Questionnaire-based prediction for type 2 diabetes

 

Background
Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.

Methods
In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.

Findings
Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.

Interpretation
Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.

You may download the full article HERE.

 

 

 

Metformin during pregnancy

 

 

During the last decades, gestational diabetes mellitus (GDM) prevalence has been on the rise. While insulin remains the gold standard treatment for GDM, metformin use during pregnancy is controversial. This review aimed to comprehensively assess the available data on the efficacy and safety of metformin during pregnancy, both for the mother and the offspring. Metformin has been validated for maternal efficacy and safety, achieving comparable glycemic control with insulin. Additionally, it reduces maternal weight gain and possibly the occurrence of hypertensive disorders. During the early neonatal period, metformin administration does not increase the risk of congenital anomalies or other major adverse effects, including lower APGAR score at 5 min, neonatal intensive care unit admissions, and respiratory distress syndrome. Several studies have demonstrated a reduction in neonatal hypoglycemia. Metformin has been associated with an increase in preterm births and lower birth weight, although this effect is controversial and depends on the indication for which it was administered. Evidence indicates possible altered fetal programming and predisposition to childhood obesity and metabolic syndrome during adulthood after use of metformin in pregnancy. With critical questions still requiring a final verdict, ongoing research on the field must be conducted.

 

You may find the full (Open Access) article HERE.

 

 

 

 

Objectives: Cardiovascular disease (CVD) is a precarious complication of type 1 diabetes (T1D). Alongside glycaemic control, lipid and blood pressure (BP) management are essential for the prevention of CVD. However, age-specific differences in lipid and BP between individuals with T1D and the general population are relatively unknown.

Design: Cross-sectional study.

Setting: Six diabetes outpatient clinics and individuals from the Lifelines cohort, a multigenerational cohort from the Northern Netherlands.

Participants: 2178 adults with T1D and 146 22 individuals without diabetes from the general population.

Primary and secondary outcome measures: Total cholesterol, low-density lipoprotein cholesterol (LDL-cholesterol), systolic BP (SBP) and diastolic BP (DBP), stratified by age group, glycated haemoglobin category, medication use and sex.

Results: In total, 2178 individuals with T1D and 146 822 without diabetes were included in this study. Total cholesterol and LDL-cholesterol were lower and SBP and DBP were higher in individuals with T1D in comparison to the background population. When stratified by age and medication use, total cholesterol and LDL-cholesterol were lower and SBP and DBP were higher in the T1D population. Men with T1D achieved lower LDL-cholesterol levels both with and without medication in older age groups in comparison to women. Women with T1D had up to 8 mm Hg higher SBP compared with the background population, this difference was not present in men.

Conclusions: Lipid and BP measurements are not comparable between individuals with T1D and the general population and are particularly unfavourable for BP in the T1D group. There are potential sex differences in the management of LDL-cholesterol and BP.

 

Read the full article at: https://bmjopen.bmj.com/content/13/10/e073690.long

 

 

 

Subtyperen van type 2 diabetes

Al jaren wordt getracht om de heterogeniteit van type 2 diabetes beter in kaart te brengen. Recentelijk zijn verschillende manieren van subtypering van type 2 diabetes gepubliceerd. De meeste aandacht gaat naar een onderverdeling in 5 subtypen, vlak na het stellen van de diagnose. Twee van deze subtypen worden gekenmerkt door insulinedeficiëntie; circa de helft van deze patiënten moet relatief vroeg beginnen met insuline. Eén subtype wordt gekenmerkt door ernstige insulineresistentie en een verhoogd risico op nierschade en niet-alcoholische leverziekte.

Subtypering kan leiden tot beter inzicht in de pathofysiologische achtergronden bij een specifiek individu, tot een beter onderbouwde aanpassing van de leefstijl, een betere inschatting van het risico op late complicaties en persoonsgerichte medicamenteuze behandeling. Er is ook meer aandacht voor specifieke factoren die de individuele respons op medicatie beïnvloeden, zoals geslacht, leeftijd en relatief lichaamsgewicht.

Het is te hopen dat deze ontwikkelingen in de nabije toekomst leiden tot een beter op het individu gerichte begeleiding en behandeling van type 2 diabetes.

 

Het volledige artikel vindt u HIER. (mogelijk moet u eerst inloggen)

 

Semaglutide in real world study

Introduction: SURE Netherlands (NCT03929679) evaluated the use of once-weekly (OW) semaglutide, a glucagon-like peptide 1 receptor agonist (GLP-1RA), in routine clinical care for individuals with type 2 diabetes (T2D).

Methods: Adults (age ≥ 18 years) with T2D were enrolled into the single-arm study. The primary endpoint was change from baseline to end of study (EOS; approx. 30 weeks) in glycated haemoglobin (HbA1c). Secondary endpoints were change from baseline to EOS in body weight (BW) and waist circumference (WC). Proportions of participants achieving predefined HbA1c targets and weight-loss responses at EOS, safety, health-related quality of life (HRQoL) and treatment satisfaction were assessed.

Results: In total, 211 participants (mean age 60.5 years; diabetes duration 13.3 years) initiated semaglutide; most were receiving metformin (82.9%) and/or basal insulin (59.2%) at baseline, and 6.2% switched from another GLP-1RA. Mean baseline HbA1c, BW and WC were 8.6%, 105.2 kg and 118.8 cm. In the 186 (88.2%) participants receiving semaglutide at EOS, mean reduction in HbA1c with semaglutide was – 1.2%-points (95% [confidence interval] CI – 1.3; – 1.0; p < 0.0001), with 124 (70.5%), 95 (54.0%) and 65 (36.9%) participants achieving HbA1c targets of < 8.0%, < 7.5% and < 7.0%, respectively. Mean reduction in BW was – 7.8 kg [95% CI – 8.7; – 6.8; p < 0.0001], corresponding to relative reduction of – 7.5% [95% CI – 8.4; – 6.6; p < 0.0001]. Improvements in WC (- 8.8 cm [95% CI – 10.4; – 7.2; p < 0.0001]), HRQoL and treatment satisfaction were observed, including across most Short-Form 36 Health Survey domains. One serious adverse drug reaction (cholecystitis) was reported. Eight participants (all receiving concomitant insulin) experienced severe or documented hypoglycaemia.

Conclusion: Individuals with T2D treated with OW semaglutide experienced significant and clinically relevant improvements in glycaemic control and BW from baseline. These results from a diverse real-world population in the Netherlands support the use of OW semaglutide in treating adults with T2D in routine clinical practice.

 

You can read the full paper for free at https://link.springer.com/article/10.1007/s12325-022-02385-x

 

 

Polygenic risk scores

The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.

 

 

You can read the full paper at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941118/pdf/41598_2023_Article_27637.pdf