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Expression-based genome-wide association study links the receptor CD44 in adipose tissue with type 2 diabetes
Open accessAbstract
Type 2 diabetes (T2D) is a complex, polygenic disease affecting nearly 300 million people worldwide. T2D is primarily characterized by insulin resistance, and growing evidence has indicated the causative link between adipose tissue inflammation and the development of insulin resistance. Genetic association studies have successfully revealed a number of important genes consistently associated with T2D to date. However, these robust T2D-associated genes do not fully elucidate the mechanisms underlying the development and progression of the disease. Here, we report an alternative approach, gene expression-based genome-wide association study (eGWAS): searching for genes repeatedly implicated in functional microarray experiments (often publicly available). We performed an eGWAS across 130 independent experiments (totally 1,175 T2D case-control microarrays) to find additional genes implicated in the molecular pathogenesis of T2D and identified the immune-cell receptor CD44 as our top candidate ( P = 8.5 × 10 −20 ). We found CD44 deficiency in a diabetic mouse model ameliorates insulin resistance and adipose tissue inflammation and also found that anti-CD44 antibody treatment decreases blood glucose levels and adipose tissue macrophage accumulation in a high-fat, diet-fed mouse model. Further, in humans, we observed CD44 is expressed in inflammatory cells in obese adipose tissue and discovered serum CD44 levels were positively correlated with insulin resistance and glycemic control. CD44 likely plays a causative role in the development of adipose tissue inflammation and insulin resistance in rodents and humans. Genes repeatedly implicated in publicly available experimental data may have unique functionally important roles in T2D and other complex diseases.
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“CD44, the main hyaluronan cell surface receptor is associated with T2DM, as shown by expression-based genome-wide association studies.”
From The Vasculature in Prediabetes (2018) · cites this paper as [176]
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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
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Inflammation and metabolic disorders
Hotamisligil, G. S. (2006) · Nature · doi:10.1038/nature05485 · Cited by 9,199
Inflammation and insulin resistance
Shoelson, S. E. (2006) · Journal of Clinical Investigation · doi:10.1172/jci29069 · Cited by 4,769
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Nishimura, S., Manabe, I., Nagasaki, M., Eto, K., Yamashita, H., Ohsugi, M., Otsu, M., Hara, K., Ueki, K., Sugiura, S., et al. (2009) · Nature Medicine · doi:10.1038/nm.1964 · Cited by 2,230
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Bhattacharya, S., Andorf, S., Gomes, L., Dunn, P. J., Schaefer, H., Pontius, J. U., Berger, P., Desborough, V., Smith, T., Campbell, J. P., et al. (2014) · Immunologic Research · doi:10.1007/s12026-014-8516-1 · Cited by 807
Self-citationPromise of personalized omics to precision medicine
Chen, R., & Snyder, M. P. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1198 · Cited by 338
Self-citationThe Role of the Immune System in Metabolic Health and Disease
Zmora, N., Bashiardes, S., Levy, M., & Elinav, E. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.02.006 · Cited by 323
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Ruiz‐Ojeda, F. J., Méndez-Gutiérrez, A., Aguilera, C. M., & Plaza‐Díaz, J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194888 · Cited by 334
The Role of CD44 in Disease Pathophysiology and Targeted Treatment
Jordan, A. R., Racine, R. R., Hennig, M. J. P., & Lokeshwar, V. B. (2015) · Frontiers in Immunology · doi:10.3389/fimmu.2015.00182 · Cited by 281
The membrane receptor CD44: novel insights into metabolism
Weng, X., Maxwell-Warburton, S., Hasib, A., Ma, L., & Kang, L. (2022) · Trends in Endocrinology & Metabolism · doi:10.1016/j.tem.2022.02.002 · Cited by 254
Hyaluronan as a therapeutic target in human diseases
Liang, J., Jiang, D., & Noble, P. W. (2015) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2015.10.017 · Cited by 245
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Lin, D., Chun, T., & Kang, L. (2016) · Biochemical Pharmacology · doi:10.1016/j.bcp.2016.05.005 · Cited by 243
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Williams, A. S., Kang, L., & Wasserman, D. H. (2015) · Trends in Endocrinology & Metabolism · doi:10.1016/j.tem.2015.05.006 · Cited by 228
Kornicka, K., Houston, J. M. I., & Marycz, K. (2018) · Stem Cell Reviews and Reports · doi:10.1007/s12015-018-9809-x · Cited by 183
Jetten, A. M., Kang, H. S., & Takeda, Y. (2013) · Frontiers in Endocrinology · doi:10.3389/fendo.2013.00001 · Cited by 170
Leveraging big data to transform target selection and drug discovery
Chen, B., & Butte, A. J. (2015) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.318 · Cited by 165
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Wasserman, D. H., Wang, T. J., & Brown, N. J. (2018) · Circulation Research · doi:10.1161/circresaha.118.311912 · Cited by 137
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Kang, L., Lantier, L., Kennedy, A., Bonner, J. S., Mayes, W. H., Bracy, D. P., Bookbinder, L. H., Hasty, A. H., Thompson, C. B., & Wasserman, D. H. (2013) · Diabetes · doi:10.2337/db12-1502 · Cited by 133
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Patouraux, S., Rousseau, D., Bonnafous, S., Lebeaupin, C., Luci, C., Canivet, C. M., Schneck, A.-S., Bertola, A., Saint‐Paul, M., Iannelli, A., et al. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2017.03.003 · Cited by 139
Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 146
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Oei, L., Rivadeneira, F., Zillikens, M. C., & Oei, E. H. G. (2015) · Current Osteoporosis Reports · doi:10.1007/s11914-015-0260-5 · Cited by 132
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Chen, R., & Snyder, M. P. (2012) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2012.07.011 · Cited by 132
Self-citationFatty pancreas: A possible risk factor for pancreatic cancer in animals and humans
Takahashi, M., Hori, M., Ishigamori, R., Mutoh, M., Imai, T., & Nakagama, H. (2018) · Cancer Science · doi:10.1111/cas.13766 · Cited by 120
Perrin, L., Loizides‐Mangold, U., Skarupelova, S., Pulimeno, P., Chanon, S., Robert, M., Bouzakri, K., Modoux, C., Roux‐Lombard, P., Vidal, H., et al. (2015) · Molecular Metabolism · doi:10.1016/j.molmet.2015.07.009 · Cited by 107
Rodriguez‐Esteban, R., & Jiang, X. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0293-y · Cited by 95
Personalized medicine—a modern approach for the diagnosis and management of hypertension
Savoia, C., Volpe, M., Grassi, G., Borghi, C., Rosei, E. A., & Touyz, R. M. (2017) · Clinical Science · doi:10.1042/cs20160407 · Cited by 97
Gong, X.-M., Li, Y., Luo, J., Wang, J., Wei, J. N., Wang, J.-Q., Xiao, T., Xie, C., Hong, J., Ning, G., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0065-4 · Cited by 93
Liu, L. F., Kodama, K., Wei, K., Tolentino, L. L., Choi, O., Engleman, E. G., Butte, A. J., & McLaughlin, T. (2015) · Diabetologia · doi:10.1007/s00125-015-3603-y · Cited by 90
Self-citationKang, H. S., Liao, G., DeGraff, L. M., Gerrish, K., Bortner, C. D., Garantziotis, S., & Jetten, A. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058417 · Cited by 85
Kodama, K., Toda, K., Morinaga, S., Yamada, S., & Butte, A. J. (2015) · Diabetes · doi:10.2337/db14-0149 · Cited by 85
Self-citationLancha, A., Rodríguez, A., Catalán, V., Becerril, S., Sáinź, N., Ramírez, B., Burrell, M. A., Salvador, J., Frühbeck, G., & Gómez‐Ambrosi, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098398 · Cited by 86
Rho, J. G., Han, H. S., Han, J. H., Lee, H., Nguyen, V. Q., Lee, W., Kwon, S., Heo, S., Yoon, J., Shin, H. H., et al. (2018) · Journal of Controlled Release · doi:10.1016/j.jconrel.2018.04.006 · Cited by 87
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Marycz, K., Kornicka, K., Marędziak, M., Golonka, P., & Nicpoń, J. (2016) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.12932 · Cited by 75
Hyaluronan in adipogenesis, adipose tissue physiology and systemic metabolism
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Kaur, P., Rizk, N. M., Ibrahim, S., Younes, N., Uppal, A., Dennis, K., Karve, T. M., Blakeslee, K., Kwagyan, J., Zirie, M. A., et al. (2012) · Journal of Proteome Research · doi:10.1021/pr300798z · Cited by 72
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Musale, V., Wasserman, D. H., & Kang, L. (2023) · Life Metabolism · doi:10.1093/lifemeta/load021 · Cited by 81
Molecular and genetic inflammation networks in major human diseases
Zhao, Y., Forst, C. V., Sayegh, C. E., Wang, I., Yang, X., & Zhang, B. (2016) · Molecular BioSystems · doi:10.1039/c6mb00240d · Cited by 72
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Jain, P., Vig, S., Datta, M., Jindel, D., Mathur, A. K., Mathur, S. K., & Sharma, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053522 · Cited by 67
Chen, J., Meng, Y., Zhou, J., Zhuo, M., Ling, F., Zhang, Y. L., Du, H., & Wang, X. (2013) · Journal of Diabetes Research · doi:10.1155/2013/970435 · Cited by 71
Sarkar, K., Bank, S., Chatterjee, A., Dutta, K., Das, A., Chakraborty, S., Paul, N., Sarkar, J., De, S., Ghosh, S., et al. (2023) · Journal of Nanobiotechnology · doi:10.1186/s12951-023-02015-w · Cited by 67
Kaplan, J. L., Marshall, M. A., McSkimming, C. C., Harmon, D. B., Garmey, J. C., Oldham, S. N., Hallowell, P. T., & McNamara, C. A. (2015) · Molecular Metabolism · doi:10.1016/j.molmet.2015.07.010 · Cited by 68
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Meng, Q., Mäkinen, V., Luk, H., & Yang, X. (2012) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-012-0280-y · Cited by 58
Alicka, M., & Marycz, K. (2018) · Stem Cells International · doi:10.1155/2018/4274361 · Cited by 59
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Hu, Z., Jujjavarapu, C., Hughey, J., Andorf, S., Lee, H.-C., Gherardini, P. F., Spitzer, M. H., Thomas, C. G., Campbell, J. P., Dunn, P. J., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.003 · Cited by 54
Self-citationContribution of Macrophage Polarization to Metabolic Diseases
Komohara, Y., Fujiwara, Y., Ohnishi, K., Shiraishi, D., & Takeya, M. (2015) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.32359 · Cited by 51
Guðmundsdóttir, V., Pedersen, H., Allebrandt, K. V., Brorsson, C., van Leeuwen, N., Banasik, K., Mahajan, A., Groves, C. J., van de Bunt, M., Dawed, A. Y., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0189886 · Cited by 50
Ahmad, K., Choi, I., & Lee, Y. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21113845 · Cited by 51
Hounkpe, B. W., Fiusa, M. M. L., Colella, M. P., da Costa, L. N. G., Benatti, R. O., Saad, S. T. O., Costa, F. F., Santos, M. N. N., & Paula, E. V. D. (2015) · Scientific Reports · doi:10.1038/srep17822 · Cited by 49
Hasib, A., HENNAYAKE, C. K., Bracy, D. P., Bugler-Lamb, A. R., Lantier, L., Khan, F., Ashford, M. L. J., McCrimmon, R. J., Wasserman, D. H., & Kang, L. (2019) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00215.2019 · Cited by 47
Neto, I. V. D. S., Durigan, J. L. Q., da Silva, A. S. R., & Marqueti, R. D. C. (2022) · Biology · doi:10.3390/biology11050765 · Cited by 53
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Hayashi, Y., Suemitsu, E., Kajimoto, K., Sato, Y., Akhter, A., Sakurai, Y., Hatakeyama, H., Hyodo, M., Kaji, N., Baba, Y., et al. (2014) · Molecular Therapy — Nucleic Acids · doi:10.1038/mtna.2014.4 · Cited by 40
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Nagy, N., Sunkari, V. G., Kaber, G., Hasbun, S., Lam, D. N., Speake, C., Sanda, S., McLaughlin, T., Wight, T. N., Long, S., et al. (2018) · Matrix Biology · doi:10.1016/j.matbio.2018.09.003 · Cited by 40
Lee, W. H., Rho, J. G., Han, H. S., Kweon, S., Nguyen, V. Q., Park, J. H., & Kim, W. (2020) · Carbohydrate Polymers · doi:10.1016/j.carbpol.2020.116161 · Cited by 39
Li, G., Yang, R., Lu, X., Liu, Y., He, W., Li, Y., Yu, H., Qin, L., Cao, Y., Zhao, Z., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms24010387 · Cited by 38
Kodama, K., Zhao, Z., Toda, K., Yip, L., Fuhlbrigge, R., Miao, D., Fathman, C. G., Yamada, S., Butte, A. J., & Yu, L. (2016) · Diabetes · doi:10.2337/db15-1308 · Cited by 36
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Tran, V., Ho, P. T., Cabrera, L., Torres, J. E., & Bhattacharya, S. K. (2013) · Current Eye Research · doi:10.3109/02713683.2013.842593 · Cited by 32
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Nasrallah, M., Pouliot, Y., Hartmann, B., Dunn, P., Thomson, E., Wiser, J. A., & Butte, A. J. (2015) · Arthritis Research & Therapy · doi:10.1186/s13075-015-0778-z · Cited by 29
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Samblas, M., Mansego, M. L., Zulet, M. Á., Milagro, F. I., & Martínez, J. A. (2018) · European Journal of Nutrition · doi:10.1007/s00394-018-1750-x · Cited by 27
New function of the CD44 gene: Lipid metabolism regulation in bovine mammary epithelial cells
Jiang, P., Xia, L., Jin, Z., Ali, S., Wang, M., Li, X., Yang, R., Fang, X., & Zhao, Z. (2020) · Journal of Dairy Science · doi:10.3168/jds.2019-17415 · Cited by 27
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Metadata integrity in bioinformatics: Bridging the gap between data and knowledge
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Hamda, C. B., Sangeda, R. Z., Mwita, L. A., Meintjes, A., Nkya, S. W., Panji, S., Mulder, N., Guizani-Tabbane, L., Benkahla, A., Makani, J., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199461 · Cited by 26
Molecular pathophysiology of diabetes mellitus during pregnancy with antenatal complications
Kopylov, A. T., Папышева, О. В., Gribova, I., Kotaysch, G., Харитонова, Л. А., Mayatskaya, T., Sokerina, E., Kaysheva, A. L., & Morozov, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-76689-9 · Cited by 26
Grigoryev, D. N., Cheranova, D. I., Heruth, D. P., Huang, P., Zhang, L. Q., Rabb, H., & Ye, S. Q. (2013) · BMC Nephrology · doi:10.1186/1471-2369-14-231 · Cited by 26
Amor, M., Moreno‐Viedma, V., Sarabi, A., Grün, N. G., Itariu, B. K., Leitner, L., Steiner, I., Bilban, M., Kodama, K., Butte, A. J., et al. (2016) · Molecular Medicine · doi:10.2119/molmed.2016.00068 · Cited by 24
Self-citationGrigoryev, D. N., Cheranova, D. I., Chaudhary, S., Heruth, D. P., Zhang, L. Q., & Ye, S. Q. (2015) · BMC Pulmonary Medicine · doi:10.1186/s12890-015-0088-x · Cited by 25
Slieker, R. C., van der Heijden, A. A. W. A., van Leeuwen, N., Mei, H., Nijpels, G., Beulens, J. W. J., & Hart, L. M. ‘. (2017) · Diabetologia · doi:10.1007/s00125-017-4467-0 · Cited by 25
García-Eguren, G., Sala‐Vila, A., Giró, O., Vega‐Beyhart, A., & Hanzu, F. A. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00516.2019 · Cited by 26
Kobayashi, N., Okazaki, S., Sampetrean, O., Irie, J., Itoh, H., & Saya, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-20973-2 · Cited by 24
Self-citationPetriv, N., Neubert, L., Vatashchuk, M., Timrott, K., Suo, H., Hochnadel, I., Huber, R., Petzold, C., Hrushchenko, A. O., Yatsenko, A. S., et al. (2021) · OncoImmunology · doi:10.1080/2162402x.2021.1874159 · Cited by 24
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Shu, J., Li, N., Wei, W., & Zhang, L. (2021) · Gene · doi:10.1016/j.gene.2021.146070 · Cited by 23
Alur, V. C., Raju, V., Vastrad, B. M., Vastrad, C. M., Kavatagimath, S., & Kotrashetti, S. (2023) · Clinical Medicine Insights Endocrinology and Diabetes · doi:10.1177/11795514231155635 · Cited by 21
Cefalu, W. T., Andersen, D. K., Arreaza-Rubín, G., Pin, C. L., Sato, S. M., Verchere, C. B., Woo, M., & Rosenblum, N. D. (2021) · Diabetes · doi:10.2337/db21-0777 · Cited by 20
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Hamid, A. R., & Tran, Q. N. (2015) · Cited by 20
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Masotti, M., Guo, B., & Wu, B. (2019) · Biometrics · doi:10.1111/biom.13076 · Cited by 17
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Gu, Y. F., Qiu, Z., Cheng, N., Chen, C., Hei, Z., & Li, X. (2018) · Journal of Cellular Biochemistry · doi:10.1002/jcb.27766 · Cited by 15
Kan, M., Shumyatcher, M., Diwadkar, A. R., Soliman, G., & Himes, B. E. (2018) · Europe PMC (PubMed Central) · Cited by 15
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Metadata
- Authors
- Kodama, K., Horikoshi, M., Toda, K., Yamada, S., Hara, K., Irie, J., Sirota, M., Morgan, A. A., Chen, R., Ohtsu, H., Maeda, S., Kadowaki, T., & Butte, A. J.
- Affiliations
- Lucile Packard Children's Hospital (US)
- The University of Tokyo (JP)
- Kitasato Institute Hospital (JP)
- Keio University (JP)
- RIKEN (JP)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-04-12
- Volume
- 109
- Issue
- 18
- Pages
- 7049-7054
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3344989
- OpenAlex
- W2044994793
- Cited by
- 171
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 16 · 2023: 18 · 2022: 9 · 2021: 10 · 2020: 14 · 2019: 8 · 2018: 17 · 2017: 10 · 2016: 10 · 2015: 20 · 2014: 11 · 2013: 14 · 2012: 7
- FWCI
- 12.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Lunds Universitet
- Kitasato University
- School of Medicine, Stanford University
- Japan Society for the Promotion of Science (20229008)
- U.S. National Library of Medicine (K22LM008261, R01LM009719, T15LM007033)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adipokines, Inflammation, and Metabolic Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
- Keywords
- type 2 diabetes, CD44, insulin resistance, adipose tissue inflammation, adipose tissue macrophages, obesity, diabetic mouse model, anti-CD44 antibody, glycemic control
- MeSH terms
- Adipose Tissue, Aged, Animals, Blood Glucose, Diabetes Mellitus, Type 2, Dietary Fats, Female, Humans, Insulin Resistance, Macrophages, Male, Mice, Inbred C57BL, Middle Aged, Obesity, Panniculitis, Gene Expression, Case-Control Studies, Mice, Knockout, Hyaluronan Receptors, Oligonucleotide Array Sequence Analysis, Mice, Genome-Wide Association Study
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