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Record W4307235023 · doi:10.1002/ejhf.2723

Optimal cardiometabolic health and risk of heart failure in type 2 diabetes: an analysis from the Look AHEAD trial

2022· article· en· W4307235023 on OpenAlexaff
Kershaw V. Patel, Muhammad Shahzeb Khan, Matthew W. Segar, Judy Bahnson, Katelyn R Garcia, Jeanne M. Clark, Ashok Balasubramanyam, Alain G. Bertoni, Muthiah Vaduganathan, Michael E. Farkouh, James L. Januzzi, Subodh Verma, Mark A. Espeland, Ambarish Pandey

Bibliographic record

VenueEuropean Journal of Heart Failure · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsSt. Michael's HospitalHeart and Stroke FoundationCanadian Heart Research CentreUniversity of Toronto
FundersNational Center on Minority Health and Health DisparitiesNational Heart, Lung, and Blood InstituteTranslational Science Center, Wake Forest UniversityIndian Health ServiceNational Center for Research ResourcesCenters for Disease Control and PreventionNational Institutes of HealthU.S. Department of Veterans AffairsMassachusetts Institute of TechnologyUniversity of Tennessee, KnoxvilleUniversity of PittsburghHarvard CatalystNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityUniversity of ColoradoNational Institute of General Medical SciencesNational Institute of Nursing ResearchMassachusetts General Hospital
KeywordsMedicineHazard ratioInternal medicineHeart failureGlycated hemoglobinType 2 diabetesConfidence intervalCardiologyProportional hazards modelEjection fractionBlood pressureCreatinineRenal functionFramingham Risk ScoreWaistDiabetes mellitusBody mass indexEndocrinologyDisease

Abstract

fetched live from OpenAlex

AIMS: To evaluate the contribution of baseline and longitudinal changes in cardiometabolic health (CMH) towards heart failure (HF) risk among adults with type 2 diabetes (T2D). METHODS AND RESULTS: Participants of the Look AHEAD trial with T2D and without prevalent HF were included. Adjusted Cox models were used to create a CMH score incorporating target levels of parameters weighted based on relative risk for HF. The associations of baseline and changes in the CMH score with risk of overall HF, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) were assessed using Cox models. Among the 5080 participants, 257 incident HF events occurred over 12.4 years of follow-up. The CMH score included 2 points each for target levels of waist circumference, glomerular filtration rate, urine albumin-to-creatinine ratio, and 1 point each for blood pressure and glycated haemoglobin at target. High baseline CMH score (6-8) was significantly associated with lower overall HF risk (adjusted hazard ratio [HR], ref = low score (0-3): 0.31, 95% confidence interval [CI] 0.21-0.47) with similar associations observed for HFpEF and HFrEF. Improvement in CMH was significantly associated with lower risk of overall HF (adjusted HR per 1-unit increase in score at 4 years: 0.80, 95% CI 0.70-0.91). In the ACCORD validation cohort, the baseline CMH score performed well for predicting HF risk with adequate discrimination (C-index 0.70), calibration (chi-square 5.53, p = 0.70), and risk stratification (adjusted HR [high (6-8) vs. low score (0-3)]: 0.35, 95% CI 0.26-0.46). In the Look AHEAD subgroup with available biomarker data, incorporating N-terminal pro-B-type natriuretic peptide to the baseline CMH score improved model discrimination (C-index 0.79) and risk stratification (adjusted HR [high (8-10) vs. low score (0-4)]: 0.18, 95% CI 0.09-0.35). CONCLUSIONS: Achieving target levels of more CMH parameters at baseline and sustained improvements were associated with lower HF risk in T2D.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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