Background And Aims: The aim is to investigate the cause-and-effect connection between metabolites found in blood/urine and the likelihood of developing periodontal disease (PD) through the utilization of a two-sample Mendelian randomization (MR) method.
Methods: Using an inverse variance weighted (IVW) method and two additional two-sample MR models, we examined the relationship between blood/urine metabolites and PD by analyzing data from a comprehensive metabolome-based genome-wide association study and the Genome-Wide Association Studies (GWAS) of PD. To assess the consistency and dependability of the findings, diversity, cross-effects, and sensitivity analyses were conducted.
Results: Out of the 35 metabolites found in blood and urine, a total of eight metabolites (C-reactive protein, Potassium in urine, Urea, Cystatin C, Non-albumin protein, Creatinine, estimated Glomerular Filtration Rate, and Phosphate) displayed a possible causal connection with the risk of dental caries/PD using the inverse variance weighted (IVW) method ( < 0.05). This includes five metabolites in the blood and three in the urine. No metabolites were statistically significant in IVW MR models ( < 3.68 × 10 ). Even after conducting sensitivity analysis with the leave-one-out method and removing the confounding instrumental variables, the impact of these factors on dental caries/PD remained significant.
Conclusion: Based on the available evidence, it is not possible to establish a significant causal link between the 35 blood metabolites and the likelihood of developing dental caries and PD.
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http://dx.doi.org/10.1002/hsr2.1895 | DOI Listing |
Nat Commun
January 2025
Folkhälsan Research Center, Helsinki, Finland.
Dissecting the genetic mechanisms underlying urinary metabolite concentrations can provide molecular insights into kidney function and open possibilities for causal assessment of urinary metabolites with risk factors and disease outcomes. Proton nuclear magnetic resonance metabolomics provides a high-throughput means for urinary metabolite profiling, as widely applied for blood biomarker studies. Here we report a genome-wide association study meta-analysed for 3 European cohorts comprising 8,011 individuals, covering both people with type 1 diabetes and general population settings.
View Article and Find Full Text PDFSurgery
December 2024
Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC; Department of Medicine (Endocrinology), Duke University School of Medicine, Durham, NC.
Objective: To characterize early physiologic stresses imposed by surgery by applying metabolomic analyses to deeply phenotype pre- and postoperative plasma and urine of patients undergoing elective surgical procedures.
Background: Patients experience perioperative stress through depletion of metabolic fuels. Bowel stasis or injury might allow more microbiome-derived uremic toxins to enter the blood, while the liver and kidney are simultaneously clearing analgesic and anesthetic drugs.
J Cancer Res Clin Oncol
December 2024
Division of Urology/Urooncology, Department of Surgery, School of Medicine, Universidad del Valle, Calle 4 B # 36-00, Cali, Colombia.
Background: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples.
View Article and Find Full Text PDFMetabolites
December 2024
Department of Biochemistry and Molecular Biology and Soil Science and Agricultural Chemistry, University of Alicante, 03690 Alicante, Spain.
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has spurred an extraordinary scientific effort to better understand the disease's pathophysiology and develop diagnostic and prognostic tools to guide more precise and effective clinical management. Among the biological samples analyzed for biomarker identification, urine stands out due to its low risk of infection, non-invasive collection, and suitability for frequent, large-volume sampling. Integrating data from omics studies with standard biochemical analyses offers a deeper and more comprehensive understanding of COVID-19.
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