Glucose patterns during an oral glucose tolerance test and associations with future diabetes, cardiovascular disease and all-cause mortality rate

Characteristics and Risk of Diabetes in People With Rare Glucose Response Curve During an Oral Glucose Tolerance Test

CONTEXT

Existing differences in persons with lower 30- or 60-minute plasma glucose (PG) levels during 75-g oral glucose tolerance test (OGTT) than fasting PG remain unclear.

OBJECTIVE

To clarify the characteristics of persons whose PG levels decrease after glucose administration during OGTT and their risk of incidence of diabetes in a Japanese general population.

METHODS

In this cohort study, a total of 3995 men and 3500 women (mean age 56.7 years) without diabetes were classified into 3 groups: (1) PG at both 30 and 60 minutes ≥ fasting PG; (2) PG at 30 minutes ≥ fasting PG and PG at 60 minutes < fasting PG; (3) PG at 30 minutes < fasting PG. The characteristics and the risk of diabetes onset were analyzed using ordered logistic regression and Cox proportional hazard regression, respectively.

RESULTS

Among 7495 participants, the numbers of individuals in the group 1, 2, and 3 were 6552, 769, and 174, respectively. The glucose response curve of the group 3 was boat shaped. Group 3 had the youngest age, lowest percentage of men, and best health condition, followed by groups 2 and 1. Among 3897 participants analyzed prospectively, 434 developed diabetes during the mean follow-up period of 5.8 years. The hazard ratio for diabetes onset in the group 2 was 0.30 with reference to the group 1. No-one in group 3 developed diabetes.

CONCLUSION

People with lower 30-minute PG than fasting PG tended to be women, young, healthy, and at low risk of diabetes onset.

Is glucose pattern of OGTT associated with late-onset gestational diabetes and adverse pregnant outcomes?

BACKGROUND

The heterogeneity of oral glucose tolerance test (OGTT) patterns during pregnancy remains unclear. This study aims to identify latent OGTT patterns in pregnant women and investigate the high-risk population for late-onset gestational diabetes mellitus (GDM).

METHODS

This study including 17,723 participants was conducted from 2018 to 2021. Latent mixture modeling was used to identify subgroups. Modified Poisson regression was performed to explore the relationship between OGTT patterns and late-onset GDM or adverse perinatal outcomes.

RESULTS

Three distinct glucose patterns, high, medium, and low glucose levels (HG, MG, and LG patterns) were identified. The HG pattern represented 28.5% of the participants and 5.5% of them developed late-onset GDM. A five-fold higher risk of late-onset GDM was found in HG pattern than in LG pattern (relative risk [RR]: 5.17, 95% confidence interval [CI]: 3.38-7.92) after adjustment. Participants in HG pattern were more likely to have macrosomia, large for gestational age, preterm birth, and cesarean deliveries, with RRs of 1.59 (1.31-1.93), 1.55 (1.33-1.82), 1.30 (1.02-1.64) and 1.15 (1.08-1.23), respectively.

CONCLUSION

Three distinct OGTT patterns presented different risks of late-onset GDM and adverse perinatal outcomes, indicating that timely monitoring of glucose levels after OGTT should be performed in pregnant women with HG pattern.

Predisposition to Type 2 Diabetes in Aspects of the Glycemic Curve and Glycated Hemoglobin in Healthy, Young Adults: A Cross-sectional Study

OBJECTIVES

Our aim in this study was to identify predictors for diabetes among the characteristics of the glycemic curve and glycated hemoglobin (A1C) in healthy, young adults.

METHODS

We used a cross-sectional study to establish predictors for diabetes based on earlier studies and evaluated occurrence of the condition in 81 healthy, young adult subjects. These volunteers underwent analysis of fasting plasma glucose, oral glucose tolerance test plasma glucose, A1C, and inflammatory markers (leukocytes, monocytes, and C-reactive protein). The nonparametric Mann-Whitney U test, Fisher's exact test, chi-square test, Kruskal-Wallis test, and multiple-comparisons test were used to analyze the data.

RESULTS

We studied 2 age groups, homogeneous in terms of family history of diabetes: one group ranged in age from ≥18 to <28 years (median 20 years; body mass index [BMI] 24 kg/m2) and the other group ranged in age from ≥28 to <45 years (median 35 years; BMI 24 kg/m2). The older group had a higher incidence of predictors (p=0.0005) and was associated with the predictors 30-minute blood glucose ≥164 mg/dL (p=0.0190), 60-minute blood glucose ≥125 mg/dL (p=0.0346), and A1C ≥5.5% (p=0.0162), with a monophasic glycemic curve (p=0.007). The younger group was associated with the 2-hour plasma glucose predictor ≥140 mg/dL (p=0.014). All subjects had fasting glucose in the normal range.

CONCLUSIONS

Healthy, young adults may already have predictors of diabetes, identified mainly by aspects of the glycemic curve and A1C, but at more modest levels than those with prediabetes.

Association between microbial composition, diversity, and function of the maternal gastrointestinal microbiome with impaired glucose tolerance on the glucose challenge test

Over the last two decades, the incidence of gestational diabetes (GDM) has almost doubled resulting in almost 9% of pregnant women diagnosed with GDM. Occurring more frequently than GDM is impaired glucose tolerance (IGT), also known as pre-diabetes, but it has been understudied during pregnancy resulting in a lack of clinical recommendations of maternal and fetal surveillance. The purpose of this retrospective, cross-sectional study was to examine the association between microbial diversity and function of the maternal microbiome with IGT while adjusting for confounding variables. We hypothesized that reduced maternal microbial diversity and increased gene abundance for insulin resistance function will be associated with IGT as defined by a value greater than 140 mg/dL on the glucose challenge test. In the examination of microbial composition between women with IGT and those with normal glucose tolerance (NGT), we found five taxa which were significantly different. Taxa higher in participants with impaired glucose tolerance were Ruminococcacea (p = 0.01), Schaalia turicensis (p<0.05), Oscillibacter (p = 0.03), Oscillospiraceae (p = 0.02), and Methanobrevibacter smithii (p = 0.04). When we further compare participants who have IGT by their pre-gravid BMI, five taxa are significantly different between the BMI groups, Enterobacteriaceae, Dialister micraerophilus, Campylobacter ureolyticus, Proteobacteria, Streptococcus Unclassified (species). All four metrics including the Shannon (p<0.00), Simpson (p<0.00), Inverse Simpson (p = 0.04), and Chao1 (p = 0.04), showed a significant difference in alpha diversity with increased values in the impaired glucose tolerance group. Our study highlights the important gastrointestinal microbiome changes in women with IGT during pregnancy. Understanding the role of the microbiome in regulating glucose tolerance during pregnancy helps clinicians and researchers to understand the importance of IGT as a marker for adverse maternal and neonatal outcomes.

No Indices of Increased Type 2 Diabetes Risk in Individuals with Reactive Postprandial Hypoglycemia

Reactive postprandial hypoglycemia (RPH) is an understudied condition that lacks clinical definition, knowledge of future health implications, and an understanding of precise underlying mechanisms. Therefore, our study aimed to assess the glycemic response after glucose ingestion in individuals several years after the initial evaluation of RPH and to compare glucose regulation in individuals with RPH vs. healthy volunteers. We assessed the inter- and intra-individual differences in glucose, insulin, and C-peptide concentrations during 5-h oral glucose tolerance tests (OGTTs); the surrogate markers of insulin resistance (HOMA-IR and Matsuda index); and beta-cell function (distribution index and insulinogenic index). The study included 29 subjects with RPH (all females, aged 39 (28, 46) years) and 11 sex-, age-, and body mass index (BMI)-matched controls. No biochemical deterioration of beta-cell secretory capacity and no progression to dysglycemia after 6.4 ± 4.2 years of follow-up were detected. RPH subjects were not insulin resistant, and their insulin sensitivity did not deteriorate. RPH subjects exhibited no differences in concentrations or in the shape of the glucose-insulin curves during the 5-h OGTTs compared to age- and BMI-matched controls. No increased incident type 2 diabetes risk indices were evident in individuals with RPH. This dictates the need for further research to investigate the magnitude of future diabetes risk in individuals experiencing RPH.

Vascular complications in prediabetes and type 2 diabetes: a continuous process arising from a common pathology

The term, "prediabetes", describes a state of hyperglycaemia that is intermediate between true normoglycaemia and the diagnostic cut-offs for indices of glycaemia that are used to diagnose type 2 diabetes. The presence of prediabetes markedly increases the risk of developing type 2 diabetes. Numerous randomized, controlled evaluations of various agents have demonstrated significant prevention or delay of the onset of type 2 diabetes in subjects with prediabetes. Intensive lifestyle interventions and metformin have been studied most widely, with the lifestyle intervention being more effective in the majority of subjects. The application of therapeutic interventions at the time of prediabetes to preserve long-term outcomes has been controversial, however, due to a lack of evidence relating to the pathogenic effects of prediabetes and the effectiveness of interventions to produce a long-term clinical benefit. Recent studies have confirmed that prediabetes, however defined, is associated with a significantly increased risk of macrovascular and microvascular complications essentially identical to those of diabetes, and also with subclinical derangements of the function of microvasculature and neurons that likely signify increased risk of compilations in future. Normoglycaemia, prediabetes and type 2 diabetes appear to be part of a continuum of increased risk of adverse outcomes. Long-term (25-30 years) post-trial follow up of two major diabetes prevention trials have shown that short-term interventions to prevent diabetes lead to long-term reductions in the risk of complications. These findings support the concept of therapeutic intervention to preserve long-term health in people with prediabetes before type 2 diabetes becomes established.

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.

Apoptotic susceptibility of pancreatic alpha cells to environmentally relevant dose levels of bisphenol-A versus dibutyl phthalate is mediated by HSP60/caspase-3 expression in male albino rats

Unfortunately, humanity is exposed to mixed plasticizers such as bisphenol-A (BPA) and dibutyl phthalate (DBP) that are leached from the daily used plastic products. Previous studies have demonstrated their potential in pancreatic beta cell injury and diabetes induction. The study hypothesized that both compounds would affect the pancreatic alpha cells in albino rats when administered at environmentally relevant doses. Heat shock protein 60 (HSP60) and caspase-3 protein expression was also investigated as potential mechanisms. Thirty-six male Wistar albino rats were separated into four equal groups: control, BPA alone, DBP alone, and BPA + DBP combined groups. BPA and DBP were given in drinking water for 45 days in a dose of 4.5 and 0.8 µg/L, respectively. Fasting blood glucose, serum insulin, pancreatic tissue levels of malondialdehyde, and superoxide dismutase were measured. Pancreatic sections were subjected to hematoxylin & eosin (H & E) staining, glucagon, HSP60, and caspase-3 immunohistochemistry. Although all three experimental groups showed diffuse islet cell HSP60 immunoreactivity, rats exposed to BPA alone showed α-cell-only apoptosis, indicated by H & E changes and caspase-3 immunoreactivity, associated with reduced glucagon immunoreaction. However, rats exposed to DBP alone showed no changes in either α or β-cells. Both combined-exposed animals displayed α and β apoptotic changes associated with islet atrophy and reduced glucagon expression. In conclusion, the study suggested HSP60/caspase-3 interaction, caspase-3 activation, and initiation of apoptosis in α-cell only for BPA-alone exposure group, meanwhile DBP alone did not progress to apoptosis. Interestingly, both α/β cell effect was observed in the mixed group implying synergetic/additive action of both chemicals when combined.

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.

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.

How Can We Adopt the Glucose Tolerance Test to Facilitate Predicting Pregnancy Outcome in Gestational Diabetes Mellitus?

BACKGROUND

We investigated how 100-g oral glucose tolerance test (OGTT) results can be used to predict adverse pregnancy outcomes in gestational diabetes mellitus (GDM) patients.

METHODS

We analyzed 1,059 pregnant women who completed the 100-g OGTT between 24 and 28 weeks of gestation. We compared the risk of adverse pregnancy outcomes according to OGTT patterns by latent profile analysis (LPA), numbers to meet the OGTT criteria, and area under the curve (AUC) of the OGTT graph. Adverse pregnancy outcomes were defined as a composite of preterm birth, macrosomia, large for gestational age, low APGAR score at 1 minute, and pregnancy-induced hypertension.

RESULTS

Overall, 257 participants were diagnosed with GDM, with a median age of 34 years. An LPA led to three different clusters of OGTT patterns; however, there were no significant associations between the clusters and adverse pregnancy outcomes after adjusting for confounders. Notwithstanding, the risk of adverse pregnancy outcome increased with an increase in number to meet the OGTT criteria (P for trend=0.011); odds ratios in a full adjustment model were 1.27 (95% confidence interval [CI], 0.72 to 2.23), 2.16 (95% CI, 1.21 to 3.85), and 2.32 (95% CI, 0.66 to 8.15) in those meeting the 2, 3, and 4 criteria, respectively. The AUCs of the OGTT curves also distinguished the patients at risk of adverse pregnancy outcomes; the larger the AUC, the higher the risk (P for trend=0.007).

CONCLUSION

The total number of abnormal values and calculated AUCs for the 100-g OGTT may facilitate tailored management of patients with GDM by predicting adverse pregnancy outcomes.

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.

Measuring Postprandial Metabolic Flexibility to Assess Metabolic Health and Disease

Metabolic abnormalities substantially increase the risk of noncommunicable diseases, which are among the leading causes of mortality globally. Mitigating and preventing these adverse consequences remains challenging due to a limited understanding of metabolic health. Metabolic flexibility, a key tenet of metabolic health, encompasses the responsiveness of interrelated pathways to maintain energy homeostasis throughout daily physiologic challenges, such as the response to meal challenges. One critical underlying research gap concerns the measurement of postprandial metabolic flexibility, which remains incompletely understood. We concisely review the methodology for assessment of postprandial metabolic flexibility in recent human studies. We identify 3 commonalities of study design, specifically the nature of the challenge, nature of the response measured, and approach to data analysis. Primary interventions were acute short-term nutrition challenges, including single- and multiple-macronutrient tolerance tests. Postmeal challenge responses were measured via laboratory assays and instrumentation, based on a diverse set of metabolic flexibility indicators [e.g., energy expenditure (whole-body indirect calorimetry), glucose and insulin kinetics, metabolomics, transcriptomics]. Common standard approaches have been diabetes-centric with single-macronutrient challenges (oral-glucose-tolerance test) to characterize the postprandial response based on glucose and insulin metabolism; or broad measurements of energy expenditure with calculated macronutrient oxidation via indirect calorimetry. Recent methodological advances have included the use of multiple-macronutrient meal challenges that are more representative of physiologic meals consumed by free-living humans, combinatorial approaches for assays and instruments, evaluation of other metabolic flexibility indicators via precision health, systems biology, and temporal perspectives. Omics studies have identified potential novel indicators of metabolic flexibility, which provide greater granularity to prior evidence from canonical approaches. In summary, recent findings indicate the potential for an expanded understanding of postprandial metabolic flexibility, based on nonclassical measurements and methodology, which could represent novel dynamic indicators of metabolic diseases.

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.

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.

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.

Effects of Interrupting Prolonged Sitting with Physical Activity Breaks on Blood Glucose, Insulin and Triacylglycerol Measures: A Systematic Review and Meta-analysis

BACKGROUND

Physical activity (PA) breaks in sitting time might attenuate metabolic markers relevant to the prevention of type 2 diabetes.

OBJECTIVES

The primary aim of this paper was to systematically review and meta-analyse trials that compared the effects of breaking up prolonged sitting with bouts of PA throughout the day (INT) versus continuous sitting (SIT) on glucose, insulin and triacylglycerol (TAG) measures. A second aim was to compare the effects of INT versus continuous exercise (EX) on glucose, insulin and TAG measures.

METHODS

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendations. Eligibility criteria consisted of trials comparing INT vs. SIT or INT vs. one bout of EX before or after sitting, in participants aged 18 or above, who were classified as either metabolically healthy or impaired, but not with other major health conditions such as chronic obstructive pulmonary disease or peripheral arterial disease.

RESULTS

A total of 42 studies were included in the overall review, whereas a total of 37 studies were included in the meta-analysis. There was a standardised mean difference (SMD) of - 0.54 (95% CI - 0.70, - 0.37, p = 0.00001) in favour of INT compared to SIT for glucose. With respect to insulin, there was an SMD of - 0.56 (95% CI - 0.74, - 0.38, p = 0.00001) in favour of INT. For TAG, there was an SMD of - 0.26 (95% CI - 0.44, - 0.09, p = 0.002) in favour of INT. Body mass index (BMI) was associated with glucose responses (β = - 0.05, 95% CI - 0.09, - 0.01, p = 0.01), and insulin (β = - 0.05, 95% CI - 0.10, - 0.006, p = 0.03), but not TAG (β = 0.02, 95% CI - 0.02, 0.06, p = 0.37). When energy expenditure was matched, there was an SMD of - 0.26 (95% CI - 0.50, - 0.02, p = 0.03) in favour of INT for glucose, but no statistically significant SMDs for insulin, i.e. 0.35 (95% CI - 0.37, 1.07, p = 0.35), or TAG i.e. 0.08 (95% CI - 0.22, 0.37, p = 0.62). It is worth noting that there was possible publication bias for TAG outcomes when PA breaks were compared with sitting.

CONCLUSION

The use of PA breaks during sitting moderately attenuated post-prandial glucose, insulin, and TAG, with greater glycaemic attenuation in people with higher BMI. There was a statistically significant small advantage for PA breaks over continuous exercise for attenuating glucose measures when exercise protocols were energy matched, but no statistically significant differences for insulin and TAG. PROSPERO Registration: CRD42017080982.

PROSPERO REGISTRATION

CRD42017080982.

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.

Heritability of Curve Patterns in Oral Glucose Tolerance Test

Type 2 diabetes, which is caused by both genetic and environmental factors, may be diagnosed using the oral glucose tolerance test (OGTT). Recent studies demonstrated specific patterns in glucose curves during OGTT associated with cardiometabolic risk profiles. As the relative contribution of genetic and environmental influences on glucose curve patterns is unknown, we aimed to investigate the heritability of these patterns. We studied twins from the Danish GEMINAKAR cohort aged 18-67 years and free from diabetes at baseline during 1997-2000; glucose concentrations were measured three times during a 2-h OGTT. Heterogeneity of the glucose response during OGTT was examined with latent class mixed-effects models, evaluating goodness of fit by Bayes information criterion. The genetic influence on curve patterns was estimated using quantitative genetic modeling based on linear structural equations. Overall, 1455 twins (41% monozygotic) had valid glucose concentrations measured from the OGTT, and four latent classes with different glucose response patterns were identified. Statistical modeling demonstrated genetic influence for belonging to a specific class or not, with heritability estimated to be between 45% and 67%. During ∼12 years of follow-up, the four classes were each associated with different incidence of type 2 diabetes. Hence, glucose response curve patterns associated with type 2 diabetes risk appear to be moderately to highly heritable.

An Acute, Placebo-Controlled, Single-Blind, Crossover, Dose-Response, Exploratory Study to Assess the Effects of New Zealand Pine Bark Extract (Enzogenol®) on Glycaemic Responses in Healthy Participants

An acute, placebo-controlled, single-blind, crossover, dose-response, exploratory study was designed to investigate the hypoglycaemic effects of New Zealand pine bark extract (Enzogenol®). Twenty-five healthy participants categorised into having a monophasic or complex (biphasic or triphasic) glucose curve shape at the control visit consumed a placebo and Enzogenol® (50 and 400 mg) on three separate occasions before an oral glucose tolerance test (OGTT). In the monophasic group, 50 and 400 mg of Enzogenol® significantly reduced the mean glucose incremental area under the curve (iAUC) compared to control 241.3 ± 20.2 vs. 335.4 ± 34.0 mmol/L·min, p = 0.034 and 249.3 ± 25.4 vs. 353.6 ± 31.5 mmol/L·min, p = 0.012, respectively. The 400 mg dose further reduced the percentage increment of postprandial glucose (%PG) 31.4% ± 7.9% vs. 47.5% ± 8.6%, p = 0.010, glucose peak 7.9 ± 0.3 vs. 8.9 ± 0.3 mmol/L, p = 0.025 and 2h-OGTT postprandial glucose (2hPG) 6.1 ± 0.3 vs. 6.7 ± 0.3 mmol/L, p = 0.027. Glucose iAUC was not significantly different in the complex group, except for reductions in %PG 28.7% ± 8.2% vs. 43.4% ± 5.9%, p = 0.012 after 50 mg dose and 27.7% ± 5.4% vs. 47.3% ± 7.2%, p = 0.025 after 400 mg dose. The results suggest that Enzogenol® may have hypoglycaemic effects in healthy participants, especially those exhibiting monophasic shapes.

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.

Disease or not, aging is easily treatable

Is aging a disease? It does not matter because aging is already treated using a combination of several clinically-available drugs, including rapamycin. Whether aging is a disease depends on arbitrary definitions of both disease and aging. For treatment purposes, aging is a deadly disease (or more generally, pre-disease), despite being a normal continuation of normal organismal growth. It must and, importantly, can be successfully treated, thereby delaying classic age-related diseases such as cancer, cardiovascular and metabolic diseases, and neurodegeneration.

The oral glucose tolerance test-derived incremental glucose peak is associated with greater arterial stiffness and maladaptive arterial remodeling: The Maastricht Study

Background

Daily glucose variability may contribute to vascular complication development irrespective of mean glucose values. The incremental glucose peak (IGP) during an oral glucose tolerance test (OGTT) can be used as a proxy of glucose variability. We investigated the association of IGP with arterial stiffness, arterial remodeling, and microvascular function, independent of HbA_1c and other confounders.

Methods

IGP was calculated as the peak minus baseline plasma glucose value during a seven-point OGTT in 2758 participants (age: 60 ± 8 years; 48% women) of The Maastricht Study, an observational population-based cohort. We assessed the cross-sectional associations between IGP and arterial stiffness (carotid-femoral pulse wave velocity [cf-PWV], carotid distensibility coefficient [carDC]), arterial remodeling (carotid intima-media thickness [cIMT]; mean [CWS_mean] and pulsatile [CWS_puls] circumferential wall stress), and microvascular function (retinal arteriolar average dilatation; heat-induced skin hyperemia) via multiple linear regression with adjustment for age, sex, HbA_1c, cardiovascular risk factors, lifestyle factors, and medication use.

Results

Higher IGP was independently associated with higher cf-PWV (regression coefficient [B]: 0.054 m/s [0.020; 0.089]) and with higher CWS_mean (B: 0.227 kPa [0.008; 0.446]). IGP was not independently associated with carDC (B: − 0.026 10⁻³/kPa [− 0.112; 0.060]), cIMT (B: − 2.745 µm [− 5.736; 0.245]), CWS_puls (B: 0.108 kPa [− 0.054; 0.270]), retinal arteriolar average dilatation (B: − 0.022% [− 0.087; 0.043]), or heat-induced skin hyperemia (B: − 1.380% [− 22.273; 19.513]).

Conclusions

IGP was independently associated with aortic stiffness and maladaptive carotid remodeling, but not with carotid stiffness, cIMT, and microvascular function measures. Future studies should investigate whether glucose variability is associated with cardiovascular disease.

Type 2 Diabetes: Multiple Genes, Multiple Diseases

PURPOSE OF REVIEW

Type 2 diabetes (T2D), which accounts for the vast majority of diabetes cases, is essentially a diagnosis of exclusion in current clinical practice. Therefore, it is not surprising that T2D is heterogenous in terms of patients' clinical presentation, disease course, and response to treatment. This review summarizes published attempts to improve diabetes subclassification, with a particular focus on the role of genetics.

RECENT FINDINGS

A handful of diabetes subclassification schemas have been proposed using clinical data (patient characteristics and laboratory values), with some subgroups associated with distinct management trends or complication risks. However, phenotypically driven classifications suffer from dependencies on time of variable measurement and are not readily linked to disease mechanism. Germline genetic data, in contrast, are essentially unchanged over a person's lifetime and rooted in mechanism. Clustering of T2D genetic loci has identified at least five groupings of loci representing mechanisms of disease that may aid in deconstructing heterogeneity of T2D, but further work is needed to determine clinical utility. Exciting progress in subclassification of diabetes has demonstrated initial steps in deconstructing disease heterogeneity. Incorporation of genetics into classification schemas will require additional research but has the potential to improve our understanding and management of T2D, both as a single disease and as a part of an integrated metabolic disease network.

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

CONTEXT

Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c).

OBJECTIVE

To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT).

DESIGN AND SETTING

We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes.

RESULTS

The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker.

CONCLUSIONS

The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.

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.

High-performance medicine: the convergence of human and artificial intelligence

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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

OBJECTIVE

Glucose measurements during an oral glucose tolerance test (OGTT) are useful in predicting diabetes and its complications. However, knowledge of the pathophysiology underlying differences in glucose curve shapes is sparse. We examined the pathophysiological characteristics that create different glucose curve patterns and studied their stability and reproducibility over 3 years of follow-up.

RESEARCH DESIGN AND METHODS

We analyzed data from participants without diabetes from the observational cohort from the European Group for the Study of Insulin Resistance: Relationship between Insulin Sensitivity and Cardiovascular Disease study; participants had a five-time point OGTT at baseline (n = 1,443) and after 3 years (n = 1,045). Measures of insulin sensitivity and secretion were assessed at baseline with a euglycemic-hyperinsulinemic clamp and intravenous glucose tolerance test. Heterogeneous glucose response patterns during the OGTT were identified using latent class trajectory analysis at baseline and at follow-up. Transitions between classes were analyzed with multinomial logistic regression models.

RESULTS

We identified four different glucose response patterns, which differed with regard to insulin sensitivity and acute insulin response, obesity, and plasma levels of lipids and inflammatory markers. Some of these associations were confirmed prospectively. Time to glucose peak was driven mainly by insulin sensitivity, whereas glucose peak size was related to both insulin sensitivity and secretion. The glucose patterns identified at follow-up were similar to those at baseline, suggesting that the latent class method is robust. We integrated our classification model into an easy-to-use online application that facilitates the assessment of glucose curve patterns for other studies.

CONCLUSIONS

The latent class analysis approach is a pathophysiologically insightful way to classify individuals without diabetes based on their response to glucose during an OGTT.

Risk of Cardiovascular Disease and Death in Individuals With Prediabetes Defined by Different Criteria: The Whitehall II Study

OBJECTIVE

We compared the risk of cardiovascular disease (CVD) and all-cause mortality in subgroups of prediabetes defined by fasting plasma glucose (FPG), 2-h plasma glucose (2hPG), or HbA_1c.

RESEARCH DESIGN AND METHODS

In the Whitehall II cohort, 5,427 participants aged 50-79 years and without diabetes were followed for a median of 11.5 years. A total of 628 (11.6%) had prediabetes by the World Health Organization (WHO)/International Expert Committee (IEC) criteria (FPG 6.1-6.9 mmol/L and/or HbA_1c 6.0-6.4%), and 1,996 (36.8%) by the American Diabetes Association (ADA) criteria (FPG 5.6-6.9 mmol/L and/or HbA_1c 5.7-6.4%). In a subset of 4,730 individuals with additional measures of 2hPG, 663 (14.0%) had prediabetes by 2hPG. Incidence rates of a major event (nonfatal/fatal CVD or all-cause mortality) were compared for different definitions of prediabetes, with adjustment for relevant confounders.

RESULTS

Compared with that for normoglycemia, incidence rate in the context of prediabetes was 54% higher with the WHO/IEC definition and 37% higher with the ADA definition (P < 0.001) but declining to 17% and 12% after confounder adjustment (P ≥ 0.111). Prediabetes by HbA_1c was associated with a doubling in incidence rate for both the IEC and ADA criteria. However, upon adjustment, excess risk was reduced to 13% and 17% (P ≥ 0.055), respectively. Prediabetes by FPG or 2hPG was not associated with an excess risk in the adjusted analysis.

CONCLUSIONS

Prediabetes defined by HbA_1c was associated with a worse prognosis than prediabetes defined by FPG or 2hPG. However, the excess risk among individuals with prediabetes is mainly explained by the clustering of other cardiometabolic risk factors associated with hyperglycemia.

One-hour glucose value as a long-term predictor of cardiovascular morbidity and mortality: the Malmö Preventive Project

OBJECTIVE

To examine the predictive capability of a 1-h vs 2-h postload glucose value for cardiovascular morbidity and mortality.

DESIGN

Prospective, population-based cohort study (Malmö Preventive Project) with subject inclusion 1974-1992.

METHODS

4934 men without known diabetes and cardiovascular disease, who had blood glucose (BG) measured at 0, 20, 40, 60, 90 and 120 min during an OGTT (30 g glucose per m² body surface area), were followed for 27 years. Data on cardiovascular events and death were obtained through national and local registries. Predictive capabilities of fasting BG (FBG) and glucose values obtained during OGTT alone and added to a clinical prediction model comprising traditional cardiovascular risk factors were assessed using Harrell's concordance index (C-index) and integrated discrimination improvement (IDI).

RESULTS

Median age was 48 (25th-75th percentile: 48-49) years and mean FBG 4.6 ± 0.6 mmol/L. FBG and 2-h postload BG did not independently predict cardiovascular events or death. Conversely, 1-h postload BG predicted cardiovascular morbidity and mortality and remained an independent predictor of cardiovascular death (HR: 1.09, 95% CI: 1.01-1.17, P = 0.02) and all-cause mortality (HR: 1.10, 95% CI: 1.05-1.16, P < 0.0001) after adjusting for various traditional risk factors. Clinical risk factors with added 1-h postload BG performed better than clinical risk factors alone, in predicting cardiovascular death (likelihood-ratio test, P = 0.02) and all-cause mortality (likelihood-ratio test, P = 0.0001; significant IDI, P = 0.0003).

CONCLUSION

Among men without known diabetes, addition of 1-h BG, but not FBG or 2-h BG, to clinical risk factors provided incremental prognostic yield for prediction of cardiovascular death and all-cause mortality.

Fiber Intake and Insulin Resistance in 6374 Adults: The Role of Abdominal Obesity

A cross-sectional design was used to evaluate the relationship between fiber intake and insulin resistance, indexed using HOMA (homeostatic model assessment), in a National Health and Nutrition Examination Study (NHANES) sample of 6374 U.S. adults. Another purpose was to test the influence of covariates on the association. A third aim was to compare HOMA levels between two groups based on the recommended intake of 14 grams of fiber per 1000 kilocalories (kcal). Fiber intake was measured using a 24-hour recall. With demographic variables controlled, results showed that HOMA differed across High, Moderate, and Low fiber categories (F = 5.4, p = 0.0072). Adjusting for the demographic variables, the possible misreporting of energy intake, smoking, and physical activity strengthened the relationship (F = 8.0, p = 0.0009), which remained significant after adjusting for body fat (F = 7.0, p = 0.0019) and body mass index (BMI) (F = 4.9, p = 0.0108), with the other covariates. However, the fiber-HOMA relationship was eliminated after adjusting for waist circumference (F = 2.3, p = 0.1050). Dividing participants based on the recommended 14-gram standard resulted in meaningful HOMA differences (F = 16.4, p = 0.0002), and the association was not eliminated after controlling for waist circumference. Apparently, adults with high fiber consumption have less insulin resistance than their counterparts. However, much of the association is due to differences in waist circumference, unless the recommended intake of fiber is attained.