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Archives of Clinical Neuropsychology logoLink to Archives of Clinical Neuropsychology
. 2024 Jul 31;39(8):1450–1456. doi: 10.1093/arclin/acae058

Associations of Post-Traumatic Stress Disorder and Objective Subtle Cognitive Difficulties in Cognitively Unimpaired Older Veterans

Mary Ellen Garcia 1,2, Peter Rantins 3,4,5, Alin Alshaheri Durazo 6,7, Uriel Urias 8,9, Alexandra J Weigand 10,11, Katherine J Bangen 12,13, Mark W Bondi 14,15, Amy J Jak 16,17, Kelsey R Thomas 18,19,✉
PMCID: PMC11586455  PMID: 39079083

Abstract

Introduction

Psychiatric conditions such as post-traumatic stress disorder (PTSD) and depression have a two-fold increased dementia risk in Veterans. Prior work has shown that psychiatric factors can both impact cognitive functioning and be early symptoms associated with Alzheimer’s disease (AD). Objectively defined subtle cognitive difficulties (Obj-SCD) has been associated with cognitive decline and AD biomarkers. However, Obj-SCD has not yet been investigated in the context of psychiatric disorders.

Methods

A total of 179 cognitively unimpaired Veterans (50–92 years old) underwent a comprehensive neuropsychological evaluation at VA San Diego and a retrospective medical record review. Chi-squared tests compared rates of psychiatric diagnoses in Veterans with and without Obj-SCD.

Results

About 21% of the sample was classified as Obj-SCD. Relative to cognitively unimpaired Veterans, Veterans classified as Obj-SCD had higher rates of PTSD, but not higher rates of other psychiatric conditions (e.g., depression). The PTSD findings appear to be driven by measures of cognitive efficiency.

Conclusion

Elevated rates of PTSD, but not other psychiatric conditions, were observed among Veterans with Obj-SCD. The prevalence and type of subtle cognitive difficulties associated with PTSD in older Veterans demonstrates a need, and informs potential targets, for intervention. Further work is needed to determine mechanisms of subtle cognitive difficulties in older Veterans with PTSD.

Keywords: Subtle cognitive decline, Post-traumatic stress disorder, Veterans, Alzheimer’s disease, Neuropsychology

INTRODUCTION

In 2020, the Department of Veterans Affairs (VA) provided medical care for over 9 million enrolled Veterans, >50% of whom were aged 65 years or older (Zhu & Sano, 2021). Accordingly, the number of Veterans with dementia is predicted to increase >29% by 2033 (U.S. Department of Veterans Affairs, 2021a), likely due to the aging of Vietnam-era Veterans. Veterans may be at an increased risk for dementia or mild cognitive impairment (MCI) and/or may experience these cognitive declines at a younger age compared with the general population due to a high prevalence of psychiatric and medical comorbidities (Bhattarai et al., 2019; Lohr et al., 2015). Notably, psychiatric conditions such as anxiety, depression, and post-traumatic stress disorder (PTSD) are associated with an approximately two-fold increased risk of dementia in Veterans (Byers et al., 2012; Qureshi et al., 2010).

Vietnam-era Veterans have been found to have a 20–30% lifetime prevalence of combat-related PTSD, and, even after >15 years following military discharge, approximately 10%–15% still meet criteria for PTSD (Beristianos et al., 2016; Dohrenwend et al., 2006; Yaffe et al., 2010). It has also been estimated that one in three Veterans who visited VA primary care clinics endorsed some symptoms of depression, and one in five endorsed symptoms requiring follow-up evaluations (U.S. Department of Veterans Affairs, 2021b). These psychiatric conditions such as PTSD and depression may contribute to accelerated biological aging and greater risk of vascular diseases and inflammatory processes, which are, in turn, risk factors for cognitive impairment and dementia (Lohr et al., 2015).

Within the field of Alzheimer’s disease (AD) research, there have been considerable efforts focused on identifying subtle cognitive changes in the preclinical stage of AD (i.e., prior to overt cognitive changes associated with MCI and dementia; Jack et al., 2018) to improve early detection and intervention efforts. A promising approach for early detection is the classification of objectively defined subtle cognitive decline (Obj-SCD), which is a “pre-MCI” diagnostic category used to identify early cognitive functions that are not severe enough to meet criteria for MCI or dementia. This method utilizes sensitive neuropsychological measures that include both traditional neuropsychological total scores as well as neuropsychological process scores, which capture the approach or types of errors made during testing (Edmonds et al., 2015; Thomas et al., 2018; Thomas et al., 2020a). Notably, as an extension of the comprehensive neuropsychological approach to classifying MCI, this pre-MCI Obj-SCD classification requires at least two scores (either total or process scores) to be >1 standard deviation (SD) below the norm-adjusted mean in order to balance reliability (i.e., requires 2 measures instead of 1) and sensitivity (i.e., −1 SD instead of −1.5 SD; Bondi et al., 2014; Jak et al., 2009). Obj-SCD classification has been associated with faster rates of progression to MCI/dementia (Thomas et al., 2018) as well as faster increases in plasma p-tau181, amyloid accumulation on positron emission tomography (PET) imaging, and entorhinal cortex thinning (Thomas et al., 2020a; Thomas et al., 2021).

Although Obj-SCD appears to be a promising classification for identifying those at risk for AD-related declines, most studies using Obj-SCD have been conducted in generally healthy convenience samples without high rates of psychiatric disorders. Therefore, we investigated the rates of Obj-SCD among older Veterans previously assessed in a VA Neuropsychology Clinic to determine whether the rates of various psychiatric conditions are higher in Obj-SCD than in cognitively unimpaired (CU) Veterans. Identifying Obj-SCD among Veterans with psychiatric disorders such as PTSD and depression is critical, as these conditions are known to increase the risk of dementia in older Veterans. Early detection of those at risk for dementia allows for earlier intervention efforts with the goal of slowing decline and improving overall quality of life. Further, understanding which psychiatric conditions have greater risk for Obj-SCD is helpful when designing interventions with the goal of improving cognition during this critical window prior to frank cognitive impairment.

METHODS

Procedure and participants

We conducted a retrospective review of 200 medical records of Veterans previously referred for a comprehensive neuropsychological evaluation at the VA San Diego Healthcare System. Data were obtained from VA medical records between January 2013 and December 2021, with information primarily obtained from the neuropsychological report to code available neuropsychological scores (total and process), existing psychiatric diagnoses, and health comorbidities. These records were coded as part of an ongoing study examining subtle cognitive decline among older Veterans with and without type 2 diabetes, and therefore Veterans with type 2 diabetes are somewhat overrepresented in this sample (37.4% vs. ~25% in general Veteran population). Notably, the proportion of individuals with type 2 diabetes did not differ across any psychiatric diagnosis in this sample (all ps > 0.34). This retrospective data collection was approved by the VA San Diego Institutional Review Board.

Records for which the Veteran met the following inclusion criteria at the time of their neuropsychological evaluation were coded: age 50 years or older, proficient in English to enable testing, medication stability (with no changes in dosage or medication type that could potentially impact cognition in the past 3 months), no diagnosis of mild or major neurocognitive disorders, no history of significant neurological disorders such as Parkinson’s disease, multiple sclerosis, epilepsy, or severe traumatic brain injury (TBI), no diagnosis of schizophrenia, psychotic disorder, or bipolar I, no recent acute alcohol or substance use that would interfere with the evaluation, and a valid neuropsychological profile based on the impression and interpretation of the clinical neuropsychologist who evaluated the patient. Performance validity measures completed during the clinical evaluation varied by assessment, but the most common were the California Verbal Learning Test-Second Edition (CVLT-II) Forced Choice Recognition, Weschler Adult Intelligence Scale-4th Edition (WAIS-IV) Reliable Digit Span, and the Test of Memory Malingering; the consistency/feasibility of the overall profile was also considered by the neuropsychologist.

Neuropsychological measures that were consistently administered across most Veterans spanned four cognitive domains. Consistent with our prior work in Obj-SCD (Thomas et al., 2018; Thomas et al., 2020a), we included two scores per cognitive domain. Specifically, measures of Memory included the CVLT-II Trials 1–5 Immediate Recall and Long Delay Free Recall; measures of Language included the Boston Naming Test and Delis–Kaplan Executive Function System (D-KEFS) Category Fluency; measures of Attention/Processing Speed included WAIS-IV Digit Span and Coding; and measures of Executive Functioning included D-KEFS Letter Fluency and Trail Making Test Number-Letter Switching. Additionally, process scores that are associated with early changes in AD were obtained from the CVLT-II for use in the Obj-SCD classification and included Trials 1–5 Learning Slope, Cued Recall Intrusion Errors, and Recency Effect (Thomas et al., 2018). The normed scores from each of these tests’ respective manuals were used for applying the actuarial MCI and Obj-SCD criteria.

While only participants without a clinical consensus diagnosis of Mild or Major Neurocognitive Disorder were included, we also applied Jak/Bondi actuarial criteria for MCI to ensure consistency of our cognitively unimpaired sample. Participants were classified as MCI using Jak/Bondi criteria if they had two impaired scores (>1SD below the normed mean) within the same cognitive domain (e.g., Memory, Language, Attention/Processing Speed, Executive Functioning; Bondi et al., 2014; Jak et al., 2009). Six participants from our analytic sample were classified as MCI based on these criteria and were excluded. There were also 15 Veterans who were missing sufficient neuropsychological data such that their MCI and/or Obj-SCD classification could not be determined based on available data. Therefore, our analytic sample included 179 Veterans classified as either CU or as Obj-SCD. A Veteran was classified as Obj-SCD if they performed in the impaired range (>1 SD below the normed mean) on (a) two total scores in different cognitive domains, (b) on two process scores, or (c) on one total score and one process score.

Current psychiatric and health diagnoses (at the time of the neuropsychological evaluation) were coded as present or absent based on a thorough review of the electronic medical record. The clinical neuropsychological report was the primary source for both psychiatric and medical diagnoses since there are consistent sections in the report template addressing both psychiatric history and medical history. To ensure diagnoses were not missed, if available, the psychiatry or psychology note nearest to the date of the neuropsychological assessment (within 6 months) was reviewed for the psychiatric diagnosis. The same process was repeated for medical diagnoses and both primary care and specialty medical clinic notes were reviewed. Psychiatric diagnoses that were coded included: major depressive disorder (MDD), other depressive disorder, PTSD, generalized anxiety disorder (GAD), bipolar II disorder, attention deficit hyperactivity disorder (ADHD), or other psychiatric disorder (e.g., obsessive compulsive disorder, adjustment disorder, etc.). Health variables included hypertension, type 2 diabetes, obstructive sleep apnea, current smoker, and history of a TBI. Of note, based on the information available in the neuropsychological report, all participants who had a history of TBI except one appeared to meet criteria for a mild TBI based on 2021 VA/DoD Clinical Practice Guidelines and/or were labeled as having a mild TBI in their neuropsychological report (Department of Veterans Affairs and Department of Defense, 2021). The one participant with a moderate TBI had a loss of consciousness of 30 s, but post-traumatic amnesia of 3 days. Additionally, continuous blood pressure readings, hemoglobin A1c (to measure diabetes severity), and body mass index (BMI) from within 6 months of the neuropsychological assessment were available from the chart.

Statistical Analyses

To examine demographic factors, health variables, and psychiatric diagnoses by CU or Obj-SCD status, we used independent sample t-tests for continuous variables and chi-squared tests for categorical variables. For chi-squared tests with expected cell sizes less than 5, a Fisher’s exact test p-value was used. In secondary follow-up analyses, t-tests were used to examine the normed neuropsychological test scores for any specific psychiatric diagnosis (compared with participants without that specific diagnosis) that was more likely to be classified as Obj-SCD.

RESULTS

Participant characteristics

Table 1 shows the characteristics of the overall sample and by cognitive status. The overall sample had a mean age of 65.59 years (SD = 9.08; range = 50–91) and mean education of 14.54 years (SD = 2.44; range = 9–20). On average, Veterans had 10.68 years of service (SD = 9.41; range = 1–34) and the distribution across military branches included 29.1% Army, 48.0% Navy, 8.9% Air Force, 12.3% Marines, 0.6% Coast Guard, and 1.1% unknown. Based on the Obj-SCD criteria, of the 179 Veterans in the sample, there were 38 who met criteria for Obj-SCD (21%) and 141 (79%) who were considered CU. Within Obj-SCD, 19 participants (50%) were classified using just the total score criterion (i.e., impaired on two total scores in different cognitive domains) and 19 (50%) were classified using a combination of total and process scores; no participants were classified as Obj-SCD based on process scores only. There were no significant differences in age, education, sex/gender, premorbid functioning estimated by the Wide Range Achievement Test 4 (WRAT-4) Word Reading subtest, vascular health factors, or history of a TBI between CU and Obj-SCD groups. There was a difference in race such that the Obj-SCD group had a lower proportion of white Veterans and higher proportion of Asian Veterans, which was largely comprised of Filipino Veterans.

Table 1.

Sample characteristics, Health Factors, and Psychiatric diagnoses by cognitive status

Total Sample (N = 179) CU
(N = 141)		 Obj-SCD
(N = 38)		 t or χ2 p
Sample characteristics
Age, mean (SD) 65.59 (9.08) 65.62 (8.86) 65.47 (9.97) t = 0.09 .928
Education, mean (SD) 14.54 (2.44) 14.63 (2.51) 14.21 (2.17) t = 0.93 .345
WRAT-4 Reading, mean (SD) 104.02 (14.23) 104.26 (14.93) 103.13 (11.46) t = 0.43 .666
Women, N (%) 13 (7.3%) 11 (7.8%) 2 (5.3%) χ2 = 0.29 .738d
Race χ2 = 12.47 .014
 American Indian/Alaska Native, N (%) 1 (0.6%) 0 (0.0%) 1 (2.6%)
 Asian, N (%)a 27 (15.1%) 16 (11.3%) 11 (28.9%)
 Black/African American, N (%) 13 (7.3%) 11 (7.8%) 2 (5.3%)
 White, N (%) 132 (73.7%) 108 (76.6%) 24 (63.2%)
 Unknown or declined, N (%) 6 (3.4%) 6 (4.3%) 0 (0.0%)
Hispanic/Latinx, N (%)b 18 (10.3%) 17 (12.4%) 1 (2.6%) χ2 = 3.08 .127d
Health factors
History of TBI, N (%) 67 (37.6%) 54 (38.6%) 13 (34.2%) χ2 = 0.01 0.946
Hypertension, N (%) 122 (68.5%) 89 (66.4%) 33 (75.0%) χ2 = 1.13 .287
Type 2 diabetes, N (%) 67 (37.4%) 47 (34.8%) 19 (43.2%) χ2 = 1.00 .318
Obstructive sleep apnea, N (%) 106 (60.6%) 79 (60.3%) 29 (65.9%) χ2 = 0.44 .508
Current smoker, N (%) 22 (12.6%) 19 (14.0%) 3 (7.9%) χ2 = 0.44 .415d
Systolic blood pressure, mean (SD) 130.36 (13.85) 130.47 (13.77) 129.97 (14.32) t = 0.19 .847
Diastolic blood pressure, mean (SD) 76.53 (10.46) 76.77 (10.72) 75.68 (9.59) t = 0.56 .574
Hemoglobin A1c, mean (SD) 6.14 (1.35) 6.14 (1.45) 6.14 (0.92) t = −0.00 .998
BMI, mean (SD) 29.87 (5.09) 29.74 (5.02) 30.35 (5.41) t = −0.65 .519
Psychiatric diagnoses
Major depressive disorder, N (%) 51 (28.5%) 39 (27.7%) 12 (31.6%) χ2 = 0.23 .635
Other depressive disorder, N (%) 39 (21.8%) 29 (20.6%) 10 (26.3%) χ2 = 0.58 .446
Post-traumatic stress disorder, N (%) 62 (34.6%) 42 (30.5%) 19 (50.0%) χ2 = 5.03 .025
Generalized anxiety disorder, N (%) 25 (14.0%) 19 (13.5%) 6 (15.8%) χ2 = 0.13 .715
Bipolar II disorder, N (%) 3 (1.7%) 3 (2.1%) 0 (0.0%) χ2 = 0.82 1.00d
Attention deficit hyperactivity disorder, N (%) 8 (4.5%) 7 (5.0%) 1 (2.6%) χ2 = 0.38 1.00d
Other psychiatric disorder, N (%)c 25 (14.0%) 19 (13.5%) 6 (15.8%) χ2 = 0.13 .715
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Note: CU=Cognitively Unimpaired; Obj-SCD=Objectively Defined Subtle Cognitive Decline; WRAT-4 = Wide Range Achievement Test, Fourth Edition; TBI = Traumatic Brain Injury; BMI = Body Mass Index. aWithin the Asian group, there was a large representation of participants who identified as Filipino specifically (total sample n = 20; CU n = 12; Obj-SCD n = 8); bFour participants’ ethnicity was unknown or declined to answer. cOther psychiatric disorder included multiple conditions such as adjustment disorder, obsessive compulsive disorder, panic disorder, etc. dFisher’s exact test was used.

Rates of psychiatric diagnoses in Obj-SCD

Compared to CU Veterans, those with Obj-SCD had a higher rate of PTSD (30.5% vs. 50.0%, p = .025). Obj-SCD did not have higher rates of other psychiatric conditions such as MDD (27.7% vs. 31.6%, p = .635), other depressive disorder (20.6% vs. 26.3%, p = .446), GAD (13.5% vs. 15.8%, p = .715), bipolar II disorder (2.1% vs. 0.0%, pFisher’s = 1.00), ADHD (5.0% vs. 2.6%, pFisher’s = 1.00), or other psychiatric diagnoses (13.5% vs. 15.8%, p = .715). Notably, when MDD, other depressive disorder, and bipolar II were combined into a “mood disorders” category, there was still no significantly difference in rate of mood disorders in CU and Obj-SCD groups (49.6% vs. 57.9%, p = .367). Additionally, results did not change when ADHD was included in “other psychiatric disorders.” To ensure that premorbid functioning was not driving the higher rate of PTSD in the Obj-SCD group, we compared WRAT-4 Reading in Veterans with PTSD (mean = 103.33, SD = 12.01) and without PTSD (mean = 104.39, SD = 15.33) and WRAT-4 Reading did not differ by PTSD status (p = .641). Analyses were also run when excluding the one participant with a moderate TBI, and the results did not change.

Associations between PTSD status and cognitive measures

In secondary follow-up analyses, we examined the performance on the specific measures used in the Obj-SCD classification by PTSD status to determine which measures may be driving the higher rate of Veterans with PTSD who were classified as Obj-SCD. WAIS-IV Coding (d = 0.655, p < .001), D-KEFS Trail Making Test Number-Letter Switching (d = 0.324, p = .046), and CVLT-II Cued Recall Intrusion Errors (d = 0.327, p = .039) differed in Veterans with (n = 62) and without (n = 117) PTSD such that Veterans with PTSD performed worse on these indices (Fig. 1). After applying a false discovery rate correction of 0.05, only the WAIS-IV Coding score remained significantly lower in the PTSD group. Given the higher proportion of Asian Veterans in the Obj-SCD group, we also examined whether there was an association between race and PTSD diagnosis (i.e., different rates of PTSD across race groups), but there was not a significant association (p = .238).

Fig. 1.

Fig. 1

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Neuropsychological test normed Z-scores by PTSD status. Note: This graph illustrates the performance of participants with and without PTSD across various cognitive tests. The dotted line represents participants with PTSD (n = 62), and the solid line represents those without PTSD (n = 117). Higher scores indicate better performance, except for CVLT CR intrusion errors, where higher scores reflect worse performance. Significant differences (p < .05) are noted with effect sizes (d-values) where applicable. Only coding was significant after a false discovery rate correction. Abbreviations: CVLT = California Verbal Learning Test-II; LDFR = long delay free recall; Learn Slope = Trials 1–5 learning slope; CR Intru = cued recall intrusion errors; BNT = Boston Naming Test; DKEFS = Delis-Kaplan Executive Function System; Cat Flu = category fluency; Letter Flu = letter fluency; Trails Switch = letter-number switching (condition 4).

DISCUSSION

Our results suggest that, in Veterans without MCI or dementia, those with PTSD were more likely to experience subtle cognitive difficulties on neuropsychological measures, a circumscribed association that was not present for other psychiatric conditions. Measures of efficiency seem to be driving the higher rates of PTSD in Obj-SCD, particularly a measure of processing speed. These findings are consistent with prior research demonstrating worse cognitive performance in older Veterans with PTSD relative to those without PTSD (Schuitevoerder et al., 2013; Thomas et al., 2024; Weiner et al., 2023), but extend this research to include Obj-SCD and process scores.

It is often difficult to disentangle the role of psychiatric symptoms in preclinical AD. For example, neuropsychiatric symptoms (e.g., depression, apathy, anxiety, agitation, and other behaviors), sometimes referred to as mild behavioral impairment (MBI), have been shown to be early indicators of emerging AD and neurodegeneration in the preclinical and prodromal phases, as pathologic changes often begin years before onset of cognitive impairment (Huang et al., 2024; Jack et al., 2018; Soto et al., 2024). Conversely, in some cases, psychiatric symptoms (e.g., depressive symptoms) may be a response to one noticing subtle cognitive declines. Further, chronic mental health conditions such as anxiety and depression may also contribute to cognitive decline via an increased cortisol response, which exerts neurotoxic effects on the hippocampus and can promote neuroinflammation (Ouanes & Popp, 2019). Regardless, psychiatric symptoms are a known risk factor for progression to dementia (Burhanullah et al., 2020). However, while neuropsychiatric factors have garnered significant attention in AD (Soto et al., 2024), the age and nature of onset (since PTSD is tied to a specific event) and trajectory of chronic PTSD symptoms and their associations with AD and other neurodegenerative biomarkers remain unclear.

PTSD has been linked to worse cognition in older adults, as well as longitudinal cognitive decline and increased risk of dementia (Prieto et al., 2023; Yaffe et al., 2010). Mechanisms for these associations need further exploration, although PTSD has been associated with structural brain changes in regions including the hippocampus, anterior cingulate, and prefrontal structures, which could potentially make one more susceptible to the effects of AD pathology (Weiner et al., 2017), alterations in the hypothalamic–pituitary–adrenal axis leading to an increased cortisol response (Ouanes & Popp, 2019), pro-inflammatory markers (Lohr et al., 2015), and increased risk of cardiovascular diseases that can also impact cognition (Beristianos et al., 2016). Interestingly, while PTSD and depression are often co-occurring and depression has similar elevated risks of dementia as PTSD in older Veterans (Byers et al., 2012; Byers & Yaffe, 2014), neither MDD nor other depressive disorder were disproportionally represented in the Obj-SCD group. Similarly, the Obj-SCD group did not have higher rates of GAD and other psychiatric disorders, although it should be noted that some of these conditions had a low prevalence in our sample that may make it difficult to detect effects.

In prior work, Obj-SCD has been associated with faster amyloid accumulation and measures of neurodegeneration (Bangen et al., 2021; Thomas et al., 2020a; Thomas et al., 2021) as well as greater cerebrovascular burden (Calcetas et al., 2022; Thomas et al., 2020b). Given recent evidence from the Department of Defense-Alzheimer’s Disease Neuroimaging Initiative that PTSD was not associated with elevated AD biomarkers of amyloid and tau (Weiner et al., 2023) and that PTSD symptoms predict cognitive decline above and beyond AD biomarkers (Prieto et al., 2023), other etiologies such as vascular and inflammatory mechanisms need to be explored in order to better identify potential targets for intervention to prevent or slow the reduced cognitive performance associated with PTSD.

In the current study, measures of cognitive efficiency especially speed of processing, differed in Veterans with and without PTSD, and likely contributed to the higher rate of Obj-SCD classification among Veterans with PTSD. Beyond any specific neurodegenerative pathologies, it is possible that PTSD-related factors (e.g., difficulty concentrating, sleep difficulties, medications) may be at least partially responsible for the subtle cognitive difficulties observed in this sample. Additional work is needed to determine whether evidence-based PTSD treatment may yield improvements in these subtle cognitive difficulties. Prior work has demonstrated that a combination of cognitive processing therapy plus compensatory cognitive training has improved both PTSD symptoms and cognition in OEF/OIF/OND Veterans with a history of TBI (Jak et al., 2019), and a similar intervention in older Veterans may also be effective at improving both the PTSD symptoms and subtle cognitive difficulties. The Obj-SCD classification may be a useful tool to identify an at-risk group of older Veterans with PTSD to enroll in such interventions prior to more notable and impairing declines that impact quality of life.

The strengths of the current study are closely related to the sample that was used. Previous literature on Obj-SCD has been mostly conducted in primarily non-Hispanic/Latinx white older adult samples in which individuals with psychiatric disorders and significant vascular disease were excluded. Our study is more representative of the older Veteran population and is inclusive of a wide range of vascular/medical comorbidities (68.5% with hypertension, 37.4% with type 2 diabetes, 60.6% with obstructive sleep apnea, 37.6% with a history of TBI, and 12.6% who are currently smoking). Additionally, another strength of the study is that everyone was considered to have a valid profile (passed cognitive validity tests) in the context of a clinical evaluation. The inclusion criteria of the study were relatively broad, and results can be generalized to Veterans who had subjective cognitive decline or who had a family member or doctor who was concerned enough about their cognition for them to be referred for a comprehensive neuropsychological assessment. To our knowledge, very few studies have the advantage of being a real-world VA clinic sample with a comprehensive neuropsychological assessment.

While this approach has strengths, the retrospective nature of the medical record review limited our ability to describe the heterogeneity within each of the psychiatric disorders’ symptom severity and profile since measures used to assess symptom severity were not consistent across participants. Additionally, while all included Veterans were determined to have a valid neuropsychological profile and some embedded measures like the CVLT-II Forced Choice and Reliable Digit Span were available on all participants, additional measures and the approach used to determine validity may have varied by neuropsychologist. The current study is cross-sectional and therefore lacks the longitudinal data needed to determine whether the Obj-SCD group is declining faster than the CU group over time and whether PTSD diagnosis impacts the rate of decline.

Taken together, our study findings showed that older Veterans with PTSD had higher rates of subtle cognitive difficulties associated with Obj-SCD classification. There was no association between Obj-SCD status and other psychiatric diagnosis, including depression, which is known to be highly comorbid with PTSD. Given the higher risk of dementia in older Veterans with PTSD, it is critical to improve early identification of those at risk for future cognitive declines in this complex population. While many older Veterans presenting to a memory clinic with PTSD may not show a pattern of cognitive impairment consistent with MCI, we see that over 20% meet criteria for Obj-SCD, which has demonstrated associations with AD and vascular biomarkers. Therefore, the Obj-SCD may identify an at-risk group of older Veterans, particularly with PTSD, who may most benefit from early intervention as well as identify potential targets (e.g., speed of processing, cognitive efficiency) for cognitive intervention. Further research is needed to determine whether these subtle cognitive difficulties can be mitigated with PTSD treatment and to explore whether Obj-SCD is linked to elevated AD and/or cerebrovascular biomarkers and accelerated longitudinal cognitive decline compared to cognitively unimpaired older Veterans, particularly among those with PTSD or other psychiatric comorbidities. This research could ultimately contribute to developing more effective prevention and individualized treatment approaches for older Veterans at-risk for cognitive decline.

Contributor Information

Mary Ellen Garcia, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA.

Peter Rantins, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA; Department of Psychology, San Diego State University, San Diego, CA 92182, USA.

Alin Alshaheri Durazo, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychology, San Diego State University, San Diego, CA 92182, USA.

Uriel Urias, Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA; Department of Psychology, San Diego State University, San Diego, CA 92182, USA.

Alexandra J Weigand, Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA; San Diego State University/University of California Joint Doctoral Program in Clinical Psychology, San Diego, CA 92182, USA.

Katherine J Bangen, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA.

Mark W Bondi, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA.

Amy J Jak, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA.

Kelsey R Thomas, VA San Diego Healthcare System, San Diego, CA 92161, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA.

FUNDING

This work was supported by the U.S. Department of Veterans Affairs Clinical Sciences Research and Development Service (1IK2CX001865 to KRT, 1I01CX001842 to KJB), National Institutes of Health/National Institute on Aging grants (R03 AG070435 to KRT, RF1 AG082726 to KRT, R01 AG063782 to KJB), and the Alzheimer’s Association (AARG-22-723000 to KRT).

CONFLICT OF INTEREST

Dr. Bondi receives royalties from Oxford University Press. The authors report no other disclosures and have no conflicts of interest.

DATA AVAILABILITY

The de-identified data could be made available on reasonable written request by a requester from the United States to the corresponding author with assurance that the recipient will not attempt to identify or re-identify any individual.

References

  1. Bangen, K. J., Thomas, K. R., Weigand, A. J., Edmonds, E. C., Clark, A. L., Solders, S., et al. (2021). Elevated plasma neurofilament light predicts a faster rate of cognitive decline over 5 years in participants with objectively-defined subtle cognitive decline and MCI. Alzheimer’s & Dementia, 17(10), 1756–1762. 10.1002/alz.12324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Beristianos, M. H., Yaffe, K., Cohen, B., & Byers, A. L. (2016). PTSD and risk of incident cardiovascular disease in aging veterans. The American Journal of Geriatric Psychiatry, 24(3), 192–200. 10.1016/j.jagp.2014.12.003. [DOI] [PubMed] [Google Scholar]
  3. Bhattarai, J., Oehlert, M. E., Multon, K. D., & Sumerall, S. W. (2019). Dementia and cognitive impairment among U.S. veterans with a history of MDD or PTSD: A retrospective cohort study based on sex and race. Journal of Aging and Health, 31(8), 1398–1422. 10.1177/0898264318781131. [DOI] [PubMed] [Google Scholar]
  4. Bondi, M. W., Edmonds, E. C., Jak, A. J., Clark, L. R., Delano-Wood, L., McDonald, C. R., et al. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42(1), 275–289. 10.3233/JAD-140276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Burhanullah, M. H., Tschanz, J. T., Peters, M. E., Leoutsakos, J.-M., Matyi, J., Lyketsos, C. G., et al. (2020). Neuropsychiatric symptoms as risk factors for cognitive decline in clinically normal older adults: The Cache County study. The American Journal of Geriatric Psychiatry, 28(1), 64–71. 10.1016/j.jagp.2019.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Byers, A. L., Covinsky, K. E., Barnes, D. E., & Yaffe, K. (2012). Dysthymia and depression increase risk of dementia and mortality among older veterans. The American Journal of Geriatric Psychiatry, 20(8), 664–672. 10.1097/JGP.0b013e31822001c1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Byers, A. L., & Yaffe, K. (2014). Depression and dementias among military veterans. Alzheimer’s & Dementia, 10(3, Supplement), S166–S173. 10.1016/j.jalz.2014.04.007. [DOI] [PubMed] [Google Scholar]
  8. Calcetas, A. T., Thomas, K. R., Edmonds, E. C., Holmqvist, S. L., Edwards, L., Bordyug, M., et al. (2022). Increased regional white matter hyperintensity volume in objectively-defined subtle cognitive decline and mild cognitive impairment. Neurobiology of Aging, 118, 1–8. 10.1016/j.neurobiolaging.2022.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Department of Veterans Affairs and Department of Defense . (2021). Clinical Practice Guideline for the Management and Rehabilitation of Post-Acute Mild Traumatic Brain Injury. https://www.healthquality.va.gov/guidelines/Rehab/mtbi/.
  10. Dohrenwend, B. P., Turner, J. B., Turse, N. A., Adams, B. G., Koenen, K. C., & Marshall, R. (2006). The psychological risks of Vietnam for U.S. veterans: A revisit with new data and methods. Science, 313(5789), 979–982. 10.1126/science.1128944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Edmonds, E. C., Delano-Wood, L., Galasko, D. R., Salmon, D. P., & Bondi, M. W. (2015). Subtle cognitive decline and biomarker staging in preclinical Alzheimer’s disease. Journal of Alzheimer’s Disease, 47(1), 231–242. 10.3233/JAD-150128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Huang, L., Huang, Q., Xie, F., & Guo, Q. (2024). Neuropsychiatric symptoms in Alzheimer’s continuum and their association with plasma biomarkers. Journal of Affective Disorders, 348, 200–206. 10.1016/j.jad.2023.12.062. [DOI] [PubMed] [Google Scholar]
  13. Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., et al. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. 10.1016/j.jalz.2018.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Jak, A. J., Bondi, M. W., Delano-Wood, L., Wierenga, C., Corey-Bloom, J., Salmon, D. P., et al. (2009). Quantification of five neuropsychological approaches to defining mild cognitive impairment. The American Journal of Geriatric Psychiatry, 17(5), 368–375. 10.1097/JGP.0b013e31819431d5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jak, A. J., Jurick, S., Crocker, L. D., Sanderson-Cimino, M., Aupperle, R., Rodgers, C. S., et al. (2019). SMART-CPT for veterans with comorbid post-traumatic stress disorder and history of traumatic brain injury: A randomised controlled trial. Journal of Neurology, Neurosurgery & Psychiatry, 90(3), 333–341. 10.1136/jnnp-2018-319315. [DOI] [PubMed] [Google Scholar]
  16. Lohr, J. B., Palmer, B. W., Eidt, C. A., Aailaboyina, S., Mausbach, B. T., Wolkowitz, O. M., et al. (2015). Is post-traumatic stress disorder associated with premature senescence? A review of the literature. The American Journal of Geriatric Psychiatry, 23(7), 709–725. 10.1016/j.jagp.2015.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Ouanes, S., & Popp, J. (2019). High cortisol and the risk of dementia and Alzheimer’s disease: A review of the literature. Frontiers in Aging Neuroscience, 11, 43. 10.3389/fnagi.2019.00043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Prieto, S., Nolan, K. E., Moody, J. N., Hayes, S. M., & Hayes, J. P. (2023). Posttraumatic stress symptom severity predicts cognitive decline beyond the effect of Alzheimer’s disease biomarkers in veterans. Translational Psychiatry, 13(1), Article 1. 10.1038/s41398-023-02354-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Qureshi, S. U., Kimbrell, T., Pyne, J. M., Magruder, K. M., Hudson, T. J., Petersen, N. J., et al. (2010). Greater prevalence and incidence of dementia in older veterans with posttraumatic stress disorder. Journal of the American Geriatrics Society, 58(9), 1627–1633. 10.1111/j.1532-5415.2010.02977.x. [DOI] [PubMed] [Google Scholar]
  20. Schuitevoerder, S., Rosen, J. W., Twamley, E. W., Ayers, C. R., Sones, H., Lohr, J. B., et al. (2013). A meta-analysis of cognitive functioning in older adults with PTSD. Journal of Anxiety Disorders, 27(6), 550–558. 10.1016/j.janxdis.2013.01.001. [DOI] [PubMed] [Google Scholar]
  21. Soto, M., Rosenberg, P., Ballard, C., Vellas, B., Miller, D., Gauthier, S., et al. (2024). Neuropsychiatric symptoms in AD: Clinical trials targeting mild behavioral impairment: A report from the international CTAD task force. The Journal of Prevention of Alzheimer’s Disease, 11(1), 56–64. 10.14283/jpad.2023.125. [DOI] [PubMed] [Google Scholar]
  22. Thomas, K. R., Bangen, K. J., Edmonds, E. C., Weigand, A. J., Walker, K. S., Bondi, M. W., et al. (2021). Objective subtle cognitive decline and plasma phosphorylated tau181: Early markers of Alzheimer’s disease-related declines. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12238. 10.1002/dad2.12238. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Thomas, K. R., Bangen, K. J., Weigand, A. J., Edmonds, E. C., Wong, C. G., Cooper, S., et al. (2020a). Objective subtle cognitive difficulties predict future amyloid accumulation and neurodegeneration. Neurology, 94(4), e397–e406. 10.1212/WNL.0000000000008838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Thomas, K. R., Clark, A. L., Weigand, A. J., Edwards, L., Durazo, A. A., Membreno, R., et al. (2024). Cognition and amyloid-β in older veterans: Characterization and longitudinal outcomes of data-derived phenotypes. Journal of Alzheimer’s Disease, 99(1), 417–427. 10.3233/JAD-240077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Thomas, K. R., Edmonds, E. C., Eppig, J., Salmon, D. P., Bondi, M. W., & Initiative, A. D. N. (2018). Using neuropsychological process scores to identify subtle cognitive decline and predict progression to mild cognitive impairment. Journal of Alzheimer’s Disease, 64(1), 195–204. 10.3233/JAD-180229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Thomas, K. R., Osuna, J. R., Weigand, A. J., Edmonds, E. C., Clark, A. L., Holmqvist, S., et al. (2020b). Regional hyperperfusion in older adults with objectively-defined subtle cognitive decline. Journal of Cerebral Blood Flow & Metabolism, 41(5), 1001–1012. 10.1177/0271678X20935171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. U.S. Department of Veterans Affairs (2021a). Statistical Projections of Alzheimer’s Dementia for VA Patients. https://www.va.gov/GERIATRICS/docs/VHA_ALZHEIMERS_DEMENTIA_Statistical_Projections_FY22_and_FY33_sgc102221.pdf. Fiscal Years 2022 and 2033. Retrieved January 25, 2023, from  VA Enrollees, and U.S. Veterans. [Google Scholar]
  28. U.S. Department of Veterans Affairs . (2021b). Depression. https://www.research.va.gov/topics/depression.cfm
  29. Weiner, M. W., Harvey, D., Hayes, J., Landau, S. M., Aisen, P. S., Petersen, R. C., et al. (2017). Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer’s disease in Vietnam veterans using the Alzheimer’s Disease Neuroimaging Initiative: Preliminary report. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 3(2), 177–188. 10.1016/j.trci.2017.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Weiner, M. W., Harvey, D., Landau, S. M., Veitch, D. P., Neylan, T. C., Grafman, J. H., et al. (2023). Traumatic brain injury and post-traumatic stress disorder are not associated with Alzheimer’s disease pathology measured with biomarkers. Alzheimer’s & Dementia, 19, 884–895. 10.1002/alz.12712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Yaffe, K., Vittinghoff, E., Lindquist, K., Barnes, D., Covinsky, K. E., Neylan, T., et al. (2010). Posttraumatic stress disorder and risk of dementia among US veterans. Archives of General Psychiatry, 67(6), 608–613. 10.1001/archgenpsychiatry.2010.61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Zhu, C. W., & Sano, M. (2021). Demographic, health, and exposure risks associated with cognitive loss, Alzheimer’s disease and other dementias in US military veterans. Frontiers in Psychiatry, 12, 610334. 10.3389/fpsyt.2021.610334. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Data Availability Statement

The de-identified data could be made available on reasonable written request by a requester from the United States to the corresponding author with assurance that the recipient will not attempt to identify or re-identify any individual.

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