Aims/hypothesis The aim of this work was to study the incidence over time of lower extremity amputations and determine variables associated with increased risk of amputations in people with type 1 diabetes. Methods Individuals with type 1 diabetes registered in the Swedish National Diabetes Registry with no previous amputation from 1 January 1998 and followed to 2 October 2019 were included. Time-updated Cox regression and gradient of risk per SD were used to evaluate the impact of risk factors on the incidence of amputation. Age- and sex-adjusted incidences were estimated over time. Results Of 46,088 people with type 1 diabetes with no previous amputation (mean age 32.5 years [SD 14.5], 25,354 [55%] male sex), 1519 (3.3%) underwent amputation. Median follow-up was 12.4 years. The standardised incidence for any amputation in 1998–2001 was 2.84 (95% CI 2.32, 3.36) per 1000 person-years and decreased to 1.64 (95% CI 1.38, 1.90) per 1000 person-years in 2017–2019. The incidence for minor and major amputations showed a similar pattern. Hyperglycaemia and renal dysfunction were the strongest risk factors for amputation, followed by older age, male sex, cardiovascular comorbidities, smoking and hypertension. Glycaemic control and age- and sex-adjusted renal function improved during the corresponding time period as amputations decreased. Conclusions/interpretation The incidence of amputation and of the most prominent risk factors for amputation, including renal dysfunction and hyperglycaemia, has improved considerably during recent years for people with type 1 diabetes. This finding has important implications for quality of life, health economics and prognosis regarding CVD, indicating a trend shift in the treatment of type 1 diabetes. Graphical abstract
OBJECTIVE This study identified variables associated with increased risk of atrial fibrillation in people with type 1 diabetes. RESEARCH DESIGN AND METHODS We performed a cohort study of people with type 1 diabetes from the Swedish National Diabetes Registry followed up between 1 January 2001 and 31 December 2013. Median follow-up was 9.7 years (interquartile range 5.2–13.0). The association between potential risk factors and incident atrial fibrillation was investigated using adjusted Cox regression. To compare the impact of each risk factor, the gradient of risk per 1 SD was estimated. RESULTS In this cohort of 36,258 patients with type 1 diabetes, 749 developed atrial fibrillation during follow-up. Older age, male sex, renal complications, increased BMI and HbA1c, coronary artery disease, heart failure, and heart valve disease increased the risk of atrial fibrillation. Age, signs of renal dysfunction with macroalbuminuria, and decreasing estimated glomerular filtration rate were associated with the highest gradient of risk for atrial fibrillation. High blood pressure, severe obesity (BMI >35 kg/m2), and elevated levels of HbA1c (>9.6%) were associated with increased risk, but no associations were found with hyperlipidemia or smoking. CONCLUSIONS The most prominent risk factors for atrial fibrillation in people with type 1 diabetes were older age, cardiovascular comorbidities, and renal complications, while obesity, hypertension, and hyperglycemia had more modest affects.
Blood pressure treatment has shown great efficacy in reducing cardiovascular events in randomized controlled trials. If this is effective in reducing cardiovascular disease in the general population, is less studied. Between 2001 and 2009 we performed an intervention to improve blood pressure control in the county of Västerbotten, using Södermanland County as a control. The intervention was directed towards primary care physicians and included lectures on blood pressure treatment, a computerized decision support system with treatment recommendations, and yearly feed back on hypertension control. Each county had approximately 255 000 inhabitants. Differences in age and incidence of cardiovascular disease were small. During follow-up, more than 400 000 patients had their blood pressure recorded. The mean number of measurements was eight per patient, yielding a total of 3.4 million blood pressure recordings. The effect of the intervention will be estimated combining the blood pressure data collected from the electronic medical records, with data on stroke, myocardial infarction and mortality from Swedish health registers. Additional variables, from health registers and Statistics Sweden, will be collected to address for confounders. The blood pressure data collected within this study will be an important asset for future epidemiological studies within the field of hypertension.
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