Metabolic syndrome is associated with mortality in elderly patients with acute respiratory distress syndrome

BACKGROUND

This study aims to evaluate the association of metabolic syndrome (MetS) with the risk of all-cause mortality in elderly patients with acute respiratory distress syndrome (ARDS).

METHODS

Elderly ARDS patients (≥ 65 years) enrolled from our hospital between January 2018 and July 2023 were divided into the MetS group or the non-MetS group. The outcomes were 28-day and 90-day all-cause mortality rates in the total population and two subgroups stratified by age (65-75 years and ≥ 75 years). Multivariate Cox regression was employed to assess the association of MetS with all-cause mortality, after controlling for potential cofounding factors.

RESULTS

A total of 946 patients were divided into the MetS group (n = 410) or the non-MetS group (n = 536). The 28-day and 90-day all-cause mortality rates were significantly higher for MetS group compared to non-MetS group in the total population and two subgroups (all P < 0.01). Multivariate Cox regression indicated that MetS was significantly associated with a higher risk of 90-day all-cause mortality in the total population (HR = 1.62, 95% CI: 1.22-2.15; P < 0.01), and subgroups of patients aged 65-75 years (HR = 1.52, 95% CI: 1.04-2.21; P = 0.03) and ≥ 75 years (HR = 1.90, 95% CI: 1.23-2.94; P < 0.01). Moreover, with each MetS criterion added from 0 to 1 to 2, 3, and 4 of 4 criteria, both 28-day and 90-day all-cause mortality rates significantly increased (both P < 0.01).

CONCLUSION

MetS was associated with higher risks of 28-day and 90-day all-cause mortality in elderly patients with ARDS.

Insulin resistance in cardiovascular disease, uremia, and peritoneal dialysis

Diabetic nephropathy is highly correlated with the occurrence of other complications of type 1 diabetes (T1D) and type 2 diabetes (T2D) mellitus; for example, hypertension with cardiovascular disease (CVD) being the most frequent cause of death in patients with end-stage renal disease and undergoing renal dialysis. Hyperglycemia and insulin resistance (IR) are responsible for the micro- and macrovascular complications of diabetes through different mechanisms. In particular, IR plays a key role in the etiology of atherosclerosis in both diabetic and non-diabetic patients. IR - exacerbated by organ-level selectivity - is more important than glycemic control per se in determining cardiovascular outcomes. This may be exacerbated by the fact that IR is organ and pathway specific due to the only selective loss of sensitivity to insulin action of specific pathways/processes. Therefore, it is counterintuitive that the use of peritoneal dialysis (PD) in (frequently) diabetic renal disease patients should involve their exposure to high daily doses of glucose peritoneally. In view of the controversy about the causal association between glucose load and CVD in PD patients, we discuss the role that selective IR may play in the progression of CVD in diabetic renal end-stage patients. In discussing these associations, we propose that reducing glucose exposure in PD solutions may be beneficial especially if coupled with strategies that address IR directly, and the avoidance of excessive use of insulin treatment in T2D.

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks

Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and difficult. Three prediction models, the logistic regression (LR) model, artificial neural network (ANN) classic model and a new structured ANN model (ANN mixed model), were constructed and evaluated using a receiver operating characteristic (ROC) curve analysis. The permutation feature importance was used to interpret the important features in the ANN models. Eight hundred fifty-nine patients were enrolled in the study. The LR model performed slightly better than the other two ANN models on the test dataset; however, in the total dataset, the ANN models fit much better. The ANN mixed model showed the best prediction performance, with area under the ROC curves (AUROCs) of 0.8 and 0.79 for the 6-month and 12-month datasets. Our study showed that age, diastolic blood pressure (DBP), and low-density lipoprotein cholesterol (LDL-c) levels were common risk factors for premature mortality in patients receiving PD. Our ANN mixed model had incomparable advantages in fitting the overall data characteristics, and age is a steady risk factor for premature mortality in patients undergoing PD. Otherwise, DBP and LDL-c levels should receive more attention for all-cause mortality during follow-up.