MétaCan
Menu
Back to cohort

Genetic Risk Score Does Not Correlate with Body Mass Index of Latina Women in a Clinical Trial

2011· article· en· W2060349705 on OpenAlexfundno aff
Kimberly R. Coenen, Sharon M. Karp, Sabina B. Gesell, Mary S. Dietrich, Thomas M. Morgan, Shari L. Barkin

Bibliographic record

VenueClinical and Translational Science · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesDNA Genotek
KeywordsBody mass indexObesityMedicineWaistDemographyClinical trialPsychological interventionGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Obesity disproportionately affects Latina women. Common genetic variants are convincingly associated with body mass index (BMI) and may be used to create genetic risk scores (GRS) for obesity that could define genetically influenced forms of obesity and alter response to clinical trial interventions. The objective of this study was (1) to identify the frequency and effect size of common obesity genetic variants in Latina women; (2) to determine the clinical utility of a GRS for obesity with Latina women participating in a community-based clinical trial. DNA from 85 Latina women was genotyped for eight genetic variants previously associated with BMI in Caucasians, but not yet assessed in Latina populations. The main outcome measure was the correlation of GRS (sum of eight risk alleles) with BMI, waist circumference, and percent body fat. A majority (83%) of participants had a BMI ≥25. Frequency of loci near FTO, MC4R, and GNPDA2 were lower in Latinas than Caucasians. Association of each locus with BMI was lower in Latinas compared to Caucasians with no significant correlations with BMI. We conclude that an eight locus GRS has no clinical utility for explaining obesity or predicting response to intervention in Latina women participating in a clinical trial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.339
Teacher spread0.286 · 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 teacher head, 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueClinical and Translational ScienceSame topicGenetic Associations and EpidemiologyFrench-language works237,207