An exposomic approach with 138 chemical and non-chemical exposures to predict 32 biomarkers of male reproductive damages: A case study of college students in Chongqing, China

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research

Outcome-wide analysis can offer several benefits, including increased power to detect weak signals and the ability to identify exposures with multiple effects on health, which may be good targets for preventive measures. Recently, advanced statistical multivariate techniques for outcome-wide analysis have been developed, but they have been rarely applied to exposome analysis. In this work, we provide an overview of a selection of methods that are well-suited for outcome-wide exposome analysis and are implemented in the R statistical software. Our work brings together six different methods presenting innovative solutions for typical problems arising from outcome-wide approaches in the context of the exposome, including dependencies among outcomes, high dimensionality, mixed-type outcomes, missing data records, and confounding effects. The identified methods can be grouped into four main categories: regularized multivariate regression techniques, multi-task learning approaches, dimensionality reduction approaches, and bayesian extensions of the multivariate regression framework. Here, we compare each technique presenting its main rationale, strengths, and limitations, and provide codes and guidelines for their application to exposome data. Additionally, we apply all selected methods to a real exposome dataset from the Human Early-Life Exposome (HELIX) project, demonstrating their suitability for exposome research. Although the choice of the best method will always depend on the challenges to be faced in each application, for an exposome-like analysis we find dimensionality reduction and bayesian methods such as reduced rank regression (RRR) or multivariate bayesian shrinkage priors (MBSP) particularly useful, given their ability to deal with critical issues such as collinearity, high-dimensionality, missing data or quantification of uncertainty.

Low-level and combined exposure to environmental metal elements affects male reproductive outcomes: Prospective MARHCS study in population of college students in Chongqing, China

BACKGROUND

Male fertility has shown a continuously declining tendency for decades. Over exposure to metal/metalloid elements has been proposed as associated with reproductive impairment. However, the hazard profile remained unclear in general public experiencing low-level and combined metal exposure.

METHODS

Based on the MARHCS cohort in Chongqing, China, 796 college students were recruited from June 2013 and 666 subjects were followed up next year. At each phase, semen and blood samples were collected for an assessment of semen quality and six sex hormones levels. Eighteen urinary metal/metalloid elements were quantified by ICP-MS as internal exposure biomarkers. Cluster analysis was conducted to characterize reproductive outcomes in the subgroups for different overall estimated exposure levels. Effects of each metal/metalloid element were analyzed using multiple statistical strategies: single-element mixed model, multiple-elements model and self before-after comparison design.

RESULTS

The urine concentration for 18 metal/metalloid elements was at a typically lower level (far away from the exposure limits) and positively associated with each other. After adjustment of the potential confounders, a decrease of 11.53% (95% CI: -18.61, -3.84%) and 10.84% (95% CI: -17.93, -3.14%) in spermatid morphology was observed in the highest quantile groups of vanadium (V) and nickel (Ni), respectively. Urinary silver (Ag) was dose-dependent associated with an increase in total sperm number (6.91%, 95% CI: 1.14, 13.00%), sperm concentration (16.38%, 95% CI: 5.15, 28.81%) and semen volume (23.73%, 95% CI: 10.46, 38.60%). Further, hormone testosterone presented a significant decrease in subgroup with higher overall estimated exposure and a stable negative association with lithium (Li). The above relationships remained significant across different statistical strategies (all p values <0.05).

CONCLUSION

Our study provided new evidences that exposure to metal/metalloid elements potentially exert bidirectional influences on semen quality at a relatively low level. And serum testosterone appears as a vulnerable index for metal exposure.

Identifying the dose response relationship between seminal metal at low levels and semen quality using restricted cubic spline function

Environmental exposure to metals, including essential and nonessential elements, may be related to semen quality. Our goal was to explore the continuous relationship between seminal metals and sperm parameters. A restricted cubic spline (RCS) was applied to automatic selection criteria to ascertain the optimal smoothing degree. We recruited 841 male volunteers from Henan Province, China. Eighteen seminal metals, namely Al, Cr, Mn, Cu, Zn, Se, As, Ni, Cd, Pb, Co, V, Rb, Ag, Ba, TI, Fe, and Li, and 21 semen parameters were detected. Seminal malondialdehyde (MDA) was also detected to express oxidative stress. We revealed a non-linear relationship of the vanadium and chromium exposure to semen parameters. There were inverse 'U' shapes found between seminal Cr and sperm concentrations, total sperm count, and semen quality. The best semen quality was observed when the seminal Cr concentration was 5.05 ppb, and an increase or decrease in chromium concentration led to decreased semen quality. The V concentration was associated with reduced sperm concentration, total sperm count, normal morphology, and progressive motility at high doses (V > 0.58 ppb). Seminal MDA had a strong adverse association with sperm motility parameters, such as curve line velocity (VCL) (P < 0.001), straight line velocity (VSL) (P = 0.004), velocity of average path (VAP) (P < 0.001), and lateral head movement (ALH) (P = 0.001), whereas it was adversely associated with seminal Zn (β [95% confidence interval (CI)], -0.28(-0.41-0.16), P < 0.001) after adjusting for confounding factors. Our findings represent the curves of the dose-response relationship between seminal Cr, seminal V, and semen quality, in which seminal MDA was a good indicator of sperm movement. These models provide new insight into the dose-relationship between metal exposure and semen quality, and further investigation is needed to validate this.

Exposure to polycyclic aromatic hydrocarbons and nicotine, and associations with sperm DNA fragmentation

Background

Tobacco smoking has been reported to cause DNA fragmentation and has been suggested to cause mutations in spermatozoa. These effects have been ascribed to the action of polycyclic aromatic hydrocarbons (PAH) present in the smoke. Simultaneously, DNA fragmentation has been associated with mutagenesis.

Objective

The aim of this study was to investigate whether levels of urinary biomarkers of PAH and nicotine exposure were associated with sperm DNA fragmentation.

Methods

In the urine of 381 men recruited from two cohorts of young men (17–21 years old) from the general Swedish population, the PAH metabolites 1‐hydroxypyrene and 2‐hydroxyphenanthrene, as well as the nicotine metabolite cotinine, were measured. The sperm DNA fragmentation index (DFI) was analysed using the sperm chromatin structure assay. Associations between the DFI, and PAH metabolite levels as continuous variables as well as in quartiles, were studied by general linear models adjusted for abstinence time. A similar analysis was carried out for cotinine levels, according to which the men were categorised as “non‐smoking” (n = 216) and “smoking” (n = 165).

Results

No association was found between levels of any of the three biomarkers and DFI, either as a continuous variable (p = 0.87–0.99), or when comparing the lowest and the highest quartiles (p = 0.11–0.61). The same was true for comparison of men categorised as non‐smoking or smoking (DFI 11.1% vs. 11.8%, p = 0.31).

Discussion

We found no evidence of PAH or nicotine exposure to be associated with DFI, which does not exclude that these exposures may have other effects on sperm DNA.

Conclusion

In these young men, levels of biomarkers of nicotine and PAH exposure were not associated with DFI.