The relationship among the age at menarche, anthropometric characteristics, and socio-economic factors in Bengali girls from Kolkata, India.

OBJECTIVES: The aim of the study was to measure the causal effect of selected socio-economic factors and anthropometrical characteristics on the menarche occurrence. METHODS: The sample consisted of 2195 Bengali girls (aged 7-21) from middle-class families, from Kolkata city, India. The age at menarche was recorded from the retrospective data and status quo method. The causal effect of anthropometric and socio-economic variables on menarche occurrence was estimated by the nonparametrical analysis of survival probability (survival random forest). RESULTS: In the examined cohort menarche occurred, on average, at 11.8 years of age. The probability of menarche occurrence amplified with the increasing values of factors such as body mass index, height-for-age z-scores, number of family members, household rooms, and toilets, but decreased when expenditures increased. The relation maintained a similar pattern of causal effect with girls' age. CONCLUSIONS: A complex pattern of relationship among sexual development, physique, and socio-economic characteristics was defined. The tendency toward early menarche, along with the observed causal relationships indicate that the analyzed sample is nearing the characteristics and standards of living noted in other middle and even high-income countries in the world.

Correction: Energy expenditure in caving.

[This corrects the article DOI: 10.1371/journal.pone.0170853.].

A Markov chain representation of the multiple testing problem.

The problem of multiple hypothesis testing can be represented as a Markov process where a new alternative hypothesis is accepted in accordance with its relative evidence to the currently accepted one. This virtual and not formally observed process provides the most probable set of non null hypotheses given the data; it plays the same role as Markov Chain Monte Carlo in approximating a posterior distribution. To apply this representation and obtain the posterior probabilities over all alternative hypotheses, it is enough to have, for each test, barely defined Bayes Factors, e.g. Bayes Factors obtained up to an unknown constant. Such Bayes Factors may either arise from using default and improper priors or from calibrating p-values with respect to their corresponding Bayes Factor lower bound. Both sources of evidence are used to form a Markov transition kernel on the space of hypotheses. The approach leads to easy interpretable results and involves very simple formulas suitable to analyze large datasets as those arising from gene expression data (microarray or RNA-seq experiments).

Energy expenditure in caving.

The aim of this study was to determine the energy expenditure of a group of cavers of both genders and different ages and experience during a 10 hour subterranean exploration, using portable metabolimeters. The impact of caving activity on body composition and hydration were also assessed through bioelectrical impedance, and nutritional habits of cavers surveyed. During cave activity, measured total energy expenditure (TEE) was in the range 225-287 kcal/h for women-men (MET = 4.1), respectively; subjects had an energy intake from food in the range 1000-1200 kcal, thus inadequate to restore lost calories. Bayesian statistical analysis estimated the effect of predictive variables on TEE, revealing that experienced subjects had a 5% lower TEE than the less skilled ones and that women required a comparatively larger energy expenditure than men to perform the same task. BIVA (bioelectrical impedance vector analysis) showed that subjects were within the range of normal hydration before and after cave activity, but bioelectrical changes indicated a reduction of extracellular water in men, which might result in hypo-osmolal dehydration in the case of prolonged underground exercise. All these facts should be considered when planning cave explorations, preparing training programs for subjects practising caving, and optimizing a diet for cavers. Further, information gathered through this study could be of value to reduce accidents in caves related to increase in fatigue.

Physical Capacity and Energy Expenditure of Cavers.

Caves are an extreme environment for humans because of the high humidity, mud, darkness, and slippery conditions. Explorations can last many hours or even days, and require extensive climbing and ropework. Very little is known about the physical capacity of cavers and their energy expenditure (EE) during caving. The physical capacity of 17 (7 females) expert cavers (age 43.9 ± 7.3 years) was assessed during an incremental cycle-ergometer test (IET) with gas exchange analysis. Moreover, a wearable metabolic band (Armband Fit Core) was used to estimate their EE during caving. In terms of physical capacity, the IET showed that cavers had a maximum oxygen uptake (VO2max) of 2,248.7 ± 657.8 ml·min-1 (i.e., 32.4 ± 6.4 ml·kg-1·min-1), while anaerobic threshold (AT) occurred on average at 74.5% of VO2max. Results from caving sessions provided an average time spent in cave of 9.4 ± 1.2 h while the average EE was 268.8 ± 54.8 kcal·h-1, which corresponded to about 40% of VO2max measured during IET. A mean distance of 10.6 ± 2.2 km was covered by subjects. Data from the present investigation provide evidence that cavers have a level of aerobic physical capacity only slightly higher than that of sedentary people, thereby suggesting that a high aerobic fitness is not needed by cavers. Moreover, during caving the EE was on average well below the level of AT. However, in absolute terms, the total EE was elevated (i.e., 2,672.3 ± 576 kcal in total) due to the long time spent in caving.

The asthma-anxiety connection.

BACKGROUND: The literature reports a significant association between various mental disorders and asthma, in particular depression and/or anxiety, with some more robust data regarding anxiety disorders. However, the nature of this association remains largely unclear. OBJECTIVES: (1) To test the hypothesis of a specific association of anxiety and depressive disorder (according to the DSM-IV) with asthma and (2) to test the bidirectional hypothesis of causality between asthma and psychiatric disorders. METHODS: Ninety-six adults were compared with 96 control subjects matched according to main socio-demographic variables (i.e., gender, age, marital status, cohabiting/non-cohabiting, and BMI). Subjects with asthma were divided according to GINA and ACT classifications. All subjects underwent Structured Clinical Interviews for DSM-IV Axis I (SCID-I) diagnosis. RESULTS: Significant association between asthma and lifetime anxiety disorders emerged (OR 3.03; p = 0.003); no significant association with other psychiatric diagnosis emerged. Moreover, lifetime and current anxiety were associated with asthma severity levels (p < 0.01 and p = 0.001 based on age). Asthma preceded anxiety in 48% of cases; in 52% of cases, anxiety preceded asthma, without significant group differences. The risk of asthma, particularly of severe, uncontrolled forms (p < 0.01), resulted higher in lifetime anxiety disorder patients (p = 0.003 and p = 0.001 based on age at onset). Current anxiety increased the risk of asthma, and that of an uncontrolled form (p < 0.05). Asthma increased the risk of lifetime anxiety disorders (p = 0.002 and p = 0.018 using ages). Intermittent asthma increased the risk of lifetime and current anxiety disorders (p < 0.01). CONCLUSIONS: Anxiety disorders, in particular Lifetime Anxiety Disorders, represent the only psychiatric disorder significantly associated with asthma, with a possible bidirectional, anxiety-asthma relationship, each of which can be caused or result from the other.

Accuracy of specific BIVA for the assessment of body composition in the United States population.

BACKGROUND: Bioelectrical impedance vector analysis (BIVA) is a technique for the assessment of hydration and nutritional status, used in the clinical practice. Specific BIVA is an analytical variant, recently proposed for the Italian elderly population, that adjusts bioelectrical values for body geometry. OBJECTIVE: Evaluating the accuracy of specific BIVA in the adult U.S. population, compared to the 'classic' BIVA procedure, using DXA as the reference technique, in order to obtain an interpretative model of body composition. DESIGN: A cross-sectional sample of 1590 adult individuals (836 men and 754 women, 21-49 years old) derived from the NHANES 2003-2004 was considered. Classic and specific BIVA were applied. The sensitivity and specificity in recognizing individuals below the 5(th) and above the 95(th) percentiles of percent fat (FMDXA%) and extracellular/intracellular water (ECW/ICW) ratio were evaluated by receiver operating characteristic (ROC) curves. Classic and specific BIVA results were compared by a probit multiple-regression. RESULTS: Specific BIVA was significantly more accurate than classic BIVA in evaluating FMDXA% (ROC areas: 0.84-0.92 and 0.49-0.61 respectively; p = 0.002). The evaluation of ECW/ICW was accurate (ROC areas between 0.83 and 0.96) and similarly performed by the two procedures (p = 0.829). The accuracy of specific BIVA was similar in the two sexes (p = 0.144) and in FMDXA% and ECW/ICW (p = 0.869). CONCLUSIONS: Specific BIVA showed to be an accurate technique. The tolerance ellipses of specific BIVA can be used for evaluating FM% and ECW/ICW in the U.S. adult population.