Patients with diabetes are at higher risk for death and cardiovascular outcomes than the general population. We investigated whether the excess risk of death and cardiovascular events among patients ...with type 2 diabetes could be reduced or eliminated.
In a cohort study, we included 271,174 patients with type 2 diabetes who were registered in the Swedish National Diabetes Register and matched them with 1,355,870 controls on the basis of age, sex, and county. We assessed patients with diabetes according to age categories and according to the presence of five risk factors (elevated glycated hemoglobin level, elevated low-density lipoprotein cholesterol level, albuminuria, smoking, and elevated blood pressure). Cox regression was used to study the excess risk of outcomes (death, acute myocardial infarction, stroke, and hospitalization for heart failure) associated with smoking and the number of variables outside target ranges. We also examined the relationship between various risk factors and cardiovascular outcomes.
The median follow-up among all the study participants was 5.7 years, during which 175,345 deaths occurred. Among patients with type 2 diabetes, the excess risk of outcomes decreased stepwise for each risk-factor variable within the target range. Among patients with diabetes who had all five variables within target ranges, the hazard ratio for death from any cause, as compared with controls, was 1.06 (95% confidence interval CI, 1.00 to 1.12), the hazard ratio for acute myocardial infarction was 0.84 (95% CI, 0.75 to 0.93), and the hazard ratio for stroke was 0.95 (95% CI, 0.84 to 1.07). The risk of hospitalization for heart failure was consistently higher among patients with diabetes than among controls (hazard ratio, 1.45; 95% CI, 1.34 to 1.57). In patients with type 2 diabetes, a glycated hemoglobin level outside the target range was the strongest predictor of stroke and acute myocardial infarction; smoking was the strongest predictor of death.
Patients with type 2 diabetes who had five risk-factor variables within the target ranges appeared to have little or no excess risk of death, myocardial infarction, or stroke, as compared with the general population. (Funded by the Swedish Association of Local Authorities and Regions and others.).
Long-term trends in excess risk of death and cardiovascular outcomes have not been extensively studied in persons with type 1 diabetes or type 2 diabetes.
We included patients registered in the ...Swedish National Diabetes Register from 1998 through 2012 and followed them through 2014. Trends in deaths and cardiovascular events were estimated with Cox regression and standardized incidence rates. For each patient, controls who were matched for age, sex, and county were randomly selected from the general population.
Among patients with type 1 diabetes, absolute changes during the study period in the incidence rates of sentinel outcomes per 10,000 person-years were as follows: death from any cause, -31.4 (95% confidence interval CI, -56.1 to -6.7); death from cardiovascular disease, -26.0 (95% CI, -42.6 to -9.4); death from coronary heart disease, -21.7 (95% CI, -37.1 to -6.4); and hospitalization for cardiovascular disease, -45.7 (95% CI, -71.4 to -20.1). Absolute changes per 10,000 person-years among patients with type 2 diabetes were as follows: death from any cause, -69.6 (95% CI, -95.9 to -43.2); death from cardiovascular disease, -110.0 (95% CI, -128.9 to -91.1); death from coronary heart disease, -91.9 (95% CI, -108.9 to -75.0); and hospitalization for cardiovascular disease, -203.6 (95% CI, -230.9 to -176.3). Patients with type 1 diabetes had roughly 40% greater reduction in cardiovascular outcomes than controls, and patients with type 2 diabetes had roughly 20% greater reduction than controls. Reductions in fatal outcomes were similar in patients with type 1 diabetes and controls, whereas patients with type 2 diabetes had smaller reductions in fatal outcomes than controls.
In Sweden from 1998 through 2014, mortality and the incidence of cardiovascular outcomes declined substantially among persons with diabetes, although fatal outcomes declined less among those with type 2 diabetes than among controls. (Funded by the Swedish Association of Local Authorities and Regions and others.).
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.
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.
Abstract
Aim
To study the characteristics and outcome among cardiac arrest cases with COVID-19 and differences between the pre-pandemic and the pandemic period in out-of-hospital cardiac arrest ...(OHCA) and in-hospital cardiac arrest (IHCA).
Method and results
We included all patients reported to the Swedish Registry for Cardiopulmonary Resuscitation from 1 January to 20 July 2020. We defined 16 March 2020 as the start of the pandemic. We assessed overall and 30-day mortality using Cox regression and logistic regression, respectively. We studied 1946 cases of OHCA and 1080 cases of IHCA during the entire period. During the pandemic, 88 (10.0%) of OHCAs and 72 (16.1%) of IHCAs had ongoing COVID-19. With regards to OHCA during the pandemic, the odds ratio for 30-day mortality in COVID-19-positive cases, compared with COVID-19-negative cases, was 3.40 95% confidence interval (CI) 1.31–11.64; the corresponding hazard ratio was 1.45 (95% CI 1.13–1.85). Adjusted 30-day survival was 4.7% for patients with COVID-19, 9.8% for patients without COVID-19, and 7.6% in the pre-pandemic period. With regards to IHCA during the pandemic, the odds ratio for COVID-19-positive cases, compared with COVID-19-negative cases, was 2.27 (95% CI 1.27–4.24); the corresponding hazard ratio was 1.48 (95% CI 1.09–2.01). Adjusted 30-day survival was 23.1% in COVID-19-positive cases, 39.5% in patients without COVID-19, and 36.4% in the pre-pandemic period.
Conclusion
During the pandemic phase, COVID-19 was involved in at least 10% of all OHCAs and 16% of IHCAs, and, among COVID-19 cases, 30-day mortality was increased 3.4-fold in OHCA and 2.3-fold in IHCA.
Graphical abstract
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.
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.
Aims/hypothesis
The reasons underlying a greater association of premature mortality with early-onset type 2 diabetes relative to late-onset disease are unclear. We evaluated the clinical ...characteristics at type 2 diabetes diagnosis and the broad trajectories in cardiometabolic risk factors over the initial years following diagnosis in relation to age at diagnosis.
Methods
Our cohort consisted of 100,606 individuals with newly diagnosed type 2 diabetes enrolled in the Swedish National Diabetes Register from 2002 to 2012. The average follow-up time was 2.8 years. Analyses were performed using a linear mixed-effects model for continuous risk factors and a mixed generalised linear model with a logistic link function for dichotomous risk factors.
Results
The individuals diagnosed at the youngest age (18–44 years) were more often male and had the highest BMI (mean of 33.4 kg/m
2
) at diagnosis and during follow-up compared with all other groups (those diagnosed at 45–59 years, 60–74 years and ≥75 years;
p
< 0.05), being ~5 kg/m
2
higher than the oldest group. Although HbA
1c
patterns were similar between all age groups, there was a difference of about 5 mmol/mol (0.45%) between the two groups at 8 years post-diagnosis (
p
< 0.05). Additionally, individuals diagnosed younger had ~0.7 mmol/l higher triacylglycerol, and ~0.2 mmol/l lower HDL-cholesterol levels at diagnosis relative to the oldest group. Such differences continued for several years post diagnosis. Yet, although more of these younger individuals were receiving oral glucose-lowering agents, other cardioprotective therapies were prescribed less often in this group. Differences in BMI, blood glucose and lipid levels remained with adjustment for potential confounders, including marital status, education and country of birth, and, where relevant, differential treatments by age, and in those with at least 5 years of follow-up.
Conclusions/interpretation
Individuals who develop type 2 diabetes at a younger age are more frequently obese, display a more adverse lipid profile, have higher HbA
1c
and a faster deterioration in glycaemic control compared with individuals who develop diabetes later in life. These differences largely remain for several years after diagnosis and support the notion that early-onset type 2 diabetes may be a more pathogenic condition than late-onset disease.
Whether infection with SARS-CoV-2 leads to excess risk of requiring hospitalization or intensive care in persons with diabetes has not been reported, nor have risk factors in diabetes associated with ...increased risk for these outcomes.
We included 44,639 and 411,976 adult patients with type 1 and type 2 diabetes alive on Jan 1, 2020, and compared them to controls matched for age, sex, and county of residence (n=204,919 and 1,948,900). Age- and sex-standardized rates for COVID-19 related hospitalizations, admissions to intensive care and death, were estimated and hazard ratios were calculated using Cox regression analyses.
There were 10,486 hospitalizations and 1,416 admissions into intensive care. A total of 1,175 patients with diabetes and 1,820 matched controls died from COVID-19, of these 53•2% had been hospitalized and 10•7% had been in intensive care. Patients with type 2 diabetes, compared to controls, displayed an age- and sex-adjusted hazard ratio (HR) of 2•22, 95%CI 2•13-2•32) of being hospitalized for COVID-19, which decreased to HR 1•40, 95%CI 1•34-1•47) after further adjustment for sociodemographic factors, pharmacological treatment and comorbidities, had higher risk for admission to ICU due to COVID-19 (age- and sex-adjusted HR 2•49, 95%CI 2•22-2•79, decreasing to 1•42, 95%CI 1•25-1•62 after adjustment, and increased risk for death due to COVID-19 (age- and sex-adjusted HR 2•19, 95%CI 2•03-2•36, complete adjustment 1•50, 95%CI 1•39-1•63). Age- and sex-adjusted HR for COVID-19 hospitalization for type 1 diabetes was 2•10, 95%CI 1•72-2•57), decreasing to 1•25, 95%CI 0•3097-1•62) after adjustment• Patients with diabetes type 1 were twice as likely to require intensive care for COVID-19, however, not after adjustment (HR 1•49, 95%CI 0•75-2•92), and more likely to die (HR 2•90, 95% CI 1•6554-5•47) from COVID-19, but not independently of other factors (HR 1•38, 95% CI 0•64-2•99). Among patients with diabetes, elevated glycated hemoglobin levels were associated with higher risk for most outcomes.
In this nationwide study, type 2 diabetes was independently associated with increased risk of hospitalization, admission to intensive care and death for COVID-19. There were few admissions into intensive care and deaths in type 1 diabetes, and although hazards were significantly raised for all three outcomes, there was no independent risk persisting after adjustment for confounding factors.
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