Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore

Are patients with newly diagnosed frozen shoulder more likely to be diagnosed with type 2 diabetes? A cohort study in UK electronic health records

AIM

To estimate the association between newly diagnosed frozen shoulder and a subsequent diagnosis of type 2 diabetes in primary care.

METHODS

We conducted an age-, gender- and practice-matched cohort study in UK primary care electronic medical records containing 31 226 adults diagnosed with frozen shoulder, matched to 31 226 without frozen shoulder. Patients with pre-existing diabetes were excluded. Variables were identified using established Read codes. A hazard ratio (HR) for the association between incident frozen shoulder and a subsequent type 2 diabetes diagnosis was estimated using shared frailty Cox regression, adjusted for age and gender. To determine whether the association could be explained by increased testing for type 2 diabetes based on other risk factors, a secondary analysis involved re-running the Cox model adjusting for the mean number of consultations per year, hyperlipidaemia, hypertension, obesity, thyroid dysfunction, ethnicity, deprivation, age, and gender.

RESULTS

Participants with frozen shoulder were more likely to be diagnosed with type 2 diabetes (1559 out of 31 226 patients [5%]) than participants without frozen shoulder (88 out of 31 226 patients [0.28%]). The HR for a diagnosis of type 2 diabetes in participants with frozen shoulder versus people without frozen shoulder was 19.4 (95% confidence interval [CI] 15.6-24.0). The secondary analysis, adjusting for other factors, produced similar results: HR 20.0 (95% CI 16.0-25.0).

CONCLUSIONS

People who have been newly diagnosed with frozen shoulder are more likely to be diagnosed with type 2 diabetes in the following 15.8 years. The value of screening patients presenting with frozen shoulder for type 2 diabetes at presentation, alongside more established risk factors, should be considered in future research.

Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome

Background

Severe fever with thrombocytopenia syndrome (SFTS) has attracted attention due to the rising incidence and high severity and mortality rates. This study aims to construct a machine learning (ML) model to identify SFTS patients at high risk of death early in hospital admission, and to provide early intensive intervention with a view to reducing the risk of death.

Methods

Data of patients hospitalized for SFTS in two hospitals were collected as training and validation sets, respectively, and six ML methods were used to construct the models using the screened variables as features. The performance of the models was comprehensively evaluated and the best model was selected for interpretation and development of an online web calculator for application.

Results

A total of 483 participants were enrolled in the study and 96 (19.88%) patients died due to SFTS. After a comprehensive evaluation, the XGBoost-based model performs best: the AUC scores for the training and validation sets are 0.962 and 0.997.

Conclusion

Using ML can be a good way to identify high risk individuals in SFTS patients. We can use this model to identify patients at high risk of death early in their admission and manage them intensively at an early stage.

An eye on equity: faricimab-driven health equity improvements in diabetic macular oedema using a distributional cost-effectiveness analysis from a UK societal perspective

BACKGROUND/OBJECTIVES

Diabetic macular oedema (DMO) is a leading cause of blindness in developed countries, with significant disease burden associated with socio-economic deprivation. Distributional cost-effectiveness analysis (DCEA) allows evaluation of health equity impacts of interventions, estimation of how health outcomes and costs are distributed in the population, and assessments of potential trade-offs between health maximisation and equity. We conducted an aggregate DCEA to determine the equity impact of faricimab.

METHODS

Data on health outcomes and costs were derived from a cost-effectiveness model of faricimab compared with ranibizumab, aflibercept and off-label bevacizumab using a societal perspective in the base case and a healthcare payer perspective in scenario analysis. Health gains and health opportunity costs were distributed across socio-economic subgroups. Health and equity impacts, measured using the Atkinson inequality index, were assessed visually on an equity-efficiency impact plane and combined into a measure of societal welfare.

RESULTS

At an opportunity cost threshold of £20,000/quality-adjusted life year (QALY), faricimab displayed an increase in net health benefits against all comparators and was found to improve equity. The equity impact increased the greater the concerns for reducing health inequalities over maximising population health. Using a healthcare payer perspective, faricimab was equity improving in most scenarios.

CONCLUSIONS

Long-acting therapies with fewer injections, such as faricimab, may reduce costs, improve health outcomes and increase health equity. Extended economic evaluation frameworks capturing additional value elements, such as DCEA, enable a more comprehensive valuation of interventions, which is of relevance to decision-makers, healthcare professionals and patients.

Development and Validation of Prediction Models for the 5-year Risk of Type 2 Diabetes in a Japanese Population: Japan Public Health Center-based Prospective (JPHC) Diabetes Study

BACKGROUND

This study aimed to develop models to predict the 5-year incidence of type 2 diabetes mellitus (T2DM) in a Japanese population and validate them externally in an independent Japanese population.

METHODS

Data from 10,986 participants (aged 46-75 years) in the development cohort of the Japan Public Health Center-based Prospective Diabetes Study and 11,345 participants (aged 46-75 years) in the validation cohort of the Japan Epidemiology Collaboration on Occupational Health Study were used to develop and validate the risk scores in logistic regression models.

RESULTS

We considered non-invasive (sex, body mass index, family history of diabetes mellitus, and diastolic blood pressure) and invasive (glycated hemoglobin [HbA1c] and fasting plasma glucose [FPG]) predictors to predict the 5-year probability of incident diabetes. The area under the receiver operating characteristic curve was 0.643 for the non-invasive risk model, 0.786 for the invasive risk model with HbA1c but not FPG, and 0.845 for the invasive risk model with HbA1c and FPG. The optimism for the performance of all models was small by internal validation. In the internal-external cross-validation, these models tended to show similar discriminative ability across different areas. The discriminative ability of each model was confirmed using external validation datasets. The invasive risk model with only HbA1c was well-calibrated in the validation cohort.

CONCLUSION

Our invasive risk models are expected to discriminate between high- and low-risk individuals with T2DM in a Japanese population.

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.

The impact of sociodemographic determinants and diabetes type-2 on oral health outcomes: An analytical cross-sectional study

OBJECTIVES

This study compared adults with type 2 diabetes (T2DM) and those without diabetes (ND) from East London in terms of sociodemographic characteristics, oral health behaviors, dietary practices, and alcohol and tobacco-related habits.

MATERIALS AND METHODS

A total of 182 participants (n = 91 for each group) were recruited and requested to complete the validated questionnaire with 33 items.

RESULTS

Results showed that the mean ± SD age was 61 ± 11.7 in the T2DM, while 51 ± 11.2 in the ND group. The mean ± SD age at T2DM diagnosis was 43 ± 10. There was a significant gender difference, with more males in the T2DM group (67.7%) and more females in the ND group (64.8%). Asian-British (38.4%) were significantly high in the T2DM group when compared to other ethnicities. 92.3% of T2DM participants were significantly more likely to use medications in comparison to the ND group (29.7%). The T2DM participants' personal statements on general health were fair (34%) and good (46.2%) when compared with the ND group (15.4% and 59.3%, respectively). The majority of T2DM and ND participants (98%) lacked dental insurance. In the T2DM group, 31.8% were receiving benefits, and 39.5% were retired, while 46% of the ND group were full-time employees. Tooth brushing twice a day was slightly less common in T2DM (68%) when compared to the ND group (78%). Nearly half of the participants in both groups failed to carry out interdental cleaning (T2DM = 52%; ND = 47%), and 38.5% of the T2DM group used mouthwash occasionally, while 30% of the ND group had it twice daily. There was a weak association between chewing paan and annual income in ND participants (r = .90, p = .49). There were significant differences in the presence of removable prostheses, juice, and sweetened juice consumptions between the two groups (p < .05).

CONCLUSION

Within the confines of this study, being male, Asian British, retired due to disability, polypharmacy, and the presence of removable prostheses were all significant factors for T2DM.

"The Critical Services Are Out of Reach": Diabetes Management and the Experiences of South Asian Immigrants in Ontario

Type 2 diabetes is a serious chronic condition affecting millions of people worldwide. South Asians (individuals originating from Pakistan, India, Bangladesh, Sri Lanka, and Nepal) represent a high-risk ethnicity for developing type 2 diabetes (T2D) and experience a high prevalence of the disease, even in migrant populations. The objective of this study was to investigate perceptions and experiences of South Asians living with T2D in Ontario, and their utilization of diabetes related services within the provincial healthcare system. Data were obtained from 20 in-depth interviews with South Asian participants diagnosed with T2D and living in the Greater Toronto Area. Our findings indicate a dissatisfaction with Ontario's coverage for diabetes services; varying uptake of recommended health tests, exams, and monitoring equipment; low utilization of additional resources (diabetes centers); and a need for primary care physicians to better facilitate awareness and utilization of available coverages and resources in the community. This study provides support for the fact that even in Canada's universal healthcare system, disparities exist, particularly for ethnic minorities, and that a universal prescription drug coverage component is a crucial step forward to ensure equitable access to health services utilization for all.

Implementation status of prediction models for type 2 diabetes

Although a prediction model is expected to identify individuals who are at a high risk of type 2 diabetes, the implementation status of prediction models has not been well examined. Our review indicates that the implementation of predictive models in practice remains low despite the increase in models being developed.

The burden of type 2 diabetes mellitus in states of the European Union and United Kingdom at the national and subnational levels: A systematic review

Type 2 diabetes mellitus (T2D) is a highly prevalent disease worldwide, with an equally increased expenditure associated with it. We aimed to longitudinally evaluate the epidemiologic and economic burden of T2D in the current member states of the European Union and the United Kingdom (EU-28). The present systematic review is registered on PROSPERO (CRD42020219894), and it followed the PRISMA guidelines. Eligibility criteria comprised original observational studies in English reporting economic and epidemiological data for T2D in member states of the EU-28. Methodological assessment was performed with the Joanna Briggs Institute (JBI) Critical Appraisal Tools. The search retrieved 2253 titles and abstracts. After study selection, 41 studies were included in the epidemiologic analysis and 25 in the economic analysis. Economic and epidemiologic studies covered only 15 member states with reported data between 1970 and 2017, resulting in an incomplete picture. For children in particular, limited information is available. The prevalence, incidence, mortality, and expenditure of the T2D population have increased across the decades in member states. Therefore, policies should aim to prevent or reduce the burden of T2D in the EU and consequently mitigate the expenditure on T2D.

Risk Amplifiers for Vascular Disease and CKD in South Asians: When Intrinsic β-Cell Dysfunction Meets a High-Carbohydrate Diet

South Asians, comprising almost one fourth of the world population, are at higher risk of type 2 diabetes mellitus, hypertension, cardiovascular disease, and CKD compared with other ethnic groups. This has major public health implications in South Asia and in other parts of the world to where South Asians have immigrated. The interplay of various modifiable and nonmodifiable risk factors confers this risk. Traditional models of cardiometabolic disease progression and CKD evaluation may not be applicable in this population with a unique genetic predisposition and phenotype. A wider understanding of dietary and lifestyle influences, genetic and metabolic risk factors, and the pitfalls of conventional equations estimating kidney function in this population are required in providing care for kidney diseases. Targeted screening of this population for metabolic and vascular risk factors and individualized management plan for disease management may be necessary. Addressing unhealthy dietary patterns, promoting physical activity, and medication management that adheres to cultural factors are crucial steps to mitigate the risk of cardiovascular disease and CKD in this population. In South Asian countries, a large rural and urban community-based multipronged approach using polypills and community health workers to decrease the incidence of these diseases may be cost-effective.

Unravelling the potential of social prescribing in individual-level type 2 diabetes prevention: a mixed-methods realist evaluation

BACKGROUND

Social prescribing (SP) usually involves linking patients in primary care with services provided by the voluntary and community sector. Preliminary evidence suggests that SP may offer a means of connecting patients with community-based health promotion activities, potentially contributing to the prevention of long-term conditions, such as type 2 diabetes (T2D).

METHODS

Using mixed-methods realist evaluation, we explored the possible contribution of SP to individual-level prevention of T2D in a multi-ethnic, socio-economically deprived population in London, UK. We made comparisons with an existing prevention programme (NHS Diabetes Prevention Programme (NDPP)) where relevant and possible. Anonymised primary care electronic health record data of 447,360 people 18+ with an active GP registration between December 2016 and February 2022 were analysed using quantitative methods. Qualitative data (interviews with 11 primary care clinicians, 11 social prescribers, 13 community organisations and 8 SP users at high risk of T2D; 36 hours of ethnographic observations of SP and NDPP sessions; and relevant documents) were analysed thematically. Data were integrated using visual means and realist methods.

RESULTS

People at high risk of T2D were four times more likely to be referred into SP than the eligible general population (RR 4.31 (95% CI 4.17-4.46)), with adjustment for socio-demographic variables resulting in attenuation (RR 1.33 (95% CI 1.27-1.39)). More people at risk of T2D were referred to SP than to NDPP, which could be explained by the broad referral criteria for SP and highly supportive (proactive, welcoming) environments. Holistic and sustained SP allowed acknowledgement of patients' wider socio-economic constraints and provision of long-term personalised care. The fact that SP was embedded within the local community and primary care infrastructure facilitated the timely exchange of information and cross-referrals across providers, resulting in enhanced service responsiveness.

CONCLUSIONS

Our study suggests that SP may offer an opportunity for individual-level T2D prevention to shift away from standardised, targeted and short-term strategies to approaches that are increasingly personalised, inclusive and long-term. Primary care-based SP seems most ideally placed to deliver such approaches where practitioners, providers and commissioners work collectively to achieve holistic, accessible, sustained and integrated services.

Performance of a Finnish Diabetes Risk Score in detecting undiagnosed diabetes among Kenyans aged 18-69 years

BACKGROUND

The application of risk scores has often effectively predicted undiagnosed type 2 diabetes in a non-invasive way to guide early clinical management. The capacity for diagnosing diabetes in developing countries including Kenya is limited. Screening tools to identify those at risk and thus target the use of limited resources could be helpful, but these are not validated for use in these settings. We, therefore, aimed to measure the performance of the Finnish diabetes risk score (FINDRISC) as a screening tool to detect undiagnosed diabetes among Kenyan adults.

METHODS

A nationwide cross-sectional survey on non-communicable disease risk factors was conducted among Kenyan adults between April and June 2015. Diabetes mellitus was defined as fasting capillary whole blood ≥ 7.0mmol/l. The performance of the original, modified, and simplified FINDRISC tools in predicting undiagnosed diabetes was assessed using the area under the receiver operating curve (AU-ROC). Non-parametric analyses of the AU-ROC, Sensitivity (Se), and Specificity (Sp) of FINDRISC tools were determined.

RESULTS

A total of 4,027 data observations of individuals aged 18-69 years were analyzed. The proportion/prevalence of undiagnosed diabetes and prediabetes was 1.8% [1.3-2.6], and 2.6% [1.9-3.4] respectively. The AU-ROC of the modified FINDRISC and simplified FINDRISC in detecting undiagnosed diabetes were 0.7481 and 0.7486 respectively, with no statistically significant difference (p = 0.912). With an optimal cut-off ≥ 7, the simplified FINDRISC had a higher positive predictive value (PPV) (7.9%) and diagnostic odds (OR:6.65, 95%CI: 4.43-9.96) of detecting undiagnosed diabetes than the modified FINDRISC.

CONCLUSION

The simple, non-invasive modified, and simplified FINDRISC tools performed well in detecting undiagnosed diabetes and may be useful in the Kenyan population and other similar population settings. For resource-constrained settings like the Kenyan settings, the simplified FINDRISC is preferred.

Household food insecurity risk indices for English neighbourhoods: Measures to support local policy decisions

BACKGROUND

In England, the responsibility to address food insecurity lies with local government, yet the prevalence of this social inequality is unknown in small subnational areas. In 2018 an index of small-area household food insecurity risk was developed and utilised by public and third sector organisations to target interventions; this measure needed updating to better support decisions in different settings, such as urban and rural areas where pressures on food security differ.

METHODS

We held interviews with stakeholders (n = 14) and completed a scoping review to identify appropriate variables to create an updated risk measure. We then sourced a range of open access secondary data to develop an indices of food insecurity risk in English neighbourhoods. Following a process of data transformation and normalisation, we tested combinations of variables and identified the most appropriate data to reflect household food insecurity risk in urban and rural areas.

RESULTS

Eight variables, reflecting both household circumstances and local service availability, were separated into two domains with equal weighting for a new index, the Complex Index, and a subset of these to make up the Simple Index. Within the Complex Index, the Compositional Domain includes population characteristics while the Structural Domain reflects small area access to resources such as grocery stores. The Compositional Domain correlated well with free school meal eligibility (rs = 0.705) and prevalence of childhood obesity (rs = 0.641). This domain was the preferred measure for use in most areas when shared with stakeholders, and when assessed alongside other configurations of the variables. Areas of highest risk were most often located in the North of England.

CONCLUSION

We recommend the use of the Compositional Domain for all areas, with inclusion of the Structural Domain in rural areas where locational disadvantage makes it more difficult to access resources. These measures can aid local policy makers and planners when allocating resources and interventions to support households who may experience food insecurity.

Estimating the potential public health impact of fibre enrichment: a UK modelling study

For improving human health, reformulation can be a tool as it allows individuals to consume products of choice while reducing intake of less desirable nutrients, such as sugars and fats, and potentially increasing intake of beneficial nutrients such as fibre. The potential effects of reformulating foods with increased fibre on diet and health need to be better understood. The objective of this statistical modelling study was to understand how fibre enrichment can affect the diet and health of consumers. The UK National Diet and Nutrition Survey datasets from 2014 to 2015 and 2015 to 2016 were utilised to evaluate intakes of fibre and kilocalories with a dietary intake model. Foods and beverages eligible for fibre enrichment were identified (n 915) based on EU legislation for fibre content claims. Those people who meet dietary reference values and fibre enrichment health outcomes such as weight, CVD and type 2 diabetes risk reductions were quantified pre- and post-fibre reformulation via Reynolds et al., D'Agostino et al. and QDiabetes algorithms, respectively. The fibre enrichment intervention showed a mean fibre intake of 19·9 g/d in the UK, signifying a 2·2 g/d increase from baseline. Modelling suggested that 5·9 % of subjects could achieve a weight reduction, 72·2 % a reduction in cardiovascular risk and 71·7 % a reduced risk of type 2 diabetes with fibre fortification (all Ps ≤ 0·05). This study gives a good overview of the potential public health benefits of reformulating food products using a straightforward enrichment scenario.

Development and validation of personalised risk prediction models for early detection and diagnosis of primary liver cancer among the English primary care population using the QResearch® database: research protocol and statistical analysis plan

BACKGROUND AND RESEARCH AIM

The incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings.

METHODS

This population-based study uses the QResearch® database (version 46) and includes adult patients aged 25-84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008-30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal-external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated.

DISCUSSION

The DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.

A nomogram model for the risk prediction of type 2 diabetes in healthy eastern China residents: a 14-year retrospective cohort study from 15,166 participants

Background

Risk prediction models can help identify individuals at high risk for type 2 diabetes. However, no such model has been applied to clinical practice in eastern China.

Aims

This study aims to develop a simple model based on physical examination data that can identify high-risk groups for type 2 diabetes in eastern China for predictive, preventive, and personalized medicine.

Methods

A 14-year retrospective cohort study of 15,166 nondiabetic patients (12-94 years; 37% females) undergoing annual physical examinations was conducted. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) models were constructed for univariate analysis, factor selection, and predictive model building. Calibration curves and receiver operating characteristic (ROC) curves were used to assess the calibration and prediction accuracy of the nomogram, and decision curve analysis (DCA) was used to assess its clinical validity.

Results

The 14-year incidence of type 2 diabetes in this study was 4.1%. This study developed a nomogram that predicts the risk of type 2 diabetes. The calibration curve shows that the nomogram has good calibration ability, and in internal validation, the area under ROC curve (AUC) showed statistical accuracy (AUC = 0.865). Finally, DCA supports the clinical predictive value of this nomogram.

Conclusion

This nomogram can serve as a simple, economical, and widely scalable tool to predict individualized risk of type 2 diabetes in eastern China. Successful identification and intervention of high-risk individuals at an early stage can help to provide more effective treatment strategies from the perspectives of predictive, preventive, and personalized medicine.

Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study

AIMS

To develop a simple multivariate predictor model of incident type 2 diabetes in general population.

METHODS

Participants were recruited from the Spanish Di@bet.es cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model.

RESULTS

156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92-106 mg/dL (OR = 3.76, 95%CI = 2.36-6.00), > 106 mg/dL (OR = 13.21; 8.26-21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89-43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs  = 97-180 mg/dL, OR = 3.12; 1.05-9.24). This model correctly classified 93.5% of individuals.

CONCLUSIONS

The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.

Establishment and validation of a nomogram that predicts the risk of type 2 diabetes in obese patients with non-alcoholic fatty liver disease: a longitudinal observational study

OBJECTIVE

This study aimed to establish and validate a nomogram for better assessment of the risk of type 2 diabetes (T2D) in obese patients with non-alcoholic fatty liver disease (NAFLD) based on independent predictors.

METHODS

Of 1820 eligible participants from the NAGALA cohort enrolled in the study. Multivariate Cox regression was employed to construct the nomogram. The performance was assessed by area under the receiver operating characteristic curve (AUC), C-index, calibration curve, decision curve analysis, and Kaplan-Meier analysis.

RESULTS

Five predictors were selected from 17 variables. The AUC values at different time points all indicated that the model constructed with these five predictors had good predictive power. Decision curves indicated that the model could be applied to clinical applications.

CONCLUSIONS

We established and validated a reasonable, economical nomogram for predicting the risk of T2D in obese NAFLD patients. This simple clinical tool can help with risk stratification and thus contribute to the development of effective prevention programs against T2D in obese patients with NAFLD.

Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

BACKGROUND

Effective targeted prevention of type 2 diabetes (T2D) depends on accurate prediction of disease risk. We assessed the role of metabolomic profiling in improving T2D risk prediction beyond conventional risk factors.

METHODS

Nuclear magnetic resonance (NMR) metabolomic profiling was undertaken on baseline plasma samples in 65,684 UK Biobank participants without diabetes and not taking lipid-lowering medication. Among a subset of 50,519 participants with data available on all relevant co-variates (sociodemographic characteristics, parental history of diabetes, lifestyle-including dietary-factors, anthropometric measures and fasting time), Cox regression yielded adjusted hazard ratios for the associations of 143 individual metabolic biomarkers (including lipids, lipoproteins, fatty acids, amino acids, ketone bodies and other low molecular weight metabolic biomarkers) and 11 metabolic biomarker principal components (PCs) (accounting for 90% of the total variance in individual biomarkers) with incident T2D. These 11 PCs were added to established models for T2D risk prediction among the full study population, and measures of risk discrimination (c-statistic) and reclassification (continuous net reclassification improvement [NRI], integrated discrimination index [IDI]) were assessed.

RESULTS

During median 11.9 (IQR 11.1-12.6) years' follow-up, after accounting for multiple testing, 90 metabolic biomarkers showed independent associations with T2D risk among 50,519 participants (1211 incident T2D cases) and 76 showed associations after additional adjustment for HbA1c (false discovery rate controlled p < 0.01). Overall, 8 metabolic biomarker PCs were independently associated with T2D. Among the full study population of 65,684 participants, of whom 1719 developed T2D, addition of PCs to an established risk prediction model, including age, sex, parental history of diabetes, body mass index and HbA1c, improved T2D risk prediction as assessed by the c-statistic (increased from 0.802 [95% CI 0.791-0.812] to 0.830 [0.822-0.841]), continuous NRI (0.44 [0.38-0.49]) and relative (15.0% [10.5-20.4%]) and absolute (1.5 [1.0-1.9]) IDI. More modest improvements were observed when metabolic biomarker PCs were added to a more comprehensive established T2D risk prediction model additionally including waist circumference, blood pressure and plasma lipid concentrations (c-statistic, 0.829 [0.819-0.838] to 0.837 [0.831-0.848]; continuous NRI, 0.22 [0.17-0.28]; relative IDI, 6.3% [4.1-9.8%]; absolute IDI, 0.7 [0.4-1.1]).

CONCLUSIONS

When added to conventional risk factors, circulating NMR-based metabolic biomarkers modestly enhanced T2D risk prediction.

Use of non-pharmacological methods in managing labour pain: experiences of nurse-midwives in two selected district hospitals in eastern Tanzania

Background

Labour pain usually brings with it many concerns for a parturient and her family. The majority of the women in labour pain may require some sort of pain relief method during this period, be it pharmacological or non-pharmacological. In Tanzania, the use of non-pharmacological methods to relief labour pain remains low among nurse-midwives. We analysed the experiences of nurse-midwives in the use of non-pharmacological methods to manage labour pain, in two selected districts of Pwani and Dar es Salaam regions in eastern Tanzania. This paper describes Non-pharmacological Methods (NPMs) currently used by nurse-midwives, the facilitators, myths and fears related to the use of NPMs.

Materials and Methods

An exploratory qualitative study using in-depth interviews was conducted with 18 purposively recruited nurse-midwives working in labour wards in two selected district hospitals in Pwani and Dar es Salaam regions in eastern Tanzania. Qualitative conventional content analysis was used to generate categories describing the experience of using non-pharmacological methods in managing labour pain.

Results

This study revealed that nurse-midwives encouraged women to tolerate labour pain and instructed them to change positions and to do deep breathing exercises as a means to relief labour pain. Nurse-midwives’ inner motives facilitated the use of non-pharmacological strategies for labour pain relief despite the fear of using them and myths that labour pain is necessary for childbirth.

Conclusion

This study generates information about the use of non-pharmacological strategies to relief labour pain. Although nurse-midwives are motivated to apply various non-pharmacological strategies to relief labour pain, fear and misconceptions about the necessity of labour pain during childbirth prohibit the effective use of these strategies. Therefore, together with capacity building the nurse-midwives in the use of non-pharmacological strategies to relief labour pain, efforts should be made to address the misconceptions that may partly be of socio-cultural origin.

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.

An Automated Risk Index for Diabetic Ketoacidosis in Pediatric Patients With Type 1 Diabetes: The RI-DKA

Identifying patients at high risk for diabetic ketoacidosis (DKA) is crucial for informing efforts at preventive intervention. This study sought to develop and validate an electronic medical record (EMR)-based tool for predicting DKA risk in pediatric patients with type 1 diabetes. Based on analysis of data from 1,864 patients with type 1 diabetes, three factors emerged as significant predictors of DKA: most recent A1C, type of health insurance (public vs. private), and prior DKA. A prediction model was developed based on these factors and tested to identify and categorize patients at low, moderate, and high risk for experiencing DKA within the next year. This work demonstrates that risk for DKA can be predicted using a simple model that can be automatically derived from variables in the EMR.

Palmitoylated small GTPase ARL15 is translocated within Golgi network during adipogenesis

The small GTPase ARF family member ARL15 gene locus is associated in population studies with increased risk of type 2 diabetes, lower adiponectin and higher fasting insulin levels. Previously, loss of ARL15 was shown to reduce insulin secretion in a human β-cell line and loss-of-function mutations are found in some lipodystrophy patients. We set out to understand the role of ARL15 in adipogenesis and showed that endogenous ARL15 palmitoylated and localised in the Golgi of mouse liver. Adipocyte overexpression of palmitoylation-deficient ARL15 resulted in redistribution to the cytoplasm and a mild reduction in expression of some adipogenesis-related genes. Further investigation of the localisation of ARL15 during differentiation of a human white adipocyte cell line showed that ARL15 was predominantly co-localised with a marker of the cis face of Golgi at the preadipocyte stage and then translocated to other Golgi compartments after differentiation was induced. Finally, co-immunoprecipitation and mass spectrometry identified potential interacting partners of ARL15, including the ER-localised protein ARL6IP5. Together, these results suggest a palmitoylation dependent trafficking-related role of ARL15 as a regulator of adipocyte differentiation via ARL6IP5 interaction.

This article has an associated First Person interview with the first author of the paper.

Summary: ARL15 (GTPase ARF family) is associated with adipose traits. ARL15 is palmitoylated, localised to Golgi in preadipocytes and translocated to other Golgi compartments during differentiation. ARL15 interacts with ER-localised ARL6IP5.

Primary care use by men with symptoms of possible prostate cancer: A multi-method study with an ethnically diverse sample in London

OBJECTIVE

The objective of this study is to investigate primary care use by men with recent onset of lower urinary tract symptoms (LUTS) to identify differences in presentation and investigation that may explain ethnic inequality in prostate cancer outcomes.

METHODS

This is a multi-method study of men presenting LUTS to primary care. Two hundred seventy-four men completed a self-administered questionnaire, and 23 participated in face-to-face interviews. Regression analyses investigated ethnic differences in (a) the period between symptom onset and first primary care presentation (patient interval) and (b) the interval between first primary care presentation and investigation with prostate-specific antigen (PSA) and digital rectal examination (DRE). Interview data were analysed using thematic analysis.

RESULTS

Half (144, 53%) reported a solitary first symptom, although multiple first symptoms were also common, particularly in Asian and Black men. There was no difference between ethnicities in patient interval or time from presentation to investigation. However, Asian men were offered less PSA testing (odds ratio 0.39; 95% confidence interval 0.17-0.92; p = 0.03). Qualitative data revealed ethnic differences in general practitioners' offer of DRE and PSA testing and highlighted limitations in doctor-patient communication and safety netting.

CONCLUSION

Our study showed only small differences in primary care experiences, insufficient to explain ethnic inequalities in prostate cancer outcomes.

Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models

OBJECTIVE

Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome.

STUDY DESIGN AND SETTING

Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis.

RESULTS

Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model).

CONCLUSION

Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems.

Development and validation of the type 2 diabetes mellitus 10-year risk score prediction models from survey data

AIMS

In this paper, we demonstrate the development and validation of the 10-years type 2 diabetes mellitus (T2DM) risk prediction models based on large survey data.

METHODS

The Survey of Health, Ageing and Retirement in Europe (SHARE) data collected in 12 European countries using 53 variables representing behavioural as well as physical and mental health characteristics of the participants aged 50 or older was used to build and validate prediction models. To account for strongly unbalanced outcome variables, each instance was assigned a weight according to the inverse proportion of the outcome label when the regularized logistic regression model was built.

RESULTS

A pooled sample of 16,363 individuals was used to build and validate a global regularized logistic regression model that achieved an area under the receiver operating characteristic curve of 0.702 (95% CI: 0.698-0.706). Additionally, we measured performance of local country-specific models where AUROC ranged from 0.578 (0.565-0.592) to 0.768 (0.749-0.787).

CONCLUSIONS

We have developed and validated a survey-based 10-year T2DM risk prediction model for use across 12 European countries. Our results demonstrate the importance of re-calibration of the models as well as strengths of pooling the data from multiple countries to reduce the variance and consequently increase the precision of the results.

Deep Learning Algorithm for Management of Diabetes Mellitus via Electrocardiogram-Based Glycated Hemoglobin (ECG-HbA1c): A Retrospective Cohort Study

BACKGROUND

glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c via ECG.

METHODS

there were 104,823 ECGs with corresponding HbA1c or fasting glucose which were utilized to train a DLM for calculating ECG-HbA1c. Next, 1539 cases from outpatient departments and health examination centers provided 2190 ECGs for initial validation, and another 3293 cases with their first ECGs were employed to analyze its contributions to DM management. The primary analysis was used to distinguish patients with and without mild to severe DM, and the secondary analysis was to explore the predictive value of ECG-HbA1c for future complications, which included all-cause mortality, new-onset chronic kidney disease (CKD), and new-onset heart failure (HF).

RESULTS

we used a gender/age-matching strategy to train a DLM to achieve the best AUCs of 0.8255 with a sensitivity of 71.9% and specificity of 77.7% in a follow-up cohort with correlation of 0.496 and mean absolute errors of 1.230. The stratified analysis shows that DM presented in patients with fewer comorbidities was significantly more likely to be detected by ECG-HbA1c. Patients with higher ECG-HbA1c under the same Lab-HbA1c exhibited worse physical conditions. Of interest, ECG-HbA1c may contribute to the mortality (gender/age adjusted hazard ratio (HR): 1.53, 95% conference interval (CI): 1.08-2.17), new-onset CKD (HR: 1.56, 95% CI: 1.30-1.87), and new-onset HF (HR: 1.51, 95% CI: 1.13-2.01) independently of Lab-HbA1c. An additional impact of ECG-HbA1c on the risk of all-cause mortality (C-index: 0.831 to 0.835, p < 0.05), new-onset CKD (C-index: 0.735 to 0.745, p < 0.01), and new-onset HF (C-index: 0.793 to 0.796, p < 0.05) were observed in full adjustment models.

CONCLUSION

the ECG-HbA1c could be considered as a novel biomarker for screening DM and predicting the progression of DM and its complications.

Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort

BACKGROUND

Previously developed prediction models for type 2 diabetes mellitus (T2DM) have limited performance. We developed a deep learning (DL) based model using a cohort representative of the Korean population.

METHODS

This study was conducted on the basis of the National Health Insurance Service-Health Screening (NHIS-HEALS) cohort of Korea. Overall, 335,302 subjects without T2DM at baseline were included. We developed the model based on 80% of the subjects, and verified the power in the remainder. Predictive models for T2DM were constructed using the recurrent neural network long short-term memory (RNN-LSTM) network and the Cox longitudinal summary model. The performance of both models over a 10-year period was compared using a time dependent area under the curve.

RESULTS

During a mean follow-up of 10.4±1.7 years, the mean frequency of periodic health check-ups was 2.9±1.0 per subject. During the observation period, T2DM was newly observed in 8.7% of the subjects. The annual performance of the model created using the RNN-LSTM network was superior to that of the Cox model, and the risk factors for T2DM, derived using the two models were similar; however, certain results differed.

CONCLUSION

The DL-based T2DM prediction model, constructed using a cohort representative of the population, performs better than the conventional model. After pilot tests, this model will be provided to all Korean national health screening recipients in the future.

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.

Cardio-metabolic outcomes in South Asians compared to White Europeans in the United Kingdom: a matched controlled population-based cohort study

BACKGROUND

There appears to be an inequality in the risk of cardio-metabolic disease between those from a South Asian (SA) background when compared to those of White Europeans (WE) descendance, however, this association has not been explored in a large European cohort. This population-based open retrospective cohort explores the incidence of cardio-metabolic disease in those without pre-existing cardiometabolic disease taken from a large UK primary care database from 1st January 2007 to 31st December 2017.

METHODS

A retrospective open cohort matched population-based study using The Health Improvement Network (THIN) database. The outcomes of this study were the incidences of cardio-metabolic events (type 2 diabetes mellitus, hypertension, ischemic heart disease, stroke, heart failure, and atrial fibrillation).

RESULTS

A total of 94,870 SA patients were matched with 189,740 WE patients. SA were at an increased risk of developing: T2DM (adjusted hazard ratio (aHR) 3.1; 95% CI 2.97-3.23); HTN (1.34; 95% CI: 1.29-1.39); ischaemic heart disease (IHD) (1.81; 95% CI: 1.68-1.93) and heart failure (HF) (1.11; 95% CI: 1.003-1.24). However, they were at a lower risk of atrial fibrillation (AF) (0.53; 95% CI: 0.48-0.59) when compared to WE. Of those of SA origin, the Bangladeshi community were at the greatest risk of T2DM, HTN, IHD and HF, but were at the lowest risk of AF in when compared to Indians and Pakistanis.

CONCLUSION

Considering the high risk of cardio-metabolic diseases in the SA cohort, differential public health measures should be considered in these patients to reduce their risk of disease, which may be furthered tailored depending on their country of origin.

A Cardiovascular Risk Score for Use in Occupational Medicine

Cardiovascular disease is one of the most frequent causes of long-term sickness absence from work. The study aims to develop and validate a score to assess the 10-year risk of unsuitability for work accounting for the cardiovascular risk. The score can be considered as a prevention tool that would improve the cardiovascular risk assessment during health surveillance visits under the assumption that a high cardiovascular risk might also translate into high risk of unsuitability for work. A total of 11,079 Italian workers were examined, as part of their scheduled occupational health surveillance. Cox proportional hazards regression models were employed to derive risk equations for assessing the 10-year risk of a diagnosis of unsuitability for work. Two scores were developed: the CROMA score (Cardiovascular Risk in Occupational Medicine) included age, sex, smoking status, blood pressure (systolic and diastolic), body mass index, height, diagnosis of hypertension, diabetes, ischemic heart disease, mental disorders and prescription of antidiabetic and antihypertensive medications. The CROMB score was the same as CROMA score except for the inclusion of only variables statistically significant at the 0.05 level. For both scores, the expected risk of unsuitability for work was higher for workers in the highest risk class, as compared with the lowest. Moreover results showed a positive association between most of cardiovascular risk factors and the risk of unsuitability for work. The CROMA score demonstrated better calibration than the CROMB score (11.624 (p-value: 0.235)). Moreover, the CROMA score, in comparison with existing CVD risk scores, showed the best goodness of fit and discrimination.

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records

Background

Predicting the risk of glycated hemoglobin (HbA_1c) elevation can help identify patients with the potential for developing serious chronic health problems, such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records data for identifying such patients can ultimately help provide better health outcomes.

Objective

Our study investigated the performance of predictive models to forecast HbA_1c elevation levels by employing several machine learning models. We also examined the use of patient electronic health record longitudinal data in the performance of the predictive models. Explainable methods were employed to interpret the decisions made by the black box models.

Methods

This study employed multiple logistic regression, random forest, support vector machine, and logistic regression models, as well as a deep learning model (multilayer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA_1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large data set from Saudi Arabia with 18,844 unique patient records.

Results

The machine learning models achieved promising results for predicting current HbA_1c elevation risk. When coupled with longitudinal data, the machine learning models outperformed the multiple logistic regression model used in the comparative study. The multilayer perceptron model achieved an accuracy of 83.22% for the area under receiver operating characteristic curve when used with historical data. All models showed a close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data.

Conclusions

This study shows that machine learning models can provide promising results for the task of predicting current HbA_1c levels (≥5.7% or less). Using patients’ longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies.

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.

Prevalence of Undiagnosed Diabetes in 2004 and 2012: Evidence From the English Longitudinal Study of Aging

BACKGROUND

In light of recent publicity campaigns to raise awareness of diabetes, we investigated changes in the prevalence of diabetes and undiagnosed diabetes in adults age 50 and older in England between 2004 and 2012, and explored risk factors for undiagnosed diabetes.

METHOD

In total, 7666 and 7729 individuals were from Wave 2 (2004-2005, mean age 66.6) and Wave 6 (2012-2013, mean age 67.6) of the English Longitudinal Study of Ageing. Diagnosed diabetes was defined as either self-reported diabetes or taking diabetic medications. Undiagnosed diabetes was defined as not self-reporting diabetes and not taking diabetic medications, but having a glycated hemoglobin measurement ≥48 mmol/mol (6.5%).

RESULTS

There were increases in both diagnosed diabetes (7.7%-11.5%) and undiagnosed diabetes (2.4%-3.4%) between 2004 and 2012. However, a small decrease in the proportion of people with diabetes who were unaware of this condition (24.5%-23.1%, p < .05) was observed. Only men aged 50-74 showed a stable prevalence of undiagnosed diabetes, with better recognition of diabetes. Age, non-white ethnicity, manual social class, higher diastolic blood pressure, and cholesterol level were factors associated with higher risks of undiagnosed diabetes, whereas greater depressive symptoms were related to lower risks.

CONCLUSION

This study suggests that the greater awareness of diabetes in the population of England has not resulted in a decline in undiagnosed cases between 2004 and 2012. A greater focus on people from lower socioeconomic groups and those with cardiometabolic risk factors may help early diagnosis of diabetes for older adults.

Differential associations of ankle and brachial blood pressures with diabetes and cardiovascular diseases: cross-sectional study

Increased brachial systolic blood-pressure (BP) predicts diabetes (T2DM) but is not fully effective. Value of absolute ankle systolic BP for T2DM compared to brachial systolic BP is not known. Our objectives were to assess independent relationships of ankle-systolic BP with T2DM and cardiovascular disease in Europeans and south Asians. Cross-sectional studies of anonymised data from registered adults (n = 1087) at inner city deprived primary care practices. Study includes 63.85% ethnic minority. Systolic BP of the left and right-brachial, posterior-tibial and dorsalis-pedis-arteries measured using a Doppler probe. Regression models' factors were age, sex, ethnicity, body mass index (BMI) and waist height ratio (WHtR). Both brachial and ankle systolic-BP increase with diabetes in Europeans and south Asians. We demonstrated that there was a significant positive independent association of ankle BP with diabetes, regardless of age and sex compared to Brachial. There was stronger negative association of ankle blood pressure with cardiovascular disease, after adjustment for BMI, WHtR and ethnicity. Additionally, we found that ankle BP were significantly associated with cardiovascular disease in south Asians more than the Europeans; right posterior tibial. Ankle systolic BPs are superior to brachial BPs to identify risks of Type 2DM and cardiovascular diseases for enhanced patient care.

Predicting the risk of prostate cancer in asymptomatic men: a cohort study to develop and validate a novel algorithm

Background

Diagnosis of prostate cancer at an early stage can potentially identify tumours when intervention may improve treatment options and survival.

Aim

To develop and validate an equation to predict absolute risk of prostate cancer in asymptomatic men with prostate specific antigen (PSA) tests in primary care.

Design and setting

Cohort study using data from English general practices, held in the QResearch database.

Method

Routine data were collected from 1098 QResearch English general practices linked to mortality, hospital, and cancer records for model development. Two separate sets of practices were used for validation. In total, there were 844 455 men aged 25–84 years with PSA tests recorded who were free of prostate cancer at baseline in the derivation cohort; the validation cohorts comprised 292 084 and 316 583 men. The primary outcome was incident prostate cancer. Cox proportional hazards models were used to derive 10-year risk equations. Measures of performance were determined in both validation cohorts.

Results

There were 40 821 incident cases of prostate cancer in the derivation cohort. The risk equation included PSA level, age, deprivation, ethnicity, smoking status, serious mental illness, diabetes, BMI, and family history of prostate cancer. The risk equation explained 70.4% (95% CI = 69.2 to 71.6) of the variation in time to diagnosis of prostate cancer (R²) (D statistic 3.15, 95% CI = 3.06 to 3.25; Harrell’s C-index 0.917, 95% CI = 0.915 to 0.919). Two-step approach had higher sensitivity than a fixed PSA threshold at identifying prostate cancer cases (identifying 68.2% versus 43.9% of cases), high-grade cancers (49.2% versus 40.3%), and deaths (67.0% versus 31.5%).

Conclusion

The risk equation provided valid measures of absolute risk and had higher sensitivity for incident prostate cancer, high-grade cancers, and prostate cancer mortality than a simple approach based on age and PSA threshold.

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.

Risk Prediction of the Diabetes Missing Million: Identifying Individuals at High Risk of Diabetes and Related Complications

Early diagnosis and effective management of type 2 diabetes (T2D) are crucial in reducing the risk of developing life-changing complications such as heart failure, stroke, kidney disease, blindness and amputation, which are also associated with significant costs for healthcare providers. However, as T2D symptoms often develop slowly it is not uncommon for people to live with T2D for years without being aware of their condition-commonly known as the undiagnosed missing million. By the time a diagnosis is received, many individuals will have already developed serious complications. While the existence of undiagnosed diabetes has long been recognised, wide-reaching awareness among the general public, clinicians and policymakers is lacking, and there is uncertainty in how best to identify high-risk individuals. In this article we have used consensus expert opinion alongside the available evidence, to provide support for the diabetes healthcare community regarding risk prediction of the missing million. Its purpose is to provide awareness of the risk factors for identifying individuals at high, moderate and low risk of T2D and T2D-related complications. The awareness of risk predictors, particularly in primary care, is important, so that appropriate steps can be taken to reduce the clinical and economic burden of T2D and its complications.

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults

Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887-0.9364) and 0.9030 (95% CI, 0.8747-0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126-0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

The contested meanings of race and ethnicity in medical research: A case study of the DynaMed Point of Care tool

Although the use of race and ethnicity for diagnostic purposes remains a controversial practice given the socially contingent meaning of the terms (Bowker and Star, 1999), health researchers continue to report possible relationships between health outcomes and race/ethnicity in the literature. As summaries of these types of studies are incorporated into commercial databases designed to provide medical practitioners with actionable information, there is a risk that the algorithms that drive the databases may unintentionally incorporate racist biases (O'Neil, 2016) in search reports that use race and ethnicity as query terms to identify findings to help in the diagnosis and treatment of particular patients. As a first step to unpacking this risk, we conducted a content analysis of the records and related citation trails in DynaMed's Point of Care (PoC) tool that refer to racial and ethnic research findings. Our analysis demonstrates that DynaMed does not control for how meanings of race and ethnicity are constructed in its entries, does not always accurately represent the nuanced and contingent nature of the findings about race/ethnicity that it cites, and relies on sources that are not always consistent with the 'evidence-based' criterion that the company self-promotes as a feature of its PoC tool. We conclude that, by failing to acknowledge the complex and contradictory ways that race and ethnicity may, or may not, correlate with the risk of a medical ailment, algorithmically-driven tools that use these concepts to establish group risks for medical ailments may unintentionally work to 'resuscitat[e] biological theories of race by modernizing old racial typologies that were based on observations of physical differences with cutting-edge genomic research' (Roberts, 2011: 567).

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Effect of novel technology-enabled multidimensional physical activity feedback in primary care patients at risk of chronic disease - the MIPACT study: a randomised controlled trial

BACKGROUND

Technological progress has enabled the provision of personalised feedback across multiple dimensions of physical activity that are important for health. Whether this multidimensional approach supports physical activity behaviour change has not yet been examined. Our objective was to examine the effectiveness of a novel digital system and app that provided multidimensional physical activity feedback combined with health trainer support in primary care patients identified as at risk of chronic disease.

METHODS

MIPACT was a parallel-group, randomised controlled trial that recruited patients at medium (≥10 and < 20%) or high (≥20%) risk of cardiovascular disease and/or type II diabetes from six primary care practices in the United Kingdom. Intervention group participants (n = 120) received personal multidimensional physical activity feedback using a customised digital system and web-app for 3 months plus five health trainer-led sessions. All participants received standardised information regarding physical activity. Control group participants (n = 84) received no further intervention. The primary outcome was device-based assessment of physical activity at 12 months.

RESULTS

Mean intervention effects were: moderate-vigorous physical activity: -1.1 (95% CI, - 17.9 to 15.7) min/day; moderate-vigorous physical activity in ≥10-min bouts: 0.2 (- 14.2 to 14.6) min/day; Physical Activity Level (PAL): 0.00 (- 0.036 to 0.054); vigorous physical activity: 1.8 (- 0.8 to 4.2) min/day; and sedentary time: 10 (- 19.3 to 39.3) min/day. For all of these outcomes, the results showed that the groups were practically equivalent and statistically ruled out meaningful positive or negative effects (>minimum clinically important difference, MCID). However, there was profound physical activity multidimensionality, and only a small proportion (5%) of patients had consistently low physical activity across all dimensions.

CONCLUSION

In patients at risk of cardiovascular disease and/or type II diabetes, MIPACT did not increase mean physical activity. Using a sophisticated multidimensional digital approach revealed enormous heterogeneity in baseline physical activity in primary care patients, and practitioners may need to screen for low physical activity across dimensions rather than rely on disease-risk algorithms that are heavily influenced by age.

TRIAL REGISTRATION

This trial is registered with the ISRCTN registry ( ISRCTN18008011 ; registration date 31 July 2013).

A Wearable Technology Delivering a Web-Based Diabetes Prevention Program to People at High Risk of Type 2 Diabetes: Randomized Controlled Trial

BACKGROUND

Intensive lifestyle interventions are effective in reducing the risk of type 2 diabetes, but the implementation of learnings from landmark studies is expensive and time consuming. The availability of digital lifestyle interventions is increasing, but evidence of their effectiveness is limited.

OBJECTIVE

This randomized controlled trial (RCT) aimed to test the feasibility of a web-based diabetes prevention program (DPP) with step-dependent feedback messages versus a standard web-based DPP in people with prediabetes.

METHODS

We employed a two-arm, parallel, single-blind RCT for people at high risk of developing diabetes. Patients with a hemoglobin A_1c (HbA_1c) level of 39-47 mmol/mol were recruited from 21 general practices in London. The intervention integrated a smartphone app delivering a web-based DPP course with SMS texts incorporating motivational interviewing techniques and step-dependent feedback messages delivered via a wearable device over 12 months. The control group received the wearable technology and access to the web-based DDP but not the SMS texts. As this was a feasibility study, the primary aim was to estimate potential sample size at different stages of the study, including the size of the target study population and the proportion of participants who consented, were randomized, and completed follow-up. We also measured the main outcomes for a full-scale RCT, namely, change in weight and physical activity at 6- and 12-month follow-ups, and secondary outcomes, including changes in the HbA_1c level, blood pressure, waist circumference, waist-to-hip ratio, and lipid levels.

RESULTS

We enrolled 200 participants: 98 were randomized to the intervention and 102 were randomized to the control group. The follow-up rate was higher in the control group (87/102, 85.3%) than in the intervention group (69/98, 70%) at 12 months. There was no treatment effect on weight at 6 months (mean difference 0.15; 95% CI -0.93 to 1.23) or 12 months (mean difference 0.07 kg; 95% CI -1.29 to 1.44) or for physical activity levels at 6 months (mean difference -382.90 steps; 95% CI -860.65 to 94.85) or 12 months (mean difference 92.64 steps; 95% CI -380.92 to 566.20). We did not observe a treatment effect on the secondary outcomes measured at the 6-month or 12-month follow-up. For the intervention group, the mean weight was 92.33 (SD 15.67) kg at baseline, 91.34 (SD 16.04) kg at 6 months, and 89.41 (SD 14.93) kg at 12 months. For the control group, the mean weight was 92.59 (SD 17.43) kg at baseline, 91.71 (SD 16.48) kg at 6 months, and 91.10 (SD 15.82) kg at 12 months. In the intervention group, the mean physical activity was 7308.40 (SD 4911.93) steps at baseline, 5008.76 (SD 2733.22) steps at 6 months, and 4814.66 (SD 3419.65) steps at 12 months. In the control group, the mean physical activity was 7599.28 (SD 3881.04) steps at baseline, 6148.83 (SD 3433.77) steps at 6 months, and 5006.30 (SD 3681.1) steps at 12 months.

CONCLUSIONS

This study demonstrates that it is feasible to successfully recruit and retain patients in an RCT of a web-based DPP.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02919397; http://clinicaltrials.gov/ct2/show/NCT02919397.

Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions

INTRODUCTION

Many predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions. In this paper, a combined T2D risk model for public health management that addresses these three issues is developed.

RESEARCH DESIGN AND METHODS

The combined T2D risk model combines eight existing predictive models by weighted average to overcome the problem of missing incidence predictions. Moreover, the combined model implements a simple recalibration strategy in which the risk scores are rescaled based on the T2D incidence in the target population. The performance of the combined model was compared with that of the eight existing models using data from two test datasets extracted from the Multi-Ethnic Study of Atherosclerosis (MESA; n=1031) and the English Longitudinal Study of Ageing (ELSA; n=4820). Metrics of discrimination, calibration, and missing incidence predictions were used for the assessment.

RESULTS

The combined T2D model performed well in terms of both discrimination (concordance index: 0.83 on MESA; 0.77 on ELSA) and calibration (expected to observed event ratio: 1.00 on MESA; 1.17 on ELSA), similarly to the best-performing existing models. However, while the existing models yielded a large percentage of missing predictions (17%-45% on MESA; 63%-64% on ELSA), this was negligible with the combined model (0% on MESA, 4% on ELSA).

CONCLUSIONS

Leveraging on existing literature T2D predictive models, a simple approach based on risk score rescaling and averaging was shown to provide accurate and robust incidence predictions, overcoming the problem of recalibration and missing predictions in practical application of predictive models.

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.

Systematic review with meta-analysis of the epidemiological evidence relating smoking to type 2 diabetes

BACKGROUND

Evidence relating tobacco smoking to type 2 diabetes has accumulated rapidly in the last few years, rendering earlier reviews considerably incomplete.

AIM

To review and meta-analyse evidence from prospective studies of the relationship between smoking and the onset of type 2 diabetes.

METHODS

Prospective studies were selected if the population was free of type 2 diabetes at baseline and evidence was available relating smoking to onset of the disease. Papers were identified from previous reviews, searches on Medline and Embase and reference lists. Data were extracted on a range of study characteristics and relative risks (RRs) were extracted comparing current, ever or former smokers with never smokers, and current smokers with non-current smokers, as well as by amount currently smoked and duration of quitting. Fixed- and random-effects estimates summarized RRs for each index of smoking overall and by various subdivisions of the data: Sex; continent; publication year; method of diagnosis; nature of the baseline population (inclusion/exclusion of pre-diabetes); number of adjustment factors; cohort size; number of type 2 diabetes cases; age; length of follow-up; definition of smoking; and whether or not various factors were adjusted for. Tests of heterogeneity and publication bias were also conducted.

RESULTS

The literature searches identified 157 relevant publications providing results from 145 studies. Fifty-three studies were conducted in Asia and 53 in Europe, with 32 in North America, and seven elsewhere. Twenty-four were in males, 10 in females and the rest in both sexes. Fifteen diagnosed type 2 diabetes from self-report by the individuals, 79 on medical records, and 51 on both. Studies varied widely in size of the cohort, number of cases, length of follow-up, and age. Overall, random-effects estimates of the RR were 1.33 [95% confidence interval (CI): 1.28-1.38] for current vs never smoking, 1.28 (95%CI: 1.24-1.32) for current vs non-smoking, 1.13 (95%CI: 1.11-1.16) for former vs never smoking, and 1.25 (95%CI: 1.21-1.28) for ever vs never smoking based on, respectively, 99, 156, 100 and 100 individual risk estimates. Risk estimates were generally elevated in each subdivision of the data by the various factors considered (exceptions being where numbers of estimates in the subsets were very low), though there was significant (P < 0.05) evidence of variation by level for some factors. Dose-response analysis showed a clear trend of increasing risk with increasing amount smoked by current smokers and of decreasing risk with increasing time quit. There was limited evidence of publication bias.

CONCLUSION

The analyses confirmed earlier reports of a modest dose-related association of current smoking and a weaker dose-related association of former smoking with type 2 diabetes risk.

Assessment of risk of diabetes by using Indian Diabetic risk score (IDRS) in Indian population

AIMS

To screen the Indian population for Type 2 Diabetes Mellitus (DM) based on Indian Diabetes Risk Score. Our main question was; Does Indian Diabetic risk score (IDRS) effectively screen diabetic subjects in Indian population?

METHODS

Multi-centric nationwide screening for DM and its risk in all populous states and Union territories of India in 2017. It is the first pan India DM screening study conducted on 240,000 subjects in a short period of 3 months based on IDRS. This was a stratified translational research study in randomly selected cluster populations from all zones of rural and urban India. Two non-modifiable (age, family history) and two modifiable (waist circumference & physical activity) were used to obtain the score. High, moderate and low risk groups were selected based on scores.

RESULTS

In this study 40.9% subjects were detected to be high risk, known or newly diagnosed DM subjects in urban and rural regions. IDRS could detect 78.1% known diabetic subjects as high risk group. Age group 50-59 (17.4%); 60-69 (22%); 70-79 (22.8%); >80 (19.2%) revealed high percentage of subjects. ROC was found to be 0.763 at CI 95% of 0.761-0.765 with statistical significance of p < 0.0001. At >50 cut off, youden index showed the sensitivity of 78.05 and specificity of 62.68. Regression analysis revealed that IDRS and Diabetes are significantly positively associated.

CONCLUSIONS

Data reveals that IDRS is a good indicator of high risk diabetic subjects.