Pandora L. Wander , Elliott Lowy , Anna Korpak , Lauren A. Beste , Steven E. Kahn , Edward J. Boyko
{"title":"SARS-CoV-2 infection is associated with higher odds of insulin treatment but not with hemoglobin A1c at 120 days in U.S. Veterans with new-onset diabetes","authors":"Pandora L. Wander , Elliott Lowy , Anna Korpak , Lauren A. Beste , Steven E. Kahn , Edward J. Boyko","doi":"10.1016/j.deman.2023.100151","DOIUrl":"10.1016/j.deman.2023.100151","url":null,"abstract":"<div><h3>Aims</h3><p>To examine associations of SARS-CoV-2 infection/COVID-19 with insulin treatment in new-onset diabetes.</p></div><div><h3>Methods</h3><p>We conducted a retrospective cohort study using Veterans Health Administration data (March 1, 2020–June 1, 2022). Individuals with ≥1 positive nasal swab for SARS-CoV-2 (<em>n</em> = 6,706) comprised the exposed group, and individuals with no positive swab and ≥1 laboratory test of any type (<em>n</em> = 20,518) the unexposed group. For exposed, the index date was the date of first positive swab, and for unexposed a random date during the month of the qualifying laboratory test. Among Veterans with new-onset diabetes after the index date, we modeled associations of SARS-CoV-2 with most recent A1c prior to insulin treatment or end of follow-up and receipt of >1 outpatient insulin prescription starting within 120 days.</p></div><div><h3>Results</h3><p>SARS-CoV-2 was associated with a 40% higher odds of insulin treatment compared to no positive test (95%CI 1.2–1.8) but not with most recent A1c (ß 0.00, 95%CI -0.04–0.04). Among Veterans with SARS-CoV-2, ≥2 vaccine doses prior to the index date was marginally associated with lower odds of insulin treatment (OR 0.6, 95%CI 0.3–1.0).</p></div><div><h3>Conclusions</h3><p>SARS-CoV-2 is associated with higher odds of insulin treatment but not with higher A1c. Vaccination may be protective.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10263386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9666788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shweta Sharma , Elliot Duong , Helen Davies , Nicholas Tutticci , Terrance Tan
{"title":"Ketosis in patients undergoing colonoscopy – more common than we think","authors":"Shweta Sharma , Elliot Duong , Helen Davies , Nicholas Tutticci , Terrance Tan","doi":"10.1016/j.deman.2023.100138","DOIUrl":"10.1016/j.deman.2023.100138","url":null,"abstract":"<div><h3>Objective</h3><p>Sodium-glucose co-transporter-2 inhibitors (SGLT2i) are associated with risk of euglycemic ketoacidosis. Guidelines recommend withholding SGLT2i prior to surgery and considering procedure delay in the presence of ketosis. Literature to support this in setting of routine outpatient colonoscopy is limited. Our aim was to clarify the incidence and range of ketosis in all individuals presenting for elective colonoscopies to help setting guidelines and threshold for concern.</p></div><div><h3>Methods</h3><p>This single-centre prospective study recruited patients ≥18 of age who underwent routine outpatient colonoscopies in a medium metropolitan hospital in Brisbane, Australia between August and November 2021. SGLT2i were withheld for 48 h prior and blood glucose and capillary ketone concentrations were recorded within 90 minutes before procedure commencement.</p></div><div><h3>Results</h3><p>315 individuals were consecutively recruited; 179 (56.8%) were female. Sixty-nine (21.9%) had a previous diagnosis of type 2 diabetes mellitus (T2DM) and 17 (5.4%) were taking SGLT2i. The mean age was 57.79 (± 15.21). Significant ketone levels defined as >1.0 mmol/L were noted in 41 individuals (13.0%). Of these, 13 (33%) were diabetic with ketosis ranging from 1.0-4.2mmol/L. The range of significant ketosis in the 28 non-diabetics was 1.0-5.7mmol/L. Only a diagnosis of T2DM and increased fasting times (>45 mins) conferred a greater trend towards ketosis risk. Patients with T2DM as a whole were 2.06 times more likely to develop ketosis with or without SGLT2i. This did not reach statistical significance (<em>p</em> = 0.05).</p></div><div><h3>Conclusion</h3><p>A wide range of periprocedural ketosis commonly occurs in patients undergoing colonoscopies with or without T2DM. This phenomenon is not unique to diabetics or in those on SGLT2i. Hence, previously defined significant ketosis cut-offs are unlikely to be useful in the unique context of colonoscopies. Avoiding procedural delays and early commencement oral intake should be a priority.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"11 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46844474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Prevalence and risk factors associated with prediabetes and undiagnosed diabetes in France: The national CONSTANCES cohort","authors":"Grégory Lailler , Sonsoles Fuentes , Sofiane Kab , Clara Piffaretti , Marie Guion , Sébastien Czernichow , Emmanuel Cosson , Sandrine Fosse-Edorh","doi":"10.1016/j.deman.2022.100121","DOIUrl":"10.1016/j.deman.2022.100121","url":null,"abstract":"<div><h3>Aims</h3><p>To assess the prevalence of prediabetes and diabetes in France between 2013 and 2014 using data from the CONSTANCES cohort, and to identify factors associated with prediabetes and undiagnosed diabetes.</p></div><div><h3>Methods</h3><p>The study population comprised participants recruited in 2013–2014 in CONSTANCES, an ongoing French national prospective cohort following participants aged 18–69 years who are covered by France's general health insurance scheme. Participants completed a questionnaire at baseline and underwent a medical examination which included providing blood samples. Undiagnosed diabetes was defined as a fasting plasma glucose (FPG) ≥ 7 mmol/l and diagnosed diabetes as self-report or identification of reimbursements for anti-diabetics. Prediabetes was defined as a FPG ≥ 6 mmol/l but < 7 mmol/l.</p></div><div><h3>Results</h3><p>25,137 participants were included in the analyses. The overall prevalence of prediabetes was 7.2% [95% confidence interval: 6.7–7.7], 1.6% [1.4–1.9] for undiagnosed diabetes, and 4.0% [3.6–4.4] for diagnosed diabetes. These rates were significantly higher in men, in older persons, in persons with obesity, and in those with lower education levels. In multivariate regression models, excessive corpulence was the variable most strongly associated with undiagnosed diabetes (adjusted Odds Ratio=9.31) and prediabetes (aOR=3.85). Additionally, male sex, older age, family history of diabetes, at-risk alcohol use, and lower education level were all positively associated with undiagnosed diabetes and prediabetes.</p></div><div><h3>Conclusion</h3><p>Diabetes and prediabetes prevention together with screening for undiagnosed diabetes must be strengthened for persons with low socioeconomic status and for those with obesity or overweight.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45989237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennifer Ko , Eva Y. Wong , Huyentran N. Tran , Rebecca J.C. Tran , Diana X. Cao
{"title":"The glycemic, cholesterol, and weight effects of L-carnitine in diabetes: A systematic review and meta-analysis of randomized controlled trials","authors":"Jennifer Ko , Eva Y. Wong , Huyentran N. Tran , Rebecca J.C. Tran , Diana X. Cao","doi":"10.1016/j.deman.2022.100122","DOIUrl":"10.1016/j.deman.2022.100122","url":null,"abstract":"<div><h3>Introduction</h3><p>L-carnitine possibly impacts insulin sensitivity and glucose metabolism. However, its therapeutic role in diabetes is poorly understood.</p></div><div><h3>Methods</h3><p>A systematic review and meta-analysis were conducted using PubMed, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) from inception through June 30, 2021. Included studies evaluated the use of L-carnitine in diabetes on fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), weight, or body mass index (BMI). Weighted mean difference (WMD) and 95% confidence intervals (CI) were calculated using the DerSimonian and Laird random-effects model.</p></div><div><h3>Results</h3><p>Seventeen studies involving 1622 patients were included. Reductions in FBG (WMD = -0.46 mmol/L, 95% CI = -0.68 to -0.23 mmol/L), HbA1c (WMD = -0.5%, 95% CI = -0.8 to -0.1%), TC (WMD = -0.29 mmol/L, 95% CI = -0.42 to -0.16 mmol/L), and LDL-C (WMD = -0.23 mmol/L, 95% CI = -0.39 to -0.07 mmol/L) were significant. Effects on HDL-C, TG, weight, or BMI were insignificant. Doses between 1001 to 2000 mg showed greatest benefit (<em>p</em> < 0.02 for all).</p></div><div><h3>Discussion/Conclusion</h3><p>L-carnitine plays a potential role as adjunctive therapy in diabetes. Additional research is necessary for patients with higher baseline HbA1c and type 1 diabetes.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41558345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roosa Perämäki , Mika Gissler , Meri-Maija Ollila , Janne Hukkanen , Marja Vääräsmäki , Jukka Uotila , Saara Metso , Heidi Hakkarainen , Reeta Rintamäki , Risto Kaaja , Heidi Immonen
{"title":"The risk of developing type 2 diabetes after gestational diabetes: A registry study from Finland","authors":"Roosa Perämäki , Mika Gissler , Meri-Maija Ollila , Janne Hukkanen , Marja Vääräsmäki , Jukka Uotila , Saara Metso , Heidi Hakkarainen , Reeta Rintamäki , Risto Kaaja , Heidi Immonen","doi":"10.1016/j.deman.2022.100124","DOIUrl":"10.1016/j.deman.2022.100124","url":null,"abstract":"<div><h3>Aims</h3><p>Women with a history of gestational diabetes (GDM) have an increased risk of developing type 2 diabetes (T2DM). We studied the risk for T2DM in women with and without GDM in relation to body mass index (BMI) and examined whether insulin treatment for GDM associates with the risk of developing T2DM. In addition, we investigated whether the risk of developing T2DM after GDM had changed in 15 years.</p></div><div><h3>Methods</h3><p>We used data by linking four registers; Medical Birth Register, Hospital Discharge Register and Primary Care Register run by THL Finnish Institute for Health and Welfare, and Medical Reimbursement Statistics run by the Social Insurance Institution of Finland (Kela). Registry data were collected from 2005 to 2020. The follow-up started from woman's delivery in 2006-2020 and ended to the diagnosis of T2DM or December 2020. Cox proportional hazard modelling was used to estimate the effect of GDM exposure to T2DM. To assess whether the risk of developing T2DM after GDM had changed in 15 years, we compared the HR between years 2006-2008 and 2018-2020.</p></div><div><h3>Results</h3><p>In total, 462 401 women were included in the study: 96 353 (21%) women had previous GDM. There were 5370 (1.2%) women who developed T2DM after childbirth during the follow-up. Among women with prior GDM, 3995 (4.1%) developed T2DM, while 1375 (0.4%) women without prior GDM developed T2DM during follow-up. The mean follow-up was 6.86 years (SD 4.21) for women with GDM and 9.07 years (SD 4.35) for women without GDM. The hazard ratio (HR) for developing T2DM after GDM was 18.49 (95% CI 17.39-19.67). The incidence of T2DM in women with a history of GDM began to rise almost steadily from the first year of follow-up. As BMI increased, T2DM incidence increased in both women with and without prior GDM but more in women with prior GDM. Insulin treatment had an independent association with increased risk of T2DM (HR 3.81, 95% CI 3.57-4.07). We did not observe any difference in HR between years 2006-2008 and 2018-2020.</p></div><div><h3>Conclusions</h3><p>The relative risk for T2DM was 11-fold for women with previous GDM compared to women without previous GDM. A higher BMI and insulin treatment increased the risk of future diabetes. All measures to prevent the conversion of GDM to T2DM should be taken especially among women with overweight or obesity.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49310821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cardiovascular and renal outcomes with SGLT2 inhibitors: Real-life observational studies in older patients with type 2 diabetes","authors":"André J. Scheen","doi":"10.1016/j.deman.2023.100135","DOIUrl":"https://doi.org/10.1016/j.deman.2023.100135","url":null,"abstract":"<div><p>Patients with type 2 diabetes mellitus (T2DM) are exposed to a high risk of atherosclerotic cardiovascular disease, heart failure and chronic kidney disease. The incidence of these complications increases markedly with the duration of diabetes and aging. Sodium-glucose cotransporter 2 inhibitors (SGLT2is) showed a remarkable reduction in hospitalization for heart failure and progression of kidney disease in large prospective placebo-controlled trials. Post hoc analyses of these trials demonstrated that cardiorenal protection occurred independently of age. The present comprehensive review analyzes the effects of SGLT2is on cardiovascular and renal outcomes among older patients with T2DM in cohort studies and real-life conditions. SGLT2is were associated with a significant reduction in hospitalization for heart failure (alone or combined with mortality) and in a composite renal outcome, including end-stage renal disease when compared to other oral glucose-lowering drugs, dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists in patients aged ≥ 65 years and even ≥ 75 years. Several observational studies worldwide compared cardiorenal outcomes in people aged ≥ 65 years versus < 65 years and showed a similar relative benefit of SGLT2is in older versus younger patients with T2DM. These favourable results were obtained while the safety profile of SGLT2is in older patients was acceptable and almost comparable with that reported in younger patients. In conclusion, observational studies in real-life conditions confirm previous results reported in placebo-controlled trials and a positive benefit/risk balance in elderly patients with T2DM at risk of heart failure and chronic kidney disease.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49759037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sonsoles Fuentes , Rok Hrzic , Romana Haneef , Sofiane Kab , Emmanuel Cosson , Sandrine Fosse-Edorh
{"title":"Identifying type 1 / type 2 diabetes in medico-administrative database to improve health surveillance, medical research and prevention in diabetes: Algorithm development and application","authors":"Sonsoles Fuentes , Rok Hrzic , Romana Haneef , Sofiane Kab , Emmanuel Cosson , Sandrine Fosse-Edorh","doi":"10.1016/j.deman.2023.100137","DOIUrl":"10.1016/j.deman.2023.100137","url":null,"abstract":"<div><h3>Introduction</h3><p>Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.</p><p>This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France.</p></div><div><h3>Methods</h3><p>The final data set comprised all diabetes cases from the CONSTANCES cohort (<em>n</em> = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age.</p></div><div><h3>Results</h3><p>Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively.</p></div><div><h3>Conclusion</h3><p>Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45046476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Caitlyn Gordon , Barbara Kamel , Lauren McKeon , Danielle Brooks , Rifka Schulman-Rosenbaum
{"title":"Dexamethasone use and insulin requirements in coronovirus-19 (COVID-19) infection stratified by Hemoglobin A1c","authors":"Caitlyn Gordon , Barbara Kamel , Lauren McKeon , Danielle Brooks , Rifka Schulman-Rosenbaum","doi":"10.1016/j.deman.2022.100123","DOIUrl":"10.1016/j.deman.2022.100123","url":null,"abstract":"<div><h3>Aims</h3><p>The study aimed to identify weight-based insulin requirements for dexamethasone-induced hyperglycemia in COVID-19 infection stratified by hemoglobin A1c (HbA1c).</p></div><div><h3>Methods</h3><p>This retrospective study assessed hospitalized patients ≥ 18 years admitted with COVID-19 and receiving ≥ 1 dose of dexamethasone 6 mG. Daily blood glucose (BG) and insulin doses were collected and organized by HbA1c.</p></div><div><h3>Results</h3><p>Among 45 patients with available HbA1c, 100% [HbA1c ≥ 7%] and 72% [HbA1c < 7%] developed hyperglycemia (BG ≥180 mG/dL). Median daily insulin (Interquartile Range) (units/kG/day) was 0.03 (0, 0.32) [HbA1c 6–6.9%], 0.1 (0.06, 0.36) [HbA1c 7–7.9%], 0.66 (0.39, 0.69) [HbA1c 8–8.9%], and 0.72 (0.63, 0.78) [HbA1c ≥ 9%]. On day 10 of dexamethasone, when majority of patients were at goal BG, patients required 0.07 (0.01, 0.31) [HbA1c 6–6.9%], 0.59 (0.11, 0.75) [HbA1c 7–7.9%], 1.15 (0.95, 1.35) [HbA1c 8–8.9%], and 1.14 units/kG/day [HbA1c ≥ 9%]. Of 24 patients completing 10 days of dexamethasone, 25% experienced hypoglycemia (BG < 70 mG/dL) upon discontinuation.</p></div><div><h3>Conclusion</h3><p>Patients with higher HbA1c experienced greater dexamethasone-induced hyperglycemia and required higher insulin doses. Inpatient insulin dosing algorithms should take into consideration baseline HbA1c to avoid delays in achieving normoglycemia.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9795281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elena Putula , Heini Huhtala , Sini Vanhamäki , Tiina Laatikainen , Aapo Tahkola , Päivi Hannula , Saara Metso
{"title":"Clinical characteristics and prognoses of patients with diabetic ketoacidosis in Finland","authors":"Elena Putula , Heini Huhtala , Sini Vanhamäki , Tiina Laatikainen , Aapo Tahkola , Päivi Hannula , Saara Metso","doi":"10.1016/j.deman.2023.100129","DOIUrl":"10.1016/j.deman.2023.100129","url":null,"abstract":"<div><h3>Aims</h3><p>To assess the prognosis and risk factors for diabetic ketoacidosis (DKA) in Tampere University Hospital (Tays) in a retrospective case-control study.</p></div><div><h3>Methods</h3><p>All 282 patients (age ≥15 years) treated for DKA in Tays during the period 2014–2020 were included. A total of 846 controls adjusted for age, gender, diabetes type and municipality, and without any DKA during follow-up were collected from the Finnish National Diabetes Registry. HbA1c, mental and behavioural disorders, and mortality obtained from the Finnish National Diabetes Registry were compared between patients with and without DKA.</p></div><div><h3>Results</h3><p>Patients’ median age was 36 years. Ten percent of the patients with DKA died during the median follow-up time of three years. Mortality rate was sixfold higher in patients with DKA than among the controls (OR 6.28; 95% CI 3.17–12.42). Patients with DKA had higher rates of substance abuse (OR 4.68; 95% CI 3.23–6.78) and depression (OR 2.24; 95% CI 1.58–3.18), and higher median HbA1c levels (84 vs. 61 mmol/mol, <em>p</em> < 0.001). Nineteen percent of the DKA patients (<em>n</em> = 53) had recurrent DKA.</p></div><div><h3>Conclusions</h3><p>DKA is a strong indicator for premature death. Poor glycaemic control, depression and substance abuse are risk factors for DKA.</p></div>","PeriodicalId":72796,"journal":{"name":"Diabetes epidemiology and management","volume":"10 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45497567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}