Glucose patterns during the OGTT and risk of future diabetes in an urban Indian population: The CARRS study

The relationship between glucose patterns in OGTT and adverse pregnancy outcomes in twin pregnancies

BACKGROUND

Traditional fixed thresholds for oral glucose tolerance test (OGTT) results may inadequately prevent adverse pregnancy outcomes in twin pregnancies. This study explores latent OGTT patterns and their association with adverse outcomes.

METHODS

This study retrospectively analyzed 2644 twin pregnancies using latent mixture models to identify glucose level patterns (high, HG; medium, MG; and low, LG) and their relationship with maternal/neonatal characteristics, gestational age at delivery, and adverse outcomes.

RESULTS

Three distinct glucose patterns, HG, MG, and LG patterns were identified. Among the participants, 16.3% were categorized in the HG pattern. After adjustment, compared with the LG pattern, the HG pattern was associated with a 1.79-fold, 1.66-fold, and 1.32-fold increased risk of stillbirth, neonatal respiratory distress, and neonatal hyperbilirubinemia, respectively. The risk of neonatal ICU admission for MG and HG patterns increased by 1.22 times and 1.32 times, respectively, compared with the LG pattern. As gestational weeks increase, although there is an overlap in the confidence intervals between the HG pattern and other patterns in the restricted cubic splines analysis, the trend suggests that pregnant women with the HG pattern are more likely to face risks of their newborns requiring neonatal intensive care unit admission, and adverse comprehensive outcomes, compared with other patterns. In addition, with age and body mass index increasing in HG mode, gestation weeks at delivery tend to be later than in other modes.

CONCLUSION

Distinct OGTT glucose patterns in twin pregnancies correlate with different risks of adverse perinatal outcomes. The HG pattern warrants closer glucose monitoring and targeted intervention.

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.

Prediabetes and Cardiometabolic Risk: The Need for Improved Diagnostic Strategies and Treatment to Prevent Diabetes and Cardiovascular Disease

The progression from prediabetes to type-2 diabetes depends on multiple pathophysiological, clinical, and epidemiological factors that generally overlap. Both insulin resistance and decreased insulin secretion are considered to be the main causes. The diagnosis and approach to the prediabetic patient are heterogeneous. There is no agreement on the diagnostic criteria to identify prediabetic subjects or the approach to those with insufficient responses to treatment, with respect to regression to normal glycemic values or the prevention of complications. The stratification of prediabetic patients, considering the indicators of impaired fasting glucose, impaired glucose tolerance, or HbA1c, can help to identify the sub-phenotypes of subjects at risk for T2DM. However, considering other associated risk factors, such as impaired lipid profiles, or risk scores, such as the Finnish Diabetes Risk Score, may improve classification. Nevertheless, we still do not have enough information regarding cardiovascular risk reduction. The sub-phenotyping of subjects with prediabetes may provide an opportunity to improve the screening and management of cardiometabolic risk in subjects with prediabetes.

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.

Lower-than-normal glycemic levels to achieve optimal reduction of diabetes risk among individuals with prediabetes: A prospective cohort study

AIMS/HYPOTHESIS

To determine whether lower than currently accepted glycemic levels could lead to optimal risk reduction of incident diabetes among individuals with prediabetes.

METHODS

We enrolled 9903 individuals with prediabetes and 16,902 individuals with normoglycemia from a prospective cohort participating health check-ups between 2006 and 2017. While classifying fasting glucose into <5.0, 5.0-5.5, and 5.6-6.9 mmol/L and postprandial glucose into <6.7, 6.7-7.7, and 7.8-11.0 mmol/L, we grouped fasting/postprandial glucose into five categories (<5.0/<6.7, <5.0/6.7-7.7, 5.0-5.5/<6.7, 5.0-5.5/6.7-7.7 mmol/L, 5.6-6.9/7.8-11.0 mmol/L). The primary outcome was incident diabetes.

RESULTS

In individuals with prediabetes, the presence of a baseline fasting glucose <5.0 mmol/L or a postprandial glucose <6.7 mmol/L led to a greater risk reduction of incident diabetes with hazard ratios of 0.34 (95% confidence interval, 0.27-0.42) and 0.47 (0.41-0.54), respectively, relative to a fasting glucose 5.6-6.9 mmol/L and a postprandial glucose 7.8-11.0 mmol/L. For individuals with prediabetes having fasting/postprandial glucose <5.0/<6.7 mmol/L, the incidence of 6.4 (4.7-8.8) per 1000 person-years corresponded to that of 5.8 (4.2-8.0) per 1000 person-years for individuals with normoglycemia having 5.0-5.5/6.7-7.7 mmol/L.

CONCLUSIONS/INTERPRETATION

Given that lower-than-normal glycemic levels were plausible for optimal risk reduction of diabetes, stringent glycemic management could be beneficial for diabetes prevention among individuals with prediabetes.

Ethnic Variability in Glucose and Insulin Response to Rice Among Healthy Overweight Adults: A Randomized Cross-Over Study

BACKGROUND

The influence of ethnicity on postprandial glucose and insulin responses has been reported earlier and rice is a major contributor to the overall glycaemic load of Asian and Arab diets. This study aims to compare postprandial glycaemic and insulinaemic responses to rice among healthy overweight Asian, Arab and European participants.

METHODS

In a randomized crossover design, 47 healthy overweight participants (23 Asian, 16 Arab, and 8 European) consumed 75 grams of glucose beverage or ate 270 grams of cooked basmati rice (75 g of available carbohydrate) on two separate occasions, separated by a one 1-week washout period. Blood glucose and insulin levels were determined at fasting 0 (fasting), 30, 60, and 120 minutes and used to determine the incremental area under the curve (iAUC).

RESULTS

The three groups were matched on body mass index and gender. Although no differences were noted statistically in most clinical features, a wide range of variation was noted in age, systolic, diastolic blood pressure. The fasting blood glucose and insulin levels were highest among Asians, followed by Arabs and Europeans (p < 0.01). According to the HOMA-IR test and the Matsuda index, Asians have a higher insulin resistance than Arabs or Europeans when consuming a glucose beverage (p < 0.001) and rice (p < 0.01). Postprandial glucose and insulin responses to glucose beverage did not differ between ethnic groups (p = 0.28; p = 0.10). Based on an unadjusted regression model, European participants had significantly lower iAUC-glucose (p = 0.02) and iAUC-insulin (p = 0.01) after rice consumption than Asian participants. In the adjusted model, the difference between the two groups remained for iAUC-insulin (p = 0.04) but not for iAUC-glucose (p = 0.07).

CONCLUSION

Our study found that ethnic differences exist among healthy overweight adults in terms of insulin resistance, glycaemic response and insulinaemic response to rice. As a result of their high insulin resistance, Asian participants had a higher postprandial insulin spike than Europeans after eating rice. These findings could have substantial implications for nutrition recommendations based on ethnicity, particularly for Asians.

Predicting Factors for Metabolic Non-Response to a Complex Lifestyle Intervention-A Replication Analysis to a Randomized-Controlled Trial

BACKGROUND

T2DM heterogeneity affects responsiveness to lifestyle treatment. Beta-cell failure and nonalcoholic fatty liver disease (NAFLD) independently predict T2DM, but NAFLD inconsistently predicts metabolic response to lifestyle intervention.

AIM

We attempt to replicate a prediction model deducted from the Tübinger Lifestyle Intervention Program by assessing similar metabolic factors to predict conversion to normal glucose regulation (NGR) in a comparable lifestyle intervention trial.

METHODS

In the Optimal Fiber Trial (OptiFiT), 131 Caucasian participants with prediabetes completed a one-year lifestyle intervention program and received a fiber or placebo supplement. We compared baseline parameters for responders and non-responders, assessed correlations of major metabolic changes and conducted a logistic regression analysis for predictors of remission to NGR.

RESULTS

NGR was achieved by 33 participants, respectively. At baseline, for the placebo group only, 1 h and 2 h glucose levels, glucose AUC and Cederholm index predicted conversion to NGR. HOMA-beta, HOMA-IR or liver fat indices did not differ between responders and non-responders of the placebo or the fiber group. Changes in waist circumference or fatty liver index correlated with changes in glycemia and insulin resistance, but not with changes in insulin secretion. Insulin-resistant NAFLD did not predict non-response. Differences in compliance did not explain the results.

CONCLUSIONS

Higher post-challenge glucose levels strongly predicted the metabolic non-response to complex lifestyle intervention in our cohort. Depending on the specific intervention and the investigated cohort, fasting glucose levels and insulin sensitivity might contribute to the risk pattern. Beta-cell function did not improve in accordance with other metabolic improvements, qualifying as a potential risk factor for non-response. We could not replicate previous data suggesting that an insulin-resistant fatty liver is a specific risk factor for treatment failure. Replication studies are required.

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.

Association of the extent of return to fasting state 2-hours after a glucose challenge with incident prediabetes and type 2 diabetes: The CARDIA study

AIM

To evaluate whether the extent of return to fasting state 2-hours after a glucose challenge among normoglycemic individuals is associated with lower risk of incident prediabetes/ type 2 diabetes in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study.

METHODS

We evaluated this association among 1879 normoglycemic adults who were categorized into three groups: 'Low post load' (2hPG < FPG); 'Medium post load' (2hPG ≥ FPG and < 75^th percentile of the difference); and 'High post load' (2hPG > FPG and ≥ 75^th percentile of the difference). We used Cox proportional hazards regression to evaluate the association of the difference in 2hPG and FPG with incident diabetes/prediabetes after adjustment for demographic and clinical covariates.

RESULTS

During 20 years of follow-up, 8% developed type 2 diabetes and 35% developed prediabetes. Compared to those with 'Low post load', the risk of type 2 diabetes was higher for participants with 'High post load' [HR: 1.56, 95% CI (1.03, 2.37)] and similar for participants with 'Medium post load' [HR: 0.99, 95% CI (0.64, 1.52)]. However, HRs for incident prediabetes among participants with 'High post load' [HR = 1.2, 95 %CI = (0.98, 1.46)] was not significantly different compared to participants with 'Low post load'.

CONCLUSION

Among normoglycemic individuals, a difference between 2hPG and FPG concentration > 0.9 mmol/L can be used to stratify individuals at higher risk for developing type 2 diabetes.

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.

Metabolic and Hormonal Profile of Adolescent Girls With Polycystic Ovary Syndrome With Concomitant Autoimmune Thyroiditis

INTRODUCTION

Both polycystic ovary syndrome (PCOS) and autoimmune thyroiditis (AT) are considered to be among the most common endocrinopathies in young women, and they are classified as diseases that affect many processes in the human body. Their role in the development of metabolic disorders and diseases of the cardiovascular system in adult women is also emphasized. However, there are no data available to assess such risk in the teenage girl population. The aim of the study was to assess the hormonal and metabolic profile of adolescent girls with PCOS, additionally diagnosed with AT, as well as to identify possible risk factors for the coexistence of AT and PCOS.

MATERIAL AND METHODS

80 euthyroidic PCOS patients were qualified for the study (chronological age 16.54 ± 1.00 years, BMI 24.60 ± 4.16 kg/m²). Eighteen girls diagnosed with AT were included in the study group and 62 girls without AT-in the control group. Each patient had biochemical and hormonal tests performed. Additionally, to diagnose AT, the level of antibodies against thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-TG), as well as the image of the thyroid gland on ultrasound examination, were taken into account.

RESULTS

Estradiol concentration was significantly higher in the study than in the control group (203.00 ± 217.00 vs. 152.00 ± 78.50 pmol/L, p=0.02). Higher DHEAS concentrations were also observed in the AT group compared with the group without AT (391.28 ± 176.40 vs. 317.93 ± 114.27 µg/dl, p=0.04). Moreover, there was a positive correlation between AT and estradiol concentration (r_y=0.27; p=0.04). It was also shown that there is a tendency toward statistical significance for the positive correlation between the positive anti-TPO titer and the glucose concentration at 120 min OGTT (r_ƴ=0.26; p=0.07) and girls with PCOS and AT had higher glucose levels in 120 min OGTT (115.29±41.70 vs. 98.56±28.02 mg/dl, p=0.08).

CONCLUSION

The study results showed no difference in the metabolic profile between the groups. The high concentration of estradiol found in girls with PCOS and AT may indicate the role of this hormone in the development of the autoimmune process. However, the numbers are small, and more research is needed to confirm our findings.

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.

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.

Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways

South Asians have a high prevalence of type 2 diabetes, even at a lower BMI. This review sets out our perspective and hypothesis on the reasons for this. Emerging data from epidemiological studies indicate that South Asians may have a lower ability to secrete insulin, and thus may have less compensatory reserves when challenged with unhealthy lifestyles. Thus, insulin resistance may not be the primary driver of type 2 diabetes in this population. Furthermore, data also suggest that South Asians, on average, have lower muscle mass, and may have a specific propensity to ectopic hepatic fat accumulation and for intramyocellular fat deposition, which cause further disruption in insulin action. We hypothesise that the high diabetes susceptibility in South Asians is evolutionarily set through dual parallel and/or interacting mechanisms: reduced beta cell function and impaired insulin action owing to low lean mass, which is further accentuated by ectopic fat deposition in the liver and muscle. These areas warrant further research.

Blood-based FTIR-ATR spectroscopy coupled with extreme gradient boosting for the diagnosis of type 2 diabetes: A STARD compliant diagnosis research

Timely diagnosis of type 2 diabetes and early intervention and treatment of it are important for controlling metabolic disorders, delaying and reducing complications, reducing mortality, and improving quality of life. Type 2 diabetes was diagnosed by Fourier transform mid-infrared (FTIR) attenuated total reflection (ATR) spectroscopy in combination with extreme gradient boosting (XGBoost). Whole blood FTIR-ATR spectra of 51 clinically diagnosed type 2 diabetes and 55 healthy volunteers were collected. For the complex composition of whole blood and much spectral noise, Savitzky-Golay smoothing was first applied to the FTIR-ATR spectrum. Then PCA was used to eliminate redundant data and got the best number of principle components. Finally, the XGBoost algorithm was used to discriminate the type 2 diabetes from healthy volunteers and the grid search algorithm was used to optimize the relevant parameters of the XGBoost model to improve the robustness and generalization ability of the model. The sensitivity of the optimal XGBoost model was 95.23% (20/21), the specificity was 96.00% (24/25), and the accuracy was 95.65% (44/46). The experimental results show that FTIR-ATR spectroscopy combined with XGBoost algorithm can diagnose type 2 diabetes quickly and accurately without reagents.

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.

Tale of two Indians: Heterogeneity in type 2 diabetes pathophysiology

AIMS

Type 2 diabetes is a heterogeneous disease and may manifest from multiple disease pathways. We examined insulin secretion and insulin resistance across two ethnicities with particularly high risk for diabetes yet with widely different distributions of weight class.

MATERIALS AND METHODS

In this population-based, cross-sectional study, Pima Indians from Southwestern United States (n = 865) and Asian Indians from Chennai, India (n = 2374) had a 75-g oral glucose tolerance test. We analysed differences in plasma glucose, plasma insulin, insulin resistance (HOMA-IR), and insulin secretion (ΔI0-30 /ΔG0-30 ) across categories of body mass index (BMI) and glycemic status per American Diabetes Association criteria.

RESULTS

Pima Indians were younger (mean 27.4 ± SD 6.6, Asian: 33.9 ± 6.7 years) and had higher BMI (33.6 ± 8.1, Asian: 25.7 ± 4.9 kg/m2 ). Among normal weight participants (mean BMI: Pima 22.4 SE 0.2; Asian 22.2 SE 0.06 kg/m2 ), fasting glucose was higher in Asian Indians (5.2 vs Pima: 4.8 mmol/L, P = .003), adjusted for age and sex. Pima Indians were three times as insulin resistant as Asian Indians (HOMA-IR: 7.7 SE 0.1, Asian: 2.5 SE 0.07), while Asian Indians had three times less insulin secretion (Pima: 2.8 SE 1.0 vs Asian: 0.9 SE 1.0 pmol/mmol), a pattern evident across age, BMI, and glycemic strata.

CONCLUSIONS

Metabolic differences between Pima and Asian Indians suggest heterogeneous pathways of type 2 diabetes in the early natural history of disease, with emphasis of insulin resistance in Pima Indians and emphasis of poor insulin secretion in Asian Indians.

Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia

BACKGROUND

Intermediate hyperglycaemia (IH) is characterised by one or more measurements of elevated blood glucose concentrations, such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and elevated glycosylated haemoglobin A1c (HbA1c). These levels are higher than normal but below the diagnostic threshold for type 2 diabetes mellitus (T2DM). The reduced threshold of 5.6 mmol/L (100 mg/dL) fasting plasma glucose (FPG) for defining IFG, introduced by the American Diabetes Association (ADA) in 2003, substantially increased the prevalence of IFG. Likewise, the lowering of the HbA1c threshold from 6.0% to 5.7% by the ADA in 2010 could potentially have significant medical, public health and socioeconomic impacts.

OBJECTIVES

To assess the overall prognosis of people with IH for developing T2DM, regression from IH to normoglycaemia and the difference in T2DM incidence in people with IH versus people with normoglycaemia.

SEARCH METHODS

We searched MEDLINE, Embase, ClincialTrials.gov and the International Clinical Trials Registry Platform (ICTRP) Search Portal up to December 2016 and updated the MEDLINE search in February 2018. We used several complementary search methods in addition to a Boolean search based on analytical text mining.

SELECTION CRITERIA

We included prospective cohort studies investigating the development of T2DM in people with IH. We used standard definitions of IH as described by the ADA or World Health Organization (WHO). We excluded intervention trials and studies on cohorts with additional comorbidities at baseline, studies with missing data on the transition from IH to T2DM, and studies where T2DM incidence was evaluated by documents or self-report only.

DATA COLLECTION AND ANALYSIS

One review author extracted study characteristics, and a second author checked the extracted data. We used a tailored version of the Quality In Prognosis Studies (QUIPS) tool for assessing risk of bias. We pooled incidence and incidence rate ratios (IRR) using a random-effects model to account for between-study heterogeneity. To meta-analyse incidence data, we used a method for pooling proportions. For hazard ratios (HR) and odds ratios (OR) of IH versus normoglycaemia, reported with 95% confidence intervals (CI), we obtained standard errors from these CIs and performed random-effects meta-analyses using the generic inverse-variance method. We used multivariable HRs and the model with the greatest number of covariates. We evaluated the certainty of the evidence with an adapted version of the GRADE framework.

MAIN RESULTS

We included 103 prospective cohort studies. The studies mainly defined IH by IFG5.6 (FPG mmol/L 5.6 to 6.9 mmol/L or 100 mg/dL to 125 mg/dL), IFG6.1 (FPG 6.1 mmol/L to 6.9 mmol/L or 110 mg/dL to 125 mg/dL), IGT (plasma glucose 7.8 mmol/L to 11.1 mmol/L or 140 mg/dL to 199 mg/dL two hours after a 75 g glucose load on the oral glucose tolerance test, combined IFG and IGT (IFG/IGT), and elevated HbA1c (HbA1c5.7: HbA1c 5.7% to 6.4% or 39 mmol/mol to 46 mmol/mol; HbA1c6.0: HbA1c 6.0% to 6.4% or 42 mmol/mol to 46 mmol/mol). The follow-up period ranged from 1 to 24 years. Ninety-three studies evaluated the overall prognosis of people with IH measured by cumulative T2DM incidence, and 52 studies evaluated glycaemic status as a prognostic factor for T2DM by comparing a cohort with IH to a cohort with normoglycaemia. Participants were of Australian, European or North American origin in 41 studies; Latin American in 7; Asian or Middle Eastern in 50; and Islanders or American Indians in 5. Six studies included children and/or adolescents.Cumulative incidence of T2DM associated with IFG5.6, IFG6.1, IGT and the combination of IFG/IGT increased with length of follow-up. Cumulative incidence was highest with IFG/IGT, followed by IGT, IFG6.1 and IFG5.6. Limited data showed a higher T2DM incidence associated with HbA1c6.0 compared to HbA1c5.7. We rated the evidence for overall prognosis as of moderate certainty because of imprecision (wide CIs in most studies). In the 47 studies reporting restitution of normoglycaemia, regression ranged from 33% to 59% within one to five years follow-up, and from 17% to 42% for 6 to 11 years of follow-up (moderate-certainty evidence).Studies evaluating the prognostic effect of IH versus normoglycaemia reported different effect measures (HRs, IRRs and ORs). Overall, the effect measures all indicated an elevated risk of T2DM at 1 to 24 years of follow-up. Taking into account the long-term follow-up of cohort studies, estimation of HRs for time-dependent events like T2DM incidence appeared most reliable. The pooled HR and the number of studies and participants for different IH definitions as compared to normoglycaemia were: IFG5.6: HR 4.32 (95% CI 2.61 to 7.12), 8 studies, 9017 participants; IFG6.1: HR 5.47 (95% CI 3.50 to 8.54), 9 studies, 2818 participants; IGT: HR 3.61 (95% CI 2.31 to 5.64), 5 studies, 4010 participants; IFG and IGT: HR 6.90 (95% CI 4.15 to 11.45), 5 studies, 1038 participants; HbA1c5.7: HR 5.55 (95% CI 2.77 to 11.12), 4 studies, 5223 participants; HbA1c6.0: HR 10.10 (95% CI 3.59 to 28.43), 6 studies, 4532 participants. In subgroup analyses, there was no clear pattern of differences between geographic regions. We downgraded the evidence for the prognostic effect of IH versus normoglycaemia to low-certainty evidence due to study limitations because many studies did not adequately adjust for confounders. Imprecision and inconsistency required further downgrading due to wide 95% CIs and wide 95% prediction intervals (sometimes ranging from negative to positive prognostic factor to outcome associations), respectively.This evidence is up to date as of 26 February 2018.

AUTHORS' CONCLUSIONS

Overall prognosis of people with IH worsened over time. T2DM cumulative incidence generally increased over the course of follow-up but varied with IH definition. Regression from IH to normoglycaemia decreased over time but was observed even after 11 years of follow-up. The risk of developing T2DM when comparing IH with normoglycaemia at baseline varied by IH definition. Taking into consideration the uncertainty of the available evidence, as well as the fluctuating stages of normoglycaemia, IH and T2DM, which may transition from one stage to another in both directions even after years of follow-up, practitioners should be careful about the potential implications of any active intervention for people 'diagnosed' with IH.

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

AIMS/HYPOTHESIS

In addition to blood glucose concentrations measured in the fasting state and 2 h after an OGTT, intermediate measures during an OGTT may provide additional information regarding a person's risk of future diabetes and cardiovascular disease (CVD). First, we aimed to characterise heterogeneity of glycaemic patterns based on three time points during an OGTT. Second, we compared the incidences of diabetes and CVD and all-cause mortality rates among those with different patterns.

METHODS

Our cohort study included 5861 participants without diabetes at baseline from the Danish Inter99 study. At baseline, all participants underwent an OGTT with measurements of plasma glucose levels at 0, 30 and 120 min. Latent class mixed-effects models were fitted to identify distinct patterns of glycaemic response during the OGTT. Information regarding incident diabetes, CVD and all-cause mortality rates during a median follow-up time of 11, 12 and 13 years, respectively, was extracted from national registers. Cox proportional hazard models with adjustment for several cardiometabolic risk factors were used to compare the risk of diabetes, CVD and all-cause mortality among individuals in the different latent classes.

RESULTS

Four distinct glucose patterns during the OGTT were identified. One pattern was characterised by high 30 min but low 2 h glucose values. Participants with this pattern had an increased risk of developing diabetes compared with participants with lower 30 min and 2 h glucose levels (HR 4.1 [95% CI 2.2, 7.6]) and participants with higher 2 h but lower 30 min glucose levels (HR 1.5 [95% CI 1.0, 2.2]). Furthermore, the all-cause mortality rate differed between the groups with significantly higher rates in the two groups with elevated 30 min glucose. Only small non-significant differences in risk of future CVD were observed across latent classes after confounder adjustment.

CONCLUSIONS/INTERPRETATION

Elevated 30 min glucose is associated with increased risk of diabetes and all-cause mortality rate independent of fasting and 2 h glucose levels. Therefore, subgroups at high risk may not be revealed when considering only fasting and 2 h glucose levels during an OGTT.

Time to glucose peak during an oral glucose tolerance test identifies prediabetes risk

CONTEXT

Morphological characteristics of the glucose curve during an oral glucose tolerance test (OGTT) (time to peak and shape) may reflect different phenotypes of insulin secretion and action, but their ability to predict diabetes risk is uncertain.

OBJECTIVE

To compare the ability of time to glucose peak and curve shape to detect prediabetes and β-cell function.

DESIGN AND PARTICIPANTS

In a cross-sectional evaluation using an OGTT, 145 adults without diabetes (age 42±9 years (mean±SD), range 24-62 years, BMI 29.2±5.3 kg/m² , range 19.9-45.2 kg/m² ) were characterized by peak (30 minutes vs >30 minutes) and shape (biphasic vs monophasic).

MAIN OUTCOME MEASURES

Prediabetes and disposition index (DI)-a marker of β-cell function.

RESULTS

Prediabetes was diagnosed in 36% (52/145) of participants. Peak>30 minutes, not monophasic curve, was associated with increased odds of prediabetes (OR: 4.0 vs 1.1; P<.001). Both monophasic curve and peak>30 minutes were associated with lower DI (P≤.01). Time to glucose peak and glucose area under the curves (AUC) were independent predictors of DI (adjR² =0.45, P<.001).

CONCLUSION

Glucose peak >30 minutes was a stronger independent indicator of prediabetes and β-cell function than the monophasic curve. Time to glucose peak may be an important tool that could enhance prediabetes risk stratification.