PD656 - CHRONONUTRITIONAL PHENOTYPES AND CLOCK GENE EXPRESSION IN A SAMPLE OF PORTUGUESE ADULTS: A PILOT STUDY

PD656

CHRONONUTRITIONAL PHENOTYPES AND CLOCK GENE EXPRESSION IN A SAMPLE OF PORTUGUESE ADULTS: A PILOT STUDY

M. Lages^1,2,3,4,5,*, S. Carmo-Silva^1,5,6, R. Barros^2,3,7, M. P. Guarino^1,8

¹ciTechCare – Center for Innovative Care and Health Technology, Polytechnic University of Leiria, Leiria, ²Faculty of Nutrition and Food Science, ³Laboratory for Integrative and Translational Research in Population Health (ITR),, University of Porto, ⁴EPIUnit – Institute of Public Health, EPIUnit – Institute of Public Health, Porto, ⁵Polytechnic University of Coimbra, Coimbra Health School, ⁶Polytechnic University of Coimbra, H&TRC- Health and Technology Research Center, Coimbra Health School, Coimbra, ⁷EPIUnit – Institute of Public Health, University of Porto, Porto, ⁸ESSLei, School of Health Sciences, Polytechnic University of Leiria, Leiria, Portugal

Rationale: Chrononutrition explores how meal timing and energy distribution interact with circadian biology. Misalignment between eating patterns and the endogenous clock may contribute to metabolic dysfunction, yet evidence integrating dietary timing, clock gene expression and metabolic markers remains limited. This study aimed to identify chrononutritional phenotypes and examine their association with clock gene expression and metabolic markers.

Methods: Cross-sectional study including adults across BMI categories. Dietary intake was assessed over 3 days. Hierarchical clustering followed by K-means was applied to chrononutritional variables. Clusters were compared using one-way ANOVA or Kruskal-Wallis. Bbiochemical markers, including lipid profile, fasting glucose and C-reactive protein (CRP), were determined. Clock gene expression (BMAL1, CLOCK, PER2, CRY) was quantified by RT-qPCR in whole blood at 8h and 16h.

Results: 18 participants were included (94.4% female; 42.7±16.2 years; BMI 26.4±7.2 kg/m²). Two clusters were identified: early energy distribution (early-ED; n=10; 22.9% energy at breakfast, 20.7% after 20h) and late energy distribution (late-ED; n=8; 13.7% energy at breakfast, 31.5% after 20h). Groups differed in breakfast energy (p<0.001) and evening energy intake (p=0.007), with trends towards higher BMI and longer eating window in Late-ED (p=0.051 and p=0.061, respectively). Clock gene expression did not differ significantly between clusters, though Late-ED showed a trend towards higher morning PER2 expression and greater CLOCK amplitude.

Conclusion: Two chrononutritional phenotypes were identified, differing in energy distribution patterns. Subtle differences in clock gene expression may suggest circadian phase differences associated with late energy distribution. These preliminary findings highlight the value of integrating meal timing and molecular circadian markers in nutritional research.

Disclosure of Interest: None declared