Type 2 Diabetes Prevention Focused on Normalization of Glycemia: A Two-Year Pilot Study.
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ABSTRACT:
SUBMITTER: McKenzie AL
PROVIDER: S-EPMC7996820 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveWe characterized fluctuations between states of glycemia in progressors to type 1 diabetes and studied whether those fluctuations are related to the early C-peptide response to oral glucose.Research design and methodsOral glucose tolerance tests (OGTTs) from differing states of glycemia were compared within individuals for glucose and C-peptide. Dysglycemic OGTTs (DYSOGTTs) were compared with normal OGTTs (NLOGTT), while transient diabetic OGTTs (TDOGTTs) were compared with subsequent nondiabetic OGTTs and with OGTTs performed at diagnosis.ResultsOf 135 progressors with four or more OGTTs, 30 (22%) went from NLOGTTs to DYSOGTTs at least twice. Area under the curve (AUC) glucose values from the second NLOGTT were higher (P < 0.001) than values from the first NLOGTT. Among 98 progressors whose DYSOGTTs and NLOGTTs were synchronized for the time before diagnosis, despite higher glucose levels (P < 0.01 at all time points) in the DYSOGTTs, 30- to 0-min C-peptide difference values changed little. Likewise, 30- to 0-min C-peptide difference values did not differ between TDOGTTs and subsequent (within 3 months) nondiabetic OGTTs in 55 progressors. In contrast, as glucose levels increased overall from the first to last OGTTs before diagnosis (P < 0.001 at every time point, n = 207), 30- to 0-min C-peptide difference values decreased (P < 0.001).ConclusionsGlucose levels fluctuate widely as they gradually increase overall with progression to type 1 diabetes. As glucose levels increase, the early C-peptide response declines. In contrast, glucose fluctuations are not related to the early C-peptide response. This suggests that changes in insulin sensitivity underlie the glucose fluctuations.
| S-EPMC3279562 | biostudies-literature
Project description:Type 2 diabetes (T2D) heterogeneity is a major determinant of complications risk and treatment response. Using cluster analysis, we aimed to stratify glycemia within metabolic multidimensionality and extract pathophysiological insights out of metabolic profiling. We performed a cluster analysis to stratify 974 subjects (PREVADIAB2 cohort) with normoglycemia, prediabetes, or non-treated diabetes. The algorithm was informed by age, anthropometry, and metabolic milieu (glucose, insulin, C-peptide, and free fatty acid (FFA) levels during the oral glucose tolerance test OGTT). For cluster profiling, we additionally used indexes of metabolism mechanisms (e.g., tissue-specific insulin resistance, insulin clearance, and insulin secretion), non-alcoholic fatty liver disease (NAFLD), and glomerular filtration rate (GFR). We found prominent heterogeneity within two optimal clusters, mainly representing normometabolism (Cluster-I) or insulin resistance and NAFLD (Cluster-II), at higher granularity. This was illustrated by sub-clusters showing similar NAFLD prevalence but differentiated by glycemia, FFA, and GFR (Cluster-II). Sub-clusters with similar glycemia and FFA showed dissimilar insulin clearance and secretion (Cluster-I). This work reveals that T2D heterogeneity can be captured by a thorough metabolic milieu and mechanisms profiling-metabolic footprint. It is expected that deeper phenotyping and increased pathophysiology knowledge will allow to identify subject's multidimensional profile, predict their progression, and treat them towards precision medicine.
| S-EPMC7463676 | biostudies-literature
Project description:BACKGROUND:In type 2 diabetes (T2DM), the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation for estimated glomerular filtration rate (eGFR) systematically underestimates the measured adjusted glomerular filtration rate (aGFR) when aGFR is high. We studied the extent to which glycemic variables associate with kidney function, and developed equations including these variables that estimate aGFR in people with T2DM. METHODS:Diabetic Pima people had aGFR measured from iothalamate clearance divided by body surface area. eGFRs <?60?ml/min/1.73m2 were excluded. Multivariate linear regression identified variables correlated with kidney function. We constructed equations for approximating aGFR. Correlation analysis and 10-fold cross-validation were used to compare the CKD-EPI equation and the new approximating equations to the measured aGFR. Ability to detect hyperfiltration, defined as aGFR >?120?ml/min/1.73m2, was compared by analysis of receiver-operating (ROC) curves. RESULTS:aGFR was measured 2798 times in 269 individuals. HbA1c, fasting plasma glucose (FPG), age, and serum creatinine (SCR) were significantly associated with aGFR. The best equations for approximating aGFR used HbA1c and FPG in addition to age and SCR. They approximate aGFR in this cohort of obese people with T2DM more precisely than the CKD-EPI equation. Analysis of ROC curves show that these equations detect hyperfiltration better than does the CKD-EPI equation. CONCLUSIONS:HbA1c, FPG, age, and SCR yielded the best equations for estimating aGFR in these subjects. The new equations identify hyperfiltration better than the CKD-EPI equation in this cohort and may inform clinical decisions regarding hyperfiltration in individuals with T2DM.
| S-EPMC6820937 | biostudies-literature
Project description:Aims/hypothesisThe aim of this work was to examine the progression to type 1 and type 2 diabetes after gestational diabetes mellitus (GDM) in a 23 year follow-up study.MethodsWe carried out a cohort study of 391 women with GDM diagnosed by an OGTT or the use of insulin treatment during pregnancy, and 391 age- and parity-matched control participants, who delivered in 1984-1994 at the Oulu University Hospital, Finland. Diagnostic cut-off levels for glucose were as follows: fasting, ≥4.8 mmol/l; 1 h, ≥10.0 mmol/l; and 2 h, ≥8.7 mmol/l. Two follow-up questionnaires were sent (in 1995-1996 and 2012-2013) to assess the progression to type 1 and type 2 diabetes. Mean follow-up time was 23.1 (range 18.7-28.8) years.ResultsType 1 diabetes developed (5.7%) during the first 7 years after GDM pregnancy and was predictable at a 2 h OGTT value of 11.9 mmol/l during pregnancy (receiver operating characteristic analysis: AUC 0.91, sensitivity 76.5%, specificity 96.0%). Type 2 diabetes increased linearly to 50.4% by the end of the follow-up period and was moderately predictable with fasting glucose (AUC 0.69, sensitivity 63.5%, specificity 68.2%) at a level of 5.1 mmol/l (identical to the fasting glucose cut-off recommended by the International Association of Diabetes and Pregnancy Study Groups [IADPSG) and WHO]).Conclusions/interpretationAll women with GDM should be intensively monitored for a decade, after which the risk for type 1 diabetes is minimal. However, the incidence of type 2 diabetes remains linear, and therefore individualised lifelong follow-up is recommended.
| S-EPMC7476985 | biostudies-literature
Project description:ObjectiveIn type 1 diabetes, small studies have found that resistance exercise (weight lifting) reduces HbA(1c). In the current study, we examined the acute impacts of resistance exercise on glycemia during exercise and in the subsequent 24 h compared with aerobic exercise and no exercise.Research design and methodsTwelve physically active individuals with type 1 diabetes (HbA(1c) 7.1 ± 1.0%) performed 45 min of resistance exercise (three sets of seven exercises at eight repetitions maximum), 45 min of aerobic exercise (running at 60% of Vo(2max)), or no exercise on separate days. Plasma glucose was measured during and for 60 min after exercise. Interstitial glucose was measured by continuous glucose monitoring 24 h before, during, and 24 h after exercise.ResultsTreatment-by-time interactions (P < 0.001) were found for changes in plasma glucose during and after exercise. Plasma glucose decreased from 8.4 ± 2.7 to 6.8 ± 2.3 mmol/L (P = 0.008) during resistance exercise and from 9.2 ± 3.4 to 5.8 ± 2.0 mmol/L (P = 0.001) during aerobic exercise. No significant changes were seen during the no-exercise control session. During recovery, glucose levels did not change significantly after resistance exercise but increased by 2.2 ± 0.6 mmol/L (P = 0.023) after aerobic exercise. Mean interstitial glucose from 4.5 to 6.0 h postexercise was significantly lower after resistance exercise versus aerobic exercise.ConclusionsResistance exercise causes less initial decline in blood glucose during the activity but is associated with more prolonged reductions in postexercise glycemia than aerobic exercise. This might account for HbA(1c) reductions found in studies of resistance exercise but not aerobic exercise in type 1 diabetes.
| S-EPMC3579339 | biostudies-literature
Project description:ObjectiveMetabolic activation of the innate immune system governed by interleukin (IL)-1β contributes to β-cell failure in type 2 diabetes. Gevokizumab is a novel, human-engineered monoclonal anti-IL-1β antibody. We evaluated the safety and biological activity of gevokizumab in patients with type 2 diabetes.Research design and methodsIn a placebo-controlled, dose-escalation study, a total of 98 patients were randomly assigned to placebo (17 subjects) or gevokizumab (81 subjects) at increasing doses and dosing schedules. The primary objective of the study was to evaluate the safety profile of gevokizumab in type 2 diabetes. The secondary objectives were to assess pharmacokinetics for different dose levels, routes of administration, and regimens and to assess biological activity.ResultsThe study drug was well tolerated with no serious adverse events. There was one hypoglycemic event whereupon concomitant insulin treatment had to be reduced. Clearance of gevokizumab was consistent with that for a human IgG(2), with a half-life of 22 days. In the combined intermediate-dose group (single doses of 0.03 and 0.1 mg/kg), the mean placebo-corrected decrease in glycated hemoglobin was 0.11, 0.44, and 0.85% after 1, 2 (P = 0.017), and 3 (P = 0.049) months, respectively, along with enhanced C-peptide secretion, increased insulin sensitivity, and a reduction in C-reactive protein and spontaneous and inducible cytokines.ConclusionsThis novel IL-1β-neutralizing antibody improved glycemia, possibly via restored insulin production and action, and reduced inflammation in patients with type 2 diabetes. This therapeutic agent may be able to be used on a once-every-month or longer schedule.
| S-EPMC3402269 | biostudies-literature
Project description:IntroductionThe prevalence of type 2 diabetes is rising steeply. National Health Service England (NHSE) is exploring the potential of a digital diabetes prevention programme (DDPP) and has commissioned a pilot with embedded evaluation.Methods and analysisThis study aims to determine whether, and if so, how, should NHSE implement a national DDPP, using a mixed-methods pretest and post-test design, underpinned by two theoretical frameworks: the Coventry, Aberdeen and London - Refined (CALO-RE) taxonomy of behavioural change techniques for the digital interventions and the Consolidated Framework for Implementation Research (CFIR) for implementation processes. In eight pilot areas across England, adults with non-diabetic hyperglycaemia (NDH) (glycated haemoglobin (HbA1c) 42-47 mmol/mol or fasting plasma glucose 5.5-6.9 mmol/L) and adults without NDH who are overweight (body mass index (BMI) >25 kg/m2) or obese (BMI >30 kg/m2) will be referred to one of five digitally delivered diabetes prevention interventions. The primary outcomes are reduction in HbA1c and weight (for people with NDH) and reduction in weight (for people who are overweight or obese) at 12 months. Secondary outcomes include use of the intervention, satisfaction, physical activity, patient activation and resources needed for successful implementation. Quantitative data will be collected at baseline, 6 months and 12 months by the digital intervention providers. Qualitative data will be collected through semistructured interviews with commissioners, providers, healthcare professionals and patients. Quantitative data will be analysed descriptively and using generalised linear models to determine whether changes in outcomes are associated with demographic and intervention factors. Qualitative data will be analysed using framework analysis, with data pertaining to implementation mapped onto the CFIR.Ethics and disseminationThe study has received ethical approval from the Public Health England Ethics and Research Governance Group (reference R&D 324). Dissemination will include a report to NHSE to inform future policy and publication in peer-reviewed journals.
| S-EPMC6538049 | biostudies-literature
Project description:OBJECTIVE:Several single nucleotide polymorphisms (SNPs) in diabetes risk genes reduce glucose- and/or incretin-induced insulin secretion. Here, we investigated interactions between glycemia and such diabetes risk polymorphisms. RESEARCH DESIGN AND METHODS:Insulin secretion was assessed by insulinogenic index and areas under the curve of C-peptide/glucose in 1,576 subjects using an oral glucose tolerance test (OGTT). Participants were genotyped for 10 diabetes risk SNPs associated with ?-cell dysfunction: rs5215 (KCNJ11), rs13266634 (SLC30A8), rs7754840 (CDKAL1), rs10811661 (CDKN2A/2B), rs10830963 (MTNR1B), rs7903146 (TCF7L2), rs10010131 (WFS1), rs7923837 (HHEX), rs151290 (KCNQ1), and rs4402960 (IGF2BP2). Furthermore, the impact of the interaction between genetic variation in TCF7L2 and glycemia on changes in insulin secretion was tested in 315 individuals taking part in a lifestyle intervention study. RESULTS:For the SNPs in TCF7L2 and WFS1, we found a significant interaction between glucose control and insulin secretion (all P ? 0.0018 for glucose × genotype). When plotting insulin secretion against glucose at 120 min OGTT, the compromising SNP effects on insulin secretion are most apparent under high glucose. In the longitudinal study, rs7903146 in TCF7L2 showed a significant interaction with baseline glucose tolerance upon change in insulin secretion (P = 0.0027). Increased glucose levels at baseline predicted an increase in insulin secretion upon improvement of glycemia by lifestyle intervention only in carriers of the risk alleles. CONCLUSIONS:For the diabetes risk genes TCF7L2 and WFS1, which are associated with impaired incretin signaling, the level of glycemia determines SNP effects on insulin secretion. This indicates the increasing relevance of these SNPs during the progression of prediabetes stages toward clinically overt type 2 diabetes.
| S-EPMC2992789 | biostudies-literature
Project description:BackgroundIn the 2.8 years of the Diabetes Prevention Program (DPP) randomised clinical trial, diabetes incidence in high-risk adults was reduced by 58% with intensive lifestyle intervention and by 31% with metformin, compared with placebo. We investigated the persistence of these effects in the long term.MethodsAll active DPP participants were eligible for continued follow-up. 2766 of 3150 (88%) enrolled for a median additional follow-up of 5.7 years (IQR 5.5-5.8). 910 participants were from the lifestyle, 924 from the metformin, and 932 were from the original placebo groups. On the basis of the benefits from the intensive lifestyle intervention in the DPP, all three groups were offered group-implemented lifestyle intervention. Metformin treatment was continued in the original metformin group (850 mg twice daily as tolerated), with participants unmasked to assignment, and the original lifestyle intervention group was offered additional lifestyle support. The primary outcome was development of diabetes according to American Diabetes Association criteria. Analysis was by intention-to-treat. This study is registered with ClinicalTrials.gov, number NCT00038727.FindingsDuring the 10.0-year (IQR 9.0-10.5) follow-up since randomisation to DPP, the original lifestyle group lost, then partly regained weight. The modest weight loss with metformin was maintained. Diabetes incidence rates during the DPP were 4.8 cases per 100 person-years (95% CI 4.1-5.7) in the intensive lifestyle intervention group, 7.8 (6.8-8.8) in the metformin group, and 11.0 (9.8-12.3) in the placebo group. Diabetes incidence rates in this follow-up study were similar between treatment groups: 5.9 per 100 person-years (5.1-6.8) for lifestyle, 4.9 (4.2-5.7) for metformin, and 5.6 (4.8-6.5) for placebo. Diabetes incidence in the 10 years since DPP randomisation was reduced by 34% (24-42) in the lifestyle group and 18% (7-28) in the metformin group compared with placebo.InterpretationDuring follow-up after DPP, incidences in the former placebo and metformin groups fell to equal those in the former lifestyle group, but the cumulative incidence of diabetes remained lowest in the lifestyle group. Prevention or delay of diabetes with lifestyle intervention or metformin can persist for at least 10 years.FundingNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
| S-EPMC3135022 | biostudies-literature
Project description:INTRODUCTION:Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups. RESEARCH DESIGN AND METHODS:Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively. RESULTS:The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21?mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58?mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ?108?mmol/mol (?12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01). CONCLUSIONS:There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.
| S-EPMC7039605 | biostudies-literature