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Record W4402618220 · doi:10.1016/j.clnu.2024.09.030

Metabolic phenotypes and vitamin D response in the critically ill: A metabolomic cohort study

2024· article· en· W4402618220 on OpenAlexaff
H. Kobayashi, Karin Amrein, Sherif Hanafy Mahmoud, Jessica Lasky‐Su, Kenneth B. Christopher

Bibliographic record

VenueClinical Nutrition · 2024
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of AlbertaSunnybrook Health Science Centre
FundersNational Institutes of HealthEuropean Society for Clinical Nutrition and MetabolismFresenius KabiOesterreichische NationalbankFoundation for the National Institutes of Health
KeywordsMedicineMetabolomicsCohortPhenotypeCritically illCohort studyVitaminPhysiologyInternal medicineBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Background & aims Although vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D 3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D 3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial. Methods In the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D 3 . To distinguish vitamin D 3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D 3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis. Results In 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N = 134; B. N = 123; C. N = 92; D. N = 104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D 3 . Further, in cluster D high-dose vitamin D 3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09–0.89); P = 0.03). In metabotype A, B, and C, high-dose vitamin D 3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment. Conclusion In this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D 3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.384
Teacher spread0.345 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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