Global estimates of undiagnosed diabetes in adults

A novel predictive model for optimizing diabetes screening in older adults

INTRODUCTION

The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels.

MATERIALS AND METHODS

This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis.

RESULTS

Nine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility.

CONCLUSIONS

We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

Examining the Impact of a Behavior Modification Program on Disease Prevention Behaviors among Individuals at Risk of Diabetes: A Quasi-Experimental Investigation

This study aimed to develop and test a behavior modification intervention to prevent type 2 diabetes (T2DM) among at-risk individuals. The primary goal was to compare diabetes prevention behaviors and fasting blood sugar levels between the intervention and comparison groups. This study utilizes a quasi-experimental design to develop a behavior modification intervention for preventing diabetes. It involves two groups, each with pre- and post-intervention assessments, comprising 60 at-risk individuals equally divided into intervention and comparison groups. The 8-week intervention includes components like risk assessment, dietary skill enhancement, exercise, stress management, and social media platforms (data recording training, follow-up visits, reminders, and visual aids). Data collection includes self-administered questionnaires and blood sugar level measurements. Statistical analysis involved paired t-tests for within-group comparisons and independent t-tests for between-group differences. The findings showed that the intervention group achieved significantly higher average scores in nutrition, exercise, and stress management, and had significantly lower average blood sugar levels compared to the comparison group. These results suggest that healthcare providers and policymakers should develop community health programs and public health policies that incorporate integrative care, leverage social media platforms, and foster collaboration with other health professionals to improve outcomes for individuals at risk of T2DM.

Diabetes mellitus and risk of incident dementia in APOE ɛ4 carriers: an updated meta-analysis

BACKGROUND AND AIM

Diabetes raises the risk of dementia, mortality, and cognitive decline in the elderly, potentially because of hereditary variables such as APOE. In this study, we aim to evaluate Diabetes mellitus and the risk of incident dementia in APOE ɛ4 carriers.

METHOD

We thoroughly searched PubMed (Medline), Scopus, and Google Scholar databases for related articles up to September 2023. The titles, abstracts, and full texts of articles were reviewed; data were extracted and analyzed.

RESULT

This meta-analysis included nine cohorts and seven cross-sectional articles with a total of 42,390 population. The study found that APOE ɛ4 carriers with type 2 diabetes (T2D) had a 48% higher risk of developing dementia compared to non-diabetic carriers (Hazard Ratio;1.48, 95%CI1.36-1.60). The frequency of dementia was 3 in 10 people (frequency: 0.3; 95%CI (0.15-0.48). No significant heterogeneity was observed. Egger's test, which we performed, revealed no indication of publication bias among the included articles (p = 0.2).

CONCLUSION

Overall, diabetes increases the risk of dementia, but further large-scale studies are still required to support the results of current research.

Analysis of risk factors and clinical implications for diabetes in first-degree relatives in the northeastern region of China

Background

The prevalence of diabetes has risen fast with a considerable weighted prevalence of undiagnosed diabetes or uncontrolled diabetes. Then it becomes more necessary to timely screen out and monitor high-risk populations who are likely to be ignored during the COVID-19 pandemic. To classify and find the common risks of undiagnosed diabetes and uncontrolled diabetes, it's beneficial to put specific risk control measures into effect for comprehensive primary care. Especially, there is a need for accurate yet accessible prediction models.

Objective

Based on a cross-sectional study and secondary analysis on the health examination held in Changchun City (2016), we aimed to evaluate the factors associated with hyperglycemia, analyze the management status of T2DM, and determine the best cutoff value of incidence of diabetes in the first-degree relatives to suggest the necessity of early diagnosis of diabetes after first screening.

Results

A total of 5658 volunteers were analyzed. Prevalence of T2DM and impaired fasting glucose were 8.4% (n=477) and 11.5% (n=648), respectively. There were 925 participants (16.3%) with a family history of T2DM in their first-degree relatives. Multivariable analysis demonstrated that family history was associated with hyperglycemia. Among the 477 patients with T2DM, 40.9% had not been previously diagnosed. The predictive equation was calculated with the following logistic regression parameters with 0.71 (95% CI: 0.67-0.76) of the area under the ROC curve, 64.0% of sensitivity and 29% of specificity (P < 0.001): P = \frac{1}{1 + e^{-z}}, where z = -3.08 + [0.89 (Family history-group) + 0.69 (age-group)+ 0.25 (BMI-group)]. Positive family history was associated with the diagnosis of T2DM, but not glucose level in the diagnosed patients. The best cutoff value of incidence of diabetes in the first-degree relatives was 9.55% (P < 0.001).

Conclusions

Family history of diabetes was independently associated with glucose dysfunction. Classification by the first-degree relatives with diabetes is prominent for targeting high-risk population. Meanwhile, positive family history of diabetes was associated with diabetes being diagnosed rather than the glycemic control in patients who had been diagnosed. It's necessary to emphasize the linkage between early diagnosis and positive family history for high proportions of undiagnosed T2DM.

Impact of Self-Monitoring Blood Glucose on Glycaemic Control Among Insulin-Treated Patients With Diabetes Mellitus in Northeastern Tanzania: A Randomised Controlled Trial

Introduction: Tracking of blood glucose levels by patients and care providers remains an integral component in the management of diabetes mellitus (DM). Evidence, primarily from high-income countries, has illustrated the effectiveness of self-monitoring of blood glucose (SMBG) in controlling DM. However, there is limited data on the feasibility and impact of SMBG among patients in the rural regions of sub-Saharan Africa. This study is aimed at assessing SMBG, its adherence, and associated factors on the effect of glycaemic control among insulin-treated patients with DM in northeastern Tanzania. Materials and Methods: This was a single-blinded, randomised clinical trial conducted from December 2022 to May 2023. The study included patients with DM who had already been on insulin treatment for at least 3 months. A total of 85 participants were recruited into the study and categorised into the intervention and control groups by a simple randomization method using numbered envelopes. The intervention group received glucose metres, test strips, logbooks, and extensive SMBG training. The control group received the usual care at the outpatient clinic. Each participant was followed for a period of 12 weeks, with glycated haemoglobin (HbA1c) and fasting blood glucose (FBG) being checked both at the beginning and at the end of the study follow-up. The primary and secondary outcomes were adherence to the SMBG schedule, barriers associated with the use of SMBG, and the ability to self-manage DM, logbook data recording, and change in HbA1c. The analysis included descriptive statistics, paired t-tests, and logistic regression. Results: Eighty participants were analysed: 39 in the intervention group and 41 in the control group. In the intervention group, 24 (61.5%) of patients displayed favourable adherence to SMBG, as evidenced by tests documented in the logbooks and glucometer readings. Education on SMBG was significantly associated with adherence. Structured SMBG improved glycaemic control with a HbA1c reduction of -1.01 (95% confidence interval (CI) -1.39, -0.63) in the intervention group within 3 months from baseline compared to controls of 0.18 (95% CI -0.07, 0.44) (p < 0.001). Conclusion: Structured SMBG positively impacted glycaemic control among insulin-treated patients with DM in the outpatient clinic. The results suggest that implementing a structured testing programme can lead to significant reductions in HbA1c and FBG levels. Trial Registration: Pan African Clinical Trials Registry identifier: PACTR202402642155729.

Adherence and Persistence to Basal Insulin Among People with Type 2 Diabetes in Europe: A Systematic Literature Review and Meta-analysis

INTRODUCTION

Diabetes is associated with a number of complications, particularly if glycaemic targets are not achieved. Glycaemic control is highly linked to treatment persistence and adherence. To understand the burden of poor persistence and adherence, this systematic literature review identified existing evidence regarding basal insulin adherence/non-adherence and persistence/non-persistence among people with diabetes in Western Europe (defined as the UK, France, Spain, Switzerland, the Netherlands, Ireland, Austria, Portugal, Denmark, Norway, Sweden, Finland, Italy, Germany, Iceland and Belgium).

METHODS

Eligible studies were systematically identified from two databases, Medline and Embase (published between 2012 and June 2022). Conference abstracts from ISPOR and EASD were manually included. Identified studies were screened by two independent reviewers in a two-step blinded process. The eligibility of studies was decided on the basis of pre-established criteria. A proportional meta-analysis and comparative narrative analyses were conducted to analyse the included studies.

RESULTS

Twelve studies were identified. Proportions of adherence/non-adherence and persistence/non-persistence varied across studies. Pooled rates of non-persistence at 6, 12 and 18 months were 20.3% (95% CI 13.8; 27.8), 33.8% (95% CI 24.1; 44.3) and 36.5% (95% CI 33.6; 39.4), respectively. In the literature, the proportion of adherent people ranged from 41% to 64% (using the outcome measure medication possession ratio (MPR) > 80%), with a pooled rate of 55.6% (95% CI 45.3; 65.6), suggesting that approximately 44% of people with type 2 diabetes (T2D) are non-adherent.

CONCLUSION

The results highlight that almost half of patients with T2D in Western Europe have poor adherence to insulin therapy and, at 18 months, one in three patients do not persist on treatment. These findings call for new basal insulin therapies and diabetes management strategies that can improve treatment persistence and adherence among people with T2D.

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm

The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.

Insights into the effect of glucose on the binding between human serum albumin and the nonsteroidal anti-inflammatory drug nimesulide

Glucose interacts with human serum albumin (HSA, the main protein responsible for the biodistribution of drugs in the bloodstream) and consequently affects the binding capacity of exogenous compounds. Thus, in this work, the interactive profile between HSA and the anti-inflammatory drug nimesulide (NMD, used mainly by patients with diabetic neuropathy to relieve acute or chronic pains) was characterized in nonglycemic, normoglycemic (80 mg/dL), and hyperglycemic (320 mg/dL) conditions by biophysics techniques. There is a spontaneous and ground-state association HSA:NMD under physiological conditions. Therefore, the Stern-Volmer constant (Ksv) can also be used to estimate the binding affinity. The Ksv values for nonglycemic, normoglycemic, and hyperglycemic conditions are around 104 M-1, indicating a moderate affinity of NMD to albumin that was slightly improved by glucose levels. Additionally, the binding is enthalpically and entropically driven mainly into subdomains IIA or IIIA. The binding perturbs weakly the α-helix content of albumin, however, glucose potentially stabilizes the tertiary structure, decreasing the structural perturbation upon NMD binding and improves the complex HSA:NMD stability. Overall, the biophysical characterization indicated that glucose levels might slightly positively impact the pharmacokinetic profile of NMD, allowing NMD to achieve its therapeutical potential without affecting drastically its effective dosages.

Multimorbidity and use of health services in a population diagnosed with COVID-19 in a municipality in the Southern Region of Brazil, 2020-2021: a cross-sectional study

OBJECTIVE

To assess association between multimorbidity and use of health services in a population diagnosed with COVID-19, in southern Brazil.

METHODS

This was a cross-sectional study with data from a longitudinal study carried out in the city of Rio Grande, Rio Grande do Sul, Brazil, in 2021 with all adult individuals diagnosed with COVID-19; descriptive analyses were performed and presented as proportions with 95% confidence intervals (95%CI); Poisson regression was performed and reported as prevalence ratios (PR) in order to assess association between multimorbidity (3 or more diseases) and healthcare service use.

RESULTS

In total, 2,919 participants were included, of which 40.4% had multimorbidity (≥ 2 diseases); the adjusted results showed that individuals with multimorbidity were more likely to use most of the services assessed, PR = 3.21 (95%CI 1.40;7.37), for Emergency Rooms.

CONCLUSION

Multimorbidity was associated with using different types of health services.

The association between the AIP and undiagnosed diabetes in ACS patients with different body mass indexes and LDL-C levels: findings from the CCC-ACS project

BACKGROUND

The atherogenic index of plasma (AIP) has been demonstrated to be significantly associated with the incidence of prediabetes and diabetes. This study aimed to investigate the association between the AIP and undiagnosed diabetes in acute coronary syndrome (ACS) patients.

METHODS

Among 113,650 ACS patients treated with coronary angiography at 240 hospitals in the Improving Care for Cardiovascular Disease in China-ACS Project from 2014 to 2019, 11,221 patients with available clinical and surgical information were included. We analyzed these patients' clinical characteristics after stratification according to AIP tertiles, body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) levels.

RESULTS

The AIP was independently associated with a greater incidence of undiagnosed diabetes. The undiagnosed diabetes was significantly greater in the T3 group than in the T1 group after adjustment for confounders [T3 OR 1.533 (1.199-1.959) p < 0.001]. This relationship was consistent within normal weight patients and patients with an LDL-C level ≥ 1.8 mmol/L. In overweight and obese patients, the AIP was significantly associated with the incidence of undiagnosed diabetes as a continuous variable after adjustment for age, sex, and BMI but not as a categorical variable. The area under the receiver operating characteristic curve (AUC) of the AIP score, triglyceride (TG) concentration, and HDL-C concentration was 0.601 (0.581-0.622; p < 0.001), 0.624 (0.603-0.645; p < 0.001), and 0.493 (0.472-0.514; p = 0.524), respectively. A nonlinear association was found between the AIP and the incidence of undiagnosed diabetes in ACS patients (p for nonlinearity < 0.001), and this trend remained consistent between males and females. The AIP may be a negative biomarker associated with undiagnosed diabetes ranging from 0.176 to 0.738.

CONCLUSION

The AIP was significantly associated with the incidence of undiagnosed diabetes in ACS patients, especially in those with normal weight or an LDL-C level ≥ 1.8 mmol/L. A nonlinear relationship was found between the AIP and the incidence of undiagnosed diabetes, and this trend was consistent between male and female patients. The AIP may be a negative biomarker associated with undiagnosed diabetes and ranges from 0.176 to 0.738.

Magnitude and predictors of pre-diabetes among adults in health facilities of Gondar city, Ethiopia: a cross-sectional study

Introduction

Impaired glucose tolerance currently affects 374 million (7.5%) people worldwide, and by 2030, this number is predicted to affect 454 million (8%). Urban inhabitants have an increased risk of developing pre-diabetes. Thus, the study aimed to assess the magnitude of pre-diabetes and associated factors among adults attending outpatient departments of the health facilities of Gondar, Ethiopia.

Method

From 3 March to 18 April 2020, an institution-based cross-sectional study was conducted. A systematic random sampling technique was used to select 992 participants. Data were gathered using an interviewer-administered questionnaire, and fasting blood glucose was assessed using capillary blood. Bivariable and multivariable binary logistic regression analyses were fitted to check the association between independent variables and pre-diabetes. Statistical significance was declared at a level of P of <0.05.

Results

The prevalence of pre-diabetes was 16.6% (95% CIs: 14.3-18.8%). Age [AOR = 3.66, 95% CIs (2.05, 6.52)], a family history of diabetes mellitus [AOR = 3.46, 95% CIs (2.16, 5.52)], waist circumference [AOR = 3.6, 95% CIs (2.26, 5.88)], physical activity [AOR: 5.02, 95% CIs (2.87, 8.77)], dietary diversity [AOR = 3.07, 95% CIs (1.95, 4.84)], and smoking [AOR = 2.9, 95% CI (1.42, 6.05)] were factors associated with pre-diabetes.

Conclusion

From our study, we can conclude that one in six adults in the health facilities have pre-diabetes. Age, family history of diabetes, waist circumference, physical activity, dietary diversity, and smoking were the factors associated with pre-diabetes. Therefore, it is recommended that adults should be educated on modifying their lifestyle, including their diet, and substantial care should be provided for older adults.

Australian general practitioners' perspectives on integrating specialist diabetes care with primary care: qualitative study

BACKGROUND

Improving the coordination and integration of health services is recognised nationally and internationally as a key strategy for improving the quality of diabetes care. The Australian Diabetes Alliance Program (DAP) is an integrated care model implemented in the Hunter New England Local Health District (HNELHD), New South Wales (NSW), in which endocrinologists and diabetes educators collaborate with primary care teams via case-conferencing, practice performance review, and education sessions. The objective of this study was to report on general practitioners' (GPs) perspectives on DAP and whether the program impacts on their skills, knowledge, and approach in delivering care to adult patients with type 2 diabetes.

METHODS

Four primary care practices with high rates of monitoring haemoglobin A1c (HbA1c) levels (> 90% of patients annually) and five practices with low rates of monitoring HbA1c levels (< 80% of patients annually) from HNELHD, NSW provided the sampling frame. A total of nine GPs were interviewed. The transcripts from the interviews were reviewed and analysed to identify emergent patterns and themes.

RESULTS

Overall, GPs were supportive of DAP. They considered that DAP resulted in significant changes in their knowledge, skills, and approach and improved the quality of diabetes care. Taking a more holistic approach to care, including assessing patients with diabetes for co-morbidities and risk factors that may impact on their future health was also noted. DAP was noted to increase the confidence levels of GPs, which enabled active involvement in the provision of diabetes care rather than referring patients for tertiary specialist care. However, some indicated the program could be time consuming and greater flexibility was needed.

CONCLUSIONS

GPs reported DAP to benefit their knowledge, skills and approach for managing diabetes. Future research will need to investigate how to improve the intensity and flexibility of the program based on the workload of GPs to ensure long-term acceptability of the program.

Awareness of the diagnosis, treatment, and control of diabetes mellitus in Brazil

OBJECTIVE

To estimate the proportions of awareness, treatment, and control of diabetes mellitus (DM) in the Brazilian adult population.

METHOD

This is a cross-sectional study, with data from a representative sample of the Brazilian population, taken from the National Health Survey(PNS 2014/2015). Outcomes were defined based on glycated hemoglobin (HbA1c) measurements, self-reported DM diagnosis, and use of hypoglycemic agents or insulin. The proportion of DM awareness, treatment, and control was estimated according to sociodemographic characteristics, health conditions, and access to health services, and their respective 95% confidence intervals.

RESULTS

DM prevalence in the Brazilian population was of 8.6% (95%CI: 7.8-9.3): 68.2% (95%CI: 63.9-72.3) were aware of their diagnosis, 92.2% (95%CI: 88.6-94.7) of those who were aware were undergoing drug treatments, and, of these, 35.8% (95%CI: 30.5-41.6) had controlled HbA1c levels. The proportions of DM awareness, control, and treatment were lower in men aged 18 to 39 years, individuals with low education, without health insurance, and beneficiaries of the Bolsa Família program.

CONCLUSION

Approximately one in ten Brazilians has DM. A little more than half of this population is aware of their diagnosis, a condition measured by HbA1c dosage and clinical diagnosis. Among those who know, the vast majority are undergoing drug treatments. However, less than half of these have their HbA1c levels controlled. Worse scenarios were found in subgroups with high social vulnerability.

Optimizing an mHealth Program to Promote Type 2 Diabetes Prevention in High-Risk Individuals: Cross-Sectional Questionnaire Study

BACKGROUND

We evaluated the outcomes of a pilot SMS text messaging-based public health campaign that identified social networking nodes and variations of response rates to develop a list of variables that could be used to analyze and develop an outreach strategy that would maximize the impact of future public health campaigns planned for Kuwait. Computational analysis of connections has been used to analyze the spread of infectious diseases, dissemination of new thoughts and ideas, efficiency of logistics networks, and even public health care campaigns. Percolation theory network analysis provides a mathematical alternative to more established heuristic approaches that have been used to optimize network development. We report on a pilot study designed to identify and treat subjects at high risk of developing type 2 diabetes mellitus in Kuwait.

OBJECTIVE

The aim of this study was to identify ways to optimize efficient deployment of resources and improve response rates in a public health campaign by using variables identified in this secondary analysis of our previously published data (Alqabandi et al, 2020). This analysis identified key variables that could be used in a computational analysis to plan for future public health campaigns.

METHODS

SMS text message screening posts were sent inviting recipients to answer 6 questions to determine their risk of developing type 2 diabetes mellitus. If subjects agreed to participate, a link to the Centers for Disease Control and Prevention prediabetes screening test was automatically transmitted to their mobile devices. The phone numbers used in this campaign were recorded and compared to the responses received through SMS text messaging and social media forwarding.

RESULTS

A total of 180,000 SMS text messages through 5 different campaigns were sent to 6% of the adult population in Kuwait. A total of 260 individuals agreed to participate, of which 153 (58.8%) completed the screening. Remarkably, 367 additional surveys were received from individuals who were not invited by the original circulated SMS text messages. These individuals were invited through forwarded surveys from the original recipients after authentication with the study center. The original SMS text messages were found to successfully identify influencers in existing social networks to improve the efficacy of the public health campaign.

CONCLUSIONS

SMS text messaging-based health care screening campaigns were found to have limited effectiveness alone; however, the increased reach through shared second-party forwarding suggests the potential of exponentially expanding the reach of the study and identifying a higher percentage of eligible candidates through the use of percolation theory. Future research should be directed toward designing SMS text messaging campaigns that support a combination of SMS text message invitations and social networks along with identification of influential nodes and key variables, which are likely unique to the environment and cultural background of the population, using percolation theory modeling and chatbots.

Evaluation of a Digital Decision Support System to Integrate Type 2 Diabetes Mellitus and Periodontitis Care: Case-Vignette Study in Simulated Environments

BACKGROUND

As highlighted by the recent World Health Organization Oral Health Resolution, there is an urgent need to better integrate primary and oral health care. Despite evidence and guidelines substantiating the relevance of integrating type 2 diabetes mellitus (T2DM) and periodontitis care, the fragmentation of primary and oral health care persists.

OBJECTIVE

This paper reports on the evaluation of a prototype digital decision support system (DSS) that was developed to enhance the integration of T2DM and periodontitis care.

METHODS

The effects of the prototype DSS were assessed in web-based simulated environments, using 2 different sets of case vignettes in combination with evaluation surveys among 202 general dental practitioners (GDPs) and 206 general practitioners (GPs). Each participant evaluated 3 vignettes, one of which, chosen at random, was assisted by the DSS. Logistic regression analyses were conducted at the participant and case levels.

RESULTS

Under DSS assistance, GPs had 8.3 (95% CI 4.32-16.03) times higher odds of recommending a GDP visit. There was no significant impact of DSS assistance on GP advice about common risk factors for T2DM and periodontal disease. GDPs had 4.3 (95% CI 2.08-9.04) times higher odds of recommending a GP visit, 1.6 (95% CI 1.03-2.33) times higher odds of giving advice on disease correlations, and 3.2 (95% CI 1.63-6.35) times higher odds of asking patients about their glycated hemoglobin value.

CONCLUSIONS

The findings of this study provide a proof of concept for a digital DSS to integrate T2DM and periodontal care. Future updating and testing is warranted to continuously enhance the functionalities of the DSS in terms of interoperability with various types of data sources and diagnostic devices; incorporation of other (oral) health dimensions; application in various settings, including via telemedicine; and further customization of end-user interfaces.

Prevalence and impact of chronic dysglycaemia among patients with COVID-19 in Swedish intensive care units: a multicentre, retrospective cohort study

OBJECTIVE

Using glycated haemoglobin A1c (HbA1c) screening, we aimed to determine the prevalence of chronic dysglycaemia among patients with COVID-19 admitted to the intensive care unit (ICU). Additionally, we aimed to explore the association between chronic dysglycaemia and clinical outcomes related to ICU stay.

DESIGN

Multicentre retrospective observational study.

SETTING

ICUs in three hospitals in Stockholm, Sweden.

PARTICIPANTS

COVID-19 patients admitted to the ICU between 5 March 2020 and 13 August 2020 with available HbA1c at admission. Chronic dysglycaemia was determined based on previous diabetes history and HbA1c.

PRIMARY AND SECONDARY OUTCOMES

Primary outcome was the actual prevalence of chronic dysglycaemia (pre-diabetes, unknown diabetes or known diabetes) among COVID-19 patients. Secondary outcome was the association of chronic dysglycaemia with 90-day mortality, ICU length of stay, duration of invasive mechanical ventilation (IMV) and renal replacement therapy (RRT), accounting for treatment selection bias.

RESULTS

A total of 308 patients with available admission HbA1c were included. Chronic dysglycaemia prevalence assessment was restricted to 206 patients admitted ICUs in which HbA1c was measured on all admitted patients. Chronic dysglycaemia was present in 82.0% (95% CI 76.1% to 87.0%) of patients, with pre-diabetes present in 40.2% (95% CI 33.5% to 47.3%), unknown diabetes in 20.9% (95% CI 15.5% to 27.1%), well-controlled diabetes in 7.8% (95% CI 4.5% to 12.3%) and uncontrolled diabetes in 13.1% (95% CI 8.8% to 18.5%). All patients with available HbA1c were included for the analysis of the relationship between chronic dysglycaemia and secondary outcomes. We found no independent association between chronic dysglycaemia and 90-day mortality, ICU length of stay or duration of IMV. After excluding patients with specific treatment limitations, no association between chronic dysglycaemia and RRT use was observed.

CONCLUSIONS

In our cohort of critically ill COVID-19 patients, the prevalence of chronic dysglycaemia was 82%. We found no robust associations between chronic dysglycaemia and clinical outcomes when accounting for treatment limitations.

The Association Between PM2.5 Exposure and Diabetes Mellitus Among Thai Army Personnel

OBJECTIVES

This study investigated the association between baseline exposures to particulate matter with a diameter < 2.5 microns (PM2.5) and subsequent temporal changes in PM2.5 exposure with the incidence of type 2 diabetes among Royal Thai Army personnel.

METHODS

A retrospective cohort study was conducted using nationwide health check-up data from 21 325 Thai Army personnel between 2018 and 2021. Multilevel mixed-effects parametric survival statistics were utilized to analyze the relationship between baseline (i.e., PM2.5-baseline) and subsequent changes (i.e., PM2.5-change) in PM2.5 exposure and the occurrence of type 2 diabetes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were employed to assess this association while considering covariates.

RESULTS

There was a significant association between both PM2.5 baseline and PM2.5-change and the incidence of type 2 diabetes in a dose-response manner. Compared to quartile 1, the HRs for quartiles 2 to 4 of PM2.5-baseline were 1.11 (95% CI, 0.74 to 1.65), 1.51 (95% CI, 1.00 to 2.28), and 1.77 (95% CI, 1.07 to 2.93), respectively. Similarly, the HRs for quartiles 2 to 4 of PM2.5-change were 1.41 (95% CI, 1.14 to 1.75), 1.43 (95% CI, 1.13 to 1.81) and 2.40 (95% CI, 1.84 to 3.14), respectively.

CONCLUSIONS

Our findings contribute to existing evidence regarding the association between short-term and long-term exposure to PM2.5 and the incidence of diabetes among personnel in the Royal Thai Army.

Chitosan-based injectable hydrogels with dual glucose sensors for precise control of insulin release and diabetes mellitus therapy

Chitosan-based injectable hydrogels were designed and fabricated through the dynamic crosslinking of dual-reversible covalent bonds (imine and phenylboronate ester) for precise insulin release. The hydrogels contain dual glucose-sensors/responsive elements, featuring high sensitivity and rapid responsiveness to glucose level variation in cumulative and half-hourly pulsed insulin release. The hydrogels demonstrated improved cytocompatibility against HSF cells and histological long-term analysis of tissue after implantation. Evaluation of the glycemic control ability in STZ-induced hyperglycemic mice revealed that the hydrogel system showed excellent glycemic control ability in the glucose tolerance test and maintained blood glucose levels in a normal range for up to 11 days after a single administration. Thus, the hydrogel system showed applicable potential in insulin replacement therapy for diabetes mellitus.

A double-Cox model for non-proportional hazards survival analysis with frailty

The Cox regression, a semi-parametric method of survival analysis, is extremely popular in biomedical applications. The proportional hazards assumption is a key requirement in the Cox model. To accommodate non-proportional hazards, we propose to parameterize the shape parameter of the baseline hazard function using the additional, separate Cox-regression term which depends on the vector of the covariates. This parametrization retains the general form of the hazard function over the strata and is similar to one in Devarajan and Ebrahimi (Comput Stat Data Anal. 2011;55:667-676) in the case of the Weibull distribution, but differs for other hazard functions. We call this model the double-Cox model. We formally introduce the double-Cox model with shared frailty and investigate, by simulation, the estimation bias and the coverage of the proposed point and interval estimation methods for the Gompertz and the Weibull baseline hazards. For real-life applications with low frailty variance and a large number of clusters, the marginal likelihood estimation is almost unbiased and the profile likelihood-based confidence intervals provide good coverage for all model parameters. We also compare the results from the over-parametrized double-Cox model to those from the standard Cox model with frailty in the case of the scale-only proportional hazards. The model is illustrated on an example of the survival after a diagnosis of type 2 diabetes mellitus. The R programs for fitting the double-Cox model are available on Github.

The magnitude of undiagnosed diabetes mellitus, prediabetes, and associated factors among adults living in Debre Tabor town, northcentral Ethiopia: A community-based cross-sectional study

Background

Diabetes mellitus (DM) is a worldwide public health problem. The burden of diabetes has been continuously increasing from day to day, especially in developing countries like Ethiopia. Globally, half of all cases of diabetes mellitus are undiagnosed. Diabetes mellitus can be easily handled if it is detected early. There is limited evidence on the magnitude of undiagnosed diabetics and prediabetes at the community level in Ethiopia, particularly in the study area.

Objective

To assess the magnitude of undiagnosed diabetes mellitus, prediabetes, and associated factors among adults living in Debre Tabor town.

Methods

A community-based cross-sectional study was conducted in Debre Tabor town from October to December 2021. A total of 407 participants were selected using a multistage sampling technique. A pretested structural questionnaire was used to collect demographic, behavioral, and clinical data. Anthropometric measurements were taken with standardized and calibrated equipment. A fasting venous blood sample was collected for blood glucose level determination. Logistic regression was used to identify risk factors. A P-value ≤0.05 was considered statistically significant.

Result

The magnitude of undiagnosed diabetes mellitus and prediabetes was found to be 4.5% (95% CI: 2.9-7.4) and 14.5% (95% CI: 11.1-18.1), respectively. Older age (AOR: 6.50, 95% CI: 1.82-23.21), abnormal body mass index (AOR: 6.84, 95% CI: 1.91-24.54), systolic hypertension (AOR: 8.74, 95% CI: 2.53-30.19), and family history of diabetes mellitus (FHDM) (AOR: 12.45, 95% CI: 3.63-42.65) were significantly associated with undiagnosed diabetes mellitus. Using saturated oil (AOR: 1.97, 95% CI: 1.09-3.55), having a high waist circumference (AOR: 2.16, 95% CI: 1.20-3.87), and being hypertensive (AOR: 2.26, 95% CI: 1.04-4.96) were all significantly associated with Prediabetes.

Conclusion

Adults in Debre Tabor town have a high prevalence of undiagnosed diabetes and prediabetes. A variety of modifiable risk factors were also identified. As a result, focusing the prevention strategy on such modifiable risk factors may help to minimize the prevalence of undiagnosed diabetes mellitus and prediabetes as well as future disease complications.

Effects of therapeutic ultrasound on the endothelial function of patients with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by endothelial dysfunction that causes micro- and macrovascular complications. Low intensity therapeutic ultrasound (LITUS) may improve endothelial function, but its effects have not been investigated in these patients. The aim of our study was to compare the effects of pulsed (PUT) and continuous (CUT) waveforms of LITUS on the endothelium-dependent vasodilation of T2DM patients. The present randomized crossover trial had a sample of twenty-three patients (7 men) diagnosed with T2DM, 55.6 (±9.1) years old, with a body mass index of 28.6 (±3.3) kg/m2. All patients were randomized and submitted to different waveforms (Placebo, CUT, and PUT) of LITUS and the arterial endothelial function was evaluated. The LITUS of 1 MHz was applied in pulsed (PUT: 20% duty cycle, 0.08 W/cm2 SATA), continuous (CUT: 0.4 W/cm2 SPTA), and Placebo (equipment off) types of waves during 5 min on the brachial artery. Endothelial function was evaluated using the flow-mediated dilation (FMD) technique. PUT (mean difference 2.08%, 95% confidence interval 0.65 to 3.51) and CUT (mean difference 2.32%, 95% confidence interval 0.89 to 3.74) increased the %FMD compared to Placebo. In the effect size analysis, PUT (d=0.65) and CUT (d=0.65) waveforms presented moderate effects in the %FMD compared to Placebo. The vasodilator effect was similar in the different types of waves. Pulsed and continuous waveforms of LITUS of 1 MHz improved the arterial endothelial function in T2DM patients.

Combined Effect of Maternal Obesity and Diabetes on Excessive Fetal Growth: Pregnancy Risk Assessment Monitoring System (PRAMS), United States, 2012-2015

Introduction

Obesity and dysregulation in glucose metabolism are risk factors for excessive fetal growth, but their combined effects are not often examined in a single study.

Methods

Data from the Centers for Disease Control and Prevention's Pregnancy Risk Assessment Monitoring System Phase 7 (2012-2015) were used. Logistic regression was used to investigate the association between maternal prepregnancy BMI and pre-existing diabetes/gestational diabetes on the odds of delivering a large-for-gestational-age infant or an infant with macrosomia.

Results

Complete data for 128,199 singleton births were used. The proportions of large-for-gestational-age infants and infants with macrosomia increased with the degree of obesity (p<0.001) and were higher in women with diabetes than in those without (p<0.001). Compared with the AOR among normal-weight women, the AOR of delivering large-for-gestational-age infants and infants with macrosomia among women with morbid obesity (BMI≥40) were 2.82 (p<0.001) and 2.67 (p<0.001), respectively. Compared with the AOR among nondiabetic women, the AOR of delivering a large-for-gestational-age infant was 1.88 (p<0.001) among those with pre-existing diabetes and 1.49 (p<0.001) among those with gestational diabetes. Except for the underweight group, women with pre-existing diabetes were nearly twice as likely to deliver a large-for-gestational-age infant as those with similar BMI without diabetes. Women with morbid obesity and gestational diabetes were twice as likely to have a large-for-gestational-age infant and an infant with macrosomia as nondiabetic women with normal BMI.

Conclusions

We have shown that when maternal obesity and diabetes, particularly pre-existing diabetes, occur together, the risk of delivering large-for-gestational-age and macrosomia increases significantly. Our findings call for public health attention to address maternal obesity and diabetes to minimize suboptimal fetal growth.

COMPARATIVE ANALYSIS OF OCT AND OCT ANGIOGRAPHY CHARACTERISTICS IN EARLY DIABETIC RETINOPATHY

PURPOSE

To assess the quantitative characteristics of optical coherence tomography (OCT) and OCT angiography (OCTA) for the objective detection of early diabetic retinopathy (DR).

METHODS

This was a retrospective and cross-sectional study, which was carried out at a tertiary academic practice with a subspecialty. Twenty control participants, 15 people with diabetics without retinopathy (NoDR), and 22 people with mild nonproliferative diabetic retinopathy (NPDR) were included in this study. Quantitative OCT characteristics were derived from the photoreceptor hyperreflective bands, i.e., inner segment ellipsoid (ISe) and retinal pigment epithelium (RPE). OCTA characteristics, including vessel diameter index (VDI), vessel perimeter index (VPI), and vessel skeleton density (VSD), were evaluated.

RESULTS

Quantitative OCT analysis indicated that the ISe intensity was significantly trending downward with DR advancement. Comparative OCTA revealed VDI, VPI, and VSD as the most sensitive characteristics of DR. Correlation analysis of OCT and OCTA characteristics revealed weak variable correlation between the two imaging modalities.

CONCLUSION

Quantitative OCT and OCTA analyses revealed photoreceptor and vascular distortions in early DR. Comparative analysis revealed that the OCT intensity ratio, ISe/RPE, has the best sensitivity for early DR detection. Weak variable correlation of the OCT and OCTA characteristics suggests that OCT and OCTA are providing supplementary information for DR detection and classification.

Association of Type 2 Diabetes Mellitus with Cognitive Function in Adults: A Prospective Cohort Study

BACKGROUND

Cognitive role of untreated type 2 diabetes mellitus (T2DM) has been less well substantiated.

OBJECTIVE

We sought to explore the prospective association of T2DM and untreated T2DM with cognitive function among middle-aged and older Chinese adults.

METHODS

Data of 7,230 participants without baseline brain damage, mental retardation, or memory-related diseases in China Health and Retirement Longitudinal Study (CHARLS) from 2011- 2012 to 2015, were analyzed. Fasting plasma glucose and self-reported information on T2DM diagnosis and treatment were assessed. Participants were categorized into normoglycemia, impaired fasting glucose (IFG), and T2DM (including untreated and treated T2DM) groups. Episodic memory and executive function were assessed by modified Telephone Interview for Cognitive Status every two years. We used generalized estimating equation model to examine the association of baseline T2DM status with cognitive function in succeedingyears.

RESULTS

Compared to those with normoglycemia, T2DM was associated with worse overall cognitive function after controlling for demographic variables, lifestyles, follow-up time, major clinical factors, and baseline cognitive function, although the associations were statistically non-significant (β= -0.19, 95% CI: -0.39, 0.00). However, a significant association was mainly observed for those with untreated T2DM (β= -0.26, 95% CI: -0.47, -0.04), especially in the domain of executive function (β= -0.19, 95% CI: -0.35, -0.03). In general, IFG and treated T2DM individuals had similar levels of cognitive function with normoglycemia participants.

CONCLUSION

Our findings supported a detrimental role of untreated T2DM on cognitive function among middle-aged and older adults. Screening and early treatment for T2DM are warranted for maintaining better cognitive function in later life.

Psychometric properties of the Diabetes Empowerment Scale Modified Version-Spanish, tested in an at-risk Latino immigrant population

BACKGROUND

Latino populations are at high risk of developing diabetes; however, few scales measure self-efficacy to change health behaviours in at-risk Latino individuals. The Diabetes Empowerment Scale was translated into Spanish, and its psychometric properties were tested.

METHODS

This descriptive investigation was conducted with adults in a predominately Latino Midwest community. Participants (N = 141) completed a modified version of the Diabetes Empowerment Scale, which measures self-efficacy in exercising and healthy eating. Factor analysis was performed on completed surveys. Recruitment in Latino cultural centres and parishes took place from June 2016 to May 2017.

RESULTS

Three factors emerged: self-efficacy to exercise, emotional eating and social eating. Three scale questions had low scores during analysis; however, overall, the tool demonstrated adequate validity.

CONCLUSIONS

The Diabetes Empowerment Scale Modified Version-Spanish demonstrated adequate reliability and validity. Healthy dietary preferences and physical activity in populations at-risk for diabetes should be assessed by nurses to ascertain level of self-efficacy in individuals by assessing confidence to engage in specific healthy behaviours.

Improving health outcomes of people with diabetes: target setting for the WHO Global Diabetes Compact

The Global Diabetes Compact is a WHO-driven initiative uniting stakeholders around goals of reducing diabetes risk and ensuring that people with diabetes have equitable access to comprehensive, affordable care and prevention. In this report we describe the development and scientific basis for key health metrics, coverage, and treatment targets accompanying the Compact. We considered metrics across four domains: factors at a structural, system, or policy level; processes of care; behaviours and biomarkers such as glycated haemoglobin (HbA1c); and health events and outcomes; and three risk tiers (diagnosed diabetes, high risk, or whole population), and reviewed and prioritised them according to their health importance, modifiability, data availability, and global inequality. We reviewed the global distribution of each metric to set targets for future attainment. This process led to five core national metrics and target levels for UN member states: (1) of all people with diabetes, at least 80% have been clinically diagnosed; and, for people with diagnosed diabetes, (2) 80% have HbA1c concentrations below 8·0% (63·9 mmol/mol); (3) 80% have blood pressure lower than 140/90 mm Hg; (4) at least 60% of people 40 years or older are receiving therapy with statins; and (5) each person with type 1 diabetes has continuous access to insulin, blood glucose meters, and test strips. We also propose several complementary metrics that currently have limited global coverage, but warrant scale-up in population-based surveillance systems. These include estimation of cause-specific mortality, and incidence of end-stage kidney disease, lower-extremity amputations, and incidence of diabetes. Primary prevention of diabetes and integrated care to prevent long-term complications remain important areas for the development of new metrics and targets. These metrics and targets are intended to drive multisectoral action applied to individuals, health systems, policies, and national health-care access to achieve the goals of the Global Diabetes Compact. Although ambitious, their achievement can result in broad health benefits for people with diabetes.

Associations of estrogen therapy and non-estrogen anti-resorptive therapy with diabetes mellitus risk: A classical and Bayesian meta-analysis

Anti-resorptive therapy (AT) increases insulin resistance and decrease insulin secretion through reduced undercarboxylated osteocalcin in mice. However, there are inconsistent findings regarding the impact of AT use on the risk of diabetes mellitus in humans. We examined the association between AT and incident diabetes mellitus using classical and Bayesian meta-analysis. We searched Pubmed, Medline, Embase, Web of Science, Cochrane, and Google Scholar for studies listed from database inception to 25 February 2022. Randomized controlled trials (RCTs) and cohort studies reporting associations of estrogen therapy (ET) and non-estrogen anti-resorptive therapy (NEAT) with incident diabetes mellitus were included. Two reviewers independently extracted research data such as ET and NEAT, diabetes mellitus, risk ratios (RRs), and 95 % confidence intervals (CIs) for incident diabetes mellitus associated with ET and NEAT from individual studies. This meta-analysis included data from nineteen original studies, consisting of fourteen ET and five NEAT studies. ET was associated with reduced risk of diabetes mellitus in the classical meta-analysis (RR: 0.90; 95 % CI: 0.81-0.99). Slightly stronger results were found in the meta-analysis of RCTs (RR: 0.83; 95 % CI: 0.77-0.89). The probability that RR < 1.0 was 95 % in the overall analysis and 99 % in RCTs under weakly informative prior. Although NEAT was associated with reduced risk of diabetes mellitus overall (RR: 0.80; 95 % CI: 0.67-0.97), this was not found in the RCT meta-analysis (RR: 0.90; 95 % CI: 0.75-1.10). Under weakly informative prior, the probabilities that NEAT reduces diabetes mellitus by >0 % were 99 %, and 73 % in the overall and RCT meta-analysis, respectively. In conclusion, meta-analysis provided consistent evidence against the hypothesis that AT increases diabetes risk. ET may reduce the risk of diabetes mellitus. Whether NEAT reduces the risk of diabetes mellitus is uncertain and requires additional evidence from RCTs.

Adult Body Height Is Associated with the Risk of Type 2 but Not Type 1 Diabetes Mellitus: A Retrospective Cohort Study of 783,029 Individuals in Germany

Background: Diabetes mellitus is a major global health burden associated with high morbidity and mortality. Although a short adult body height has been associated with increased risk of type 2 diabetes (T2D), there are large inconsistencies between the studies. Therefore, we aimed to investigate the association between body height and T2D in a large cohort of adult outpatients in Germany. Methods: A total of 783,029 adult outpatients with available body height data from the Disease Analyzer (IQVIA) database were included in Germany between 2010 and 2020. The incidence of diabetes mellitus (type 1 and type 2) was evaluated as a function of the patients’ body height stratified by age, sex, and body-mass-index (BMI). Results: In both women and men in all age groups, incidence of T2D decreased with the increasing body height (<50, 51–60, 61–70, and >70 years). There was no association between the body height and the individual HbA1c value. In multivariable Cox regression analyses adjusted for patient age and BMI, hazard ratios for the development of T2D were 1.15 (95% CI: 1.13–1.17) for each 10 cm decrease in body height in women and 1.10 (95% CI: 1.09–1.12) in men. No significant association was found between body height and the development of T1D. Conclusions: We present the first data from a large cohort of outpatients in Germany, providing strong evidence for an association between adult body height and T2D. These data add to the current literature and might help in implementing body height into existing diabetes risk stratification tools to further reduce morbidity and mortality worldwide.

Implications of an HbA1c-based Diabetes Screening on Prevalence and Effect of Dysglycemia in Patients With COVID-19

CONTEXT

In patients with severe acute respiratory syndrome coronavirus type 2 infection, diabetes is associated with poor COVID-19 prognosis. However, case detection strategy is divergent and reported prevalence varies from 5% to 35%.

OBJECTIVE

We examined how far the choice of screening tools affects the detection rate of dysglycemia and in consequence the estimation of diagnosis-associated risk for moderate (mo) or severe (s) COVID-19.

METHODS

Non-intensive care unit inpatients with COVID-19 were screened systematically at admission for diabetes (D) and prediabetes (PreD) by glycated hemoglobin A1c (HbA1c) (A), random blood glucose (B), and known history (C) from November 1, 2020 to March 8, 2021. Dysglycemia rate and effect on COVID-19 outcome were analyzed in 2 screening strategies (ABC vs BC).

RESULTS

A total of 578 of 601 (96.2%) of admitted patients were screened and analyzed. In ABC, prevalence of D and PreD was 38.2% and 37.5%, respectively. D was significantly associated with an increased risk for more severe COVID-19 (adjusted odds ratio [aOR] [moCOVID-19]: 2.27, 95% CI, 1.16-4.46 and aOR [sCOVID-19]: 3.26, 95% CI, 1.56-6.38). Patients with PreD also presented more often with more severe COVID-19 than those with normoglycemia (aOR [moCOVID-19]: 1.76, 95% CI, 1.04-2.97 and aOR [sCOVID-19]: 2.41, 95% CI, 1.37-4.23). Screening with BC failed to identify only 96% of PreD (206/217) and 26.2% of D diagnosis (58/221) and missed associations of dysglycemia and COVID-19 severity.

CONCLUSION

Pandemic conditions may hamper dysglycemia detection rate and in consequence the awareness of individual patient risk for COVID-19 severity. A systematic diabetes screening including HbA1c reduces underdiagnosis of previously unknown or new-onset dysglycemia, and enhances the quality of risk estimation and access of patients at risk to a diabetes-specific intervention.

Berberine inhibits gluconeogenesis in spontaneous diabetic rats by regulating the AKT/MAPK/NO/cGMP/PKG signaling pathway

This work was aimed to investigate the action mechanism of berberine (BBR) on gluconeogenesis. The effects of BBR were examined in rat primary hepatocytes and confirmed in vivo in spontaneous diabetic rats. Protein levels were assessed by Western blot. Immunofluorescence staining was utilized for visualizing protein expression, while qRT-PCR helped for the determination of gene expression at the mRNA level. Besides, cGMP concentration was measured using ELISA, whereas NO level was assessed by spectrophotometry. BBR inhibited gluconeogenesis by downregulating G6Pase and PEPCK via inhibition of CREB phosphorylation. Moreover, BBR enhanced NO and cGMP concentrations, leading to the activation of the NO/cGMP/PKG signaling via activating AKT1/MAPK axis. The in vivo experiments were consistent with the findings obtained in vitro. Hence, BBR represents a drug candidate for diabetic patients and its mechanism of action may be driven via the AKT/MAPK/NO/cGMP/PKG pathway.

Global estimates of diabetes-related amputations incidence in 2010-2020: A systematic review and meta-analysis

AIMS/HYPOTHESIS

This study sought to provide up-to-date pooled global estimates of diabetes-related amputation incidence from 2010 to 2020.

METHODS

Embase and Medline databases were searched for studies reporting the incidence rate (IR) of diabetes-related amputations from 2010 to 2020. IR estimates of diabetes-related amputations with associated 95% confidence interval (CI) per 100,000 individuals with diabetes were calculated.

RESULTS

23 studies were included, reporting 505,390 diabetes-related lower extremity amputations. IR of minor amputations was 139.97 (95% CI 88.18-222.16) per 100,000 individuals with diabetes, among patients with type 1 diabetes was 148.59 (95% CI 65.00-339.68) and in type 2 diabetes was 75.53 (95% CI 29.94-190.54). IR of major amputations was 94.82 (95% CI 56.62-158.80) per 100,000 individuals with diabetes, among patients with type 1 diabetes was 100.76 (95% CI 53.71-189.01) and among type 2 diabetes was 40.58 (95% CI 11.03-149.28). There were 83.84 annual amputations (95% CI 41.67-168.65) per 100,000 women with diabetes and 178.04 (95% CI 81.16-390.55) per 100,000 men.

CONCLUSIONS

Globally, annual incidence of diabetes-related amputations from 2010 to 2020 has shown to disproportionately affect men and individuals with type 1 diabetes mellitus, although its incidence is not uniform across countries.

Impact of cumulative hyperglycemic burden on the pancreatic cancer risk: A nationwide cohort study

AIMS

We aimed to investigate how much cumulative hyperglycemia exposure increases pancreatic cancer risk.

METHODS

This study used the National Health Insurance Service Database of Claims and included 3,138,099 individuals who underwent four consecutive annual health screenings between 2009 and 2013. We defined hyperglycemic burden in two ways. First, the hyperglycemic burden was given a score from 0 to 4, with one point assigned for each time blood glucose was ≥100 mg/dL or the use of an antidiabetic drug. Furthermore, we performed semiquantitative scoring of a pre-diabetic (100-125; 1 point) and diabetic level (≥126; 2 points) and categorized into one of nine groups (hyperglycemic score 0-8).

RESULTS

During the median 6.2 years of follow-up, groups with a hyperglycemic burden of 1, 2, 3, and 4 had a 15%, 30%, 26%, and 67% increased pancreatic cancer risk compared with normal subjects. In semiquantitative analyses, individuals with a pre-diabetic glucose level on at least one occasion had a 14% increased the risk. Furthermore, individuals with a burden score of 8 had an 89% higher risk than subjects with a normal range.

CONCLUSIONS

The pancreatic cancer incidence increased significantly according to the hyperglycemic burden, defined as sustained hyperglycemic exposure, including pre-diabetic levels.

Prevalence of diabetic retinopathy in patients with newly diagnosed type 2 diabetes: A systematic review and meta-analysis

AIMS

Type 2 diabetes mellitus (T2DM) can remain undiagnosed for many years, during which micro- and macro-vascular complications may develop. This study aimed to assess the worldwide prevalence of diabetic retinopathy (DR) in patients with newly diagnosed T2DM.

MATERIALS AND METHODS

We systematically searched electronic databases for relevant studies published from inception to 01 January 2022. Selected studies reported the prevalence of DR among patients with newly diagnosed T2DM, specifying the case definition used. Random-effects meta-analysis was used to derive the pooled prevalence. Subgroup and meta-regression analyses were used to investigate variations in the prevalence estimates in terms of available variables.

RESULTS

Data from 77 studies including 99,847 patients with newly diagnosed T2DM were included from 26 countries. The pooled prevalence of DR among patients with newly diagnosed T2DM was 13.1% (95% CI, 11.1%-15.1%; I²  = 97.0%). DR was higher in clinic-based samples compared with community-based samples (15.0%, 95% CI = 12.4%-17.8% vs. 11.5%, 95% CI = 8.9%-14.5%; p = 0.05; I²  = 97.0%) and was higher in countries in the WHO African 19.2% (95% CI, 14.6%-24.3%; I²  = 76.0%), South-East Asia 15.4% (95% CI, 10.0%-21.6%; I²  = 79.1%), and European 15.0% (95% CI, 11.2%-19.2%; I²  = 82.0%) regions. A higher proportion of female patients was significantly associated with a lower prevalence of DR in patients with newly diagnosed T2DM. We observed that the prevalence of DR in patients with newly diagnosed T2DM has remained unchanged over time.

CONCLUSIONS

Globally, DR is a prevalent complication among patients with newly diagnosed T2DM indicating the importance of establishing effective strategies to promote regular screening for the early diagnosis of T2DM alongside routine ophthalmic assessment at the time of T2DM diagnosis to reduce the burden of vision-threatening retinopathy.

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.

Socioeconomic disparities in diabetes prevalence and management among the adult population in Bangladesh

BACKGROUND

Diabetes, one of the major metabolic disorders, is rising in Bangladesh. Studies indicate there is inequality in prevalence and care-seeking behavior, which requires further exploration to understand the socioeconomic disparities in the pathophysiology of diabetes. This study examined the latest nationally representative estimates of diabetes prevalence, awareness, and management among adults aged 18 years and above in Bangladesh and its association with socioeconomic status in 2017-18.

METHODS

We used the 2017-18 Bangladesh Demographic and Health Survey data. Diabetic status of 12,092 adults aged 18 years and above was measured in the survey using fasting plasma glucose levels. We applied multivariate logistic regressions to examine the role of socioeconomic status on diabetes prevalence, awareness, and management, after controlling for relevant covariates.

RESULTS

Overall, 10% of adults had diabetes in Bangladesh in 2017-18, with the highest prevalence of 16% in the age group 55-64 years. Our analyses found statistically significant disparities by socioeconomic status in the prevalence of diabetes as well as the person's awareness of his/her diabetic condition. However, the effect of socioeconomic status on receiving anti-diabetic medication only approached significance (p = 0.07), and we found no significant association between socioeconomic status and control of diabetes.

CONCLUSIONS

We expect to see an 'accumulation' of the number of people with diabetes to continue in the coming years. The rising prevalence of diabetes is only the tip of an iceberg; a large number of people with uncontrolled diabetes and a lack of awareness of their condition will lead to increased morbidity and mortality, and that could be the real threat. Immediate measures to increase screening coverage and exploration of poor control of diabetes are required to mitigate the situation.

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

INTRODUCTION

Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%-75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status.

RESEARCH DESIGN AND METHODS

Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes.

RESULTS

The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications.

CONCLUSIONS

A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening.

Evaluation of motor vehicle crashes between scooter riders and car drivers after diagnosis of type 2 diabetes in Taiwan

BACKGROUND

Despite the plausibility that diabetes may increase the risk of motor vehicle crashes (MVCs) due to various diabetes related complications and co-morbidity, findings from epidemiological studies on the relationship between diabetes and MVCs remained inclusive mainly due to heterogeneity in the study design and failure to complete consideration of potential confounders. This study re-visited this putative association with an improved study design.

METHOD

This study employed a controlled before-after study design and included 1,264,280 people aged 18-75 years with T2D newly diagnosed from 2009-2014 and an equal number of age-, sex-, and time-matched controls. The rate ratios (RRs) of vehicle type-specific incidence rates of MVCs in the 1 and 2 years before and after diabetes diagnosis (or the matched dates) were compared between the individuals with type 2 diabetes (T2D) and their matched controls.

RESULTS

The rate of MVCs increased slightly among people with T2D over 1 and 2 years following diabetes diagnosis, with RRs of 1.04 (95% confidence interval [CI]=1.02-1.07) and 1.11 (95% CI=1.09-1.13), respectively. These RRs were comparable to those obtained for controls (1.06 and 1.12, respectively). By contrast, the RRs of scooter crashes were significantly higher in the T2D group than in the control group during the 1 year (1.28 vs. 1.08, p < 0.001) and 2 years (1.32 vs. 1.08, p < 0.001) following diabetes diagnosis.

CONCLUSION

T2D diagnosis was associated with a moderate but significant increase in the risk of MVCs among scooter drivers, but not among car drivers.

Immediate effect of textured insoles on the balance in patients with diabetic neuropathy

AIMS/INTRODUCTION

Neuropathy is a common complication of diabetes that reduces balance by disrupting vision, and the sensory and vestibular systems. This is important in older adults who are more at risk of falling. Studies show that improving the sensory mechanisms through insoles can improve balance in people with balance disorders. As textured insoles have recently been considered for improving balance disorders, this study aimed to investigate the immediate effect of textured insoles on the balance of patients with diabetic neuropathy.

MATERIALS AND METHODS

A total of 17 patients with diabetic neuropathy participated in this quasi-experimental study. The studied variables were the general balance index, posterior-anterior balance index and medial-lateral balance index, which were measured in three conditions: (i) soft textured insoles; (ii) hard textured insoles; and (iii) without textured insoles. A Biodex balance device was used for this purpose.

RESULTS

In the general balance index, there was a significant difference between all the studied conditions. In the posterior-anterior balance index, there was a significant difference between without textured insole and hard textured insoles, and also soft and hard textured insoles. In the medial-lateral balance index, there was a significant difference between the hard textured insoles and without textured insoles, and also soft and hard textured insoles (P < 0.05).

CONCLUSIONS

Wearing textured insoles can increase the balance in patients with diabetic neuropathy. This can be due to its effect on the sensory feedback of the soles, improving the proprioception and tactile sensors that are the main sources of balance.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose

Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.

Design

Cross sectional study.

Participants

Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.

Methods

The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.

Main Outcome Measures

The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.

Results

For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.

Conclusions

Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Can the level of HbA1C predict diabetic retinopathy among type II diabetic patients?

Background

Hemoglobin A1C (HbA₁C) test is the best care evaluation measurement due to a strong correlation between the test results and diabetic complications. So, this cross-sectional study aimed to assess whether the level of HbA₁C can predict Diabetic Retinopathy (DR) among Type 2 diabetes mellitus (T2DM) in the Iranian population.

Method

One hundred sixty-eight diabetic patients were selected via the convenience sampling method. Data were collected by research made questionnaire scale and laboratory test had been done. To estimate the cut off point for some variables statistical tests, formal measures of classification performance, model evaluation criteria and a decision Tree were used.

Results

The prevalence of DR was 29.8%. The Receiver Operating Characteristic (ROC) curve and decision tree showed the optimal cut-off point for the HbA1C variable that separates the patient with and without DR is HbA₁C = 8.15.

Conclusion

Current study showed an appropriate cutoff point for detecting the development of DR among diabetic patients. So, this cutoff point can be used as guide evidence in several clinical judgments on the Iranian population.

Epidemiology of Poor Glycaemic Control among Patients with Type 2 Diabetes Mellitus in Terengganu State of Malaysia

Background: Type 2 diabetes mellitus (T2DM) is among the most common non-communicable diseases worldwide and Malaysia. Poor glycaemic control among T2DM patients lead to serious life-long complications. Therefore, it is imperative to study the prevalence of poor glycaemic control and its risk factors to facilitate public health physicians and clinicians in developing the best strategies to optimize glycaemic control among T2DM patients. Materials and Methods: A comparative cross-sectional study between groups of good glycaemic control and poor glycaemic control was conducted among type 2 diabetes mellitus (T2DM) patients who fulfilled study criteria in Terengganu state of Malaysia. Eligible samples in the National Diabetes Registry registered from 1st January 2021 until 31st December 2021 were included into the study. Descriptive statistics, simple and multiple logistic regressions were employed for data analysis. Result: A total of 17,165 samples were recruited in the descriptive part of the study. The prevalence of patients with poor glycaemic control in Terengganu state was 65.3% (95% CI: 0.62, 0.67). In the inferential part of the study, a total 3,700 samples were randomly selected. Multivariable analysis using multiple logistic regression revealed age, duration of diabetes, body mass index, cigarette smoking, presence of retinopathy and presence of hypertension were the significant factors associated with poor glycaemic control among T2DM patients in Terengganu state with an adjusted odds ratio (AOR) of 0.95 (95%CI:0.94, 0.96); p<0.001), AOR 1.15 (95%CI:1.13, 1.17; p<0.001), AOR 1.03 (95%CI:1.01, 1.04; p<0.001), AOR 1.45 (95%CI:1.01, 2.10; p=0.047), AOR 1.32 (95%CI:1.01, 1.73; p=0.043) and AOR 1.39 (95%CI:1.16, 1.67; p<0.001) respectively. Conclusion: Strategies focusing on the identified risk factors may improve diabetes mellitus management and avert life-long diabetic complications.

Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes

BACKGROUND

The prevention of type 2 diabetes is challenging due to the variable effects of risk factors at an individual level. Data-driven methods could be useful to detect more homogeneous groups based on risk factor variability. The aim of this study was to derive characteristic phenotypes using cluster analysis of common risk factors and to assess their utility to stratify the risk of type 2 diabetes.

METHODS

Data on 7317 diabetes-free adults from Sweden were used in the main analysis and on 2332 diabetes-free adults from Mexico for external validation. Clusters were based on sex, family history of diabetes, educational attainment, fasting blood glucose and insulin levels, estimated insulin resistance and β-cell function, systolic and diastolic blood pressure, and BMI. The risk of type 2 diabetes was assessed using Cox proportional hazards models. The predictive accuracy and long-term stability of the clusters were then compared to different definitions of prediabetes.

RESULTS

Six risk phenotypes were identified independently in both cohorts: very low-risk (VLR), low-risk low β-cell function (LRLB), low-risk high β-cell function (LRHB), high-risk high blood pressure (HRHBP), high-risk β-cell failure (HRBF), and high-risk insulin-resistant (HRIR). Compared to the LRHB cluster, the VLR and LRLB clusters showed a lower risk, while the HRHBP, HRBF, and HRIR clusters showed a higher risk of developing type 2 diabetes. The high-risk clusters, as a group, had a better predictive accuracy than prediabetes and adequate stability after 20 years.

CONCLUSIONS

Phenotypes derived using cluster analysis were useful in stratifying the risk of type 2 diabetes among diabetes-free adults in two independent cohorts. These results could be used to develop more precise public health interventions.

Clinical evaluation of AI-assisted screening for diabetic retinopathy in rural areas of midwest China

BACKGROUND

Although numerous studies have described the application of artificial intelligence (AI) in diabetic retinopathy (DR) screening among diabetic populations, studies among populations in rural areas are rare. The purpose of this study was to evaluate the application value of an AI-based diagnostic system for DR screening in rural areas of midwest China.

METHODS

In this diagnostic accuracy study, diabetes mellitus (DM) patients in the National Basic Public Health Information Systems of Licheng County and Lucheng County of Changzhi city from July to December 2020 were selected as the target population. A total of 7824 eyes of 3933 DM patients were enrolled in this screening; the patients included 1395 males and 2401 females, with an average age of 19-87 years (63±8.735 years). All fundus photographs were collected by a professional ophthalmologist under natural pupil conditions in a darkroom using the Zhiyuan Huitu fundus image AI analysis software EyeWisdom. The AI-based diagnostic system and ophthalmologists were tasked with diagnosing the photos independently, and the consistency rate, sensitivity and specificity of the two methods in diagnosing DR were calculated and compared.

RESULTS

The prevalence rates of DR according to the ophthalmologist and AI diagnoses were 22.7% and 22.5%, respectively; the consistency rate was 81.6%. The sensitivity and specificity of the AI system relative to the ophthalmologists' grades were 81.2% (95% confidence interval [CI]: 80.3% 82.1%) and 94.3% (95% CI: 93.7% 94.8%), respectively. There was no significant difference in diagnostic outcomes between the methods (χ2 = 0.329, P = 0.566, P>0.05), and the AI-based diagnostic system had high consistency with the ophthalmologists' diagnostic results (κ = 0.752).

CONCLUSION

Our research demonstrated that DR patients in rural area hospitals can be screened feasibly. Compared with that of the ophthalmologists, however, the accuracy of the AI system must be improved. The results of this study might lend support to the large-scale application of AI in DR screening among different populations.

Impact of a reduction in sugar-sweetened beverage consumption on the burden of type 2 diabetes in Brazil: A modeling study

AIM

To model the impact of a 20 % tax on sugar-sweetened beverages (SSBs) on the disease burden of T2DM among Brazilian adults.

METHODS

This is an ex-ante risk comparative study. The model applied a 20 % tax on SSB and projected the incidence, prevalence, and mortality of T2DM over a 20-year period (2019 to 2039). Using recent national data on consumption, previously published cross- and own-price elasticities of SSBs and diabetes relative risk we estimated changes on T2DM burden.

RESULTS

With a 20 % tax on SSBs, after 10 years, we estimated a reduction of 37,303 new cases of T2DM for men and 56,757 for women; 184,129 prevalent cases for men and 219,236 for women; and 5,386 and 6,075 deaths for men and women, respectively. After 20 years, 8.6 % and 12.4 % new cases of T2DM will have been prevented, 4.0 % and 5.5 % prevalent cases, and 13.7 % and 12.7 % deaths among men and women, respectively.

CONCLUSIONS

SSB taxes have the potential to reduce the burden of and deaths attributable to T2DM. Our results show that a fiscal policy may significantly impact strategic plans to tackle noncommunicable diseases.

Efficacy of methanolic extracts of some medicinal plants on wound healing in diabetic rats

Background

Materials and methods

Results

Conclusion

Diabetes Mellitus among Adult Outpatients Visiting a Tertiary Care Centre: A Descriptive Cross-sectional Study

Introduction

Diabetes mellitus is a chronic metabolic disease characterised by elevated blood sugar levels and is a pandemic of public health importance. Screening programs can help reduce morbidity and mortality by preventing or delaying complications. This study aimed to find out the prevalence of diabetes mellitus among adult outpatients visiting a tertiary care centre.

Methods

This descriptive cross-sectional study was conducted among outpatients visiting a tertiary care centre between 1 January 2019 to 31 December 2019. Ethical approval was obtained from the Ethical Review Board (Registration number: 408/2020 P). Patients with unknown history of diabetes participating in free random blood sugar examinations were included in the study. Systematic random sampling was used. Point estimate and 95% Confidence Interval were calculated.

Results

Among 385 adult outpatients, 17 (4.42%) (2.37-6.47, 95% Confidence Interval) had diabetes. The mean random blood sugar level of the diabetic patients was 281.41±57.49 mg/dl.

Conclusions

The prevalence of diabetes mellitus among adult outpatients was similar to previous studies conducted in similar settings. Random blood sugar test in hospital outpatient settings is feasible to identify people with diabetes mellitus.

Keywords

diabetes mellitus; hospital; Nepal; outpatient.

The Impact of Diabetes and Prediabetes on Prevalence of Mycobacterium tuberculosis Infection Among Household Contacts of Active Tuberculosis Cases in Ethiopia

Background

It is uncertain whether diabetes affects the risk of developing latent tuberculosis infection (LTBI) following exposure to Mycobacterium tuberculosis (Mtb). We assessed the relationship of diabetes or prediabetes and LTBI among close and household contacts (HHCs) of patients with active pulmonary tuberculosis (TB) disease in Addis Ababa, Ethiopia.

Methods

In this cross-sectional study, we performed interferon-γ release assays, TB symptom screening, and point-of-care glycolated hemoglobin (HbA1c) testing among HHCs of active TB cases. Diabetes status was classified into diabetes (HbA1c ≥6.5% or self-reported diagnosis), prediabetes (5.7%-6.4%), and euglycemia (≤5.6%). Multivariable logistic regression was used to determine the association of diabetes with LTBI.

Results

Among 597 study participants, 123 (21%) had dysglycemia including diabetes (n = 31) or prediabetes (n = 92); 423 (71%) participants were diagnosed with LTBI. Twelve of 31 (39%) HHCs with diabetes were previously undiagnosed with diabetes. The prevalence of LTBI among HHCs with diabetes, prediabetes, and euglycemia was 87% (27/31), 73% (67/92), and 69% (329/474), respectively. In multivariable analysis adjusted for age, sex, and HIV status, the odds of LTBI among HHCs with diabetes were 2.33 (95% confidence interval [CI], .76-7.08) times the odds of LTBI without diabetes. When assessing interaction with age, the association of diabetes and LTBI was robust among participants aged ≥40 years (adjusted odds ratio [aOR], 3.68 [95% CI, .77-17.6]) but not those <40 years (aOR, 1.15 [95% CI, .22-6.1]).

Conclusions

HHCs with diabetes may be more likely to have LTBI than those with euglycemia. Further investigations are needed to assess mechanisms by which diabetes may increase risk of LTBI after Mtb exposure.

Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards

Background

Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

Methods

In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40-69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

Results

The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77-0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5-66). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85-0.90) and a deciles' OR of ×48 (95% CI 12-109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4-7%); 3% (2-4%); 10% (8-12%); and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74-0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models.

Conclusions

The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

Funding

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.