Personalized Intervention Based on Early Detection of Atherosclerosis: JACC State-of-the-Art Review.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required-moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health.

The association of cardiometabolic, diet and lifestyle parameters with plasma glucagon-like peptide-1: An IMI DIRECT study.

CONTEXT: The role of glucagon-like peptide-1(GLP-1) in Type 2 diabetes (T2D) and obesity is not fully understood. OBJECTIVE: We investigate the association of cardiometabolic, diet and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. METHOD: We analysed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1(n=2127) individuals at risk of diabetes; cohort 2 (n=789) individuals with new-onset of T2D. RESULTS: Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin resistant phenotype and observe a strong independent relationship with male sex, increased adiposity and liver fat particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycaemia, higher adiposity, liver fat, male sex and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit and vegetables inpeople with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. CONCLUSION: These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D.

Precision medicine for cardiometabolic disease: a framework for clinical translation.

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic 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.

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study.

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired ß cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). ß-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.

Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity.

CONTEXT: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity. OBJECTIVE: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies. DESIGN: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models. RESULTS: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10-9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10-9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity. CONCLUSION: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.

OBJECTIVE: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), ß-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS: Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R 2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS: Deteriorating insulin sensitivity and ß-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, ß-cell function, and insulin clearance may be relevant to prevent progression.

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study.

BACKGROUND: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. METHODS: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. RESULTS: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. CONCLUSIONS: Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study.

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.

Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain.

BACKGROUND: Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches. METHODS AND RESULTS: Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m2) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10-8) and 5 suggestively (p < 1 × 10-6) significant loci, several of which have been previously linked to obesity-related phenotypes. CONCLUSIONS: We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium.

AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.

Empagliflozin compared with glimepiride in metformin-treated patients with type 2 diabetes: 208-week data from a masked randomized controlled trial.

AIM: To report results at week 208, including a 104-week masked extension, of the EMPA-REG H2H-SU trial in patients with type 2 diabetes with inadequate glycaemic control on metformin, in which empagliflozin 25 mg given for 104 weeks provided a sustained reduction in glycated haemoglobin (HbA1c) with a small but statistically significant benefit vs glimepiride, sustained reductions in weight and blood pressure, and low risk of hypoglycaemia. RESEARCH DESIGN AND METHODS: Patients with type 2 diabetes and HbA1c 53-86 mmol/mol (7% to 10%) were randomized to empagliflozin 25 mg or glimepiride 1 to 4 mg for 104 weeks as add-on to metformin. Patients who completed the randomized treatment period could participate in a 104-week extension in which they continued the double-blind treatment allocated at randomization. RESULTS: Of 765 and 780 patients treated with empagliflozin and glimepiride, 576 and 549 patients, respectively, entered the extension period of the study. At week 208, the adjusted mean difference in change from baseline in HbA1c with empagliflozin vs glimepiride was -1.96 mmol/mol, 95% CI -3.57, -0.35 (-0.18%, 95% CI -0.33, -0.03); P = 0.0172. Rescue therapy was given to 23% of patients on empagliflozin and 34% on glimepiride (odds ratio 0.56 [95% CI 0.45, 0.71]; P < 0.0001). Confirmed hypoglycaemic adverse events (plasma glucose ≤3.9 mmol/L and/or requiring assistance) occurred in 3% of patients on empagliflozin and 28% on glimepiride (odds ratio 0.08 [95% CI 0.05, 0.13]; P < 0.0001). CONCLUSIONS: In patients with type 2 diabetes, empagliflozin 25 mg as add-on to metformin for 208 weeks reduced HbA1c with a significantly lower risk of hypoglycaemia and a significantly smaller proportion of patients receiving rescue therapy compared with glimepiride.

Correction to: Comparison of Adipose Distribution Indices with Gold Standard Body Composition Assessments in the EMPA-REG H2H SU Trial: A Body Composition Sub-Study.

In the original publication, disclosure statement for the author Ian J. Neeland was published incorrectly. The correct statement should read as "Ian J. Neeland received financial assistance for publishing/article processing fees from Boehringer Ingelheim".

Stratification of type 2 diabetes based on routine clinical markers.

AIMS: We hypothesized that patients with dysregulated type 2 diabetes may be stratified based on routine clinical markers. METHODS: In this retrospective cohort study, diabetes related clinical measures including age at onset, diabetes duration, HbA1c, BMI, HOMA2-ß, HOMA2-IR and GAD65 autoantibodies, were used for sub-grouping patients by K-means clustering and for adjusting. Probability of diabetes complications (95% confidence interval), were calculated using logistic regression. RESULTS: Based on baseline data from patients with type 2 diabetes (n = 2290), the cluster analysis suggested up to five sub-groups. These were primarily characterized by autoimmune ß-cell failure (3%), insulin resistance with short disease duration (21%), non-autoimmune ß-cell failure (22%), insulin resistance with long disease duration (32%), and presence of metabolic syndrome (22%), respectively. Retinopathy was more common in the sub-group characterized by non-autoimmune ß-cell failure (52% (47.7-56.8)) compared to other sub-groups (22% (20.1-24.1)), adj. p < 0.001. The prevalence of cardiovascular disease, nephropathy and neuropathy also differed between sub-groups, but significance was lost after adjustment. CONCLUSIONS: Patients with type 2 diabetes cluster into clinically relevant sub-groups based on routine clinical markers. The prevalence of diabetes complications seems to be sub-group specific. Our data suggests the need for a tailored strategy for the treatment of type 2 diabetes.

Impact of a multifactorial treatment programme on clinical outcomes and cardiovascular risk estimates: a retrospective cohort study from a specialised diabetes centre in Denmark.

OBJECTIVES: To investigate the impact of a multifactorial treatment programme in a real-life setting on clinical outcomes and estimated cardiovascular disease (CVD) risk. DESIGN: A retrospective observational cohort study, using data from the electronic medical records and national registers. SETTING: Tertiary diabetes centre in Denmark. PARTICIPANTS: Patients with type 2 diabetes (n=4299) referred to a programme with focus on treatment of hyperglycaemia, hypertension and dyslipidaemia between 1 January 2001 and 1 April 2016. OUTCOMES: Primary outcomes were changes in haemoglobin A1c (HbA1c), blood pressure (BP) and low-density lipoprotein (LDL) cholesterol as well as proportion reaching treatment targets. Our secondary outcome was to investigate changes in antidiabetic, antihypertensive and lipid-lowering treatment, together with the impact on estimated CVD risk. Linear mixed model for repeated measurements were used for continuous variables and logistic regression for dichotomous variables. RESULTS: The patients achieved a mean±SD decrease in HbA1c, systolic and diastolic BP and LDL cholesterol of 1.0%±0.04% (10.6±0.4 mmol/mol), 6.3±0.4 mm Hg, 2.7±0.2 mm Hg and 0.32±0.02 mmol/L, respectively (p<0.0001). The proportion of patients who met the treatment goal for HbA1c (<7% (<53 mmol/mol)) increased from 31% to 58% (p<0.0001); for BP (<130/80 mm Hg) from 24% to 34% (p<0.0001), and for LDL cholesterol (<2.5 mmol/L (patients without previous CVD) or <1.8 mmol/L (patients with previous CVD)) from 52% to 65%. Those reaching all three guideline treatment targets increased from 4% to 15% (p<0.0001), and when relaxing the BP target to <140/85 from 8% to 24%. The estimated CVD risk was relatively reduced by 15.2% using the Swedish National Diabetes Register risk engine and 30.9% using the UK Prospective Diabetes Study risk engine. CONCLUSIONS: Our data support that short-term multifactorial treatment of patients with glycaemic dysregulation in a specialist outpatient setting is both achievable and effective, and associated with a clinically meaningful improvement in CVD risk.

Treatment Modality-Dependent Risk of Diabetic Ketoacidosis in Patients with Type 1 Diabetes: Danish Adult Diabetes Database Study.

BACKGROUND AND AIMS: The aim of this study was to evaluate the incidence rates of diabetic ketoacidosis (DKA) according to treatment modality in patients with type 1 diabetes (T1D) in Denmark, either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). MATERIALS AND METHODS: A total of 20,902 T1D registered in the Danish Adult Diabetes Database were followed for an average of 5.4 years. Poisson regression analyses with risk time as offset were used to compare differences in rates of DKA between CSII and MDI. Model was adjusted for age, sex, diabetes duration, previous DKA events, and hemoglobin A1c (HbA1c). A modifying effect of number of CSII patients on the DKA rates was tested. RESULTS: During 113,731 person-years, 3100 DKA events were registered (53 among CSII). CSII patients were younger (42.3 vs. 47.9 years), a larger proportion was female (59% vs. 43%), had a shorter diabetes duration (19 vs. 21 years), and a lower HbA1c (61.9 vs. 66.6 mmol/mol). There was no significant difference in the incidence rate of DKA between CSII and MDI (rate ratio: 1.30, 95% confidence interval: 0.97-1.76). However, in clinics with at least 250 CSII patients, rates of DKA events were lower among CSII users, while the opposite was true for the smaller clinics (P = 0.016). CONCLUSIONS: Delivery of CSII in large diabetes clinics with sufficient support and patient education may ensure that CSII treatment does not lead to an increased risk of DKA.

Adult-onset type 1 diabetes: Predictors of glycaemic control.

AIMS: Knowledge about adult-onset (AO) type 1 diabetes remains insufficient. We sought to characterize the initial 5 years of AO type 1 diabetes and hypothesized that initial factors predictive of subsequent glycaemic control might exist. MATERIALS AND METHODS: A retrospective cohort study based on electronic medical records of 280 subjects with newly diagnosed AO type 1 diabetes (>18 years of age, excluding secondary and latent autoimmune diabetes) with available data for the initial 5-year treatment. RESULTS: Characteristics at diagnosis: 61% men, mean age 37 ± 12 years, BMI 23 ± 3.3 (kg/m2), systolic/diastolic blood pressure: 123 ± 15/76 ± 9 mm Hg and LDL cholesterol: 2.9 ± 0.9 mmol/L. HbA1c decreased from 106 mmol/mol (11.8%) at diagnosis to 52 mmol/mol (6.9%) at 6 months and then increased gradually to 67 mmol/mol (8.3%) after 5 years. Strict glycaemic control (<53 mmol/mol (7%)) was achieved by 66% within 6-9 months and 30% after 5 years. Comparing patients with and without strict glycaemic control after 5 years revealed no differences in HbA1c, C-peptide or any other diabetes-related parameter at the time of diagnosis. However, reaching strict control within 6-9 months after diagnosis was strongly associated with remaining in strict control after 5 years (OR: 9.2 (CI-95% 4.0-20.9; P < 0.0001)). Conversely, patients who did not achieve early strict control were very unlikely to be well controlled after 5 years. CONCLUSIONS: Long-term tight glycaemic control in subjects with AO type 1 diabetes is both achievable and to some extent predictable. Whether alternative strategies shortly after diagnosis would benefit patients with insufficient glycaemic control should be investigated.