The development and validation of the Portuguese risk score for detecting type 2 diabetes and impaired fasting glucose

Predicting diabetes-related conditions in need of intervention: Lolland-Falster Health Study, Denmark

In the Danish population, about one-in-ten adults have prediabetes, undiagnosed, poorly or potentially sub-regulated diabetes, for short DMRC. It is important to offer these citizens relevant healthcare intervention. We therefore built a model for prediction of prevalent DMRC. Data were derived from the Lolland-Falster Health Study undertaken in a rural-provincial area of Denmark with disadvantaged health. We included variables from public registers (age, sex, age, citizenship, marital status, socioeconomic status, residency status); from self-administered questionnaires (smoking status, alcohol use, education, self-rated health, dietary habits, physical activity); and from clinical examinations (body mass index (BMI), pulse rate, blood pressure, waist-to-hip ratio). Data were divided into training/testing datasets for development and testing of the prediction model. The study included 15,801 adults; of whom 1,575 with DMRC. Statistically significant variables in the final model included age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. In the testing dataset this model had an area under the curve (AUC) = 0.77 and a sensitivity of 50% corresponding to a specificity of 84%. In a health disadvantaged Danish population, presence of prediabetes, undiagnosed, or poorly or potentially sub-regulated diabetes could be predicted from age, self-rated health, smoking status, BMI, waist-to-hip ratio, and pulse rate. Age is known from the Danish personal identification number, self-rated health and smoking status can be obtained from simple questions, and BMI, waist-to-hip ratio, and pulse rate can be measured by any person in health care and potentially by the person him/her-self. Our model might therefore be useful as a screening tool.

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review

Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.

Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

OBJECTIVES

This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).

DATA SOURCES

Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.

STUDY SELECTION

Articles published between December 2011 and October 2019 were considered.

DATA EXTRACTION

For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.

RESULTS

The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.

CONCLUSIONS

Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

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

Background:

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

Objective(s):

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

Methods:

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

Conclusions:

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

A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening

BACKGROUND AND PURPOSE

Diagnostic algorithms are invaluable tools for screening diabetes. This review aimed to evaluate and identify the most robust methodological approaches for developing diagnostic algorithms for screening diabetes.

METHODS

Following a literature search, methodological quality of algorithm development studies was evaluated using the TRIPOD guidelines (Collins, Reitsma, Altman, & Moons, 2015).

RESULTS

Methods used for developing the algorithms included logistic regression models, classification and regression trees, Random Forest and TreeNet, Artificial Neural Networks, and Naïve Bayes. Methodological issues for algorithm development studies were related to handling of missing values, reporting recruitment methods, categorization of continuous variables, and statistical controls.

CONCLUSIONS

Most studies exhibited critical methodological flaws and poor adherence to reporting standards. Diabetes screening algorithms can easily be availed electronically and utilized by nurses at minimal cost even in underserved areas.

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques

The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

Utility of nonblood-based risk assessment for predicting type 2 diabetes mellitus: A meta-analysis

OBJECTIVE

Nonblood-based risk assessment for type 2 diabetes mellitus (T2DM) that depends on data based on a questionnaire and anthropometry is expected to avoid unnecessary diagnostic testing and overdiagnosis due to blood testing. This meta-analysis aims to assess the predictive ability of nonblood-based risk assessment for future incident T2DM.

METHODS

Electronic literature search was conducted using EMBASE and MEDLINE (from January 1, 1997 to October 1, 2014). Included studies had to use at least 3 predictors for T2DM risk assessment and allow reproduction of 2×2 contingency table data (i.e., true positive, true negative, false positive, false negative) to be pooled with a bivariate random-effects model and hierarchical summary receiver-operating characteristic model. Considering the importance of excluding individuals with a low likelihood of T2DM from diagnostic blood testing, we especially focused on specificity and LR-.

RESULTS

Eighteen eligible studies consisting of 184,011 participants and 7038 cases were identified. The pooled estimates (95% confidence interval) were as follows: sensitivity=0.73 (0.66-0.79), specificity=0.66 (0.59-0.73), LR+=2.13 (1.81-2.50), and LR-=0.41 (0.34-0.50).

CONCLUSIONS

Nonblood-based assessment of risk of T2DM could produce acceptable results although the feasibility of such a screener needs to be determined in future studies.

Analysis of the Japanese Diabetes Risk Score and fatty liver markers for incident diabetes in a Japanese cohort

AIMS

We examined the effectiveness of the Japanese Diabetes Risk Score (JPDRISC) and fatty liver markers for predicting incident diabetes.

METHODS

We created the JPDRISC. The study periods I and II were January 2007 to May 2009 and June 2009 to December 2011, respectively. A total of 2084 people (1389 men, 695 women; mean age: 46 years) were included. People with diabetes in the Period I and those with ethanol intake >140 g/week were excluded. A total of 1515 people were included. Fatty liver using ultrasonography scores (FLUS) were assigned.

RESULTS

The mean observation period was 26.3 months, and 24 people had developed diabetes between the Periods I and II. In logistic regression analysis, the JPDRISC (OR=1.197, 95% C.I.: 1.062-1.350, p=0.003) and FLUS (OR=2.591, 95% C.I.: 1.411-4.758, p=0.002) in the Period I were independent determinants of incident diabetes. In receiver operating characteristic analysis, sensitivity and specificity for incident diabetes were 0.885 and 0.536, respectively, in people with both FLUS≥1 and the total JPDRISC≥6 in the Period I. The sensitivity was better than the JPDRISC alone (sensitivity 0.696) and FLUS alone (sensitivity 0.750).

CONCLUSIONS

JPDRISC and FLUS were independently associated with incident diabetes and their combination is useful.

Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes

AIMS/INTRODUCTION

To develop and evaluate a simple, non-invasive, diabetes risk score for detecting individuals at high risk for type 2 diabetes in rural Bangladesh.

MATERIALS AND METHODS

Data from 2,293 randomly selected individuals aged ≥20 years from a cross-sectional study in a rural community of Bangladesh (2009 Chandra Rural Study) was used for model development. The validity of the model was assessed in another rural cross-sectional study (2009 Thakurgaon Rural Study). The logistic regression model used included age, sex, body mass index, waist-to-hip ratio and hypertension status to predict individuals who were at high risk for type 2 diabetes.

RESULTS

On applying the developed model to both cohorts, the area under the receiver operating characteristic curve was 0.70 (95% confidence interval 0.68-0.72) for the Chandra cohort and 0.71 (95% confidence interval 0.68-0.74) for the Thakurgaon cohort. The risk score of >9 was shown to have the optimal cut-point to detect diabetes. This score had a sensitivity of 62.4 and 75.7%, and specificity of 67.4 and 61.6% in the two cohorts, respectively. This risk score was shown to have improved sensitivity and specificity to detect type 2 diabetes cases compared with the Thai, Indian, Omani, UK, Dutch, Portuguese and Pakistani diabetes risk scores.

CONCLUSIONS

This simple, non-invasive risk score can be used to detect individuals at high risk for type 2 diabetes in rural Bangladesh. Subjects with a score of 9 or above (out of 15) should undergo an oral glucose tolerance test for definitive diagnosis of diabetes.

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.