Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance. 137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype. Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.

The COVID-19 pandemic had a profound global impact, characterized by a high fatality rate and the emergence of enduring consequences known as Long COVID. Our study sought to determine the prevalence of Long COVID syndrome within a population of Northeastern Mexico, correlating it with patients' comorbidities, number of COVID-19 reinfection, and vaccination status. Employing an observational cross-sectional approach, we administered a comprehensive questionnaire covering medical history, demographics, vaccination status, COVID-related symptoms, and treatment. Our participant cohort included 807 patients, with an average age of 41.5 (SD 13.6) years, and women accounting 59.3% of the cohort. The follow-up was 488 (IQR 456) days. One hundred sixty-eight subjects (20.9%) met Long COVID criteria. Long COVID-19 was more prevalent when subjects had reinfections (p = 0.02) and less frequent when they had a complete vaccination scheme (p = 0.05). Through logistic regression, we found that male gender (OR 0.5, p ≤ 0.001), blood types of AB− (OR 0.48, p = 0.003) and O− (OR 0.27, p ≤ 0.001) in comparison with A+ and two doses of vaccines (OR 0.5, p = 006) to be protective factors against Long COVID; while higher BMI (OR 1.04, p = 0.005) was a risk factor. We saw that the prevalence of Long COVID was different within vaccinated patients and specific blood types, while being female and a higher BMI were associated with an increased risk of having long-COVID.

Background: Vaccination is our main strategy to control SARS-CoV-2 infection. Given the decrease in quantitative SARS-CoV-2 spike 1–2 IgG antibody titers three months after the second BNT162b2 dose, healthcare workers received a third booster six months after completing the original protocol. This study aimed to analyze the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers and the safety of the third dose. Material and methods: A prospective longitudinal cohort study included healthcare workers who received a third booster six months after completing the BNT162b2 regimen. We assessed the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers 21–28 days after the first and second dose, three months after the completed protocol, 1–7 days following the third dose, and 21–28 days after booster administration. Results: The cohort comprised 168 participants aged 41(10) years old, 67% of whom were female. The third dose was associated with an increase in quantitative antibody titers, regardless of previous SARS-CoV-2 history. In cases with a negative SARS-CoV-2 history, the median (IQR) antibody titer values increased from 379 (645.4) to 2960 (2010) AU/ml, whereas in cases with a positive SARS-CoV-2 history, from 590 (1262) to 3090 (2080) AU/ml (p<0.001). The third dose caused a lower number of total (local and systemic) adverse events following immunization (AEFI) compared with the first two vaccines. However, in terms of specific symptoms such as fatigue, myalgia, arthralgia, fever, and adenopathy, the proportion was higher in comparison with the first and second doses (p<0.05). The most common AEFI after the third BNT162b2 vaccine was pain at the injection site (n = 82, 84.5%), followed by fatigue (n = 45, 46.4%) of mild severity (n = 36, 37.1%). Conclusion: The third dose applied six months after the original BNT162b2 regimen increased the quantitative SARS-CoV-2 spike 1–2 IgG antibody titers. The booster dose was well tolerated and caused no severe AEFI.

The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (<60%) and sensitivity (30%). We set forth the hypothesis that the Machine Learning (ML) C5.0 algorithm could optimize the ECG in the prediction of LVH by echocardiography (Echo) while also establishing ECG-LVH phenotypes. We used Echo as the standard diagnostic tool to detect LVH and measured the ECG abnormalities found in Echo-LVH. We included 432 patients (power = 99%). Of these, 202 patients (46.7%) had Echo-LVH and 240 (55.6%) were males. We included a wide range of ventricular masses and Echo-LVH severities which were classified as mild (n = 77, 38.1%), moderate (n = 50, 24.7%) and severe (n = 75, 37.1%). Data was divided into a training/testing set (80%/20%) and we applied logistic regression analysis on the ECG measurements. The logistic regression model with the best ability to identify Echo-LVH was introduced into the C5.0 ML algorithm. We created multiple decision trees and selected the tree with the highest performance. The resultant five-level binary decision tree used only six predictive variables and had an accuracy of 71.4% (95%CI, 65.5–80.2), a sensitivity of 79.6%, specificity of 53%, positive predictive value of 66.6% and a negative predictive value of 69.3%. Internal validation reached a mean accuracy of 71.4% (64.4–78.5). Our results were reproduced in a second validation group and a similar diagnostic accuracy was obtained, 73.3% (95%CI, 65.5–80.2), sensitivity (81.6%), specificity (69.3%), positive predictive value (56.3%) and negative predictive value (88.6%). We calculated the Romhilt-Estes multilevel score and compared it to our model. The accuracy of the Romhilt-Estes system had an accuracy of 61.3% (CI95%, 56.5–65.9), a sensitivity of 23.2% and a specificity of 94.8% with similar results in the external validation group. In conclusion, the C5.0 ML algorithm surpassed the accuracy of current ECG criteria in the detection of Echo-LVH. Our new criteria hinge on ECG abnormalities that identify high-risk patients and provide some insight on electrogenesis in Echo-LVH.

Background: Scarce information exists in relation to the comparison of seroconversion and adverse events following immunization (AEFI) with different SARS-CoV-2 vaccines. Our aim was to correlate the magnitude of the antibody response to vaccination with previous clinical conditions and AEFI.Methods: A multicentric comparative study where SARS-CoV-2 spike 1-2 IgG antibodies IgG titers were measured at baseline, 21-28 days after the first and second dose (when applicable) of the following vaccines: BNT162b2 mRNA, mRNA-1273, Gam-COVID-Vac, Coronavac, ChAdOx1-S, Ad5-nCoV and Ad26.COV2. Mixed model and Poisson generalized linear models were performed.Results: We recruited 1867 individuals [52 (SD 16.8) years old, 52% men]. All vaccines enhanced anti-S1 and anti-S2 IgG antibodies over time (p<0.01). The highest increase after the first and second dose was observed in mRNA-1273 (p<0.001). There was an effect of previous SARS-CoV-2 infection; and an interaction of age with previous SARS-CoV-2 infection, Gam-COVID-Vac and ChAdOx1-S (p<0.01). There was a negative correlation of Severe or Systemic AEFI (AEs) of naïve SARS-CoV-2 subjects with age and sex (p<0.001); a positive interaction between the delta of antibodies with Gam-COVID-Vac (p=0.002). Coronavac, Gam-COVID-Vac and ChAdOx1-S had less AEs compared to BNT162b (p<0.01). mRNA-1273 had the highest number of AEFIs. The delta of the antibodies showed an association with AEFIs in previously infected individuals (p<0.001).Conclusions: The magnitude of seroconversion is predicted by age, vaccine type and SARS-CoV-2 exposure. AEs are correlated with age, sex, and vaccine type. The delta of the antibody response only correlates with AEs in patients previously exposed to SARS-CoV-2.Registration number: ClinicalTrials.gov, identifier NCT05228912.

Background: My Dose Coach (MDC) is a mobile application combined with a web portal that can suggest optimized basal insulin (BI) injection doses using Self-Measured Plasma Glucose (SMPG) and hypoglycemia data. This study aimed to evaluate its efficacy on patients reaching SMPG and Fasting blood glucose (FBG) target range 90-130 mg/dl (5-7.2 mmol/L) goals without severe hypoglycemic episodes. We also addressed the mean reduction in glycated hemoglobin (A1C), FBG, and SMPG and the improvement in the WHO’s Five Well Being Index (WBI). Methods: This prospective pilot study involved the use of MDC in outpatients with type 2 diabetes (T2DM) from a Hospital in Northern Mexico. Patients on treatment with any BI were included in the study. The follow-up was of 16 weeks. Student t -tests or McNemar test were used for effect comparisons. Results: We included 158 patients (46.8% women), mean (SD) age 51 (10.3) years. We achieved SMPG target range in 58.9% [mean (95CI) reduction of 30.9 mg/dl (22.5-37.7; P < .001)] of the patients [66(28) days], with no severe hypoglycemia events. FBG goal was reached in 55.7% [mean (95CI) reduction of 63.4 mg/dl (49.6-77.2; P < .001)]. The mean (95CI) reduction of A1C was 1.78% (1.47-2, P < .01) with the last observation carried forward. There was a mean (95CI) increase of 2.23 (−3, −1.4, P < .01) points in WBI scale. Conclusions: MDC successfully helped to achieve FBG and SMPG goals, reduced A1C, and increased WBI with no severe hypoglycemia events.