Non-coronary arterial outcomes in people with type 1 diabetes mellitus: a Swedish retrospective cohort study.

Background: Observational studies on long-term trends, risk factor association and importance are scarce for type 1 diabetes mellitus and peripheral arterial outcomes. We set out to investigate trends in non-coronary complications and their relationships with cardiovascular risk factors in persons with type 1 diabetes mellitus compared to matched controls. Methods: 34,263 persons with type 1 diabetes mellitus from the Swedish National Diabetes Register and 164,063 matched controls were included. Incidence rates of extracranial large artery disease, aortic aneurysm, aortic dissection, lower extremity artery disease, and diabetic foot syndrome were analyzed using standardized incidence rates and Cox regression. Findings: Between 2001 and 2019, type 1 diabetes mellitus incidence rates per 100,000 person-years were as follows: extracranial large artery disease 296.5-84.3, aortic aneurysm 0-9.2, aortic dissection remained at 0, lower extremity artery disease 456.6-311.1, and diabetic foot disease 814.7-77.6. Persons with type 1 diabetes mellitus with cardiometabolic risk factors at target range did not exhibit excess risk of extracranial large artery disease [HR 0.83 (95% CI, 0.20-3.36)] or lower extremity artery disease [HR 0.94 (95% CI, 0.30-2.93)], compared to controls. Persons with type 1 diabetes with all risk factors at baseline, had substantially elevated risk for diabetic foot disease [HR 29.44 (95% CI, 3.83-226.04)], compared to persons with type 1 diabetes with no risk factors. Persons with type 1 diabetes mellitus continued to display a lower risk for aortic aneurysm, even with three cardiovascular risk factors at baseline [HR 0.31 (95% CI, 0.15-0.67)]. Relative importance analyses demonstrated that education, glycated hemoglobin (HbA1c), duration of diabetes and lipids explained 54% of extracranial large artery disease, while HbA1c, smoking and systolic blood pressure explained 50% of lower extremity artery disease and HbA1c alone contributed to 41% of diabetic foot disease. Income, duration of diabetes and body mass index explained 66% of the contribution to aortic aneurysm. Interpretation: Peripheral arterial complications decreased in persons with type 1 diabetes mellitus, except for aortic aneurysm which remained low. Besides glycemic control, traditional cardiovascular risk factors were associated with incident outcomes. Risk of these outcomes increased with additional risk factors present. Persons with type 1 diabetes mellitus exhibited a lower risk of aortic aneurysm compared to controls, despite presence of cardiovascular risk factors. Funding: Swedish Governmental and the county support of research and education of doctors, the Swedish Heart and Lung Foundation, Sweden and Åke-Wibergs grant.

Non-coronary peripheral arterial complications in people with type 2 diabetes: a Swedish retrospective cohort study.

Background: Few studies have explored long-term trends and risk factors for peripheral arterial complications in type 2 diabetes compared to the general population. Our research focuses on identifying optimal risk factors, their significance, risk associated with multifactorial risk factor control, and trends for these complications in diabetic patients versus general controls. Methods: This study included persons with type 2 diabetes mellitus entered into the Swedish National Diabetes Register 2001-2019 and controls matched for age-, sex- and county of residence. Outcomes comprised of extracranial large artery disease, aortic aneurysm, aortic dissection, lower extremity arterial disease and diabetes foot disease. Standardized incidence rates and Cox regression were used for analyses. Findings: The study comprises 655,250 persons with type 2 diabetes mellitus; average age 64.2; 43.8% women. Among persons with type 2 diabetes mellitus, the incidence rates per 100,000 person years for each non-coronary peripheral arterial complication event changed between 2001 and 2019 as follows: extracranial large artery disease 170.0-84.9; aortic aneurysm 40.6-69.2; aortic dissection 9.3 to 5.6; lower extremity artery disease from 338.8 to 190.8; and diabetic foot disease from 309.8 to 226.8. Baseline hemoglobin A1c (HbA1c), systolic blood pressure (SBP), smoking status and lipid levels were independently associated with all outcomes in the type 2 diabetes mellitus cohort. Within the cohort with type 2 diabetes mellitus, the risk for extracranial large artery disease and lower extremity artery disease increased in a stepwise fashion for each risk factor not within target. Excess risk for non-coronary peripheral arterial complications in the entire cohort for persons with type 2 diabetes mellitus, compared to matched controls, were as follows: extracranial large artery disease adjusted hazard ratio (HR) 1.69 (95% confidence interval (CI), 1.65-1.73), aortic aneurysm HR 0.89 (95% CI, 0.87-0.92), aortic dissection HR 0.51 (95% CI, 0.46-0.57) and lower extremity artery disease HR 2.59 (95% CI, 2.55-2.64). Interpretation: The incidence of non-coronary peripheral arterial complications has declined significantly among persons with type 2 diabetes mellitus, with the exception of aortic aneurysm. HbA1c, smoking and blood pressure demonstrated greatest relative contribution for outcomes and lower levels of cardiometabolic risk factors are associated with reduced relative risk of outcomes. Funding: Swedish Governmental and the County support of research and education of doctors, the Swedish Heart-Lung Foundation and Åke-Wibergs grant.

Risk factors for and risk of all-cause and atherosclerotic cardiovascular disease mortality in people with type 2 diabetes and peripheral artery disease: an observational, register-based cohort study.

BACKGROUND: Type 2 diabetes (T2D) and peripheral artery disease (PAD) are recognized as independent risk factors contributing to excess mortality. Contemporary observational studies exploring the associations of risk factors, and risk of all-cause and atherosclerotic cardiovascular disease mortality in persons with T2D following the onset of incident peripheral artery disease are limited. The objectives of this study were to investigate the associations of risk factors, and assess mortality risks in people with T2D compared with controls without T2D after the onset of PAD. METHODS: All persons with T2D (n = 150,215) registered in the Swedish National Diabetes Register between 2005 and 2009 were included, along with 346,423 controls without T2D matched for sex and age. Data were retrieved from several national registries, capturing information on risk factors, onset of incident peripheral artery disease, other comorbidities, socioeconomic factors, and outcomes. To compare persons with T2D and controls following the onset of peripheral artery disease regarding the risk of all-cause, and atherosclerotic cardiovascular disease mortality, Cox proportional hazard models and Kaplan-Meier curves were employed. A gradient-boosting model was utilized to estimate the relative statistical contribution of risk factors to the modeling of incident mortality risk in people with both T2D and peripheral artery disease. RESULTS: Crude rates of incident all-cause mortality were higher in individuals with T2D compared with controls, following the onset of PAD (600.4 (95% CI, 581.4-619.8) per 10,000 person-years versus 549.1 (95% CI, 532.1-566.5) per 10,000 person-years). Persons with T2D had an adjusted hazard ratio (HR) for all-cause mortality of 1.12 (95% CI, 1.05-1.19, P < 0.01) compared with controls after onset of incident PAD. The comparable adjusted HR for cardiovascular mortality was 1.13 (95% CI, 1.07-1.19, P < 0.01). High age and hyperglycemia at baseline played a significant role in contributing to the predictive models for incident all-cause and cardiovascular mortality among individuals with both T2D and PAD. CONCLUSIONS: The presence of T2D with concomitant PAD is related to an increased risk of both all-cause and cardiovascular mortality compared with individuals with only PAD. This argues for implementing optimized and intensive treatment strategies for individuals with both conditions.

Early ICD implantation following out-of-hospital cardiac arrest: a retrospective cohort study from the Swedish Registry for Cardiopulmonary Resuscitation.

BACKGROUND: It is unclear whether an implantable cardioverter-defibrillator (ICD) is generally beneficial in survivors of out-of-hospital cardiac arrest (OHCA). OBJECTIVE: We studied the association between ICD implantation prior to discharge and survival in patients with cardiac aetiology or initial shockable rhythm in OHCA. DESIGN: We conducted a retrospective cohort study in the Swedish Registry for Cardiopulmonary Resuscitation. Treatment associations were estimated using propensity scores. We used gradient boosting, Bayesian additive regression trees, neural networks, extreme gradient boosting and logistic regression to generate multiple propensity scores. We selected the model yielding maximum covariate balance to obtain weights, which were used in a Cox regression to calculate HRs for death or recurrent cardiac arrest. PARTICIPANTS: All cases discharged alive during 2010 to 2020 with a cardiac aetiology or initial shockable rhythm were included. A total of 959 individuals were discharged with an ICD, and 2046 were discharged without one. RESULTS: Among those experiencing events, 25% did so within 90 days in the ICD group, compared with 52% in the other group. All HRs favoured ICD implantation. The overall HR (95% CI) for ICD versus no ICD was 0.38 (0.26 to 0.56). The HR was 0.42 (0.28 to 0.63) in cases with initial shockable rhythm; 0.18 (0.06 to 0.58) in non-shockable rhythm; 0.32 (0.20 to 0.53) in cases with a history of coronary artery disease; 0.36 (0.22 to 0.61) in heart failure and 0.30 (0.13 to 0.69) in those with diabetes. Similar associations were noted in all subgroups. CONCLUSION: Among survivors of OHCA, those discharged with an ICD had approximately 60% lower risk of death or recurrent cardiac arrest. A randomised trial is warranted to study this further.

No obesity paradox in out-of-hospital cardiac arrest: Data from the Swedish registry of cardiopulmonary resuscitation.

Background: Although an "obesity paradox", which states an increased chance of survival for patients with obesity after myocardial infarction has been proposed, it is less clear whether this phenomenon even exists in patients suffering out-of-hospital cardiac arrest (OHCA) and if diabetes, which is often associated with obesity, implies an additional risk. Objective: To investigate if and how obesity, with or without diabetes, affects the survival of patients with OHCA. Methods: This study included 55,483 patients with OHCA reported to the Swedish Registry of Cardiopulmonary Resuscitation between 2010 and 2020. Patients were classified in five groups: obesity only (Ob), type 1 diabetes only (T1D), type 2 diabetes only (T2D), obesity and any diabetes (ObD), or belonging to the group other (OTH). Patient characteristics and outcomes were studied using descriptive statistics, logistic, and Cox proportional regression. Results: Obesity only was found in 2.7% of the study cohort, while 3.2% had obesity and any type of diabetes. Ob patients were significantly younger than all other patients (p ≤ 0.001); the 30 day-survival was 9.6% in Ob, and 10.6%, 7.3%, 6.9%, and 12.7% in T1D, T2D, ObD, and OTH, respectively, with OR (95% CI) of 0.69 (0.57-0.82), 0.78 (0.56-1.05), 0.65 (0.59-0.71), and 0.55 (0.45-0.66) for Ob, T1D, T2D, and ObD, respectively (reference group OTH). No time-related trends in 30-days survival were found. Conclusion: Obesity was present in 6% of the population and was associated with younger age and a 30% reduction in survival; a combination of obesity and diabetes further reduced the survival rate.

Left-sided valvular heart disease and survival in out-of-hospital cardiac arrest: a nationwide registry-based study.

Survival in left-sided valvular heart disease (VHD; aortic stenosis [AS], aortic regurgitation [AR], mitral stenosis [MS], mitral regurgitation [MR]) in out-of-hospital cardiac arrest (OHCA) is unknown. We studied all cases of OHCA in the Swedish Registry for Cardiopulmonary Resuscitation. All degrees of VHD, diagnosed prior to OHCA, were included. Association between VHD and survival was studied using logistic regression, gradient boosting and Cox regression. We studied time to cardiac arrest, comorbidities, survival, and cerebral performance category (CPC) score. We included 55,615 patients; 1948 with AS (3,5%), 384 AR (0,7%), 17 MS (0,03%), and 704 with MR (1,3%). Patients with MS were not described due to low case number. Time from VHD diagnosis to cardiac arrest was 3.7 years in AS, 4.5 years in AR and 4.1 years in MR. ROSC occurred in 28% with AS, 33% with AR, 36% with MR and 35% without VHD. Survival at 30 days was 5.2%, 10.4%, 9.2%, 11.4% in AS, AR, MR and without VHD, respectively. There were no survivors in people with AS presenting with asystole or PEA. CPC scores did not differ in those with VHD compared with no VHD. Odds ratio (OR) for MR and AR showed no difference in survival, while AS displayed OR 0.58 (95% CI 0.46-0.72), vs no VHD. AS is associated with halved survival in OHCA, while AR and MR do not affect survival. Survivors with AS have neurological outcomes comparable to patients without VHD.

Association between exercise load, resting heart rate, and maximum heart rate and risk of future ST-segment elevation myocardial infarction (STEMI).

OBJECTIVE: This study aimed to examine the association between exercise workload, resting heart rate (RHR), maximum heart rate and the risk of developing ST-segment elevation myocardial infarction (STEMI). METHODS: The study included all participants from the UK Biobank who had undergone submaximal exercise stress testing. Patients with a history of STEMI were excluded. The allowed exercise load for each participant was calculated based on clinical characteristics and risk categories. We studied the participants who exercised to reach 50% or 35% of their expected maximum exercise tolerance. STEMI was adjudicated by the UK Biobank. We used Cox regression analysis to study how exercise tolerance and RHR were related to the risk of STEMI. RESULTS: A total of 66 949 participants were studied, of whom 274 developed STEMI during a median follow-up of 7.7 years. After adjusting for age, sex, blood pressure, smoking, forced vital capacity, forced expiratory volume in 1 s, peak expiratory flow and diabetes, we noted a significant association between RHR and the risk of STEMI (p=0.015). The HR for STEMI in the highest RHR quartile (>90 beats/min) compared with that in the lowest quartile was 2.92 (95% CI 1.26 to 6.77). Neither the maximum achieved exercise load nor the ratio of the maximum heart rate to the maximum load was significantly associated with the risk of STEMI. However, a non-significant but stepwise inverse association was noted between the maximum load and the risk of STEMI. CONCLUSION: RHR is an independent predictor of future STEMI. An RHR of >90 beats/min is associated with an almost threefold increase in the risk of STEMI.

Triglyceride-rich lipoprotein remnants, low-density lipoproteins, and risk of coronary heart disease: a UK Biobank study.

AIMS: The strength of the relationship of triglyceride-rich lipoproteins (TRL) with risk of coronary heart disease (CHD) compared with low-density lipoprotein (LDL) is yet to be resolved. METHODS AND RESULTS: Single-nucleotide polymorphisms (SNPs) associated with TRL/remnant cholesterol (TRL/remnant-C) and LDL cholesterol (LDL-C) were identified in the UK Biobank population. In a multivariable Mendelian randomization analysis, TRL/remnant-C was strongly and independently associated with CHD in a model adjusted for apolipoprotein B (apoB). Likewise, in a multivariable model, TRL/remnant-C and LDL-C also exhibited independent associations with CHD with odds ratios per 1 mmol/L higher cholesterol of 2.59 [95% confidence interval (CI): 1.99-3.36] and 1.37 [95% CI: 1.27-1.48], respectively. To examine the per-particle atherogenicity of TRL/remnants and LDL, SNPs were categorized into two clusters with differing effects on TRL/remnant-C and LDL-C. Cluster 1 contained SNPs in genes related to receptor-mediated lipoprotein removal that affected LDL-C more than TRL/remnant-C, whereas cluster 2 contained SNPs in genes related to lipolysis that had a much greater effect on TRL/remnant-C. The CHD odds ratio per standard deviation (Sd) higher apoB for cluster 2 (with the higher TRL/remnant to LDL ratio) was 1.76 (95% CI: 1.58-1.96), which was significantly greater than the CHD odds ratio per Sd higher apoB in cluster 1 [1.33 (95% CI: 1.26-1.40)]. A concordant result was obtained by using polygenic scores for each cluster to relate apoB to CHD risk. CONCLUSION: Distinct SNP clusters appear to impact differentially on remnant particles and LDL. Our findings are consistent with TRL/remnants having a substantially greater atherogenicity per particle than LDL.

Twenty Years of Cardiovascular Complications and Risk Factors in Patients With Type 2 Diabetes: A Nationwide Swedish Cohort Study.

BACKGROUND: The goal of this work was to investigate trends (2001-2019) for cardiovascular events and cardiometabolic risk factor levels in individuals with type 2 diabetes (T2D) and matched control subjects. METHODS: This study included 679 072 individuals with T2D from the Swedish National Diabetes Register and 2 643 800 matched control subjects. Incident outcomes comprised coronary artery disease, acute myocardial infarction, cerebrovascular disease, and heart failure (HF). Trends in time to first event for each outcome were analyzed with Cox regression and standardized incidence rates. In the group with T2D, Cox regression was also used to assess risk factor levels beyond target and outcomes, as well as the relative importance of each risk factor to each model. RESULTS: Among individuals with T2D, incidence rates per 10 000 person-years in 2001 and 2019 were as follows: acute myocardial infarction, 73.9 (95% CI, 65.4-86.8) and 41.0 (95% CI, 39.5-42.6); coronary artery disease, 205.1 (95% CI, 186.8-227.5) and 80.2 (95% CI, 78.2-82.3); cerebrovascular disease, 83.9 (95% CI, 73.6-98.5) and 46.2 (95% CI, 44.9-47.6); and HF, 98.3 (95% CI, 89.4-112.0) and 75.9 (95% CI, 74.4-77.5). The incidence for HF plateaued around 2013, a trend that then persisted. In individuals with T2D, glycated hemoglobin, systolic blood pressure, estimated glomerular filtration rate, and lipids were independently associated with outcomes. Body mass index alone potentially explained >30% of HF risk in T2D. For those with T2D with no risk factor beyond target, there was no excess cardiovascular risk compared with control subjects except for HF, with increased hazard with T2D even when no risk factor was above target (hazard ratio, 1.50 [95% CI, 1.35-1.67]). Risk for coronary artery disease and cerebrovascular disease increased in a stepwise fashion for each risk factor not within target. Glycated hemoglobin was most prognostically important for incident atherosclerotic events, as was body mass index for incident of HF. CONCLUSIONS: Risk and rates for atherosclerotic complications and HF are generally decreasing among individuals with T2D, although HF incidence has notably plateaued in recent years. Modifiable risk factors within target levels were associated with lower risks for outcomes. This was particularly notable for systolic blood pressure and glycated hemoglobin for atherosclerotic outcomes and body mass index for heart failure.

Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model.

BACKGROUND: A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms. METHODS: We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation set (20% of data), and test set (20% of data). We assessed the 30-day survival and cerebral performance category (CPC) score at discharge using several machine learning frameworks with hyperparameter tuning. Parsimonious models with the top 1 to 20 strongest predictors were tested. We calibrated the decision threshold to assess the cut-off yielding 95% sensitivity for survival. The final model was deployed as a web application. FINDINGS: We included 55,615 cases of OHCA. Initial presentation, prehospital interventions, and critical time intervals variables were the most important. At a sensitivity of 95%, specificity was 89%, positive predictive value 52%, and negative predictive value 99% in test data to predict 30-day survival. The area under the receiver characteristic curve was 0.97 in test data using all 393 predictors or only the ten most important predictors. The final model showed excellent calibration. The web application allowed for near-instantaneous survival calculations. INTERPRETATION: Thirty-day survival and neurological outcome in OHCA can rapidly and reliably be estimated during ongoing cardiopulmonary resuscitation in the emergency room using a machine learning model incorporating widely available variables. FUNDING: Swedish Research Council (2019-02019); Swedish state under the agreement between the Swedish government, and the county councils (ALFGBG-971482); The Wallenberg Centre for Molecular and Translational Medicine.

Cardiac arrhythmias and conduction abnormalities in patients with type 2 diabetes.

The association between type 2 diabetes (T2D) and the development of cardiac arrhythmias and conduction disturbances has not been extensively studied. Arrhythmia was defined as atrial fibrillation and flutter (AF/AFl), ventricular tachycardia (VT) and ventricular fibrillation (VF), and conduction abnormality as sinus node disease (SND), atrioventricular (AV) block or pacemaker implantation, and intraventricular conduction blocks (IVCB). Incidence rates and Cox regression were used to compare outcomes, and to assess optimal levels for cardiometabolic risk factors and risk associated with multifactorial risk factor control (i.e., HbA1c, LDL-C, systolic blood pressure (SBP), BMI and eGFR), between patients with versus without T2D. The analyses included data from 617,000 patients with T2D and 2,303,391 matched controls. Patients with diabetes and the general population demonstrated a gradual increase in rates for cardiac conduction abnormalities and virtually all age-groups for AF/AFI showed increased incidence during follow-up. For patients with versus without T2D, risks for cardiac arrhythmias were higher, including for AF/AFl (HR 1.17, 95% CI 1.16-1.18), the composite of SND, AV-block or pacemaker implantation (HR 1.40, 95% CI 1.37-1.43), IVCB (HR 1.23, 95% CI 1.18-1.28) and VT/VF (HR 1.08, 95% CI 1.04-1.13). For patients with T2D who had selected cardiometabolic risk factors within target ranges, compared with controls, risk of arrythmia and conduction abnormalities for T2D vs not were: AF/AFl (HR 1.09, 95% CI 1.05-1.14), the composite of SND, AV-block or pacemaker implantation (HR 1.06, 95% CI 0.94-1.18), IVCB (HR 0.80, 95% CI 0.60-0.98), and for VT/VF (HR 0.97, 95% CI 0.80-1.17). Cox models showed a linear risk increase for SBP and BMI, while eGFR showed a U-shaped association. Individuals with T2D had a higher risk of arrhythmias and conduction abnormalities than controls, but excess risk associated with T2D was virtually not evident among patients with T2D with all risk factors within target range. BMI, SBP and eGFR displayed significant associations with outcomes among patients with T2D.

Predicting recurrent cardiac arrest in individuals surviving Out-of-Hospital cardiac arrest.

BACKGROUND: Despite improvements in short-term survival for Out-of-Hospital Cardiac Arrest (OHCA) in the past two decades, long-term survival is still not well studied. Furthermore, the contribution of different variables on long-term survival have not been fully investigated. AIM: Examine the 1-year prognosis of patients discharged from hospital after an OHCA. Furthermore, identify factors predicting re-arrest and/or death during 1-year follow-up. METHODS: All patients 18 years or older surviving an OHCA and discharged from the hospital were identified from the Swedish Register for Cardiopulmonary Resuscitation (SRCR). Data on diagnoses, medications and socioeconomic factors was gathered from other Swedish registers. A machine learning model was constructed with 886 variables and evaluated for its predictive capabilities. Variable importance was gathered from the model and new models with the most important variables were created. RESULTS: Out of the 5098 patients included, 902 (∼18%) suffered a recurrent cardiac arrest or death within a year. For the outcome death or re-arrest within 1 year from discharge the model achieved an ROC (receiver operating characteristics) AUC (area under the curve) of 0.73. A model with the 15 most important variables achieved an AUC of 0.69. CONCLUSIONS: Survivors of an OHCA have a high risk of suffering a re-arrest or death within 1 year from hospital discharge. A machine learning model with 15 different variables, among which age, socioeconomic factors and neurofunctional status at hospital discharge, achieved almost the same predictive capabilities with reasonable precision as the full model with 886 variables.

Range of Risk Factor Levels, Risk Control, and Temporal Trends for Nephropathy and End-stage Kidney Disease in Patients With Type 1 and Type 2 Diabetes.

OBJECTIVE: To investigate trends, optimal levels for cardiometabolic risk factors, and multifactorial risk control in diabetic nephropathy and end-stage kidney disease (ESKD) in patients with diabetes and matched control subjects. RESEARCH DESIGN AND METHODS: This study included 701,622 patients with diabetes from the Swedish National Diabetes Register and 2,738,137 control subjects. Trends were analyzed with standardized incidence rates. Cox regression was used to assess excess risk, optimal risk factor levels, and risk according to the number of risk factors, in diabetes. RESULTS: ESKD incidence among patients with and without diabetes initially declined until 2007 and increased thereafter, whereas diabetic nephropathy decreased throughout follow-up. In patients with diabetes, baseline values for glycated hemoglobin, systolic blood pressure (SBP), triglycerides, and BMI were associated with outcomes. Hazard ratio (HR) for ESKD for patients with type 2 diabetes who had all included risk factors at target was 1.60 (95% CI 1.49-1.71) compared with control subjects and for patients with type 1 diabetes 6.10 (95% CI 4.69-7.93). Risk for outcomes increased in a stepwise fashion for each risk factor not at target. Excess risk for ESKD in type 2 diabetes showed a HR of 2.32 (95% CI 2.30-2.35) and in type 1 diabetes 10.92 (95% CI 10.15-11.75), compared with control. CONCLUSIONS: Incidence of diabetic nephropathy has declined substantially, whereas ESKD incidence has increased. Traditional and modifiable risk factors below target levels were associated with lower risks for outcomes, particularly notable for the causal risk factors of SBP and HbA1c, with potential implications for care.

BMP4 and Gremlin 1 regulate hepatic cell senescence during clinical progression of NAFLD/NASH.

The role of hepatic cell senescence in human non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) is not well understood. To examine this, we performed liver biopsies and extensive characterization of 58 individuals with or without NAFLD/NASH. Here, we show that hepatic cell senescence is strongly related to NAFLD/NASH severity, and machine learning analysis identified senescence markers, the BMP4 inhibitor Gremlin 1 in liver and visceral fat, and the amount of visceral adipose tissue as strong predictors. Studies in liver cell spheroids made from human stellate and hepatocyte cells show BMP4 to be anti-senescent, anti-steatotic, anti-inflammatory and anti-fibrotic, whereas Gremlin 1, which is particularly highly expressed in visceral fat in humans, is pro-senescent and antagonistic to BMP4. Both senescence and anti-senescence factors target the YAP/TAZ pathway, making this a likely regulator of senescence and its effects. We conclude that senescence is an important driver of human NAFLD/NASH and that BMP4 and Gremlin 1 are novel therapeutic targets.

Left-Sided Degenerative Valvular Heart Disease in Type 1 and Type 2 Diabetes.

BACKGROUND: The role of diabetes in the development of valvular heart disease, and, in particular, the relation with risk factor control, has not been extensively studied. METHODS: We included 715 143 patients with diabetes registered in the Swedish National Diabetes Register and compared them with 2 732 333 matched controls randomly selected from the general population. First, trends were analyzed with incidence rates and Cox regression, which was also used to assess diabetes as a risk factor compared with controls, and, second, separately in patients with diabetes according to the presence of 5 risk factors. RESULTS: The incidence of valvular outcomes is increasing among patients with diabetes and the general population. In type 2 diabetes, systolic blood pressure, body mass index, and renal function were associated with valvular lesions. Hazard ratios for patients with type 2 diabetes who had nearly all risk factors within target ranges, compared with controls, were as follows: aortic stenosis 1.34 (95% CI, 1.31-1.38), aortic regurgitation 0.67 (95% CI, 0.64-0.70), mitral stenosis 1.95 (95% CI, 1.76-2.20), and mitral regurgitation 0.82 (95% CI, 0.79-0.85). Hazard ratios for patients with type 1 diabetes and nearly optimal risk factor control were as follows: aortic stenosis 2.01 (95% CI, 1.58-2.56), aortic regurgitation 0.63 (95% CI, 0.43-0.94), and mitral stenosis 3.47 (95% CI, 1.37-8.84). Excess risk in patients with type 2 diabetes for stenotic lesions showed hazard ratios for aortic stenosis 1.62 (95% CI, 1.59-1.65), mitral stenosis 2.28 (95% CI, 2.08-2.50), and excess risk in patients with type 1 diabetes showed hazard ratios of 2.59 (95% CI, 2.21-3.05) and 11.43 (95% CI, 6.18-21.15), respectively. Risk for aortic and mitral regurgitation was lower in type 2 diabetes: 0.81 (95% CI, 0.78-0.84) and 0.95 (95% CI, 0.92-0.98), respectively. CONCLUSIONS: Individuals with type 1 and 2 diabetes have greater risk for stenotic lesions, whereas risk for valvular regurgitation was lower in patients with type 2 diabetes. Patients with well-controlled cardiovascular risk factors continued to display higher risk for valvular stenosis, without a clear stepwise decrease in risk between various degrees of risk factor control.

Cardiorenal function and survival in in-hospital cardiac arrest: A nationwide study of 22,819 cases.

BACKGROUND: We studied the association between cardiorenal function and survival, neurological outcome and trends in survival after in-hospital cardiac arrest (IHCA). METHODS: We included cases aged ≥ 18 years in the Swedish Cardiopulmonary Resuscitation Registry during 2008 to 2020. The CKD-EPI equation was used to calculate estimated glomerular filtration rate (eGFR). A history of heart failure was defined according to contemporary guideline criteria. Logistic regression was used to study survival. Neurological outcome was assessed using cerebral performance category (CPC). RESULTS: We studied 22,819 patients with IHCA. The 30-day survival was 19.3%, 16.6%, 22.5%, 28.8%, 39.3%, 44.8% and 38.4% in cases with eGFR < 15, 15-29, 30-44, 45-59, 60-89, 90-130 and 130-150 ml/min/1.73 m2, respectively. All eGFR levels below and above 90 ml/min/1.73 m2 were associated with increased mortality. Probability of survival at 30 days was 62% lower in cases with eGFR < 15 ml/min/1.73 m2, compared with normal kidney function. At every level of eGFR, presence of heart failure increased mortality markedly; patients without heart failure displayed higher mortality only at eGFR below 30 ml/min/1.73 m2. Among survivors with eGFR < 15 ml/min/1.73 m2, good neurological outcome was noted in 87.2%. Survival increased in most groups over time, but most for those with eGFR < 15 ml/min/1.73 m2, and least for those with normal eGFR. CONCLUSIONS: All eGFR levels below and above normal range are associated with increased mortality and this association is modified by the presence of heart failure. Neurological outcome is good in the majority of cases, across kidney function levels and survival is increasing.

Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach.

The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.

Trajectories in HbA1c and other risk factors among adults with type 1 diabetes by age at onset.

INTRODUCTION: In type 1 diabetes, potential loss of life-years is greatest in those who are youngest at the time of onset. Using data from a nationwide cohort of patients with type 1 diabetes, we aimed to study risk factor trajectories by age at diagnosis. RESEARCH DESIGN AND METHODS: We stratified 30 005 patients with type 1 diabetes aged 18-75 years into categories based on age at onset: 0-10, 11-15, 16-20, 21-25, and 26-30 years. HbA1c, albuminuria, estimated glomerular filtration rate (eGFR), body mass index (BMI), low-denisty lipoprotein (LDL)-cholesterol, systolic blood pressure (SBP), and diastolic blood pressure trends were analyzed using mixed models. Variable importance for baseline HbA1c was analyzed using conditional random forest and gradient boosting machine approaches. RESULTS: Individuals aged ≥16 years at onset displayed a relatively low mean HbA1c level (~55-57 mmol/mol) that gradually increased. In contrast, individuals diagnosed at ≤15 years old entered adulthood with a mean HbA1c of approximately 70 mmol/mol. For all groups, HbA1c levels stabilized at a mean of approximately 65 mmol/mol by about 40 years old. In patients who were young at the time of onset, albuminuria appeared at an earlier age, suggesting a more rapid decrease in eGFR, while there were no distinct differences in BMI, SBP, and LDL-cholesterol trajectories between groups. Low education, higher age, and poor risk factor control were associated with higher HbA1c levels. CONCLUSIONS: Young age at the diabetes onset plays a substantial role in subsequent glycemic control and the presence of albuminuria, where patients with early onset may accrue a substantial glycemic load during this period.

Severe COVID-19 in people with type 1 and type 2 diabetes in Sweden: A nationwide retrospective cohort study.

BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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.

A national observation study of cancer incidence and mortality risks in type 2 diabetes compared to the background population over time.

We examined changing patterns in cancer incidence and deaths in diabetes compared to the background population. A total of 457,473 patients with type 2 diabetes, included between 1998 and 2014, were matched on age, sex, and county to five controls from the population. Incidence, trends in incidence and post-cancer mortality for cancer were estimated with Cox regression and standardised incidence rates. Causes of death were estimated using logistic regression. Relative importance of risk factors was estimated using Heller's relative importance model. Type 2 diabetes had a higher risk for all cancer, HR 1.10 (95% CI 1.09-1.12), with highest HRs for liver (3.31), pancreas (2.19) and uterine cancer (1.78). There were lesser increases in risk for breast (1.05) and colorectal cancers (1.20). Type 2 diabetes patients experienced a higher HR 1.23 (1.21-1.25) of overall post-cancer mortality and mortality from prostate, breast, and colorectal cancers. By the year 2030 cancer could become the most common cause of death in type 2 diabetes. Persons with type 2 diabetes are at greater risk of developing cancer and lower chance of surviving it. Notably, hazards for specific cancers (e.g. liver, pancreas) in type 2 patients cannot be explained by obesity alone.