Air Pollutants and Mortality Risk in Patients with Aortic Dissection: Evidence from a Clinical Cohort, Single-Cell Sequencing, and Proteomics.

We aimed to evaluate the association between air pollutants and mortality risk in patients with acute aortic dissection (AAD) in a longitudinal cohort and to explore the potential mechanisms of adverse prognosis induced by fine particulate matter (PM2.5). Air pollutants data, including PM2.5, PM10.0, nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were collected from official monitoring stations, and multivariable Cox regression models were applied. Single-cell sequencing and proteomics of aortic tissue were conducted to explore the potential mechanisms. In total, 1,267 patients with AAD were included. Exposure to higher concentrations of air pollutants was independently associated with an increased mortality risk. The high-PM2.5 group carried approximately 2 times increased mortality risk. There were linear associations of PM10, NO2, CO, and SO2 exposures with long-term mortality risk. Single-cell sequencing revealed an increase in mast cells in aortic tissue in the high-PM2.5 exposure group. Enrichment analysis of the differentially expressed genes identified the inflammatory response as one of the main pathways, with IL-17 and TNF signaling pathways being among the top pathways. Analysis of proteomics also identified these pathways. This study suggests that exposure to higher PM2.5, PM10, NO2, CO, and SO2 are associated with increased mortality risk in patients with AAD. PM2.5-related activation and degranulation of mast cells may be involved in this process.

Abnormal adiposity indices are associated with an increased risk of diabetes in a non-obese Asian population.

OBJECTIVES: The aim of this study was to evaluate the association between adiposity indices and the risk of incident diabetes and to compare their predictive ability in non-obese healthy individuals. STUDY DESIGN: Population-based cohort study. METHODS: Data were taken from the NAGALA research study, which enrolled Japanese adults aged 18-79 years. Cox regression was used to evaluate the association between adiposity indices (including waist circumference [WC], waist-to-height ratio [WHtR], lipid accumulation product index [LAP], body roundness index [BRI], visceral adiposity index [VAI] and Chinese visceral adiposity index [CVAI]) and diabetes risk. The performance of the indices for predicting diabetes was explored using area under the receiver operating characteristic curve (AUC). A Chinese community-based population was used for validation. RESULTS: A total of 12,940 healthy Japanese individuals with normal body mass index and glycaemic levels were included and were followed up for a median of 6 years. Multivariable Cox models revealed a positive and significant association between all indices and incident diabetes, with the hazard ratios for the highest quartile of the indices ranging from 1.89 to 2.90 (all P-values < 0.01). A non-linear association between WC, BRI and VAI and a linear association between WHtR, LAP and CVAI and diabetes risk were observed. CVAI, VAI and LAP had comparable ability in predicting diabetes, with the highest AUC being 0.733 for CVAI. Data from 10,830 Chinese individuals confirmed these results. CONCLUSIONS: Adiposity indices are associated with incident diabetes in healthy non-obese individuals. Participants in the highest quartile of WC, WHtR, LAP, BRI, VAI and CVAI had an increased risk of developing diabetes.

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine.

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00342-4.

Visceral fat and its dynamic change are associated with renal damage: Evidence from two cohorts.

BACKGROUND AND AIMS: To evaluate the association of Chinese visceral adiposity index (CVAI) and its dynamic trends with risk of renal damage, and to compare its prediction performance with that of other obesity indices. METHODS AND RESULTS: A community-based population with 23 905 participants from Shantou city was included in the cross-sectional analysis. A total of 9,778 individuals from two separated cohort were included in the longitudinal portion. Five patterns of CVAI change were predefined (low-stable, decreasing, moderate, increasing, and persistent-high). Logistic and Cox regressions were used to evaluate the association between CVAI and renal damage. We explored potential mechanisms using the mediating effect method, and the prediction performance was determined by receiver operating characteristic curve analysis. Results from both cross-sectional and longitudinal data revealed a positive and linear association between CVAI and risk of renal damage. Pooled analysis of the two cohorts showed that per unit increase in Z score of CVAI induced 18% increased risk of renal damage (P = .008). Longitudinal trends of CVAI were also associated with renal damage, and the moderate, increasing, and persistent-high patterns showing a higher risk. Blood pressure and glucose had a mediating effect on renal damage induced by CVAI. Among several obesity indices, CVAI was the optimal for predicting renal damage. CONCLUSION: A higher level of immediate CVAI and longitudinal increasing and persistent-high patterns of CVAI were independently associated with increased risk of renal damage. Monitoring immediate level and long-term trend of CVAI may contribute to the prevention of renal damage.

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.

In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.

The association of coagulation indicators with in-hospital acute kidney injury and malignant events of patients with acute aortic dissection: a retrospective cohort study.

Background: Acute kidney injury (AKI) is a prevalent complication of acute aortic dissection (AAD) and is associated with poor outcomes. The onset of AAD may result in endothelial injury due to the formation of the false lumen, which can activate the coagulation pathway and lead to coagulation dysfunction. It serves as a valuable diagnostic and prognostic marker for AAD, but also plays a role in the pathological mechanisms underlying AKI. We aimed to investigate the potential value of coagulation indicators at admission for assessing in-hospital AKI and malignant events after AAD. Methods: We identified patients with AAD admitted to the First Affiliated Hospital of Shantou University Medical College from January 2015 to October 2020 and divided them into two groups according to coagulation function. Univariable and multivariable analyses were used to analyze the association between coagulation indicators and AKI and malignant events in patients with AAD. Chi-squared or Fisher exact test and receiver operating characteristic (ROC) curve analysis was conducted to assess the value of coagulation indicators in predicting in-hospital AKI and malignant events. Results: A total of 487 patients were enrolled in this study, including 309 cases with normal coagulation. After the multivariable adjustment, the incidence of in-hospital AKI in the abnormal coagulation group was significantly higher [model 1: 2.061 (1.214-3.501), P=0.007; model 2: 1.833 (1.058-3.177), P=0.031; model 3: 1.836 (1.048-3.216), P=0.034]. The incidence of malignant events was higher in the abnormal prothrombin time (PT) group [model 1: 4.283 (0.983-18.665), P=0.053; model 2: 7.342 (1.467-36.749), P=0.015; model 3: 6.996 (1.377-35.537), P=0.019]. Chi-squared and Fisher exact test showed that PT and abnormal coagulation score (ACS) were statistically different among the AKI groups and malignant event groups. Under ROC analysis, coagulation indicators were helpful to predict AKI (AUC =0.668; P<0.001). Conclusions: Our study confirmed the presence of coagulation dysfunction is associated with an increased risk of AKI and malignant events. It suggested the severity of coagulation dysfunction is positively correlated with the incidence of in-hospital AKI in AAD patients. These results highlight the importance of considering coagulation dysfunction as a potential mechanism underlying AKI and malignant events after AAD.

Dysnatremia is associated with increased risk of all-cause mortality within 365 days post-discharge in patients with atrial fibrillation without heart failure: A prospective cohort study.

Aim: The aim of this study is to evaluate the association between serum sodium concentrations at hospital admission and all-cause mortality within 365 days post-discharge in patients with atrial fibrillation (AF) without heart failure (HF). Methods: The prospective cohort study enrolled 1,446 patients with AF without HF between November 2018 and October 2020. A follow-up was performed 30, 90, 180, and 365 days after enrollment through outpatient visits or telephone interviews. All-cause mortality was estimated in three groups according to serum sodium concentrations: hyponatremia (< 135 mmol/L), normonatremia (135-145 mmol/L), and hypernatremia (> 145 mmol/L). We estimated the risk of all-cause mortalities using univariable and multivariable Cox proportional hazards models with normonatremia as the reference. Results: The all-cause mortalities of hyponatremia, normonatremia, and hypernatremia were 20.6, 9.4, and 33.3% within 365 days post-discharge, respectively. In the univariable analysis, hyponatremia (HR: 2.19, CI 1.5-3.2) and hypernatremia (HR: 4.03, CI 2.32-7.02) increased the risk of all-cause mortality. The HRs for hyponatremia and hypernatremia were 1.55 (CI 1.05-2.28) and 2.55 (CI 1.45-4.46) after adjustment for age, diabetes mellitus, loop diuretics, antisterone, antiplatelet drugs, and anticoagulants in the patients with AF without HF. The association between serum sodium concentrations and the HRs of all-cause mortality was U-shaped. Conclusion: Dysnatremia at hospital admission was an independent factor for all-cause mortality in patients with AF without HF within 365 days post-discharge.

Does Warfarin or Rivaroxaban at Low Anticoagulation Intensity Provide a Survival Benefit to Asian Patients With Atrial Fibrillation?

Background: Low-dose rivaroxaban and low-intensity warfarin are widely used in Asia for patients with atrial fibrillation (AF). However, in Asians, it is unclear whether low-dose rivaroxaban and low-intensity warfarin can improve the prognosis of AF. In this study, we investigate the survival benefits of low-dose rivaroxaban and low-intensity warfarin in Asian patients with AF in clinical practice. Methods: This cohort study used medical records in a single tertiary hospital in China, between 2019 and 2020, to identify patients with AF who used rivaroxaban or warfarin, or had no anticoagulant therapy. Follow-ups were performed through telephone contact or medical record review. Cox proportional hazards models were used to compare the risk of mortality of patients in the anticoagulant-untreated group vs. warfarin-treated groups and rivaroxaban-treated groups. Results: A total of 1727 AF patients, discharged between 2019 and 2020, were enrolled in this cohort, of which 873 patients did not use any anticoagulant, 457 patients received warfarin and 397 patients used rivaroxaban. Multivariable analysis showed that, of all the warfarin groups, patients with an international normalized ratio (INR) below 2, good INR control, or poor INR control had a significantly lower risk of mortality compared with that of patients without anticoagulants (HR 0.309, p = 0.0001; HR 0.387, p = 0.0238; HR 0.363, p < 0.0001). Multivariable Cox proportional hazard analyses also demonstrated that, compared with the no anticoagulant group, all rivaroxaban dosage groups (≤10 mg, HR 0.456, p = 0.0129; 15 mg, HR 0.246, p = 0.0003; 20 mg, HR 0.264, p = 0.0237) were significantly associated with a lower risk of mortality. Conclusion: Despite effects being smaller than observed with recommended optimal anticoagulation, the use of warfarin with an INR below 2, poor INR control and the use of low-dose rivaroxaban may still provide survival benefits, suggesting viable alternatives that enable physicians to better resolve decisional conflicts concerning the risks and benefits of anticoagulant therapies, as well as for patients in need of but unable to receive standard anticoagulant therapy due to bleeding risk or other factors, such as financial burden, concerns of adverse outcomes, as well as low treatment compliance and persistence.

Predicting All-Cause Mortality Risk in Atrial Fibrillation Patients: A Novel LASSO-Cox Model Generated From a Prospective Dataset.

Background: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF. Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients. Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations. Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA2DS2-VASc and HAS-BLED models. Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.

Efficiency of atorvastatin on in-hospital mortality of patients with acute aortic dissection (AAD): study protocol for a randomized, open-label, superiority clinical trial.

BACKGROUND: Dyslipidemia and local inflammation at sites of lipid deposition on blood vessel walls have been demonstrated to be risk factors for patients with acute aortic dissection (AAD). Statins have anti-inflammatory and lipid-lowering effects, which suggest that statins may play an important role in the prevention and treatment of AAD. Some retrospective studies show that statins can protect patients with aortic dissection. However, the effect of statins on the survival of AAD patients has been scarcely investigated, especially in randomized trials. In this study, we will perform a randomized clinical trial to understand whether statins can reduce in-hospital mortality of AAD patients. METHODS: A total of 384 subjects diagnosed with AAD in the First Affiliated Hospital of Shantou University Medical College will be recruited. Participants will be randomly divided into an atorvastatin-treated or control group. The primary outcome will be the in-hospital mortality at 30 days. DISCUSSION: This study is designed to verify the efficacy of atorvastatin on reducing in-hospital mortality of patients with AAD. The aim is to provide a new means of improving survival as a complement to conventional drug therapy. TRIAL REGISTRATION: Chinese Clinical Trials Registry ChiCTR1900023515 . Registered on 1 June 2019.

Non-O blood group is associated with lower risk of in-hospital mortality in non-surgically managed patients with type A aortic dissection.

BACKGROUND: The association between different ABO blood groups and mortality of aortic dissection (AD) remains controversial. This study aimed to examine whether different ABO blood groups affect the prognosis of AD. METHODS: Demographic and clinical data were collected from 877 patients diagnosed with AD from 2015 to 2019 in the First Affiliated Hospital of Shantou University Medical College. The association between in-hospital mortality of AD patients and ABO blood group was analyzed using Cox proportional hazards regression models. RESULTS: This retrograde cohort study demonstrated that for 877 patients, male gender, non-O blood group, Stanford type B AD (TBAD), higher presenting systolic and diastolic blood pressure, and being a recipient of aortic arch replacement surgery (surgery) or endovascular stent-graft implantation (stent-graft) were associated with decreased in-hospital mortality of AD. In Cox proportional hazards models, non-O blood group was associated with lower risk of early mortality regardless of adjustment (HR = 0.668, 95% confidence interval [CI] 0.473-0.944 before adjustment, HR = 0.662, 95% CI 0.468-0.935 after adjustment for age and sex, and HR = 0.641, 95% CI 0.453-0.906 after adjustment for AD types, SBP and surgery). Further analyses revealed that for patients diagnosed with type A AD (TAAD), non-O blood group renders a significant 34.3% decrease in the risk of in-hospital mortality compared with blood group O. Specifically, this difference in mortality risk was found among TAAD patients who did not undergo surgery (HR = 0.579, 95% CI 0.377-0.889), rather than those who did. There was no significant difference in early mortality for patients with TBAD, whether or not stent-grafts were implanted. CONCLUSIONS: Non-O blood type decreases the risk of in-hospital mortality, especially for TAAD, in AD patients without surgical intervention. More attention must be paid to blood type O TAAD patients without surgical interventions, and early surgical intervention may be an effective means to decrease in-hospital mortality of TAAD.