Non-linear relationship between pulse pressure and the risk of prediabetes: a 5-year cohort study in Chinese adults.

Previous research has established a strong link between pulse pressure (PP) and diabetes, but there is limited investigation into the connection between PP and prediabetes. This study aims to explore the potential association between PP and prediabetes. A retrospective cohort study encompassed 202,320 Chinese adults who underwent health check-ups between 2010 and 2016. Prediabetes was defined in accordance with the World Health Organization criteria, indicating impaired fasting glucose, with fasting blood glucose levels ranging from 6.1 to 6.9 mmol/L. To assess the PP-prediabetes relationship, we employed Cox regression analysis, sensitivity analysis, and subgroup analysis. Cox proportional hazards regression, coupled with cubic spline functions and smooth curve fitting, helped elucidate the non-linear PP-prediabetes relationship. Upon adjusting for confounding factors, we observed a positive association between PP and prediabetes (HR 1.15, 95% CI 1.11-1.18, P < 0.0001). Participants in the fourth quartile (PP ≥ 51 mmHg) had a 73% higher likelihood of developing prediabetes compared to those in the first quartile (PP < 36 mmHg) (HR 1.73, 95% CI 1.52-1.97, P < 0.0001). Moreover, the relationship between PP and prediabetes was non-linear. A two-piece Cox proportional hazards regression model identified an inflection point at 40 mmHg for PP (P for log-likelihood ratio test = 0.047). Sensitivity and subgroup analyses corroborated the robustness of our findings. Our study reveals a non-linear correlation between PP and prediabetes, signifying an increased risk of prediabetes when PP levels exceed 40 mmHg. This discovery has significant clinical implications for early prediabetes prevention and intervention, ultimately contributing to improved patient outcomes and quality of life.

Blood urea nitrogen has a nonlinear association with 3-month outcomes with acute ischemic stroke: A second analysis based on a prospective cohort study.

BACKGROUND: Evidence regarding the relationship between blood urea nitrogen (BUN) and 3-month outcomes in acute ischemic stroke (AIS) patients is still scarce. Therefore, the present study was preformed to explore the link between the BUN and 3-month poor outcomes in patients with AIS. METHODS: A retrospective study of 1866 participants with AIS enrolled from January 2010 to December 2016 at a hospital in South Korea. Binary logistic regression, smooth curve fitting, and a set of sensitivity analyses were used to analyze the association between BUN and 3-month poor outcomes. RESULTS: After adjusting covariates, the results of the binary logistic regression model suggested that the relationship between the BUN and the risk of 3-month poor outcomes for AIS patients was not statistically significant. However, there was a special nonlinear relationship between them, and the inflection point of the BUN was 13 mg/dl. On the left side of the inflection point, every unit increase in the BUN reduces the risk of 3-month poor outcomes by 14.1 % (OR = 0.859, 95%CI: 0.780-0.945, p = 0.0019). On the right side of the inflection point, the relationship is not statistically significant. CONCLUSION: There is a nonlinear relationship with saturation effect between BUN level and 3-month poor outcomes in AIS patients. Maintaining the BUN at around 13 mg/dl can reduce the risk of 3-month poor outcome in AIS patients.

Nonlinear relationship between platelet count and 30-day in-hospital mortality in ICU acute respiratory failure patients: a multicenter retrospective cohort study.

BACKGROUND: Limited evidence exists regarding the link between platelet count and 30-day in-hospital mortality in acute respiratory failure (ARF) patients. Thus, this study aims to investigate this association among ICU patients experiencing acute respiratory failure. METHODS: We conducted a retrospective cohort study across multiple centers, utilizing data from the US eICU-CRD v2.0 database covering 22,262 patients with ARF in the ICU from 2014 to 2015. Our aim was to investigate the correlation between platelet count and 30-day in-hospital mortality using binary logistic regression, subgroup analyses, and smooth curve fitting. RESULTS: The 30-day in-hospital mortality rate was 19.73% (4393 out of 22,262), with a median platelet count of 213 × 109/L. After adjusting for covariates, our analysis revealed an inverse association between platelet count and 30-day in-hospital mortality (OR = 0.99, 95% CI 0.99, 0.99). Subgroup analyses supported the robustness of these findings. Furthermore, a nonlinear relationship was identified between platelet count and 30-day in-hospital mortality, with the inflection point at 120 × 109/L. Below the inflection point, the effect size (OR) was 0.89 (0.87, 0.91), indicating a significant association. However, beyond this point, the relationship was not statistically significant. CONCLUSION: This study establishes a clear negative association between platelet count and 30-day in-hospital mortality among ICU patients with ARF. Furthermore, we have identified a nonlinear relationship with saturation effects, indicating that among ICU patients with acute respiratory failure, the lowest 30-day in-hospital mortality rate occurs when the baseline platelet count is approximately 120 × 109/L.

Evaluation of Sampling Algorithms Used for Bayesian Uncertainty Quantification of Molecular Dynamics Force Fields.

New Bayesian parameter estimation methods have the capability to enable more physically realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates of uncertainties of force-field (FF) parameters and associated properties. However, the choice of which Bayesian parameter estimation algorithm to use has not been widely investigated, despite its impact on the effective exploration of parameter space. Here, using a case example of the Embedded Atom Method (EAM) FF parameters, we investigated the ramifications of several of the algorithm choices. We found that Ensemble Slice Sampling (ESS) and Affine-Invariant Ensemble Sampling (AIES) demonstrate a new level of superior performance, culminating in more accurate parameter and property estimations with tighter uncertainty bounds, compared to traditional methods such as Metropolis-Hastings (MH), Gradient Search (GS), and Uniform Random Sampler (URS). We demonstrate that Bayesian Uncertainty Quantification with ESS and AIES leads to significantly more accurate and reliable predictions of the FF parameters and properties. The results suggest that ESS and AIES should be used to obtain more accurate parameter and uncertainty estimations while providing deeper physical insights.