Animal studies have shown the reproductive toxicity of a number of heavy metals. Very few human observational studies have analyzed the relationship between male reproductive function and heavy metal concentrations in diverse biological fluids.

In animals, anogenital distance (AGD) at birth reflects androgen levels during pregnancy and predicts adult AGD. Little is known about AGD in relation to female reproductive characteristics in humans, a question this study was designed to explore.

Objective: Animal models have suggested that anogenital distance (AGD) at birth reflects androgen levels during in utero development and predicts adult AGD. A recent study showed an association between perineal length and androgen levels in men, suggesting that serum testosterone levels in adulthood will depend on factors involved during the fetal period. The aim of this study is to assess the relationship between AGD measures and reproductive hormone levels in women.Design: Cross‐sectional study conducted between February and November 2011.Setting: University‐affiliated fertility clinics.Population: 100 young college students.Methods: Physical and gynaecological examinations were conducted on university students. All participants provided a blood sample for determination of reproductive hormones and completed an epidemiological questionnaire on lifestyles and gynaecological history. We used multiple linear regression analysis to examine the associations between perineal length measurements [anus‐fourchette (AGD_AF) and anus‐clitoris (AGD_AC)] and reproductive hormone levels.Main outcome measures: Anogenital distance measurements and reproductive hormone levels.Results: In the multiple linear regression analyses, AGD_AF was positively associated with serum testosterone levels. Serum testosterone increased 0.06 ng/ml (95%CI 0.01, 0.10; P = 0.02) for each 1‐cm increase in AGD_AF. None of the measurements was associated with other reproductive hormones.Conclusions: Anogenital distance may predict normal reproductive development in women, and may be a new tool of potential clinical interest to evaluate ovarian function. Our results suggest that serum testosterone levels in adulthood may depend on factors operating in the prenatal period.

It has been hypothesized that exposure to heavy metals may impair male reproduction. To measure the effect produced by low doses of heavy metals on semen parameters, it is necessary to clarify in which body fluids those measurements must be performed. Sixty-one men attending infertility clinics participated in our study. Concentrations of lead, cadmium, and mercury were measured in whole blood, blood plasma, and seminal plasma using spectroanalytical and electrochemical methods. Semen analyses were performed according to World Health Organization criteria. For statistical analysis, Spearman's rank correlations, mean comparison tests, and discriminant analysis were calculated. Significant correlations between the measured concentrations of the three heavy metals in the same biological fluids were observed. However, no similar relationship was seen when comparing the concentrations in different body fluids of the same metal. According to our results and previous publications, seminal plasma might be the best body fluid for assessing impairment of human semen parameters.

Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.