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. 2024 Dec;63(8):3047-3060.
doi: 10.1007/s00394-024-03488-7. Epub 2024 Sep 4.

Longitudinal associations between vitamin D status and biomarkers of inflammation in a pan-European cohort of children and adolescents

Affiliations

Longitudinal associations between vitamin D status and biomarkers of inflammation in a pan-European cohort of children and adolescents

Maike Wolters et al. Eur J Nutr. 2024 Dec.

Abstract

Purpose: To investigate longitudinal associations between the vitamin D status and inflammatory markers in children and adolescents.

Methods: Children from eight European countries from the IDEFICS/I.Family cohort with repeated measurements were included in this study. A linear mixed-effect model was used to model the association of serum 25(OH)D as independent variable and z-scores of inflammatory markers [CRP, cytokines, adipokines, combined inflammation score] as dependent variables, where one level accounts for differences between individuals and the other for changes over age within individuals.

Results: A total of 1,582 children were included in the study. In the adjusted model, 25(OH)D levels were positively associated with adiponectin (β = 0.11 [95% CI 0.07; 0.16]) and negatively with the inflammation score (β = - 0.24 [95% CI - 0.40; - 0.08]) indicating that the adiponectin z-score increased by 0.11 units and the inflammation score decreased by 0.24 units per 12.5 nmol/l increase in 25(OH)D. In children with overweight or obesity, only a positive association between 25(OH)D and IP-10 was observed while in children with normal weight adiponectin was positively and the inflammation score was negatively associated. Associations of vitamin D with adiponectin and the inflammation score were stronger in girls than in boys and a positive association with TNF-α was observed only in girls.

Conclusion: Our results suggest that an increase in vitamin D concentrations may help to regulate inflammatory biomarkers. However, it seems to be no benefit of a better vitamin D status in children with overweight/obesity unless their weight is managed to achieve an improved inflammatory marker status.

Keywords: Adipokines; CRP; Children cohort; Cytokines; Inflammatory markers; Vitamin D.

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Conflict of interest statement

The authors declare that there are no conflict of interest.

Figures

Fig. 1
Fig. 1
Posthoc analyses to evaluate the marginal effect of 25(OH)D on inflammatory markers at different ages. 25(OH)D, 25-hydroxyvitamin D; CI, confidence interval; CRP, C-reactive protein; IL, interleukin; IL-1Ra, interleukin-1 receptor antagonist; IP-10, interferon gamma inducible protein; TNFα, tumor necrosis factor alpha. Inflammation score = sum of z-scores of proinflammatory markers (CRP, leptin, TNF-α, IP-10, IL-8, IL-6) - sum of z-scores of anti-inflammatory markers (IL-1Ra, IL-15, adiponectin, ghrelin). Marginal effects are presented as regression coefficient that represent the ß unit change in the z-score of inflammatory markers per 12.5 nmol/l increase in 25(OH)D at different ages. Associations are adjusted for age, sex, study region, lifetime smoking and alcohol status, membership in sports club, screen time/week, BMI, month of blood sample collection and parental education status with interaction term between 25(OH)D and age with age as a random slope
Fig. 2
Fig. 2
Association between serum 25-hydroxyvitamin D and inflammatory markers stratified on BMI. Abbreviations: 25(OH)D, 25-hydroxyvitamin D; BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; IL, interleukin; IL-1Ra, interleukin-1 receptor antagonist; IP-10, interferon gamma inducible protein; TNF-α, tumor necrosis factor alpha. Inflammation score = sum of z-scores of proinflammatory markers (CRP, leptin, TNF-α, IP-10, IL-8, IL-6) − sum of z-scores of anti-inflammatory markers (IL-1Ra, IL-15, adiponectin, ghrelin). Normal weight children: 18.5 ≤ BMI < 25; Overweight/obese children: BMI ≥ 25 [32]. The ß coefficient represents the ß unit change in the z-score of inflammatory markers per 12.5 nmol/l increase in 25(OH)D. Associations are adjusted for age, sex, study region, lifetime smoking and alcohol status, membership in sport club, screen time/week, month of blood sample collection and parental education status with age as a random slope

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