Articles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)

Contemporary longitudinal data on dietary patterns (DP) during adolescence are scarce. This study aimed to identify trajectories of adherence to an obesogenic DP and changes in diet quality (DQ), related food consumption, and adiposity markers during adolescence.

Methods

A cohort of 600 adolescents (293 girls, 48.8%) attending 24 secondary schools enrolled on the SI! Program for Secondary Schools trial in Spain was assessed when participants were approximately 12, 14, and 16 years old. An energy-dense, high-fat, and low-fiber (obesogenic) DP was derived at each time point by reduced rank regression (RRR) using the percentage energy intake from fat, fiber density, and dietary energy density as intermediate variables. Based on each participant's resulting scores, trajectories of adherence to the obesogenic DP were identified by latent class trajectory modeling. Adjusted associations between trajectories, DQ and food consumption changes, and adiposity markers during adolescence were analyzed with generalized linear models.

Results

Based on adherence to the obesogenic DP during adolescence, four stable trajectory groups (from lowest to highest adherence) were identified: trajectory 1 (44 participants [7.3%]), trajectory 2 (180 participants [30.0%]), trajectory 3 (292 participants [48.7%]), and trajectory 4 (84 participants [14.0%]). Overall DQ was moderate, but showed a gradient across trajectories, with trajectory 1 having the best quality. Although the identified trajectories were stable, individuals in the group with the lowest adherence to the obesogenic DP (trajectory 1) significantly improved their overall DQ over time, whereas those with the highest adherence (trajectory 4) showed the opposite trend. The group of adolescents in trajectory 4 had the least healthy central adiposity profile when ∼16 years old.

Conclusion

Four stable trajectories of adherence to an obesogenic DP were identified in a large cohort of adolescents, with DQ decreasing as adherence to the DP increased. Although adherence to the DP was stable, differences in food intake between trajectories widened over time, resulting in increased central adiposity in participants with the highest adherence to the pattern at the end of the study. Further research is needed to explore the determinants of adherence to obesogenic DPs in adolescence and to evaluate their effects on adiposity and overall health later in life.

RCW, particularly at high doses (RCW-HD), increased soil organic carbon and microbial biomass at an early stage. Despite a 50% reduction in organic fertiliser use, RCW-HD did not reduce N availability and crop productivity, suggesting improved N use efficiency. Several N-cycling gene abundances were elevated under CTL compared to RCW-HD, including the nitrification-related pmoA-amoA (+42%) and pmoC-amoC (+72%), and the denitrification-related nosZ (+14%). The RCW-HD no-till system increased nitrate reduction assimilation (+13% nrtABC) and favoured N-fixing bacterial genera such as Terrihabitans, Ferriphaselus, Azospira and Rhodopseudomonas. Soil depth significantly influenced 72% of the N-cycling genes, with key genes being more abundant at the surface. These results highlight the potential of RCW to improve N retention and soil fertility, while reducing fertiliser dependence and greenhouse gas emissions. They also support sustainable practices in regenerative agriculture by highlighting how microbiomes contribute to the efficiency of nitrogen cycling.

Keywords: Shotgun metagenomics; Biogeochemical process; Microbial activity; Organic agriculture; Soil organic matter; Soil regeneration.

fingerprinting for authenticating virgin olive oil (VOO) geographical origin. Both methods were selected for

being among the most promising targeted and untargeted approaches, respectively. These methods were applied

to the same sample set of nearly 400 VOO samples, covering diverse harvest years, cultivars and producers. PLSDA

classification models were developed to differentiate between Italian and non-Italian VOOs, as well as VOOs

from three closely located Italian regions. Isotopic models based on bulk δ13C, δ18O and δ2H achieved over 75 %

classification accuracy in distinguishing Italian from non-Italian VOOs, while SH fingerprinting outperformed

with over 90 % accuracy and greater sensitivity to regional differences, as assessed in external validation. This

systematic comparison provides insights into the strengths and weaknesses of each method, and the results will

guide future research to enhance their reliability in VOO geographical authentication.