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Record W4390196173 · doi:10.1002/alz.076811

Effect of genetic clusters related to insulin resistance on neurological traits in diverse populations from the Trans‐Omics for Precision Medicine Program

2023· article· en· W4390196173 on OpenAlexaff
Chloé Sarnowski, Yixin Zhang, Farah Ammous, Lincoln M. P. Shade, Xueqiu Jian, Josée Dupuis, Marie‐France Hivert, José C. Florez, Sudha Seshadri, Alanna C. Morrison

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsInsulin resistanceObesityType 2 diabetesGenome-wide association studyBiologyGeneticsBioinformaticsInternal medicineDiabetes mellitusEndocrinologyMedicineGenotypeGeneSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Abstract Background Insulin resistance (IR) is a major risk factor for Alzheimer’s disease (AD) and is primarily driven by obesity, another AD risk factor. The biological mechanism by which IR predisposes to AD is unknown. Genetic clustering analyses of type 2 diabetes (T2D) loci can help characterize which mechanism of impaired insulin action may be involved in AD and related traits. Method We constructed five genetic clusters related to IR (Obesity, Lipodystrophy, Liver/Lipid, ALP [alkaline phosphatase] negative, and Hyper‐Insulin Secretion) based on a recent clustering analysis of T2D loci (PMID: 36538063). We evaluated the association of each cluster with the Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR) and neurological traits (AD, dementia, general cognitive function, and four brain MRI volumes) in >38k participants (36% men, mean age 55yrs (14.5)) of diverse race or ethnicity, from the Trans‐Omics for Precision Medicine (TOPMed) Program. We conducted pooled and race/ethnicity‐stratified association analyses (European‐50%, African‐American‐22.5%, and Hispanic/Latino‐21%). We used logistic or linear mixed‐effect models, adjusted for age, sex, study, and the first 11 genetic principal components. We accounted for relatedness and allowed for heterogeneous variances across studies. Result We confirmed the association of each IR genetic cluster with HOMA‐IR in the pooled analysis (2.6E‐66≤P≤2E‐4) and detected significant to suggestive associations in group‐stratified analyses (6.3E‐39≤P≤0.05), except for the Hyper‐Insulin Secretion cluster in African‐Americans (P = 0.30). The genetic clusters were not significantly associated with neurological outcomes after multiple testing adjustment (P = 0.05/Ngroups/Nclusters/Noutcomes = 0.05/4/5/7 = 3.6×10−4). We observed nominal associations (P<0.05) for two genetic clusters. The Lipodystrophy cluster was negatively associated with intracranial volume in the pooled analysis (beta = ‐0.51, P = 0.003) as well as in European (beta = ‐0.47, P = 0.02) and Hispanic/Latino (beta = ‐1.23, P = 0.01) participants. The Hyper‐Insulin Secretion cluster was negatively associated with hippocampal volume in Hispanic/Latino participants (beta = ‐0.005, P = 0.03), and positively associated with AD and dementia (OR = 1.02 [1.001‐1.038], P = 0.03) in European participants. Conclusion Our findings are consistent with the literature suggesting that IR may be associated with lower brain volumes and increased AD risk. Our analyses shed light on two biological mechanisms of impaired insulin action potentially involved in AD and related traits, with genetic associations that may differ by race or ethnicity. Funding: R00AG066849

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.320
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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