The DOI matches its Crossref record; 331 of the 333 references it cites were found in a registry or an index.
The Role of Mitochondria in the Pathophysiology of Skeletal Muscle Insulin Resistance
Open accessAbstract
Multiple organs contribute to the development of peripheral insulin resistance, with the major contributors being skeletal muscle, liver, and adipose tissue. Because insulin resistance usually precedes the development of type 2 diabetes mellitus (T2DM) by many years, understanding the pathophysiology of insulin resistance should enable development of therapeutic strategies to prevent disease progression. Some subjects with mitochondrial genomic variants/defects and a subset of lean individuals with hereditary predisposition to T2DM exhibit skeletal muscle mitochondrial dysfunction early in the course of insulin resistance. In contrast, in the majority of subjects with T2DM the plurality of evidence implicates skeletal muscle mitochondrial dysfunction as a consequence of perturbations associated with T2DM, and these mitochondrial deficits then contribute to subsequent disease progression. We review the affirmative and contrarian data regarding skeletal muscle mitochondrial biology in the pathogenesis of insulin resistance and explore potential therapeutic options to intrinsically modulate mitochondria as a strategy to combat insulin resistance. Furthermore, an overview of restricted molecular manipulations of skeletal muscle metabolic and mitochondrial biology offers insight into the mitochondrial role in metabolic substrate partitioning and in promoting innate adaptive and maladaptive responses that collectively regulate peripheral insulin sensitivity. We conclude that skeletal muscle mitochondrial dysfunction is not generally a major initiator of the pathophysiology of insulin resistance, although its dysfunction is integral to this pathophysiology and it remains an intriguing target to reverse/delay the progressive perturbations synonymous with T2DM.
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“Consequently, an observed decrease in fatty acid oxidation and an increase in cytosolic levels of free fatty acids ultimately lead to pathologies such as type 2 diabetes mellitus (T2DM), muscle dysfunction and insulin resistance125.”
From Mitochondrial and metabolic dysfunction in ageing and age-related diseases (2022) · cites this paper as [125]
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Gentleman R: R: A language for data analysis and graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,747
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Biochemistry and molecular cell biology of diabetic complications
Brownlee, M. (2001) · Nature · doi:10.1038/414813a · Cited by 9,107
Effects of Intensive Glucose Lowering in Type 2 Diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0802743 · Cited by 7,888
Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0802987 · Cited by 7,397
The Pathobiology of Diabetic Complications
Brownlee, M. (2005) · Diabetes · doi:10.2337/diabetes.54.6.1615 · Cited by 5,339
Insulin signalling and the regulation of glucose and lipid metabolism
Saltiel, A. R., & Kahn, C. R. (2001) · Nature · doi:10.1038/414799a · Cited by 5,326
Resveratrol improves health and survival of mice on a high-calorie diet
Baur, J. A., Pearson, K. J., Price, N. L., Jamieson, H. A., Lerin, C., Kalra, A., Prabhu, V. V., Allard, J. S., Lopez-Lluch, G., Lewis, K., et al. (2006) · Nature · doi:10.1038/nature05354 · Cited by 4,290
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“Excessive lipid production and impaired disposal may promote the development of insulin resistance and diabetes in the skeletal muscle [47, 48].”
BackgroundMitochondrial and metabolic dysfunction in ageing and age-related diseases
Amorim, J. A., Coppotelli, G., Rolo, A. P., Palmeira, C. M., Ross, J. M., & Sinclair, D. A. (2022) · Nature Reviews Endocrinology · doi:10.1038/s41574-021-00626-7 · Cited by 990
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The role of mitochondria in insulin resistance and type 2 diabetes mellitus
Szendroedi, J., Phielix, E., & Roden, M. (2012) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2011.138 · Cited by 600
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Giorgi, C., Marchi, S., Simões, I. C. M., Ren, Z., Morciano, G., Perrone, M., Patalas‐Krawczyk, P., Borchard, S., Jędrak, P., Pierzynowska, K., et al. (2018) · International review of cytology · doi:10.1016/bs.ircmb.2018.05.006 · Cited by 484
Metadata
- Authors
- Pagel-Langenickel, I., Bao, J., Pang, L., & Sack, M. N.
- Affiliations
- Translational Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892-1454
- National Institutes of Health (US)
- ORCID
- Sack, Michael N.
- Venue
- Endocrine Reviews
- Publisher
- The Endocrine Society
- Published
- 2009-10-27
- Volume
- 31
- Issue
- 1
- Pages
- 25-51
- ISSN
- 0163-769X (ISSN-L)
- 1945-7189
- PMC
- PMC2852205
- OpenAlex
- W2161339546
- Cited by
- 154
- References
- 333
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 5 · 2021: 3 · 2020: 13 · 2019: 7 · 2018: 8 · 2017: 8 · 2016: 11 · 2015: 7 · 2014: 15 · 2013: 17 · 2012: 11
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Endocrine Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Mitochondrial Function and Pathology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
- Keywords
- skeletal muscle insulin resistance, mitochondrial dysfunction, type 2 diabetes, peripheral insulin resistance, mitochondrial DNA variants, mitochondrial-targeted therapy, insulin sensitivity, skeletal muscle metabolism
- MeSH terms
- Animals, Diabetes Mellitus, Type 2, Humans, Insulin Resistance, Mitochondria, Muscle, Organ Specificity, Prediabetic State, Muscle, Skeletal, Metabolic Syndrome, Mitochondrial Diseases
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