Development and validation of a risk-score model for subjects with impaired glucose tolerance for the assessment of the risk of type 2 diabetes mellitus-The STOP-NIDDM risk-score

Risk assessment of diabetes using the Indian Diabetes Risk Score among older adults: Secondary analysis from the Longitudinal Ageing Study in India

BACKGROUND

The Indian Diabetes Risk Score (IDRS) is a simple tool to assess the probability of an individual having type 2 diabetes (T2DM) but its applicability in community-dwelling older adults is lacking. This study aimed to estimate the risk of T2DM and its determinants among older adults without prior diabetes (DM) using the IDRS, while also assessing its sensitivity and specificity in individuals with a history of diabetes.

METHODS

We analyzed cross-sectional data from the Longitudinal Ageing Study in India (LASI) wave-1 (2017-18). IDRS was calculated amongst individuals aged ≥45 years considering waist circumference, physical activity, age and family history of DM. Risk was categorized as high (≥60), moderate (30-50), and low (<30).

RESULTS

Among 64541 individuals, 7.27 % (95 % CI: 6.78, 7.80) were at low risk, 61.80 % (95 % CI: 60.99, 62.61) at moderate risk, and 30.93 % (95 % CI: 30.19, 31.67) at high risk for T2DM. Adjusted analysis showed higher risk of T2DM among men, widowed/divorced, urban residents, minority religions, overweight, obese, and individuals with hypertension. ROC curve yielded an AUC of 0.67 (95 % CI: 0.66, 0.67, P < 0.001). The IDRS cutoff ≥50 had 73.69 % sensitivity and 51.40 % specificity for T2DM detection.

CONCLUSION

More than 9 in 10 older adults in India without history of DM have high-moderate risk of T2DM when assessed with the IDRS risk-prediction tool. However, the low specificity and moderate sensitivity of IDRS in existing DM cases constraints its practical utility as a decision tool for screening.

Development and validation of risk prediction models for new-onset type 2 diabetes in adults with impaired fasting glucose

AIMS

To develop and validate sex-specific risk prediction models based on easily obtainable clinical data for predicting 5-year risk of type 2 diabetes (T2D) among individuals with impaired fasting glucose (IFG), and generate practical tools for public use.

METHODS

The data used for model training and internal validation came from a large prospective cohort (N = 18,384). Two independent cohorts were used for external validation. A two-step approach was applied to screen variables. Coefficient-based models were constructed by multivariate Cox regression analyses, and score-based models were subsequently generated. The predictive power was evaluated by the area under the curve (AUC).

RESULTS

During a median follow-up of 7.55 years, 5697 new-onset T2D cases were identified. Predictor variables included age, body mass index, waist circumference, diastolic blood pressure, triglycerides, fasting plasma glucose, and fatty liver. The proposed models outperformed five existing models. In internal validation, the AUCs of the coefficient-based models were 0.741 (95% CI 0.723-0.760) for men and 0.762 (95% CI 0.720-0.802) for women. External validation yielded comparable prediction performance. We finally constructed a risk scoring system and a web calculator.

CONCLUSIONS

The risk prediction models and derived tools had well-validated performance to predict the 5-year risk of T2D in IFG adults.

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

INTRODUCTION

Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%-75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status.

RESEARCH DESIGN AND METHODS

Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes.

RESULTS

The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications.

CONCLUSIONS

A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening.

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BACKGROUND

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

METHODS

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test).

RESULTS

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

CONCLUSIONS

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

2022 Prevention of chronic non-communicable diseases in Of the Russian Federation. National guidelines

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Lower miR-21/ROS/HNE levels associate with lower glycemia after habit-intervention: DIAPASON study 1-year later

Background

The prevalence of prediabetes is increasing in the global population and its metabolic derangements may expose to a higher risk to develop type 2 diabetes (T2D) and its cardiovascular burden. Lifestyle modifications might have considerable benefits on ameliorating metabolic status. Alternative biomarkers, such as circulating miR-21, has been recently discovered associated with dysglycemia. Here we evaluated, in a longitudinal cohort of dysglycemic population the relation between the circulating miR-21/ROS/HNE levels and the habit-intervention (HI) after 1 year of follow-up.

Methods

1506 subjects from DIAPASON study were screened based on the Findrisc score. Of them, 531 subjects with Findrisc ≥ 9 were selected for dysglycemia (ADA criteria) and tested for circulating miR-21, ROS and HNE levels, as damaging-axis. 207 subjects with dysglycemia were re-evaluated after 1-year of habit intervention (HI). Repeated measures tests were used to evaluate changes from baseline to 1-year of follow-up. The associations between glycemic parameters and miR-21/ROS/HNE were implemented by linear regression and logistic regression models.

Results

After HI, we observed a significant reduction of miR-21/ROS/HNE axis in dysglycemic subjects, concomitantly with ameliorating of metabolic parameters, including insulin resistance, BMI, microalbuminuria, reactive hyperemia index and skin fluorescence. Significant positive interaction was observed between miR-21 axis with glycaemic parameters after HI. Lower miR-21 levels after HI, strongly associated with a reduction of glycemic damaging-axis, in particular, within-subjects with values of 2hPG < 200 mg/dL.

Conclusions

Our findings demonstrated that HI influenced the epigenetic changes related to miR-21 axis, and sustain the concept of reversibility from dysglycemia. These data support the usefulness of novel biological approaches for monitoring glycemia as well as provide a screening tool for preventive programmes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01465-0.

Predicting the risk of developing type 2 diabetes in Chinese people who have coronary heart disease and impaired glucose tolerance

AIMS

Robust diabetes risk estimates in Asian patients with impaired glucose tolerance (IGT) and coronary heart disease (CHD) are lacking. We developed a Chinese type 2 diabetes risk calculator using Acarbose Cardiovascular Evaluation (ACE) trial data.

METHODS

There were 3105 placebo-treated ACE participants with requisite data for model development. Clinically relevant variables, and those showing nominal univariate association with new-onset diabetes (P < .10), were entered into BASIC (clinical variables only), EXTENDED (clinical variables plus routinely available laboratory results), and FULL (all candidate variables) logistic regression models. External validation was performed using the Luzhou prospective cohort of 1088 Chinese patients with IGT.

RESULTS

Over median 5.0 years, 493 (15.9%) ACE participants developed diabetes. Lower age, higher body mass index, and use of corticosteroids or thiazide diuretics were associated with higher diabetes risk. C-statistics for the BASIC (using these variables), EXTENDED (adding male sex, fasting plasma glucose, 2-hour glucose, and HbA1c), and FULL models were 0.610, 0.757, and 0.761 respectively. The EXTENDED model predicted a lower 13.9% 5-year diabetes risk in the Luzhou cohort than observed (35.2%, 95% confidence interval 31.3%-39.5%, C-statistic 0.643).

CONCLUSION

A risk prediction model using routinely available clinical variables can be used to estimate diabetes risk in Chinese people with CHD and IGT.

One-hour plasma glucose combined with skin autofluorescence identifies subjects with pre-diabetes: the DIAPASON study

INTRODUCTION

The major challenge for diabetes prevention is early identification of individuals at risk to allow for implementation of measures to delay the onset of future disease. Measures such as fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG), and glycosylated hemoglobin (HbA1c) are equally appropriate for identifying pre-diabetes and diabetes, but do not all identify the disease in the same individual. We tested the utility of a diagnostic method combining FPG, 2hPG and HbA1c for early evaluation and easy identification of pre-diabetes.

RESEARCH DESIGN AND METHODS

531 subjects underwent skin autofluorescence (SAF) and glycemia analyses. We created two classification groups based on the American Diabetes Association diagnosis guidelines: (1) based on 2hPG and (2) based on a new combination of three glycemia parameters (the three-criteria strategy (3-c)). Logistic regression modeling was used to estimate the associations.

RESULTS

SAF showed high associations for both 3-c definition and 2hPG definition alone. These associations appeared stronger in 3-c than those in 2hPG. The non-invasive SAF measurement outperformed 2hPG in the detection of dysglycemia or pre-diabetes. Stepwise selections identified 1-hour postload glucose (1hPG) as variable identifying pre-diabetes using the 2hPG criterion, and the model based on 1hPG plus SAF appeared to be the best association using the 3-c strategy.

CONCLUSIONS

1hPG coupled with SAF showed a strong association in the evaluation of pre-diabetes using the 3-c method.

Hypoglycemic Effects of Oat Oligopeptides in High-Calorie Diet/STZ-Induced Diabetic Rats

The study was aimed to determine whether treatment with oat oligopeptides (OOPs) could modulate hyperglycemia related to type 2 diabetes mellitus (T2DM) in Sprague–Dawley (SD) rats. Diabetic SD rats modeling by a joint effect of high-calorie diet for 45 days and twice intraperitoneal injection of 30 mg/kg streptozotocin at one-week interval were observed with or without OOPs administration (0.25, 0.50, 1.00, and 2.00 g/kg Body Weight) for 12 weeks. Fasting blood glucose (FBG), oral glucose test tolerance (OGTT), serum insulin, level of antioxidant, and hepatic enzymes were measured. In addition, frequency of micturition was recorded in this study for the first time. It was observed that the administration of OOPs (2.00 g/kg Body Weight) resulted in a significant decrease (p < 0.05) in FBG since 6th week and a significant decrease (p < 0.05) in the OGTT-AUC on 6th and 10th week. In addition, the administration of OOPs (2.00 g/kg Body Weight) reduced HOMA-IR index and 24-h urine volume significantly (p < 0.05) whereas increased SOD activity significantly (p < 0.05). These results suggested that OOPs may have a hypoglycemic effect in diabetic rats.

Determining type 2 diabetes risk factors for the adults: A community based study from Turkey

AIMS

This study aimed to determine risk factors for type 2 diabetes among adults who were not diagnosed with diabetes.

METHODS

Adults were included in this study within the public activities performed on World Diabetes Day (n=1872). Data were collected using the FINDRISC questionnaire and a short questionnaire.

RESULTS

Participants' mean age was 39.35±10.40. The mean FINDRISC score was 7.46±4.62, women's mean score was higher than that for men. The FINDRISC score indicates that 7.4% of the participants were in the highrisk group. Among participants, BMI value of 65.1% was 25kg/m² and higher, waist circumference of 58% was over the threshold value; and 50.7% did not engage in sufficient physical activity. Of the participants, 9.5% had a history of high blood glucose, families of 38.9% had a history of diabetes. The mean FINDRISC score was in the slightly high category, 121 participants were found likely to be diagnosed with diabetes within ten years if no action was taken.

CONCLUSIONS

It is recommended the risk screening studies to be conducted and the FINDRISC tool to be used in Turkey, where diabetes prevalence is increasing rapidly, to determine diabetes risks in the early period and to raise social awareness for diabetes.

Utility of Tissue Inhibitor Metalloproteinase-1 and Osteopontin as Prospective Biomarkers of Early Cardiovascular Complications in Type 2 Diabetes

AIM:

This work investigated associations between tissue inhibitor metalloproteinase-1 and diabetic cardiovascular diseases in type 2 diabetic patients; also it investigated the role of osteopontin in the diagnosis of type 2 cardiovascular diabetes complications.

SUBJECTS AND METHODS:

These were examined on eighty subjects, divided into three groups as follows: twenty volunteer healthy control subjects, thirty type 2 diabetes mellitus (DM) patients, and thirty cardiovascular, diabetic patients. Full clinical measurements were carried out, and the expression level of tissue inhibitor metalloproteinase-1 in blood samples was analysed by real-time PCR, using gene-specific primer pairs. Also osteopontin concentrations had been measured by the enzyme-linked immunosorbent assay. Data were tested statistically by parametric tests.

RESULTS:

The concentrations of osteopontin and the expression levels of tissue inhibitor metalloproteinase-1 were significantly increased in diabetic and cardiovascular diabetic groups compared to control group also they were significantly increased in the cardiovascular diabetic group compared to the diabetic group.

CONCLUSION:

Tissue inhibitor metalloproteinase-1 and osteopontin concentrations were significantly increased in diabetic patients with cardiovascular complications than other groups.

Effectiveness and safety of bifidobacteria and berberine in people with hyperglycemia: study protocol for a randomized controlled trial

Background

Berberine is one of the most important examples of a Chinese traditional medicine that has hypoglycemic effects but there have been no randomized controlled trials of the drug in a larger sample. In addition, the use of probiotic biotherapy to maintain an appropriate intestinal flora may represent an effective early intervention for hyperglycemia. Unfortunately, there has been a shortage of relevant research on this possibility at the population level. This study was designed to determine the hypoglycemic effect and safety of both bifidobacteria and berberine administration to newly diagnosed patients with pre-diabetes or diabetes mellitus.

Methods/design

This is a multicenter, double-blind, randomized, and parallel-controlled study that includes a run-in period of 2 weeks and a treatment period of 16 weeks, which will be conducted between June 2015 and October 2018. The 300 randomized patients will be assigned to the following four groups for 16 weeks’ treatment: Bifidobacterium, berberine, Bifidobacterium combined berberine, and placebo control groups. The primary outcome is the absolute value of fasting plasma glucose compared with baseline after 16 weeks of treatment.

Discussion

This is the first randomized controlled trial to determine the hypoglycemic effect and safety of both bifidobacteria and berberine administration to newly diagnosed patients with pre-diabetes or diabetes mellitus. It may provide support for the use of berberine and bifidobacteria in the treatment of diabetes.

Trial registration

ClinicalTrials.gov, ID: NCT03330184. Retrospectively registered on 18 October 2017.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-018-2438-5) contains supplementary material, which is available to authorized users.

Using Neighborhood-Level Census Data to Predict Diabetes Progression in Patients with Laboratory-Defined Prediabetes

CONTEXT

Research on predictors of clinical outcomes usually focuses on the impact of individual patient factors, despite known relationships between neighborhood environment and health.

OBJECTIVE

To determine whether US census information on where a patient resides is associated with diabetes development among patients with prediabetes.

DESIGN

Retrospective cohort study of all 157,752 patients aged 18 years or older from Kaiser Permanente Northern California with laboratory-defined prediabetes (fasting plasma glucose, 100 mg/dL-125 mg/dL, and/or glycated hemoglobin, 5.7%-6.4%). We assessed whether census data on education, income, and percentage of households receiving benefits through the US Department of Agriculture's Supplemental Nutrition Assistance Program (SNAP) was associated with diabetes development using logistic regression controlling for age, sex, race/ethnicity, blood glucose levels, and body mass index.

MAIN OUTCOME MEASURE

Progression to diabetes within 36 months.

RESULTS

Patients were more likely to progress to diabetes if they lived in an area where less than 16% of adults had obtained a bachelor's degree or higher (odds ratio [OR] =1.22, 95% confidence interval [CI] = 1.09-1.36), where median annual income was below $79,999 (OR = 1.16 95% CI = 1.03-1.31), or where SNAP benefits were received by 10% or more of households (OR = 1.24, 95% CI = 1.1-1.4).

CONCLUSION

Area-level socioeconomic and food assistance data predict the development of diabetes, even after adjusting for traditional individual demographic and clinical factors. Clinical interventions should take these factors into account, and health care systems should consider addressing social needs and community resources as a path to improving health outcomes.

Impact of correlation of predictors on discrimination of risk models in development and external populations

Background

The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations.

Methods

We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity.

Results

For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects.

Conclusions

Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-017-0345-1) contains supplementary material, which is available to authorized users.

A Review of Emerging Technologies for the Management of Diabetes Mellitus

OBJECTIVE

High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making.

METHODS

A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels.

RESULTS

Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM.

CONCLUSION

Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care.

SIGNIFICANCE

The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

Literature-based discovery of salivary biomarkers for type 2 diabetes mellitus

The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM-specific salivary biomarkers by literature-based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM-specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM-associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM-associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening.

A home-based method for the detection of impaired glucose tolerance in hypertensive primary care patients

OBJECTIVE

The aim of this project was to compare an oral glucose tolerance test (OGTT) partly performed in the patient's home (OGTTh) with a clinic-obtained OGTT with regard to the ability of the tests to identify patients with impaired glucose tolerance (IGT) and type 2 diabetes mellitus (DM-2).

DESIGN

A method comparison.

SETTING

The study was completed at two primary health care centres.

SUBJECTS

Fifty-one patients with hypertension aged 50-79 years completed both OGTT tests.

MAIN OUTCOME MEASURES

Values for capillary P-glucose obtained two hours after a glucose load were compared between the two OGTT tests. Fasting plasma glucose (fP-glucose) and HbA1c were also measured.

RESULTS

Thirty-seven patients were classified in the same group (normal/IGT/DM-2) by the two tests. The index of validity based on the test's ability to identify normal or pathological values (≥ 8.9 mmol/l) was 0.75. The value for kappa was 0.66 with a sensitivity of 0.54 and a specificity of 0.82.

CONCLUSION

OGTTh may be a useful screening method for IGT in risk groups such as hypertensive patients.

Managing hyperglycemia in patients with Cushing's disease treated with pasireotide: medical expert recommendations

To recommend an approach to monitoring and treating hyperglycemia in pasireotide-treated patients with Cushing's disease, a severe clinical condition caused by a pituitary adenoma hypersecreting adrenocorticotropic hormone. Advisory Board meeting of ten European experts in pituitary disease and diabetes mellitus in Munich, Germany, on February 23, 2012, to obtain expert recommendations. Cushing's disease presents a number of management challenges. Pasireotide, a novel agent for the treatment of Cushing's disease with proven biochemical and clinical efficacy, improves outcomes and expands treatment options. Clinical trials have shown that the pasireotide adverse event profile is similar to that of other somatostatin analogs, except for a higher frequency of hyperglycemia. Mechanistic studies in healthy volunteers suggest that pasireotide-associated hyperglycemia is due to reduced secretion of glucagon-like peptide (GLP)-1, glucose-dependent insulinotropic polypeptide, and insulin; however, it is associated with intact postprandial glucagon secretion. Individual patients' results demonstrate effective hyperglycemia management by following standard guidelines for the treatment of diabetes mellitus with individual adaptation to the specific underlying pathophysiology, i.e., preferential use of GLP-1 based-medications. Patients on pasireotide treatment should be monitored for changes in glucose metabolism and hyperglycemia. Diabetes mellitus should be managed by initiation of medical therapy with metformin and staged treatment intensification with a dipeptidyl peptidase-4 inhibitor, with a switch to a GLP-1 receptor agonist and initiation of insulin, as required, to achieve and maintain glycemic control. Further research into hyperglycemia following pasireotide treatment will help refine the optimal strategy in Cushing's disease.

Chinese herbal medicine Tianqi reduces progression from impaired glucose tolerance to diabetes: a double-blind, randomized, placebo-controlled, multicenter trial

CONTEXT

Living in a prediabetes state significantly increases a patient's risk for both diabetes and cardiovascular disease. Tianqi capsule, containing 10 Chinese herbal medicines, is used in China for the treatment of type 2 diabetes mellitus (T2DM).

OBJECTIVE

The purpose of this study was to assess whether Tianqi prevented T2DM in subjects with impaired glucose tolerance (IGT) over the course of a 12-month treatment.

METHODS

Individuals with IGT were randomly allocated in a double-blind manner to receive Tianqi (n = 210) or a placebo (n = 210) for 12 months. Oral glucose tolerance tests were conducted every 3 months to assess the development of diabetes or restoration to normal glucose tolerance. All subjects received the same lifestyle education. The primary endpoint was the conversion of IGT to T2DM. Body weight and body mass index were observed. Adverse effects were monitored.

RESULTS

Of the 420 enrolled subjects with IGT, 389 completed the trial (198 in the Tianqi group and 191 in the placebo group). At the end of the 12-month trial, 36 subjects in the Tianqi group (18.18%) and 56 in the placebo group (29.32%) had developed diabetes (P = .01). There was a significant difference in the number of subjects who had normal glucose tolerance at the end of the study between the Tianqi and placebo groups (n = 125, 63.13%, and n = 89, 46.60%, respectively; P = .001). Cox's proportional hazards model analysis showed that Tianqi reduced the risk of diabetes by 32.1% compared with the placebo. No severe adverse events occurred in the trial. There were no statistical differences in body weight and body mass index changes between the Tianqi group and the placebo group during the 12-month trial.

CONCLUSIONS

Treatment with a Tianqi capsule for 12 months significantly decreased the incidence of T2DM in subjects with IGT, and this herbal drug was safe to use.

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.

Survey of diabetes risk assessment tools: concepts, structure and performance

The objective of this study is to review the effectiveness and limitations of existing diabetes risk screening tools to assess the need for further developing of such tools. An electronic search of the EMBASE, MEDLINE, and Cochrane library supplemented by a manual search was performed from 1995-2010. The search retrieved a total of 2168 articles reporting diabetes risk assessment tools which, after culling, produced 41 tools developed in 22 countries, with the majority (n = 26) developed in North America and Europe. All are short questionnaires of 2-16 questions incorporating common variables including age, gender, waist circumference, BMI, family history of diabetes, history of hypertension or antihypertensive medications. While scoring format and cut-offs point are diverse between questionnaires, overall accuracy value range of 40-97%, 24-86% and 62-87% were reported for sensitivity, specificity and receiver operating characteristic curve respectively. In summary, there is a trend of increasing availability of diabetes prediction tools with the existing risk assessment tools being generally a short questionnaire aiming for ease of use in clinical practice. The overall performance of existing tools showed moderate to high accuracy in their predictive performance. However, further detailed comparison of existing questionnaires is needed to evaluate whether they can serve adequately as diabetes risk assessment tool in clinical practice.

A simple screening score for diabetes for the Korean population: development, validation, and comparison with other scores

OBJECTIVE

We developed and validated a self-assessment score for diabetes risk in Korean adults and compared it with other established screening models.

RESEARCH DESIGN AND METHODS

The Korea National Health and Nutrition Examination Survey (KNHANES) 2001 and 2005 data were used to develop a diabetes screening score. After excluding patients with known diabetes, 9,602 participants aged ≥ 20 years were selected. Undiagnosed diabetes was defined as a fasting plasma glucose ≥ 126 mg/dL and/or nonfasting plasma glucose ≥ 200 mg/dL. The SAS Survey Logistic Regression analysis was used to determine predictors of undiagnosed diabetes (n = 341). We validated our model and compared it with other existing methods using the KNHANES 2007-2008 data (n = 8,391).

RESULTS

Age, family history of diabetes, hypertension, waist circumference, smoking, and alcohol intake were independently associated with undiagnosed diabetes. We calculated a diabetes screening score (range 0-11), and a cut point of ≥ 5 defined 47% of adults as being at high risk for diabetes and yielded a sensitivity of 81%, specificity of 54%, positive predictive value of 6%, and positive likelihood ratio of 1.8 (area under the curve [AUC] = 0.73). Comparable results were obtained in validation datasets (sensitivity 80%, specificity 53%, and AUC = 0.73), showing better performance than other non-Asian models from the U.S. or European population.

CONCLUSIONS

This self-assessment score may be useful for identifying Korean adults at high risk for diabetes. Additional studies are needed to evaluate the utility and feasibility of this score in various settings.

An integrated health care standard for the management and prevention of obesity in The Netherlands

The Partnership Overweight Netherlands (PON) is a collaboration between 18 partners, which are national organizations of health care providers, health insurance companies and patient organizations. The PON published an integrated health care standard for obesity in November 2010. The integrated health care standard for obesity involves strategies for diagnosis and early detection of high-risk individuals as well as appropriate combined lifestyle interventions for those who are overweight and obese and, when appropriate, additional medical therapies. The PON works towards a standard that transcends traditional boundaries of conventional health care systems and health care professions but, instead, focuses on competences of groups of health professionals who organize care from a patient-oriented perspective.

Risk models and scores for type 2 diabetes: systematic review

OBJECTIVE

To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.

DESIGN

Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.

DATA SOURCES

Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.

DATA EXTRACTION

Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.

RESULTS

8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.

CONCLUSION

Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

BACKGROUND

The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.

METHODS

We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.

RESULTS

Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).

CONCLUSIONS

We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

Screening for type 2 diabetes and dysglycemia

Type 2 diabetes mellitus (T2DM) and dysglycemia (impaired glucose tolerance and/or impaired fasting glucose) are increasingly contributing to the global burden of diseases. The authors reviewed the published literature to critically evaluate the evidence on screening for both conditions and to identify the gaps in current understanding. Acceptable, relatively simple, and accurate tools can be used to screen for both T2DM and dysglycemia. Lifestyle modification and/or medication (e.g., metformin) are cost-effective in reducing the incidence of T2DM. However, their application is not yet routine practice. It is unclear whether diabetes-prevention strategies, which influence cardiovascular risk favorably, will also prevent diabetic vascular complications. Cardioprotective therapies, which are cost-effective in preventing complications in conventionally diagnosed T2DM, can be used in screen-detected diabetes, but the magnitude of their effects is unknown. Economic modeling suggests that screening for both T2DM and dysglycemia may be cost-effective, although empirical data on tangible benefits in preventing complications or death are lacking. Screening for T2DM is psychologically unharmful, but the specific impact of attributing the label of dysglycemia remains uncertain. Addressing these gaps will inform the development of a screening policy for T2DM and dysglycemia within a holistic diabetes prevention and control framework combining secondary and high-risk primary prevention strategies.

External validation of QDSCORE(®) for predicting the 10-year risk of developing Type 2 diabetes

BACKGROUND

A small number of risk scores for the risk of developing diabetes have been produced but none has yet been widely used in clinical practice in the UK. The aim of this study is to independently evaluate the performance of QDSCORE(®) for predicting the 10-year risk of developing diagnosed Type 2 diabetes in a large independent UK cohort of patients from general practice.

METHODS

A prospective cohort study of 2.4 million patients (13.6 million person years) aged between 25 and 79 years from 364 practices from the UK contributing to The Health Improvement Network (THIN) database between 1 January 1993 and 20 June 2008.

RESULTS

QDSCORE(®) showed good performance data when evaluated on a large external data set. The score is well calibrated with reasonable agreement between observed and predicted outcomes. There is a slight underestimation of risk in both men and women aged 60 years and above, although the magnitude of underestimation is small. The ability of the score to differentiate between those who develop diabetes and those who do not is good, with values for the area under the receiver operating characteristic curve exceeding 0.8 for both men and women. Performance data in this external validation are consistent with those reported in the development and internal validation of the risk score.

CONCLUSIONS

QDSCORE(®) has shown to be a useful tool to predict the 10-year risk of developing Type 2 diabetes in the UK.

The new puzzle about the treatment of type 2 diabetes after the ACCORD and Da Qing studies

INTRODUCTION

There is a dramatic increase in the worldwide incidence of obesity, diabetes mellitus type 2, and other cardiovascular risk factors, summarized previously under the term "metabolic syndrome". Although preventive lifestyle modifications are effective, they are hard to implement and are therefore associated with a high number needed to treat. In most cases, intervention studies with hard endpoints such as myocardial infarction, stroke, or death are missing.

RESULTS

For example, the Da Qing study proved the efficacy of lifestyle modification with respect to manifestation of diabetes, but failed to show clear benefits regarding cardiovascular mortality. Several studies raised doubt, whether the concept of optimally reducing glucose is the optimal treatment for improving cardiovascular endpoints. Moreover other studies, such as Steno-2, showed an impressive effect of a multimodal therapy on hard endpoints.

CONCLUSIONS

In the future, the focus on new strategies for individualized therapies will increase. Additionally, approaches targeting novel molecular pathways are on the horizon, since plasma levels of posttranslationally modified proteins such as HbA1c are strong cardiovascular risk predictors despite normal glucose levels. For the clinician, it now becomes obvious that epidemiologically proven associations do not necessarily reflect causality. Studies addressing defined clinical endpoints, such as micro- and macrovascular morbidity and mortality are needed, as well as basic research, investigating other pathophysiological mechanisms, e.g., reactive metabolites and the digestive tract. The unexplained reduction in diabetes and its complications by bariatric surgery will give further insight not only into new therapeutic approaches, but also into mechanisms yet to be discovered.