BACKGROUND: Atherogenic index of plasma (AIP) is a non-traditional lipid parameter that can reflect the burden of atherosclerosis. A lipid profile resembling atherosclerosis emerged during pregnancy. Although lipid metabolism is pivotal in diabetes pathogenesis, there is no evidence linking AIP to gestational diabetes mellitus (GDM). Therefore, our objective was to explore the relationship between AIP and GDM and assess AIP\'s predictive capability for GDM.
METHODS: This was a secondary analysis based on data from a prospective cohort study in Korea involving 585 single pregnant women. AIP was calculated as log10 (TG/HDL). We examined the relationship between AIP and GDM using logistic regression models, curve fitting, sensitivity analyses, and subgroup analyses. Receiver operating characteristic (ROC) analysis was also used to determine the ability of AIP to predict GDM.
RESULTS: The average age of the participants was 32.06 ± 3.76 years. The AIP was 0.24 ± 0.20 on average. The GDM incidence was 6.15%. After adjustment for potentially confounding variables, AIP showed a positive linear relationship with GDM (P for non-linearity: 0.801, OR 1.58, 95% CI 1.27-1.97). The robustness of the connection between AIP and GDM was demonstrated by sensitivity analyses and subgroup analyses. An area under the ROC curve of 0.7879 (95% CI 0.7087-0.8671) indicates that AIP is an excellent predictor of GDM. With a specificity of 75.41% and sensitivity of 72.22%, the ideal AIP cut-off value for identifying GDM was 0.3557.
CONCLUSIONS: This study revealed that the AIP at 10-14 weeks of gestation was independently and positively correlated with GDM risk. AIP could serve as an early screening and monitoring tool for pregnant women at high risk of GDM, thereby optimizing GDM prevention strategies.
BACKGROUND: ClinicalTrials.gov registration no. NCT02276144.

This study aimed to identify hub genes and elucidate the molecular mechanisms underlying low bone mineral density (BMD) in perimenopausal women. R software was used to normalize the dataset and screen the gene set associated with BMD in perimenopausal women from the Gene Expression Omnibus database. Cytoscape software was used to identify 7 critical genes. Gene enrichment analysis and protein interaction was employed to further analyze the core genes, and the CIBERSORT deconvolution algorithm was used to perform immune infiltration analysis of 22 immune genes in the samples. Furthermore, an analysis of the immune correlations of 7 crucial genes was conducted. Subsequently, a receiver operating characteristic curve was constructed to assess the diagnostic efficacy of these essential genes. A total of 171 differentially expressed genes were identified that were primarily implicated in the signaling pathways associated with apoptosis. Seven crucial genes (CAMP, MMP8, HMOX1, CTNNB1, ELANE, AKT1, and CEACAM8) were effectively filtered. The predominant functions of these genes were enriched in specific granules. The pivotal genes displayed robust associations with activated dendritic cells. The developed risk model showed a remarkable level of precision, as evidenced by an area under the curve of 0.8407 and C-index of 0.854. The present study successfully identified 7 crucial genes that are significantly associated with low BMD in perimenopausal women. Consequently, this research offers a solid theoretical foundation for clinical risk prediction, drug sensitivity analysis, and the development of targeted drugs specifically tailored for addressing low BMD in perimenopausal women.

BACKGROUND: Associations between metabolic status and metabolic changes with the risk of cardiovascular outcomes have been reported. However, the role of genetic susceptibility underlying these associations remains unexplored. We aimed to examine how metabolic status, metabolic transitions, and genetic susceptibility collectively impact cardiovascular outcomes and all-cause mortality across diverse body mass index (BMI) categories.
METHODS: In our analysis of the UK Biobank, we included a total of 481,576 participants (mean age: 56.55; male: 45.9%) at baseline. Metabolically healthy (MH) status was defined by the presence of < 3 abnormal components (waist circumstance, blood pressure, blood glucose, triglycerides, and high-density lipoprotein cholesterol). Normal weight, overweight, and obesity were defined as 18.5 ≤ BMI < 25 kg/m2, 25 ≤ BMI < 30 kg/m2, and BMI ≥ 30 kg/m2, respectively. Genetic predisposition was estimated using the polygenic risk score (PRS). Cox regressions were performed to evaluate the associations of metabolic status, metabolic transitions, and PRS with cardiovascular outcomes and all-cause mortality across BMI categories.
RESULTS: During a median follow-up of 14.38 years, 31,883 (7.3%) all-cause deaths, 8133 (1.8%) cardiovascular disease (CVD) deaths, and 67,260 (14.8%) CVD cases were documented. Among those with a high PRS, individuals classified as metabolically healthy overweight had the lowest risk of all-cause mortality (hazard ratios [HR] 0.70; 95% confidence interval [CI] 0.65, 0.76) and CVD mortality (HR 0.57; 95% CI 0.50, 0.64) compared to those who were metabolically unhealthy obesity, with the beneficial associations appearing to be greater in the moderate and low PRS groups. Individuals who were metabolically healthy normal weight had the lowest risk of CVD morbidity (HR 0.54; 95% CI 0.51, 0.57). Furthermore, the inverse associations of metabolic status and PRS with cardiovascular outcomes and all-cause mortality across BMI categories were more pronounced among individuals younger than 65 years (Pinteraction < 0.05). Additionally, the combined protective effects of metabolic transitions and PRS on these outcomes among BMI categories were observed.
CONCLUSIONS: MH status and a low PRS are associated with a lower risk of adverse cardiovascular outcomes and all-cause mortality across all BMI categories. This protective effect is particularly pronounced in individuals younger than 65 years. Further research is required to confirm these findings in diverse populations and to investigate the underlying mechanisms involved.

BACKGROUND: Transfer to the ICU is common following non-cardiac surgeries, including radical colorectal cancer (CRC) resection. Understanding the judicious utilization of costly ICU medical resources and supportive postoperative care is crucial. This study aimed to construct and validate a nomogram for predicting the need for mandatory ICU admission immediately following radical CRC resection.
METHODS: Retrospective analysis was conducted on data from 1003 patients who underwent radical or palliative surgery for CRC at Ningxia Medical University General Hospital from August 2020 to April 2022. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression in the training cohort to construct the nomogram. An online prediction tool was developed for clinical use. The nomogram\'s calibration and discriminative performance were assessed in both cohorts, and its clinical utility was evaluated through decision curve analysis (DCA).
RESULTS: The final predictive model comprised age (P = 0.003, odds ratio [OR] 3.623, 95% confidence interval [CI] 1.535-8.551); nutritional risk screening 2002 (NRS2002) (P = 0.000, OR 6.129, 95% CI 2.920-12.863); serum albumin (ALB) (P = 0.013, OR 0.921, 95% CI 0.863-0.982); atrial fibrillation (P = 0.000, OR 20.017, 95% CI 4.191-95.609); chronic obstructive pulmonary disease (COPD) (P = 0.009, OR 8.151, 95% CI 1.674-39.676); forced expiratory volume in 1 s / Forced vital capacity (FEV1/FVC) (P = 0.040, OR 0.966, 95% CI 0.935-0.998); and surgical method (P = 0.024, OR 0.425, 95% CI 0.202-0.891). The area under the curve was 0.865, and the consistency index was 0.367. The Hosmer-Lemeshow test indicated excellent model fit (P = 0.367). The calibration curve closely approximated the ideal diagonal line. DCA showed a significant net benefit of the predictive model for postoperative ICU admission.
CONCLUSIONS: Predictors of ICU admission following radical CRC resection include age, preoperative serum albumin level, nutritional risk screening, atrial fibrillation, COPD, FEV1/FVC, and surgical route. The predictive nomogram and online tool support clinical decision-making for postoperative ICU admission in patients undergoing radical CRC surgery.
BACKGROUND: Despite the retrospective nature of this study, we have proactively registered it with the Chinese Clinical Trial Registry. The registration number is ChiCTR2200062210, and the date of registration is 29/07/2022.

UNASSIGNED: Despite advancements in surgical techniques, operations for infective endocarditis (IE) remain associated with relatively high mortality. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing cardiac surgery for infective endocarditis based on the preoperative clinical features.
UNASSIGNED: We retrospectively analyzed the clinical data of 357 patients with IE who underwent surgeries at our center between January 2007 and June 2023. Independent risk factors for early postoperative mortality were identified using univariate and multivariate logistic regression models. Based on these factors, a predictive model was developed and presented in a nomogram. The performance of the nomogram was evaluated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Internal validation was performed utilizing the bootstrapping method.
UNASSIGNED: The nomogram included nine predictors: age, stroke, pulmonary embolism, albumin level, cardiac function class IV, antibotic use <4weeks, vegetation size ≥1.5 cm, perivalvular abscess and preoperative dialysis. The area under the ROC curve (AUC) of the model was 0.88 (95%CI:0.80-0.96). The calibration plot indicated strong prediction consistency of the nomogram with satisfactory Hosmer-Lemeshow test results (χ2 = 13.490, p = 0.142). Decision curve analysis indicated that the nomogram model provided greater clinical net benefits compared to \"operate-all\" or \"operate-none\" strategies.
UNASSIGNED: The innovative nomogram model offers cardiovascular surgeons a tool to predict the risk of early postoperative mortality in patients undergoing IE operations. This model can serve as a valuable reference for preoperative decision-making and can enhance the clinical outcomes of IE patients.

Objectives. Temporary mechanical circulatory support (TMCS) has become a component in the therapeutic strategy for treatment of cardiogenic shock as a bridge-to-decision. TMCS can facilitate recovery of cardiopulmonary function, end-organ function, and potentially reduce the surgical risk of left ventricular assist device (LVAD) implantation. Despite the improvements of hemodynamics and end-organ function, post-LVAD operative morbidity might be increased in these high-risk patients. The aim of the study was to compare outcomes after Heartmate 3 (HM3) implantation in patients with and without TMCS prior to HM3 implant. Methods. In this retrospective cohort study of all HM3 patients in the period between November 2015 and October 2021, patients with and without prior TMCS were compared. Patients\' demographics, baseline clinical characteristics, laboratory tests, intraoperative variables, postoperative outcomes, and adverse events were collected from patient records. Results. The TMCS group showed an improvement in hemodynamics prior to LVAD implantation. Median TMCS duration was 19.5 (14-26) days. However, the TMCS group were more coagulopathic, had more wound infections, neurological complications, and more patients were on dialysis compared with patient without TMCS prior to HM3 implantation. Survival four years after HM3 implantation was 80 and 82% in the TMCS (N = 22) and non-TMCS group (N = 41), respectively. Conclusion. Patients on TMCS had an acceptable short and long-term survival and comparable to patients receiving HM3 without prior TMCS. However, they had a more complicated postoperative course.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

BACKGROUND: Oocyte donation (OD) pregnancy is accompanied by a high incidence of hypertensive complications, with serious consequences for mother and child. Optimal care management, involving early recognition, optimisation of suitable treatment options and possibly eventually also prevention, is in high demand. Prediction of patient-specific risk factors for hypertensive complications in OD can provide the basis for this. The current project aims to establish the first prediction model on the risk of hypertensive complications in OD pregnancy.
METHODS: The present study is conducted within the DONation of Oocytes in Reproduction project. For this multicentre cohort study, at least 541 OD pregnancies will be recruited. Baseline characteristics and obstetric data will be collected. Additionally, one sample of maternal peripheral blood and umbilical cord blood after delivery or a saliva sample from the child will be obtained, in order to determine the number of fetal-maternal human leucocyte antigen mismatches. Following data collection, a multivariate logistic regression model will be developed for the binary outcome hypertensive complication \'yes\' and \'no\'. The Prediction model Risk Of Bias ASsessment Tool will be used as guide to minimise the risk of bias. The study will be reported in line with the \'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis\' guideline. Discrimination and calibration will be determined to assess model performance. Internal validation will be performed using the bootstrapping method. External validation will be performed with the \'DONation of Oocytes in Reproduction individual participant data\' dataset.
BACKGROUND: This study is approved by the Medical Ethics Committee LDD (Leiden, Den Haag, Delft), with protocol number P16.048 and general assessment registration (ABR) number NL56308.058.16. Further results will be shared through peer-reviewed journals and international conferences.

While the coronavirus disease-2019 (COVID-19) might have increased acute episodes in people living with sickle cell disease (SCD), it may also have changed their reliance on emergency department (ED) services. We assessed the impact of the COVID-19 pandemic and lockdowns on ED visits in adult SCD people followed in five French reference centres, with a special focus on \'high users\' (≥10 visits in 2019). We analysed the rate of ED visits from 1 January 2015 to 31 December 2021, using a self-controlled case series. Among 1530 people (17 829 ED visits), we observed a significant reduction in ED visits during and after lockdowns, but the effect vanished over time. Compared to pre-pandemic, incidence rate ratios for ED visits were 0.59 [95% CI 0.52-0.67] for the first lockdown, 0.66 [95% CI 0.58-0.75] for the second and 0.85 [95% CI 0.73-0.99] for the third. High users (4% of people but 33.7% of visits) mainly drove the reductions after the first lockdown. COVID-19 lockdowns were associated with reduced ED visits. While most people returned to their baseline utilization by April 2021, high users had a lasting decrease in ED visits. Understanding the factors driving the drop in ED utilization among high users might inform clinical practice and health policy.

BACKGROUND: Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions.
METHODS: Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically.
RESULTS: From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including \'limited resources\', \'low maternal health priority\', \'lack of knowledge\', \'ineffective systems\' and \'new mum syndrome\'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities.
CONCLUSIONS: Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.