Cluster Profiles of Health Metabolic Markers and Vitamin D †
by
, , and
Ángela Alcalá-Santiago
1,2,3,*, 
Miguel Rodríguez-Barranco
2,4,5,
Celia Rodríguez-Pérez
1,2,3
,
María José Sánchez
2,4,5 and
Esther Molina-Montes
1,2,3,5 1
Department of Nutrition and Food Science, Faculty of Pharmacy, University of Granada, 18071 Granada, Spain
2
Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
3
Institute of Nutrition and Food Technology (INYTA) ‘José Mataix’, Biomedical Research Centre, University of Granada, Avenida del Conocimiento s/n, 18071 Granada, Spain
4
Andalusian School of Public Health, 18011 Granada, Spain
5
CIBER de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
*
Author to whom correspondence should be addressed.
†
Presented at the 14th European Nutrition Conference FENS 2023, Belgrade, Serbia, 14–17 November 2023.
Proceedings 2023, 91(1), 414; https://doi.org/10.3390/proceedings2023091414
Published: 15 March 2024
(This article belongs to the Proceedings of The 14th European Nutrition Conference FENS 2023)
Abstract
:Vitamin D (VD) is an essential nutrient for which deficiency is highly prevalent and worthy of attention. In fact, VD deficiency may increase the risk of developing chronic diseases, including cardiovascular disease, diabetes and metabolic syndrome, and cancer. Recent studies have also reported a link between VD deficiency, comorbid conditions, and infectious diseases such as COVID-19, which is caused by the Sars-CoV-2 virus. The impact of VD deficiency on the metabolomic profiles of some of these diseases is poorly understood. The aim of this study was to analyse the relationship between VD and some metabolomics/biochemical markers. Metabolomics data (249 NMR-derived Nightingale Health markers) and some common biochemical markers related to VD and inflammation (VD, CRP, IGF-1, GGT, and steroid hormones, among others) were taken from the UK BIOBANK database. Two sets of markers were subjected to a hierarchical clustering analysis after data normalization: (i) the metabolomics-derived markers with VD (N = 10,000 randomly selected subjects) and (ii) the metabolomics-derived markers with all other biochemical markers (N = 674 subjects with complete data). Ward’s inter-cluster linkages and Euclidean and Manhattan distances were applied to group the markers and subjects based on their similarity. The silhouette method was considered to choose the optimal number of clusters. The results showed three distinctive clusters of subjects and three clusters of metabolites. The first cluster of HDL-related metabolites defined subjects with high, intermediate, and low levels of these metabolites. The second cluster of metabolites included VD, inflammatory markers (CRP and IGF-1), branched-chain amino acids (Valine, Isoleucine, and Leucine), polyunsaturated fatty acids, markers of the acetate metabolism, and LDL-related markers. VD showed a heterogeneous trend across the clusters of subjects. The third cluster comprised other cholesterol-related markers. Results were consistent in both sets of markers and distance matrixes. In conclusion, this exploratory study suggests that VD aggregates with key metabolic markers of energy metabolism and inflammation, pointing to synergistic mechanisms through which these markers could modulate metabolic disorders. These markers, however, do not seem to define subgroups of subjects with VD deficiency. Analyses are underway to explore the influence of other VD-related variables on these results.
Author Contributions
Conceptualization, E.M.-M., M.R.-B., C.R.-P., M.J.S. and Á.A.-S.; methodology, Á.A.-S.; formal analysis, Á.A.-S.; investigation, Á.A.-S. and E.M.-M.; resources, Á.A.-S.; data curation, Á.A.-S. and E.M.-M.; writing—original draft preparation, Á.A.-S.; writing—review and editing, E.M.-M., M.R.-B., C.R.-P. and M.J.S.; supervision, E.M.-M.; project administration, E.M.-M.; funding acquisition, E.M.-M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Project PECOVID-0200-2020, funded by Consejería de Salud y Consumo de la Junta de Andalucía and cofunded by the European Regional Development Fund (ERDF-FEDER).
Data Availability Statement
This research was conducted using the UK Biobank resource under. Application number 76564. Data can be obtained upon application to the UK Biobank.
Conflicts of Interest
The authors declare no conflict of interest.
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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MDPI and ACS Style
Alcalá-Santiago, Á.; Rodríguez-Barranco, M.; Rodríguez-Pérez, C.; Sánchez, M.J.; Molina-Montes, E. Cluster Profiles of Health Metabolic Markers and Vitamin D. Proceedings 2023, 91, 414. https://doi.org/10.3390/proceedings2023091414
AMA Style
Alcalá-Santiago Á, Rodríguez-Barranco M, Rodríguez-Pérez C, Sánchez MJ, Molina-Montes E. Cluster Profiles of Health Metabolic Markers and Vitamin D. Proceedings. 2023; 91(1):414. https://doi.org/10.3390/proceedings2023091414
Chicago/Turabian StyleAlcalá-Santiago, Ángela, Miguel Rodríguez-Barranco, Celia Rodríguez-Pérez, María José Sánchez, and Esther Molina-Montes. 2023. "Cluster Profiles of Health Metabolic Markers and Vitamin D" Proceedings 91, no. 1: 414. https://doi.org/10.3390/proceedings2023091414
