Precision Diabetes Is Slowly Becoming a Reality

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

Advancements in pharmacotherapy options for treating diabetes ‎in children and adolescents

INTRODUCTION

This study compares diabetes management between pediatric and adult patients and identifies treatment challenges and gaps.

AREAS COVERED

We searched PubMed and Clinicaltrails.gov databases for studies published from 2001 to 2023 on diabetes management in different age groups.

EXPERT OPINION

Research shows children have lower insulin sensitivity, clearance, and β cell function than adults. The US FDA only allows insulin, metformin, and liraglutide as antidiabetic medication options for children. However, some off-label drugs, like meglitinides, sulfonylureas, and alogliptin, have demonstrated positive results in treating certain types of diabetes caused by gene mutations. It's crucial to adopt personalized and precise approaches to managing diabetes in pediatrics, which vary from those used for adult patients. New studies support the classification of type 2 diabetes into several subtypes based on age, BMI, glycemia, homeostasis model estimates, varying insulin resistance, ‎different rates of complications‎, and islet ‎autoantibodies. With this insight, prevention, treatment, and precision medicine of diabetes might be changed. More research is necessary to assess the safety and efficacy of different antidiabetic drugs and improve diabetes treatment for children and adolescents.

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.

The Mutation Spectrum of Rare Variants in the Gene of Adenosine Triphosphate (ATP)-Binding Cassette Subfamily C Member 8 in Patients with a MODY Phenotype in Western Siberia

During differential diagnosis of diabetes mellitus, the greatest difficulties are encountered with young patients because various types of diabetes can manifest themselves in this age group (type 1, type 2, and monogenic types of diabetes mellitus, including maturity-onset diabetes of the young (MODY)). The MODY phenotype is associated with gene mutations leading to pancreatic-β-cell dysfunction. Using next-generation sequencing technology, targeted sequencing of coding regions and adjacent splicing sites of MODY-associated genes (HNF4A, GCK, HNF1A, PDX1, HNF1B, NEUROD1, KLF11, CEL, PAX4, INS, BLK, KCNJ11, ABCC8, and APPL1) was carried out in 285 probands. Previously reported missense variants c.970G>A (p.Val324Met) and c.1562G>A (p.Arg521Gln) in the ABCC8 gene were found once each in different probands. Variant c.1562G>A (p.Arg521Gln) in ABCC8 was detected in a compound heterozygous state with a pathogenic variant of the HNF1A gene in a diabetes patient and his mother. Novel frameshift mutation c.4609_4610insC (p.His1537ProfsTer22) in this gene was found in one patient. All these variants were detected in available family members of the patients and cosegregated with diabetes mellitus. Thus, next-generation sequencing of MODY-associated genes is an important step in the diagnosis of rare MODY subtypes.

Evolution of the diagnostic value of "the sugar of the blood": hitting the sweet spot to identify alterations in glucose dynamics

In this paper, we provide an overview of the evolution of the definition of hyperglycemia during the past century and the alterations in glucose dynamics that cause fasting and postprandial hyperglycemia. We discuss how extensive mechanistic, physiological research into the factors and pathways that regulate the appearance of glucose in the circulation and its uptake and metabolism by tissues and organs has contributed knowledge that has advanced our understanding of different types of hyperglycemia, namely prediabetes and diabetes and their subtypes (impaired fasting plasma glucose, impaired glucose tolerance, combined impaired fasting plasma glucose, impaired glucose tolerance, type 1 diabetes, type 2 diabetes, gestational diabetes mellitus), their relationships with medical complications, and how to prevent and treat hyperglycemia.

Machine-learning models utilizing CYP3A4*1G show improved prediction of hypoglycemic medication in Type 2 diabetes

The effectiveness and side effects of Type 2 diabetes (T2D) medication are related to individual genetic background. SNPs CYP3A4 and CYP2C19 were introduced to machine-learning models to improve the performance of T2D medication prediction. Two multilabel classification models, ML-KNN and WRank-SVM, trained with clinical data and CYP3A4/ CYP2C19 SNPs were evaluated. Prediction performance was evaluated with Hamming loss, one-error, coverage, ranking loss and average precision. The average precision of ML-KNN and WRank-SVM using clinical data was 92.74% and 92.9%, respectively. Combined with CYP2C19*2*3, the average precision dropped to 88.84% and 89.93%, respectively. While combined with CYP3A4*1G, the average precision was enhanced to 97.96% and 97.82%, respectively. Results suggest that CYP3A4*1G can improve the performance of ML-KNN and WRank-SVM models in predicting T2D medication performance.

Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

CONTEXT

Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease.

OBJECTIVE

This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years.

METHODS

Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria.

RESULTS

Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate.

CONCLUSION

The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.

Circulating microRNAs Signature for Predicting Response to GLP1-RA Therapy in Type 2 Diabetic Patients: A Pilot Study

Type 2 diabetes (T2D) represents one of the major health issues of this century. Despite the availability of an increasing number of anti-hyperglycemic drugs, a significant proportion of patients are inadequately controlled, thus highlighting the need for novel biomarkers to guide treatment selection. MicroRNAs (miRNAs) are small non-coding RNAs, proposed as useful diagnostic/prognostic markers. The aim of our study was to identify a miRNA signature occurring in responders to glucagon-like peptide 1 receptor agonists (GLP1-RA) therapy. We investigated the expression profile of eight T2D-associated circulating miRNAs in 26 prospectively evaluated diabetic patients in whom GLP1-RA was added to metformin. As expected, GLP1-RA treatment induced significant reductions of HbA1c and body weight, both after 6 and 12 months of therapy. Of note, baseline expression levels of the selected miRNAs revealed two distinct patient clusters: “high expressing” and “low expressing”. Interestingly, a significantly higher percentage of patients in the high expression group reached the glycemic target after 12 months of treatment. Our findings suggest that the evaluation of miRNA expression could be used to predict the likelihood of an early treatment response to GLP1-RA and to select patients in whom to start such treatment, paving the way to a personalized medicine approach.

Physical activity attenuates postprandial hyperglycaemia in homozygous TBC1D4 loss-of-function mutation carriers

AIMS/HYPOTHESIS

The common muscle-specific TBC1D4 p.Arg684Ter loss-of-function variant defines a subtype of non-autoimmune diabetes in Arctic populations. Homozygous carriers are characterised by elevated postprandial glucose and insulin levels. Because 3.8% of the Greenlandic population are homozygous carriers, it is important to explore possibilities for precision medicine. We aimed to investigate whether physical activity attenuates the effect of this variant on 2 h plasma glucose levels after an oral glucose load.

METHODS

In a Greenlandic population cohort (n = 2655), 2 h plasma glucose levels were obtained after an OGTT, physical activity was estimated as physical activity energy expenditure and TBC1D4 genotype was determined. We performed TBC1D4-physical activity interaction analysis, applying a linear mixed model to correct for genetic admixture and relatedness.

RESULTS

Physical activity was inversely associated with 2 h plasma glucose levels (β[main effect of physical activity] -0.0033 [mmol/l] / [kJ kg-1 day-1], p = 6.5 × 10-5), and significantly more so among homozygous carriers of the TBC1D4 risk variant compared with heterozygous carriers and non-carriers (β[interaction] -0.015 [mmol/l] / [kJ kg-1 day-1], p = 0.0085). The estimated effect size suggests that 1 h of vigorous physical activity per day (compared with resting) reduces 2 h plasma glucose levels by an additional ~0.7 mmol/l in homozygous carriers of the risk variant.

CONCLUSIONS/INTERPRETATION

Physical activity improves glucose homeostasis particularly in homozygous TBC1D4 risk variant carriers via a skeletal muscle TBC1 domain family member 4-independent pathway. This provides a rationale to implement physical activity as lifestyle precision medicine in Arctic populations.

DATA REPOSITORY

The Greenlandic Cardio-Metabochip data for the Inuit Health in Transition study has been deposited at the European Genome-phenome Archive ( https://www.ebi.ac.uk/ega/dacs/EGAC00001000736 ) under accession EGAD00010001428.

Precision Health Care Elements, Definitions, and Strategies for Patients with Diabetes: A Literature Review

Diabetes is a prevalent disease with a high risk of complications. The number of people with diabetes worldwide was reported to increase every year. However, new integrated individualized health care related to diabetes is insufficiently developed. Purpose: The objective of this study was to conduct a literature review and discover precision health care elements, definitions, and strategies. Methods: This study involved a 2-stage process. The first stage comprised a systematic literature search, evidence evaluation, and article extraction. The second stage involved discovering precision health care elements and defining and developing strategies for the management of patients with diabetes. Results: Of 1337 articles, we selected 35 relevant articles for identifying elements and definitions of precision health care for diabetes, including personalized genetic or lifestyle factors, biodata- or evidence-based practice, glycemic target, patient preferences, glycemic control, interdisciplinary collaboration practice, self-management, and patient priority direct care. Moreover, strategies were developed to apply precision health care for diabetes treatment based on eight elements. Conclusions: We discovered precision health care elements and defined and developed strategies of precision health care for patients with diabetes. precision health care is based on team foundation, personalized glycemic target, and control as well as patient preferences and priority, thus providing references for future research and clinical practice.

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

BACKGROUND

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. TEDDY has collected heterogenous data longitudinally to gain insights into the environmental and biological mechanisms driving the progression to persistent islet autoantibodies.

METHODS

We developed a machine learning model to predict imminent transition to the development of persistent islet autoantibodies based on time-varying metabolomics data integrated with time-invariant risk factors (eg, gestational age). The machine learning was initiated with 221 potential features (85 genetic, 5 environmental, 131 metabolomic) and an ensemble-based feature evaluation was utilized to identify a small set of predictive features that can be interrogated to better understand the pathogenesis leading up to persistent islet autoimmunity.

RESULTS

The final integrative machine learning model included 42 disparate features, returning a cross-validated receiver operating characteristic area under the curve (AUC) of 0.74 and an AUC of ~0.65 on an independent validation dataset. The model identified a principal set of 20 time-invariant markers, including 18 genetic markers (16 single nucleotide polymorphisms [SNPs] and two HLA-DR genotypes) and two demographic markers (gestational age and exposure to a prebiotic formula). Integration with the metabolome identified 22 supplemental metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies.

CONCLUSIONS

The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to three pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate, suggesting that these metabolic processes may play a role in triggering islet autoimmunity.

Metabolomics: The Stethoscope for the Twenty-First Century

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

Diagnosis of hepatic nuclear factor 1A monogenic diabetes mellitus (HNF1A-MODY) impacts antihyperglycemic treatment

Monogenic mutations of the hepatocyte nuclear factor 1 homeobox A maturity onset diabetes of the young (HNF1A-MODY) is characterized by early onset, typically before the age of 25 years. Patients are often not clinically recognized; however, the identification of HNF1A-MODY patients is crucial because they require different antihyperglycemic medical treatment than patients with type 1 or type 2 diabetes mellitus. We describe two adult patients with monogenic diabetes, both identified as HNF1A-MODY, genetically c.815G>A, p.Arg272His and c675delC, p.Ser225Argfs*8, respectively. They were misdiagnosed as having type 1 diabetes mellitus, and consequently, initiating insulin therapy led to hypoglycemia and unstable blood glucose control. Usually, sulfonylureas represent the basis of antidiabetic treatment in patients with HNF1A-MODY; however, all medical personnel involved in diabetes care should be aware of monogenic diabetes mellitus and the possibilities for genetic testing. The patients observed have shown the necessity of the identification and appropriate genetic diagnosis of HNF1A-MODY in order to discontinue insulin therapy and to initiate adjusted diabetes management.

Challenges in the diagnosis of diabetes type in pediatrics

The incidence of diabetes, both type 1 and type 2, is increasing. Health outcomes in pediatric diabetes are currently poor, with trends indicating that they are worsening. Minority racial/ethnic groups are disproportionately affected by suboptimal glucose control and have a higher risk of acute and chronic complications of diabetes. Correct clinical management starts with timely and accurate classification of diabetes, but in children this is becoming increasingly challenging due to high prevalence of obesity and shifting demographic composition. The growing obesity epidemic complicates classification by obesity's effects on diabetes. Since the prevalence and clinical characteristics of diabetes vary among racial/ethnic groups, migration between countries leads to changes in the distribution of diabetes types in a certain geographical area, challenging the clinician's ability to classify diabetes. These challenges must be addressed to correctly classify diabetes and establish an appropriate treatment strategy early in the course of disease for all. This may be the first step in improving diabetes outcomes across racial/ethnic groups. This review will discuss the pitfalls in the current diabetes classification scheme that is leading to increasing overlap between diabetes types and heterogeneity within each type. It will also present proposed alternative classification schemes and approaches to understanding diabetes type that may improve the timely and accurate classification of pediatric diabetes type.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

Advances in Research on Diabetes by Human Nutriomics

The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.