Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology

https://doi.org/10.1016/j.jpsychires.2016.07.006Get rights and content

Highlights

  • There is scarce information about the molecular mechanisms late-life depression (LLD).

  • We investigated the molecular biosignatures of LLD using proteomic analysis.

  • Also uncovered abnormalities in biological processes dysregulation of proteostasis, nutrient sensing and cell-to-cell interaction in LLD.

  • Our findings attest to the underlying neurobiological heterogeneity of LLD.

Abstract

There is scarce information about the pathophysiological processes underlying Late-Life Depression (LLD). We aimed to determine the neurobiological abnormalities related to LLD through a multi-modal biomarker approach combining a large, unbiased peripheral proteomic panel and structural brain imaging. We examined data from 44 LLD and 31 control participants. Plasma proteomic analysis was performed using a multiplex immunoassay. We evaluated the differential protein expression between groups with random intercept models. We carried out enrichment pathway analyses (EPA) to uncover biological pathways and processes related to LLD. Machine learning analysis was applied to the combined dataset to determine the accuracy with which specific proteins could correctly discriminate LLD versus control participants. Sixty-one proteins were differentially expressed in LLD (p < 0.05 and FDR < 0.01). EPA showed that these proteins were related to abnormal immune-inflammatory control, cell survival and proliferation, proteostasis control, lipid metabolism, intracellular signaling. Machine learning analysis showed that a panel of three proteins (C-peptide, FABP-liver, ApoA-IV) discriminated LLD and control participants with 100% accuracy. The plasma proteomic profile in LLD revealed dysregulation in biological processes essential to the maintenance of homeostasis at cellular and systemic levels. These abnormalities increase brain and systemic allostatic load leading to the downstream negative outcomes of LLD, including increased risk of medical comorbidities and dementia. The peripheral biosignature of LLD has predictive power and may suggest novel putative therapeutic targets for prevention, treatment, and neuroprotection in LLD.

Introduction

Late-life major depression (LLD) is a common psychiatric disorder in older adults, with one-year prevalence rates ranging from 4% to 12% in developed and developing countries (Byers et al., 2010; do Nascimento et al., 2015). It is a clinically heterogeneous disorder, associated with negative health outcomes, e.g., higher rates of medical comorbidities and mortality risk (including suicide), disability, and increased risk for dementia (Alexopoulos et al., 2002, Diniz et al., 2013a, Diniz et al., 2014a).

Despite high public health burden and significance, there is still only sparse information about basic neurobiological abnormalities related to this disorder. Structural neuroimaging studies have consistently shown that individuals with LLD have higher cerebrovascular disease burden and higher rates of whole brain, caudate and hippocampal atrophy compared to non-depressed individuals (Culang-Reinlieb et al., 2011, Butters et al., 2009, Taylor et al., 2014). LLD is associated with significantly higher levels of pro-inflammatory and lower levels of anti-inflammatory markers, reduced neurotrophic support, and higher levels of oxidative stress markers and activity of glycogen synthase kinase 3β compared to non-depressed older adults (Alexopoulos and Morimoto, 2011, Diniz et al., 2011, Diniz et al., 2012, Diniz et al., 2014b, Pomara et al., 2012, Xiong et al., 2015).

Although these studies have increased our understanding of neurobiological abnormalities associated with LLD, our current knowledge is nonetheless fragmented. One potential reason is that most studies have investigated single or a few biomarkers in isolation, and thus their results do not provide an integrated view of related biological and molecular processes. The recent development of large biomarker panels analyzed by multiplex technology and other “omics” methods (e.g. metabolomics, genomics) now permits simultaneous measurement of most relevant biological pathways, helping overcome some of the current conceptual and methodological limitations of single biomarker studies (Arnold et al., 2012, Diniz et al., 2015, Wu et al., 2006, Paige et al., 2016).

No study thus far has attempted to identify a circulating proteomic signature for LLD. Within this context, the current study sought to evaluate blood-based biomarker abnormalities related to LLD, using a plasma-based, unbiased, data-driven, comprehensive multiplex proteomic analysis. We also sought to elucidate biological pathways and molecular processes related to these peripheral biomarkers. Although we had no a priori hypotheses, given the intentionally data-driven design of the study, we expected to confirm the association of LLD with markers of inflammation and vascular disease, and, mainly, to uncover novel circulating peripheral biomarkers related to major depression. We anticipate that observations from such an approach will inform subsequent confirmatory studies. Finally, we applied a machine learning approach to identify putative predictive biomarkers for LLD.

Section snippets

Subject recruitment and cognitive assessment

Forty-four older adults age ≥65 years with remitted LLD and 31 older adults with no history of major depression or other major psychiatric disorder (control group) were included in this analysis. All of the participants were enrolled in a research clinic based at the University of Pittsburgh’s NIMH-sponsored Advanced Center for Intervention and Services Research for Late-Life Mood Disorders (P30 MH90333). All LLD participants had previously met DSM-IV criteria for current unipolar Major

Results

Table 1 shows the socio-demographic, clinical, and cognitive characteristics of the LLD and control participants. Participants with LLD had a lower frequency of males and higher scores on the HDRS-17 and CIRS-G scales (for medical comorbidity) compared to control participants.

Sixty-one proteins were significantly associated with LLD (p-value < 0.05 and q-value < 0.01) (Table 2). Pathway enrichment analysis showed that these proteins were related to known biological processes associated with

Discussion

In the present study, an unbiased, multi-modal, data-driven analysis of peripheral circulating proteins showed that LLD was associated with abnormal expression of a large set of circulating proteins spanning distinct biological pathways, e.g., immune-inflammatory control, proteostasis, lipid metabolism, cell survival and apoptosis, and nutrient sensing. Our results provide evidence that the neurobiological abnormalities in LLD are extensive and involve several distinct, but interrelated

Contributors

Breno Satler Diniz, MD, PhD: Study concept, statistical analysis, interpretation of the results and writing the manuscript.

Chien-Wei Lin, PhD: statistical analysis and writing the manuscript.

Etienne Sibille, PhD: statistical analysis, interpretation of the results, and writing the manuscript.

George Tseng, PhD: statistical analysis and writing the manuscript.

Francis Lotrich, MD, PhD: Study concept and writing the manuscript.

Howard J. Aizenstein, MD, PhD: Study concept and writing the manuscript.

Role of funding sources

The funding sources did not have any role in the conception of the study, data collection and analysis, interpretation of the results, writing of the manuscript and in the final decision to publish the present study.

Financial disclosure

The authors have no financial conflict of interest related to this work to disclose.

Acknowledgment

this work was support by NIH grants P30MH90333 (Reynolds), MH080240, MH080240-S1 & S2(Butters), P50 AG05133 (Butters, Aizenstein); MH093723 (Sibille), the UPMC Endowment in Geriatric Psychiatry (Reynolds), The John A. Hartford Foundation Center of Excellence in Geriatric Psychiatry (Reynolds); CNPq 472138/2013-8 and 466623/2014-3(Diniz). We thank Drs. James Becker and Oscar Lopez for participating in the cognitive adjudication of the study participants.

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