Evidence map›Paper›PMID 35350557›Full record

ArticleFrontiers in neuroscience2022

Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer's Disease Based on Major Depressive Disorder Risk Variants.

Suraj Upadhya, Hongliang Liu, Sheng Luo, Michael W Lutz, Ornit Chiba-Falek

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Suraj UpadhyaDivision of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States.
Hongliang LiuDepartment of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States.
Sheng LuoDepartment of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States.
Michael W LutzDivision of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States.
Ornit Chiba-FalekDivision of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States.
Duke Medical Center · USDuke University Hospital · US

Funding

Deciphering the regulation of gene expression in the etiology of LOADR01AG057522 · NIA · DUKE UNIVERSITY · PI CHIBA-FALEK, ORNIT, LUTZ, MICHAEL WILLIAM · 2017 to 2021
$3.6M
NIA NIH HHS R01 AG057522
6 · The paper itself

Abstract

Introduction: Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer's Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown. Methods: Major Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, Results: The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and Conclusion: This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.

Indexed as

depressionheterogeneitylate-onset Alzheimer’s diseasemajor depressive disorderneuropsychiatric symptomspolygenic risk scores

Identifiers

PMID35350557
PMCPMC8957806
OpenAlexW4220869574

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.