Iron and Testosterone: Interplay and Clinical Implications

Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning

The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning and to identify the differences between sexes, academic years, socioeconomic strata, and the generated profiles. A total of 542 healthy and physically active students (445 males, 97 females; 19.8 [2.2] years; 66.0 [10.3] kg; 169.5 [7.8] cm) participated in this cross-sectional study. Their indirect VO2max (Cooper and Shuttle-Run 20 m tests), lower-limb power (horizontal jump), sprint (30 m), agility (shuttle run), and flexibility (sit-and-reach) were assessed. The participants were profiled using clustering algorithms after setting the optimal number of clusters through an internal validation using R packages. Non-parametric tests were used to identify the differences (p < 0.05). The higher percentage of the population were freshmen (51.4%) and middle-income (64.0%) students. Seniors and juniors showed a better physical fitness than first-year students. No significant differences were found between their socioeconomic strata (p > 0.05). Two profiles were identified using hierarchical clustering (Cluster 1 = 318 vs. Cluster 2 = 224). The matching analysis revealed that physical fitness explained the variation in the data, with Cluster 2 as a sex-independent and more physically fit group. All variables differed significantly between the sexes (except the body mass index [p = 0.218]) and the generated profiles (except stature [p = 0.559] and flexibility [p = 0.115]). A multidimensional analysis showed that the body mass, cardiorespiratory fitness, and agility contributed the most to the data variation so that they can be used as profiling variables. This profiling method accurately identified the relevant variables to reinforce exercise recommendations in a low physical performance and overweight majors.

Trace Element Interactions, Inflammatory Signaling, and Male Sex Implicated in Reduced Growth Following Excess Oral Iron Supplementation in Pre-Weanling Rats

Iron supplements are frequently provided to infants in high-income countries despite low incidence of iron deficiency. There is growing concern regarding adverse health and development outcomes of excess iron provision in early life. Excess iron may directly damage developing organs through the formation of reactive oxygen species, alter systemic inflammatory signaling, and/or dysregulate trace mineral metabolism. To better characterize the in vivo effects of excess iron on development, we utilized a pre-weanling rat pup model. Lewis rat litters were culled to eight pups (four males and four females) and randomly assigned to daily supplementation groups receiving either vehicle control (CON; 10% w/v sucrose solution) or ferrous sulfate (FS) iron at one of the following doses: 10, 30, or 90 mg iron/kg body weight—FS-10, FS-30, and FS-90, respectively—from postnatal day (PD) 2 through 9. FS-90 litters, but not FS-30 or FS-10, failed to thrive compared to CON litters and had smaller brains on PD 10. Among the groups, FS-90 liver iron levels were highest, as were white blood cell counts. Compared to CON, circulating MCP-1 and liver zinc were increased in FS-90 pups, whereas liver copper was decreased. Growth defects due to excess FS provision in pre-weanling rats may be related to liver injury, inflammation, and altered trace mineral metabolism.

Effects of Ashwagandha (Withania somnifera) on Physical Performance: Systematic Review and Bayesian Meta-Analysis

Ashwagandha (Withania somnifera) is considered a potent adaptogen and anti-stress agent that could have some potential to improve physical performance. This preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based comprehensive systematic review and Bayesian meta-analysis aimed to evaluate clinical trials up to 2020 from PubMed, ScienceDirect, and Google Scholar databases regarding the effect of Ashwagandha supplementation on physical performance in healthy individuals. Besides implementing estimation statistics analysis, we developed Bayesian hierarchical models for a pre-specified subgroup meta-analysis on strength/power, cardiorespiratory fitness and fatigue/recovery variables. A total of 13 studies met the requirements of this systematic review, although only 12 were included in the quantitative analysis. A low-to-moderate overall risk of bias of the trials included in this study was detected. All Bayesian hierarchical models converged to a target distribution (Ȓ = 1) for both meta-analytic effect size (μ) and between-study standard deviation (τ). The meta-analytic approaches of the included studies revealed that Ashwagandha supplementation was more efficacious than placebo for improving variables related to physical performance in healthy men and female. In fact, the Bayesian models showed that future interventions might be at least in some way beneficial on the analyzed outcomes considering the 95% credible intervals for the meta-analytic effect size. Several practical applications and future directions are discussed, although more comparable studies are needed in exercise training, and athletic populations are needed to derive a more stable estimate of the true underlying effect.

Age-Related Changes and Sex-Related Differences in Brain Iron Metabolism

Iron is an essential element that participates in numerous cellular processes. Any disruption of iron homeostasis leads to either iron deficiency or iron overload, which can be detrimental for humans' health, especially in elderly. Each of these changes contributes to the faster development of many neurological disorders or stimulates progression of already present diseases. Age-related cellular and molecular alterations in iron metabolism can also lead to iron dyshomeostasis and deposition. Iron deposits can contribute to the development of inflammation, abnormal protein aggregation, and degeneration in the central nervous system (CNS), leading to the progressive decline in cognitive processes, contributing to pathophysiology of stroke and dysfunctions of body metabolism. Besides, since iron plays an important role in both neuroprotection and neurodegeneration, dietary iron homeostasis should be considered with caution. Recently, there has been increased interest in sex-related differences in iron metabolism and iron homeostasis. These differences have not yet been fully elucidated. In this review we will discuss the latest discoveries in iron metabolism, age-related changes, along with the sex differences in iron content in serum and brain, within the healthy aging population and in neurological disorders such as multiple sclerosis, Parkinson's disease, Alzheimer's disease, and stroke.

Assessment of Gender Effects and Reference Values of Mane Hair Trace Element Content in English Thoroughbred Horses (North Caucasus, Russia) Using ICP-DRC-MS

The objective of the present study was assessment of gender differences in hair trace element content in English Thoroughbred horses (North Caucasus, Russia) using ICP-DRC-MS and calculation of the reference values. Trace element content in mane hair of 190 stallions and 94 mares (3-7 years old) bred in North Caucasus (Russia) was assessed using inductively coupled plasma mass spectrometry. Mane hair Co, Cr, Mn, Li, Si, and Sr levels in mares exceeded those in stallions by 77%, 63%, 64%, 42%, 39%, and 64%, respectively. Hair Fe and Si content was nearly twofold higher in female horses as compared to the males. Only hair Zn content was 5% higher in stallions as compared to mares. In addition, mares were characterized by 63%, 65%, 29%, and 40% higher levels of As, Pb, Sn, and Ni levels in hair as compared to the respective values in stallions. In turn, hair Al and Hg were more than twofold higher in mares than in stallions. The reference intervals of mane hair content (μg/g) for Co (0.006-0.143), Cr (0.028-0.551), Cu (4.17-6.84), Fe (10.11-442.2), I (0.026-3.69), Mn (0.551-12.55), Se (0.108-0.714), Zn (97.43-167), Li (0.011-0.709), Ni (0.060-0.589), Si (0.665-29.12), V (0.006-0.584), Al (1.98-168.5), As (0.006-0.127), Cd (0.002-0.033), B (0.494-16.13), Pb (0.018-0.436), Sn (0.002-0.144), Sr (1.0-9.46), and Hg (0.0018-0.017) in the total cohort of horses were estimated using the American Society for Veterinary Clinical Pathology Quality Assurance and Laboratory Standard Guidelines. The reference intervals were also estimated for stallions and mares bred in North Caucasus (Russia) and may be used for interpretation of the results of hair trace element analysis in horses.