PTSD, major depression, and advanced transcriptomic age in brain tissue

An EWAS of dementia biomarkers and their associations with age, African ancestry, and PTSD

BACKGROUND

Large-scale cohort and epidemiological studies suggest that PTSD confers risk for dementia in later life but the biological mechanisms underlying this association remain unknown. This study examined this question by assessing the influences of PTSD, APOE ε4 genotypes, DNA methylation, and other variables on the age- and dementia-associated biomarkers Aβ40, Aβ42, GFAP, NfL, and pTau-181 measured in plasma. Our primary hypothesis was that PTSD would be associated with elevated levels of these markers.

METHODS

Analyses were based on data from a PTSD-enriched cohort of 849 individuals. We began by performing factor analyses of the biomarkers, the results of which identified a two-factor solution. Drawing from the ATN research framework, we termed the first factor, defined by Aβ40 and Aβ42, "Factor A" and the second factor, defined by GFAP, NfL and pTau-181, "Factor TN." Next, we performed epigenome-wide association analyses (EWAS) of the two-factor scores. Finally, using structural equation modeling (SEM), we evaluated (a) the influence of PTSD, age, APOE ε4 genotype and other covariates on levels of the ATN factors, and (b) tested the mediating influence of the EWAS-significant DNAm loci on these associations.

RESULTS

The Factor A EWAS identified one significant locus, cg13053408, in FANCD2OS. The Factor TN analysis identified 3 EWAS-significant associations: cg26033520 near ASCC1, cg23156469 in FAM20B, and cg15356923 in FAM19A4. The SEM showed age to be related to both factors, more so with Factor TN (β = 0.581, p < 0.001) than Factor A (β = 0.330, p < 0.001). Genotype-determined African ancestry was associated with lower Factor A (β = 0.196, p < 0.001). Contrary to our primary hypothesis, we found a modest negative bivariate correlation between PTSD and the TN factor scores (r = - 0.133, p < 0.001) attributable primarily to reduced levels of GFAP (r = - 0.128, p < 0.001).

CONCLUSIONS

This study identified novel epigenetic associations with ATN biomarkers and demonstrated robust age and ancestral associations that will be essential to consider in future efforts to develop the clinical applications of these tests. The association between PTSD and reduced GFAP, which has been reported previously, warrants further investigation.

Longitudinal study of traumatic-stress related cellular and cognitive aging

Traumatic stress is associated with both accelerated epigenetic age and increased risk for dementia. Accelerated epigenetic age might link symptoms of traumatic stress to dementia-associated biomarkers, such as amyloid-beta (Aβ) proteins, neurofilament light (NFL), and inflammatory molecules. We tested this hypothesis using longitudinal data obtained from 214 trauma-exposed military veterans (85 % male, mean age at baseline: 53 years, 75 % White) who were assessed twice over the course of an average of 5.6 years. Cross-lagged panel mediation models evaluated measures of lifetime posttraumatic stress disorder and internalizing and externalizing comorbidity (assessed at Time 1; T1) in association with T1 epigenetic age (per the GrimAge algorithm) and T1 plasma markers of neuropathology along with bidirectional temporal paths between T1 and T2 epigenetic age and the plasma markers. Results revealed that a measure of externalizing comorbidity was associated with accelerated epigenetic age (β = 0.30, p <.01), which in turn, was associated with subsequent increases in Aβ-40 (β = 0.20, p <.001), Aβ-42 (β = 0.18, p <.001), and interleukin-6 (β = 0.18, p <.01). T1 advanced epigenetic age and the T1 neuropathology biomarkers NFL and glial fibrillary acidic protein predicted worse performance on T2 neurocognitive tasks assessing working memory, executive/attentional control, and/or verbal memory (ps = 0.03 to 0.009). Results suggest that advanced GrimAge is predictive of subsequent increases in neuropathology and inflammatory biomarkers as well as worse cognitive function, highlighting the clinical significance of this biomarker with respect to cognitive aging and brain health over time. The finding that advanced GrimAge mediated the association between psychiatric comorbidity and future neuropathology is important for understanding potential pathways to neurodegeneration and early identification of those at greatest risk.

Decoding the role of transcriptomic clocks in the human prefrontal cortex

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.

Association between Perceived Health-Related Quality of Life and Depression with Frailty in the FRASNET Study

Frailty is a major challenge facing the aging world. The phenotype of the frail subject is still far from being satisfactorily defined. We report data on mood, cognition, and quality of life (QoL) in relation to anamnestic factors, health, and socio-economic status in the FRASNET geriatric population (1204 subjects in stable health conditions), which is an observational cohort study that includes fairly balanced groups of Italian frail (421, 35%), pre-frail (449, 37.3%) and robust (334, 27.7%) subjects. A conditional inference tree analysis revealed a substantial influence of psychological variables on frailty. The physical indicator of QoL (Short Form Survey-36-Physical Component Summary, SF-36-PCS) was the predominant variable in the full model (threshold at 39.9, p < 0.001): higher frailty was found in subjects with a caregiver and lower SF-36-PCS. Frailty was also associated with the mental indicator of QoL (Short Form Survey-36-Mental Component Summary, SF-36-MCS), depression (Geriatric Depression Scale, GDS-15), leisure activities, and level of education. In support of the prominent role of inflammation in aging and mental illness, the SF-36-PCS score was correlated with the blood concentration of C-X-C motif chemokine ligand 10 (CXCL10) (r Pearson -0.355, p = 0.015), a critical signal in cell senescence and inflammaging, while the rs7567647 variant in FN1 gene encoding a glycoprotein in the extracellular matrix was significantly associated with frailty in a multivariable model (p = 0.0006). The perception of health-related QoL and subclinical depression contribute to frailty. Their assessment could improve the identification of older patients at increased risk of adverse outcomes.