1. Comparing the influence of early and late time-restricted eating with energy restriction and energy restriction alone on cardiometabolic markers, metabolic hormones and appetite in adults with overweight/obesity : per-protocol analysis of a 3-month randomized clinical trialBernarda Habe, Tanja Črešnovar, Ana Petelin, Saša Kenig, Nina Mohorko, Zala Jenko Pražnikar, 2025, original scientific article Abstract: Background It remains unclear whether adding time-restricted eating (TRE) to energy restriction (ER) offers additional cardiometabolic benefits, particularly in metabolic hormone regulation, and insulin sensitivity. This per-protocol analysis assessed whether early TRE (eTRE) or late TRE (lTRE), when combined with ER, additionally improves insulin resistance indexes, and cardiovascular and liver biomarkers compared to ER alone. Methods We analysed per-protocol data of 90 participants, 31 from the eTRE with ER (eTRE + ER) group, 28 from the lTRE with ER (lTRE + ER) group and 31 from the ER group. As chronotype-adapted diets have already been shown to produce better outcomes than non-adapted ones and in line with real-life behaviour, randomisation was performed on the basis of the individuals’ chronotype. Anthropometric and biochemical measurements for analysis were taken at baseline, and after first and third month of intervention. The primary outcome was mean change in body mass, while the secondary outcomes were mean changes in glycaemic markers (fasting glucose, fasting insulin), indexes of insulin resistance, cardiovascular and liver markers and metabolic hormones (adiponectin, ghrelin, leptin, leptin/ghrelin ratio). Additionally, participant’s subjective appetite was also assessed at baseline and in third month of the intervention. Results We confirmed that participants who adhered to eTRE + ER for 3 months showed greater improvements in % of fat mass, BMI, and fasting glucose compared to those in the lTRE + ER and/or ER group. These greater reductions in % of the fat mass and BMI were accompanied by more pronounced decreases in leptin levels, with eTRE + ER showing larger leptin reductions than lTRE + ER or ER. Additionally, the eTRE group showed a significantly greater decrease in desire for food and greater reduction in capacity to eat than ER. However, insulin levels, insulin resistance indexes, lipid profiles, adiponectin, ghrelin, visceral fat indexes, and liver enzymes showed similar changes across all groups. Conclusions This analysis showed that eTRE + ER is more effective weight management strategy, while eTRE + ER, lTRE + ER and ER are comparable effective on cardiovascular, liver and insulin resistance markers. Trial registration https://clinicaltrials.gov/study/NCT05730231 (NCT05730231, registered on February 6, 2023).
Keywords: obesity, nutrition, metabolism
Published in RUP: 30.07.2025; Views: 467; Downloads: 9
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2. Identifying risk factors for sarcopenia using machine learning : insights from multimodal dataFelicita Urzi, Domen Šoberl, Ornella Caputo, Marco Vincenzo Narici, 2025, original scientific article Abstract: Purpose This study aims to identify key risk factors for sarcopenia using machine learning models, leveraging anthropomet- ric, biochemical, functional, nutritional, and genetic data. By developing predictive models, the research seeks to improve early detection, enhance diagnostic accuracy, and facilitate personalized interventions for individuals at risk of sarcopenia. Methods We analysed multimodal data from 484 older adults. Two scenarios: Set-a (including SARC–CalF, excluding SARC-F) and Set-b (including SARC-F, excluding SARC–CalF) were applied in a three-stage modeling process with progressively reduced features and optimized predictive performance using machine learning models. Key predictors were ranked using SHAP values, and model performance was evaluated using AUC, accuracy, sensitivity, and specificity. Internal validation and DeLong’s test were applied to assess robustness and statistical differences. Results The most predictive risk factors included functional measures (chair stand, gait speed), nutritional indicators (pro- tein, folate, copper, vitamin B7), clinical conditions (diabetes, comorbidities, low-density lipoprotein (LDL)), and anthro- pometric markers (body mass index (BMI), calf circumference). Genetic features also contributed to risk stratification. The best-performing model Set-b (with screening test SARC-F) achieved an AUC of 0.951 and an accuracy of 93.62%. While SARC–CalF showed higher individual feature importance, the model achieved an AUC of 0.945 and accuracy of 92.2%. Conclusions This study highlights that traditional sarcopenia screening can be enhanced by capturing complex interplay of functional, nutritional, clinical, and genetic factors, offering clinicians a more accurate and tailored tool for early detec- tion and risk stratification. Future research should focus on validating these models in larger, independent, and longitudinal cohorts to assess their predictive utility across diverse populations and over time.
Keywords: genetics, nutrition, risk factors, sarcopenia
Published in RUP: 23.07.2025; Views: 423; Downloads: 6
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3. Obesity measures and dietary parameters as predictors of gut microbiota phyla in healthy individualsKatja Kranjc, Ana Petelin, Jure Pražnikar, Esther Nova, Noemi Redondo, Marcos Ascensión, Zala Jenko Pražnikar, 2020, original scientific article Abstract: : The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host%s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25%50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.
Keywords: gut microbiota, nutrition, obesity, lifestyle parameters, clustering
Published in RUP: 10.09.2020; Views: 2587; Downloads: 80
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