Established and emerging biomarkers for the prediction of type 1 diabetes: a systematic review

Type 1 diabetes (T1D) is an autoimmune disease with a prolonged and variable latent period that culminates in the destruction of pancreatic β-cells and the development of hyperglycemia. There is a need for diagnostic biomarkers to detect more accurately detect individuals with prediabetes to expedite targeting for prevention and intervention strategies. To assess the current ability to predict the insidious development of T1D, we conducted a comprehensive systematic review for established and prospective predictive markers of T1D using the Medline, OVID, and EMBASE databases. Resulting citations were screened for relevance to subject. Our research generated five major categories of markers that are either currently used or forthcoming: genetic, autoantibodies, risk score quantification, cellular immunity, and β-cell function. The current standard used to assess T1D onset or predisposition focuses on autoimmune pathology and disease-associated autoantibodies. Research studies in general go beyond autoantibody screening and assess genetic predisposition, and quantitate risk of developing disease based on additional factors. However, there are few currently used techniques that assess the root of T1D: β-cell destruction. Thus, novel techniques are discussed with the potential to gauge degrees of β-cell stress and failure via protein, RNA, and DNA analyses

General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine

This report is the collective product of word-leading experts working in the branches of integrative medicine by predictive, preventive and personalised medicine (PPPM) under the coordination of the European Association for Predictive, Preventive and Personalised Medicine. The general report has been prepared as the consortium document proposed at the EPMA World Congress 2011 which took place in Bonn, Germany. This forum analyzed the overall deficits and trends relevant for the top-science and daily practice in PPPM focused on the patient. Follow-up consultations resulted in a package of recommendations for consideration by research units, educators, healthcare industry, policy-makers, and funding bodies to cover the current knowledge deficit in the field and to introduce integrative approaches for advanced diagnostics, targeted prevention, treatments tailored to the person and cost-effective healthcare

Towards Metabolic Biomarkers for the Diagnosis and Prognosis of CKD

Chronic kidney disease, the gradual loss of renal function, is an increasingly recognized burden for patients and health care systems; globally, it has a high and rapidly growing prevalence, a significant mortality, and causes disproportionately high costs, particularly for hemodialysis and kidney transplantations. Yet, the available diagnostic tools are either impractical in clinical routine or have serious shortcomings preventing a well-informed disease management, although optimized treatment strategies with impressive benefits for patients have been established. Advances in bioanalytics have facilitated the identification of many genomic, proteomic, and metabolic biomarker candidates, some of which have been validated in independent cohorts. Summarizing the markers discovered so far, this chapter focuses on compounds or pathways, for which quantitative data, substantiating evidence from translational research, and a mechanistic understanding is available. Also, multiparametric marker panels have been suggested with promising diagnostic and prognostic performance in initial analyses, although the data basis from prospective trials is very limited. Large-scale studies, however, are underway and will validate certain sets of parameters and discard others. Finally, the path from clinical research to routine application is discussed, focusing on potential obstacles such as the use of mass spectrometry, and the feasibility of obtaining regulatory approval for metabolomics assays

Gestational Diabetes Mellitus

The incidence of gestational diabetes mellitus (GDM) is increasing, and this pathological condition is strongly associated with some serious adverse pregnancy outcomes and important miscellaneous long-term complications. Therefore, it is important that GDM is timely recognized and adequately managed. Although much knowledge has been acquired regarding the prevention, diagnosis, implications, and management of GDM, the exact mechanisms of its genesis are still under investigation. This book provides a comprehensive overview of recent advances in gestational diabetes mellitus. It includes three major sections directing the reader’s attention to the etiology, management, and consequences of the disorder

Branched Chain Amino Acids and Risk of Type 2 Diabetes Mellitus: A Literature Review

INTRODUCTION Type 2 diabetes mellitus (T2DM) is recognized as a major public health problem in the modern world, with its prevalence increasing each year. Consistently poor lifestyle habits — namely, nutritional excess coupled with sedentary behavior — are the leading causes of obesity, which in turn leverages the gradual desensitization of cells to insulin, followed by the onset of insulin resistance (IR) and the subsequent development of T2DM. Countless studies and ongoing research have confirmed that nutrition plays a definitive role in contributing to the development and onset of T2DM. However, in recent years, there has been increasing controversy surrounding the role that branched-chain amino acids (BCAAs) may play in influencing IR and the development of T2DM. AIM To review existing literature regarding both the purportedly harmful and beneficial roles and impacts of BCAAs on metabolic health, in order to better understand the contradictory nature of BCAAs and their effects on IR and T2DM. METHODS Relevant research, review articles and epidemiological studies spanning the time frame from 2004 to 2020 were collected, analyzed and summarized with the goal of underscoring and delineating the conflicting roles of BCAAs. RESULTS Evidence of beneficial effects of BCAAs includes enhanced muscle protein synthesis, more efficient glucose homeostasis, increased satiety, better body composition and improved body weight regulation. Evidence of harmful effects of BCAAs includes elevated fasting concentrations of circulating BCAAs correlating with an increased risk of IR and T2DM in human and rodent models. DISCUSSION In spite of the various studies that have been undertaken to shed further light on BCAAs, it still remains unclear whether they are simply markers of metabolic disturbances that ultimately lead to the development of T2DM, or if they are, at least in part, the actual cause of metabolic disturbances leading to T2DM. The general consensus amongst the scientific community is that more research is needed on this topic

Developing clinical prediction models for diabetes classification and progression

Patients with type 1 and type 2 diabetes have very different treatment and care requirements. Overlapping phenotypes and lack of clear classification guidelines make it difficult for clinicians to differentiate between type 1 and type 2 diabetes at diagnosis. The rate of glycaemic deterioration is highly variable in patients with type 2 diabetes but there is no single test to accurately identify which patients will progress rapidly to requiring insulin therapy. Incorrect treatment and care decisions in diabetes can have life-threatening consequences. The aim of this thesis is to develop clinical prediction models that can be incorporated into routine clinical practice to assist clinicians with the classification and care of patient diagnosed with diabetes. We addressed the problem first by integrating features previously associated with classification of type 1 and type 2 diabetes to develop a diagnostic model using logistic regression to identify, at diagnosis, patients with type 1 diabetes. The high performance achieved by this model was comparable to that of machine learning algorithms. In patients diagnosed with type 2 diabetes, we found that patients who were GADA positive and had genetic susceptibility to type 1 diabetes progressed more rapidly to requiring insulin therapy. We built upon this finding to develop a prognostic model integrating predictive features of glycaemic deterioration to predict early insulin requirement in adults diagnosed with type 2 diabetes. The three main findings of this thesis have the potential to change the way that patients with diabetes are managed in clinical practice. Use of the diagnostic model developed to identify patients with type 1 diabetes has the potential to reduce misclassification. Classifying patients according to the model has the benefit of being more akin to the treatment needs of the patient rather than the aetiopathological definitions used in current clinical guidelines. The design of the model lends itself to implementing a triage-based approach to diabetes subtype diagnosis. Our second main finding alters the clinical implications of a positive GADA test in patients diagnosed with type 2 diabetes. For identifying patients likely to progress rapidly to insulin, genetic testing is only beneficial in patients who test positive for GADA. In clinical practice, a two-step screening process could be implemented - only patients who test positive for GADA in the first step would go on for genetic testing. The prognostic model can be used in clinical practice to predict a patient’s rate of glycaemic deterioration leading to a requirement for insulin. The availability of this data will enable clinical practices to more effectively manage their patient lists, prioritising more intensive follow up for those patients who are at high risk of rapid progression. Patients are likely to benefit from tailored treatment. Another key clinical use of the prognostic model is the identification of patients who would benefit most from GADA testing saving both inconvenience to the patient and a cost-benefit to the health service

Clinical problems caused by obesity

Over the past few decades the incidence of obesity has doubled worldwide and current estimates classify more than 1.5 billion adults as overweight and at least 500 million of them as clinically obese, with body mass index (BMI) over 25 kg/m2 and 30 kg/m2, respectively. Obesity prevalence rates are steadily rising in the majority of the modern Western societies, as well as in the developing world. Moreover, alarming trends of weight gain are reported for children and adolescents, undermining the present and future health status of the pediatric population. To highlight the related threat to public health, the World Health Organization has declared obesity a global epidemic, also stressing that it remains an under-recognized problem of the public health agenda