Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

Nuclear magnetic resonance-based metabolomics and risk of pancreatic cancer: a prospective analysis in the UK Biobank

BACKGROUND

Plasma metabolite levels in patients with pancreatic cancer (PC) have changed, but the relationship between the altered plasma metabolites and the risk for PC occurrence is not fully clear, as well as the predictive value of the specific metabolites.

METHODS

In this study, we obtained the metabolomics data of 243,145 people from the UK Biobank. An extreme gradient boosting (XGBoost) model, least absolute shrinkage and selection operator (Lasso) regression, and covariate-adjusted Cox proportional hazard regression models were used to evaluate the relationship between metabolites and PC risk. We also evaluated conventional risks, metabolites, and combination models for PC risk by comparing the area under the receiver operating characteristic curve (AUC).

RESULTS

The average follow-up time was 13.8 (± 2.1) years; 1,026 of 243,145 participants developed PC. Fourteen metabolites were significantly associated with PC, including glucose-related metabolites, lipids, lipoproteins, and amino acids. Increased PC risk was associated with citrate, glucose, and the percentage of triglycerides to total lipids in intermediate-density lipoprotein or small low-density lipoprotein. Glycine, histidine, cholesterol, and cholesterol ester subclasses were associated with lower PC risk. Predicting PC risk improved when the newly identified metabolites were added to conventional PC risk factors (AUC: 0.705 vs 0.711, p = 0.037). The Kaplan-Meier cumulative incidence curves showed that these metabolites were good predictors of PC risk (all log-rank p < 0.05).

CONCLUSION

We identified novel metabolites that were significantly associated with the occurrence of PC, which may aid in the early diagnosis of PC.

New composite phenotypes enhance chronic kidney disease classification and genetic associations

Chronic kidney disease (CKD) is a multifactorial condition driven by diverse etiologies that lead to a gradual loss of kidney function. Although genome-wide association studies (GWAS) have identified numerous genetic loci linked to CKD, a large portion of its genetic basis remains unexplained. This knowledge gap may partly arise from the reliance on single biomarkers, such as estimated glomerular filtration rate (eGFR), to assess kidney function. To address this limitation, we developed and applied a novel multi-phenotype approach, combinatorial Principal Component Analysis (cPCA), to better understand the complex genetic architecture of CKD. Using UK Biobank dataset (n = 337,112), we analyzed 21 CKD-related phenotypes, generating over 2 million composite phenotypes (CPs) through cPCA. Nearly 50,000 of these CPs demonstrated significantly higher classification power for clinical CKD compared to individual biomarkers. The top-ranked CP-a combination of albumin, cystatin C, eGFR, gamma-glutamyltransferase, HbA1c, low-density lipoprotein, and microalbuminuria, achieved an AUC of 0.878 (95% CI: 0.873-0.882), significantly outperforming eGFR alone (AUC: 0.830, 95% CI: 0.825-0.835). Genetic association analysis of the ~ 50,000 high-performing CPs identified all major eGFR-associated loci, except for the SH2B3 locus rs3184504, a loss-of-function variant, which was uniquely identified in CPs (p = 3.1[Formula: see text]10-56) but not in eGFR within the same sample size. In addition, SH2B3 locus showed strong evidence of colocalization with eGFR, supporting its role in kidney function. These results highlight the power of the multi-phenotype cPCA approach in understanding the genetic basis of CKD, with potential applications to other complex diseases.

Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI

Chronic kidney disease (CKD) remains a significant global health burden, often diagnosed at advanced stages due to the limitations of traditional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR). This review aims to critically evaluate recent advancements in novel biomarkers, multi-omics technologies, and artificial intelligence (AI)-driven diagnostic strategies, specifically addressing existing gaps in early CKD detection and personalized patient management. We specifically explore key advancements in CKD diagnostics, focusing on emerging biomarkers-including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), soluble urokinase plasminogen activator receptor (suPAR), and cystatin C-and their clinical applications. Additionally, multi-omics approaches integrating genomics, proteomics, metabolomics, and transcriptomics are reshaping disease classification and prognosis. Artificial intelligence (AI)-driven predictive models further enhance diagnostic accuracy, enabling real-time risk assessment and treatment optimization. Despite these innovations, challenges remain in biomarker standardization, large-scale validation, and integration into clinical practice. Future research should focus on refining multi-biomarker panels, improving assay standardization, and facilitating the clinical adoption of precision-driven diagnostics. By leveraging these advancements, CKD diagnostics can transition toward earlier intervention, individualized therapy, and improved patient outcomes.

Metabolomic signature of retinal ageing, polygenetic susceptibility, and major health outcomes

BACKGROUND/AIMS

To identify the metabolic underpinnings of retinal aging and examine how it is related to mortality and morbidity of common diseases.

METHODS

The retinal age gap has been established as essential aging indicator for mortality and systemic health. We applied neural network to train the retinal age gap among the participants in UK Biobank and used nuclear magnetic resonance (NMR) to profile plasma metabolites. The metabolomic signature of retinal ageing (MSRA) was identified using an elastic network model. Multivariable Cox regressions were used to assess associations between the signature with 12 serious health conditions. The participants in Guangzhou Diabetic Eye Study (GDES) cohort were analyzed for validation.

RESULTS

This study included 110 722 participants (mean age 56.5±8.1 years at baseline, 53.8% female), and 28 plasma metabolites associated with retinal ageing were identified. The MSRA revealed significant correlations with each 12 serious health conditions beyond traditional risk factors and genetic predispositions. Each SD increase in MSRA was linked to a 24%-76% higher risk of mortality, cardiovascular diseases, dementia and diabetes mellitus. MSRA showed dose-response relationships with risks of these diseases, with seven showing non-linear and five showing linear increases. Validation in the GDES further established the relation between retinal ageing-related metabolites and increased risks of cardiovascular and chronic kidney diseases (all p<0.05).

CONCLUSIONS

The metabolic connections between ocular and systemic health offer a novel tool for identifying individuals at high risk of premature ageing, promoting a more holistic view of human health.

Systolic blood pressure variability: risk of cardiovascular events, chronic kidney disease, dementia, and death

BACKGROUND AND AIMS

Earlier studies evaluated the association between systolic blood pressure variability (SBPV) measured during a single period and risk of health outcomes. This study expanded upon existing evidence by examining the association between changes in SBPV over time and clinical outcomes in primary care settings.

METHODS

Visit-to-visit SBPV was determined as standard deviation of ≥3 systolic blood pressure values measured at 5-10 (Period 1) and 0-5 (Period 2) years before enrolment in the UK Biobank. Cox proportional hazards models were used to evaluate associations of absolute changes in SBPV and SBPV change patterns between these two periods with risk of cardiovascular disease (CVD), coronary heart disease (CHD), stroke, atrial fibrillation and flutter (AF), heart failure (HF), chronic kidney disease (CKD), dementia, and overall mortality.

RESULTS

A total of 36 251 participants were included with a median follow-up time of 13.9 years. In the fully adjusted models, an increased SBPV from Period 1 to Period 2 was significantly associated with an increased risk of CVD, CHD, stroke, CKD, and overall mortality (all P for trend < .005), reflecting a 23%-33% increased risk comparing participants with an increase in SBPV above Tertile 3 with those below Tertile 1. An increase in SBPV from Period 1 to Period 2 appeared to be associated with an increased risk of AF, HF, and dementia; however, the associations did not reach statistical significance at P < .005. The restricted cubic spline analysis did not reveal non-linear associations, as all P-values for non-linearity were >.05. Regarding SBPV change patterns, compared with the participants with consistently low SBPV, participants with a consistently high SBPV during the two periods had an increased risk of CVD, CHD, stroke, AF, HF, CKD, and overall mortality, with a risk evaluation of 28%-46%. The observed associations remained largely unchanged across subgroup and sensitivity analyses.

CONCLUSIONS

An increase in SBPV over time was associated with an elevated risk of CVD, CKD, and overall mortality. These findings provide compelling evidence to inform the importance for the management of SBPV in clinical practice.

Lipid Biomarkers and Cardiometabolic Diseases: Critical Knowledge Gaps and Future Research Directions

The past decade has witnessed transformative changes in our understanding of various lipid or lipid-related biomarkers (Table 1) and their relationships with cardiometabolic diseases [...].

Machine learning-based plasma metabolomics for improved cirrhosis risk stratification

BACKGROUND

Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis.

METHODS

This study used proton nuclear magnetic resonance (1 H-NMR) serum metabolomics data from the UK Biobank (UKB) and employed elastic net-regularized Cox proportional hazards models to explore the role of metabolomics in cirrhosis risk stratification in patients with CLD. Metabolomic data were integrated with aspartate aminotransferase to platelet ratio index (APRI) and fibrosis-4 score (FIB-4) to construct predictive models for cirrhosis risk. The model performance was assessed in both the derivation and validation cohorts.

RESULTS

A total of 2,738 eligible patients were included in the analysis. Several metabolites showed an independent association with cirrhosis events (68 out of 168 metabolites after adjustment for age and sex, and 21 out of 168 metabolites after full adjustment). The integration of metabolomics with FIB-4 improved the predictive performance compared to FIB-4 alone (Harrell's C: 0.717 vs. 0.696, ΔC = 0.021, 95% confidence interval [CI] 0.014-0.028, Net Reclassification Improvement [NRI]: 0.504 [0.488-0.520]). Similarly, the combination of metabolomics with APRI also improved predictive performance compared to APRI alone (Harrell's C: 0.747 vs. 0.718, ΔC = 0.029, 95% CI 0.022-0.035, NRI: 0.378 [0.366-0.389]). Key metabolites, including branched-chain amino acids (BCAAs), lipids, and markers of oxidative stress, were identified as significant predictors. Pathway enrichment analysis revealed that disruptions in lipid and amino acid metabolism play a central role in the progression of cirrhosis.

CONCLUSION

1 H-NMR serum metabolomics significantly improves the prediction of cirrhosis risk in patients with CLD. The APRI + Metabolomics model demonstrated strong discriminatory power, with key metabolites involved in fatty acid and amino acid metabolism, providing a promising tool for the early screening of cirrhosis risk.

The association between metabolomic profiles of lifestyle and the latent phase of incident chronic kidney disease in the UK Population

Chronic kidney disease (CKD) is a global health challenge associated with lifestyle factors such as diet, alcohol, BMI, smoking, sleep, and physical activity. Metabolomics, especially nuclear magnetic resonance(NMR), offers insights into metabolic profiles' role in diseases, but more research is needed on its connection to CKD and lifestyle factors. Therefore, we utilized the latest metabolomics data from the UK Biobank to explore the relationship between plasma metabolites and lifestyle factors, as well as to investigate the associations between various factors, including lifestyle-related metabolites, and the latent phase of CKD onset. The study enrolled approximately 500,000 participants from the UK Biobank (UKB) between 2006 and 2010, excluding 447,163 individuals with missing data for any metabolite in the NMR metabolomics, any biomarker in the blood chemistry (including eGFR, albumin, or cystatin C), any factor required for constructing the lifestyle score, or a baseline diagnosis of CKD. Lifestyle scores (LS) were calculated based on several factors, including diet, alcohol consumption, smoking, BMI, physical activity, and sleep. Each healthy lifestyle component contributed to the overall score, which ranged from 0 to 6. A total of 249 biological metabolites covering multiple categories were determined by the NMR Metabolomics Platform. Random forest algorithms and LASSO regression were employed to identify lifestyle-related metabolites. Subsequently, accelerated failure time models(AFT) were used to assess the relationship between multiple factors, including traditional CKD-related biomarkers (such as eGFR, cystatin C, and albumin) and lifestyle-related metabolites, with the latent phase of incident CKD. Finally, we performed Kaplan-Meier survival curve analysis on the significant variables identified in the AFT model. Over a mean follow-up period of 13.86 years, 2,279 incident chronic kidney disease (CKD) cases were diagnosed. Among the 249 metabolites analyzed, 15 were identified as lifestyle-related, primarily lipid metabolites. Notably, among these metabolites, each 1 mmol/L increase in triglycerides in large LDL particles accelerated the onset of CKD by 24%. Diabetes, hypertension, and smoking were associated with a 56.6%, 31.5% and 22.3% faster onset of CKD, respectively. Additionally, each unit increase in age, BMI, TDI, and cystatin C was linked to a 3.2%, 1.4%, 1.6% and 32.3% faster onset of CKD. In contrast, higher levels of albumin and eGFR slowed the onset of CKD, reducing the speed of progression by 3.0% and 3.9% per unit increase, respectively. Nuclear magnetic resonance metabolomics offers new insights into renal health, though further validation studies are needed in the future.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes

Background

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes.

Methods

This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites.

Results

During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes.

Conclusions

Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.

Funding

Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Research hotspots and future trends in lipid metabolism in chronic kidney disease: a bibliometric and visualization analysis from 2004 to 2023

Background

Disorders of lipid metabolism play a key role in the initiation and progression of chronic kidney disease (CKD). Recently, research on lipid metabolism in CKD has rapidly increased worldwide. However, comprehensive bibliometric analyses in this field are lacking. Therefore, this study aimed to evaluate publications in the field of lipid metabolism in CKD over the past 20 years based on bibliometric analysis methods to understand the important achievements, popular research topics, and emerging thematic trends.

Methods

Literature on lipid metabolism in CKD, published between 2004 and 2023, was retrieved from the Web of Science Core Collection. The VOSviewer (v.1.6.19), CiteSpace (v.6.3 R1), R language (v.4.3.2), and Bibliometrix (v.4.1.4) packages (https://www.bibliometrix.org) were used for the bibliometric analysis and visualization. Annual output, author, country, institution, journal, cited literature, co-cited literature, and keywords were also included. The citation frequency and H-index were used to evaluate quality and influence.

Results

In total, 1,285 publications in the field of lipid metabolism in CKD were identified in this study. A total of 7,615 authors from 1,885 institutions in 69 countries and regions published articles in 466 journals. Among them, China was the most productive (368 articles), and the United States had the most citations (17,880 times) and the highest H-index (75). Vaziri Nosratola D, Levi Moshe, Fornoni Alessia, Zhao Yingyong, and Merscher Sandra emerged as core authors. Levi Moshe (2,247 times) and Vaziri Nosratola D (1,969 times) were also authors of the top two most cited publications. The International Journal of Molecular Sciences and Kidney International are the most published and cited journals in this field, respectively. Cardiovascular disease (CVD) and diabetic kidney disease (DKD) have attracted significant attention in the field of lipid metabolism. Oxidative stress, inflammation, insulin resistance, autophagy, and cell death are the key research topics in this field.

Conclusion

Through bibliometric analysis, the current status and global trends in lipid metabolism in CKD were demonstrated. CVD and DKD are closely associated with the lipid metabolism of patients with CKD. Future studies should focus on effective CKD treatments using lipid-lowering targets.

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.

The key role of altered tubule cell lipid metabolism in kidney disease development

Kidney epithelial cells have very high energy requirements, which are largely met by fatty acid oxidation. Complex changes in lipid metabolism are observed in patients with kidney disease. Defects in fatty acid oxidation and increased lipid uptake, especially in the context of hyperlipidemia and proteinuria, contribute to this excess lipid build-up and exacerbate kidney disease development. Recent studies have also highlighted the role of increased de novo lipogenesis in kidney fibrosis. The defect in fatty acid oxidation causes energy starvation. Increased lipid uptake, synthesis, and lower fatty acid oxidation can cause toxic lipid build-up, reactive oxygen species generation, and mitochondrial damage. A better understanding of these metabolic processes may open new treatment avenues for kidney diseases by targeting lipid metabolism.