BMC Medicine
BioMed Central
Open Access
Research article
The association of posttraumatic stress disorder and metabolic
syndrome: a study of increased health risk in veterans
Pia S Heppner*1,2, Eric F Crawford3, Uzair A Haji1, Niloofar Afari1,2,
Richard L Hauger1,2, Boris A Dashevsky4, Paul S Horn4,5, Sarah E Nunnink1
and Dewleen G Baker1,2
Address: 1Veterans Affairs San Diego Health Care System, Research Service, MC 151, La Jolla Village Drive, San Diego, CA 92161, USA,
of Psychiatry, University of California at San Diego, Gilman Drive, MC:0603, La Jolla, CA 92093-0603, USA, 3Durham Veterans
Affairs Medical Center, Felton Street, Durham, NC 27705, USA, 4Psychiatry Service, Cincinnati Veterans Affairs Medical Center, Vine Street,
Cincinnati, OH 45220, USA and 5Department of Mathematical Sciences, University of Cincinnati, Old Chemistry Building, Cincinnati, OH 452210025, USA
2Department
Email: Pia S Heppner* - pheppner@ucsd.edu; Eric F Crawford - Eric.Crawford@va.gov; Uzair A Haji - uzair.haji2@va.gov;
Niloofar Afari - nafari@ucsd.edu; Richard L Hauger - rhauger@ucsd.edu; Boris A Dashevsky - boris_dashevsky@hotmail.com;
Paul S Horn - paul.horn@uc.edu; Sarah E Nunnink - sarah.nunnink@va.gov; Dewleen G Baker - dgbaker@ucsd.edu
* Corresponding author
Published: 9 January 2009
BMC Medicine 2009, 7:1
doi:10.1186/1741-7015-7-1
Received: 6 November 2008
Accepted: 9 January 2009
This article is available from: http://www.biomedcentral.com/1741-7015/7/1
© 2009 Heppner et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: There is accumulating evidence for a link between trauma exposure, posttraumatic
stress disorder (PTSD) and diminished health status. To assess PTSD-related biological burden, we
measured biological factors that comprise metabolic syndrome, an important established predictor
of morbidity and mortality, as a correlate of long-term health risk in PTSD.
Methods: We analyzed clinical data from 253 male and female veterans, corresponding to five
factors linked to metabolic syndrome (systolic and diastolic blood pressure, waist-to-hip ratio and
fasting measures of high-density lipoprotein (HDL) cholesterol, serum triglycerides and plasma
glucose concentration). Clinical cut-offs were defined for each biological parameter based on
recommendations from the World Health Organization and the National Cholesterol Education
Program. Controlling for relevant variables including sociodemographic variables, alcohol/
substance/nicotine use and depression, we examined the impact of PTSD on metabolic syndrome
using a logistic regression model.
Results: Two-fifths (40%) of the sample met criteria for metabolic syndrome. Of those with PTSD
(n = 139), 43% met criteria for metabolic syndrome. The model predicted metabolic syndrome well
(-2 log likelihood = 316.650, chi-squared = 23.731, p = 0.005). Veterans with higher severity of
PTSD were more likely to meet diagnostic criteria for metabolic syndrome (Wald = 4.76, p = 0.03).
Conclusion: These findings provide preliminary evidence linking higher severity of PTSD with risk
factors for diminished health and increased morbidity, as represented by metabolic syndrome.
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Background
Several lines of research suggest that stress and post-stress
adaptational responses are related to long-term health
outcomes. Studies of survivors of disasters, veterans and
prisoners of war, and others exposed to severe trauma,
suggest higher rates of physical morbidity and mortality
and increased healthcare utilization related to lifetime
prevalence of trauma [1-7]. There is accumulating evidence from epidemiological studies that chronic posttraumatic stress disorder (PTSD) may moderate the link
between trauma and secondary negative health outcomes
such as cardiovascular, metabolic and autoimmune conditions [4-9]. Furthermore, Chrousos and colleagues have
linked maladaptive neuro-endocrine-immune responses
to psychiatric, endocrine and/or autoimmune disease or
vulnerability to such diseases [10,11]. Evidence for neuroendocrine-immune abnormalities associated with PTSD,
also provides a biological rationale for downstream
health effects observed in the epidemiological studies [1216].
Metabolic syndrome or syndrome × is composed of a cluster of clinical signs including dyslipidemia, hyperglycemia
(insulin resistance), central obesity and hypertension
[17]. Recent research on the impact of stress on health has
focused on metabolic syndrome as a possible consequence of pathophysiological adaptations to chronic
stress [18]. Brunner and colleagues studied metabolic syndrome status in the Whitehall II cohort and found markers
associated
with
increased
stress-related
neuroendocrine and autonomic activation such as lowered heart rate variability, increased cortisol output and
higher levels of IL-6, C-reactive protein and blood viscosity among cases with metabolic syndrome as compared
with those who did not meet diagnostic criteria [19].
Additional analyses of prospective data from the Whitehall cohort also suggest a dose-response relationship
between stress and the presence of metabolic syndrome
such that participants with chronic exposure to work
stress were found to be more than twice as likely to have
the syndrome after controlling for age, sex and health
behaviors [20]. In a recent study of 2189 Gulf War I veterans, those with chronic multisymptom illness, as defined
by presence of fatigue, musculoskeletal pain, mood or
cognitive abnormalities for at least 6 months, had significantly higher prevalence of metabolic syndrome [21].
While there is a lack of consensus in specific clinical definitions of metabolic syndrome, prospective studies have
shown it to be an important predictor of physical morbidity and mortality [22-25]. For example, in a comparison of
four definitions based on guidelines from the World
Health Organization (WHO) and the National Cholesterol Education Program (NCEP), metabolic syndrome
was found to predict incidence and prevalence of diabetes
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mellitus in a prospective study [24]. Similarly, in a sample
of 5128 men, metabolic syndrome was found to be a significant predictor of the development of cardiovascular
disease (CVD) and diabetes mellitus [25].
In this study, we examined physical measures and laboratory values in a sample of veterans, to examine the association between PTSD severity and the presence of
metabolic syndrome. We hypothesized that greater severity of PTSD would be associated with a higher likelihood
of metabolic syndrome.
Methods
Participants
Comprehensive medical and mental health examinations
were completed by 355 veterans entering Gulf War
Screening and PTSD Programs at the Cincinnati Veterans
Affairs, of whom 341 signed informed consents permitting use of their data for a health-related study. The study
was conducted in accordance with the Helsinki Declaration, and was approved by the Institutional Review Board
of the University of Cincinnati Medical Center and the
Research Committee of the Cincinnati Veterans Affairs
Medical Center (Protocol #00-03-07-01-E; "Healthrelated quality of life in post-deployment veterans").
Of the 341 veterans with signed consent, we excluded 68
veterans who had missing data from at least one of the
variables of interest, 18 veterans whose laboratory values
were greater than 3 standard deviations (SD) from the
group mean, and 2 veterans who had both missing data
and laboratory values greater than 3 SD from the mean.
Cases with extreme laboratory values were excluded to
allow for a more conservative analysis of quantitative
physiological burden. These exclusions yielded a remaining sample size of 253 veterans.
Measures
Sociodemographics
Veterans provided sociodemographic information including years of education, military service and deployment
history via written questionnaires.
Psychiatric diagnoses
PTSD severity was measured using the Clinician Administered PTSD Scale (CAPS), which ranges in scores from 0 to
136 (see [26]). A score of 65 or above on the CAPS has
been shown to be optimally specific and efficient in predicting PTSD and was used, along with the Diagnostic and
Statistical Manual of Mental Disorders, 4th Edition (DSM-IV)
criteria, to identify veterans with PTSD [27]. Lifetime and
current diagnoses of major depressive disorder (MDD),
substance, alcohol and nicotine abuse or dependence
were determined using a structured diagnostic interview
[28,29].
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Physical measures
Physical examinations including waist-to-hip ratio
(WHR), blood pressure and laboratory tests (12-hour fasting lipids, glucose) were conducted on each veteran. Phlebotomy was scheduled between 7 and 9 AM. All samples
were assayed within the hospital clinical laboratory.
Procedure
In the context of a cross-sectional study, metabolic syndrome status was determined based on the clinical criteria
for each factor outlined in Table 1. Systolic and diastolic
blood pressure values were combined to represent one
condition (hypertension as represented by systolic blood
pressure (SBP) ≥ 130 mm/Hg and diastolic blood pressure
(DBP) ≥ 85 mm/Hg) yielding a total of five criteria that
contributed to metabolic syndrome. While the cut-off for
elevated serum triglycerides was based on NCEP criteria,
increased blood pressure, high-density lipoprotein
(HDL), WHR and plasma glucose concentration were
based on a modified set of criterion scores recommended
by the WHO and the NCEP, similar to those used by
Lakka and colleagues to evaluate CVD-related mortality
and metabolic syndrome [23,30-32].
Statistical analyses
We examined the relationship between PTSD and metabolic syndrome using a logistic regression model. In addition, we conducted a series of independent-sample t-tests
to determine whether those with PTSD differed on these
five criteria from those without PTSD. Statistical analyses
were conducted using SPSS (Statistical Package for Social
Sciences) version 15.0. Metabolic syndrome was regressed
on the following predictors: PTSD severity (CAPS), age,
gender, race (white, black/other), years of education, history of substance abuse/dependence, history of alcohol
abuse/dependence, nicotine abuse/dependence (lifetime)
and current or past diagnosis of MDD. All predictors were
simultaneously entered into the regression model as a sinTable 1: Clinically determined criterion scores for metabolic
syndrome
Measures
Serum triglycerides
High-density lipoprotein
Blood pressurea
Systolic
Diastolic
Waist-to-hip ratio
Men
Women
Plasma glucose concentration
Criterion score
≥ 150 mg dl-1
< 35 mg dl-1
≥ 130 mm/Hg
≥ 85 mm/Hg
≥ 0.90
≥ 0.85
≥ 110 mg dl-1
and diastolic blood pressure values were combined to
represent one condition (hypertension as represented by SBP ≥ 130
mm/Hg and DBP ≥ 85 mm/Hg).
Metabolic syndrome status was defined as meeting of three or more
of the five criteria.
aSystolic
gle block in order to determine whether PTSD severity was
a valid predictor of metabolic syndrome status, while controlling for all other demographic, behavioral and psychiatric variables.
Results
Participants
Table 2 describes the sociodemographic, medical and psychiatric characteristics of participants. The sample was primarily male and white, with an average age of 52 ± 9
years. A large proportion served in the US Army and more
than 70% served in Vietnam; 55% of the veterans met criteria for PTSD, as defined by a CAPS score of more than
65 and all met DSM-IV criteria for PTSD based on the
presence of intrusion, avoidance and hyperarousal scores.
There were 54 veterans (21%) who did not meet criteria
for PTSD based on the three DSM-IV symptom clusters
and another 60 veterans (24%) who had subthreshold
PTSD (reported all three types of symptom, but had a total
CAPS score less than 65). Close to two-thirds of the participants met criteria for MDD. Abuse or dependence of nicotine, alcohol and other substances were also prevalent in
this sample. In addition, there was high comorbidity
between PTSD and MDD, lifetime nicotine abuse or
dependence, past substance abuse or dependence, and
past alcohol abuse or dependence.
The overall prevalence of current metabolic syndrome in
our sample was 39.9%. Among those with PTSD only,
34.3% (n = 12) met criteria for metabolic syndrome as
compared with 28.8% (n = 17) of those with MDD only.
For those with PTSD and MDD, 46.2% (n = 48) met criteria for metabolic syndrome. Mean values for physical and
laboratory measures, shown in Table 3, indicate that, on
average, the sample had elevated serum triglycerides, high
total cholesterol/HDL ratios and central obesity. Group
differences between those with and without PTSD on
these physical and laboratory measures were not significant except for those with PTSD had significantly higher
DBP measures than those without PTSD (t = -2.15, p =
0.033). These multiple t-tests evaluating group differences
were not corrected for possible inflation of Type I error
(that is, the Bonferroni principle).
PTSD and metabolic syndrome
Table 4 presents the results of the logistic regression
model used to predict presence of metabolic syndrome.
The overall regression model produced an adequate
model fit (-2 log likelihood = 316.650, chi-squared =
23.731, p = 0.005). CAPS total score (Wald = 4.76, p =
0.03, odds ratio (OR) = 1.01) was a significant predictor
of metabolic syndrome. Regression coefficients indicated
that veterans' risk for metabolic syndrome increased one
percentage point for each point obtained on the CAPS.
Gender was also a significant, unique predictor of risk for
metabolic syndrome with women having lower risk
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Table 2: Sociodemographic, medical and psychiatric characteristics of veterans (N = 253)
Sociodemographic data
Number (percentage) of all veterans
Male
Race
White
Black
Other
Branch of service
Army
Marines
Navy
Air Force
Service era
World War II
Korea
Vietnam
Gulf War I
Other conflicts
Medical and psychiatric diagnoses
PTSD
At least moderate severity
Subthreshold severity
MDD
Nicotine abuse/dependence (lifetime)
Substance abuse/dependence (past)
Alcohol abuse/dependence (past)
Co-morbid PTSD and MDD
Co-morbid PTSD and lifetime nicotine abuse/dependence
Co-morbid PTSD and past substance abuse/dependence
Co-morbid PTSD and past alcohol abuse/dependence
Metabolic syndrome (prevalence)
PTSD only and metabolic syndrome
MDD only and metabolic syndrome
PTSD, MDD and metabolic syndrome
Other factors:
Age (mean ± standard deviation (SD)) = 51.5 ± 9.0 years
Length of education (mean ± SD) = 12.9 ± 2.4 years
CAPS score (mean ± SD) = 62.8 ± 29.4 years.
233 (92%)
193 (76.3%)
47 (18.6%)
13 (5.1%)
153 (60.5%)
53 (20.9%)
26 (10.3%)
21 (8.3%)
7 (2.8%)
8 (3.2%)
180 (71.1%)
36 (14.2%)
22 (8.7%)
139 (54.9%)a
60 (24.0%)a
163 (64.4%)
98 (38.7%)
102 (40.3%)
174 (68.8%)
104 (41.1%)b
53 (20.9%)
67 (26.5%)
107 (42.3%)
101 (39.9%)c
12 (34.3%)d
17 (28.8%)e
48 (46.2%)f
identified as meeting Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for posttraumatic stress disorder
(PTSD) were subclassified into subthreshold (Clinician Administered PTSD Scale (CAPS) < 65) or moderate to severe PTSD (CAPS ≥ 65).
bDiagnosis of PTSD if CAPS ≥ 65; diagnosis of major depressive disorder (MDD) determined by structured psychodiagnostic clinical interview.
cMetabolic syndrome status if three of five criteria are present (serum triglycerides at least 150 mg dl-1, high-density lipoprotein less than 35 mg dl1, blood pressure at least 130/85 mm/Hg, waist-to-hip ratio at least 0.90 for men and at least 0.85 for women, and plasma glucose concentration at
least 110 mg dl-1).
dPercentage of those with PTSD only who meet criteria for metabolic syndrome.
ePercentage of those with MDD only who meet criteria for metabolic syndrome.
fPercentage of those with both PTSD and MDD who meet criteria for metabolic syndrome.
aThose
(Wald = 4.852, p = 0.028, OR = 0.17). To determine
whether the comorbidity between PTSD and MDD in our
sample (41%) would introduce problems related to multicollinearity to our model, we conducted diagnostic tests
(variance inflation factor (VIF) estimates and tolerance)
and found no evidence for multicollinearity (VIF = 1.053,
tolerance = 0.950).
Discussion
In a sample of 253 male and female veterans, greater
severity of PTSD was associated with higher likelihood of
metabolic syndrome after controlling for relevant demo-
graphic, behavioral and psychiatric factors. This finding
aligns with and extends existing research examining metabolic syndrome in populations vulnerable to PTSD.
Violanti and colleagues found metabolic syndrome to be
three times more likely in police officers with severe PTSD
symptoms as compared with police officers in the lowest
PTSD severity category [33], while others have found that
that 31–35% of male war veteran samples with combat
PTSD evidenced concomitant metabolic syndrome
[34,35]. Of note, the current study utilized the largest
sample size to date and the most diverse in terms of gender, given the small but extant presence of women.
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Table 3: Physical measures and laboratory values used to determine metabolic syndrome
Measures
All Veterans (N = 253)
With PTSD (N = 139)
Without PTSD (N = 114)
p
Serum triglycerides
High-density lipoprotein
Blood pressure
Systolic
Diastolic
Waist-to-hip ratio
Men
Women
Plasma glucose concentration
189.5 ± 141.8
42.7 ± 11.3
194.6 ± 154.7
42.5 ± 10.6
183.3 ± 124.7
43.0 ± 12.0
ns
ns
130.8 ± 15.3
81.7 ± 10.0
132.2 ± 16.0
82.9 ± 9.9
129.1 ± 14.4
80.2 ± 10.0
ns
0.033
0.97 ± 0.07
0.85 ± 0.06
106.4 ± 26.8
0.98 ± 0.07
0.87 ± 0.03
105.6 ± 26.3
0.97 ± 0.06
0.84 ± 0.06
107.5 ± 27.5
ns
ns
ns
All values are mean ± standard deviation. Diagnosis of posttraumatic stress disorder (PTSD) if Clinician Administered PTSD Scale (CAPS) ≥ 65 and
Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for PTSD are met. ns, not significant.
The importance of the association between PTSD and
metabolic syndrome becomes apparent when one considers the fact that metabolic syndrome has been shown to
predict CVD-related morbidity and mortality [23,25]. Our
findings suggest that metabolic syndrome provides a useful framework for assessing and describing the physiologic burden of PTSD and can be used prospectively to
evaluate the increased health risk associated with combat
exposure and PTSD.
123.1 mm/Hg), higher DBP (81.7 mm/Hg versus 71.2
and 74.4 mm/Hg), higher fasting glucose (106.4 mg dl-1
versus 98.8 and 103.4 mg dl-1), and lower HDL (42.7 mg
dl-1 versus 55.8 and 45.3 mg dl-1). While our sample
appeared to be 'less healthy' based on the pattern of mean
values, our participants also tended to be older (mean age
of 51.5 years as compared with mean ages of 47.2 and
44.7 years for women and men, respectively, in
NHANES).
The prevalence of metabolic syndrome in our sample
(40%) and specifically among those with PTSD (43%)
were slightly higher than rates observed among adults in
the general population as reported in the National Health
and Nutrition Examination Survey (NHANES) which
ranged from 20% to 31% [36-38]. Loucks and colleagues
reported mean values from men and women ages 25 years
and over on individual metabolic syndrome components
also collected in our sample (HDL, serum triglycerides,
SBP, DBP, and fasting glucose) [36]. Compared with the
women and men surveyed in NHANES, our sample of veterans tended to have higher mean triglycerides (189.5 mg
dl-1 versus 132.3 and 164.0 mg dl-1 for women and men,
respectively), higher SBP (130.8 mm/Hg versus 122.1 and
In spite of the growing attention on the importance and
surveillance of metabolic syndrome, it should be noted
that there is still considerable controversy regarding its
definition and utility. Recent reviews pose questions
regarding the need for a consensus in the definition of
metabolic syndrome, lack of certainty about the underlying pathophysiological process, whether the syndrome
itself conveys more information about health risk than
the sum of its individual components, and how different
combinations of risk factors should guide clinical management [39-41]. In addition to research examining longterm health risk from PTSD, prospective studies are similarly needed to continue to address these remaining questions about metabolic syndrome.
Table 4: Logistic regression predicting metabolic syndrome score
Variable
Estimate
Standard error
Wald
P
OR
Lower
Age
Race
Gender
Years of education
Nicotine abuse
Substance abuse
Alcohol abuse
MDD
CAPS
0.032
-0.329
-1.772
0.013
-0.439
0.418
-0.317
-0.274
0.011
0.017
0.324
0.805
0.059
0.281
0.294
0.332
0.293
0.005
3.710
1.031
4.852
0.051
2.441
2.015
0.915
0.876
4.759
0.054
0.310
0.028
0.821
0.118
0.156
0.339
0.349
0.029
1.033
0.719
0.170
1.014
0.645
1.519
0.728
0.760
1.011
0.999
0.381
0.035
0.903
0.372
0.853
0.380
0.429
1.001
95% CI
Upper
1.068
1.358
0.823
1.138
1.118
2.704
1.395
1.349
1.022
'Race' reflects the difference in metabolic risk comparing veterans of non-white to those of white ethnicity. MDD, major depressive disorder.
'CAPS' reflects one unit change in metabolic risk associated with one unit change in Clinician Administered PTSD Scale (CAPS) score.
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A closely related concept, allostatic load, represents the
cumulative biological burden of exposure to repeated
cycles of adaptation to stress [42]. It incorporates the
notion of stress, as well as the contribution of genetic,
developmental, and lifestyle factors that might underpin
the physiologic response to daily life events and traumatic
stressors [42-45]. The selected factors that contribute to
metabolic syndrome in this study closely approximate the
secondary mediators of allostatic load, with the exception
of serum triglycerides [46,47]. Our cross-sectional findings show a relationship between PTSD and metabolic
syndrome suggesting that perhaps metabolic syndrome in
itself is a useful clinical tool in quantifying the cardiovascular and metabolic impact of PTSD. In comparing those
with and without PTSD on individual measures of metabolic syndrome components, we only found significant
group differences for DBP, suggesting the utility of considering these measures collectively as the presence or
absence of metabolic syndrome. It should be noted, however, that these multiple comparisons were not adjusted
according to the Bonferroni principle, thereby potentially
inflating Type I error in our analysis. Several studies
appear to support the dysregulating effect of PTSD on a
number of physiologic systems [12-16,33-35,48]. Given
this association, future prospective studies examining
physical morbidity and mortality related to PTSD should
also assess metabolic syndrome to determine whether the
pattern of dysregulation represented by metabolic syndrome mediates long-term health risk in PTSD.
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Conclusion
This study extends the current knowledge of PTSD and
diminished health status by examining metabolic syndrome while controlling for relevant health and psychiatric variables. After controlling for MDD, and alcohol,
nicotine and substance abuse or dependence, we found a
robust and significant association between PTSD severity
and health risk as measured by metabolic syndrome. Further exploration, using prospective studies with assessment of CVD- and metabolic-related conditions, are
indicated to determine whether metabolic syndrome can
sufficiently account for the higher morbidity and mortality observed among those exposed to trauma or with
PTSD.
Abbreviations
CAPS: Clinician Administered PTSD Scale; CVD: cardiovascular disease; DBP: diastolic blood pressure; DSM-IV:
Diagnostic and Statistical Manual of Mental Disorders, 4th
Edition; HDL: high-density lipoprotein; HPA: hypothalamic-pituitary-adrenocortical axis; MDD: major depressive disorder; NCEP: National Cholesterol Education
Program; NHANES: National Health and Nutrition Examination Survey; OR: odds ratio; PTSD: posttraumatic stress
disorder; SBP: systolic blood pressure; SD: standard deviations; SPSS: Statistical Package for Social Sciences; VIF:
variance inflation factor; WHO: World Health Organization; WHR: waist-to-hip ratio.
Competing interests
Interestingly, we did not find MDD to be a significant and
unique predictor of risk for metabolic syndrome, in spite
of preliminary findings suggesting increased HPA activity
with depression [49-55]. These previous studies, however,
focused on depression and did not assess or control for
PTSD. Our findings may provide preliminary evidence for
a higher relative contribution of PTSD to metabolic syndrome after controlling for MDD.
One limitation of the current study is the under-representation of women and minority groups. While female gender was found to be associated with decreased risk of
metabolic syndrome, in spite of the small number of
women in the study (7.9%), race did not appear to have
unique effects on metabolic syndrome. A more diverse
sample may yield important information on whether the
relationship between PTSD and metabolic syndrome is
robust across different racial/ethnic groups.
Finally, given the cross-sectional design of our study, we
cannot fully establish a causal relationship between PTSD
and metabolic syndrome. Prospective studies are clearly
needed in this area to fully examine the long-term health
risk related to PTSD.
The authors declare that they have no competing interests.
Authors' contributions
PSH (1) conducted statistical analyses and participated in
drafting the manuscript. EFC was involved with statistical
analysis and writing the manuscript. UAH was involved in
data collection and study execution. NA participated in
interpretation of the findings, as well as manuscript drafting and editing. RLH participated in editing the manuscript. BAD was involved with data processing and
analysis and database construction. PSH (2) participated
in statistical analyses. SEN assisted with manuscript editing and formatting. DGB carried out study design, data
gathering and participated in manuscript drafting and
editing.
Acknowledgements
DGB and RLH are supported by VACO Research funds (VA Merit Review,
VA-DOD, MIRECC, HSR&D, and the Office of Environmental Hazards).
This work is supported in part by these funds. NA is supported in part by
NIH R01AR51524. PSH (1), NA, DGB, RLH, and SEN are also supported
by the VA Center of Excellence for Stress and Mental Health. We thank the
clinicians at the Veterans Affairs of Cincinnati who participated in the collection of data and Laura Harder who provided assistance with manuscript
formatting.
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