Physical activity highly impacts the neuroendocrine system and hormonal secretion. Numerous variables, both those related to the individual, including genetics, age, sex, biological rhythms, nutritional status, level of training, intake of drugs or supplements, and previous or current pathologies, and those related to the physical activity in terms of type, intensity, and duration of exercise, or environmental conditions can shape the hormonal response to physical exercise. The aim of this review is to provide an overview of the effects of physical exercise on hormonal levels in the human body, focusing on changes in concentrations of hormones such as cortisol, testosterone, and insulin in response to different types and intensities of physical activity. Regular monitoring of hormonal responses in athletes could be a potential tool to design individual training programs and prevent overtraining syndrome.

Athletic performance results from complex interactions between genetic and environmental factors. This review compiles and synthesizes available literature on polymorphic genes associated with endurance, power, and strength performance, as well as their links to injury susceptibility and chronic metabolic diseases. Endurance performance is modulated by ACE, PPARGC1A, HFE, UCP2, UCP3, CDKN1A, and PPARA, regulating mitochondrial biogenesis, oxygen utilization, and muscle fiber composition. Power performance involves ACTN3, MCT1, IGF1, AMPD1, AGT, and AGTR2, affecting anaerobic metabolism, lactate clearance, and fast-twitch fiber recruitment. Strength performance is influenced by AR, PPARG, ARK2N, MMS22L, LRPPRC, PHACTR1, and MTHFR, related to androgen signaling, muscle hypertrophy, and recovery. Injury-related genes (COL1A1, COL5A1, IL6, VEGFA, NOG) and metabolic risk genes (FTO, PPARG, ADRB3) further highlight the clinical relevance of genomics. Collectively, these insights support the application of genetic information to personalize training, enhance performance, prevent injuries, and guide exercise interventions to mitigate metabolic disease risk.

Article: From athletic performance to clinical care: the role of genetics in physical activity - Biochimica Clinica 2025 Sep 26 - Minerva Medica - Journals

Background: Athletic performance is a result of interaction between genetic and environmental factors, including training, nutrition, rest, hormonal and psychophysiological status. Numerous genes and polymorphisms can influence key physiological processes, such as muscle contractility, energy metabolism, oxidative capacity, and cardiovascular efficiency. Among these, the ACTN3 gene, and in particular the polymorphism rs1815739 (NM_001104.4:c.1729C>T; p.Arg577Ter), is well known for its association with athletic performance, but emerging evidence suggests broader effects on metabolic and inflammatory phenotypes. Methods: For this study, 109 elite athletes were enrolled and genotyped for ACTN3 c.1729C>T by Sanger sequencing. Moreover, biochemical markers, such as creatine kinase (CK), C-reactive protein (CRP), lipid profile, adiponectin, and neutrophil-to-lymphocyte ratio (NLR), were examined. Finally, to better investigate the association between genotype and athletic performance, the genotypes and the results of standardized physical tests were correlated in a small exploratory subgroup of 28 professional basketball athletes. Normality was assessed using the Shapiro–Wilk test; non-normally distributed variables were summarized as median [IQR] and analyzed with non-parametric methods. Genotype comparisons were adjusted for multiple testing using the Holm procedure, and principal biochemical models were adjusted for age, BMI, sex, and ancestry. Results: The c.1729C>T genotype distribution differed between the two ancestry groups (exact p = 0.049), and all three variants were compatible with Hardy–Weinberg equilibrium within the ancestry groups. No biochemical genotype association remained statistically significant after Holm correction. In the performance subgroup, no genotype–performance association remained significant after Holm correction. Conclusions: In this cohort, the corrected analyses did not provide robust evidence for genotype-dependent biochemical or performance effects. The results are exploratory and require replication in larger cohorts, considering ancestry and gender differences.

Autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDDs) are characterized by a complex genetic architecture involving both rare high-impact variants and cumulative low-effect alterations. The contribution of borderline copy number variants (CNVs) to disease susceptibility and phenotypic variability remains incompletely understood. Reflecting a real-world clinical setting rather than a prospectively recruited research cohort, we performed a retrospective analysis of a tertiary care registry comprising 328 individuals referred for suspected ASD or NDDs who underwent array comparative genomic hybridization (a-CGH), identifying 612 CNVs classified according to ACMG/ClinGen guidelines. CNVs were evaluated by type, size, genomic distribution, and clinical correlation, and patients were stratified based on CNV burden and the presence of borderline variants. Duplications were more frequent than deletions, and medium-sized CNVs (100–500 kb) were the most common. Borderline CNVs represented the largest category. Within this descriptive cohort, increased CNV burden, in both number and cumulative genomic size, co-occurred more frequently in individuals with severe phenotypes, including ASD with intellectual disability, epilepsy, and broader NDDs. Individuals with isolated ASD showed a lower burden and enrichment of small borderline variants, which often co-occurred alongside CNVs. In contrast, severe phenotypes were frequently associated with single large pathogenic variants. Recurrent loci included 15q11.2–q13, 16p11.2, and 22q11.21. Overall, these exploratory observations from a real-world clinical registry illustrate how cumulative genomic burden and borderline CNVs co-occur across different neurodevelopmental presentations, providing descriptive context for future mechanistic studies.