The effects of aerobic and resistant exercises on the lipid profile in healthy women: a systematic review and meta-analysis.

Exercise can have a wide range of health benefits, including improving blood lipid profiles. For women to achieve optimal cardiovascular health, it is vital to determine the effect of exercise on their health and whether different exercise intensities can affect their blood lipid profile. A systematic review and meta-analysis were conducted to examine the effects of exercise on improving the lipid profile of healthy women. A database search was conducted using PubMed, Google Scholar, Embase, Scopus, and Web of Science from inception until July 2, 2021, for randomized controlled trials (RCTs) investigating exercise's effects on healthy women's blood lipid profiles. A total of 10 eligible articles (or 17 trials) with 576 participants were identified as eligible for the study. Overall, the meta-analysis shows that physical activity significantly improved total cholesterol (TC), triglycerides (TG), and high-density lipoprotein (HDL-C) levels: TC [WMD = -5.77 mg/dL, 95% CI: -10.41, -1.13, P < 0.01]; TG [WMD = -5.60 mg/dL, 95% CI: -8.96, -2.23, P < 0.01]; HDL [WMD = 4.49 mg/dL, 95% CI: 0.33, 8.65, P = 0.03]. Additionally, sub-group analyses indicated that combined exercise training improved TG and TC (p 0.05), and aerobic exercise significantly increased HDL. In this study, physical activity appears to be one of the most effective non-pharmacological means for improving HDL, TG, and TC in healthy women. In terms of TG and TC, CT was the most effective.

The NOS1AP gene rs10494366 common genetic variant does not modify the risk of sudden cardiac death in users of digoxin.

AIMS: Common genetic variations in the nitric oxide synthase-1 adaptor protein (NOS1AP) gene are associated with QT-interval prolongation. In a previous study, we observed an association between the rs10494366 variant of this gene and an increased QT-interval shortening in digoxin users. As QT-interval shortening is a risk factor for sudden cardiac death (SCD), in this study, we investigated whether the association between digoxin use and risk of SCD differs in participants with different NOS1AP rs10494366 genotypes. METHODS: We included 11 377 individuals from the prospective population-based cohort of the Rotterdam Study. We used Cox proportional hazard regression analysis with digoxin as time-dependent exposure to estimate the associations between current digoxin use and the risk of SCD among different rs10494366 genotype groups in the adjusted models. We also studied whether such an association was dose-dependent, comparing high dosage (≥ 0.250 mg), moderate dosage (0.125 mg ≤ dose< 0.250 mg) and low dosage (< 0.125 mg) digoxin users with non-users. RESULTS: The median baseline age of the total study population was 62 (interquartile range [IQR] 58-71) years. The cumulative incidence of SCD was 4.1% (469 cases), and among them, 74 (15.7%) individuals were current digoxin users at the time of death, during a median follow-up of 11.5 (IQR 6.5-17) years. Current digoxin users had an increased risk of SCD (multivariable adjusted model hazard ratio [HR]: 3.07; 95% confidence interval [CI]: 2.38-3.98), with no significant differences between the three genotype groups. The adjusted HRs were 4.03 [95% CI: 1.98-8.21] in the minor homozygous GG, 3.46 [95% CI: 2.37-5.04] in the heterozygous TG and 2.56 [95%CI: 1.70-3.86] in the homozygous TT genotype groups. Compared to low- and moderate-dose, high-dose digoxin users with GG genotype had the highest risk of SCD (HR: 5.61 [95% CI: 1.34-23.47]). CONCLUSIONS: Current use of digoxin is associated with a significantly increased risk of SCD. The NOS1AP gene rs10494366 variant did not modify the digoxin-associated risk of SCD in a population of European ancestry.

Statin therapy and cardiovascular protection in type 2 diabetes: The role of baseline LDL-Cholesterol levels. A systematic review and meta-analysis of observational studies.

AIM: The guidelines recommend statins to prevent cardiovascular events in patients with type 2 diabetes (T2D) however, the importance of baseline LDL-Cholesterol (LDL-C) levels remains controversial. This study aimed to determine the association of statin use in T2D patients with major adverse cardiovascular events (MACE) and all-cause mortality and whether this association differs by baseline LDL-C levels. DATA SYNTHESIS: Medline, Embase, and Web of Science were systematically searched from inception until January 2022. Observational studies in patients with T2D comparing statin users vs non-users, with reports of the baseline LDL-C levels, were included. Random-effects meta-analysis and meta-regression were performed to estimate the overall effect on the risk of all-cause mortality and MACE (a composite of myocardial infarction, heart failure, stroke, and revascularization events) and the modification in the association by baseline LDL-C levels. We categorized studies according to their baseline LDL-C levels into 1) <100 mg/dl (2.59 mmol/l), 2) 100-130 mg/dl (2.59-3.37 mmol/l) and 3) >130 mg/dl (3.37 mmol/l) categories. A total of 9 cohort studies (n = 403,411 individuals) fulfilled our criteria. The follow-up duration ranged from 1.7 to 8 years. The overall combined estimate showed that statin therapy was associated with a significantly lower risk of MACE (Hazard Ratio (HR): 0.70 [95% CI 0.59 to 0.83], Absolute risk reduction percentage (ARR%): 3.19% [95%CI 0.88 to 5.50%) and all-cause mortality (HR: 0.60 [95% CI 0.46 to 0.79], ARR%: 5.23% [95% CI 2.18 to 8.28%), but varied, albeit not statistically significant, by baseline LDL-C levels. Studies with baseline LDL-C levels higher than 130 mg/dl had the greatest reduction of MACE (HR: 0.58 [95% CI 0.37 to 0.90]) and all-cause mortality risk (HR: 0.51 [95% CI [ 0.29 to 0.90]). The HRs of MACE in studies with LDL-C levels of 100-130 mg/dl and <100 mg/dl categories were respectively (0.70 [95% CI 0.59 to 0.83]) and (0.83 [95% CI [0.68 to 1.00]); and that of all-cause mortality were respectively (0.62 [95% CI 0.38 to 1.01]) and (0.67 [95% CI [0.44 to 1.02]). Statin use changes the HRs of MACE (0.99 [95%CI, 0.98 to 0.99]; P = 0.04) and all-cause mortality (0.99 [95% CI 0.98 to 1.01]; P = 0.8) per each mg/dl increase in baseline LDL-C level in meta-regression analyses. CONCLUSION: Statin therapy in patients with T2D was associated with reduced risk of MACE and all-cause mortality. Significant differences across studies with different baseline LDL-C levels were not observed.

Respiratory syncytial virus vaccination during pregnancy for improving infant outcomes.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections (LRTIs) in infants. Maternal RSV vaccination is a preventive strategy of great interest, as it could have a substantial impact on infant RSV disease burden. In recent years, the clinical development of maternal RSV vaccines has advanced rapidly. OBJECTIVES: To assess the efficacy and safety of maternal respiratory syncytial virus (RSV) vaccination for preventing RSV disease in infants. SEARCH METHODS: We searched Cochrane Pregnancy and Childbirth's Trials Register and two other trials registries on 21 October 2022. We updated the search on 27 July 2023, when we searched MEDLINE, Embase, CENTRAL, CINAHL, and two trials registries. Additionally, we searched the reference lists of retrieved studies and conference proceedings. There were no language restrictions on our searches. SELECTION CRITERIA: We included randomised controlled trials (RCTs) comparing maternal RSV vaccination with placebo or no intervention in pregnant women of any age. The primary outcomes were hospitalisation with clinically confirmed or laboratory-confirmed RSV disease in infants. The secondary outcomes covered adverse pregnancy outcomes (intrauterine growth restriction, stillbirth, and maternal death) and adverse infant outcomes (preterm birth, congenital abnormalities, and infant death). DATA COLLECTION AND ANALYSIS: We used standard Cochrane methods and assessed the certainty of evidence using the GRADE approach. MAIN RESULTS: We included six RCTs (25 study reports) involving 17,991 pregnant women. The intervention was an RSV pre-F protein vaccine in four studies, and an RSV F protein nanoparticle vaccine in two studies. In all studies, the comparator was a placebo (saline, formulation buffer, or sterile water). We judged four studies at overall low risk of bias and two studies at overall high risk (mainly due to selection bias). All studies were funded by pharmaceutical companies. Maternal RSV vaccination compared with placebo reduces infant hospitalisation with laboratory-confirmed RSV disease (risk ratio (RR) 0.50, 95% confidence interval (CI) 0.31 to 0.82; 4 RCTs, 12,216 infants; high-certainty evidence). Based on an absolute risk with placebo of 22 hospitalisations per 1000 infants, our results represent 11 fewer hospitalisations per 1000 infants from vaccinated pregnant women (15 fewer to 4 fewer). No studies reported infant hospitalisation with clinically confirmed RSV disease. Maternal RSV vaccination compared with placebo has little or no effect on the risk of congenital abnormalities (RR 0.96, 95% CI 0.88 to 1.04; 140 per 1000 with placebo, 5 fewer per 1000 with RSV vaccination (17 fewer to 6 more); 4 RCTs, 12,304 infants; high-certainty evidence). Maternal RSV vaccination likely has little or no effect on the risk of intrauterine growth restriction (RR 1.32, 95% CI 0.75 to 2.33; 3 per 1000 with placebo, 1 more per 1000 with RSV vaccination (1 fewer to 4 more); 4 RCTs, 12,545 pregnant women; moderate-certainty evidence). Maternal RSV vaccination may have little or no effect on the risk of stillbirth (RR 0.81, 95% CI 0.38 to 1.72; 3 per 1000 with placebo, no difference with RSV vaccination (2 fewer to 3 more); 5 RCTs, 12,652 pregnant women). There may be a safety signal warranting further investigation related to preterm birth. This outcome may be more likely with maternal RSV vaccination, although the 95% CI includes no effect, and the evidence is very uncertain (RR 1.16, 95% CI 0.99 to 1.36; 6 RCTs, 17,560 infants; very low-certainty evidence). Based on an absolute risk of 51 preterm births per 1000 infants from pregnant women who received placebo, there may be 8 more per 1000 infants from pregnant women with RSV vaccination (1 fewer to 18 more). There was one maternal death in the RSV vaccination group and none in the placebo group. Our meta-analysis suggests that RSV vaccination compared with placebo may have little or no effect on the risk of maternal death (RR 3.00, 95% CI 0.12 to 73.50; 3 RCTs, 7977 pregnant women; low-certainty evidence). The effect of maternal RSV vaccination on the risk of infant death is very uncertain (RR 0.81, 95% CI 0.36 to 1.81; 6 RCTs, 17,589 infants; very low-certainty evidence). AUTHORS' CONCLUSIONS: The findings of this review suggest that maternal RSV vaccination reduces laboratory-confirmed RSV hospitalisations in infants. There are no safety concerns about intrauterine growth restriction and congenital abnormalities. We must be careful in drawing conclusions about other safety outcomes owing to the low and very low certainty of the evidence. The evidence available to date suggests RSV vaccination may have little or no effect on stillbirth, maternal death, and infant death (although the evidence for infant death is very uncertain). However, there may be a safety signal warranting further investigation related to preterm birth. This is driven by data from one trial, which is not fully published yet. The evidence base would be much improved by more RCTs with substantial sample sizes and well-designed observational studies with long-term follow-up for assessment of safety outcomes. Future studies should aim to use standard outcome measures, collect data on concomitant vaccines, and stratify data by timing of vaccination, gestational age at birth, race, and geographical setting.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.

BACKGROUND: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. METHODS: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35-70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). RESULTS: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. CONCLUSION: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

A Federated Database for Obesity Research: An IMI-SOPHIA Study.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Machine-learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study.

INTRODUCTION: The application of machine learning (ML) is increasingly growing in biomedical sciences. This study aimed to evaluate factors associated with type 2 diabetes mellitus (T2DM) and compare the performance of ML methods in identifying individuals with the disease in an Iranian setting. METHODS: Using the baseline data from Fasa Adult Cohort Study (FACS) and in a sex-stratified manner, we studied factors associated with T2DM by applying seven different ML methods including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbours (KNN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB) and Bagging classifier (BAG). We further compared the performance of these methods; for each algorithm, accuracy, precision, sensitivity, specificity, F1 score, and Area Under Curve (AUC) were calculated. RESULTS: 10,112 participants were recruited between 2014 and 2016, of whom 1246 had T2DM at baseline. 4566 (45%) participants were males, aged between 35 and 70 years. For males, age, sugar consumption, and history of hospitalization were the most weighted variables regarding their importance in screening for T2DM using the GBM model, respectively; these variables were sugar consumption, urine blood, and age for females. GBM outperformed other models for both males and females with AUC of 0.75 (0.69-0.82) and 0.76 (0.71-0.80), and F1 score of 0.33 (0.27-0.39) and 0.42 (0.38-0.46), respectively. GBM also showed a sensitivity of 0.24 (0.19-0.29) and a specificity of 0.98 (0.96-1.0) in males and a sensitivity of 0.38 (0.34-0.42) and specificity of 0.92 (0.89-0.95) in females. Notably, close performance characteristics were detected among other ML models. CONCLUSIONS: GBM model might achieve better performance in screening for T2DM in a south Iranian population.