Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search

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.

Comparing non-invasive diabetes risk scores for detecting patients in clinical practice: a cross-sectional validation study

Background: Type 2 diabetes (T2DM) is a significant cause of morbidity and mortality, thus early identification is of paramount importance. A high proportion of T2DM cases are undiagnosed highlighting the importance of effective detection methods such as non-invasive diabetes risk scores (DRSs). Thus far, no DRS has been validated in an Irish population. Therefore, the aim of this study was to compare the ability of nine DRSs to detect T2DM cases in an Irish population. Methods: This was a cross-sectional study of 1,990 men and women aged 46–73 years. Data on DRS components were collected from questionnaires and clinical examinations. T2DM was determined according to a fasting plasma glucose level ≥7.0 mmol/l or a glycated haemoglobin A1c level ≥6.5% (≥48 mmol/mol). Receiver operating characteristic curve analysis assessed the ability of DRSs and their components to discriminate T2DM cases. Results: Among the examined scores, area under the curve (AUC) values ranged from 0.71–0.78, with the Cambridge Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Leicester Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Rotterdam Predictive Model 2 (AUC=0.78, 95% CI: 0.74–0.82) and the U.S. Diabetes Risk Score (AUC=0.78, 95% CI: 0.74–0.81) demonstrating the largest AUC values as continuous variables and at optimal cut-offs. Regarding individual DRS components, anthropometric measures displayed the largest AUC values. Conclusions: The best performing DRSs were broadly similar in terms of their components; all incorporated variables for age, sex, BMI, hypertension and family diabetes history. The Cambridge Diabetes Risk Score, had the largest AUC value at an optimal cut-off, can be easily accessed online for use in a clinical setting and may be the most appropriate and cost-effective method for case-finding in an Irish population.

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.

Alternative indices of glucose homeostasis as biochemical diagnostic tests for abnormal glucose tolerance in an African setting

AIMS

Accurate diabetes diagnosis is important in Africa, where rates are increasing, and the disease largely undiagnosed. The cumbersome oral glucose tolerance test (OGTT) remains the reference standard, while alternative diagnostic methods are not yet established in Africans. We assessed the ability of fasting plasma glucose (FPG), HbA1c and fructosamine, to diagnose OGTT-based abnormal glucose tolerance in mixed-ancestry South Africans.

METHODS

Mixed-ancestry adults, residing in Cape Town were examined between February and November 2015. OGTT values were used to classify glucose tolerance status as: screen-detected diabetes, prediabetes, dysglycaemia (combination of diabetes and prediabetes) and normal glucose tolerance.

RESULTS

Of the 793 participants included, 65 (8.2%) had screen-detected diabetes, 157 (19.8%) prediabetes and 571 (72.0%) normal glucose tolerance. Correlations of FPG and 2-h glucose with HbA1c (r=0.51 and 0.52) were higher than those with fructosamine (0.34 and 0.30), both p<0.0001. The highest c-statistic for the prediction of abnormal glucose tolerance was recorded with 2-h glucose [c-statistic=0.997 (screen-detected diabetes), 0.979 (prediabetes) and 0.984 (dysglycaemia)] and the lowest with fructosamine (0.865, 0.596 and 0.677). At recommended or data-specific optimal cut-offs, no combination of FPG, HbA1c and fructosamine did better than 2-h glucose, while FPG was better than HbA1c and fructosamine on a range of performance measures.

CONCLUSIONS

Abnormal glucose tolerance in this population is overwhelmingly expressed through 2-h glucose's abnormalities; and no combination of FPG, HbA1c and fructosamine was effective at accurately discriminating OGTT-defined abnormal glucose tolerance. Tested non-glucose based strategies are unreliable alternatives to OGTT for dysglycaemia diagnosis in this population.

Type 2 diabetes mellitus unawareness, prevalence, trends and risk factors: National Health and Nutrition Examination Survey (NHANES) 1999-2010

Objectives To determine whether the associations with key risk factors in patients with diagnosed and undiagnosed type 2 diabetes mellitus (T2DM) are different using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010. Methods The study analysed the prevalence and association with risk factors of undiagnosed and diagnosed T2DM using a regression model and a multinomial logistic regression model. Data from the NHANES 1999-2010 were used for the analyses. Results The study analysed data from 10 570 individuals. The overall prevalence of diagnosed and undiagnosed T2DM increased significantly from 1999 to 2010. The prevalence of undiagnosed T2DM was significantly higher in non-Hispanic whites, in individuals <30 years old and in those with near optimal (130-159 mg/dl) or very high (≥220 mg/dl) non-high-density lipoprotein cholesterol levels compared with diagnosed T2DM. Body mass index, low economic status or low educational level had no effect on T2DM diagnosis rates. Though diagnosed T2DM was associated with favourable diet/carbohydrate intake behavioural changes, it had no effect on physical activity levels. Conclusion The overall T2DM prevalence increased between 1999 and 2010, particularly for undiagnosed T2DM in patients that were formerly classified as low risk.

Cardiovascular disease risk factors in a Nigerian population with impaired fasting blood glucose level and diabetes mellitus

Background

Diabetes is a risk factor for cardiovascular diseases (CVDs) and there are reports of increasing prevalence of prediabetes in Nigeria. This study therefore characterised CVDs risk factors in subjects with impaired fasting glucose (IFG) and diabetes.

Methods

Data from 4 population-based cross-sectional studies on 2447 apparently healthy individuals from 18 - 89 years were analysed. Anthropometric, blood pressure and biochemical parameters were collected and classified. Individuals with IFG (prediabetes) and diabetes were merged each for positive cases of dyslipidaemia, high blood pressure (HBP) or obesity. Optimal Discriminant and Hierarchical Optimal Classification Tree Analysis (HO-CTA) were employed.

Results

Overall prevalence of IFG and diabetes were 5.8% (CI: 4.9 – 6.7%) and 3.1% (CI: 2.4 – 3.8%), respectively. IFG co-morbidity with dyslipidaemia (5.0%; CI: 4.1 – 5.8%) was the highest followed by overweight/obese (3.1%; CI: 2.5 – 3.8%) and HBP (1.8%; CI: 1.3 – 2.4%). The predicted age of IFG or diabetes and their co-morbidity with other CVD risk factors were between 40 – 45 years. Elevated blood level of total cholesterol was the most predictive co-morbid risk factor among IFG and diabetes subjects. Hypertriglyceridaemia was an important risk factor among IFG-normocholesterolaemic-overweight/obese individuals.

Conclusion

The higher prevalence of co-morbidity of CVD risk factors with IFG than in diabetes plus the similar age of co-morbidity between IFG and diabetes highlights the need for risk assessment models for prediabetes and education of individuals at risk about factors that mitigate development of diabetes and CVDs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3910-3) contains supplementary material, which is available to authorized users.

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

This was a cross-sectional study designed to assess the validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. Participants aged ≥20 years without previously diagnosed diabetes were screened by (1) an 8-item Finnish diabetes risk assessment questionnaire and (2) Haemoglobin A1c test. Data from 291 participants were analyzed (74.2% were females). The mean age of the participants was 50.1 (SD = ±11) years, and the prevalence of undiagnosed diabetes was 42 (14.4%) with no significant differences between the gender (20% versus 12.5%, P = 0.26). The area under curve for detecting undiagnosed diabetes was 0.63 (95% CI 0.55-0.72) for the total population, 0.65 (95% CI: 0.56-0.75) for women, and 0.67 (95% CI: 0.52-0.83) for men. The optimal cut-off point for detecting undiagnosed diabetes was 17 (sensitivity = 48% and specificity = 73%) for the total population, 17 (sensitivity = 56% and specificity = 66%) for females, and 13 (sensitivity = 53% and specificity = 77%) for males. The positive predictive value and negative predictive value were 20% and 89.5%, respectively. The findings indicate that the Finnish questionnaire was only modestly effective in predicting undiagnosed diabetes among outpatients in Botswana.

Developing a Screening Algorithm for Type II Diabetes Mellitus in the Resource-Limited Setting of Rural Tanzania

BACKGROUND

Noncommunicable diseases are on pace to outnumber infectious disease as the leading cause of death in sub-Saharan Africa, yet many questions remain unanswered with concern toward effective methods of screening for type II diabetes mellitus (DM) in this resource-limited setting. We aim to design a screening algorithm for type II DM that optimizes sensitivity and specificity of identifying individuals with undiagnosed DM, as well as affordability to health systems and individuals.

METHODS

Baseline demographic and clinical data, including hemoglobin A1c (HbA1c), were collected from 713 participants using probability sampling of the general population. We used these data, along with model parameters obtained from the literature, to mathematically model 8 purposed DM screening algorithms, while optimizing the sensitivity and specificity using Monte Carlo and Latin Hypercube simulation.

RESULTS

An algorithm that combines risk assessment and measurement of fasting blood glucose was found to be superior for the most resource-limited settings (sensitivity 68%, sensitivity 99% and cost per patient having DM identified as $2.94). Incorporating HbA1c testing improves the sensitivity to 75.62%, but raises the cost per DM case identified to $6.04. The preferred algorithms are heavily biased to diagnose those with more severe cases of DM.

CONCLUSIONS

Using basic risk assessment tools and fasting blood sugar testing in lieu of HbA1c testing in resource-limited settings could allow for significantly more feasible DM screening programs with reasonable sensitivity and specificity.

Effects of Single and Multiple Blood Pressure Measurement Strategies on the Prediction of Prevalent Screen-Detected Diabetes Mellitus: A Population-Based Survey

The authors investigated the effects of single and multiple blood pressure (BP) measurements during the same encounter on screen-detected diabetes risk. Data for 9018 Cameroonian adults from a community-based survey were used. Resting BP was measured three times 5 minutes apart. Logistic regressions were used to compute the odd ratio (OR) per standard deviation (SD) higher BP variables. Systolic BP, diastolic BP, and mean arterial pressure (MAP), but not pulse pressure, were related to prevalent diabetes. The highest OR (95% confidence interval [CI]) per SD higher pressure were recorded for MAP (OR, 1.16; 95% CI, 1.05-1.28) and systolic BP (OR, 1.15; 95% CI, 1.04-1.27). Estimates of the association were highest for the first, then third, and lastly the second BP measurements. Estimates from average BP measurements were not better than those from single measurement. Single BP measurement is more effective for diabetes risk screening than multiple measurements. Community-based diabetes strategies utilizing a single measurement are simple without compromising the yield.

Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting

Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey (n = 2,472) and the CRONICAS Cohort Study (n = 2,945). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62-0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61-0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.