The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance
Autoři:
Zong Miao aff001; Marcus Alvarez aff001; Arthur Ko aff003; Yash Bhagat aff001; Elior Rahmani aff004; Brandon Jew aff002; Sini Heinonen aff005; Linda Liliana Muñoz-Hernandez aff006; Miguel Herrera-Hernandez aff009; Carlos Aguilar-Salinas aff006; Teresa Tusie-Luna aff010; Karen L. Mohlke aff011; Markku Laakso aff012; Kirsi H. Pietiläinen aff005; Eran Halperin aff001; Päivi Pajukanta aff001
Působiště autorů:
Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
aff001; Bioinformatics Interdepartmental Program, UCLA, Los Angeles, California, United States of America
aff002; Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
aff003; Computer Science Department in the School of Engineering, UCLA, Los Angeles, California, United States of America
aff004; Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
aff005; Unidad de Investigación en Enfermedades Metabólicas, Dirección de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
aff006; Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
aff007; Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, México
aff008; Departamento de Cirugía, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico
aff009; Unidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM / Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubiran, Mexico City, Mexico
aff010; Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
aff011; Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
aff012; Obesity Center, Endocrinology, Abdominal Center, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
aff013; Department of Computational Medicine, UCLA, Los Angeles, California, United States of America
aff014; Department of Anesthesiology and Perioperative Medicine, UCLA, Los Angeles, California, United States of America
aff015; Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
aff016
Vyšlo v časopise:
The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance. PLoS Genet 16(9): e32767. doi:10.1371/journal.pgen.1009018
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009018
Souhrn
Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2×10−3 and p = 3.1×10−24). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p≤0.05 for each) when adjusting for the decomposed adipose cell-type proportions. Next, we showed that these 3 factors, adipose MT gene expression, body fat percent, and adipose cell types, explain a substantial amount (44.39%) of variance in insulin resistance and can be used to predict it (p≤2.64×10−5 in 3 independent human cohorts). In summary, we demonstrated that obesity is a strong determinant of both prediabetes and insulin resistance, and discovered that individuals’ adipose cell-type composition, adipose MT gene expression, and body fat percent predict their insulin resistance, emphasizing the critical role of adipose tissue in systemic insulin resistance.
Klíčová slova:
Adipose tissue – Body Mass Index – Forecasting – Gene expression – Genome-wide association studies – Insulin resistance – Obesity – Type 2 diabetes
Zdroje
1. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, et al. Obesity and cardiovascular disease: Pathophysiology, evaluation, and effect of weight loss. Circulation. 2006;113(6):898–918. doi: 10.1161/CIRCULATIONAHA.106.171016 16380542
2. Zamora-Mendoza R, Rosas-Vargas H, Ramos-Cervantes MT, Garcia-Zuniga P, Perez-Lorenzana H, Mendoza-Lorenzo P, et al. Dysregulation of mitochondrial function and biogenesis modulators in adipose tissue of obese children. Int J Obes. 2018;42(4):618–24. doi: 10.1038/ijo.2017.274 29158541
3. Martinez KE, Tucker LA, Bailey BW, LeCheminant JD. Expanded normal weight obesity and insulin resistance in US adults of the national health and nutrition examination survey. J Diabetes Res. 2017;2017. doi: 10.1155/2017/9502643 28812029
4. Chung JO, Cho DH, Chung DJ, Chung MY. Associations among Body Mass Index, Insulin Resistance, and Pancreatic β-Cell Function in Korean Patients with New-Onset Type 2 Diabetes FAU—Chung, Jin Ook FAU Cho, Dong Hyeok FAU—Chung, Dong Jin FAU—Chung, Min Young. Korean J Intern Med. 2012;27(1):66–71. doi: 10.3904/kjim.2012.27.1.66 22403502
5. Meah FA, DiMeglio LA, Greenbaum CJ, Blum JS, Sosenko JM, Pugliese A, et al. The relationship between BMI and insulin resistance and progression from single to multiple autoantibody positivity and type 1 diabetes among TrialNet Pathway to Prevention participants. Diabetologia. 2016;59(6):1186–95. doi: 10.1007/s00125-016-3924-5 26995649
6. Cheng YH, Tsao YC, Tzeng IS, Chuang HH, Li WC, Tung TH, et al. Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese. Med (United States). 2017;96(39):1–6. doi: 10.1097/MD.0000000000008126 28953643
7. Neeland I. J., Turer A. T., Ayers C. R., Powell-Wiley T. M., Vega G. L., Farzaneh-Far R., … De Lemos J. A. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA. 2012; 308(11), 1150–1159. doi: 10.1001/2012.jama.11132 22990274
8. Goran M. I., Lane C., Toledo-Corral C., & Weigensberg M. J. Persistence of pre-diabetes in overweight and obese hispanic children; Association with progressive insulin resistance, Poor β-cell function, and increasing visceral fat. Diabetes. 2008; 57(11), 3007–3012. doi: 10.2337/db08-0445 18678615
9. Tabák A. G., Herder C., Rathmann W., Brunner E. J., & Kivimäki M. Prediabetes: A high-risk state for diabetes development. The Lancet. 2012;379(9833): 2279–2290. doi: 10.1016/S0140-6736(12)60283-9 22683128
10. Dandona P, Aljada A, Bandyopadhyay A. Inflammation the link between insulin resistance,. Trends Immunol. 2004;25(1):4–7. doi: 10.1016/j.it.2003.10.013
11. Yang WM, Jeong HJ, Park SW, Lee W. Obesity-induced miR-15b is linked causally to the development of insulin resistance through the repression of the insulin receptor in hepatocytes. Mol Nutr Food Res. 2015;59(11):2303–14. doi: 10.1002/mnfr.201500107 26179126
12. Pedersen DJ, Guilherme A, Danai LV., Heyda L, Matevossian A, Cohen J, et al. A major role of insulin in promoting obesity-associated adipose tissue inflammation. Mol Metab. 2015;4(7):507–18. doi: 10.1016/j.molmet.2015.04.003 26137438
13. Adabimohazab R, Garfinkel A, Milam EC, Frosch O, Mangone A, Convit A. Does Inflammation Mediate the Association Between Obesity and Insulin Resistance? Inflammation. 2016;39(3):994–1003. doi: 10.1007/s10753-016-0329-z 26956471
14. Saltiel AR, Olefsky JM, Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease Find the latest version: Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017;127(1):1–4. doi: 10.1172/JCI92035 28045402
15. Li P, Oh DY, Bandyopadhyay G, Lagakos WS, Talukdar S, Osborn O, et al. LTB4 promotes insulin resistance in obese mice by acting on macrophages, hepatocytes and myocytes. Nat Med [Internet]. 2015;21(3):239–47. doi: 10.1038/nm.3800 25706874
16. Yang WM, Jeong HJ, Park SW, Lee W. Obesity-induced miR-15b is linked causally to the development of insulin resistance through the repression of the insulin receptor in hepatocytes. Mol Nutr Food Res. 2015;59(11):2303–14. doi: 10.1002/mnfr.201500107 26179126
17. Wensveen FM, Jelenčić V, Valentić S, Šestan M, Wensveen TT, Theurich S, et al. NK cells link obesity-induced adipose stress to inflammation and insulin resistance. Nat Immunol. 2015;16(4):376–85. doi: 10.1038/ni.3120 25729921
18. Pedersen DJ, Guilherme A, Danai LV., Heyda L, Matevossian A, Cohen J, et al. A major role of insulin in promoting obesity-associated adipose tissue inflammation. Mol Metab. 2015;4(7):507–18. doi: 10.1016/j.molmet.2015.04.003 26137438
19. Adabimohazab R, Garfinkel A, Milam EC, Frosch O, Mangone A, Convit A. Does Inflammation Mediate the Association Between Obesity and Insulin Resistance? Inflammation. 2016;39(3):994–1003 doi: 10.1007/s10753-016-0329-z 26956471
20. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. doi: 10.1038/s41586-018-0579-z 30305743
21. Laakso M, Kuusisto J, Stančáková A, Kuulasmaa T, Pajukanta P, Lusis AJ, et al. The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. J Lipid Res. 2017;58(3):481–93. doi: 10.1194/jlr.O072629 28119442
22. Weisberg SP, Leibel RL, Anthony WF Jr, Weisberg SP, Mccann D, et al. Obesity is associated with macrophage accumulation in adipose tissue Find the latest version: Obesity is associated with. J Clin Invest. 2003;112(12):1796–808. doi: 10.1172/JCI19246 14679176
23. Van Harmelen V, Skurk T, Röhrig K, Lee YM, Halbleib M, Aprath-Husmann I, et al. Effect of BMI and age on adipose tissue cellularity and differentiation capacity in women. Int J Obes. 2003; doi: 10.1038/sj.ijo.0802314 12861228
24. Reilly SM, Saltiel AR. Adapting to obesity with adipose tissue inflammation. Nat Rev Endocrinol. 2017;13(11):633–43. doi: 10.1038/nrendo.2017.90 28799554
25. Majka SM, Miller HL, Helm KM, Acosta AS, Childs CR, Kong R, et al. Analysis and isolation of adipocytes by flow cytometry. Methods in Enzymology 2014; 537:281–296 p. doi: 10.1016/B978-0-12-411619-1.00015-X 24480352
26. Ehrlund A, Acosta JR, Björk C, Hedén P, Douagi I, Arner P, et al. The cell-type specific transcriptome in human adipose tissue and influence of obesity on adipocyte progenitors. Sci Data. 2017;4:1–11.
27. Hagberg CE, Li Q, Kutschke M, Bhowmick D, Kiss E, Shabalina IG, et al. Flow Cytometry of Mouse and Human Adipocytes for the Analysis of Browning and Cellular Heterogeneity. Cell Rep. 2018;24(10):2746–2756.e5. doi: 10.1016/j.celrep.2018.08.006 30184507
28. Wu M, Singh AK. Single-cell protein analysis. Curr Opin Biotechnol. 2012;23(1):83–8. doi: 10.1016/j.copbio.2011.11.023 22189001
29. Hu P, Zhang W, Xin H, Deng G. Single Cell Isolation and Analysis. Front Cell Dev Biol. 2016 Oct 25;4(October):135–82. doi: 10.3389/fcell.2016.00116 27826548
30. Vink RG, Roumans NJ, Fazelzadeh P, Tareen SHK, Boekschoten M V., Van Baak MA, et al. Adipose tissue gene expression is differentially regulated with different rates of weight loss in overweight and obese humans. Int J Obes. 2017;41(2):309–16. doi: 10.1038/ijo.2016.201 27840413
31. Heinonen S, Buzkova J, Muniandy M, Kaksonen R, Ollikainen M, Ismail K, et al. Impaired mitochondrial biogenesis in adipose tissue in acquired obesity. Diabetes. 2015;64(9):3135–45. doi: 10.2337/db14-1937 25972572
32. Lindinger PW, Christe M, Eberle AN, Kern B, Peterli R, Peters T, et al. Important mitochondrial proteins in human omental adipose tissue show reduced expression in obesity. J Proteomics. 2015;124:79–87. doi: 10.1016/j.jprot.2015.03.037 25865306
33. Mardinoglu A, Kampf C, Asplund A, Fagerberg L, Hallström BM, Edlund K, et al. Defining the human adipose tissue proteome to reveal metabolic alterations in obesity. J Proteome Res. 2014;13(11):5106–19. doi: 10.1021/pr500586e 25219818
34. Vernochet C, Damilano F, Mourier A, Bezy O, Mori MA, Smyth G, et al. Adipose tissue mitochondrial dysfunction triggers a lipodystrophic syndrome with insulin resistance, hepatosteatosis, and cardiovascular complications. FASEB J. 2014;28(10):4408–19. doi: 10.1096/fj.14-253971 25005176
35. Paglialunga S, Ludzki A, Root-McCaig J, Holloway GP. In adipose tissue, increased mitochondrial emission of reactive oxygen species is important for short-term high-fat diet-induced insulin resistance in mice. Diabetologia. 2015;58(5):1071–80. doi: 10.1007/s00125-015-3531-x 25754553
36. Petersen KF, Petersen KF, Befroy D, Dufour S, Dipietro L, Cline GW, et al. Mitochondrial Dysfunction in the Elderly: Possible Role in Insulin. Science. 2007;1140(2003):1140–3. doi: 10.1126/science.1082889 12750520
37. Sanyal AJ, Campbell-Sargent C, Mirshahi F, Rizzo WB, Contos MJ, Sterling RK, et al. Nonalcoholic steatohepatitis: Association of insulin resistance and mitochondrial abnormalities. Gastroenterology. 2001;120(5):1183–92. doi: 10.1053/gast.2001.23256 11266382
38. American Diabetes Association. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2010; Jan; 33(Supplement 1): S62–S69. doi: 10.2337/dc10-S062 20042775
39. Shea J. L., King M. T. C., Yi Y., Gulliver W., & Sun G. Body fat percentage is associated with cardiometabolic dysregulation in BMI-defined normal weight subjects. Nutrition, Metabolism and Cardiovascular Diseases. 2012; 22(9): 741–747. doi: 10.1016/j.numecd.2010.11.009 21215604
40. Gómez-Ambrosi J., Silva C., Galofré J. C., Escalada J., Santos S., Gil M. J., … Frühbeck G. Body adiposity and type 2 diabetes: Increased risk with a high body fat percentage even having a normal BMI. Obesity. 2011; 19(7): 1439–1444. doi: 10.1038/oby.2011.36 21394093
41. Howard B. V., Ruotolo G., & Robbins D. C. Obesity and dyslipidemia. Endocrinology and Metabolism Clinics of North America. 2003; 32(4): 855–867. doi: 10.1016/s0889-8529(03)00073-2 14711065
42. Ferrannini E. Insulin and blood pressure: Connected on a circumference? Hypertension. 2005; 45(3): 347–348. doi: 10.1161/01.HYP.0000155464.44905.6c 15668355
43. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. doi: 10.1038/s41588-018-0099-7 29686387
44. Bowden J., Fabiola Del Greco M., Minelli C., Smith G. D., Sheehan N. A., & Thompson J. R. Assessing the suitability of summary data for two-sample mendelian randomization analyses using MR-Egger regression: The role of the I 2 statistic. International Journal of Epidemiology. 2016; 45(6): 1961–1974. doi: 10.1093/ije/dyw220 27616674
45. Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: Implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013;34(1):1–11. doi: 10.1016/j.mam.2012.10.001 23068073
46. Lynes MD, Tseng YH. Deciphering adipose tissue heterogeneity. Ann N Y Acad Sci. 2018;1411(1):5–20. doi: 10.1111/nyas.13398 28763833
47. Kaaman M, Sparks LM, Van Harmelen V, Smith SR, Sjölin E, Dahlman I, et al. Strong association between mitochondrial DNA copy number and lipogenesis in human white adipose tissue. Diabetologia. 2007;50(12):2526–33. doi: 10.1007/s00125-007-0818-6 17879081
48. Heinonen S, Buzkova J, Muniandy M, Kaksonen R, Ollikainen M, Ismail K, et al. Impaired mitochondrial biogenesis in adipose tissue in acquired obesity. Diabetes. 2015;64(9):3135–45. doi: 10.2337/db14-1937 25972572
49. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(5):768.
50. Holmes M V., Lange LA, Palmer T, Lanktree MB, North KE, Almoguera B, et al. Causal effects of body mass index on cardiometabolic traits and events: A Mendelian randomization analysis. Am J Hum Genet. 2014;94(2):198–208. doi: 10.1016/j.ajhg.2013.12.014 24462370
51. Khera A V., Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019;177(3):587–596.e9. doi: 10.1016/j.cell.2019.03.028 31002795
52. Stancáková A, Javorsky M, Kuulasmaa T, Haffner SM, Kuusisto J, Stančáková A, et al. Changes in Insulin Sensitivity and Insulin Release in Relation to Glycemia and Glucose Tolerance in 6,414 Finnish Men. Diabetes. 2009;58(5):1212–21. doi: 10.2337/db08-1607 19223598
53. Risso D., Ngai J., Speed T. P., & Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nature Biotechnology.2014; 32(9): 896–902. doi: 10.1038/nbt.2931 25150836
54. Matsuda M, DeFronzo R. Insulin Sensitivity Indices Obtained Fro m Comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462–70. doi: 10.2337/diacare.22.9.1462 10480510
55. Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5. doi: 10.1038/ng.2653 23715323
56. Ardlie KG, Deluca DS, Segre a. V., Sullivan TJ, Young TR, Gelfand ET, et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science 2015;348(6235):648–60. doi: 10.1126/science.1262110 25954001
57. Jukarainen S, Heinonen S, Rämö JT, Rinnankoski-Tuikka R, Rappou E, Tummers M, et al. Obesity is associated with low nad+/sirt pathway expression in adipose tissue of BMI-discordant monozygotic twins. J Clin Endocrinol Metab. 2016;101(1):275–83. doi: 10.1210/jc.2015-3095 26574954
58. Granér M, Seppälä-Lindroos A, Rissanen A, Hakkarainen A, Lundbom N, Kaprio J, et al. Epicardial fat, cardiac dimensions, and low-grade inflammation in young adult monozygotic twins discordant for obesity. Am J Cardiol. 2012;109(9):1295–302. doi: 10.1016/j.amjcard.2011.12.023 22325087
59. Loh P. R., Tucker G., Bulik-Sullivan B. K., Vilhjálmsson B. J., Finucane H. K., Salem R. M., Price A. L. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nature Genetics. 2015; 47(3), 284–290. doi: 10.1038/ng.3190 25642633
60. Loh P. R., Kichaev G., Gazal S., Schoech A. P. & Price A. L. Mixed-model association for biobank-scale datasets. Nature Genetics. 2018; 50, 906–908. doi: 10.1038/s41588-018-0144-6 29892013
61. Bowden J., Smith G. D., & Burgess S. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. International Journal of Epidemiology. 2015; 44(2): 512–525. doi: 10.1093/ije/dyv080 26050253
62. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:1–12. doi: 10.1038/ncomms14049 28091601
63. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20. doi: 10.1038/nbt.4096 29608179
64. Wang X, Park J, Susztak K, Zhang NR, Li M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019;10(1). doi: 10.1038/s41467-018-08023-x 30670690
65. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
66. Dobin A, Davis C a., Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886
67. Liao Y, Smyth GK, Shi W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. doi: 10.1093/bioinformatics/btt656 24227677
68. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17(1):13. doi: 10.1186/s13059-016-0881-8 26813401
69. The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature. 2015; 526: 68–74. doi: 10.1038/nature15393 26432245
70. http://broadinstitute.github.io/picard
71. Zou H, Hastie T. Erratum: Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(5):768.
72. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models. J Stat Software. 2010;33(1):1–3.
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