The BDNF-Increasing Allele is Associated With Increased Proportion of Fast-Twitch Muscle Fibers, Handgrip Strength, and Power Athlete Status

A critical examination of sport discipline typology: identifying inherent limitations and deficiencies in contemporary classification systems

Exercise scientists (especially in the field of biomolecular research) frequently classify athletic cohorts into categories such as endurance, strength, or mixed, and create a practical framework for studying diverse athletic populations between seemingly similar groups. It is crucial to recognize the limitations and complexities of these classifications, as they may oversimplify the multidimensional characteristics of each sport. If so, the validity of studies dealing with such approaches may become compromised and the comparability across different studies challenging or impossible. This perspective critically examines and highlights the issues associated with current sports typologies, critiques existing sports classification systems, and emphasizes the imperative for a universally accepted classification model to enhance the quality of biomolecular research of sports in the future.

Exploratory analysis using machine learning algorithms to predict pinch strength by anthropometric and socio-demographic features

Objectives. This study examines the role of different machine learning (ML) algorithms to determine which socio-demographic factors and hand-forearm anthropometric dimensions can be used to accurately predict hand function. Methods. The cross-sectional study was conducted with 7119 healthy Iranian participants (3525 males and 3594 females) aged 10-89 years. Seventeen hand-forearm anthropometric dimensions were measured by JEGS digital caliper and a measuring tape. Tip-to-tip, key and three-jaw chuck pinches were measured using a calibrated pinch gauge. Subsequently, 21 features pertinent to socio-demographic factors and hand-forearm anthropometric dimensions were used for classification. Furthermore, 12 well-known classifiers were implemented and evaluated to predict pinches. Results. Among the 21 features considered in this study, hand length, stature, age, thumb length and index finger length were found to be the most relevant and effective components for each of the three pinch predictions. The k-nearest neighbor, adaptive boosting (AdaBoost) and random forest classifiers achieved the highest classification accuracy of 96.75, 86.49 and 84.66% to predict three pinches, respectively. Conclusions. Predicting pinch strength and determining the predictive hand-forearm anthropometric and socio-demographic characteristics using ML may pave the way to designing an enhanced tool handle and reduce common musculoskeletal disorders of the hand.

Genomic predictors of physical activity and athletic performance

Physical activity and athletic performance are complex phenotypes influenced by environmental and genetic factors. Recent advances in lifestyle and behavioral genomics led to the discovery of dozens of DNA polymorphisms (variants) associated with physical activity and allowed to use them as genetic instruments in Mendelian randomization studies for identifying the causal links between physical activity and health outcomes. On the other hand, exercise and sports genomics studies are focused on the search for genetic variants associated with athlete status, sports injuries and individual responses to training and supplement use. In this review, the findings of studies investigating genetic markers and their associations with physical activity and athlete status are reported. As of the end of September 2023, a total of 149 variants have been associated with various physical activity traits (of which 42 variants are genome-wide significant) and 253 variants have been linked to athlete status (115 endurance-related, 96 power-related, and 42 strength-related).

Genes and Athletic Performance: The 2023 Update

Phenotypes of athletic performance and exercise capacity are complex traits influenced by both genetic and environmental factors. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status summarises recent advances in sports genomics research, including findings from candidate gene and genome-wide association (GWAS) studies, meta-analyses, and findings involving larger-scale initiatives such as the UK Biobank. As of the end of May 2023, a total of 251 DNA polymorphisms have been associated with athlete status, of which 128 genetic markers were positively associated with athlete status in at least two studies (41 endurance-related, 45 power-related, and 42 strength-related). The most promising genetic markers include the AMPD1 rs17602729 C, CDKN1A rs236448 A, HFE rs1799945 G, MYBPC3 rs1052373 G, NFIA-AS2 rs1572312 C, PPARA rs4253778 G, and PPARGC1A rs8192678 G alleles for endurance; ACTN3 rs1815739 C, AMPD1 rs17602729 C, CDKN1A rs236448 C, CPNE5 rs3213537 G, GALNTL6 rs558129 T, IGF2 rs680 G, IGSF3 rs699785 A, NOS3 rs2070744 T, and TRHR rs7832552 T alleles for power; and ACTN3 rs1815739 C, AR ≥21 CAG repeats, LRPPRC rs10186876 A, MMS22L rs9320823 T, PHACTR1 rs6905419 C, and PPARG rs1801282 G alleles for strength. It should be appreciated, however, that elite performance still cannot be predicted well using only genetic testing.

Brain-derived neurotrophic factor associated with kidney function

BACKGROUND

We examined the relationship between brain-derived neurotrophic factor (BDNF) and chronic kidney disease (CKD).

METHODS

First, a cross-sectional study was conducted in 480 participants without known diabetes. An oral glucose tolerance test (OGTT) was administered after overnight fasting, and blood samples were collected at 0, 30, and 120 min. Second, a total of 3003 participants were enrolled for the case-control genetic analysis. After assigning them to a case or a control group based on age and CKD status, we investigated the association between BDNF gene variants and susceptibility to CKD.

RESULTS

A higher fasting serum BDNF quartile was significantly associated with a lower prevalence of CKD (P value for trend  < 0.001). Based on the receiver operating characteristic analysis, the fasting BDNF level had a larger area under the curve for differentiating CKD (0.645, 95% CI 0.583‒0.707) than the BDNF levels at both 30 min (0.547, 95% CI 0.481‒0.612) and 120 min (0.598, 95% CI 0.536‒0.661). A significantly lower CKD prevalence (odds ratio = 0.30, 95% CI 0.12‒0.71) was observed in the highest quartile of fasting BDNF level than that in the lowest quartile, whereas no interquartile differences were observed for BDNF levels determined at 30 or 120 min during the OGTT. Furthermore, BDNF-associated variants, including rs12098908, rs12577517, and rs72891405, were significantly associated with CKD.

CONCLUSIONS

The BDNF level at fasting, but not at 30 and 120 min after glucose intake, was an independent indicator of CKD. In addition, significant associations were observed between three BDNF gene variants and CKD.

Identification and Characterization of Genomic Predictors of Sarcopenia and Sarcopenic Obesity Using UK Biobank Data

The substantial decline in skeletal muscle mass, strength, and gait speed is a sign of severe sarcopenia, which may partly depend on genetic risk factors. So far, hundreds of genome-wide significant single nucleotide polymorphisms (SNPs) associated with handgrip strength, lean mass and walking pace have been identified in the UK Biobank cohort; however, their pleiotropic effects on all three phenotypes have not been investigated. By combining summary statistics of genome-wide association studies (GWAS) of handgrip strength, lean mass and walking pace, we have identified 78 independent SNPs (from 73 loci) associated with all three traits with consistent effect directions. Of the 78 SNPs, 55 polymorphisms were also associated with body fat percentage and 25 polymorphisms with type 2 diabetes (T2D), indicating that sarcopenia, obesity and T2D share many common risk alleles. Follow-up bioinformatic analysis revealed that sarcopenia risk alleles were associated with tiredness, falls in the last year, neuroticism, alcohol intake frequency, smoking, time spent watching television, higher salt, white bread, and processed meat intake; whereas protective alleles were positively associated with bone mineral density, serum testosterone, IGF1, and 25-hydroxyvitamin D levels, height, intelligence, cognitive performance, educational attainment, income, physical activity, ground coffee drinking and healthier diet (muesli, cereal, wholemeal or wholegrain bread, potassium, magnesium, cheese, oily fish, protein, water, fruit, and vegetable intake). Furthermore, the literature data suggest that single-bout resistance exercise may induce significant changes in the expression of 26 of the 73 implicated genes in m. vastus lateralis, which may partly explain beneficial effects of strength training in the prevention and treatment of sarcopenia. In conclusion, we have identified and characterized 78 SNPs associated with sarcopenia and 55 SNPs with sarcopenic obesity in European-ancestry individuals from the UK Biobank.

MicroRNA profiling of different exercise interventions for alleviating skeletal muscle atrophy in naturally aging rats

BACKGROUND

Exercise is an affordable and practical strategy to alleviate several detrimental outcomes from the aging process, including sarcopenia. The elucidation of molecular mechanisms to alleviate sarcopenia is one of the most important steps towards understanding human aging. Although microRNAs (miRNAs) regulate muscle growth, regeneration and aging, the potential role of exercise-mediated miRNAs during the prevention and rehabilitation of skeletal muscle atrophy upon exercise interventions remains unclear.

METHODS

A miRNA profile by miRNA sequencing for gastrocnemius muscle of a 24-month-old aged male rat model mimicking the naturally aging process was established through screening the differentially expressed miRNAs (DEMs) for alleviating aging-induced skeletal muscle atrophy upon optimal exercise intervention. The screened miRNAs and hub genes, as well as biomarkers with the most significantly enriched pathways, were validated by quantitative real-time polymerase chain reaction and western blotting.

RESULTS

The sarcopenia index (SI) value and cross-sectional area (CSA) of rats from the old control (OC) group significantly decreased when compared with the youth control (YC) group (P < 0.001, P < 0.01), whereas an increased SI value and an enlarged CSA of rats from the old-aerobic exercise (OE), old-resistance exercise (OR) and old-mixed exercise (OM) groups were determined (P < 0.01, P < 0.001, P < 0.05; P < 0.01, P < 0.01, P < 0.05). Our results demonstrate that 764 known miRNAs, 201 novel miRNAs and 505 miRNA-mRNA interaction networks were identified to be related to aging-induced muscular atrophy. Among them, 13 miRNAs were differentially expressed (P < 0.05 and log₂ |fold change| > 1) between the YC group and the OC group. Compared with the OC group, 7, 2 and 11 miRNAs were differentially expressed in the OE, OR and OM groups after exercise interventions, respectively. Meanwhile, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the identified DEMs were primarily related to apoptosis, autophagy and the NF-κB/MuRF1 signalling pathways (P < 0.05). Meanwhile, four DEMs (miR-7a-1-3p, miR-135a-5p, miR-151-5p and miR-196b-5p), six hub genes (Ar, Igf1, Hif1a, Bdnf, Fak and Nras) and several biomarkers (LC3, Beclin1, p62, Bax, Bcl-2 and NF-κB/MuRF1) with the most significantly enriched pathways were confirmed, which may play a key role in muscular atrophy during the aging process.

CONCLUSIONS

These findings are closely correlated with the progression of sarcopenia and could act as potential biomarkers for the diagnosis and interventional monitoring of aging-induced skeletal muscle atrophy.

Genome-Wide Association Study Identifies CDKN1A as a Novel Locus Associated with Muscle Fiber Composition

Muscle fiber composition is associated with physical performance, with endurance athletes having a high proportion of slow-twitch muscle fibers compared to power athletes. Approximately 45% of muscle fiber composition is heritable, however, single nucleotide polymorphisms (SNP) underlying inter-individual differences in muscle fiber types remain largely unknown. Based on three whole genome SNP datasets, we have shown that the rs236448 A allele located near the cyclin-dependent kinase inhibitor 1A (CDKN1A) gene was associated with an increased proportion of slow-twitch muscle fibers in Russian (n = 151; p = 0.039), Finnish (n = 287; p = 0.03), and Japanese (n = 207; p = 0.008) cohorts (meta-analysis: p = 7.9 × 10−5. Furthermore, the frequency of the rs236448 A allele was significantly higher in Russian (p = 0.045) and Japanese (p = 0.038) elite endurance athletes compared to ethnically matched power athletes. On the contrary, the C allele was associated with a greater proportion of fast-twitch muscle fibers and a predisposition to power sports. CDKN1A participates in cell cycle regulation and is suppressed by the miR-208b, which has a prominent role in the activation of the slow myofiber gene program. Bioinformatic analysis revealed that the rs236448 C allele was associated with increased CDKN1A expression in whole blood (p = 8.5 × 10−15) and with greater appendicular lean mass (p = 1.2 × 10−5), whereas the A allele was associated with longer durations of exercise (p = 0.044) reported amongst the UK Biobank cohort. Furthermore, the expression of CDKN1A increased in response to strength (p < 0.0001) or sprint (p = 0.00035) training. Accordingly, we found that CDKN1A expression is significantly (p = 0.002) higher in the m. vastus lateralis of strength athletes compared to endurance athletes and is positively correlated with the percentage of fast-twitch muscle fibers (p = 0.018). In conclusion, our data suggest that the CDKN1A rs236448 SNP may be implicated in the determination of muscle fiber composition and may affect athletic performance.

Advances in sports genomics

Sports genomics is the scientific discipline that focuses on the organization and function of the genome in elite athletes, and aims to develop molecular methods for talent identification, personalized exercise training, nutritional need and prevention of exercise-related diseases. It postulates that both genetic and environmental factors play a key role in athletic performance and related phenotypes. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status and soft-tissue injuries covers advances in research reported in recent years, including one whole genome sequencing (WGS) and four genome-wide association (GWAS) studies, as well as findings from collaborative projects and meta-analyses. At end of 2020, the total number of DNA polymorphisms associated with athlete status was 220, of which 97 markers have been found significant in at least two studies (35 endurance-related, 24 power-related, and 38 strength-related). Furthermore, 29 genetic markers have been linked to soft-tissue injuries in at least two studies. The most promising genetic markers include HFE rs1799945, MYBPC3 rs1052373, NFIA-AS2 rs1572312, PPARA rs4253778, and PPARGC1A rs8192678 for endurance; ACTN3 rs1815739, AMPD1 rs17602729, CPNE5 rs3213537, CKM rs8111989, and NOS3 rs2070744 for power; LRPPRC rs10186876, MMS22L rs9320823, PHACTR1 rs6905419, and PPARG rs1801282 for strength; and COL1A1 rs1800012, COL5A1 rs12722, COL12A1 rs970547, MMP1 rs1799750, MMP3 rs679620, and TIMP2 rs4789932 for soft-tissue injuries. It should be appreciated, however, that hundreds and even thousands of DNA polymorphisms are needed for the prediction of athletic performance and injury risk.

Perspectives in Sports Genomics

Human athletic performance is a complex phenotype influenced by environmental and genetic factors, with most exercise-related traits being polygenic in nature. The aim of this article is to outline some of the challenge faced by sports genetics as this relatively new field moves forward. This review summarizes recent advances in sports science and discusses the impact of the genome, epigenome and other omics (such as proteomics and metabolomics) on athletic performance. The article also highlights the current status of gene doping and examines the possibility of applying genetic knowledge to predict athletes' injury risk and to prevent the rare but alarming occurrence of sudden deaths during sporting events. Future research in large cohorts of athletes has the potential to detect new genetic variants and to confirm the previously identified DNA variants believed to explain the natural predisposition of some individuals to certain athletic abilities and health benefits. It is hoped that this article will be useful to sports scientists who seek a greater understanding of how genetics influences exercise science and how genomic and other multi-omics approaches might support performance analysis, coaching, personalizing nutrition, rehabilitation and sports medicine, as well as the potential to develop new rationale for future scientific investigation.

Are Resistance Training-Induced BDNF in Hemodialysis Patients Associated with Depressive Symptoms, Quality of Life, Antioxidant Capacity, and Muscle Strength? An Insight for the Muscle-Brain-Renal Axis

Background: Hemodialysis patients are suffering from depressive symptoms. Brain-derived neurotrophic factor (BDNF) levels are negatively associated with depressive symptoms and decrease during a single hemodialysis session. Resistance training (RT) might be an additional non-pharmacological tool to increase BDNF and promote mental health. Methods: Two randomized groups of hemodialysis patients: control (CTL, n = 76/F36; 66.33 ± 3.88 years) and RT (n = 81/F35; 67.27 ± 3.24 years). RT completed six months of training thrice a week under the supervision of strength and conditioning professional immediately before the dialysis session. Training loads were adjusted using the OMNI rating of perceived exertion. The total antioxidant capacity (TROLOX), glutathione (GSH), thiobarbituric acid reactive substance (TBARS), and BDNF levels were analyzed in serum samples. Quality of life (assessed through Medical Outcomes—SF36), and Beck Depression Inventory was applied. Results: RT improved handgrip strength (21.17 ± 4.38 vs. 27.17 ± 4.34; p = 0.001) but not for CTL (20.09 ± 5.19 vs. 19.75 ± 5.54; p = 0.001). Post-training, RT group had higher values as compared to CTL related to TROLOX (RT,680.8 ± 225.2 vs. CTL,589.5 ± 195.9; p = 0.001) and GSH (RT, 9.33 ± 2.09 vs. CTL,5.00 ± 2.96; p = 0.001). RT group had lower values of TBARS as compared to CTL at post-training (RT, 11.06 ± 2.95 vs. CTL, 13.66 ± 2.62; p = 0.001). BDNF increased for RT (11.66 ± 5.20 vs. 19.60 ± 7.23; p = 0.001), but decreased for CTL (14.40 ± 4.99 vs. 10.84 ± 5.94; p = 0.001). Quality of life and mental health increased (p = 0.001) for RT, but did not change for CTL (p = 0.001). BDNF levels were associated with emotional dimensions of SF36, depressive symptoms, and handgrip (p = 0.001). Conclusions: RT was effective as a non-pharmacological tool to increased BDNF levels, quality of life, temper the redox balance and decrease depressive symptoms intensity in hemodialysis patients.