Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)-mass spectroscopy (MS) and flow injection analysis (FIA)-MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

AIMS/HYPOTHESIS: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. METHODS: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster. RESULTS: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. CONCLUSIONS/INTERPRETATION: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.

Evaluating glucose-lowering treatment in older people with diabetes: Lessons from the IMPERIUM trial.

Understanding the benefits and risks of treatments to be used by older individuals (≥65 years old) is critical for informed therapeutic decisions. Glucose-lowering therapy for older patients with diabetes should be tailored to suit their clinical condition, comorbidities and impaired functional status, including varying degrees of frailty. However, despite the rapidly growing population of older adults with diabetes, there are few dedicated clinical trials evaluating glucose-lowering treatment in older people. Conducting clinical trials in the older population poses multiple significant challenges. Despite the general agreement that individualizing treatment goals and avoiding hypoglycaemia is paramount for the therapy of older people with diabetes, there are conflicting perspectives on specific glycaemic targets that should be adopted and on use of specific drugs and treatment strategies. Assessment of functional status, frailty and comorbidities is not routinely performed in diabetes trials, contributing to insufficient characterization of older study participants. Moreover, significant operational barriers and problems make successful enrolment and completion of such studies difficult. In this review paper, we summarize the current guidelines and literature on conducting such trials, as well as the learnings from our own clinical trial (IMPERIUM) that assessed different glucose-lowering strategies in older people with type 2 diabetes. We discuss the importance of strategies to improve study design, enrolment and attrition. Apart from summarizing some practical advice to facilitate the successful conduct of studies, we highlight key gaps and needs that warrant further research.

Glycaemic outcomes of an Individualized treatMent aPproach for oldER vulnerable patIents: A randomized, controlled stUdy in type 2 diabetes Mellitus (IMPERIUM).

AIMS: To compare the glycaemic outcomes of 2 glucose-lowering treatment strategies in vulnerable (moderately ill and/or frail) patients aged ≥65 years with type 2 diabetes whose individual HbA1c targets were not met with diet/exercise and/or oral anti-hyperglycaemic medications (OAMs). METHODS: The primary endpoint of this study was a composite of achieving/maintaining individualized HbA1c targets without "clinically significant" hypoglycaemia (severe hypoglycaemia or repeated hypoglycaemia causing interruption of patients' activities or blood glucose <54 mg/dL). Strategy-A comprised glucose-dependent therapies (n = 99) with a non-sulphonylurea OAM and a glucagon-like peptide-1 receptor agonist as the first injectable. Strategy-B comprised non-glucose-dependent therapies (n = 93) with sulphonylurea as the preferred OAM and insulin glargine as the first injectable. RESULTS: There was no significant difference between Strategy-A and Strategy-B in percentages of patients achieving the primary endpoint (64.5% vs 54.9%; P = .190). Mean incidences (A vs B) of total (10.2% vs 53.8%), documented symptomatic (5.1% vs 36.6%), and asymptomatic (8.2% vs 32.3%) hypoglycaemia were lower for Strategy-A (P < .001 each). Proportions of patients achieving/maintaining HbA1c target (A, 63.3% vs B, 55.9%) were similar. CONCLUSION: Similar proportions of older, vulnerable aged ≥65 years patients with type 2 diabetes achieved/maintained glycaemic treatment goals without clinically significant hypoglycaemia with Strategies A or B. However, Strategy-A resulted in lower risk of total, documented symptomatic, and asymptomatic hypoglycaemia. These results identify an approach of potential clinical benefit in this age group and will inform future clinical research in older patients with type 2 diabetes.

Individual serum saturated fatty acids and markers of chronic subclinical inflammation: the Insulin Resistance Atherosclerosis Study.

Recent evidence has documented distinct effects of individual saturated FAs (SFAs) on cardiometabolic outcomes, with potential protective effects from odd- and very long-chain SFAs (VLSFAs). Cross-sectional and prospective associations of individual serum SFAs (12:0, 14:0, 15:0, 16:0, 18:0, 20:0, 22:0, and total SFA) with proinflammatory biomarkers and adiponectin were investigated in 555 adults from the IRAS. Principal component analysis (PCA) of proinflammatory markers yielded three clusters: principal component (PC) 1: fibrinogen, white cell count, C-reactive protein; PC 2: plasminogen activator inhibitor-1 (PAI-1), TNF-α, IL-18; PC 3: IL-6 and IL-8. Cross-sectional analyses on proinflammatory PCs and adiponectin, and prospective analyses on 5 year PAI-1 and fibrinogen concentrations were conducted with multiple regression. Total SFA and 16:0 were positively associated with PC 1 and PC 2, and negatively associated with adiponectin. The 14:0 was positively associated with PC 1 and negatively associated with adiponectin. In contrast, 15:0, 20:0, and 22:0 were negatively associated with PC 2, and 20:0 and 22:0 were positively associated with adiponectin. The 18:0 was negatively associated with PC 3. Prospectively, 15:0, 18:0, 20:0, and 22:0 were negatively associated with 5 year PAI-1 concentrations. The results demonstrate that individual SFAs have distinct roles in subclinical inflammation, highlighting the unique metabolic impacts of individual SFAs.

Lipoprotein heterogeneity may help to detect individuals with insulin resistance.

AIMS/HYPOTHESIS: The triacylglycerol (TG)-to-HDL-cholesterol ratio has been shown to detect insulin resistance. However, the added predictive value of a more comprehensive assessment of lipoprotein composition is unknown. METHODS: We analysed cross-sectional data from 882 non-diabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS). Lipoproteins were measured by nuclear magnetic resonance (NMR) spectroscopy. Determined by the frequently sampled intravenous glucose tolerance test, insulin resistance was defined as the lowest sex-specific quartile of insulin sensitivity. RESULTS: The AUC of the receiver operating characteristic curve of HDL-cholesterol and TG levels for detecting insulin resistance was similar to that of the TG-to-HDL-cholesterol ratio (0.676 vs 0.673; p = 0.685), but smaller than the AUC of NMR-detected lipoproteins (0.676 vs 0.745; p < 0.001). NMR lipoproteins added discriminative value to HDL-cholesterol and TG levels (net reclassification improvement of 40.0%; p < 0.001; and integrated discrimination improvement of 9.5%; p < 0.001), with net benefit within predicted probabilities of between 10% and 50% by Vickers' decision-curve analysis. We also demonstrated additive value to demographic variables, BMI and levels of fasting glucose, TG, and HDL-cholesterol (net reclassification improvement of 14.0%; p < 0.001; and integrated discrimination improvement of 4.5%; p < 0.001). CONCLUSIONS/INTERPRETATION: NMR lipoproteins, which can be measured in the fasting state, add information to the TG and HDL-cholesterol ratio across a broad range on insulin resistance. Depending on the other risk factors of insulin resistance that are incorporated, NMR lipoproteins permit the correct reclassification of an additional 14-40% of individuals.

Long-term changes in cardiovascular risk markers during administration of exenatide twice daily or glimepiride: results from the European exenatide study.

OBJECTIVE: The risk of cardiovascular morbidity and mortality is significantly increased in patients with diabetes; thus, it is important to determine whether glucose-lowering therapy affects this risk over time. Changes in cardiovascular risk markers were examined in patients with type 2 diabetes treated with exenatide twice daily (a glucagon-like peptide-1 receptor agonist) or glimepiride (a sulfonylurea) added to metformin in the EURopean EXenAtide (EUREXA) study. RESEARCH DESIGN AND METHODS: Patients with type 2 diabetes failing metformin were randomized to add-on exenatide twice daily (n = 515) or glimepiride (n = 514) until treatment failure defined by hemoglobin A1C. Anthropomorphic measures, blood pressure (BP), heart rate, lipids, and high-sensitivity C-reactive protein (hsCRP) over time were evaluated. RESULTS: Over 36 months, twice-daily exenatide was associated with improved body weight (-3.9 kg), waist circumference (-3.6 cm), systolic/diastolic BP (-2.5/-2.6 mmHg), high-density lipoprotein (HDL)-cholesterol (0.05 mmol/L), triglycerides (-0.2 mmol/L), and hsCRP (-1.7 mg/L). Heart rate did not increase (-0.3 beats/minute), and low-density lipoprotein-cholesterol (0.2 mmol/L) and total cholesterol (0.1 mmol/L) increased slightly. Between-group differences were significantly in favor of exenatide for body weight (P < 0.0001), waist circumference (P < 0.001), systolic BP (P < 0.001), diastolic BP (P = 0.023), HDL-cholesterol (P = 0.001), and hsCRP (P = 0.004). Fewer patients randomized to exenatide twice daily versus glimepiride required the addition of at least one antihypertensive (20.4 vs 26.4%; P = 0.026) or lipid-lowering medication (8.4 vs 12.8%; P = 0.025). CONCLUSIONS: Add-on exenatide twice daily was associated with significant, sustained improvement in several cardiovascular risk markers in patients with type 2 diabetes versus glimepiride. CLINICAL TRIAL REGISTRATION: NCT00359762, http://www.ClinicalTrials.gov.

Exenatide twice daily versus glimepiride for prevention of glycaemic deterioration in patients with type 2 diabetes with metformin failure (EUREXA): an open-label, randomised controlled trial.

BACKGROUND: Glycaemic control deteriorates progressively over time in patients with type 2 diabetes. Options for treatment escalation remain controversial after failure of first-line treatment with metformin. We compared add-on exenatide with glimepiride for durability of glycaemic control in patients with type 2 diabetes inadequately controlled by metformin alone. METHODS: We did an open-label, randomised controlled trial at 128 centres in 14 countries between Sept 5, 2006, and March 29, 2011. Patients aged 18-85 years with type 2 diabetes inadequately treated by metformin were randomly assigned via a computer-generated randomisation sequence to receive exenatide twice daily or glimepiride once daily as add-on to metformin. Randomisation was stratified by predetermined categories of glycated haemoglobin (HbA(1C)) concentration. The primary outcome was time to inadequate glycaemic control and need for alternative treatment, defined as an HbA(1c) concentration of more than 9% after the first 3 months of treatment, or more than 7% at two consecutive visits after the first 6 months. Analysis was by intention to treat. This trial is registered with EudraCT, number 2005-005448-21, and ClinicalTrials.gov, number NCT00359762. FINDINGS: We randomly assigned 515 patients to the exenatide group and 514 to the glimepiride group, of whom 490 versus 487 were the intention-to-treat population. 203 (41%) patients had treatment failure in the exenatide group compared with 262 (54%) in the glimepiride group (risk difference 12·4 [95% CI 6·2-18·6], hazard ratio 0·748 [0·623-0·899]; p=0·002). 218 (44%) of 490 patients in the exenatide group, and 150 (31%) of 487 in the glimepiride group achieved an HbA(1c) concentration of less than 7% (p<0·0001), and 140 (29%) versus 87 (18%) achieved concentrations of 6·5% and less (p=0·0001). We noted a significantly greater decrease in bodyweight in patients given exenatide than in those given glimepiride (p<0·0001). Five patients in each treatment group died from causes unrelated to treatment. Significantly fewer patients in the exenatide group than in the glimepiride group reported documented symptomatic (p<0·0001), nocturnal (p=0·007), and non-nocturnal (p<0·0001) hypoglycaemia. Discontinuation because of adverse events (mainly gastrointestinal) was significantly higher (p=0·0005) in the exenatide group than in the glimepiride group in the first 6 months of treatment, but not thereafter. INTERPRETATION: These findings provide evidence for the benefits of exenatide versus glimepiride for control of glycaemic deterioration in patients with type-2 diabetes inadequately controlled by metformin alone. FUNDING: Eli Lilly and Company; Amylin Pharmaceuticals.

[Other specific types of diabetes and exocrine pancreatic insufficiency (update 2023)]. / Andere spezifische Diabetesformen und exokrine Pankreasinsuffizienz (Update 2023).

The heterogenous category "specific types of diabetes due to other causes" encompasses disturbances in glucose metabolism due to other endocrine disorders such as acromegaly or hypercortisolism, drug-induced diabetes (e.g. antipsychotic medications, glucocorticoids, immunosuppressive agents, highly active antiretroviral therapy (HAART), checkpoint inhibitors), genetic forms of diabetes (e.g. Maturity Onset Diabetes of the Young (MODY), neonatal diabetes, Down­, Klinefelter- and Turner Syndrome), pancreatogenic diabetes (e.g. postoperatively, pancreatitis, pancreatic cancer, haemochromatosis, cystic fibrosis), and some rare autoimmune or infectious forms of diabetes. Diagnosis of specific diabetes types might influence therapeutic considerations. Exocrine pancreatic insufficiency is not only found in patients with pancreatogenic diabetes but is also frequently seen in type 1 and long-standing type 2 diabetes.

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.

Declining Frequency of Acute Complications Associated with Tubeless Insulin Pump Use: Data from 2,911 Patients in the German/Austrian Diabetes Patienten Verlaufsdokumentation Registry.

Objective: To characterize patients with diabetes treated with a tubeless insulin pump (Omnipod® Insulin Management System; Insulet Corp., Acton, MA), and to evaluate the frequency of acute complications with long-term use of the system. Methods: This retrospective analysis of the German/Austrian Diabetes Patienten Verlaufsdokumentation (DPV) registry included data from 3657 patients with diabetes (n = 3582 type 1, n = 25 type 2, n = 50 latent autoimmune diabetes in adults/other) treated with a tubeless insulin pump. Hemoglobin A1c (HbA1c) levels and frequency of diabetic ketoacidosis (DKA) and severe hypoglycemia (SH) were compared between 1 year pre- and 1 year (n = 2911) or up to 3 years (n = 1311) post-tubeless insulin pump initiation and compared with a contemporary cohort on multiple daily injections (MDI) with 3-year data (n = 1874). Results: Patients using tubeless insulin pump therapy had a median age of 13.7 years [interquartile range 10.8, 17.3], diabetes duration 3.7 years [1.7, 8.0], and HbA1c 7.5% [6.9, 8.2]. In patients with 3 years of follow-up data (n = 1311), the percentage with ≥1 episode of DKA, SH (Level 3, requiring assistance), and SH (coma) event with prior treatment was 6.3%, 5.5%, and 1.7%, respectively. After 3 years of tubeless insulin pump therapy, the frequency of DKA, SH (Level 3), and SH (coma) decreased to 2.2%, 4.1%, and 0.5%, respectively. Both DKA and SH remained significantly lower compared with MDI after adjustment in multiple regression analysis. High treatment retention rates (>90%) were observed. Conclusion: Real-world registry data document that tubeless insulin pump therapy is associated with good glycemic control and a low frequency of DKA and SH in an age group prone to acute complications.

Distinct Molecular Signatures of Clinical Clusters in People With Type 2 Diabetes: An IMI-RHAPSODY Study.

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Reduced Glucose Variability With Glucose-Dependent Versus Glucose-Independent Therapies Despite Similar Glucose Control and Hypoglycemia Rates in a Randomized, Controlled Study of Older Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Few studies have evaluated continuous glucose monitoring (CGM) in older patients with type 2 diabetes mellitus (T2DM) not using injectable therapy. CGM is useful for investigating hypoglycemia and glycemic variability, which is associated with complications in T2DM. METHODS: A CGM substudy of Individualized treatMent aPproach for oldER patIents in a randomized trial in type 2 diabetes Mellitus (IMPERIUM)) was conducted. Patients were vulnerable (moderately ill and/or frail) older (≥65 years) individuals with suboptimally controlled T2DM. Strategy A comprised glucose-dependent therapies (n = 26) with a nonsulfonylurea oral antihyperglycemic medication (OAM) and a glucagon-like peptide-1 receptor agonist as the first injectable. Strategy B comprised non-glucose-dependent therapies (n = 21) with sulfonylurea as the preferred OAM and insulin glargine as the first injectable. Primary endpoints were duration and percentage of time spent with blood glucose (BG) ≤70 mg/dL over 24 hours at week 24. RESULTS: Duration and percentage of time spent with hypoglycemia at ≤70 mg/dL were similar for Strategy A and Strategy B; glycemic control improved similarly in both arms (LSM change in HbA1c at week 24; A = -1.2%, B = -1.4%). Duration and percentage time spent with euglycemia and hyperglycemia were also similar in both arms. However, Strategy A was associated with lower within-day (21.1 ± 1.2 vs 25.1 ± 1.4, P = .046) and between-day (5.4 ± 1.0 vs 9.1 ± 1.3, P = .038) BG variability (coefficient of variance [LSM ± SE]) at week 24. CONCLUSIONS: This CGM substudy in older patients with T2DM showed lower within- and between-day BG variability with glucose-dependent therapies but similar HbA1c reductions and hypoglycemia duration with glucose-independent strategies.

Progression of plasminogen activator inhibitor-1 and fibrinogen levels in relation to incident type 2 diabetes.

BACKGROUND: Several studies have shown that fibrinolytic and coagulation abnormalities as well as low-grade inflammation predict cardiovascular disease and type 2 diabetes. We studied in the Insulin Resistance Atherosclerosis Study the relation of incident diabetes to dynamic changes of plasminogen activator inhibitor-1 (PAI-1) and fibrinogen. METHODS AND RESULTS: After a follow-up of 5.2 years, diabetes developed in 140 (16.6%) of 843 individuals (57% women; mean age [range], 54.7 [40, 69] years) (converters versus nonconverters). Baseline and follow-up levels of PAI-1 and fibrinogen (demographically and smoking adjusted) were higher in converters versus nonconverters (mean [SE]): at baseline, 23.7 ng/mL (1.5) versus 14.5 (0.4) and 286.2 mg/dL (4.8) versus 273.6 (2.1); at follow-up, 45.3 ng/mL (3.2) versus 25.9 (0.8) and 292.0 mg/dL (5.6) versus 275.2 (2.5); all P<0.05. In a demographically and smoking-adjusted logistic regression model, the change in PAI-1 was related to incident diabetes (OR for a 1-SD change [CI], 1.75 [1.37, 2.22]; P<0.001) after adjusting for baseline PAI-1 levels. After further adjusting for insulin sensitivity (SI) or waist, change in PAI-1 remained significantly related to incident diabetes (OR, 1.66 [1.28, 2.15], and 1.64 [1.28, 2.10]; P<0.001). In contrast, change in fibrinogen was not significantly related to incident diabetes. CONCLUSIONS: Progression of PAI-1 levels over time, in addition to high baseline PAI-1 levels, is associated with incident diabetes. PAI-1 levels (but not fibrinogen) further increase with the rising glucose levels and the development of diabetes. These findings extend the current knowledge on the relation of fibrinolysis and coagulation abnormalities to the development of type 2 diabetes.

The natural course of beta-cell function in nondiabetic and diabetic individuals: the Insulin Resistance Atherosclerosis Study.

Data from the UKPDS (U.K. Prospective Diabetes Study) indicate a continuous decline in beta-cell function in patients with type 2 diabetes. We studied longitudinal changes in beta-cell function (follow-up of 5.2 years) in subjects with normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and type 2 diabetes, using acute insulin response (AIR) and insulin sensitivity index (S(i)) from a frequently sampled intravenous glucose tolerance test among African-American, Hispanic, and non-Hispanic white subjects aged 40-69 years. At baseline, decreasing levels of both S(i) and AIR (either unadjusted or adjusted for S(i)) mirrored deteriorating glucose tolerance status at baseline and at follow-up. A different pattern was found with respect to longitudinal changes; S(i) declined in each glucose tolerance category, ranging from -0.81 x10(-4) min(-1) x muU(-1) x ml(-1) in NGT at baseline and NGT at follow-up (NGT/NGT) to -1.06 x10(-4) in NGT/diabetes, whereas the directional change in AIR principally determined the glucose tolerance status at follow-up. In NGT/NGT S(i) decreased by 35% and AIR increased by 34%. Results were similar in each of the three ethnic groups. These data shed light on the natural course of beta-cell function; over 5.2 years, mean insulin sensitivity declined in each glucose tolerance category. The change in AIR, however, principally determined glucose tolerance status at follow-up; NGT was maintained by a compensatory increase in insulin secretion. Failure to increase insulin secretion led to IGT, and a decrease in insulin secretion led to overt diabetes. This data may have important implications for the prevention and treatment of type 2 diabetes.

Association between mild and severe hypoglycemia in people with type 2 diabetes initiating insulin.

AIMS: Primary objective: Identify risk factors associated with severe hypoglycemia (SH) and investigate the association between mild hypoglycemia and SH in people with type 2 diabetes starting insulin. Secondary objectives: Investigate the association of demographics and clinical factors with SH incidence. METHODS: Integrated trial database data were obtained for 3 randomized controlled trials that included insulin-naïve people with type 2 diabetes initiating basal (insulin glargine) versus biphasic (insulin lispro mixture) insulin. Standard definitions were used for SH; mild hypoglycemia was defined as all non-SH. Cox regression identified risk factors associated with SH and the correlation between SH and mild hypoglycemia. RESULTS: Data were pooled (N=2931). During 24-48weeks' treatment, 2127 (72.6%) participants experienced ≥1 mild hypoglycemic event but no SH (mean mild hypoglycemia rate=2.33/month). 56 participants (1.9%) experienced ≥1 SH event plus mild hypoglycemia (mean mild hypoglycemia rate=3.95/month); 748 participants (25.5%) had no hypoglycemia. Among factors tested, only mild hypoglycemia rate/month was associated with SH. SH risk was higher (HR=4.24; 95%CI=2.57-6.99;p<0.0001) for participants experiencing multiple mild hypoglycemic events/month compared with those experiencing ≤1 mild hypoglycemic event/month. CONCLUSIONS: Mild hypoglycemia may predict the first SH event, which is important because SH is a strong and consistent risk factor for morbidity/mortality.

Novel Protein Glycan-Derived Markers of Systemic Inflammation and C-Reactive Protein in Relation to Glycemia, Insulin Resistance, and Insulin Secretion.

OBJECTIVE: N-acetylglucosamine/galactosamine (GlycA) and sialic acid (GlycB) moieties of glycosylated serum proteins are nonspecific measures of inflammation, but conclusive data on their relationship with insulin resistance or insulin secretion are missing. Therefore, we aimed to examine the relation of GlycA, GlycB, and C-reactive protein (CRP) to direct measures of insulin sensitivity (insulin sensitivity index [SI]) and insulin secretion (acute insulin response [AIR]). RESEARCH DESIGN AND METHODS: This study used cross-sectional analyses and included 1,225 participants with and without type 2 diabetes in the Insulin Resistance Atherosclerosis Study (IRAS). SI and AIR were measured using the frequently sampled intravenous glucose tolerance test, and GlycA and GlycB were measured using nuclear magnetic resonance spectroscopy. RESULTS: GlycA and GlycB had a strong correlation with CRP (r = 0.60 [P < 0.001] and r = 0.46 [P < 0.001], respectively). In a linear regression model with both GlycA and CRP as independent variables, GlycA (ß × 1 SD, -0.04 ± 0.02; P < 0.01) and CRP (-0.06 ± 0.02; P < 0.001) were independently associated with SI even after adjusting for demographics, smoking, physical activity, plasma glucose, and BMI. However, neither CRP nor GlycA had an independent relationship with AIR. CONCLUSIONS: GlycA may complement CRP in evaluating the relationship between inflammation, glucose tolerance, and insulin resistance.

Nuclear magnetic resonance lipoprotein abnormalities in prediabetic subjects in the Insulin Resistance Atherosclerosis Study.

BACKGROUND: Subjects with type 2 diabetes have smaller LDL and HDL particles in addition to higher levels of triglycerides and lower HDL cholesterol. Elevated insulin resistance, blood pressure, and dyslipidemia (including small dense LDL) predicted incident diabetes. In the Insulin Resistance Atherosclerosis Study (IRAS) we studied nuclear magnetic resonance (NMR) lipoprotein particle measures in prediabetic individuals, considering potentially modifying covariates, including insulin resistance, as directly measured using a frequently sampled intravenous glucose tolerance test. METHODS AND RESULTS: Of 830 subjects who were nondiabetic at baseline, 130 (15.7%) developed diabetes after a mean follow-up of 5.2 years. Various lipoprotein abnormalities were found in prediabetic subjects compared with subjects who stayed nondiabetic at follow-up. In logistic regression analyses (demographically adjusted), VLDL particles, large VLDL, LDL particles, small LDL, large HDL, small HDL, VLDL size, LDL size, and HDL size were related to incident diabetes. The relation of VLDL size and small HDL to incident diabetes was independent of waist (odds ratio [OR] [95% CI], 1.43 [1.18 to 1.73] and 1.23 [1.01 to 1.51] for VLDL size and small HDL, respectively) and independent of conventionally (chemically) measured triglycerides and HDL cholesterol (OR [95% CI], 1.45 [1.18 to 1.78] and 1.30 [1.06 to 1.60], respectively). Insulin sensitivity attenuated the relation to incident diabetes of VLDL size (OR [95% CI], 1.25 [1.01 to 1.53]) but not of small HDL particles (OR [95% CI], 1.25 [1.02 to 1.54]). CONCLUSIONS: We have shown a range of lipoprotein abnormalities in prediabetic individuals, including compositional changes in HDL and VLDL. These findings extend previous work indicating a proatherogenic state in healthy, nondiabetic subjects who subsequently develop diabetes.

Prediction of type 2 diabetes mellitus with alternative definitions of the metabolic syndrome: the Insulin Resistance Atherosclerosis Study.

BACKGROUND: In addition to predicting cardiovascular disease (CVD) morbidity and mortality, the metabolic syndrome is strongly associated with the development of type 2 diabetes mellitus (DM), itself an important risk factor for CVD. Our objective was to compare the ability of various metabolic syndrome criteria (including those recently proposed by the International Diabetes Federation), markers of insulin resistance (IR) and inflammation, and impaired glucose tolerance (IGT) in the prediction of DM and to determine whether various proposed modifications to the National Cholesterol Education program (NCEP) metabolic syndrome definition improved predictive ability. METHODS AND RESULTS: We examined 822 subjects in the Insulin Resistance Atherosclerosis Study aged 40 to 69 years who were nondiabetic at baseline. After 5.2 years, 148 individuals had developed DM. IGT, metabolic syndrome definitions, and IR markers all significantly predicted DM, with odds ratios ranging from 3.4 to 5.4 (all P<0.001), although there were no significant differences in the areas under the receiver operator characteristic (AROC) curves between the definitions. Modifying or requiring obesity, glucose, or IR components in NCEP-defined metabolic syndrome did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). Similarly, although IR and inflammation variables were significantly associated with incident DM when included in multivariate models with NCEP-defined metabolic syndrome (all P<0.01), expanding the definition by adding these variables as components did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). CONCLUSIONS: The International Diabetes Federation and NCEP metabolic syndrome definitions predicted DM at least as well as the World Health Organization definition, despite not requiring the use of oral glucose tolerance testing or measures of IR or microalbuminuria. Modifications or additions to the NCEP metabolic syndrome definition had limited impact on the prediction of DM.