Characteristics of men classified at high-risk for type 2 diabetes mellitus using the AUSDRISK screening tool

A meta-analysis of diabetes risk prediction models applied to prediabetes screening

AIM

To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.

METHODS

The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.

RESULTS

A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.

CONCLUSIONS

Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

Predicting the Risk of Type 2 Diabetes Mellitus with the New Chinese Diabetes Risk Score in a Cohort Study

Objectives: The New Chinese Diabetes Risk Score (NCDRS) is a noninvasive tool to assess the risk of type 2 diabetes mellitus (T2DM) in the Chinese population. Our study aimed to evaluate the performance of the NCDRS in predicting T2DM risk with a large cohort. Methods: The NCDRS was calculated, and participants were categorized into groups by optimal cutoff or quartiles. Hazard ratios (HRs) and 95% confidential intervals (CIs) in Cox proportional hazards models were used to estimate the association between the baseline NCDRS and the risk of T2DM. The performance of the NCDRS was assessed by the area under the curve (AUC). Results: The T2DM risk was significantly increased in participants with NCDRS ≥25 (HR = 2.12, 95% CI 1.88-2.39) compared with NCDRS <25 after adjusting for potential confounders. T2DM risk also showed a significant increasing trend from the lowest to the highest quartile of NCDRS. The AUC was 0.777 (95% CI 0.640-0.786) with a cutoff of 25.50. Conclusion: The NCDRS had a significant positive association with T2DM risk, and the NCDRS is valid for T2DM screening in China.

What is the effectiveness of a personalised video story after an online diabetes risk assessment? A Randomised Controlled Trial

Background

Online risk assessment tools for type 2 diabetes communicate risk information to motivate individuals to take actions and reduce their risk if needed. The impact of these tools on follow-up behaviours (e.g., General Practitioner (GP) visits, improvement in health behaviours) is unknown. This study examined effectiveness of a personalised video story and text-based message on GP and health professional visitations and health behaviours, of individuals assessed as ‘high risk’ following completion of the online Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK).

Methods

A Randomised Controlled Trial (conducted between October 2018 and April 2019) included 477 participants with a high score (≥12). The control group received a text-based message (TM) and the intervention group received both the text-based message and a personalised video story (TM+VS) encouraging them to take follow-up action. Participants reported follow-up actions (one- and three months), and physical activity (PA), dietary behaviours and body weight (baseline, one and three months). Generalized Linear Mixed Models and chi-squared tests were used to test differences in outcomes between groups over time.

Results

The intervention was not more effective for the TM+VS group compared to the TM only group (p-values>0.05 for all outcomes). More participants in the TM only group (49.8% compared to 40.0% in the VS+TM group) visited either a GP or health professional (p = 0.18). During the 3-month follow-up: 44.9% of all participants visited a GP (36.7%) and/or other health professional (31.0%). Significant improvements were found between baseline and three months, in both groups for weekly physical activity, daily fruit and vegetable intake and weight status.

Conclusions

Messages provided with online diabetes risk assessment tools to those with high-risk, positively influence GP and health professional visitations and promote short-term improvements in health behaviours that may contribute to an overall reduction in the development of type 2 diabetes.

Trial registration

Australia New Zealand Clinical Trials Registry; ACTRN12619000809134.

Noninvasive Prototype for Type 2 Diabetes Detection

The present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance (p < 0.05). The Kappa coefficient values were higher than 0.9, showing that the device has a good predictive capacity which would allow the screening process of type 2 diabetes. This development contributes to preventive medicine and makes it possible to determine at a low cost, comfortably, without medical preparation, and in less than 2 minutes whether a person has type 2 diabetes.

Using the AUSDRISK score to screen for pre-diabetes and diabetes in GP practices: a case-finding approach

OBJECTIVE

To identify the optimal AUSDRISK threshold score to screen for pre-diabetes and diabetes.

METHODS

A total of 406 adult patients not diagnosed with diabetes were screened in General Practices (GP) between May and October 2019. All patients received a point of care (POC) HbA1c test. HbA1c test results were categorised into diabetes (≥6.5% or ≥48 mmol/mol), pre-diabetes (5.7-6.4% or 39-47 mmol/mol), or normal (<5.7% or 39 mmol/mol).

RESULTS

Of these patients, 9 (2%) had undiagnosed diabetes and 60 (15%) had pre-diabetes. A Receiver Operator Characteristic (ROC) curve was constructed to predict the presence of pre-diabetes and diabetes; the area under the ROC curve was 0.72 (95%CI 0.65-0.78) indicating modest predictive ability. The optimal threshold cut point for AUSDRISK score was 17 (sensitivity 76%, specificity 61%, + likelihood ratio (LR) 1.96, - likelihood ratio of 0.39) while the accepted cut point of 12 performed less well (sensitivity 94%, specificity 23%, +LR=1.22 -LR+0.26).

CONCLUSIONS

The AUSDRISK tool has the potential to be used as a screening tool for pre-diabetes/diabetes in GP practices. A cut point of ≥17 would potentially identify 75% of all people at risk and three in 10 sent for further testing would be positive for prediabetes or diabetes. Implications for public health: Routine case-finding in high-risk patients will enable GPs to intervene early and prevent further public health burden from the sequelae of diabetes.

Type 2 Diabetes Risk Among University Students in Malaysia

BACKGROUND

Type 2 diabetes (T2D) is a preventable condition. Targeting those who are at risk of getting this disease is essential.

OBJECTIVE

To examine T2D risk among university students in Malaysia and determine its relationship with socio-demographic characteristics and physical activity.

METHODS

The study was conducted cross-sectionally on 390 students selected using quota sampling method from 13 faculties in Universiti Kebangsaan Malaysia. A short form of the International Physical Activity Questionnaire and Finnish Diabetes Risk Score were used to measure the physical activity and T2D risk.

RESULTS

The T2D risk was found to be low (M = 5.23, SD = 3.32) with more than two-third of the student population at the low risk level while a significant proportion of 23.8%, 5.6% and 0.3% having slightly elevated, moderate and high risk respectively. The T2D risk was significantly related to their age (rho = 0.197, p < 0.000), gender (U = 12641, p = 0.011), ethnic group (Χ2 = 18.86, p < 0.000), marital status (Χ2 = 6.597, p = 0.037), residence (U = 10345, p = 0.008), academic year (Χ2 = 14.24, p = 0.007) and physical activity (rho = -0.205, p < 0.000 and Χ2 = 13.515, p = 0.001). Of these, only age (β=0.130) and physical activity (β=-0.159) remained significant in the regression analysis.

CONCLUSION

The findings call for a radical change in the nursing practice to target the amendable factors that are significant in order to prevent the progression of the risk towards type 2 diabetes.

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential 'hotspots' of T2D risk were examined using Local Moran's I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

Diabetes Risk Assessment Among the City Population in Azad Kashmir: A Cross-sectional Study

Objective

To determine the frequency of people at risk of developing diabetes mellitus type 2 (DMT2) and their risk of developing the disease over the next five years, using the Australian type 2 diabetes risk assessment (AUSDRISK) tool.

Methods

A cross-sectional study was done involving 152 adults; both males and females were randomly selected from city populations in Rawalakot and Muzaffarabad of the Azad Kashmir, irrespective of weight, family history and dietary habits. Patients with the apparent clinical features of DMT2 were excluded from the study. Data were collected over a nine-month period from April 2017 using an interviewer-administered questionnaire based on the AUSDRISK tool.

Results

Statistical analysis was done using SPSS version 23.0 (IBM, Armonk, NY, USA). Descriptive statistics were used to calculate the frequencies and percentages. Fifty-four (35.5%) participants had a low risk, 88 (57.9%) had an intermediate risk, and 10 (6.6%) had a high risk of developing DMT2 over the next five years.

Conclusion

Most of the city occupants had an intermediate-to-high risk of developing DMT2 (64.5%) over the next five years.

The clinical utility of the AUSDRISK tool in assessing change in type 2 diabetes risk in overweight/obese volunteers undertaking a healthy lifestyle intervention

The objective of this study was to assess the clinical utility of the AUSDRISK tool for determining risk of Type 2 diabetes mellitus (T2DM). In this secondary analysis from the HealthTrack study, the AUSDRISK tool was applied to data from overweight/obese volunteers completing a lifestyle intervention trial. Participants were volunteer residents of the Illawarra region recruited in 2014–2015. From 377 trial participants (BMI 25–40 kg/m², 25–54 yr), 161 provided data required for measurement of AUSDRISK, collected at 0 and 12 months. They had been randomised to one of two lifestyle interventions (±a healthy food sample, 30 g walnuts/day, I and IW) delivered by dietitians, or a control intervention (C) delivered by nurse practitioners. HbA1c measures were considered for comparison. At baseline the AUSDRISK score indicated n = 83 (51.5%) were at high risk of T2DM within 5 years (≥12 points). After 12 months the proportion scored as high risk significantly decreased in the IW group (51.5% vs 33.3%; p = 0.005), but not I (51.2% vs 39.0%; p = 0.063) or C group (51.9% vs 38.9%; p = 0.065). By comparison, HbA1c measures indicated high risk in n = 24 (17%) of 139 participants at baseline and borderline non-significant changes over time in the randomised groups. In conclusion, the AUSDRISK tool has reasonable clinical utility in identifying T2DM risk in clinical samples of overweight/obese individuals.

Process Evaluation of the Type 2 Diabetes Mellitus PULSE Program Randomized Controlled Trial: Recruitment, Engagement, and Overall Satisfaction

Background: Men are underrepresented in weight loss and type 2 diabetes mellitus (T2DM) prevention studies. Purpose: To determine the effectiveness of recruitment, and acceptability of the T2DM Prevention Using LifeStyle Education (PULSE) Program—a gender-targeted, self-administered intervention for men. Methods: Men (18–65 years, high risk for T2DM) were randomized to intervention (n = 53) or wait-list control groups (n = 48). The 6-month PULSE Program intervention focused on weight loss, diet, and exercise for T2DM prevention. A process evaluation questionnaire was administered at 6 months to examine recruitment and selection processes, and acceptability of the intervention’s delivery and content. Associations between self-monitoring and selected outcomes were assessed using Spearman’s rank correlation. Results: A pragmatic recruitment and online screening process was effective in identifying men at high risk of T2DM (prediabetes prevalence 70%). Men reported the trial was appealing because it targeted weight loss, T2DM prevention, and getting fit, and because it was perceived as “doable” and tailored for men. The intervention was considered acceptable, with men reporting high overall satisfaction (83%) and engagement with the various components. Adherence to self-monitoring was poor, with only 13% meeting requisite criteria. However, significant associations were observed between weekly self-monitoring of weight and change in weight (r_s = −.47, p = .004) and waist circumference (r_s = −.38, p = .026). Men reported they would have preferred more intervention contact, for example, by phone or email. Conclusions: Gender-targeted, self-administered lifestyle interventions are feasible, appealing, and satisfying for men. Future studies should explore the effects of additional non-face-to-face contact on motivation, accountability, self-monitoring adherence, and program efficacy.