Proteomic signatures for identification of impaired glucose tolerance

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Enhanced interactions among gut mycobiomes with the deterioration of glycemic control

BACKGROUND

The gut mycobiome is closely linked to health and disease; however, its role in the progression of type 2 diabetes mellitus (T2DM) remains obscure. Here, a multi-omics approach was employed to explore the role of intestinal fungi in the deterioration of glycemic control.

METHODS

350 participants without hypoglycemic therapies were invited for a standard oral glucose tolerance test to determine their status of glycemic control. The gut mycobiome was identified through internal transcribed spacer sequencing, host genetics were determined by genotyping array, and plasma metabolites were measured with untargeted liquid chromatography mass spectrometry.

FINDINGS

The richness of fungi was higher, whereas its dissimilarity was markedly lower, in participants with T2DM. Moreover, the diversity and composition of fungi were closely associated with insulin sensitivity and pancreatic β-cell functions. With the exacerbation of glycemic control, the co-occurrence network among fungus taxa became increasingly complex, and the complexity of the interaction network was inversely associated with insulin sensitivity. Mendelian randomization analysis further demonstrated that the Archaeorhizomycetes class, Fusarium genus, and Neoascochyta genus were causally linked to impaired glucose metabolism. Furthermore, integrative analysis with metabolomics showed that increased 4-hydroxy-2-oxoglutaric acid, ketoleucine, lysophosphatidylcholine (20:3/0:0), and N-lactoyl-phenylalanine, but decreased lysophosphatidylcholine (O-18:2), functioned as key molecules linking the adverse effect of Fusarium genus on insulin sensitivity.

CONCLUSIONS

Our study uncovers a strong association between disturbance in gut fungi and the progression of T2DM and highlights the potential of targeting the gut mycobiome for the management of T2DM.

FUNDINGS

This study was supported by MOST and NSFC of China.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Human cord plasma proteomic analysis reveals sexually dimorphic proteins associated with intrauterine growth restriction

Intrauterine growth restriction (IUGR) is associated with increased risk of cardiometabolic disease later in life and has been shown to affect female and male offspring differently, but the mechanisms remain unclear. The purpose of this study was to identify proteomic differences and metabolic risk markers in IUGR male and female neonates when compared to appropriate for gestational age (AGA) babies that will provide a better understanding of IUGR pathogenesis and its associated risks. Our results revealed alterations in IUGR cord plasma proteomes with most of the differentially abundant proteins implicated in peroxisome pathways. This effect was evident in females but not in males. Furthermore, we observed that catalase activity, a peroxisomal enzyme, was significantly increased in females (p < 0.05) but unchanged in males. Finally, we identified risk proteins associated with obesity, type-2 diabetes, and glucose intolerance such as EGF containing fibulin extracellular matrix protein 1 (EFEMP1), proprotein convertase subtilisin/kexin type 9 (PCSK9) and transforming growth factor beta receptor 3 (TGFBR3) proteins unique to females while coagulation factor IX (C9) and retinol binding protein 4 (RBP4) are unique in males. In conclusion, IUGR may display sexual dimorphism which may be associated with differences in lifelong risk for cardiometabolic disease between males and females.

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.

Genetic determinants of micronucleus formation in vivo

Genomic instability arising from defective responses to DNA damage1 or mitotic chromosomal imbalances2 can lead to the sequestration of DNA in aberrant extranuclear structures called micronuclei (MN). Although MN are a hallmark of ageing and diseases associated with genomic instability, the catalogue of genetic players that regulate the generation of MN remains to be determined. Here we analyse 997 mouse mutant lines, revealing 145 genes whose loss significantly increases (n = 71) or decreases (n = 74) MN formation, including many genes whose orthologues are linked to human disease. We found that mice null for Dscc1, which showed the most significant increase in MN, also displayed a range of phenotypes characteristic of patients with cohesinopathy disorders. After validating the DSCC1-associated MN instability phenotype in human cells, we used genome-wide CRISPR-Cas9 screening to define synthetic lethal and synthetic rescue interactors. We found that the loss of SIRT1 can rescue phenotypes associated with DSCC1 loss in a manner paralleling restoration of protein acetylation of SMC3. Our study reveals factors involved in maintaining genomic stability and shows how this information can be used to identify mechanisms that are relevant to human disease biology1.

Growth Hormone Mediators and Glycemic Control in Youths With Type 2 Diabetes: A Secondary Analysis of a Randomized Clinical Trial

Importance

Youth-onset type 2 diabetes (T2D) has a more aggressive phenotype than adult-onset T2D, including rapid loss of glycemic control and increased complication risk.

Objective

To identify associations of growth hormone mediators with glycemic failure, beta cell function, and insulin sensitivity in youth-onset T2D.

Design, Setting, and Participants

This post hoc secondary analysis of the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) randomized clinical trial, which enrolled participants from July 2004 to February 2009, included 398 participants from 15 university-affiliated medical centers with available plasma samples from baseline and 36 months. Participants were youths aged 10 to 17 years with a duration of T2D of less than 2 years who were randomized to metformin, metformin plus lifestyle intervention, or metformin plus rosiglitazone. Participants were followed up for a mean (SD) of 3.9 (1.5) years during the trial, ending in 2011. Statistical analysis was performed from August 2022 to November 2023.

Exposure

Plasma insulin-like growth factor-1 (IGF-1), growth hormone receptor (GHR), and insulin-like growth factor binding protein 1 (IGFBP-1).

Main Outcomes and Measures

Main outcomes were (1) loss of glycemic control during the TODAY study, defined as hemoglobin A1c (HbA1c) level of 8% or more for 6 months or inability to wean from insulin therapy, and (2) baseline and 36-month measures of glycemia (fasting glucose, HbA1c), insulin sensitivity (1/fasting C-peptide), high-molecular-weight adiponectin, and beta cell function (C-peptide index, C-peptide oral disposition index).

Results

This analysis included 398 participants (mean [SD] age, 13.9 [2.0] years; 248 girls [62%]; 166 Hispanic participants [42%]; 134 non-Hispanic Black participants [34%], and 84 non-Hispanic White participants [21%]). A greater increase in IGF-1 level between baseline and 36 months was associated with lower odds of glycemic failure (odds ratio [OR], 0.995 [95% CI, 0.991-0.997]; P < .001) and higher C-peptide index per 100-ng/mL increase in IGF-1 (β [SE], 0.015 [0.003]; P < .001). A greater increase in log2 GHR level between baseline and 36 months was associated with higher odds of glycemic failure (OR, 1.75 [95% CI, 1.05-2.99]; P = .04) and lower C-peptide index (β [SE], -0.02 [0.006]; P < .001). A greater increase in log2 IGFBP-1 level between baseline and 36 months was associated with higher odds of glycemic failure (OR, 1.37 [95% CI, 1.09-1.74]; P = .007) and higher high-molecular-weight adiponectin (β [SE], 431 [156]; P = .007).

Conclusions and Relevance

This study suggests that changes in plasma growth hormone mediators are associated with loss of glycemic control in youth-onset T2D, with IGF-1 associated with lower risk and GHR and IGFBP-1 associated with increased risk.

Trial Registration

ClinicalTrials.gov Identifier: NCT00081328.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Higher systemic immune-inflammation index is associated with sarcopenia in individuals aged 18-59 years: a population-based study

The association between the systemic immune-inflammation index (SII) and the risk of sarcopenia has not yet been revealed. The purpose of this study was to investigate the relationship between the SII and sarcopenia in individuals aged 18-59 years. All data for this study are from the National Health and Nutrition Examination Survey (NHANES) database, including 7258 participants (age range: 18-59 years). We divided SII values by quartiles (quartiles 1-4: 0.3-3.1, 3.2-4.4, 4.4-6.2, and 6.2-58.5). We constructed a multivariate logistic regression model to assess the association between the SII and the risk of sarcopenia, and an interaction test was run to test the stability of the model and identify high-risk individuals with sarcopenia. Compared to nonsarcopenia participants, sarcopenia patients had a significantly higher SII value (weighted average: 6.65 vs. 5.16) (P = 0.002). Multivariate logistic regression results showed a positive linear relationship between the SII and sarcopenia (OR [odds ratio] = 1.12, 95% CI [confidence interval] 1.03-1.21). Compared to the quartile 1 group, the quartile 4 group was associated with a higher risk of sarcopenia (OR = 3.94, 95% CI 1.42-10.94). Compared with the quartile 1 group, the OR value of the quartile 2 to quartile 4 groups showed an upwards trend (Ptrend < 0.001) as the level of SII increased. Subgroup analysis also indicate that the correlation between higher SII values and the risk of sarcopenia was stable. There was a significant positive linear relationship between SII and sarcopenia, indicating that higher SII values can increase the risk of sarcopenia in individuals aged 18-59 in the United States. The findings of this study will be beneficial in promoting the use of SII alone or in combination with other tools for the risk screening of sarcopenia in communities or large populations.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Machine learning in precision diabetes care and cardiovascular risk prediction

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

Proteomic Predictors of Incident Diabetes: Results From the Atherosclerosis Risk in Communities (ARIC) Study

OBJECTIVE

The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis.

RESEARCH DESIGN AND METHODS

In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments.

RESULTS

There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk.

CONCLUSIONS

We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.