A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature

Omics Approach for Personalised Prevention of Type 2 Diabetes Mellitus for African and European Populations (OPTIMA): a protocol paper

INTRODUCTION

The prevalence of type 2 diabetes (T2D) within sub-Saharan Africa (SSA) is increasing. Despite the pathophysiology of T2D differing by ethnicity and sex, risk stratification and guidelines for the prevention of T2D are generic, relying on evidence from studies including predominantly Europeans. Accordingly, this study aims to develop ethnic-specific and sex-specific risk prediction models for the early detection of dysglycaemia (impaired glucose tolerance and T2D) to inform clinically feasible, culturally acceptable and cost-effective risk management and prevention strategies using dietary modification in SSA and European populations.

METHODS AND ANALYSIS

This multinational collaboration will include the prospective cohort data from two African cohorts, the Middle-Aged Soweto Cohort from South Africa and the Research on Obesity and Diabetes among African Migrants Prospective cohort from Ghana and migrants living in Europe, and a Swedish cohort, the Pre-Swedish CArdioPulmonary bioImage Study. Targeted proteomics, as well as targeted and untargeted metabolomics, will be performed at baseline to discover known and novel ethnic-specific and sex-specific biomarkers that predict incident dysglycaemia in the different longitudinal cohorts. Dietary patterns that explain maximum variation in the biomarker profiles and that associate with dysglycaemia will be identified in the SSA and European cohorts and used to build the prototypes for dietary interventions to prevent T2D. A comparative cost-effectiveness analysis of the dietary interventions will be estimated in the different populations. Finally, the perceptions of at-risk participants and healthcare providers regarding ethnic-specific and sex-specific dietary recommendations for the prevention of T2D will be assessed using focus group discussions and in-depth interviews in South Africa, Ghana, Germany (Ghanaian migrants) and Sweden.

ETHICS AND DISSEMINATION

Ethical clearance has been obtained from all participating sites. The study results will be disseminated at scientific conferences and in journal publications, and through community engagement events and diabetes organisations in the respective countries.

Physiological Fatty Acid-Stimulated Insulin Secretion and Redox Signaling Versus Lipotoxicity

Significance: Type 2 diabetes as a world-wide epidemic is characterized by the insulin resistance concomitant to a gradual impairment of β-cell mass and function (prominently declining insulin secretion) with dysregulated fatty acids (FAs) and lipids, all involved in multiple pathological development. Recent Advances: Recently, redox signaling was recognized to be essential for insulin secretion stimulated with glucose (GSIS), branched-chain keto-acids, and FAs. FA-stimulated insulin secretion (FASIS) is a normal physiological event upon postprandial incoming chylomicrons. This contrasts with the frequent lipotoxicity observed in rodents. Critical Issues: Overfeeding causes FASIS to overlap with GSIS providing repeating hyperinsulinemia, initiates prediabetic states by lipotoxic effects and low-grade inflammation. In contrast the protective effects of lipid droplets in human β-cells counteract excessive lipids. Insulin by FASIS allows FATP1 recruitment into adipocyte plasma membranes when postprandial chylomicrons come late at already low glycemia. Future Directions: Impaired states of pancreatic β-cells and peripheral organs at prediabetes and type 2 diabetes should be revealed, including the inter-organ crosstalk by extracellular vesicles. Details of FA/lipid molecular physiology are yet to be uncovered, such as complex phenomena of FA uptake into cells, postabsorptive inactivity of G-protein-coupled receptor 40, carnitine carrier substrate specificity, the role of carnitine-O-acetyltransferase in β-cells, and lipid droplet interactions with mitochondria. Antioxid. Redox Signal. 42, 566-622.

The relationship between hepatic enzymes, prediabetes, and diabetes in the Azar cohort population

BACKGROUND

Early prediabetes screening holds immense significance in decreasing the incidence of diabetes. Therefore, we aimed to evaluate the association of hepatic enzymes with prediabetes and diabetes in the Azar cohort population in Iran.

METHODS

This cross-sectional study utilized data from the Azar cohort study, initiated in 2014, with 14,865 participants aged 35-70 years. This study defines prediabetes, according to the American Diabetes Association (ADA), as fasting blood sugar (FBS) of 100-125 mg/dl. An FBS ≥ 126 mg/dL or a history of diabetes indicates diabetes. Serum liver enzymes including alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP) were measured, and associations with prediabetes and diabetes were analyzed using binary logistic regression.

RESULTS

In a study of 14,865 participants, 16% had prediabetes and 14.1% had diabetes. The serum levels of ALT, AST, GGT, and ALP were significantly higher (P < 0.05) in the prediabetic and diabetic patients. The adjusted logistic regression model showed a dose-response increase for all hepatic enzymes, with the highest ORs in the fourth quartile for both prediabetes and diabetes. The highest OR for prediabetes and diabetes was in the fourth GGT quartile.

CONCLUSION

Our findings suggest that serum ALT, GGT, and ALP levels are strongly associated with prediabetes and diabetes. These hepatic enzymes may be considered easy and valuable early indicators of diabetes risk, prompting timely interventions to slow disease progression.

Longitudinal metabolite and protein trajectories prior to diabetes mellitus diagnosis in Danish blood donors: a nested case-control study

AIMS/HYPOTHESIS

Metabolic risk factors and plasma biomarkers for diabetes have previously been shown to change prior to a clinical diabetes diagnosis. However, these markers only cover a small subset of molecular biomarkers linked to the disease. In this study, we aimed to profile a more comprehensive set of molecular biomarkers and explore their temporal association with incident diabetes.

METHODS

We performed a targeted analysis of 54 proteins and 171 metabolites and lipoprotein particles measured in three sequential samples spanning up to 11 years of follow-up in 324 individuals with incident diabetes and 359 individuals without diabetes in the Danish Blood Donor Study (DBDS) matched for sex and birth year distribution. We used linear mixed-effects models to identify temporal changes before a diabetes diagnosis, either for any incident diabetes diagnosis or for type 1 and type 2 diabetes mellitus diagnoses specifically. We further performed linear and non-linear feature selection, adding 28 polygenic risk scores to the biomarker pool. We tested the time-to-event prediction gain of the biomarkers with the highest variable importance, compared with selected clinical covariates and plasma glucose.

RESULTS

We identified two proteins and 16 metabolites and lipoprotein particles whose levels changed temporally before diabetes diagnosis and for which the estimated marginal means were significant after FDR adjustment. Sixteen of these have not previously been described. Additionally, 75 biomarkers were consistently higher or lower in the years before a diabetes diagnosis. We identified a single temporal biomarker for type 1 diabetes, IL-17A/F, a cytokine that is associated with multiple other autoimmune diseases. Inclusion of 12 biomarkers improved the 10-year prediction of a diabetes diagnosis (i.e. the area under the receiver operating curve increased from 0.79 to 0.84), compared with clinical information and plasma glucose alone.

CONCLUSIONS/INTERPRETATION

Systemic molecular changes manifest in plasma several years before a diabetes diagnosis. A particular subset of biomarkers shows distinct, time-dependent patterns, offering potential as predictive markers for diabetes onset. Notably, these biomarkers show shared and distinct patterns between type 1 diabetes and type 2 diabetes. After independent replication, our findings may be used to develop new clinical prediction models.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study

Aims

This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

Methods

Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves.

Results

44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR.

Conclusion

Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

Tools for assessing quality and risk of bias in Mendelian randomization studies: a systematic review

BACKGROUND

The use of Mendelian randomization (MR) in epidemiology has increased considerably in recent years, with a subsequent increase in systematic reviews of MR studies. We conducted a systematic review of tools designed for assessing risk of bias and/or quality of evidence in MR studies and a review of systematic reviews of MR studies.

METHODS

We systematically searched MEDLINE, Embase, the Web of Science, preprints servers and Google Scholar for articles containing tools for assessing, conducting and/or reporting MR studies. We also searched for systematic reviews and protocols of systematic reviews of MR studies. From eligible articles we collected data on tool characteristics and content, as well as details of narrative description of bias assessment.

RESULTS

Our searches retrieved 2464 records to screen, from which 14 tools, 35 systematic reviews and 38 protocols were included in our review. Seven tools were designed for assessing risk of bias/quality of evidence in MR studies and evaluation of their content revealed that all seven tools addressed the three core assumptions of instrumental variable analysis, violation of which can potentially introduce bias in MR analysis estimates.

CONCLUSION

We present an overview of tools and methods to assess risk of bias/quality of evidence in MR analysis. Issues commonly addressed relate to the three standard assumptions of instrumental variables analyses, the choice of genetic instrument(s) and features of the population(s) from which the data are collected (particularly in two-sample MR), in addition to more traditional non-MR-specific epidemiological biases. The identified tools should be tested and validated for general use before recommendations can be made on their widespread use. Our findings should raise awareness about the importance of bias related to MR analysis and provide information that is useful for assessment of MR studies in the context of systematic reviews.

Longitudinal changes in vitamin D concentrations and the association with type 2 diabetes mellitus: the Tromsø Study

AIM

We aimed to investigate the relationship between pre- and post-diagnostic 25-hydroxyvitamin D (25(OH)D) concentrations and type 2 diabetes (T2DM) over a period of 30 years in individuals who developed T2DM compared to healthy controls.

METHODS

This case-control study included 254 participants with blood samples collected at five different time-points (T1-T5) between 1986 and 2016. Of the 254 participants, 116 were diagnosed with T2DM between T3 and T4, and were considered cases; the remaining 138 were controls. Linear mixed regression models were used to examine pre- and post-diagnostic changes in 25(OH)D concentrations, and logistic regression was used to examine associations between these concentrations and T2DM at each time-point.

RESULTS

25(OH)D concentrations at different time-points and the longitudinal change in concentrations differed between cases and controls, and by sex. For women, each 5-nmol/l increase in 25(OH)D concentrations was inversely associated with T2DM at T3 (odds-ratio, OR, 0.79), whereas for men, this same increase was positively associated with T2DM at T1 (OR 1.12). Cases experienced a significant decrease in pre-diagnostic 25(OH)D concentrations (p value < 0.01 for women, p value = 0.02 for men) and a significant increase in post-diagnostic 25(OH)D concentrations (p value < 0.01 for women, p value = 0.01 for men). As such, each 1-unit increase in month-specific z-score change between T1 and T3 was significantly inversely associated with T2DM (OR 0.51 for women, OR 0.52 for men), and each such increase between T3 and T5 was significantly positively associated with T2DM in women (OR 2.48).

CONCLUSIONS

25(OH)D concentrations seem to be affected by disease progression and type 2 diabetes diagnosis.

Prediction of bullying at work: A data-driven analysis of the Finnish public sector cohort study

AIM

To determine the extent to which change in (i.e., start and end of) workplace bullying can be predicted by employee responses to standard workplace surveys.

METHODS

Responses to an 87-item survey from 48,537 Finnish public sector employees at T1 (2017-2018) and T2 (2019-2020) were analyzed with least-absolute-shrinkage-and-selection-operator (LASSO) regression. The predictors were modelled both at the individual- and the work unit level. Outcomes included both the start and the end of bullying. Predictive performance was evaluated with C-indices and density plots.

RESULTS

The model with best predictive ability predicted the start of bullying with individual-level predictors, had a C-index of 0.68 and included 25 variables, of which 6 remained in a more parsimonious model: discrimination at work unit, unreasonably high workload, threat that some work tasks will be terminated, working in a work unit where everyone did not feel they are understood and accepted, having a supervisor who was not highly trusted, and a shorter time in current position. Other models performed even worse, either from the point of view of predictive performance, or practical useability.

DISCUSSION

While many bivariate associations between socioeconomic characteristics, work characteristics, leadership, team climate, and job satisfaction were observed, reliable individualized detection of individuals at risk of becoming bullied at workplace was not successful. The predictive performance of the developed risk scores was suboptimal, and we do not recommend their use as an individual-level risk prediction tool. However, they might be useful tool to inform decision-making when planning the contents of interventions to prevent bullying at an organizational level.

Targeted proteomics identifies potential biomarkers of dysglycaemia, beta cell function and insulin sensitivity in Black African men and women

AIMS/HYPOTHESIS

Using a targeted proteomics approach, we aimed to identify and validate circulating proteins associated with impaired glucose metabolism (IGM) and type 2 diabetes in a Black South African cohort. In addition, we assessed sex-specific associations between the validated proteins and pathophysiological pathways of type 2 diabetes.

METHODS

This cross-sectional study included Black South African men (n=380) and women (n=375) who were part of the Middle-Aged Soweto Cohort (MASC). Dual-energy x-ray absorptiometry was used to determine fat mass and visceral adipose tissue, and fasting venous blood samples were collected for analysis of glucose, insulin and C-peptide and for targeted proteomics, measuring a total of 184 pre-selected protein biomarkers. An OGTT was performed on participants without diabetes, and peripheral insulin sensitivity (Matsuda index), HOMA-IR, basal insulin clearance, insulin secretion (C-peptide index) and beta cell function (disposition index) were estimated. Participants were classified as having normal glucose tolerance (NGT; n=546), IGM (n=116) or type 2 diabetes (n=93). Proteins associated with dysglycaemia (IGM or type 2 diabetes) in the MASC were validated in the Swedish EpiHealth cohort (NGT, n=1706; impaired fasting glucose, n=550; type 2 diabetes, n=210).

RESULTS

We identified 73 proteins associated with dysglycaemia in the MASC, of which 34 were validated in the EpiHealth cohort. Among these validated proteins, 11 were associated with various measures of insulin dynamics, with the largest number of proteins being associated with HOMA-IR. In sex-specific analyses, IGF-binding protein 2 (IGFBP2) was associated with lower HOMA-IR in women (coefficient -0.35; 95% CI -0.44, -0.25) and men (coefficient -0.09; 95% CI -0.15, -0.03). Metalloproteinase inhibitor 4 (TIMP4) was associated with higher insulin secretion (coefficient 0.05; 95% CI 0.001, 0.11; p for interaction=0.025) and beta cell function (coefficient 0.06; 95% CI 0.02, 0.09; p for interaction=0.013) in women only. In contrast, a stronger positive association between IGFBP2 and insulin sensitivity determined using an OGTT (coefficient 0.38; 95% CI 0.27, 0.49) was observed in men (p for interaction=0.004). A posteriori analysis showed that the associations between TIMP4 and insulin dynamics were not mediated by adiposity. In contrast, most of the associations between IGFBP2 and insulin dynamics, except for insulin secretion, were mediated by either fat mass index or visceral adipose tissue in men and women. Fat mass index was the strongest mediator between IGFBP2 and insulin sensitivity (total effect mediated 40.7%; 95% CI 37.0, 43.6) and IGFBP2 and HOMA-IR (total effect mediated 39.1%; 95% CI 31.1, 43.5) in men.

CONCLUSIONS/INTERPRETATION

We validated 34 proteins that were associated with type 2 diabetes, of which 11 were associated with measures of type 2 diabetes pathophysiology such as peripheral insulin sensitivity and beta cell function. This study highlights biomarkers that are similar between cohorts of different ancestry, with different lifestyles and sociodemographic profiles. The African-specific biomarkers identified require validation in African cohorts to identify risk markers and increase our understanding of the pathophysiology of type 2 diabetes in African populations.

Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes

Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. In this study, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of 5 years, the area under the receiver operating characteristic curve was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D.

Identification of Putative Causal Relationships Between Type 2 Diabetes and Blood-Based Biomarkers in East Asians by Mendelian Randomization

Observational studies have revealed phenotypic associations between type 2 diabetes (T2D) and many biomarkers. However, causality between these conditions in East Asians is unclear. We leveraged genome-wide association study (GWAS) summary statistics on T2D (n = 77,418 cases; n = 356,122 controls) from the Asian Genetic Epidemiology Network (sample recruited during 2001-2011) and GWAS summary statistics on 42 biomarkers (n = 12,303-143,658) from BioBank Japan (sample recruited during 2003-2008) to investigate causal relationships between T2D and biomarkers. Applications of Mendelian randomization approaches consistently revealed genetically instrumented associations of T2D with increased serum potassium levels (liability-scale β = 0.04-0.10; P = 6.41 × 10-17-9.85 × 10-5) and decreased serum chloride levels (liability-scale β = -0.16 to -0.06; P = 5.22 × 10-27-3.14 × 10-5), whereas these 2 biomarkers showed no causal effects on T2D. Heritability Estimation Using Summary Statistics (ρ-HESS) and summary-data-based Mendelian randomization highlighted 27 genomic regions and 3 genes (α-1,3-mannosyl-glycoprotein 2-β-N-acetylglucosaminyltransferase (MGAT1), transducing-like enhancer (TLE) family member 1, transcriptional corepressor (TLE1), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR)) that interactively associated with the shared genetics underlying T2D and the 2 biomarkers. Thus, T2D may causally affect serum potassium and chloride levels among East Asians. In contrast, the relationships of potassium and chloride with T2D are not causal, suggesting the importance of monitoring electrolyte disorders for T2D patients.

Therapeutic Potential of miRNAs for Type 2 Diabetes Mellitus: An Overview

MicroRNA(miRNA)s have been identified as an emerging class for therapeutic interventions mainly due to their extracellularly stable presence in humans and animals and their potential for horizontal transmission and action. However, treating Type 2 diabetes mellitus using this technology has yet been in a nascent state. MiRNAs play a significant role in the pathogenesis of Type 2 diabetes mellitus establishing the potential for utilizing miRNA-based therapeutic interventions to treat the disease. Recently, the administration of miRNA mimics or antimiRs in-vivo has resulted in positive modulation of glucose and lipid metabolism. Further, several cell culture-based interventions have suggested beta cell regeneration potential in miRNAs. Nevertheless, few such miRNA-based therapeutic approaches have reached the clinical phase. Therefore, future research contributions would identify the possibility of miRNA therapeutics for tackling T2DM. This article briefly reported recent developments on miRNA-based therapeutics for treating Type 2 Diabetes mellitus, associated implications, gaps, and recommendations for future studies.

Factor structure of the diabetes knowledge questionnaire and the assessment of the knowledge of risk factors, causes, complications, and management of diabetes mellitus: A national population-based study in Singapore

This study evaluated the knowledge of diabetes mellitus and predictors of the level of diabetes knowledge among the general public of Singapore. Confirmatory factor analysis and exploratory factor analysis were used to evaluate the fit of different factor models for the diabetes knowledge questionnaire. Multiple linear regressions were performed to determine the sociodemographic characteristics associated with diabetes knowledge. The final factor model identified three domains for diabetes knowledge: general knowledge, diabetes specific knowledge and causes of diabetes, and complications of untreated diabetes. Overall knowledge scores were 23.8 ± 2.4 for general diabetes knowledge, 2.3 ± 0.8 for diabetes specific knowledge, 2.3 ± 1.2 for causes, and 5.2 ± 1.2 for complications of untreated diabetes. Patients with diabetes were more knowledgeable than adults without diabetes in the population. While the general public in Singapore has adequate knowledge of diabetes, misconceptions were identified in both groups which underscores the need to tailor specific educational initiatives to reduce these diabetes knowledge gaps.

Predictive significance of joint plasma fibrinogen and urinary alpha-1 microglobulin-creatinine ratio in patients with diabetic kidney disease

Background

Although many biomarkers have high diagnostic and predictive power for diabetic kidney disease (DKD), less studies were performed for the predictive assessment in DKD and its progression with combined blood and urinary biomarkers. This study aims to explore the predictive significance of joint plasma fibrinogen (FIB) concentration and urinary alpha-1 microglobulin-creatinine (α1-MG/CR) ratio in DKD.

Methods

A total of 234 patients with type 2 diabetes were enrolled, and their clinical and laboratory data were retrospectively assessed. A ROC curve analysis was performed to evaluate the power of plasma FIB and urinary α1-MG/CR ratio for identifying DKD and advanced DKD, respectively. The predictive power for DKD and advanced DKD was analyzed by regression analysis.

Results

Plasma FIB and urinary α1-MG/CR levels were higher in patients with DKD than with pure T2D (p<0.001). The multivariate-adjusted odds ratios (ORs) were 5.047 (95%CI: 2.276–10.720) and 2.192 (95%CI: 1.539–3.122) (p<0.001) for FIB and α1-MG/CR as continuous variables for DKD prediction, respectively. The optimal cut-off values were 3.21 g/L and 2.11mg/mmol for identifying DKD, and 5.58 g/L and 11.07 mg/mmol for advanced DKD from ROC curves. At these cut-off values, the sensitivity and specificity of joint FIB and α1-MG/CR were 0.95 and 0.92 for identifying DKD, and 0.62 and 0.67 for identifying advanced DKD, respectively. The area under curve was 0.972 (95%CI: 0.948–0.995) (p<0.001) and 0.611, 95%CI: 0.488–0.734) (p>0.05). The multivariate-adjusted ORs for joint FIB and α1-MG/CR at the cut-off values were 214.500 (95%CI: 58.054–792.536) and 3.252 (95%CI: 1.040–10.175) (p<0.05), respectively.

Conclusion

The present study suggests that joint plasma FIB concentration and urinary α1-MG/CR ratio can be used as a powerful predictor for general DKD, but it is less predictive for advanced DKD.

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.

Longitudinal changes in blood biomarkers and their ability to predict type 2 diabetes mellitus—The Tromsø study

Introduction

Methods

Results

Conclusion

Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes

Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.

Plasma triacylglycerols are biomarkers of β-cell function in mice and humans

Objectives

To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues.

Methods

We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules.

Results

We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion.

Conclusion

TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion.

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Plasma triacylglycerols correlated with genes controlling β-cell mass and function.

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Plasma triacylglycerols correlated with genes controlling liver β-oxidation.

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In humans, triacylglycerols also correlated with key regulators of insulin secretion.

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Mouse and human data identified PITPNC1 as a candidate regulator of insulin secretion.

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Triacylglycerols are biomarkers of the liver-to-β-cell axis and β-cell function.

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population

CONTEXT

Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most.

OBJECTIVE

This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c).

METHODS

We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c).

RESULTS

Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies.

CONCLUSION

The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.

Personizing the prediction of future susceptibility to a specific disease

A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

Clinical and predictive significance of Plasma Fibrinogen Concentrations combined Monocyte-lymphocyte ratio in patients with Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the most common causes of blindness and visual impairment. Therefore, early prediction of its occurrence and progression is important. This study aimed to assess the clinical and predictive significance of plasma fibrinogen concentrations combined monocyte-lymphocyte ratio (FC-MLR) in patients with DR. A total of 307 patients with type 2 diabetes (T2D) were enrolled. Plasma fibrinogen concentrations and peripheral white blood cells were measured, and MLR was calculated, and the associations of FC-MLR with DR and severity of disease were assessed. Regression analysis and receiver operating characteristic (ROC) curves were performed to evaluate the risk factors and predictive power of FC-MLR for DR and severity of disease, respectively. DR patients showed higher fibrinogen concentrations and a higher MLR than did T2D patients without complications (P<0.01); Moreover, DR patients in proliferative stage also showed higher fibrinogen concentrations and a higher MLR than did those in non-proliferative stage (P<0.01). FC-MLR was closely associated with occurrence and severity of DR (P<0.01), and was an independent risk factor for them (OR=6.123, 95%CI: 3.122-17.102; and 7.932, 95%CI: 4.315-16.671, respectively; P<0.001). The predictive sensitivity and specificity for DR and severity of disease were 0.86 and 0.68, and 0.85 and 0.73, respectively. The study suggests that FC-MLR may be used as a predictor for the risk and progression of diabetic retinopathy.

Next-generation epidemiology: the role of high-resolution molecular phenotyping in diabetes research

Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before. Studies employing these technologies have identified gene variants or causal loci, and linked these to other omics data that help define the molecular processes mediating the effects of genetic variation in the expression of clinical phenotypes. The scale of these epidemiological studies is rapidly growing; a trend that is set to continue as the public and private sectors invest heavily in omics data generation. Many are banking on this massive volume of diverse molecular data for breakthroughs in drug discovery and predicting sensitivity to risk factors, response to therapies and susceptibility to diabetes complications, as well as the development of disease-monitoring tools and surrogate outcomes. To realise these possibilities, it is essential that omics technologies are applied to well-designed epidemiological studies and that the emerging data are carefully analysed and interpreted. One might view this as next-generation epidemiology, where complex high-dimensionality data analysis approaches will need to be blended with many of the core principles of epidemiological research. In this article, we review the literature on omics in diabetes epidemiology and discuss how this field is evolving. Graphical abstract.

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

PURPOSE OF THE REVIEW

Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).

RECENT FINDINGS

Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

A Newly Developed Diabetes Risk Index, Based on Lipoprotein Subfractions and Branched Chain Amino Acids, is Associated with Incident Type 2 Diabetes Mellitus in the PREVEND Cohort

Objective: Evaluate the ability of a newly developed diabetes risk score, the Diabetes Risk Index (DRI), to predict incident type 2 diabetes mellitus (T2D) in a large adult population. Methods: The DRI was developed by combining the Lipoprotein Insulin Resistance Index (LP-IR), calculated from 6 lipoprotein subspecies and size parameters, and the branched chain amino acids, valine and leucine, all of which have been shown previously to be associated with future T2D. DRI scores were calculated in a total of 6134 nondiabetic men and women in the Prevention of Renal and Vascular End-Stage Disease (PREVEND) Study. Cox proportional hazards regression was used to evaluate the association of DRI scores with incident T2D. Results: During a median follow-up of 8.5 years, 306 new T2D cases were ascertained. In analyses adjusted for age and sex, there was a significant association between DRI scores and incident T2D with the hazard ratio (HR) for the highest versus lowest quartile being 12.07 (95% confidence interval: 6.97–20.89, p < 0.001). After additional adjustment for body mass index (BMI), family history of T2D, alcohol consumption, diastolic blood pressure, total cholesterol, triglycerides, HDL cholesterol and HOMA-IR, the HR was attenuated but remained significant (HR 3.20 (1.73–5.95), p = 0.001). Similar results were obtained when DRI was analyzed as HR per 1 SD increase (HR 1.37 (1.14–1.65), p < 0.001). The Kaplan–Meier plot demonstrated that patients in the highest quartile of DRI scores presented at higher risk (p-value for log-rank test <0.001). Conclusions: Higher DRI scores are associated with an increased risk of T2D. The association is independent of clinical risk factors for T2D including HOMA-IR, BMI and conventional lipids.

Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes

The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-sample Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.

Relationship between plasma total homocysteine and the severity of renal function in Chinese patients with type 2 diabetes mellitus aged ≥75 years

We aimed to investigate the relationship between total homocysteine (tHcy) levels in the plasma and renal function severity in patients with type 2 diabetes mellitus (T2DM) aged ≥75 years.We included 221 patients with T2DM aged ≥60 years (59 aged ≥75 years).tHcy levels among the 4 groups of patients aged ≥60 years significantly differed, but not in those aged ≥75 years. tHcy levels in patients aged ≥60 years were negatively correlated with the estimated glomerular filtration rate. The area under the receiver operating characteristic curve of tHcy for predicting diabetic kidney disease (DKD) was 0.636. Fasting c-peptide and creatinine were independently associated with tHcy levels in patients aged ≥60 years, whereas insulin and creatinine were independently associated with tHcy levels in those aged ≥75 years.tHcy concentrations were elevated in T2DM and can potentially serve as a risk factor for DKD, but it is not an ideal biomarker.

Non-genetic biomarkers and colorectal cancer risk: Umbrella review and evidence triangulation

Several associations between non-genetic biomarkers and colorectal cancer (CRC) risk have been detected, but the strength of evidence and the direction of associations are not confirmed. We aimed to evaluate the evidence of these associations and integrate results from different approaches to assess causal inference. We searched Medline and Embase for meta-analyses of observational studies, meta-analyses of randomized clinical trials (RCTs), and Mendelian randomization (MR) studies measuring the associations between non-genetic biomarkers and CRC risk and meta-analyses of RCTs on supplementary micronutrients. We repeated the meta-analyses using random-effects models and categorized the evidence based on predefined criteria. We described each MR study and evaluated their credibility. Seventy-two meta-analyses of observational studies and 18 MR studies on non-genetic biomarkers and six meta-analyses of RCTs on micronutrient intake and CRC risk considering 65, 42, and five unique associations, respectively, were identified. No meta-analyses of RCTs on blood level biomarkers have been found. None of the associations were classified as convincing or highly suggestive, three were classified as suggestive, and 26 were classified as weak. For three biomarkers explored in MR studies, there was evidence of causality and seven were classified as likely noncausal. For the first time, results from both observational and MR studies were integrated by triangulating the evidence for a wide variety of non-genetic biomarkers and CRC risk. At blood level, lower vitamin D, higher homeostatic model assessment-insulin resistance, and human papillomavirus infection were associated with higher CRC risk while increased linoleic acid and oleic acid and decreased arachidonic acid were likely causally associated with lower CRC risk. No association was found convincing in both study types.

Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients' information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.

Monocyte-lymphocyte ratio is a valuable predictor for diabetic nephropathy in patients with type 2 diabetes

Diabetic nephropathy (DN) is serious threat to human health. Therefore, early prediction of its occurrence is important. This study aimed to assess the predictive significance of monocyte-lymphocyte ratio (MLR) for DN.A total of 301 patients with type 2 diabetes (T2D), including 212 T2D patients without diabetic-related complications and 99 DN patients, were enrolled. Peripheral white blood cells were measured before treatment to calculate MLR, and the risk factors and predictive significance for T2D and DN were assessed.T2D patients without diabetic-related complications had higher MLR than control patients (P < .01). However, MLR was significantly higher in DN patients than in T2D patients without diabetic-related complications (P < .001). According to MLR quartiles, higher MLR in DN patients was correlated with higher serum creatinine, estimated glomerular filtration rate, and urinary albumin excretion (UAE) levels (P < .01 or P < .001). Furthermore, MLR was positively correlated with UAE level (R = 0.5973; P < .01) and an independent predictor for DN (odds ratio: 7.667; 95% confidence interval [CI]: 3.689-21.312; P < .001). The area under the receiver-operating characteristic (ROC) curve for MLR was 0.874 (95%CI: 0.830-0.918, P < .001). When the optimal cutoff value was 0.23, the sensitivity and specificity of MLR for DN prediction were 0.85 and 0.74, respectively.The present findings suggest that MLR is a powerful independent predictor for DN.

The impact of phenotype, ethnicity and genotype on progression of type 2 diabetes mellitus

AIM

To conduct a comprehensive review of studies of glycaemic deterioration in type 2 diabetes and identify the major factors influencing progression.

METHODS

We conducted a systematic literature search with terms linked to type 2 diabetes progression. All the included studies were summarized based upon the factors associated with diabetes progression and how the diabetes progression was defined.

RESULTS

Our search yielded 2785 articles; based on title, abstract and full-text review, we included 61 studies in the review. We identified seven criteria for diabetes progression: 'Initiation of insulin', 'Initiation of oral antidiabetic drug', 'treatment intensification', 'antidiabetic therapy failure', 'glycaemic deterioration', 'decline in beta-cell function' and 'change in insulin dose'. The determinants of diabetes progression were grouped into phenotypic, ethnicity and genotypic factors. Younger age, poorer glycaemia and higher body mass index at diabetes diagnosis were the main phenotypic factors associated with rapid progression. The effect of genotypic factors on progression was assessed using polygenic risk scores (PRS); a PRS constructed from the genetic variants linked to insulin resistance was associated with rapid glycaemic deterioration. The evidence of impact of ethnicity on progression was inconclusive due to the small number of multi-ethnic studies.

CONCLUSION

We have identified the major determinants of diabetes progression-younger age, higher BMI, higher HbA1c and genetic insulin resistance. The impact of ethnicity is uncertain; there is a clear need for more large-scale studies of diabetes progression in different ethnic groups.

Alcohol and Human Health: What Is the Evidence?

Alcohol consumption has long been a part of human culture. However, alcohol consumption levels and alcohol consumption patterns are associated with chronic diseases. Overall, light and moderate alcohol consumption (up to 14 g per day for women and up to 28 g per day for men) may be associated with reduced mortality risk, mainly due to reduced risks for cardiovascular disease and type-2 diabetes. However, chronic heavy alcohol consumption and alcohol abuse lead to alcohol-use disorder, which results in physical and mental diseases such as liver disease, pancreatitis, dementia, and various types of cancer. Risk factors for alcohol-use disorder are largely unknown. Alcohol-use disorder and frequent heavy drinking have detrimental effects on personal health.

Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease-a comparison in the MONICA/KORA study

Background

Biomarkers may contribute to our understanding of the pathophysiology of various diseases. Type 2 diabetes (T2D) and coronary heart disease (CHD) share many clinical and lifestyle risk factors and several biomarkers are associated with both diseases. The current analysis aims to assess the relevance of biomarkers combined to pathway groups for the development of T2D and CHD in the same cohort.

Methods

Forty-seven serum biomarkers were measured in the MONICA/KORA case-cohort study using clinical chemistry assays and ultrasensitive molecular counting technology. The T2D (CHD) analyses included 689 (568) incident cases and 1850 (2004) non-cases from three population-based surveys. At baseline, the study participants were 35–74 years old. The median follow-up was 14 years. We computed Cox regression models for each biomarker, adjusted for age, sex, and survey. Additionally, we assigned the biomarkers to 19 etiological pathways based on information from literature. One age-, sex-, and survey-controlled average variable was built for each pathway. We used the R²_PM coefficient of determination to assess the explained disease risk.

Results

The associations of many biomarkers, such as several cytokines or the iron marker soluble transferrin receptor (sTfR), were similar in strength for T2D and CHD, but we also observed important differences. Lipoprotein (a) (Lp(a)) and N-terminal pro B-type natriuretic peptide (NT-proBNP) even demonstrated opposite effect directions. All pathway variables together explained 49% of the T2D risk and 21% of the CHD risk. The insulin-like growth factor binding protein 2 (IGFBP-2, IGF/IGFBP system pathway) best explained the T2D risk (about 9% explained risk, independent of all other pathway variables). For CHD, the myocardial-injury- and lipid-related-pathways were most important and both explained about 4% of the CHD risk.

Conclusions

The biomarker-derived pathway variables explained a higher proportion of the T2D risk compared to CHD. The ranking of the pathways differed between the two diseases, with the IGF/IGFBP-system-pathway being most strongly associated with T2D and the myocardial-injury- and lipid-related-pathways with CHD. Our results help to better understand the pathophysiology of the two diseases, with the ultimate goal of pointing out targets for lifestyle intervention and drug development to ideally prevent both T2D and CHD development.

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model

AIMS

Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whether the use of a machine-learning model can improve the prediction of incident diabetes utilizing patient data from electronic medical records.

METHODS

A machine-learning model predicting the progression from pre-diabetes to diabetes was developed using a gradient boosted trees model. The model was trained on data from The Health Improvement Network (THIN) database cohort, internally validated on THIN data not used for training, and externally validated on the Canadian AppleTree and the Israeli Maccabi Health Services (MHS) data sets. The model's predictive ability was compared with that of a logistic-regression model within each data set.

RESULTS

A cohort of 852 454 individuals with pre-diabetes (glucose ≥ 100 mg/dL and/or HbA1c ≥ 5.7) was used for model training including 4.9 million time points using 900 features. The full model was eventually implemented using 69 variables, generated from 11 basic signals. The machine-learning model demonstrated superiority over the logistic-regression model, which was maintained at all sensitivity levels - comparing AUC [95% CI] between the models; in the THIN data set (0.865 [0.860,0.869] vs 0.778 [0.773,0.784] P < .05), the AppleTree data set (0.907 [0.896, 0.919] vs 0.880 [0.867, 0.894] P < .05) and the MHS data set (0.925 [0.923, 0.927] vs 0.876 [0.872, 0.879] P < .05).

CONCLUSIONS

Machine-learning models preserve their performance across populations in diabetes prediction, and can be integrated into large clinical systems, leading to judicious selection of persons for interventional programmes.

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories

BACKGROUND

The ubiquity of electronic health records (EHR) offers an opportunity to observe trajectories of laboratory results and vital signs over long periods of time. This study assessed the value of risk factor trajectories available in the electronic health record to predict incident type 2 diabetes.

STUDY DESIGN AND METHODS

Analysis was based on a large 13-year retrospective cohort of 71,545 adult, non-diabetic patients with baseline in 2005 and median follow-up time of 8 years. The trajectories of fasting plasma glucose, lipids, BMI and blood pressure were computed over three time frames (2000-2001, 2002-2003, 2004) before baseline. A novel method, Cumulative Exposure (CE), was developed and evaluated using Cox proportional hazards regression to assess risk of incident type 2 diabetes. We used the Framingham Diabetes Risk Scoring (FDRS) Model as control.

RESULTS

The new model outperformed the FDRS Model (.802 vs .660; p-values <2e-16). Cumulative exposure measured over different periods showed that even short episodes of hyperglycemia increase the risk of developing diabetes. Returning to normoglycemia moderates the risk, but does not fully eliminate it. The longer an individual maintains glycemic control after a hyperglycemic episode, the lower the subsequent risk of diabetes.

CONCLUSION

Incorporating risk factor trajectories substantially increases the ability of clinical decision support risk models to predict onset of type 2 diabetes and provides information about how risk changes over time.

In search of causal pathways in diabetes: a study using proteomics and genotyping data from a cross-sectional study

AIMS/HYPOTHESIS

The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome 'diabetes' and whether these associations were causal.

METHODS

In 2467 individuals of the population-based, cross-sectional EpiHealth study (45-75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of ≥7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study.

RESULTS

Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, α-L-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes.

CONCLUSIONS/INTERPRETATION

The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways.

Identification of Novel Causal Blood Biomarkers Linking Metabolically Favorable Adiposity With Type 2 Diabetes Risk

OBJECTIVE

Observations of a metabolically unhealthy normal weight phenotype suggest that a lack of favorable adiposity contributes to an increased risk of type 2 diabetes. We aimed to identify causal blood biomarkers linking favorable adiposity with type 2 diabetes risk for use in cardiometabolic risk assessments.

RESEARCH DESIGN AND METHODS

A weighted polygenic risk score (PRS) underpinning metabolically favorable adiposity was validated in the UK Biobank (n = 341,872) and the Outcome Reduction With Initial Glargine Intervention (ORIGIN Trial) (n = 8,197) and tested for association with 238 blood biomarkers. Associated biomarkers were investigated for causation with type 2 diabetes risk using Mendelian randomization and for its performance in predictive models for incident major adverse cardiovascular events (MACE).

RESULTS

Of the 238 biomarkers tested, only insulin-like growth factor-binding protein (IGFBP)-3 concentration was associated with the PRS, where a 1 unit increase in PRS predicted a 0.28-SD decrease in IGFBP-3 blood levels (P < 0.05/238). Higher IGFBP-3 levels causally increased type 2 diabetes risk (odds ratio 1.26 per 1 SD genetically determined IGFBP-3 level [95% CI 1.11-1.43]) and predicted a higher incidence of MACE (hazard ratio 1.13 per 1 SD IGFBP-3 concentration [95% CI 1.07-1.20]). Adding IGFBP-3 concentrations to the standard clinical assessment of metabolic health enhanced the prediction of incident MACE, with a net reclassification improvement of 11.5% in normal weight individuals (P = 0.004).

CONCLUSIONS

We identified IGFBP-3 as a novel biomarker linking a lack of favorable adiposity with type 2 diabetes risk and a predictive marker for incident cardiovascular events. Using IGFBP-3 blood concentrations may improve the risk assessment of cardiometabolic diseases.

Decision models of prediabetes populations: A systematic review

AIMS

With evidence supporting the use of preventive interventions for prediabetes populations and the use of novel biomarkers to stratify the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. Our aim is to summarize and assess the quality and validity of decision models and model-based economic evaluations of populations with prediabetes, to evaluate their potential use for the assessment of novel prevention strategies and to discuss the knowledge gaps, challenges and opportunities.

MATERIALS AND METHODS

We searched Medline, Embase, EconLit and NHS EED between 2000 and 2018 for studies reporting computer simulation models of the natural history of individuals with prediabetes and/or we used decision models to evaluate the impact of treatment strategies on these populations. Data were extracted following PRISMA guidelines and assessed using modelling checklists. Two reviewers independently assessed 50% of the titles and abstracts to determine whether a full text review was needed. Of these, 10% was assessed by each reviewer to cross-reference the decision to proceed to full review. Using a standardized form and double extraction, each of four reviewers extracted 50% of the identified studies.

RESULTS

A total of 29 published decision models that simulate prediabetes populations were identified. Studies showed large variations in the definition of prediabetes and model structure. The inclusion of complications in prediabetes (n = 8) and type 2 diabetes (n = 17) health states also varied. A minority of studies simulated annual changes in risk factors (glycaemia, HbA1c, blood pressure, BMI, lipids) as individuals progressed in the models (n = 7) and accounted for heterogeneity among individuals with prediabetes (n = 7).

CONCLUSIONS

Current prediabetes decision models have considerable limitations in terms of their quality and validity and do not allow evaluation of stratified strategies using novel biomarkers, highlighting a clear need for more comprehensive prediabetes decision models.

Association of gamma-glutamyl transferase and alanine aminotransferase with type 2 diabetes mellitus incidence in middle-aged Japanese men: 12-year follow up

AIMS/INTRODUCTION

To prospectively investigate whether simultaneous elevation of gamma-glutamyl transferase (GGT) and alanine aminotransferase (ALT) is associated with the increase of type 2 diabetes mellitus incidence independent of alcohol drinking, body mass index and triglycerides.

METHODS

A total of 2,775 Japanese male workers who had no history of type 2 diabetes mellitus were followed. High GGT and ALT were defined as the top tertiles (GGT cutpoint: 49 IU/L, ALT cutpoint: 28 IU/L). Three groups were created using these dichotomized GGT and ALT cutpoints: both low, either high or both high. Multivariable Cox proportional hazards models were carried out adjusted for potential confounding factors.

RESULTS

A total of 276 type 2 diabetes mellitus cases were identified during 12 years (27,040 person-years) of follow up. Participants with simultaneously elevated GGT and ALT had a significantly higher incidence of type 2 diabetes mellitus, even after adjustment for fasting insulin and fasting blood glucose compared with the group without GGT or ALT elevation. Similar associations were observed in non- or light-to-moderate alcohol drinkers, as well as in participants with normal weight. However, the association was weaker in participants with triglycerides <150 mg/dL. We then evaluated whether the addition of GGT and ALT would improve the prediction of type 2 diabetes mellitus incidence, and found that their inclusion significantly increased the C-statistic, net reclassification improvement and integrated discrimination improvement.

CONCLUSIONS

Simultaneous elevation of GGT and ALT was significantly associated with type 2 diabetes mellitus incidence, independent of potential confounding factors, including alcohol drinking and obesity, although the association might require concomitant elevation of triglycerides. Inclusion of GGT and ALT improved type 2 diabetes mellitus risk prediction.

Predictive Estimates of Risks Associated with Type 2 Diabetes Mellitus on the Basis of Biochemical Biomarkers and Derived Time-Dependent Parameters

This work contributes to the development of effective statistical methods of big data analysis for type 2 diabetes mellitus (T2DM) risk assessment to be employed in routine clinical practice. The objective of this study to be reached via machine-learning analysis is twofold: investigation of a possible application of biochemical biomarkers for the T2DM risk prediction in case of a limited knowledge of biometrical parameters of an individual, as well as study on the predictive ability of a derived parameter (rate of a biomarker change over time) in T2DM risk prediction. Obtained statistical parameters (AUC, p-value, etc.) justify a relatively high quality of the model. Nevertheless, a further improvement may be addressed through the following avenues: analysis of adding new factors and models, including lifestyle/habits, and genetic parameters.

Vegetarian diets and risk of hospitalisation or death with diabetes in British adults: results from the EPIC-Oxford study

BACKGROUND

The global prevalence of diabetes is high and rapidly increasing. Some previous studies have found that vegetarians might have a lower risk of diabetes than non-vegetarians.

OBJECTIVE

We examined the association between vegetarianism and risk of hospitalisation or death with diabetes in a large, prospective cohort study of British adults.

METHODS

The analysed cohort included participants from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford study who were diabetes free at recruitment (1993-2001), with available dietary intake data at baseline, and linked hospital admissions and death data for diabetes over follow-up (n = 45,314). Participants were categorised as regular meat eaters (≥50 g per day: n = 15,181); low meat eaters (<50 g of meat per day: n = 7615); fish eaters (ate no meat but consumed fish: n = 7092); and vegetarians (ate no meat or fish, including vegans: n = 15,426). We used multivariable Cox proportional hazards models to assess associations between diet group and risk of diabetes.

RESULTS

Over a mean of 17.6 years of follow-up, 1224 incident cases of diabetes were recorded. Compared with regular meat eaters, the low meat eaters, fish eaters, and vegetarians were less likely to develop diabetes (hazard ratio (HR) = 0.63, 95% confidence interval (CI) 0.54-0.75; HR = 0.47, 95% CI 0.38-0.59; and HR = 0.63, 95% CI 0.54-0.74, respectively). These associations were substantially attenuated after adjusting for body mass index (BMI) (low meat eaters: HR = 0.78, 95% CI 0.66-0.92; fish eaters: HR = 0.64, 95% CI 0.51-0.80; and vegetarians: HR = 0.89, 95% CI 0.76-1.05).

CONCLUSIONS

Low meat and non-meat eaters had a lower risk of diabetes, in part because of a lower BMI.

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study

The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42–81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age, and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.

Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables

BACKGROUND

The present study aims to identify the patients at risk of type 2 diabetes (T2D). There is a body of literature that uses machine learning classification algorithms to predict development of T2D among patients. The current study compares the performance of these classification algorithms to identify patients who are at risk of developing T2D in short, medium and long terms. In addition, the list of predictor variables important for prediction for T2D progression is provided.

METHODS

This study uses 10,911 records generated in 36 clinics from the 15th of November 2008-15th of November 2016. Syntactic minority oversampling and random under sampling were used to create a balanced dataset. The performance of Neural Networks, Support Vector Machines, Decision Tress and Logistic Regression to identify patients developing T2D in short, medium and long terms was compared. The measures were Area Under Curve, Sensitivity, Specificity, Matthew correlation coefficient and Mean Calibration Error. Through importance analysis and information fusion techniques the predictors of developing T2D were identified for short, medium and long-term risk analysis.

RESULTS

The findings show that the performance of analytics techniques depends on both period and purpose of prediction whether the prediction is to identify people who will not develop T2D or to determine at risk patients. Oversampling as opposed to under sampling improved performance. 16 predictors and their importance to determine patients at risk of T2D in short, medium and long terms were identified.

CONCLUSIONS

This study provides guidelines for an automated system to prompt patients for screening. Several predictors are reportable by patients, others can be examined by physicians or ordered for further lab examination, which offers a potential reduction of the burden placed upon the clinical settings.

Spousal cardiometabolic risk factors and incidence of type 2 diabetes: a prospective analysis from the English Longitudinal Study of Ageing

AIMS/HYPOTHESIS

In the UK, more than one million people have undiagnosed diabetes and an additional five million are at high risk of developing the disease. Given that early identification of these people is key for both primary and secondary prevention, new screening approaches are needed. Since spouses resemble each other in cardiometabolic risk factors related to type 2 diabetes, we aimed to investigate whether diabetes and cardiometabolic risk factors in one spouse can be used as an indicator of incident type 2 diabetes in the other spouse.

METHODS

We analysed data from 3649 men and 3478 women from the English Longitudinal Study of Ageing with information on their own and their spouse's diabetes status and cardiometabolic risk factors. We modelled incidence rates and incidence rate ratios with Poisson regression, using spousal diabetes status or cardiometabolic risk factors (i.e. BMI, waist circumference, systolic and diastolic BP, HDL- and LDL-cholesterol and triacylglycerols) as exposures and type 2 diabetes incidence in the index individual as the outcome. Models were adjusted for two nested sets of covariates.

RESULTS

Spousal BMI and waist circumference were associated with incident type 2 diabetes, but with different patterns for men and women. A man's risk of type 2 diabetes increased more steeply with his wife's obesity level, and the association remained statistically significant even after adjustment for the man's own obesity level. Having a wife with a 5 kg/m² higher BMI (30 kg/m² vs 25 kg/m²) was associated with a 21% (95% CI 11%, 33%) increased risk of type 2 diabetes. In contrast, the association between incident type 2 diabetes in a woman and her husband's BMI was attenuated after adjusting for the woman's own obesity level. Findings for waist circumference were similar to those for BMI. Regarding other risk factors, we found a statistically significant association only between the risk of type 2 diabetes in women and their husbands' triacylglycerol levels.

CONCLUSIONS/INTERPRETATION

The main finding of this study is the sex-specific effect of spousal obesity on the risk of type 2 diabetes. Having an obese spouse increases an individual's risk of type 2 diabetes over and above the effect of the individual's own obesity level among men, but not among women. Our results suggest that a couples-focused approach may be beneficial for the early detection of type 2 diabetes and individuals at high risk of developing type 2 diabetes, especially in men, who are less likely than women to attend health checks.

DATA AVAILABILITY

Data were accessed via the UK Data Service under the data-sharing agreement no. 91400 ( https://discover.ukdataservice.ac.uk/catalogue/?sn=5050&type=Data%20catalogue ).

Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches

PURPOSE OF THE REVIEW

Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D.

RECENT FINDINGS

Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking. Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D.

Clinical Significance of Hemostatic Parameters in the Prediction for Type 2 Diabetes Mellitus and Diabetic Nephropathy

It would be important to predict type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN). This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT), platelet (PLT), and D-dimer (D-D) levels compared with controls (P < 0.01). Fibrinogen (FIB), PLT, and D-D increased in DN patients compared with those without complications (P < 0.001). Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P < 0.01, resp.), and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P < 0.01, resp.). The area under ROC curve (AUC) of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85%) and specificity (76%) for DN, and that of PLT was 0.564, with sensitivity (60%) and specificity (89%) based on the cutoff values of 3.15 g/L and 245 × 10⁹/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.