The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
MINMOD Millennium: A Computer Program to Calculate Glucose Effectiveness and Insulin Sensitivity from the Frequently Sampled Intravenous Glucose Tolerance Test
Abstract
The Bergman Minimal Model enables estimation of two key indices of glucose/insulin dynamics: glucose effectiveness and insulin sensitivity. In this paper we describe MINMOD Millennium, the latest Windows-based version of minimal model software. Extensive beta testing of MINMOD Millennium has shown that it is user-friendly, fully automatic, fast, accurate, reproducible, repeatable, and highly concordant with past versions of MINMOD. It has a simple interface, a comprehensive help system, an input file editor, a file converter, an intelligent processing kernel, and a file exporter. It provides publication-quality charts of glucose and insulin and a table of all minimal model parameters and their error estimates. In contrast to earlier versions of MINMOD and some other minimal model programs, Millennium provides identified estimates of insulin sensitivity and glucose effectiveness for almost every subject.
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.
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
STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
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
Global and societal implications of the diabetes epidemic
Zimmet, P., Alberti, K. G. M. M., & Shaw, J. (2001) · Nature · doi:10.1038/414782a · Cited by 5,883
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874
Self-citationBuchanan, T. A., Xiang, A. H., Peters, R. K., Kjos, S. L., Marroquin, A., Goico, J., Ochoa, C., Tan, S., Berkowitz, K., Hodis, H. N., et al. (2002) · Diabetes · doi:10.2337/diabetes.51.9.2796 · Cited by 1,339
Rudic, R. D., McNamara, P., Curtis, A.-M., Boston, R. C., Panda, S., Hogenesch, J. B., & FitzGerald, G. A. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020377 · Cited by 1,059
Self-citation
Cited by
Rudic, R. D., McNamara, P., Curtis, A.-M., Boston, R. C., Panda, S., Hogenesch, J. B., & FitzGerald, G. A. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020377 · Cited by 1,059
Self-citationSunny, N. E., Parks, E. J., Browning, J. D., & Burgess, S. C. (2011) · Cell Metabolism · doi:10.1016/j.cmet.2011.11.004 · Cited by 672
Leproult, R., Holmbäck, U., & Van Cauter, E. (2014) · Diabetes · doi:10.2337/db13-1546 · Cited by 607
“Minimal model analyses (21) were performed using the Minmod Millennium software (22) and provided SI, the acute insulin response to glucose (AIRg), a measure of b-cell response, and the disposition index (DI = AIRg 3 SI), a marker of diabetes risk.”
MethodLarson-Meyer, D. E., Heilbronn, L. K., Redman, L. M., Newcomer, B. R., Frisard, M. I., Anton, S., Smith, S. R., Alfonso, A., Ravussin, E., & the Pennington CALERIE Team (2006) · Diabetes Care · doi:10.2337/dc05-2565 · Cited by 504
Spencer, M., Yao-Borengasser, A., Unal, R., Rasouli, N., Gurley, C. M., Zhu, B., Peterson, C. A., & Kern, P. A. (2010) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00329.2010 · Cited by 439
Effects of acute intermittent hypoxia on glucose metabolism in awake healthy volunteers.
Louis, M., & Punjabi, N. M. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.91523.2008 · Cited by 342
Clinical trial: pilot study of metformin for the treatment of non-alcoholic steatohepatitis
LOOMBA, R., LUTCHMAN, G., KLEINER, D. E., RICKS, M., FELD, J. J., BORG, B. B., MODI, A., NAGABHYRU, P., SUMNER, A. E., LIANG, T. J., et al. (2009) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2008.03869.x · Cited by 299
Self-citationVarma, V., Yao-Borengasser, A., Rasouli, N., Nolen, G. T., Phanavanh, B., Starks, T., Gurley, C., Simpson, P., McGehee, R. E., Kern, P. A., et al. (2009) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.90885.2008 · Cited by 229
Varma, V., Yao-Borengasser, A., Bodles, A. M., Rasouli, N., Phanavanh, B., Nolen, G. T., Kern, E. M., Nagarajan, R., Spencer, H. J., Lee, M.-J., et al. (2008) · Diabetes · doi:10.2337/db07-0840 · Cited by 194
Muscle sphingolipids during rest and exercise: a C18:0 signature for insulin resistance in humans
Bergman, B. C., Brozinick, J. T., Strauss, A., Bacon, S., Kerege, A., Bui, H. H., Sanders, P., Siddall, P., Wei, T., Thomas, M. K., et al. (2016) · Diabetologia · doi:10.1007/s00125-015-3850-y · Cited by 148
Sleep architecture and glucose and insulin homeostasis in obese adolescents
Koren, D., Levitt Katz, L. E., Brar, P. C., Gallagher, P. R., Berkowitz, R. I., & Brooks, L. J. (2011) · Diabetes Care · doi:10.2337/dc11-1093 · Cited by 134
“The MINMOD Millenium software program (14)was used to estimate indices of glucose and insulin dynamics from the FSIGT.”
InfluentialEndurance training-induced changes in insulin sensitivity and gene expression
Teran-Garcia, M., Rankinen, T., Koza, R. A., Rao, D. C., & Bouchard, C. (2005) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00467.2004 · Cited by 116
Vidal, J., Nicolau, J., Romero, F., Casamitjana, R., Momblan, D., Conget, I., Morínigo, R., & Lacy, A. M. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2008-1620 · Cited by 94
SARZYNSKI, M. A., RICE, T. K., DESPRÉS, J.-P., PÉRUSSE, L., TREMBLAY, A., STANFORTH, P. R., TCHERNOF, A., BARBER, J. L., FALCIANI, F., CLISH, C., et al. (2022) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002859 · Cited by 62
An, P., Teran-Garcia, M., Rice, T., Rankinen, T., Weisnagel, S. J., Bergman, R. N., Boston, R. C., Mandel, S., Stefanovski, D., Leon, A. S., et al. (2005) · Diabetologia · doi:10.1007/s00125-005-1769-4 · Cited by 54
Self-citationChow, C. C., Periwal, V., Csako, G., Ricks, M., Courville, A. B., Miller, B. V., Vega, G. L., & Sumner, A. E. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-0532 · Cited by 50
Self-citationMager, U., Kolehmainen, M., de Mello, V. D. F., Schwab, U., Laaksonen, D. E., Rauramaa, R., Gylling, H., Atalay, M., Pulkkinen, L., & Uusitupa, M. (2008) · European Journal of Endocrinology · doi:10.1530/eje-07-0862 · Cited by 49
Ness, K. M., Strayer, S. M., Nahmod, N. G., Chang, A.-M., Buxton, O. M., & Shearer, G. C. (2019) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00336.2018 · Cited by 38
Obesity, insulin resistance, and skeletal muscle nitric oxide synthase
Kraus, R. M., Houmard, J. A., Kraus, W. E., Tanner, C. J., Pierce, J. R., Choi, M. D., & Hickner, R. C. (2012) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01018.2011 · Cited by 28
Trinh, M. D., Plihalova, A., Gojda, J., Westlake, K., Spicka, J., Lattova, Z., Pretl, M., & Polak, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-83018-1 · Cited by 20
Oh, T. J., Kim, Y. G., Kang, S., Moon, J. H., Kwak, S. H., Choi, S. H., Lim, S., Park, K. S., Jang, H. C., Hong, J.-S., et al. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2018.0086 · Cited by 17
Pancreatic β-cell dysfunction in polycystic ovary syndrome: the role of metformin
Messer, C., Boston, R., Leroith, D., Geer, E., Miller, J. D., Messer, M., & Futterweit, W. (2012) · Endocrine Practice · doi:10.4158/ep11375.or · Cited by 16
Self-citationRickels, M. R., Kong, S. M., Fuller, C., Dalton-Bakes, C., Ferguson, J. F., Reilly, M. P., Teff, K. L., & Naji, A. (2014) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00667.2013 · Cited by 16
Metadata
- Authors
- Boston, R. C., Stefanovski, D., Moate, P. J., Sumner, A. E., Watanabe, R. M., & Bergman, R. N.
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2003-12
- Volume
- 5
- Issue
- 6
- Pages
- 1003-1015
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- OpenAlex
- W2176172321
- Cited by
- 431
- References
- 6
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
