Metabolome-wide association study of estimated glomerular filtration rates in Hispanics

Blood metabolites and chronic kidney disease: a Mendelian randomization study

BACKGROUND

Human blood metabolites have demonstrated close associations with chronic kidney disease (CKD) in observational studies. Nonetheless, the causal relationship between metabolites and CKD is still unclear. This study aimed to assess the associations between metabolites and CKD risk.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to evaluate relationships between 1400 blood metabolites and eight phenotypes (outcomes) (CKD, estimated glomerular filtration rate(eGFR), urine albumin to creatinine ratio, rapid progress to CKD, rapid decline of eGFR, membranous nephropathy, immunoglobulin A nephropathy, and diabetic nephropathy). The inverse variance weighted (IVW), MR-Egger, and weighted median were used to investigate the causal relationship. Sensitivity analyses were performed with Cochran's Q, MR-Egger intercept, MR-PRESSO Global test, and leave-one-out analysis. Bonferroni correction was used to test the strength of the causal relationship.

RESULTS

Through the MR analysis of 1400 metabolites and eight clinical phenotypes, a total of 48 metabolites were found to be associated with various outcomes. Among them, N-acetylleucine (OR = 0.923, 95%CI: 0.89-0.957, PIVW = 1.450 × 10-5) has a strong causal relationship with lower risk of CKD after the Bonferroni-corrected test, whereas Glycine to alanine ratio has a strong causal relationship with higher risk of CKD (OR = 1.106, 95%CI: 1.063-1.151, PIVW = 5.850 × 10-7). No horizontal pleiotropy and heterogeneity were detected.

CONCLUSION

Our study offers groundbreaking insights into the integration of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for CKD. It underscores 48 metabolites as potential causal candidates, meriting further investigation.

Untargeted Metabolome Atlas for Sleep Phenotypes in the Hispanic Community Health Study/Study of Latinos

Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep phenotypes and blood metabolites. Utilizing data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep phenotypes, grouped in several domains (i.e., sleep disordered breathing (SDB), sleep duration, timing, insomnia symptoms, and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualize and interpret the associations between sleep phenotypes and metabolites. The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, some xenobiotic metabolites were associated with sleep duration and heart rate phenotypes (e.g. 1H-indole-7-acetic acid, 4-allylphenol sulfate), while ketone bodies and fatty acid metabolism metabolites were associated with sleep timing measures (e.g. 3-hydroxybutyrate (BHBA), 3-hydroxyhexanoylcarnitine (1)). Heart rate phenotypes had the overall largest number of detected metabolite associations. Many of these associations were shared with both SDB and with sleep timing phenotypes, while SDB phenotypes shared relatively few metabolite associations with sleep duration measures. A number of metabolites were associated with multiple sleep phenotypes, from a few domains. The amino acids vanillylmandelate (VMA) and 1-carboxyethylisoleucine were associated with the greatest number of sleep phenotypes, from all domains other than insomnia. This atlas of sleep-metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health.

Circulating Metabolites Associated with Albuminuria in a Hispanic/Latino Population

BACKGROUND

Albuminuria is associated with metabolic abnormalities, but these relationships are not well understood. We studied the association of metabolites with albuminuria in Hispanic/Latino people, a population with high risk for metabolic disease.

METHODS

We used data from 3736 participants from the Hispanic Community Health Study/Study of Latinos, of which 16% had diabetes and 9% had an increased urine albumin-to-creatinine ratio (UACR). Metabolites were quantified in fasting serum through nontargeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Spot UACR was inverse normally transformed and tested for the association with each metabolite or combined, correlated metabolites, in covariate-adjusted models that accounted for the study design. In total, 132 metabolites were available for replication in the Hypertension Genetic Epidemiology Network study ( n =300), and 29 metabolites were available for replication in the Malmö Offspring Study ( n =999).

RESULTS

Among 640 named metabolites, we identified 148 metabolites significantly associated with UACR, including 18 novel associations that replicated in independent samples. These metabolites showed enrichment for D-glutamine and D-glutamate metabolism and arginine biosynthesis, pathways previously reported for diabetes and insulin resistance. In correlated metabolite analyses, we identified two modules significantly associated with UACR, including a module composed of lipid metabolites related to the biosynthesis of unsaturated fatty acids and alpha linolenic acid and linoleic acid metabolism.

CONCLUSIONS

Our study identified associations of albuminuria with metabolites involved in glucose dysregulation, and essential fatty acids and precursors of arachidonic acid in Hispanic/Latino population.

PODCAST

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Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.