Precision medicine in diabetes prevention, classification and management

Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.

Novel Therapies in Diabetes: A Comprehensive Narrative Review of GLP-1 Receptor Agonists, SGLT2 Inhibitors, and Beyond

Diabetes mellitus, a widespread metabolic illness with increasing global occurrence, continues to have a significant impact on public health. Diabetes is a condition marked by long-term high blood sugar levels. It is caused by a combination of genetic, environmental, and lifestyle factors, which lead to problems with insulin production and insulin resistance. This dysfunctional state disturbs the delicate balance of glucose regulation, promoting the emergence of problems in both large and small blood vessels that have a substantial impact on illness and death rates. Traditional therapy methods have traditionally given more importance to managing blood sugar levels by using insulin sensitizers, secretagogues, and other medications that lower glucose levels. Advancements in our understanding of the underlying mechanisms of diabetes have led to a significant change in approach, focusing on comprehensive therapies that target not only high blood sugar levels but also the accompanying dangers to the heart and kidneys. This study examines the evolving field of diabetes therapies, explicitly highlighting the significance of GLP-1 receptor agonists and SGLT2 inhibitors. These two types of drugs have become essential components in modern diabetes management. GLP-1 receptor agonists replicate the effects of natural glucagon-like peptide-1, leading to insulin production that is reliant on glucose levels, reducing the release of glucagon, and providing cardiovascular advantages that go beyond controlling blood sugar levels. SGLT2 inhibitors, however, act on the process of renal glucose reabsorption, leading to increased excretion of glucose in the urine and showing significant benefits for cardiovascular and renal protection. This extensive investigation seeks to contribute to the ongoing discourse on diabetes therapies by synthesizing existing research. This review aims to provide clinicians, researchers, and policymakers with a comprehensive understanding of the disease background and the specific pharmacological details of GLP-1 receptor agonists, SGLT2 inhibitors, and other related treatments. The goal is to assist them in developing more effective and personalized strategies to tackle the complex challenges presented by diabetes.

Insulin Resistance and Coronary Artery Disease: Untangling the Web of Endocrine-Cardiac Connections

The relationship between insulin resistance and coronary artery disease (CAD) is a crucial study area in understanding the complex connection between metabolic dysregulation and cardiovascular morbidity. This scholarly investigation examines the intricate relationship between insulin resistance, a key characteristic of metabolic syndrome, and CAD development. The goal is to understand the detailed molecular and physiological connections that underlie the dangerous connection between the endocrine and cardiac systems. The recognition of insulin resistance as a key player in cardiovascular disease highlights the need to study the complex relationships between insulin signaling pathways and the development of atherosclerosis. This research analyzes the molecular processes by which insulin resistance leads to disruptions in lipid metabolism, inflammatory reactions, and malfunction of the blood vessel's inner lining. These processes create an environment that promotes the development and advancement of CAD. As we begin this scientific exploration, it becomes clear that insulin resistance acts as a metabolic indicator and a potent mediator of endothelial dysfunction, oxidative stress, and systemic inflammation. The complex interaction between insulin-sensitive tissues and the vascular endothelium plays a crucial role in defining the pathophysiological landscape of CAD. Furthermore, this discussion highlights the mutual interaction between the endocrine and cardiac systems, where CAD produced by myocardial ischemia worsens insulin resistance through complex molecular pathways. Discovering new therapeutic targets that disrupt the harmful cycle between insulin resistance and the development of CAD shows potential for creating specific therapies to reduce cardiovascular risk in people with insulin resistance. This study aims to clarify the complexities of the connection between the endocrine system and the heart, establishing the basis for a thorough comprehension of how insulin resistance contributes to the development and advancement of CAD.

Endothelial progenitor cells as biomarkers of diabetes-related cardiovascular complications

Diabetes mellitus (DM) constitutes a chronic metabolic disease characterized by elevated levels of blood glucose which can also lead to the so-called diabetic vascular complications (DVCs), responsible for most of the morbidity, hospitalizations and death registered in these patients. Currently, different approaches to prevent or reduce DM and its DVCs have focused on reducing blood sugar levels, cholesterol management or even changes in lifestyle habits. However, even the strictest glycaemic control strategies are not always sufficient to prevent the development of DVCs, which reflects the need to identify reliable biomarkers capable of predicting further vascular complications in diabetic patients. Endothelial progenitor cells (EPCs), widely known for their potential applications in cell therapy due to their regenerative properties, may be used as differential markers in DVCs, considering that the number and functionality of these cells are affected under the pathological environments related to DM. Besides, drugs commonly used with DM patients may influence the level or behaviour of EPCs as a pleiotropic effect that could finally be decisive in the prognosis of the disease. In the current review, we have analysed the relationship between diabetes and DVCs, focusing on the potential use of EPCs as biomarkers of diabetes progression towards the development of major vascular complications. Moreover, the effects of different drugs on the number and function of EPCs have been also addressed.

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data.

Transform diabetes care with precision medicine

Background and Aims

Diabetes is a global concern. This article took a closer look at diabetes and precision medicine.

Methods

A literature search of studies related to the use of precision medicine in diabetes care was conducted in various databases (PubMed, Google Scholar, and Scopus).

Results

Precision medicine encompasses the integration of a wide array of personal data, including clinical, lifestyle, genetic, and various biomarker information. Its goal is to facilitate tailored treatment approaches using contemporary diagnostic and therapeutic techniques that specifically target patients based on their genetic makeup, molecular markers, phenotypic traits, or psychosocial characteristics. This article not only highlights significant advancements but also addresses key challenges, particularly focusing on the technologies that contribute to the realization of personalized and precise diabetes care.

Conclusion

For the successful implementation of precision diabetes medicine, collaboration and coordination among multiple stakeholders are crucial.

Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review

BACKGROUND

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D.

METHODS

We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier.

RESULTS

The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition.

CONCLUSIONS

We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

A 2-dimensional model framework for blood glucose prediction based on iterative learning control architecture

The accurate, timely, and personalized prediction for future blood glucose (BG) levels is undoubtedly needed for further advancement of diabetes management technologies. Human inherent circadian rhythm and regular lifestyle resulting in similarity of daily glycemic dynamics play a positive role in the prediction of blood glucose. Inspired by the iterative learning control (ILC) method in the field of automatic control, a 2-dimensional (2-D) model framework is constructed to predict the future blood glucose levels by taking both the short-range information within a day (intra-day) and long-range information between days (inter-day) into account. In this framework, the radial basis function neural network was applied to capture nonlinear relationships in glycemic metabolism, that is, short-range temporal dependence and long-range contemporaneous dependence on previous days. We build models for each patient, and the models were tested on the in silico datasets at various prediction horizons (PHs). The learning model developed in the 2-D framework successfully increases the accuracy and reduces the delay of predictions. This modeling framework provides a new point of view for BG level prediction and contributes to the development of personalized glucose management, such as hypoglycemia warning and glycemic control.

Advances in the Management of Diabetes Mellitus: A Focus on Personalized Medicine

Diabetes mellitus poses a substantial global health challenge, necessitating innovative approaches to improve patient outcomes. Conventional one-size-fits-all treatment strategies have shown limitations in addressing the diverse nature of the disease. In recent years, personalized medicine has emerged as a transformative solution, tailoring treatment plans based on individual genetic makeup, lifestyle factors, and health characteristics. This review highlights the role of genetic screening in predicting diabetes susceptibility and response to treatment, as well as the potential of pharmacogenomics in optimizing medication choices. Moreover, it discusses the incorporation of lifestyle modifications and behavioral interventions to empower patients in their health journey. Telemedicine and remote patient monitoring are also examined for their role in enhancing accessibility and adherence. Ethical considerations and challenges in implementing personalized medicine are addressed. The review envisions a future where personalized medicine becomes a cornerstone in diabetes management, ensuring improved patient outcomes and fostering more effective and patient-centric care on a global scale.

Exploring the Complex Connection Between Diabetes and Cardiovascular Disease: Analyzing Approaches to Mitigate Cardiovascular Risk in Patients With Diabetes

Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in individuals diagnosed with diabetes mellitus. This narrative review offers a comprehensive examination of the complex correlation between diabetes and cardiovascular complications. The objective of this review is to analyze the most recent evidence on preventive measures and treatment options for mitigating cardiovascular risk in patients with diabetes, by synthesizing existing literature. Insulin resistance plays a crucial role in connecting diabetes and CVD, leading to the development of dyslipidemia and atherogenesis. As a result, the risk of cardiovascular events in individuals with diabetes is significantly elevated. Moreover, the presence of hyperglycemia-induced oxidative stress and inflammation serves to intensify endothelial dysfunction and vascular damage, thereby exacerbating the risk of cardiovascular complications. The interaction between diabetes and CVD frequently speeds up the development of atherosclerotic plaque, making the plaque more prone to rupture. This can lead to severe cardiovascular events such as myocardial infarction and stroke. It is crucial to comprehend the intricate relationship between diabetes and CVD in order to formulate effective strategies aimed at enhancing patient outcomes and mitigating the burden associated with these interconnected chronic conditions. Healthcare practitioners can enhance the quality of life and reduce mortality rates associated with CVD in diabetic patients by thoroughly examining evidence-based preventive measures and treatment options. This approach allows them to make informed decisions when managing cardiovascular risk. In summary, this narrative review provides a valuable resource for healthcare professionals and researchers, presenting a comprehensive analysis of the complex relationship between diabetes and CVD. By providing a comprehensive analysis of the latest evidence and elucidating the underlying mechanisms, this review seeks to establish a foundation for the development of innovative strategies in diabetes management. These strategies have the potential to significantly improve cardiovascular outcomes and enhance overall patient care.

Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Ferroptosis is involved in corpus cavernosum smooth muscle cells impairment in diabetes mellitus-induced erectile dysfunction

BACKGROUNDS

Erectile dysfunction (ED) is a common andrological disorder that tends to afflict diabetic patients, among others. Pharmacological therapy of diabetes mellitus-induced ED (DMED) is ineffective, as it is linked with smooth muscle cell loss in the corpus cavernosum. Ferroptosis is a recently identified kind of cell death evoked by lipid peroxidation, and it is connected with a number of diabetic complications.

OBJECTIVES

To investigate the role of ferroptosis in DMED.

MATERIALS AND METHODS

We established the rat model of DMED and conducted a combined analysis of RNA sequencing (RNA-seq) and Gene Expression Omnibus (GEO) data to identify differentially expressed genes (DEGs). Next, DMED disease targets were determined by cross-referencing DEGs and DMED-related genes in the DisGeNET, GenCLiP3, and GeneCards databases. Additionally, these targets were analyzed using "clusterProfiler" in R utilizing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. Immunohistochemistry (IHC) staining of rat penile tissues was used to validate several targets. Notably, the Cell Counting Kit-8 assay, Western blotting, oxidative stress (OS) level, and iron concentration were tested in corpus cavernosum smooth muscle cells (CCSMCs) stimulated with high glucose (HG), and treated with Ferrostatin-1 (Fer-1).

RESULTS

Sixty-nine disease targets of DMED were identified. According to KEGG analysis, these targets were primarily enriched in the ferroptosis pathway. Additionally, IHC results revealed that the expression of GPX4, SLC7A11, and ACSL4 was deregulated in the DMED group compared to the control group. Significantly, HG decreased cell viability and increased OS and iron levels in CCSMCs, which could be reversed by Fer-1 treatment.

DISCUSSION AND CONCLUSION

Our study revealed that ferroptosis may indeed exist in DMED. GPX4, SLC7A11, and ACSL4 all have a role in controlling the viability of CCSMCs, making them potential therapeutic targets.

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

Natural Products as Outstanding Alternatives in Diabetes Mellitus: A Patent Review

Diabetes mellitus (DM) is a metabolic syndrome that can be considered a growing health problem in the world. High blood glucose levels are one of the most notable clinical signs. Currently, new therapeutic alternatives have been tackled from clinicians' and scientists' points of view. Natural products are considered a promising source, due to the huge diversity of metabolites with pharmaceutical applications. Therefore, this review aimed to uncover the latest advances in this field as a potential alternative to the current therapeutic strategies for the treatment of DM. This purpose is achieved after a patent review, using the Espacenet database of the European Patent Office (EPO) (2016-2022). Final screening allowed us to investigate 19 patents, their components, and several technology strategies in DM. Plants, seaweeds, fungi, and minerals were used as raw materials in the patents. Additionally, metabolites such as tannins, organic acids, polyphenols, terpenes, and flavonoids were found to be related to the potential activity in DM. Moreover, the cellular transportation of active ingredients and solid forms with special drug delivery profiles is also considered a pharmaceutical technology strategy that can improve their safety and efficacy. From this perspective, natural products can be a promissory source to obtain new drugs for DM therapy.

Type 2 diabetes: an exploratory genetic association analysis of selected metabolizing enzymes and transporters and effects on cardiovascular and renal biomarkers

OBJECTIVES

This study sought to identify potential pharmacogenetic associations of selected enzymes and transporters with type 2 diabetes (T2D). In addition, pharmacogenomic profiles, concentrations of asymmetric dimethylarginine (ADMA) or kidney injury molecule-1 (KIM-1), and several covariates were investigated.

METHODS

Whole blood was collected from 63 patients, with 32 individuals with T2D. A pharmacogenomic panel was used to assay genetic profiles, and biomarker ELISAs were run to determine subject concentrations of ADMA and KIM-1. Additive genetic modeling with multiple linear and logistic regressions were performed to discover potential SNPs-outcome associations using PLINK.

RESULTS

Ten SNPs were found to be significant (p<0.05) depending on the inclusion or exclusion of covariates. Of these, four were found in association with the presence of T2D, rs2231142, rs1801280, rs1799929, and rs1801265 depending on covariate inclusion or exclusion. Regarding ADMA, one SNP was found to be significant without covariates, rs1048943. Five SNPs were identified in association with KIM-1 and T2D in the presence of covariates, rs12208357, rs34059508, rs1058930, rs1902023, and rs3745274. Biomarker concentrations were not significantly different in the presence of T2D.

CONCLUSIONS

This exploratory study found several SNPs related to T2D; further research is required to validate and understand these relationships.

Type 2 diabetes

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

A randomized clinical trial of genetic testing and personalized risk counselling in patients with type 2 diabetes receiving integrated care -The genetic testing and patient empowerment (GEM) trial

AIMS

We evaluated the effect of personalized risk counseling incorporating clinical and genetic risk factors on patient empowerment and risk factor control in diabetes.

METHODS

Patients with type 2 diabetes (T2D) with suboptimal glycaemic control (HbA1c ≥ 7.5%) were randomized to a genetic counselling (GC) or control group. All patients underwent genetic testing for alleles at three loci associated with diabetic complications. The GC group received additional explanation of the joint associations of genetic and modifiable risk factors on risk of complications. All patients were reassessed at 12 months including validated questionnaires for patient reported outcomes. The primary outcome was proportion of patients reaching ≥ 3 of 5 predefined treatment targets (HbA1c < 7%, BP < 130/80 mmHg, LDL-C < 2.6 mmol/L, Triglyceride < 2.0 mmol/L, use of renin-angiotensin system inhibitors). Secondary outcomes included new-onset chronic kidney disease or microalbuminuria and patient reported outcome measures.

RESULTS

A total of 435 patients were randomized and 420 patients were included in the modified intention-to-treat analysis. At 12 months, the proportion of patients who attained ≥ 3 targets increased from 41.6% to 52.3% in the GC group (p = 0.007) versus 49.5% to 62.6% in the control group (p = 0.003), without between-group difference. Both groups had similar reduction in HbA1c, LDL-C and increased use of medications. In per protocol analysis, the GC group had higher diabetes empowerment, with reduced diabetes distress. In the GC group, the greatest improvement in positive attitude and self-care activities was observed in the intermediate to high genetic risk score (GRS) groups.

CONCLUSIONS

In patients with T2D receiving integrated care, additional counselling on genetic risk of complications did not further improve risk factor control, although the improvement in self-efficacy warrants long-term evaluation.

Evaluation of the Clinical Efficacy of the Treatment of Overweight and Obesity in Type 2 Diabetes Mellitus by the Telemedicine Management System Based on the Internet of Things Technology

Objective

To explore the application value of medical intelligent electronic system under the background of Internet of Things in the clinical study of the treatment of overweight/obesity in type 2 diabetes mellitus (T2DM) with empagliflozin combined with liraglutide; 50 overweight and obese adult T2DM patients in our hospital were randomly divided into the combined group and the control group, 25 cases in each group. The control group was treated with liraglutide alone, while the combined group was treated with empagliflozin on the basis of liraglutide. Based on the Internet of Things technology, with diabetes management as the core, the functions of information collection, transmission, and storage of T2DM patients are realized. Doctors pass the diabetes management plan to T2DM patients through the platform, supervise the implementation, and finally compare the clinical efficacy of the two groups.

Results

Compared with before treatment, the body mass index (BMI), fasting blood glucose (FPG), postprandial blood glucose (2hPG), glycosylated hemoglobin (HbAlc), islet beta cell secretion function index (HOMA-β), islet resistance index (HOMA-IR), total cholesterol (TC), and triglyceride (TG) in both groups decreased significantly after treatment. After combined treatment, systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, 2hPG, HbA1c, and HOMA-IR in the combined group were significantly lower than those in the control group (P < 0.05). Hypoglycemia occurred in both groups, with 2 cases in the control group and 4 cases in the combined group.

Conclusion

The telemedicine management system based on Internet of Things technology can improve patients' self-management ability and provide a new choice for individualized treatment of overweight/obesity T2DM patients. The combination therapy of empagliflozin and liraglutide can effectively reduce blood sugar, weight, blood pressure, blood lipid, and hypoglycemia and effectively improve insulin resistance and secretion function of islet β cells in T2DM patients.

The Impact of Nutritional Supplementation During Pregnancy on the Incidence of Gestational Diabetes and Glycaemia Control

The nutritional state before and throughout pregnancy has a critical impact on the women's health and the baby's development and growth. The release of placental hormones during pregnancy induces/ increases maternal insulin resistance and promotes nutrition utilization by the fetus. Gestational Diabetes Mellitus (GDM) is the most common medical complication in pregnancy and is associated with significant maternal and fetal morbidity. Several studies have examined the effect of physical activity, healthy eating, and various food supplements on the risk of developing gestational diabetes (GDM) and related outcomes. Among those, Myo-Inositol supplementation has shown encouraging results in the prevention of GDM. Maternal vitamin D deficiency has been associated with an elevated risk of GDM, and supplementation can improve glucose haemostasis by lowering fasting blood glucose, HbA1c, and serum insulin concentration. Probiotics modulate the gut microbiota leading to an improved glucose and lipid metabolism, which is proposed to reduce the risk of GDM. We aim to review the strength and limitation of the current evidence for using some nutritional supplements either as single agents or in combinations on the risk of developing GDM and on glycaemic control.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Locational effects on oral microbiota among long-term care patients

Background

Dysbiosis of oral microbiota is the cause of many diseases related to oral and general health. However, few Asia-based studies have evaluated the role of oral microbiota in patients receiving long-term care. Thus, new indications are needed for early prevention and risk management based on information derived from the oral microbiota.

Methods

We used next-generation sequencing (NGS) to identify the oral bacterial composition and abundance in patients receiving long-term care: 20 from the outpatient department (OPD) and 20 home-care patients. Their microbial compositions, taxonomy, and alpha/beta diversity were characterized.

Results

Microbiota from the two groups showed different diversity and homogeneity, as well as distinct bacterial species. A more diverse and stable microbial population was observed among OPD patients. Our findings indicated that home-care patients had a higher risk of oral diseases due to the existence of dominant species and a less stable microbial community.

Conclusion

This work was the first in Taiwan to use NGS to investigate the oral microbiota of long-term care patients. Our study demonstrated the potential use of dominant bacterial species as biomarkers for the risk management of posttreatment complications.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

Measuring Exercise-Induced Secreted Protein Acidic and Rich in Cysteine Expression as a Molecular Tool to Optimize Personalized Medicine

The numerous exercise benefits for health as well as applications for diseases has lead to exercise being prescribed in many pathological conditions. Secreted protein acidic and rich in cysteine (SPARC) gene expression is stimulated by exercise and SPARC has been suggested as a molecular mediator of exercise. Therefore, we suggest using this property for personalized medicine. This can be achieved by prescribing the exercise with a pattern (duration, intensity, etc.) that corresponds to the optimum SPARC/Sparc expression. We expect this approach to optimize the exercise therapy in both the preventive and curative contexts. In the research field, measuring exercise -dependent expression of Sparc would represent a molecular tool to further optimize the selection of exercise animal models as well.

Precision Medicine and Triple-Negative Breast Cancer: Current Landscape and Future Directions

Simple Summary

The implementation of precision medicine will revolutionize cancer treatment paradigms. Notably, this goal is not far from reality: genetically similar cancers can be treated similarly. The heterogeneous nature of triple-negative breast cancer (TNBC) made it a suitable candidate to practice precision medicine. Using TNBC molecular subtyping and genomic profiling, a precision medicine-based clinical trial is ongoing. This review summarizes the current landscape and future directions of precision medicine and TNBC.

Abstract

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer associated with a high recurrence and metastasis rate that affects African-American women disproportionately. The recent approval of targeted therapies for small subgroups of TNBC patients by the US ‘Food and Drug Administration’ is a promising development. The advancement of next-generation sequencing, particularly somatic exome panels, has raised hopes for more individualized treatment plans. However, the use of precision medicine for TNBC is a work in progress. This review will discuss the potential benefits and challenges of precision medicine for TNBC. A recent clinical trial designed to target TNBC patients based on their subtype-specific classification shows promise. Yet, tumor heterogeneity and sub-clonal evolution in primary and metastatic TNBC remain a challenge for oncologists to design adaptive precision medicine-based treatment plans.

Differentiating Among Type 1, Type 2 Diabetes, and MODY: Raising Awareness About the Clinical Implementation of Genetic Testing in Latin America

Objective

To describe a case of maturity-onset diabetes of the young (MODY) to highlight the importance of a correct diabetes diagnosis.

Methods

We describe a Mexican family misdiagnosed with T1D and T2D.

Results

A 36-year-old woman with diabetes and adverse outcomes during 2 pregnancies had been diagnosed with T2D 10 years ago. Genetic testing was performed due to clinical and family history, which showed a pathogenic heterozygous variant c.544G>T (p.Val182Leu) in the GCK gene. This mutation was also confirmed in most of the family members who had been diagnosed with diabetes.

Conclusion

This case highlights the need for a correct diabetes classification. Reassessment of diabetes etiology is justified, especially in individuals with unclear clinical presentation or when family history is suggestive.

Waist-to-height ratio and non-alcoholic fatty liver disease in adults

Background

The waist-to-height ratio (WHtR) has been recognised as a powerful indicator to evaluate non-alcoholic fatty liver disease (NAFLD) in recent years, but few related studies are available. Thus, clarifying the association between the WHtR and NAFLD may be beneficial to the prevention and treatment of NAFLD.

Methods

The cross-sectional study population was from a large-scale health examination programme called ‘human dock’ in Japan. In this study, 14,125 participants in this health examination programme were included. To understand the association between the WHtR and NAFLD more intuitively, we grouped the WHtR values into quintiles and used a multivariable logistic regression model to assess WHtR and its quintile with NAFLD risk. Moreover, we used the generalised additive model to model the association between WHtR and NAFLD to explore their non-linear relationship.

Results

The prevalence of NAFLD among participants in this study was 17.59%, with an average age of 43.53 ± 8.89 years. After adjusting for all non-collinear covariables, we observed a 66% increase in the NAFLD risk per SD increase in WHtR. Furthermore, in the quintile groups of WHtR, the participants in quintile 2, quintile 3, quintile 4, and quintile 5 had 3.62-fold, 5.98-fold, 9.55-fold, and 11.08-fold increased risks of NAFLD, respectively, compared with those in quintile 1 (P_trend < 0.0001). Non-linear relationship analysis revealed threshold and saturation effects between WHtR and NAFLD in which a WHtR of approximately 0.4 might be the threshold effect of NAFLD risk, 0.6 might be the saturation effect of NAFLD risk. Additionally, subgroup analysis showed that the interaction between WHtR and BMI was significant.

Conclusions

Our results suggest that in adults, the WHtR is associated with NAFLD, and the association is not purely linear but non-linear, with significant threshold and saturation effects.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01824-3.

Demographic stratification of Type 2 diabetes and comorbidities in district healthcare in KwaZulu-Natal

Background

Diabetes has been reported as the second leading cause of death and the top leading cause of death amongst women in South Africa; it is important to evaluate any epidemiological or demographic transition related to diabetes. This study evaluated the demographically stratified prevalence of type 2 diabetes mellitus (T2DM) and existing comorbidities amongst an outpatient population in a district healthcare facility in Kwazulu-Natal (KZN).

Methods

This retrospective cross-sectional study was conducted at a district hospital, and a retrospective record review of all outpatients who reported to the hospital to be treated for T2DM between the period, August 2018–January 2019, was used. Data, such as age, sex, ethnicity and any coexisting morbidity, were collected from outpatient hospital registers and electronically captured using a record review tool.

Results

There were significantly more female patients (3072) compared to male patients (1050) (p < 0.001) with a mean age of 59.21 years. Hypertension (77.9%) and cardiovascular problems (11.16%) were most frequent. Approximately 84% of women presented with T2DM and either one or two morbidities simultaneously. Female patients were at significantly higher risk of presenting with hypertension (odds ratio [OR] = 1.44, 95% confidence interval [CI]: 1.20;1.71), whilst their risk for cardiovascular problems was significantly lower compared to male patients (OR = 0.67, 95% CI: 0.54;0.83).

Conclusion

The prevalence of T2DM and comorbidities differed by demographic factors, such as sex, ethnicity and age. There is a need for flexible and adaptive approaches for the prevention and management of T2DM cases in order to allocate medical resources efficiently and according to the true burden of disease because of T2DM complications.

Effects of L-arginine supplementation on biomarkers of glycemic control: a systematic review and meta-analysis of randomised clinical trials

The present meta-analysis aimed to determine the effectiveness of L-arginine supplementation in improving biomarkers of glycemic control in adults. Electronic databases including PubMed, ISI Web of Science, Scopus, and the Cochrane Collaboration Library were searched up to January 2020. The meta-analysis of twelve randomised clinical trials indicated that L-arginine had no significant effect on serum fasting blood sugar (FBS) (weighted mean difference [WMD]: -3.38 mg/dl, 95% CI: -6.79 to 0.04, p = .53), serum insulin (WMD: -0.12 Hedges' g 95% CI: -0.33 to 0.09, p = .27), glycated haemoglobin A1c (HbA1c; WMD: -0.04%, 95% CI: -0.25 to 0.17, p = .71), and homeostasis model assessment for insulin resistance (WMD: -0.48, 95% CI: -1.15 to 0.19, p = .15). Although several animal studies have proposed that L-arginine supplementation might improve blood glucose control, the present study could not confirm this benefit in humans.

Pharmacogenomic Studies of Current Antidiabetic Agents and Potential New Drug Targets for Precision Medicine of Diabetes

Diabetes is a major threat to people's health and has become a burden worldwide. Current drugs for diabetes have limitations, such as different drug responses among individuals, failure to achieve glycemic control, and adverse effects. Exploring more effective therapeutic strategies for patients with diabetes is crucial. Currently pharmacogenomics has provided potential for individualized drug therapy based on genetic and genomic information of patients, and has made precision medicine possible. Responses and adverse effects to antidiabetic drugs are significantly associated with gene polymorphisms in patients. Many new targets for diabetes also have been discovered and developed, and even entered clinical trial phases. This review summarizes pharmacogenomic evidence of some current antidiabetic agents applied in clinical settings, and highlights potential drugs with new targets for diabetes, which represent a more effective treatment in the future.

Type 2 Diabetes Mellitus: A Review of Multi-Target Drugs

Diabetes Mellitus (DM) is a multi-factorial chronic health condition that affects a large part of population and according to the World Health Organization (WHO) the number of adults living with diabetes is expected to increase. Since type 2 diabetes mellitus (T2DM) is suffered by the majority of diabetic patients (around 90-95%) and often the mono-target therapy fails in managing blood glucose levels and the other comorbidities, this review focuses on the potential drugs acting on multi-targets involved in the treatment of this type of diabetes. In particular, the review considers the main systems directly involved in T2DM or involved in diabetes comorbidities. Agonists acting on incretin, glucagon systems, as well as on peroxisome proliferation activated receptors are considered. Inhibitors which target either aldose reductase and tyrosine phosphatase 1B or sodium glucose transporters 1 and 2 are taken into account. Moreover, with a view at the multi-target approaches for T2DM some phytocomplexes are also discussed.

Utility of Precision Medicine in the Management of Diabetes: Expert Opinion from an International Panel

AIM

The primary objective of this review is to develop a practice-based expert group opinion on the role of precision medicine with a specific focus on sulfonylureas (SUs) in diabetes management.

BACKGROUND

The clinical etiology, presentation and complications of diabetes vary from one patient to another, making the management of the disease challenging. The pre-eminent feature of diabetes mellitus (DM) are chronically elevated blood glucose concentrations; however, in clinical practice, the exclusion of autoimmunity, pregnancy, pancreatic disease or injury and rare genetic forms of diabetes is crucial. Within this framework, precision medicine provides unique insights into the risk factors and natural history of DM. Precision medicine goes beyond genomics and encompasses patient-centered care, molecular technologies and data sharing. Precision medicine has evolved in the field of diabetology. It has helped improve the efficacy of SUs, a class of drugs, which have been effectively used in the management of diabetes mellitus for decades, and it has enabled the expansion of SUs use in diabetes patients with genetic mutations.

REVIEW RESULTS

After due discussions, the expert group analyzed studies that focused on the use of SUs in diabetes patients with genomic variations and rare mutations. The expert group opined that SUs are important glucose-lowering drugs and that precision medicine helps in improving the efficacy of SUs by matching them to those patients who will benefit most.

CONCLUSION

Precision medicine opens new vistas for the effective use of SUs in unexpected patient populations, such as those with genetic mutations.

Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

We review current applications of Big Data in diabetes care and consider the future potential by carrying out a scoping study of the academic literature on Big Data and diabetes care. Healthcare data are being produced at ever-increasing rates, and this information has the potential to transform the provision of diabetes care. Big Data is beginning to have an impact on diabetes care through data research. The use of Big Data for routine clinical care is still a future application. Vast amounts of healthcare data are already being produced, and the key is harnessing these to produce actionable insights. Considerable development work is required to achieve these goals.

Mixed methods pilot study of a low-carbohydrate diabetes prevention programme among adults with pre-diabetes in the USA

OBJECTIVES

(1) To estimate weight change from a low-carbohydrate diabetes prevention programme (LC-DPP) and (2) to evaluate the feasibility and acceptability of an LC-DPP.

RESEARCH DESIGN

Single-arm, mixed methods (ie, integration of quantitative and qualitative data) pilot study.

SETTING

Primary care clinic within a large academic medical centre in the USA.

PARTICIPANTS

Adults with pre-diabetes and Body Mass Index of ≥25 kg/m².

INTERVENTION

We adapted the Centers for Disease Control and Prevention's National Diabetes Prevention Program (NDPP)-an evidence-based, low-fat dietary intervention-to teach participants to follow a very low-carbohydrate diet (VLCD). Participants attended 23 group-based classes over 1 year.

OUTCOME MEASURES

Primary outcome measures were (1) weight change and (2) percentage of participants who achieved ≥5% wt loss. Secondary outcome measures included intervention feasibility and acceptability (eg, attendance and qualitative interview feedback).

RESULTS

Our enrolment target was 22. One person dropped out before a baseline weight was obtained; data from 21 individuals were analysed. Mean weight loss in kilogram was 4.3 (SD 4.8) at 6 months and 4.9 (SD 5.8) at 12 months. Mean per cent body weight changes were 4.5 (SD 5.0) at 6 months and 5.2 (SD 6.0) at 12 months; 8/21 individuals (38%) achieved ≥5% wt loss at 12 months. Mean attendance was 10.3/16 weekly sessions and 3.4/7 biweekly or monthly sessions. Among interviewees (n=14), three factors facilitated VLCD adherence: (1) enjoyment of low-carbohydrate foods, (2) diminished hunger and cravings and (3) health benefits beyond weight loss. Three factors hindered VLCD adherence: (1) enjoyment of high-carbohydrate foods, (2) lack of social support and (3) difficulty preplanning meals.

CONCLUSIONS

An LC-DPP is feasible, acceptable and may be an effective option to help individuals with pre-diabetes to lose weight. Data from this pilot will be used to plan a fully powered randomised controlled trial of weight loss among NDPP versus LC-DPP participants.

TRIAL REGISTRATION NUMBER

NCT03258918.

Atypical hyperglycemia presentation suggests considering a diagnostic of other types of diabetes: first reported GCK-MODY in Perú

Background

Prevalence of maturity-onset diabetes of the young (MODY) is estimated between 1 and 2% of all diabetes cases. In Latin-America little information has been described about the frequency of the disease, perhaps due to limited access to genetic studies.

Case presentation

We present the case of a male patient with a history of two years of fatigue, mild hyperglycemia and intermittent polyuria, accompanied by a recent history of weight loss. He was diagnosed initially as type 2 diabetes, but in the follow-up as a patient with type 1 diabetes. He required relatively low doses of insulin and was evaluated in the endocrinology service at a hospital in Lima. The results of glucose, insulin and C-peptide in the oral glucose tolerance test (OGTT) performed were not consistent with a type 1 diabetes. Moreover, the age of the patient and the clinical characteristics did not strongly suggest a diagnosis of type 2 diabetes either. These clinical features had prompted us to carry out the genetic study. The genetic test performed with a genetic MODY panel through a massive sequencing. Heterozygous pathogenic for a variant in GCK gene was found c.629C>T p.(Thr210Met). His parents were negative for this variant after performed the genetic test.

Conclusions

This is the first case of MODY for a pathogenic variant in the GCK gene reported in Perú. The genetic evaluation of a clinical suspicion of MODY is important to confirm the diagnosis and establish an adequate treatment in patients.

Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks

Background: Electronic health records (EHR) play an important role for the redefinition of phenotypes in view of the wealth and heterogeneity of information now available from disparate data sources. A recent cross-sectional retrospective study has described the potential of EHR toward type 2 diabetes mellitus (T2D) screening when ad hoc models are used. About 10,000 US patients have been analyzed through a variety of inference techniques applied to all records with a variable degree of completeness. The analyses conducted in the reference study have indicated that EHR phenotypes significantly improved T2D detection.

Methods: With these US patients and the T2D data evidenced in the above study, we propose an integrative inference approach that leverages the prediction power of EHR features selected by two well-known methods, Random Forests and Lasso. The goal is 2-fold: reducing the Big Data redundancies potentially harmful to the predictive learning task and exploiting the interconnectivity of EHR features. A mutual information (MI) network is the inference tool used to identify communities useful to prioritize significant T2D features underlying the similarity between patients.

Results: Endowed with a different degree of granularity, the communities detected after the application of both methods were centered especially on T2D comorbidities and risk factors. As such, they appear very relevant for assessment of two main issues, T2D disease burden, and prevention.

Conclusions: Our analytical approach offers a solution for managing the EHR scale factor in a complex disease context. EHR are rich sources of phenotypic diversity through which novel stratifications of patients are expected. To enable these results, both pre-screening of variables and calibration of risk prediction methods become necessary steps in EHR analyses. We have presented networks identifying major T2D communities. The specific significance assigned to comorbidities and risk factors in relation to T2D can be inferred with accuracy from just a suitably reduced number of EHR features.

Epidemiological characteristics, medical costs and healthcare resource utilization of diabetes-related complications among Chinese patients with type 2 diabetes mellitus

Objectives: To estimate the direct medical costs (DMCs) and healthcare resource utilization (HRU) of type 2 diabetes mellitus (T2DM)-related complications in China. Methods: Data from a total of 74,507 patients were extracted from the 2015 China Health Insurance Research Association Claims Database. The complications determined by primary diagnoses were categorized into three groups: 1) for mild acute and local chronic complications, both outpatients and inpatients were considered; 2) for severe acute complications, only inpatiens were considered; 3) for systemic chronic complications, a 1:1 propensity-score matching was performed to calculate the incremental DMCs and HRU of preexisting and new-onset patients. Results: Among the mild acute and local chronic complications, the DMCs and HRU per event were the highest for gangrene and laser treatment. Of the severe acute complications, the DMCs and HRU per event were highest for hyperosmotic nonketonic diabetic coma (HNDC), followed by severe hypoglycemia and ketosis. For systemic chronic complications, the DMCs and HRU associated with dialysis and myocardial infarction were the highest both in patients with new-onset complications and preexisting complications. Conclusions: The estimated economic data are required for policy decisions to optimize resource allocation and to evaluate different approaches for disease management.

Long noncoding RNA: an emerging player in diabetes and diabetic kidney disease

Diabetic kidney disease (DKD) is among the most common complications of diabetes mellitus (DM), and remains the leading cause of end-stage renal diseases (ESRDs) in developed countries, with no definitive therapy yet available. It is imperative to decipher the exact mechanisms underlying DKD and identify novel therapeutic targets. Burgeoning evidence indicates that long non-coding RNAs (lncRNAs) are essential for diverse biological processes. However, their roles and the mechanisms of action remain to be defined in disease conditions like diabetes and DKD. The pathogenesis of DKD is twofold, so is the principle of treatments. As the underlying disease, diabetes per se is the root cause of DKD and thus a primary focus of therapy. Meanwhile, aberrant molecular signaling in kidney parenchymal cells and inflammatory cells may directly contribute to DKD. Evidence suggests that a number of lncRNAs are centrally involved in development and progression of DKD either via direct pathogenic roles or as indirect mediators of some nephropathic pathways, like TGF-β1, NF-κB, STAT3 and GSK-3β signaling. Some lncRNAs are thus likely to serve as biomarkers for early diagnosis or prognosis of DKD or as therapeutic targets for slowing progression or even inducing regression of established DKD. Here, we elaborated the latest evidence in support of lncRNAs as a key player in DKD. In an attempt to strengthen our understanding of the pathogenesis of DKD, and to envisage novel therapeutic strategies based on targeting lncRNAs, we also delineated the potential mechanisms of action as well as the efficacy of targeting lncRNA in preclinical models of DKD.

Inflammatory cytokines in type 2 diabetes mellitus as facilitators of hypercoagulation and abnormal clot formation

BACKGROUND

The global burden of type 2 diabetes mellitus (T2DM), together with the presence of cardiovascular risk in this population, is reaching pandemic levels. A prominent feature of T2DM is chronic and systemic inflammation, with the accompanying presence of circulating and dysregulated inflammatory biomarkers; which in turn is associated with abnormal clot formation.

METHODS

Here, we investigate the correlation between abnormal blood clotting, using thromboelastography (TEG), clot ultrastructure using scanning electron microscopy (SEM) and the presence of a dysregulated inflammatory cytokine profile, by examining various circulating biomarkers.

RESULTS

Our results show that many biomarkers, across TEG, cytokine and lipid groups, were greatly dysregulated in the T2DM sample. Furthermore, our T2DM sample's coagulation profiles were significantly more hypercoagulable when compared to our heathy sample, and ultrastructural analysis confirmed a matted and denser clot structure in the T2DM sample.

CONCLUSIONS

We suggest that dysregulated circulating molecules may in part be responsible for a hypercoagulable state and vascular dysfunction in the T2DM sample. We propose further that a personalized approach could be of great value when planning treatment and tracking the patient health status after embarking on a treatment regimes, and that looking to novel inflammatory and vascular biomarkers might be crucial.

Excess Burden of Mental Illness and Hospitalization in Young-Onset Type 2 Diabetes: A Population-Based Cohort Study

BACKGROUND

Type 2 diabetes (T2D) increases hospitalization risk. Young-onset T2D (YOD) (defined as onset before age 40 years) is associated with excess morbidity and mortality, but its effect on hospitalizations is unknown.

OBJECTIVE

To determine hospitalization rates among persons with YOD and to examine the effect of age at onset on hospitalization risk.

DESIGN

Prospective cohort study.

SETTING

Hong Kong.

PARTICIPANTS

Adults aged 20 to 75 years in population-based (2002 to 2014; n = 422 908) and registry-based (2000 to 2014; n = 20 886) T2D cohorts.

MEASUREMENTS

All-cause and cause-specific hospitalization rates. Negative binomial regression models estimated effect of age at onset on hospitalization rate and cumulative bed-days from onset to age 75 years for YOD.

RESULTS

Patients with YOD had the highest hospitalization rates by attained age. In the registry cohort, 36.8% of YOD bed-days before age 40 years were due to mental illness. The adjusted rate ratios showed increased hospitalization in YOD versus usual-onset T2D (onset at age ≥40 years) (all-cause, 1.8 [95% CI, 1.7 to 2.0]; renal, 6.7 [CI, 4.2 to 10.6]; diabetes, 3.7 [CI, 3.0 to 4.6]; cardiovascular, 2.1 [CI, 1.8 to 2.5]; infection, 1.7 [CI, 1.4 to 2.1]; P < 0.001 for all). Models estimated that intensified risk factor control in YOD (hemoglobin A1c level <6.2%, systolic blood pressure <120 mm Hg, low-density lipoprotein cholesterol level <2.0 mmol/L [<77.3 mg/dL], triglyceride level <1.3 mmol/L [<115.1 mg/dL], waist circumference of 85 cm [men] or 80 cm [women], and smoking cessation) was associated with a one-third reduction in cumulative bed-days from onset to age 75 years (97 to 65 bed-days).

LIMITATION

Possible residual confounding.

CONCLUSION

Adults with YOD have excess hospitalizations across their lifespan compared with persons with usual-onset T2D, including an unexpectedly large burden of mental illness in young adulthood. Efforts to prevent YOD and intensify cardiometabolic risk factor control while focusing on mental health are urgently needed.

PRIMARY FUNDING SOURCE

Asia Diabetes Foundation.

Engineering Precision Medicine

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs-on-chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential.