The type 2 diabetes 'modern preventable pandemic' and replicable lessons from the COVID-19 crisis

Strengthening the Evidence for a Causal Link between Type 2 Diabetes Mellitus and Pancreatic Cancer: Insights from Two-Sample and Multivariable Mendelian Randomization

This two-sample Mendelian randomization (MR) study was conducted to investigate the causal associations between type 2 diabetes mellitus (T2DM) and the risk of pancreatic cancer (PaCa), as this causal relationship remains inconclusive in existing MR studies. The selection of instrumental variables for T2DM was based on two genome-wide association study (GWAS) meta-analyses from European cohorts. Summary-level data for PaCa were extracted from the FinnGen and UK Biobank databases. Inverse variance weighted (IVW) and four other robust methods were employed in our MR analysis. Various sensitivity analyses and multivariable MR approaches were also performed to enhance the robustness of our findings. In the IVW and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analyses, the odds ratios (ORs) for each 1-unit increase in genetically predicted log odds of T2DM were approximately 1.13 for PaCa. The sensitivity tests and multivariable MR supported the causal link between T2DM and PaCa without pleiotropic effects. Therefore, our analyses suggest a causal relationship between T2DM and PaCa, shedding light on the potential pathophysiological mechanisms of T2DM's impact on PaCa. This finding underscores the importance of T2DM prevention as a strategy to reduce the risk of PaCa.

A Hybrid Model of In-Person and Telemedicine Diabetes Education and Care for Management of Patients with Uncontrolled Type 2 Diabetes Mellitus: Findings and Implications from a Multicenter Prospective Study

Background

Patients with uncontrolled type 2 diabetes mellitus (T2DM) require close follow-up, support, and education to achieve glycemic control, especially during the initiation or intensification of insulin therapy and self-care management. This study aimed to describe and evaluate the impact of implementing a hybrid model of in-person and telemedicine care and education on glycemic control for patients with uncontrolled T2DM (hemoglobin A1c [HbA1c] ≥9%) during the coronavirus disease pandemic.

Methods

This prospective multicenter-cohort pre-/post-intervention study was conducted on patients with uncontrolled T2DM. This study included three chronic illness centers affiliated with the Family and Community Medicine Department at Prince Sultan Military Medical City in Riyadh, Saudi Arabia. A hybrid model of in-person (onsite) and telemedicine care and education was developed. This involved implementing initial in-person care at the physicians' clinic and initial in-person education at the diabetes education clinic, followed by telemedicine services of tele-follow-ups, support, and education for an average 4-month follow-up period.

Results

Of the enrolled 181 patients, more than half of the participants were women (n = 103, 56.9%). The mean age of participants (standard deviation) was 58.64 ± 11.23 years and the mean duration of diabetes mellitus was 13.80 ± 8.55 years. The majority of the patients (n = 144; 79.6%) were on insulin therapy. Overall, in all three centers, the hybrid model had significantly reduced HbA1c from 10.47 ± 1.23% to 7.87 ± 1.59% (mean difference of reduction 2.59% [95% confidence interval (CI) = 2.34-2.85%], p < 0.001). At the level of each center, HbA1c was reduced significantly with mean differences of 3.17% (95% CI = 2.81-3.53%), 2.49% (95% CI = 1.92-3.06%), and 2.16% (95% CI = 1.76-2.57%) at centers A, B, and C, respectively (all p < 0.001).

Conclusion

The findings showed that the hybrid model of in-person and telemedicine care and education effectively managed uncontrolled T2DM. Consequently, the role of telemedicine in diabetes management could be further expanded as part of routine diabetes care in primary settings to achieve better glycemic control and minimize nonessential in-person visits when appropriate.

Addressing Technical Failures in a Diabetic Retinopathy Screening Program

Purpose

Diabetic retinopathy (DR) is a preventable cause of blindness detectable through screening using retinal digital photography. The Irish National Diabetic Retina Screening (DRS) programme, Diabetic RetinaScreen, provides free screening services to patients with diabetes from aged 12 years and older. A technical failure (TF) occurs when digital retinal imaging is ungradable, resulting in delays in the diagnosis and treatment of sight-threatening disease. Despite their impact, the causes of TFs, and indeed the utility of interventions to prevent them, have not been extensively examined.

Aim

Primary analysis aimed to identify factors associated with TF. Secondary analysis examined a subset of cases, assessing patient data from five time points between 2019 and 2021 to identify photographer/patient factors associated with TF.

Methods

Patient data from the DRS database for one provider were extracted for analysis between 2018 and 2022. Information on patient demographics, screening results, and other factors previously associated with TF were analyzed. Primary analysis involved using mixed-effects logistic regression models with nested patient-eye random effects. Secondary analysis reviewed a subset of cases in detail, checking for causes of TF.

Results

The primary analysis included a total of 366,528 appointments from 104,407 patients over 5 years. Most patients had Type 2 diabetes (89.2%), and the overall TF rate was 4.9%. Diabetes type and duration, dilate pupil status, and the presence of lens artefacts on the camera were significantly associated with TF. The Secondary analysis identified the primary cause of TF was found to be optically dense cataracts, accounting for over half of the TFs.

Conclusion

This study provides insight into the causes of TF within the Irish DRS program, highlighting cataracts as the primary contributing factor. The identification of patient-level factors associated with TF facilitates appropriate interventions that can be put in place to improve patient outcomes and minimize delays in treatment and diagnosis.

Factors Influencing Medication Adherence Among Adults Living with Diabetes and Comorbidities: a Qualitative Systematic Review

PURPOSE OF REVIEW

Medication adherence plays an important role in improving health outcomes related to diabetes and comorbidity. The potential factors influencing medication adherence and how they contribute to health behaviors have not been synthesized to date. This review synthesized qualitative studies that identified factors influencing medication adherence among adults living with diabetes and comorbidity.

RECENT FINDINGS

Twenty-eight findings were extracted and synthesized into four themes: perceived support, lack of knowledge, medication issues, and the importance of routine. The findings highlight the factors that support medication adherence and areas that can be targeted to support and promote medication adherence. The findings also support the potential role of healthcare providers in supporting people living with diabetes and comorbidity to adhere to and maintain medication regimes. Several factors were identified that are amenable to intervention within the clinical practice setting and have the potential to enhance medication adherence and improve health outcomes for people living with diabetes and comorbidities. The development of acceptable and effective interventions could have a positive effect on medication adherence and health outcomes.

Association of Obesity With Type 2 Diabetes Mellitus: A Hospital-Based Unmatched Case-Control Study

Background The prevalence of type 2 diabetes mellitus (T2DM) and obesity is alarmingly increasing with the accessibility of the modern lifestyle. This study aimed to assess the association of obesity with T2DM among the patients visiting the Medicine Department of Ayub Teaching Hospital, Abbottabad, Pakistan. Method This hospital-based, unmatched case-control study was conducted from March 2022 to September 2022. A total of 200 patients (age ≥ 18) (100 cases and 100 controls) were recruited. Those patients with a history of T2DM were selected as cases, and those without diabetes were selected as controls after taking informed written consent. Patients with BMI ≥ 25 were considered obese. Data were collected through a non-probability convenience sampling technique using a self-structured non-validated questionnaire. Data were organized and analyzed through IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY). Results We found a significant positive association of obesity with T2DM with a crude odds ratio of 3.6 (95% CI: 2.0-6.6), a p-value of 0.000, and an adjusted odd ratio of 3.7 (95% CI: 1.9 - 7.1), with a p-value of 0.004 (adjusted for potential confounders, including gender, age group, stress, and status of physical activeness) using a logistic regression model. Conclusion It is concluded that obesity is strongly associated with developing T2DM and lack of physical activity, people over 45 years, and males with obesity have a higher chance of developing T2DM.

Bromocriptine and Colesevelam Hydrochloride: Novel Therapies for Type II Diabetes Mellitus

The increasing prevalence of type II diabetes mellitus (T2DM) is a worldwide healthcare concern. Over the years, our understanding of T2DM has grown considerably in uncovering the pathophysiology of the disease and, in turn, understanding how improved treatment methods can be used to slow disease progression. Some long-term complications that are responsible for most T2DM mortalities include cardiovascular disease, neurological decline, and renal failure. In treating T2DM, it is important that not only glycemic control be obtained but also control of associated complications. Bromocriptine and colesevelam hydrochloride have both been approved by the Food and Drug Administration (FDA) to treat T2DM but are not readily used in practice. These medications are known to treat glycemic dysregulation via unconventional mechanisms, which might contribute to their potential to provide protection against common diabetic complications such as cardiovascular disease. In order to ensure that these overlooked medications become more readily used, it is vital that more research be performed to further elucidate their efficacy in a clinical setting. Future studies should continue to provide clinicians a better understanding of the role these medications have on the treatment of T2DM such as their ability to be used in combination with other commonly used T2DM medications or as monotherapies.

EndoC-βH5 cells are storable and ready-to-use human pancreatic beta cells with physiological insulin secretion

OBJECTIVES

Readily accessible human pancreatic beta cells that are functionally close to primary adult beta cells are a crucial model to better understand human beta cell physiology and develop new treatments for diabetes. We here report the characterization of EndoC-βH5 cells, the latest in the EndoC-βH cell family.

METHODS

EndoC-βH5 cells were generated by integrative gene transfer of immortalizing transgenes hTERT and SV40 large T along with Herpes Simplex Virus-1 thymidine kinase into human fetal pancreas. Immortalizing transgenes were removed after amplification using CRE activation and remaining non-excized cells eliminated using ganciclovir. Resulting cells were distributed as ready to use EndoC-βH5 cells. We performed transcriptome, immunological and extensive functional assays.

RESULTS

Ready to use EndoC-βH5 cells display highly efficient glucose dependent insulin secretion. A robust 10-fold insulin secretion index was observed and reproduced in four independent laboratories across Europe. EndoC-βH5 cells secrete insulin in a dynamic manner in response to glucose and secretion is further potentiated by GIP and GLP-1 analogs. RNA-seq confirmed abundant expression of beta cell transcription factors and functional markers, including incretin receptors. Cytokines induce a gene expression signature of inflammatory pathways and antigen processing and presentation. Finally, modified HLA-A2 expressing EndoC-βH5 cells elicit specific A2-alloreactive CD8 T cell activation.

CONCLUSIONS

EndoC-βH5 cells represent a unique storable and ready to use human pancreatic beta cell model with highly robust and reproducible features. Such cells are thus relevant for the study of beta cell function, screening and validation of new drugs, and development of disease models.

Artificial intelligence with temporal features outperforms machine learning in predicting diabetes

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities.

Exploring the option of student-run free health clinics to support people living with type 2 diabetes mellitus: a scoping review

Diabetes is a major cause of morbidity and premature mortality worldwide and now identified as a 'public health emergency' and a 'modern and preventable pandemic'. Indigenous populations are disproportionately affected by type 2 diabetes mellitus (T2DM) and associated complications. Student run free clinics (SRFCs) may play an important role in the prevention and management of T2DM. The primary objective of this scoping review was to investigate the opportunity for curriculum enhancement through the role and effectiveness of SRFCs in managing T2DM. Electronic databases such as PubMed, CINAHL, Science Direct and Cochrane Library were searched from inception to October 2022. Identified records from database literature searches were imported into Covidence®. Two independent reviewers screened and extracted the data. The research team collectively created a data charting table/form to standardize data collection. A narrative synthesis was used to summarize the evidence. Six studies (total of 319 participants) that met our eligibility criteria were included in this scoping review. SRFCs can provide high-quality diabetic care, especially for uninsured and economically weaker population. Preliminary evidence further indicate that shared medical appointments and telehealth may facilitate diabetic care especially during times where access to care may be difficult (e.g., COVID lockdown). However, no study included in the review explored or discussed family centred/culturally sensitive interventions. Hence, such interventions should be made part of the curriculum in the future with students in SRFCs exposed to such an approach.

Sex-Based Differences in Clinical Profile and Complications among Individuals with Type 2 Diabetes Seen at a Private Tertiary Diabetes Care Centre in India

This study aimed to compare the clinical and biochemical profiles as well as the complications in males and females with type 2 diabetes (T2DM) presenting to a private tertiary diabetes care centre in India. This is a retrospective study, conducted between 1 January 2017 and 31 December 2019, and included 72,980 individuals with T2DM, aged ≥ 18 years (age and sex-matched-males-36,490; females-36,490). Anthropometric measurements, blood pressure, fasting plasma glucose (FPG), post-prandial plasma glucose (PPPG), glycated haemoglobin (HbA1c), lipids, urea, and creatinine were measured. Retinopathy was screened using retinal photography, neuropathy using biothesiometry, nephropathy measuring urinary albumin excretion, peripheral vascular disease (PVD) using Doppler, and coronary artery disease (CAD) based on the history of myocardial infarction and/or drug treatment for CAD and/or electrocardiographic changes. Obesity (73.6% vs. 59.0%) rates were significantly higher in females compared to males. FPG, PPPG, and HbA1c were higher among younger age groups among both sexes, with males having higher values compared to females. However, after the age of 44 years, control of diabetes was worse among females. In addition, only 18.8% of the females achieved glycemic control (HbA1c < 7%) compared to 19.9% in males (p < 0.001). Males had higher prevalence of neuropathy (42.9% vs. 36.9%), retinopathy (36.0% vs. 26.3%), and nephropathy (25.0% vs. 23.3%) compared to females. Males had 1.8- and 1.6-times higher risk of developing CAD and retinopathy compared to females. Hypothyroidism (12.5% vs. 3.5%) and cancers (1.3% vs. 0.6%) were significantly higher in females compared to males. In this large sample of T2DM seen at a chain of private tertiary diabetes centres, females had higher prevalence of metabolic risk factors and poorer diabetes control compared to males, emphasizing the need for better control of diabetes in females. However, males had higher prevalence of neuropathy, retinopathy, nephropathy, and CAD compared to females.

In Vivo and In Vitro Antidiabetic Efficacy of Aqueous and Methanolic Extracts of Orthosiphon Stamineus Benth

Orthosiphon stamineus is a popular folk herb used to treat diabetes and some other disorders. Previous studies have shown that O. stamineus extracts were able to balance blood glucose levels in diabetic rat animal models. However, the antidiabetic mechanism of O. stamineus is not fully known. This study was carried out to test the chemical composition, cytotoxicity, and antidiabetic activity of O. stamineus (aerial) methanol and water extracts. GC/MS phytochemical analysis of O. stamineus methanol and water extracts revealed 52 and 41 compounds, respectively. Ten active compounds are strong antidiabetic candidates. Oral treatment of diabetic mice with O. stamineus extracts for 3 weeks resulted significant reductions in blood glucose levels from 359 ± 7 mg/dL in diabetic non-treated mice to 164 ± 2 mg/dL and 174 ± 3 mg/dL in water- and methanol-based-extract-treated mice, respectively. The efficacy of O. stamineus extracts in augmenting glucose transporter-4 (GLUT4) translocation to the plasma membrane (PM) was tested in a rat muscle cell line stably expressing myc-tagged GLUT4 (L6-GLUT4myc) using enzyme-linked immunosorbent assay. The methanol extract was more efficient in enhancing GLUT4 translocation to the PM. It increased GLUT4 translocation at 250 µg/mL to 279 ± 15% and 351 ± 20% in the absence and presence of insulin, respectively. The same concentration of water extract enhanced GLUT4 translocation to 142 ± 2.5% and 165 ± 5% in the absence and presence of insulin, respectively. The methanol and water extracts were safe up to 250 µg/mL as measured with a Methylthiazol Tetrazolium (MTT) cytotoxic assay. The extracts exhibited antioxidant activity as measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. O. stamineus methanol extract reached the maximal inhibition of 77 ± 10% at 500 µg/mL, and O. stamineus water extract led to 59 ± 3% inhibition at the same concentration. These findings indicate that O. stamineus possesses antidiabetic activity in part by scavenging the oxidants and enhancing GLUT4 translocation to the PM in skeletal muscle.

Diabetic retinopathy as a potential risk factor for ptosis: A 13-year nationwide population-based cohort study in Taiwan

Purpose

To determine the risk of ptosis among diabetic retinopathy (DR) patients.

Methods

This is a population-based, retrospective, matched-cohort study where DR patients were recruited from the Taiwan National Health Insurance Research Database (NHIRD) to investigate the risk of developing ptosis. Preexisting co-factors of interest included smoking status and medical comorbidities of hyperlipidemia and hypertension. Statistical analysis was performed using T-test, Cox-proportional hazard ratios adjusted for comorbidities (aHR), Wilcoxon rank sum test, Kaplan-Meier estimators, and log rank tests.

Results

Follow-up data of 9,494 patients with DR and 37,976 matched control cohort (non-DR) from 2000 to 2012 were analyzed. DR patients were found to have significantly increased risk of developing ptosis (adjusted hazard ratio (HR) [95% CI]: 2.76 [1.74-4.38], p < 0.001) when compared to the control cohort. From analysis in different strata, adult age and non-smokers were shown to have higher risk for ptosis development among DR patients. Furthermore, DR patients was also found to have increased risk of developing ptosis when compared to matched controls, regardless of whether they had medical comorbidities of lipid metabolism disorders or hypertension.

Conclusions

In this large-scale study using real-world data, our results showed that DR patients were found to have increased risk of developing ptosis. Female gender, adult age, and non-smokers were also shown to increase the risk of ptosis among DR patients. This has implications towards the care of diabetic patients, complications such as ptosis should be properly screened for when encountering such patients. Before ptosis surgery, the possibility of underlying diabetes or DR should be also scrutinized and treated properly to avoid undesirable postoperative dissension.

Adherence to Oral Antidiabetic Drugs in Patients with Type 2 Diabetes: Systematic Review and Meta-Analysis

Poor adherence to oral antidiabetic drugs (OADs) in patients with type 2 diabetes (T2D) can lead to therapy failure and risk of complications. The aim of this study was to produce an adherence proportion to OADs and estimate the association between good adherence and good glycemic control in patients with T2D. We searched in MEDLINE, Scopus, and CENTRAL databases to find observational studies on therapeutic adherence in OAD users. We calculated the proportion of adherent patients to the total number of participants for each study and pooled study-specific adherence proportions using random effect models with Freeman-Tukey transformation. We also calculated the odds ratio (OR) of having good glycemic control and good adherence and pooled study-specific OR with the generic inverse variance method. A total of 156 studies (10,041,928 patients) were included in the systematic review and meta-analysis. The pooled proportion of adherent patients was 54% (95% confidence interval, CI: 51-58%). We observed a significant association between good glycemic control and good adherence (OR: 1.33; 95% CI: 1.17-1.51). This study demonstrated that adherence to OADs in patients with T2D is sub-optimal. Improving therapeutic adherence through health-promoting programs and prescription of personalized therapies could be an effective strategy to reduce the risk of complications.

Prediction of Weight Loss in Filipino Americans to Decrease Risk for Type 2 Diabetes: Using Multi-Dimensional Data (Preprint)

BACKGROUND

Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy.

OBJECTIVE

The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D.

METHODS

The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss.

RESULTS

Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]).

CONCLUSIONS

Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

Factors Related to the Continuity of Care and Self-Management of Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in Taiwan

BACKGROUND

Most diabetic patients suffer from chronic diseases affecting their self-management status. This study aims to explore the relationship between the CoC and the self-management of patients with Type 2 Diabetes Mellitus (T2DM) and analyze the predictive factors affecting their self-management.

METHODS

Structured questionnaires were used for data collection. Convenient sampling was adopted to recruit inpatients diagnosed with T2DM in the endocrine ward of a medical hospital in central Taiwan.

RESULTS

A total of 160 patients were recruited. The average age of the patients is 66.60 ± 14.57 years old. Among the four dimensions of the self-management scale, the average score of the problem-solving dimension was the highest, and that of the self-monitoring of blood glucose was the lowest. The analysis results showed that the overall regression model could explain 20.7% of the total variance in self-management.

CONCLUSIONS

Healthcare providers should attach importance to the CoC of T2DM patients and encourage patients to maintain good interaction with healthcare providers during their hospitalization. It is recommended to strengthen CoC for patients with diabetes who are single or with low educational levels in clinical practice to enhance their blood glucose control and improve diabetes self-management.