Daily probiotic's (lactobacillus casei shirota) reduction of infection incidence in athletes

The purpose of this study was to examine the effects of a probiotic supplement during 4 mo of winter training in men and women engaged in endurance-based physical activities on incidence of upper respiratory-tract infections (URTIs) and immune markers. Eighty-four highly active individuals were randomized to probiotic (n = 42) or placebo (n = 42) groups and, under double-blind procedures, received probiotic (PRO: Lactobacillus casei Shirota [LcS]) or placebo (PLA) daily for 16 wk. Resting blood and saliva samples were collected at baseline and after 8 and 16 wk. Weekly training and illness logs were kept. Fifty-eight subjects completed the study (n = 32 PRO, n = 26 PLA). The proportion of subjects on PLA who experienced 1 or more weeks with URTI symptoms was 36% higher than those on PRO (PLA 0.90, PRO 0.66; p = .021). The number of URTI episodes was significantly higher (p < .01) in the PLA group (2.1 ± 1.2) than in the PRO group (1.2 ± 1.0). Severity and duration of symptoms were not significantly different between treatments. Saliva IgA concentration was higher on PRO than PLA, significant treatment effect F(1, 54) = 5.1, p = .03; this difference was not evident at baseline but was significant after 8 and 16 wk of supplementation. Regular ingestion of LcS appears to be beneficial in reducing the frequency of URTI in an athletic cohort, which may be related to better maintenance of saliva IgA levels during a winter period of training and competition

Sex differences in immune variables and respiratory infection incidence in an athletic population

The purpose of this study was to examine sex differences in immune variables and upper respiratory tract infection (URTI) incidence in 18-35 year-old athletes engaged in endurance-based physical activity during the winter months. Eighty physically active individuals (46 males, 34 females) provided resting venous blood samples for determination of differential leukocyte counts, lymphocyte subsets and whole blood culture multi-antigen stimulated cytokine production. Timed collections of unstimulated saliva were also made for determination of saliva flow rate, immunoglobulin A (IgA) concentration and IgA secretion rate. Weekly training and illness logs were kept for the following 4 months. Training loads averaged 10 h/week of moderate-vigorous physical activity and were not different for males and females. Saliva flow rates, IgA concentration and IgA secretion rates were significantly higher in males than females (all P < 0.01). Plasma IgA, IgG and IgM concentrations and total blood leukocyte, neutrophil, monocyte and lymphocyte counts were not different between the sexes but males had higher numbers of B cells (P < 0.05) and NK cells (P < 0.001). The production of interleukins 1β, 2, 4, 6, 8 and 10, interferon-γ and tumour necrosis factor-α in response to multi-antigen challenge were not significantly different in males and females (all P > 0.05). The average number of weeks with URTI symptoms was 1.7 ± 2.1 (mean ± SD) in males and 2.3 ± 2.5 in females (P = 0.311). It is concluded that most aspects of immunity are similar in men and women in an athletic population and that the observed differences in a few immune variables are not sufficient to substantially affect URTI incidence. Sex differences in immune function among athletes probably do not need to be considered in future mixed gender studies on exercise, infection and immune function unless the focus is on mucosal immunity

Effects of a Lactobacillus salivarius probiotic intervention on infection, cold symptom duration and severity, and mucosal immunity in endurance athletes

The purpose of this study was to examine the effects of a probiotic supplement during 4 mo of spring training in men and women engaged in endurance-based physical activities on incidence of upper respiratory tract infections (URTI) and mucosal immune markers. Sixty-six highly active individuals were randomized to probiotic (n = 33) or placebo (n = 33) groups and, under double-blind procedures, received probiotic (PRO: Lactobacillus salivarius, 2 × 1010 bacterium colony-forming units) or placebo (PLA) daily for 16 wk. Resting blood and saliva samples were collected at baseline and after 8 and 16 wk. Weekly training and illness logs were kept. Fifty-four subjects completed the study (n = 27 PRO, n = 27 PLA). The proportion of subjects on PRO who experienced 1 or more wk with URTI symptoms was not different from that of those on PLA (PRO .58, PLA .59; p = .947). The number of URTI episodes was similar in the 2 groups (PRO 1.6 ± 0.3, PLA 1.4 ± 0.3; p = .710). Severity and duration of symptoms were not significantly different between treatments. Blood leukocyte, neutrophil, monocyte, and lymphocyte counts; saliva IgA; and lysozyme concentrations did not change over the course of the study and were not different on PRO compared with PLA. Regular ingestion of L. salivarius does not appear to be beneficial in reducing the frequency of URTI in an athletic cohort and does not affect blood leukocyte counts or levels of salivary antimicrobial proteins during a spring period of training and competition

Influence of training load on upper respiratory tract infection incidence and antigen-stimulated cytokine production

This study examined the effect of training load on upper respiratory tract infection (URTI) incidence in men and women engaged in endurance-based physical activity during winter and sought to establish if there are training-associated differences in immune function related to patterns of illness. Seventy-five individuals provided resting blood and saliva samples for determination of markers of systemic immunity. Weekly training and illness logs were kept for the following 4 months. Comparisons were made between subjects (n = 25) who reported that they exercised 3–6 h/week (LOW), 7–10 h/week (MED) or ≥ 11 h/week (HIGH). The HIGH and MED groups had more URTI episodes than the LOW group (2.4 ± 2.8 and 2.6 ± 2.2 vs 1.0 ± 1.6, respectively: P < 0.05). The HIGH group had approximately threefold higher interleukin (IL)-2, IL-4 and IL-10 production (all P < 0.05) by antigen-stimulated whole blood culture than the LOW group and the MED group had twofold higher IL-10 production than the LOW group (P < 0.05). Other immune variables were not influenced by training load. It is concluded that high levels of physical activity are associated with increased risk of URTI and this may be related to an elevated anti-inflammatory cytokine response to antigen challenge

Respiratory infection risk in athletes: association with antigen-stimulated IL-10 production and salivary IgA secretion

The purpose of this study was to examine factors influencing susceptibility to upper respiratory tract infections (URTI) in 18–35-year-old men and women engaged in endurance-based physical activity during the winter months. Eighty individuals (46 males, 34 females) provided resting blood and saliva samples for determination of markers of systemic immunity. Weekly training and illness logs were kept for the following 4 months. Thirty subjects did not experience an URTI episode and 24 subjects experienced 3 or more weeks of URTI symptoms. These illness-prone subjects had higher training loads and had ∼2.5-fold higher interleukin (IL)-4 and IL-10 production by antigen-stimulated whole blood culture than the illness-free subjects. Illness-prone subjects also had significantly lower saliva S-IgA secretion rate and higher plasma IgM (but not IgA or IgG) concentration than the illness-free subjects. There were no differences in circulating numbers of leukocyte subtypes or lymphocyte subsets between the illness-prone and illness-free subjects. The production of IL-10 was positively correlated and the S-IgA secretion rate was negatively correlated with the number of weeks with infection symptoms. It is concluded that high IL-10 production in response to antigen challenge and low S-IgA secretion are risk factors for development of URTI in physically active individuals

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

<div><p>Background</p><p>Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation.</p><p>Methods</p><p>Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis.</p><p>Results</p><p>The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (<i>ρ</i> = 0:87 vs. <i>ρ</i> = 0:86 for the whole sample and <i>ρ</i> = 0:88 vs. <i>ρ</i> = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE).</p><p>Conclusions</p><p>There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.</p></div

Regression from linear model 3a relating the percentage of body fat to Log 10 BMI (Body Mass Index)(<i>kg</i>/<i>m</i>²).

<p>The regression model 3a adjusted for a 2-level factor variable for Sex, and age. The points are coloured by Sex groups.</p

Residuals from linear model 3a relating observed—predicted percentage of body fat to Log 10 BMI (Body Mass Index)(<i>kg</i>/<i>m</i>²).

<p>The residuals are adjusted for a 2-level factor variable for Sex, and age in model 3a. The residuals are coloured by Sex groups.</p

Multi regression models obtained to predict BF, and the different correlations between the model and BF obtained using BIA.

<p>BIA: Bioelectrical Impedance Analysis.</p><p>Models a and b have been obtained with the whole dataset (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122291#pone.0122291.s001" target="_blank">S1 Dataset</a>), and models c and d have been obtained with the whole dataset (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122291#pone.0122291.s001" target="_blank">S1 Dataset</a>) constrained to overweight/obese subjects (datasetB). Models a and c include BMI as predictor variable, models b and d include BAI as predictor variable.</p><p>Multi regression models obtained to predict BF, and the different correlations between the model and BF obtained using BIA.</p

% Body fat (from bioelectrical impedance analysis (BIA)) vs. body adiposity index (BAI) for males. Figure 1b. % Body fat (from bioelectrical impedance analysis (BIA)) vs. body adiposity index (BAI) for females.

<p>% Body fat (from bioelectrical impedance analysis (BIA)) vs. body adiposity index (BAI) for males. Figure 1b. % Body fat (from bioelectrical impedance analysis (BIA)) vs. body adiposity index (BAI) for females.</p