Available via license: CC BY 4.0
Content may be subject to copyright.
Gender differences in the relationship between
depressive symptoms and diabetes associated
with cognitive-affective symptoms
Shakila Meshkat
†
, Vanessa K. Tassone
†
, Sarah Dunnett
†
, Hilary Pang, Michelle Wu, Josheil K. Boparai,
Hyejung Jung, Wendy Lou and Venkat Bhat
Background
Despite the frequent co-occurrence of depression and diabetes,
gender differences in their relationship remain unclear.
Aims
This exploratory study examined if gender modifies the associ-
ation between depressive symptoms, prediabetes and diabetes
with cognitive-affective and somatic depressive symptom
clusters.
Method
Cross-sectional analyses were conducted on 29 619 participants
from the 2007–2018 National Health and Nutrition Examination
Survey. Depressive symptoms were measured by the nine-item
Patient Health Questionnaire. Multiple logistic regression was
used to analyse the relationship between depressive symptoms
and diabetes. Multiple linear regression was used to analyse the
relationship between depressive symptom clusters and
diabetes.
Results
The odds of having depressive symptoms were greater in those
with diabetes compared to those without. Similarly, total symp-
tom cluster scores were higher in participants with diabetes.
Statistically significant diabetes–gender interactions were found
in the cognitive-affective symptom cluster model. Mean cogni-
tive-affective symptom scores were higher for females with
diabetes (coefficient =0.23, CI: 0.10, 0.36, P= 0.001) than males
with diabetes (coefficient = −0.05, CI: −0.16, 0.07, P= 0.434)
when compared to the non-diabetic groups.
Conclusions
Diabetes was associated with higher cognitive-affective symp-
tom scores in females than in males. Future studies should
examine gender differences in causal pathways and how dia-
betic states interact with gender and influence symptom profiles.
Keywords
Depressive disorder; diabetes mellitus; prediabetic state; gender
differences; NHANES.
Copyright and usage
© Venkat Bhat, St. Michael’s Hospital-Unity Health Toronto, 2024.
Published by Cambridge University Press on behalf of Royal
College of Psychiatrists. This is an Open Access article, distrib-
uted under the terms of the Creative Commons Attribution
licence (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted re-use, distribution and reproduction, pro-
vided the original article is properly cited.
Depression and diabetes are significant contributors to disability glo-
bally
1
and are frequently comorbid, with 28% of individuals with dia-
betes also suffering from a depressive disorder.
2
When both
conditions are present, patients report significantly greater functional
impairment than those with either depression or diabetes alone.
3
The
presence of depression in diabetes can reduce treatment adherence
and increase complications and risk of death.
4
Individuals with pre-
diabetes can reverse their condition through lifestyle modifications.
5
However, when co-occurring with depression, these changes may be
more difficult to implement,
6
and the two conditions synergistically
interact to increase the risk of diabetes.
7
There are significant gender differences in the development,
psychological impact and management of diabetes.
8,9
As such, it
is essential to examine the relationship between diabetes and
depressive symptoms with this in mind. While the prevalence of
depression is higher among females with diabetes as compared to
males,
10
meta-analyses that stratify results by gender and include
predominantly longitudinal analyses have found greater associa-
tions between depression and diabetes in males than in
females.
10,11
Other research has shown a relationship between
depression and diabetes among females but not males,
12,13
or sig-
nificantly greater odds of depression for females versus males.
14
Moreover, there is evidence for relationships between depression
and both insulin resistance
15
and prediabetes
16
in both males and
females, though gender-based differences are not clear.
Considering depression as a combination of two symptom clus-
ters (i.e. somatic and cognitive-affective) rather than one uniform
condition may aid in understanding how the relationship with dia-
betes differs by gender. Based on the Diagnostic and Statistical
Manual of Mental Disorders, Fifth Edition
17
(DSM) criteria,
somatic symptoms relate to sleep, energy, appetite and psychomotor
slowing/restlessness, whereas symptoms relating to anhedonia,
mood, guilt, concentration and suicidal ideation are cognitive-
affective. Analyses that consider such symptom clusters have been
shown to provide significant benefits over those that examine
total depression only,
18
particularly when investigating health out-
comes.
19
The cognitive-affective symptom cluster has been shown
to be associated with an individual’s perception of their unmet psy-
chological care needs, while somatic symptoms are not.
20
Without
distinguishing by gender, research has found diabetes,
21
insulin
resistance
22
and metabolic syndrome (MetS)
23
–which are corre-
lated with diabetes
24
–to be primarily associated with somatic
symptoms of depression. Only one study has examined depression
symptom clusters by gender, finding that associations were primar-
ily driven by somatic symptoms in both males and females.
23
However, that study examined MetS and not diabetes or predia-
betes. Further, despite research suggesting that the association
between diabetes and depression differs according to gender,
10,11
no study has examined how depressive symptom clusters may
differ by gender in people with diabetes or prediabetes.
To fill these knowledge gaps, this exploratory study examined
the associations of depressive symptoms and symptom cluster
†
Joint first authors.
BJPsych Open (2024)
10, e192, 1–7. doi: 10.1192/bjo.2024.764
1
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
scores with diabetes status, including gender-based interactions. We
hypothesised that (a) individuals with known prediabetes and dia-
betes would have statistically significantly higher odds of having
depressive symptoms than non-diabetic individuals, and that this
would occur to a greater extent in females than in males, (b)
somatic symptom scores would be statistically significantly higher
in prediabetic and diabetic individuals compared to non-diabetic
individuals, with no gender differences, and (c) cognitive-affective
symptom scores would not be statistically significantly higher in
prediabetic and diabetic individuals compared to non-diabetic indi-
viduals, but that scores would be statistically significantly higher in
females with diabetes and prediabetes than in males.
Method
Study population
This study used data from the 2007–2018 cycles of the National
Health and Nutrition Examination Survey (NHANES). NHANES
is an annual cross-sectional survey administered by the National
Center for Health Statistics (NCHS), part of the Centers for
Disease Control and Prevention (CDC). Data were collected from
a sample of the non-institutionalised US population. The
NHANES data collection protocols are approved each year by the
NCHS Ethics Review Board and informed consent is obtained
from all participants. More information on the protocols and sam-
pling methods is available on the CDC website (https://www..cdc.
gov/nchs/nhanes/analyticguidelines.aspx#). The study sample in-
cluded males and females aged 20+ years who completed the
Mental Health –Depression Screener (items DPQ010 to DPQ090)
and the Diabetes Questionnaire (items DIQ010 and/or DIQ160).
While NHANES does not differentiate between the type of diabetes,
participants who met all of the following criteria were excluded, as
type 1 diabetes was likely: (a) diagnosed with diabetes prior to age 30
(DID040), (b) started insulin therapy within 1 year of diabetes diag-
nosis (i.e. age minus length of time taking insulin [DID060] =
within 1 year of age first diagnosed with diabetes [DID040]) and
(c) taking insulin when surveyed (DIQ050).
25
Exposure variable
Diabetes status was categorised as (a) no diabetes, (b) known predia-
betes or (c) diabetes, assessed through self-report via the questions
DIQ010, ‘Other than during pregnancy, have you ever been told by a
doctor or health professional that you have diabetes or sugar dia-
betes?’and DIQ160, ‘Have you ever been told by a doctor or
other health professional that you have any of the following: predia-
betes, impaired fasting glucose, impaired glucose tolerance, border-
line diabetes or that your blood sugar is higher than normal but not
high enough to be called diabetes or sugar diabetes?’Individuals
responding ‘yes’to DIQ010 were categorised as having diabetes.
Those who responded ‘no’to DIQ010 were asked DIQ160.
Individuals responding ‘borderline’to DIQ010 or ‘yes’to DIQ160
were categorised as having known prediabetes. Individuals respond-
ing ‘no’to DIQ160 were categorised as non-diabetic.
Outcome variable
The presence of depressive symptoms was measured using the
Mental Health –Depression Screener, which asks participants to
complete the Patient Health Questionnaire-9 (PHQ-9). The PHQ-
9 comprises nine items, which assess the frequency of depressive
symptoms experienced over the past 2 weeks, based on diagnostic
criteria for major depressive disorder (MDD) from the DSM –
Fourth Edition.
26
The items on this scale are scored from 0 (experi-
enced no days) to 3 (experienced nearly every day). Answers to
items 1–9 on the PHQ-9 were summed and participants were cate-
gorised as having depressive symptoms (score ≥10) or not (score <
10). Symptom clusters were determined by summing scores to ques-
tions within each cluster (questions 1, 2, 6, 7 and 9 for cognitive-
affective and questions 3, 4, 5 and 8 for somatic) and were kept as
continuous values. Symptom clusters were defined in this way to
align with existing diabetes/prediabetes and depression-related
studies.
26,27
Statistical analysis
Statistical analyses were performed using R, version 4.2.1 for
MacOS, and the package ‘survey’to account for survey weights.
Mobile exam centre survey weights were divided by six to account
for the merging of six survey cycles. Categorical variables were
described as raw frequency and weighted percent in the study popu-
lation demographic characteristics table, while continuous variables
were described as weighted mean and s.d. A chi-square test of inde-
pendence was used to check for statistically significant (P-value
≤0.05) differences in categorical demographic characteristics
among the gender-dependent diabetes groups, and a t-test was
used to test for differences in continuous variables. Multiple logistic
regression was conducted to assess the relationship between dia-
betes status and presence of depressive symptoms (yes/no). A sen-
sitivity analysis with depressive symptoms as a continuous
measure, rather than categorical, was run using a multiple linear
regression model. Multiple linear regression models were also
used to assess the relationship between diabetes status and cogni-
tive-affective or somatic symptom cluster scores. An additional sen-
sitivity analysis was run with item 8 (psychomotor retardation)
included in the cognitive-affective symptom cluster rather than
the somatic symptom cluster.
27
Next, additional models were run
to test the interaction effects between diabetes status and gender
for all previous models. Where interaction terms were statistically
significant, a subgroup analysis by gender was conducted to
further investigate the relationship between presence of depressive
symptoms or symptom cluster scores and diabetes status.
Statistical significance was set to P< 0.01 to account for multiple
testing and reduce Type 1 error for all models.
Shortlisted covariates based on prior literature included age
(continuous by year), gender (female or male), body mass index
(BMI; <25 kg/m
2
,≥25 to <30 kg/m
2
,≥30 kg/m
2
), race (non-
Mexican White, non-Mexican Black, Mexican Hispanic, other
Hispanic, other [including multi-racial]), poverty-income ratio
(PIR; low income ≤1.3, mid-to-high income >1.3),
28
and sedentary
activity (continuous by minutes per week). Backward stepwise selec-
tion was used to validate which covariates would be included in the
multiple regression models with a cutoff of P< 0.10. All covariates
were selected in all models, with the exception of the cognitive-
affective model, which did not include sedentary activity.
Symptom cluster models also controlled for the opposite
symptom cluster.
Results
Descriptive statistics
The study population included 29 619 participants. Figure 1 shows
participants’inclusion into the study. The mean age of participants
was 47.64 years (s.d. = 17) and 15 052 (51.32%) were female.
Moreover, 23 224 (82%) participants were non-diabetic, 2548
(8.52%) were prediabetic and 3847 (9.48%) were diabetic (Table 1,
Supplementary Table 1 available at https://doi.org/10.1192/bjo.
2024.764). The overall prevalence of depressive symptoms in this
sample was 8.03%, with a prevalence of 10.12 and 5.82% in
Meshkat et al
2
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
59 842 participants from the 2007–2018 NHANES
cycles
25 072 participants aged <20 years excluded
4926 participants were excluded because they refused
to answer, responded with 'I don’t know', or had
missing data on the Depression screener
225 participants were excluded:
• 43 refused to answer, responded 'I don’t know', or had
missing values on the the Diabetes questionnaire (items
DIQ010 and DIQ060)
• 182 for type 1 diabetes
34 770 participants who are > 20 years old
29 844 participants who have completed the
Depression screener (DPQ)
29 619 participants included in analysis
Fig. 1 Flowchart of National Health Examination and Nutrition Examination Survey (NHANES) participants included in the final study population.
Table 1 Demographics of study sample, based on diabetes status and gender
No diabetes Prediabetes Diabetes
Females Males P-value Females Males P-value Females Males P-value
Sample size 11 773 11 451 1428 1120 1851 1996
Age, years (mean,
s.d.)
46.44 (17.06) 44.32 (16.32) <0.001 53.81 (15.56) 54.70 (14.16) 0.226 60.76 (13.31) 61.44 (11.85) 0.197
Depressive
symptoms –yes
1201 (8.92) 668 (5.29) <0.001 186 (13.09) 119 (8.84) 0.020 337 (17.94) 199 (7.93) <0.001
PHQ-9 score (mean,
s.d.)
3.32 (4.19) 2.47 (3.64) <0.001 4.26 (4.62) 3.28 (4.48) <0.001 5.04 (5.25) 3.04 (4.15) <0.001
Cognitive-affective
symptom
scores (mean,
s.d.)
1.26 (2.24) 0.98 (1.97) <0.001 1.64 (2.56) 1.33 (2.50) 0.034 2.06 (2.86) 1.15 (2.24) <0.001
Somatic symptom
scores (mean,
s.d.)
2.06 (2.35) 1.49 (2.05) <0.001 2.61 (2.53) 1.95 (2.43) <0.001 2.98 (2.88) 1.89 (2.33) <0.001
Race <0.001 0.073 0.002
Non-Hispanic White 5061 (68.37) 4930 (66.85) 524 (65.43) 480 (70.21) 588 (58.77) 738 (64.17)
Non-Hispanic Black 2390 (11.09) 2346 (10.10) 325 (11.93) 242 (9.32) 529 (17.49) 508 (12.77)
Mexican Hispanic 1715 (7.56) 1694 (9.39) 216 (7.73) 154 (7.92) 334 (9.32) 331 (8.89)
Other Hispanic 1292 (5.73) 1113 (5.95) 164 (5.92) 98 (4.99) 220 (5.87) 196 (5.17)
Other race –
including
multiracial
1315 (7.25) 1368 (7.70) 199 (9.00) 146 (7.56) 180 (8.56) 223 (9.01)
Poverty-income
ratio
<0.001 0.020 <0.001
Mid-to-high income
(>1.3)
7212 (77.36) 7271 (79.72) 900 (79.99) 759 (84.25) 965 (69.38) 1235 (78.86)
Body mass index <0.001 0.004 <0.001
<25 kg/m
2
3940 (36.72) 3430 (28.33) 234 (16.56) 200 (14.72) 207 (9.91) 270 (11.47)
≥25 to <30 kg/m
2
3443 (29.26) 4369 (38.71) 363 (27.71) 408 (37.20) 414 (20.13) 662 (30.18)
≥30 kg/m
2
4294 (34.03) 3553 (32.96) 825 (55.73) 502 (48.08) 1197 (69.95) 1024 (58.35)
Minutes of
sedentary
activity/week
(mean, s.d.)
366.39 (198.50) 367.89 (203.69) 0.681 391.15 (205.56) 398.01 (209.32) 0.617 387.48 (212.20) 407.99 (212.13) 0.025
P-values < 0.05 denote statistically significant differences between genders, within each diabetes status group (i.e. no diabetes, prediabetes, diabetes). For statistically significant differences
across diabetes status groups, see Supplementary Table 1. Categorical variables presented as unweighted frequencies and weighted percentages. Continuous variables presented as
weighted mean and s.d.
PHQ-9, Patient Health Questionnaire-9.
Gender differences in the relationship between depressive symptoms
3
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
females and males, respectively. Across the total sample, mean cog-
nitive-affective symptom scores were 1.20 (s.d. =2.21) and mean
somatic symptom scores were 1.89 (s.d. = 2.31). Respective female
and male mean scores were 1.37 (s.d. = 2.35) and 1.02 (s.d. = 2.05)
for cognitive-affective symptoms, and 2.19 (s.d. = 2.44) and 1.57
(s.d. = 2.12) for somatic symptoms.
Depressive symptoms
Individuals with known prediabetes (adjusted odds ratio (aOR):
1.63, 95% CI: 1.30, 2.04, P< 0.001) or diabetes (aOR: 1.85, CI:
1.58, 2.17, P< 0.001) had statistically significantly higher odds of
having depressive symptoms compared to those without diabetes
(Table 2, Supplementary Table 2). There was no interaction effect
between diabetes status and gender for prediabetes (Table 3,
Supplementary Table 3). The interaction between diabetes status
and gender was not statistically significant for diabetes in the
main analysis based on our threshold for statistical significance
(P=0.036) (Table 3). However, in the sensitivity analysis wherein
depressive symptoms were treated continuously, this interaction
was statistically significant (Supplementary Table 3). Females with
diabetes had an average total depressive symptom score that was
1.44 points greater than females without diabetes (adjusted coeffi-
cient (aCoeff.) = 1.44, CI: 1.10, 1.78, P< 0.001), whereas males
with diabetes showed a smaller difference in mean scores than
their non-diabetic counterparts (aCoeff. =0.71, CI: 0.44, 0.98,
P< 0.001) (Supplementary Table 4). As such, subsequent subgroup
analyses by gender were conducted for the main analysis based on
the overall trend of results and its potential clinical relevance in sug-
gesting that the association between diabetes status and depressive
symptoms may differ by gender. In the main analysis, the odds of
depressive symptoms for females with diabetes were more than
two times the odds of depressive symptoms for females without dia-
betes (aOR: 2.05, CI: 1.65, 2.54, P< 0.001), whereas the odds of
depressive symptoms for males with diabetes was 52% higher
than males without diabetes (aOR: 1.52, CI: 1.19, 1.93, P= 0.001)
(Table 4, Supplementary Table 4).
Cognitive-affective symptom cluster score
Based on our threshold forstatistical significance, neither individuals
with diabetes (aCoeff. = 0.09, CI: 0.00, 0.18, P= 0.039) or known pre-
diabetes (aCoeff. = 0.07, CI: −0.07, 0.22, P= 0.315) had statistically
significantly higher mean cognitive-affective symptom cluster
scores than non-diabetic individuals in the main analysis; this
became significant for diabetes in the sensitivity analysis wherein
psychomotor retardation was included in the cognitive-affective
symptom cluster (Table 2, Supplementary Table 2). The interaction
between diabetes status and gender was statistically significant for
diabetes (P= 0.001), but not known prediabetes (Table 3), which
was consistent in the sensitivity analysis with psychomotor retard-
ation included in the cognitive-affective cluster (Supplementary
Table 3). In adjusted subgroup analyses, females with diabetes had
a mean cognitive-affective score that was statistically significant, at
0.23 points higher compared to females without diabetes (CI: 0.10,
0.36, P= 0.001), whereas males with diabetes had a mean cogni-
tive-affective score that was 0.05 points lower than non-diabetic
males (CI: −0.16, 0.07, P= 0.434). The association in males was stat-
istically insignificant (Table 4, Supplementary Table 4).
Somatic symptom cluster score
Individuals with known prediabetes (aCoeff. =0.24, CI: 0.13, 0.35,
P< 0.001) and diabetes (aCoeff. = 0.30, CI: 0.19, 0.41, P< 0.001)
had statistically significantly higher mean somatic symptom
cluster scores than non-diabetic individuals (Table 2). Similarly,
in the model including an interaction term between gender and
known prediabetes or diabetes, the mean somatic symptom scores
were statistically significantly higher for the individuals with
known prediabetes and diabetes than those with no diabetes;
however, these associations did not differ by gender (Table 3).
The sensitivity analysis wherein psychomotor retardation was
included in the cognitive-affective symptom cluster yielded the
same finding (Supplementary Table 3).
Discussion
Using a population-based sample, the current study investigated the
association between diabetes status and depressive symptoms (total
overall and subset into clusters) in males and females. Statistically
significant interactions between gender and diabetes were found
in the total depressive symptom (when treated as a continuous
measure) and cognitive-affective symptom analyses, but not in the
somatic symptom analysis. Compared to participants without dia-
betes, females with diabetes had higher mean total depressive
symptom scores and cognitive-affective symptom scores than
males with diabetes, though cognitive-affective symptom scores
were not statistically significantly related to diabetes in males.
Gender did not modify the relationship between known prediabetes
and depressive symptoms or symptom cluster scores.
Depressive symptoms were statistically significantly greater in
females than in males with diabetes, which is in line with previous
research.
13,14
Several mechanisms can explain this finding, includ-
ing gender dimorphic risk factors that influence both diabetes and
depression. For example, depression risk factors such as lower deci-
sion latitude and higher job strain,
29
lower education level
30
and
lower socioeconomic status in childhood
31
have been found to be
associated with diabetes in females than in males.
8
Sociocultural
gender differences in coping with such stressors may be one explan-
ation behind the higher depressive symptom scores found in
females compared to males, as it has been suggested that men are
more likely to cope by becoming aggressive and participating in
activities while women are more likely to ruminate, decrease phys-
ical activity and eat more.
12
Menopause and associated hormone
changes also confer a unique risk for both diabetes and depression
on females.
12
Both factors may play a role in gender-based associa-
tions between diabetes and depression, as research has found that
Table 2 Results of main effect multiple logistic and linear regressions
Exposure
Depressive symptoms Cognitive-affective symptom cluster Somatic symptom cluster
aOR (95% CI) P-value aCoeff. estimate (95% CI) P-value aCoeff. estimate (95% CI) P-value
Diabetes status
No diabetes 1 (ref) –0 (ref) –0 (ref) –
Prediabetes 1.63 (1.30, 2.04) <0.001 0.07 (−0.07, 0.22) 0.315 0.24 (0.13, 0.35) <0.001
Diabetes 1.85 (1.58, 2.17) <0.001 0.09 (0.00, 0.18) 0.039 0.30 (0.19, 0.41) <0.001
aOR, adjusted odds ratio; aCoeff., adjusted coefficient; ref, reference. P-values < 0.01 (shown in bold) denote statistical significance. Depressive symptom and somatic symptom cluster
models adjusted for age, gender, body mass index, race, poverty-income ratio, sedentary activity; cognitive-affective symptom cluster model adjusted for the same variables with the
exception of sedentary activity; somatic and cognitive-affective symptom cluster models additionally controlled for the opposite symptom cluster.
Meshkat et al
4
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
this relationship differs by age.
13,14,32,33
In contrast with the results
presented in this study, meta-analyses including predominantly
longitudinal studies examined depression as a predictor of diabetes
and found greater associations between the two conditions in
males.
11
This suggests that heterogeneity in gender-based associa-
tions between depressive symptoms and diabetes may in part
stem from variations in study design and from which condition is
examined as the precipitating factor. This is supported by a longitu-
dinal study that found a greater positive association in females when
diabetes predicted the development of depressed mood than when
depressed mood predicted the development of diabetes.
34
Future
research should consider how gender dimorphic risk factors play
into gender differences in the diabetes–depression relationship, as
well as how causal directions in this relationship may differ.
Previous research has suggested that cognitive-affective symp-
toms of depression, independent of somatic symptoms, are not
associated with diabetes
35
or insulin resistance.
22
While this was
the case for males in our sample, we found that females with dia-
betes had statistically significantly higher mean cognitive-affective
scores than females without diabetes. This may be explained by
the psychological impact of the complications or burdens that can
accompany a diabetes diagnosis, known as ‘diabetes distress’.
Measures of diabetes distress, including feelings of worry, guilt
and frustration with the diagnosis,
34
are more aligned with cogni-
tive-affective symptoms of depression than somatic symptoms.
Existing research supports a greater magnitude of this effect in
females than in males.
36,37
Further, the lack of significance for
females with known prediabetes in our sample suggests that
known prediabetes is not associated with the same cognitive dis-
tress, perhaps because the condition can be reversed. The psycho-
logical burden of awareness of diabetes may also explain why our
results differ from previous research using similar methods.
Specifically, Wiltink et al.
35
found no significant association
between cognitive-affective symptoms and diabetes; however,
both those with known (identified through self-report) and
unknown (identified through blood tests as part of the study) dia-
betes were included in the diabetic group. In contrast, the present
study examined only known diabetes or prediabetes, suggesting
that knowledge of diabetes might be a key factor influencing cogni-
tive-affective symptoms of depression.
Somatic depressive symptoms did not differ between males or
femaleswitheitherknownprediabetesordiabeteswhencompared
to their non-diabetic counterparts. Research suggests that females
with depression may be more likely than males to exhibit somatic
symptoms as part of MDD
38,39
and endorse somatic symptoms at a
greater rate.
39
Our results suggest that the presence of diabetes or
known prediabetes does not statistically significantly alter this pattern.
Study findings did not support our hypothesis that the relation-
ship between depressive symptoms and prediabetes would mirror
that of diabetes. Results showed a trend in the opposite direction,
with males experiencing greater odds of having depressive symp-
toms and higher mean cognitive-affective scores, though the asso-
ciations themselves and the interactions did not reach
significance. This may be due to differential types of prediabetes
experienced across genders, where males more commonly experi-
ence combined impaired glucose tolerance and impaired fasting
glucose type prediabetes,
40,41
which is most strongly associated
with depression.
16
However, further research is needed to investi-
gate the role of gender in the relationship between prediabetes
and depressive symptoms and how this relationship may differ
from that of diabetes.
This study is not without limitations. While the nationally rep-
resentative sample allows for the inclusion of individuals who are
not seeking care for diabetes or depressive symptoms, the self-
Table 3 Results of multiple logistic and linear regressions with interaction effects
Exposure
Depressive symptoms Cognitive-affective symptom cluster Somatic symptom cluster
aOR (95% CI) P-value aCoeff. estimate (95% CI) P-value aCoeff. estimate (95% CI) P-value
Diabetes status
No diabetes 1 (ref) –0 (ref) –0 (ref) –
Prediabetes 1.55 (1.16, 2.07) 0.004 0.06 (−0.13, 0.25) 0.515 0.26 (0.10, 0.42) 0.001
Diabetes 2.08 (1.68, 2.57) <0.001 0.25 (0.12, 0.39) <0.001 0.31 (0.16, 0.46) <0.001
Gender interaction
Prediabetes × male 1.16 (0.78, 1.72) 0.449 0.03 (−0.23, 0.29) 0.824 −0.05 (−0.30, 0.20) 0.682
Diabetes × male 0.73 (0.55, 0.98) 0.036 −0.31 (−0.49, −0.13) 0.001 −0.02 (−0.19, 0.15) 0.832
aOR, adjusted odds ratio; aCoeff., adjusted coefficient; ref, reference. P-values < 0.01 (shown in bold) denote statistical significance. Depressive symptom and somatic symptom cluster
models adjusted for age, body mass index, race, poverty-income ratio, sedentary activity; cognitive-affective symptom cluster model adjusted for the same variables with the exception of
sedentary activity; somatic and cognitive-affective symptom cluster models additionally controlled for the opposite symptom cluster.
Table 4 Subgroup models following statistically significant interaction between diabetes status and gender
Depressive symptoms
Female Male
aOR (95% CI) P-value aOR (95% CI) P-value
No diabetes 1 (ref) –1 (ref) –
Prediabetes 1.50 (1.13, 2.01) 0.006 1.83 (1.34, 2.50) <0.001
Diabetes 2.05 (1.65, 2.54) <0.001 1.52 (1.19, 1.93) 0.001
Cognitive-affective symptom cluster
Female Male
aCoeff. estimate (95% CI) P-value aCoeff. estimate (95% CI) P-value
No diabetes 0 (ref) –0 (ref) –
Prediabetes 0.05 (−0.14, 0.24) 0.601 0.10 (−0.10, 0.30) 0.334
Diabetes 0.23 (0.10, 0.36) 0.001 −0.05 (−0.16, 0.07) 0.434
Note: aOR, adjusted odds ratio; aCoeff., adjusted coefficient; ref, reference. P-values < 0.01 (shown in bold) denote statistical significance. Depressive symptom model adjusted for age, body
mass index (BMI), race, povery-income ratio (PIR), sedentary activity; cognitive-affective symptom model adjusted for age, BMI, race, PIR, somatic symptom cluster.
Gender differences in the relationship between depressive symptoms
5
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
report of diabetes status limits diabetes and prediabetes groups to
those who are aware of their condition. However, the awareness
of diabetes status is associated with depression regardless of meta-
bolic status, and self-report measures of diabetes have been shown
to have high sensitivity and specificity.
42
Moreover, the measure-
ment of depressive symptoms was based solely on total PHQ-9
scores. Using a self-report scale, rather than a semi-structured inter-
view or confirmed clinical diagnosis, limits our study to investigat-
ing the association of diabetes and prediabetes with presence of
depressive symptoms rather than MDD. Several other conditions
(e.g. bereavement, adjustment disorder and substance use disorder)
may be associated with elevated PHQ-9 scores, and, as such, it is
possible that participants who were categorised as having depressive
symptoms were experiencing a condition other than MDD. Finally,
the use of cross-sectional data limits the conclusions that can be
drawn to correlation and presents the possibility of non-response
bias. This study is also limited by the lack of comorbidity analysis.
Results from this study suggest that diabetes is associated with
higher total depressive symptom scores and cognitive-affective
symptom scores in females. Future studies should examine causal
pathways in the diabetes-depressive symptom relationship, while
also considering gender to determine how differing trajectories
explain the heterogeneity of research in this area. Further, studies
should examine gender differences in the distinction between dia-
betes distress and depressive symptoms. Studies that examine
depression symptom profiles and diabetes status should consider
how results may differ across genders, as well as how prediabetic
or diabetic state and diagnosed or undiagnosed diabetes influence
these symptom profiles.
Shakila Meshkat, Interventional Psychiatry Program, St. Michael’s Hospital, Toronto,
Canada; Vanessa K. Tassone, Interventional Psychiatry Program, St. Michael’s Hospital,
Toronto, Canada; Sarah Dunnett, Interventional Psychiatry Program, St. Michael’s
Hospital, Toronto, Canada; Hilary Pang, Department of Psychiatry, University of Toronto,
Toronto, Canada; Michelle Wu, Interventional Psychiatry Program, St. Michael’s
Hospital, Toronto, Canada; Josheil K. Boparai, Interventional Psychiatry Program,
St. Michael’s Hospital, Toronto, Canada; Hyejung Jung, Department of Biostatistics,
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; Wendy Lou,
Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto,
Toronto, Canada; Venkat Bhat , Interventional Psychiatry Program, St. Michael’s
Hospital, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto,
Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto,
Toronto, Canada; and Mental Health and Addictions Services, St. Michael’s Hospital,
Toronto, Canada
Correspondence: Venkat Bhat. Email: venkat.bhat@utoronto.ca
First received 17 Aug 2023, final revision 31 Jul 2024, accepted 1 Aug 2024
Supplementary material
Supplementary material is available online at https://doi.org/10.1192/bjo.2024.764
Data availability
The data that support the findings of this study are available from the corresponding author,
V.B., upon reasonable request.
Author contributions
V.B. conceptualised the study along with the rest of the team. The investigation was led by S.M.,
S.D., V.K.T. and M.W. who were also responsible for writing the original draft. M.W. was respon-
sible for the methodology, data curation, formal analysis and visualisation. W.L. and H.J. super-
vised formal analysis. Study conceptualisation and manuscript writing were supervised by V.B.
All authors provided critical revisions to the manuscript for intellectual content and contributed
to editing. All authors read and approved the final manuscript.
Funding
This research did not receive any specific grant from funding agencies in the public, commer-
cial or non-for-profit sectors.
Declaration of interest
V.B. is supported by an Academic Scholar Award from the Department of Psychiatry, University
of Toronto, Canada, and has received research support from the Canadian Institutes of Health
Research, Brain & Behavior Foundation (USA), Ministry of Health Innovation Funds (Canada),
Royal College of Physicians and Surgeons of Canada, Department of National Defence
(Canada) and an investigator-initiated trial from Roche Canada.
Ethics and consent statement
The National Health and Nutrition Examination Survey (NHANES) data collection protocols are
approved each year by the National Center for Health Statistics (NCHS) Ethics Review Board and
informed consent is obtained from all participants
References
1GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disabil-
ity-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life
expectancy (HALE), 1990–2015: a systematic analysis for the global burden of
disease study 2015. Lancet Lond Engl 2016; 388(10053): 1603–58.
2Khaledi M, Haghighatdoost F, Feizi A, Aminorroaya A. The prevalence of
comorbid depression in patients with type 2 diabetes: an updated systematic
review and meta-analysis on huge number of observational studies. Acta
Diabetol 2019; 56(6): 631–50.
3Egede LE. Diabetes, major depression, and functional disability among U.S.
Adults. Diabetes Care 2004; 27(2): 421–8.
4Egede LE, Ellis C. Diabetes and depression: global perspectives. Diabetes Res
Clin Pract 2010; 87(3): 302–12.
5Galaviz KI, Weber MB, Suvada K, Gujral UP, Wei J, Merchant R, et al.
Interventions for reversing prediabetes: a systematic review and meta-
analysis. Am J Prev Med 2022; 62(4): 614–25.
6Sumlin LL, Garcia TJ, Brown SA, Winter MA, García AA, Brown A, et al.
Depression and adherence to lifestyle changes in type 2 diabetes: a systematic
review. Diabetes Educ 2014; 40(6): 731–44.
7Deschênes SS, Burns RJ, Graham E, Schmitz N. Prediabetes, depressive and
anxiety symptoms, and risk of type 2 diabetes: a community-based cohort
study. J Psychosom Res 2016; 89:85–90.
8Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, patho-
physiology and complications of type 2 diabetes Mellitus. Endocr Rev 2016;
37(3): 278–316.
9Kautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes.
Diabetologia 2023; 66(6): 986–1002.
10 Ali S, Stone MA, Peters JL, Davies MJ, Khunti K. The prevalence of co-morbid
depression in adults with type 2 diabetes: a systematic review and meta-
analysis. Diabet Med 2006; 23(11): 1165–73.
11 Zhuang QS, Shen L, Ji HF. Quantitative assessment of the bidirectional relation-
ships between diabetes and depression. Oncotarget 2017; 8(14): 23389–400.
12 Demmer RT, Gelb S, Suglia SF, Keyes KM, Aiello AE, Colombo PC, et al. Sex dif-
ferences in the association between depression, anxiety, and type 2 diabetes
Mellitus. Psychosom Med 2015; 77(4): 467–77.
13 Zhao W, Chen Y, Lin M, Sigal RJ. Association between diabetes and depression:
sex and age differences. Public Health 2006; 120(8): 696–704.
14 Deischinger C, Dervic E, Leutner M, Kosi-Trebotic L, Klimek P, Kautzky A, et al.
Diabetes mellitus is associated with a higher risk for major depressive disorder
in women than in men. BMJ Open Diabetes Res Care 2020; 8(1): e001430.
15 Pearson S, Schmidt M, Patton G, Dwyer T, Blizzard L, Otahal P, et al. Depression
and insulin resistance. Diabetes Care 2010; 33(5): 1128–33.
16 Krysiak R, Szkróbka W, OkopienB. Sexual functioning and depressive symp-
toms in men with various types of prediabetes: a pilot study. Int J Impot Res
2018; 30(6): 327–34.
17 American Psychiatric Association. Diagnostic and Statistical Manual of Mental
Disorders (5th ed.). American Psychiatric Publishing, 2013.
18 Elhai JD, Contractor AA, Tamburrino M, Fine TH, Prescott MR, Shirley E, et al.
The factor structure of major depression symptoms: a test of four competing
models using the patient health questionnaire-9. Psychiatry Res 2012; 199(3):
169–73.
19 Stewart JC, Zielke DJ, Hawkins MA, Williams DR, Carnethon MR, Knox SS, et al.
Depressive symptom clusters and 5-year incidence of coronary artery calcifica-
tion: the CARDIA study. Circulation 2012; 126(4): 410–7.
20 van der Donk LJ, Fleer J, Sanderman R, Emmelkamp PMG, Links TP, Tovote KA,
et al. Is type of depressive symptoms associated with patient-perceived need
for professional psychological care in depressed individuals with diabetes?
PLoS One 2019; 14(2): e0212304.
Meshkat et al
6
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
21 Nicolau J, Simó R, Conchillo C, Sanchís P, Blanco J, Romerosa JM, et al.
Differences in the cluster of depressive symptoms between subjects with
type 2 diabetes and individuals with a major depressive disorder and without
diabetes. J Endocrinol Invest 2019; 42(8): 881–8.
22 Khambaty T, Stewart JC, Muldoon MF, Kamarck TW. Depressive symptom clus-
ters as predictors of 6-year increases in insulin resistance: data from the
Pittsburgh healthy heart project. Psychosom Med 2014; 76(5): 363–9.
23 Marijnissen RM, Smits JE, Schoevers RA, van den Brink RH, Holewijn S, Franke
B, et al. Association between metabolic syndrome and depressive symptom
profiles—Sex-specific? J Affect Disord 2013; 151(3): 1138–42.
24 James M, Varghese TP, Sharma R, Chand S. Association between metabolic
syndrome and diabetes Mellitus according to international diabetic feder-
ation and national cholesterol education program adult treatment panel
III criteria: a cross-sectional study. J Diabetes Metab Disord 2020; 19(1):
437.
25 Gujral UP, Mohan V, Pradeepa R, Deepa M, Anjana RM, Narayan KV. Ethnic dif-
ferences in the prevalence of diabetes in underweight and normal weight indi-
viduals: the CARRS and NHANES studies. Diabetes Res Clin Pract 2018; 146:
34–40.
26 Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression
severity measure. J Gen Intern Med 2001; 16(9): 606–13.
27 Krause JS, Bombardier C, Carter RE. Assessment of depressive symptoms dur-
ing inpatient rehabilitation for spinal cord injury: is there an underlying somatic
factor when using the PHQ? Rehabil Psychol 2008; 53: 513–20.
28 Center on Budget and Policy Priorities. Policy Basics: The Supplemental
Nutrition Assistance Program (SNAP). Center on Budget and Policy Priorities,
2008.
29 Theorell T, Hammarström A, Aronsson G, Träskman Bendz L, Grape T, Hogstedt
C, et al. A systematic review including meta-analysis of work environment and
depressive symptoms. BMC Public Health 2015; 15(1): 738.
30 Chang-Quan H, Zheng-Rong W, Yong-Hong L, Yi-Zhou X, Qing-Xiu L. Education
and risk for late life depression: a meta-analysis of published literature. Int J
Psychiatry Med 2010; 40(1): 109–24.
31 Gilman SE, Kawachi I, Fitzmaurice GM, Buka SL. Socioeconomic status in child-
hood and the lifetime risk of major depression. Int J Epidemiol 2002; 31(2):
359–67.
32 Chen PC, Chan YT, Chen HF, Ko MC, Li CY. Population-Based cohort analyses of
the bidirectional relationship between type 2 diabetes and depression.
Diabetes Care 2013; 36(2): 376–82.
33 Berge LI, Riise T, Tell GS, Iversen MM, Østbye T, Lund A, et al. Depression in per-
sons with diabetes by age and antidiabetic treatment: a cross-sectional analysis
with data from the Hordaland Health Study. PLoS One 2015; 10(5): e0127161.
34 Polonsky WH, Anderson BJ, Lohrer PA, Welch G, Jacobson AM, Aponte JE, et al.
Assessment of diabetes-related distress. Diabetes Care 1995; 18(6): 754–60.
35 Wiltink J, Michal M, Wild PS, Schneider A, König J, Blettner M, et al. Associations
between depression and diabetes in the community: do symptom dimensions
matter? Results from the Gutenberg Health Study. PLoS One;9(8): e105499.
36 Brooks RJ, Roxburgh S. Gender differences in the effect of the subjective experi-
enceof diabetes and senseof control on distress.Health (N Y) 1999; 3(4): 399–420.
37 KausarR, Awan B, KhanN. Gender differencesin risk perceptionand emotional dis-
tress in patients with type2 diabetes. JIndianAcadApplPsychol2013; 39: 222–7.
38 Delisle VC, Beck AT, Dobson KS, Dozois DJA, Thombs BD. Revisiting gender dif-
ferences in somatic symptoms of depression: much Ado about nothing? PLoS
One 2012; 7(2): e32490.
39 Kroenke K, Spitzer RL. Gender differences in the reporting of physical and
somatoform symptoms. Psychosom Med 1998; 60(2): 150.
40 Bhowmik B, Binte Munir S, Ara Hossain I, Siddiquee T, Diep LM, Mahmood S,
et al. Prevalence of type 2 diabetes and impaired glucose regulation with asso-
ciated cardiometabolic risk factors and depression in an urbanizing rural com-
munity in Bangladesh: a population-based cross-sectional study. Diabetes
Metab J 2012; 36(6): 422–32.
41 Færch K, Borch-Johnsen K, Vaag A, Jørgensen T, Witte DR. Sex differences in
glucose levels: a consequence of physiology or methodological convenience?
The Inter99 study. Diabetologia 2010; 53(5): 858–65.
42 Seligman HK, Bindman AB, Vittinghoff E, Kanaya AM, Kushel MB. Food insecur-
ity is associated with diabetes Mellitus: results from the National Health
Examination and Nutrition Examination Survey (NHANES) 1999–2002. J Gen
Intern Med 2007; 22(7): 1018–23.
Gender differences in the relationship between depressive symptoms
7
https://doi.org/10.1192/bjo.2024.764 Published online by Cambridge University Press
