex), these estimates were combined using
random effects and then that pooled estimate
was used for the meta-analysis. For
studies that presented graded relationships
(e.g., low, medium, high depressive symptoms),
only the estimate for the highest category
was selected. Forest plots of the
estimates and 95% CIs, with the weight of
each point estimate indicated by the relative
size of the marker, were used to visualize the
range of effects.Weused subgroup analyses
to explore potential variability in the relationships
by demographic characteristics
(e.g., age, sex, race) and conducted sensitivity
analyses to assess the robustness of our
results (11). We used Egger’s test and funnel
plots to assess publication bias. If such
bias was evident, we used the trim and fill
approach (12) to generate a pooled estimate
that accounted for the unpublished negative
findings. All analyses were conducted
using Stata v9.0 (StataCorp, College Station,
TX), and statistical significance was set
a priori at P _ 0.05.
RESULTS
Study selection
A total of 21,190 original-research articles
were retrieved by the searches (the titles
of which were examined by two independent
reviewers). Manuscripts that did not
reference either depression or diabetes in
the title (or specifically referred to type 1
diabetes) were excluded at this phase, and
titles that referred to “development,”
“risk/incidence,” or “comorbidity/cooccurrence”
and similar terms were selected
for additional review (n _ 1,168).
From this set, 42 articles were retrieved
for full abstraction. Of these, 24 articles
did not meet selection criteria and were
excluded (see Supplemental Fig. 1 and
supplemental search results in the online
appendix available at http://dx.doi.org/
10.2337/dc08-0985). Ten studies were
excluded for failing to remove prevalent
cases of depression/diabetes at baseline.
One was excluded for using a measure of
“burnout” rather than depression. Ten reports
were excluded because they did not
provide enough data to generate a risk
estimate. Two studies used the same sample
(13,14), and only the most recent
publication (Arroyo et al. [14]) was retained.
Two studies used samples selected
for the presence of specific diabetes or depression
risk factors (antipsychotic medications
[15] and coronary heart failure
[16]), respectively). We determined that
these samples would introduce bias and
heterogeneity into the pooled estimate
and therefore excluded them. However,
we conducted sensitivity analyses to assess
the effect of this decision. The remaining
18 articles (two of which
examined both depression predicting diabetes
and diabetes predicting depression)
were retained for analysis and are
described in Tables 1 and 2.
Depression predicting type 2
diabetes
The results from 13 studies of depression
predicting incident diabetes, representing
6,916 cases of diabetes, are consistent
(Fig. 1A). Even in those instances in
which the association was not statistically
significant, the trend for depression to increase
risk of subsequent diabetes was
present. Assessment of heterogeneity indicated
that the random effects model was
appropriate (Cochrane Q statistic [13
d.f.]: 37.63, P _ 0.001), generating a
pooled relative risk (RR) of 1.60 (95% CI
1.37–1.88).
Measurement