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In an observational study, patients with type 2 diabetes who had glycated hemoglobin, LDL cholesterol, albuminuria, and blood pressure in target ranges and did not smoke had minimal excess risk of death, myocardial infarction, and stroke as compared with a general population.

People with type 1 diabetes are at elevated risk of mortality and cardiovascular disease, yet current guidelines do not consider age of onset as an important risk stratifier. We aimed to examine how age at diagnosis of type 1 diabetes relates to excess mortality and cardiovascular risk. We did a nationwide, register-based cohort study of individuals with type 1 diabetes in the Swedish National Diabetes Register and matched controls from the general population. We included patients with at least one registration between Jan 1, 1998, and Dec 31, 2012. Using Cox regression, and with adjustment for diabetes duration, we estimated the excess risk of all-cause mortality, cardiovascular mortality, non-cardiovascular mortality, acute myocardial infarction, stroke, cardiovascular disease (a composite of acute myocardial infarction and stroke), coronary heart disease, heart failure, and atrial fibrillation. Individuals with type 1 diabetes were categorised into five groups, according to age at diagnosis: 0–10 years, 11–15 years, 16–20 years, 21–25 years, and 26–30 years. 27 195 individuals with type 1 diabetes and 135 178 matched controls were selected for this study. 959 individuals with type 1 diabetes and 1501 controls died during follow-up (median follow-up was 10 years). Patients who developed type 1 diabetes at 0–10 years of age had hazard ratios of 4·11 (95% CI 3·24–5·22) for all-cause mortality, 7·38 (3·65–14·94) for cardiovascular mortality, 3·96 (3·06–5·11) for non-cardiovascular mortality, 11·44 (7·95–16·44) for cardiovascular disease, 30·50 (19·98–46·57) for coronary heart disease, 30·95 (17·59–54·45) for acute myocardial infarction, 6·45 (4·04–10·31) for stroke, 12·90 (7·39–22·51) for heart failure, and 1·17 (0·62–2·20) for atrial fibrillation. Corresponding hazard ratios for individuals who developed type 1 diabetes aged 26–30 years were 2·83 (95% CI 2·38–3·37) for all-cause mortality, 3·64 (2·34–5·66) for cardiovascular mortality, 2·78 (2·29–3·38) for non-cardiovascular mortality, 3·85 (3·05–4·87) for cardiovascular disease, 6·08 (4·71–7·84) for coronary heart disease, 5·77 (4·08–8·16) for acute myocardial infarction, 3·22 (2·35–4·42) for stroke, 5·07 (3·55–7·22) for heart failure, and 1·18 (0·79–1·77) for atrial fibrillation; hence the excess risk differed by up to five times across the diagnosis age groups. The highest overall incidence rate, noted for all-cause mortality, was 1·9 (95% CI 1·71–2·11) per 100 000 person-years for people with type 1 diabetes. Development of type 1 diabetes before 10 years of age resulted in a loss of 17·7 life-years (95% CI 14·5–20·4) for women and 14·2 life-years (12·1–18·2) for men. Age at onset of type 1 diabetes is an important determinant of survival, as well as all cardiovascular outcomes, with highest excess risk in women. Greater focus on cardioprotection might be warranted in people with early-onset type 1 diabetes. Swedish Heart and Lung Foundation.

Patients with type 1 or type 2 diabetes in Sweden were studied to examine trends in mortality and cardiovascular disease incidence between 1998 and 2014. Both outcomes declined substantially, although fatal outcomes declined less among patients with type 2 diabetes than among controls. Diabetes mellitus is a complex and heterogeneous group of chronic metabolic diseases that are characterized by hyperglycemia. Type 1 diabetes occurs predominantly in young people (diagnosis at 30 years of age or younger) and is generally thought to be precipitated by an immune-associated destruction of insulin-producing pancreatic beta cells, leading to insulin deficiency and an absolute need for exogenous insulin replacement. 1 Type 2 diabetes is a progressive metabolic disease that is characterized by insulin resistance and eventual functional failure of pancreatic beta cells. 2 The prevalence of type 2 diabetes has been increasing dramatically over the past few decades, 3 with projections . . .

The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40–69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.

Objective30-day survival after cardiac arrest is low, 12.4% and 36% for out-of-hospital and in-hospital cardiac arrest, respectively. Heart failure is a known risk condition for cardiac arrest. Improving our ability to identify patients at high risk of cardiac arrest would enable prevention. We aimed to develop a prediction model for cardiac arrest to be used in patients newly diagnosed with heart failure.DesignA nationwide registry-based observational study.SettingData were sourced from the Swedish Heart Failure Registry (1 January 2005 to 31 December 2021).ParticipantsThis cohort included 45 068 patients discharged from hospital after first hospitalisation for newly diagnosed heart failure. Patients discharged from hospital with palliative care and/or implantable defibrillators were excluded.Outcome measure and analysisThe primary outcome was defined as cardiac arrest registered in the Swedish Registry for Cardiopulmonary Resuscitation until final follow-up (15 November 2022). Patients who died without resuscitation were treated as competing events. A Random Survival Forest model for competing risk was developed using predictors from the heart failure registry. The model was evaluated with Brier score, observed versus predicted cumulative incidence, Concordance-index (C-index) and time-dependent area under the curve of a receiver operating characteristics graph (AUC-ROC).ResultsIn this cohort, 2399 (5%) patients had received cardiopulmonary resuscitation (CPR) (5%), and 31 989 (71%) patients died without resuscitation. Our model with 82 predictors had a low Brier score indicating a capacity to accurately predict cumulative incidence of cardiac arrest on a group level. However, the model also had a low C-index 0.52 and low AUC-ROC 0.63–0.65.ConclusionOur Random Survival Forest model for competing risk could not accurately predict cardiac arrest in individual patients newly diagnosed with heart failure, because the event death without attempted resuscitation was treated as a competing event. The lack of information on transitions to palliative care and Do-Not-Attempt-CPR-orders limits the clinical relevance of any cardiac arrest prediction model.

We evaluated the importance of body composition, amount of subcutaneous and visceral fat, liver and heart ectopic fat, adipose tissue distribution and cell size as predictors of cardio-metabolic risk in 53 non-obese male individuals. Known family history of type 2 diabetes was identified in 25 individuals. The participants also underwent extensive phenotyping together with measuring different biomarkers and non-targeted serum metabolomics. We used ensemble learning and other machine learning approaches to identify predictors with considerable relative importance and their intricate interactions. Visceral fat and age were strong individual predictors of ectopic fat accumulation in liver and heart along with markers of lipid oxidation and reduced glucose tolerance. Subcutaneous adipose cell size was the strongest individual predictor of whole-body insulin sensitivity and also a marker of visceral and ectopic fat accumulation. The metabolite 3-MOB along with related branched-chain amino acids demonstrated strong predictability for family history of type 2 diabetes.

[This corrects the article DOI: 10.1371/journal.pone.0309241.].

Guidelines for the management of cardiac arrest play a crucial role in guiding clinical decisions and care. We examined the strength and quality of evidence underlying these recommendations in order to elucidate strengths and gaps in knowledge. Using the 2020 American Heart Association (AHA) Guidelines for Adult CPR, we subdivided all recommendations into advanced life support (ALS), basic life support (BLS), and recovery after cardiac arrest, as well as a more granular categorization by topic (i.e. the intervention or evaluation recommended). The Class of Recommendation (COR) and Level of Evidence (LOE) for each were reviewed. Additionally, we reviewed the 2023 guidelines to ensure the inclusion of the most recent updates. We noted 254 recommendations, of which 181 were ALS, 69 were BLS, and 4 were recovery after resuscitation. In total, only 2 (1%) had the most robust evidence (LOE A), while 23% were at LOE B-NR (Non-Randomized), 15% at LOE B-R (Randomized), 50% at LOE C-LD (Limited Data), and 12% relied on expert opinion LOE C-EO (Expert Opinion). Despite the strength of ALS recommendations (Class 1, 2a, or 2b), none had LOE A. In BLS, no recommendations were supported by LOE A. For BLS, 7% of recommendations had LOE C (C-LD or C-EO). The evidence for specific BLS topics, such as airway management, was notably low. Among ALS topics, neurological prognostication had relatively stronger evidence. Only 26 out of the 81 COR 1 recommendations (32%) were supported by LOE A or B, indicating a strong discrepancy between the strength of recommendation and the underlying evidence in cardiac arrest guidelines. The findings underscore a pressing need for more rigorous research, particularly randomized trials.

Data on de novo aneurysm formation after treatment for intracranial aneurysms remains scarce. We studied the incidence of de novo aneurysm formation in patients who had undergone aneurysm treatment more than 18 years prior to follow-up. As it is a disease affecting a younger patient population more specific guidelines are needed when planning a follow-up regime. The rate of de novo aneurysm formation was assessed with Magnetic Resonance Angiography (MRA) follow-up >18 years after endovascular or microsurgical treatment for an intracranial aneurysm. Variables associated with de novo aneurysm formation were studied using logistic regression. Missing data were imputed using chained random forests. A data-driven model for the prediction of de novo aneurysm was created to calculate the relative variable importance of ten clinical features. De novo aneurysms were identified in 11/81 (13.6 %) patients, of whom 1 was male, over a median follow-up of 20 years. Sex was the most important variable associated with de novo aneurysm formation. Regarding the development of de novo aneurysm, men displayed an odds ratio (OR) of 0.16 (0.01–0.97), compared with women. OR for mRS score 2 or more was 0.20 (95 % CI 0.01–1.34), and OR for smokers was 3.70 (0.54–31.18). Six out of 11 patients (54.5 %) needed treatment; 1 underwent endovascular treatment (EVT) and 5 underwent microsurgical treatment (MST). The overall annual de novo aneurysm formation rate was 0.92 %. This study highlights the need for a longer follow-up imaging monitoring of patients that have previously undergone treatment for an intracranial aneurysm. These data are useful to take into consideration when planning a follow-up strategy. •The overall annual de novo aneurysm formation rate was 0.92 %.•Sex was the most important variable associated with de novo aneurysm formation rate.•A longer follow-up is deemed necessary especially in the younger population suffering an aneurysmal Subarachnoid haemorrhage.

ObjectivesTo evaluate the effects of diabetes mellitus (DM) and body mass index (BMI) on long-term all-cause mortality in chronic total occlusion (CTO) patients.DesignRetrospective, nationwide cohort study.SettingSwedish Coronary Angiography and Angioplasty Registry, between June 2015 and December 2021.Participants24 284 patients with angiographically confirmed CTO. Prior coronary artery bypass graft surgery excluded. Subgroups were defined by DM status and BMI categories (underweight, healthy weight, overweight, obesity).Primary outcome measuresLong-term all-cause mortality, assessed by Kaplan-Meier analysis and multivariable Cox proportional hazards regression.ResultsDM was present in 30.3% of patients and conferred a 31% higher risk of mortality (HR: 1.31, 95% CI: 1.20 to 1.42; p<0.001). Insulin use among patients with diabetes added a 52% increase in hazard (HR: 1.52; 95% CI: 1.38 to 1.67; p<0.001). BMI demonstrated a non-linear association with mortality: overweight (HR: 0.70, 95% CI: 0.64 to 0.77; p<0.001) and obese (HR: 0.74, 95% CI: 0.68 to 0.81; p<0.001) groups had lower risk compared with the healthy-weight group, whereas underweight individuals faced the highest risk (HR: 1.61, 95% CI: 1.25 to 2.08; p<0.001). A continuous BMI spline revealed an asymmetric U-shaped association: a steep increase in mortality below 23 kg/m2, lowest risk (nadir) at 32 kg/m2 and modest rise above 35 kg/m2.ConclusionsIn this nationwide CTO cohort, DM independently predicted higher long-term mortality, accompanied by more severe comorbidities and greater CTO complexity, and insulin therapy further elevated hazard. Overweight and obese patients had better survival, while underweight individuals had the poorest prognosis. These findings underscore the importance of individualised risk assessment and management strategies in CTO patients, particularly those with DM or low BMI.