Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study
Autoři:
Mi Lai aff001; Ying Liu aff001; Gabriele V. Ronnett aff003; Anne Wu aff001; Brian J. Cox aff001; Feihan F. Dai aff001; Hannes L. Röst aff004; Erica P. Gunderson aff005; Michael B. Wheeler aff001
Působiště autorů:
Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
aff001; Metabolism Research Group, Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario, Canada
aff002; Janssen Research & Development, World Without Disease Accelerator, Spring House, Pennsylvania, United States of America
aff003; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
aff004; Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America
aff005
Vyšlo v časopise:
Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: A metabolic profiling study. PLoS Med 17(5): e32767. doi:10.1371/journal.pmed.1003112
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003112
Souhrn
Background
Women with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D) during midlife and an elevated risk of developing hypertension and cardiovascular disease. Glucose tolerance reclassification after delivery is recommended, but fewer than 40% of women with GDM are tested. Thus, improved risk stratification methods are needed, as is a deeper understanding of the pathology underlying the transition from GDM to T2D. We hypothesize that metabolites during the early postpartum period accurately distinguish risk of progression from GDM to T2D and that metabolite changes signify underlying pathophysiology for future disease development.
Methods and findings
The study utilized fasting plasma samples collected from a well-characterized prospective research study of 1,035 women diagnosed with GDM. The cohort included racially/ethnically diverse pregnant women (aged 20–45 years—33% primiparous, 37% biparous, 30% multiparous) who delivered at Kaiser Permanente Northern California hospitals from 2008 to 2011. Participants attended in-person research visits including 2-hour 75-g oral glucose tolerance tests (OGTTs) at study baseline (6–9 weeks postpartum) and annually thereafter for 2 years, and we retrieved diabetes diagnoses from electronic medical records for 8 years. In a nested case–control study design, we collected fasting plasma samples among women without diabetes at baseline (n = 1,010) to measure metabolites among those who later progressed to incident T2D or did not develop T2D (non-T2D). We studied 173 incident T2D cases and 485 controls (pair-matched on BMI, age, and race/ethnicity) to discover metabolites associated with new onset of T2D. Up to 2 years post-baseline, we analyzed samples from 98 T2D cases with 239 controls to reveal T2D-associated metabolic changes. The longitudinal analysis tracked metabolic changes within individuals from baseline to 2 years of follow-up as the trajectory of T2D progression. By building prediction models, we discovered a distinct metabolic signature in the early postpartum period that predicted future T2D with a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI 0.820–0.945, p < 0.001). At baseline, the most striking finding was an overall increase in amino acids (AAs) as well as diacyl-glycerophospholipids and a decrease in sphingolipids and acyl-alkyl-glycerophospholipids among women with incident T2D. Pathway analysis revealed up-regulated AA metabolism, arginine/proline metabolism, and branched-chain AA (BCAA) metabolism at baseline. At follow-up after the onset of T2D, up-regulation of AAs and down-regulation of sphingolipids and acyl-alkyl-glycerophospholipids were sustained or strengthened. Notably, longitudinal analyses revealed only 10 metabolites associated with progression to T2D, implicating AA and phospholipid metabolism. A study limitation is that all of the analyses were performed with the same cohort. It would be ideal to validate our findings in an independent longitudinal cohort of women with GDM who had glucose tolerance tested during the early postpartum period.
Conclusions
In this study, we discovered a metabolic signature predicting the transition from GDM to T2D in the early postpartum period that was superior to clinical parameters (fasting plasma glucose, 2-hour plasma glucose). The findings suggest that metabolic dysregulation, particularly AA dysmetabolism, is present years prior to diabetes onset, and is revealed during the early postpartum period, preceding progression to T2D, among women with GDM.
Trial registration
ClinicalTrials.gov Identifier: NCT01967030.
Klíčová slova:
Blood plasma – Carbohydrate metabolism – diabetes mellitus – Glucose metabolism – Hexoses – Metabolic pathways – Metabolites – Type 2 diabetes
Zdroje
1. Chen Q, Francis E, Hu G, Chen L. Metabolomic profiling of women with gestational diabetes mellitus and their offspring: review of metabolomics studies. J Diabetes Complications. 2018;32(5):512–23. doi: 10.1016/j.jdiacomp.2018.01.007 29506818
2. Melchior H, Kurch-Bek D, Mund M. The prevalence of gestational diabetes. Dtsch Arztebl Int. 2017;114(24):412–8. doi: 10.3238/arztebl.2017.0412 28669379
3. Gunderson EP, Hurston SR, Ning X, Lo JC, Crites Y, Walton D, et al. Lactation and progression to type 2 diabetes mellitus after gestational diabetes mellitus: a prospective cohort study. Ann Intern Med. 2015;163(12):889–98. doi: 10.7326/M15-0807 26595611
4. Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009;373(9677):1773–9. doi: 10.1016/S0140-6736(09)60731-5 19465232
5. Gunderson EP, Lewis CE, Tsai A-L, Chiang V, Carnethon M, Quesenberry CP, et al. A 20-year prospective study of childbearing and incidence of diabetes in young women, controlling for glycemia before conception: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Diabetes. 2007;56(12):2990–6. doi: 10.2337/db07-1024 17898128
6. Tobias DK. Prediction and prevention of type 2 diabetes in women with a history of GDM. Curr Diab Rep. 2018;18(10):78. doi: 10.1007/s11892-018-1063-8 30117058
7. Magee MS, Walden CE, Benedetti TJ, Knopp RH. Influence of diagnostic criteria on the incidence of gestational diabetes and perinatal morbidity. JAMA. 1993;269(5):609–15. 8421365
8. Beharier O, Shoham-Vardi I, Pariente G, Sergienko R, Kessous R, Baumfeld Y, et al. Gestational diabetes mellitus is a significant risk factor for long-term maternal renal disease. J Clin Endocrinol Metab. 2015;100(4):1412–6. doi: 10.1210/jc.2014-4474 25668200
9. Shah BR, Retnakaran R, Booth GL. Increased risk of cardiovascular disease in young women following gestational diabetes mellitus. Diabetes Care. 2008;31(8):1668–9. doi: 10.2337/dc08-0706 18487472
10. Retnakaran R, Shah BR. Role of type 2 diabetes in determining retinal, renal, and cardiovascular outcomes in women with previous gestational diabetes mellitus. Diabetes Care. 2017;40(1):101–8. doi: 10.2337/dc16-1400 27821407
11. Fadl H, Magnuson A, Östlund I, Montgomery S, Hanson U, Schwarcz E. Gestational diabetes mellitus and later cardiovascular disease: a Swedish population based case-control study. BJOG. 2014;121(12):1530–6. doi: 10.1111/1471-0528.12754 24762194
12. Gunderson EP, Jaffe MG. Pregnancy and subsequent glucose intolerance in women of childbearing age. JAMA Intern Med. 2017;177(12):1742. doi: 10.1001/jamainternmed.2017.4768 29049465
13. Tobias DK, Stuart JJ, Li S, Chavarro J, Rimm EB, Rich-Edwards J, et al. Association of history of gestational diabetes with long-term cardiovascular disease risk in a large prospective cohort of US women. JAMA Intern Med. 2017;177(12):1735. doi: 10.1001/jamainternmed.2017.2790 29049820
14. Ajmera VH, Gunderson EP, VanWagner LB, Lewis CE, Carr JJ, Terrault NA. Gestational diabetes mellitus is strongly associated with non-alcoholic fatty liver disease. Am J Gastroenterol. 2016;111(5):658–64. doi: 10.1038/ajg.2016.57 27002796
15. Gunderson EP, Jacobs DR Jr, Chiang V, Lewis CE, Tsai A, Quesenberry CP Jr, et al. Childbearing is associated with higher incidence of the metabolic syndrome among women of reproductive age controlling for measurements before pregnancy: the CARDIA study. Am J Obstet Gynecol. 2009;201(2):177.e1–9.
16. American Diabetes Association. 12. Management of diabetes in pregnancy. Diabetes Care. 2016;39(Suppl 1):S94–8.
17. Janghorbani M, Almasi SZ, Amini M. The product of triglycerides and glucose in comparison with fasting plasma glucose did not improve diabetes prediction. Acta Diabetol. 2015;52(4):781–8. doi: 10.1007/s00592-014-0709-5 25572334
18. Abdul-Ghani MA, Lyssenko V, Tuomi T, DeFronzo RA, Groop L. Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study. Diabetes Care. 2009;32(2):281–6. doi: 10.2337/dc08-1264 19017778
19. Allalou A, Nalla A, Prentice KJ, Liu Y, Zhang M, Dai FF, et al. A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes. Diabetes. 2016;65(9):2529–39. doi: 10.2337/db15-1720 27338739
20. Blatt AJ, Nakamoto JM, Kaufman HW. Gaps in diabetes screening during pregnancy and postpartum. Obstet Gynecol. 2011;117(1):61–8. doi: 10.1097/AOG.0b013e3181fe424b 21173645
21. Russell MA, Phipps MG, Olson CL, Welch HG, Carpenter MW. Rates of postpartum glucose testing after gestational diabetes mellitus. Obstet Gynecol. 2006;108(6):1456–62. doi: 10.1097/01.AOG.0000245446.85868.73 17138780
22. Jones EJ, Roche CC, Appel SJ. A review of the health beliefs and lifestyle behaviors of women with previous gestational diabetes. J Obstet Gynecol Neonatal Nurs. 2009;38(5):516–26. doi: 10.1111/j.1552-6909.2009.01051.x 19883473
23. Kim C, McEwen LN, Piette JD, Goewey J, Ferrara A, Walker EA. Risk perception for diabetes among women with histories of gestational diabetes mellitus. Diabetes Care. 2007;30(9):2281–6. doi: 10.2337/dc07-0618 17575087
24. Menni C, Fauman E, Erte I, Perry JR, Kastenmuller G, Shin SY, et al. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes. 2013;62(12):4270–6. doi: 10.2337/db13-0570 23884885
25. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17(4):448–53. doi: 10.1038/nm.2307 21423183
26. Chen T, Ni Y, Ma X, Bao Y, Liu J, Huang F, et al. Branched-chain and aromatic amino acid profiles and diabetes risk in Chinese populations. Sci Rep. 2016;6(1):20594.
27. Lappas M, Mundra PA, Wong G, Huynh K, Jinks D, Georgiou HM, et al. The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics. Diabetologia. 2015;58(7):1436–42. doi: 10.1007/s00125-015-3587-7 25893729
28. Khan SR, Mohan H, Liu Y, Batchuluun B, Gohil H, Al Rijjal D, et al. The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes. Diabetologia. 2019;62(4):687–703. doi: 10.1007/s00125-018-4800-2 30645667
29. Batchuluun B, Al Rijjal D, Prentice KJ, Eversley JA, Burdett E, Mohan H, et al. Elevated medium-chain acylcarnitines are associated with gestational diabetes mellitus and early progression to type 2 diabetes and induce pancreatic beta-cell dysfunction. diabetes. 2018;67(5):885–97. doi: 10.2337/db17-1150 29436377
30. Lynch CJ, Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol. 2014;10:723. doi: 10.1038/nrendo.2014.171 25287287
31. Carpenter MW, Coustan DR. Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982;144(7):768–73. doi: 10.1016/0002-9378(82)90349-0 7148898
32. Gunderson EP, Matias SL, Hurston SR, Dewey KG, Ferrara A, Quesenberry CP Jr, et al. Study of Women, Infant Feeding, and Type 2 Diabetes Mellitus after GDM Pregnancy (SWIFT), a prospective cohort study: methodology and design. BMC Public Health. 2011;11:952. doi: 10.1186/1471-2458-11-952 22196129
33. Gunderson EP, Kim C, Quesenberry CP Jr, Marcovina S, Walton D, Azevedo RA, et al. Lactation intensity and fasting plasma lipids, lipoproteins, non-esterified free fatty acids, leptin and adiponectin in postpartum women with recent gestational diabetes mellitus: the SWIFT cohort. Metabolism. 2014;63(7):941–50. doi: 10.1016/j.metabol.2014.04.006 24931281
34. Gunderson EP, Hedderson MM, Chiang V, Crites Y, Walton D, Azevedo RA, et al. Lactation intensity and postpartum maternal glucose tolerance and insulin resistance in women with recent GDM: the SWIFT cohort. Diabetes Care. 2012;35(1):50–6. doi: 10.2337/dc11-1409 22011407
35. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care. 2003;26(Suppl 1):S5–20.
36. Yang Q, Vijayakumar A, Kahn BB. Metabolites as regulators of insulin sensitivity and metabolism. Nat Rev Mol Cell Biol. 2018;19(10):654–72. doi: 10.1038/s41580-018-0044-8 30104701
37. Park JE, Lim HR, Kim JW, Shin KH. Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: a systematic review and meta-analysis. Diabetes Res Clin Pract. 2018;140:216–27. doi: 10.1016/j.diabres.2018.03.045 29626587
38. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J, et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care. 2016;39(5):833–46. doi: 10.2337/dc15-2251 27208380
39. Gar C, Rottenkolber M, Prehn C, Adamski J, Seissler J, Lechner A. Serum and plasma amino acids as markers of prediabetes, insulin resistance, and incident diabetes. Crit Rev Clin Lab Sci. 2018;55(1):21–32. doi: 10.1080/10408363.2017.1414143 29239245
40. Lobner K, Knopff A, Baumgarten A, Mollenhauer U, Marienfeld S, Garrido-Franco M, et al. Predictors of postpartum diabetes in women with gestational diabetes mellitus. Diabetes. 2006;55(3):792–7. doi: 10.2337/diabetes.55.03.06.db05-0746 16505245
41. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost H-G, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62(2):639–48. doi: 10.2337/db12-0495 23043162
42. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002;25(10):1862–8. doi: 10.2337/diacare.25.10.1862 12351492
43. St John-Williams L, Blach C, Toledo JB, Rotroff DM, Kim S, Klavins K, et al. Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci Data. 2017;4:170140. doi: 10.1038/sdata.2017.140 29039849
44. Piening BD, Zhou W, Contrepois K, Rost H, Gu Urban GJ, Mishra T, et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 2018;6(2):157–70.e8. doi: 10.1016/j.cels.2017.12.013 29361466
45. Carter TC, Rein D, Padberg I, Peter E, Rennefahrt U, David DE, et al. Validation of a metabolite panel for early diagnosis of type 2 diabetes. Metabolism. 2016;65(9):1399–408. doi: 10.1016/j.metabol.2016.06.007 27506746
46. Liu J, Semiz S, van der Lee SJ, van der Spek A, Verhoeven A, van Klinken JB, et al. Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study. Metabolomics. 2017;13(9):104. doi: 10.1007/s11306-017-1239-2 28804275
47. Padberg I, Peter E, Gonzalez-Maldonado S, Witt H, Mueller M, Weis T, et al. A new metabolomic signature in type-2 diabetes mellitus and its pathophysiology. PLoS ONE. 2014;9(1):e85082. doi: 10.1371/journal.pone.0085082 24465478
48. Wurtz P, Tiainen M, Makinen VP, Kangas AJ, Soininen P, Saltevo J, et al. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care. 2012;35(8):1749–56. doi: 10.2337/dc11-1838 22563043
49. Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, et al. Untargeted metabolic profiling identifies altered serum metabolites of type 2 diabetes mellitus in a prospective, nested case control study. Clin Chem. 2015;61(3):487–97. doi: 10.1373/clinchem.2014.228965 25524438
50. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE. 2010;5(11):e13953. doi: 10.1371/journal.pone.0013953 21085649
51. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab. 2009;9(4):311–26. doi: 10.1016/j.cmet.2009.02.002 19356713
52. Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen M, et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care. 2013;36(3):648–55. doi: 10.2337/dc12-0895 23129134
53. Shah SH, Crosslin DR, Haynes CS, Nelson S, Turer CB, Stevens RD, et al. Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss. Diabetologia. 2012;55(2):321–30. doi: 10.1007/s00125-011-2356-5 22065088
54. Palmer ND, Stevens RD, Antinozzi PA, Anderson A, Bergman RN, Wagenknecht LE, et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J Clin Endocrinol Metab. 2015;100(3):E463–8. doi: 10.1210/jc.2014-2357 25423564
55. Tai E, Tan M, Stevens R, Low Y, Muehlbauer M, Goh D, et al. Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia. 2010;53(4):757–67. doi: 10.1007/s00125-009-1637-8 20076942
56. Dean ED, Li M, Prasad N, Wisniewski SN, Von Deylen A, Spaeth J, et al. Interrupted glucagon signaling reveals hepatic alpha cell axis and role for L-glutamine in alpha cell proliferation. Cell Metab. 2017;25(6):1362–73.e5. doi: 10.1016/j.cmet.2017.05.011 28591638
57. Kim J, Okamoto H, Huang Z, Anguiano G, Chen S, Liu Q, et al. Amino acid transporter Slc38a5 controls glucagon receptor inhibition-induced pancreatic alpha cell hyperplasia in mice. Cell Metab. 2017;25(6):1348–61.e8. doi: 10.1016/j.cmet.2017.05.006 28591637
58. Okamoto H, Kim J, Aglione J, Lee J, Cavino K, Na E, et al. Glucagon receptor blockade with a human antibody normalizes blood glucose in diabetic mice and monkeys. Endocrinology. 2015;156(8):2781–94. doi: 10.1210/en.2015-1011 26020795
59. Kazda C, Garhyan P, Kelly R, Shi C, Lim C, Fu H, et al. A randomized, double-blind, placebo-controlled phase 2 study of the glucagon receptor antagonist LY2409021 in patients with type 2 diabetes. Diabetes Care. 2015;39(7):1241. doi: 10.2337/dc15-1643
60. Anderson SG, Dunn WB, Banerjee M, Brown M, Broadhurst DI, Goodacre R, et al. Evidence that multiple defects in lipid regulation occur before hyperglycemia during the prodrome of type-2 diabetes. PLoS ONE. 2014;9(9):e103217. doi: 10.1371/journal.pone.0103217 25184286
61. Wang-Sattler R, Yu Y, Mittelstrass K, Lattka E, Altmaier E, Gieger C, et al. Metabolic profiling reveals distinct variations linked to nicotine consumption in humans—first results from the KORA study. PLoS ONE. 2008;3(12):e3863. doi: 10.1371/journal.pone.0003863 19057651
62. Lemaitre RN, Yu C, Hoofnagle A, Hari N, Jensen PN, Fretts AM, et al. Circulating sphingolipids, insulin, HOMA-IR, and HOMA-B: the Strong Heart Family Study. Diabetes. 2018;67(8):1663–72. doi: 10.2337/db17-1449 29588286
63. Alshehry ZH, Mundra PA, Barlow CK, Mellett NA, Wong G, McConville MJ, et al. Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus. Circulation. 2016;134(21):1637–50. doi: 10.1161/CIRCULATIONAHA.116.023233 27756783
64. Favennec M, Hennart B, Caiazzo R, Leloire A, Yengo L, Verbanck M, et al. The kynurenine pathway is activated in human obesity and shifted toward kynurenine monooxygenase activation. Obesity. 2015;23(10):2066–74. doi: 10.1002/oby.21199 26347385
65. Oxenkrug GF, Turski WA, Zgrajka W, Weinstock JV, Summergrad P. Tryptophan-kynurenine metabolism and insulin resistance in hepatitis C patients. Hepat Res Treat. 2013;2013:149247. doi: 10.1155/2013/149247 24083022
66. Oxenkrug GF. Increased plasma levels of xanthurenic and kynurenic acids in type 2 diabetes. Mol Neurobiol. 2015;52(2):805–10. doi: 10.1007/s12035-015-9232-0 26055228
67. Connick JH, Stone TW. The role of kynurenines in diabetes mellitus. Med Hypotheses. 1985;18(4):371–6. doi: 10.1016/0306-9877(85)90104-5 3912651
68. Munipally PK, Agraharm SG, Valavala VK, Gundae S, Turlapati NR. Evaluation of indoleamine 2,3-dioxygenase expression and kynurenine pathway metabolites levels in serum samples of diabetic retinopathy patients. Arch Physiol Biochem. 2011;117(5):254–8. doi: 10.3109/13813455.2011.623705 22034910
69. Oxenkrug G. Insulin resistance and dysregulation of tryptophan-kynurenine and kynurenine-nicotinamide adenine dinucleotide metabolic pathways. Mol Neurobiol. 2013;48(2):294–301. doi: 10.1007/s12035-013-8497-4 23813101
70. Stone TW, Darlington LG. Endogenous kynurenines as targets for drug discovery and development. Nat Rev Drug Discov. 2002;1(8):609–20. doi: 10.1038/nrd870 12402501
71. Gunderson EP, Quesenberry CP Jr, Jacobs DR Jr, Feng J, Lewis CE, Sidney S. Longitudinal study of prepregnancy cardiometabolic risk factors and subsequent risk of gestational diabetes mellitus: the CARDIA study. Am J Epidemiol. 2010;172(10):1131–43. doi: 10.1093/aje/kwq267 20929958
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