Sex disparities in blood pressure development: time for action

Machine Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals

Background

Noncommunicable diseases continue to pose a substantial health challenge globally, with hyperglycemia serving as a prominent indicator of diabetes.

Objective

This study employed machine learning algorithms to predict hyperglycemia in a cohort of individuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification.

Methods

This dataset included an extensive array of clinical and demographic data obtained from 195 adults who were asymptomatic and residing in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model for predicting hyperglycemia. Moreover, we explored feature importance to pinpoint correlates of high blood glucose levels within the cohort.

Results

Elevated blood pressure and prehypertension were recorded in 8 (4.1%) and 18 (9.2%) of the 195 participants, respectively. A total of 41 (21%) participants presented with hypertension, of which 34 (83%) were female. However, sex adjustment showed that 34 of 118 (28.8%) female participants and 7 of 77 (9%) male participants had hypertension. Age-based analysis revealed an inverse relationship between normotension and age (r=-0.88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50-59 years. Of the 195 participants, isolated systolic hypertension and isolated diastolic hypertension were recorded in 16 (8.2%) and 15 (7.7%) participants, respectively, with female participants recording a higher prevalence of isolated systolic hypertension (11/16, 69%) and male participants reporting a higher prevalence of isolated diastolic hypertension (11/15, 73%). Following class rebalancing, the random forest classifier gave the best performance (accuracy score 0.89; receiver operating characteristic-area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as important variables associated with hyperglycemia.

Conclusions

The random forest classifier identified significant clinical correlates associated with hyperglycemia, offering valuable insights for the early detection of diabetes and informing the design and deployment of therapeutic interventions. However, to achieve a more comprehensive understanding of each feature's contribution to blood glucose levels, modeling additional relevant clinical features in larger datasets could be beneficial.

Associations between long-term exposure to air pollution, diabetes, and hypertension in metropolitan Iran: an ecologic study

Epidemiological studies on air pollution, diabetes, and hypertension conflict. This study examined air pollution, diabetes, and hypertension in adults in 11 metropolitan areas of Iran (2012-2016). Local environment departments and the Tehran Air Quality Control Company provided air quality data. The VIZIT website and Stepwise Approach to Chronic Disease Risk Factor Surveillance study delivered chronic disease data. Multiple logistic regression and generalized estimating equations evaluated air pollution-related diabetes and hypertension. In Isfahan, Ahvaz, and Tehran, PM2.5 was linked to diabetes. In all cities except Urmia, Yasuj, and Yazd, PM2.5 was statistically related to hypertension. O3 was connected to hypertension in Ahvaz, Tehran, and Shiraz, whereas NO2 was not. BMI and gender predict hypertension and diabetes. Diabetes, SBP, and total cholesterol were correlated. Iran's largest cities' poor air quality may promote diabetes and hypertension. PM2.5 impacts many cities' outcomes. Therefore, politicians and specialists have to control air pollution.

Evaluating Sex Differences in the Effect of Increased Systolic Blood Pressure on the Risk of Cardiovascular Disease in Asian Populations: A Systematic Review and Meta-Analysis

Background

Cardiovascular disease (CVD) is a serious health concern worldwide, and half of the cases of CVD occur in Asia. Because hypertension or high blood pressure has been confirmed to be an important risk factor for CVD, controlling blood pressure is helpful for CVD prevention. Although many studies have shown a sex difference in the impact of blood pressure on the risk of CVD, the risk threshold of blood pressure remained the same for both sexes in the latest global guidelines.

Objective

The study aimed to evaluate sex differences in the effect of increased blood pressure on the risk of CVD in Asian populations.

Methods

In this study, we performed a systematic review via PubMed, Embase, and MEDLINE to select studies conducted with Asian populations published before 30 June 2021.

Results

Six female and eleven male effect sizes for CVD risk from six articles were identified. The unadjusted pooled effect size for CVD risk per 10-mmHg increase in systolic blood pressure was estimated to be 1.20 for females (95% confidence interval: [1.10, 1.32]) and 1.19 for males (95% confidence interval: [1.11, 1.27]). Furthermore, using meta-regression to adjust for the significant effect of smoking, we showed that the impact of a 10-mmHg systolic blood pressure increase on CVD risk among females was 1.232 times that among males, corresponding to a significant sex difference (95% confidence interval: [1.065, 1.426]; P = 0.02). In summary, the effect of an increased systolic blood pressure on the risk of CVD in females was significantly higher than that in males in the Asian population.