Association patterns of volatile metabolites in urinary excretions among Type-2 Non-Insulin dependent diabetes patients

Background: Patterns of volatile metabolites in urine are important to detect abnormalities associated with diabetes. Present study was conducted to find out the excretion patterns of endogenously produced alcohols in urine for type 2 (Non-Insulin Dependent) diabetes mellitus. A cross sectional analytical study was conducted with duration extended from Jan to Mar 2015.Methods: The current study included 40 patients with chronic type 2 diabetes mellitus. In total, 10 sex and age matched subjects with no history of any disease were considered as controls. Blood sugar was estimated by autoanalyzer using standard kit of Merck following manufacturer`s instructions. Urine sugar was quantitatively detected by biuret reagent using titration technique. Urinary alcohol was identified and estimated by gas chromatography.  Urinary ketone bodies were estimated by urinary strip.Results: It was observed that level of fasting blood sugar was significantly increased (P<0.001) in patients as compared to their controls. The blood sugar and urinary alcohol in patients were 3.0% and 6.0% respectively. Urinary ketone bodies were found to be 2+. On the other hand urine sugar, alcohol and ketone bodies were not detected in the negative control subjects.Conclusions: It is concluded that urinary alcohol is endogenously produced in patients with type 2 diabetes due to uncontrolled hyperglycemia. However further work is needed to find out the ratio of urinary and blood alcohol which may confirm the present findings

Clinical evaluation of a novel adaptive bolus calculator and safety system in Type 1 diabetes

Bolus calculators are considered state-of-the-art for insulin dosing decision support for people with Type 1 diabetes (T1D). However, they all lack the ability to automatically adapt in real-time to respond to an individual’s needs or changes in insulin sensitivity. A novel insulin recommender system based on artificial intelligence has been developed to provide personalised bolus advice, namely the Patient Empowerment through Predictive Personalised Decision Support (PEPPER) system. Besides adaptive bolus advice, the decision support system is coupled with a safety system which includes alarms, predictive glucose alerts, predictive low glucose suspend for insulin pump users, personalised carbohydrate recommendations and dynamic bolus insulin constraint. This thesis outlines the clinical evaluation of the PEPPER system in adults with T1D on multiple daily injections (MDI) and insulin pump therapy. The hypothesis was that the PEPPER system is safe, feasible and effective for use in people with TID using MDI or pump therapy. Safety and feasibility of the safety system was initially evaluated in the first phase, with the second phase evaluating feasibility of the complete system (safety system and adaptive bolus advisor). Finally, the whole system was clinically evaluated in a randomised crossover trial with 58 participants. No significant differences were observed for percentage times in range between the PEPPER and Control groups. For quality of life, participants reported higher perceived hypoglycaemia with the PEPPER system despite no objective difference in time spent in hypoglycaemia. Overall, the studies demonstrated that the PEPPER system is safe and feasible for use when compared to conventional therapy (continuous glucose monitoring and standard bolus calculator). Further studies are required to confirm overall effectiveness.Open Acces

Associations of dietary macronutrient and fibre intake with glycaemia in individuals with Type 1 diabetes

Aims To study the association between dietary intake and glycaemia in Type 1 diabetes. Methods Data on energy and nutrient intakes, and the mean and coefficient of variation of self-monitored blood glucose measurements were obtained from records completed by 1000 adults with Type 1 diabetes. Associations between these measures of glycaemia and dietary intake were investigated using generalized linear regression, with and without macronutrient substitution. Results In the first set of analyses, fibre intake was associated with lower mean self-monitored blood glucose values (beta = -0.428, 95% CI -0.624 to -0.231; PPeer reviewe

Mendelian randomization study of B-type natriuretic peptide and type 2 diabetes: evidence of causal association from population studies

&lt;p&gt;Background: Genetic and epidemiological evidence suggests an inverse association between B-type natriuretic peptide (BNP) levels in blood and risk of type 2 diabetes (T2D), but the prospective association of BNP with T2D is uncertain, and it is unclear whether the association is confounded.&lt;/p&gt; &lt;p&gt;Methods and Findings: We analysed the association between levels of the N-terminal fragment of pro-BNP (NT-pro-BNP) in blood and risk of incident T2D in a prospective case-cohort study and genotyped the variant rs198389 within the BNP locus in three T2D case-control studies. We combined our results with existing data in a meta-analysis of 11 case-control studies. Using a Mendelian randomization approach, we compared the observed association between rs198389 and T2D to that expected from the NT-pro-BNP level to T2D association and the NT-pro-BNP difference per C allele of rs198389. In participants of our case-cohort study who were free of T2D and cardiovascular disease at baseline, we observed a 21% (95% CI 3%-36%) decreased risk of incident T2D per one standard deviation (SD) higher log-transformed NT-pro-BNP levels in analysis adjusted for age, sex, body mass index, systolic blood pressure, smoking, family history of T2D, history of hypertension, and levels of triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The association between rs198389 and T2D observed in case-control studies (odds ratio = 0.94 per C allele, 95% CI 0.91-0.97) was similar to that expected (0.96, 0.93-0.98) based on the pooled estimate for the log-NT-pro-BNP level to T2D association derived from a meta-analysis of our study and published data (hazard ratio = 0.82 per SD, 0.74-0.90) and the difference in NT-pro-BNP levels (0.22 SD, 0.15-0.29) per C allele of rs198389. No significant associations were observed between the rs198389 genotype and potential confounders.&lt;/p&gt; &lt;p&gt;Conclusions: Our results provide evidence for a potential causal role of the BNP system in the aetiology of T2D. Further studies are needed to investigate the mechanisms underlying this association and possibilities for preventive interventions.&lt;/p&gt

Multimorbidity of cardiometabolic diseases and effectiveness of integrated healthcare system response in sub-Saharan Africa

This thesis aims to strengthen the responsiveness of healthcare systems to the management of cardiometabolic multimorbidity in sub-Saharan Africa (SSA). More specifically, four main issues on cardiometabolic multimorbidity in SSA were investigated: the burden of cardiometabolic multimorbidity, chronic care models, the readiness of healthcare facilities to provide integrated care, and the effect of multimorbidity on self-care interventions. A latent class analysis and hierarchical agglomerative cluster analysis in part one show that cardiometabolic diseases occur in distinct clusters of concordant and discordant multimorbidity. These clusters are significant predictors of outpatient visits, hospitalisation, functional disability and quality of life. Multimorbidity is disproportionately highest among persons of high socioeconomic status, women, the middle and old-aged, and those with sedentary lifestyles and obesity. A systematic review and meta-analysis in part two shows that integrated care versus standard care improved systolic blood pressure control in people with multimorbidity. In part three, a national facility assessment survey in Kenya shows that only one in every four healthcare facilities (at all levels) was ready to provide integrated care for cardiovascular diseases and type 2 diabetes. The clinical integration barriers included vertical and unresponsive healthcare services. In part four, a quasi-experimental study of patients with hypertension undergoing a home-based self-care program in Kenya shows that multimorbidity attenuated the effectiveness of patient support groups for hypertension. Overall, the findings of this thesis provide crucial evidence for multimorbidity risk stratification and underscore the importance of tailoring patient-centered care interventions to match the needs of people with cardiometabolic multimorbidity in SSA

Social epidemiology

Social epidemiology is the branch of epidemiology concerned with understanding how social and economic characteristics influence states of health in populations. There has been a resurgence recently in interest among epidemiologists about the roles that social and economic factors play in determining health, leading to valuable synergies with the social sciences. The determinants of health commonly studied in social epidemiology include absolute poverty, income inequality, as well as race and discrimination. Recently, social epidemiologists have been at the forefront of conceptual developments within the discipline that view the determinants of health at different levels of social organization. © 2008 Copyright © 2008 Elsevier Inc. All rights reserved

Prognostic Predictive Model to Estimate the Risk of Multiple Chronic Diseases: Constructing Copulas Using Electronic Medical Record Data

Introduction: Multimorbidity, the presence of two or more chronic diseases in an individual, is a pressing medical condition. Novel prevention methods are required to reduce the incidence of multimorbidity. Prognostic predictive models estimate a patient’s risk of developing chronic disease. This thesis developed a single predictive model for three diseases associated with multimorbidity: diabetes, hypertension, and osteoarthritis. Methods: Univariate logistic regression models were constructed, followed by an analysis of the dependence that existed using copulas. All analyses were based on data from the Canadian Primary Care Sentinel Surveillance Network. Results: All univariate models were highly predictive, as demonstrated by their discrimination and calibration. Copula models revealed the dependence between each disease pair. Discussion: By estimating the risk of multiple chronic diseases, prognostic predictive models may enable the prevention of chronic disease through identification of high-risk individuals or delivery of individualized risk assessments to inform patient and health care provider decision-making

An adaptive real-time intelligent system to enhance self-care of chronic disease (ARISES)

Diabetes mellitus is an increasingly prevalent chronic metabolic condition characterised by impaired glucose homeostasis and raised blood glucose levels (hyperglycaemia). Broadly categorised as either type 1 (T1DM) or type 2 diabetes (T2DM), people with diabetes are largely responsible for self-managing their blood glucose levels. Despite the development of diabetes technologies such as real time continuous glucose monitoring (RT-CGM), many individuals are frequently exposed to iatrogenic low blood glucose levels (hypoglycaemia). Severe hypoglycaemia is associated with an increased risk of recurrent hypoglycaemia, impaired symptomatic awareness of hypoglycaemia, and potentially death if left untreated. This thesis affirmed the existing clinical impact of severe hypoglycaemia and its recurrent risk in a six-month analysis of severe hypoglycaemia attended by the London Ambulance Service NHS Trust (LAS). Fewer incidents of severe hypoglycaemia observed in a date matched repeat analysis during the 2020 COVID-19 lockdown suggested improved self-management possibly motivated by a proximal fear of hospitalisation and improved structure at home. Finally, a 12-week randomised control trial demonstrating a significant difference in time spent in hypoglycaemia <3mmol/L, is the first study to prove the immediate provision of RT-CGM significantly reduces the risk of recurrent hypoglycaemia. Moreover, it highlighted the impact of socioeconomic disparity as a barrier to effective hypoglycaemia risk modification. This guided the design of an adaptive real time intelligent system to enhance self-care of chronic disease (ARISES) aimed to deliver therapeutic and lifestyle decision support for people with T1DM. The ARISES graphic user interface (GUI) design was a collaborative process conceived in a series of focus group meetings including people with T1DM. Finally, a 12-week observational study using RT-CGM, a physiological sensor wristband, and a mobile diary app, allowed for a sub-analysis identifying measurable physiological parameters associated with current and impending hypoglycaemia in people with T1DM.Open Acces