The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance
Open accessAbstract
Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.
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Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
Matsuda, M., & DeFronzo, R. A. (1999) · Diabetes Care · doi:10.2337/diacare.22.9.1462 · Cited by 5,621
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
Metabolite 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
Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity.
Stumvoll, M., Mitrakou, A., Pimenta, W., Jenssen, T., Yki-Järvinen, H., Van Haeften, T., Renn, W., & Gerich, J. (2000) · Diabetes Care · doi:10.2337/diacare.23.3.295 · Cited by 1,042
Role of oxidative stress in the pathogenesis of nonalcoholic steatohepatitis
Rolo, A. P., Teodoro, J. S., & Palmeira, C. M. (2012) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2011.10.003 · Cited by 955
Clinical risk factors, DNA variants, and the development of type 2 diabetes
Lyssenko, V., Jonsson, A., Almgren, P., Pulizzi, N., Isomaa, B., Tuomi, T., Berglund, G., Altshuler, D., Nilsson, P., & Groop, L. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801869 · Cited by 938
Self-citationFatty acids, obesity, and insulin resistance: Time for a reevaluation
Karpe, F., Dickmann, J. R., & Frayn, K. N. (2011) · Diabetes · doi:10.2337/db11-0425 · Cited by 896
Cauter, E. V., Mestrez, F., Sturis, J., & Polonsky, K. S. (1992) · Diabetes · doi:10.2337/diab.41.3.368 · Cited by 890
Plasma Amino Acid Levels and Insulin Secretion in Obesity
Felig, P., Marliss, E., & Cahill, G. F. (1969) · New England Journal of Medicine · doi:10.1056/nejm196910092811503 · Cited by 812
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Chambers, E. S., Byrne, C. S., Morrison, D. J., Murphy, K. G., Preston, T., Tedford, C., Garcia-Perez, I., Fountana, S., Serrano-Contreras, J. I., Holmes, E., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318424 · Cited by 443
Human Insulin Resistance Is Associated With Increased Plasma Levels of 12α-Hydroxylated Bile Acids
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Self-citationMorze, J., Wittenbecher, C., Schwingshackl, L., Danielewicz, A., Rynkiewicz, A., Hu, F. B., & Guasch-Ferré, M. (2022) · Diabetes Care · doi:10.2337/dc21-1705 · Cited by 298
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Metadata
- Authors
- Ferrannini, E., Natali, A., Camastra, S., Nannipieri, M., Mari, A., Adam, K.-P., Milburn, M. V., Kastenmüller, G., Adamski, J., Tuomi, T., Lyssenko, V., Groop, L., & Gall, W. E.
- Venue
- Diabetes
- Publisher
- American Diabetes Association
- Published
- 2013-04-16
- Volume
- 62
- Issue
- 5
- Pages
- 1730-1737
- ISSN
- 0012-1797 (ISSN-L)
- 1939-327X
- OpenAlex
- W2099491564
- Cited by
- 349
- References
- 40
- FWCI
- 15.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Ferrannini, E., Natali, A., Camastra, S., Nannipieri, M., Mari, A., Adam, K.-P., Milburn, M. V., Kastenmüller, G., Adamski, J., Tuomi, T., Lyssenko, V., Groop, L., & Gall, W. E. (2013). Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance. Diabetes. https://doi.org/10.2337/db12-0707
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