Validity of the Finnish Diabetes Risk Score for Detecting Undiagnosed Type 2 Diabetes among General Medical Outpatients in Botswana

Identifying diabetes risks among Indonesians: A cross-sectional study in a community setting

Background

There is an upward surge in diabetes patients worldwide, including in Indonesia, annually. Diabetes can lead to new diseases that burden patients' lives further. Nurses can reduce this problem by identifying people at risk of developing diabetes and educating them on how to prevent diabetes.

Objective

The study aimed to determine the risk of diabetes in the Indonesian population.

Methods

The descriptive research involved a sample of 1216 Indonesians living in North Sumatra Province. Participants were nondiabetic individuals selected using the convenience method from May to October 2020. This study utilized the Indonesian version of the Finnish Diabetes Risk Score (FINDRISC) tool and employed various statistical analyses, including frequencies, percentages, chi-square test, and Fisher's exact test.

Results

Of the total samples, 372 were males (30.6%), and 844 were females (69.4%). The risk of developing diabetes was classified as low (57.1%), slightly elevated (36.4%), moderate (5.3%), high (1.0%), and very high (0.2%). Only one of the eight risk factors that differed significantly between men and women was a history of elevated blood glucose levels, with a p-value of 0.02.

Conclusion

The study identified a portrait of the number and percentage of diabetes risk factors in a community setting in Indonesia. Nurses must provide education on diabetes prevention to not only members of the local community at the research site but also the general public, nationally and globally.

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.

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.

External Validation of Finnish Diabetes Risk Score and Australian Diabetes Risk Assessment Tool Prediction Models to Identify People with Undiagnosed Type 2 Diabetes: A Cross-sectional Study in Iran

BACKGROUND

Noninvasive risk prediction models have been widely used in various settings to identify individuals with undiagnosed diabetes.

OBJECTIVES

We aimed to evaluate the discrimination, calibration, and clinical usefulness of the Finnish Diabetes Risk Score (FINDRISC) and Australian Diabetes Risk Assessment (AUSDRISK) to screen undiagnosed diabetes in Kerman, Iran.

METHODS

We analyzed data from 2014 to 2018 in the second round of the Kerman Coronary Artery Disease Risk Factors Study (KERCADRS), Iran. Participants aged 35 - 65 with no history of confirmed diabetes were eligible. The area under the receiver operating characteristic curve (AUROC) and decision curve analysis were applied to evaluate the discrimination power and clinical usefulness of the models, respectively. The calibration was assessed by the Hosmer-Lemeshow test and the calibration plots.

RESULTS

Out of 3262 participants, 145 (4.44%) had undiagnosed diabetes. The estimated AUROCs were 0.67 and 0.62 for the AUSDRISK and FINDRISC models, respectively (P < 0.001). The chi-square test results for FINDRISC and AUSDRISC were 7.90 and 16.47 for the original model and 3.69 and 14.61 for the recalibrated model, respectively. Based on the decision curves, useful threshold ranges for the original models of FINDRIS and AUSDRISK were 4% to 10% and 3% to 13%, respectively. Useful thresholds for the recalibrated models of FINDRISC and AUSDRISK were 4% to 8% and 4% to 9%, respectively.

CONCLUSIONS

The original AUSDRISK model performs better than FINDRISC in identifying patients with undiagnosed diabetes and could be used as a simple and noninvasive tool where access to laboratory facilities is costly or limited.

Prevalence of cardiovascular risk factors by HIV status in a population-based cohort in South Central Uganda: a cross-sectional survey

INTRODUCTION

Cardiovascular disease is one of the leading causes of mortality for people living with HIV, but limited population-based data are available from sub-Saharan Africa. This study aimed to determine the prevalence of key cardiovascular disease risk factors, 10-year risk of cardiovascular disease and type 2 diabetes mellitus through risk scores by HIV status, as well as investigate factors associated with hyperglycaemia, hypertension and dyslipidaemia in South-Central Uganda.

METHODS

A cross-sectional study was conducted in 37 communities of the population-based Rakai Community Cohort Study from May 2016 to May 2018. In total, 990 people living with HIV and 978 HIV-negative participants aged 35-49 years were included. Prevalence estimates and 10-year cardiovascular and type 2 diabetes risk were calculated by sex and HIV serostatus. Multivariable logistic regression was used to determine associations between socio-demographic, lifestyle and body composition risk factors and hyperglycaemia, hypertension and dyslipidaemia.

RESULTS

Overweight (21%), obesity (9%), abdominal obesity (15%), hypertension (17%) and low high-density lipoprotein (HDL) (63%) were the most common cardiovascular risk factors found in our population. These risk factors were found to be less common in people living with HIV apart from hypertension. Ten-year risk for cardiovascular and type 2 diabetes mellitus risk was low in this population with <1% categorized as high risk. In HIV-adjusted multivariable analysis, obesity was associated with a higher odds of hypertension (odds ratio [OR] = 2.31, 95% confidence interval [CI] 1.35-3.96) and high triglycerides (OR = 2.08, CI 1.25-3.47), and abdominal obesity was associated with a higher odds of high triglycerides (OR = 2.55, CI 1.55-4.18) and low HDL (OR = 1.36, CI 1.09-1.71). A positive HIV status was associated with a lower odds of low HDL (OR = 0.43, CI 0.35-0.52).

CONCLUSIONS

In this population-based study in Uganda, cardiovascular risk factors of obesity, abdominal obesity, hypertension and dyslipidaemia were found to be common, while hyperglycaemia was less common. Ten-year risk for cardiovascular and type 2 diabetes mellitus risk was low. The majority of cardiovascular risk factors were not affected by HIV status. The high prevalence of dyslipidaemia in our study requires further research.

Type 2 diabetes mellitus risk assessment among doctors in Ondo state

Introduction

Diabetes Mellitus (DM) has become a disease of public health importance in Nigeria. Early identification of DM risk is important in the reduction of this disease burden. This study assessed ten-year risk of developing type 2 DM among some medical doctors in Ondo State.

Methods

This was a cross-sectional study that assessed ten-year risk of developing type 2 DM among some doctors using the Finland Diabetic Risk Score form. Known diabetics were excluded from the study. Body mass index (BMI), waist circumference (WC), blood pressure and total DM risk score were determined for each participant.

Results

One hundred and ninety-two doctors participated in the study with a male: female ratio of 1.3:1. Majority (92.2%) were below 55 years, 22 (11.5%) were obese, 32(16.7%) had central obesity, 46(24%) reported physical inactivity, 49(25.5%) had family history of DM, 141(73.4%) do not take fruits and vegetables regularly. Forty-three (22.4%) were found to have elevated blood pressure while 6(3.1%) had elevated blood glucose. Fifty-seven (29.7%) of the participants had increased ten-year DM risk. Significant predictors of increase DM risk were age ≥ 45 years (AOR:9.08; CI 3.13–26.33; p = <0.001); BMI ≥25kg/m² (AOR:11.41; CI:4.14–31.45; p = <0.001); family history of DM (AOR:9.93; CI:3.25–30.39; p = <0.001); abdominal obesity (AOR:6.66; CI:2.08–21.29; p= < 0.001); and infrequent dietary intake of fruits and vegetable (AOR:3.11;CI:1.03:9.37: p = 0.04)

Conclusion

There was increased 10-year DM risk in about 30% of the participants. Lifestyle modification such as physical activity and regular consumption of fruits and vegetables should be encouraged among doctors.

Comparative Assessment of ADA, IDRS, and FINDRISC in Predicting Prediabetes and Diabetes Mellitus in South Indian Population

Introduction  Diabetes risk-screening tools are validated and implemented across various countries. There is a need for improvement in these risk scores with suitable modifications so as to make them more sensitive, specific, and suitable to the local population.

Objectives  The aim of this study was to evaluate and compare the diagnostic accuracy and clinical utility of the Indian diabetes risk score (IDRS), the American diabetic association (ADA) risk score, and the Finnish Diabetes Risk Score in healthy subjects of South Indian origin in predicting the risk of diabetes and to correlate these risk scores with the blood glucose and hemoglobin A1c (HbA1c) levels in the study population.

Materials and Methods  A total of 160 subjects attending the master health checkup/outpatient department of a tertiary care hospital were included in the study. Each subject was asked to fill a questionnaire. Details obtained using the questionnaire were assessed as per the three diabetic risk scores. Fasting blood sugar/random blood sugar and HbA1c were estimated.

Statistical Analysis Used  Data analysis was done using SPSS 22/23. Pearson correlation was used to compare continuous variables, with p < 0.05 considered statistically significant. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell’s clinical utility indices were calculated for each risk tool.

Results  We found the prevalence of diabetes to be 11.9%. ADA risk score was the only risk score that showed a statistically significant difference ( p -value = 0.05) between the low- and high-risk subjects.

Conclusions  ADA or IDRS risk scores can be used for screening diabetes in the South Indian population. We suggest that inclusion of the history of gestational diabetes and hypertension in the IDRS risk score might improve its sensitivity as a screening tool in our local population.

Screening of undiagnosed hyperglycaemia in the dental setting: The DiabetRisk study. A field trial

AIM

To evaluate the efficacy of different screening protocols for undiagnosed hyperglycaemia in a Research Network of Dental Clinics coordinated by the Spanish Society of Periodontology (SEPA).

MATERIAL AND METHODS

A total of 1143 patients were included in the study. Participants filled a questionnaire considering diabetes risk factors (FINDRISC) and received a periodontal screening examination. Patients with a slightly elevated score according to the Findrisc (≥7), received a point-of-care HbA1c and were eventually referred to their physician for confirmatory diagnosis. Receiver Operating Characteristic (ROC) curves were used to assess the performance of various predictive models with confirmed hyperglycaemia as outcome.

RESULTS

From this population, 97 (8.5%) were finally diagnosed of diabetes (n = 28; 2.5%) or prediabetes (n = 69; 6.0%). When only including the results from the FINDRISC questionnaire, the model reported an area under the curve (AUC) of 0.866 (95% confidence interval - CI [0.833; 0.900]). This model significantly improved when a basic periodontal examination (EPB Code; AUC = 0.876; 95% CI [0.845: 0.906]; p = .042) or a point-of-care HbA1c were added (AUC = 0.961; 95% CI [0.941; 0.980]; p < .001).

CONCLUSIONS

The tested protocol, combining the FINDRISC questionnaire and a point-of-care HbA1c, showed to be feasible when carried out in a dental clinic setting and was efficient to identify subjects with undiagnosed diabetes or prediabetes.

A systematic analysis on prevalence and sub-regional distribution of undiagnosed diabetes mellitus among adults in African countries

BACKGROUND

Despite the high prevalence of diabetes in Africa, the extent of undiagnosed diabetes in the region is still poorly understood. This systematic review and meta-analysis was designed to determine the pooled prevalence of undiagnosed diabetes mellitus among adults in Africa.

METHODS

We conducted a systematic desk review and electronic web-based search of PubMed, Google Scholar, EMBASE, and the World Health Organization's Hinari portal (which includes the SCOPUS, African Index Medicus, and African Journals Online databases), identifying peer-reviewed research studies on the prevalence of undiagnosed diabetes among adult individuals using pre-defined quality and inclusion criteria. We ran our search from June 1, 2018 to Jun 14, 2020. We extracted relevant data and presented descriptive summaries of the studies in tabular form. The I² test was used to assess heterogeneity across studies. A random effects model was used to estimate the pooled prevalence of undiagnosed diabetes mellitus at a 95% confidence interval (CI). Funnel plot asymmetry and Egger's tests were used to check for publication bias. The final effect size was determined by applying a trim and fill analysis in a random-effects model.

RESULTS

Our search identified 1442 studies amongst which 23 articles were eligible for inclusion in the final meta-analysis. The average pooled prevalence of undiagnosed diabetes mellitus among adults was 3.85 (95% CI: 3.10-4.60). The pooled prevalence of undiagnosed diabetes mellitus based on geographic location was 4.43 (95% CI: 3.12-5.74) in Eastern Africa; 4.72 (95% CI: 2.64-6.80) in Western Africa; 4.27 (95% CI: 1.77-6.76) in Northern Africa and 1.46 (95%CI: 0.57-2.34) in southern Africa respectively.

CONCLUSION

Our findings indicate a high prevalence of undiagnosed diabetes in Africa and suggest that it may be more prevalent in Western Africa than the rest of the regions. Given the high levels of undiagnosed diabetes in the Africa region, more attention should be paid to incorporating diabetes screening and treatment services into existing diabetes related programs to reduce the prevalence of undiagnosed cases.

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.

Statin prescription among patients with type 2 diabetes in Botswana: findings and implications

BACKGROUND

There is evidence of statin benefit among patients with diabetes regardless of cholesterol levels or prior cardiovascular disease history. Despite the evidence, there is under-prescription of statins in clinical practice. This study aimed to assess statin prescriptions and associated factors among patients with type 2 diabetes in Botswana.

METHODS

The study was a secondary data analysis of 500 randomly selected type 2 diabetes patients at a specialised diabetes clinic at Gaborone, Botswana. We assessed the proportion of statin-eligible patients who are prescribed statins and evaluated the adjusted associations between various factors and statin prescriptions.

RESULTS

Overall, 477 (95.4%) participants were eligible for a statin prescription. Clinicians prescribed statins in 217 (45.5%) of eligible participants, and only one (4.4%) ineligible participant. The probability of a statin prescription was higher in participants with high baseline low-density lipoprotein cholesterol (risk ratio [RR]: 1.49; 95%CI: 1.17-1.89), increasing duration of diabetes (RR: 1.01; 95%CI 1.00-1.03) and the presence of chronic kidney disease (RR: 1.35; 95%CI: 1.06-1.74).

CONCLUSION

A large proportion with type 2 diabetes in Gaborone is not receiving statins. Clinicians did not consider most guideline-recommended indications for statin prescriptions. The findings call for improvement in diabetes quality of care by implementing evidence-based guideline recommendations.

Can Self-Determination Explain Dietary Patterns Among Adults at Risk of or with Type 2 Diabetes? A Cross-Sectional Study in Socio-Economically Disadvantaged Areas in Stockholm

Type 2 Diabetes (T2D) is a major health concern in Sweden, where prevalence rates have been increasing in socioeconomically disadvantaged areas. Self-Determination Theory (SDT) is posited as an optimal framework to build interventions targeted to improve and maintain long-term healthy habits preventing and delaying the onset of T2D. However, research on SDT, T2D and diet has been widely overlooked in socio-economically disadvantaged populations. This study aims to identify the main dietary patterns of adults at risk of and with T2D from two socio-economically disadvantaged Stockholm areas and to determine the association between those patterns and selected SDT constructs (relatedness, autonomy motivation and competence). Cross-sectional data of 147 participants was collected via questionnaires. Exploratory Factor Analysis was used to identify participants’ main dietary patterns. Multiple linear regressions were conducted to assess associations between the SDT and diet behaviours, and path analysis was used to explore mediations. Two dietary patterns (healthy and unhealthy) were identified. Competence construct was most strongly associated with healthy diet. Autonomous motivation and competence mediated the effect of relatedness on diet behaviour. In conclusion, social surroundings can promote adults at high risk of or with T2D to sustain healthy diets by supporting their autonomous motivation and competence.

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.

Type 2 diabetes mellitus prevalence and risk scores in treated PLWHIV: a cross-sectional preliminary study

Objective

This was a preliminary study whose objective was to estimate the prevalence and risk of developing type 2 diabetes mellitus (T2DM) among people living with HIV (PLWHIV) based on diabetes risk assessment scores.

Results

The study was composed of 234 PLWHIV with median age (interquartile range, IQR) of 44 (36, 52) and a female preponderance of 66%. The median risk scores (IQR) for developing T2DM was 5 (2, 9). Based on the risk scores, 5% of PLWHIV were at high risk for developing T2DM close to 3.4% actual prevalence in the study population. This study demonstrated the importance of using a cheap and fast method for identifying high risk individuals for developing T2DM.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4183-6) contains supplementary material, which is available to authorized users.

A Review of Diabetes Prediction Equations in African Descent Populations

Background: Predicting undiagnosed diabetes is a critical step toward addressing the diabetes epidemic in populations of African descent worldwide. Objective: To review characteristics of equations developed, tested, or modified to predict diabetes in African descent populations. Methods: Using PubMed, Scopus, and Embase databases, a scoping review yielded 585 research articles. After removal of duplicates (n = 205), 380 articles were reviewed. After title and abstract review 328 articles did not meet inclusion criteria and were excluded. Fifty-two articles were retained. However, full text review revealed that 44 of the 52 articles did not report findings by AROC or C-statistic in African descent populations. Therefore, eight articles remained. Results: The 8 articles reported on a total of 15 prediction equation studies. The prediction equations were of two types. Prevalence prediction equations (n = 9) detected undiagnosed diabetes and were based on non-invasive variables only. Non-invasive variables included demographics, blood pressure and measures of body size. Incidence prediction equations (n = 6) predicted risk of developing diabetes and used either non-invasive variables or both non-invasive and invasive. Invasive variables required blood tests and included fasting glucose, high density lipoprotein-cholesterol (HDL), triglycerides (TG), and A1C. Prevalence prediction studies were conducted in the United States, Africa and Europe. Incidence prediction studies were conducted only in the United States. In all these studies, the performance of diabetes prediction equations was assessed by area under the receiver operator characteristics curve (AROC) or the C-statistic. Therefore, we evaluated the efficacy of these equations based on standard criteria, specifically discrimination by either AROC or C-statistic were defined as: Poor (0.50 - 0.69); Acceptable (0.70 - 0.79); Excellent (0.80 - 0.89); or Outstanding (0.90 - 1.00). Prediction equations based only on non-invasive variables reported to have poor to acceptable detection of diabetes with AROC or C-statistic 0.64 - 0.79. In contrast, prediction equations which were based on both non-invasive and invasive variables had excellent diabetes detection with AROC or C-statistic 0.80 - 0.82. Conclusion: Equations which use a combination of non-invasive and invasive variables appear to be superior in the prediction of diabetes in African descent populations than equations that rely on non-invasive variables alone.

Circulating miRNAs as Predictive Biomarkers of Type 2 Diabetes Mellitus Development in Coronary Heart Disease Patients from the CORDIOPREV Study

Circulating microRNAs (miRNAs) have been proposed as type 2 diabetes biomarkers, and they may be a more sensitive way to predict development of the disease than the currently used tools. Our aim was to identify whether circulating miRNAs, added to clinical and biochemical markers, yielded better potential for predicting type 2 diabetes. The study included 462 non-diabetic patients at baseline in the CORDIOPREV study. After a median follow-up of 60 months, 107 of them developed type 2 diabetes. Plasma levels of 24 miRNAs were measured at baseline by qRT-PCR, and other strong biomarkers to predict diabetes were determined. The ROC analysis identified 9 miRNAs, which, added to HbA1c, have a greater predictive value in early diagnosis of type 2 diabetes (AUC = 0.8342) than HbA1c alone (AUC = 0.6950). The miRNA and HbA1c-based model did not improve when the FINDRISC was included (AUC = 0.8293). Cox regression analyses showed that patients with low miR-103, miR-28-3p, miR-29a, and miR-9 and high miR-30a-5p and miR-150 circulating levels have a higher risk of disease (HR = 11.27; 95% CI = 2.61-48.65). Our results suggest that circulating miRNAs could potentially be used as a new tool for predicting the development of type 2 diabetes in clinical practice.

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.