Jie Min

Sleep apnoea (SA) is a common condition associated with obesity, conferring significant cardiovascular and metabolic risks. This study investigates the causal effects of obesity phenotypes throughout the life course, from birth weight to adult obesity, on SA risk using Mendelian randomization (MR). A two-sample MR approach was utilized, employing genome-wide association study (GWAS) data from individuals of European ancestry. Exposures included birth weight, childhood obesity, adult obesity classifications (overweight and obesity classes 1-3), and fat distribution characteristics (waist/hip circumference and MRI-derived abdominal adipose tissue volumes). Outcome data were sourced from the FinnGen database. Analyses were conducted using inverse variance weighting (IVW), MR-Egger regression, and sensitivity analyses. A replication analysis using another GWAS dataset on SA was also performed. The analysis demonstrated a positive causal relationship between birth weight and SA risk (OR = 1.142, P < 0.001). Features of childhood obesity, including BMI (OR = 1.218, P < 0.001) and early life body size (OR = 1.533, P < 0.001), were associated with increased SA risk. In adulthood, BMI was strongly associated with SA risk (OR = 2.419, P < 0.001). Fat distribution measures, notably MRI-derived subcutaneous adipose tissue volume (OR = 1.119, P = 0.003) and waist circumference (OR = 1.953, P < 0.001), were also predictors of SA risk. Sensitivity analyses affirmed these findings, suggesting minimal horizontal pleiotropy. The replication analysis confirmed a positive correlation between obesity indicators and SA. This study supports the causal relationship of life-course obesity, including birth weight and obesity, in childhood and adulthood, on SA risk, highlighting the importance of fat distribution metrics in understanding SA determinants.

Renal replacement therapy (RRT) plays a crucial role in managing acute pancreatitis (AP). This study aimed to develop and evaluate predictive models for determining the need for RRT among patients with AP in the intensive care unit (ICU). A retrospective selection of patients with AP was made from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version V2.0). The cohort was randomly divided into a training set (447 patients) and a validation set (150 patients). The least absolute shrinkage and selection operator (LASSO) regression cross-validation method was utilized to identify key features for model construction. Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. The mean age of the cohort was 59.17 years, with an average Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 17.55. Acute kidney injury (AKI) was observed in 52.43% of patients with AP, and 9.05% required RRT. After feature selection, four of 41 clinical factors were ultimately chosen for use in model construction. The Lasso-Logistic Regression (Lasso-LR) model showed a high discriminative ability to predict RRT risk in patients with AP, with an area under the receiver operating characteristic (AUROC) of 0.955 (95% CI 0.924–0.987) in the training set. In the validation set, it maintained its discriminative performance, achieving an AUROC of 0.985 (95% CI 0.970–1.000). Calibration curves indicated an excellent fit in both sets (Brier scores: 0.039 and 0.032, respectively), suggesting high consistency. Decision curve analysis (DCA) highlighted the Lasso-LR model's significant clinical utility in predicting RRT likelihood in patients with AP. Developed via the LASSO regression cross-validation method, the Lasso-LR model significantly excels in predicting the requirement for RRT in patients with AP, demonstrating its potential for clinical application.

Elderly acute kidney injury (AKI) occurring in the intensive care unit (ICU), particularly when caused or accompanied by sepsis, is linked to extended hospital stays, increased mortality rates, heightened prevalence of chronic diseases, and diminished quality of life. This study primarily utilizes a comprehensive critical care database to examine the correlation of albumin corrected anion gap (ACAG) levels with short-term prognosis in elderly patients with AKI caused or accompanied by sepsis, thus assisting physicians in early identification of high-risk patients. This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database. The patient population was divided into death and survival groups based on a 14-day prognosis. Subsequently, the entire population was further categorized into a normal ACAG group (12–20 mmol/L) and a high ACAG group (> 20 mmol/L) based on ACAG levels. The LASSO regression cross-validation method was employed to identify significant risk factors for inclusion in multivariate Cox regression analyses. A restricted cubic spline (RCS) was then employed to visually represent the correlation between ACAG levels and the risk of mortality in patients. Kaplan–Meier curves were utilized to plot the cumulative survival rates at 14 and 30 days for both patient groups. The robustness of the findings was subsequently evaluated through subgroup analyses. Our study identified a total of 3741 eligible subjects, revealing higher all-cause mortality rates at both 14-day and 30-day intervals in the high ACAG group compared to the normal ACAG group (χ2 = 87.023, P < 0.001; χ2 = 90.508, P < 0.001). Cox regression analysis further demonstrated that an elevated ACAG on ICU admission independently posed a risk factor for both 14- and 30-day prognosis within this population. In addition, the analysis conducted using RCS revealed a non-linear association between the levels of ACAG and the risk of mortality at both 14 and 30 days in the patient cohort (χ2 = 18.220, P < 0.001; χ2 = 18.360, P < 0.001). The application of Kaplan–Meier analysis demonstrated a statistically significant decrease in cumulative survival rates among individuals with high ACAG levels (P < 0.001). Subgroup analyses indicated that ACAG levels interacted with cerebrovascular disease and acute pancreatitis on 14-day mortality (P < 0.05 for interaction). Elevated ACAG levels at ICU admission are an independent risk factor for poor short-term prognosis, correlating with increased all-cause mortality at 14 and 30 days in elderly patients with AKI caused or accompanied by sepsis. This highlights the importance of monitoring ACAG in critically ill patients to identify those at higher risk of adverse outcomes early.

Heart failure is a multifaceted clinical syndrome, with obesity identified as a significant modifiable risk factor. This study employed a two-sample Mendelian randomization (MR) design, incorporating obesity data across life stages, to elucidate the causal link between obesity and heart failure. Data on heart failure from the 2023 Finngen database and genetic predictors of obesity from the IEU OpenGWAS project were analyzed using the IVW method, MR-Egger regression, weighted median, simple mode, weighted mode, and scatter plots. Heterogeneity was assessed with Cochran’s Q test, and horizontal pleiotropy with MR-Egger intercept test. Sensitivity to single-nucleotide polymorphisms (SNPs) was tested via leave-one-out analysis, and funnel plots were utilized for visual inspection of horizontal pleiotropy. Statistical powers were also calculated. The MR analysis findings indicate a significant relationship between birth weight and the likelihood of developing heart failure (Odds Ratio [OR] 1.134, 95% Confidence Interval [CI] 1.033–1.245, P = 0.008). In addition, a heightened childhood BMI was found to be a significant predictor of heart failure risk (OR 1.307, 95% CI 1.144–1.494, P = 8.51E-05), as was childhood obesity (OR 1.123, 95% CI 1.074–1.173, P = 2.37E-07). Furthermore, adult BMI sex-combined exhibited a strong correlation with the risk of heart failure (OR 2.365, 95% CI 2.128–2.629, P = 1.91E-57). Sensitivity analyses provided further support for the reliability of these results, with no significant indication of horizontal pleiotropy observed. This study shows that obesity, including childhood obesity, is linked to a higher risk of heart failure. These findings highlight the urgent need for early weight management interventions in public health and clinical settings to reduce heart failure rates. Graphical Abstract

The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT). Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed. In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p < 0.05) in AKI patients who were receiving CRRT. These findings remained consistent even after IPTW adjustment, thereby ensuring a reliable and robust outcome. Kaplan-Meier survival curves exhibited a gradual decline in cumulative survival rates at both 28 and 360 days as the LAR values increased (log-rank test, χ2 = 48.630, p < 0.001; χ2 = 33.530, p < 0.001). A high LAR (>1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

Background: Patients admitted to the surgical intensive care unit (SICU) often suffer from multi-organ dysfunction and have a high mortality rate. Therefore, finding a simple but effective clinical indicator to predict the prognosis of patients is essential to improve their survival. The aim of this study was to investigate the relationship between blood urea nitrogen to serum albumin ratio (B/A) and short-term mortality among patients from the SICU. Methods: All eligible adult patients admitted to the SICU from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were recruited for this study. Participants were divided into a death group (n = 638) and a survival group (n = 2,048) based on the 90-day prognosis, and then grouped by B/A quartiles. We used restricted cubic splines (RCS) to visually analyze the correlation of B/A with 30- and 90-day risk of death. Cumulative survival rates were estimated using Kaplan–Meier survival curves according to B/A quartiles and evaluated using the log-rank test. Cox proportional risk models were developed and sensitivity analyses were performed to explore whether B/A was independently associated with short-term outcomes in SICU patients. Receiver operating characteristic (ROC) curves were analyzed to ascertain the value of B/A for prognosticating 90-day outcome. Results: A total of 2686 participants were included in the final study, and their 30-day and 90-day all-cause mortality rates were 17.61% and 23.75%, respectively. The differences in 30-day and 90-day mortality rates were statistically significant among the four groups of patients (all p < 0.001). RCS curves showed that B/A was linearly associated with the risk of 30-day and 90-day all-cause mortality in SICU patients (χ2 = 0.960, p = 0.811; χ2 = 1.940, p = 0.584). Kaplan–Meier analysis showed that the 90-day cumulative survival rate gradually decreased as B/A increased, with patients in the highest quartile of B/A having the lowest survival rate (p < 0.001). Cox regression indicated that elevated B/A (> 9.69) was an independent risk factor for 30-day and 90-day all-cause mortality in SICU patients. The analysis of ROC curves demonstrated that B/A exhibited a significant predictive ability for 90-day mortality, with an optimal threshold of 6.587, a sensitivity of 56.9%, and a specificity of 64.8%. Conclusions: Elevated B/A (> 9.69) on admission was an independent risk factor for short-term mortality in SICU patients, and clinicians should pay more attention to this group of patients and intervene clinically at an early stage to reduce mortality.

The early identification of patients at high risk for acute kidney injury (AKI) with a poor prognosis is crucial to prevent complications and minimize mortality. This study sought to investigate the association between albumin-corrected anion gap (ACAG) and all-cause mortality among critically ill patients with AKI. All eligible AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV version 2.0) database were considered for participation in this study. We employed Kaplan-Meier curves to assess the 30-d and 360-d cumulative survival rates among various groups. Flexibly visualizing the connection between ACAG and mortality, we utilize restricted cubic splines (RCS) and multivariate Cox regression models. Result robustness underwent assessment through subgroup analyses and sensitivity analyses. Receiver-operating characteristic (ROC) curves were generated to evaluate the predictive performance of ACAG. The study included 9625 AKI participants, of whom 58.60% were male, and the 360-d all-cause mortality rate was 39.89%. According to Kaplan-Meier analysis, the 30-d and 360-d cumulative survival rates for AKI patients were significantly lower in the high ACAG group than in the normal ACAG group. RCS analysis indicated that ACAG levels had a non-linear correlation with the risk of 30-d and 360-d mortality for AKI patients. Cox regression analysis demonstrated that ACAG is an independent risk indicator for 30-d and 360-d prognosis in AKI patients in the ICU. Elevated ACAG levels (> 20 mmol/L) at ICU admission were associated with 30-d and 360-d all-cause mortality in critically ill patients with AKI.