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This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

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BACKGROUND: Short tandem repeats (STRs) are the most widely used genetic markers in forensic genetics. Therefore, it is essential to document genetic population data of new kits designed for human identification purposes to enable laboratories to use these genetic systems to interpret and solve forensic casework. However, in Mexico, there are no studies with the PowerPlex Fusion 6C System, which includes 26 STRs (23 autosomal STRs and 3 Y-STRs). METHODS AND RESULTS: 600 DNA samples from Mexico City were subjected to genotyping using the PowerPlex Fusion 6C System. For autosomal STRs, 312 different alleles were observed. Combined PE and PD were 99.999999809866% and 99.99999999999999999999999818795%, respectively. Genetic distances and AMOVA test showed low but significant differentiation between Mexican populations. CONCLUSIONS: The results reported in this work demonstrate the efficacy of this system for human identification purposes in the population studied and justify its possible application in other Mexican Mestizo populations.

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We conducted a spatial and temporal analysis of housing patterns in Mexico City by utilizing an extensive database of 16,000 prices for flats and houses, covering the period from 2000 to 2022. Our findings reveal a striking trend: The average housing prices have quadrupled over a 20-y period, without considering inflation. In contrast, the per capita labor income of Mexican citizens has declined relative to inflation. As a result, the average family encountered four times greater challenges in accessing housing in 2015 as compared to 2005. Furthermore, our research demonstrates that areas that have undergone significant gentrification or super-gentrification contribute to a widespread increase in land value on neighboring zones, leading to the emergence of clusters of highly expensive neighborhoods.

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BACKGROUND: Few epidemiologic studies have focused on the specific source of ambient air pollution and adverse health effects in early life. Here, we investigated whether air pollutants from different emission sources were associated with decreased birth anthropometry parameters and increased DNA adduct formation in mother-child pairs residing in the Mexico City Metropolitan Area (MCMA). METHODS: This cross-sectional study included 190 pregnant women recruited during their last trimester of pregnancy from two hospitals at MCMA, and a Modeling Emissions Inventory (MEI) to calculate exposure to ambient air pollutants from different emissions sources (area, point, mobile, and natural) for two geographical buffers 250 and 750 m radii around the participants households. RESULTS: Contaminants were positively correlated with umbilical cord blood (UCB) adducts, but not with maternal blood (MB) adducts. PM10 emissions (area and point sources, overall emissions), PM2.5 (point sources), volatile organic compounds (VOC), total organic compounds (TOC) from point sources were positively correlated with UCB adducts. Air pollutants emitted from natural sources were correlated with a decrease in MB and UCB adducts. PM10 and PM2.5 were correlated (p < 0.05) with a decrease in birth weight (BW), birth length (BL) and gestational age at term (GA). In multivariate analyses adjusted for potential confounders, PM10 was associated with an increase in UCB adducts. PM10 and PM2.5 from overall emissions were associated with a decrease in BW, BL and GA at term. IMPACT: Results suggested higher susceptibility of newborns compared to mothers to damage related to ambient air pollution. PMs are associated with birth anthropometry parameters and DNA damage in adjusted models, highlighting the need for more strict regulation of PM emissions.

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OBJECTIVE: To assess SARS-CoV-2 antibody prevalence and titers in people living with HIV (PLWHIV) on antiretroviral treatment (ART) enrolled at a tertiary reference hospital in Mexico. METHODS: Two plasma aliquots per person, used for HIV viral load follow-up between 01/2020 and 09/2021, were used to assess total anti-N and neutralizing SARS-CoV-2 antibodies. Sociodemographic, clinical, and SARS-CoV-2 exposure risk information were collected. The risk associated with SARS-CoV-2 exposure and associations with antibody titers were analyzed with logistic, Cox, and linear multivariable models. RESULTS: 803 PLWHIV participated; 233 had detectable SARS-CoV-2 antibodies (prevalent cases), and 132 seroconverted (incident cases). Overall, the adjusted prevalence was 46.45%, with an incidence rate of 3.78 cases/100 person-months. Factors associated with prevalent cases included lower age, location (western zone of Mexico City and the neighboring Mexico State), use of public transport, attendance at meetings without social distancing, and higher CD4 + T cell counts (p < 0.05; multivariable logistic model). BNT162b2 vaccination reduced incident cases (Cox adjusted HR = 0.4; p = 0.013). Notably, previously infected and vaccinated individuals showed maximization of neutralizing activity (p < 0.001). No associations between SARS-CoV-2 neutralization and HIV-related variables (CD4 + T cell counts, viral load, number of years in viral suppression, ART regimen) were found in multivariable analysis. CONCLUSIONS: SARS-CoV-2 infection was associated with community risk rather than HIV-associated variables in PLWH on ART and clinical follow-up. Antibody neutralization activity in vaccinated participants was maximized with previous SARS-CoV-2 infection.

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Introduction: Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment. Methods: In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity. Results: Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics. Discussion: The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

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Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City's air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005-2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

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This Review emphasizes the impact of APOE4-the most significant genetic risk factor for Alzheimer's disease (AD)-on peripheral and neural effects starting in childhood. We discuss major mechanistic players associated with the APOE alleles' effects in humans to understand their impact from conception through all life stages and the importance of detrimental, synergistic environmental exposures. APOE4 influences AD pathogenesis, and exposure to fine particulate matter (PM2.5), manufactured nanoparticles (NPs), and ultrafine particles (UFPs) associated with combustion and friction processes appear to be major contributors to cerebrovascular dysfunction, neuroinflammation, and oxidative stress. In the context of outdoor and indoor PM pollution burden-as well as Fe, Ti, and Al alloys; Hg, Cu, Ca, Sn, and Si UFPs/NPs-in placenta and fetal brain tissues, urban APOE3 and APOE4 carriers are developing AD biological disease hallmarks (hyperphosphorylated-tau (P-tau) and amyloid beta 42 plaques (Aß42)). Strikingly, for Metropolitan Mexico City (MMC) young residents ≤ 40 y, APOE4 carriers have 4.92 times higher suicide odds and 23.6 times higher odds of reaching Braak NFT V stage versus APOE4 non-carriers. The National Institute on Aging and Alzheimer's Association (NIA-AA) framework could serve to test the hypothesis that UFPs and NPs are key players for oxidative stress, neuroinflammation, protein aggregation and misfolding, faulty complex protein quality control, and early damage to cell membranes and organelles of neural and vascular cells. Noninvasive biomarkers indicative of the P-tau and Aß42 abnormal protein deposits are needed across the disease continuum starting in childhood. Among the 21.8 million MMC residents, we have potentially 4 million APOE4 carriers at accelerated AD progression. These APOE4 individuals are prime candidates for early neuroprotective interventional trials. APOE4 is key in the development of AD evolving from childhood in highly polluted urban centers dominated by anthropogenic and industrial sources of pollution. APOE4 subjects are at higher early risk of AD development, and neuroprotection ought to be implemented. Effective reductions of PM2.5, UFP, and NP emissions from all sources are urgently needed. Alzheimer's Disease prevention ought to be at the core of the public health response and physicians-scientist minority research be supported.

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BACKGROUND: PM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AIM: To evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. METHODS: This study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 µg/m3 increase) at different lag years (1-4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). RESULTS: The median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) µm, respectively. Annual average PM2.5 exposure was 26.64 µg/m3, with median and IQR, of 24.46 (23.5-25.46) µg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; -0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. CONCLUSIONS: Our results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

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Anti-inflammatory and analgesic medications (AAMs) are widely used in Mexico and the rest of the world. Their excessive acquisition can lead to waste, representing an unnecessary expense for families and the public health system. The aim of this study was to estimate the economic cost of the waste of unused AAMs collected by the National System for the Collection of Residues of Containers and Medications (SINGREM, the acronym in Spanish) in Mexico City during 2019. Data from SINGREM on discarded AAMs in Mexico City were classified by the type and quantity of drug, pharmaceutical dosage form, origin, dose, and the complete or incomplete condition of the package. The unitary cost for each medication was based on public tenders of the Mexican Social Security Institute (IMSS) for the public sector and the prices in large drug store franchises for the private sector. A decision-making model was constructed to appraise the total cost of discarded AAMs. The economic cost of the 48924 units of discarded AAMs in SINGREM containers in Mexico City during 2019 was approx. USD$143500, of which over USD$127000 corresponded to the private health sector. The current findings evidence an enormous accumulation of unneeded or expired AAMs in Mexico City. According to the present data, the cost of such waste is substantial. The estimated cost was 8-fold higher for discarded medications originating from the private versus the public healthcare sector. It is important to implement measures to prevent this waste and increase awareness of the consequences of inadequate drug disposal.

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