Pathophysiological Characteristics Underlying Different Glucose Response Curves: A Latent Class Trajectory Analysis From the Prospective EGIR-RISC Study

Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning

The classification of type 2 diabetes and prediabetes does not consider heterogeneity in the pathophysiology of glucose dysregulation. Here we show that prediabetes is characterized by metabolic heterogeneity, and that metabolic subphenotypes can be predicted by the shape of the glucose curve measured via a continuous glucose monitor (CGM) during standardized oral glucose-tolerance tests (OGTTs) performed in at-home settings. Gold-standard metabolic tests in 32 individuals with early glucose dysregulation revealed dominant or co-dominant subphenotypes (muscle or hepatic insulin-resistance phenotypes in 34% of the individuals, and β-cell-dysfunction or impaired-incretin-action phenotypes in 40% of them). Machine-learning models trained with glucose time series from OGTTs from the 32 individuals predicted the subphenotypes with areas under the curve (AUCs) of 95% for muscle insulin resistance, 89% for β-cell deficiency and 88% for impaired incretin action. With CGM-generated glucose curves obtained during at-home OGTTs, the models predicted the muscle-insulin-resistance and β-cell-deficiency subphenotypes of 29 individuals with AUCs of 88% and 84%, respectively. At-home identification of metabolic subphenotypes via a CGM may aid the risk stratification of individuals with early glucose dysregulation.

Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework

Type 2 diabetes (T2D) and prediabetes are classically defined by the level of fasting glucose or surrogates such as hemoglobin HbA1c. This classification does not take into account the heterogeneity in the pathophysiology of glucose dysregulation, the identification of which could inform targeted approaches to diabetes treatment and prevention and/or predict clinical outcomes. We performed gold-standard metabolic tests in a cohort of individuals with early glucose dysregulation and quantified four distinct metabolic subphenotypes known to contribute to glucose dysregulation and T2D: muscle insulin resistance, β-cell dysfunction, impaired incretin action, and hepatic insulin resistance. We revealed substantial inter-individual heterogeneity, with 34% of individuals exhibiting dominance or co-dominance in muscle and/or liver IR, and 40% exhibiting dominance or co-dominance in β-cell and/or incretin deficiency. Further, with a frequently-sampled oral glucose tolerance test (OGTT), we developed a novel machine learning framework to predict metabolic subphenotypes using features from the dynamic patterns of the glucose time-series ("shape of the glucose curve"). The glucose time-series features identified insulin resistance, β-cell deficiency, and incretin defect with auROCs of 95%, 89%, and 88%, respectively. These figures are superior to currently-used estimates. The prediction of muscle insulin resistance and β-cell deficiency were validated using an independent cohort. We then tested the ability of glucose curves generated by a continuous glucose monitor (CGM) worn during at-home OGTTs to predict insulin resistance and β-cell deficiency, yielding auROC of 88% and 84%, respectively. We thus demonstrate that the prediabetic state is characterized by metabolic heterogeneity, which can be defined by the shape of the glucose curve during standardized OGTT, performed in a clinical research unit or at-home setting using CGM. The use of at-home CGM to identify muscle insulin resistance and β-cell deficiency constitutes a practical and scalable method by which to risk stratify individuals with early glucose dysregulation and inform targeted treatment to prevent T2D.

Dysregulation of Lipid and Glucose Metabolism in Nonalcoholic Fatty Liver Disease

Non-Alcoholic Fatty Liver Disease (NAFLD) is a highly prevalent condition affecting approximately a quarter of the global population. It is associated with increased morbidity, mortality, economic burden, and healthcare costs. The disease is characterized by the accumulation of lipids in the liver, known as steatosis, which can progress to more severe stages such as steatohepatitis, fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC). This review focuses on the mechanisms that contribute to the development of diet-induced steatosis in an insulin-resistant liver. Specifically, it discusses the existing literature on carbon flux through glycolysis, ketogenesis, TCA (Tricarboxylic Acid Cycle), and fatty acid synthesis pathways in NAFLD, as well as the altered canonical insulin signaling and genetic predispositions that lead to the accumulation of diet-induced hepatic fat. Finally, the review discusses the current therapeutic efforts that aim to ameliorate various pathologies associated with NAFLD.

Post-Load Insulin Secretion Patterns are Associated with Glycemic Status and Diabetic Complications in Patients with Type 2 Diabetes Mellitus

BACKGROUND

To examine whether the different patterns of post-load insulin secretion can identify the heterogeneity of type 2 diabetes mellitus (T2DM).

METHODS

Six hundred twenty-five inpatients with T2DM at Jining No. 1 People's Hospital were recruited from January 2019 to October 2021. The 140 g steamed bread meal test (SBMT) was conducted on patients with T2DM, and glucose, insulin, and C-peptide levels were recorded at 0, 60, 120, and 180 min. To avoid the effect of exogenous insulin, patients were categorized into three different classes by latent class trajectory analysis based on the post-load secretion patterns of C-peptide. The difference in short- and long-term glycemic status and prevalence of complications distributed among the three classes were compared by multiple linear regression and multiple logistic regression, respectively.

RESULTS

There were significant differences in long-term glycemic status (e. g., HbA1c) and short-term glycemic status (e. g., mean blood glucose, time in range) among the three classes. The difference in short-term glycemic status was similar in terms of the whole day, daytime, and nighttime. The prevalence of severe diabetic retinopathy and atherosclerosis showed a decreasing trend among the three classes.

CONCLUSIONS

The post-load insulin secretion patterns could well identify the heterogeneity of patients with T2DM in short- and long-term glycemic status and prevalence of complications, providing recommendations for the timely adjustment in treatment regimes of patients with T2DM and promotion of personalized treatment.

A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future.

Incidence and status of insulin secretion in pregnant women with flat plasma glucose profiles during oral glucose tolerance test

OBJECTIVES

Flat shaped glucose curves (FC) during oral glucose tolerance test (OGTT) in pregnant women (PW) are a not uncommon finding. We aimed to define the FC incidence in a large PW cohort and to describe the status of insulin and C-peptide secretion in women with FC when compared with a well-matched control group.

METHODS

1050 PW performing OGTT for gestational diabetes screening were enrolled. An increase <6 % in plasma glucose (PG) during OGTT defined a FC. Serum samples for measuring insulin and C-peptide were also obtained.

RESULTS

61 (5.8 %) women showed a FC. 60 of them, paired to a group of 60 no-FC women matched for age, body mass index and gestational age, were further investigated. C-peptide and insulin concentrations were significantly lower (P < 0.001) in FC in both 1-h and 2-h OGTT samples. When incremental area under the curves (AUC) normalized to PG were estimated, only AUCinsulin remained however significantly lower. The insulin sensitivity index was higher in FC.

CONCLUSIONS

PW with FC showed a hypersensitivity to insulin with normal β-cell function. Moreover, a delayed glucose absorption could be hypothesised because of the slight but continuously increasing shape of insulin curve found in FC group. Both phenomena could occur in parallel and contribute to FC.

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis

OBJECTIVES

Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them.

MATERIALS AND METHODS

We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G.

RESULTS

More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3).

CONCLUSION

Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.

Diabetic family history in young Japanese persons with normal glucose tolerance associates with k-means clustering of glucose response to oral glucose load, insulinogenic index and Matsuda index

Aims

The present study aimed to clarify the relationships between diabetic family history (FH), and dysglycemic response to the oral glucose tolerance test (OGTT), insulin secretion, and insulin sensitivity in young Japanese persons with normal glucose tolerance (NGT).

Methods

We measured plasma glucose (PG) and immunoreactive insulin levels in 1,309 young Japanese persons (age <40 years) with NGT before and at 30, 60, and 120 min during a 75-g OGTT. Dysglycemia during OGTT was analyzed by k-means clustering analysis. Body mass index (BMI), blood pressure (BP), and lipids were measured. Insulin secretion and sensitivity indices were calculated.

Results

PG levels during OGTT were classified by k-means clustering analysis into three groups with stepwise decreases in glucose tolerance even among individuals with NGT. In these clusters, proportion of males, BMI, BP and frequency of FH were higher, and lipid levels were worse, together with decreasing glucose tolerance. Subjects with a diabetic FH showed increases in PG after glucose loading and decreases in insulinogenic index and Matsuda index.

Conclusions

Dysglycemic response to OGTT by k-means clustering analysis was associated with FH in young Japanese persons with NGT. FH was also associated with post-loading glucose, insulinogenic index, and Matsuda index.

Mechanistic Insights Into the Heterogeneity of Glucose Response Classes in Youths With Obesity: A Latent Class Trajectory Approach

OBJECTIVE

In a large, multiethnic cohort of youths with obesity, we analyzed pathophysiological and genetic mechanisms underlying variations in plasma glucose responses to a 180 min oral glucose tolerance test (OGTT).

RESEARCH DESIGN AND METHODS

Latent class trajectory analysis was used to identify various glucose response profiles to a nine-point OGTT in 2,378 participants in the Yale Pathogenesis of Youth-Onset T2D study, of whom 1,190 had available TCF7L2 genotyping and 358 had multiple OGTTs over a 5 year follow-up. Insulin sensitivity, clearance, and β-cell function were estimated by glucose, insulin, and C-peptide modeling.

RESULTS

Four latent classes (1 to 4) were identified based on increasing areas under the curve for glucose. Participants in class 3 and 4 had the worst metabolic and genetic risk profiles, featuring impaired insulin sensitivity, clearance, and β-cell function. Model-predicted probability to be classified as class 1 and 4 increased across ages, while insulin sensitivity and clearance showed transient reductions and β-cell function progressively declined. Insulin sensitivity was the strongest determinant of class assignment at enrollment and of the longitudinal change from class 1 and 2 to higher classes. Transitions between classes 3 and 4 were explained only by changes in β-cell glucose sensitivity.

CONCLUSIONS

We identified four glucose response classes in youths with obesity with different genetic risk profiles and progressive impairment in insulin kinetics and action. Insulin sensitivity was the main determinant in the transition between lower and higher glucose classes across ages. In contrast, transitions between the two worst glucose classes were driven only by β-cell glucose sensitivity.

How measurements affected by medication use are reported and handled in observational research: A literature review

PURPOSE

In epidemiological research, measurements affected by medication, for example, blood pressure lowered by antihypertensives, are common. Different ways of handling medication are required depending on the research questions and whether the affected measurement is the exposure, the outcome, or a confounder. This study aimed to review handling of medication use in observational research.

METHODS

PubMed was searched for etiological studies published between 2015 and 2019 in 15 high-ranked journals from cardiology, diabetes, and epidemiology. We selected studies that analyzed blood pressure, glucose, or lipid measurements (whether exposure, outcome or confounder) by linear or logistic regression. Two reviewers independently recorded how medication use was handled and assessed whether the methods used were in accordance with the research aim. We reported the methods used per variable category (exposure, outcome, confounder).

RESULTS

A total of 127 articles were included. Most studies did not perform any method to account for medication use (exposure 58%, outcome 53%, and confounder 45%). Restriction (exposure 22%, outcome 23%, and confounders 10%), or adjusting for medication use using a binary indicator were also used frequently (exposure: 18%, outcome: 19%, confounder: 45%). No advanced methods were applied. In 60% of studies, the methods' validity could not be judged due to ambiguous reporting of the research aim. Invalid approaches were used in 28% of the studies, mostly when the affected variable was the outcome (36%).

CONCLUSION

Many studies ambiguously stated the research aim and used invalid methods to handle medication use. Researchers should consider a valid methodological approach based on their research question.

Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies

BACKGROUND

Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories.

METHODS

This expository paper provides an illustrative guide to summarising nonlinear growth trajectories for repeatedly measured continuous outcomes using (i) linear spline and (ii) natural cubic spline linear mixed-effects (LME) models, (iii) Super Imposition by Translation and Rotation (SITAR) nonlinear mixed effects models, and (iv) latent trajectory models. The underlying model for each approach, their similarities and differences, and their advantages and disadvantages are described. Their application and correct interpretation of their results is illustrated by analysing repeated bone mass measures to characterise bone growth patterns and their sex differences in three cohort studies from the UK, USA, and Canada comprising 8500 individuals and 37,000 measurements from ages 5-40 years. Recommendations for choosing a modelling approach are provided along with a discussion and signposting on further modelling extensions for analysing trajectory exposures and outcomes, and multiple cohorts.

RESULTS

Linear and natural cubic spline LME models and SITAR provided similar summary of the mean bone growth trajectory and growth velocity, and the sex differences in growth patterns. Growth velocity (in grams/year) peaked during adolescence, and peaked earlier in females than males e.g., mean age at peak bone mineral content accrual from multicohort SITAR models was 12.2 years in females and 13.9 years in males. Latent trajectory models (with trajectory shapes estimated using a natural cubic spline) identified up to four subgroups of individuals with distinct trajectories throughout adolescence.

CONCLUSIONS

LME models with linear and natural cubic splines, SITAR, and latent trajectory models are useful for describing nonlinear growth trajectories, and these methods can be adapted for other complex traits. Choice of method depends on the research aims, complexity of the trajectory, and available data. Scripts and synthetic datasets are provided for readers to replicate trajectory modelling and visualisation using the R statistical computing software.

Characteristics of Workplace Psychosocial Resources and Risk of Diabetes: A Prospective Cohort Study

OBJECTIVE

To examine whether characteristics of workplace psychosocial resources are associated with the risk of type 2 diabetes among employees.

RESEARCH DESIGN AND METHODS

Participants were 49,835 employees (77% women, aged 40-65 years, and diabetes free at baseline) from the Finnish Public Sector cohort study. Characteristics of horizontal (culture of collaboration and support from colleagues) and vertical (leadership quality and organizational procedural justice) psychosocial resources were self-reported. Incident type 2 diabetes (n = 2,148) was ascertained through linkage to electronic health records from national registers. We used latent class modeling to assess the clustering of resource characteristics. Cox proportional hazards models were used to examine the relationship between the identified clusters and risk of type 2 diabetes during 10.9 years of follow-up, adjusting for age, sex, marital status, educational level, type of employment contract, comorbidity, and diagnosed mental disorders.

RESULTS

We identified four patterns of workplace psychosocial resources: unfavorable, favorable vertical, favorable horizontal, and favorable vertical and horizontal. Compared with unfavorable, favorable vertical (hazard ratio 0.87 [95% CI 0.78; 0.97]), favorable horizontal (0.77 [0.67; 0.88]), and favorable vertical and horizontal (0.77 [0.68; 0.86]) resources were associated with a lower risk of type 2 diabetes, with the strongest associations seen in employees at age ≥55 years (Pinteraction = 0.03). These associations were robust to multivariable adjustments and were not explained by reverse causation.

CONCLUSIONS

A favorable culture of collaboration, support from colleagues, leadership quality, and organizational procedural justice are associated with a lower risk of employees developing type 2 diabetes than in those without such favorable workplace psychosocial resources.

Difference in Insulin Resistance Assessment between European Union and Non-European Union Obesity Treatment Centers (ESPE Obesity Working Group Insulin Resistance Project)

INTRODUCTION

The obesity epidemic has become one of the most important public health issues of modern times. Impaired insulin sensitivity seems to be the cornerstone of multiple obesity related comorbidities. However, there is no accepted definition of impaired insulin sensitivity.

OBJECTIVE

We hypothesize that assessment of insulin resistance differs between centers.

METHODS

The ESPE Obesity Working Group (ESPE ObWG) Scientific Committee developed a questionnaire with a focus on the routine practices of assessment of hyperinsulinemia and insulin resistance, which was distributed through Google Docs platform to the clinicians and researchers from the current ESPE ObWG database (n = 73). Sixty-one complete responses (84% response rate) from clinicians and researchers were analyzed: 32 from European Union (EU) centers (representatives of 14 countries) and 29 from Non-EU centers (representatives from 10 countries). Standard statistics were used for the data analysis.

RESULTS

The majority of respondents considered insulin resistance (IR) as a clinical tool (85.2%) rather than a research instrument. For the purpose of IR assessment EU specialists prefer analysis of the oral glucose tolerance test (OGTT) results, whereas non-EU ones mainly use Homeostatic Model Assessment of Insulin Resistance (HOMA-IR; p = 0.032). There was no exact cutoff for the HOMA-IR in either EU or non-EU centers. A variety of OGTT time points and substances measured per local protocol were reported. Clinicians normally analyzed blood glucose (88.52% of centers) and insulin (67.21%, mainly in EU centers, p = 0.0051). Furthermore, most participants (70.5%) considered OGTT insulin levels as a more sensitive parameter of IR than glucose. Meanwhile, approximately two-thirds (63.9%) of the centers did not use any cutoffs for the insulin response to the glucose load.

CONCLUSIONS

Since there is no standard for the IR evaluation and uniform accepted indication of performing, an OGTT the assessment of insulin sensitivity varies between EU and non-EU centers. A widely accepted standardized protocol is needed to allow comparison between centers.

Utility of oral glucose tolerance test in predicting type 2 diabetes following gestational diabetes: Towards personalized care

BACKGROUND

Women with gestational diabetes mellitus (GDM) are at a seven-fold higher risk of developing type 2 diabetes (T2D) within 7-10 years after childbirth, compared with those with normoglycemic pregnancy. Although raised fasting blood glucose (FBG) levels has been said to be the main significant predictor of postpartum progression to T2D, it is difficult to predict who among the women with GDM would develop T2D. Therefore, we conducted a cross-sectional retrospective study to examine the glycemic indices that can predict postnatal T2D in Emirati Arab women with a history of GDM.

AIM

To assess how oral glucose tolerance test (OGTT) can identify the distinct GDM pathophysiology and predict possible distinct postnatal T2D subtypes.

METHODS

The glycemic status of a cohort of 4603 pregnant Emirati Arab women, who delivered in 2007 at both Latifa Women and Children Hospital and at Dubai Hospital, United Arab Emirates, was assessed retrospectively, using the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. Of the total, 1231 women were followed up and assessed in 2016. The FBG and/or the 2-h blood glucose (2hrBG) levels after a 75-g glucose load were measured to assess the prevalence of GDM and T2D, according to the IADPSG and American Diabetes Association (ADA) criteria, respectively. The receiver operating characteristic curve for the OGTT was plotted and sensitivity, specificity, and predictive values of FBG and 2hrBG for T2D were determined.

RESULTS

Considering both FBG and 2hrBG levels, according to the IADPSG criteria, the prevalence of GDM in pregnant Emirati women in 2007 was 1057/4603 (23%), while the prevalence of pre-pregnancy T2D among them, based on ADA criteria, was 230/4603 (5%). In the subset of women (n = 1231) followed up in 2016, the prevalence of GDM in 2007 was 362/1231 (29.6%), while the prevalence of pre-pregnancy T2D was 36/1231 (2.9%). Of the 362 pregnant women with GDM in 2007, 96/362 (26.5%) developed T2D; 142/362 (39.2%) developed impaired fasting glucose; 29/362 (8.0%) developed impaired glucose tolerance, and the remaining 95/362 (26.2%) had normal glycemia in 2016. The prevalence of T2D, based on ADA criteria, stemmed from the prevalence of 36/1231 (2.9%) in 2007 to 141/1231 (11.5%), in 2016. The positive predictive value (PPV) for FBG suggests that if a woman tested positive for GDM in 2007, the probability of developing T2D in 2016 was approximately 24%. The opposite was observed when 2hrBG was used for diagnosis. The PPV value for 2hrBG suggests that if a woman was positive for GDM in 2007 then the probability of developing T2D in 2016 was only 3%.

CONCLUSION

FBG and 2hrBG could predict postpartum T2D, following antenatal GDM. However, each test reflects different pathophysiology and possible T2D subtype and could be matched with a relevant T2D prevention program.

Time to Peak Glucose and Peak C-Peptide During the Progression to Type 1 Diabetes in the Diabetes Prevention Trial and TrialNet Cohorts

OBJECTIVE

To assess the progression of type 1 diabetes using time to peak glucose or C-peptide during oral glucose tolerance tests (OGTTs) in autoantibody-positive relatives of people with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We examined 2-h OGTTs of participants in the Diabetes Prevention Trial Type 1 (DPT-1) and TrialNet Pathway to Prevention (PTP) studies. We included 706 DPT-1 participants (mean ± SD age, 13.84 ± 9.53 years; BMI Z-score, 0.33 ± 1.07; 56.1% male) and 3,720 PTP participants (age, 16.01 ± 12.33 years; BMI Z-score, 0.66 ± 1.3; 49.7% male). Log-rank testing and Cox regression analyses with adjustments (age, sex, race, BMI Z-score, HOMA-insulin resistance, and peak glucose/C-peptide levels, respectively) were performed.

RESULTS

In each of DPT-1 and PTP, higher 5-year diabetes progression risk was seen in those with time to peak glucose >30 min and time to peak C-peptide >60 min (P < 0.001 for all groups), before and after adjustments. In models examining strength of association with diabetes development, associations were greater for time to peak C-peptide versus peak C-peptide value (DPT-1: χ2 = 25.76 vs. χ2 = 8.62; PTP: χ2 = 149.19 vs. χ2 = 79.98; all P < 0.001). Changes in the percentage of individuals with delayed glucose and/or C-peptide peaks were noted over time.

CONCLUSIONS

In two independent at-risk populations, we show that those with delayed OGTT peak times for glucose or C-peptide are at higher risk of diabetes development within 5 years, independent of peak levels. Moreover, time to peak C-peptide appears more predictive than the peak level, suggesting its potential use as a specific biomarker for diabetes progression.

Oral glucose tolerance test in diabetes, the old method revisited

The oral glucose tolerance test (OGTT) has been widely used both in clinics and in basic research for a long time. It is applied to diagnose impaired glucose tolerance and/or type 2 diabetes mellitus in individuals. Additionally, it has been employed in research to investigate glucose utilization and insulin sensitivity in animals. The main aim of each was quite different, and the details are also somewhat varied. However, the time or duration of the OGTT was the same, using the 2-h post-glucose load glycemia in both, following the suggestions of the American Diabetes Association. Recently, the use of 30-min or 1-h post-glucose load glycemia in clinical practice has been recommended by several studies. In this review article, we describe this new view and suggest perspectives for the OGTT. Additionally, quantification of the glucose curve in basic research is also discussed. Unlike in clinical practice, the incremental area under the curve is not suitable for use in the studies involving animals receiving repeated treatments or chronic treatment. We discuss the potential mechanisms in detail. Moreover, variations between bench and bedside in the application of the OGTT are introduced. Finally, the newly identified method for the OGTT must achieve a recommendation from the American Diabetes Association or another official unit soon. In conclusion, we summarize the recent reports regarding the OGTT and add some of our own perspectives, including machine learning and others.

Time to the Peak, Shape of the Curve and Combination of These Glucose Response Characteristics During Oral Glucose Tolerance Test as Indicators of Early Beta-cell Dysfunction in Obese Adolescents

OBJECTIVE

Characteristics of the glucose response during oral glucose tolerance test (OGTT) may reflect differences in insulin secretion and action. The aim was to examine whether timing of the glucose peak, shape of the glucose curve and their combination could be indicators of beta-cell dysfunction in obese/severely obese adolescents with normal glucose tolerance (NGT).

METHODS

Data from 246 obese/severely obese adolescents who completed OGTT were reviewed. Out of 184 adolescents with NGT, 174 could be further classified into groups based on timing of the glucose peak (early/30 minutes vs late/≥60 minutes) and shape of the glucose curve (monophasic vs biphasic). Groups were compared with respect to insulin sensitivity (whole body insulin sensitivity index - WBISI), early-phase insulin secretion (insulinogenic index - IGI) and beta-cell function relative to insulin sensitivity (oral disposition index - oDI).

RESULTS

Late glucose peak (p=0.004) and monophasic glucose curve (p=0.001) were both associated with lower oDI after adjustment for age, sex, puberty stage and body mass index z-score. Among obese/severely obese adolescents with NGT, those with coexistent late glucose peak and monophasic glucose curve had lower oDI than those with early glucose peak and biphasic glucose curve (p=0.002). Moreover, a combination of late glucose peak and monophasic glucose curve was the most powerful predictor of the lowest oDI quartile [odds ratio (OR): 11.68, 95% confidence interval: 3.048-44.755, p<0.001].

CONCLUSION

Late timing of the glucose peak, monophasic shape of the glucose curve and, in particular, a combination of those characteristics during OGTT may indicate early beta-cell dysfunction in obese/severely obese adolescents with NGT.

Waist-to-height ratio and metabolic phenotype compared to the Matsuda index for the prediction of insulin resistance

Current screening algorithms for type 2 diabetes (T2D) rely on fasting plasma glucose (FPG) and/or HbA1c. This fails to identify a sizeable subgroup of individuals in early stages of metabolic dysregulation who are at high risk for developing diabetes or cardiovascular disease. The Matsuda index, a combination of parameters derived from a fasting and postprandial insulin assay, is an early biomarker for metabolic dysregulation (i.e. insulin resistance/compensatory hyperinsulinemia). The aim of this analysis was to compare four widely available anthropometric and biochemical markers indicative of this condition [waist-to-height ratio (WHtR), hypertriglyceridemic-waist phenotype (HTW), triglycerides-to-HDL-C ratio (TG/HDL-C) and FPG] to the Matsuda index. This cross-sectional analysis included 2231 individuals with normal fasting glucose (NFG, n = 1333), impaired fasting glucose (IFG, n = 599) and T2D (n = 299) from an outpatient diabetes clinic in Germany and thus extended a prior analysis from our group done on the first two subgroups. We analyzed correlations of the Matsuda index with WHtR, HTW, TG/HDL-C and FPG and their predictive accuracies by correlation and logistic regression analyses and receiver operating characteristics. In the entire group and in NFG, IFG and T2D, the best associations were observed between the Matsuda index and the WHtR (r = - 0.458), followed by HTW phenotype (r = - 0.438). As for prediction accuracy, WHtR was superior to HTW, TG/HDL-C and FPG in the entire group (AUC 0.801) and NFG, IFG and T2D. A multivariable risk score for the prediction of insulin resistance was tested and demonstrated an area under the ROC curve of 0.765 for WHtR and its interaction with sex as predictor controlled by age and sex. The predictive power increased to 0.845 when FPG and TG/HDL-C were included. Using as a comparator the Matsuda index, WHtR, compared to HTW, TG/HDL-C and FPG, showed the best predictive value for detecting metabolic dysregulation. We conclude that WHtR, a widely available anthropometric index, could refine phenotypic screening for insulin resistance/hyperinsulinemia. This may ameliorate early identification of individuals who are candidates for appropriate therapeutic interventions aimed at addressing the twin epidemic of metabolic and cardiovascular disease in settings where more extended testing such as insulin assays are not feasible.

Towards precision medicine in diabetes? A critical review of glucotypes

In response to a study previously published in PLOS Biology, this Formal Comment thoroughly examines the concept of ’glucotypes’ with regard to its generalisability, interpretability and relationship to more traditional measures used to describe data from continuous glucose monitoring.

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

OGTT Glucose Response Curves, Insulin Sensitivity, and β-Cell Function in RISE: Comparison Between Youth and Adults at Randomization and in Response to Interventions to Preserve β-Cell Function

We examined the glucose response curves (biphasic [BPh], monophasic [MPh], incessant increase [IIn]) during an oral glucose tolerance test (OGTT) and their relationship to insulin sensitivity (IS) and β-cell function (βCF) in youth versus adults with impaired glucose tolerance or recently diagnosed type 2 diabetes.RESEARCH DESIGN AND METHODSThis was both a cross-sectional and a longitudinal evaluation of participants in the RISE study randomized to metformin alone for 12 months or glargine for 3 months followed by metformin for 9 months. At baseline/randomization, OGTTs (85 youth, 353 adults) were categorized as BPh, MPh, or IIn. The relationship of the glucose response curves to hyperglycemic clamp-measured IS and βCF at baseline and the change in glucose response curves 12 months after randomization were assessed.RESULTSAt randomization, the prevalence of the BPh curve was significantly higher in youth than adults (18.8% vs. 8.2%), with no differences in MPh or IIn. IS did not differ across glucose response curves in youth or adults. However, irrespective of curve type, youth had lower IS than adults (P < 0.05). βCF was lowest in IIn versus MPh and BPh in youth and adults (P < 0.05), yet compared with adults, youth had higher βCF in BPh and MPh (P < 0.005) but not IIn. At month 12, the change in glucose response curves did not differ between youth and adults, and there was no treatment effect.CONCLUSIONSDespite a twofold higher prevalence of the more favorable BPh curve in youth at randomization, RISE interventions did not result in beneficial changes in glucose response curves in youth compared with adults. Moreover, the typical β-cell hypersecretion in youth was not present in the IIn curve, emphasizing the severity of β-cell dysfunction in youth with this least favorable glucose response curve.

Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study

AIM

To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.

METHODS

We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.

RESULTS

At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13-0.58), Subgroup 3 (β = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.

CONCLUSIONS

Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

OGTT Glucose Response Curves, Insulin Sensitivity, and β-Cell Function in RISE: Comparison Between Youth and Adults at Randomization and in Response to Interventions to Preserve β-Cell Function

OBJECTIVE

We examined the glucose response curves (biphasic [BPh], monophasic [MPh], incessant increase [IIn]) during an oral glucose tolerance test (OGTT) and their relationship to insulin sensitivity (IS) and β-cell function (βCF) in youth versus adults with impaired glucose tolerance or recently diagnosed type 2 diabetes.RESEARCH DESIGN AND METHODSThis was both a cross-sectional and a longitudinal evaluation of participants in the RISE study randomized to metformin alone for 12 months or glargine for 3 months followed by metformin for 9 months. At baseline/randomization, OGTTs (85 youth, 353 adults) were categorized as BPh, MPh, or IIn. The relationship of the glucose response curves to hyperglycemic clamp-measured IS and βCF at baseline and the change in glucose response curves 12 months after randomization were assessed.RESULTSAt randomization, the prevalence of the BPh curve was significantly higher in youth than adults (18.8% vs. 8.2%), with no differences in MPh or IIn. IS did not differ across glucose response curves in youth or adults. However, irrespective of curve type, youth had lower IS than adults (P < 0.05). βCF was lowest in IIn versus MPh and BPh in youth and adults (P < 0.05), yet compared with adults, youth had higher βCF in BPh and MPh (P < 0.005) but not IIn. At month 12, the change in glucose response curves did not differ between youth and adults, and there was no treatment effect.CONCLUSIONSDespite a twofold higher prevalence of the more favorable BPh curve in youth at randomization, RISE interventions did not result in beneficial changes in glucose response curves in youth compared with adults. Moreover, the typical β-cell hypersecretion in youth was not present in the IIn curve, emphasizing the severity of β-cell dysfunction in youth with this least favorable glucose response curve.

Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes

The state of intermediate hyperglycemia is indicative of elevated risk of developing type 2 diabetes1. However, the current definition of prediabetes neither reflects subphenotypes of pathophysiology of type 2 diabetes nor is predictive of future metabolic trajectories. We used partitioning on variables derived from oral glucose tolerance tests, MRI-measured body fat distribution, liver fat content and genetic risk in a cohort of extensively phenotyped individuals who are at increased risk for type 2 diabetes2,3 to identify six distinct clusters of subphenotypes. Three of the identified subphenotypes have increased glycemia (clusters 3, 5 and 6), but only individuals in clusters 5 and 3 have imminent diabetes risks. By contrast, those in cluster 6 have moderate risk of type 2 diabetes, but an increased risk of kidney disease and all-cause mortality. Findings were replicated in an independent cohort using simple anthropomorphic and glycemic constructs4. This proof-of-concept study demonstrates that pathophysiological heterogeneity exists before diagnosis of type 2 diabetes and highlights a group of individuals who have an increased risk of complications without rapid progression to overt type 2 diabetes.

Patterns of Insulin Secretion During First-Phase Insulin Secretion in Normal Chinese Adults

BACKGROUND

The increase in diabetes worldwide is alarming. Decreased acute insulin response to intravenous glucose tolerance test (IVGTT) during first-phase insulin secretion (FPIS) is a characteristic of diabetes. However, knowledge of the insulin secretion characteristics identified by different time to glucose peak in subjects with different metabolic state is sparse.

AIMS

This study aimed to find different patterns of FPIS in subjects with normal glucose tolerance (NGT) and analyzed the relationship between insulin secretion patterns and the risk for development of type 2 diabetes mellitus (T2DM).

METHODS

A total of 126 subjects were divided into three groups during a 10-min IVGTT, including NGT with time to glucose peak after 3 min (G1, n = 21), NGT with time to glucose peak at 3 min (G2, n = 95), and prediabetes or diabetes with time to glucose peak at 3 min (G3, n = 10). Glucose, insulin, and C-peptide concentrations at 0, 3, 5, 7, and 10 min during the IVGTT were tested. IVGTT-based indices were calculated to evaluate the insulin secretion and insulin sensitivity.

RESULTS

Age, body mass index (BMI), waist-to-hip ratio, triglyceride (TG), and hemoglobin A1c (HbA1c) of subjects were gradually higher, while high-density lipoprotein cholesterol (HDL-C) was gradually lower from G1 to G3 (p for linear trend <0.05), and the differences between G1 and G2 were also statistically significant (p < 0.05). Glucose peak of most participants in G1 converged at 5 min, and the curves shape of insulin and C-peptide in G2 were the sharpest among three groups. There was no significant difference in all IVGTT-based indices between G1 and G2, but AUC_Ins, AUC_Ins/AUC_Glu, and △Ins₃/△Glu₃ in G2 were the highest, and the p-value for linear trend of those indices among three groups were statistically significant (p < 0.05).

CONCLUSIONS

Two patterns of FPIS were in subjects with NGT, while subjects with later time to glucose peak during FPIS might be less likely to develop T2DM in the future.

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.

The Oral Glucose Tolerance Test: 100 Years Later

For over 100 years, the oral glucose tolerance test (OGTT) has been the cornerstone for detecting prediabetes and type 2 diabetes (T2DM). In recent decades, controversies have arisen identifying internationally acceptable cut points using fasting plasma glucose (FPG), 2-h post-load glucose (2-h PG), and/or HbA1c for defining intermediate hyperglycemia (prediabetes). Despite this, there has been a steadfast global consensus of the 2-h PG for defining dysglycemic states during the OGTT. This article reviews the history of the OGTT and recent advances in its application, including the glucose challenge test and mathematical modeling for determining the shape of the glucose curve. Pitfalls of the FPG, 2-h PG during the OGTT, and HbA1c are considered as well. Finally, the associations between the 30-minute and 1-hour plasma glucose (1-h PG) levels derived from the OGTT and incidence of diabetes and its complications will be reviewed. The considerable evidence base supports modifying current screening and diagnostic recommendations with the use of the 1-h PG. Measurement of the 1-h PG level could increase the likelihood of identifying high-risk individuals when the pancreatic ß-cell function is substantially more intact with the added practical advantage of potentially replacing the conventional 2-h OGTT making it more acceptable in the clinical setting.

The intestinal microbiome is a co-determinant of the postprandial plasma glucose response

Elevated postprandial plasma glucose is a risk factor for development of type 2 diabetes and cardiovascular disease. We hypothesized that the inter-individual postprandial plasma glucose response varies partly depending on the intestinal microbiome composition and function. We analyzed data from Danish adults (n = 106), who were self-reported healthy and attended the baseline visit of two previously reported randomized controlled cross-over trials within the Gut, Grain and Greens project. Plasma glucose concentrations at five time points were measured before and during three hours after a standardized breakfast. Based on these data, we devised machine learning algorithms integrating bio-clinical, as well as shotgun-sequencing-derived taxa and functional potentials of the intestinal microbiome to predict individual postprandial glucose excursions. In this post hoc study, we found microbial and clinical features, which predicted up to 48% of the inter-individual variance of postprandial plasma glucose responses (Pearson correlation coefficient of measured vs. predicted values, R = 0.69, 95% CI: 0.45 to 0.84, p<0.001). The features were age, fasting serum triglycerides, systolic blood pressure, BMI, fasting total serum cholesterol, abundance of Bifidobacterium genus, richness of metagenomics species and abundance of a metagenomic species annotated to Clostridiales at order level. A model based only on microbial features predicted up to 14% of the variance in postprandial plasma glucose excursions (R = 0.37, 95% CI: 0.02 to 0.64, p = 0.04). Adding fasting glycaemic measures to the model including microbial and bio-clinical features increased the predictive power to R = 0.78 (95% CI: 0.59 to 0.89, p<0.001), explaining more than 60% of the inter-individual variance of postprandial plasma glucose concentrations. The outcome of the study points to a potential role of the taxa and functional potentials of the intestinal microbiome. If validated in larger studies our findings may be included in future algorithms attempting to develop personalized nutrition, especially for prediction of individual blood glucose excursions in dys-glycaemic individuals.

Type 2 diabetes: one disease, many pathways

Diabetes is a chronic, progressive disease that calls for longitudinal data and analysis. We introduce a longitudinal mathematical model that is capable of representing the metabolic state of an individual at any point in time during their progression from normal glucose tolerance to type 2 diabetes (T2D) over a period of years. As an application of the model, we account for the diversity of pathways typically followed, focusing on two extreme alternatives, one that goes through impaired fasting glucose (IFG) first and one that goes through impaired glucose tolerance (IGT) first. These two pathways are widely recognized to stem from distinct metabolic abnormalities in hepatic glucose production and peripheral glucose uptake, respectively. We confirm this but go beyond to show that IFG and IGT lie on a continuum ranging from high hepatic insulin resistance and low peripheral insulin resistance to low hepatic resistance and high peripheral resistance. We show that IFG generally incurs IGT and IGT generally incurs IFG on the way to T2D, highlighting the difference between innate and acquired defects and the need to assess patients early to determine their underlying primary impairment and appropriately target therapy. We also consider other mechanisms, showing that IFG can result from impaired insulin secretion, that non-insulin-dependent glucose uptake can also mediate or interact with these pathways, and that impaired incretin signaling can accelerate T2D progression. We consider whether hyperinsulinemia can cause insulin resistance in addition to being a response to it and suggest that this is a minor effect.

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.

The present and future scope of real-world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?

The last decade has witnessed an exponential growth in the opportunities to collect and link health-related data from multiple resources, including primary care, administrative, and device data. The availability of these "real-world," "big data" has fuelled also an intense methodological research into methods to handle them and extract actionable information. In medicine, the evidence generated from "real-world data" (RWD), which are not purposely collected to answer biomedical questions, is commonly termed "real-world evidence" (RWE). In this review, we focus on RWD and RWE in the area of diabetes research, highlighting their contributions in the last decade; and give some suggestions for future RWE diabetes research, by applying well-established and less-known tools to direct RWE diabetes research towards better personalized approaches to diabetes care. We underline the essential aspects to consider when using RWD and the key features limiting the translational potential of RWD in generating high-quality and applicable RWE. Only if viewed in the context of other study designs and statistical methods, with its pros and cons carefully considered, RWE will exploit its full potential as a complementary or even, in some cases, substitutive source of evidence compared to the expensive evidence obtained from randomized controlled trials.

β-cell function, incretin response, and insulin sensitivity of glucose and fat metabolism in obese youth: Relationship to OGTT-time-to-glucose-peak

BACKGROUND

In adults, the time-to-glucose-peak at or after 30 minutes during an oral glucose tolerance test (OGTT) identifies physiologically distinct groups with differences in insulin sensitivity, β-cell function and risk for type 2 diabetes. In obese non-diabetic adolescents, we investigated if the OGTT-time-to-glucose-peak also reflects incretin and free fatty acid (FFA) responses besides insulin sensitivity and β-cell function, measured by the clamp.

METHODS

Obese adolescents (n = 278) were categorized according to their OGTT-time-to-glucose-peak by Early-peak (at 30 minutes) vs Late-peak (>30 minutes) groups. Body composition, visceral adipose tissue, oral disposition index and OGTT-area under the curve (AUC) were examined. A subset of 102 participants had both hyperinsulinemic-euglycemic and hyperglycemic clamps to measure in vivo insulin sensitivity, insulin secretion, and β-cell function relative to insulin sensitivity.

RESULTS

Compared with the Early-peak group, the Late-peak group had impaired β-cell function relative to insulin sensitivity, lower glucose-dependent insulinotropic polypeptide-AUC, and higher FFA-AUC despite higher insulin- and C-peptide-AUC. They also had lower hepatic and peripheral insulin sensitivity despite similar percent body fat and visceral adipose tissue, and had higher prevalence of impaired glucose tolerance (all P < .05).

CONCLUSIONS

In obese non-diabetic youth, those with a Late-peak vs an Early-peak glucose during an OGTT showed diminished β-cell function, blunted incretin secretion, and lower insulin sensitivity of glucose and FFA metabolism. It remains to be determined if Late-peak glucose predicts the future development of type 2 diabetes in these high-risk youth.

Metabolic and Genetic Determinants of Glucose Shape After Oral Challenge in Obese Youths: A Longitudinal Study

CONTEXT

The time-to-glucose-peak following the oral glucose tolerance test (OGTT) is a highly reproducible marker for diabetes risk. In obese youths, we lack evidence for the mechanisms underlying the effects of the TCF7L2 rs7903146 variant on glucose peak.

METHODS

We analyzed the metabolic phenotype and the genotype for the TCF7L2 rs7903146 in 630 obese youths with normal (NGT) and impaired (IGT) glucose tolerance. Participants underwent a 3-hour, 9-point OGTT to estimate, using the oral minimal model, the disposition index (DI), the static (φstatic) and dynamic (φdynamic) components β-cell responsiveness and insulin sensitivity (SI). In a subgroup (n = 241) longitudinally followed for 2 years, we estimated the effect of time-to-glucose-peak on glucose tolerance change.

RESULTS

Participants were grouped into early (<30 minutes) and late (≥30 minutes) glucose peakers. A delayed glucose peak was featured by a decline in φstatic (P < .001) in the absence of a difference in φdynamic. The prevalence of T-risk allele for TCF7L2 rs7903146 variant significantly increased in the late peak group. A lower DI was correlated with higher glucose concentration at 1 and 2 hours, whereas SI was inversely associated with 1-hour glucose. Glucose peak <30 minutes was protective toward worsening of glucose tolerance overtime (odds ratio 0.35 [0.15-0.82]; P = .015), with no subjects progressing to NGT or persisting IGT, in contrast to the 40% of progressor in those with late glucose peak.

CONCLUSION

The prevalence of T-risk allele for the TCF7L2 rs7903146 prevailed in the late time-to-glucose peak group, which in turn is associated with impaired β-cell responsiveness to glucose (φ), thereby predisposing to prediabetes and diabetes in obese youths.

The Effect of Prior Gestational Diabetes on the Shape of the Glucose Response Curve during an Oral Glucose Tolerance Test 3 Years after Delivery

OBJECTIVE

Monophasic glucose response (MGR) during an oral glucose tolerance test (OGTT) and gestational diabetes mellitus (GDM) are predictors of type 2 diabetes mellitus (T2DM). We investigated the association between current MGR and (1) glucose tolerance during a pregnancy 3 years before and (2) current glucose tolerance status. We also sought (3) other determinants of MGR. Research Design and Methods. We conducted a nested case-control study of GDM (n = 47 early GDM, diagnosed between 16 and 20 weeks of gestation; n = 40 late GDM, diagnosed between 24 and 28 weeks of gestation) and matched healthy controls (n = 37, normal glucose tolerance during pregnancy) all free from diabetes at follow-up 3.4 ± 0.6 years after delivery. Glucose tolerance was determined by 2-hour 75 g OGTT. Monophasic and biphasic groups were defined based on serum glucose measurements during OGTT.

RESULTS

The biphasic group was younger, had lower triglyceride levels and area under the OGTT glucose curve, and was less frequently diagnosed with early GDM (25 vs. 45%, all p < 0.05). Women with a biphasic response also tended to have lower systolic blood pressure (p < 0.1). No differences were found in fasting and 2-hour glucose and insulin levels, or BMI. According to multiple logistic regression, MGR was associated with prior early GDM (OR 2.14, 95% CI 0.92-4.99) and elevated triglyceride levels (OR 2.28, 95% CI 1.03-5.03/log (mmol/l)).

CONCLUSIONS

We found that more severe, early-onset GDM was an independent predictor of monophasic glucose response suggesting that monophasic response may represent an intermediate state between GDM and manifest type 2 diabetes.

Metabolic characteristics of Africans with normal glucose tolerance and elevated 1-hour glucose: insight from the Africans in America study

INTRODUCTION

Risk of insulin resistance, dyslipidemia, diabetes and cardiac death is increased in Asians and Europeans with normal glucose tolerance (NGT) and 1-hour glucose ≥8.6 mmol/L. As African descent populations often have insulin resistance but a normal lipid profile, the implications for Africans with NGT and glucose ≥8.6 mmol/L (NGT-1-hour-high) are unknown.

OBJECTIVE

We performed oral glucose tolerance tests (OGTTs) in 434 African born-blacks living in Washington, DC (male: 66%, age 38±10 years (mean±SD)) and determined in the NGT group if either glucometabolic or lipid profiles varied according to a 1-hour-glucose threshold of 8.6 mmol/L.

METHODS

Glucose tolerance category was defined by OGTT criteria. NGT was subdivided into NGT-1-hour-high (glucose ≥8.6 mmol/L) and NGT-1-hour-normal (glucose <8.6 mmol/L). Second OGTT were performed in 27% (119/434) of participants 10±7 days after the first. Matsuda Index and Oral Disposition Index measured insulin resistance and beta-cell function, respectively. Lipid profiles were obtained. Comparisons were by one-way analysis of variance with Bonferonni corrections for multiple comparisons. Duplicate tests were assessed by к-statistic.

RESULTS

One-hour-glucose ≥8.6 mmol/L occurred in 17% (47/272) with NGT, 72% (97/134) with pre-diabetes and in 96% (27/28) with diabetes. Both insulin resistance and beta-cell function were worse in NGT-1-hour-high than in NGT-1-hour-normal. Dyslipidemia occurred in both the diabetes and pre-diabetes groups but not in either NGT group. One-hour glucose concentration ≥8.6 mmol/L showed substantial agreement for the two OGTTs (к=0.628).

CONCLUSIONS

Although dyslipidemia did not occur in either NGT group, insulin resistance and beta-cell compromise were worse in NGT-1 hour-high. Subdividing the NGT group at a 1-hour glucose threshold of 8.6 mmol/L may stratify risk for diabetes in Africans.

Clusters of Glycemic Response to Oral Glucose Tolerance Tests Explain Multivariate Metabolic and Anthropometric Outcomes of Bariatric Surgery in Obese Patients

Glycemic responses to bariatric surgery are highly heterogeneous among patients and defining response types remains challenging. Recently developed data-driven clustering methods have uncovered subtle pathophysiologically informative patterns among patients without diabetes. This study aimed to explain responses among patients with and without diabetes to bariatric surgery with clusters of glucose concentration during oral glucose tolerance tests (OGTTs). We assessed 30 parameters at baseline and at four subsequent follow-up visits over one year on 154 participants in the Bialystok Bariatric Surgery Study. We applied latent trajectory classification to OGTTs and multinomial regression and generalized linear mixed models to explain differential responses among clusters. OGTT trajectories created four clusters representing increasing dysglycemias that were discordant from standard diabetes diagnosis criteria. The baseline OGTT cluster increased the predictive power of regression models by over 31% and aided in correctly predicting more than 83% of diabetes remissions. Principal component analysis showed that the glucose homeostasis response primarily occurred as improved insulin sensitivity concomitant with improved the OGTT cluster. In sum, OGTT clustering explained multiple, correlated responses to metabolic surgery. The OGTT is an intuitive and easy-to-implement index of improvement that stratifies patients into response types, a vital first step in personalizing diabetic care in obese subjects.

Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Glycemia and Childhood Glucose Metabolism

OBJECTIVE

This study examined associations of maternal glycemia during pregnancy with childhood glucose outcomes in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) cohort.

RESEARCH DESIGN AND METHODS

HAPO was an observational international investigation that established associations of maternal glucose with adverse perinatal outcomes. The HAPO Follow-up Study included 4,832 children ages 10-14 years whose mothers had a 75-g oral glucose tolerance test (OGTT) at ∼28 weeks of gestation. Of these, 4,160 children were evaluated for glucose outcomes. Primary outcomes were child impaired glucose tolerance (IGT) and impaired fasting glucose (IFG). Additional outcomes were glucose-related measures using plasma glucose (PG), A1C, and C-peptide from the child OGTT.

RESULTS

Maternal fasting plasma glucose (FPG) was positively associated with child FPG and A1C; maternal 1-h and 2-h PG were positively associated with child fasting, 30 min, 1-h, and 2-h PG, and A1C. Maternal FPG, 1-h, and 2-h PG were inversely associated with insulin sensitivity, whereas 1-h and 2-h PG were inversely associated with disposition index. Maternal FPG, but not 1-h or 2-h PG, was associated with child IFG, and maternal 1-h and 2-h PG, but not FPG, were associated with child IGT. All associations were independent of maternal and child BMI. Across increasing categories of maternal glucose, frequencies of child IFG and IGT, and timed PG measures and A1C were higher, whereas insulin sensitivity and disposition index decreased.

CONCLUSIONS

Across the maternal glucose spectrum, exposure to higher levels in utero is significantly associated with childhood glucose and insulin resistance independent of maternal and childhood BMI and family history of diabetes.

Heterogeneity of Metabolic Defects in Type 2 Diabetes and Its Relation to Reactive Oxygen Species and Alterations in Beta-Cell Mass

Type 2 diabetes (T2D) is a complex and heterogeneous disease which affects millions of people worldwide. The classification of diabetes is at an interesting turning point and there have been several recent reports on sub-classification of T2D based on phenotypical and metabolic characteristics. An important, and perhaps so far underestimated, factor in the pathophysiology of T2D is the role of oxidative stress and reactive oxygen species (ROS). There are multiple pathways for excessive ROS formation in T2D and in addition, beta-cells have an inherent deficit in the capacity to cope with oxidative stress. ROS formation could be causal, but also contribute to a large number of the metabolic defects in T2D, including beta-cell dysfunction and loss. Currently, our knowledge on beta-cell mass is limited to autopsy studies and based on comparisons with healthy controls. The combined evidence suggests that beta-cell mass is unaltered at onset of T2D but that it declines progressively. In order to better understand the pathophysiology of T2D, to identify and evaluate novel treatments, there is a need for in vivo techniques able to quantify beta-cell mass. Positron emission tomography holds great potential for this purpose and can in addition map metabolic defects, including ROS activity, in specific tissue compartments. In this review, we highlight the different phenotypical features of T2D and how metabolic defects impact oxidative stress and ROS formation. In addition, we review the literature on alterations of beta-cell mass in T2D and discuss potential techniques to assess beta-cell mass and metabolic defects in vivo.

A1C Underperforms as a Diagnostic Test in Africans Even in the Absence of Nutritional Deficiencies, Anemia and Hemoglobinopathies: Insight From the Africans in America Study

Introduction: To improve detection of undiagnosed diabetes in Africa, there is movement to replace the OGTT with A1C. The performance of A1C in the absence of hemoglobin-related micronutrient deficiencies, anemia and heterozygous hemoglobinopathies is unknown. Therefore, we determined in 441 African-born blacks living in America [male: 65% (281/441), age: 38 ± 10 y (mean ± SD), BMI: 27.5 ± 4.4 kg/m²] (1) nutritional and hematologic profiles and (2) glucose tolerance categorization by OGTT and A1C. Methods: Hematologic and nutritional status were assessed. Hemoglobin <11 g/dL occurred in 3% (11/441) of patients and led to exclusion. A1C and OGTT were performed in the remaining 430 participants. ADA thresholds for A1C and OGTT were used. Diagnosis by A1C required meeting either A1C-alone or A1C&OGTT criteria. Diagnosis by OGTT-alone required detection by OGTT and not A1C. Results: Hemoglobin, mean corpuscular volume and red blood cell distribution width were 14.0 ± 1.3 g/dL, 85.5 ± 5.3 fL, and 13.2 ± 1.2% respectively. B12, folate, and iron deficiency occurred in 1% (5/430), 0% (0/430), and 4% (12/310), respectively. Heterozygous hemoglobinopathy prevalence was 18% (78/430). Overall, diabetes prevalence was 7% (32/430). A1C detected diabetes in 32% (10/32) but OGTT-alone detected 68% (22/32). Overall prediabetes prevalence was 41% (178/430). A1C detected 57% (102/178) but OGTT-alone identified 43% (76/178). After excluding individuals with heterozygous hemoglobinopathies, the rate of missed diagnosis by A1C of abnormal glucose tolerance did not change (OR: 0.99, 95% CI: 0.61, 1.62). Conclusions: In nutritionally replete Africans without anemia or heterozygous hemoglobinopathy, if only A1C is used, ~60% with diabetes and ~40% with prediabetes would be undiagnosed. Clinical Trial Registration:: www.ClinicalTrials.gov, Identifier: NCT00001853.

The Shape of the Glucose Response Curve During an Oral Glucose Tolerance Test: Forerunner of Heightened Glycemic Failure Rates and Accelerated Decline in β-Cell Function in TODAY

OBJECTIVE

Obese youth without diabetes with monophasic oral glucose tolerance test (OGTT) glucose response curves have lower insulin sensitivity and impaired β-cell function compared with those with biphasic curves. The OGTT glucose response curve has not been studied in youth-onset type 2 diabetes. Here we test the hypothesis that the OGTT glucose response curve at randomization in youth in the TODAY (Treatment Options for Type 2 Diabetes in Adolescents and Youth) study forecasts heightened glycemic failure rates and accelerated decline in β-cell function.

RESEARCH DESIGN AND METHODS

OGTTs (n = 662) performed at randomization were categorized as monophasic, biphasic, or incessant increase. Demographics, insulin sensitivity (1/fasting insulin), C-peptide index (△C₃₀/△G₃₀), and β-cell function relative to insulin sensitivity (oral disposition index [oDI]) were compared among the three groups.

RESULTS

At randomization, 21.7% had incessant increase, 68.6% monophasic, and 9.7% biphasic glucose response curves. The incessant increase group had similar insulin sensitivity but significantly lower C-peptide index and lower oDI, despite similar diabetes duration, compared with the other two groups. Glycemic failure rates were higher in the incessant increase group (58.3%) versus the monophasic group (42.3%) versus the biphasic group (39.1%) (P < 0.0001). The 6-month decline in C-peptide index (32.8% vs. 18.1% vs. 13.2%) and oDI (32.2% vs. 11.6% vs. 9.1%) was greatest in incessant increase versus monophasic and biphasic with no difference in insulin sensitivity.

CONCLUSIONS

In the TODAY study cohort, an incessant increase in the OGTT glucose response curve at randomization reflects reduced β-cell function and foretells increased glycemic failure rates with accelerated deterioration in β-cell function independent of diabetes duration and treatment assignment compared with monophasic and biphasic curves. The shape of the OGTT glucose response curve could be a metabolic biomarker prognosticating the response to therapy in youth with type 2 diabetes.