AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures

Are Nutrition Literacy and Sustainable Dietary Habits Associated with Cardiovascular Disease and Diabetes Developmental Risks?

OBJECTIVE

This study aimed to examine the association of nutritional literacy levels and sustainable nutritional behaviors with the risk of developing cardiovascular diseases and diabetes in the Turkish adult population.

METHODS

Sociodemographic information, disease history, nutritional habits, and physical activity levels of 3146 volunteer individuals (male = 1590, female = 1556) between the ages of 40-75 were collected using a questionnaire form and face-to-face interviews. The sustainable nutritional behaviors of the participants were evaluated using Turkish validated scales for Sustainable and Healthy Eating Behavior (SHE) and nutritional literacy levels with the Evaluation Instrument of Nutrition Literacy on Adults (EINLA). Cardiovascular disease risks of the participants were assessed with the Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator program and the Heart Score (SCORE) scale and type-2 diabetes risk with the Finnish Diabetes Risk Score (FINDRISC). Each participant's 24-h food consumption record was obtained using the retrospective recall method.

RESULTS

It was determined that ASCVD and SCORE levels were significantly higher in males compared to females. It was observed that individuals with lower cardiovascular and diabetes risk scores had higher educational levels, and the risks increased significantly with age (p < 0.05). Anthropometric measurements such as body mass index, and waist hip circumference were significantly higher in those with higher cardiovascular and diabetes risk scores. Furthermore, in individuals with higher SCORE and FINDRISC levels, SHE and EINLA scores were significantly lower (p < 0.05). It was also observed that SCORE and diabetes risk scores increased with higher energy and macronutrient intakes.

CONCLUSION

The risk of developing cardiovascular disease and diabetes was associated with sustainable nutritional behaviors and nutritional literacy. It may be suggested that increasing nutritional literacy and encouraging sustainable nutritional behaviors may be effective strategies in the management and reduction of the prevalence of certain chronic diseases.KEY TEACHING POINTSCardiovascular diseases and diabetes are two major chronic conditions that can be managed and treated through proper nutrition.Increased nutritional literacy levels and sustainable dietary habits may result in reduced risk of cardiovascular diseases and diabetes.Nutritionists should assess the patients' nutrition literacy levels and implement sustainable, health-focused nutrition education programs to enhance their understanding of nutrition.

Diabetes Screening in the Emergency Department: Development of a Predictive Model for Elevated Hemoglobin A1c

Aims: We developed a prediction model for elevated hemoglobin A1c (HbA1c) among patients presenting to the emergency department (ED) at risk for diabetes to identify important factors that may influence follow-up patient care. Methods: Retrospective electronic health records data among patients screened for diabetes at the ED in May 2021 was used. The primary outcome was elevated HbA1c (≥ 5.7%). The data was divided into a derivation set (80%) and a test set (20%) stratified by elevated HbA1c. In the derivation set, we estimated the optimal significance level for backward elimination using a 10-fold cross-validation method. A final model was derived using the entire derivation set and validated on the test set. Performance statistics included C-statistic, sensitivity, specificity, predictive values, Hosmer-Lemeshow test, and Brier score. Results: There were 590 ED patients screened for diabetes in May 2021. The final model included nine variables: age, race/ethnicity, insurance, chief complaints of back pain and fever/chills, and a past medical history of obesity, hyperlipidemia, chronic obstructive pulmonary disease, and substance misuse. Adequate model discrimination (C-statistic = 0.75; sensitivity, specificity, and predictive values > 0.70), no evidence of model ill fit (Hosmer-Lemeshow test = 0.29), and moderate Brier score (0.21) suggest acceptable model performance. Conclusion: In addition to age, obesity, and hyperlipidemia, a history of substance misuse was identified as an important predictor of elevated HbA1c levels among patients screened for diabetes in the ED. Our findings suggest that substance misuse may be an important factor to consider when facilitating follow-up care for patients identified with prediabetes or diabetes in the ED and warrants further investigation. Future research efforts should also include external validation in larger samples of ED patients.

Self-Guided Blood Pressure Screening in the Community: Opportunities, and Challenges

BACKGROUND

Community-based health check kiosks provide opportunities to improve the detection and long-term monitoring of hypertension. We describe the sociodemographic and cardiovascular characteristics of first-time and repeat users of these kiosks.

METHOD

This was an observational study. Deidentified data collected from 430 SiSU Health consumer-facing health check stations in pharmacies across Australia between January 2018 and November 2020 were analyzed. Using a logistic regression, we identified factors associated with repeat checks in the overall cohort and in those with possible hypertension presented as adjusted odds ratios (aOR) and 95% CIs.

RESULTS

A total of 982 122 unique checks were conducted; 54% (n=530 139) of the health check users were female, and the average age of all users was 38.2 (SD 16.0) years. Of those that used the kiosks, 13% used them more than once. Overall, 22% met the definition of possible hypertension, 16% (n=136 345) had blood pressure (BP) ≥140/90 mm Hg, 4% (n=34 349) had BP >160/100 mm Hg, and 13% (121 282) reported taking BP medicines. In the adjusted analysis, first-time users who were aged 50 to 69 years (aOR 0.91 [95% CI 0.87-0.96]) or ≥70 years (aOR 0.68 [95% CI 0.62-0.74]) were less likely than young users (18-29 years) to return for a second health check. Those in very remote areas were 61% (aOR 0.39 [95% CI 0.19-0.72]), and smokers were 13% less likely to return (aOR 0.87 [95% CI 0.83-0.91]). People taking BP medications were more likely to return (aOR 1.16 [95% CI 1.09-1.22]).

CONCLUSIONS

Community-based health checks may identify people with high BP and could provide an option for self-monitoring. Broader implementation is needed to increase the reach in rural areas and among the elderly population.

The association between chemosensitivity and the 10-year risk of type 2 diabetes in male patients with obstructive sleep apnea

PURPOSE

Obstructive sleep apnea (OSA) is associated with a variety of diseases, including type 2 diabetes (T2D). Chemosensitivity is an important component of the pathophysiological mechanisms of OSA, and it is not only elevated in patients with OSA but also in those with T2D. This study aimed to investigate the association between chemosensitivity and the risk of developing T2D in patients with OSA.

METHODS

A total of 135 male participants with OSA and without pre-existing T2D were enrolled in this study. Peripheral chemosensitivity was evaluated using the rebreathing test. Data on demographics, polysomnographic parameters, and clinical characteristics were collected. The QDiabetes-2018 risk calculator was employed to calculate the 10-year T2D risk. The association between peripheral chemosensitivity and 10-year T2D risk was examined using multivariate logistic regression.

RESULTS

A total of 64 participants had moderate-to-high 10-year risk of T2D. In the fully adjusted model, participants situated within the second and fifth quantiles of peripheral chemosensitivity levels demonstrated a higher risk of developing T2D, with OR of 4.87 (95% CI, 1.22-19.43) and 5.26 (95% CI, 1.27-21.68) respectively. However, across varying levels of peripheral chemosensitivity, no significant difference in the 10-year T2D risk was observed among different severities of OSA.

CONCLUSION

Higher peripheral chemosensitivity was associated with an increased 10-year T2D risk, as calculated using a risk calculator based on clinical variables. For outcomes that reflect a moderate-to-high 10-year risk of T2D, the severity of OSA did not significantly affect the risk, irrespective of whether patients exhibited relatively low or high chemosensitivity.

Indirect estimation of the prevalence of type 2 diabetes mellitus in the sub-population of Tehran: using non-laboratory risk-score models in Iran

BACKGROUND

The prevalence of type 2 diabetes mellitus (T2DM) in the population covered by the Tehran University of Medical Sciences is unclear but crucial for healthcare programs. This study aims to validate four non-laboratory risk-score models, the American Diabetes Association (ADA) Risk Score, Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), Finnish Diabetes Risk Score (FINDRISC), and TOPICS Diabetes Screening Score, for identifying undiagnosed diabetes and indirectly estimate the prevalence of T2DM in a subset of the Tehranian population using the selected model.

METHODS

This research consisted of two main parts. In the first part, non-laboratory risk-score models to identify undiagnosed T2DM were validated using Iranian data from STEPs 2016 survey. The model performance was evaluated through the Area Under the Curve (AUC) and calibration via the observed-to-expected (O/E) ratio. Additional independent data from STEPs 2011 survey in Iran were utilized to test the model results by comparing indirect prevalence estimates with observed estimates. In the second part, the prevalence of T2DM was estimated indirectly by applying the selected model to a representative random sample from a Tehranian population telephone survey conducted in 2023.

RESULTS

Among the different models used, AUSDRISK showed the best performance in both discrimination (AUC (95% confidence interval (CI)): 0.80 (0.78, 0.81)) and calibration (O/E ratio = 1.01). After updating the original model, there was no change in the AUC value or calibration. Additionally, our findings indicate that the indirect estimates are nearly identical to the observed values in STEPs 2011 survey. In the second part of the study, by applying the recalibrated model to a subsample, the indirect prevalence of undiagnosed diabetes and T2DM (95% CI) were estimated at 4.18% (3.87, 4.49) and 11.1% (9.34, 13.1), respectively.

CONCLUSION

Given the strong performance of the model, it appears that indirect method can provide a cost-effective and simple approach to assess disease prevalence and intervention effectiveness.

Diabetic Foot Care: Assessing the Knowledge and Practices of Diabetic Patients at Aldaraga Centre, Gezira State, Sudan, 2021

Introduction

Improving diabetic patients' foot care behaviours is crucial in the incidence reduction of diabetic foot ulceration-associated complications.

Objective

This study assessed the knowledge and practice of diabetic patients towards diabetic foot care and their general understanding of diabetes causes, complications, and treatment.

Methods

A cross-sectional study was conducted at Aldaraga Clinic Centre, Sudan, with a sample size of 100 diabetic patients. A questionnaire and checklist were used to collect data for this study. The data was analyzed through SPSS Version 16 software.

Results

The majority of respondents were females (62%), above 40 years old (66%), married, with a low educational level, and moderate-income (76%). The study revealed that most respondents did not attend any educational program about diabetes, indicating poor or no knowledge about diabetes mellitus. However, respondents had good knowledge of most signs and symptoms of diabetes, with the highest percentage (88%) for extreme thirst. Concerning the knowledge of respondents about complications of diabetes, it was generally poor, except for retinal diseases (70%). Participants' knowledge of signs and symptoms of hypoglycemia was found to be poor at 25%. The study showed that most respondents did not know what diabetes gangrene is. Foot infections were the most dominant cause of hospitalization among diabetic patients, often leading to amputations.

Conclusion

Enhancing foot care behaviours in diabetic patients is crucial to reduce diabetic foot ulceration risks. Patient-friendly educational interventions and regular physician reinforcement are urgently needed, including awareness programs, specialized diabetes centres, and health education through mass media to improve foot care practices and prevent complications like amputations.

Pros and cons of live kidney donation in prediabetics: A critical review and way forward

There is shortage of organs, including kidneys, worldwide. Along with deceased kidney transplantation, there is a significant rise in live kidney donation. The prevalence of prediabetes (PD), including impaired fasting glucose and impaired glucose tolerance, is on the rise across the globe. Transplant teams frequently come across prediabetic kidney donors for evaluation. Prediabetics are at risk of diabetes, chronic kidney disease, cardiovascular events, stroke, neuropathy, retinopathy, dementia, depression and nonalcoholic liver disease along with increased risk of all-cause mortality. Unfortunately, most of the studies done in prediabetic kidney donors are retrospective in nature and have a short follow up period. There is lack of prospective long-term studies to know about the real risk of complications after donation. Furthermore, there are variations in recommendations from various guidelines across the globe for donations in prediabetics, leading to more confusion among clinicians. This increases the responsibility of transplant teams to take appropriate decisions in the best interest of both donors and recipients. This review focuses on pathophysiological changes of PD in kidneys, potential complications of PD, other risk factors for development of type 2 diabetes, a review of guidelines for kidney donation, the potential role of diabetes risk score and calculator in kidney donors and the way forward for the evaluation and selection of prediabetic kidney donors.

Development of a gestational diabetes risk assessment scale: a validity and reliability study

Background/aim

The aim of this study was to develop a gestational diabetes risk assessment scale (GEDRISK).

Materials and methods

This methodological study included 652 pregnant women who presented to six public health institutions in İstanbul between September 2021 and February 2022. Content validity was evaluated using the content validity index, while construct validity was assessed through exploratory and confirmatory factor analyses. Item discrimination was examined using Cronbach's alpha coefficients, Spearman-Brown and Guttman coefficients, item-total correlation tests, and 27% lower and upper quartile t tests. Reliability was determined through test-retest analysis methods.

Results

The scale-level content validity index demonstrated strong coherence at 0.83, confirming its robustness. In the EFA, the scale, comprising 18 items and 5 subdimensions, accounted for 57.48% of the variance (n = 652). The results of the confirmatory factor analysis were as follows: χ2/df = 2.28; RMR = 0.01; CFI, GFI, and IFI = 0.95; AGFI = 0.93; NFI = 0.92; RFI = 0.90; and RMSEA = 0.04. The Spearman-Brown and Guttman split-half coefficient equal length analysis produced a result of 0.826, Cronbach's alpha value was 0.756, and the temporal consistency of the scale was evaluated with the test-retest method (p = 0.184). The structure of the scale was evaluated with a validity and reliability analysis and was found to have acceptable, valid, and reliable properties. The mean total GEDRISK score of the pregnant women participating in the study was 33.57 ± 4.71. It was observed that the GEDRISK scale identified 51% of the respondents diagnosed with diabetes through an oral glucose tolerance test.

Conclusion

The GEDRISK scale was found to be a valid and reliable tool for the measurement of gestational diabetes risk in a sample of the Turkish population.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Developing a risk assessment tool for insulin resistance in European adolescents: the HELENA-IR score

OBJECTIVES

To develop and validate an easy-to-use screening tool for identifying adolescents at high-risk for insulin resistance (IR).

METHODS

Α total of 1,053 adolescents (554 females), aged 12.5 to 17.5 years with complete data on glucose and insulin levels were included. Body mass index (BMI), fat mass index (FMI) and the homeostasis model assessment for insulin resistance (HOMA-IR) were calculated. VO₂max was predicted using 20 m multi-stage fitness test. The population was randomly separated into two cohorts for the development (n=702) and validation (n=351) of the index, respectively. Factors associated with high HOMA-IR were identified by Spearman correlation in the development cohort; multiple logistic regression was performed for all identified independent factors to develop a score index. Finally, receiver operating characteristic (ROC) analysis was performed in the validation cohort and was used to define the cut-off values that could identify adolescents above the 75th and the 95th percentile for HOMA-IR.

RESULTS

BMI and VO₂max significantly identified high HOMA-IR in males; and FMI, TV watching and VO₂max in females. The HELENA-IR index scores range from 0 to 29 for males and 0 to 43 for females. The Area Under the Curve, sensitivity and specificity for identifying males above the 75th and 95th of HOMA-IR percentiles were 0.635 (95%CI: 0.542-0.725), 0.513 and 0.735, and 0.714 (95%CI: 0.499-0.728), 0.625 and 0.905, respectively. For females, the corresponding values were 0.632 (95%CI: 0.538-0.725), 0.568 and 0.652, and 0.708 (95%CI: 0.559-0.725), 0.667 and 0.617, respectively. Simple algorithms were created using the index cut-off scores.

CONCLUSIONS

Paediatricians or physical education teachers can use easy-to-obtain and non-invasive measures to apply the HELENA-IR score and identify adolescents at high risk for IR, who should be referred for further tests.

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.

Performance of Stepwise Screening Methods in Identifying Individuals at High Risk of Type 2 Diabetes in an Iranian Population

BACKGROUND

Recent evidence recommended stepwise screening methods for identifying individuals at high risk of type 2 diabetes to be recruited in the lifestyle intervention programs for the prevention of the disease. This study aims to assess the performance of different stepwise screening methods that combine non-invasive measurements with lab-based measurements for identifying those with 5-years incident type 2 diabetes.

METHODS

3037 participants aged ≥30 years without diabetes at baseline in the Tehran Lipid and Glucose Study (TLGS) were followed. Thirty-two stepwise screening methods were developed by combining a non-invasive measurement (an anthropometric measurement (waist-to-height ratio, WtHR) or a score based on a non-invasive risk score [Australian Type 2 Diabetes Risk Assessment Tool, AUSDRISK]) with a lab-based measurement (different cut-offs of fasting plasma glucose [FPG] or predicted risk based on three lab-based prediction models [Saint Antonio, SA; Framingham Offspring Study, FOS; and the Atherosclerosis Risk in Communities, ARIC]). The validation, calibration, and usefulness of lab-based prediction models were assessed before developing the stepwise screening methods. Cut-offs were derived either based on previous studies or decision-curve analyses.

RESULTS

203 participants developed diabetes in 5 years. Lab-based risk prediction models had good discrimination power (area under the curves [AUCs]: 0.80-0.83), achieved acceptable calibration and net benefits after recalibration for population's characteristics and were useful in a wide range of risk thresholds (5%-21%). Different stepwise methods had sensitivity ranged 20%-68%, specificity 70%-98%, and positive predictive value (PPV) 14%-46%; they identified 3%-33% of the screened population eligible for preventive interventions.

CONCLUSION

Stepwise methods have acceptable performance in identifying those at high risk of incident type 2 diabetes.

Oral health professionals screening for undiagnosed type-2 diabetes and prediabetes: the iDENTify study

BACKGROUND

As part of an evaluation of an oral healthcare practice-based model that identifies patients with prediabetes or type-2 diabetes, this study reports on the proportion of patients identified with clinically confirmed type-2 diabetes (T2D)/prediabetes and barriers of implementation of the model.

METHODOLOGY

Urban and rural oral healthcare practices were invited to participate. Participating practices invited eligible patients to participate in the screening program using the Australian Type-2 Diabetes Risk Assessment Tool (AUSDRISK). Participants were categorised as low, intermediate, or high-risk for prediabetes/T2D. Patients in the intermediate or high-risk category were referred to their General Medical Practitioner (GP) for further investigation.

RESULTS

Fifty-one oral healthcare practices and 76 Oral Health Professionals (OHP) participated (60 Dentists, 8 Dental Hygienists, 8 Oral Health Therapists). 797 patients were screened; 102 were low-risk; 331 intermediate-risk; and 364 high-risk for T2D. Of the 695 participants in the intermediate or high-risk groups, 386 (55.5%) were referred to their GP for T2D assessment. Of them, 96 (25.0%) results were returned to OHPs. Of the returned results, six were (6.3%) diagnosed with pre-T2D.

CONCLUSION

Patients found to have undiagnosed T2D/prediabetes (6.3%) were within the expected range reported in the literature. Findings indicate that identifying individuals at an elevated risk of having or developing T2D is effective, feasible and could be incorporated into oral healthcare settings. However, this integration may require additional OHPs training and education to ensure that patients at elevated risk of T2D are referred for further assessment.

Remotely Conducted App-Based Intervention for Cardiovascular Disease and Diabetes Risk Awareness and Prevention: Single-Group Feasibility Trial

BACKGROUND

Cardiovascular disease and type 2 diabetes mellitus are two of the most prevalent chronic conditions worldwide. An unhealthy lifestyle greatly contributes to someone's risk of developing these conditions. Mobile health is an emerging technology that can help deliver health promotion interventions to the population, for example, in the form of health apps.

OBJECTIVE

The aim of this study was to test the feasibility of an app-based intervention for cardiovascular and diabetes risk awareness and prevention by measuring nonusage, dropout, adherence to app use, and usability of the app over 3 months.

METHODS

Participants were eligible if they were aged 45 years or older, resided in Australia, were free of cardiovascular disease and diabetes, were fluent in English, and owned a smartphone. In the beginning, participants received an email with instructions on how to install the app and a user guide. After 3 months, they received an email with an invitation to an end-of-study survey. The survey included questions about general smartphone use and the user version of the Mobile Application Rating Scale. We analyzed app-generated and survey data by using descriptive and inferential statistics as well as thematic analysis for open-text comments.

RESULTS

Recruitment took place between September and October 2021. Of the 46 participants who consented to the study, 20 (44%) never used the app and 15 (33%) dropped out. The median age of the app users at baseline was 62 (IQR 56-67) years. Adherence to app use, that is, using the app at least once a week over 3 months, was 17% (8/46) of the total sample and 31% (8/26) of all app users. The mean app quality rating on the user version of the Mobile Application Rating Scale was 3.5 (SD 0.6) of 5 points. The app scored the highest for the information section and the lowest for the engagement section of the scale.

CONCLUSIONS

Nonusage and dropouts were too high, and the adherence was too low to consider the intervention in its current form feasible. Potential barriers that we identified include the research team not actively engaging with participants early in the study to verify that all participants could install the app, the intervention did not involve direct contact with health care professionals, and the app did not have enough interactive features.

A Mobile App for Prevention of Cardiovascular Disease and Type 2 Diabetes Mellitus: Development and Usability Study

BACKGROUND

Cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) are posing a huge burden on health care systems worldwide. Mobile apps can deliver behavior change interventions for chronic disease prevention on a large scale, but current evidence for their effectiveness is limited.

OBJECTIVE

This paper reported on the development and user testing of a mobile app that aims at increasing risk awareness and engaging users in behavior change. It would form part of an intervention for primary prevention of CVD and T2DM.

METHODS

The theoretical framework of the app design was based on the Behaviour Change Wheel, combined with the capability, opportunity, and motivation for behavior change system and the behavior change techniques from the Behavior Change Technique Taxonomy (version 1). In addition, evidence from scientific literature has guided the development process. The prototype was tested for user-friendliness via an iterative approach. We conducted semistructured interviews with individuals in the target populations, which included the System Usability Scale. We transcribed and analyzed the interviews using descriptive statistics for the System Usability Scale and thematic analysis to identify app features that improved utility and usability.

RESULTS

The target population was Australians aged ≥45 years. The app included 4 core modules (risk score, goal setting, health measures, and education). In these modules, users learned about their risk for CVD and T2DM; set goals for smoking, alcohol consumption, diet, and physical activity; and tracked them. In total, we included 12 behavior change techniques. We conducted 2 rounds of usability testing, each involving 5 participants. The average age of the participants was 58 (SD 8) years. Totally, 60% (6/10) of the participants owned iPhone Operating System phones, and 40% (4/10) of them owned Android phones. In the first round, we identified a technical issue that prevented 30% (3/10) of the participants from completing the registration process. Among the 70% (7/10) of participants who were able to complete the registration process, 71% (5/7) rated the app above average, based on the System Usability Scale. During the interviews, we identified some issues related to functionality, content, and language and clarity. We used the participants' feedback to improve these aspects.

CONCLUSIONS

We developed the app using behavior change theory and scientific evidence. The user testing allowed us to identify and remove technical errors and integrate additional functions into the app, which the participants had requested. Next, we will evaluate the feasibility of the revised version of the app developed through this design process and usability testing.

Machine learning for diabetes clinical decision support: a review

Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

Diabetes Mellitus Diagnosis and Screening in Australian General Practice: A National Study

Aims

To investigate the epidemiology of diabetes diagnosis and screening in Australian general practice.

Methods

Cross-sectional study using electronic health records of 1,522,622 patients aged 18+ years attending 544 Australian general practices (MedicineInsight database). The prevalence of diagnosed diabetes and diabetes screening was explored using all recorded diagnoses, laboratory results, and prescriptions between 2016 and 2018. Their relationship with patient sociodemographic and clinical characteristics was also investigated.

Results

Overall, 7.5% (95% CI 7.3, 7.8) of adults had diabetes diagnosis, 0.7% (95% CI 0.6, 0.7) prediabetes, and 0.3% (95% CI 0.3, 0.3) unrecorded diabetes/prediabetes (elevated glucose levels without a recorded diagnosis). Patients with unrecorded diabetes/prediabetes had clinical characteristics similar to those with recorded diabetes, except for a lower prevalence of overweight/obesity (55.5% and 69.9%, respectively). Dyslipidaemia was 1.8 times higher (36.2% vs. 19.7%), and hypertension was 15% more likely (38.6% vs. 33.8%) among patients with prediabetes than with diabetes. Diabetes screening (last three years) among people at high risk of diabetes was 55.2% (95% CI 52.7, 57.7), with lower rates among young or elderly males.

Conclusions

Unrecorded diabetes/prediabetes is infrequent in Australian general practice, but prediabetes diagnosis was also lower than expected. Diabetes screening among high-risk individuals can be improved, especially in men, to enhance earlier diabetes diagnosis and management.

Diabetic yoga protocol improves glycemic, anthropometric and lipid levels in high risk individuals for diabetes: a randomized controlled trial from Northern India

PURPOSE

To study the effectiveness of diabetic yoga protocol (DYP) against management of cardiovascular risk profile in a high-risk community for diabetes, from Chandigarh, India.

METHODS

The study was a randomized controlled trial, conducted as a sub study of the Pan India trial Niyantrita Madhumeha Bharath (NMB). The cohort was identified through the Indian Diabetes Risk Scoring (IDRS) (≥ 60) and a total of 184 individuals were randomized into intervention (n = 91) and control groups (n = 93). The DYP group underwent the specific DYP training whereas the control group followed their daily regimen. The study outcomes included changes in glycemic and lipid profile. Analysis was done under intent-to-treat principle.

RESULTS

The 3 months DYP practice showed diverse results showing glycemic and lipid profile of the high risk individuals. Three months of DYP intervention was found to significantly reduce the levels of post-prandial glucose levels (p = 0.035) and LDL-c levels (p = 0.014) and waist circumference (P = 0.001).

CONCLUSION

The findings indicate that the DYP intervention could improve the metabolic status of the high-diabetes-risk individuals with respect to their glucose tolerance and lipid levels, partially explained by the reduction in abdominal obesity. The study highlights the potential role of yoga intervention in real time improvement of cardiovascular profile in a high diabetes risk cohort.

TRIAL REGISTRATION

CTRI, CTRI/2018/03/012804. Registered 01 March 2018-Retrospectively registered, http://www.ctri.nic.in/ CTRI/2018/03/012804.

Ten-year prediction model for post-bronchodilator airflow obstruction and early detection of COPD: development and validation in two middle-aged population-based cohorts

BACKGROUND

Classifying individuals at high chronic obstructive pulmonary disease (COPD)-risk creates opportunities for early COPD detection and active intervention.

OBJECTIVE

To develop and validate a statistical model to predict 10-year probabilities of COPD defined by post-bronchodilator airflow obstruction (post-BD-AO; forced expiratory volume in 1 s/forced vital capacity<5th percentile).

SETTING

General Caucasian populations from Australia and Europe, 10 and 27 centres, respectively.

PARTICIPANTS

For the development cohort, questionnaire data on respiratory symptoms, smoking, asthma, occupation and participant sex were from the Tasmanian Longitudinal Health Study (TAHS) participants at age 41-45 years (n=5729) who did not have self-reported COPD/emphysema at baseline but had post-BD spirometry and smoking status at age 51-55 years (n=2407). The validation cohort comprised participants from the European Community Respiratory Health Survey (ECRHS) II and III (n=5970), restricted to those of age 40-49 and 50-59 with complete questionnaire and spirometry/smoking data, respectively (n=1407).

STATISTICAL METHOD

Risk-prediction models were developed using randomForest then externally validated.

RESULTS

Area under the receiver operating characteristic curve (AUCROC) of the final model was 80.8% (95% CI 80.0% to 81.6%), sensitivity 80.3% (77.7% to 82.9%), specificity 69.1% (68.7% to 69.5%), positive predictive value (PPV) 11.1% (10.3% to 11.9%) and negative predictive value (NPV) 98.7% (98.5% to 98.9%). The external validation was fair (AUCROC 75.6%), with the PPV increasing to 17.9% and NPV still 97.5% for adults aged 40-49 years with ≥1 respiratory symptom. To illustrate the model output using hypothetical case scenarios, a 43-year-old female unskilled worker who smoked 20 cigarettes/day for 30 years had a 27% predicted probability for post-BD-AO at age 53 if she continued to smoke. The predicted risk was 42% if she had coexistent active asthma, but only 4.5% if she had quit after age 43.

CONCLUSION

This novel and validated risk-prediction model could identify adults aged in their 40s at high 10-year COPD-risk in the general population with potential to facilitate active monitoring/intervention in predicted 'COPD cases' at a much earlier age.

The Validity of the American Diabetes Association's Diabetes Risk Test in a Saudi Arabian Population

Introduction

The prevalence of type 2 diabetes (T2D) is growing worldwide. This study aimed to assess the sensitivity and specificity of the American Diabetes Association (ADA) and the United States Centers for Disease Control and Prevention's diabetes risk test in identifying Saudi Arabian patients at risk of developing T2D.

Methods

We conducted a one-month cross-sectional study that included patients older than 18 years who visited primary care facilities for any health concern in Riyadh City, Saudi Arabia. We used the Arabic language version of the ADA Prediabetes Risk Test questionnaire, a validated and reliable tool, to collect data. For this study, we analyzed the data using IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, New York). Moreover, we calculated sensitivity and specificity, positive predictive values (PPV), negative predictive value (NPV), the area under the curve (AUC), and Youden’s index.

Results

A total of 180 participants were included in the study (121 women and 59 men; mean age = 45 years). The ADA Prediabetes Risk Test sensitivity was 78.9, specificity was 82, PPV was 32, and NPV was 76. Youden’s index was 60.9 and the AUC was 0.6.

Conclusion

The ADA prediabetes risk assessment tool is highly sensitive and specific for determining the disease. It is a reliable and valid tool that has not yet been implemented to a great extent in Saudi Arabia. Therefore, future work should study the tool’s effectiveness in risk assessment in additional local Saudi Arabian communities.

Development and Validation of a Prevalence Model for Latent Autoimmune Diabetes in Adults (LADA) Among Patients First Diagnosed with Type 2 Diabetes Mellitus (T2DM)

Background

We designed this study to develop and validate a prevalence model for latent autoimmune diabetes in adults (LADA) among people initially diagnosed with type 2 diabetes mellitus (T2DM).

Material/Methods

The study recruited 930 patients aged ≥18 years who were diagnosed with T2DM within the past year. Demographic information, medical history, and clinical biochemistry records were collected. Logistic regression was used to develop a regression model to distinguish LADA from T2DM. Predictors of LADA were identified in a subgroup of patients (n=632) by univariate logistic regression analysis. From this we developed a prediction model using multivariate logistic regression analysis and tested its sensitivity and specificity among the remaining patients (n=298).

Results

Among 930 recruited patients, 880 had T2DM (96.4%) and 50 had LADA (5.4%). Compared to T2DM patients, LADA patients had fewer surviving β cells and reduced insulin production. We identified age, ketosis, history of tobacco smoking, 1-hour plasma glucose (1hPG-AUC), and 2-hour C-peptide (2hCP-AUC) as the main predictive factors for LADA (P<0.05). Based on this, we developed a multivariable logistic regression model: Y=−8.249−0.035(X1)+1.755(X2)+1.008(X3)+0.321(X4)−0.126(X5), where Y is diabetes status (0=T2DM, 1=LADA), X1 is age, X2 is ketosis (1=no, 2=yes), X3 is history of tobacco smoking (1=no, 2=yes), X4 is 1hPG-AUC, and X5 is 2hCP-AUC. The model has high sensitivity (78.57%) and selectivity (67.96%).

Conclusions

This model can be applied to people newly diagnosed with T2DM. When Y ≥0.0472, total autoantibody screening is recommended to assess LADA.

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.

A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study

Background

The prevention of type 2 diabetes (T2D) and its associated complications has become a major priority of global public health. In addition, there is growing evidence that nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of diabetes. Therefore, the purpose of this study was to develop and validate a nomogram based on independent predictors to better assess the 8-year risk of T2D in Japanese patients with NAFLD.

Methods

This is a historical cohort study from a collection of databases that included 2741 Japanese participants with NAFLD without T2D at baseline. All participants were randomized to a training cohort (n = 2058) and a validation cohort (n = 683). The data of the training cohort were analyzed using the least absolute shrinkage and selection operator method to screen the suitable and effective risk factors for Japanese patients with NAFLD. A cox regression analysis was applied to build a nomogram incorporating the selected features. The C-index, receiver operating characteristic curve (ROC), calibration plot, decision curve analysis, and Kaplan-Meier analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. The results were reevaluated by internal validation in the validation cohort.

Results

We developed a simple nomogram that predicts the risk of T2D for Japanese patients with NAFLD by using the parameters of smoking status, waist circumference, hemoglobin A1c, and fasting blood glucose. For the prediction model, the C-index of training cohort and validation cohort was 0.839 (95% confidence interval (CI), 0.804-0.874) and 0.822 (95% CI, 0.777-0.868), respectively. The pooled area under the ROC of 8-year T2D risk in the training cohort and validation cohort was 0.811 and 0.805, respectively. The calibration curve indicated a good agreement between the probability predicted by the nomogram and the actual probability. The decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusions

We developed and validated a nomogram for the 8-year risk of incident T2D among Japanese patients with NAFLD. Our nomogram can effectively predict the 8-year incidence of T2D in Japanese patients with NAFLD and helps to identify people at high risk of T2D early, thus contributing to effective prevention programs for T2D.

Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking

OBJECTIVES

Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning.

METHODS

A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013-2016 (n = 6673) was used for external validation. Undiagnosed type 2 diabetes was determined by a negative response to the question "Have you ever been told by a doctor that you have diabetes?" and a positive glycaemic response to one or more of the three diagnostic tests (HbA1c > 6.4% or FPG >125 mg/dl or 2-hr post-OGTT glucose > 200mg/dl). Following comprehensive literature search, 114 potential nutritional markers were modelled with 13 behavioural and 12 socio-economic variables. We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. From this, the derived 12 predictive models were validated on internal- and external validation cohorts. Magnitudes of associations were gauged through odds ratios in logistic models and variable importance in others. Models were benchmarked against the ADA diabetes risk test.

RESULTS

The prevalence of undiagnosed type 2 diabetes was 5.26%. Four best-performing models (AUROC range: 74.9%-75.7%) classified 39 markers of undiagnosed type 2 diabetes; 28 via one or more of the three best-performing non-linear/ensemble models and 11 uniquely by the logistic model. They comprised 14 nutrient-based, 12 anthropometry-based, 9 socio-behavioural, and 4 diet-associated markers. AUROC of all models were on a par with ADA diabetes risk test on both internal and external validation cohorts (p>0.05).

CONCLUSIONS

Models performed comparably to the chosen benchmark. Novel behavioural markers such as the number of meals not prepared from home were revealed. This approach may be useful in nutritional epidemiology to unravel new associations with type 2 diabetes.

A Comparison of Nutritional Status, Knowledge and Type 2 Diabetes Risk Among Malaysian Young Adults With and Without Family History of Diabetes

BACKGROUND

Genetic factors increase the risk of type 2 diabetes mellitus (T2DM). Thus, family history status may be a useful public health tool for disease prevention. This study compared the nutritional status, knowledge level, and T2DM risk among young adults with and without a family history of diabetes in Malaysia.

METHODS

A total of 288 university students aged 18 to 29 years participated in this comparative cross-sectional study. We assessed dietary intake, level of physical activity, knowledge of diabetes and T2DM risk.

RESULTS

Respondents with a family history of diabetes had significantly higher weight (P = 0.003), body mass index (P < 0.001), waist circumference (P < 0.001), diabetes knowledge level (P < 0.005) and T2DM risk (P < 0.001). Ethnicity, fibre intake, T2DM risk score and knowledge about diabetes were significant contributors toward family history of diabetes (P = 0.025, 0.034, < 0.001 and 0.004, respectively).

CONCLUSION

Young adults with a family history of diabetes had suboptimal nutritional status. Despite being more knowledgeable about diabetes, they did not practice a healthy lifestyle. Family history status can be used to screen young adults at the risk of developing T2DM for primary disease prevention.

NDSP 09: Risk Assessment of Pakistani Individual for Diabetes (RAPID) - Findings from Second National Diabetes Survey of Pakistan (NDSP) 2016-2017

OBJECTIVE

To elucidate the effectiveness of Risk Assessment of Pakistani individuals with diabetes (RAPID) tool in epidemiological and population-based second National Diabetes Survey of Pakistan (NDSP) 2016-2017 for identifying risk of developing type 2 diabetes.

METHODOLOGY

This observational study was a sub-analysis of the second National Diabetes Survey of Pakistan (NDSP) 2016-2017 conducted from February 2016 to August 2017 in all four provinces of Pakistan. Ethical approval was obtained from National Bioethics Committee Pakistan. RAPID score, a validated and published scoring scale to assess risk of diabetes, originally developed from community-based surveys was used. The risk score is assessed by parameters namely: age, waist circumference, and positive family history of diabetes. Subjects with score greater ≥4 were considered at risk of diabetes.

RESULTS

A total of 4904 individuals were assessed (2205 males and 2699 females). Mean age of participants was 41.8±14.2 years. Positive family history of diabetes was seen in 1379 (28.1%) people. According to RAPID score 1268 (25.9%) individuals scored ≥4 and were at risk of diabetes. OGTT status of people at risk of diabetes according to RAPID score showed that 18.1% people with diabetes and 29.2% were prediabetic. Whereas, OGTT status of people not at risk of diabetes showed that only 7.6% people with diabetes, 20% were prediabetic.

CONCLUSION

A simple diabetes risk score can be used for identification of high-risk individuals for diabetes so that timely intervention can be implemented. Community-based awareness programs are needed to educate people regarding healthy lifestyle in order to reduce risk of diabetes.

Nomogram Predicting the Risk of Progression from Prediabetes to Diabetes After a 3-Year Follow-Up in Chinese Adults

PURPOSE

To develop a nomogram for predicting the risk of progression from prediabetes to diabetes and provide a quantitative predictive tool for early clinical screening of high-risk populations of diabetes.

MATERIALS AND METHODS

This study was a retrospective cohort study and part of the investigation conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 1857 prediabetic participants at baseline underwent oral glucose tolerance test and hemoglobin A1c (HbA1c) testing after 3 years. The areas under the receiver operating characteristic curves (AUCs) were adopted to measure the predictive value of progression to diabetes, using baseline fasting plasma glucose (FPG), 2-hr postprandial plasma glucose (2hPG), HbA1c or combined models. Decision curve analysis determined the model with the best discriminative ability. A nomogram was formulated and internally validated, providing an individualized predictive tool by calculating total scores.

RESULTS

After 3 years, 145 participants developed diabetes, and the annual incidence was estimated to be 2.60%. Among the three single indicators and four combined models, model 4 combined of FPG, 2hPG, and HbA1c showed the best performance in risk predication, with an AUC of 0.742. The nomogram constructed via model 4 was validated and demonstrated good prediction for the risk of diabetes. The nomogram score/predicted probability was a numeric value representing the prediction model score of individual patients. Notably, all nomogram scores showed relatively high negative predictive values.

CONCLUSION

The nomogram constructed in this study effectively predicts and quantifies the risk of progression from prediabetes to diabetes after a 3-year follow-up and could be adopted to identify Chinese patients at high risk for diabetes in order to provide timely interventions.

Prevalence, incidence and risk factors of diabetes in Australian adults aged ≥45 years: A cohort study using linked routinely-collected data

Aims

To use linked routinely-collected health data to estimate diabetes prevalence and incidence in an Australian cohort of adults aged ≥45 years, and examine risk factors associated with incident disease.

Research design and methods

The EXamining ouTcomEs in chroNic Disease in the 45 and Up Study (EXTEND45) Study is a linked data study that combines baseline questionnaire responses from the population-based 45 and Up Study (2006–2009, n = 267,153) with multiple routinely-collected health databases up to December 2014. Among participants with ≥1 linked result for any laboratory test, diabetes status was determined from multiple data sources according to standard biochemical criteria, use of glucose-lowering medication or self-report, and the prevalence and incidence rate calculated. Independent risk factors of incident diabetes were examined using multivariable Cox regression.

Results

Among 152,169 45 and Up Study participants with ≥1 linked laboratory result in the EXTEND45 database (mean age 63.0 years; 54.9% female), diabetes prevalence was 10.8% (95% confidence interval [CI] 10.6%–10.9%). Incident disease in those without diabetes at baseline (n = 135,810; mean age 62.5 years; 56.1% female) was 10.0 per 1,000 person-years (95% CI 9.8–10.2). In all age groups, diabetes incidence was lower in women compared to men, an association that persisted in the fully adjusted analyses. Other independent risk factors of diabetes were older age, being born outside of Australia (with the highest rate of 19.2 per 1,000 person-years observed in people born in South and Central Asia), lower education status, lower annual household income, residence in a major city, family history of diabetes, personal history of cardiovascular disease or hypertension, higher body mass index, smoking and long sleeping hours.

Conclusions

Our study represents an efficient approach to assessing diabetes frequency and its risk factors in the community. The infrastructure provided by the EXTEND45 Study will be useful for diabetes surveillance and examining other important clinical and epidemiological questions.

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

BACKGROUND

Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology.

OBJECTIVE

We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017.

METHODS

We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model.

RESULTS

Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001).

CONCLUSIONS

A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

Adapting a Prediction Rule for Metabolic Syndrome Risk Assessment Suitable for Developing Countries

Background: Metabolic syndrome (MetS) is a cluster of cardiometabolic disturbances that increases the risk of cardiovascular diseases (CVD) and type 2 diabetes mellitus (DM). The early identification of high-risk individuals is the key for halting these conditions. The world is facing a growing epidemic MetS although the magnitude in Egypt is unknown. Objectives: To describe MetS and its determinants among apparently healthy individuals residing in urban and rural communities in Egypt and to establish a model for MetS prediction. Methods: A cross-sectional study was conducted with 270 adults from rural and urban districts in Alexandria, Egypt. Participants were clinically evaluated and interviewed for sociodemographic and lifestyle factors and dietary habits. MetS was defined according to the harmonized criteria set by the AHA/NHLBI. The risk of ischemic heart diseases (IHDs), DM and fatty liver were assessed using validated risk prediction charts. A multiple risk model for predicting MetS was developed, and its performance was compared. Results: In total, 57.8% of the study population met the criteria for MetS and were at high risk for developing IHD, DM, and fatty liver. Silent CVD risk factors were identified in 20.4% of the participants. In our proposed multivariate logistic regression model, the predictors of MetS were obesity [OR (95% CI) = 16.3 (6.03-44.0)], morbid obesity [OR (95% CI) = 21.7 (5.3-88.0)], not working [OR (95% CI) = 2.05 (1.1-3.8)], and having a family history of chronic diseases [OR (95% CI) = 4.38 (2.23-8.61)]. Consumption of caffeine once per week protected against MetS by 27.8-fold. The derived prediction rule was accurate in predicting MetS, fatty liver, high risk of DM, and, to a lesser extent, a 10-year lifetime risk of IHD. Conclusion: Central obesity and sedentary lifestyles are accountable for the rising rates of MetS in our society. Interventions are needed to minimize the potential predisposition of the Egyptian population to cardiometabolic diseases.

Characteristics of participants utilising a telephone-based coaching service for chronic disease health risk behaviours: A retrospective examination comparing those with and without a mental health condition

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People with mental health conditions engage with health coaching phone services.

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The prevalence and extent of some health risk behaviours is higher in this group.

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Confidence in ability to change health risk behaviours is lower in this group.

The NSW Get Healthy Service® (GHS) is a free telephone-based coaching service in NSW, Australia, which supports behaviour change around healthy eating and physical activity. The aims of this study were to 1) assess the proportion of coaching participants within GHS who report having had a mental health condition, and 2) describe and compare the health risk profiles and confidence for behaviour change of coaching participants with and without a mental health condition. Secondary data analysis was conducted on information collected via participant self-report as a part of the coaching process for 11,925 participants who enrolled in a GHS coaching program for the first time between January 2015 and December 2017. Twenty six percent (n = 3106) of participants reported having had a significant mental health condition that required treatment from a health professional. Participants who reported a mental health condition were significantly less likely (54%) to be meeting guidelines for physical activity than participants without a mental health condition (64%); more likely to be overweight/obese (89%) compared to those without (81%); and reported lower confidence for changing exercise, nutrition and weight. There were no significant differences in proportions meeting fruit or vegetable intake recommendations. People with a mental health condition represent approximately a quarter of GHS participants. This group of participants presented higher levels of health risks and expressed lower confidence in behaviour change than program participants without such a condition. Future service planning and development may consider this variation in participant profiles.

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.

Development and Validation of Two Self-Reported Tools for Insulin Resistance and Hypertension Risk Assessment in A European Cohort: The Feel4Diabetes-Study

Early identification of type 2 diabetes mellitus (T2DM) and hypertension (HTN) risk may improve prevention and promote public health. Implementation of self-reported scores for risk assessment provides an alternative cost-effective tool. The study aimed to develop and validate two easy-to-apply screening tools identifying high-risk individuals for insulin resistance (IR) and HTN in a European cohort. Sociodemographic, lifestyle, anthropometric and clinical data obtained from 1581 and 1350 adults (baseline data from the Feel4Diabetes-study) were used for the European IR and the European HTN risk assessment index respectively. Body mass index, waist circumference, sex, age, breakfast consumption, alcohol, legumes and sugary drinks intake, physical activity and sedentary behavior were significantly correlated with Homeostatic Model Assessment of IR (HOMA-IR) and/or HTN and incorporated in the two models. For the IR index, the Area Under the Curve (AUC), sensitivity and specificity for identifying individuals above the 75th and 95th of HOMA-IR percentiles were 0.768 (95%CI: 0.721-0.815), 0.720 and 0.691 and 0.828 (95%CI: 0.766-0.890), 0.696 and 0.778 respectively. For the HTN index, the AUC, sensitivity and specificity were 0.778 (95%CI: 0.680-0.876), 0.667 and 0.797. The developed risk assessment tools are easy-to-apply, valid, and low-cost, identifying European adults at high risk for developing T2DM or having HTN.

Cohort Profile: Effectiveness of a 12-Month Patient-Centred Medical Home Model Versus Standard Care for Chronic Disease Management among Primary Care Patients in Sydney, Australia

Evidence suggests that patient-centred medical home (PCMH) is more effective than standard general practitioner care in improving patient outcomes in primary care. This paper reports on the design, early implementation experiences, and early findings of the 12-month PCMH model called ‘WellNet’ delivered across six primary care practices in Sydney, Australia. The WellNet study sample comprises 589 consented participants in the intervention group receiving enhanced primary care in the form of patient-tailored chronic disease management plan, improved self-management support, and regular monitoring by general practitioners (GPs) and trained clinical coordinators. The comparison group consisted of 7750 patients who were matched based on age, gender, type and number of chronic diseases who received standard GP care. Data collected include sociodemographic characteristics, clinical measures, and self-reported health assessments at baseline and 12 months. Early study findings show the mean age of the study participants was 70 years with nearly even gender distribution of males (49.7%) and females (50.3%). The most prevalent chronic diseases in descending order were circulatory system disorders (69.8%), diabetes (47.4%), musculoskeletal disorders (43.5%), respiratory diseases (28.7%), mental illness (18.8%), and cancer (13.6%). To our knowledge, the WellNet study is the first study in Australia to generate evidence on the feasibility of design, recruitment, and implementation of a comprehensive PCMH model. Lessons learned from WellNet study may inform other medical home models in Australian primary care settings.

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient’s evolving health condition is imperative to comprehending the patient’s current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton’s Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.

A Novel Risk Score for Type 2 Diabetes Containing Sleep Duration: A 7-Year Prospective Cohort Study among Chinese Participants

BACKGROUND

Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration.

METHODS

We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration.

RESULTS

After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C-index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit.

CONCLUSIONS

The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50.

MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States

Diabetes results from the inability of pancreatic islets to maintain blood glucose concentrations within a normal physiological range. Clinical features are usually not observed until islets begin to fail and irreversible damage has occurred. Diabetes is generally diagnosed based on elevated glucose, which does not distinguish between type 1 and 2 diabetes. Thus, new diagnostic approaches are needed to detect different modes of diabetes before manifestation of disease. During prediabetes (pre-DM), islets undergo stress and release micro (mi) RNAs. Here, we review studies that have measured and tracked miRNAs in the blood for those with recent-onset or longstanding type 1 diabetes, obesity, pre-diabetes, type 2 diabetes, and gestational diabetes. We summarize the findings on miRNA signatures with the potential to stage progression of different modes of diabetes. Advances in identifying selective biomarker signatures may aid in early detection and classification of diabetic conditions and treatments to prevent and reverse diabetes.

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes

Background

This study proposes the use of machine learning algorithms to improve the accuracy of type 2 diabetes predictions using non-invasive risk score systems.

Methods

We evaluated and compared the prediction accuracies of existing non-invasive risk score systems using the data from the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals: A Longitudinal Study). Two simple risk scores were established on the bases of logistic regression. Machine learning techniques (ensemble methods) were used to improve prediction accuracies by combining the individual score systems.

Results

Existing score systems from Western populations performed worse than the scores from Eastern populations in general. The two newly established score systems performed better than most existing scores systems but a little worse than the Chinese score system. Using ensemble methods with model selection algorithms yielded better prediction accuracy than all the simple score systems.

Conclusion

Our proposed machine learning methods can be used to improve the accuracy of screening the undiagnosed type 2 diabetes and identifying the high-risk patients.

Community-Based Chronic Disease Prevention and Management for Aboriginal People in New South Wales, Australia: Mixed Methods Evaluation of the 1 Deadly Step Program

BACKGROUND

Chronic diseases account for over 70% of health gaps between Aboriginal people and the rest of the Australian population. The 1 Deadly Step program involves community-based events that use a sporting platform and cultural ambassadors to improve chronic disease prevention and management in New South Wales (NSW).

OBJECTIVE

This study aimed to evaluate the feasibility and acceptability of a community-based chronic disease screening program for Aboriginal people.

METHODS

In 2015, the program was enhanced to include an iPad app for screening assessments, a results portal for nominated care providers, and a reporting portal for program administrators and implemented in 9 NSW community events. A mixed methods evaluation comprising survey data, analytics obtained from iPad and Web portal usage, and key informant interviews was conducted.

RESULTS

Overall, 1046 people were screened between April 2015 and April 2016 (mean age 40.3 years, 640 (61.19%) female, 957 (91.49%) Aboriginal or Torres Strait Islander). High chronic disease rates were observed (231 [22.08%] participants at high cardiovascular disease (CVD) risk, 173 [16.54%] with diabetes, and 181 [17.30%] with albuminuria). A minority at high risk of CVD (99/231 [42.9%]) and with diabetes (73/173 [42.2%]) were meeting guideline-recommended management goals. Overall, 297 participants completed surveys (response rate 37.4%) with 85.1% reporting satisfaction with event organization and information gained and 6.1% experiencing problems with certain screening activities. Furthermore, 21 interviews were conducted. A strong local working group and processes that harnessed community social networks were key to implementation success. Although software enhancements facilitated screening and data management, some technical difficulties (eg, time delays in processing blood test results) impeded smooth processing of information. Only 51.43% of participants had a medical review recorded postevent with wide intersite variability (10.5%-85.6%). Factors associated with successful follow-up included clinic managers with overall program responsibility and availability of medical staff for immediate discussion of results on event day. The program was considered highly resource intensive to implement and support from a central coordinating body and integration with existing operational processes was essential.

CONCLUSIONS

1 Deadly Step offers an effective and acceptable strategy to engage Aboriginal communities in chronic disease screening. High rates of risk factors and management gaps were encountered, including people with no previous knowledge of these issues. Strategies to improve linkage to primary care could enhance the program's impact on reducing chronic disease burden.

Effectiveness of a type 2 diabetes prevention program combining FINDRISC scoring and telephone-based coaching in the French population of bakery/pastry employees

Background/objectives

Preventive actions targeting the risk of type 2 diabetes mellitus (T2D) and deployed from the workplace are scarce. This study aimed to measure this T2D risk in a large sample of the bakery/pastry employees in France and to assess the effectiveness of a telephone coaching program in participants with the highest risk.

Subjects/methods

A screening survey using the FINDRISC score was conducted by phone among the employees. Those with a moderate risk (score ≥ 12 and <15; body mass index ≥ 25 kg/m²) or high/very high risk (score ≥ 15) were invited to participate in a 6-month coaching program including 6 monthly interviews together with a final evaluation interview three months later. The effects and impact were evaluated using 8 questions on dietary knowledge/behavior as well as the GPAQ (physical activity) and SF-12 (quality of life) questionnaires.

Results

There were 19,951 employees eligible for screening (age: 38.0 ± 13.5 years, men 49.6%, mean FINDRISC score 5.9 ± 4.4). A high/very high score was found in 4% of individuals. Overall, 1,348 (among 2,018) eligible employees agreed to participate in the coaching program, 630 of whom participated in all interviews. Of the latter, dietary knowledge/behavior (+1.60) and quality of life (+1.83) improved (P < 0.0001), with a favorable trend for physical activity (+0.06, P = 0.0756). Dietary knowledge/behavior continued to improve in the 581 completers (+0.17, P = 0.0001).

Conclusions

This two-step prevention program associating T2D risk estimation and a 6-month telephone coaching was deployed in the French craft bakery/pastry sector with significant adhesion. Such program appears beneficial for enhancing knowledge and mobilizing skills associated with T2D prevention.

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.

Massive transfusion in upper gastrointestinal bleeding: a new scoring system

Background: Massive transfusion in patients with upper gastrointestinal bleeding (UGIB) was not investigated. We developed a new scoring system to predict massive transfusion and to enhance care and early resource mobilization. Methods: Massive transfusion was defined as transfusion with ≥10 units of red blood cells within the first 24 h. Data were extracted from a 10-year, six-hospital database. Logistic regression was applied to derive a risk score for massive transfusion using data from 2006 to 2010, in 24,736 patients (developmental cohort). The score was then validated using data from 2011 to 2015 in 27,449 patients (validation cohort). Area under the receiver operating characteristic (AUROC) curve was performed to assess prediction accuracy. Results: Five characteristics were independently associated (p < .001) with massive transfusion: presence of band-form cells among white blood cells (band form >0), international normalized ratio (INR) >1.5, pulse >100 beats per minute or systolic blood pressure <100 mmHg (shock), haemoglobin <8.0 g/dL and endoscopic therapy. The new scoring system successfully discriminated well between UGIB patients requiring massive transfusion and those who did not in both cohorts (AUROC: 0.831, 95%CI: 0.827-0.836; AUROC: 0.822, 95% CI: 0.817-0.826, respectively). Conclusions: The new scoring system predicts massive transfusion requirement in patients with UGIB well. Key messages Massive transfusion is a life-saving management in massive upper gastrointestinal bleeding. How to identify patients requiring massive transfusion in upper gastrointestinal bleeding is poorly documented. Approximately 3.9% of upper gastrointestinal bleeding patients require massive transfusion. A new scoring system is developed to identify patients requiring massive transfusion with high accuracy.

Development and validation of a predictive model for incident type 2 diabetes in middle-aged Mexican adults: the metabolic syndrome cohort

BACKGROUND

Type 2 diabetes mellitus (T2D) is a leading cause of morbidity and mortality in Mexico. Here, we aimed to report incidence rates (IR) of type 2 diabetes in middle-aged apparently-healthy Mexican adults, identify risk factors associated to ID and develop a predictive model for ID in a high-risk population.

METHODS

Prospective 3-year observational cohort, comprised of apparently-healthy adults from urban settings of central Mexico in whom demographic, anthropometric and biochemical data was collected. We evaluated risk factors for ID using Cox proportional hazard regression and developed predictive models for ID.

RESULTS

We included 7636 participants of whom 6144 completed follow-up. We observed 331 ID cases (IR: 21.9 per 1000 person-years, 95%CI 21.37-22.47). Risk factors for ID included family history of diabetes, age, abdominal obesity, waist-height ratio, impaired fasting glucose (IFG), HOMA2-IR and metabolic syndrome. Early-onset ID was also high (IR 14.77 per 1000 person-years, 95%CI 14.21-15.35), and risk factors included HOMA-IR and IFG. Our ID predictive model included age, hypertriglyceridemia, IFG, hypertension and abdominal obesity as predictors (Dxy = 0.487, c-statistic = 0.741) and had higher predictive accuracy compared to FINDRISC and Cambridge risk scores.

CONCLUSIONS

ID in apparently healthy middle-aged Mexican adults is currently at an alarming rate. The constructed models can be implemented to predict diabetes risk and represent the largest prospective effort for the study metabolic diseases in Latin-American population.

Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes

Primary prevention in individuals at Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of the field, which is mostly attributed to its epidemiological weakness. The latter, in turn, underlies the limited identification power of at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. In the first part, this perspective review discusses these limitations and traces a new approach to overcome them. Theoretical concepts to support a Psychosis Polyrisk Score (PPS) integrating genetic and non-genetic risk and protective factors for psychosis are presented. The PPS hinges on recent findings indicating that risk enrichment in CHR-P samples is accounted for by the accumulation of non-genetic factors such as: parental and sociodemographic risk factors, perinatal risk factors, later risk factors, and antecedents. In the second part of this perspective review we present a prototype of a PPS encompassing core predictors beyond genetics. The PPS prototype may be piloted in the next generation of CHR-P research and combined with genetic information to refine the detection of individuals at-risk of psychosis and the prediction of their outcomes, and ultimately advance clinical research in this field.

Public knowledge, attitudes and practices toward diabetes mellitus: A cross-sectional study from Jordan

AIMS

To assess the knowledge and practices toward diabetes in the Jordanian community.

METHODS

This study was conducted as a public based cross-sectional study in different cities in Jordan. A previously published validated questionnaire about knowledge, attitudes, and practices (KAP) toward diabetes mellitus (DM) was translated from the Arabic version and used in this study with very minor modification to be suitable for this study of the Jordanian population.

RESULTS

A total of 1,702 participants were recruited in the present study. About half of the participants (53.3%) had good knowledge scores. The respondents' knowledge scores were significantly correlated with attitudes (p < 0.001). The education level (university or higher) and education related to a field were predictors for good knowledge and positive attitudes. About 46.3% of participants had positive attitudes toward the disease. As for practices, 37.7% of participants did not engage in regular exercise while more than half of the study subjects had never checked their blood glucose level on an annual basis. The factors influencing the practice of checking blood glucose level have been investigated.

CONCLUSION

This study has highlighted the need for more educational interventions to address negative attitudes and promote healthy lifestyle practices and regular health checks especially in certain subgroups of patients, such as those not having a degree related to the medical field and not having a first-degree relative with DM.

Incident Type 2 Diabetes Among Individuals With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Few studies have examined incident type 2 diabetes mellitus (T2DM) in chronic kidney disease (CKD). Our objective was to examine rates of and risk factors for T2DM in CKD, using several alternative measures of glycemic control.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

1,713 participants with reduced glomerular filtration rates and without diabetes at baseline, enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study.

PREDICTORS

Measures of kidney function and damage, fasting blood glucose, hemoglobin A1c (HbA1c), HOMA-IR (homeostatic model assessment of insulin resistance), demographics, family history of diabetes mellitus (DM), smoking status, medication use, systolic blood pressure, triglyceride level, high-density lipoprotein cholesterol level, body mass index, and physical activity.

OUTCOME

Incident T2DM (defined as fasting blood glucose ≥ 126mg/dL or prescription of insulin or oral hypoglycemic agents).

ANALYTICAL APPROACH

Concordance between fasting blood glucose and HbA1c levels was assessed using κ. Cause-specific hazards modeling, treating death and end-stage kidney disease as competing events, was used to predict incident T2DM.

RESULTS

Overall T2DM incidence rate was 17.81 cases/1,000 person-years. Concordance between fasting blood glucose and HbA1c levels was low (κ for categorical versions of fasting blood glucose and HbA1c = 13%). Unadjusted associations of measures of kidney function and damage with incident T2DM were nonsignificant (P ≥ 0.4). In multivariable models, T2DM was significantly associated with fasting blood glucose level (P = 0.002) and family history of DM (P = 0.03). The adjusted association of HOMA-IR with T2DM was comparable to that of fasting blood glucose level; the association of HbA1c level was nonsignificant (P ≥ 0.1). Harrell's C for the models ranged from 0.62 to 0.68.

LIMITATIONS

Limited number of outcome events; predictors limited to measures taken at baseline.

CONCLUSIONS

The T2DM incidence rate among individuals with CKD is markedly higher than in the general population, supporting the need for greater vigilance in this population. Measures of glycemic control and family history of DM were independently associated with incident T2DM.

Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait

Objective: In recent decades, the Arab population has experienced an increase in the prevalence of type 2 diabetes (T2DM), particularly within the Gulf Cooperation Council. In this context, early intervention programmes rely on an ability to identify individuals at risk of T2DM. We aimed to build prognostic models for the risk of T2DM in the Arab population using machine-learning algorithms vs. conventional logistic regression (LR) and simple non-invasive clinical markers over three different time scales (3, 5, and 7 years from the baseline). Design: This retrospective cohort study used three models based on LR, k-nearest neighbours (k-NN), and support vector machines (SVM) with five-fold cross-validation. The models included the following baseline non-invasive parameters: age, sex, body mass index (BMI), pre-existing hypertension, family history of hypertension, and T2DM. Setting: This study was based on data from the Kuwait Health Network (KHN), which integrated primary health and hospital laboratory data into a single system. Participants: The study included 1,837 native Kuwaiti Arab individuals (equal proportion of men and women) with mean age as 59.5 ± 11.4 years. Among them, 647 developed T2DM within 7 years of the baseline non-invasive measurements. Analytical methods: The discriminatory power of each model for classifying people at risk of T2DM within 3, 5, or 7 years and the area under the receiver operating characteristic curve (AUC) were determined. Outcome measures: Onset of T2DM at 3, 5, and 7 years. Results: The k-NN machine-learning technique, which yielded AUC values of 0.83, 0.82, and 0.79 for 3-, 5-, and 7-year prediction horizons, respectively, outperformed the most commonly used LR method and other previously reported methods. Comparable results were achieved using the SVM and LR models with corresponding AUC values of (SVM: 0.73, LR: 0.74), (SVM: 0.68, LR: 0.72), and (SVM: 0.71, LR: 0.70) for 3-, 5-, and 7-year prediction horizons, respectively. For all models, the discriminatory power decreased as the prediction horizon increased from 3 to 7 years. Conclusions: Machine-learning techniques represent a useful addition to the commonly reported LR technique. Our prognostic models for the future risk of T2DM could be used to plan and implement early prevention programmes for at risk groups in the Arab population.

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.

Integrated motivational interviewing and cognitive behaviour therapy can increase physical activity and improve health of adult ambulatory care patients in a regional hospital: the Healthy4U randomised controlled trial

BACKGROUND

The aim of this study was to determine whether a twelve-week, health coaching intervention could result in changes in physical activity, anthropometrics and health-related outcomes in adults presenting to an ambulatory hospital clinic.

METHODS

Seventy-two participants who reported being insufficiently active were recruited from an ambulatory hospital clinic and randomised to an intervention group that received an education session and eight 30-min telephone sessions of integrated motivational interviewing and cognitive behaviour therapy (MI-CBT), or to a control group that received the education session only. ActiGraph GT3X accelerometers were used to measure moderate-to-vigorous physical activity at baseline, post-intervention (3-months) and follow-up (6-months). Secondary outcome measures (anthropometrics, physical activity self-efficacy, health-related quality of life, type 2 diabetes risk) were also assessed at the three time points.

RESULTS

At baseline, the mean age and body mass index of participants (n = 72, 75% females) were 53 ± 8 years and 30.8 ± 4.1 kg/m2, respectively. Treatment group influenced the pattern of physical activity over time (p < 0.001). The intervention group increased moderate-to-vigorous physical activity from baseline to post-intervention and remained elevated at follow-up by 12.9 min/day (95%CI: 6.5 to 19.5 min/day). In contrast, at follow-up the control group decreased moderate-to-vigorous physical activity by 9.9 min/day (95%CI: -3.7 to -16.0 min/day). Relative to control, at follow-up the intervention group exhibited beneficial changes in body mass (p < 0.001), waist circumference (p < 0.001), body mass index (p < 0.001), physical activity self-efficacy (p < 0.001), type 2 diabetes risk (p < 0.001), and health-related quality of life (p < 0.001).

CONCLUSIONS

This study demonstrates that a low contact coaching intervention results in beneficial changes in physical activity, anthropometrics and health-related outcomes that were maintained at follow-up in adults who report being insufficiently active to an ambulatory care clinic.

TRIAL REGISTRATION

ANZCTR: ACTRN12616001331426 . Registered 23 September 2016.

Premorbid risk perception, lifestyle, adherence and coping strategies of people with diabetes mellitus: A phenomenological study in the Brong Ahafo Region of Ghana

Background

One of the non-communicable diseases which is on the rise is type 2 diabetes (T2D). T2D is largely preventable with healthy lifestyle. We therefore conducted this study to explore premorbid perception of risk, behavioural practices and the coping strategies of patients with T2D.

Methods

Using descriptive phenomenology approach to qualitative enquiry, we conducted eight focus group discussions (N = 73) with diabetic patients; four among males (N = 36) and four among females (N = 37). In addition, we conducted in-depth interviews with 15 patients, seven caretakers and three physicians. We adopted Colaizzi’s descriptive phenomenology approach to analyse the data with the aid of NVivo 11.

Results

We found that respondents believed diabetes was a condition for the aged and rich and this served as a premorbid risk attenuator. Majority of them engaged in diabetes-related high risk behaviours such as lack of exercise, sedentary lifestyle and unhealthy eating despite their foreknowledge about the role of lifestyle in diabetes pathogenesis. We also found that patients used moringa, noni, prekese, and garlic concurrently with orthodox medications. Adherence to dietary changes and exercises was a challenge with females reporting better adherence than males. The study also revealed that patients believed biomedical health facilities paid little attention to psychosocial aspects of care despite its essential role in coping with the condition.

Conclusion

Diabetic patients had low premorbid perception of risk and engaged in diabetes-related risky behaviours. Diabetic patients had challenges adhering to lifestyle changes and use both biomedical and local remedies in the management of the condition. Psychosocial support is necessary to enhance coping with the condition.