Alzheimer’s disease (AD) is a disabling neurodegenerative disorder that leads to long-term functional and cognitive impairment and greatly reduces life expectancy. Early genetic studies focused on tracking variations in genome-wide DNA sequences discovered several polymorphisms and novel susceptibility genes associated with AD. However, despite the numerous risk factors already identified, there is still no fully satisfactory explanation for the mechanisms underlying the onset of the disease. Also, as with other complex human diseases, the causes of low heritability are unclear. Epigenetic mechanisms, in which changes in gene expression do not depend on changes in genotype, have attracted considerable attention in recent years and are key to understanding the processes that influence age-related changes and various neurological diseases. With the recent use of massive sequencing techniques, methods for studying epigenome variations in AD have also evolved tremendously, allowing the discovery of differentially expressed disease traits under different conditions and experimental settings. This is important for understanding disease development and for unlocking new potential AD therapies. In this work, we outline the genomic and epigenomic components involved in the initiation and development of AD and identify potentially effective therapeutic targets for disease control.

Nutrition is an essential component when promoting human health. Without a doubt, improving the quality of one’s diet can improve one’s quality of life as a whole and help postpone the onset or control of many chronic diseases. The volume of publications in this field has increased in recent years, in line with increased awareness of the importance of nutrition in health; however, the quality of the evidence on which most nutritional guidelines are based remains low, due to errors in conducting nutritional interventions or because the information is primarily derived from observational studies. To enhance the evidence supporting clinical guidelines in nutrition, the quality of randomized clinical trials (RCT) based on nutritional interventions must be improved; nevertheless, due to their heterogeneous nature and a lack of specific guidelines for designing, performing, documenting, and reporting on this type of intervention, conducting a nutritional intervention is a real challenge. Following a review of the literature on the methodological and ethical standards, as well as four extensions of the CONSORT (Consolidated Standards of Reporting Trials) guidelines that should be considered when implementing a nutritional intervention, seven essential aspects were identified. The current narrative review includes definitions, examples, diagrams, and algorithms regarding aspects of the appropriate study design, the intervention of the control group, the randomization and blinding processes, the study population selection, as well as a description of the type of intervention and the personnel involved in carrying out the study in order to make the implementation of a nutritional intervention easier.

Objective: Since vitamin D is an important regulator of muscle function, the effect of vitamin D deficiency on frailty syndrome has been recently studied. This cross-sectional study aimed to determine the association between 25(OH)-vitamin D levels and frailty status in Mexican community-dwelling elderly. Methods: Sample of 331 community-dwelling elderly aged 70 or older, a subset of those included in the “Coyoacán cohort” were included. 25(OH)-vitamin D assay and frailty status were measured. Results: Mean age was 79.3 years and 54.1% were women. Those classified as frail were more likely to have lower Mini-Mental State Examination score (p = 0.015), more disability for instrumental activities of daily living (p < 0.001) and for activities of daily living (p < 0.001). Serum 25(OH)-vitamin D levels were lower in the frail subgroup when compared with the non-frail one (p < 0.001). Multivariate logistic regression analyses showed a significant association between intermediate tertile [odds ratios (OR) = 4.13; 95% confidence intervals (CI) 2.00–8.56] or insufficient tertile (OR = 8.95; 95% CI 2.41–33.30) of vitamin D levels and frailty even after adjusting for potential confounders. Conclusion: These results suggest that older adults with low 25(OH)-vitamin D levels are associated with the probability to being frail compared with those with sufficient vitamin D levels.

Microorganisms have a close relationship with humans, whether it is commensal, symbiotic, or pathogenic. Recently, it has been documented that microorganisms may influence the response to drug therapy. Pharmacomicrobiomics is an emerging field that focuses on the study of how variations in the microbiome affect the disposition, action, and toxicity of drugs. Two additional sciences have been added to complement pharmacomicrobiomics, namely toxicomicrobiomics, which explores how the microbiome influences drug metabolism and toxicity, and pharmacoecology, which refers to modifications in the microbiome as a result of drug administration. In this context, we introduce the concept of “drug-infection interaction” to describe the influence of pathogenic microorganisms on drug response. This review analyzes the current state of knowledge regarding the relevance of microorganisms in the host’s response to drugs. It also highlights promising areas for future research and proposes the term “drug-infection interaction” as an extension of pharmacomicrobiomics.

Metabolic syndrome (MetS) is a complex condition characterized by a group of interconnected metabolic abnormalities. Due to its increasing prevalence, better predictive markers are needed. Therefore, this study aims to develop predictive models for MetS by integrating adipokines, metabolic and cardiovascular risk factors, and anthropometric indices. Data were collected from 381 subjects aged 20 to 59 years (242 women and 139 men) from Guadalajara, Jalisco, Mexico, who were classified as having MetS or non-MetS based on the ATP-III criteria. Four supervised machine learning models were developed—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—and their performance was evaluated using the Area under the Curve (AUC), calibration curves, Decision Curve Analysis (DCA), and local interpretability analysis. The RF and XGBoost models achieved the highest AUCs (0.940 and 0.954). The RF and LR models were the best calibrated and showed the highest net benefit in DCA. Key variables included age, anthropometric indices (BRI and DAI), insulin resistance measures (HOMA-IR), lipid profiles (sdLDL-C and LDL-C), and high-molecular-weight adiponectin, used to classify the presence of MetS. The results highlight the usefulness of specific models and the importance of anthropometric variables, cardiovascular risk factors, metabolic profiles, and adiponectin as indicators of MetS.