Abstract

Background
People with diabetes have an increased risk of heart failure (HF), compared to those without diabetes. However, no comprehensive systematic review and meta-analysis has explored whether these associations could differ in relation to prevalent cardiovascular disease (CVD).

Aims
To estimate the association between diabetes and incident heart failure (HF), compared to without diabetes, in individuals with and without CVD.

Methods
PubMed, Scopus, and Web of Science were searched for observational cohort studies from the earliest dates to 22nd March 2023. A random-effects model calculated the pooled relative risk (RR).

Results
Of 11,609 articles, 31 and 6 studies reported data in people with type 2 diabetes (T2D) and type 1 diabetes (T1D) respectively. Individuals with T2D had an increased risk of HF irrespective of CVD prevalence: 1.61 (95% CI: 1.35–1.92) in those with CVD; 1.78 (1.60–1.99) without CVD; and 2.02 (1.75–2.33) with unspecified CVD prevalence. Meta-regression did not identify a significant difference comparing HF risk in T2D individuals with vs. without CVD (p = 0.232).

Conclusion
People with T2D, compared to those without diabetes, have similar increased risk of HF, regardless of CVD prevalence. Strategies proven to lower HF risk in T2D individuals should be prioritized for those with and without CVD.

Introduction

Diabetes mellitus and heart failure (HF) tend to exist as common comorbid conditions [1], with approximately 4.3 to 28 % of HF individuals with prevalent type 2 diabetes (T2D) and 12–57 % of T2D individuals presenting with HF [2]. People with diabetes also have an increased risk of HF, which subsequently increases their risk of hospitalizations [3], [4] and mortality [5], [6] to further burden global economic and healthcare resources [7]. Nearly 537 million adults are currently living with diabetes world-wide, estimated to rise to 643 million by 2030 [8]; HF currently affects 64.3 million people globally [9], predicted to rise by 50 % in the next 20 years [10]. Hence, early prevention and management strategies to lower HF risk in individuals with diabetes is of public health importance [11], [12]. This is further reinforced in evidence showing HF as the most common initial presentation of cardiovascular disease (CVD) in people with diabetes [13].

A previous meta-analysis from 2018 [14] showed that persons with diabetes have an increased risk of HF, relative to without diabetes (relative risk, RR: 2.06, 95 % confidence interval, CI: 1.73 to 2.46). However, this study pooled estimates from heterogeneous study designs (e.g., case-control and cohort), which could have led to biased estimates. To address this limitation, a recent 2020 meta-analysis [15], exclusively using data from observational cohort studies, reported that individuals with diabetes have an increased risk of new-onset HF, relative to without diabetes (RR: 2.14; 95 % CI: 1.96 to 2.34). However, the author’s included studies that did not adjust for at minimum age and sex, which could have led to biased results. The study also found an attenuated association when restricting the population to individuals with prevalent coronary heart disease (CHD) (RR: 1.94, 95 % CI: 1.77 to 2.12) [15]. It remains unclear whether, and to what extent, the association between diabetes and incident HF could differ in populations with and without broad CVD.

Results

3.1. Study characteristics
Of the 11,609 studies, 31 articles [6,13,22–50] consisting of 34 cohorts for T2D were identified (prevalent CVD: 11; without CVD: 12; unspecified CVD prevalence: 11) and 6 articles [6,24,27,29,46,51] consisting of 7 cohorts were identified for T1D (prevalent CVD: 1; without CVD: 3; unspecified CVD prevalence: 3) (Fig. 1). The total number of individuals were 501,536 for cohorts of T2D with CVD, 27,855,580 for T2D without CVD, 9,046,486 for T2D with unspecified CVD prevalence; 251,538 for T1D with CVD, 18,580,888 for T1D without CVD, and 3,690,911 for T1D unspecified CVD prevalence (Table S3). Of studies investigating T2D, mean age was 57.99, 61.25 and 64.74 in those with CVD, without CVD and unspecified CVD prevalence respectively. Mean baseline recruitment (years) was higher in those with prevalent CVD (2007), compared to without CVD (2004) and unspecified CVD (2001). Most studies were from Europe at 45.45 %, 50.00 % and 54.55 % in those with CVD, without CVD and unspecified CVD prevalence. In T2D, most studies had low risk of bias, with 90.91 %, 91.67 % and 72.73 % reported in populations with CVD, without CVD, and unspecified CVD prevalence. Table S3 summarises the characteristics of all included studies.

Summary

Background
In spite of new treatments, the incidence of type 2 diabetes (T2D) and its morbidities continue to rise. The key feature of T2D is resistance of adipose tissue and other organs to insulin. Approaches to overcome insulin resistance are limited due to a poor understanding of the mechanisms and inaccessibility of drugs to relevant intracellular targets. We previously showed in mice and humans that CD248, a pre/adipocyte cell surface glycoprotein, acts as an adipose tissue sensor that mediates the transition from healthy to unhealthy adipose, thus promoting insulin resistance.
Methods
Molecular mechanisms by which CD248 regulates insulin signaling were explored using in vivo insulin clamp studies and biochemical analyses of cells/tissues from CD248 knockout (KO) and wild-type (WT) mice with diet-induced insulin resistance. Findings were validated with human adipose tissue specimens.
Findings
Genetic deletion of CD248 in mice, overcame diet-induced insulin resistance with improvements in glucose uptake and lipolysis in white adipose tissue depots, effects paralleled by increased adipose/adipocyte GLUT4, phosphorylated AKT and GSK3β, and reduced ATGL. The insulin resistance of the WT mice could be attributed to direct interaction of the extracellular domains of CD248 and the insulin receptor (IR), with CD248 acting to block insulin binding to the IR. This resulted in dampened insulin-mediated autophosphorylation of the IR, with reduced downstream signaling/activation of intracellular events necessary for glucose and lipid homeostasis.
Interpretation
Our discovery of a cell-surface CD248-IR complex that is accessible to pharmacologic intervention, opens research avenues toward development of new agents to prevent/reverse insulin resistance.
Funding
Funded by Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundations for Innovation (CFI), the Swedish Diabetes Foundation, Family Ernfors Foundation and Novo Nordisk Foundation.

Introduction

Type 2 diabetes (T2D) is a chronic inflammatory disease that is associated with multiple morbidities, including in particular, a heightened risk of cardio-cerebro-vascular disorders, thrombosis and cancer.1,  2,  3 More than 450 million people are living with diabetes, a number expected to grow to ∼650 million by 2040. A key feature of T2D is resistance of adipose tissue and other organs to insulin, often accompanied by hyperinsulinemia.4

Insulin controls key metabolic activities, including inducing glucose uptake, glycogenesis and lipogenesis, inhibiting lipolysis, stimulating protein synthesis, and enhancing adipogenesis.5,  6,  7 Its effects are mediated via cell surface-expressed tyrosine kinase receptors, triggering signaling cascades leading to a plethora of cellular responses, dysregulation of which are believed to result in the associated morbidities. Thus, for example, the increased risk of cancer in T2D is likely due in part to sustained insulin-induced signaling that promotes pathologic angiogenesis, dysregulated cell differentiation and proliferation, with alterations in cell metabolism.8 Mechanisms that regulate insulin signaling are incompletely understood, as are those that underly insulin resistance.9 It is known however, that with obesity and white adipose tissue (WAT) expansion, extracellular matrix remodeling occurs.10 This is associated with inadequate angiogenesis11 due to microenvironmental hypoxia, the latter which is accompanied by dampened adipogenesis and dysregulated expression and/or activation of the insulin receptor (IR).12,13 Adipocytes become dysfunctional with multiple changes, including mitochondrial disturbances, reduced adiponectin release, upregulation of pro-apoptotic and pro-fibrotic genes, increased release of cytokines and adipokines, infiltration with pro-inflammatory cells and suppression of preadipocyte differentiation.14 The associated disturbances in glucose metabolism and insulin sensitivity may occur early, even before evidence of inflammation.

CD248 is a multi-domain, transmembrane glycoprotein expressed on the surface of pre/adipocytes, perivascular cells and macrophages. From studies with gene targeted mice, we and others showed that CD248 participates in hypoxic regulation and angiogenesis,15,  16,  17,  18 and promotes inflammation, fibrosis, and tumorigenesis. Mice lacking CD248 (KO) are healthy and protected against tumor growth,16,19,20 atherosclerosis,21 arthritis,16 thrombosis,22 liver and renal fibrosis23,  24,  25 and lipid accumulation.26 We also recently reported that mice lacking CD248 either globally or specifically in mature adipocytes, are resistant to high fat diet (HFD)-induced weight gain, and are protected against insulin resistance, glucose intolerance and steatosis.18 Notably, WAT health, insulin sensitivity and glucose tolerance could be restored to normal after onset of HFD-induced diabetes by genetically excising CD248 from the mature adipocyte. The relevance of CD248 in glucometabolic health in humans was confirmed by studies of several independent clinical cohorts, showing that CD248 expression in adipocytes strongly and directly correlates with dysfunctional WAT and insulin resistance.18

Results

We previously showed that global lack of CD248 in mice (KO) confers protection of male mice against 9-weeks of a HFD that induced obesity, insulin resistance, glucose intolerance, steatosis, WAT hypoxia, inflammation and fibrosis.18 For the current studies, we found that even exposure to 2 weeks of the HFD induced differences in glucometabolism between male KO mice and their wild-type (WT) counterparts (Supplemental Figures S1–S4). Thus, the KO mice had significantly lower fasting plasma insulin and insulin growth factor (IGF)-1 levels (Supplemental Figure S1) as compared to sibling control, sex matched HFD-fed WT mice, with significantly better responses in glucose tolerance and insulin tolerance tests (GTT & ITT, respectively) (Supplemental Figure S2). ]Immunohistochemical analyses of epididymal (e)WAT from the HFD-fed KO mice followed by fasting x 5 h, revealed less evidence of fibrosis, less infiltration with F480 inflammatory monocytes, and reduced hypoxia, as compared to fasting HFD-fed wild-type (WT) mice (Supplemental Figures S3 and S4). There was no evidence of steatosis in either group (not shown) following the 2 week HFD. Based on these findings, we used this 2-week HFD model to study the mechanisms by which CD248 regulates glucometabolism and insulin sensitivity.

Abstract

Diabetes is a common chronic disease and a major public health problem approaching epidemic proportions globally. People with diabetes are more likely to suffer from glaucoma than people without diabetes. Glaucoma can lead to loss of vision if not diagnosed at an early stage. This study proposes an intelligent computer-aided triage system with a deep neural network and machine learning to develop and analyze color retinal fundus images and classify glaucomatous retinal images. Deep features of retinal images from the fundus retinal image are extracted using a deep neural network, and the classification of features is performed and analyzed using different machine learning classifiers. Experimental results show that the combination of deep neural network and logistic regression-based classifier outperforms all existing glaucomatous triage systems, improving classification accuracy, sensitivity, and specificity.

Introduction

According to the World Health Organization (WHO) report [1], glaucoma is one of major eye disease which is affecting millions of people in developing countries such as India. The Glaucoma damages the retina in a progressive manner and is less detected by the person and finally causes blindness. As per the survey of glaucoma society of India [2], around 12 million people in India are suffering from this disease. Therefore, early detection and treatment of glaucoma to reduce the risk of blindness is the need of the hour. The acquisition of retinal information of eye is usually performed by gonioscopy and ophthalmoscopy. Then, the analysis of glaucoma for physical condition of the optical nerve is done by filed vision test, intraocular pressure, etc. [3], [4], [5]. The glaucoma detection can be performed using optical coherence tomography (OCT), visual test chart and color fundus camera. The glaucoma detection can also be done by analyzing features such as cup-to-disk (CDR) and disk rim thickness rule of inferior, superior, nasal, and temporal (ISNT).

Recently, computer-aided systems [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] based on various image processing and machine learning algorithms are gaining importance for intelligent detection of glaucoma [19], [20]. While supervised machine learning algorithms are used for classification of the normal image and glaucomatous image for a given dataset of the retinal image, the unsupervised machine learning algorithms are used mainly for segmentation of disk and cup in the enhanced retinal image. Recently, the various schemes are proposed by various researchers for detection of glaucoma in retinal images.

Conclusion

In this paper, a deep neural network and machine learning based computer-aided system for classification of the glaucomatous retinal image is proposed. Here, 512 deep features of retinal images are explored using DNN. The proposed system tested and analyzed all images from the public datasets such as DRISTHI-GS1 and ORIGA. The classification of the glaucomatous retinal image is performed using six machine learning-based classifiers: kNN, SVM, DT, RF, NB, and LR. It is observed that the combination of DNN with LR based machine learning-based classifier outperforms all existing glaucomatous screening systems, with improvement in classification accuracy and sensitivity.

Future work will be focused on the assessment of deep learning-based classifier combination for classification of glaucomatous retinal images. The work may also focus on detection and screening of vascular bleeding in the retina due to macular degeneration and diabetic retinopathy (DR) using machine learning techniques. Also, experimental results particularly F1 score indicates that proposed system need to test with more balance dataset to improve this score and acceptance for practical implementation in future.

Summary

Background
Some epidemiological studies have suggested an increase in incidence of type 1 diabetes during the COVID-19 pandemic, however the mechanism(s) behind such an increase have yet to be identified. In this study we aimed to evaluate the possible role of the SARS-CoV-2 virus in the reported increase in the rate of type 1 diabetes.
Methods
In this observational cohort study using data from the Finnish Pediatric Diabetes Register (FPDR), we assessed the incidence of type 1 diabetes (number of children with newly diagnosed type 1 diabetes per 100 000 person-years during the pandemic and the reference period) during the first 18 months of the COVID-19 pandemic in children in Finland younger than 15 years old compared with a reference period which included three corresponding pre-pandemic periods also obtained from the FPDR. Children with confirmed monogenic diabetes were excluded. We also compared the phenotype and HLA genotype of the disease between these two cohorts, and analysed the proportion of newly diagnosed people with type 1 diabetes testing positive for SARS-CoV-2 antibodies.
Findings
785 children and adolescents in Finland were diagnosed with type 1 diabetes from March 1, 2020, to Aug 31, 2021. In the reference period, which comprised three similar 18-month terms (from March 1, 2014, to Aug 31, 2015; March 1, 2016, to Aug 31, 2017; and March 1, 2018, to Aug 31, 2019) 2096 children and adolescents were diagnosed. The incidence of type 1 diabetes was 61·0 per 100 000 person-years (95% CI 56·8–65·4) among children younger than 15 years old during the pandemic, which was significantly higher than during the reference period (52·3 per 100 000 person-years; 50·1–54·6). The incidence rate ratio adjusted for age and sex for the COVID-19 pandemic was 1·16 (1·06–1·25; p=0·0006) when compared with the reference period. The children diagnosed during the COVID-19 pandemic had more often diabetic ketoacidosis (p<0·001), had a higher HbA1c (p<0·001), and tested more frequently positive for glutamic acid debarboxylase antibodies at diagnosis (p<0·001) than those diagnosed before the pandemic. There were no significant differences in the distribution of HLA genotypes between the two periods. Only five of those diagnosed during the pandemic (0·9%) of 583 tested positive for infection-induced SARS-CoV-2 antibodies.
Interpretation
Children and adolescents diagnosed with type 1 diabetes during the pandemic had a more severe disease at diagnosis. The observed increase in type 1 diabetes incidence during the first 18 months could be a consequence of lockdown and physical distancing rather than a direct effect of SARS-CoV-2 infection.
Funding
Helsinki University Hospital Research Funds, EU Horizon 2020 (Versatile emerging infectious disease observatory project), Academy of Finland, Sigrid Jusélius Foundation, Jane & Aatos Erkko Foundation, and Medicinska understödsföreningen Liv och Hälsa.

Introduction

A series of epidemiological studies have reported that the number of people with newly diagnosed type 1 diabetes has increased during the COVID-19 pandemic.1,  2 According to one meta-analysis, the global incidence of type 1 diabetes among children increased by 9·5% from 2019 to 2020.3 However, these findings have been questioned due to methodological weaknesses.4 An increase in diabetic ketoacidosis at diagnosis has also been observed during the pandemic.5,  6,  7 The reasons behind these findings are unknown. There is an ongoing discussion as to whether these observations are a direct effect of a SARS-CoV-2 infection or a consequence of the lockdown and social isolation due to the pandemic.

A direct effect has been implied, either through an injury to the pancreatic β cells by the SARS-CoV-2 virus or through virus-induced precipitation or acceleration of the disease process leading to type 1 diabetes.8 An increase in diabetic ketoacidosis at diagnosis suggests a possible delayed presentation to health-care services, whereas the indirect effect on the incidence of type 1 diabetes might be mediated through an earlier unmasking of the disease process because of the substantially reduced infection load and decreased physical activity, particularly in children. A decreased infection load might affect the immune system, favouring the development of type 1 diabetes, whereas reduced physical activity increases β-cell stress.9 In April 2020, all schools were closed in Finland and parents with children attending day-care centres were asked to keep their children at home, if possible. The biodiversity hypothesis assumes that reduced microbial exposure in early life increases the risk of immune-mediated diseases.10

Results

785 children diagnosed with type 1 diabetes were enrolled during the pandemic period, comprising of 18 months from March 1, 2020, to Aug 31, 2021 (appendix 3 p 5). The reference periods comprised three similar 18-month terms (from March 1, 2014, to Aug 31, 2015; March 1, 2016, to Aug 31, 2017; and March 1, 2018, to Aug 31, 2019). Altogether, 2096 children diagnosed with type 1 diabetes during the combined 54-month reference period joined the FPDR. The baseline characteristics of the participants in the pandemic and reference cohorts are shown in table 1.

Abstract

Introduction

Background

Systematic literature review methodologies

Related works

Methods

Search strategy

Eligibility criteria

Data extraction

Risk of bias analyses

Results

Quality assessment

Risk of bias analyses

Discussion

Conclusions

References

Abstract

     Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

Introduction

     Diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both [1]. In particular, type 2 diabetes is associated with insulin resistance (insulin action defect), i.e., where cells respond poorly to insulin, affecting their glucose intake [2]. The diagnostic criteria established by the American Diabetes Association are: (1) a level of glycated hemoglobin (HbA1c) greater or equal to 6.5%; (2) basal fasting blood glucose level greater than 126 mg/dL, and; (3) blood glucose level greater or equal to 200 mg/dL 2 h after an oral glucose tolerance test with 75 g of glucose [1].

     Diabetes mellitus is a global public health issue. In 2019, the International Diabetes Federation estimated the number of people living with diabetes worldwide at 463 million and the expected growth at 51% by the year 2045. Moreover, it is estimated that there is one undiagnosed person for each diagnosed person with a diabetes diagnosis [2].

     The early diagnosis and treatment of type 2 diabetes are among the most relevant actions to prevent further development and complications like diabetic retinopathy [3]. According to the ADDITION-Europe Simulation Model Study, an early diagnosis reduces the absolute and relative risk of suffering cardiovascular events and mortality [4]. A sensitivity analysis on USA data proved a 25% relative reduction in diabetes-related complication rates for a 2-year earlier diagnosis.

Results

Search results and reduction
     The initial search generated 1327 records, 925 from PubMed and 402 from Web of Science. Only 130 records were excluded when filtering by publication year (2017–2021). Therefore, further searches were conducted using fine-tuned search strings and options for both databases to narrow down the results. The new search was carried out using the original keywords but restricting the word ‘diabetes’ to be in the title, which generated 517 records from both databases. Fifty-one duplicates were discarded. Therefore, 336 records were selected for further screening.

     Further selection was conducted by applying the exclusion criteria to the 336 records above. Thirty-seven records were excluded since the study reported used non-omittable genetic attributes as model inputs, something out of this review’s scope. Thirty-eight records were excluded as they were review papers. All in all, 261 articles that fulfill the criteria were included in the quality assessment.

Abstract

Background
There are limited population-wide trend data on kidney failure and kidney replacement therapy (KRT) in people with diabetes. We conducted a retrospective cohort study to report incidence trends of kidney failure and KRT and related mortality in people with diabetes in Hong Kong between 2002 and 2015.

Methods
We used territory-wide electronic medical records including laboratory, diagnostic and procedural data to identify people with kidney failure and KRT. We used Joinpoint regression models to estimate the average annual percent change (AAPC) of kidney failure and KRT incidence for entire study period, and annual percent change (APC) for each linear trend segment, along with 1-year and 5-year mortality rates.

Findings
During 4.9 million person-years of follow-up of 712,222 people with diabetes, 31,425 developed kidney failure, among whom 23.0% (n=7,233) received KRT. The incidence of kidney failure declined by 46.8% from 2002 to 2007 (APC: -11.6, 95% CI: -16.3, -6.7), then flattened from 2007 to 2015 (APC: -0.9, 95% CI: -3.1, 1.3). The incidence of KRT remained constant (AAPC: -1.6, 95% CI: -4.4, 1.2). The 1-year mortality rates declined statistically significantly after both kidney failure and KRT. The 5-year mortality rates declined after kidney failure but the decline was not statistically significant after KRT.

Interpretation
The findings of our study highlight the importance of developing new strategies to prevent a looming epidemic of kidney failure in people with diabetes in Hong Kong.

1. Introduction

Kidney failure is a major contributor to chronic disability, impaired quality of life and premature death in people with diabetes[1], [2], [3] . In recent estimates, between 40% and 50% of people newly initiating kidney replacement therapy (KRT) had underlying diabetes4,5 . In Hong Kong, the proportion of people entering KRT attributable to diabetes has increased from 26.2% in 1996 to 49.6% in 20136. The rapidly rising worldwide burden of diabetes in the past 20 years has contributed to increased number of people at high risk of kidney failure and related morbidity and mortality7,8. In the United States, population surveillance revealed a decline in the incidence of kidney failure with KRT in people with diabetes across a 20-year period between 1990 and 2010 which levelled off after 20109,10. In Germany, the incidence of KRT in people with diabetes was stable between 2002 and 201611, whilst that in Australia, increased between 2002 and 201312. Population-wide studies reporting trends in incidence of kidney failure among people with diabetes in Asia are sparse. Understanding the epidemiology of kidney failure in people with diabetes allows us to evaluate the quality of diabetes care, to develop approaches to primary and secondary prevention of kidney disease, and to plan health services to address unmet needs.

 Highlights

Abstract

Keywords

1. Introduction

2. Materials and methods

3. Results

4. Discussion

5. Conclusion

Financial support and sponsorship

Declaration of Competing interest

Appendix A. Lifestyle related behaviour questionnaire

Appendix B. Scoring instructions for the questionnaire to assess impact of COVID 19 on individual’s lifestyle-related behaviour

References

Abstract

Background and aims
The lasting impact of COVID 19 pandemic and associated restrictions are bound to be significant on lifestyle-related behaviour including diet, physical activity and sleep which is one of the important components in the management of diabetes mellitus and metabolic syndrome. This study was conducted to develop and validate a questionnaire to assess changes in individual’s lifestyle-related behaviour during COVID 19 pandemic.

Materials and methods
The questionnaire was developed through a standardised methodology including literature review, focus group discussion, expert evaluation, pre-testing and validation. The face validity and content validity of the questionnaire were analysed. A cross-sectional survey was carried out on 103 participants to validate the questionnaire that used a 5-point Likert scale for the response option. Exploratory factor analysis was performed to establish construct validity. Cronbach’s alpha was calculated to test the internal consistency of the whole questionnaire.

Results
A questionnaire with 20 items to assess the lifestyle-related behaviour of people was developed. The questionnaire shows a satisfactory validity and a good internal consistency with the Cronbach’s alpha value of 0.72.

Conclusion
The developed tool is valid and reliable to assess the changes in lifestyle-related behaviour of individuals during COVID 19 pandemic.

1. Introduction
COVID pandemic has led to life-changing challenges among people across the globe. Terms like “social distancing” and “selfisolation” have become a reality. As people are trying to get accustomed with this, the confinement has drastically impacted citizens’ lives bringing about a sudden and radical change in their daily routine and lifestyle [1].
Social isolation for extended periods can lead to boredom and stress which may further subject individuals to develop a greater tendency of overeating especially “comfort foods” which are usually high in calories [2,3]. Moreover, prolonged confinement at home may induce an increased screen time and reduced outdoor time thus, augmenting sedentarism [4]. Apart from this, being homebound and facing disruptions in daily routines may affect the sleeping patterns as well as the quality of sleep [5].

1. Introduction

2. Methods

3. Results

4. Discussion

Acknowledgements

References

Abstract

Aims: To investigate levels and changes in diabetes distress over the course of the PRIORITY (Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In people with TYpe 2 diabetes and normoalbuminuria) randomised controlled trial of screening for diabetic kidney disease (DKD) risk among people with type 2 diabetes (T2D) at a specialist diabetes clinic in Denmark.

Methods: Of 436 trial participants with T2D, 216 were invited to complete the 17-item diabetes distress scale at the time of screening (T1, n = 180), immediately after receiving the screening results at 6–8 weeks (T2, n = 169), and at 12 months follow up (T3, n = 107). Linear mixed models were used to explore changes in diabetes distress.

Results: No significant changes in diabetes distress were observed between the time of screening, receiving results, and at 12 months. Changes in diabetes distress were not influenced by diabetes empowerment, sense of coherence, or perceived support for diabetes self-management.

Conclusions: In contrast to previous studies demonstrating that screening programmes can have negative psychological consequences, our findings indicate that participating in this screening programme for DKD does not influence emotional burden or physician-related distress among people with T2D

Introduction

People with diabetes have increased mortality and morbidity. Diabetes-related complications include macrovascular complications such as cardiovascular disease and microvascular complications such as nephropathy, neuropathy, and retinopathy, which are associated with lower quality of life and high healthcare costs.1 Identification and appropriate treatment of people with type 2 diabetes mellitus (T2D) at high risk of developing diabetes-related complications are core disease management activities.

Identification of people at risk of diabetic kidney disease (DKD) typically occurs through screening for albuminuria and kidney function at routine visits when people are still without symptoms of DKD.3 However, the urinary proteomics classifier CKD273 has been suggested as an improved marker for early identification of people at high risk of DKD compared to traditional methods.4 The clinical multicentre intervention trial PRIORITY (Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In people with TYpe 2 diabetes and normoalbuminuria) used this method to screen for future risk of DKD in people with T2D and normal kidney function.

Ramadan is the fasting month in Islam. Muslims around the world observe Ramadan every year, including people with diabetes. Data on the association of fasting in people with diabetes are sparse. The purpose of this study is to assess the association of fasting on cardiovascular risk factors and events in people with diabetes. A comprehensive search was conducted in the following database: Embase, Medline, Cochrane library and CINAHL. The following key terms were used: Ramadan, Ramazan, Ramadhan, Muslim, Islam and fasting. Studies were eligible if they included people with Type 2 diabetes who fasted during Ramadan and reporting results on cardiovascular risk factors or events. Overall 22 studies met inclusion criteria for the review; five studies reported cardiovascular outcomes and 17 reported changes in risk factors. There is insufficient evidence to link Ramadan fasting with increased or reduced incidence of cardiovascular events in people with diabetes, though there were some indication stroke risk may be increased. Findings were inconsistent in term of risk factors as some favoured Ramadan and others did not.

Introduction

Ramadan occurs in the ninth month of the Islamic calendar. During this month, Muslims around the world abstain from food and drinks, including medications, from sunrise to sunset. Because Ramadan follows the lunar calendar, the length of fasting varies depending on the season [1]. Fasting is obligatory for all adults who are capable, but individuals who may be placed at risk by fasting, such as certain high risk patients with diabetes mellitus, are exempt from fasting [2]. However, as Ramadan fasting is one of the five pillars of Islam, many Muslims insist on fasting in some cases against medical advice [3]. There are about 148 million Muslims globally with diabetes [4]. Most of the available guidelines for people with diabetes planning to fast on Ramadan are based on expert opinions. However, there is a lack of scientific evidence on the safety of fasting in people with diabetes [4,5]. During Ramadan, there is a sudden change in daily routines, including eating and sleeping patterns as well as physical activity levels. Muslims who are fasting consume two meals a day, the first before sunrise (suhur) and the second, which is the main meal, after sunset (iftar). These changes in meal time can be associated with changes in sleeping patterns, such as reduced sleep duration, delayed sleep time and increased sleeping and reduced physical activity levels during the day [6–8]. Additionally, these changes can result in changes in insulin resistance as well as multiple neurohormonal changes, including the activation of the hypothalamic-pituitary axis and increased catecholamines, which can lead to endothelial dysfunction and increased cardiovascular events [4,9,10]. Furthermore activation of the HPA axis leads to the secretion of cortisol which results in insulin resistance, increased hepatic glucose output and increased gluconeogenesis [11].

1- Introduction

2- Related study

3- Methodology

4- Experimental result

5- Discussion

6- Conclusion and future work

References

Abstract

Diabetes causes a large number of deaths each year and a large number of people living with the disease do not realize their health condition early enough. In this study, we propose a data mining based model for early diagnosis and prediction of diabetes using the Pima Indians Diabetes dataset. Although K-means is simple and can be used for a wide variety of data types, it is quite sensitive to initial positions of cluster centers which determine the final cluster result, which either provides a sufficient and efficiently clustered dataset for the logistic regression model, or gives a lesser amount of data as a result of incorrect clustering of the original dataset, thereby limiting the performance of the logistic regression model. Our main goal was to determine ways of improving the k-means clustering and logistic regression accuracy result. Our model comprises of PCA (principal component analysis), k-means and logistic regression algorithm. Experimental results show that PCA enhanced the k-means clustering algorithm and logistic regression classifier accuracy versus the result of other published studies, with a k-means output of 25 more correctly classified data, and a logistic regression accuracy of 1.98% higher. As such, the model is shown to be useful for automatically predicting diabetes using patient electronic health records data. A further experiment with a new dataset showed the applicability of our model for the predication of diabetes.

Introduction

Diabetes stands among the top 10 causes of death for 2016. Diabetes killed 1.6 million people in 2016, up from less than 1 million in 2000. With this figure diabetes replaced HIV/AIDS as the seventh top cause of death [1]. The number of people with diabetes has risen from 108 million in 1980 to 422 million in 2014, with the global prevalence of diabetes among adults over 18 years of age rising from 4.7% in 1980 to 8.5% in 2014 [2]. By 2040, 642 million adults (1 in 10 adults) are expected to have diabetes. Also, 46.5% of those with diabetes have not been diagnosed [3]. In order to reduce the number of deaths attributable to diabetes, it is essential that methods and techniques that will aid in early diagnosis of diabetes be devised, because a large number of deaths in diabetic patients are due to late diagnosis. In order to achieve cutting-edge techniques for the early diagnosis of diabetes, we need to utilize advanced information technology, and data mining is a suitable field for this. Data mining offers the ability to extract and discover previously unknown, hidden, but interesting patterns from a large database repository. These patterns can aid medical diagnosis and decision-making. Various techniques and algorithms have been designed for application in extracting knowledge and information in the diagnosis and treatment of disease from medical databases. PCA is a simple, nonparametric method for extracting relevant information from confusing data sets [4]. When a large dataset is to be clustered into a user specified number of clusters (k), which are represented by their centroids, k-means will cluster the data by minimizing the squared error function [5], and often misclassifies some data due to outliers; also the time complexity will be greater. To overcome these problems, principal components analysis (PCA) can be used to reduce the dataset to a lower dimension, while ensuring that the least information is lost, and providing a better centroid point for clustering. K-means clustering partitions a dataset into different groups of similar objects. Clusters that are highly dissimilar from the others are regarded as outliers and discarded. Logistic regression is an efficient regression predictive analysis algorithm. Its application is efficient when the dependent variable of a dataset is dichotomous (binary).

2- Materials and Methods

3- Results

4- Discussion

5- Conclusions

References

Introduction

More than 330 million individuals worldwide are estimated to have prediabetes and up to 70% will develop diabetes within a decade of initial diagnosis [1]. According to the US Centers for Disease Control (CDC), 84 million Americans (more than 1 out of 3) have prediabetes. Of these, 90% are unaware of their prediabetic status and up to 70% of them will eventually progress to diabetes [2]. Te average risk of developing diabetes is approximately 5–10% per year in individuals with IFG or IGT, which is 10-15 times higher compared to individuals with normoglycemia. Prediabetes is a condition in which a person’s blood glucose level is higher than normal physiological levels but lower than individuals with diabetes. Prediabetes increases the risk of diabetes and cerebrocardiovascular disorders, which are the major causes of death worldwide [3]. Despite the worldwide prevalence of prediabetes and diabetes, many patients remain undiagnosed. Tis may be due to the current reference standards for fasting glucose (FG), or the inconvenience for individuals taking the 2horal glucose tolerance test (OGTT). In 2010, the American Diabetes Association approved the more expensive assay of glycated hemoglobin A1c (HbA1c) as one of the three tests for diabetes and prediabetes screening. However, recent studies have shown substantial discordance between results from FG, OGTT, and HbA1c, with OGTT probably being the more accurate diagnostic test for pre/diabetes associated with age, BMI, physical activity, income, education, race/ethnicity, etc. [4–6]. Conversely, recent studies have shown that biomarkers for food intake and nutrient status were associated with glucose tolerance status and development of diabetes in the elderly [7]. Te utility of these new dietary biomarkers for determining pre/diabetes status has not been comprehensively evaluated in diferent ethnic populations. Oxidative stress, infammation, and peripheral insulin resistance lead to pancreatic beta cell overproduction; however eventual depletion of insulin secretion is thought to underlie the development of prediabetes and diabetes [8–13]. Overweight, being 45 years or older, and lower physically active are associated with type 2 diabetes and are also the main risk factors for prediabetes [2, 14]. Modifcation in lifestyle, increase in physical activity, having a healthy diet, and maintaining a healthy body weight can lower the risk of developing prediabetes and diabetes [15–17]. However, many people have difculties to adhere to these recommendations. Herbal supplements have long been used for disease prevention and treatment. Recent cellular and animal model studies have suggested that herbal supplements could improve glucose metabolism and protect from oxidative stress-induced diabetes [18–24].

2- Case Presentation

3- Discussion

4- Conclusion

References

Introduction

Te proportion of patients with diabetes currently on any insulin (includes insulin only and insulin plus oral diabetes medications) was 29.1% in 2005–2012 and has been relatively stable since 1988 with 30.3% [1]. Diabetes secondary to pancreatic diseases is commonly referred to as pancreatogenic diabetes or type 3c diabetes mellitus; it is a clinically relevant condition with a prevalence of 5%-10% among all diabetic subjects in Western populations. Tose with type 3c diabetes require insulin therapy more urgently than those with type 2, and most of them are discharged on insulin from the hospital [2].

Case Presentation

A 30-year-old male presented in December 2017 to the emergency department complaining of abdominal pain, which started around 3 am, and the pain was characterized as sharp, epigastric, nonradiating, and 9/10 in intensity. He admitted drinking alcohol, used to drink 5-6 glasses of wine/cocktails a day, but claimed he stopped a week ago, and he denied smoking or another illicit drug use. Laboratory exams on admission were signifcant for lactic acid 3.7 (0.7-2.0 mmol/L), lipase >3000IU/L (11-82 IU/L), WBC 33,300 mmcu (4,000-11,000mmcu), and glucose 147mg/dl (70-99mg/dl). Patient underwent CT abdomen that showed acute pancreatitis. Te next day patient glucose increased to 502mg/dl, bicarbonate was 18mg/dl (21-31mg/dl), and anion gap was 14. Patient then was transferred to Intensive Care Unit (ICU) for insulin infusion and management of diabetic ketoacidosis (DKA). His hemoglobin A1c was 5.5%. Repeat CT abdomen with contrast (Figure 2) was concerning for necrotizing pancreatitis. Afer few days he was switched to subcutaneous insulin glargine/aspart and transferred back to general medical foor. Because patient developed fever and had persistent leukocytosis, and he was started on meropenem. His conditioned improved and antibiotics were changed to oral doxycycline and levofoxacin; he was discharged on insulin glargine 20U at bedtime and correctional insulin.