The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index.
Interplay between Lipids and Branched-Chain Amino Acids in Development of Insulin Resistance
Open accessAbstract
Fatty acids (FA) and FA-derived metabolites have long been implicated in the development of insulin resistance and type 2 diabetes. Surprisingly, application of metabolomics technologies has revealed that branched-chain amino acids (BCAA) and related metabolites are more strongly associated with insulin resistance than many common lipid species. Moreover, the BCAA-related signature is predictive of incident diabetes and intervention outcomes and uniquely responsive to therapeutic interventions. Nevertheless, in animal feeding studies, BCAA supplementation requires the background of a high-fat diet to promote insulin resistance. This Perspective develops a model to explain how lipids and BCAA may synergize to promote metabolic diseases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Thus, chronic elevations in incomplete oxidation of fatty acids and branched chain amino acids (Newgard, 2012) might foster a mitochondrial microenvironment that is conducive to ROS production.”
From Lipid-Induced Mitochondrial Stress and Insulin Action in Muscle (2012) · cites this paper as Newgard, 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Prevalence and Trends in Obesity Among US Adults, 1999-2008
Flegal, K. M. (2010) · JAMA · doi:10.1001/jama.2009.2014 · Cited by 7,580
Prevalence and trends in obesity among US adults, 1999–2000
Flegal, K. M. (2002) · JAMA · doi:10.1001/jama.288.14.1723 · Cited by 6,190
Newgard, C. B., An, J., Bain, J. R., Muehlbauer, M. J., Stevens, R. D., Lien, L. F., Haqq, A. M., Shah, S. H., Arlotto, M., Slentz, C. A., et al. (2009) · Cell Metabolism · doi:10.1016/j.cmet.2009.02.002 · Cited by 3,165
Self-citationMetabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
Koves, T. R., Ussher, J. R., Noland, R. C., Slentz, D., Mosedale, M., Ilkayeva, O., Bain, J., Stevens, R., Dyck, J. R. B., Newgard, C. B., et al. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.10.013 · Cited by 1,990
Self-citationMcGarry, J. D. (2002) · Diabetes · doi:10.2337/diabetes.51.1.7 · Cited by 1,435
Hypothalamic mTOR Signaling Regulates Food Intake
Cota, D., Proulx, K., Smith, K. A. B., Kozma, S. C., Thomas, G., Woods, S. C., & Seeley, R. J. (2006) · Science · doi:10.1126/science.1124147 · Cited by 1,278
Molecular and metabolic mechanisms of insulin resistance and β-cell failure in type 2 diabetes
Muoio, D. M., & Newgard, C. B. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2327 · Cited by 1,177
Self-citationFatty acid-induced β cell apoptosis: A link between obesity and diabetes
Shimabukuro, M., Zhou, Y.-T., Levi, M., & Unger, R. H. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.5.2498 · Cited by 1,142
Disordered lipid metabolism and the pathogenesis of insulin resistance.
Savage, D. B., Petersen, K. F., & Shulman, G. I. (2007) · Physiological Reviews · doi:10.1152/physrev.00024.2006 · Cited by 1,107
Flagged first, then by citations
1 of the 58 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 401–500 of 998
Abdualkader, A. M., Karwi, Q. G., Lopaschuk, G. D., & Batran, R. A. (2024) · Journal of Pharmacy & Pharmaceutical Sciences · doi:10.3389/jpps.2024.13040 · Cited by 49
Application of combined omics platforms to accelerate biomedical discovery in diabesity
Kurland, I. J., Accili, D., Burant, C., Fischer, S. M., Kahn, B. B., Newgard, C. B., Ramagiri, S., Ronnett, G. V., Ryals, J. A., Sanders, M., et al. (2013) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12116 · Cited by 33
Self-citationAustdal, M., Silva, G. B., Bowe, S., Thomsen, L. C. V., Tangerås, L. H., Bjørge, L., Bathen, T. F., & Iversen, A. (2019) · Hypertension · doi:10.1161/hypertensionaha.119.13184 · Cited by 33
Stevens, V. L., Carter, B. D., McCullough, M. L., Campbell, P. T., & Wang, Y. (2019) · Obesity · doi:10.1002/oby.22670 · Cited by 35
Small intestinal metabolism is central to whole-body insulin resistance
Angelini, G., Salinari, S., Castagneto‐Gissey, L., BERTUZZI, A., Casella-Mariolo, J. R., Ahlin, S., Boškoski, I., Gaggini, M., Raffaelli, M., Costamagna, G., et al. (2020) · Gut · doi:10.1136/gutjnl-2020-322073 · Cited by 33
Obesity Connected Metabolic Changes in Type 2 Diabetic Patients Treated With Metformin
Aleidi, S. M., Dahabiyeh, L. A., Gu, X., Dubayee, M. A., Alshahrani, A. S., Benabdelkamel, H., Mujammami, M. H., Li, L., Aljada, A., & Rahman, A. M. A. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.616157 · Cited by 35
Orsso, C. E., Peng, Y., Deehan, E. C., Tan, Q., Field, C. J., Madsen, K. L., Walter, J., Prado, C. M. M., Tun, H. M., & Haqq, A. M. (2021) · Microorganisms · doi:10.3390/microorganisms9071490 · Cited by 33
Current progress in metabolomics of gestational diabetes mellitus
Wang, Q., You, L., Xiang, L.-L., Zhu, Y., & Zeng, Y. (2021) · World Journal of Diabetes · doi:10.4239/wjd.v12.i8.1164 · Cited by 35
Metabolic Impairment in Coronary Artery Disease: Elevated Serum Acylcarnitines Under the Spotlights
Gander, J., Carrard, J., Gallart‐Ayala, H., Borreggine, R., Teav, T., Infanger, D., Colledge, F., Streese, L., Wagner, J., Klenk, C., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.792350 · Cited by 36
Gadhia, M. M., Maliszewski, A. M., O'Meara, M. C., Wesolowski, S. R., Lavezzi, J. R., Limesand, S. W., Hay, W. W., Brown, L. D., & Rozance, P. J. (2012) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00377.2012 · Cited by 33
Stroeve, J. H. M., Saccenti, E., Bouwman, J., Dane, A. D., Strassburg, K., Vervoort, J. J. M., Hankemeier, T., Astrup, A. V., Smilde, A. K., van Ommen, B., et al. (2016) · Obesity · doi:10.1002/oby.21361 · Cited by 33
Metabolic role of the hepatic valine/3-hydroxyisobutyrate (3-HIB) pathway in fatty liver disease
Bjune, M. S., Lawrence‐Archer, L., Laupsa‐Borge, J., Sommersten, C. H., McCann, A., Glastad, R. C., Johnston, I. G., Kern, M., Blüher, M., Mellgren, G., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104569 · Cited by 35
Altered plasma acylcarnitine and amino acid profiles in type 2 diabetic kidney disease
Chuang, W.-H., Arivajiagane, A., Lu, C., Chen, C.-C., Wu, W.-C., Susanto, H., Purnomo, J. D. T., & Wang, C.-H. (2016) · Metabolomics · doi:10.1007/s11306-016-1049-y · Cited by 32
Li, Q., Gu, W., Ma, X., Liu, Y., Jiang, L., Feng, R., & Liu, L. (2016) · Nutrients · doi:10.3390/nu8060379 · Cited by 32
Human splanchnic amino-acid metabolism
Neis, E. P. J. G., Sabrkhany, S., Hundscheid, I. H. R., Schellekens, D. H. S. M., Lenaerts, K., Damink, S. W. M. O., Blaak, E. E., Dejong, C. H. C., & Rensen, S. S. (2016) · Amino Acids · doi:10.1007/s00726-016-2344-7 · Cited by 32
Schlecht, I., Gronwald, W., Behrens, G., Baumeister, S. E., Hertel, J., Hochrein, J., Zacharias, H. U., Fischer, B., Oefner, P. J., & Leitzmann, M. F. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175133 · Cited by 34
Almario, R. U., Buchan, W., Rocke, D. M., & Karakas, S. E. (2017) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2017-000420 · Cited by 32
Cavallari, J. F., & Schertzer, J. D. (2017) · Journal of Obesity & Metabolic Syndrome · doi:10.7570/jomes.2017.26.3.161 · Cited by 32
Panjwani, A. A., Ji, Y., Fahey, J. W., Palmer, A., Wang, G., Hong, X., ZUCKERMAN, B. S., & Wang, X. (2019) · Autism Research · doi:10.1002/aur.2177 · Cited by 32
Pillai, S. M., Herzog, B., Seebeck, P., Pellegrini, G., Roth, E., & Verrey, F. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01460 · Cited by 32
Pathogenesis and remission of type 2 diabetes: what has the twin cycle hypothesis taught us?
Al-Mrabeh, A. H. (2020) · Cardiovascular Endocrinology & Metabolism · doi:10.1097/xce.0000000000000201 · Cited by 35
Endotoxemia is associated with an adverse metabolic profile
Määttä, A.-M., Salminen, A., Pietiäinen, M., Leskelä, J., Palviainen, T., Sattler, W., Sinisalo, J. P., Salomaa, V. V., Kaprio, J. A., & Pussinen, P. J. (2020) · Innate Immunity · doi:10.1177/1753425920971702 · Cited by 32
Müllner, E., Röhnisch, H. E., von Brömssen, C., & Moazzami, A. A. (2021) · Metabolomics · doi:10.1007/s11306-020-01759-y · Cited by 35
Lépine, G., Fouillet, H., Rémond, D., Huneau, J., Mariotti, F., & Polakof, S. (2021) · Advances in Nutrition · doi:10.1093/advances/nmab073 · Cited by 36
Aberrant branched-chain amino acid catabolism in cardiovascular diseases
Xiong, Y., Jiang, L., & Li, T. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.965899 · Cited by 32
Metabolomic signatures in lipid‐loaded HepaRGs reveal pathways involved in steatotic progression
Brown, M. V., Compton, S. A., Milburn, M. V., Lawton, K. A., & Cheatham, B. (2013) · Obesity · doi:10.1002/oby.20440 · Cited by 34
Amino Acid Signatures to Evaluate the Beneficial Effects of Weight Loss
Geidenstam, N., Magnusson, M., Danielsson, A. P. H., Gerszten, R. E., Wang, T. J., Reinius, L. E., Mulder, H., Melander, O., & Ridderstråle, M. (2017) · International Journal of Endocrinology · doi:10.1155/2017/6490473 · Cited by 33
A Metabolite Perspective on the Involvement of the Gut Microbiota in Type 2 Diabetes
Fu, Y., Li, S., Xiao, Y., Liu, G., & Fang, J. Q. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914991 · Cited by 32
Amino acid-induced impairment of insulin sensitivity in healthy and obese rats is reversible
Jeganathan, S., Abdullahi, A., Zargar, S., Maeda, N., Riddell, M. C., & Adegoke, O. A. J. (2014) · Physiological Reports · doi:10.14814/phy2.12067 · Cited by 29
Tekwe, C. D., Lei, J., Yao, K., Rezaei, R., Li, X., DAHANAYAKA, S. A., Carroll, R. J., Meininger, C. J., Bazer, F. W., & Wu, G. (2013) · BioFactors · doi:10.1002/biof.1113 · Cited by 31
Fan, J., Cui, M., Than, M. T., & Han, M. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.676650 · Cited by 31
Advances in Gut Microbiome Research, Opening New Strategies to Cope with a Western Lifestyle
Rodríguez-Castaño, G. P., Caro‐Quintero, A., Reyes, A., & Lizcano, F. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2016.00224 · Cited by 36
Lee, S., Yim, Y.-S., Lee, Y., Lee, B., Kim, H.-S., Kim, K., Lee, Y.-W., & Kim, J. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.03.028 · Cited by 32
Regulation of Metabolic Disease-Associated Inflammation by Nutrient Sensors
Yamashita, A. S., Belchior, T., Lira, F. S., BISHOP, N. C., Wessner, B., Neto, J. C. R., & Festuccia, W. T. (2018) · Mediators of Inflammation · doi:10.1155/2018/8261432 · Cited by 32
Remchak, M. E., Heiston, E. M., Ballantyne, A. C., Dotson, B. L., Stewart, N. R., Spaeth, A. M., & Malin, S. K. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac233 · Cited by 31
Branched‐Chain Amino Acids and Mitochondrial Biogenesis: An Overview and Mechanistic Summary
Hinkle, J. S., Rivera, C. N., & Vaughan, R. A. (2022) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202200109 · Cited by 31
Sigurdsson, M. I., Kobayashi, H., Amrein, K., Nakahira, K., Rogers, A. J., Pinilla-Vera, M., Baron, R. M., Fredenburgh, L. E., Lasky-Su, J. A., & Christopher, K. B. (2022) · Critical Care · doi:10.1186/s13054-022-04174-y · Cited by 31
Mitochondrial target of thiazolidinediones
Colca, J. R., McDonald, W. G., & Kletzien, R. F. (2014) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12308 · Cited by 32
Mladenović, D. R., Vesković, M. N., Šutulović, N., Hrnčić, D., Stanojlović, O. P., Radić, L., Macut, J. B., & Μacut, D. (2024) · Endocrine · doi:10.1007/s12020-024-03702-w · Cited by 32
Determination of the safety of leucine supplementation in healthy elderly men
Rasmussen, B., Gilbert, E., Turki, A., Madden, K. M., & Elango, R. (2016) · Amino Acids · doi:10.1007/s00726-016-2241-0 · Cited by 30
Fagotti, J., Targa, A., Rodrigues, L. S., Noseda, A. C. D., Dorieux, F. W. C., Scarante, F. F., Ilkiw, J. L., Louzada, F. M., Chowdhury, N. R., van der Veen, D. R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37657-6 · Cited by 29
Foodomics evaluation of the anti-proliferative potential of Passiflora mollissima seeds
Ballesteros-Vivas, D., Álvarez‐Rivera, G., León, C., Morantes, S. J., Ibáñez, E., Parada‐Alfonso, F., Cifuentes, A., & Valdés, A. (2019) · Food Research International · doi:10.1016/j.foodres.2019.108938 · Cited by 30
Developmental plasticity and the response to nutrient stress in Caenorhabditis elegans
Rashid, S., Wong, C., & Roy, R. (2021) · Developmental Biology · doi:10.1016/j.ydbio.2021.01.015 · Cited by 30
Margolis, L. M., Karl, J. P., Wilson, M. A., Coleman, J. L., Whitney, C. C., & Pasiakos, S. M. (2021) · Metabolites · doi:10.3390/metabo11120828 · Cited by 30
Ďásková, N., Módos, I., Krbcová, M., Kuzma, M., Pelantová, H., Hradecký, J., Heczková, M., Brátová, M., Videňská, P., Šplíchalová, P., et al. (2023) · Nutrition and Diabetes · doi:10.1038/s41387-023-00235-5 · Cited by 31
New insights into the role of the branched‐chain aminotransferase proteins in the human brain
Hull, J., Patel, V. B., Hutson, S. M., & Conway, M. E. (2015) · Journal of Neuroscience Research · doi:10.1002/jnr.23558 · Cited by 30
Targeting skeletal muscle mitochondria to prevent type 2 diabetes in youth
Gordon, J. W., Dolinsky, V. W., Mughal, W., Gordon, G. R., & McGavock, J. (2015) · Biochemistry and Cell Biology · doi:10.1139/bcb-2015-0012 · Cited by 30
Ning, Y., Zhang, F., Tang, L., Song, J., Ding, J., Chang, Y., & Zuo, R. (2021) · Aquaculture · doi:10.1016/j.aquaculture.2021.737688 · Cited by 29
Werner, C., Doenst, T., & Schwarzer, M. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-802394-5.00004-2 · Cited by 29
Koh, A. S.-M., Gao, F., Leng, S., Kovalik, J., Zhao, X., Tan, R., Fridianto, K. T., Ching, J., Chua, S. J., Yuan, J., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-26456-8 · Cited by 29
Serum free amino acid levels in rheumatoid arthritis according to therapy and physical disability
Urbaniak, B., Plewa, S., Klupczyńska, A., Sikorska, D., Samborski, W., & Kokot, Z. J. (2018) · Cytokine · doi:10.1016/j.cyto.2018.10.002 · Cited by 28
Cormier, R. J., Strang, R., Menail, H. A., Touaibia, M., & Pichaud, N. (2021) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2021.103556 · Cited by 32
Amino Acid-Induced Impairment of Insulin Signaling and Involvement of G-Protein Coupling Receptor
Zakaria, N., Hamid, M., & Khayat, M. E. (2021) · Nutrients · doi:10.3390/nu13072229 · Cited by 33
Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men
Comte, B., Monnerie, S., Brandolini-Bunlon, M., Canlet, C., Castelli, F. A., Chu‐Van, E., Colsch, B., Fenaille, F., Joly, C., Jourdan, F., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103440 · Cited by 32
Harstad, R. K., & Bowser, M. T. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b01846 · Cited by 29
Wei, S., Zhao, J., Wang, S., Huang, M., Wang, Y., & Chen, Y. (2018) · Heliyon · doi:10.1016/j.heliyon.2018.e00830 · Cited by 29
Tao, H., Yang, X., Wang, W.-X., Yue, S., Pu, Z., Huang, Y., Shi, X.-Q., Chen, J., Zhou, G., Chen, Y., et al. (2019) · Phytomedicine · doi:10.1016/j.phymed.2019.153162 · Cited by 29
Ding, Y., Wang, S., & Lu, J. (2023) · Metabolites · doi:10.3390/metabo13091017 · Cited by 30
Nutritional Challenges and Treatment After Bariatric Surgery
Moize, V. L., Laferrère, B., & Shapses, S. A. (2024) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-061121-101547 · Cited by 33
Metabolomics in nutrition research–a powerful window into nutritional metabolism
Brennan, L. (2016) · Essays in Biochemistry · doi:10.1042/ebc20160029 · Cited by 29
Precision Nutrition and Childhood Obesity: A Scoping Review
Wu, Y., Perng, W., & Peterson, K. E. (2020) · Metabolites · doi:10.3390/metabo10060235 · Cited by 28
Kitsy, A., Carney, S. T., Vivar, J. C., Knight, M. S., Pointer, M. A., Gwathmey, J. K., & Ghosh, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102615 · Cited by 27
Hyperplasia and Cellularity Changes in IGF-1-Overexpressing Skeletal Muscle of Crucian Carp
Li, D., Lou, Q., Zhai, G., Peng, X., Cheng, X., Dai, X., Zhuo, Z., Shang, G., Jin, X., Chen, X., et al. (2014) · Endocrinology · doi:10.1210/en.2013-1938 · Cited by 28
Normal pregnancy induced glucose metabolic stress in a longitudinal cohort of healthy women
Wang, M., Xia, W., Li, H., Liu, F., Li, Y., Sun, X., Lu, S., & Xu, S. (2018) · Medicine · doi:10.1097/md.0000000000012417 · Cited by 28
The role of gut microbiota in the occurrence and progression of non-alcoholic fatty liver disease
Mai, H., Yang, X. H., Xie, Y., Zhou, J., Wang, Q., Wei, Y., Yang, Y., Lu, D., Ye, L., Cui, P., et al. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1257903 · Cited by 27
The role of lysine acetylation in metabolic sensing and proteostasis
Eftekhari, A., Sabir, U., & Kasumov, T. (2025) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2025.108908 · Cited by 30
Ross, L. M., Slentz, C. A., Sudnick, A. M., Huffman, K. M., SHALAUROVA, I. Y., Otvos, J. D., Connelly, M. A., Kraus, V. B., Bales, C. W., Houmard, J. A., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.626142 · Cited by 28
Hu, W., Yang, P., Fu, Z., Wang, Y., Zhou, Y., Ye, Z., Gong, Y., Huang, A., Sun, L., Zhao, Y., et al. (2022) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s336736 · Cited by 27
Nichols, C. G., York, N., & Remedi, M. S. (2022) · Diabetes · doi:10.2337/db21-0755 · Cited by 31
Matye, D. J., Gunewardena, S., Chen, J., Wang, H., Wang, Y., Hasan, M. N., Gu, L., Clayton, Y. D., Du, Y., Chen, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33465-9 · Cited by 27
Doestzada, M., Zhernakova, D. V., van den Munckhof, I., Wang, D., Kurilshikov, A., Chen, L., Bloks, V. W., van Faassen, M. H. J. R., Rutten, J. H. W., Joosten, L. A. B., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02688-4 · Cited by 28
Yang, J., Wu, J., Ayele, F. T., Li, L., Bremer, A. A., Lu, R., Rahman, M. L., Weir, N. L., Pang, W. W., Chen, Z., et al. (2023) · Diabetes Care · doi:10.2337/dc22-1892 · Cited by 28
Plasma amino acid and metabolite signatures tracking diabetes progression in the UCD-T2DM rat model
Piccolo, B. D., Graham, J. L., Stanhope, K. L., Fiehn, O., Havel, P. J., & Adams, S. H. (2016) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00052.2016 · Cited by 27
A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction
Merino, J., Udler, M. S., Leong, A., & Meigs, J. B. (2017) · Current Diabetes Reports · doi:10.1007/s11892-017-0958-0 · Cited by 28
Acylcarnitine profiling by low-resolution LC-MS
Meierhofer, D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221342 · Cited by 27
Cuomo, P., Papaianni, M., Sansone, C., Iannelli, A., Iannelli, D., Medaglia, C., Paris, D., Motta, A., & Capparelli, R. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218369 · Cited by 27
Bartsch, M., Hahn, A., & Berkemeyer, S. (2023) · Metabolites · doi:10.3390/metabo13121182 · Cited by 28
Cardiac fibrogenesis: an immuno-metabolic perspective
Hoque, M. M., Gbadegoye, J. O., Hassan, F. O., Raafat, A., & Lebeche, D. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1336551 · Cited by 30
Tini, S., Baima, J., Pigni, S., Antoniotti, V., Caputo, M., Palma, E. D., Cerbone, L., Grosso, F., Vecchia, M. L., Bona, E., et al. (2025) · Nutrients · doi:10.3390/nu17172898 · Cited by 28
Fetal Serum Metabolites Are Independently Associated with Gestational Diabetes Mellitus
Lu, Y.-P., Reichetzeder, C., Prehn, C. P., von Websky, K., Slowinski, T., Chen, Y.-P., Yin, L., Kleuser, B., Yang, X., Adamski, J., et al. (2018) · Cellular Physiology and Biochemistry · doi:10.1159/000487119 · Cited by 27
Zhang, Y., Keerthisinghe, T. P., Yuan, H., Liu, M., Wanjaya, E. R., & Fang, M. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.8b02629 · Cited by 26
Effect of valine on myotube insulin sensitivity and metabolism with and without insulin resistance
Rivera, M. E., Lyon, E. S., Johnson, M. A., Sunderland, K., & Vaughan, R. A. (2020) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-020-03720-y · Cited by 25
Zhang, H., Xiang, L., Huo, M., Wu, Y., Yu, M., Lau, C. W., Tian, D., Gou, L., Huang, Y., Luo, J., et al. (2022) · Obesity · doi:10.1002/oby.23394 · Cited by 26
An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers
Razo-Azamar, M., Nambo‐Venegas, R., Meraz‐Cruz, N., Guevara‐Cruz, M., Ibarra‐González, I., Vela-Amieva, M. B., Delgadillo‐Velazquez, J., Santiago, X. C., Escobar, R. F., Vadillo‐Ortega, F., et al. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01098-7 · Cited by 29
Personal genomes, quantitative dynamic omics and personalized medicine
Mias, G. I., & Snyder, M. P. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0005-3 · Cited by 29
Leruez, S., Bresson, T., de la Barca, J. M. C., Marill, A., Martin, G. D. S., Buisset, A., Muller, J., Tessier, L., Gadras, C., Verny, C., et al. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-23055 · Cited by 26
The Regulation of Insulin-Stimulated Cardiac Glucose Transport via Protein Acetylation
Renguet, E., Bultot, L., Beauloye, C., Horman, S., & Bertrand, L. (2018) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2018.00070 · Cited by 26
Gao, R., DUFF, W. R. D., Chizen, D. R., Zello, G. A., & Chilibeck, P. D. (2019) · Nutrients · doi:10.3390/nu11092012 · Cited by 26
Hasenour, C. M., Ridley, D. E., James, F. D., Hughey, C. C., Donahue, E. P., Viollet, B., Foretz, M., Young, J. D., & Wasserman, D. H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170382 · Cited by 26
Zhao, J., Duan, L., Li, J., Yao, C., Wang, G., Mi, J., Yu, Y., Ding, L., Zhao, Y., Yan, G., et al. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.116807 · Cited by 26
Kobayashi, H., Amrein, K., Lasky-Su, J. A., & Christopher, K. B. (2021) · Scientific Reports · doi:10.1038/s41598-021-02679-0 · Cited by 26
Suzuki, M., Shimizu‐Hirota, R., Mita, M., Hamase, K., & Sasabe, J. (2022) · Amino Acids · doi:10.1007/s00726-022-03140-w · Cited by 25
Moskaleva, N. E., Shestakova, K. M., Kukharenko, A. V., Markin, P. A., Kozhevnikova, M. V., Korobkova, E. O., Alex Brito, O., Baskhanova, S. N., Mesonzhnik, N. V., Belenkov, Y. N., et al. (2022) · Metabolites · doi:10.3390/metabo12121185 · Cited by 26
Cooper‐DeHoff, R. M., Hou, W., Weng, L., Baillie, R., Beitelshees, A. L., Gong, Y., Shahin, M. H., Turner, S. T., Chapman, A. B., Gums, J. G., et al. (2014) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.113.000421 · Cited by 25
Sun, L., Hu, C., Yang, R., Lv, Y., Yuan, H., Liang, Q., He, B., Pang, G., Jiang, M., Dong, J., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.21489 · Cited by 25
miR-188 promotes liver steatosis and insulin resistance via the autophagy pathway
Liu, Y., Zhou, X., Xiao, Y., Li, C., Huang, Y., Guo, Q., Su, T., Fu, L., & Luo, L. (2020) · Journal of Endocrinology · doi:10.1530/joe-20-0033 · Cited by 25
Kale, D., Fatangare, A., Phapale, P. B., & Sickmann, A. (2023) · Cells · doi:10.3390/cells12242796 · Cited by 24
The Metabolic Syndrome: An Overview and Proposed Mechanisms
Thomaz, F. S., John, O. D., Sinha, P., Shafie, S. R., & Worrall, S. (2024) · Obesities · doi:10.3390/obesities4030020 · Cited by 29
Insulin Resistance in Obese Children: What Can Metabolomics and Adipokine Modelling Contribute?
Rupérez, F. J., Martos‐Moreno, G. Á., Chamoso‐Sánchez, D., Barbas, C., & Argente, J. (2020) · Nutrients · doi:10.3390/nu12113310 · Cited by 26
Lin, S., Miao, Y., Zheng, X., Dong, Y., Yang, Q., Yang, Q., Du, S., Xu, J., Zhou, S., & Yuan, T. (2022) · Cell Death Discovery · doi:10.1038/s41420-022-01029-x · Cited by 24
Metadata
- Authors
- Newgard, C. B.
- Affiliations
- Duke University (US)
- Corresponding author
- Newgard, Christopher B.
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2012-05
- Volume
- 15
- Issue
- 5
- Pages
- 606-614
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC3695706
- OpenAlex
- W2079195246
- Cited by
- 1,080
- References
- 58
- Citations by year
- 2026: 54 · 2025: 72 · 2024: 66 · 2023: 80 · 2022: 106 · 2021: 116 · 2020: 86 · 2019: 82 · 2018: 83 · 2017: 71 · 2016: 89 · 2015: 48 · 2014: 69 · 2013: 46 · 2012: 12
- FWCI
- 7.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Pfizer
- GlaxoSmithKline
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (P01DK058398)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- branched-chain amino acids, insulin resistance, type 2 diabetes, fatty acids, lipid metabolism, high-fat diet, BCAA supplementation, metabolomics, metabolic disorders, predictive biomarkers
- MeSH terms
- Amino Acids, Branched-Chain, Animals, Humans, Insulin Resistance, Metabolic Diseases, Lipid Metabolism
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review