Abstract
Uncertainty remains about the effect of vitamin D therapy on biomarkers of health status in obesity. The molecular basis underlying this controversy is largely unknown. To address the existing gap, our study sought to compare changes in metabolomic profiles of obesity phenotypes (metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO)) patients with sub-optimal levels of vitamin D following vitamin D supplementation. We conducted two randomized double-blind clinical trials on participants with either of the two obesity phenotypes from Tehran province. These phenotypes were determined by the Adult Treatment Panel-III criteria. Patients in each of the MHO (n = 110) and MUHO (n = 105) groups were separately assigned to receive either vitamin D (4000 IU/d) or placebo for 4 months. Pre- and post-supplementation plasma metabolomic profiling were performed using Liquid chromatography coupled to a triple quadrupole mass spectrometry. Multivariable linear regression was used to explore the association of change in each metabolite with the trial assignment (vitamin D/placebo) across obesity phenotypes. Metabolites (n = 104) were profiled in 82 MHO and 78 MUHO patients. After correction for multiple comparisons, acyl-lysophosphatidylcholines C16:0, C18:0, and C18:1, diacyl-phosphatidylcholines C32:0, C34:1, C38:3, and C38:4, and sphingomyelin C40:4 changed significantly in response to vitamin D supplementation only in MUHO phenotype. The interaction analysis revealed that vitamin D therapy was different between the two obesity phenotypes based on acyl-lysophosphatidylcholines C16:0 and C16:1 and citrulline which were altered significantly after supplementation. Changes in metabolites were associated with changes in cardiometabolic biomarkers after the intervention. Vitamin D treatment influenced the obesity-related plasma metabolites only in adults with obesity and metabolically unhealthy phenotype. Therefore, not all patients with obesity may benefit from an identical strategy for vitamin D therapy. These findings provide mechanistic basis highlighting the potential of precision medicine to mitigate diseases in health-care settings.
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