Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform

Background: The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensitivity for early-stage detection, thereby limiting improvements in patient survival rates. Methods: In this study, we developed an advanced Mass Spectrometry-based volatilomics platform, complemented by an enhanced breath sampler. The platform integrates artificial intelligence (AI)-assisted algorithms to detect multiple volatile organic compounds (VOCs) biomarkers in human breath. Subsequently, we applied this platform to analyze 364 clinical CRC and normal exhaled samples. Results: The diagnostic signatures, including 2-methyl, octane, and butyric acid, generated by the platform effectively discriminated CRC patients from normal controls with high sensitivity (89.7%), specificity (86.8%), and accuracy (AUC = 0.91). Furthermore, the metastatic signature correctly identified over 50% of metastatic patients who tested negative for carcinoembryonic antigen (CEA). Fecal validation indicated that elevated breath biomarkers correlated with an inflammatory response guided by Bacteroides fragilis in CRC. Conclusion: This study introduces a sophisticated AI-aided Mass Spectrometry-based platform capable of identifying novel and feasible breath biomarkers for early-stage CRC detection. The promising results position the platform as an efficient noninvasive screening test for clinical applications, offering potential advancements in early detection and improved survival rates for CRC patients.

Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort

BACKGROUND

Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition.

METHODS

The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform.

RESULTS

Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10- 45), innate immune system (q = 1.1 × 10- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls.

CONCLUSIONS

The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

Shared Genetics between Age at Menarche and Type 2 Diabetes Mellitus: Genome-Wide Genetic Correlation Study

Background: Age at menarche (AAM) has been associated with type 2 diabetes mellitus (T2DM). However, little is known about their shared heritability. Methods: Our data comes from the Taiwan Biobank. Genome-wide association studies (GWASs) were conducted to identify single-nucleotide polymorphisms (SNPs) related to AAM-, T2DM-, and T2DM-related phenotypes, such as body fat percentage (BFP), fasting blood glucose (FBG), and hemoglobin A1C (HbA1C). Further, the conditional false discovery rate (cFDR) method was applied to examine the shared genetic signals. Results: Conditioning on AAM, Quantile-quantile plots showed an earlier departure from the diagonal line among SNPs associated with BFP and FBG, indicating pleiotropic enrichments among AAM and these traits. Further, the cFDR analysis found 39 independent pleiotropic loci that may underlie the AAM-T2DM association. Among them, FN3KRP rs1046896 (cFDR = 6.84 × 10-49), CDKAL1 rs2206734 (cFDR = 6.48 × 10-10), B3GNTL1 rs58431774 (cFDR = 2.95 × 10-10), G6PC2 rs1402837 (cFDR = 1.82 × 10-8), and KCNQ1 rs60808706 (cFDR = 9.49 × 10-8) were highlighted for their significant genetic enrichment. The protein-protein interaction analysis revealed a significantly enriched network among novel discovered genes that were mostly found to be involved in the insulin and glucagon signaling pathways. Conclusions: Our study highlights potential pleiotropic effects across AAM and T2DM. This may shed light on identifying the genetic causes of T2DM.

Metabolic rewiring and epigenetic reprogramming in leptin receptor-deficient db/db diabetic nephropathy mice

BACKGROUND

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease in the United States. Emerging evidence suggests that mitochondrial metabolism and epigenetics play an important role in the development and progression of DN and its complications. For the first time, we investigated the regulation of cellular metabolism, DNA methylation, and transcriptome status by high glucose (HG) in the kidney of leptin receptor-deficient db/db mice using multi-omics approaches.

METHODS

The metabolomics was performed by liquid-chromatography-mass spectrometry (LC-MS), while epigenomic CpG methylation coupled with transcriptomic gene expression was analyzed by next-generation sequencing.

RESULTS

LC-MS analysis of glomerular and cortex tissue samples of db/db mice showed that HG regulated several cellular metabolites and metabolism-related signaling pathways, including S-adenosylmethionine, S-adenosylhomocysteine, methionine, glutamine, and glutamate. Gene expression study by RNA-seq analysis suggests transforming growth factor beta 1 (TGFβ1) and pro-inflammatory pathways play important roles in early DN. Epigenomic CpG methyl-seq showed HG revoked a list of differentially methylated regions in the promoter region of the genes. Integrated analysis of DNA methylation in the promoter regions of genes and gene expression changes across time points identified several genes persistently altered in DNA methylation and gene expression. Cyp2d22, Slc1a4, and Ddah1 are some identified genes that could reflect dysregulated genes involved in renal function and DN.

CONCLUSION

Our results suggest that leptin receptor deficiency leading to HG regulates metabolic rewiring, including SAM potentially driving DNA methylation and transcriptomic signaling that could be involved in the progression of DN.

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.