Obesity is a serious medical condition worldwide, which needs new approaches and recognized international consensus in treating diseases leading to morbidity. The aim of this review was to examine heterogeneous links among the various phenotypes of obesity in adults. Proteins and associated genes in each group were analysed to differentiate between biomarkers. A variety of terms for classification and characterization within this pathology are currently in use; however, there is no clear consensus in terminology. The most significant groups reviewed include metabolically healthy obese, metabolically abnormal obese, metabolically abnormal, normal weight and sarcopenic obese. These phenotypes do not define particular genotypes or epigenetic gene regulation, or proteins related to inflammation. There are many other genes linked to obesity, though the value of screening all of those for diagnosis has low predictive results, as there are no significant biomarkers. It is important to establish a consensus in the terminology used and the characteristics attributed to obesity subtypes. The identification of specific molecular biomarkers is also required for better diagnosis in subtypes of obesity.

The debate regarding the cutoff point in the treatment of patients with subclinical hypothyroidism (Shypo) is ongoing. Generally, two different groups are identified for treatment by levels of 10 and 20 mIU/L. Nevertheless, the question remains, “what cutoff point should be chosen?” We have written a selective nonsystematic review focused on the 97.5 percentile reference value reported in healthy subjects in a number of countries and observed important disparities, which partly show the challenge of identifying a single cutoff point for those patients needing medication. We identified studies of TSH on the natural history of subclinical hypothyroidism from population-based prospective cohort studies, which follow up patients for several years. The evolution of TSH levels in these patients is variable. Some cases of TSH may return to lower levels at different stages over the years, but others may not, possibly even developing into overt thyroid failure, also variable. We analyzed factors that may explain the normalization of serum TSH levels. In addition, we found that thorough population-based prospective cohort studies following up on TSH levels, thyroid antibodies, and ultrasonography are important in decisions made in the treatment of patients. However, the 97.5 percentile reference value varies in different countries; therefore, an international cutoff point for subclinical hypothyroidism cannot be recommended.

Ageing is associated with changes in body composition, such as low muscle mass (sarcopenia), decreased grip strength or physical function (dynapenia), and accumulation of fat mass. When the accumulation of fat mass synergistically accompanies low muscle mass or reduced grip strength, it results in sarcopenic obesity and dynapenic obesity, respectively. These types of obesity contribute to the increased risk of cardiovascular disease and mortality in the elderly, which could increase the damage caused by COVID-19. In this review, we associated factors that could generate a higher risk of COVID-19 complications in dynapenic obesity and sarcopenic obesity. For example, skeletal muscle regulates the expression of inflammatory cytokines and supports metabolic stress in pulmonary disease; hence, the presence of dynapenic obesity or sarcopenic obesity could be related to a poor prognosis in COVID-19 patients.

(1) Background: Obesity and overweight are defined as an abnormal or excessive accumulation of fat that can be harmful to health. These are conditions that can lead to a lifetime of diseases, including cancer, diabetes, and heart disease. The diagnosis of overweight and obesity in children and adolescents depends on the international reference used. (2) Objectives: In this study aimed to determine the level of concordance of the prevalence of underweight, normal weight, overweight, and obesity were estimated with three international references in Mexican children and adolescents between 2 and 18 years of age. (3) Methods: We used specific the body mass index (BMI) cut-off points of the ENSANUT Continua 2022 database. The weight category was measured using the World Health Organization (WHO) criteria (<−2 standard deviation (SD), underweight; −2 and +0.99 SD, normal weight; +1 and +1.99 SD, overweight; and ≥+2 SD, obesity); Centers for Disease Control and Prevention (CDC) criteria (<percentile 5, underweight; percentile 5 and <percentile 85, normal weight; ≥percentile 85, overweight; and ≥percentile 95, obesity); and the Obesity Task Force (IOTF) criteria (specific limit values). To determine agreement among these three criteria, Cohen’s Kappa index was used. (4) Results: There were differences in the estimation of weight categories according to the international reference used. Substantial (WHO-IOTF: 0.639; CDC-IOTF: 0.785) and almost perfect (WHO-CDC: 0.806) levels of agreement were found between the references used; however, agreement varied according to age. (5) Conclusions: The weight category depends on the reference used, so each one should be used with caution since the results inform our actions of prevention, surveillance, and the control of nutrition in childhood and adolescence for the timely detection of chronic health problems and effects of social deficiencies.

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise.

Preeclampsia represents an unmasking of underlying vascular and metabolic vulnerability during the physiologic stress of pregnancy. Current first-trimester screening relies heavily on body mass index which incompletely captures qualitative metabolic dysfunction, particularly in non-obese women. In this narrative perspective, we examine the atherogenic index of plasma (AIP; log ₁₀ [TG/HDL-C]), which reflects triglyceride-rich lipoprotein burden and has emerged as a marker of lipid-driven vascular stress independent of adiposity. This article does not follow systematic review methodology (e.g., PRISMA) and is intended to provide a conceptual synthesis rather than an exhaustive review. Emerging obstetric evidence suggests that first-trimester AIP (≤14 weeks) is associated with an increased risk of subsequent preeclampsia, with uric acid (UA) potentially amplifying this relationship through redox-dependent mechanisms. We propose that elevated AIP and UA define an early, detectable oxidative dyslipidemia phenotype characterized by the convergence of lipotoxic and oxidative pathways, leading to endothelial dysfunction, nitric oxide (NO) depletion, platelet activation, and impaired placental perfusion. We further delineate druggable mechanistic nodes within this axis and outline a precision pharmacology framework for biomarker-enriched prevention trials. These nodes include cyclooxygenase-1 (COX-1)–mediated platelet activation (targeted by low-dose aspirin), NO pathway dysregulation (amenable to NO-supportive strategies), and xanthine oxidase–driven oxidative amplification (a potential target of redox-modulating agents). By explicitly linking early metabolic biomarkers to defined vascular pathways, this framework reframes first-trimester screening as a platform for mechanism-guided pharmacologic investigation rather than risk categorization alone. Before clinical integration, essential steps include independent validation across diverse populations, establishment of pregnancy-specific thresholds, demonstration of incremental predictive value beyond existing screening algorithms, and evidence that biomarker-guided strategies improve maternal and perinatal outcomes. Overall, these observations should be considered hypothesis-generating and require further validation before AIP-based assessment can support biologically informed, phenotype-guided prevention strategies within obstetric pharmacology.