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.
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.
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.
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.
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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.
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Mijn laatste column in het HYPOnieuws van November 2023.
Hypothyroidism is associated with a decreased health-related quality of life (HRQoL). Wehypothesized that individuals with hypothyroidism (defined as use of thyroid hormone (TH)) and especially those having an impaired HRQoL are characterized by a high prevalence of comorbid disorders, and that the impact of hypothyroidism and comorbidity on HRQoL is synergistic. Presence of comorbidity was based on data obtained using structured questionnaires, physical examination, biochemical measurements and verified medication use. Single morbidities were clustered into 14 different disease domains. HRQoL was measured using the RAND-36. Logistic regression analyses were used to determine the effect of TH-use on the odds of having an affected disease domain and a lower score than an age-and sex-specific reference value for HRQoL. TH was used by 4537/147201 participants of the population-based Lifelines cohort with a mean(±SD) age of 51.0±12.8 years (88% females). 85% of the TH-users had ≥1 affected disease domain, in contrast to 71% of non-users. TH-use was associated with a higher odds of 13/14 affected disease domains, independent of age and sex. In a multivariable model, TH-use was associated with a decreased HRQoL across 6/8 dimensions. No significant interactions between TH-use and affected disease domains were observed. TH-users with an impaired HRQoL had significantly more comorbidity than those not having an impaired HRQoL. In this large, population-based study, we demonstrated that TH-users had more comorbidity than individuals not using TH. The co-existence of other chronic medical conditions in subjects with TH-use led to further lowering of HRQoL in an additive manner.
Find the current accepted version of this article at: https://ec.bioscientifica.com/view/journals/ec/aop/ec-23-0266/ec-23-0266.xml
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