Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Type 2 Diabetes, the Epidemic: Trends in Prevalence and Incidence, 2004-2020.

Aims/Hypothesis: Only a few studies reported the incidence of type 2 diabetes (T2D). Understanding recent trends in diabetes is vital for planning future diabetes care. This study updated national trends in the prevalence and incidence of type 2 diabetes (T2D) in the Netherlands from 2004-2020. Methods: The DIAbetes, MANagement and Treatment (DIAMANT) cohort was used. A cross-sectional design with yearly measurements for the study period was used. The prevalence was calculated by dividing the total number of people with T2D by the total number of all residents. The incidence was calculated by dividing new cases of T2D by the resident population at risk during the calendar year of interest. Results: Among men, the prevalence of T2D in the Netherlands increased from 2.3% in 2004 to 6.3% in 2020. Women's prevalence increased from 2.3% in 2004 to 5.3% in 2020. During 2005-2009, the incidence rate for both men and women was relatively stable Between 2010 and 2020, the incidence rate fell about 1.5 per 1000 in both men and women. Conclusion: From 2004-2020, the prevalence of T2D in the Netherlands more than doubled, with a decreasing incidence from 2010 onwards.

Research in context What is already known about this subject? Many studies have reported the increasing prevalence of type 2 diabetes (T2D). However, only a few studies reported the incidence.In a recent systematic review of all these studies, the incidence fell in over a third of the most high-income populations and increased in a minority of populations. Data from the Netherlands were included, but they date back to 1996.Understanding recent trends in diabetes, the prevalence and incidence are vital for planning future diabetes care.What is the key question? To update national trends in the prevalence and incidence of T2D in the Netherlands for 2004-2020.What are the new findings? During 2004-2020, the prevalence of T2D in the Netherlands more than doubled, with a decreasing incidence from 2010 onwards.How might this impact on clinical practice in the foreseeable future? It demonstrates the effectiveness of preventive strategies, public health education and awareness campaigns contributing to this trend.

The yield of routine laboratory examination in osteoporosis evaluation in primary care.

This study evaluated the yield of routine laboratory examination in a large population of older women in primary care. The prevalence of laboratory abnormalities was low and the clinical consequences in follow-up were limited. There was a weak association of laboratory abnormalities with osteoporosis but no association with vertebral fractures and recent fractures. PURPOSE: Most osteoporosis guidelines advice routine laboratory examination. We have investigated the yield of laboratory examinations in facture risk evaluation of elderly women in primary care. METHODS: We assessed the prevalence of laboratory abnormalities and their association with risk factors for fractures, recent fractures, low bone mineral density (BMD), and prevalent vertebral fracture in 8996 women ≥ 65 years of age participating in a primary care fracture risk screening study. In a sample of 2208 of these participants, we also evaluated the medical consequences in the medical records during a follow-up period of ≥ 1 year. RESULTS: Vitamin D deficiency (< 30 nmol/L) was present in 13% and insufficiency (< 50 nmol/L) in 43% of the study sample. The prevalence of other laboratory abnormalities (ESR, calcium, creatinine, FT4) was 4.6% in women with risk factors for fractures, 6.1% in women with low BMD (T-score ≤ - 2.5), 6.0% after a prevalent vertebral fracture, 5.2% after a recent fracture and 2.6% in the absence of important risk factors for fractures. Laboratory abnormalities other than vitamin D were associated with low BMD (OR 1.4, 95%CI 1.1-1.8) but not with prevalent vertebral fractures nor recent fractures. Low BMD was associated with renal failure (OR 2.0, 95%CI 1.3-3.4), vitamin D insufficiency (OR 1.2, 95%CI 1.0-1.3) and deficiency (OR 1.3, 95%CI 1.1-.5). In the follow-up period, 82% of the laboratory abnormalities did not result in a new diagnosis or treatment reported in the medical records. CONCLUSIONS: We identified a low prevalence of laboratory abnormalities in a primary care population of older women and the majority of these findings had no medical consequences.

Increased interleukin-6 is associated with higher risk of heart failure in people with type 2 diabetes.

AIMS: We aimed to determine the association between serum interleukin-6 (IL-6) concentrations and new-onset heart failure (HF) in persons with type 2 diabetes (T2D). METHODS AND RESULTS: We performed a case-control study nested in the Diabetes Care System Cohort, a prospective cohort of persons with T2D in primary care. We included 724 participants, of whom 141 developed HF during 5 years of follow-up and 583 were age- and sex-matched controls. IL-6 was measured at baseline and categorized into four groups: Group 1 was composed of participants with IL-6 below the detection limit of 1.5 pg/mL, and the remainder were divided into tertiles. We performed logistic regression analyses with categorized IL-6 or continuous IL-6 as the determinant and new-onset HF as the outcome adjusted for follow-up time, age, sex, glycated haemoglobin, estimated glomerular filtration rate, albumin/creatinine ratio, and cardiovascular disease at baseline. Effect modification by sex was tested. Participants were 70.7 ± 9.0 years, and 38% were women. In comparison with Group 1, all tertiles were associated with an increased risk of HF with odds ratios of 2.1 [95% confidence interval (CI): 1.2-2.9], 2.8 (95% CI: 2.0-3.7), and 2.1 (95% CI: 1.3-3.0), respectively, for Tertiles 1-3. Continuous IL-6 was associated with the development of HF with an odds ratio of 1.2 (95% CI: 1.0-1.5). No effect modification by sex was observed. CONCLUSIONS: Higher IL-6 levels are associated with the development of HF in persons with T2D. Further research should determine whether IL-6-lowering interventions could prevent the development of HF.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Is the association between social jetlag and BMI mediated by lifestyle? A cross-sectional survey study in the Dutch general population.

OBJECTIVE: Social jetlag is a discordance between the social and biological rhythm and is associated with higher HbA1c, higher BMI, and higher odds of obesity. The pathways that could explain these associations are still debated. This study aims to assess the mediating role of several lifestyle factors in the cross-sectional association between social jetlag and BMI. METHODS: We used cross-sectional data from 1784 adults from urban areas in the Netherlands, collected in 2019. Social jetlag (difference in midpoint of sleep between week and weekend nights) was categorized as low(<1 h), moderate(1-2h), and high(>2 h). BMI(kg/m2) was calculated from self-reported height and weight. The association between social jetlag and BMI was assessed using linear regression, adjusted for sex, age, education, and sleep duration and stratified for the effect modifier stress (high vs. low). Mediation analysis was performed for self-reported smoking, physical activity, alcohol consumption, and adherence to a healthy diet. RESULTS: High social jetlag was associated with higher BMI (0.69 kg/m2,95%CI 0.05;1.33). This association was stronger in people with high stress (0.93 kg/m2,95%CI 0.09;1.76). Social jetlag was also associated with higher odds of smoking, lower physical activity, higher alcohol consumption, and lower healthy diet adherence. In people with high stress, these factors mediated 10-15% of the association between social jetlag and BMI. CONCLUSIONS: Social jetlag is associated with higher BMI and this association is stronger in people with high stress. In people with high stress, healthy diet adherence mediated 12% of this association. Other pathways involved in this association should be further investigated.

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

AIMS/HYPOTHESIS: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. METHODS: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic. RESULTS: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. CONCLUSIONS/INTERPRETATION: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. DATA AVAILABILITY: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .

Epidemiology of heart failure in diabetes: a disease in disguise.

Left ventricular diastolic dysfunction (LVDD) without symptoms, and heart failure (HF) with preserved ejection fraction (HFpEF) represent the most common phenotypes of HF in individuals with type 2 diabetes mellitus, and are more common than HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and left ventricular systolic dysfunction (LVSD) in these individuals. However, diagnostic criteria for HF have changed over the years, resulting in heterogeneity in the prevalence/incidence rates reported in different studies. We aimed to give an overview of the diagnosis and epidemiology of HF in type 2 diabetes, using both a narrative and systematic review approach; we focus narratively on diagnosing (using the 2021 European Society of Cardiology [ESC] guidelines) and screening for HF in type 2 diabetes. We performed an updated (2016-October 2022) systematic review and meta-analysis of studies reporting the prevalence and incidence of HF subtypes in adults ≥18 years with type 2 diabetes, using echocardiographic data. Embase and MEDLINE databases were searched and data were assessed using random-effects meta-analyses, with findings presented as forest plots. From the 5015 studies found, 209 were screened using the full-text article. In total, 57 studies were included, together with 29 studies that were identified in a prior meta-analysis; these studies reported on the prevalence of LVSD (n=25 studies, 24,460 individuals), LVDD (n=65 studies, 25,729 individuals), HFrEF (n=4 studies, 4090 individuals), HFmrEF (n=2 studies, 2442 individuals) and/or HFpEF (n=8 studies, 5292 individuals), and on HF incidence (n=7 studies, 17,935 individuals). Using Hoy et al's risk-of-bias tool, we found that the studies included generally had a high risk of bias. They showed a prevalence of 43% (95% CI 37%, 50%) for LVDD, 17% (95% CI 7%, 35%) for HFpEF, 6% (95% CI 3%, 10%) for LVSD, 7% (95% CI 3%, 15%) for HFrEF, and 12% (95% CI 7%, 22%) for HFmrEF. For LVDD, grade I was found to be most prevalent. Additionally, we reported a higher incidence rate of HFpEF (7% [95% CI 4%, 11%]) than HFrEF 4% [95% CI 3%, 7%]). The evidence is limited by the heterogeneity of the diagnostic criteria over the years. The systematic section of this review provides new insights on the prevalence/incidence of HF in type 2 diabetes, unveiling a large pre-clinical target group with LVDD/HFpEF in which disease progression could be halted by early recognition and treatment.Registration PROSPERO ID CRD42022368035.

Association between lower extremity arterial calcification and coronary arterial calcification in a population at increased risk of cardiovascular disease.

INTRODUCTION: There is conflicting evidence whether lower extremity arterial calcification coincides with coronary arterial calcification (CAC). The aims of this study were to investigate the associations between (1) femoral and crural calcification with CAC, and (2) femoral and crural calcification pattern with CAC. RESEARCH DESIGN AND METHODS: This cross-sectional study included 405 individuals (74% men, 62.6±10.9 years) from the ARTEMIS cohort study at high risk of cardiovascular disease (CVD) who underwent a CT scan of the femoral, crural and coronary arteries. High CVD risk was defined as history/presence of cerebrovascular disease, coronary artery disease, abdominal aortic aneurysm, renal artery stenosis, peripheral artery disease or CVD risk factors: diabetes mellitus type 2, hypertension, hyperlipidemia. Calcification score within each arterial bed was expressed in Agatston units. Dominant calcification patterns (intimal, medial, absent/indistinguishable) were determined via a CT-guided histologically validated scoring algorithm. Multivariable-adjusted multinomial logistic regression analyses were used. Replication was performed in an independent population of individuals with diabetes mellitus type 2 (Early-HFpEF cohort study). RESULTS: Every 100-point increase in femoral and crural calcification score was associated with 1.23 (95% CI=1.09 to 1.37, p<0.001) and 1.28 (95% CI=1.11 to 1.47, p=0.001) times higher odds of having CAC within tertile 3 (high) versus tertile 1 (low), respectively. The association appeared stronger for crural versus femoral arteries. Moreover, the presence of femoral intimal (OR=10.81, 95% CI=4.23 to 27.62, p<0.001), femoral medial (OR=10.37, 95% CI=3.92 to 27.38, p<0.001) and crural intimal (OR=6.70, 95% CI=2.73 to 16.43, p<0.001) calcification patterns were associated with higher odds of having CAC within tertile 3 versus tertile 1, independently from concomitant calcification score. This association appeared stronger for intimal versus medial calcification patterns. The replication analysis yielded similar results. CONCLUSIONS: Higher femoral and crural calcification scores were associated with higher CAC. Moreover, the presence of femoral intimal, femoral medial and crural intimal calcification patterns was associated with increased CAC. It appears that arterial calcification is a systemic process which occurs simultaneously in various arterial beds.

A meta-analysis of previous falls and subsequent fracture risk in cohort studies.

The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm. INTRODUCTION: Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD). METHODS: The resource comprised 906,359 women and men (66.9% female) from 46 prospective cohorts. Previous falls were uniformly defined as any fall occurring during the previous year in 43 cohorts; the remaining three cohorts had a different question construct. The association between previous falls and fracture risk (any clinical fracture, osteoporotic fracture, major osteoporotic fracture, and hip fracture) was examined using an extension of the Poisson regression model in each cohort and each sex, followed by random-effects meta-analyses of the weighted beta coefficients. RESULTS: Falls in the past year were reported in 21.4% of individuals. During a follow-up of 9,102,207 person-years, 87,352 fractures occurred of which 19,509 were hip fractures. A previous fall was associated with a significantly increased risk of any clinical fracture both in women (hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.33-1.51) and men (HR 1.53, 95% CI 1.41-1.67). The HRs were of similar magnitude for osteoporotic, major osteoporotic fracture, and hip fracture. Sex significantly modified the association between previous fall and fracture risk, with predictive values being higher in men than in women (e.g., for major osteoporotic fracture, HR 1.53 (95% CI 1.27-1.84) in men vs. HR 1.32 (95% CI 1.20-1.45) in women, P for interaction = 0.013). The HRs associated with previous falls decreased with age in women and with duration of follow-up in men and women for most fracture outcomes. There was no evidence of an interaction between falls and BMD for fracture risk. Subsequent risk for a major osteoporotic fracture increased with each additional previous fall in women and men. CONCLUSIONS: A previous self-reported fall confers an increased risk of fracture that is largely independent of BMD. Previous falls should be considered as an additional risk factor in future iterations of FRAX to improve fracture risk prediction.

Cultural competency of GP trainees and GP trainers: a cross-sectional survey study.

OBJECTIVE: To assess the cultural competence (CC) of GP trainees and GP trainers.Design and setting: A cross-sectional survey study was conducted at the GP Training Institute of Amsterdam UMC. SUBJECTS: We included 92 GP trainees and 186 GP trainers. MAIN OUTCOME MEASURES: We measured the three domains of cultural competency: 1) knowledge, 2) culturally competent attitudes and 3) culturally competent skills. Regression models were used to identify factors associated with levels of CC. Participants rated their self-perceived CC at the beginning and end of the survey, and the correlation between self-perceived and measured CC was assessed. RESULTS: Approximately 94% of the GP trainees and 81% of the GP trainers scored low on knowledge; 45% and 42%, respectively, scored low on culturally competent attitudes. The level of culturally competent skills was moderate (54.3%) or low (48.4%) for most GP trainees and GP trainers. The year of residency and the GP training institute were significantly associated with one or more (sub-)domains of CC in GP trainees. Having >10% migrant patients and experience as a GP trainer were positively associated with one or more (sub-) domains of cultural competence in GP trainers. The correlation between measured and self-perceived CC was positive overall but very weak (Spearman correlation coefficient ranging from -0.1-0.3). CONCLUSION: The level of cultural competence was low in both groups, especially in the knowledge scores. Cultural competence increased with experience and exposure to an ethnically diverse patient population. Our study highlights the need for cultural competence training in the GP training curricula.

General practitioner (GP) trainees find cross-cultural consultations stressful due to a self-perceived lack of cultural competence (CC). The level of CC in general practice is as yet unknown.On average, the level of CC was low for the majority of GP trainees and GP trainers, especially for the scores on knowledge.CC increased with experience and exposure to an ethnically diverse patient population.GP trainees and trainers perceived a lack of covered education on various topics related to the care of migrants.Our study highlights the need for cultural competence training in the GP training curricula.

Apolipoprotein-CIII O-Glycosylation, a Link between GALNT2 and Plasma Lipids.

Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII0a), and glycosylated apo-CIII with zero, one, or two sialic acids (apo-CIII0c, apo-CIII1 and apo-CIII2). Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease.

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits.

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Cost-Effectiveness of SGLT2 Inhibitors in a Real-World Population: A MICADO Model-Based Analysis Using Routine Data from a GP Registry.

OBJECTIVE: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been shown to reduce the risk of cardiovascular complications, which largely drive diabetes' health and economic burdens. Trial results indicated that SGLT2i are cost effective. However, these findings may not be generalizable to the real-world target population. This study aims to evaluate the cost effectiveness of SGLT2i in a routine care type 2 diabetes population that meets Dutch reimbursement criteria using the MICADO model. METHODS: Individuals from the Hoorn Diabetes Care System cohort (N = 15,392) were filtered to satisfy trial inclusion criteria (including EMPA-REG, CANVAS, and DECLARE-TIMI58) or satisfy the current Dutch reimbursement criteria for SGLT2i. We validated a health economic model (MICADO) by comparing simulated and observed outcomes regarding the relative risks of events in the intervention and comparator arm from three trials, and used the validated model to evaluate the long-term health outcomes using the filtered cohorts' baseline characteristics and treatment effects from trials and a review of observational studies. The incremental cost-effectiveness ratio (ICER) of SGLT2i, compared with care-as-usual, was assessed from a third-party payer perspective, measured in euros (2021 price level), using a discount rate of 4% for costs and 1.5% for effects. RESULTS: From Dutch individuals with diabetes in routine care, 15.8% qualify for the current Dutch reimbursement criteria for SGLT2i. Their characteristics were significantly different (lower HbA1c, higher age, and generally more preexisting complications) than trial populations. After validating the MICADO model, we found that lifetime ICERs of SGLT2i, when compared with usual care, were favorable (< €20,000/QALY) for all filtered cohorts, resulting in an ICER of €5440/QALY using trial-based treatment effect estimates in reimbursed population. Several pragmatic scenarios were tested, the ICERs remained favorable. CONCLUSIONS: Although the Dutch reimbursement indications led to a target group that deviates from trial populations, SGLT2i are likely to be cost effective when compared with usual care.

Prolongation of the QTc interval is associated with an increased risk of cardiovascular diseases: The Hoorn study.

BACKGROUND/OBJECTIVE: Prolonged heart rate-corrected QT interval (QTc) on the electrocardiogram (ECG) is maybe associated with the occurrence of cardiovascular diseases (CVD), but the evidence is inconsistent. Therefore, we investigated whether baseline prolongation of the QTc interval is associated with CVD morbidity and mortality and its subtypes and whether glucose tolerance modifies this association in a population-based cohort study with a mean follow-up of 10.8 years. METHODS: We analyzed a glucose tolerance stratified sample (N = 487) from the longitudinal population-based Hoorn Study cohort (age 64 ± 7 years, 48% female). Cox regression was used to investigate the association between sex-specific baseline QTc quartiles and CVD morbidity and mortality. The risk was also estimated per 10 ms increase in QTc. All analyses were adjusted for age, sex, smoking status, systolic blood pressure, prevalent CVD, glucose tolerance status, hypertension and total cholesterol. In addition, stratified analyses were conducted for glucose tolerance status. RESULTS: During a mean follow-up of 10.8 years, 351 CVD events were observed. The adjusted hazard ratios (95% CI) for each 10 ms increase in QTc interval were 1.06 (95% CI: 1.02-1.10) for CVD, 1.06 (95% CI: 0.97-1.15) for acute myocardial infarction, 1.07 (95% CI: 1.01-1.13) for stroke, 1.12 (95% CI: 1.06-1.19) for heart failure, 1.04 (95% CI: 0.96-1.12) for peripheral arterial disease and 1.01 (95% CI:0.95-1.08) for coronary heart disease. Glucose tolerance status did not modify the association (P > 0.2). CONCLUSION/INTERPRETATION: Prolongation of the QTc interval is associated with morbidity and mortality due to general CVD. Glucose tolerance status did not modify these associations.

Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups.

OBJECTIVE: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c. RESEARCH DESIGN AND METHODS: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. RESULTS: Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained. CONCLUSIONS: Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes.

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Patient-reported outcomes for people with diabetes: what and how to measure? A narrative review.

Patient-reported outcomes (PROs) are valuable for shared decision making and research. Patient-reported outcome measures (PROMs) are questionnaires used to measure PROs, such as health-related quality of life (HRQL). Although core outcome sets for trials and clinical practice have been developed separately, they, as well as other initiatives, recommend different PROs and PROMs. In research and clinical practice, different PROMs are used (some generic, some disease-specific), which measure many different things. This is a threat to the validity of research and clinical findings in the field of diabetes. In this narrative review, we aim to provide recommendations for the selection of relevant PROs and psychometrically sound PROMs for people with diabetes for use in clinical practice and research. Based on a general conceptual framework of PROs, we suggest that relevant PROs to measure in people with diabetes are: disease-specific symptoms (e.g. worries about hypoglycaemia and diabetes distress), general symptoms (e.g. fatigue and depression), functional status, general health perceptions and overall quality of life. Generic PROMs such as the 36-Item Short Form Health Survey (SF-36), WHO Disability Assessment Schedule (WHODAS 2.0), or Patient-Reported Outcomes Measurement Information System (PROMIS) measures could be considered to measure commonly relevant PROs, supplemented with disease-specific PROMs where needed. However, none of the existing diabetes-specific PROM scales has been sufficiently validated, although the Diabetes Symptom Self-Care Inventory (DSSCI) for measuring diabetes-specific symptoms and the Diabetes Distress Scale (DDS) and Problem Areas in Diabetes (PAID) for measuring distress showed sufficient content validity. Standardisation and use of relevant PROs and psychometrically sound PROMs can help inform people with diabetes about the expected course of disease and treatment, for shared decision making, to monitor outcomes and to improve healthcare. We recommend further validation studies of diabetes-specific PROMs that have sufficient content validity for measuring disease-specific symptoms and consider generic item banks developed based on item response theory for measuring commonly relevant PROs.

Validation of the ARIC prediction model for sudden cardiac death in the European population: The ESCAPE-NET project.

BACKGROUND: Sudden cardiac death is responsible for 10% to 20% of all deaths in Europe. The current study investigates how well the risk of sudden cardiac death can be predicted. To this end, we validated a previously developed prediction model for sudden cardiac death from the Atherosclerosis Risk in Communities study (USA). METHODS: Data from participants of the Copenhagen City Heart Study (CCHS) (n=9988) was used to externally validate the previously developed prediction model for sudden cardiac death. The model's performance was assessed through discrimination (C-statistic) and calibration by the Hosmer-Lemeshow goodness-of-fit (HL) statistics suited for censored data and visual inspection of calibration plots. Additional validation was performed using data from the Hoorn Study (N=2045), employing the same methods. RESULTS: During ten years of follow-up of CCHS participants (mean age: 58.7 years, 56.2% women), 425 experienced SCD (4.2%). The prediction model showed good discrimination for sudden cardiac death risk (C-statistic: 0.81, 95% CI: 0.79-0.83). Calibration was robust (HL statistic: P=0.8). Visual inspection of the calibration plot showed that the calibration could be improved. Sensitivity was 89.8%, and specificity was 60.6%. The positive and negative predictive values were 10.1% and 99.2%. Model performance was similar in the Hoorn Study (C-statistic: 0.81, 95% CI: 0.77-0.85 and the HL statistic: 1.00). CONCLUSION: Our study showed that the previously developed prediction model in North American adults performs equally well in identifying those at risk for sudden cardiac death in a general North-West European population. However, the positive predictive value is low.