Comparison of screening scores for diabetes and prediabetes

A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies

BACKGROUND

The prevalence of type 2 diabetes mellitus (DM) and pre-diabetes mellitus (pre-DM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth pre-DM/DM data.

OBJECTIVE

We aimed to first build a high-quality, comprehensive epidemiological data set focused on youth pre-DM/DM. Subsequently, we aimed to make these data accessible by creating a user-friendly web portal to share them and the corresponding codes. Through this, we hope to address this significant gap and facilitate youth pre-DM/DM research.

METHODS

Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to pre-DM/DM (fasting plasma glucose level ≥100 mg/dL or glycated hemoglobin  ≥5.7%) for youth aged 12-19 years (N=15,149). We identified individual factors associated with pre-DM/DM risk using bivariate statistical analyses and predicted pre-DM/DM status using our Ensemble Integration (EI) framework for multidomain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth Online Dashboard (POND) to share the data and codes.

RESULTS

We extracted 95 variables potentially relevant to pre-DM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of pre-DM/DM (P<.001, Bonferroni adjusted), including race or ethnicity, health insurance, BMI, added sugar intake, and screen time. Among these factors, 16 factors were also identified based on the EI methodology (Fisher P of overlap=7.06×106). In addition to those, the EI approach identified 11 additional predictive variables, including some known (eg, meat and fruit intake and family income) and less recognized factors (eg, number of rooms in homes). The factors identified in both analyses spanned across all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND.

CONCLUSIONS

Using NHANES data, we built one of the largest public epidemiological data sets for studying youth pre-DM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of pre-DM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth pre-DM/DM studies.

Reframing prediabetes: A call for better risk stratification and intervention

Prediabetes is an intermediate state of glucose homeostasis whereby plasma glucose concentrations are above normal but below the threshold of diagnosis for diabetes. Over the last several decades, criteria for prediabetes have changed as the cut points for normal glucose concentration and diagnosis of diabetes have shifted. Global consensus does not exist for prediabetes criteria; as a result, the clinical course and risk for type 2 diabetes vary. At present, we can identify individuals with prediabetes based on three glycemic tests (hemoglobin A1c, fasting plasma glucose, and 2-h plasma glucose during an oral glucose tolerance test). The majority of individuals diagnosed with prediabetes meet only one of these criteria. Meeting one, two, or all glycemic criteria changes risk for type 2 diabetes, but this information is not widely known and does not currently guide intervention strategies for individuals with prediabetes. This review summarizes current epidemiology, prognosis, and intervention strategies for individuals diagnosed with prediabetes and suggests a call for more precise risk stratification of individuals with prediabetes as elevated (one prediabetes criterion), high risk (two prediabetes criteria), and very high risk (three prediabetes criteria). In addition, the roles of oral glucose tolerance testing and continuous glucose monitoring in the diagnostic criteria for prediabetes need reassessment. Finally, we must reframe our goals for prediabetes and prioritize intensive interventions for those at high and very high risk for type 2 diabetes.

A meta-analysis of diabetes risk prediction models applied to prediabetes screening

AIM

To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.

METHODS

The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.

RESULTS

A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.

CONCLUSIONS

Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Optimizing an mHealth Program to Promote Type 2 Diabetes Prevention in High-Risk Individuals: Cross-Sectional Questionnaire Study

BACKGROUND

We evaluated the outcomes of a pilot SMS text messaging-based public health campaign that identified social networking nodes and variations of response rates to develop a list of variables that could be used to analyze and develop an outreach strategy that would maximize the impact of future public health campaigns planned for Kuwait. Computational analysis of connections has been used to analyze the spread of infectious diseases, dissemination of new thoughts and ideas, efficiency of logistics networks, and even public health care campaigns. Percolation theory network analysis provides a mathematical alternative to more established heuristic approaches that have been used to optimize network development. We report on a pilot study designed to identify and treat subjects at high risk of developing type 2 diabetes mellitus in Kuwait.

OBJECTIVE

The aim of this study was to identify ways to optimize efficient deployment of resources and improve response rates in a public health campaign by using variables identified in this secondary analysis of our previously published data (Alqabandi et al, 2020). This analysis identified key variables that could be used in a computational analysis to plan for future public health campaigns.

METHODS

SMS text message screening posts were sent inviting recipients to answer 6 questions to determine their risk of developing type 2 diabetes mellitus. If subjects agreed to participate, a link to the Centers for Disease Control and Prevention prediabetes screening test was automatically transmitted to their mobile devices. The phone numbers used in this campaign were recorded and compared to the responses received through SMS text messaging and social media forwarding.

RESULTS

A total of 180,000 SMS text messages through 5 different campaigns were sent to 6% of the adult population in Kuwait. A total of 260 individuals agreed to participate, of which 153 (58.8%) completed the screening. Remarkably, 367 additional surveys were received from individuals who were not invited by the original circulated SMS text messages. These individuals were invited through forwarded surveys from the original recipients after authentication with the study center. The original SMS text messages were found to successfully identify influencers in existing social networks to improve the efficacy of the public health campaign.

CONCLUSIONS

SMS text messaging-based health care screening campaigns were found to have limited effectiveness alone; however, the increased reach through shared second-party forwarding suggests the potential of exponentially expanding the reach of the study and identifying a higher percentage of eligible candidates through the use of percolation theory. Future research should be directed toward designing SMS text messaging campaigns that support a combination of SMS text message invitations and social networks along with identification of influential nodes and key variables, which are likely unique to the environment and cultural background of the population, using percolation theory modeling and chatbots.

Facilitating youth diabetes studies with the most comprehensive epidemiological dataset available through a public web portal

The prevalence of type 2 diabetes mellitus (DM) and prediabetes (preDM) is rapidly increasing among youth, posing significant health and economic consequences. To address this growing concern, we created the most comprehensive youth-focused diabetes dataset to date derived from National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018. The dataset, consisting of 15,149 youth aged 12 to 19 years, encompasses preDM/DM relevant variables from sociodemographic, health status, diet, and other lifestyle behavior domains. An interactive web portal, POND (Prediabetes/diabetes in youth ONline Dashboard), was developed to provide public access to the dataset, allowing users to explore variables potentially associated with youth preDM/DM. Leveraging statistical and machine learning methods, we conducted two case studies, revealing established and lesser-known variables linked to youth preDM/DM. This dataset and portal can facilitate future studies to inform prevention and management strategies for youth prediabetes and diabetes.

Comparing the detection rate of postpartum diabetes in early and 4-12 week postpartum screening tests in women with gestational diabetes mellitus: A systematic review and meta-analysis

Gestational Diabetes Mellitus (GDM) is one of the critical risk factors for diabetes mellitus (DM). An early postpartum test done in the first few postpartum days can increase the screening rate in women with GDM. Therefore, this systematic review and meta-analysis aimed to combine and analyze data from different studies reporting the detection rate of postpartum diabetes in early and 4-12 week postpartum screening tests in women with GDM. ProQuest, Web of Science, EMBASE, PubMed, Cochrane, and Scopus were searched for English articles from January 1985 to January 2021. Two independent reviewers selected the eligible studies, and the outcomes of interest were extracted. The quality of studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for diagnostic test accuracy studies. Sensitivity and specificity, negative likelihood ratio (NLR), and positive likelihood ratio (PLR) were calculated for the early postpartum oral glucose tolerance test (OGTT). Of 1944 initially identified articles, four studies were included. The sensitivity and specificity of the early test were 74% and 56%, respectively, and the PLR and NLR were calculated as 1.7 and 0.4, respectively. The sensitivity of the early test was higher than the specificity. Based on this sensitivity and specificity, normal cases could be distinguished from abnormal cases, including diabetes and glucose intolerance. Early postpartum OGTT can be advised before hospital discharge. Early testing is a practical option in patients with GDM. Further studies are required to evaluate the early test detection rate for DM and glucose intolerance separately.

Improving screening of prediabetes and undiagnosed diabetes

BACKGROUND

Type II diabetes mellitus is a chronic medical condition affecting societies worldwide. The duration of hyperglycemia is a strong predictor of adverse outcomes and imposes immense clinical and public health concerns. The best way to prevent complications and reduce the economic burden is by capturing asymptomatic individuals early in the disease process.

LOCAL PROBLEM

Patients at a large urban academic medical center were not consistently identified as having a high risk of hyperglycemia.

METHODS

The project used a pretest-posttest design. Retrospective data on new-onset hyperglycemia incidence were compared for all individuals seeking primary care services 6 weeks before and after the intervention.

INTERVENTION

Patients without a known hyperglycemia history were provided the screening tool to determine risk status. Additional screening measures were implemented for patients identified as high risk on the initial screening.

RESULTS

A total of 52 (61.6%) of the 84 individuals who met inclusion criteria during the intervention period were diagnosed with new-onset chronic hyperglycemia. In contrast, 20 (22.5%) of the 89 individuals identified during the retrospective period resulted in a statistically significant difference ( p < .001) in the frequency and accuracy of patients diagnosed with hyperglycemia between groups.

CONCLUSION

A diabetes risk assessment tool is quick and reliable in capturing high-risk individuals who would benefit from additional screening measures.

The Role of Pharmacists and Community Pharmacies in the Screening, Knowledge, and Awareness of Diabetes Mellitus Type 2 in Jordanian People Visiting Community Pharmacies

BACKGROUND

According to the International Diabetes Federation (IDF), diabetes is increasing exponentially worldwide and will become more prevalent than ever in the Middle East by 2045, with a 110% increase. This study aims to clarify the role of pharmacists and community pharmacies in the screening, knowledge, and awareness of Type 2 diabetes among Jordanian people who visit community pharmacies in Amman, Jordan.

METHODS

Study design: This was a cross-sectional prospective study that was conducted from September to December 2021 in Amman, Jordan. Data were collected using a standardized questionnaire that was composed of multiple parts. The first part collected information on demographics, residence, educational level, and insurance status; the second part was composed of 14 knowledge assessing questions; the last part was composed of the American Diabetes Association (ADA) diabetes risk score card test. Additionally, after confirming that each participant had returned their completed sheets, participants who scored greater than 5 had their blood sugar levels checked using a finger-prick blood test. The questionnaire was administered in person by a trained researcher. Using Slovin's formula, a 95% confidence interval (CI), and a 0.05 margin of error, the sample size was determined to be 267 participants. The study included 305 participants. Descriptive and regression analyses were performed by using the Statistical Package for Social Science (SPSS) with a significance level of p < 0.05.

RESULTS

A significant relationship was found between specialty (medical education) and the knowledge of risk factors for Type 2 diabetes mellitus (T2DM), (p < 0.012). In terms of knowledge, from a total of 13 correct knowledge points (13 marks for correct answers out of 14), some subjects scored slightly higher than others (n = 175; 57.4% of participants scored above 7, nearly over half of the correct answers, compared to n = 130; 42.6% scoring below 7). We found 132 individuals (44%) with risk scores of five or above (high risk for developing T2DM according to ADA). Smokers comprised n = 138, 45%, and nonsmokers comprised n = 148, 48%. Although 50.5% of the participants (n = 154) held a bachelor's, master's, or doctorate degree, these degrees did not improve the participants' overall general knowledge levels. The association was tested using chi-squared analysis, but no significance was found.

CONCLUSIONS

Random visitors to Jordanian community pharmacies are expected to benefit from awareness and educational campaigns. These test results revealed a lack of knowledge, indicating the need for education to dispel myths and highlight the serious risks associated with T2DM. The study discovered that participants' understanding of diabetes disease prevention through lifestyle and dietary changes was inadequate. A specialist-led educational program may increase knowledge among visitors who participate. In order to prevent the spread of diabetes, more campaigns and health-promoting and prevention educational activities are required.

2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Is hemoglobin A1c valuable for predicting concurrent endometrial cancer in diabetic women with endometrial intraepithelial neoplasia?

AIM

The purpose of this study was to determine the predictive value of preoperative hemoglobin A1c (HgA1c) level for endometrial cancer in diabetic women with endometrial intraepithelial neoplasia (EIN).

MATERIALS AND METHODS

Six hundred patients with EIN were retrospectively studied in a tertiary referral center in Turkey between January 2014 and December 2021. One hundred and thirteen diabetic patients with EIN who met the inclusion criteria were enrolled in the study and divided into three groups according to the final pathological results: Group 1 with benign findings (n = 29), Group 2 with EIN (n = 34) and Group 3 with endometrial cancer (n = 50). Demographic, clinical and biochemical characteristics were compared among the three groups. Receiver operating characteristic analysis (ROC) was used to evaluate the predictive value of HgA1c for concurrent endometrial cancer in EIN.

RESULTS

Mean preoperative HgA1c levels were different among three groups (5.41 ± 0.64, 6.01 ± 0.72, 6.65 ± 1.15, p < 0.001, respectively). The highest value of HgA1c level was found in cancer group and difference within pairs was statistically significant (p < 0.001). Age and duration of menopause were also different among groups (p < 0.005). After adjustment of HgA1c level for age and duration of menopause differences were maintained (p < 0.001), the cutoff value was detected as ≥6.05% for HgA1c and sensitivity, specificity was 60%, 70%, respectively (p < 0.001).

CONCLUSIONS

HgA1c could be used in prediction of endometrial cancer. The optimal cutoff value determined in our study could be considered in predicting endometrial cancer in diabetic women with EIN.

Early prediction of diabetes by applying data mining techniques: A retrospective cohort study

BACKGROUND

Saudi Arabia ranks 7th globally in terms of diabetes prevalence, and its prevalence is expected to reach 45.36% by 2030. The cost of diabetes is expected to increase to 27 billion Saudi riyals in cases where undiagnosed individuals are also documented. Prevention and early detection can effectively address these challenges.

OBJECTIVE

To improve healthcare services and assist in building predictive models to estimate the probability of diabetes in patients.

METHODS

A chart review, which was a retrospective cohort study, was conducted at the National Guard Health Affairs in Riyadh, Saudi Arabia. Data were collected from 5 hospitals using National Guard Health Affairs databases. We used 38 attributes of 21431 patients between 2015 and 2019. The following phases were performed: (1) data collection, (2) data preparation, (3) data mining and model building, and (4) model evaluation and validation. Subsequently, 6 algorithms were compared with and without the synthetic minority oversampling technique.

RESULTS

The highest performance was found in the Bayesian network, which had an area under the curve of 0.75 and 0.71.

CONCLUSION

Although the results were acceptable, they could be improved. In this context, missing data owing to technical issues played a major role in affecting the performance of our model. Nevertheless, the model could be used in prevention, health monitoring programs, and as an automated mass population screening tool without the need for extra costs compared to traditional methods.

Performance of Diabetes and Kidney Disease Screening Scores in Contemporary United States and Korean Populations

BACKGROUND

Risk assessment tools have been actively studied, and they summarize key predictors with relative weights/importance for a disease. Currently, standardized screening scores for type 2 diabetes mellitus (DM) and chronic kidney disease (CKD)-two key global health problems-are available in United States and Korea. We aimed to compare and evaluate screening scores for DM (or combined with prediabetes) and CKD, and assess the risk in contemporary United States and Korean populations.

METHODS

Four (2×2) models were evaluated in the United States-National Health and Nutrition Examination Survey (NHANES 2015-2018) and Korea-NHANES (2016-2018)-8,928 and 16,209 adults. Weighted statistics were used to describe population characteristics. We used logistic regression for predictors in the models to assess associations with study outcomes (undiagnosed DM and CKD) and diagnostic measures for temporal and cross-validation.

RESULTS

Korean adult population (mean age 47.5 years) appeared to be healthier than United States counterpart, in terms of DM and CKD risks and associated factors, with exceptions of undiagnosed DM, prediabetes and prehypertension. Models performed well in own country and external populations regarding predictor-outcome association and discrimination. Risk tests (high vs. low) showed area under the curve >0.75, sensitivity >84%, specificity >45%, positive predictive value >8%, and negative predictive value >99%. Discrimination was better for DM, compared to the combined outcome of DM and prediabetes, and excellent for CKD due to age.

CONCLUSION

Four easy-to-use screening scores for DM and CKD are well-validated in contemporary United States and Korean populations. Prevention of DM and CKD may serve as first-step in public health, with these self-assessment tools as basic tools to help health education and disparity.

Implementation of a Prediabetes Risk Test for an Underserved Population in a Federally Qualified Health Center

Consistent screening for prediabetes is imperative to ensure early detection and timely intervention to prevent progression to diabetes. Adopting a standardized approach such as a screening tool can streamline the screening process. This articles describes a quality improvement project conducted at a federally qualified health center to assess whether implementation of the Prediabetes Risk Test improved early detection of prediabetes in an underserved population. Use of the risk test was found to improve detection of prediabetes in this population.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The Validity of the American Diabetes Association's Diabetes Risk Test in a Saudi Arabian Population

Introduction

The prevalence of type 2 diabetes (T2D) is growing worldwide. This study aimed to assess the sensitivity and specificity of the American Diabetes Association (ADA) and the United States Centers for Disease Control and Prevention's diabetes risk test in identifying Saudi Arabian patients at risk of developing T2D.

Methods

We conducted a one-month cross-sectional study that included patients older than 18 years who visited primary care facilities for any health concern in Riyadh City, Saudi Arabia. We used the Arabic language version of the ADA Prediabetes Risk Test questionnaire, a validated and reliable tool, to collect data. For this study, we analyzed the data using IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, New York). Moreover, we calculated sensitivity and specificity, positive predictive values (PPV), negative predictive value (NPV), the area under the curve (AUC), and Youden’s index.

Results

A total of 180 participants were included in the study (121 women and 59 men; mean age = 45 years). The ADA Prediabetes Risk Test sensitivity was 78.9, specificity was 82, PPV was 32, and NPV was 76. Youden’s index was 60.9 and the AUC was 0.6.

Conclusion

The ADA prediabetes risk assessment tool is highly sensitive and specific for determining the disease. It is a reliable and valid tool that has not yet been implemented to a great extent in Saudi Arabia. Therefore, future work should study the tool’s effectiveness in risk assessment in additional local Saudi Arabian communities.

Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes

AIMS

The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia.

METHODS

We use NHANES cross-sectional data over 10 years (2007-2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables.

RESULTS

Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3%. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data.

CONCLUSIONS

The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.

Monitoring Prediabetes Screening in Two Primary Care Offices in Rural Appalachia: A Quality Improvement Process

BACKGROUND

One-third of the U.S. population has prediabetes, but 90% remain undiagnosed because healthcare providers are not screening for this condition.

OBJECTIVE

The purpose of this quality improvement project was to monitor prediabetes screening and identification, and implement evidence-based recommendations including registered dietician referral.

METHODS

This project involved using an evidence-based screening tool to measure individual risk of prediabetes. Aggregate data was collected to evaluate screening implementation, evidence-based recommendations offered by providers, and assess patient risk factors.

RESULTS

The percentage of patients at risk for prediabetes was 41.3% (n = 111). The most frequent risks were identified as overweight, history of hypertension, family history of type 2 diabetes mellitus (T2DM), and older age. Providers offered education on weight loss 68.5% (n = 76) and exercise 76.6% (n = 85) but referred 33.3% (n = 37) patients for nutrition education. The screening rates were 52.3% (n = 176) and 72.5% (n = 244) in clinics A and B respectively.

CONCLUSIONS

A gap remains in using evidence-based recommendations to decrease risk of prediabetes. Prediabetes screening identified a greater percentage of persons in this population.

IMPLICATIONS FOR NURSING

There is a need for consistent practice of evidence-based recommendations. This project set the benchmark for future efforts to educate, encourage, and measure providers successes.

A combined strategy of feature selection and machine learning to identify predictors of prediabetes

OBJECTIVE

To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

MATERIALS AND METHODS

We analyzed n = 6346 men and women enrolled in the National Health and Nutrition Examination Survey 2013-2014. Prediabetes was defined using American Diabetes Association guidelines. The sample was randomly partitioned to training (n = 3174) and internal validation (n = 3172) sets. Feature selection algorithms were run on training data containing 156 preselected exposure variables. Four machine learning algorithms were applied on 46 exposure variables in original and resampled training datasets built using 4 resampling methods. Predictive models were tested on internal validation data (n = 3172) and external validation data (n = 3000) prepared from National Health and Nutrition Examination Survey 2011-2012. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC). Predictors were assessed by odds ratios in logistic models and variable importance in others. The Centers for Disease Control (CDC) prediabetes screening tool was the benchmark to compare model performance.

RESULTS

Prediabetes prevalence was 23.43%. The CDC prediabetes screening tool produced 64.40% AUROC. Seven optimal (≥ 70% AUROC) models identified 25 predictors including 4 potentially novel associations; 20 by both logistic and other nonlinear/ensemble models and 5 solely by the latter. All optimal models outperformed the CDC prediabetes screening tool (P < 0.05).

DISCUSSION

Combined use of feature selection and machine learning increased predictive performance outperforming the recommended screening tool. A range of predictors of prediabetes was identified.

CONCLUSION

This work demonstrated the value of combining feature selection with machine learning to identify a wide range of predictors that could enhance prediabetes prediction and clinical decision-making.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Prediabetes/diabetes screening strategy at the periodontal clinic

Objective

The aim of the study was to propose an efficient chairside clinical strategy for the identification of undiagnosed hyperglycaemia in periodontal clinics.

Material and methods

Α chairside system was used for assessment of glycated hemoglobin 1c (HbA1c) and active Matrix Metalloproteinase‐8 levels (aMMP‐8) were analyzed by immunotest in patients (n = 150) who fulfilled the criteria for screening of the Centers for Disease Control and Prevention. Full‐mouth periodontal parameters were assessed and various data such as Body Mass Index (BMI), smoking and education were recorded.

Results

Thirty‐one patients out of 150 tested were found with unknown hyperglycaemia (20.7%). Regarding sex, education, parent with diabetes, normal BMI, smoking, age ≥45 years and prior testing for diabetes, no differences were observed between subjects displaying HbA1c < 5.7 and ≥5.7% (Pearson's Chi‐square test, p > .05). Subgroups differed regarding BMI (kg/m²), tooth count, percentages of 4 and 5 mm pockets (Mann–Whitney and z‐test, p < .05). The diagnostic performance for HbA1c ≥5.7 was tested by Receiving Operator Characteristic curves and Areas Under the Curve (AUC) for the following: age ≥ 45 years and BMI (AUC 0.651, p = .010), the above and aMMP‐8 (AUC 0.660, p = .006), age ≥ 45 years, BMI and Stage of Periodontitis (AUC 0.711, p < .001) and age ≥ 45 years, BMI, aMMP‐8 and stage of periodontitis (AUC 0.713, p < .001).

Conclusions

Findings of the study suggest that the combination of stage of periodontitis, increasing age, BMI and aMMP‐8, without chairside HbA1c assessment appears to be a viable screening strategy for referring dental patients for testing for prediabetes/diabetes.

The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P < 0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee (https://doi.org/10.2337/dc20-SPPC), a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc20-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents

Aims

To develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population.

Methods

A 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction.

Results

The 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m², 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively.

Conclusion

Our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.

Electronic supplementary material

The online version of this article (10.1007/s13167-019-00181-2) contains supplementary material, which is available to authorized users.

Evaluating the utility of self-reported questionnaire data to screen for dysglycemia in young adults: Findings from the US National Health and Nutrition Examination Survey

Dysglycemia, including prediabetes and type 2 diabetes, is dangerous and widespread. Yet, the condition is transiently reversible and sequelae preventable, prompting the use of prediction algorithms to quickly assess dysglycemia status through self-reported data. However, as current algorithms have largely been developed in older populations, their application to younger adults is uncertain considering associations between risk factors and dysglycemia vary by age. We sought to identify sex-specific predictors of current dysglycemia among young adults and evaluate their ability to screen for prediabetes and undiagnosed diabetes. We analyzed 2005-2014 data from the National Health and Nutrition Examination Survey for 3251 participants aged 20-39, who completed an oral glucose tolerance test (OGTT), had not been diagnosed with diabetes, and, for females, were not pregnant. Sex-specific stepwise logistic models were fit with predictors identified from univariate analyses. Risk scores were developed using adjusted odds ratios and model performance was assessed using area under the curve (AUC) measures. The OGTT identified 906 (27.9%) and 78 (2.4%) participants with prediabetes or undiagnosed diabetes, respectively. Predictors of dysglycemia status for males were BMI, age, race, and first-degree family history of diabetes, and, in addition to those, education, delivered baby weight, waist circumference, and vigorous physical activity for females. Our male- and female-specific models demonstrated improved validity to assess dysglycemia presence among young adults relative to the widely-used American Diabetes Association test (AUC = 0.69 vs. 0.61; 0.92 vs. 0.71, respectively). Thus, age-specific scoring algorithms employing questionnaire data show promise and are effective in identifying dysglycemia among young adults.

Prediabetes Risk Evaluation Scoring System [PRESS]: A simplified scoring system for detecting undiagnosed Prediabetes

OBJECTIVE

To develop a simplified scoring system for detecting undiagnosed prediabetes among healthy adult population of Haryana, India.

MATERIAL & METHODS

This cross-sectional study was a household survey which involved 892 healthy adults >18year of age who were subjected to an oral glucose tolerance test. Sensitivity, specificity, positive and negative predictive value and area under the receiver operating characteristic (ROC) curve and the optimal cut off points for identifying undiagnosed prediabetes were calculated.

RESULTS

Out of 892 subjects studied, 459 were males and 433 were females and 160 were found to be prediabetic (17.93%). At an optimal score of >45 the area under the ROC curve is 0.785 with sensitivity, specificity, positive and negative predictive value of 84.37%, 58.47%, 30.75 and 94.48% respectively.

CONCLUSIONS

Prediabetes Risk Evaluation Scoring System (PRESS) a simple and non-invasive screening tool to identify adult individuals having undiagnosed prediabetes.

Prediabetes and Diabetes Screening in Dental Care Settings: NHANES 2013 to 2016

Introduction

Early recognition of prediabetes may prevent progression to diabetes, yet not all adults are aware of their prediabetes risk. To reach all adults unaware of their risk, additional risk assessment strategies are warranted.

Objectives

The objective of this study was to evaluate the potential scope of benefit from prediabetes risk assessment in the dental care setting and to identify characteristics of dental patients likely to unknowingly have prediabetes or diabetes.

Methods

Data from 10,472 adults in the National Health and Nutrition Examination Survey from 2013 to 2014 and 2015 to 2016 were analyzed for associations among prediabetes/diabetes risk factors, health care use, and hemoglobin A1C levels according to chi-square tests and multivariate logistic regression.

Results

A total of 7.73% of US adults had seen a dentist but not a medical provider in the past 12 mo. The composition of this subpopulation was significantly different from that who saw a medical provider, in ways that might affect their diabetes risk. In addition, 31.27% of this subpopulation would be identified as being at high risk for prediabetes according to the CDC Prediabetes Screening Test (Centers for Disease Control and Prevention), and 15.83% had hemoglobin A1C levels indicative of undiagnosed prediabetes or diabetes. Screening in a dental setting would have the highest odds of identifying someone unaware of his or her diabetes risk among those who were non-White, obese, or ≥45 y old.

Conclusion

Extrapolation from this analysis indicates that screening for prediabetes at dental visits has the potential to alert an estimated 22.36 million adults of their risk for prediabetes or diabetes. Incorporating prediabetes or diabetes risk assessment into routine dental visits may enable 1) those with prediabetes to take action to decrease their risk of developing diabetes and 2) those with diabetes to engage in treatment to decrease their risk of diabetes-related complications.

Knowledge Transfer Statement

Screening for prediabetes and diabetes during dental visits has the potential to raise patients' awareness of diabetes risk and prevent prediabetes from progressing to diabetes. For some patients, the dental visit may be the only point of contact with the health care system, which heightens the importance of including diabetes risk assessment for patient well-being.

2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2019

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Novel nomogram for screening the risk of developing diabetes in a Korean population

AIMS

We propose a novel nomogram, which graphically expresses the numerical relationship between type 2 diabetes (T2D) and disease-related risk factors.

METHODS

Data of 8999 patients from the 2013-2014 Korean National Health and Nutrition Examination Survey were analyzed. Multiple logistic regression analysis was performed to assess risk factors for T2D and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) and calibration plot were created to evaluate the accuracy of the nomogram.

RESULTS

The risk factor with the greatest impact on the prevalence of T2D was age over 60 years (95% CI 5.97-15.00, OR = 9.46), followed by presence of dyslipidemia and cardiovascular disease (95% CI 5.90-13.68, OR = 8.98), family history of T2D (95% CI 2.33-3.64, OR = 2.92), abdominal obesity (OR = 1.76), hypertension (OR = 1.75), male gender (OR = 1.55), current-smoking status (OR = 1.52), lower education level (OR = 1.42), and lower income (OR = 1.30). The area under the ROC curve (AUC) showed statistically significant determination (AUC = 0.83). The equation of the calibration plot was drawn along the ideal line; coefficient of determination was 0.864.

CONCLUSION

Our proposed nomogram could accurately predict the risk of T2D from nationwide data. The novel nomogram can be a useful tool for screening patients with T2D risk in a Korean population.

Fitting a square peg into a round hole: Perceptions of Appalachian physicians on the incorporation of chronic disease prevention into their practice

This study used a focus group in August 2017 (n = 9) to explore the perceptions of rural physicians to a state request to incorporate diabetes prevention screening into their West Virginia medical practice. Analysis of the data revealed that the participants did not think private physicians were equipped to incorporate diabetes prevention programming into their practice. Three categories emerged from the data analysis to explain the reasoning of the health practitioners on the incorporation of pre-diabetes screening and management into their practice.⁎

The practice of medicine

⁎

Prevention is a mismatch

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Social determinants of health

In the end, the study revealed that a request for physicians to identify and refer at risk patients to a diabetes prevention program is problematic due to conceptual and structural issues. Based on the findings it does not appear at this time that private physicians in rural settings can incorporate diabetes prevention into their existing practice. To address conceptual and structural barriers the invitation to rural physicians must: 1) present evidence on how physicians may be effective in a diabetes management team; 2) include a model that demonstrates a limited, specific role and duties for the physician within a team setting; and last, 3) integrate physicians into an existing community-based network of social and human service providers set up to provide diabetes prevention services.

Protocol for a cluster randomized controlled trial to study the effectiveness of an obesity and diabetes intervention (PASOS) in an immigrant farmworker population

BACKGROUND

Studies have shown that the prevalence of overweight, obesity and diabetes are higher in the largely Hispanic, immigrant farmworker population in California. Though to date, few interventional studies have focused on these issues in this at-risk population. The objective of this paper is to describe the study design of a cluster randomized controlled trial aimed at evaluating the effectiveness of an obesity and diabetes work place intervention in an immigrant farm worker population.

METHODS

PASOS is an obesity and diabetes intervention program that will be implemented on ranches where immigrant farmworkers spend a considerable amount of time each day. This cluster randomized controlled study will enroll approximately 600 farmworkers. Using a uniform distribution for random number generation, ranches are randomized using a 1:1 ratio as either control or intervention. Baseline data will be taken from eligible participants and analyzed against data collected at the post-intervention, 6-month, 1-year, and 1.5-year follow-ups. The enrollment period is 1.5 years.

DISCUSSION

Few studies have been conducted that aim to evaluate the effectiveness of a worksite intervention for obesity and diabetes prevention in a largely Hispanic, farmworker population. This study has been tailored to this population in order to enhance the feasibility of implementation and retention. If successful in reducing obesity and increasing healthy lifestyle choices to reduce diabetes, this study design can be implemented on a larger scale.

TRIAL REGISTRATION

NCT02480244 . Registered 24 June 2015.

Diabetes Risk Assessment in Latinas: Effectiveness of a Brief Diabetes Risk Questionnaire for Detecting Prediabetes in a Community-Based Sample

OBJECTIVE

Numerous validated questionnaires use self-reported data to quantify individuals' risk of having diabetes or developing it in the future. Evaluations of these tools have primarily used nationally representative data, limiting their application in clinical and community settings. This analysis tested the effectiveness of the American Diabetes Association (ADA) risk questionnaire for identifying prediabetes in a community-based sample of Latinas.

METHODS

Data were collected using the ADA risk questionnaire and assessing A1C. Among 204 participants without diabetes, we examined the association between individual characteristics and glycemic status. We then calculated the performance characteristics (sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) of the ADA risk questionnaire for detecting prediabetes, using A1C results as the gold standard to define the outcome.

RESULTS

All participants were women of self-reported Hispanic/Latino ethnicity. Their mean ADA risk score was 5.6 ± 1.6. Latinas who had prediabetes were older, with significantly higher rates of hypertension and a higher ADA risk score than those without prediabetes. At a risk score ≥5-the threshold for high risk set by the ADA-the questionnaire had the following test performance characteristics: sensitivity 77.8%, specificity 41.7%, PPV 76.2%, and NPV 43.9%.

CONCLUSION

The ADA risk questionnaire demonstrates reasonable performance for identifying prediabetes in a community-based sample of Latinas. Our data may guide other groups' use of this tool in the same target population. Future research should examine the effectiveness of this questionnaire for recruiting diverse populations into diabetes prevention programs. In addition, unique diabetes risk assessment tools for specific target populations are needed and may outperform questionnaires developed using nationally representative data.