The search for genetic risk factors of type 2 diabetes mellitus

Diabetes: Risk factor and translational therapeutic implications for Alzheimer's disease

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) commonly co-occur. T2DM increases the risk for AD by approximately twofold. Animal models provide one means of interrogating the relationship of T2DM to AD and investigating brain insulin resistance in the pathophysiology of AD. Animal models show that persistent hyperglycaemia results in chronic low-grade inflammation that may contribute to the development of neuroinflammation and accelerate the pathobiology of AD. Epidemiological studies suggest that patients with T2DM who received treatment with specific anti-diabetic agents have a decreased risk for the occurrence of AD and all-cause dementia. Agents such as metformin ameliorate T2DM and may have other important systemic effects that lower the risk of AD. Glucagon-like peptide 1 (GLP-1) agonists have been associated with a decreased risk for AD in patients with T2DM. Both insulin and non-insulin anti-diabetic treatments have been evaluated for the treatment of AD in clinical trials. In most cases, patients included in the trials have clinical features of AD but do not have T2DM. Many of the trials were conducted prior to the use of diagnostic biomarkers for AD. Trials have had a wide range of durations and population sizes. Many of the agents used to treat T2DM do not cross the blood brain barrier, and the effects are posited to occur via lowering of peripheral hyperglycaemia and reduction of peripheral and central inflammation. Clinical trials of anti-diabetic agents to treat AD are ongoing and will provide insight into the therapeutic utility of these agents.

Association of gene polymorphisms with body weight changes in prediabetic patients

BACKGROUND

Recent research has demonstrated that Type 2 Diabetes (T2D) risk is influenced by a number of common polymorphisms, including MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146. Knowledge of the association between these single nucleotide polymorphisms (SNPs) and body weight changes in different forms of prediabetes treatment is still limited. The aim of this study was to investigate the association of polymorphisms within the MC4R, PPARG, and TCF7L2 genes on the risk of carbohydrate metabolism disorders and body composition changes in overweight or obese patients with early carbohydrate metabolism disorders.

METHODS AND RESULTS

From 327 patients, a subgroup of 81 prediabetic female patients (48.7 ± 14.8 years) of Eastern European descent participated in a 3-month study comprised of diet therapy or diet therapy accompanied with metformin treatment. Bioelectrical impedance analysis and genotyping of MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146 polymorphisms were performed. The MC4R CC and TCF7L2 TT genotypes were associated with increased risk of T2D (OR = 1.46, p = 0.05 and OR = 2.47, p = 0.006, respectively). PPARG CC homozygotes experienced increased weight loss; however, no additional improvements were experienced with the addition of metformin. MC4R TT homozygotes who took metformin alongside dietary intervention experienced increased weight loss and reductions in fat mass (p < 0.05).

CONCLUSIONS

We have shown that the obesity-protective alleles (MC4R T and PPARG C) were positively associated with weight loss efficiency. Furthermore, we confirmed the previous association of the MC4R C and TCF7L2 T alleles with T2D risk.

Associations between new and old anthropometric indices with type 2 diabetes mellitus and risk of metabolic complications: a cross-sectional analytical study

BACKGROUND

Obesity can increase the risk of diabetes mellitus and complications associated with it.

OBJECTIVES

The aim of this study was to estimate the associations between new and old anthropometric indices and the risk of type 2 diabetes mellitus (T2DM) and its metabolic complications.

METHODS

In this cross-sectional analytical study, 110 T2DM subjects and 110 healthy controls were selected by convenience sampling. Metabolic factors were evaluated including the atherogenic index of plasma (AIP), glycemic status, lipid profile, blood pressure, kidney indices, new anthropometric indices (abdominal volume index [AVI], body shape index [ABSI], lipid accumulation product [LAP], body adiposity index [BAI], and conicity index [CI]), and old anthropometric indices (weight, body mass index [BMI], and waist and hip circumference [WC and HC]).

RESULTS

Significant positive correlations were observed between AVI, LAP, and BAI and fasting blood glucose and HbA1c in the T2DM group (p < 0.001 for all associations). The odds ratio (OR) for T2DM elevated significantly with increasing BMI (OR: 1.30, 95% CI: 1.20-1.42), LAP (OR: 1.20, 95% CI: 1.13-1.27), and BAI (OR: 1.32, 95% CI: 1.21-1.43). The indices AVI (OR: 1.90, 95% CI: 1.57-2.29), LAP (OR: 1.19, 95% CI: 1.13-1.27), BAI (OR: 1.19, 95% CI: 1.12-1.26), WC (OR: 1.29, 95% CI: 1.18, 1.42), and HC (OR: 1.07, 95% CI: 1.01, 1.14) significantly increased the risk of metabolic syndrome (MetS).

CONCLUSIONS

Associations were identified between obesity indices and diabetes. These indices could be used in clinical practice for evaluation and control of T2DM.

From Pre-Diabetes to Diabetes: Diagnosis, Treatments and Translational Research

Diabetes, a silent killer, is one of the most widely prevalent conditions of the present time. According to the 2017 International Diabetes Federation (IDF) statistics, the global prevalence of diabetes among the age group of 20-79 years is 8.8%. In addition, 1 in every 2 persons is unaware of the condition. This unawareness and ignorance lead to further complications. Pre-diabetes is the preceding condition of diabetes, and in most of the cases, this ultimately leads to the development of diabetes. Diabetes can be classified into three types, namely type 1 diabetes, type 2 diabetes mellitus (T2DM) and gestational diabetes. The diagnosis of both pre-diabetes and diabetes is based on glucose criteria; the common modalities used are fasting plasma glucose (FPG) test and oral glucose tolerance test (OGTT). A glucometer is commonly used by diabetic patients to measure blood glucose levels with fast and rather accurate measurements. A few of the more advanced and minimally invasive modalities include the glucose-sensing patch, SwEatch, eyeglass biosensor, breath analysis, etc. Despite a considerable amount of data being collected and analyzed regarding diabetes, the actual molecular mechanism of developing type 2 diabetes mellitus (T2DM) is still unknown. Both genetic and epigenetic factors are associated with T2DM. The complications of diabetes can predominantly be classified into two categories: microvascular and macrovascular. Retinopathy, nephropathy, and neuropathy are grouped under microvascular complications, whereas stroke, cardiovascular disease, and peripheral artery disease (PAD) belong to macrovascular complications. Unfortunately, until now, no complete cure for diabetes has been found. However, the treatment of pre-diabetes has shown significant success in preventing the further progression of diabetes. To prevent pre-diabetes from developing into T2DM, lifestyle intervention has been found to be very promising. Various aspects of diabetes, including the aforementioned topics, have been reviewed in this paper.

Seminal Oxidative Stress Markers, Calcium, Magnesium, and Semen Profile of Infertile Diabetic and Nondiabetic Nigerian Men

Context

Oxidative stress, vitamin, and macroelement deficiencies have been implicated in male infertility. It is unknown if diabetes mellitus with its attendant increased oxidative stress makes the seminal quality of the diabetic infertile men worse compared to their nondiabetic counterparts.

Aims

The study investigated semen parameters, seminal plasma calcium, magnesium, Vitamins C and E, and total antioxidant capacity (TAC) in diabetic and nondiabetic infertile men.

Settings and Design

This was a cross-sectional study involving 30 infertile men with type 2 diabetes, 30 infertile nondiabetic men and 30 fertile men.

Subjects and Methods

Fasting plasma glucose, glycated hemoglobin, seminal plasma calcium, magnesium, TAC, Vitamin C, Vitamin E, semen analysis, and cultures carried out using the standard procedures.

Statistical Analysis Used

Data were analyzed by the analysis of variance and Student's t-test; the level of significance was set at P < 0.05.

Results

Both infertile groups had significantly lower (P < 0.0001) sperm count, percentage motility, TAC, Vitamin E and C, magnesium and calcium when compared to the fertile group. However, there was no statistically significant difference (P > 0.05) in the mean values of these parameters among the two infertile groups. The infertile men had a significantly higher (P = 0.034) frequency of bacterial isolates compared to the fertile men. Staphylococcus aureus was the most frequent organism isolated.

Conclusions

Seminal calcium, magnesium, TAC, and Vitamin E and C were lower in both infertile diabetic and nondiabetic men as compared to that of fertile men, but the levels of these analytes were comparable in the infertile diabetic and nondiabetic men.

Protective effects of asiatic acid in a spontaneous type 2 diabetic mouse model

Asiatic acid (AA) has been demonstrated to exhibit anti-diabetic activity. However, the mechanisms and underlying signaling pathways remain to be elucidated. The present study was performed to confirm the protective effect of AA and demonstrate its ability to regulate the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/glycogen synthase kinase-3β (GSK-3β) signaling pathway in db/db mice. Db/db mice fed on a high-fat diet were used to model diabetes mellitus. Modeled mice were divided randomly into the model control, pioglitazone hydrochloride tablet (PH) and AA groups. Age-matched C57 BL/6J mice served as normal controls. Lipid and glucose levels, and glycogen synthesis rates were assessed following treatment. Pathological changes were detected using hematoxylin and eosin staining. Expression of the PI3K/AKT/GSK-3β signaling pathway at the mRNA level was measured using quantitative polymerase chain reaction analysis. The model control group revealed typical characteristics of obesity and diabetes, including high glucose and lipid levels, and decreased glycogen synthesis. Four weeks of treatment with AA or PH ameliorated these abnormalities. AA and PH treatments mitigated the upregulation of PI3K, AKT, insulin receptor, and insulin receptor substrate-1 mRNA expression in modeled mice. Furthermore, AA and PH treatments decreased GSK-3β and glucose-6-phosphatase mRNA expression compared with the normal control group. The results of the present study confirmed that AA possesses anti-diabetic activity in db/db mice. The PI3K/AKT/GSK-3β signaling pathway may mediate this protective effect.

Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study

OBJECTIVE

The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models.

DESIGN

Prospective cohort study.

SETTING

Tehran Lipid and Glucose Study (TLGS).

METHODS

A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure.

PRIMARY OUTCOME MEASURE

T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication.

RESULTS

During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence.

CONCLUSIONS

Our study emphasises the utility of DT for exploring interactions between predictor variables.

A multicenter clinical study to determine the efficacy of a novel fenugreek seed (Trigonella foenum-graecum) extract (Fenfuro™) in patients with type 2 diabetes

Background

Trigonella foenum-graecum (fenugreek) seeds are known to exhibit potent antioxidant, hypoglycemic, and nephroprotective activities, as well as serve as excellent membrane stabilizers especially because of their content of novel furostanolic saponins. Our previous studies exhibited the broad spectrum safety and efficacy of Fenfuro, a novel T. foenum-graecum seed extract enriched in furostanolic saponins, in type 2 diabetes (T2D) in rats.

Design

This multicenter, randomized, placebo-controlled, double-blind, add-on clinical study evaluated over a period of 90 consecutive days the efficacy of Fenfuro (daily dosage: 500 mg bid) in 154 subjects (male: 108; female: 46; age: 25–60 years) with T2D.

Methods

This study examined the body weight, blood pressure, and pulse rate, as well as the efficacy of Fenfuro on fasting and post-prandial plasma sugar (mg/dL), glycosylated hemoglobin (HbA1c), and fasting and post-prandial C-peptide levels.

Results

Fenfuro caused significant reduction in both fasting plasma and post-prandial blood sugar levels. Approximately 83% of the subjects reported decreases in fasting plasma sugar levels in the Fenfuro-treated group as compared to 62% in the placebo group, while 89% of the subjects demonstrated reduction in post-prandial plasma sugar levels in the Fenfuro-treated group as compared to 72% in the placebo group. HbA1c levels were reduced in both placebo and treatment groups. The decrease in HbA1c levels was significant in both groups as compared to respective baseline values. A significant increase in fasting and post-prandial C-peptide levels compared to the respective baseline values was observed, while no significant changes in fasting and post-prandial C-peptide levels were observed between the two groups. No significant adverse effects were observed by blood chemistry analyses. Furthermore, 48.8% of the subjects reported reduced dosage of anti-diabetic therapy in the Fenfuro-treated group, whereas 18.05% reported reduced dosage of anti-diabetic therapy in the placebo group.

Conclusion

In summary, Fenfuro proved safe and efficacious in ameliorating the symptoms of T2D in humans.

Are We in the Same Risk of Diabetes Mellitus? Gender- and Age-Specific Epidemiology of Diabetes in 2001 to 2014 in the Korean Population

In the early 2000s, the prevalence of diabetes in adults aged ≥30 years in Korea was about 9% to 10%, and it remained stable. However, a nationwide survey showed that this prevalence increased over the past few years. After age-standardization using the Korean population of the year 2010, the prevalence of diabetes in adults aged ≥30 years was 10.0% to 10.8% between 2001 and 2012, which increased to 12.5% in 2013 and 11.6% in 2014. During that period, there have been changes in the gender- and age-specific prevalence of diabetes in Korean adults. The prevalence of diabetes in the elderly population increased significantly, while this prevalence in young adults, especially in young women, did not change significantly. The contribution of each diabetic risk factor, such as obesity, β-cell dysfunction, sarcopenia, and socioeconomic status, in developing diabetes has also changed during that period in each gender and age group. For young women, obesity was the most important risk factor; by contrast, for elderly diabetic patients, sarcopenia was more important than obesity as a risk factor. Considering the economic burden of diabetes and its associated comorbidities, a public health policy targeting the major risk factors in each population might be more effective in preventing diabetes.

Genetic polymorphisms associated with overweight and obesity in uncontrolled Type 2 diabetes mellitus

Generally, obese and overweight individuals display higher free fatty acid levels, which stimulate insulin resistance. The combination of overweight or obesity with insulin resistance can trigger Type 2 diabetes mellitus (T2DM) and are primary contributing factors to the development of uncontrolled T2DM. Genetic polymorphisms also play an important role as they can impact a population's susceptibility to becoming overweight or obese and developing related chronic complications, such as uncontrolled T2DM. This review specifically examines the genetic polymorphisms associated with overweight and obesity in patients with uncontrolled T2DM. Particularly, gene polymorphisms in ADIPOQ (rs1501299 and rs17300539), LepR (rs1137101 and rs1045895), IRS2 (rs1805092), GRB14 (rs10195252 and rs3923113) and PPARG (rs1801282) have been associated with overweight and obesity in uncontrolled T2DM.

Association between -308G/A TNFA Polymorphism and Susceptibility to Type 2 Diabetes Mellitus: A Systematic Review

Diabetes mellitus (DM) is considered to be a worldwide epidemic disease and its type 2 form comprises more than 95% of all cases. Tumor necrosis factor-alpha (TNF-α) is a proinflammatory cytokine. Its dysregulation has been implicated in a variety of human diseases, including type 2 diabetes mellitus (T2DM). The control of expression of this cytokine is associated with insulin resistance and has a strong genetic influence. In order to understand this relationship, the literature from all case-control studies since 2000 to date was reviewed. The genotypes frequency results presented in ten publications with different ethnicities were compared. The correlation between the TNFA promoter genotypes and the risk of developing T2DM remains controversial due to the many discrepancies between the different studies available. Ethnic differences may play a role in these conflicting results, since the distribution of TNFA promoter polymorphisms is distinctive between individuals of dissimilar racial origin. Hence, although the relationship between T2DM incidence and presence of polymorphisms at position -308 of the TNFA gene is not entirely clear, the results of these studies suggest the need for further investigation.

Predictive modeling for incident and prevalent diabetes risk evaluation

With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.

Metabolomics – the complementary field in systems biology: a review on obesity and type 2 diabetes

This paper highlights the metabolomic roles in systems biology towards the elucidation of metabolic mechanisms in obesity and type 2 diabetes.

Frequency of fat mass and obesity-associated gene rs9939609 and peroxisome proliferator-activated receptor gamma 2 gene rs1801282 polymorphisms among Trinidadian neonates of different ethnicities and their relationship to anthropometry at birth

BACKGROUND

The fat mass and obesity-associated gene (FTO) rs9939609 and peroxisome proliferator-activated receptor gamma 2 gene (PPARG2) rs1801282 polymorphisms are type 2 diabetes mellitus susceptibility gene variants associated with obesity. This study examined whether these variants are associated with anthropometry at birth among a representative multi-ethnic sample of Trinidadian neonates.

METHODS

Cord blood was obtained from consecutive term live births and DNA was genotyped for FTO and PPARG2 variants using polymerase chain reaction. Associations between neonate anthropometry at birth and genotype frequency were assessed using the χ(2) test and linear regression.

RESULTS

Significant associations were observed between neonate ethnicity and PPARG2 (p = 0.005) and FTO (p = 0.017) variants: high-risk alleles were more prevalent among African than South Asian neonates for both variants. The allelic and genotypic frequencies for mixed neonates were between those for the African and those for the South Asian neonates. No significant relationship was observed between rs9939609 and rs1801282 and anthropometric measures. For both variants, the allelic and genotypic frequencies among the African and South Asian neonates mirrored those found elsewhere for similar ethnic groups.

CONCLUSIONS

Neonates of African ethnicity possess the highest frequency of rs9939609 and rs1801282 alleles and genotypes; this may be associated with ethnic differences in the risk of lifestyle diseases.

Translational medicine as a new clinical tool and application which improves metabolic diseases: perspectives from 2012 Sino-American symposium on clinical and translational medicine

Because of the economic growth and changes in lifestyle, metabolic diseases have become a major public health problem, which impose heavy economic burdens on individuals, families and health systems. However, its precise mediators and mechanisms remain to be fully understood. Clinical translational medicine (CTM) is an emerging area comprising multidisciplinary research from basic science to medical applications and as a new tool to improve human health by reducing disease incidence, morbidity and mortality. It can bridge knowledge of metabolic diseases processes, gained by in vitro and experimental animal models, with the disease pathways found in humans, further to identify their susceptibility genes and enable patients to achieve personalized medicament treatment. Thus, we have the reasons to believe that CTM will play even more roles in the development of new diagnostics, therapies, healthcare, and policies and the Sino-American Symposium on Clinical and Translational Medicine (SAS-CTM) will become a more and more important platform for exchanging ideas on clinical and translational research and entails a close collaboration among hospital, academia and industry.

The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings

The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.

Genetics of type 2 diabetes and potential clinical implications

Type 2 diabetes (T2DM) is a common complex metabolic disorder that has a strong genetic component. Recent advances in genome-wide association studies have revolutionized our knowledge regarding the genetics of T2DM. There are at least 64 common genetic variants that are strongly associated with T2DM. However, the pathophysiologic roles of these variants are mostly unknown and require further functional characterization. The variants identified so far have a small effect size and their added effect explains less than 10 % of the T2DM heritability. The current ongoing whole exome and whole genome studies of T2DM are focused on identifying functionally important rare variants that have a stronger effect. Through these efforts, we will have a better understanding of the genetic architecture of T2DM and its pathophysiology. The potential clinical applications of genetic studies of T2DM include risk prediction, identification of novel therapeutic targets, genetic prediction of efficacy and toxicity of anti-diabetic medications, and eventually optimization of patient care through personalized genomic medicine. We hope further research in genetics of T2DM could aid patient care and improve outcomes of T2DM patients.

Genome-wide association studies with metabolomics

Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.

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.