A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: OGTT is not needed

Elevated levels of PAI-1 precede the occurrence of type 2 diabetes mellitus

AIMS

Plasminogen activator inhibitor-1 (PAI-1) is the main inhibitor of the fibrinolytic system and is mainly secreted from adipose tissue. It is associated with cardiovascular disease and has also been considered a possible early risk marker for type 2 diabetes. Here, we present the results of a large prospective study investigating PAI-1 levels in relation to incident type 2 diabetes mellitus.

METHODS

We conducted a prospective incident case-referent study within the Västerbotten Intervention Programme (VIP). Data on cardiovascular risk factors, fasting plasma glucose (FPG) and 2-hour plasma glucose (2-hPG) were collected at baseline health examination 1990-2005. Blood samples were collected and stored for future analyses. Participants were followed and 484 cases developed type 2 diabetes. Referents without type 2 diabetes were matched for sex, age, and year of participation, n = 484. Baseline plasma samples were analysed for PAI-1. Subgroup analysis was performed for 201 cases and 201 matched referents with normal baseline glucose levels (FPG < 6.1 and 2hPG < 8.9 mmol/L).

RESULTS

Elevated baseline levels of PAI-1 were associated with incident type 2 diabetes after adjustments for BMI, family history of diabetes, smoking status, hypertension, FPG and 2hPG (PAI-1; OR = 1.87, 95% CI: 1.06-3.29). A similar result was shown in the subgroup analysis with 201 participants who had normal glucose levels at time of the health examination (PAI-1; OR = 2.68, 95% CI: 1.03-6.95).

CONCLUSIONS

Elevated PAI-1 levels in non-diabetic persons precede the manifestation of type 2 diabetes and can be detected before an elevation of FPG or 2-hPG is observed.

Continuous glucose monitoring and intrapersonal variability in fasting glucose

Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40-70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dl-1, rising by 0.234 mg dl-1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dl-1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.

The combination of body mass index and fasting plasma glucose is associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis

Background

Body mass index (BMI) and fasting plasma glucose (FPG) are known risk factors for type 2 diabetes mellitus (T2DM), but data on the prospective association of the combination of BMI and FPG with T2DM are limited. This study sought to characterize the association of the combination of BMI and FPG (ByG) with T2DM.

Methods

The current study used the NAGALA database. We categorized participants by tertiles of ByG. The association of ByG with T2DM was expressed with hazard ratios (HRs) with 95% confidence intervals (CIs) after adjustment for potential risk factors.

Results

During a median follow-up of 6.19 years in the normoglycemia cohort and 5.58 years in the prediabetes cohort, the incidence of T2DM was 0.75% and 7.79%, respectively. Following multivariable adjustments, there were stepwise increases in T2DM with increasing tertiles of ByG. After a similar multivariable adjustment, the risk of T2DM was 2.57 (95% CI 2.26 - 2.92), 1.97 (95% CI 1.53 - 2.54) and 1.50 (95% CI 1.30 - 1.74) for a per-SD change in ByG in all populations, the normoglycemia cohort and the prediabetes cohort, respectively.

Conclusion

ByG was associated with an increased risk of T2DM in Japan. The result reinforced the importance of the combination of BMI and FPG in assessing T2DM risk.

Is the Risk Perception Attitude Framework Associated with the Accuracy of Self-Reported vs Actual Cardiometabolic Risk and Physical Activity in Young Adults with Overweight/Obesity?

We examined the accuracy of perceived vs actual cardiometabolic risk and physical activity within the Risk Perception Attitude Framework (RPA). We analyzed baseline data from 343 young adults (23.3 ± 4.4 years) participating in a weight management clinical trial. Cardiometabolic risk factors were measured according to standard clinical procedures. A cardiometabolic risk score was created from five biomarkers according to whether or not a standard clinical risk cut point was exceeded. Physical activity was determined by ActiGraph and self-report. Perceived risk and physical activity self-efficacy were assessed by validated measures. The Proactive cluster (low perceived risk/high self-efficacy) was most accurate regarding actual vs perceived risk awareness (54%), while the Responsive cluster (high perceived risk/high self-efficacy) was the least accurate (16%). All RPA clusters underestimated their actual physical activity, self-reporting less than half the moderate-to-vigorous physical activity that was captured via accelerometry. The RPA Framework can identify young adults unlikely to be aware of their cardiometabolic risk. Given the growing prevalence of metabolic syndrome, efforts early in adulthood to increase knowledge and awareness of cardiometabolic risk, and behaviors necessary to reduce that risk, can have substantial impact on future health.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.

A Comparative analysis of type 2 diabetes management quality indicators in cancer survivors

Objective

This study aimed to assess indicators of type 2 diabetes mellitus (DM) management, including adequate DM control, and treatment rates, in cancer survivors according to the time of DM diagnosis and to compare them with the DM management indicators of a non-cancer control group.

Methods

We used the 2013-2019 data of the Korea National Health and Nutrition Examination Survey for this study. To compare their adequate DM control, and treatment rates, we identified 4918 patients with type 2 DM aged ≥ 30 years and classified them into pre-existing diabetes, pre-existing cancer, and diabetes without cancer groups. Predictors of adequate glycemic control and diabetes treatment were analyzed using binary logistic regression.

Results

Diabetes without cancer group had higher fasting blood glucose and glycosylated hemoglobin A1c levels and lower adequate glycemic control than did the other two groups. The preexisting cancer group had low treatment rates. After adjusting for age, gender, employment status, and duration of diabetes, the preexisting cancer group had 0.51-fold lower odds of receiving treatment, such as insulin injection or oral diabetes medications, than the other two groups (adjusted odds ratio, 0.50; 95% confidence interval, 0.38-0.66).

Conclusions

Cancer survivors had lower fasting glucose and HbA1c than those with diabetes without cancer. However, as a result of the sub-analysis, the treatment rate of the pre-existing cancer group was significantly lower than that of diabetes without cancer. Based on these results, cancer survivors' care-related healthcare workers should be aware of the need for monitoring blood sugar even in cancer survivors without underlying diabetes mellitus and pay more attention to early detection and active treatment of diabetes.

Differences in the prevalence of intermediate hyperglycaemia and the associated incidence of type 2 diabetes mellitus by ethnicity: The HELIUS study

AIMS

We aimed to describe differences in the prevalence of intermediate hyperglycaemia (IH) between six ethnic groups. Moreover, to investigate differences in the association of the classifications of IH with the incidence of T2DM between ethnic groups.

METHODS

We included 3759 Dutch, 2826 African Surinamese, 1646 Ghanaian, 2571 Turkish, 2691 Moroccan and 1970 South Asian Surinamese origin participants of the HELIUS study. IH was measured by fasting plasma glucose (FPG) and HbA1c. We calculated age-, BMI and physical-activity-adjusted prevalence of IH by sex, and calculated age and sex-adjusted hazard ratios (HR)for the association between IH and T2DM in each ethnic group.

RESULTS

The prevalence of IH was higher among ethnic minority groups (68.6-41.7%) than the Dutch majority (34.9%). The prevalence of IH categories varied across subgroups. Combined increased FPG and HbA1c was most prevalent in South-Asian Surinamese men (27.6%, 95 %CI: 24.5-30.9%), and in Dutch women (4.2%, 95 %CI: 3.4-5.1%). The HRs for T2DM for each IH-classification did not differ significantly between ethnic groups. HRs were highest for the combined classification, e.g., HR = 8.1, 95 %CI: 2.5-26.6 in the Dutch.

CONCLUSION

We found a higher prevalence of IH in ethnic minority versus majority groups, but did not find evidence for a differential association of IH with incident T2DM.

What moves young people? Applying the risk perception attitude framework to physical activity behavior and cardiometabolic risk

Cardiometabolic disease risk factors, including metabolic syndrome and physical inactivity, are prevalent among young adults. However, few young adults are aware of their risk status. The risk perception attitude (RPA) framework was used to categorize participants (n = 456) enrolled in a three-arm randomized controlled weight management trial by their baseline values of cardiometabolic risk perceptions and physical activity self-efficacy. Trial recruitment occurred at two universities from 2015 to 2018 and participants were randomly assigned to one of three weight management interventions: Tailored, Targeted, Control. Cross-sectional and longitudinal analyses were conducted to examine associations between RPA category (i.e., Responsive, Indifferent, Avoidant, Proactive) and physical activity behavior. At baseline, the Responsive group had the highest amount of physical activity (mean [95% CI]: 379.2 [332.6 to 425.8] min/week), the Indifferent group had the lowest (296.7 [261.98 to 331.32] min/week), and the Avoidant/Proactive groups showed intermediate values. Over 6 months, there was a significant interaction between RPA group and intervention arm on change in physical activity adjusted for age, sex, race/ethnicity, baseline body mass index, and baseline moderate-to-vigorous physical activity (p = .017). Among Tailored intervention participants only, the Proactive participants were the only group to have an increase in physical activity (19.97 min/week) and the Indifferent participants had the most significant decrease in physical activity (127.62 min/week). Results suggest the importance of early screening for young adults to help raise awareness of cardiometabolic risk and ultimately support them in health promotion efforts.

Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.

Efficacy of telemedicine on glycaemic control in patients with type 2 diabetes: A meta-analysis

BACKGROUND

Telemedicine is defined as the delivery of health services via remote communication and technology. It is a convenient and cost-effective method of intervention, which has shown to be successful in improving glyceamic control for type 2 diabetes patients. The utility of a successful diabetes intervention is vital to reduce disease complications, hospital admissions and associated economic costs.

AIM

To evaluate the effects of telemedicine interventions on hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), post-prandial glucose (PPG), fasting plasma glucose (FPG), weight, cholesterol, mental and physical quality of life (QoL) in patients with type 2 diabetes. The secondary aim of this study is to determine the effect of the following subgroups on HbA1c post-telemedicine intervention; telemedicine characteristics, patient characteristics and self-care outcomes.

METHODS

PubMed Central, Cochrane Library, Embase and Scopus databases were searched from inception until 18th of June 2020. The quality of the 43 included studies were assessed using the PEDro scale, and the random effects model was used to estimate outcomes and I 2 for heterogeneity testing. The mean difference and standard deviation data were extracted for analysis.

RESULTS

We found a significant reduction in HbA1c [-0.486%; 95% confidence interval (CI) -0.561 to -0.410, P < 0.001], DBP (-0.875 mmHg; 95%CI -1.429 to -0.321, P < 0.01), PPG (-1.458 mmol/L; 95%CI -2.648 to -0.268, P < 0.01), FPG (-0.577 mmol/L; 95%CI -0.710 to -0.443, P < 0.001), weight (-0.243 kg; 95%CI -0.442 to -0.045, P < 0.05), BMI (-0.304; 95%CI -0.563 to -0.045, P < 0.05), mental QoL (2.210; 95%CI 0.053 to 4.367, P < 0.05) and physical QoL (-1.312; 95%CI 0.545 to 2.080, P < 0.001) for patients following telemedicine interventions in comparison to control groups. The results of the meta-analysis did not show any significant reductions in SBP and cholesterol in the telemedicine interventions compared to the control groups. The telemedicine characteristic subgroup analysis revealed that clinical treatment models of intervention, as well as those involving telemonitoring, and those provided via modes of videoconference or interactive telephone had the greatest effect on HbA1c reduction. In addition, interventions delivered at a less than weekly frequency, as well as those given for a duration of 6 mo, and those lead by allied health resulted in better HbA1c outcomes. Furthermore, interventions with a focus on biomedical parameters, as well as those with an engagement level > 70% and those with a drop-out rate of 10%-19.9% showed greatest HbA1c reduction. The patient characteristics investigation reported that Hispanic patients with T2DM had a greater HbA1c reduction post telemedicine intervention. For self-care outcomes, telemedicine interventions that resulted in higher post-intervention glucose monitoring and self-efficacy were shown to have better HbA1c reduction.

CONCLUSION

The findings indicate that telemedicine is effective for improving HbA1c and thus, glycemic control in patients with type 2 diabetes. In addition, telemedicine interventions were also found to significantly improved other health outcomes as well as QoL scores. The results of the subgroup analysis emphasized that interventions in the form of telemonitoring, via a clinical treatment model and with a focus on biomedical parameters, delivered at a less than weekly frequency and 6 mo duration would have the largest effect on HbA1c reduction. This is in addition to being led by allied health, through modes such as video conference and interactive telephone, with an intervention engagement level > 70% and a drop-out rate between 10%-19.9%. Due to the high heterogeneity of included studies and limitations, further studies with a larger sample size is needed to confirm our findings.

Physical Activity and Cardiometabolic Risk Factor Clustering in Young Adults with Obesity

INTRODUCTION

There is a paucity of information on the clustering of cardiometabolic risk factors in young adults and how this clustering may vary based on whether or not they perform sufficient levels of physical activity.

METHODS

We analyzed baseline data from 346 young adults (23.3 ± 4.4 yr) participating in the Healthy Body Healthy U clinical trial from 2015 to 2018. Cardiometabolic risk factors were measured according to standard procedures and moderate- to vigorous-intensity physical activity (MVPA) was determined by accelerometry. A cardiometabolic clustering score (ranging from 0 to 5) was created from five biomarkers according to whether or not a standard clinical risk cut point was exceeded (0, no; 1, yes): abdominal circumference (>102 cm (men) or >88 cm (women)), hemoglobin A1c (≥5.7%), HDL cholesterol (<40 mg·dL (men) or <50 mg·dL (women)), systolic blood pressure (≥130 mm Hg), and diastolic blood pressure (≥85 mm Hg). Cardiometabolic dysregulation (CD) was defined as a cardiometabolic clustering score ≥3. Multiple logistic regression determined the independent association between level of MVPA and CD, while adjusting for sex, race/ethnicity, sedentary time, and smoking.

RESULTS

The prevalence of CD was 18% (22% in men, 17% in women). We observed a nonlinear graded association between MVPA and CD. Participants performing 150-300 min·wk of MVPA significantly lowered their odds of CD by 66% (odds ratio, 0.34; 95% confidence interval, 0.16-0.75), whereas those exceeding 300 min·wk lowered their odds by 61% (odds ratio, 0.39; 95% confidence interval, 0.18-0.86) compared with those performing <150 min·wk, independent of obesity and the other covariables.

CONCLUSION

Recommended levels of moderate-intensity physical activity is significantly associated with lower odds of CD and thus may prevent or diminish the need for expensive pharmaceutical treatment over the remainder of the life-span.

Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics

BACKGROUND

Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development.

METHODS

Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classification (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) models with tenfold cross validation.

RESULTS

Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~ 84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P = 0.003) and CpG29 (chr10:58385324, P = 0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classification sets.

CONCLUSIONS

Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discovery.

Survey to estimate the prevalence of type 2 diabetes mellitus in hospital patients in Germany by systematic HbA1c measurement upon admission

OBJECTIVES

The objective of this survey was to estimate the prevalence of type 2 diabetes mellitus (T2DM) in hospitalised patients ≥55 years based on routine HbA1c measurement upon admission, using the diagnosis algorithm according to the German National Diabetes Care Guideline.

DESIGN

Non-interventional survey.

SETTING

Four German maximum care hospitals.

POPULATION

Consecutive patients ≥55 years of age admitted to hospital.

MAIN OUTCOME MEASURES

Participating hospitals measured HbA1c upon admission and applied the algorithm for diagnosing T2DM per the clinical recommendations of the American Diabetes Association (ADA) and the German National Diabetes Care Guideline as part of the clinical routine and allocated patients to three diagnostic categories: T2DM, increased risk for T2DM, no T2DM.

RESULTS

Between Oct 2014 and May 2015, the survey documented data from 6092 patients; the analyses included 5820 patients fulfilling validity criteria (95.5%). Of these, 1906 (32.7%) had a known history of T2DM. Among the 3914 remaining patients, 2181 had no T2DM (55.8%), 1180 an increased risk for T2DM (30.1%) and 553 unrecognised T2DM (14.1%; 95% CI: 13.1%-15.3%). The overall prevalence of known and unrecognised T2DM was 42.3% (95% CI: 41.0%-43.5%). Patients with previously unrecognised T2DM were admitted to hospital predominantly for cardiac disorders (21.9%), nervous system disorders such as cerebral infarction (15.0%) and infections/infestations (13.4%).

CONCLUSIONS

This survey revealed an overall prevalence of known and unrecognised T2DM of more than 40%. Among patients with unrecognised T2DM on admission, the prevalence of T2DM was 14%. These data indicate that systematic documentation of T2DM in in-patients is clinically useful. Hospitals should consider using the diagnostic algorithm and to streamline pathways of care to secure adequate care considering patients' diabetic risk profiles, and to manage related additional costs.

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.

Prediction of Type 2 Diabetes by Hemoglobin A1c in Two Community-Based Cohorts

OBJECTIVE

Hemoglobin A_1c (HbA_1c) can be used to assess type 2 diabetes (T2D) risk. We asked whether HbA_1c was associated with T2D risk in four scenarios of clinical information availability: 1) HbA_1c alone, 2) fasting laboratory tests, 3) clinic data, and 4) fasting laboratory tests and clinic data.

RESEARCH DESIGN AND METHODS

We studied a prospective cohort of white (N = 11,244) and black (N = 2,294) middle-aged participants without diabetes in the Framingham Heart Study and Atherosclerosis Risk in Communities study. Association of HbA_1c with incident T2D (defined by medication use or fasting glucose [FG] ≥126 mg/dL) was evaluated in regression models adjusted for 1) age and sex (demographics); 2) demographics, FG, HDL, and triglycerides; 3) demographics, BMI, blood pressure, and T2D family history; or 4) all preceding covariates. We combined results from cohort and race analyses by random-effects meta-analyses. Subsidiary analyses tested the association of HbA_1c with developing T2D within 8 years or only after 8 years.

RESULTS

Over 20 years, 3,315 individuals developed T2D. With adjustment for demographics, the odds of T2D increased fourfold for each percentage-unit increase in HbA_1c. The odds ratio (OR) was 4.00 (95% CI 3.14, 5.10) for blacks and 4.73 (3.10, 7.21) for whites, resulting in a combined OR of 4.50 (3.35, 6.03). After adjustment for fasting laboratory tests and clinic data, the combined OR was 2.68 (2.15, 3.34) over 20 years, 5.79 (2.51, 13.36) within 8 years, and 2.23 (1.94, 2.57) after 8 years.

CONCLUSIONS

HbA_1c predicts T2D in different common scenarios and is useful for identifying individuals with elevated T2D risk in both the short- and long-term.

Use of HbA1c to diagnose type 2 diabetes mellitus among high risk Sri Lankan adults

AIM

Even though, glycosylated hemoglobin (HbA1c) was found to be effective in predicting diabetes especially in Caucasians there is limited evidence of its diagnostic utility in high risk Sri Lankan adults. This study aimed to determine the optimal HbA1c cut-off points for detecting diabetes in a high risk population in Sri Lanka.

MATERIALS AND METHODS

This community based study consisted of 254 previously healthy adults with history of diabetes in one or more first-degree relatives. Fasting plasma glucose (FPG) , glucose tolerance test (GTT) and HbA1c were measured in all and GTT was used as a reference to diagnose diabetes. Receiver operating characteristic curve was created to find the optimum HbA1c cut-off value to predict diabetes.

RESULTS

Prevalence of diabetes was 12.2% (n=31) with FPG and 16.1% (n=41) with GTT. Prevalence rose to 27.6% (P<0.01) when HbA1c with cut-off of ≥6.5% was used as the diagnostic test. The ROC curves showed the HbA1c threshold of 6.3% provided the optimum balance between sensitivity (80.5%) and specificity (79%). In compared to GTT, FPG had only a modest sensitivity (65%) in diagnosing diabetes in this high risk population.

CONCLUSION

Our study showed that optimum HbA1C cut-off for detecting diabetes was 6.3% and it had better sensitivity, but lower specificity than FPG. This study further showed that the prevalence of diabetes would become double if HbA1c is used over FPG to screen this high risk population.

Mitochondrial DNA copy number augments performance of A1C and oral glucose tolerance testing in the prediction of type 2 diabetes

Here, we tested the performance of the mitochondrial DNA copy number (mtDNA-CN) in predicting future type 2 diabetes (n = 1108). We used the baseline clinical data (age, sex, body mass index, waist-to-hip ratio, systolic and diastolic blood pressure) and the mtDNA-CN, hemoglobin A1c (A1C) levels and results of oral glucose tolerance test (OGTT) including fasting plasma glucose, 1-hour glucose, and 2-hour glucose levels, to predict future diabetes. We built a prediction model using the baseline data and the diabetes status at biannual follow-up of 8 years. The mean area under curve (AUC) for all follow-ups of the full model including all variables was 0.92 ± 0.04 (mean ± standard deviation), while that of the model excluding the mtDNA-CN was 0.90 ± 0.03. The sensitivity of the f4ull model was much greater than that of the model not including mtDNA-CN: the mean sensitivities of the model with and without mtDNA-CN were 0.60 ± 0.06 and 0.53 ± 0.04, respectively. We found that the mtDNA-CN of peripheral leukocytes is a biomarker that augments the predictive power for future diabetes of A1C and OGTT. We believe that these results could provide invaluable information for developing strategies for the management of diabetes.

Development and Validation of a Risk-Score Model for Type 2 Diabetes: A Cohort Study of a Rural Adult Chinese Population

Some global models to predict the risk of diabetes may not be applicable to local populations. We aimed to develop and validate a score to predict type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Data for a cohort of 12,849 participants were randomly divided into derivation (n = 11,564) and validation (n = 1285) datasets. A questionnaire interview and physical and blood biochemical examinations were performed at baseline (July to August 2007 and July to August 2008) and follow-up (July to August 2013 and July to October 2014). A Cox regression model was used to weigh each variable in the derivation dataset. For each significant variable, a score was calculated by multiplying β by 100 and rounding to the nearest integer. Age, body mass index, triglycerides and fasting plasma glucose (scores 3, 12, 24 and 76, respectively) were predictors of incident T2DM. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC), with optimal cut-off value 936. With the derivation dataset, sensitivity, specificity and AUC of the model were 66.7%, 74.0% and 0.768 (95% CI 0.760–0.776), respectively. With the validation dataset, the performance of the model was superior to the Chinese (simple), FINDRISC, Oman and IDRS models of T2DM risk but equivalent to the Framingham model, which is widely applicable in a variety of populations. Our model for predicting 6-year risk of T2DM could be used in a rural adult Chinese population.

The cost-effectiveness of testing strategies for type 2 diabetes: a modelling study

BACKGROUND

An estimated 850,000 people have diabetes without knowing it and as many as 7 million more are at high risk of developing it. Within the NHS Health Checks programme, blood glucose testing can be undertaken using a fasting plasma glucose (FPG) or a glycated haemoglobin (HbA1c) test but the relative cost-effectiveness of these is unknown.

OBJECTIVES

To estimate and compare the cost-effectiveness of screening for type 2 diabetes using a HbA1c test versus a FPG test. In addition, to compare the use of a random capillary glucose (RCG) test versus a non-invasive risk score to prioritise individuals who should undertake a HbA1c or FPG test.

DESIGN

Cost-effectiveness analysis using the Sheffield Type 2 Diabetes Model to model lifetime incidence of complications, costs and health benefits of screening.

SETTING

England; population in the 40-74-years age range eligible for a NHS health check.

DATA SOURCES

The Leicester Ethnic Atherosclerosis and Diabetes Risk (LEADER) data set was used to analyse prevalence and screening outcomes for a multiethnic population. Alternative prevalence rates were obtained from the literature or through personal communication.

METHODS

(1) Modelling of screening pathways to determine the cost per case detected followed by long-term modelling of glucose progression and complications associated with hyperglycaemia; and (2) calculation of the costs and health-related quality of life arising from complications and calculation of overall cost per quality-adjusted life-year (QALY), net monetary benefit and the likelihood of cost-effectiveness.

RESULTS

Based on the LEADER data set from a multiethnic population, the results indicate that screening using a HbA1c test is more cost-effective than using a FPG. For National Institute for Health and Care Excellence (NICE)-recommended screening strategies, HbA1c leads to a cost saving of £12 and a QALY gain of 0.0220 per person when a risk score is used as a prescreen. With no prescreen, the cost saving is £30 with a QALY gain of 0.0224. Probabilistic sensitivity analysis indicates that the likelihood of HbA1c being more cost-effective than FPG is 98% and 95% with and without a risk score, respectively. One-way sensitivity analyses indicate that the results based on prevalence in the LEADER data set are insensitive to a variety of alternative assumptions. However, where a region of the country has a very different joint HbA1c and FPG distribution from the LEADER data set such that a FPG test yields a much higher prevalence of high-risk cases relative to HbA1c, FPG may be more cost-effective. The degree to which the FPG-based prevalence would have to be higher depends very much on the uncertain relative uptake rates of the two tests. Using a risk score such as the Leicester Practice Database Score (LPDS) appears to be more cost-effective than using a RCG test to identify individuals with the highest risk of diabetes who should undergo blood testing.

LIMITATIONS

We did not include rescreening because there was an absence of required relevant evidence.

CONCLUSIONS

Based on the multiethnic LEADER population, among individuals currently attending NHS Health Checks, it is more cost-effective to screen for diabetes using a HbA1c test than using a FPG test. However, in some localities, the prevalence of diabetes and high risk of diabetes may be higher for FPG relative to HbA1c than in the LEADER cohort. In such cases, whether or not it still holds that HbA1c is likely to be more cost-effective than FPG depends on the relative uptake rates for HbA1c and FPG. Use of the LPDS appears to be more cost-effective than a RCG test for prescreening.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Type 2 Diabetes Prevention: Implications of Hemoglobin A1c Genetics

Hemoglobin A1c (HbA1c) is a biomarker used for population-level screening of type 2 diabetes (T2D) and risk stratification. Large-scale, genome-wide association studies have identified multiple genomic loci influencing HbA1c. We discuss the challenges of classifying these genomic loci as influencing HbA1c through glycemic or nonglycemic pathways, based on their probable biology and pleiotropic associations with erythrocyte traits. We show that putative nonglycemic genetic variants have a measurable, albeit small, impact on the classification of T2D status by HbA1c in white and Asian populations. Accounting for their effect on HbA1c may be relevant when screening populations with higher frequencies of nonglycemic HbA1c-altering alleles. As carriers of such HbA1c-altering alleles have HbA1c levels that may not accurately reflect overall glycemia, we describe how accounting for genotype may improve the performance of HbA1c in T2D prediction models and risk stratification, allowing for lifestyle intervention strategies to be directed towards those who are truly at elevated risk for developing T2D. In a Mendelian randomization framework, genetic variants can be used as instrumental variables to estimate causal relationships between HbA1c and T2D-related complications. This approach may help to support or refute HbA1c as an appropriate biomarker for long-term health outcomes in the general population.

Primary care screening for individuals with impaired glucose metabolism with focus on impaired glucose tolerance

AIM

To evaluate the utility of three short questions (the Skövde Form) combined with a random plasma glucose, and HbA1c as alternative tools for detection of individuals with impaired glucose metabolism (IGM), and particularly impaired glucose tolerance (IGT).

METHODS

Three questions concerning BMI ≥ 25 kg/m(2), heredity for type 2 diabetes, and known hypertension were asked in a random population of 573 individuals. All with two positive answers or one positive answer and a random plasma glucose > 7.2 mmol/l were invited for an oral glucose tolerance test and an HbA1c examination. FINDRISC was completed for comparison.

RESULTS

The positive predictive value (PPV) for IGM, using the Skövde Form, was 31% while sensitivity and specificity were 59% and 73%, respectively. Corresponding values for IGT were 11%, 50% and 69%. Using HbA1c ≥ 42 mmol/mol, the PPV for IGM was 64% while sensitivity and specificity were 28% and 97%, respectively. The corresponding values for IGT were 15%, 16% and 94%.

CONCLUSION

The Skövde Form combined with a random plasma glucose may be used as an alternative tool for detection of individuals with IGM and IGT in particular. HbA1c may be used to identify individuals with type 2 diabetes but fails to detect most individuals with prediabetes.

Comparison of fasting plasma glucose and haemoglobin A1c point-of-care tests in screening for diabetes and abnormal glucose regulation in a rural low income setting

AIMS

Glycated haemoglobin (HbA1C) has been suggested to replace glucose tests in identifying diabetes and pre-diabetes. We assessed agreement between fasting plasma glucose (FPG) and HbA1C rapid tests in classifying abnormal glucose regulation (AGR), and their utility for preventive screening in rural Africa.

METHODS

A population-based survey of 795 people aged 35-60 years was conducted in a mainly rural district in Uganda. FPG was measured using On-Call® Plus glucometers, and classified using World Health Organization (WHO) and American Diabetes Association (ADA) criteria. HbA1C was measured using A1cNow® kits and classified using ADA criteria. Body mass index and blood pressure were measured. Percentage agreement between the two tests was computed.

RESULTS

Using HbA1C, 11.3% of participants had diabetes compared with 4.8% for FPG. Prevalence of HbA1C-defined pre-diabetes (26.4%) was 1.2 times and 2.5 times higher than FPG-defined pre-diabetes using ADA (21.8%) and WHO (10.1%) criteria, respectively. With FPG as the reference, agreement between FPG and HbA1C in classifying diabetes status was moderate (Kappa=22.9; Area Under the Curve (AUC)=75%), while that for AGR was low (Kappa=11.0; AUC=59%). However, agreement was high (over 90%) among negative tests and among participants with risk factors for type 2 diabetes (obesity, overweight or hypertension). HbA1C had more procedural challenges than FPG.

CONCLUSIONS

Although low in the general sample, agreement between HbA1C and FPG is excellent among persons who test negative with either test. A single test can therefore identify the majority at lower risk for type 2 diabetes. Nurses if trained can conduct these tests.

Use of high-normal levels of haemoglobin A(1C) and fasting plasma glucose for diabetes screening and for prediction: a meta-analysis

BACKGROUND

Using high-normal levels of haemoglobin A1C (Abnormal-A1C ) or fasting plasma glucose (FPG) (Abnormal-FPG) for diabetes screening are expected to improve the ability to detect persons with or at high risk of diabetes. We assessed the diagnostic and predictive capacity for diabetes of Abnormal-A1C and Abnormal-FPG. We compared these to the combined use of the two measures to the single use of either measurement.

METHODS

We analysed 31 eligible cross-sectional or cohort studies that assessed diagnostic or predictive ability, respectively, by using lower A1C and FPG cutoff values than recommended by current diabetes criteria. Positive and negative likelihood ratios (LR+ and LR-) were calculated to assess the ability to confirm or exclude diabetes, respectively, on the basis of a bivariate random-effects model.

RESULTS

With both Abnormal-A1C and Abnormal-FPG, the pooled LR+ was above 4 for diagnosing diabetes and above 3 for predicting diabetes. However, the pooled LR- for predicting diabetes was higher with Abnormal-A1C (0.48) and Abnormal-FPG (0.49) in comparison with that for diagnosing diabetes (0.27, Abnormal-A1C ; 0.28, Abnormal-FPG). In eight studies that assessed the predictive ability of the combination of A1C and FPG, using either Abnormal-A1C or Abnormal-FPG could lower LR- to 0.17 from 0.43 for only Abnormal-A1C and from 0.38 for only Abnormal-FPG. Accordingly, LR+ was also lowered to 2.37 from 3.36 for only Abnormal-A1C and from 3.84 for only-Abnormal-FPG.

CONCLUSION

The use of the two blood glucose tests had insufficient capacity to identify subjects at high risk for diabetes but had considerable capacity to identify undiagnosed diabetes.

Risk equations for the development of worsened glucose status and type 2 diabetes mellitus in a Swedish intervention program

Background

Several studies investigated transitions and risk factors from impaired glucose tolerance (IGT) to type 2 diabetes mellitus (T2D). However, there is a lack of information on the probabilities to transit from normal glucose tolerance (NGT) to different pre-diabetic states and from these states to T2D. The objective of our study is to estimate these risk equations and to quantify the influence of single or combined risk factors on these transition probabilities.

Methods

Individuals who participated in the VIP program twice, having the first examination at ages 30, 40 or 50 years of age between 1990 and 1999 and the second examination 10 years later were included in the analysis. Participants were grouped into five groups: NGT, impaired fasting glucose (IFG), IGT, IFG&IGT or T2D. Fourteen potential risk factors for the development of a worse glucose state (pre-diabetes or T2D) were investigated: sex, age, education, perceived health, triglyceride, blood pressure, BMI, smoking, physical activity, snus, alcohol, nutrition and family history. Analysis was conducted in two steps. Firstly, factor analysis was used to find candidate variables; and secondly, logistic regression was employed to quantify the influence of the candidate variables. Bootstrap estimations validated the models.

Results

In total, 29 937 individuals were included in the analysis. Alcohol and perceived health were excluded due to the results of the factor analysis and the logistic regression respectively. Six risk equations indicating different impacts of different risk factors on the transition to a worse glucose state were estimated and validated. The impact of each risk factor depended on the starting or ending pre-diabetes state. High levels of triglyceride, hypertension and high BMI were the strongest risk factors to transit to a worsened glucose state.

Conclusions

The equations could be used to identify individuals with increased risk to develop any of the three pre-diabetic states or T2D and to adapt prevention strategies.

Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome

OBJECTIVE

To investigate the predictive value of different biomarkers for the incidence of type 2 diabetes mellitus (T2DM) in subjects with metabolic syndrome.

METHODS

A prospective study of 525 non-diabetic, middle-aged Lithuanian men and women with metabolic syndrome but without overt atherosclerotic diseases during a follow-up period of two to four years. We used logistic regression to develop predictive models for incident cases and to investigate the association between various markers and the onset of T2DM.

RESULTS

Fasting plasma glucose (FPG), body mass index (BMI), and glycosylated haemoglobin can be used to predict diabetes onset with a high level of accuracy and each was shown to have a cumulative predictive value. The estimated area under the receiver-operating characteristic curve (AUC) for this combination was 0.92. The oral glucose tolerance test (OGTT) did not show cumulative predictive value. Additionally, progression to diabetes was associated with high values of aortic pulse-wave velocity (aPWV).

CONCLUSION

T2DM onset in middle-aged metabolic syndrome subjects can be predicted with remarkable accuracy using the combination of FPG, BMI, and HbA1c, and is related to elevated aPWV measurements.

Implications of risk stratification for diabetes prevention: the case of hemoglobin A1c

Although glycated hemoglobin (HbA1c) has been widely recommended for the diagnosis of diabetes, considerable ambiguity remains about how HbA1c should be used to identify people with prediabetes or other high-risk states for preventive interventions. The current paper provides a synthesis of the epidemiologic basis and the health and economic implications of using various HbA1c-based risk-stratification approaches for diabetes prevention. HbA1c predicts diabetes and related outcomes across a wide range of HbA1c values. However, the authors estimate that, among U.S. adults, the top 15% of the nondiabetic HBA1c distribution (HbA1c of 5.7%-6.4%) accounts for 47% of diabetes cases over 5 years, and the top 30% (5.5%-6.4%) accounts for about 70% of cases. Although this clustering of eventual cases at the high end of the HbA1c risk distribution means that intervention resources will be more efficient when applied to the upper end of the distribution, no obvious threshold exists to prioritize people for preventive interventions. Thus, the choice of optimal thresholds is a tradeoff, wherein selecting a lower HbA1c cut-point will lead to a higher rate of eligibility and health benefits for more people, and a higher HbA1c cut-point will lead to fewer cases of diabetes prevented but greater "economic efficiency" in terms of diabetes cases prevented per intervention participant. Selection of optimal HbA1c thresholds also may change with the evolving science, as better evidence on the biologic effectiveness of lower-intensity interventions and effects of lifestyle interventions on additional outcomes could pave the way for a more comprehensive, tiered approach to risk stratification.

An Integrative, Multilevel, and Transdisciplinary Research Approach to Challenges of Work, Family, and Health

Recognizing a need for rigorous, experimental research to support the efforts of workplaces and policymakers in improving the health and wellbeing of employees and their families, the National Institutes of Health and the Centers for Disease Control and Prevention formed the Work, Family & Health Network (WFHN). The WFHN is implementing an innovative multisite study with a rigorous experimental design (adaptive randomization, control groups), comprehensive multilevel measures, a novel and theoretically based intervention targeting the psychosocial work environment, and translational activities. This paper describes challenges and benefits of designing a multilevel and transdisciplinary research network that includes an effectiveness study to assess intervention effects on employees, families, and managers; a daily diary study to examine effects on family functioning and daily stress; a process study to understand intervention implementation; and translational research to understand and inform diffusion of innovation. Challenges were both conceptual and logistical, spanning all aspects of study design and implementation. In dealing with these challenges, however, the WFHN developed innovative, transdisciplinary, multi-method approaches to conducting workplace research that will benefit both the research and business communities.

Signs of dysregulated fibrinolysis precede the development of type 2 diabetes mellitus in a population-based study

Background

Diabetic patients experience stimulated coagulation and dysfibrinolysis, which is associated with an increased risk of cardiovascular events. This imbalance may precede the manifest diagnosis. We investigated whether elevated antigen levels of tissue plasminogen activator (tPA), plasminogen activator inhibitor-1 (PAI-1), the tPA/PAI-1 complex, or von Willebrand Factor (VWF) precede type 2 diabetes mellitus (T2DM) diagnosis, and whether this elevation occurs before increased fasting plasma glucose (FPG) or 2-hour plasma glucose (2hPG) in individuals who later develop T2DM.

Methods

We conducted a prospective incident case-referent study within the Västerbotten Intervention Programme. Cardiovascular risk factor data as well as FPG and 2hPG and blood samples for future research were collected at a baseline health examination between 1989 and 2000, (n= 28 736). During follow-up in January 2001, 157 cases had developed T2DM. Referents without T2DM were matched for sex, age, and year of participation (n=277). Subgroup analysis was performed for cases with normal baseline glucose levels (FPG <6.1 mmol/L and 2hPG < 8.9 mmol/L) and cases with elevated levels (FPG 6.1-6.9 mmol/L and/or 2hPG 8.9-12.1 mmol/L).

Results

After adjusting for BMI, family history of diabetes, physical activity, smoking, systolic blood pressure and levels of C-reactive protein and triglycerides, independent associations were found between incident T2DM and elevated levels of tPA (OR=1.54, 95% CI 1.06-2.23), PAI-1 (OR=1.61, 95% CI 1.14-2.28), and tPA/PAI-1 complex (OR=2.45, 95% CI 1.56-3.84). In participants with normal glucose levels, PAI-1 (OR=2.06, 95% CI 1.10 - 3.86) exhibited an independent relationship with incident T2DM after the adjustments.

Conclusions

Elevated levels of fibrinolytic variables precede the manifestation of T2DM after adjusting for metabolic and cardiovascular risk factors and can be detected several years before changes in glucose tolerance.

Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6)

AIMS/HYPOTHESIS

The aims of this study were to assess the clinical significance of introducing HbA(1c) into a risk score for diabetes and to develop a scoring system to predict the 5 year incidence of diabetes in Japanese individuals.

METHODS

The study included 7,654 non-diabetic individuals aged 40-75 years. Incident diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l, HbA(1c) ≥6.5% (48 mmol/mol) or self-reported clinician-diagnosed diabetes. We constructed a risk score using non-laboratory assessments (NLA) and evaluated improvements in risk prediction by adding elevated FPG, elevated HbA(1c) or both to NLA.

RESULTS

The discriminative ability of the NLA score (age, sex, family history of diabetes, current smoking and BMI) was 0.708. The difference in discrimination between the NLA + FPG and NLA + HbA(1c) scores was non-significant (0.836 vs 0.837; p = 0.898). A risk score including family history of diabetes, smoking, obesity and both FPG and HbA(1c) had the highest discrimination (0.887, 95% CI 0.871, 0.903). At an optimal cut-off point, sensitivity and specificity were high at 83.7% and 79.0%, respectively. After initial screening using NLA scores, subsequent information on either FPG or HbA(1c) resulted in a net reclassification improvement of 42.7% or 52.3%, respectively (p < 0.0001). When both were available, net reclassification improvement and integrated discrimination improvement were further improved at 56.7% (95% CI 47.3%, 66.1%) and 10.9% (9.7%, 12.1%), respectively.

CONCLUSIONS/INTERPRETATION

Information on HbA(1c) or FPG levels after initial screening by NLA can precisely refine diabetes risk reclassification.

Biomarkers in Type 2 diabetes: improving risk stratification with the PreDx ® Diabetes Risk Score

Type 2 diabetes is a chronic, debilitating and often deadly disease that has reached epidemic proportions. The onset of diabetes can be delayed or prevented in high-risk individuals by diet and lifestyle changes and medications, and hence a key element for addressing the diabetes epidemic is to identify those most at risk of developing diabetes so that preventative measures can be effectively focused. The PreDx(®) Diabetes Risk Score is a multimarker tool for assessing a patient's risk of developing diabetes within the next 5 years. Requiring a simple blood draw using standard sample collection and handling procedures, the PreDx Diabetes Risk Score is easily implemented in clinical practice and provides an assessment of diabetes risk that is superior to other measures, including fasting plasma glucose, glycated hemoglobin, measures of insulin resistance and other clinical measures. In this article, we provide an overview of the PreDx Diabetes Risk Score.

HbA(1c) values for defining diabetes and impaired fasting glucose in Asian Indians

AIM

To determine the glycosylated haemoglobin (HbA(1c)) cut-points for diabetes and impaired fasting glucose (IFG) among Asian Indians.

METHODS

Participants (n=525) were a random sample selected from the India Health Study. Based on history and fasting plasma glucose (FPG), participants were classified into known diabetes, newly diagnosed diabetes (NDD), impaired fasting glucose (IFG) [ADA and WHO criteria] or normal fasting glucose (NFG). Receiver Operating Characteristic curves were used to identify the optimum sensitivity and specificity for defining HbA(1c) cut-points for NDD and IFG against the FPG criteria.

RESULTS

There were 64 participants with a known history of diabetes. Of the remaining 461, IFG was present in 44.7% (ADA) and 18.2% (WHO), and 10.4% were NDD. Mean HbA(1c) were 5.4 (±0.04)% for NFG; 5.7 (±0.06)% among IFG-ADA, 5.8 (±0.09)% among IFG-WHO; 7.5 (±0.33)% for NDD and 8.4 (±0.32)% for known diabetes. Optimal HbA(1c) cut-point for NDD was 5.8% (sensitivity=75%, specificity=75.5%, AUC=0.819). Cut-point for IFG (ADA) was 5.5% (sensitivity=59.7%, specificity=59.9%, AUC=0.628) and for IFG (WHO) was 5.6% (sensitivity=60.7%, specificity=65.1%, AUC=0.671).

CONCLUSION

In this study population from north and south regions of India, the HbA(1c) cut-point that defines NDD (≥5.8%) was much lower than that proposed by an international expert committee and the American Diabetes Association (≥6.5%). A cut-point of ≥5.5% or ≥5.6% defined IFG, and was slightly lower than the ≥5.7% for high risk proposed, but accuracy was less than 70%.

Risk assessment tools for identifying individuals at risk of developing type 2 diabetes

Trials have demonstrated the preventability of type 2 diabetes through lifestyle modifications or drugs in people with impaired glucose tolerance. However, alternative ways of identifying people at risk of developing diabetes are required. Multivariate risk scores have been developed for this purpose. This article examines the evidence for performance of diabetes risk scores in adults by 1) systematically reviewing the literature on available scores and 2) their validation in external populations; and 3) exploring methodological issues surrounding the development, validation, and comparison of risk scores. Risk scores show overall good discriminatory ability in populations for whom they were developed. However, discriminatory performance is more heterogeneous and generally weaker in external populations, which suggests that risk scores may need to be validated within the population in which they are intended to be used. Whether risk scores enable accurate estimation of absolute risk remains unknown; thus, care is needed when using scores to communicate absolute diabetes risk to individuals. Several risk scores predict diabetes risk based on routine noninvasive measures or on data from questionnaires. Biochemical measures, in particular fasting plasma glucose, can improve prediction of such models. On the other hand, usefulness of genetic profiling currently appears limited.

Elevated proinflammatory cytokine production by a skewed T cell compartment requires monocytes and promotes inflammation in type 2 diabetes

An appropriate balance between proinflammatory (Th17 and Th1) and anti-inflammatory (regulatory T cells [Tregs] and Th2) subsets of T cells is critical to maintain homeostasis and avoid inflammatory disease. Type 2 diabetes (T2D) is a chronic inflammatory disease promoted by changes in immune cell function. Recent work indicates T cells are important mediators of inflammation in a mouse model of T2D. These studies identified an elevation in the Th17 and Th1 subsets with a decrease in the Treg subset, which culminates in inflammation and insulin resistance. Based on these data, we tested the hypothesis that T cells in T2D patients are skewed toward proinflammatory subsets. Our data show that blood from T2D patients has increased circulating Th17 cells and elevated activation of Th17 signature genes. Importantly, T cells required culture with monocytes to maintain Th17 signatures, and fresh ex vivo T cells from T2D patients appeared to be poised for IL-17 production. T cells from T2D patients also have increased production of IFN-γ, but produce healthy levels of IL-4. In contrast, T2D patients had decreased percentages of CD4(+) Tregs. These data indicate that T cells in T2D patients are naturally skewed toward proinflammatory subsets that likely promote chronic inflammation in T2D through elevated cytokine production. Potential therapies targeted toward resetting this balance need to be approached with caution due to the reciprocal relationship between Th17 cells and Tregs. Understanding the unique aspects of T2D T cells is essential to predict outcomes of such treatments.

A1C level and future risk of diabetes: a systematic review

We examined ranges of A1C useful for identifying persons at high risk for diabetes prior to preventive intervention by conducting a systematic review. From 16 included studies, we found that annualized diabetes incidence ranged from 0.1% at A1C <5.0% to 54.1% at A1C >or=6.1%. Findings from 7 studies that examined incident diabetes across a broad range of A1C categories showed 1) risk of incident diabetes increased steeply with A1C across the range of 5.0 to 6.5%; 2) the A1C range of 6.0 to 6.5% was associated with a highly increased risk of incident diabetes, 25 to 50% incidence over 5 years; 3) the A1C range of 5.5 to 6.0% was associated with a moderately increased relative risk, 9 to 25% incidence over 5 years; and 4) the A1C range of 5.0 to 5.5% was associated with an increased incidence relative to those with A1C <5%, but the absolute incidence of diabetes was less than 9% over 5 years. Our systematic review demonstrated that A1C values between 5.5 and 6.5% were associated with a substantially increased risk for developing diabetes.

The Västerbotten Intervention Programme: background, design and implications

Background and objective

In Sweden, mortality from cardiovascular diseases (CVD) increased steadily during the 20th century and in the mid-1980s it was highest in the county of Västerbotten. Therefore, a community intervention programme was launched – the Västerbotten Intervention Programme (VIP) – with the aim of reducing morbidity and mortality from CVD and diabetes.

Design

The VIP was first developed in the small municipality of Norsjö in 1985. Subsequently, it was successively implemented across the county and is now integrated into ordinary primary care routines. A population-based strategy directed towards the public is combined with a strategy to reach all middle-aged persons individually at ages 40, 50 and 60 years, by inviting them to participate in systematic risk factor screening and individual counselling about healthy lifestyle habits. Blood samples for research purposes are stored at the Umeå University Medical Biobank.

Results

Overall, 113,203 health examinations have been conducted in the VIP and 6,500–7,000 examinations take place each year. Almost 27,000 subjects have participated twice. Participation rates have ranged between 48 and 67%. A dropout rate analysis in 1998 indicated only a small social selection bias. Cross-sectional, nested case-control studies and prospective studies have been based on the VIP data. Linkages between the VIP and local, regional and national databases provide opportunities for interdisciplinary research, as well as national and international collaborations on a wide range of disease outcomes. A large number of publications are based on data that are collected in the VIP, many of which also use results from analysed stored blood samples. More than 20 PhD theses have been based primarily on the VIP data.

Conclusions

The concept of the VIP, established as a collaboration between politicians and health care providers on the one hand and primary care, functioning as the operating machinery, and the public on the other, forms the basis for effective implementation and endurance over time. After more than 20 years of the VIP, there is a large comprehensive population-based database, a stable organisation to conduct health surveys and collect data, and a solid structure to enable widespread multidisciplinary and scientific collaborations.

The relative importance of modifiable potential risk factors of type 2 diabetes: a meta-analysis of two cohorts

Lifestyle factors predict type 2 diabetes occurrence, but their effect in high- and low-risk populations is poorly known. This study determines the prediction of low-risk lifestyle on type 2 diabetes in those with and without metabolic syndrome in a pooled sample of two representative Finnish cohorts, collected in 1978-1980 and 2000-2001. Altogether 8,627 individuals, aged 40-79 years, and free of diabetes and cardiovascular disease at baseline were included in this study. A low-risk lifestyle was defined based on body mass index, exercise, alcohol consumption, smoking, and serum vitamin D concentration. The metabolic syndrome was defined according to the International Diabetes Federation including obesity, blood pressure, serum HDL cholesterol, serum triglycerides, and fasting glucose. During a 10-year follow-up, altogether 226 type 2 diabetes cases occurred. Overweight was the strongest predictor of type 2 diabetes (population attributable fraction (PAF) = 77%, 95% confidence interval (CI): 53, 88%). Together with lack of exercise, unsatisfactory alcohol consumption, smoking, and low vitamin D concentration it explained 82% of the cases. Altogether 62% (CI: 47, 73%) of the cases were attributable to the metabolic syndrome and 92% (CI: 67, 98%) to the most unfavourable combination of its components. The metabolic syndrome did not modify the prediction of lifestyle factors but persons with normal blood pressure benefited more from positive changes in exercise, alcohol consumption, and smoking than those with elevated blood pressure (P for interaction = 0.01). In conclusion, modification of lifestyle factors apparently reduces type 2 diabetes risk, especially in persons with normal blood pressure.

An accurate risk score for estimation 5-year risk of type 2 diabetes based on a health screening population in Taiwan

This study aimed to provide the epidemiological model evaluating the risk of developing type 2 diabetes (T2DM) in Taiwan periodic health-check population. We derived risk functions using multivariate Cox regression in a random half of the sample. Rules based on these risk functions were evaluated in another half. Model coefficients were used to assign each variable a score. 73,961 subjects aged 35-74, were included and followed up with a median 3.15 years. Six predictive models (PMs) were developed. PM1 contained simple clinical information, while PM2 contained fasting plasma glucose (FPG) based on PM1, and PM3 further added variables indicating lipid level, liver and kidney. PM4 only included FPG. The capability of published ARIC score model was also evaluated. Eventually we considered score defined nine predictors by PM2. The area under the ROC curve (AUC) was 0.848 (95% CI, 0.829-0.868) predicting diabetes within 5 years, and also had adequate performance in validation subsample (AUC=0.833, 95% CI, 0.811-0.855). The 5-year T2DM probability can be calculated by: 1-0.9743960037 exp((score points -15.0281284)). We concluded that this diabetes risk score, derived from clinical information combined with FPG is a simple, effective tool to identify individuals at high risk for undiagnosed T2DM.

Predictors of development of diabetes in patients with chronic heart failure in the Candesartan in Heart Failure Assessment of Reduction in Mortality and Morbidity (CHARM) program

OBJECTIVE

The purpose of this study was to identify predictors of incident diabetes during follow-up of nondiabetic patients with chronic heart failure (CHF) in the Candesartan in Heart Failure Assessment of Reduction in Mortality and Morbidity (CHARM) program.

RESEARCH DESIGN AND METHODS

A total of 1,620 nondiabetic patients had full baseline datasets. We compared baseline demographic, medication, and laboratory data for patients who did or did not develop diabetes and conducted logistic regression and receiver operator characteristic curve analyses.

RESULTS

Over a median period of 2.8 years, 126 of the 1,620 patients (7.8%) developed diabetes. In multiple logistic regression analysis, the following baseline characteristics were independently associated with incident diabetes in decreasing order of significance by stepwise selection: higher A1C (odds ratio [OR] 1.78 per 1 SD increase; P < 0.0001), higher BMI (OR 1.64 per 1 SD increase; P < 0.0001), lipid-lowering therapy (OR 2.05; P = 0.0005), lower serum creatinine concentration (OR 0.68 per 1 SD increase; P = 0.0018), diuretic therapy (OR 4.81; P = 0.003), digoxin therapy (OR 1.65; P = 0.022), higher serum alanine aminotransferase concentration (OR 1.15 per 1 SD increase; P = 0.027), and lower age (OR 0.81 per 1 SD increase; P = 0.048). Using receiver operating characteristic curve analysis, A1C and BMI yielded areas under the curve of 0.723 and 0.712, respectively, increasing to 0.788 when combined. Addition of other variables independently associated with diabetes risk minimally improved prediction of diabetes.

CONCLUSIONS

In nondiabetic patients with CHF in CHARM, A1C and BMI were the strongest predictors of the development of diabetes. Other minor predictors in part reflected CHF severity or drug-associated diabetes risk. Identifying patients with CHF at risk of diabetes through simple criteria appears possible and could enable targeted preventative measures.

Advanced glycation end products of DNA: quantification of N2-(1-Carboxyethyl)-2'-deoxyguanosine in biological samples by liquid chromatography electrospray ionization tandem mass spectrometry

Methylglyoxal (MG) and related alpha-oxoaldehydes react with proteins, lipids, and DNA to give rise to covalent adducts known as advanced glycation end products (AGEs). Elevated levels of AGEs have been implicated in the pathological complications of diabetes, uremia, Alzheimer's disease, and possibly cancer. There is therefore widespread interest in developing sensitive methods for the in vivo measurement of AGEs as prognostic biomarkers and for treatment monitoring. The two diastereomeric MG-DNA adducts of N(2)-(1-carboxyethyl)-2'-deoxyguanosine (CEdG) are the primary glycation products formed in DNA; however, accurate assessment of their distribution in vivo has not been possible since there is no readily available quantitative method for CEdG determination in biological samples. To address these issues, we have developed a sensitive and quantitative liquid chromatography electrospray ionization tandem mass spectrometry assay using the stable isotope dilution method with an (15)N(5)-CEdG standard. Methods for CEdG determination in urine or tissue extracted DNA are described. Changes in urinary CEdG in diabetic rats in response to oral administration of the AGE inhibitor LR-90 are used to demonstrate the potential utility of the method for treatment monitoring. Both stereoisomeric CEdG adducts were detected in a human breast tumor and normal adjacent tissue at levels of 3-12 adducts/10(7) dG, suggesting that this lesion may be widely distributed in vivo. Strategies for dealing with artifactual adduct formation due to oxoaldehyde generation during DNA isolation and enzymatic workup procedures are described.

Fatty acid profile of the erythrocyte membrane preceding development of Type 2 diabetes mellitus

BACKGROUND AND AIMS

The respective roles of dietary fatty acids in the pathogenesis of diabetes are as yet unclear. Erythrocyte membrane fatty acid (EMFA) composition may provide an estimate of dietary fatty acid intake. This study investigates the relation between EMFA composition and development of Type 2 diabetes mellitus.

METHODS AND RESULTS

In a nested case-referent design we studied 159 individuals tested as non-diabetic at baseline who after a mean observation time of 5.4+/-2.6years were diagnosed with Type 2 diabetes mellitus and 291 sex- and age-matched referents. Higher proportions of pentadecanoic acid (15:0) and heptadecanoic acid (17:0) were associated with a lower risk of diabetes. In accordance with earlier findings, higher proportions of palmitoleic (16:1 n-7), dihomo-gamma-linolenic (20:3 n-6) and adrenic (22:4 n-6) acids were associated with increased risk, whereas linoleic (18:2 n-6) and clupanodonic (22:5 n-3) acids were inversely associated with diabetes. After adjustment for BMI, HbA1c, alcohol intake, smoking and physical activity the only significant predictors were 15:0 and 17:0 as protective factors and 22:4 n6 as risk factor.

CONCLUSION

In accordance with previous studies, our results indicate that EMFA-patterns predict development of Type 2 diabetes mellitus. The inverse association with two saturated fatty acids, previously shown to reflect consumption of dairy products, is a new finding.

Does HbA1C predict isolated impaired fasting glycaemia in the oral glucose tolerance test in subjects with impaired fasting glycaemia?

Background

To assess the usefulness of erythrocyte glycated haemoglobin (HbA1C) as a screening tool to identify those subjects with impaired fasting glycaemia (IFG) who do not have impaired glucose tolerance (IGT) or diabetes mellitus (DM) on a 75 g oral glucose tolerance test (OGTT).

Design and methods

All subjects undergoing an OGTT had HbA1C measured at baseline. Receiver operator characteristics analysis was used to identify optimal HbA1C cut-off values for diagnosing and excluding IGT and DM.

Results

We studied 140 subjects (69 women) with IFG (fasting capillary plasma glucose between 6.1–6.9 mmol/L). Using World Health Organisation criteria, 27 had isolated IFG, 56 had IGT and 57 had DM. HbA1C was higher ( P < 0.001) in patients with DM (6.8 ± 0.93%) when compared with those with IGT (6.3 ± 0.68%) and isolated IFG (6.2 ± 0.30%), but HbA1C was similar in those with IGT and isolated IFG. There was no HbA1C cut-off value differentiating isolated IFG from IGT or DM. None of the subjects with isolated IFG had HbA1C concentration of >6.8%, but 76% and 54% subjects with IGT and DM, respectively, had HbA1C of ≤6.8%.

Conclusions

HbA1C measurement is of limited value in differentiating isolated IFG, IGT and DM in subjects with IFG. It cannot be used to identify which subjects with IFG do not require an OGTT.

Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities?

This review critically appraises studies examining the association of novel factors with diabetes. We show that many of the most studied novel and apparently 'independent' risk factors are correlated with each other by virtue of their common origins or pathways, and that residual confounding is likely. Available studies also have other limitations, including differences in methodology or inadequate statistical analyses. Furthermore, although most relevant work in this area has focused on improving our understanding of the pathogenesis of diabetes, association studies in isolation cannot prove causality; intervention studies with specific agents (if available) are required, and genetic studies may help. With respect to the potential value of novel risk factors for diabetes risk prediction, we illustrate why this work is very much in its infancy and currently not guaranteed to reach clinical utility. Indeed, the existence of several more easily measured powerful predictors of diabetes, suggests that the additional value of novel markers may be limited. Nevertheless, several suggestions to improve relevant research are given. Finally, we show that several risk factors for diabetes are only weakly associated with the risk of incident vascular events, an observation that highlights the limitations of attempting to devise unified criteria (e.g. metabolic syndrome) to identify individuals at risk of both CHD and diabetes.