Home › Research › Digital health tools
Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology
Diniz BS, Lin CW, Sibille E, Tseng G, Lotrich F, Aizenstein HJ, Reynolds CF, Butters MA.. Journal of psychiatric research, 2016-07-11
Topic: Risk, prevention and care in population studies
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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Diniz, B. S., Lin, C. W., Sibille, E., Tseng, G., Lotrich, F., Aizenstein, H. J., et al. (2016). Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology. Journal of psychiatric research. https://doi.org/10.1016/j.jpsychires.2016.07.006
Vancouver
Diniz BS, Lin CW, Sibille E, Tseng G, Lotrich F, Aizenstein HJ, et al. Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology. Journal of psychiatric research. 2016. doi:10.1016/j.jpsychires.2016.07.006. PMID: 27447786.
BibTeX
@article{diniz2016-27447786,
author = {Diniz, B. S. and Lin, C. W. and Sibille, E. and Tseng, G. and Lotrich, F. and Aizenstein, H. J. and Reynolds, C. F. and Butters, M. A.},
title = {{Circulating biosignatures of late-life depression (LLD): Towards a comprehensive, data-driven approach to understanding LLD pathophysiology}},
journal = {Journal of psychiatric research},
year = {2016},
doi = {10.1016/j.jpsychires.2016.07.006},
pmid = {27447786},
url = {https://doi.org/10.1016/j.jpsychires.2016.07.006}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Machine learning analysis was applied to the combined dataset to determine the accuracy with which specific proteins could correctly discriminate LLD versus control participants.
- 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.
Why it matters
In 44 people with late-life depression and 31 controls, a panel of three proteins discriminated the groups with 100% accuracy in this small sample. This is about understanding late-life depression biology, not Alzheimer's disease, and does not test any treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of psychiatric research) |
| Journal's citation record | 2 / 4 | Journal of Psychiatric Research: journal h-index 220 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2016 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 51 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.2 citations a year over 12 year(s) since 2016 |
| Impact for its field and year | 3 / 6 | Cited 4.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 2 / 4 | Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Meryl A. Butters: h-index 74 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Toronto: institution h-index 1,923 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 27447786) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.jpsychires.2016.07.006
- PubMed: 27447786
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Related studies in Digital health tools
Also tagged “delirium”
858 studies in the library carry the topic tag “delirium”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 858 tagged studies.