American Diabetes Association Risk Research Articles

Derivation and Validation of D-RISK: An Electronic Health Record-Driven Risk Score to Detect Undiagnosed Dysglycemia in Clinical Practice.

We derive and validate D-RISK, an electronic health record (EHR)-driven risk score to optimize and facilitate screening for undiagnosed dysglycemia (prediabetes + diabetes) in clinical practice. We used retrospective EHR data (derivation sample) and a prospective diabetes screening study (validation sample) to develop D-RISK. Logistic regression with backward selection was used to predict dysglycemia (HbA1c ≥5.7%) using diabetes risk factors consistently captured in structured EHR data. Model coefficients were converted to a points-based risk score. We report discrimination, sensitivity, and specificity and compare D-RISK to the American Diabetes Association (ADA) risk test and the ADA and United States Preventive Services Task Force (USPSTF) screening guidelines. The derivation cohort included 11,387 patients (mean age 48 years; 65% female; 42% Hispanic; 32% non-Hispanic Black; mean BMI 32; 29% with hypertension). D-RISK included age, race, BMI, hypertension, and random glucose. The area under curve (AUC) for the risk score was 0.75 (95% CI 0.74-0.76). In the validation screening study (n = 519), the AUC was 0.71 (95% CI 0.66-0.75) which was better than the ADA and USPSTF diabetes screening guidelines (AUC = 0.52 and AUC = 0.58, respectively; P < 0.001 for both). Discrimination was similar to the ADA risk test (AUC = 0.67) using patient-reported data to supplement EHR data, although D-RISK was more sensitive (75% vs. 61%) at the recommended screening thresholds. Designed for use in EHR, D-RISK performs better than commonly used screening guidelines and risk scores and may help detect undiagnosed cases of dysglycemia in clinical practice.

A voice-based algorithm can predict type 2 diabetes status in USA adults: Findings from the Colive Voice study.

The pressing need to reduce undiagnosed type 2 diabetes (T2D) globally calls for innovative screening approaches. This study investigates the potential of using a voice-based algorithm to predict T2D status in adults, as the first step towards developing a non-invasive and scalable screening method. We analyzed pre-specified text recordings from 607 US participants from the Colive Voice study registered on ClinicalTrials.gov (NCT04848623). Using hybrid BYOL-S/CvT embeddings, we constructed gender-specific algorithms to predict T2D status, evaluated through cross-validation based on accuracy, specificity, sensitivity, and Area Under the Curve (AUC). The best models were stratified by key factors such as age, BMI, and hypertension, and compared to the American Diabetes Association (ADA) score for T2D risk assessment using Bland-Altman analysis. The voice-based algorithms demonstrated good predictive capacity (AUC = 75% for males, 71% for females), correctly predicting 71% of male and 66% of female T2D cases. Performance improved in females aged 60 years or older (AUC = 74%) and individuals with hypertension (AUC = 75%), with an overall agreement above 93% with the ADA risk score. Our findings suggest that voice-based algorithms could serve as a more accessible, cost-effective, and noninvasive screening tool for T2D. While these results are promising, further validation is needed, particularly for early-stage T2D cases and more diverse populations.

Abstract P432: Validation and Optimization of the American Diabetes Association Score for Undiagnosed Type 2 Diabetes Risk in Hispanic or Latino Adults

Background: The prevalence of type 2 diabetes (T2D) is increasing in Hispanic or Latino adults. The American Diabetes Association (ADA) score to identify current risk of T2D was developed with limited Hispanic or Latino representation. Therefore, we sought to validate the ADA score in a cohort of Hispanic or Latino adults. Methods: At enrollment in the Sangre Por Salud Biobank, 3,625 Hispanic or Latino adults from Phoenix, Arizona completed a baseline questionnaire. Current risk of undiagnosed T2D was based on non-laboratory risk factors and ADA score ≥5 (Table). Participants also completed T2D screening (ie, fasting glucose, 2-hour oral glucose tolerance test, and/or hemoglobin A1c). Undiagnosed T2D was based on 'no self-reported T2D but meeting T2D diagnostic’ criteria. Multivariable logistic regression analyzed associations of ADA risk factors and undiagnosed T2D, and model performance was based on sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: At enrollment, 7.3% of participants had undiagnosed T2D (n=265/3,625). In multivariable analysis, most risk factors described for the ADA score were significantly associated with T2D, except for sex, which showed no association. ADA score ≥5 had sensitivity 50%, specificity 79%, PPV 18%, and NPV 95%. However, ADA score ≥4 had sensitivity 72%, specificity 61%, PPV 14%, and NPV 96%. ADA score ≥4 vs ≥5 identified 22% (n=59) additional cases of undiagnosed T2D. Furthermore, using the revised definition for elevated blood pressure (&gt;120/80 mmHg) vs ≥140/90 mmHg (current ADA score) further improved the ADA score ≥4 (sensitivity 81%; specificity 52%). Other scores had lower performance using both hypertension definitions. Conclusion: In Hispanic or Latino adults, a lower ADA score than currently recommended identified additional cases of undiagnosed T2D. The ADA score requires validation and recalibration, including for the updated hypertension guidelines.

THU260 Development Of Plantar Thermography For Screening Of Diabetic Neuropathy

Abstract Disclosure: V. Panamonta: None. R. Jerawatana: None. P. Ariyaprayoon: None. P. Looareesuwan: None. B. Ongphiphadhanakul: None. C. Sriphrapradang: None. B. Ongphiphadhanakul: None. Background: Diabetic foot ulcers are a major complication of diabetes. They are also the leading cause of nontraumatic lower extremity amputation. The incidence of diabetic foot ulcers is highest in those patients with poor glycemic control and neuropathy. In people with diabetic neuropathy, the feet temperature may change due to decreased blood flow and nerve damage. Thermography has been proposed as a noninvasive modality to identify patients at risk for diabetic foot ulcers. In this study, we used thermography and deep learning to stratify patients with diabetes at risk for developing a foot ulcer. Methods: We prospectively record clinical data and plantar thermogram in adult diabetic patients underwent diabetic foot screening at outpatient clinics at Ramathibodi hospital, Bangkok, Thailand during September to December 2022. Altogether, there were 245 thermal images were analyzed using a deep learning algorithm to determine the risk for diabetic foot ulcers by transfer learning using the pre-trained VGG16 model which is a convolutional neural network for image recognition. Twenty percent of the images were set aside for testing purposes while 20% of the images served as the validation set during training of the neural network. The neural network was trained and weighed more toward higher sensitivity of identifying at risk feet for screening purpose. Results: The study sample consisted of individuals with a mean age of 62.5±12.5 years, 57.1% who had diabetes for more than 10 years. The majority of the participants were females (57.1%). The average body mass index (BMI) was 27.8±9.6 (kg/m2). There were 186 thermal images classified as category 0 and 59 images classified as categories 1 to 3 according to the American Diabetes Association risk classification. The trained neural network achieved 75 % accuracy with 95 % sensitivity and 67 % specificity for classifying thermograms as higher than normal risk in the training dataset. For the testing dataset, the sensitivity and specificity was 75% and 41%, respectively. The accuracy, however, decreased to 50% due mainly to the misclassification of normal thermograms from abnormal. Conclusions: These results suggest that thermography combined with deep learning could potentially be developed for screening purpose to stratify patients at risk of developing diabetic foot ulcers. Further research is needed to validate the results in larger datasets and to explore alternative algorithms for predicting diabetic foot ulcers. Presentation: Thursday, June 15, 2023

Predictive model for diabetes mellitus occurrence in Iran’s southeastern region: a study based on American diabetes association guidelines

To control diabetes in a society, risk assessment tools are used to predict disease risk. We aimed to assess the value of different risk factors for diabetes mellitus in a remarkable community in the city of Kerman, one of the vast areas in the southeast of Iran, with the final goal of designing a predictive model for diabetes in this region. This study was a cross-sectional study with the aim of investigating the predictive value of risk factors indicating the presence of diabetes in the population of Kerman City based on the guidelines of the American Diabetes Association (ADA) risk assessment tool. The information of 4000 people participating in the comprehensive screening plan for cardiovascular risk factors in Kerman City was extracted by reviewing the relevant data registry. According to the ADA guideline, 32.5% of participants were at risk for diabetes mellitus. The hazard ratio of diabetes mellitus in the subgroup with the ADA final score ≥5 as compared to those with a lower final score was 1.9. Advanced age, history of gestational diabetes, family history of diabetes mellitus, history of hypertension, low physical activity, and higher body mass index were the main determinants of diabetes mellitus. According to ADA guidelines and the diabetes mellitus risk assessment tool, 32.5% of the population residents in Kerman City are potentially at risk for diabetes mellitus that can be successfully predicted aide by the ADA risk assessment tool.

Comparing a New Scoring System to the American Diabetes Association Risk Assessment to Identify Adults with Prediabetes for Randomized Clinical Trials

Comparing a New Scoring System to the American Diabetes Association Risk Assessment to Identify Adults with Prediabetes for Randomized Clinical Trials

PO-04-210 NEW-ONSET DIABETES SCREENING USING ARTIFICIAL INTELLIGENCE-ENHANCED ELECTROCARDIOGRAM

Diabetes has a long asymptomatic period which can remain undiagnosed for years. Glycated hemoglobin (HbA1c) enables the discovery of asymptomatic diabetes. Yet, undiagnosed diabetes is present in 23.0% of all US adults with Diabetes and 3.4% of all US adults. American Diabetes Association (ADA) guidlines promote the use of risk tests that help identify candidates for diagnostic testing. To develop and evaluate an artificial intelligence (AI)-enhanced electrocardiogram algorithm (ECGAI-DM) for screening of Diabetes. We trained a neural network to estimate HbA1c using a 12-lead ECG and readily available demographics (ECG model), and evaluated its ability to screen for Diabetes in a complete outpatient population. The dataset comprised of patients (aged ≥18 years) with paired 12-lead, 10-second ECG and HbA1c test acquired during an outpatient encounter. Our primary outcome was the performance of the AI-enhanced ECG at classifying new-onset diabetes (HbA1c ≥ 6.5 in patients without a prior history of diabetes), which was assessed using the area under the receiver operator curve (AUC) and positive predictive value (PPV) at the sensitivity defined by the ADA risk test, with 95% bootstrap CIs. Population bias was addressed by reconstruction of a pseudopopulation with adjusted individual patient weights. We trained on 198,857 ECGs from 160,788 patients, and our test set comprised 34,106 ECGs from 30,593 patients, recorded between January 1, 2013 and September 17, 2021. The prevalence of Diabetes (HbA1c ≥ 6.5) was 4.9%. The ECGAI-DM significantly outperformed the ADA Risk test (AUC, 0.80 [95% CI, 0.79-0.81] vs. AUC, 0.68 [95% CI, 0.68-0.69]; p-value <0.001). The ECGAI-DM model restricted to using a single lead (Lead-I) outperformed the ADA risk test as well (AUC, 0.78 [95% CI, 0.78-0.79] vs. 0.68 [95% CI, 0.68-0.69]; p-value <0.001). Patients with false positive prediction by ECGAI-DM had a 3-fold risk for Diabetes over a 1 year follow up as compared to the true negative population, suggesting unmasking of latent high risk. In a separate test set, ECGAI-DM significantly outperformed 2 human Cardiologists (AUC 0.82 vs. 0.61, p<0.001) assessed on a class balanced 100 randomly sampled ECGs. An AI-enhanced ECG can automate the screening of patients for Diabetes with fewer false positives than questionnaire-based assessment. This also applies to single-lead ECGs commonly found on wearable devices, which suggests an avenue for community-wide opportunistic Diabetes screening.

Investigation of the Effectiveness of the ADA Prediabetes Risk Test in Identifying Prediabetic Turkish Patients and Determination of the Frequency of Retinopathy in Prediabetic Patients

Aim: In this study, the Turkish version of the American Diabetes Association (ADA) prediabetes risk test and the 75-g oral glucose tolerance test (OGTT) were administered to patients to determine the rate of prediabetic patients in our geographical region and investigate its consistency with the ADA risk test. In addition, the presence of retinopathy in prediabetic patients was examined.&#x0D; Material and Method: &#x0D; The study included a total of 342 patients with a fasting plasma glucose value of 100-125 mg/dl. The 75-g OGTT test was performed on the patients. The ADA prediabetes risk test was also administered, and the scores were noted. The OGTT and risk test results were compared. According to the ADA prediabetes risk test, the patients were classified into those at risk for type 2 diabetes mellitus (DM) and those diagnosed with prediabetes. In the evaluation of retinopathy, visual acuity, intraocular pressure measurement, and anterior segment and fundus examinations were performed in each patient. The data of the patients were compared with statistical analysis methods.&#x0D; Results: When the OGTT results of the patients with negative ADA prediabetes risk test scores were compared, the sensitivity was determined as 54% and the specificity as 63% (p0.05). There was no retinopathy finding in the eye examination of 262 of the 342 patients included in the study (p&gt;0.05).&#x0D; Conclusion: In this study, we found that a positive ADA prediabetes risk score was effective in predicting prediabetes, but it was not sufficient. However, prediabetes diagnosed according to OGTT was found to be higher in the patients with negative test scores. Therefore, the prediabetes risk test was not effective in predicting prediabetes among the patients with negative scores.

Detection of high risk people for diabetes by American diabetes association risk score in PERSIAN Guilan cohort study

BackgroundDiabetes mellitus (DM) is known as one of the most prevalent non communicable diseases with high cost of health services in the world. Present study was conducted to assess the frequency of high risk people for diabetes mellitus based on American Diabetes Association (ADA) risk score among Iranian people.MethodsPresent study was a cross sectional study on non-diabetic subjects aged 35–70 years from 10,520 PERSIAN Guilan Cohort Study (PGCS). ADA risk score was calculated for every individual through an online calculator. Receiver operating characteristic (ROC) curves was used to assess diagnostic accuracy of the anthropometric indices to identify individuals with high risk ADA score for developing DM, represented by the area under the curve (AUC).ResultsFrom 7989 study subjects, ADA risk score found 3874 (48.5%) and 1912 (23%) at risk for developing PreDM and DM, respectively. The results of ROC curve analyses showed the highest diagnostic value was related to waist circumference (WC) in total population and Waist to Height Ratio in both sex (0.695 total, 0.743 female, 0.744 male). The cut-points of WC in total population to identifying high risk group were 97 cm.ConclusionsA considerable number of populations were classified as high ADA risk for developing DM and PreDM that provide the importance of prevention strategies. Present study showed WC and Waist to Height Ratio have the highest diagnostic value to identify high risk people for DM.

Not So Sweet: Preoperative Diabetes Screening in the Total Joint Population.

Clearly identifying patients with prediabetes and diabetes prior to surgery allows the clinical team to target interventions and reduce the risks of complications. Yet, protocols for preoperative diabetes screening vary. The purpose of this article is to present an evidence-based practice project examining the implementation of preoperative diabetes screening in an elective total joint patient population. The American Diabetes Association (ADA) Risk Test was used to assess diabetes risk and guide further testing. A total of 121 patients were screened. Of the sample, 55 were undiagnosed and at risk for diabetes according to the instrument. Twelve patients (21.8%) who screened at risk also revealed elevated fasting blood glucose levels. These patients were identified as potentially having prediabetes. The findings of this project support adoption of the ADA Risk Test in the preoperative setting, emphasize the feasibility of its integration in order to obtain valuable patient information, and assist with optimizing patients for surgery.

Risk of Developing Diabetes in Patients Undergoing Coronary Artery Angiography Based on American Diabetes Association Risk Score: A Cross-Sectional Study

Aims The high prevalence of undiagnosed diabetes and the high risk for diabetes in the general population is alarming. In addition, the frequency of coronary artery diseases (CAD) in patients with undiagnosed diabetes is higher than in non-diabetic patients. This study aimed to investigate the frequency of people at risk of diabetes in patients undergoing coronary artery angiography (CAG) in a university referral hospital in Guilan Province, Iran. Methods &amp; Materials In this cross-sectional study, data from 300 non-diabetic patients who were candidates for CAG were collected using a simple sampling method. The risk of developing diabetes was calculated according to American Diabetes Association (ADA) risk score. ADA scores higher than five were considered high risk, and ADA scores equal to 4 and lower than 4 were as moderate and low risk, respectively. Findings The mean age of the study population was 59.44±15.7 years. Most subjects were male (59.3%). Only 24.3% of patients were in the low-risk group while 32% were in moderate risk and 43.7% were in the high-risk group. The frequency of patients with at least two coronary artery involvement was significantly higher in the high-risk group (43.1%) than in the moderate group (34.4%), and the low-risk (22.5%) group. Conclusion A high proportion of the patients undergoing CAG were at high risk of developing diabetes. Hence, it seems essential to address cardiometabolic risk factors in coronary artery diseases (CAD) after discharge from the hospital.

1133-P: High Prediabetes Risk and Associated Risk Factors in Indian Population Based on Online Prediabetes Risk Test

Background: In India, a significant number of people are still diagnosed with diabetes at a later stage or after the complications have started. The unawareness about the disease and financial burden associated with the diagnostic testing remains a challenge. The study was aimed at the digital administration of a prediabetes risk test questionnaire to understand the people who are at high risk and more prone to developing prediabetes and eventually diabetes. Methods: A prediabetes risk questionnaire was developed based on the American Diabetes association risk score test. The test was completed by 1722 participants across India. The test was based on risk factors like age, gender, race, BMI, family history of diabetes, history of high blood pressure, and physical activity status. The scoring was done for three groups including high risk (score &amp;gt;5) , medium risk (score 3-5) , and low risk (score &amp;lt;3) . Binomial logistic regression was used for risk factor analysis among groups with low score and high-medium score. Results: The risk of prediabetes was low in 21 %, medium in 65 % and high in 12 % of participants. Out of all the participants 44 % had age ≥ 40 years, 71% were male, 96 % were asian, 74 % had BMI ≥ 23 kg/m2, 59 % of total participants have a family history of diabetes (first-degree relatives) , 17 % had high blood pressure, and 44 % were performing physical activity below recommended level of 150 mins per week. Among people with BMI ≥ 23 kg/m2, 89 % were having either high or medium risk scores. Among young people (age &amp;lt; 40 years) , 67 % were having high or medium scores. The odds ratio for score in high or medium range for people with BMI ≥ 23 kg/m2 and age ≥ 40 years was 38.51 (95% CI: 24.19-61.30, P&amp;lt;0.001) and 78.89 (95% CI: 41.21-136.06, P&amp;lt;0.001) respectively. Conclusion: The study showed that out of all the participants, 77% had a high or medium risk score. The study highlights the importance of using a prediabetes risk calculator as a tool for improving awareness about the risk of developing prediabetes. Disclosure R.Verma: Employee; Fitterfly Healthtech Pvt Ltd, Wellthy Therapeutics Pvt Ltd. A.K.Singal: Other Relationship; Fitterfly Healthtech Pvt Ltd. R.Sahay: Advisory Panel; Torrent Pharmaceuticals , Speaker's Bureau; Dr. Reddy’s Laboratories Ltd., Eli Lilly and Company, Eris Lifesciences Ltd., Intas Pharmaceuticals Ltd., Novo Nordisk, USV Private Limited. G.Bantwal: None. S.Kalra: Speaker's Bureau; Boehringer Ingelheim International GmbH, Novo Nordisk A/S, Sanofi. A.D.Agnihotri: None. M.H.Tiwaskar: None. S.F.Ade: None. A.M.Juneja: None. O.J.Lakhani: Advisory Panel; FITTERFLY, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk Global Business Services, Sanofi, Stock/Shareholder; Zydus Cadilla.

Uptake of guideline-recommended postpartum diabetes screening among diverse women with gestational diabetes: associations with patient factors in an integrated health system in USA

IntroductionClinical guidelines urge timely postpartum screening for diabetes among women with gestational diabetes mellitus (GDM), yet patient factors associated with screening uptake remain unclear. We aimed to identify patient factors...

Implementation and analysis of an enhanced screening and testing protocol for prediabetes in a dental school patient population

This project aimed to enhance the identification of patients at-risk for prediabetes or diabetes within a dental school patient population by introduction of a modified screening tool and related training of dental residents and students. The American Diabetes Association Risk Tool (ADART) was modified by addition of three diabetes-linked oral health questions. Of the 1477 dental patients screened, 551 (37.3%) indicated an at-risk status using the modified tool. A subset of 138 patients received follow-up chairside HbA1c blood testing conducted by dental residents and students. Data was analysed to determine a) the influence of the modifications on the tool's discrimination strength and b) change in the tool's predictive value. The addition of the 3 oral health questions to the 7-item ADART resulted in a 9.4% increase in identification of patients at-risk for pre-diabetes/diabetes. The predictive value of the tool remained stable. Residents and students successfully incorporated the new screening activities within their assigned clinics. This project demonstrates that screening for risk for prediabetes/diabetes is both prudent and practical in the dental setting. Dental personnel, including trainees, can successfully incorporate enhanced screening methods within their traditional activities. Further, screening tools used in the dental setting might be enhanced by inclusion of certain oral health variables associated with diabetes. These findings add to emerging knowledge on the importance of screening for prediabetes/diabetes in dental settings and have particular relevance and application to institutional practice.

Adapting Diabetes Risk Scores for Jordan

BackgroundDiabetes risk score can be used as a simple non-invasive screening tool for identifying people with high risk of diabetes. This study aimed to assess the predictive power of various risk-scoring systems to predict pre-diabetes and diabetes in Jordanian adults.MethodsThis cross-sectional study was conducted among people attending 54 primary health care centers distributed throughout the 12 governorates of Jordan. Diabetes risk scores using the American Diabetes Association risk score, Canadian risk score, Finland risk score (FINDRISC), British Risk score, German and Australian risk score were calculated for each patient. Fasting blood sugar (FBS) was measured for all participants.ResultsThis study included 392 participants: 231 patients with normal fasting blood sugar (FBG), 101 patients with pre-diabetes and 60 patients with type 2 diabetes. The FINDRISC, British, and Australian risk scores were strongly inter-correlated and weakly correlated with other systems’ risk scores. Moreover, they correlated moderately and significantly with FBS. In contrast, other systems risk scores were associated weekly with FBS. Based on receiving operating characteristics (ROC) analysis and multivariate logistic regression, the FINDRISC risk score was superior to other risk scores to predict high FBS and identify pre-diabetes and diabetes.ConclusionFINDRISC risk score performed the best compared to other risk scores for predicting pre-diabetes, diabetes, and absence of diabetes. We recommend using the FINDRISC risk score assessment in Jordan.

CORRELATION OF HEMOGLOBIN A1c AND DIABETES RISK USING THE THAI DIABETES RISK SCORE

Background: Early detection of diabetes allows prompt access to interventions that can improve microvascular and macrovascular disease outcomes. Multiple strategies have been employed, i.e., the use of diabetes risk scores including blood testing.&#x0D; Objective: The study aimed to evaluate the correlation between point-of-care hemoglobin A1c (POC HbA1c) and Thai diabetes risk score.&#x0D; Methods: A cross-sectional study was conducted consisting of 252 individuals without diabetes over the age of 35. Demographic data and anthropometric measures were recorded and the blood test for POC HbA1c including plasma glucose were performed.&#x0D; Results: Of 252 participants, the mean HbA1c was 5.56 ± 0.73%, the median Thai diabetes risk score was 7 [5-10] and American Diabetes Association (ADA) risk score was 3 [2.3-4]. Males had higher risk scores than females. Weak positive correlations were observed between POC HbA1c and both Thai and ADA risk score (r = 0.226 and 0.279, respectively, p&lt;0.001). The predictors of higher HbA1c among males were high BMI and waist circumference.&#x0D; Conclusion: A weak correlation of POC HbA1c and Thai diabetes risk score suggested that POC HbA1c may not be beneficial in screening diabetes in out-of-clinic situations; however, male participants with WC &gt;100 cm and BMI &gt;27.5 kg/m2 were associated with highest HbA1c.

302-OR: Medical Prescriptions for Vegetables Are Associated with Reduced Consumption of Unhealthy Foods, Improved Food Security, and Lowered Cardiometabolic Risk among Adults with or at Risk of Type 2 Diabetes

Previously, we have reported that improving access to fresh vegetables reduces cardio-metabolic risk for predominantly Mexican American adults with or at risk of type 2 diabetes (T2D). This analysis examined changes in self-reported consumption of less healthy foods (soda, sugary drinks, and tortillas) and the association with medical prescriptions for locally sourced fresh vegetables. In this observational cohort study, 202 adults (152 female, 80% Hispanic/Latino, mean ±SD age of 53.8 ±12.6 years) with (n=62) or at risk (American Diabetes Association risk calculator) of T2D received 21 servings per week of locally sourced fresh vegetables prescribed by a physician for 10 weeks. No nutrition education was provided. For participants with paired data, there were reductions in waist circumference (-0.9 ±3.7 cm, p=0.003); systolic blood pressure for those with baseline ≥ 130 mmHg (-8.9±16.5 mmHg, p=0.001); and HbA1c values for participants with a baseline &amp;gt;7.0 % (-0.4 [-0.8, -0.1] %, p=0.016). Fewer participants had low or very low food security (USDA 6-item Food Security Module) after the intervention (25 versus 76, p&amp;lt;0.0001), associated with reductions in consumption of tortillas (p&amp;lt;0.00001); soda and other sugar-sweetened fruit drinks, sweet tea, and sports or energy drinks (p&amp;lt;0.001); and their weekly frequency of eating out (p=0.01). The improvement in food security was also associated with better sleep (ρ=0.22, p=0.007). The use of medical prescriptions for vegetables is associated with greater food security and lower cardiometabolic risk in predominantly Mexican-American adults with or at risk of T2D, possibly related to less consumption of unhealthy foods. Disclosure N. M. Glantz: Research Support; Self; Abbott, Eli Lilly and Company. C. Conneely: Research Support; Self; Abbott, Eli Lilly and Company. A. J. Larez: Research Support; Self; Abbott, Eli Lilly and Company. W. C. Bevier: Research Support; Self; Abbott Diabetes, Eli Lilly and Company. D. Kerr: Advisory Panel; Self; Novo Nordisk, Sanofi-Aventis, Research Support; Self; Abbott Diabetes, Elsevier, Lilly Diabetes, Stock/Shareholder; Self; Glooko, Inc. Funding U.S. Department of Agriculture (2018-33800-28404)

Factors associated with abnormal glucose readings in a pharmacy-led community health fair using the ADA risk assessment tool

ObjectivesThe objective of this study was to evaluate whether the American Diabetes Association (ADA) risk tool correctly identified high-risk patients with abnormal glucose readings requiring referral to a health care provider in an underserved population at a large student pharmacist–led health fair. In addition, the association of abnormal glucose readings compared with alternative ADA risk score cut point values and other collected variables was evaluated. MethodsThis was a retrospective, cross-sectional study using deidentified data from a large student pharmacist–led health fair. ResultsA total of 35 of 188 (19%) patients were considered high risk per the ADA risk tool, and 11 of those 35 (31%) had abnormal glucose results. After controlling for ADA risk score, no additional collected clinical variables were independently associated with abnormal glucose results. Although the ADA cut point associated with an abnormal glucose screening with the highest area under the curve was greater than or equal to 4, a cut point of 3 or greater resulted in a sensitivity of 91.2%. ConclusionThe optimal method to identify patients who are at risk for an abnormal glucose screening is the ADA risk tool compared with the individual components of the tool or other evaluated risk factors. We suggest using an ADA risk cut point of greater than or equal to 3 instead of greater than or equal to 5 to identify patients likely to have abnormal glucose results in the health fair setting as this would greatly increase the chance of identifying patients who would need to be referred to their primary care provider for diagnostic testing.

Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: A nationwide cohort and modeling study.

Obesity is closely related to the development of insulin resistance and type 2 diabetes (T2D). The prevention of T2D has become imperative to stem the rising rates of this disease. Weight loss is highly effective in preventing T2D; however, the at-risk pool is large, and a clinically meaningful metric for risk stratification to guide interventions remains a challenge. The objective of this study is to predict T2D risk using full-information continuous analysis of nationally sampled data from white and black American adults age ≥45 years. A sample of 12,043 black (33%) and white individuals from a population-based cohort, REasons for Geographic And Racial Differences in Stroke (REGARDS) (enrolled 2003-2007), was observed through 2013-2016. The mean participant age was 63.12 ± 8.62 years, and 43.7% were male. Mean BMI was 28.55 ± 5.61 kg/m2. Risk factors for T2D regularly recorded in the primary care setting were used to evaluate future T2D risk using Bayesian logistic regression. External validation was performed using 9,710 participants (19% black) from Atherosclerotic Risk in Communities (ARIC) (enrolled 1987-1989), observed through 1996-1998. The mean participant age in this cohort was 53.86 ± 5.65 years, and 44.6% were male. Mean BMI was 27.15 ± 4.92 kg/m2. Predictive performance was assessed using the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. The primary outcome was incident T2D. By 2016 in REGARDS, there were 1,602 incident cases of T2D. Risk factors used to predict T2D progression included age, sex, race, BMI, triglycerides, high-density lipoprotein, blood pressure, and blood glucose. The Bayesian logistic model (AUC = 0.79) outperformed the Framingham risk score (AUC = 0.76), the American Diabetes Association risk score (AUC = 0.64), and a cardiometabolic disease system (using Adult Treatment Panel III criteria) (AUC = 0.75). Validation in ARIC was robust (AUC = 0.85). Main limitations include the limited generalizability of the REGARDS sample to black and white, older Americans, and no time to diagnosis for T2D. Our results show that a Bayesian logistic model using full-information continuous predictors has high predictive discrimination, and can be used to quantify race- and sex-specific T2D risk, providing a new, powerful predictive tool. This tool can be used for T2D prevention efforts including weight loss therapy by allowing clinicians to target high-risk individuals in a manner that could be used to optimize outcomes.

Factors Associated With a Higher Risk of Undiagnosed Prediabetes in a Community-Dwelling Medicare Population.

OBJECTIVE: To identify characteristics in an ambulatory Medicare population that are significantly more likely to be associated with a high risk of undiagnosed prediabetes.<br/> DESIGN: Cross-sectional study.<br/> SETTING: Fourteen health clinics targeting Medicare beneficiaries were held throughout northern and central California during the fall of 2017.<br/> PATIENTS, PARTICIPANTS: Noninstitutionalized Medicare beneficiaries receiving medication therapy management services without self-reported diabetes.<br/> INTERVENTIONS: Beneficiaries were screened for their risk of type 2 diabetes mellitus (T2DM) through the use of the American Diabetes Association (ADA) risk assessment (score of ≥ 5 indicates increased risk of developing type 2 diabetes) by pharmacy students. For this study, patients with a score of ≥ 5 were considered to be at high risk for undiagnosed prediabetes.<br/> MAIN OUTCOME MEASURE(S): Characteristics significantly more likely to be identified in patients at high risk for undiagnosed prediabetes.<br/> RESULTS: A total of 683 Medicare beneficiaries without self-reported diabetes completed the ADA risk assessment, with 457 (66.9%) receiving a score of 5 or more. In those, the presence of hyperlipidemia, hypertension, obesity, coronary heart disease, and use of aspirin were all characteristics researchers identified as significantly more likely to be found in this group. In contrast, those of Asian race or who took dietary supplements were significantly less likely to score 5 or higher in the questionnaire.<br/> CONCLUSION: Identification of older adults at higher risk for undiagnosed prediabetes through the use of appropriate screening tools allows for targeted preventive interventions, potentially lowering risk of developing T2DM for selected patients.