Evidence map›Paper›PMID 41374098›Full record

ArticleNutrients2025

Exploring Vitamin D Trends Through Big Data Analysis.

Szilvia Racz, Miklos Emri, Ervin Berenyi, Laszlo Horvath, Bela E Toth, Sandor Barat, Edit Kalina, Luca Jozsa, Amrit Pal Bhattoa-Buzas, William B Grant and 1 more

Abstract read
In one paragraph

Article in Nutrients, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Szilvia RaczDepartment of Medical Imaging, Faculty of Medicine, Kalman Laki Doctoral School of the University of Debrecen, University of Debrecen, 4032 Debrecen, Hungary.
Miklos EmriDepartment of Medical Imaging, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0001-9884-9202
Ervin BerenyiDepartment of Medical Imaging, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Laszlo HorvathDepartment of Pharmaceutical Surveillance and Economics, Faculty of Pharmacy, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0003-1466-7052
Bela E TothDepartment of Pharmaceutical Surveillance and Economics, Faculty of Pharmacy, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0009-0002-7188-2082
Sandor BaratDepartment of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Edit KalinaDepartment of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Luca JozsaDepartment of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
Amrit Pal Bhattoa-BuzasDepartment of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.
William B GrantSunlight, Nutrition, and Health Research Center, 1745 Pacific Ave., Suite 504, San Francisco, CA 94109, USA.ORCID 0000-0002-1439-3285
Harjit Pal BhattoaDepartment of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary.ORCID 0000-0002-4909-0065

Funding

National Research, Development and Innovation Fund TKP2021-NKTA
6 · The paper itself

Abstract

BACKGROUND/

objectivesBig data analysis has revolutionized medical research, making it possible to analyze vast amounts of data and gain valuable insights that were previously impossible to obtain. Our knowledge of the characteristics of vitamin D sufficiency is primarily based on data from a limited number of observations, generally spanning a few years at most.

methodsHere at the Medical Faculty of the University of Debrecen, the big data approach has allowed us to analyze trends in vitamin D status using nearly 60,000 25-hydroxyvitamin D (25(OH)D) concentration results from 2000 onwards.

resultsApart from analyzing the well-known phenomenon of seasonality in 25(OH)D concentration, we observed a trend in test requests, which increased from a few hundred in 2000 to almost 10,000 in 2020. Of particular interest is the change in the gender gap in test requests. In previous years, test requests were primarily from women, but by the end of the analysis period, a significant number of requests were from men as well. Since the data set includes all age groups, we analyzed 25(OH)D concentration for incremental age sets of five years, from a few months to 100 years old. The prevalence of vitamin D insufficiency (<75 nmol/L) was clearly demarcated among various years of observation, age groups, sexes, and seasons. Our data was particularly valuable for analyzing the effect of the methodology used for 25(OH)D determination. Three different methodologies were used during the study period, and clear, statistically significant bias was observed.

conclusionsOur results clearly demonstrate the effect of the methodology used to determine 25(OH)D concentrations on vitamin D status, explicitly highlighting the urgent need to standardize the various platforms used to measure this important analyte and its consequences for public health.

Indexed as

Big DataData AnalysisVitamin DVitamin D DeficiencyAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPrevalence25-hydroxyvitamin DVitamin D25-hydroxyvitamin Dagebig datagendermethodologyseasonal variationsvitamin D

Identifiers

PMID41374098
PMCPMC12693762

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.