Diabetes Risk Prediction Research Articles

Postprandial Saturated Fatty Acids Increase the Risk of Type 2 Diabetes: A Cohort Study in a Chinese Population.

Experimental evidence suggests saturated fatty acids (SFAs) are associated with insulin resistance, but results from epidemiological studies on fasting SFAs-diabetes risk are inconsistent. We investigated SFA (fasting and 2-hour postprandial) profiles and diabetes risk. A total of 8940 participants were recruited for the Harbin People's Health Study in 2008. Serum SFAs (fasting and 2-hour postprandial) at baseline in Chinese men and women without diabetes were profiled, and type 2 diabetes was ascertained using World Health Organization criteria after 4 to 7 years of follow-up. Associations between 2-hour postprandial SFA (2h-SFA) and diabetes. At baseline, incident cases of diabetes were older with a higher body mass index and waist circumference. After a mean follow-up of 6.7 years, 658 incident cases of diabetes occurred. After propensity score computation and inverse probability of treatment weighting (IPTW) estimation, fasting SFAs were unrelated to diabetes risk but IPTW-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the highest tertile of 2-hour postprandial stearic acid (2h-SA), 2-hour postprandial palmitic acid (2h-PA), and 2h-SFA for diabetes risk were 2.50 (2.08 to 3.16), 1.56 (1.23 to 2.02), and 1.70 (1.34 to 2.17), respectively (P-trend < 0.0001). Similarly, 2h-SA/fasting SA, 2h-PA/fasting PA, and 2h-SFA/fasting SFA ratios [IPTW-adjusted OR (95% CI): 2.94 (2.39 to 3.58), 2.31 (1.80 to 2.93), and 2.42 (1.91 to 3.11), respectively; P-trend < 0.0001] predicted the diabetes risk. Higher serum 2h-SFA (but not fasting SFA) independently predicted diabetes risk.

Large Cohort Data Based Group or Community Disease Prevention Design Strategy: Strong Heart Study.

Background and ObjectiveA multitude of large cohort studies have data on incidence rates and predictors of various chronic diseases. However, approaches for utilization of these costly collected data and translation of these valuable results to inform and guide clinical disease prevention practice are not well developed. In this paper we proposed a novel conceptual group/community disease prevention design strategy based on large cohort study data.Methods and ResultsThe data from participants (n = 3516; 2056 women) aged 45 to 74 years and the diabetes risk prediction model from Strong Heart Study were used. The Strong Heart Study is a population-based cohort study of cardiovascular disease and its risk factors in American Indians. A conceptual group/community disease prevention design strategy based on large cohort data was initiated. The application of the proposed strategy for group diabetes prevention was illustrated.DiscussionThe strategy may provide reasonable solutions to the prevention design issues. These issues include complex associations of a disease with its combined and correlated risk factors, individual differences, choosing intervention risk factors and setting their appropriate, attainable, gradual and adaptive goal levels for different subgroups, and assessing effectiveness of the prevention program.ConclusionsThe strategy and methods shown in the illustration example can be analogously adopted and applied for other diseases preventions. The proposed strategy for a target group/community in a population provides a way to translate and apply epidemiological study results to clinical disease prevention practice.

Large Cohort Data Based Cost-Effective Disease Prevention Design Strategy: Strong Heart Study.

Background and Objective:A multitude of large cohort studies have collected data on incidence and covariates/risk factors of various chronic diseases. However, approaches for utilization of these large data and translation of the valuable results to inform and guide clinical disease prevention practice are not well developed. In this paper, we proposed, based on large cohort study data, a novel conceptual cost-effective disease prevention design strategy for a target group when it is not affordable to include everyone in the target group for intervention.Methods and Results:Data from American Indian participants (n = 3516; 2056 women) aged 45 – 74 years in the Strong Heart Study, the diabetes risk prediction model from the study, a utility function, and regression models were used. A conceptual cost-effective disease prevention design strategy based on large cohort data was initiated. The application of the proposed strategy for diabetes prevention was illustrated.Discussion:The strategy may provide reasonable solutions to address cost-effective prevention design issues. These issues include complex associations of a disease with its significant risk factors, cost-effectively selecting individuals at high risk of developing disease to undergo intervention, individual differences in health conditions, choosing intervention risk factors and setting their appropriate, attainable, gradual and adaptive goal levels for different subgroups, and assessing effectiveness of the prevention program.Conclusions:The strategy and methods shown in the illustrative example can also be analogously adopted and applied to other diseases preventions. The proposed strategy provides a way to translate and apply epidemiological study results to clinical disease prevention practice.

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction.

The purpose of this review was to summarize and reflect on advances over the past decade in human genetic and metabolomic discovery with particular focus on their contributions to type 2 diabetes (T2D) risk prediction. In the past 10years, a combination of advances in genotyping efficiency, metabolomic profiling, bioinformatics approaches, and international collaboration have moved T2D genetics and metabolomics from a state of frustration to an abundance of new knowledge. Efforts to control and prevent T2D have failed to stop this global epidemic. New approaches are needed, and although neither genetic nor metabolomic profiling yet have a clear clinical role, the rapid pace of accumulating knowledge offers the possibility for "multi-omic" prediction to improve health.

Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction

The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute. Support Vector Machines and Logistic Regression have also been used since they are two other machine learning techniques that are well-established in the health community. Their performance has been compared to that achieved by RF. Furthermore, the relevance of the attributes obtained through the use of RF has then been used to perform predictions with k-Nearest Neighbour method weighting attributes in the similarity measure according to the relevance of the attributes with RF. Testing is performed on a set of 677 subjects. RF is able to handle the complexity of features' interactions, overfitting, and unknown attribute values, providing the SNPs' relevance with an up to 0.89 area under the ROC curve in terms of risk prediction. RF outperforms all the other tested machine learning techniques in terms of prediction accuracy, and in terms of the stability of the estimated relevance of the attributes. The Random Forest is a useful method for learning predictive models and the relevance of SNPs without any underlying assumption.

Body mass index and type 2 diabetes in Thai adults: defining risk thresholds and population impacts

BackgroundBody mass index (BMI) cut-off values (>25 and >30) that predict diabetes risk have been well validated in Caucasian populations but less so in Asian populations. We aimed to determine the BMI threshold associated with increased type 2 diabetes (T2DM) risk and to calculate the proportion of T2DM cases attributable to overweight and obesity in the Thai population.MethodsParticipants were those from the Thai Cohort Study who were diabetes-free in 2005 and were followed-up in 2009 and 2013 (n = 39,021). We used multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the BMI-T2DM association. We modelled non-linear associations using restricted cubic splines. We estimated population attributable fractions (PAF) and the number of T2DM incident cases attributed to overweight and obesity. We also calculated the impact of reducing the prevalence of overweight and obesity on T2DM incidence in the Thai population.ResultsNon-linear modelling indicated that the points of inflection where the BMI-T2DM association became statistically significant compared to a reference of 20.00 kg/m2 were 21.60 (OR = 1.27, 95% CI 1.00–1.61) and 20.03 (OR = 1.02, 95% CI 1.02–1.03) for men and women, respectively. Approximately two-thirds of T2DM cases in Thai adults could be attributed to overweight and obesity. Annually, if prevalent obesity was 5% lower, ~13,000 cases of T2DM might be prevented in the Thai population.ConclusionsA BMI cut-point of 22 kg/m2, one point lower than the current 23 kg/m2, would be justified for defining T2DM risk in Thai adults. Lowering obesity prevalence would greatly reduce T2DM incidence.

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

Morphological characteristics of the glucose curve during an oral glucose tolerance test (OGTT) (time to peak and shape) may reflect different phenotypes of insulin secretion and action, but their ability to predict diabetes risk is uncertain. To compare the ability of time to glucose peak and curve shape to detect prediabetes and β-cell function. In a cross-sectional evaluation using an OGTT, 145 adults without diabetes (age 42±9years (mean±SD), range 24-62years, BMI 29.2±5.3kg/m2 , range 19.9-45.2kg/m2 ) were characterized by peak (30minutes vs >30minutes) and shape (biphasic vs monophasic). Prediabetes and disposition index (DI)-a marker of β-cell function. Prediabetes was diagnosed in 36% (52/145) of participants. Peak>30minutes, not monophasic curve, was associated with increased odds of prediabetes (OR: 4.0 vs 1.1; P<.001). Both monophasic curve and peak>30minutes were associated with lower DI (P≤.01). Time to glucose peak and glucose area under the curves (AUC) were independent predictors of DI (adjR2 =0.45, P<.001). Glucose peak >30minutes was a stronger independent indicator of prediabetes and β-cell function than the monophasic curve. Time to glucose peak may be an important tool that could enhance prediabetes risk stratification.

LMNA Sequences of 60,706 Unrelated Individuals Reveal 132 Novel Missense Variants in A-Type Lamins and Suggest a Link between Variant p.G602S and Type 2 Diabetes.

Mutations in LMNA, encoding nuclear intermediate filament proteins lamins A and C, cause multiple diseases (‘laminopathies’) including muscular dystrophy, dilated cardiomyopathy, familial partial lipodystrophy (FPLD2), insulin resistance syndrome and progeria. To assess the prevalence of LMNA missense mutations (‘variants’) in a broad, ethnically diverse population, we compared missense alleles found among 60,706 unrelated individuals in the ExAC cohort to those identified in 1,404 individuals in the laminopathy database (UMD-LMNA). We identified 169 variants in the ExAC cohort, of which 37 (∼22%) are disease-associated including p.I299V (allele frequency 0.0402%), p.G602S (allele frequency 0.0262%) and p.R644C (allele frequency 0.124%), suggesting certain LMNA mutations are more common than previously recognized. Independent analysis of LMNA variants via the type 2 diabetes (T2D) Knowledge Portal showed that variant p.G602S associated significantly with type 2 diabetes (p = 0.02; odds ratio = 4.58), and was more frequent in African Americans (allele frequency 0.297%). The FPLD2-associated variant I299V was most prevalent in Latinos (allele frequency 0.347%). The ExAC cohort also revealed 132 novel LMNA missense variants including p.K108E (limited to individuals with psychiatric disease; predicted to perturb coil-1B), p.R397C and p.R427C (predicted to perturb filament biogenesis), p.G638R and p.N660D (predicted to perturb prelamin A processing), and numerous Ig-fold variants predicted to perturb phenotypically characteristic protein–protein interactions. Overall, this two-pronged strategy— mining a large database for missense variants in a single gene (LMNA), coupled to knowledge about the structure, biogenesis and functions of A-type lamins— revealed an unexpected number of LMNA variants, including novel variants predicted to perturb lamin assembly or function. Interestingly, this study also correlated novel variant p.K108E with psychiatric disease, identified known variant p.I299V as a potential risk factor for metabolic disease in Latinos, linked variant p.G602 with type 2 diabetes, and identified p.G602S as a predictor of diabetes risk in African Americans.

Possible type 1 diabetes risk prediction: Using ultrasound imaging to assess pancreas inflammation in the inducible autoimmune diabetes BBDR model.

Background/AimsStudies of human cadaveric pancreas specimens indicate that pancreas inflammation plays an important role in type 1 diabetes pathogenesis. Due to the inaccessibility of pancreas in living patients, imaging technology to visualize pancreas inflammation is much in need. In this study, we investigated the feasibility of utilizing ultrasound imaging to assess pancreas inflammation longitudinally in living rats during the progression leading to type 1 diabetes onset.MethodsThe virus-inducible BBDR type 1 diabetes rat model was used to systematically investigate pancreas changes that occur prior to and during development of autoimmunity. The nearly 100% diabetes incidence upon virus induction and the highly consistent time course of this rat model make longitudinal imaging examination possible. A combination of histology, immunoblotting, flow cytometry, and ultrasound imaging technology was used to identify stage-specific pancreas changes.ResultsOur histology data indicated that exocrine pancreas tissue of the diabetes-induced rats underwent dramatic changes, including blood vessel dilation and increased CD8+ cell infiltration, at a very early stage of disease initiation. Ultrasound imaging data revealed significant acute and persistent pancreas inflammation in the diabetes-induced rats. The pancreas micro-vasculature was significantly dilated one day after diabetes induction, and large blood vessel (superior mesenteric artery in this study) dilation and inflammation occurred several days later, but still prior to any observable autoimmune cell infiltration of the pancreatic islets.ConclusionsOur data demonstrate that ultrasound imaging technology can detect pancreas inflammation in living rats during the development of type 1 diabetes. Due to ultrasound’s established use as a non-invasive diagnostic tool, it may prove useful in a clinical setting for type 1 diabetes risk prediction prior to autoimmunity and to assess the effectiveness of potential therapeutics.

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

Updated risk factors should be used to predict development of diabetes

AimsPredicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. MethodsAmong non-diabetic participants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a “landmark” model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. ResultsA total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72–0.74]) than for the baseline model (0.67 [95% CI 0.66–0.68]). The landmark model improved classification to modest (<20%), moderate (20%–40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10–0.16) and an integrated discrimination index of 0.01 (95% CI 0.003–0.013). ConclusionsUsing historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.

Associations of hyperglycemia and insulin resistance with biomarkers of endothelial dysfunction in Hispanic/Latino youths: Results from the Hispanic Community Children's Health Study/Study of Latino Youth (SOL Youth)

AimsWe hypothesized that Hispanic/Latino youth at high risk for diabetes would have elevated biomarkers of endothelial dysfunction. MethodsAmong 1316 children 8–16years old from the Study of Latino Youth (SOL Youth), we used Poisson regression to obtain prevalence ratios (PRs) and 95% CIs for the cross-sectional association of quartiles of fasting glucose, HbA1c, and insulin resistance with E-selectin and plasminogen activator inhibitor-1 (PAI-1) levels above the median (≥48.1 and ≥2.02ng/mL, respectively). ResultsLevels of E-selectin and PAI-1 were higher in children who were obese or had higher levels of hs-CRP (p<0.05). Insulin resistance was independently associated with higher levels of PAI-1 (adjusted PR and 95% CI for the highest versus lowest quartile (Q4 vs Q1): 2.25 [1.64, 3.09]). We found stronger evidence of associations of insulin resistance with higher levels of PAI-1 among boys as compared with girls (p-interaction = 0.10). ConclusionsInsulin resistance was associated with endothelial dysfunction, as measured by higher levels of PAI-1, in Hispanic/Latino youth. These biomarkers may be useful in risk stratification and prediction of diabetes and cardiovascular disease in high-risk youth.

The cost of diabetes in Canada over 10 years: applying attributable health care costs to a diabetes incidence prediction model.

Our objective was to estimate the future direct health care costs due to diabetes for a 10-year period in Canada using national survey data, a validated diabetes risk prediction tool and individual-level attributable cost estimates. We used the Diabetes Population Risk Tool to predict the number of new diabetes cases in those aged 20 years and above over a 10-year period (to 2022), using 2011 and 2012 Canadian Community Health Survey data. We derived attributable costs due to diabetes from a propensity-matched case control study using the Ontario Diabetes Database and other administrative data. We calculated total costs by applying the respective attributable costs to the incident cases, accounting for sex, year of diagnosis and annual disease-specific mortality rates. The predicted 10-year risk of developing diabetes for the Canadian population in 2011/12 was 9.98%, corresponding to 2.16 million new cases. Total health care costs attributable to diabetes during this period were $7.55 billion for females and $7.81 billion for males ($15.36 billion total). Acute hospitalizations accounted for the greatest proportion of costs (43.2%). A population intervention resulting in 5% body weight loss would save $2.03 billion in health care costs. A 30% risk-reduction intervention aimed at individuals with the highest diabetes risk (i.e. the top 10% of the highest-risk group) would save $1.48 billion. Diabetes represents a heavy health care cost burden in Canada through to the year 2022. Our future cost calculation method can provide decision makers and planners with an accessible and transparent tool to predict future expenditures attributable to the disease and the corresponding cost savings associated with interventions.

Diabetes risk prediction using machine learning: prospect and challenges

Diabetes risk prediction using machine learning: prospect and challenges

Functional Investigations of HNF1A Identify Rare Variants as Risk Factors for Type 2 Diabetes in the General Population.

Variants in HNF1A encoding hepatocyte nuclear factor 1α (HNF-1A) are associated with maturity-onset diabetes of the young form 3 (MODY 3) and type 2 diabetes. We investigated whether functional classification of HNF1A rare coding variants can inform models of diabetes risk prediction in the general population by analyzing the effect of 27 HNF1A variants identified in well-phenotyped populations (n = 4,115). Bioinformatics tools classified 11 variants as likely pathogenic and showed no association with diabetes risk (combined minor allele frequency [MAF] 0.22%; odds ratio [OR] 2.02; 95% CI 0.73–5.60; P = 0.18). However, a different set of 11 variants that reduced HNF-1A transcriptional activity to <60% of normal (wild-type) activity was strongly associated with diabetes in the general population (combined MAF 0.22%; OR 5.04; 95% CI 1.99–12.80; P = 0.0007). Our functional investigations indicate that 0.44% of the population carry HNF1A variants that result in a substantially increased risk for developing diabetes. These results suggest that functional characterization of variants within MODY genes may overcome the limitations of bioinformatics tools for the purposes of presymptomatic diabetes risk prediction in the general population.

Findrisk score, pulse wave velocity and detecting dysglycemia in a risk population of diabetes

Findrisk score has been validated for prediction of diabetes risk in many populations. The aim of this study was to examine the value of this score for the detection of dysglycemia (impaired fasting glucose and/or glucose intolerance or diabetes) in a high-risk population in Constantine (Algeria). We had recruited 397 patients (281 women) with risk factors of diabetes, mean age 50.2 ± 11.7 years. All had fasting glycemia (FG) and oral post charge glycemia (PCG). Findrisk score was calculated. Carotid femoral pulse wave velocity (PWV) was measured by Complior® device. FG and PCG were abnormal in 159 and 157 patients, respectively. Dysglycemia was found in 204 patients (51.4%), with diabetes in 17.6% and prediabetes in 33.8% of them. Findrisk score strongly correlated to FG (r = 0.129, P = 0.01) and PCG (r = 0.193, P = 10−5) but also to systolic (r = 0.33) and diastolic blood pressure (r = 0.20), and to PWV (r = 0.29). Discriminating threshold of this score for predicting dysglycemia was 13. It was significantly associated with prediabetes (P = 0.001) and dysglycemia (P = 10−5), this association was found in women but not in men. Sensibility and specificity of this score for detecting dysglycemia were 76% and 37%, respectively. Their positive and negative predictive values were 54.7% and 60.7%, respectively. FG and PCG detected dysglycemia in similar proportions. Identification of dysglycemia through Findrisk score was validated only in women. The correlation between Findrisk score and PWV suggests that a high score testified also of a high cardiovascular risk in these patients without glycemic abnormality or known cardiovascular history.

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study

AimsTo evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population. MethodsSurveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic. ResultsAll risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11–2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%–88%), but poor sensitivity (event NRIs: −23% to −3%). ConclusionsFive diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed.

The Link Between Adverse Childhood Experiences and Diabetes.

Exposure to adversity in childhood (adverse childhood experiences [ACEs]) is linked to a number of chronic diseases in adulthood, yet there is limited research examining the impact of ACEs on diabetes. The current review sought to examine the association between ACEs, other trauma exposure or posttraumatic stress disorder (PTSD) diagnosis, and risk for diabetes. Thirty-eight studies are reviewed. Unlike in other diseases, several studies in diabetes show a threshold-response versus a dose-response relation, while other studies show a relation between greater abuse severity and diabetes risk. There were mixed results for studies examining abuse type and frequency. Chronic or comorbid PTSD was also related to increased diabetes risk among veterans, but in community samples, only trauma exposure predicted diabetes risk. While the research is still limited, diabetes researchers and clinicians should consider screening for ACEs and examine severity and frequency across abuse type as a predictor of both diabetes and poor diabetes outcomes.

Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

BackgroundPredictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings.MethodsThis paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year.ResultsFor the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year.ConclusionsOur demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

Abstract P026: Associations of Biomarkers of Endothelial Dysfunction with Hyperglycemia and Insulin Resistance in Hispanic/Latino Youths: Results From the Hispanic Community Children’s Health Study/Study of Latino Youth (SOL Youth)

Background: Endothelial dysfunction may contribute to the long-term development of diabetes and cardiovascular disease (CVD). However, little is known about the correlates of biomarkers of endothelial dysfunction in youths, which is significant because early markers of endothelial damage might signal high future CVD risk and could be reversible. Methods: We included 1,318 participants from the Hispanic Community Children’s Health Study/Study of Latino Youth (SOL Youth) (2012-14) aged 8-16 years with complete data who were fasting ≥8 hours. We calculated unadjusted Pearson correlations for e-selectin and PAI-1, biomarkers of endothelial dysfunction, with sociodemographic and clinical variables. We conducted multivariable logistic regression and used predictive margins to obtain prevalence ratios (PRs) and 95% CIs for the association of quartiles of e-selectin and PAI-1, separately and in combination with one another, with fasting glucose≥100 mg/dL, HbA1c≥5.7%, and HOMA-IR≥2.6. Results: Higher levels of e-selectin and PAI-1 were associated with higher body mass index, systolic blood pressure, and dyslipidemia (P&lt;0.05 for all). After adjustment for sociodemographic and clinical variables, higher levels of PAI-1 were associated with elevated fasting glucose and HOMA-IR, and higher levels of e-selectin were associated with elevated HOMA-IR ( Figure ). Associations were relatively unchanged when including both e-selectin and PAI-1 in the same model. Adjusted PR and 95% CI for Q4 of e-selectin with HOMA-IR was 1.36 (1.13, 1.60); and for Q4 of PAI-1 with fasting glucose and HOMA-IR were 2.50 (1.16, 3.85) and 1.59 (1.26, 1.91), respectively. Conclusions: Biomarkers of endothelial dysfunction were independently associated with insulin resistance in Hispanic/Latino youths. Since the endothelium may help regulate insulin secretion, these biomarkers may be useful in risk stratification and prediction of diabetes and CVD in this population.