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Rossmeislová L, Gojda J, Smolková K. Pancreatic cancer: branched-chain amino acids as putative key metabolic regulators? Cancer Metastasis Rev 2021; 40:1115-1139. [PMID: 34962613 DOI: 10.1007/s10555-021-10016-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/18/2021] [Indexed: 02/06/2023]
Abstract
Branched-chain amino acids (BCAA) are essential amino acids utilized in anabolic and catabolic metabolism. While extensively studied in obesity and diabetes, recent evidence suggests an important role for BCAA metabolism in cancer. Elevated plasma levels of BCAA are associated with an increased risk of developing pancreatic cancer, namely pancreatic ductal adenocarcinoma (PDAC), a tumor with one of the highest 1-year mortality rates. The dreadful prognosis for PDAC patients could be attributable also to the early and frequent development of cancer cachexia, a fatal host metabolic reprogramming leading to muscle and adipose wasting. We propose that BCAA dysmetabolism is a unifying component of several pathological conditions, i.e., obesity, insulin resistance, and PDAC. These conditions are mutually dependent since PDAC ranks among cancers tightly associated with obesity and insulin resistance. It is also well-established that PDAC itself can trigger insulin resistance and new-onset diabetes. However, the exact link between BCAA metabolism, development of PDAC, and tissue wasting is still unclear. Although tissue-specific intracellular and systemic metabolism of BCAA is being intensively studied, unresolved questions related to PDAC and cancer cachexia remain, namely, whether elevated circulating BCAA contribute to PDAC etiology, what is the biological background of BCAA elevation, and what is the role of adipose tissue relative to BCAA metabolism during cancer cachexia. To cover those issues, we provide our view on BCAA metabolism at the intracellular, tissue, and whole-body level, with special emphasis on different metabolic links to BCAA intermediates and the role of insulin in substrate handling.
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Affiliation(s)
- Lenka Rossmeislová
- Department of Pathophysiology, Center for Research On Nutrition, Metabolism, and Diabetes, Third Faculty of Medicine, Charles University, Prague, Czech Republic
- Franco-Czech Laboratory for Clinical Research On Obesity, Third Faculty of Medicine, Prague, Czech Republic
| | - Jan Gojda
- Franco-Czech Laboratory for Clinical Research On Obesity, Third Faculty of Medicine, Prague, Czech Republic
- Department of Internal Medicine, Královské Vinohrady University Hospital and Third Faculty of Medicine, Prague, Czech Republic
| | - Katarína Smolková
- Laboratory of Mitochondrial Physiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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Leal Yepes FA, Mann S, Overton TR, Ryan CM, Bristol LS, Granados GE, Nydam DV, Wakshlag JJ. Effect of rumen-protected branched-chain amino acid supplementation on production- and energy-related metabolites during the first 35 days in milk in Holstein dairy cows. J Dairy Sci 2019; 102:5657-5672. [PMID: 30928273 DOI: 10.3168/jds.2018-15508] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 02/09/2019] [Indexed: 12/27/2022]
Abstract
Essential AA are critical for multiple physiological processes. Branched-chain AA (BCAA) supplementation has beneficial effects on body weight, lipogenesis, and insulin resistance in several species. The BCAA are used for milk and body protein synthesis as well as being oxidized by the tricarboxylic acid cycle to produce ATP during catabolic states. The objective was to evaluate the effect of rumen-protected BCAA (375 g of 27% l-Leu, 85 g of 48% l-Ile, and 91 g of 67% l-Val) with or without propylene glycol (PG) oral administration on milk production, dry matter intake, nonesterified fatty acids, β-hydroxybutyrate, and plasma urea nitrogen during the first 35 d in milk (DIM) in dairy cattle. Multiparous Holstein cows were enrolled in blocks of three 28 d before expected calving and assigned randomly to either the control or 1 of 2 treatments. The control (n = 26) received 200 g/d of dry molasses, the BCAA treatment (n = 23) received BCAA mixed with 200 g/d of dry molasses from calving until 35 DIM, and the BCAA plus PG (BCAAPG) treatment (n = 25) received BCAA mixed with 200 g/d of dry molasses from calving until 35 DIM plus 300 mL of PG once daily from calving until 7 DIM. Postpartum, dry matter intake least squares means (LSM; 95% confidence interval) were 20.7 (19.9, 21.7), 21.3 (20.4, 22.3), and 21.9 (20.9, 22.8) kg for control, BCAA, and BCAAPG, respectively. Milk yield (1-35 DIM) LSM were 41.7 (39.4, 44.0), 42.7 (40.3, 45.0), and 43.7 (41.4, 46.0) kg for control, BCAA, and BCAAPG, respectively. Energy-corrected milk LSM were 50.3 (46.8, 53.7), 52.4 (48.9, 55.8), and 52.9 (49.5, 56.4) kg for control, BCAA, and BCAAPG, respectively. Milk urea nitrogen LSM in milk for control, BCAA, and BCAAPG were 8.60 (8.02, 9.22), 9.70 (9.01, 10.45), and 9.75 (9.08, 10.47) mg/dL. Plasma urea nitrogen concentrations LSM for control, BCAA, and BCAAPG were 8.3 (7.7, 8.9), 10.1 (9.4, 10.9), and 9.6 (9.4, 10.3) mg/dL, respectively. The numbers of plasma samples classified as hyperketonemia were 77, 44, and 57 in control, BCAA, and BCAAPG, respectively. The BCAA supplementation increased plasma urea nitrogen and milk urea nitrogen, free valine concentration in plasma, and decreased hyperketonemia events during the postpartum period.
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Affiliation(s)
- F A Leal Yepes
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - S Mann
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
| | - T R Overton
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - C M Ryan
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - L S Bristol
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
| | - G E Granados
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - D V Nydam
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
| | - J J Wakshlag
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853.
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Lacruz ME, Kluttig A, Tiller D, Medenwald D, Giegling I, Rujescu D, Prehn C, Adamski J, Frantz S, Greiser KH, Emeny RT, Kastenmüller G, Haerting J. Cardiovascular Risk Factors Associated With Blood Metabolite Concentrations and Their Alterations During a 4-Year Period in a Population-Based Cohort. ACTA ACUST UNITED AC 2016; 9:487-494. [DOI: 10.1161/circgenetics.116.001444] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 10/03/2016] [Indexed: 01/22/2023]
Abstract
Background—
The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years.
Methods and Results—
One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines.
Conclusions—
This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.
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Affiliation(s)
- Maria Elena Lacruz
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Alexander Kluttig
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Daniel Tiller
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Daniel Medenwald
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Ina Giegling
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Dan Rujescu
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Cornelia Prehn
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Jerzy Adamski
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Stefan Frantz
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Karin Halina Greiser
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Rebecca Thwing Emeny
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Gabi Kastenmüller
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
| | - Johannes Haerting
- From the Institute of Medical Epidemiology, Biostatistics and Informatics (M.E.L., A.K., D.T., D.M., J.H.), Clinic of Psychiatry, Psychotherapy, and Psychosomatics (I.G., D.R.), and Department of Medicine III, Martin-Luther University Halle-Wittenberg, Halle Saale, Germany (S.F.); Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg (C.P., J.A.); Lehrstuhl für Experimentelle Genetik, Technische Universität
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