BACKGROUND: Accumulating evidence shows that free fatty acids (FFA) are associated with gestational diabetes mellitus (GDM). However, most of the studies focus on a few specific types of FFA, such as α-linolenic acid (C18:3n3) and Arachidonic acid (C20:4n6) or a total level of FFA.
OBJECTIVE: This study aimed to test the association between a variety of FFAs during the first trimester and the risk of GDM.
METHODS: The participants came from the Zhoushan Pregnant Women Cohort (ZWPC). A 1:2 nested case-control study was conducted: fifty mothers with GDM were matched with 100 mothers without GDM by age, pre-pregnancy body mass index (BMI), month of oral glucose tolerance test (OGTT) and parity. Thirty-seven FFAs (including 17 saturated fatty acids (SFA), 8 monounsaturated fatty acids (MUFA), 10 polyunsaturated fatty acids (PUFA) and 2 trans fatty acids (TFA)) in maternal plasma during the first trimester were tested by Gas Chromatography-Mass Spectrometry (GC-MS). Conditional logistic regression models were performed to assess the associations of FFA with the risk of GDM.
RESULTS: Nine FFAs were respectively associated with an increased risk of GDM (P < 0.05), and four FFAs were respectively associated with a decreased risk of GDM (P < 0.05). SFA risk score was associated with a greater risk of GDM (OR = 1.34, 95% CI: 1.12-1.60), as well as UFA risk score (OR = 1.26, 95% CI: 1.11-1.44), MUFA risk score (OR = 1.70, 95%CI: 1.27-2.26), PUFA risk score (OR = 1.32, 95%CI: 1.09-1.59) and TFA risk score (OR = 2.51, 95%CI: 1.23-5.13). Moreover, joint effects between different types of FFA risk scores on GDM were detected. For instance, compared with those with low risk scores of SFA and UFA, women with high risk scores of SFA and UFA had the highest risk of GDM (OR = 8.53, 95%CI: 2.41-30.24), while the Odds ratio in those with a low risk score of SFA and high risk score of UFA and those with a high risk score of SFA and low risk score of UFA was 6.37 (95%CI:1.33- 30.53) and 4.25 (95%CI: 0.97-18.70), respectively.
CONCLUSIONS: Maternal FFAs during the first trimester were positively associated with the risk of GDM. Additionally, there were joint effects between FFAs on GDM risk.
CONCLUSIONS: Elevated FFA levels in the first trimester increased the risk of GDM.

OBJECTIVE: In patients with atrial fibrillation (AF), recurrent AF and sinus rhythm during follow-up are determined by interactions between cardiovascular disease processes and rhythm-control therapy. Predictors of attaining sinus rhythm at follow-up are not well known.
METHODS: To quantify the interaction between cardiovascular disease processes and rhythm outcomes, 14 biomarkers reflecting AF-related cardiovascular disease processes in 1586 patients in the EAST-AFNET 4 biomolecule study (71 years old, 46% women) were quantified at baseline. Mixed logistic regression models including clinical features were constructed for each biomarker. Biomarkers were interrogated for interaction with early rhythm control. Outcome was sinus rhythm at 12 months. Results were validated at 24 months and in external datasets.
RESULTS: Higher baseline concentrations of three biomarkers were independently associated with a lower chance of sinus rhythm at 12 months: angiopoietin 2 (ANGPT2) (odds ratio [OR] 0.76 [95% confidence interval 0.65-0.89], p=0.001), bone morphogenetic protein 10 (BMP10) (OR 0.83 [0.71-0.97], p=0.017) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) (OR 0.73 [0.60-0.88], p=0.001). Analysis of rhythm at 24 months confirmed the results. Early rhythm control interacted with the predictive potential of NT-proBNP (pinteraction=0.033). The predictive effect of NT-proBNP was reduced in patients randomized to early rhythm control (usual care: OR 0.64 [0.51-0.80], p<0.001; early rhythm control: OR 0.90 [0.69-1.18], p=0.453). External validation confirmed that low concentrations of ANGPT2, BMP10 and NT-proBNP predict sinus rhythm during follow-up.
CONCLUSIONS: Low concentrations of ANGPT2, BMP10 and NT-proBNP identify patients with AF who are likely to attain sinus rhythm during follow-up. The predictive ability of NT-proBNP is attenuated in patients receiving rhythm control.

UNASSIGNED: A neonatal mortality prediction score can assist clinicians in making timely clinical decisions to save neonates\' lives by facilitating earlier admissions where needed. It can also help reduce unnecessary admissions.
UNASSIGNED: The study aimed to develop and validate a prognosis risk score for neonatal mortality within 28 days in public hospitals in the Amhara region, Ethiopia.
UNASSIGNED: The model was developed using a validated neonatal near miss assessment scale and a prospective cohort of 365 near-miss neonates in six hospitals between July 2021 and January 2022. The model\'s accuracy was assessed using the area under the receiver operating characteristics curve, calibration belt, and the optimism statistic. Internal validation was performed using a 500-repeat bootstrapping technique. Decision curve analysis was used to evaluate the model\'s clinical utility.
UNASSIGNED: In total, 63 of the 365 neonates died, giving a neonatal mortality rate of 17.3% (95% CI: 13.7-21.5). Six potential predictors were identified and included in the model: anemia during pregnancy, pregnancy-induced hypertension, gestational age less than 37 weeks, birth asphyxia, 5 min Apgar score less than 7, and birth weight less than 2500 g. The model\'s AUC was 84.5% (95% CI: 78.8-90.2). The model\'s predictive ability while accounting for overfitting via internal validity was 82%. The decision curve analysis showed higher clinical utility performance.
UNASSIGNED: The neonatal mortality predictive score could aid in early detection, clinical decision-making, and, most importantly, timely interventions for high-risk neonates, ultimately saving lives in Ethiopia.
Main findings: This prognosis risk score for neonatal mortality tested in Ethiopia had high performance accuracy and the decision curve analysis showed increased clinical utility performance.Added knowledge: The tool developed here can aid healthcare providers in identifying high-risk neonates and making timely clinical decisions to save lives.Global health impact for policy and action: The findings have the potential to be applied in local contexts to identify high-risk neonates and make treatment decisions that could improve child survival rates.

OBJECTIVE: Reversible cognitive frailty (RCF) is an ideal target to prevent asymptomatic cognitive impairment and dependency. This study aimed to develop and validate prediction models for incident RCF.
METHODS: A total of 1230 older adults aged ≥60 years from China Health and Retirement Longitudinal Study 2011-2013 survey were included as the training set. The modified Poisson regression and three machine learning algorithms including eXtreme Gradient Boosting, support vector machine and random forest were used to develop prediction models. All models were evaluated internally with fivefold cross-validation, and evaluated externally using a temporal validation method through the China Health and Retirement Longitudinal Study 2013-2015 survey.
RESULTS: The incidence of RCF was 27.4% in the training set and 27.5% in the external validation set. A total of 13 important predictors were selected to develop the model, including age, education, contact with their children, medical insurance, vision impairment, heart diseases, medication types, self-rated health, pain locations, loneliness, self-medication, night-time sleep and having running water. All models showed acceptable or approximately acceptable discrimination (AUC 0.683-0.809) for the training set, but fair discrimination (AUC 0.568-0.666) for the internal and external validation. For calibration, only modified Poisson regression and eXtreme Gradient Boosting were acceptable in the training set. All models had acceptable overall prediction performance and clinical usefulness. Older adults were divided into three groups by the risk scoring tool constructed based on modified Poisson regression: low risk (≤24), median risk (24-29) and high risk (>29).
CONCLUSIONS: This risk tool could assist healthcare providers to predict incident RCF among older adults in the next 2 years, facilitating early identification of a high-risk population of RCF. Geriatr Gerontol Int 2024; ••: ••-••.

UNASSIGNED: Clinically, the diagnosis and treatment of cholangiocarcinoma are generally different according to the location of occurrence, and the studies rarely consider the differences between different pathological types. Cholangiocarcinomas in large- and middle-sized intrahepatic bile ducts are mostly mucinous, while in small sized bile duct are not; mucinous extrahepatic cholangiocarcinomas are also more common than mucinous intrahepatic cholangiocarcinoma. However, it is unclear whether these pathological type differences are related to the prognosis.
UNASSIGNED: Data of total 22509 patients was analyzed from Surveillance, Epidemiology, and End Results program database out of which 22299 patients were diagnosed with common adeno cholangiocarcinoma while 210 were diagnosed with mucinous cholangiocarcinoma. Based on the propensity score matching (PSM) analysis, between these two groups\' clinical, demographic, and therapeutic features were contrasted. The data were analyzed using Cox and LASSO regression analysis and Kaplan-Meier survival curves. Ultimately, overall survival (OS) and cancer specific survival (CSS) related prognostic models were established and validated in test and external datasets and nomograms were created to forecast these patients\' prognosis.
UNASSIGNED: There was no difference in prognosis between mucinous cholangiocarcinoma and adeno cholangiocarcinoma. Therefore, we constructed prognostic model and nomogram that can be used for mucinous and adeno cholangiocarcinoma at the same time. By comparing the 9 independent key characteristics i.e. Age, tumor size, the number of primary tumors, AJCC stage, Grade, lymph node status, metastasis, surgery and chemotherapy, risk scores were calculated for each individual. By integrating these two pathological types in OS and CSS prognostic models, effective prognosis prediction results could be achieved in multiple datasets (OS: AUC 0.70-0.87; CSS: AUC 0.74-0.89).
UNASSIGNED: Age, tumor size, the number of primary tumors, AJCC stage, Grade, lymph node status, metastasis, surgery and chemotherapy are the independent prognostic factors in OS or CSS of the patients with mucinous and ordinary cholangiocarcinoma. Nomogram that can be used for mucinous and adeno cholangiocarcinoma at the same time is of significance in clinical practice and management of cholangiocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and lethal cancers worldwide, signifying a critical need for improved prognostic tools. A growing number of studies have highlighted the role of microRNAs (miRNAs) and their regulatory functions in tumorigenesis and cancer progression. In this context, we performed an extensive analysis of bulk RNA- and miRNA-sequencing to identify LUAD-associated prognostic genes. A risk score system based on 11 miRNA-regulated and surface-protein genes was developed, which was later validated by internally and externally using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Further single-cell RNA sequencing analysis revealed significant interactions between various cellular subpopulations within the tumor microenvironment, with the most pronounced differences observed between endothelial and epithelial cells. The mutational analysis highlighted TP53 as a key signaling pathway associated with the risk score. The study underscores that immune suppression, indicated by a positive association with regulatory T cells (Tregs) and an inverse correlation with M1-type macrophages, is prevalent in high-risk LUAD patients. These findings provide a promising prognostic tool for clinical outcomes of LUAD patients, facilitating future development of therapeutic strategies and enhancing our understanding of the regulatory function of miRNAs in LUAD.

UNASSIGNED: Breast cancer (BRCA) is the most common type of cancer and the second leading cause of cancer-related death in women all over the world. Metastasis to bone is an indicator of poor prognosis in BRCA patients. This study aimed to develop a prognostic score model for predicting bone metastasis in patients with BRCA.
UNASSIGNED: BRCA-related RNA sequencing datasets and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were screened using Limma package of R software. A risk score based predictive model was constructed based on the key genes identified through univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression. The gene expression profiles in BRCA patients were analyzed by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Random survival forest (RSF) analysis of BRCA patients with bone metastasis was conducted to identify the key DEGs.
UNASSIGNED: Based on DEG analysis, a total of 677 genes were identified as genes related to bone metastasis in BRCA. By univariate Cox regression and LASSO regression, 28 DEGs were identified as signature genes to develop the prognostic model. A risk score for each patient was created by incorporating the expression values of each specific gene and weighting them with the corresponding estimated regression coefficients. Patients were divided into a low-risk and a high-risk group based on the median risk score. Overall survival (OS) was significantly lower in the high-risk group. The receiver operating characteristic (ROC) curve and multi-omics analysis indicated that the model had high training/testing accuracy and a good clinical predictive value. We used extra data from GEO database to verify the robustness of the prognostic model, and the lower OS in high-risk group and area under the curve (AUC) value indicated the model had strong predictive efficacy for prognosis of BRCA.
UNASSIGNED: A prognostic prediction model was constructed based on 28 key DEGs identified through multi-omics analysis of studies on bone metastasis. The model may provide a promising method for distinguishing the high-risk BRCA patients and help on decision making in addition to prognosis prediction for BRCA patients.

BACKGROUND: It remains unclear today whether risk scores created specifically to predict early mortality after cardiac operations for infective endocarditis (IE) outperform or not the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II).
METHODS: Perioperative data and outcomes from a European multicenter series of patients undergoing surgery for definite IE were retrospectively reviewed. Only the cases with known pathogen and without missing values for all considered variables were retained for analyses. A comparative validation of EuroSCORE II and 5 specific risk scores for early mortality after surgery for IE-(1) STS-IE (Society of Thoracic Surgeons for IE); (2) PALSUSE (Prosthetic valve, Age ≥70, Large intracardiac destruction, Staphylococcus spp, Urgent surgery, Sex (female), EuroSCORE ≥10); (3) ANCLA (Anemia, New York Heart Association class IV, Critical state, Large intracardiac destruction, surgery on thoracic Aorta); (4) AEPEI II (Association pour l\'Étude et la Prévention de l\'Endocardite Infectieuse II); (5) APORTEI (Análisis de los factores PROnósticos en el Tratamiento quirúrgico de la Endocarditis Infecciosa)-was carried out using calibration plot and receiver-operating characteristic curve analysis. Areas under the curve (AUCs) were compared 1:1 according to the Hanley-McNeil\'s method. The agreement between APORTEI score and EuroSCORE II of the 30-day mortality prediction after surgery was also appraised.
RESULTS: A total of 1,012 patients from 5 European university-affiliated centers underwent 1,036 cardiac operations, with a 30-day mortality after surgery of 9.7%. All IE-specific risk scores considered achieved better results than EuroSCORE II in terms of calibration; AEPEI II and APORTEI score showed the best performances. Despite poor calibration, EuroSCORE II overcame in discrimination every specific risk score (AUC, 0.751 vs 0.693 or less, P = .01 or less). For a higher/lesser than 20% expected mortality, the agreement of prediction between APORTEI score and EuroSCORE II was 86%.
CONCLUSIONS: EuroSCORE II discrimination for 30-day mortality after surgery for IE was higher than 5 established IE-specific risk scores. AEPEI II and APORTEI score showed the best results in terms of calibration.

OBJECTIVE: Oral anticoagulants (OACs) reduce the risk of ischemic stroke but may increase the risk of major bleeding in patients with non-valvular atrial fibrillation (NVAF). Various risk scores, such as HAS-BLED, ATRIA, ORBIT, and DOAC, have been proposed to assess the risk of major bleeding in patients with NVAF receiving OACs. However, limited data are available regarding bleeding risk stratification in Japanese patients with NVAF.
METHODS: Of the 16,098 NVAF patients from the J-RISK AF study, the combined data of the five major AF registries in Japan (J-RHYTHM Registry, Fushimi AF Registry, Shinken Database, Keio interhospital Cardiovascular Studies, and Hokuriku-Plus AF Registry), we analyzed 11,539 patients receiving OACs (median age, 71 years old; women, 29.6%; median CHA2DS2-VASc score, 3).
RESULTS: During the 2-year follow-up period, major bleeding occurred in 274 patients (1.3% per patient-year). In a multivariate Cox proportional hazards analysis, an advanced age, hypertension (systolic blood pressure ≥ 150 mmHg), bleeding history, anemia, thrombocytopenia, and concomitant antiplatelet agents were significantly associated with a higher incidence of major bleeding. We developed a novel risk stratification system, HED-[EPA]2-B3 score, which had a better predictive performance for major bleeding (C-statistics 0.67, [95% confidence interval, 0.63-0.70]) than the HAS-BLED (0.64, [0.60-0.67], P for difference 0.02) and ATRIA (0.63, [0.60-0.66], P for difference ＜0.01) scores. Furthermore, it was non-significantly higher than the ORBIT (0.65, [0.62-0.68], P for difference 0.07) and DOAC (0.65, [0.62-0.68], P for difference 0.17) scores.
CONCLUSIONS: Our novel risk stratification system, the HED-[EPA]2-B3 score, may be useful for identifying Japanese patients receiving OACs at a risk of major bleeding.

BACKGROUND: The Indian Diabetes Risk Score (IDRS) is a simple tool to assess the probability of an individual having type 2 diabetes (T2DM) but its applicability in community-dwelling older adults is lacking. This study aimed to estimate the risk of T2DM and its determinants among older adults without prior diabetes (DM) using the IDRS, while also assessing its sensitivity and specificity in individuals with a history of diabetes.
METHODS: We analyzed cross-sectional data from the Longitudinal Ageing Study in India (LASI) wave-1 (2017-18). IDRS was calculated amongst individuals aged ≥45 years considering waist circumference, physical activity, age and family history of DM. Risk was categorized as high (≥60), moderate (30-50), and low (<30).
RESULTS: Among 64541 individuals, 7.27 % (95 % CI: 6.78, 7.80) were at low risk, 61.80 % (95 % CI: 60.99, 62.61) at moderate risk, and 30.93 % (95 % CI: 30.19, 31.67) at high risk for T2DM. Adjusted analysis showed higher risk of T2DM among men, widowed/divorced, urban residents, minority religions, overweight, obese, and individuals with hypertension. ROC curve yielded an AUC of 0.67 (95 % CI: 0.66, 0.67, P < 0.001). The IDRS cutoff ≥50 had 73.69 % sensitivity and 51.40 % specificity for T2DM detection.
CONCLUSIONS: More than 9 in 10 older adults in India without history of DM have high-moderate risk of T2DM when assessed with the IDRS risk-prediction tool. However, the low specificity and moderate sensitivity of IDRS in existing DM cases constraints its practical utility as a decision tool for screening.