The performance of the Finnish Diabetes Risk Score, a modified Finnish Diabetes Risk Score and a simplified Finnish Diabetes Risk Score in community-based cross-sectional screening of undiagnosed type 2 diabetes in the Philippines

Current status of diabetes mellitus care and management in the Philippines

AIMS

In this paper, we discuss the existing data on the burden of diabetes in the Philippines and present the status of management, prevention, and control of diabetes in the country.

METHODS

A review of literature was conducted to synthesize the status of diabetes mellitus in the Philippines.

RESULTS

An estimated 4.3 million Filipinos were diagnosed with diabetes, while 2.8 million remained undiagnosed in 2021. Diabetic retinopathy is a top cause of preventable blindness in Region 3, Philippines. Diabetic nephropathy contributes to 38% of renal disease cases in the Philippines. The 2021 Philippine Guidelines on Periodic Health Examination (PhEX) advocate for the utilization of fasting blood sugar (FBS) as a screening measure for Type 2 Diabetes Mellitus (T2DM) in healthy adults aged 40 years and older or in those with specified risk factors. The alternative option of hemoglobin A1c is (HbA1c) deemed appropriate but comes with a conditional recommendation due to its uneven accessibility across different regions of the country. Treatment guidelines align between the Philippines and the US. Initial medical nutrition therapy involves healthy habits, progressing to pharmacologic treatment if necessary. Financial constraints, seen in limited insurance coverage and high out-of-pocket costs, impede care, amplifying disease impact. The complex diabetes care, encompassing pharmacotherapy, nutrition, exercise, and monitoring, faced challenges during COVID-19 quarantines.

CONCLUSION

In conclusion, the paper outlines diabetes care principles-screening, diagnostics, and multidisciplinary care-alongside economic implications. Local and national initiatives are discussed to mitigate diabetes trends and reduce its burden in the Philippines.

Finnish diabetes risk score outperformed triglyceride-glucose index in diabetes risk prediction

Purpose

Triglyceride-Glucose (TyG) index is a surrogate marker of insulin resistance. This study compared the performance of TyG index and the Finnish diabetes risk score (FINDRISC) in diabetes risk prediction.

Methods

This cross-sectional study involved 122 young adults (aged 15-35 years) in Asaba, Delta State, Nigeria. Anthropometric measurements and biochemical analysis were done following standard protocols. Diabetes risk scoring was done using the FINDRISC questionnaire. TyG index was calculated logarithmically. Discrimination between TyG index and FINDRISC was done by plotting receiver operating characteristic (ROC) curves.

Results

High risk participants had significantly (p < 0.001) higher mean values of body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) relative to the lower risk categories. Participants in TyG index Quartile 4 had significantly higher mean values of triglyceride (p < 0.001) and fasting plasma glucose (p < 0.05). BMI and triglyceride had the most significant (p < 0.001) positive correlation with FINDRISC and TyG index, respectively. A moderately elevated to high risk (FINDRISC ≥ 12) of developing diabetes was found in 14.8% of the participants; with a female preponderance (20.6%) relative to males (7.4%). More than half of the participants (52.5%) had slightly elevated risk and differences in diabetes risk susceptibility were significant (p < 0.001) across gender. FINDRISC had an AUC value of 0.826 while TyG index had an AUC value of 0.628 for diabetes risk prediction.

Conclusion

FINDRISC had a better performance than TyG index in the prediction of diabetes risk in this population. The use of other TyG-related parameters rather than TyG index is recommended in future studies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-023-01252-y.

Performance of a Finnish Diabetes Risk Score in detecting undiagnosed diabetes among Kenyans aged 18-69 years

BACKGROUND

The application of risk scores has often effectively predicted undiagnosed type 2 diabetes in a non-invasive way to guide early clinical management. The capacity for diagnosing diabetes in developing countries including Kenya is limited. Screening tools to identify those at risk and thus target the use of limited resources could be helpful, but these are not validated for use in these settings. We, therefore, aimed to measure the performance of the Finnish diabetes risk score (FINDRISC) as a screening tool to detect undiagnosed diabetes among Kenyan adults.

METHODS

A nationwide cross-sectional survey on non-communicable disease risk factors was conducted among Kenyan adults between April and June 2015. Diabetes mellitus was defined as fasting capillary whole blood ≥ 7.0mmol/l. The performance of the original, modified, and simplified FINDRISC tools in predicting undiagnosed diabetes was assessed using the area under the receiver operating curve (AU-ROC). Non-parametric analyses of the AU-ROC, Sensitivity (Se), and Specificity (Sp) of FINDRISC tools were determined.

RESULTS

A total of 4,027 data observations of individuals aged 18-69 years were analyzed. The proportion/prevalence of undiagnosed diabetes and prediabetes was 1.8% [1.3-2.6], and 2.6% [1.9-3.4] respectively. The AU-ROC of the modified FINDRISC and simplified FINDRISC in detecting undiagnosed diabetes were 0.7481 and 0.7486 respectively, with no statistically significant difference (p = 0.912). With an optimal cut-off ≥ 7, the simplified FINDRISC had a higher positive predictive value (PPV) (7.9%) and diagnostic odds (OR:6.65, 95%CI: 4.43-9.96) of detecting undiagnosed diabetes than the modified FINDRISC.

CONCLUSION

The simple, non-invasive modified, and simplified FINDRISC tools performed well in detecting undiagnosed diabetes and may be useful in the Kenyan population and other similar population settings. For resource-constrained settings like the Kenyan settings, the simplified FINDRISC is preferred.

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.

Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population

AIMS

This study aimed to compare the diagnostic accuracy of the metabolic syndrome with the Finnish Diabetes Risk Score (FINDRISC) to screen for type 2 diabetes mellitus (T2DM) in a Shanghai population.

METHODS

Participants aged 25-64 years were recruited from a Shanghai population from July 2019 to March 2020. Each participant underwent a standard metabolic work-up, including clinical examination with anthropometry. Glucose status was tested using hemoglobin A1c (HbAlc), 2h-post-load glucose (2hPG), and fasting blood glucose (FBG). The FINDRISC questionnaire and the metabolic syndrome were examined. The performance of the FINDRISC was assessed using the area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

Of the 713 subjects, 9.1% were diagnosed with prediabetes, whereas 5.2% were diagnosed with T2DM. A total of 172 subjects had the metabolic syndrome. A higher FINDRISC score was positively associated with the prevalence of T2DM and the metabolic syndrome. Multivariable linear regression analysis demonstrated that the FINDRISC had a linear regression relationship with 2hPG levels (b'= 036, p < 0.0001). The AUC-ROC of the FINDRISC to identify subjects with T2DM among the total population was 0.708 (95% CI 0.639-0.776), the sensitivity was 44.6%, and the specificity was 90.1%, with 11 as the cut-off point. After adding FBG or 2hPG to the FINDRISC, the AUC-ROC among the total population significantly increased to 0.785 (95% CI 0.671-0.899) and 0.731 (95% CI 0.619-0.843), respectively, while the AUC-ROC among the female group increased to 0.858 (95% CI 0.753-0.964) and 0.823 (95% CI 0.730-0.916), respectively (p < 0.001). The AUC-ROC of the metabolic syndrome to identify subjects with T2DM among the total and female population was 0.805 (95% CI 0.767-0.844) and 0.830 (95% CI 0.788-0.872), respectively, with seven as the cut-off point.

CONCLUSIONS

The metabolic syndrome performed better than the FINDRISC model. The metabolic syndrome and the FINDRISC with FBG or 2hPG in a two-step screening model are both efficacious clinical practices for predicting T2DM in a Shanghai population.

Bittersweet: How prices of sugar-rich foods contribute to the diet-related disease epidemic in Mexico

I provide new evidence on how price changes of nutritionally similar foods, such as those rich in sugar or fats, change obesity and diet-related diseases in the context of Mexico between 1996-2010. I merge a bar-code level price dataset with product-specific nutritional composition to two datasets with health outcomes: state-level administrative and nationally representative individual-level panel data. Exploiting within-city variation in prices using fixed effects models, I show that decreased prices of sugar-rich foods increase obesity, type 2 diabetes, and hypertension prevalence; yet the prices of foods rich in other nutrients do not. Health responses to price changes are the largest for those abdominally obese or at the highest risk for chronic disease. The association between prices of sugary foods and chronic disease is meaningful: I estimate that in Mexico, price reductions of sugary foods explain roughly 15 percent of the rise in obesity and diabetes during the 15-year study period.

Health literacy and health-promoting behaviors among adults at risk for diabetes in a remote Filipino community

Background

Diabetes risk assessment is an essential preboarding tool before implementing health literacy programs to change an adult's health behavior positively. Research has shown an association between health literacy and health behaviors, but there is a dearth of literature that delineates the difference between the health literacy and health behaviors of adults according to their diabetes risks; high risk vs. low risk.

Objective

This study aimed to determine the difference between the health literacy and health behaviors of adults and establish the relationship between the two variables when classified according to their diabetes risks.

Methods

This study utilized a descriptive cross-sectional design with 400 adults in a remote Filipino community in November 2019. Data were gathered using the Health Promoting Lifestyle Profile II (HPLP II) and Health Literacy Survey-Short Form 12 (HLS-SF12) questionnaires. Descriptive statistics, independent t-test, and Pearson's r were used to analyze the data.

Results

There is a significant difference between the health literacy index scores (p < .05); but no significant difference between the health behavior mean scores (p > .05) of adults when grouped according to their diabetes risks. Health literacy is significantly (p < .05) correlated with health behaviors of adults, with a moderate positive correlation in the high-risk group (r = .43), and both weak positive correlation in the low-risk group (r = .13) and entire group (r = .17).

Conclusion

All adult inclusion efforts in promoting health literacy, with emphasis on the high-risk group, are needed to improve awareness of the degree of diabetes risks. Nurses should take an active role in the assessment of diabetes risks, evaluation of results, and implementation of interventions that could increase health literacy to facilitate the development of healthy behaviors. Stakeholders are urged to advance the availability of evidence-based lifestyle interventions to reduce the growth in new cases of diabetes.

Performance of the Finnish Diabetes Risk Score (FINDRISC) and Modified Asian FINDRISC (ModAsian FINDRISC) for screening of undiagnosed type 2 diabetes mellitus and dysglycaemia in primary care

AIMS

To evaluate the performance of FINDRISC and ModAsian FINDRISC for the screening of undiagnosed diabetes and dysglycaemia in primary care. To compare the performance of FINDRISC with the recommendations of the American Diabetes Association (ADA) and US Preventive Services Task Force (USPSTF) guidelines.

METHODS

This cross-sectional study was carried out on 293 patients without a prior history of diabetes at a primary care clinic in Malaysia. Questions on body mass index and waist circumference were modified based on the Asian standard in ModAsian FINDRISC. Haemoglobin A1c of ≥6.5% (48 mmol/mol) was used to diagnose diabetes. Areas under the receiver operating curve (ROC-AUC) for FINDRISC and ModAsian FINDRISC were analyzed.

RESULTS

The prevalence of undiagnosed diabetes was 7.5% and prediabetes was 32.8%. The ROC-AUC of FINDRISC was 0.76 (undiagnosed diabetes) and 0.79 (dysglycaemia). There was no statistical difference between FINDRISC and ModAsian FINDRISC. The recommended optimal FINDRISC cut-off point for undiagnosed diabetes was ≥11 (Sensitivity 86.4%, Specificity 48.7%). FINDRISC ≥11 point has higher sensitivity compared to USPSTF criteria (72.7%) and higher specificity compared to the ADA (9.6%).

CONCLUSIONS

FINDRISC is a useful diabetes screening tool to identify those at risk of diabetes in primary care in Malaysia.

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.

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.

Choosing the most appropriate existing type 2 diabetes risk assessment tool for use in the Philippines: a case-control study with an urban Filipino population

Background

As the prevalence of type 2 diabetes (T2DM) increases in low- to middle-income countries, the burden on individuals and health care systems also increases. The use of diabetes risk assessment tools could identify those at risk, leading to prevention or early detection of diabetes. The aim of this study was to evaluate the appropriateness of 6 existing T2DM risk screening tools in detecting dysglycemia in Zamboanga City, Philippines.

Methods

This study used a case-control design in an urban setting in the southern Philippines. There were 200 participants in two groups: 1) those diagnosed with diabetes (n = 50; recruited from diabetes clinics) and 2) those with no previous diagnosis of diabetes (n = 150; recruited from community locations). Participants completed six tools (the Finnish Diabetes Risk Score [FINDRISC], the Canadian Diabetes Risk Score [CANRISK], the Indian Diabetes Risk Score [IDRS], the American Diabetes Association [ADA] risk score, an Indonesian undiagnosed diabetes mellitus [UDDM] scoring system, and a Filipino tool). Scores were compared to fasting plasma glucose levels, which are recommended in Philippines clinical practice guidelines as a valid, available, and low cost option for T2DM diagnosis. Appropriateness of tools was determined through accuracy, sensitivity, specificity, positive/negative predictive value (PPV, NPV), and positive/negative likelihood ratios.

Results

The Filipino tool had the highest specificity (0.73) and PPV (0.27), but lowest sensitivity (0.68). The IDRS and Indonesian UDDM tool had the highest NPV at 0.96, but were not amongst the highest in other scores. The CANRISK tied for highest area under the receiver operating characteristic (ROC) curve (AUC), AUC (0.80), but other scores were not noteworthy. Overall, the FINDRISC was the most effective with highest sensitivity (0.94), tied for highest AUC (0.80), and with middle scores in other variables (specificity: 0.45, PPV: 0.20, NPV: 0.95), when using the published cut-off score of 9. When increasing the cut-off score to 11, specificity increased (0.71) and sensitivity was not greatly affected (0.86).

Conclusions

Our results suggest that the FINDRISC is more suitable than other known diabetes risk assessment tools in an urban Filipino population; effectiveness increased with a higher cut-off score.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7402-0) contains supplementary material, which is available to authorized users.

The Community Health Assessment Program in the Philippines (CHAP-P) diabetes health promotion program for low- to middle-income countries: study protocol for a cluster randomized controlled trial

Background

Type 2 diabetes is increasing globally, with the highest burden in low- to middle-income countries (LMICs) such as the Philippines. Developing effective interventions could improve detection, prevention, and treatment of diabetes. The Cardiovascular Health Awareness Program (CHAP), an evidence-based Canadian intervention, may be an appropriate model for LMICs due to its low cost, ease of implementation, and focus on health promotion and disease prevention. The primary aim of this study is to adapt the CHAP model to a Philippine context as the Community Health Assessment Program in the Philippines (CHAP-P) and evaluate the effect of CHAP-P on glycated hemoglobin (HbA1c) compared to a random sample of community residents in control communities.

Methods

Six-month, 26-community (13 intervention, 13 control) parallel cluster randomized controlled trial in Zamboanga Peninsula, an Administrative Region in the southern Philippines. Criteria for community selection include: adequate political stability, connection with local champions, travel feasibility, and refrigerated space for materials. The community-based intervention, CHAP-P sessions, are volunteer-led group sessions with chronic condition assessment, blood pressure monitoring, and health education. Three participant groups will be involved: 1) Random sample of community participants aged 40 or older, 100 per community (1300 control, 1300 intervention participants total); 2) Community members aged 40 years or older who attended at least one CHAP-P session; 3) Community health workers and staff facilitating sessions. Primary outcome: mean difference in HbA1c at 6 months in intervention group individuals compared to control. Secondary outcomes: modifiable risk factors, health utilization and access (individual); diabetes detection and management (cluster). Evaluation also includes community process evaluation and cost-effectiveness analysis.

Discussion

CHAP has been shown to be effective in a Canadian setting. Individual components of CHAP-P have been piloted locally and shown to be acceptable and feasible. This study will improve understanding of how best to adapt this model to an LMIC setting, in order to maximize prevention, detection, and management of diabetes. Results may inform policy and practice in the Philippines and have the potential to be applied to other LMICs.

Trial registration

ClinicalTrials.gov (NCT03481335), registered March 29, 2018.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6974-z) contains supplementary material, which is available to authorized users.

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.

A Mobile App for Identifying Individuals With Undiagnosed Diabetes and Prediabetes and for Promoting Behavior Change: 2-Year Prospective Study

BACKGROUND

To decrease the burden of diabetes in society, early screening of undiagnosed diabetes and prediabetes is needed. Integrating a diabetes risk score into a mobile app would provide a useful platform to enable people to self-assess their risk of diabetes with ease.

OBJECTIVE

The objectives of this study were to (1) assess the profile of Diabetes Risk Score mobile app users, (2) determine the optimal cutoff value of the Finnish Diabetes Risk Score to identify undiagnosed diabetes and prediabetes in the Chinese population, (3) estimate users' chance of developing diabetes within 2 years of using the app, and (4) investigate high-risk app users' lifestyle behavior changes after ascertaining their risk level from the app.

METHODS

We conducted this 2-phase study among adults via mobile app and online survey from August 2014 to December 2016. Phase 1 adopted a cross-sectional design, with a descriptive analysis of the app users' profile. We used a Cohen kappa score to show the agreement between the risk level (as shown in the app) and glycated hemoglobin test results. We used sensitivity, specificity, and area under the curve to determine the optimal cutoff value of the diabetes risk score in this population. Phase 2 was a prospective cohort study. We used a logistic regression model to estimate the chance of developing diabetes after using the app. Paired t tests compared high-risk app users' lifestyle changes.

RESULTS

A total of 13,289 people used the app in phase 1a. After data cleaning, we considered 4549 of these as valid data. Most users were male, and 1811 (39.81%) had tertiary education or above. Among them, 188 (10.4%) users agreed to attend the health assessment in phase 1b. We recommend the optimal value of the diabetes risk score for identifying persons with undiagnosed diabetes and prediabetes to be 9, with an area under the receiver operating characteristic curve of 0.67 (95% CI 0.60-0.74), sensitivity of 0.70 (95% CI 0.58-0.80), and specificity of 0.57 (95% CI 0.47-0.66). At the 2-year follow-up, people in the high-risk group had a higher chance of developing diabetes (odds ratio 4.59, P=.048) than the low-risk group. The high-risk app users improved their daily intake of vegetables (baseline: mean 0.76, SD 0.43; follow-up: mean 0.93, SD 0.26; t₈₁=-3.77, P<.001) and daily exercise (baseline: mean 0.40, SD 0.49; follow-up: mean 0.54, SD 0.50; t₈₁=-2.08, P=.04).

CONCLUSIONS

The Diabetes Risk Score app has been shown to be a feasible and reliable tool to identify persons with undiagnosed diabetes and prediabetes and to predict diabetes incidence in 2 years. The app can also encourage high-risk people to modify dietary habits and reduce sedentary lifestyle.

Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population

AIM

To develop and validate a simplified screening test for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose for the Slovenian population (SloRisk) to be used in the general population.

METHODS

Data on 11 391 people were collected from the electronic health records of comprehensive medical examinations in five Slovenian healthcare centres. Fasting plasma glucose as well as information related to the Finnish Diabetes Risk Score questionnaire, FINDRISC, were collected for 2073 people to build predictive models. Bootstrapping-based evaluation was used to estimate the area under the receiver-operating characteristic curve performance metric of two proposed logistic regression models as well as the Finnish Diabetes Risk Score model both at recommended and at alternative cut-off values.

RESULTS

The final model contained five questions for undiagnosed Type 2 diabetes prediction and achieved an area under the receiver-operating characteristic curve of 0.851 (95% CI 0.850-0.853). The impaired fasting glucose prediction model included six questions and achieved an area under the receiver-operating characteristic curve of 0.840 (95% CI 0.839-0.840). There were four questions that were included in both models (age, sex, waist circumference and blood sugar history), with physical activity selected only for undiagnosed Type 2 diabetes and questions on family history and hypertension drug use selected only for the impaired fasting glucose prediction model.

CONCLUSIONS

This study proposes two simplified models based on FINDRISC questions for screening of undiagnosed Type 2 diabetes and impaired fasting glucose in the Slovenian population. A significant improvement in performance was achieved compared with the original FINDRISC questionnaire. Both models include waist circumference instead of BMI.

Risk of Type 2 Diabetes Mellitus Development in the Native Population of Low- and High-Altitude Regions of Kyrgyzstan: Finnish Diabetes Risc Score Questionnaire Results

Moldobaeva, Marina S., Anastasiya V. Vinogradova, and Marina K. Esenamanova. Risk of type 2 diabetes mellitus development in the native population of low- and high-altitude regions of Kyrgyzstan: Finnish Diabetes Risc Score questionnaire results. High Alt Med Biol. 18:428-435, 2017.

OBJECTIVE

The number of patients with diabetes is steadily growing, but likely only half of all cases are ever identified. The Kyrgyz, native inhabitants of Central Asia, live in the mountainous area and have a particular lifestyle and nutrition. However, the risk of type 2 diabetes mellitus (T2DM) in our population is not well defined. Therefore, we aimed at determining the risk of T2DM development in the Kyrgyz population residing in low- and high-altitude (HAlt) regions by using the Finnish Diabetes Risc Score (FINDRISC) questionnaire.

METHODS

We included in the study 3190 randomly selected participants, including 1780 low-altitude (LAlt) residents (Chu region, 500-1200 m) and 1410 HAlt residents (Naryn region, 2000-4500 m), among whom there were 1207 men and 1983 women. Assessment of T2DM development was conducted by using the FINDRISC questionnaire and risk stratification was performed by region of residency, gender, and age.

RESULTS

An irregular intake of vegetables and fruits, increased waist circumference (WC), and increased body mass index (BMI) were identified as leading risk factors of T2DM development in native residents of Chu and Naryn regions of Kyrgyzstan. The 10-year risk stratification of T2DM development revealed the absence of a very high-risk group; high-risk status was more frequently identified among residents of the LAlt Chu district (4.7% of women and 2.1% of men), as compared with the HAlt population (1.9% of women and 1% of men) (p = 0.0018 for women and p = 0.09 for men).

CONCLUSIONS

In the Kyrgyz population, a 10-year high risk of T2DM development is greater among residents of LAlts as compared with HAlts, irrespective of gender. No very high-risk group was detected in residents of low or HAlts. The leading composites of FINDRISC score are increased WC and BMI, possibly due to irregular intake of vegetables and fruits that are dependent on the altitude of residence and age.

Skin autofluorescence improves the Finnish Diabetes Risk Score in the detection of diabetes in a large population-based cohort: The LifeLines Cohort Study

AIM

The aim of the present study was to investigate whether skin autofluorescence would improve the Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes in a large population-based cohort.

METHODS

Included were participants from the Dutch LifeLines Cohort Study. Skin autofluorescence was assessed in an unselected subset of participants using the AGE Reader. After the exclusion of participants with previously diagnosed diabetes (n=1635), pregnant women (n=58) and those using corticosteroids (n=345), 79,248 subjects were eligible for analysis. Diabetes was defined as fasting plasma glucose ≥7.0mmol/L, non-fasting plasma glucose ≥11.1mmol/L or HbA1c ≥6.5% (48mmol/mol).

RESULTS

Diabetes was detected in 1042 participants (aged 55±12 years; 54% male). Skin autofluorescence improved the area under the receiver operating characteristic (AUROC) curve of the FINDRISC model from 0.802 to 0.811 (P<0.001). Furthermore, the addition of skin autofluorescence to FINDRISC reclassified 8-15% of all participants into more accurate risk categories (NRI: 0.080, 95% CI: 0.052-0.110). The proportion of reclassified participants was especially high (>30%) in the intermediate (1% to <5% and 5% to<10%) risk categories. When skin autofluorescence was added to a simplified model (age+body mass index), its discriminatory performance was similar to the full model+skin autofluorescence (AUROC: 0.806, P=0.062).

CONCLUSION

Skin autofluorescence is a non-invasive tool that can be used to further improve the FINDRISC for diabetes detection. The new resultant model is especially useful for reclassifying people in the intermediate-risk categories, where additional blood glucose testing is needed to confirm the presence of diabetes.

Risk Score for Detecting Dysglycemia: A Cross-Sectional Study of a Working-Age Population in an Oil Field in China

Background

Dysglycemia (pre-diabetes or diabetes) in young adults has increased rapidly. However, the risk scores for detecting dysglycemia in oil field staff and workers in China are limited. This study developed a risk score for the early identification of dysglycemia based on epidemiological and health examination data in an oil field working-age population with increased risk of diabetes.

Material/Methods

Multivariable logistic regression was used to develop the risk score model in a population-based, cross-sectional study. All subjects completed the questionnaires and underwent physical examination and oral glucose tolerance tests. The performance of the risk score models was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

The study population consisted of 1995 participants, 20–64 years old (49.4% males), with undiagnosed diabetes or pre-diabetes who underwent periodic health examinations from March 2014 to June 2015 in Dagang oil field, Tianjin, China. Age, sex, body mass index, history of high blood glucose, smoking, triglyceride, and fasting plasma glucose (FPG) constituted the Dagang dysglycemia risk score (Dagang DRS) model. The performance of Dagang DRS was superior to m-FINDRISC (AUC: 0.791; 95% confidence interval (CI), 0.773–0.809 vs. 0.633; 95% CI, 0.611–0.654). At the cut-off value of 5.6 mmol/L, the Dagang DRS (AUC: 0.616; 95% CI, 0.592–0.641) was better than the FPG value alone (AUC: 0.571; 95% CI, 0.546–0.596) in participants with FPG <6.1 mmol/L (n=1545, P=0.028).

Conclusions

Dagang DRS is a valuable tool for detecting dysglycemia, especially when FPG <6.1 mmol/L, in oil field workers in China.

Co-expression analysis among microRNAs, long non-coding RNAs, and messenger RNAs to understand the pathogenesis and progression of diabetic kidney disease at the genetic level

Diabetic kidney disease (DKD) is a serious disease that presents a major health problem worldwide. There is a desperate need to explore novel biomarkers to further facilitate the early diagnosis and effective treatment in DKD patients, thus preventing them from developing end-stage renal disease (ESRD). However, most regulation mechanisms at the genetic level in DKD still remain unclear. In this paper, we describe our innovative methodologies that integrate biological, computational, and statistical approaches to investigate important roles performed by regulations among microRNAs (miRs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs) in DKD. We conducted fully transparent, rigorously designed experiments. Our robust and reproducible results identified hsa-miR-223-3p as a candidate novel biomarker performing important roles in DKD disease process.

Finnish Diabetes Risk Score Is Associated with Impaired Insulin Secretion and Insulin Sensitivity, Drug-Treated Hypertension and Cardiovascular Disease: A Follow-Up Study of the METSIM Cohort

We investigated the association of the Finnish Diabetes Risk Score (FINDRISC) with insulin secretion, insulin sensitivity, and risk of type 2 diabetes, drug-treated hypertension, cardiovascular (CVD) events and total mortality in a follow-up study of the Metabolic Syndrome in Men (METSIM) cohort. The METSIM study includes 10,197 Finnish men, aged 45-73 years, and examined in 2005-2010. Of 8,749 non-diabetic participants of the METSIM study 693 developed incident type 2 diabetes, 225 started antihypertensive medication, 351 had a CVD event, and 392 died during a 8.2-year follow-up. The FINDRISC was significantly associated with decreases in insulin secretion and insulin sensitivity (P<0.0001), and with a 4.14-fold increased risk of incident type 2 diabetes, 2.43-fold increased risk of drug-treated hypertension, 1.61-fold increased risk of CVD, and 1.55-increased risk of total mortality (the FINDRISC ≥12 vs. < 12 points). In conclusion, the FINDRISC predicts impairment in insulin secretion and insulin sensitivity, the conversion to type 2 diabetes, drug-treated hypertension, CVD events and total mortality.

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.

Impact of sex and age on the performance of FINDRISC: the HUNT Study in Norway

OBJECTIVE

The Finnish Diabetes Risk Score (FINDRISC) is recommended as a screening tool for diabetes risk. However, there is a lack of well-powered studies examining the performance of FINDRISC by sex and age. We aim to estimate, by sex and age, the prevalence of elevated FINDRISC and positive predictive value (PPV) of FINDRISC for identifying impaired glucose metabolism (IGM) in a general Norwegian population.

RESEARCH DESIGN AND METHODS

We estimated the prevalence of elevated FINDRISC (≥15) among 47 694 adults in the third survey of the Nord-Trøndelag Health Study (HUNT3, 2006-08). Among 2559 participants who participated in oral glucose tolerance testing, we estimated the PPV of elevated FINDRISC for identifying unknown prevalent diabetes and other forms of IGM.

RESULTS

The prevalence of elevated FINDRISC was 12.1% in women, 9.6% in men, and increased from 1.5% at age 20-39 to 25.1% at age 70-79 years. The PPVs of elevated FINDRISC were 9.8% for diabetes, 16.9% for impaired glucose tolerance, 8.2% for impaired fasting glucose, and 34.9% for any form of IGM. The PPV for IGM was lower in women (31.2%) than in men (40.4%), and increased from 19.1% at age 20-39 to 55.5% at age ≥80 years.

CONCLUSIONS

FINDRISC identified more women than men as high-risk individuals for diabetes. FINDRISC had a high PPV for detecting prevalent IGM, and the PPV was higher in men than in women and in the older individuals. Our data indicate that the impact of sex and age on diabetes risk is not fully captured by FINDRISC, and that refinements to it might improve diabetes prediction.

Mobile Applications for Type 2 Diabetes Risk Estimation: a Systematic Review

Screening for chronical diseases like type 2 diabetes can be done using different methods and various risk tests. This study present a review of type 2 diabetes risk estimation mobile applications focusing on their functionality and availability of information on the underlying risk calculators. Only 9 out of 31 reviewed mobile applications, featured in three major mobile application stores, disclosed the name of risk calculator used for assessing the risk of type 2 diabetes. Even more concerning, none of the reviewed applications mentioned that they are collecting the data from users to improve the performance of their risk estimation calculators or offer users the descriptive statistics of the results from users that already used the application. For that purpose the questionnaires used for calculation of risk should be upgraded by including the information on the most recent blood sugar level measurements from users. Although mobile applications represent a great future potential for health applications, developers still do not put enough emphasis on informing the user of the underlying methods used to estimate the risk for a specific clinical condition.

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.

Content, participants and outcomes of three diabetes care programmes in three low and middle income countries

AIMS

To improve access and quality of diabetes care for people in low-income countries, it is important to understand which elements of diabetes care are effective. This paper analyses three diabetes care programmes in the DR Congo, Cambodia and the Philippines.

METHODS

Three programmes offering diabetes care and self-management were selected. Programme information was collected through document review and interviews. Data about participants' characteristics, health outcomes, care utilisation, expenditures, care perception and self-management were extracted from a study database. Comparative univariate analyses were performed.

RESULTS

Kin-réseau (DR Congo) is an urban primary care network with 8000 patients. MoPoTsyo (Cambodia) is a community-based peer educator network, covering 7000 patients. FiLDCare (Philippines) is a programme in which 1000 patients receive care in a health facility and self-management support from a community health worker. Content of care of the programmes is comparable, the focus on self-management largest in MoPoTsyo. On average, Kin-réseau patients have a higher age, longer diabetes history and more overweight. MoPoTsyo includes most female, most illiterate and most lean patients. Health outcomes (HbA1C level, systolic blood pressure, diabetes foot lesions) were most favourable for MoPoTsyo patients. Diabetes-related health care expenditure was highest for FiLDCare patients.

CONCLUSIONS

This study shows it possible to maintain a diabetes programme with minimal external resources, offering care and self-management support. It also illustrates that health outcomes of persons with diabetes are determined by their bio-psycho-social characteristics and behaviour, which are each subject to the content of care and the approach to chronic illness and self-management of the programme, in turn influenced by the larger context.

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.

Implementing elements of a context-adapted chronic care model to improve first-line diabetes care: effects on assessment of chronic illness care and glycaemic control among people with diabetes enrolled to the First-Line Diabetes Care (FiLDCare) Project in the Northern Philippines

Aim

The purpose of this study was to investigate the effects of implementing elements of a context-adapted chronic disease-care model (CACCM) in two local government primary healthcare units of a non-highly urbanized city and a rural municipality in the Philippines on Patients’ Assessment of Chronic Illness Care (PACIC) and glycaemic control (HbA1c) of people with diabetes.

Background

Low-to-middle income countries like the Philippines are beset with rising prevalence of chronic conditions but their healthcare systems are still acute disease oriented. Attention towards improving care for chronic conditions particularly in primary healthcare is imperative and ways by which this can be done amidst resource constraints need to be explored.

Methods

A chronic care model was adapted based on the context of the Philippines. Selected elements (community sensitization, decision support, minor re-organization of health services, health service delivery-system re-design, and self-management education and support) were implemented. PACIC and HbA1c were measured before and one year after the start of implementation.

Findings

The improvements in the PACIC (median, from 3.2 to 3.5) as well as in four of the five subsets of the PACIC were statistically significant (P-values: PACIC=0.009; ‘patient activation’=0.026; ‘goal setting’=0.017; ‘problem solving’&lt;0.001; ‘follow-up’&lt;0.001). The decrease in HbA1c (median, from 7.7% to 6.9%) and the level of diabetes control of the project participants (increase of optimally controlled diabetes from 37.2% to 50.6%) were likewise significant (P&lt;0.000 andP=0.014). A significantly higher rating of the post-implementation PACIC subsets ‘problem solving’ (P=0.027) and ‘follow-up’ (P=0.025) was noted among those participants whose HbA1c improved. The quality of chronic care in general and primary diabetes care in particular may be improved, as measured through the PACIC and glycaemic control, in resource-constrained settings applying selected elements of a CACCM and without causing much strain on an already-burdened healthcare system.

Disparity in diabetes risk across Native Hawaiians and different Asian groups: the multiethnic cohort

We evaluated the impact of body mass index (BMI) and lifestyle risk factors on ethnic disparity in diabetes incidence among 89 198 Asian, Native Hawaiian, and white participants of the Multiethnic Cohort who completed multiple questionnaires. After 12 years of follow-up, 11 218 new cases were identified through self-report and health plan linkages. BMI was lowest in Chinese/Koreans, Japanese, and Filipinos (22.4, 23.5, and 23.9 kg/m(2)). Using Cox regression, the unadjusted hazard ratios were 1.9 (Chinese/Korean), 2.1 (Japanese, Mixed-Asian), 2.2 (Filipino), 2.5 (Native Hawaiian), and 2.6 (part-Asian) as compared with whites. With BMI added, the risk for Japanese, Filipinos, Chinese/Koreans, and mixed-Asians increased (8%-42%) but declined in part-Asians and Native Hawaiians (17%-31%). When lifestyle and dietary factors were also included, the risk was attenuated in all groups (6%-14%). Despite their lower BMI, Asian Americans have a higher diabetes risk than whites, but dietary and lifestyle factors do not account for the excess risk.

Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010

Objective

To evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race.

Methods

This cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999–2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity.

Results

Among the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes.

Conclusions

FINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults.