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Record W4383904036 · doi:10.1016/j.cdnut.2023.101971

Vitamin D and Zinc Supplementation to Improve Treatment Outcomes among COVID-19 Patients in India: Results from a Double-Blind Randomized Placebo-Controlled Trial

2023· article· en· W4383904036 on OpenAlexafffund
Uttara Partap, Kamal Kant Sharma, Yogesh N. Marathe, Molin Wang, Sanaa Shaikh, Pradeep D’Costa, Gaurav Gupta, Sabri Bromage, Elena C. Hemler, Nerges Mistry, Kevin C. Kain, Yatin Dholakia, Wafaie Fawzi

Bibliographic record

VenueCurrent Developments in Nutrition · 2023
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research Chairs
KeywordsMedicineInterquartile rangePlaceboHazard ratioVitaminInternal medicineRandomized controlled trialVitamin D and neurologyConfidence intervalGastroenterology

Abstract

fetched live from OpenAlex

There remains a need to identify low-cost interventions to improve COVID-19 outcomes. Vitamin D and zinc play a role in respiratory infections, and could hold value as part of therapeutic regimens. To determine the effect of vitamin D or zinc supplementation on recovery from COVID-19. We conducted a double-blind randomized 2x2 factorial placebo-controlled trial with 1:1:1:1 allocation ratio, enrolling non-pregnant adults with COVID-19 from hospitals in Mumbai and Pune, India (NCT04641195). Participants (N=181) were randomized to vitamin D3 (180,000IU bolus, then 2000IU daily), zinc (40mg daily), vitamin D3 and zinc, or placebo, for 8 weeks. Participants were followed until 8 weeks. The primary outcome was time to resolution of fever, cough, and shortness of breath. Secondary outcomes were duration of individual symptoms; need for assisted ventilation; duration of hospital stay; all-cause mortality; and blood biomarkers including nutritional, inflammatory and immunological markers. We observed no effect of vitamin D or zinc supplementation on time to resolution of all three symptoms (vitamin D hazard ratio [HR]: 0.92, 95% confidence interval [95% CI]: 0.66-1.30, P=0.650; zinc HR: 0.94, 95% CI: 0.67-1.33, P=0.745). Neither vitamin D nor zinc supplementation was associated with secondary outcomes, except for increased endline serum vitamin D with vitamin D supplementation (median [interquartile range] difference between endline and baseline for vitamin D: 5.3ng/mL [-2.3-13.7]; for no vitamin D: -1.4ng/mL [-5.6-3.9]; P=0.003). We observed non-significant increases in serum zinc at endline following zinc supplementation. There was no evidence of interaction between vitamin D and zinc supplementation; no effect of either on hypercalcemia; and no adverse events. Results suggest that neither vitamin D nor zinc supplementation improve COVID-19 treatment outcomes in this population. However, much larger-scale evidence, particularly from populations with vitamin D or zinc deficiency and severe infection, is required to corroborate our findings. NCT04641195, CTRI/2021/04/032593.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.054
GPT teacher head0.386
Teacher spread0.332 · 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 designRandomized trial
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

Citations9
Published2023
Admission routes2
Has abstractyes

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