Diabetes epidemiology and management最新文献

Aims

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.

Methods

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.

Results

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.

Conclusion

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.

Introduction

L-carnitine possibly impacts insulin sensitivity and glucose metabolism. However, its therapeutic role in diabetes is poorly understood.

Methods

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.

Results

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 (p < 0.02 for all).

Discussion/Conclusion

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.

Aims

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.

Methods

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.

Results

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.

Conclusions

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.

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.

Introduction

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.

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.

Methods

The final data set comprised all diabetes cases from the CONSTANCES cohort (n = 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.

Results

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.

Conclusion

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.

Aims

The study aimed to identify weight-based insulin requirements for dexamethasone-induced hyperglycemia in COVID-19 infection stratified by hemoglobin A1c (HbA1c).

Methods

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.

Results

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.

Conclusion

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.

Aims

To assess the prognosis and risk factors for diabetic ketoacidosis (DKA) in Tampere University Hospital (Tays) in a retrospective case-control study.

Methods

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.

Results

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, p < 0.001). Nineteen percent of the DKA patients (n = 53) had recurrent DKA.

Conclusions

DKA is a strong indicator for premature death. Poor glycaemic control, depression and substance abuse are risk factors for DKA.

Background

Gold and Clarke questionnaire are originally developed to assess impaired awareness of hypoglycaemia (IAH) in type 1 diabetes. Present study examined the similarities and differences between the two questionnaires when administered to insulin-treated type 2 diabetes patients.

Methods

A total of 153 insulin-treated type 2 diabetes patients with mean age of 61.0±9.4 years and mean HbA1c of 8.4±1.5% completed questionnaire in diabetes outpatient clinics of tertiary-care hospital. Factor analysis was conducted to examine the psychometric properties of Clarke questionnaire. Spearman's correlation was used to examine convergent validity of Clarke questionnaire with Gold method.

Results

Bifactorial structure for Clarke questionnaire was identified, namely Awareness of Hypoglycaemia (Factor 1) and Experience of Hypoglycaemia (Factor 2). Clarke Factor 1 correlated strongly with Gold scores (r_s=0.77, p<0.001), and yielded 22.9% prevalence of IAH using cut-off score of ≥2.5, which is comparable to Gold method of 19.6%.

Conclusions

Gold single-item questionnaire assesses hypoglycaemia awareness only while Clarke questionnaire assesses both hypoglycaemia awareness and severe hypoglycaemia events. There is a high degree of convergence between Gold and Clarke in hypoglycaemia awareness assessment among insulin-treated type 2 diabetes. Hence, these two questionnaires are similar but not interchangeable due to bifactorial nature of Clarke questionnaire.