Epidemiological studies are essential in medicine and public health as they help identify risk factors and causes of diseases. Additionally, they are key to planning, implementing, and evaluating health interventions aimed at preventing and controlling the spread of diseases. Among these studies, analytical observational studies, such as cross-sectional, case–control, and cohort studies, are the most used. The validity of their results largely depends on the robustness of the design, execution, and statistical analysis. Objective: The objective of this study is to examine the most common errors in the selection of methodological design and statistical tests in analytical observational studies and to provide recommendations to correct them. Methodology: A comprehensive review of the available literature on methodology in epidemiological observational studies was conducted, focusing on cross-sectional, case–control, and cohort studies. Common errors in the selection of designs and statistical tests were identified and analyzed. Results and Conclusions: Errors in the selection of methodological design and statistical tests are common in epidemiological observational studies. Based on the identified errors, a series of recommendations is provided to improve the selection of methodological design and statistical tests, thereby increasing the reliability of the results in cross-sectional, case–control, and cohort studies.

Background/Objectives: The purpose of the current study was to compare the methylation of five regions of the CpG island of MLH1 with the presence of microsatellite instability (MSI) in colorectal cancer (CRC) patients. Methods: The study analyzed 138 CRC tumor samples. DNA extraction was performed, followed by bisulfite conversion. MLH1 gene methylation was assessed by methylation-specific PCR (MS-PCR), and the resulting fragments were analyzed using polyacrylamide gels. MSI was evaluated using multiplex PCR, and the fragments were run through capillary electrophoresis. R studio (v4.4.1) and SPSS (v29.0) software were used for the statistical analysis, and values of p < 0.05 were considered statistically significant. Results: The study showed 75.4% unmethylated, 21% partially methylated, and 3.6% fully methylated samples, with region A frequently methylated. MSI was observed in 7.2% of cases (MSI-H: 5.8%, MSI-L: 1.4%). BAT-26 was the most unstable marker. A significant difference between MLH1 methylation and MSI-H (p < 0.01) was identified, but there was no relationship with specific MLH1 regions. Conclusions: No differences were identified when analyzing specific methylation regions in relation to MSI. This study is the first to describe MSI frequency in Mexican patients regardless of age.

Forensic DNA phenotyping (FDP) is an important innovation approach in forensics sciences, especially when traditional DNA profiling results are limited, mostly due to the absence of reference samples. FDP is based on the detection of genetic variants in specific genes whose function is related to pigmentation mechanisms and uses the genotypes found in the sample to determine the externally visible traits (EVT) such as the iris, hair, and skin tone or color of the individual; this prediction would help and expedite human identification processes and solve criminal cases. Several technologies have been developed to facilitate EVT prediction; however, most of them have been validated only in European populations. Implementing techniques for FDP in Latin American countries is essential given the problems of disappearance and human identification that have persisted for years. Nonetheless, scientists have a great challenge due to the admixed genetic structure of the population. This review explores the current application of FDP, emphasizing its significance, practical uses, and limitations within Latin American populations.

Bladder cancer (BC) is the most common neoplasm of the urinary system and ranks tenth in global cancer incidence. Due to its high recurrence rate and the need for continuous monitoring, it is the cancer with the highest cost per patient. Cystoscopy is the traditional method for its detection and surveillance; however, this is an invasive technique, while non-invasive methods, such as cytology, have a limited sensitivity. For this reason, new non-invasive strategies have emerged, analyzing useful markers for BC detection from urine samples. The identification of tumor markers is essential for early cancer detection and treatment. Urine analysis offers a non-invasive method to identify these markers. Microsatellite instability (MSI) has been proposed as a promising marker for tumor cell detection and guided targeted therapies. Therefore, this review aims to explore the evidence supporting the identification of MSI in exfoliated bladder tumor cells (EBTCs) in the urine, emphasizing its potential as a non-invasive and clinically effective alternative for tumor identification. Furthermore, establishing clinical guidelines is crucial for standardizing its application in oncological screening and validating its clinical utility.

Background/Objectives: Alzheimer’s disease (AD) exhibits marked genetic heterogeneity between early-onset (EOAD) and late-onset (LOAD) forms. EOAD is typically associated with highly penetrant variants, whereas LOAD follows a polygenic architecture dominated by non-coding variation. However, the tissue-specific regulatory consequences of these variants remain insufficiently characterized. This study aimed to compare the regulatory genomic architectures underlying EOAD and LOAD using a multi-tissue integrative approach. Methods: GWAS-associated variants for EOAD and LOAD were retrieved from the GWAS Catalog using a relaxed significance threshold (p < 1 × 10⁻⁵). Variants were functionally annotated and integrated with GTEx v8 eQTL data across 13 neurologically relevant tissues and peripheral blood. Regulatory effects were evaluated using eQTL slope estimates. Basal gene expression patterns were assessed using GTEx RNA-seq data, and protein–protein interaction and functional enrichment analyses were performed using the STRING database. Results: A total of 287 variants were analyzed (32 EOAD, 255 LOAD), with minimal overlap. EOAD exhibited a highly focal regulatory profile, identifying GSE1 as the sole eQTL-regulated gene, restricted to the dorsolateral prefrontal cortex (BA9). In contrast, LOAD displayed a broad multi-tissue regulatory architecture involving APH1B, APOE, CEP63, and HAVCR2, with heterogeneous tissue-specific effects. LOAD-regulated genes converged on pathways related to γ-secretase activity, amyloid precursor protein processing, and Notch signaling, whereas GSE1-associated interactions were enriched for chromatin organization and epigenetic repression. Conclusions: EOAD and LOAD exhibit distinct regulatory genomic architectures, with EOAD characterized by focal, region-specific regulation and LOAD by widespread, tissue-dependent effects, highlighting stage-specific molecular mechanisms contributing to AD heterogeneity.

Human adenovirus 36 (HAdV-36) is associated with obesity, potentially by promoting adipocyte proliferation and differentiation. Although linked to increased fat storage, HAdV-36 is also correlated with improved insulin sensitivity. Given its potential role in modulating adipose tissue and promoting a less inflammatory metabolic profile, its impacts on pro- and anti-inflammatory cytokine secretion remain unclear. Methods: This nested case-control study compared cytokine levels (IL-10, IL-2, IL-6, IL-8, and TNF-α) between patients with and without HAdV-36 infection. A total of 76 participants were included, with 37 in the control group (HAdV-36 negative) and 39 classified as cases (HAdV-36 positive). Results: HAdV-36 seropositive individuals exhibited significantly lower IL-6 levels and higher IL-8 levels than seronegative participants. Additionally, they had lower glucose levels, suggesting a potential link between HAdV-36 and metabolic regulation. Conclusions: These findings support the hypothesis that HAdV-36 may influence inflammatory and metabolic responses by modulating cytokine expression and glucose levels. Further research is needed to clarify the underlying mechanisms and their implications for metabolic health.