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

Proteomic analysis identifies novel biological pathways that may link dietary quality to type 2 diabetes risk: evidence from African American and Asian cohorts

BACKGROUND

Diet affects the development of chronic diseases such as type 2 diabetes, but the underlying biological mechanisms are only partly understood.

OBJECTIVES

This study aimed to identify proteomic markers of the Alternative Healthy Eating Index (AHEI) and the Dietary Approaches to Stop Hypertension (DASH) diet and their association with type 2 diabetes risk.

METHODS

We examined the associations between the AHEI and DASH diet quality scores and 1317 plasma proteins in African American participants of the Jackson Heart Study (JHS, n = 1878). These findings were validated in a Singapore Multi-Ethnic Cohort (n = 2395) and examined in relation to type 2 diabetes incidence (n = 539 cases). We adjusted for multiple testing by using false discovery rate-adjusted q values.

RESULTS

We identified 13 proteins consistently associated with the AHEI or DASH scores with the strongest associations for the AHEI score and epidermal growth factor receptor (β:0.089; SE: 0.017; q < 0.001) and for the DASH score and tissue factor (β: -0.114; SE: 0.022; q < 0.001). Most of these proteins were related to inflammation, thrombosis, adipogenesis, and glucose metabolism. Concentrations of myeloperoxidase, epidermal growth factor receptor, hepatocyte growth factor receptor, coagulation factor Xa, contactin 4, kynureninase, neurogenic locus notch homolog protein 1, and vesicular integral-membrane protein VIP36 were associated with the risk of type 2 diabetes in the Asian cohort. The diabetes odds ratio for a 2-fold higher protein abundance concentration ranged from 0.03 (95% CI: 0.01, 0.08) for neurogenic locus notch homolog protein 1 to 3.04 (95% CI: 2.13, 4.33) for kynureninase. Furthermore, genetic markers for myeloperoxidase and hepatocyte growth factor receptor were significantly associated with diabetes risk.

CONCLUSIONS

Our study across geographically and ethnically diverse populations identified robust protein biomarkers for healthy dietary patterns. Furthermore, our findings suggest novel biological mechanisms linking dietary patterns with type 2 diabetes development.

Microbial and proteomic signatures of type 2 diabetes in an Arab population

BACKGROUND

The rising prevalence of Type 2 diabetes mellitus (T2D) in the Qatari population presents a significant public health challenge, highlighting the need for innovative approaches to early detection and management. While most efforts are centered on using blood samples for biomarker discovery, the use of saliva remains underexplored.

METHODS

Using noninvasive saliva samples from 2974 Qatari subjects, we analyzed the microbial communities from diabetic, pre-diabetic, and non-diabetic participants based on their HbA1C levels. The salivary microbiota was assessed in all subjects by sequencing the V1-V3 regions of 16S rRNA gene. For the proteomics profiling, we randomly selected 50 gender and age-matched non-diabetic and diabetic subjects and compared their proteome with SOMAscan. Microbiota and proteome profiles were then integrated to reveal candidate biomarkers for T2D.

RESULTS

Our results indicate that the salivary microbiota of pre-diabetic and diabetic individuals differs significantly from that of non-diabetic subjects. Specifically, a significant increase in the abundance of Campylobacter, Dorea, and Bacteroidales was observed in the diabetic subjects compared to their non-diabetic controls. Metabolic pathway prediction analysis for these bacteria revealed a significant overrepresentation of genes associated with fatty acid and lipid biosynthesis, as well as aromatic amino acid metabolism in the diabetic group. Additionally, we observed distinct differences in salivary proteomic profiles between diabetic and non-diabetic subjects. Notably, levels of Haptoglobin, Plexin-C1, and MCL-1 were elevated, while Osteopontin (SPP1), Histone1H3A (HIST3H2A), and Histone H1.2 were reduced in diabetic individuals. Furthermore, integrated correlation analysis of salivary proteome and microbiota data demonstrated a strong positive correlation between HIST1H3A and HIST3H2A with Porphyromonas sp., all of which were decreased in the diabetic group.

CONCLUSION

This is the first study to assess the salivary microbiota in T2D patients from a large cohort of the Qatari population. We found significant differences in the salivary microbiota of pre-diabetic and diabetic individuals compared to non-diabetic controls. Our study is also the first to assess the salivary proteome using SOMAScan in diabetic and non-diabetic subjects. Integration of the microbiota and proteome profiles revealed a unique signature for T2D that can be used as potential T2D biomarkers.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus

This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts

BACKGROUND

Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black).

MEDTHODS AND FINDINGS

A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.68 and 1.36 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals.

CONCLUSIONS

In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.

Cardiorespiratory fitness, body composition, diabetes, and longevity: a two-sample Mendelian randomization study

CONTEXT

Cardiorespiratory fitness, commonly assessed as maximal volume of oxygen consumption (VO2max), has emerged as an important predictor of morbidity and mortality.

OBJECTIVE

We investigated the causality and directionality of the associations of VO2max with body composition, physical activity, diabetes, performance enhancers, and longevity.

METHODS

Using publicly available summary statistics from the largest genome-wide association studies publicly available, we conducted a bidirectional two-sample Mendelian randomization (MR) study. Bidirectional MR tested directionality, and estimated the total causal effects, whereas multivariable MR (MVMR) estimated independent causal effects. Cardiorespiratory fitness (VO2max) was estimated from a submaximal cycle ramp test (N≈90,000) and scaled to total body weight, and in additional analyses to fat-free mass (mL/min/kg).

RESULTS

Genetically predicted higher (per one standard deviation increase) body fat percentage was associated with lower VO2max (β=-0.36 [95% CI: -0.40, -0.32], p=6E-77). Meanwhile, genetically predicted higher appendicular lean mass (0.10 [0.08,0.13] p=3E-16), physical activity (0.29 [0.07,0.52]), and performance enhancers (fasting insulin, hematocrit, and free testosterone in men) were all positively associated with VO2max (p<0.01). Genetic predisposition to diabetes had no effect on VO2max. MVMR showed independent causal effects of body fat percentage, appendicular lean mass, physical activity, and hematocrit on VO2max, as well as of body fat percentage and type 2 diabetes (T2D) on longevity. Genetically predicted VO2max showed no associations.

CONCLUSION

Cardiorespiratory fitness can be improved by favorable body composition, physical activity, and performance enhancers. Despite being a strong predictor of mortality, VO2max is not causally associated with T2D or longevity.

Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes

OBJECTIVE

Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D.

RESULTS

Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D.

CONCLUSIONS

Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.

Plasma Neuronal Growth Regulator 1 May Link Physical Activity to Reduced Risk of Type 2 Diabetes: A Proteome-Wide Study of ARIC Participants

Habitual physical activity (PA) impacts the plasma proteome and reduces the risk of developing type 2 diabetes (T2D). Using a large-scale proteome-wide approach in Atherosclerosis Risk in Communities study participants, we aimed to identify plasma proteins associated with PA and determine which of these may be causally related to lower T2D risk. PA was associated with 92 plasma proteins in discovery (P < 1.01 × 10-5), and 40 remained significant in replication (P < 5.43 × 10-4). Eighteen of these proteins were independently associated with incident T2D (P < 1.25 × 10-3), including neuronal growth regulator 1 (NeGR1; hazard ratio per SD 0.85; P = 7.5 × 10-11). Two-sample Mendelian randomization (MR) inverse variance weighted analysis indicated that higher NeGR1 reduces T2D risk (odds ratio [OR] per SD 0.92; P = 0.03) and was consistent with MR-Egger, weighted median, and weighted mode sensitivity analyses. A stronger association was observed for the single cis-acting NeGR1 genetic variant (OR per SD 0.80; P = 6.3 × 10-5). Coupled with previous evidence that low circulating NeGR1 levels promote adiposity, its association with PA and potential causal role in T2D shown here suggest that NeGR1 may link PA exposure with metabolic outcomes. Further research is warranted to confirm our findings and examine the interplay of PA, NeGR1, adiposity, and metabolic health.

ARTICLE HIGHLIGHTS

Proteomic and Metabolomic Signatures of Diet Quality in Young Adults

The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.

A framework towards digital twins for type 2 diabetes

Introduction

A digital twin is a virtual representation of a patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction of disease progression, optimization of care delivery, and improvement of outcomes.

Methods

Here, we introduce a digital twin framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, and mechanistic models. By analyzing a substantial multiomic and clinical dataset, we constructed predictive machine learning models to forecast disease progression. Furthermore, knowledge graphs were employed to elucidate and contextualize multiomic-disease relationships.

Results and discussion

Our findings not only reaffirm known targetable disease components but also spotlight novel ones, unveiled through this integrated approach. The versatile components presented in this study can be incorporated into a digital twin system, enhancing our grasp of diseases and propelling the advancement of precision medicine.

Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses

We conduct proteome-wide Mendelian randomization and colocalization analyses to decipher the associations of blood proteins with the risk of type 2 diabetes and diabetic complications. Genetic data on plasma proteome are obtained from 54,306 UK Biobank participants and 35,559 Icelanders. Summary-level data on type 2 diabetes are obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis consortium) consortium (74,124 cases) and FinnGen study (33,043 cases). Data on 10 diabetic complications are obtained from FinnGen and corresponding studies. Among 1,886 proteins, genetically predicted levels of 47 plasma proteins are associated with type 2 diabetes. Eleven of these proteins have strong support of colocalization. Seventeen proteins are associated with at least one diabetic complication, although a few have colocalization support. HLA-DRA, AGER, HSPA1A, and HSPA1B are associated with most microvascular complications. This study reveals causal proteins for the onset of type 2 diabetes and diabetic complications, which enhances the understanding of molecular etiology and development of therapeutics.