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      Associations of plasma TMAO and its precursors with stroke risk in the general population: A nested case‐control study

      1 , 2 , 2 , 1 , 1 , 3
      Journal of Internal Medicine
      Wiley

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          Abstract

          Background

          Trimethylamine N‐oxide (TMAO) is a gut‐derived atherogenic metabolite. However, the role of TMAO and its precursors in the development of stroke remains unclear. We aimed to examine the associations between metabolites in TMAO biosynthesis and stroke risk.

          Methods

          A nested case‐control study was performed in a community‐based cohort (2013–2018, n = 16,113). We included 412 identified stroke cases and 412 controls matched by age and sex. Plasma carnitine, choline, betaine, trimethyl lysine (TML), and TMAO were measured by ultrahigh performance liquid chromatography–tandem mass spectrometry. Conditional logistic regression analyses were used to calculate odds ratios (ORs) and their 95% confidence intervals (CIs) between these biomarkers and stroke risk.

          Results

          After adjustment for body mass index, smoking, hypertension, educational attainment, and estimated glomerular filtration rate, the corresponding OR for the highest versus lowest quartile was 1.74 (95% CI: 1.16–2.61, P trend = 0.006) for total stroke and 1.81 (95% CI: 1.14–2.86, P trend = 0.020) for ischemic stroke in an essentially linear dose–response fashion. A significant association between TMAO and nonischemic stroke was shown as a J‐shape with OR for the highest versus second quartile of 5.75 (95% CI: 1.73–19.1). No meaningful significant risk association was found among plasma carnitine, choline, betaine, and TML with stroke risk.

          Conclusions

          Increased TMAO was associated with higher stroke risk in the community‐based population, whereas the TMAO precursors carnitine, choline, betaine, and TML were not associated. Further studies are warranted to confirm these findings and to further elucidate the role of TMAO in the development of stroke.

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          Most cited references49

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease

            Metabolomics studies hold promise for discovery of pathways linked to disease processes. Cardiovascular disease (CVD) represents the leading cause of death and morbidity worldwide. A metabolomics approach was used to generate unbiased small molecule metabolic profiles in plasma that predict risk for CVD. Three metabolites of the dietary lipid phosphatidylcholine, namely choline, trimethylamine N-oxide (TMAO), and betaine, were identified and then shown to predict risk for CVD in an independent large clinical cohort. Dietary supplementation of mice with choline, TMAO or betaine promoted up-regulation of multiple macrophage scavenger receptors linked to atherosclerosis, and supplementation with choline or TMAO promoted atherosclerosis. Studies using germ-free mice confirmed a critical role for dietary choline and gut flora in TMAO production, augmented macrophage cholesterol accumulation and foam cell formation. Suppression of intestinal microflora in atherosclerosis-prone mice inhibited dietary choline-enhanced atherosclerosis. Genetic variations controlling expression of flavin monooxygenases (FMOs), an enzymatic source of TMAO, segregated with atherosclerosis in hyperlipidemic mice. Discovery of a relationship between gut flora-dependent metabolism of dietary phosphatidylcholine and CVD pathogenesis provides opportunities for development of both novel diagnostic tests and therapeutic approaches for atherosclerotic heart disease.
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              Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis

              Intestinal microbiota metabolism of choline/phosphatidylcholine produces trimethylamine (TMA), which is further metabolized to a proatherogenic species, trimethylamine-N-oxide (TMAO). Herein we demonstrate that intestinal microbiota metabolism of dietary L-carnitine, a trimethylamine abundant in red meat, also produces TMAO and accelerates atherosclerosis. Omnivorous subjects are shown to produce significantly more TMAO than vegans/vegetarians following ingestion of L-carnitine through a microbiota-dependent mechanism. Specific bacterial taxa in human feces are shown to associate with both plasma TMAO and dietary status. Plasma L-carnitine levels in subjects undergoing cardiac evaluation (n = 2,595) predict increased risks for both prevalent cardiovascular disease (CVD) and incident major adverse cardiac events (MI, stroke or death), but only among subjects with concurrently high TMAO levels. Chronic dietary L-carnitine supplementation in mice significantly altered cecal microbial composition, markedly enhanced synthesis of TMA/TMAO, and increased atherosclerosis, but not following suppression of intestinal microbiota. Dietary supplementation of TMAO, or either carnitine or choline in mice with intact intestinal microbiota, significantly reduced reverse cholesterol transport in vivo. Intestinal microbiota may thus participate in the well-established link between increased red meat consumption and CVD risk.
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                Author and article information

                Contributors
                Journal
                Journal of Internal Medicine
                J Intern Med
                Wiley
                0954-6820
                1365-2796
                January 2023
                October 06 2022
                January 2023
                : 293
                : 1
                : 110-120
                Affiliations
                [1 ] School of Public Health Suzhou Medical College of Soochow University Suzhou China
                [2 ] Department of Chronic Disease Control and Prevention Changshu Center for Disease Control and Prevention Suzhou China
                [3 ] Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases Suzhou Medical College of Soochow University Suzhou China
                Article
                10.1111/joim.13572
                3b27dd12-733a-4b93-8cf1-4dad1922cc5b
                © 2023

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