Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis

AIM

This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes.

METHODS

Pre-diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t-tests.

RESULTS

Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody-positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA-positivity was higher in insulin-treated relatives than in non-insulin-treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA-positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody-positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072).

CONCLUSION

This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis.

Coordination chemistry suggests that independently observed benefits of metformin and Zn2+ against COVID-19 are not independent

Independent trials indicate that either oral Zn2+ or metformin can separately improve COVID-19 outcomes by approximately 40%. Coordination chemistry predicts a mechanistic relationship and therapeutic synergy. Zn2+ deficit is a known risk factor for both COVID-19 and non-infectious inflammation. Most dietary Zn2+ is not absorbed. Metformin is a naked ligand that presumably increases intestinal Zn2+ bioavailability and active absorption by cation transporters known to transport metformin. Intracellular Zn2+ provides a natural buffer of many protease reactions; the variable "set point" is determined by Zn2+ regulation or availability. A Zn2+-interactive protease network is suggested here. The two viral cysteine proteases are therapeutic targets against COVID-19. Viral and many host proteases are submaximally inhibited by exchangeable cell Zn2+. Inhibition of cysteine proteases can improve COVID-19 outcomes and non-infectious inflammation. Metformin reportedly enhances the natural moderating effect of Zn2+ on bioassayed proteome degradation. Firstly, the dissociable metformin-Zn2+ complex could be actively transported by intestinal cation transporters; thereby creating artificial pathways of absorption and increased body Zn2+ content. Secondly, metformin Zn2+ coordination can create a non-natural protease inhibitor independent of cell Zn2+ content. Moderation of peptidolytic reactions by either or both mechanisms could slow (a) viral multiplication (b) viral invasion and (c) the pathogenic host inflammatory response. These combined actions could allow development of acquired immunity to clear the infection before life-threatening inflammation. Nirmatrelvir (Paxlovid®) opposes COVID-19 by selective inhibition the viral main protease by a Zn2+-independent mechanism. Pending safety evaluation, predictable synergistic benefits of metformin and Zn2+, and perhaps metformin/Zn2+/Paxlovid® co-administration should be investigated.

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals

INTRODUCTION

None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.

RESEARCH DESIGN AND METHODS

In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden.

RESULTS

Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset.

CONCLUSIONS

In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives

OBJECTIVE

We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis.

METHODS

We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.

RESULTS

Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses.

CONCLUSIONS

The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Protective effects of Huang-Qi-Ge-Gen decoction against diabetic liver injury through regulating PI3K/AKT/Nrf2 pathway and metabolic profiling

ETHNOPHARMACOLOGICAL RELEVANCE

Huang-Qi-Ge-Gen decoction (HGD) is a traditional Chinese medicine prescription that has been used for centuries to treat "Xiaoke" (the name of diabetes mellitus in ancient China). However, the ameliorating effects of HGD on diabetic liver injury (DLI) and its mechanisms are not yet fully understood.

AIM OF THE STUDY

To elucidate the ameliorative effect of HGD on DLI and explore its material basis and potential hepatoprotective mechanism.

MATERIALS AND METHODS

A diabetic mice model was induced by feeding a high-fat diet and injecting intraperitoneally with streptozotocin (40 mg kg-1) for five days. After the animals were in confirmed diabetic condition, they were given HGD (3 or 12 g kg-1, i. g.) for 14 weeks. The effectiveness of HGD in treating DLI mice was evaluated by monitoring blood glucose and blood lipid levels, liver function, and pathological conditions. Furthermore, UPLC-MS/MS was used to identify the chemical component profile in HGD and absorption components in HGD-treated plasma. Network pharmacology and molecular docking were performed to predict the potential pathway of HGD intervention in DLI. Then, the results of network pharmacology were validated by examining biochemical parameters and using western blotting. Lastly, urine metabolites were analyzed by metabolomics strategy to explore the effect of HGD on the metabolic profile of DLI mice.

RESULTS

HGD exerted therapeutic potential against the disorders of glucose metabolism and lipid metabolism, liver dysfunction, liver steatosis, and fibrosis in a DLI model mice induced by HFD/STZ. A total of 108 chemical components in HGD and 18 absorption components in HGD-treated plasma were preliminarily identified. Network pharmacology and molecular docking results of the absorbed components in plasma indicated PI3K/AKT as a potential pathway for HGD to intervene in DLI mice. Further experiments verified that HGD markedly reduced liver oxidative stress in DLI mice by modulating the PI3K/AKT/Nrf2 signaling pathway. Moreover, 19 differential metabolites between normal and DLI mice were detected in urine, and seven metabolites could be significantly modulated back by HGD.

CONCLUSIONS

HGD could ameliorate diabetic liver injury by modulating the PI3K/AKT/Nrf2 signaling pathway and urinary metabolic profile.

Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review

Type 2 diabetes mellitus (T2DM) poses a significant global health burden. This is particularly due to its macrovascular complications, such as coronary artery disease, peripheral vascular disease, and cerebrovascular disease, which have emerged as leading contributors to morbidity and mortality. This review comprehensively explores the pathophysiological mechanisms underlying these complications, protective strategies, and both existing and emerging secondary preventive measures. Furthermore, we delve into the applications of experimental models and methodologies in foundational research while also highlighting current research limitations and future directions. Specifically, we focus on the literature published post-2020 concerning the secondary prevention of macrovascular complications in patients with T2DM by conducting a targeted review of studies supported by robust evidence to offer a holistic perspective.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

The Multifaceted S100B Protein: A Role in Obesity and Diabetes?

The S100B protein is abundant in the nervous system, mainly in astrocytes, and is also present in other districts. Among these, the adipose tissue is a site of concentration for the protein. In the light of consistent research showing some associations between S100B and adipose tissue in the context of obesity, metabolic disorders, and diabetes, this review tunes the possible role of S100B in the pathogenic processes of these disorders, which are known to involve the adipose tissue. The reported data suggest a role for adipose S100B in obesity/diabetes processes, thus putatively re-proposing the role played by astrocytic S100B in neuroinflammatory/neurodegenerative processes.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries

Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.