Metal Homeostasis and Exposure in Distinct Phenotypic Subtypes of Insulin Resistance among Children with Obesity

Association between childhood obesity, trace elements, and heavy metals: Recent discoveries and future perspectives

Trace elements and heavy metals play pivotal roles in health status by regulating a myriad of vital biological functions. Abnormal metal homeostasis has been linked to a constellation of pathogenic complications, including oxidative stress, inflammatory processes, dyslipidemia, and impaired insulin-mediated metabolism of carbohydrates, thereby increasing the odds of developing childhood obesity and related comorbidities. Herein, we provide a comprehensive revision of recent literature on the association between childhood obesity, trace elements, and heavy metals. Further, we emphasize on the crucial importance of addressing the influence that interindividual variability factors (e.g., sex, age, genetic determinants, concomitance of comorbidities, and environmental factors) may have in modulating the susceptibility to disease development. Altogether, this review article represents a concise guide to better understand the involvement of metals in childhood obesity pathogenesis and discusses future needs with the aim of establishing robust biomarkers in the context of precision medicine.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data

The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.

Parental obesity predisposes to exacerbated metabolic and inflammatory disturbances in childhood obesity within the framework of an altered profile of trace elements

BACKGROUND

Family history of obesity is known to increase the odds of developing childhood obesity in the offspring, but its influence in underlying molecular complications remains unexplored.

SUBJECTS/METHODS

Here, we investigated a population-based cohort comprising children with obesity, with and without parental obesity (PO+, N = 20; PO-, N = 29), and lean healthy children as controls (N = 30), from whom plasma and erythrocyte samples were collected to characterize their multi-elemental profile, inflammatory status, as well as carbohydrate and lipid metabolisms.

RESULTS

We found parental obesity to be associated with unhealthier outcomes in children, as reflected in increased blood insulin levels and reduced insulin sensitivity, unfavorable lipid profile, and pro-inflammatory milieu. This was accompanied by moderate alterations in the content of trace elements, including increased copper-to-zinc ratios and iron deficiency in circulation, as well as metal accumulation within erythrocytes.

CONCLUSIONS

Therefore, we hypothesize that family history of obesity could be an important risk factor in modulating the characteristic multi-elemental alterations behind childhood obesity, which in turn could predispose to boost related comorbidities and metabolic complications.

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.