Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review

HbA1c comparable to fasting glucose in the external validation of the African Diabetes Risk Score and other established risk prediction models in Black South Africans

BACKGROUND

The use of non-invasive risk scores to detect undiagnosed type 2 diabetes (T2D) ensures the restriction of invasive and costly blood tests to those most likely to be diagnosed with the disease. This study assessed and compared the performance of the African Diabetes Risk Score (ADRS) with three other diabetes risk prediction models for identifying screen-detected diabetes based on fasting plasma glucose (FPG) or glycated haemoglobin (HBA1c).

METHODS

Age, sex, waist circumference, body mass index, blood pressure, history of diabetes and physical activity levels from the SA-NW-PURE study were used to externally validate the ADRS and other established risk prediction models. Discrimination was assessed and compared using C-statistics and nonparametric methods. Calibration was assessed using calibration plots, before and after recalibration.

RESULTS

Nine hundred and thirty-seven participants were included; 14% had prevalent undiagnosed T2D according to FPG and 26% according to HbA1c. Discrimination was acceptable and was mostly similar between models for both diagnostic measures. The C-statistics for diagnosis by FPG ranged from 0.69 for the Simplified FINDRISC model to 0.77 for the ADRS model and 0.77 for the Simplified FINDRISC model to 0.79 for the ADRS model for diagnosis by HbA1c. Calibration ranged from acceptable to good, though over- and underestimation were present. All models improved significantly following recalibration.

CONCLUSIONS

The models performed comparably, with the ADRS offering a non-invasive way to identify up to 79% of cases. Based on its ease of use and performance, the ADRS is recommended for screening for T2D in certain Black population groups in South Africa. HbA1c as a means of diagnosis also showed comparable performance with FPG. Therefore, further validation studies can potentially use HbA1c as the standard to compare to.

Factors Related to Diagnosis of Diabetes After Detecting High Blood Glucose Levels Through Screening: One-Year Follow-up Among Publicly Insured Adults in Gunma, Japan

This study aimed to assess the diagnosis of diabetes after detecting high blood glucose levels through screening among insured individuals in Gunma, Japan. Data for men and women 35 to 74 years of age were provided by Japan Health Insurance Association, and 4133 individuals with high blood glucose levels while not currently being treated for diabetes were included in the study. About 13% received a diagnosis of diabetes at a subsequent physician visit, and individuals who were under treatment for hypertension were less likely to receive the added diagnosis of diabetes compared with those not being treated for hypertension (odds ratio = 0.42 from a logistic regression model). Fasting blood glucose levels were significantly improved in the next year only among individuals with a confirmed diagnosis of diabetes.

Hepatitis C risk score as a tool to identify individuals with HCV infection: a demonstration and cross-sectional epidemiological study in Egypt

OBJECTIVES

Hepatitis C virus (HCV) infection poses a global health challenge. By the end of 2021, the WHO estimated that less than a quarter of global HCV infections had been diagnosed. There is a need for a public health tool that can facilitate the identification of people with HCV infection and link them to testing and treatment, and that can be customised for each country.

METHODS

We derived and validated a risk score to identify people with HCV in Egypt and demonstrated its utility. Using data from the 2008 and 2014 Egypt Demographic and Health Surveys, two risk scores were constructed through multivariable logistic regression analysis. A range of diagnostic metrics was then calculated to evaluate the performance of these scores.

RESULTS

The 2008 and 2014 risk scores exhibited similar dependencies on sex, age and type of place of residence. Both risk scores demonstrated high and similar areas under the curve of 0.77 (95% CI: 0.76 to 0.78) and 0.78 (95% CI: 0.77 to 0.80), respectively. For the 2008 risk score, sensitivity was 73.7% (95% CI: 71.5% to 75.9%), specificity was 68.5% (95% CI: 67.5% to 69.4%), positive predictive value (PPV) was 27.8% (95% CI: 26.4% to 29.2%) and negative predictive value (NPV) was 94.1% (95% CI: 93.5% to 94.6%). For the 2014 risk score, sensitivity was 64.0% (95% CI: 61.5% to 66.6%), specificity was 78.2% (95% CI: 77.5% to 78.9%), PPV was 22.2% (95% CI: 20.9% to 23.5%) and NPV was 95.7% (95% CI: 95.4% to 96.1%). Each score was validated by applying it to a different survey database than the one used to derive it.

CONCLUSIONS

Implementation of HCV risk scores is an effective strategy to identify carriers of HCV infection and to link them to testing and treatment at low cost to national programmes.

Impaired health-related quality of life due to elevated risk of developing diabetes: A cross-sectional study in Indonesia

BACKGROUND

This study investigated the association between elevated risk of developing diabetes and impaired health-related quality of life (HRQoL) in the Indonesian population.

METHODS

A cross-sectional study was conducted on 1,336 Indonesians from the general population who had no previous diagnosis of diabetes. Utility score to represent HRQoL was measured using the EuroQol 5-dimension, while the risk for developing diabetes was determined using the Finnish Diabetes Risk Score (FINDRISC) instrument. All participants underwent a blood glucose test after fasting for 8 hours. The association between FINDRISC score and HRQoL adjusted for covariates was analysed using multivariate Tobit regression models. Minimal clinically important differences were used to facilitate interpretation of minimal changes in utility score that could be observed.

RESULTS

The median (interquartile range) of the overall FINDRISC score was 6 (7), while the mean (95% confidence intervals) of the EQ-5D utility score was 0.93 (0.93-0.94). Once adjusted for clinical parameters and socio-demographic characteristics, participants with a higher FINDRISC score showed a significantly lower HRQoL. No significant association was detected between fasting blood glucose level categories and HRQoL. A difference of 4-5 points in the FINDRISC score was considered to reflect meaningful change in HRQoL in clinical practice.

CONCLUSION

An elevated risk of developing diabetes is associated with a lower HRQoL. Therefore, attention should be paid not only to patients who have already been diagnosed with diabetes, but also to members of the general population who demonstrate an elevated risk of developing diabetes. This approach will assist in preventing the onset of diabetes and any further deterioration of HRQoL in this segment of the Indonesian population.

Patterns and trends of hepatitis C virus infection in Jordan: an observational study

Background

Hepatitis C virus (HCV) infection levels in Jordan remain uncertain. No HCV national population-based survey has ever been conducted in the country. To meet the World Health Organization's target of reducing HCV incidence to ≤5 per 100,000 people per year by 2030, it is essential to determine the infection levels, identify affected individuals and populations, and provide appropriate treatment using direct-acting antivirals to individuals carrying the virus.

Methods

The study utilized the HCV testing database of 28,798 attendees of Biolab Diagnostic Laboratories in Jordan, covering the period from January 19, 2010, to May 26, 2023. Cross-sectional and cohort study analyses were conducted, including estimating HCV antibody (Ab) prevalence, examining associations with HCV Ab positivity, determining the HCV viremic rate, and estimating HCV incidence rate using a retrospective cohort study design.

Results

A total of 27,591 individuals, with a median age of 31.3 and 52.9% being females, underwent HCV Ab testing, while 1,450 individuals, with a median age of 42.2 and 32.8% being females, underwent HCV RNA PCR testing. The study sample HCV Ab prevalence was 4.0% (95% CI: 3.7-4.2%). After applying probability weights, the weighted HCV Ab prevalence was 5.8% (95% CI: 4.6-7.3%). Age was strongly associated with HCV Ab positivity, particularly among individuals aged 50 years or older, who had 10-fold higher odds of being HCV Ab positive compared to those aged 10-19 years. Males had 2.41-fold higher odds of testing positive for HCV Ab compared to females. The HCV viremic rate was 54.1% (95% CI: 43.0-65.0%). The cumulative incidence of HCV infection, after 5 years of follow-up, was estimated to be 0.41% (95% CI: 0.17-0.99%). The HCV incidence rate was calculated at 1.19 per 1,000 person-years (95% CI, 0.50-2.87).

Conclusion

Prevalence and incidence of HCV infection were substantial, estimated at ~5% and 1 per 1,000 person-years, respectively, and highlighting the presence of core groups actively engaged in the virus' acquisition and transmission. The high observed viremic rate indicates the need for expanding HCV treatment efforts to effectively control HCV transmission in Jordan. Utilizing quality diagnostic laboratories and innovative testing strategies is key to identifying infection carriers and facilitating linkage to treatment and care.

Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study

Background

Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings.

Aims

This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants.

Study design

A multicentered cross-sectional study.

Methods

This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots.

Results

All six models had acceptable discrimination (0.70 ≤ AUC <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept.

Conclusion

Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.

ModAsian FINDRISC as a Screening Tool for People with Undiagnosed Type 2 Diabetes Mellitus in Vietnam: A Community-Based Cross-Sectional Study

Purpose

Our study aims to evaluate the risk of developing type 2 diabetes mellitus in the next 10 years using ModAsian FINDRISC and additionally explore associated factors among the Vietnam population.

Participants and Methods

A cross-sectional study was conducted on 2258 participants aged 25 years old or above in Thua Thien Hue Province, Vietnam. The sample size is calculated based on the estimated sensitivity, and participants were randomly selected from different geographical and socio-economic areas. All participants were thoroughly medically examined, taking blood lipid profile and fasting blood glucose, taking blood pressure, anthropometric indexes, 12-lead electrocardiogram, and behavioral factors were investigated using the Vietnamese version of the WHO STEPS toolkit. The risk of developing T2DM was made based on the ModAsian FINDRISC.

Results

The incidence of developing type 2 diabetes mellitus among the study population was 4.21%. The group with a high or very high risk of developing type 2 diabetes mellitus in the next 10 years accounted for 2.52%. Body mass index (AUC = 0.840, 95% CI: 0.792-0.888), waist circumference (AUC = 0.824, 95% CI: 0.777-0.871), family history of diabetes mellitus (AUC = 0.751, 95% CI = 0.668-0.833), and history of antihypertensive medication use regularly (AUC = 0.708, 95% CI: 0.632-0.784) are the most associated factors of the ModAsian FINDRISC. Residential location (OR = 5.62, 95% CI: 1.91-16.54) and occupational status (OR = 0.35, 95% CI: 0.20-0.62) were significant factors associated with a high and very high risk of developing type 2 diabetes mellitus in the next 10 year.

Conclusion

Screening for the risk of type 2 diabetes mellitus and implementing intervention programs focusing on controlling weight, waist circumference, and blood pressure are essential for reducing type 2 diabetes mellitus incidence and burden in Vietnam.

Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population

A diabetes risk score cannot directly be translated and applied in different populations, and its performance should be evaluated in the target population. This study aimed to translate the Finnish Diabetes Risk Score (FINDRISC) instrument and compare its performance with the modified version for detecting undiagnosed type 2 diabetes mellitus (T2DM) and dysglycaemia among the Indonesian adult population. Forward and backward translations were performed and followed by cultural adaptation. In total, 1,403 participants were recruited. The FINDRISC-Bahasa Indonesia (FINDRISC-BI) was scored according to the original FINDRISC instrument, while a Modified FINDRISC-BI was analyzed using a specific body mass index and waist circumference classification for Indonesians. The area under the receiver operating characteristic curve, sensitivity, specificity, and the optimal cut-offs of both instruments were estimated. The area under the receiver operating characteristic curve for detecting undiagnosed T2DM was 0.73 (0.67-0.78) for the FINDRISC-BI with an optimal cut-off score of ≥9 (sensitivity = 63.0%; specificity = 67.3%) and 0.72 (0.67-0.78) for the Modified FINDRISC-BI with an optimal cut-off score of ≥11 (sensitivity = 59.8%; specificity = 74.9%). The area under the receiver operating characteristic curve for detecting dysglycaemia was 0.72 (0.69-0.75) for the FINDRISC-BI instrument with an optimal cut-off score of ≥8 (sensitivity = 66.4%; specificity = 67.0%), and 0.72 (0.69-0.75) for the Modified FINDRISC-BI instrument with an optimal cut-off score ≥9 (sensitivity = 63.8%; specificity = 67.6%). The Indonesian version of the FINDRISC instrument has acceptable diagnostic accuracy for screening people with undiagnosed T2DM or dysglycaemia in Indonesia. Modifying the body mass index and waist circumference classifications in the Modified FINDRISC-BI results in a similar diagnostic accuracy; however, the Modified FINDRISC-BI has a higher optimal cut-off point than the FINDRISC-BI. People with an above optimal cut-off score are suggested to take a further blood glucose test.

COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar

We developed a Coronavirus Disease 2019 (COVID-19) risk score to guide targeted RT-PCR testing in Qatar. The Qatar national COVID-19 testing database, encompassing a total of 2,688,232 RT-PCR tests conducted between February 5, 2020-January 27, 2021, was analyzed. Logistic regression analyses were implemented to derive the COVID-19 risk score, as a tool to identify those at highest risk of having the infection. Score cut-off was determined using the ROC curve based on maximum sum of sensitivity and specificity. The score’s performance diagnostics were assessed. Logistic regression analysis identified age, sex, and nationality as significant predictors of infection and were included in the risk score. The ROC curve was generated and the area under the curve was estimated at 0.63 (95% CI: 0.63–0.63). The score had a sensitivity of 59.4% (95% CI: 59.1%-59.7%), specificity of 61.1% (95% CI: 61.1%-61.2%), a positive predictive value of 10.9% (95% CI: 10.8%-10.9%), and a negative predictive value of 94.9% (94.9%-95.0%). The concept and utility of a COVID-19 risk score were demonstrated in Qatar. Such a public health tool can have considerable utility in optimizing testing and suppressing infection transmission, while maximizing efficiency and use of available resources.

Developing a risk score for undiagnosed prediabetes or type 2 diabetes among Saharawi refugees in Algeria

AIMS

To prevent type 2 diabetes mellitus (T2D) and reduce the risk of complications, early identification of people at risk of developing T2D, preferably through simple diabetes risk scores, is essential. The aim of this study was to create a risk score for identifying subjects with undiagnosed prediabetes or T2D among Saharawi refugees in Algeria and compare the performance of this score to the Finnish diabetes risk score (FINDRISC).

METHODS

A cross-sectional survey was carried out in five Saharawi refugee camps in Algeria in 2014. A total of 180 women and 175 men were included. HbA1c and cut-offs proposed by the American Diabetes Association (ADA) were used to define cases. Variables to include in the risk score were determined by backwards elimination in logistic regression. Simplified scores were created based on beta coefficients from the multivariable model after internal validation with bootstrapping and shrinkage. The empirical cut-off value for the simplified score and FINDRISC was determined by Area Under the Receiver Operating Curve (AUROC) analysis.

RESULTS

Variables included in the final risk score were age, body mass index (BMI), and waist circumference. The area under the curve (AUC) (C.I) was 0.82 (0.76, 0.88). The sensitivity, specificity, and positive and negative predictive values were 89, 65, 28, and 97%, respectively. AUC and sensitivity were slightly higher and specificity somewhat lower than for FINDRISC.

CONCLUSIONS

The risk score developed is a helpful tool to decide who should be screened for prediabetes or T2D by blood sample analysis. The performance of the risk score was adequate based on internal validation with bootstrap analyses, but should be confirmed in external validation studies.

A simple nomogram for identifying individuals at high risk of undiagnosed diabetes in rural population

AIMS

To sought for an easily applicable nomogram for detecting individuals at high risk of undiagnosed type 2 diabetes.

METHODS

The development cohort included 2542 participants recruited randomly from a rural population in 2011.The glycemic status of subjects was determined using the fasting plasma glucose test and the oral glucose tolerance test. The Bayesian Model Average approach was used to search for a parsimonious model with minimum number of predictor and maximum discriminatory power. The corresponding prediction nomograms were constructed and checked for discrimination, calibration, clinical usefulness, and generalizability in nationwide population in 2012.

RESULTS

The non-lab nomogram including waist circumference and systolic blood pressure was the most parsimonious with the area under receiver operating characteristic curve (AUC) of 0.71 (95 %CI = 0.64-0.76). Adding low-density lipoprotein cholesterol in the non-lab nomogram generated the lab-based nomogram with significantly improved AUC of 0.83 (0.78-0.87, P < 0.001). The nomograms had a positive net benefit at threshold probability between 0.01 and 0.15. Applying the non-lab nomogram to the national population yielded the AUC of 0.66 (0.63-0.70) and 0.68 (0.65-0.71) in the cohorts aged 40-64 and 30-69 years, respectively.

CONCLUSIONS

The novel nomograms could help promote the early detection of undiagnosed diabetes in rural Vietnamese population.

Machine learning risk score for prediction of gestational diabetes in early pregnancy in Tianjin, China

AIMS

This study aimed to develop a machine learning-based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women.

MATERIALS AND METHODS

We used an established population-based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination.

RESULTS

In total, 1484 (7.6%) women developed GDM. Pre-pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model-predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer-Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm_risk_calculator/).

CONCLUSION

The XGBoost model achieved better performance than the logistic model.

Predicting youth diabetes risk using NHANES data and machine learning

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06-0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10-5).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

Examining social determinants of undiagnosed diabetes in Namibia and South Africa using a behavioral model of health services use

AIMS

To examine factors associated with undiagnosed diabetes in Namibia and South Africa.

METHODS

This study used the most recent Demographic and Health Surveys (DHS) from Namibia (2013) and South Africa (2016). This study focused on adults at 35-64 years old. Using Andersen's Behavioral Model, potential contributing factors were categorized into predisposing factors (sex and education), enabling factors (wealth, health insurance, and residence), and a need factor (age, BMI, and high blood pressure). Separate multivariable logistic regression models were used to examine factors associated with undiagnosed diabetes in Namibia (N = 242) and South Africa (N = 525).

RESULTS

In Namibia, higher odds of having undiagnosed diabetes were associated with rural residence (adjusted odds ratio (aOR) = 2.21) and age younger than 45 years old (aOR = 3.20). In South Africa, odds of having undiagnosed diabetes were higher among the poorest-to-poorer group than it was in the richer-to-richest group (aOR = 2.33). In both countries, having high blood pressure was associated with lower odds of having undiagnosed diabetes (aOR = 0.31 in Namibia; aOR = 0.21 in South Africa).

DISCUSSION

Different enabling and need factors were associated with undiagnosed diabetes in these two countries, which implies potentially-different mechanisms driving the high prevalence of undiagnosed diabetes, as well as the needs for different solutions.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study

Background:

Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups.

Objective(s):

We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands.

Methods:

A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one.

Conclusions:

Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.

A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.

Identifying Africans with undiagnosed diabetes: Fasting plasma glucose is similar to the hemoglobin A1C updated Atherosclerosis Risk in Communities diabetes prediction equation

AIMS

Seventy percent of Africans living with diabetes are undiagnosed. Identifying who should be referred for testing is critical. Therefore we evaluated the ability of the Atherosclerosis Risk in Communities (ARIC) diabetes prediction equation with A1C added (ARIC + A1C) to identify diabetes in 451 African-born blacks living in America (66% male; age 38 ± 10y (mean ± SD); BMI 27.5 ± 4.4 kg/m²).

METHODS

All participants denied a history of diabetes. OGTTs were performed. Diabetes diagnosis required 2-h glucose ≥200 mg/dL. The five non-invasive (Age, parent history of diabetes, waist circumference, height, systolic blood pressure) and four invasive variables (Fasting glucose (FPG), A1C, triglycerides (TG), HDL) were obtained. Four models were tested: Model-1: Full ARIC + A1C equation; Model-2: All five non-invasive variables with one invasive variable excluded at a time; Model-3: All five non-invasive variables with one invasive variable included at a time; Model-4: Each invasive variable singly. Area under the receiver operator characteristic curve (AROC) predicted diabetes. Youden Index identified optimal cut-points.

RESULTS

Diabetes occurred in 7% (30/451). Model-1, the full ARIC + A1C equation, AROC = 0.83. Model-2: With FPG excluded, AROC = 0.77 (P = 0.038), but when A1C, HDL or TG were excluded AROC remained unchanged. Model-3 with all non-invasive variables and FPG alone, AROC=0.87; but with A1C, TG or HDL included AROC declined to ≤0.76. Model-4: FPG as a single predictor, AROC = 0.87. A1C, TG, or HDL as single predictors all had AROC ≤ 0.74. Optimal cut-point for FPG was 100 mg/dL.

CONCLUSIONS

To detect diabetes, FPG performed as well as the nine-variable updated ARIC + A1C equation.

Performance and costs of multiple screening strategies for type 2 diabetes: two population-based studies in Shanghai, China

INTRODUCTION

To compare the performance and the costs of various assumed screening strategies for type 2 diabetes mellitus (T2DM) among Chinese adults, and identify an optimal one for the population.

RESEARCH DESIGN AND METHODS

Two multistage-sampling surveys were conducted in Shanghai, China, in 2009 and 2017. All participants were interviewed, had anthropometry, measured fasting plasma glucose (FPG), hemoglobin A1c (A1c) and/or postprandial glucose. The 1999 WHO diagnostic criteria was used to identify undiagnosed T2DM. A previously developed Chinese risk assessment system and a specific risk assessment system developed in this study were applied to calculate diabetes risk score (DRS) 1 and 2. Optimal screening strategies were selected based on the sensitivity, Youden index and the costs using the 2009 survey data as the training set and the 2017 survey data as the validation set. A twofold cross-validation was also performed.

RESULTS

Of numerous assumed strategies, FPG ≥5.6 mmol/L alone performed well (Youden index of 71.8%) and cost least (US$18.4 for each case detected), followed by the strategy of DRS2 ≥8 combining with FPG ≥5.6 mmol/L (Youden index of 71.7% and US$20.2 per case detected) and the strategy of DRS1 ≥17 combining with FPG ≥5.6 mmol/L (Youden index of 72.0% and US$21.6 per case detected). However, FPG alone resulted in more subjects requiring oral glucose tolerance test (OGTT) than did combining with DRS. The strategy of FPG ≥5.6 mmol/L combining with A1c ≥4.7% achieved a Youden index of 72.1%, but had a cost as high as US$48.8 for each case identified. Twofold cross-validation also supported the use of FPG alone, but with an optimal cut-off of 6.1 mmol/L.

CONCLUSIONS

Our results support the use of FPG alone in T2DM screening in Chinese adults. DRS may be used combining with FPG in populations with available electronic health records to reduce the number of OGTT and save costs of screening.

Comparative analysis of screening models for undiagnosed diabetes in Mexico

BACKGROUND

It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease.

OBJECTIVES

To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements.

MATERIALS AND METHODS

Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators.

RESULTS

Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set.

CONCLUSIONS

Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.

Findings from Community-Based Screenings for Type 2 Diabetes Mellitus in at Risk Communities in Cape Town, South Africa: A Pilot Study

Completed and ongoing implementation activities globally advocate for community-based approaches to improve strategies for type 2 diabetes prevention. However, little is known about such strategies in the African region where there are higher relative increases in diabetes prevalence. We reported findings from the first 8-month pilot phase of the South African diabetes prevention program. The study was conducted across eight townships (four black and four mixed-ancestry communities) in Cape Town, South Africa, between August 2017 and March 2018. Participants were recruited using both random and self-selected sampling techniques because the former approach proved to be ineffective; <10% of randomly selected individuals consented to participate. Non-laboratory-based diabetes risk screening, using the African diabetes risk score, and based on targeted population specific cut-offs, identified potentially high-risk adults in the community. This was followed by an oral glucose tolerance test (OGTT) to confirm prevalent pre-diabetes. Among the 853 adults without prior diabetes who were screened in the community, 354 (43.4%) were classified as high risk, and 316 presented for further screening. On OGTT, 13.1% had dysglycemia, including 10% with screen-detected diabetes and 67.9% with glycated haemoglobin (HbA1c)-defined high risk. Participants with pre-diabetes (n = 208) had high levels of common cardiovascular risk factors, i.e., obesity (73.7%), elevated total cholesterol (51.9%), and hypertension (29.4%). Self-referral is likely an efficient method for selecting participants for community-based diabetes risk screening in Africa. Post-screening management of individuals with pre-diabetes must include attention to co-morbid cardiovascular risk factors.

Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

PURPOSE OF REVIEW

Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential.

RECENT FINDINGS

ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner. ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University

BACKGROUND

Risk scores were mainly proved to predict undiagnosed type 2 diabetes mellitus (UT2DM) in a non-invasive manner and to guide earlier clinical treatment. The objective of the present study was to assess the performance of the Finnish Diabetes Risk Score (FINDRISC) for detecting three outcomes: UT2DM, prediabetes, and the metabolic syndrome (MS).

METHODS

This was a prospective, cross-sectional study during which employees aged between 30 and 64, with no known diabetes and working within the faculties of the Lebanese University (LU) were conveniently recruited. Participants completed the FINDRISC questionnaire and their glucose levels were examined using both fasting blood glucose (FBG) and oral glucose tolerance tests (OGTT). Furthermore, they underwent lipid profile tests with anthropometry.

RESULTS

Of 713 subjects, 397 subjects (55.2% female; 44.8% male) completed the blood tests and thus were considered as the sample population. 7.6% had UT2DM, 22.9% prediabetes and 35.8% had MS, where men had higher prevalence than women for these 3 outcomes (P = 0.001, P = 0.003 and P = 0.001) respectively. The AUROC value with 95% Confidence Interval (CI) for detecting UT2DM was 0.795 (0.822 in men and 0.725 in women), 0.621(0.648 in men and 0.59 in women) for prediabetes and 0.710 (0.734 in men and 0.705 in women) for MS. The correspondent optimal cut-off point for UT2DM was 11.5 (sensitivity = 83.3% and specificity = 61.3%), 9.5 for prediabetes (sensitivity = 73.6% and specificity = 43.1%) and 10.5 (sensitivity = 69.7%; specificity = 56.5%) for MS.

CONCLUSION

The FINDRISC can be considered a simple, quick, inexpensive, and non-invasive instrument to use in a Lebanese community of working people who are unaware of their health status and who usually report being extremely busy because of their daily hectic work for the screening of UT2DM and MS. However, it poorly screens for prediabetes in this context.

Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study

OBJECTIVE

We aimed to investigate which clinical and metabolic tests offer optimal accuracy and acceptability to help diagnose diabetes among a large sample of people with serious mental illness in receipt of antipsychotic medication.

METHODS

A prospective observational study design of biochemical and clinical factors was used. Biochemical measures were fasting glucose, insulin and lipids, oral glucose tolerance testing (OGTT), hemoglobin A1c, and insulin resistance assessed with the homeostatic model (HOMA-IR) were determined in a consecutive cohort of 798 adult psychiatric inpatients receiving antipsychotics. Clinical variables were gender, age, global assessment of functioning (GAF), mental health clinicians' global impression (CGI), duration of severe mental illness, height, weight, BMI and waist/hip ratio. In addition, we calculated the risk using combined clinical predictors using the Leicester Practice Risk Score (LPRS) and the Topics Diabetes Risk Score (TDRS). Diabetes was defined by older criteria (impaired fasting glucose (IFG) or OGTT) as well as2010 criteria (IFG or OGTT or Glycated haemoglobin (HBA1c)) at conventional cut-offs.

RESULTS

Using the older criteria, 7.8% had diabetes (men: 6.3%; women: 10.3%). Using the new criteria, 10.2% had diabetes (men: 8.2%, women: 13.2%), representing a 30.7% increase (p = 0.02) in the prevalence of diabetes. Regarding biochemical predictors, conventional OGTT, IFG, and HbA1c thresholds used to identify newly defined diabetes missed 25%, 50% and 75% of people with diabetes, respectively. The conventional HBA1c cut-point of ≥6.5% (48 mmol/mol) missed 7 of 10 newly defined cases of diabetes while a cut-point of ≥5.7% improved sensitivity from 44.4% to up to 85%. Specific algorithm approaches offered reasonable accuracy. Unfortunately no single clinical factor was able to accurately rule-in a diagnosis of diabetes. Three clinical factors were able to rule-out diabetes with good accuracy namely: BMI, waist/hip ratio and height. A BMI < 30 had a 92% negative predictive value in ruling-out diabetes. Of those not diabetic, 20% had a BMI ≥ 30. However, for complete diagnosis a specific biochemical protocol is still necessary.

CONCLUSIONS

Patients with SMI maintained on antipsychotic medication cannot be reliably screened for diabetes using clinical variables alone. Accurate assessment requires a two-step algorithm consisting of HBA1c ≥5.7% followed by both FG and OGTT which does not require all patients to have OGTT and FG.

Evaluation of the Finnish Diabetes Risk Score as a screening tool for undiagnosed type 2 diabetes and dysglycaemia among early middle-aged adults in a large-scale European cohort. The Feel4Diabetes-study

AIM

To assess the diagnostic accuracy of the FINDRISC for undiagnosed type 2 diabetes mellitus (T2DM) and dysglycaemia (i.e. the presence of prediabetes or T2DM) among early middle-aged adults from vulnerable groups in a large-scale European cohort.

METHODS

Participants were recruited from low-socioeconomic areas in high-income countries (HICs) (Belgium-Finland) and in HICs under austerity measures (Greece-Spain) and from the overall population in low/middle-income countries (LMICs) (Bulgaria-Hungary). Study population comprised of 2116 parents of primary-school children from families identified at increased risk of T2DM, based on parental self-reported FINDRISC. Sensitivity (Se), specificity (Sp), area under the receiver operating characteristic curves (AUC-ROC) and the optimal cut-offs of FINDRISC that indicate an increased probability for undiagnosed T2DM or dysglycaemia were calculated.

RESULTS

The AUC-ROC for undiagnosed T2DM was 0.824 with optimal cut-off ≥14 (Se = 68%, Sp = 81.7%) for the total sample, 0.839 with optimal cut-off ≥15 (Se = 83.3%, Sp = 86.9%) for HICs, 0.794 with optimal cut-off ≥12 (Se = 83.3%, Sp = 61.1%) for HICs under austerity measures and 0.882 with optimal cut-off ≥14 (Se = 71.4%, Sp = 87.8%) for LMICs. The AUC-ROC for dysglycaemia was 0.663 with optimal cut-off ≥12 (Se = 58.3%, Sp = 65.7%) for the total sample, 0.656 with optimal cut-off ≥12 (Se = 54.5%, Sp = 64.8%) for HICs, 0.631 with optimal cut-off ≥12 (Se = 59.7%, Sp = 62.0%) for HICs under austerity measures and 0.735 with optimal cut-off ≥11 (Se = 72.7%, Sp = 70.2%) for LMICs.

CONCLUSION

FINDRISC can be applied for screening primarily undiagnosed T2DM but also dysglycaemia among vulnerable groups across Europe, considering the use of different cut-offs for each subpopulation.

Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

Background

Prediction model updating methods are aimed at improving the prediction performance of a model in a new setting. This study sought to critically assess the impact of updating techniques when applying existent prevalent diabetes prediction models to a population different to the one in which they were developed, evaluating the performance in the mixed-ancestry population of South Africa.

Methods

The study sample consisted of 1256 mixed-ancestry individuals from the Cape Town Bellville-South cohort, of which 173 were excluded due to previously diagnosed diabetes and 162 individuals had undiagnosed diabetes. The primary outcome, undiagnosed diabetes, was based on an oral glucose tolerance test. Model updating techniques and prediction models were identified via recent systematic reviews. Model performance was assessed using the C-statistic and expected/observed (E/O) events rates ratio.

Results

Intercept adjustment and logistic calibration improved calibration across all five models (Cambridge, Kuwaiti, Omani, Rotterdam and Simplified Finnish diabetes risk models). This was improved further by model revision, where likelihood ratio tests showed that the effect of body mass index, waist circumference and family history of diabetes required additional adjustment (Omani, Rotterdam and Finnish models). However, discrimination was poor following internal validation of these models. Re-estimation of the regression coefficients did not increase performance, while the addition of new variables resulted in the highest discriminatory and calibration performance combination for the models it was undertaken in.

Conclusions

While the discriminatory performance of the original existent models during external validation were higher, calibration was poor. The highest performing models, based on discrimination and calibration, were the Omani diabetes model following model revision, and the Cambridge diabetes risk model following the addition of waist circumference as a predictor. However, while more extensive methods incorporating development population information were superior over simpler methods, the increase in model performance was not great enough for recommendation.

Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population

AIMS

To assess the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM and to compare its performance with the Latin-American FINDRISC (LA-FINDRISC) and the Peruvian Risk Score.

MATERIALS AND METHODS

A population-based study was conducted. T2DM and undiagnosed T2DM were defined using oral glucose tolerance test (OGTT). Risk scores assessed were FINDRISC, LA-FINDRISC and Peruvian Risk Score. Diagnostic accuracy of risk scores was estimated using the c-statistic and the area under the ROC curve (aROC). A simplified version of FINDRISC was also derived.

RESULTS

Data from 1609 individuals, mean age 48.2 (SD: 10.6), 810 (50.3%) women, were collected. A total of 176 (11.0%; 95%CI: 9.4%-12.5%) were classified as having T2DM, and 71 (4.7%; 95%CI: 3.7%-5.8%) were classified as having undiagnosed T2DM. Diagnostic accuracy of the FINDRISC (aROC=0.69), LA-FINDRISC (aROC=0.68), and Peruvian Risk Score (aROC=0.64) was similar (p=0.15). The simplified FINDRISC, with 4 variables, had a slightly better performance (aROC=0.71) than the other scores.

CONCLUSION

The performance of FINDRISC, LA-FINDRISC and Peruvian Risk Score for undiagnosed T2DM was similar. A simplified FINDRISC can perform as well or better for undiagnosed T2DM. The FINDRISC may be useful to detect cases of undiagnosed T2DM in resource-constrained settings.

Gestational diabetes mellitus risk score: A practical tool to predict gestational diabetes mellitus risk in Tanzania

BACKGROUND

Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania.

METHODS

We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives).

RESULTS

Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals.

CONCLUSION

The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.

Determining type 2 diabetes risk factors for the adults: A community based study from Turkey

AIMS

This study aimed to determine risk factors for type 2 diabetes among adults who were not diagnosed with diabetes.

METHODS

Adults were included in this study within the public activities performed on World Diabetes Day (n=1872). Data were collected using the FINDRISC questionnaire and a short questionnaire.

RESULTS

Participants' mean age was 39.35±10.40. The mean FINDRISC score was 7.46±4.62, women's mean score was higher than that for men. The FINDRISC score indicates that 7.4% of the participants were in the highrisk group. Among participants, BMI value of 65.1% was 25kg/m² and higher, waist circumference of 58% was over the threshold value; and 50.7% did not engage in sufficient physical activity. Of the participants, 9.5% had a history of high blood glucose, families of 38.9% had a history of diabetes. The mean FINDRISC score was in the slightly high category, 121 participants were found likely to be diagnosed with diabetes within ten years if no action was taken.

CONCLUSIONS

It is recommended the risk screening studies to be conducted and the FINDRISC tool to be used in Turkey, where diabetes prevalence is increasing rapidly, to determine diabetes risks in the early period and to raise social awareness for diabetes.

Accuracy of diabetes screening methods used for people with tuberculosis, Indonesia, Peru, Romania, South Africa

Objective

To evaluate the performance of diagnostic tools for diabetes mellitus, including laboratory methods and clinical risk scores, in newly-diagnosed pulmonary tuberculosis patients from four middle-income countries.

Methods

In a multicentre, prospective study, we recruited 2185 patients with pulmonary tuberculosis from sites in Indonesia, Peru, Romania and South Africa from January 2014 to September 2016. Using laboratory-measured glycated haemoglobin (HbA1c) as the gold standard, we measured the diagnostic accuracy of random plasma glucose, point-of-care HbA1c, fasting blood glucose, urine dipstick, published and newly derived diabetes mellitus risk scores and anthropometric measurements. We also analysed combinations of tests, including a two-step test using point-of-care HbA1cwhen initial random plasma glucose was ≥ 6.1 mmol/L.

Findings

The overall crude prevalence of diabetes mellitus among newly diagnosed tuberculosis patients was 283/2185 (13.0%; 95% confidence interval, CI: 11.6–14.4). The marker with the best diagnostic accuracy was point-of-care HbA1c (area under receiver operating characteristic curve: 0.81; 95% CI: 0.75–0.86). A risk score derived using age, point-of-care HbA1c and random plasma glucose had the best overall diagnostic accuracy (area under curve: 0.85; 95% CI: 0.81–0.90). There was substantial heterogeneity between sites for all markers, but the two-step combination test performed well in Indonesia and Peru.

Conclusion

Random plasma glucose followed by point-of-care HbA1c testing can accurately diagnose diabetes in tuberculosis patients, particularly those with substantial hyperglycaemia, while reducing the need for more expensive point-of-care HbA1c testing. Risk scores with or without biochemical data may be useful but require validation.

Diabetes risk scores for Hispanics living in the United States: A systematic review

AIM

Undiagnosed diabetes is more prevalent among racial/ethnic minorities in the United States (U.S.). Despite the proliferation of risk scores, few have been validated in Hispanics populations. The aim of this study is to systematically review published studies that developed risk scores to identify undiagnosed Type 2 Diabetes Mellitus based on self-reported information that were validated for Hispanics in the U.S.

METHODS

The search included PubMed, EMBASE, Cochrane and CINAHL from inception to 2016 without language restrictions. Risk scores whose main outcome was undiagnosed Type 2 diabetes reporting performance measures for Hispanics were included.

RESULTS

We identified three studies that developed and validated risk scores for undiagnosed diabetes based on questionnaire data. Two studies were conducted in Latin America and one in the U.S. All three studies reported adequate performance (area under the receiving curve (AUC) range between0.68and 0.78). The study conducted in the U.S. reported a higher sensitivity of their risk score for Hispanics than whites. The limited number of studies, small size and heterogeneity of the combined cohorts provide limited evidence of the validity of risk scores for Hispanics.

CONCLUSIONS

Efforts to develop and validate risk prediction models in Hispanic populations in the U.S are needed, particularly given the diversity of thisfast growing population. Healthcare professionals should be aware of the limitations of applying risk scores developed for the general population on Hispanics.

Screening models for undiagnosed diabetes in Mexican adults using clinical and self-reported information

BACKGROUND

Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications.

OBJECTIVE

To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults.

SUBJECTS AND METHODS

Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS).

RESULTS

The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively.

CONCLUSIONS

Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries.

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.

Evaluation of the modified FINDRISC to identify individuals at high risk for diabetes among middle-aged white and black ARIC study participants

OBJECTIVE

To evaluate a modified Finnish Diabetes Risk Score (FINDRISC) for predicting the risk of incident diabetes among white and black middle-aged participants from the Atherosclerosis Risk in Communities (ARIC) study.

RESEARCH DESIGN AND METHODS

We assessed 9754 ARIC cohort participants who were free of diabetes at baseline. Logistic regression and receiver operator characteristic (ROC) curves were used to evaluate a modified FINDRISC for predicting incident diabetes after 9 years of follow-up, overall and by race/gender group. The modified FINDRISC used comprised age, body mass index, waist circumference, blood pressure medication and family history.

RESULTS

The mean FINDRISC (range, 2 [lowest risk] to 17 [highest risk]) for black women was higher (9.9 ± 3.6) than that for black men (7.6 ± 3.9), white women (8.0 ± 3.6) and white men (7.6 ± 3.5). The incidence of diabetes increased generally across deciles of FINDRISC for all 4 race/gender groups. ROC curve statistics for the FINDRISC showed the highest area under the curve for white women (0.77) and the lowest for black men (0.70).

CONCLUSIONS

We used a modified FINDRISC to predict the 9-year risk of incident diabetes in a biracial US population. The modified risk score can be useful for early screening of incident diabetes in biracial populations, which may be helpful for early interventions to delay or prevent diabetes.

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study

CONTEXT AND OBJECTIVE:

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task.

DESIGN AND SETTING:

Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil.

METHODS:

After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest.

RESULTS:

The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step.

CONCLUSION:

Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Identifying risk for type 2 diabetes in different age cohorts: does one size fit all?

OBJECTIVE

To estimate age-specific risk equations for type 2 diabetes onset in young, middle-aged, and older US adults, and to compare the performance of simple equations based on readily available demographic information alone, against enhanced equations that require both demographic and clinical information (fasting plasma glucose, high-density lipoprotein, and triglyceride levels).

RESEARCH DESIGN AND METHODS

We estimated the probability of developing diabetes by age group using data from the Coronary Artery Risk Development in Young Adults (for ages 18-40 years), Atherosclerosis Risk in Communities (for ages 45-64 years), and the Cardiovascular Health Study (for ages 65 years and older). Simple and enhanced equations were estimated using logistic regression models, and performance was compared by age group. Thresholds based on these risk equations were evaluated using split-sample bootstraps and calibrating the constant of one age cohort to others.

RESULTS

Simple risk equations had an area under the receiver-operating curve (AUROC) of 0.72, 0.79, 0.75, and 0.69 for age groups 18-30, 28-40, 45-64, and 65 and older, respectively. The corresponding AUROCs for enhanced equations were 0.75, 0.85, 0.85, and 0.81. Risk equations based on younger populations, when applied to older cohorts, underpredict diabetes incidence and risk. Conversely, risk equations based on older populations overpredict the likelihood of diabetes in younger cohorts.

CONCLUSIONS

In general, risk equations are more successful in middle-aged adults than in young and old populations. The results demonstrate the importance of applying age-specific risk equations to identify target populations for intervention. While the predictive capacity of equations that include biomarkers is better than of those based solely on self-reported variables, biomarkers are more important in older populations than in younger ones.

Preventing Diabetes: Early Versus Late Preventive Interventions

There are a number of arguments in support of early measures for the prevention of type 2 diabetes (T2D), as well as for concepts and strategies at later intervention stages. Diabetes prevention is achievable when implemented in a sustainable manner. Sustainability within a T2D prevention program is more important than the actual point in time or disease process at which prevention activities may start. The quality of intervention, as well as its intensity, should vary with the degree of the identified T2D risk. Nevertheless, preventive interventions should start as early as possible in order to allow a wide variety of relatively low- and moderate-intensity programs. The later the disease risk is identified, the more intensive the intervention should be. Public health interventions for diabetes prevention represent an optimal model for early intervention. Late interventions will be targeted at people who already have significant pathophysiological derangements that can be considered steps leading to the development of T2D. These derangements may be difficult to reverse, but the worsening of dysglycemia may be halted, and thus the clinical onset of T2D can be delayed.

Performance of the Finnish Diabetes Risk Score and a Simplified Finnish Diabetes Risk Score in a Community-Based, Cross-Sectional Programme for Screening of Undiagnosed Type 2 Diabetes Mellitus and Dysglycaemia in Madrid, Spain: The SPREDIA-2 Study

Aim

To evaluate the performance of the Finnish Diabetes Risk Score (FINDRISC) and a simplified FINDRISC score (MADRISC) in screening for undiagnosed type 2 diabetes mellitus (UT2DM) and dysglycaemia.

Methods

A population-based, cross-sectional, descriptive study was carried out with participants with UT2DM, ranged between 45–74 years and lived in two districts in the north of metropolitan Madrid (Spain). The FINDRISC and MADRISC scores were evaluated using the area under the receiver operating characteristic curve method (ROC-AUC). Four different gold standards were used for UT2DM and any dysglycaemia, as follows: fasting plasma glucose (FPG), oral glucose tolerance test (OGTT), HbA1c, and OGTT or HbA1c. Dysglycaemia and UT2DM were defined according to American Diabetes Association criteria.

Results

The study population comprised 1,426 participants (832 females and 594 males) with a mean age of 62 years (SD = 6.1). When HbA1c or OGTT criteria were used, the prevalence of UT2DM was 7.4% (10.4% in men and 5.2% in women; p<0.01) and the FINDRISC ROC-AUC for UT2DM was 0.72 (95% CI, 0.69–0.74). The optimal cut-off point was ≥13 (sensitivity = 63.8%, specificity = 65.1%). The ROC-AUC of MADRISC was 0.76 (95% CI, 0.72–0.81) with ≥13 as the optimal cut-off point (sensitivity = 84.8%, specificity = 54.6%). FINDRISC score ≥12 for detecting any dysglycaemia offered the best cut-off point when HbA1c alone or OGTT and HbA1c were the criteria used.

Conclusions

FINDRISC proved to be a useful instrument in screening for dysglycaemia and UT2DM. In the screening of UT2DM, the simplified MADRISC performed as well as FINDRISC.

Detecting people at high risk of type 2 diabetes- How do we find them and who should be treated?

Diabetes is a chronic disease characterized by its silent and progressive nature. The prevalence of type 2 diabetes (T2DM) increases with age but with a worrying trend of increasingly young age of diagnosis. Compared to their counterparts with late onset of disease, these younger subjects face long disease duration with increased risk of diabetes-related complications. Besides, there is marked phenotypic heterogeneity which can interact with different interventions to give rise to variable clinical outcomes. Recognized at-risk groups include those with known atherosclerosis and vascular disease, genetic background (family history and non-White ethnic groups), phenotypes of insulin resistance (obesity, metabolic syndrome, women with gestational diabetes or polycystic ovarian syndrome, and men with androgen deficiency) and "pre-diabetes" (impaired glucose tolerance and impaired fasting glucose). These risk factors interact to amplify the risk for diabetes, thus emphasizing the importance of comprehensive assessment. Raising awareness and health literacy, regular screening of high risk subjects, structured lifestyle modification program including early use of pharmacological agents, targeting at predominant pathophysiological defects offers a personalized approach to prevent this global hazard.

Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

BACKGROUND

The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non-diabetic subjects based on non-laboratory- and laboratory-based risk algorithms.

METHODS

Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non-diabetic subjects aged 18-70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non-laboratory- and laboratory-based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample (n = 839). The Hosmer-Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms.

RESULTS

Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2-h post-load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non-laboratory-based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory-based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM.

CONCLUSION

A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms.

Does Future Diabetes Risk Impair Current Quality of Life? A Cross-Sectional Study of Health-Related Quality of Life in Relation to the Finnish Diabetes Risk Score (FINDRISC)

OBJECTIVES

Present study examines the relationship between the estimated risk of developing type 2 diabetes (T2D) and health-related quality of life (HRQoL). We quantify the association between Finnish Diabetes Risk Score (FINDRISC) and HRQoL, and examine the potential use of FINDRISC as tool to evaluate HRQoL indirectly.

METHODS

We conducted a cross-sectional study comprising 707 Finnish people without a diagnosis of T2D between the ages of 51 and 75 years. The risk of developing T2D was assessed using the validated and widely used FINDRISC (range 0-26 points), and quality of life was measured using two preference-based HRQoL instruments (15D and SF-6D) and one health profile instrument (SF-36). Effects of the individual FINDRISC items and demographic and clinical characteristics, such as co-morbidities, on HRQoL were studied using multivariable Tobit regression models.

RESULTS

Low HRQoL was significantly and directly associated with the estimated risk of developing T2D. An approximate 4-5 point change in FINDRISC score was observed to be associated with clinically noticeable changes in the preference-based instrument HRQoL index scores. The association between HRQoL and the risk of developing T2D was also observed for most dimensions of HRQoL in all applied HRQoL instruments. Overall, old age, lack of physical activity, obesity, and history of high blood glucose were the FINDRISC factors most prominently associated with lower HRQoL.

CONCLUSIONS

The findings may help the health care professionals to substantiate the possible improvement in glucose metabolism and HRQoL potentially achieved by lifestyle changes, and better convince people at high risk of T2D to take action towards healthier lifestyle habits. FINDRISC may also provide an accurate proxy for HRQoL, and thus by estimating the risk of T2D with the FINDRISC, information about patients' HRQoL may also be obtained indirectly, when it is not feasible to use HRQoL instruments.

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa

Background

Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation.

Methods

Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models’ discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment.

Results

The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4%) had missing data. Family history had the highest proportion of missing data (25%). Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals). Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods.

Conclusions

Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation.

Validating prediction scales of type 2 diabetes mellitus in Spain: the SPREDIA-2 population-based prospective cohort study protocol

INTRODUCTION

The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. When diagnosed, many patients already have organ damage or advance subclinical atherosclerosis. An early diagnosis could allow the implementation of lifestyle changes and treatment options aimed at delaying the progression of the disease and to avoid cardiovascular complications. Different scores for identifying undiagnosed diabetes have been reported, however, their performance in populations of southern Europe has not been sufficiently evaluated. The main objectives of our study are: to evaluate the screening performance and cut-off points of the main scores that identify the risk of undiagnosed T2DM and prediabetes in a Spanish population, and to develop and validate our own predictive models of undiagnosed T2DM (screening model), and future T2DM (prediction risk model) after 5-year follow-up. As a secondary objective, we will evaluate the atherosclerotic burden of the population with undiagnosed T2DM.

METHODS AND ANALYSIS

Population-based prospective cohort study with baseline screening, to evaluate the performance of the FINDRISC, DANISH, DESIR, ARIC and QDScore, against the gold standard tests: Fasting plasma glucose, oral glucose tolerance and/or HbA1c. The sample size will include 1352 participants between the ages of 45 and 74 years.

ANALYSIS

sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio positive, likelihood ratio negative and receiver operating characteristic curves and area under curve. Binary logistic regression for the first 700 individuals (derivation) and last 652 (validation) will be performed. All analyses will be calculated with their 95% CI; statistical significance will be p<0.05.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Research Ethics Committee of the Carlos III Hospital (Madrid). The score performance and predictive model will be presented in medical conferences, workshops, seminars and round table discussions. Furthermore, the predictive model will be published in a peer-reviewed medical journal to further increase the exposure of the scores.

Predictive modeling for incident and prevalent diabetes risk evaluation

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

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining

Objectives:

The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors.

Methods:

The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as “Inputs” were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as “Class”. We applied the “Decision Tree” technique and “J48” algorithm in the WEKA (3.6.10 version) software to develop the model.

Results:

After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons.

Conclusions:

We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.

Factors associated with untreated diabetes: analysis of data from 20,496 participants in the Japanese National Health and Nutrition Survey

Objective

We aimed to examine factors associated with untreated diabetes in a nationally representative sample of the Japanese population.

Research Design and Methods

We pooled data from the Japanese National Health and Nutrition Survey from 2005 to 2009 (n = 20,496). Individuals aged 20 years and older were included in the analysis. We classified participants as having diabetes if they had HbA1c levels ≥6.5% (≥48 mmol/mol). People with diabetes who self-reported that they were not currently receiving diabetic treatment were considered to be untreated. We conducted a multinomial logistic regression analysis to determine factors associated with untreated diabetes relative to non-diabetic individuals.

Results

Of 20,496 participants who were included in the analysis, untreated diabetes was present in 748 (3.6%). Among participants with untreated diabetes, 48.3% were previously diagnosed with diabetes, and 46.5% had HbA1c levels ≥7.0% (≥53 mmol/mol). Participants with untreated diabetes were significantly more likely than non-diabetic participants to be male, older, and currently smoking, have lower HDL cholesterol levels and higher BMI, non-HDL cholesterol levels, and systolic blood pressure.

Conclusions

A substantial proportion of people in Japan with untreated diabetes have poor glycemic control. Targeting relevant factors for untreated diabetes in screening programs may be effective to enhance the treatment and control of diabetes.

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND

Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans.

METHODS

Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment.

RESULTS

Seven hundred thirty-seven participants (27 % male), mean age, 52.2 years, were included, among whom 130 (17.6 %) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95 % confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment.

CONCLUSIONS

The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings.