Background: Despite the extensive scientific evidence accumulating on the epidemiological risk factors for non-alcoholic fatty liver disease (NAFLD), evidence exploring sex- and age-related differences remains insufficient. The present cross-sectional study aims to investigate possible sex differences in the prevalence of FLI-defined NAFLD as well as in its association with common risk factors across different age groups, in a large sample of Spanish working adults. Methods: This cross-sectional study included data from 33,216 Spanish adult workers (18–65 years) randomly selected during voluntary routine occupational medical examinations. Sociodemographic characteristics (age and social class), anthropometric (height, weight, and waist circumference) and clinical parameters (blood pressure and serum parameters) were collected. NAFLD was determined by the validated fatty liver index (FLI) with a cut-off value of ≥ 60. The presence of metabolic syndrome (MetS) was assessed according to the diagnostic criteria of the International Diabetes Federation. Cardiovascular risk was determined using the REGICOR-Framingham equation. The association between FLI-defined NAFLD and risk factors by sex and age was evaluated by multivariate logistic regression. Results: The prevalence of FLI-defined NAFLD (FLI ≥ 60) was 19.1% overall, 27.9% (95% CI 23.3–28.5%) for men and 6.8% (95% CI 6.4–7.3%) for women and increasing across age intervals. As compared to women, men presented worse cardiometabolic and anthropometric profiles. The multivariate analysis model showed that hepatic steatosis assessed by FLI was strongly associated with age, HDL-cholesterol, social class, prediabetes, diabetes, prehypertension, hypertension, and smoking status for both men and women. The association between diabetes and hypertension with FLI-defined NAFLD was stronger in women than in men at both univariate and multivariate analyses. Conclusions: Men presented a higher prevalence of NAFLD than women across all age intervals, as well as a worse cardiometabolic profile and a higher cardiovascular risk. Nevertheless, the association between FLI-defined NAFLD and diabetes or hypertension was significantly stronger in women than in men, possibly indicating that the presence of a dysmetabolic state might affect women more than men with regard to liver outcomes.

The psychological factors of sports injuries constitute a growing field of study, even from the point of view of the prediction of their occurrence. Most of them, however, do not take into account the likelihood of the injuries’ occurrence and the weight and role of the psychological variables on it. We conducted a study building up a Bayesian Network on a big sample of athletes, trying to assess these probabilistic links among several relevant psychological variables and the injuries’ occurrence. The sample was constituted by 297 athletes (239 males, 58 females) from a wide range of sports: track and field; judo; fencing; karate; boxing; swimming; kayaking; artistic rollerskating, and team sports as football, basketball, and handball (Mean age: 25.10 ±-3.87; range: 21–38 years). Several psychological variables, such as anxiety, social support, and self-efficacy were studied. Also, we recorded the history of injuries as well the body mass index and personal epidemiological data. The overall picture of the generated graph and Bayesian Network and its analysis – including the use of hypothetical data by means of several instantiations – includes the nuclear role of the Self-Efficacy regarding the injuries’ occurrence likelihood; the decreasing impact of the competitive anxiety previous to the injury; the probabilistic independence of the players’ risk behaviors, and the relevance of the environmental clues such the use of coping strategies and social support in order to build up a good level of Self-Efficacy after the occurrence of an injury. All these data are relevant when designing both preventive and recovery interventions from the multidisciplinary as well as from the psychological point of view.

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. 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. 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 (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). 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.