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Precision Medicine in Endocrinology Practice

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Precision Medicine in Clinical Practice

Abstract

The precision medicine approach can be used in endocrinology, which provides improved clinical outcomes and cost-effectiveness in the healthcare system. The new technologies of genotyping have significantly increased genetic understanding, providing an interesting opportunity to use genetic information to predispose disease and to subclassify the patients whom have the greatest benefit of therapies. Precision diabetes medicine consists of the following components: precision diagnosis, precision therapeutics, precision prevention, precision treatment, precision prognostics, and precision monitoring. The treatment of metastatic or advanced thyroid cancer has undergone a substantial change. It evolved from a one-size-fits-all approach to cytotoxic chemotherapy for these patients to an age of personalized targeted therapy based on tumor type and genomic profile. In this chapter, we present a review of precision medicine approach in endocrinology.

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References

  1. Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff (Millwood). 2018;37(5):694–701.

    Article  Google Scholar 

  2. National Library of Medicine (NIH). What is precision medicine? https://ghrnlmnihgov/primer/precisionmedicine/definition.

    Google Scholar 

  3. Meybodi HRA, Hasanzad M, Larijani B. Path to personalized medicine for type 2 diabetes mellitus: reality and hope. Acta Med Iran. 2017;55:166–74.

    Google Scholar 

  4. Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med. 2012;366(6):489–91.

    Article  PubMed  Google Scholar 

  5. Tuomi T, Santoro N, Caprio S, Cai M, Weng J, Groop L. The many faces of diabetes: a disease with increasing heterogeneity. Lancet (London, England). 2014;383(9922):1084–94.

    Article  Google Scholar 

  6. Barrea L, Annunziata G, Bordoni L, Muscogiuri G, Colao A, Savastano S. Nutrigenetics—personalized nutrition in obesity and cardiovascular diseases. Int J Obes Suppl. 2020;10(1):1–13.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Merino J. Florez JCJAotNYAoS. Precision medicine in diabetes: an opportunity for clinical translation. Ann N Y Acad Sci. 2018;1411(1):140–52.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shepherd M, Shields B, Ellard S, Rubio-Cabezas O, Hattersley AT. A genetic diagnosis of HNF1A diabetes alters treatment and improves glycaemic control in the majority of insulin-treated patients. Diabet Med. 2009;26(4):437–41.

    Article  CAS  PubMed  Google Scholar 

  9. Chung WK, Erion K, Florez JC, Hattersley AT, Hivert MF, Lee CG, et al. Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2020;43(7):1617–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Linsley PS, Greenbaum CJ, Nepom GT. Uncovering pathways to personalized therapies in type 1 diabetes. Diabetes. 2021;70(4):831–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Dufort MJ, Greenbaum CJ, Speake C, Linsley PS. Cell type-specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes. JCI Insight. 2019;4(4):e125556.

    Article  PubMed Central  Google Scholar 

  12. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science (New York, NY). 2012;337(6099):1190–5.

    Article  CAS  Google Scholar 

  13. Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50(11):1505–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Scott RA, Freitag DF, Li L, Chu AY, Surendran P, Young R, et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease Sci Transl Med. 2016;8(341):341ra76.

    Google Scholar 

  15. Reddy MA, Zhang E, Natarajan R. Epigenetic mechanisms in diabetic complications and metabolic memory. Diabetologia. 2015;58(3):443–55.

    Article  CAS  PubMed  Google Scholar 

  16. Pearson ER, Flechtner I, Njølstad PR, Malecki MT, Flanagan SE, Larkin B, et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N Engl J Med. 2006;355(5):467–77.

    Article  CAS  PubMed  Google Scholar 

  17. Thomas NJ, Jones SE, Weedon MN, Shields BM, Oram RA, Hattersley AT. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 2018;6(2):122–9.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Dimas AS, Lagou V, Barker A, Knowles JW, Mägi R, Hivert MF, et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes. 2014;63(6):2158–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gooding JR, Jensen MV, Newgard CB. Metabolomics applied to the pancreatic islet. Arch Biochem Biophys. 2016;589:120–30.

    Article  CAS  PubMed  Google Scholar 

  20. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes. 2009;58(11):2429–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Del Prato S. Heterogeneity of diabetes: heralding the era of precision medicine. Lancet Diabetes Endocrinol. 2019;7(9):659–61.

    Article  PubMed  Google Scholar 

  22. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361–9.

    Article  PubMed  Google Scholar 

  23. Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442–51.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Insel RA, Dunne JL, Atkinson MA, Chiang JL, Dabelea D, Gottlieb PA, et al. Staging presymptomatic type 1 diabetes: a scientific statement of JDRF, the Endocrine Society, and the American Diabetes Association. Diabetes Care. 2015;38(10):1964–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Krischer JP. The use of intermediate endpoints in the design of type 1 diabetes prevention trials. Diabetologia. 2013;56(9):1919–24.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ziegler AG, Rewers M, Simell O, Simell T, Lempainen J, Steck A, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013;309(23):2473–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Skyler JS, Krischer JP, Becker DJ, Rewers M. Prevention of Type 1 Diabetes. Diabetes in America. Diabetes in America. 3rd ed. Bethesda, MD: National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018. CHAPTER 37

    Google Scholar 

  28. DECODE Study Group, the European Diabetes Epidemiology Group. Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria. Arch Intern Med. 2001;161(3):397–405.

    Article  Google Scholar 

  29. Diabetes Prevention Program Research Group. Within-trial cost-effectiveness of lifestyle intervention or metformin for the primary prevention of type 2 diabetes. Diabetes Care. 2003;26(9):2518–23.

    Article  Google Scholar 

  30. Langenberg C, Sharp SJ, Franks PW, Scott RA, Deloukas P, Forouhi NG, et al. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med. 2014;11(5):e1001647.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Hivert MF, Christophi CA, Franks PW, Jablonski KA, Ehrmann DA, Kahn SE, et al. Lifestyle and metformin ameliorate insulin sensitivity independently of the genetic burden of established insulin resistance variants in diabetes prevention program participants. Diabetes. 2016;65(2):520–6.

    Article  CAS  PubMed  Google Scholar 

  32. Godino JG, van Sluijs EM, Marteau TM, Sutton S, Sharp SJ, Griffin SJ. Lifestyle advice combined with personalized estimates of genetic or phenotypic risk of type 2 diabetes, and objectively measured physical activity: a randomized controlled trial. PLoS Med. 2016;13(11):e1002185.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Committee ADAPP, Care ADAPPCJD 9. Pharmacologic approaches to glycemic treatment: Standards of Medical Care in Diabetes—2022. Diabetes Care. 2022;45(Supplement_1):S125–43.

    Article  Google Scholar 

  34. DiStefano JK, Watanabe RMJP. Pharmacogenetics of anti-diabetes drugs. Pharmaceuticals (Basel). 2010;3(8):2610–46.

    Article  CAS  Google Scholar 

  35. Ahmed S, Zhou Z, Zhou J, Chen SQ. Pharmacogenomics of drug metabolizing enzymes and transporters: relevance to precision medicine. Genomics Proteomics Bioinformatics. 2016;14(5):298–313.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pearson ER. Pharmacogenetics and target identification in diabetes. Curr Opin Genet Dev. 2018;50:68–73.

    Article  CAS  PubMed  Google Scholar 

  37. Ordelheide AM, Hrabe de Angelis M, Haring HU, Staiger H. Pharmacogenetics of oral antidiabetic therapy. Pharmacogenomics. 2018;19(6):577–87.

    Article  CAS  PubMed  Google Scholar 

  38. Lancia P, Adam de Beaumais T, Jacqz-Aigrain E. Pharmacogenetics of posttransplant diabetes mellitus. Pharmacogenomics J. 2017;17(3):209–21.

    Article  CAS  PubMed  Google Scholar 

  39. Karras SN, Rapti E, Koufakis T, Kyriazou A, Goulis DG, Kotsa K. Pharmacogenetics of glucagon-like Peptide-1 agonists for the treatment of type 2 diabetes mellitus. Curr Clin Pharmacol. 2017;12(4):202–9.

    Article  CAS  PubMed  Google Scholar 

  40. Florez JC. The pharmacogenetics of metformin. Diabetologia. 2017;60(9):1648–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Florez JC. Pharmacogenetics in type 2 diabetes: precision medicine or discovery tool? Diabetologia. 2017;60(5):800–7.

    Article  CAS  PubMed  Google Scholar 

  42. Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nat Rev Endocrinol. 2016;12(6):337–46.

    Article  CAS  PubMed  Google Scholar 

  43. Singh S, Usman K, Banerjee M. Pharmacogenetic studies update in type 2 diabetes mellitus. World J Diabetes. 2016;7(15):302–15.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Scheen AJ. Precision medicine: The future in diabetes care? Diabetes Res Clin Pract. 2016;117:12–21.

    Article  PubMed  Google Scholar 

  45. Pearson ER. Personalized medicine in diabetes: the role of ‘omics’ and biomarkers. Diabet Med. 2016;33(6):712–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Dawed AY, Zhou K, Pearson ER. Pharmacogenetics in type 2 diabetes: influence on response to oral hypoglycemic agents. Pharmgenomics Pers Med. 2016;9:17–29.

    PubMed  PubMed Central  Google Scholar 

  47. Pirmohamed M. Pharmacogenetics and pharmacogenomics. Br J Clin Pharmacol. 2001;52(4):345–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pearson ER. Diabetes: is there a future for pharmacogenomics guided treatment? Clin Pharmacol Ther. 2019;106(2):329–37.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Zhou K, Donnelly L, Burch L, Tavendale R, Doney AS, Leese G, et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin Pharmacol Ther. 2010;87(1):52–6.

    Article  CAS  PubMed  Google Scholar 

  50. Shu Y, Sheardown SA, Brown C, Owen RP, Zhang S, Castro RA, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. J Clin Invest. 2007;117(5):1422–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Shah HS, Gao H, Morieri ML, Skupien J, Marvel S, Paré G, et al. Genetic predictors of cardiovascular mortality during intensive glycemic control in type 2 diabetes: findings from the ACCORD clinical trial. Diabetes Care. 2016;39(11):1915–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Nasykhova YA, Tonyan ZN, Mikhailova AA, Danilova MM, Glotov AS. Pharmacogenetics of type 2 diabetes-Progress and prospects. Int J Mol Sci. 2020;21(18):6842.

    Article  CAS  PubMed Central  Google Scholar 

  53. Daniels MA, Kan C, Willmes DM, Ismail K, Pistrosch F, Hopkins D, et al. Pharmacogenomics in type 2 diabetes: oral antidiabetic drugs. Pharmacogenomics J. 2016;16(5):399–410.

    Article  CAS  PubMed  Google Scholar 

  54. Mannino GC, Andreozzi F, Sesti G. Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine. Diabetes Metab Res Rev. 2019;35(3):e3109.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Bedene A, Mencej Bedrač S, Ješe L, Marc J, Vrtačnik P, Preželj J, et al. MiR-148a the epigenetic regulator of bone homeostasis is increased in plasma of osteoporotic postmenopausal women. Wien Klin Wochenschr. 2016;128(7):519–26.

    Article  CAS  PubMed  Google Scholar 

  56. Johnell O, Kanis J. An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int. 2006;17(12):1726–33.

    Article  CAS  PubMed  Google Scholar 

  57. Ralston SH, De Crombrugghe BJG. Development. Genetic regulation of bone mass and susceptibility to osteoporosis. Genes Dev. 2006;20(18):2492–506.

    Article  CAS  PubMed  Google Scholar 

  58. US Food and Drug Adminstration. Drug development tools (DDT) qualification programs. https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tool-ddt-qualification-programs

  59. O’Brien J, Hayder H, Zayed Y, Peng C. Overview of microRNA biogenesis, mechanisms of actions, and circulation. Front Endocrinol (Lausanne). 2018;9:402.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Van Wijnen AJ, Van De Peppel J, Van Leeuwen JP, Lian JB, Stein GS, Westendorf JJ, et al. MicroRNA functions in osteogenesis and dysfunctions in osteoporosis. Curr Osteoporos Rep. 2013;11(2):72–82.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Yahata T, Quan J, Tamura N, Nagata H, Kurabayashi T, Tanaka K. Association between single nucleotide polymorphisms of estrogen receptor α gene and efficacy of HRT on bone mineral density in post-menopausal Japanese women. Hum Reprod. 2005;20(7):1860–6.

    Article  CAS  PubMed  Google Scholar 

  62. Kurabayashi T, Matsushita H, Tomita M, Kato N, Kikuchi M, Nagata H, et al. Association of vitamin D and estrogen receptor gene polymorphism with the effects of longterm hormone replacement therapy on bone mineral density. J Bone Miner Metab. 2004;22(3):241–7.

    Article  CAS  PubMed  Google Scholar 

  63. Li W-F, Hou S-X, Yu B, Li M-M, Férec C, Chen J-M. Genetics of osteoporosis: accelerating pace in gene identification and validation. Hum Genet. 2010;127(3):249–85.

    Article  CAS  PubMed  Google Scholar 

  64. Li W-F, Hou S-X, Yu B, Jin D, Férec C, Chen J-M. Genetics of osteoporosis: perspectives for personalized medicine. Pers Med. 2010;7(6):655–68.

    Article  CAS  Google Scholar 

  65. Marini F, Brandi ML. Pharmacogenetics of osteoporosis. Best Pract Res Clin Endocrinol Metab. 2014;28(6):783–93.

    Article  CAS  PubMed  Google Scholar 

  66. Giguère Y, Dodin S, Blanchet C, Morgan K, Rousseau F. The association between heel ultrasound and hormone replacement therapy is modulated by a two-locus vitamin D and estrogen receptor genotype. J Bone Miner Res. 2000;15(6):1076–84.

    Article  PubMed  Google Scholar 

  67. Simsek M, Cetin Z, Bilgen T, Taskin O, Luleci G, Keser I. Effects of hormone replacement therapy on bone mineral density in Turkish patients with or without COL1A1 Sp1 binding site polymorphism. J Obstet Gynaecol Res. 2008;34(1):73–7.

    CAS  PubMed  Google Scholar 

  68. Heilberg IP, Hernandez E, Alonzo E, Valera R, Ferreira LG, Gomes SA, et al. Estrogen receptor (ER) gene polymorphism may predict the bone mineral density response to raloxifene in postmenopausal women on chronic hemodialysis. Ren Fail. 2005;27(2):155–61.

    Article  CAS  PubMed  Google Scholar 

  69. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev. 2012;33(6):981–1030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Azziz R. Controversy in clinical endocrinology: diagnosis of polycystic ovarian syndrome: the Rotterdam criteria are premature. J Clin Endocrinol Metab. 2006;91(3):781–5.

    Article  CAS  PubMed  Google Scholar 

  71. Zawdaki J, Dunaif A. Diagnostic criteria for polycystic ovarian syndrome: towards a rational approach. In: Polycystic ovarian syndrome current issues in endocrinology and metabolism. Boston, MA: Blackwell Scientific; 1992.

    Google Scholar 

  72. Kahsar-Miller MD, Nixon C, Boots LR, Go RC, Azziz RJF. Sterility. Prevalence of polycystic ovary syndrome (PCOS) in first-degree relatives of patients with PCOS. Fertil Steril. 2001;75(1):53–8.

    Article  CAS  PubMed  Google Scholar 

  73. Hayes MG, Urbanek M, Ehrmann DA, Armstrong LL, Lee JY, Sisk R, et al. Genome-wide association of polycystic ovary syndrome implicates alterations in gonadotropin secretion in European ancestry populations. Nat Commun. 2015;6(1):1–13.

    Article  CAS  Google Scholar 

  74. Day FR, Hinds DA, Tung JY, Stolk L, Styrkarsdottir U, Saxena R, et al. Causal mechanisms and balancing selection inferred from genetic associations with polycystic ovary syndrome. Nat Commun. 2015;6(1):1–7.

    Article  Google Scholar 

  75. Chen Z-J, Zhao H, He L, Shi Y, Qin Y, Shi Y, et al. Genome-wide association study identifies susceptibility loci for polycystic ovary syndrome on chromosome 2p16. 3, 2p21 and 9q33. 3. Nat Genet. 2011;43(1):55–9.

    Article  PubMed  Google Scholar 

  76. Mutharasan P, Galdones E, Peñalver Bernabé B, Garcia OA, Jafari N, Shea LD, et al. Evidence for chromosome 2p16. 3 polycystic ovary syndrome susceptibility locus in affected women of European ancestry. J Clin Endocrinol Metab. 2013;98(1):E185–E90.

    Article  CAS  PubMed  Google Scholar 

  77. Shi Y, Zhao H, Shi Y, Cao Y, Yang D, Li Z, et al. Genome-wide association study identifies eight new risk loci for polycystic ovary syndrome. Nat Genet. 2012;44(9):1020–5.

    Article  CAS  PubMed  Google Scholar 

  78. Pau C, Saxena R, Welt CKJF. Sterility. Evaluating reported candidate gene associations with polycystic ovary syndrome. Fertil Steril. 2013;99(6):1774–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Lee H, Oh J-Y, Sung Y-A, Chung H, Kim H-L, Kim GS, et al. Genome-wide association study identified new susceptibility loci for polycystic ovary syndrome. Hum Reprod. 2015;30(3):723–31.

    Article  CAS  PubMed  Google Scholar 

  80. Hong S-H, Hong YS, Jeong K, Chung H, Lee H, Sung Y-A. Relationship between the characteristic traits of polycystic ovary syndrome and susceptibility genes. Sci Rep. 2020;10(1):10479.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Day F, Karaderi T, Jones MR, Meun C, He C, Drong A, et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet. 2018;14(12):e1007813.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  83. Kitahara CM, Schneider AB, Brenner AV. Thyroid cancer. In: Thun M, Linet MS, Cerhan JR, Haiman CA, Schottenfeld D, eds. Cancer Epidemiology and Prevention. 4th ed. New York, NY: Oxford University Press; 2016:839–860.

    Google Scholar 

  84. Cooper DS, Doherty GM, Haugen BR, Kloos RT, Lee SL, Mandel SJ, et al. Management guidelines for patients with thyroid nodules and differentiated thyroid cancer: The American Thyroid Association Guidelines Taskforce. Thyroid. 2006;16(2):109–42.

    Article  PubMed  Google Scholar 

  85. Salter KD, Andersen PE, Cohen JI, Schuff KG, Lester L, Shindo ML, et al. Central nodal metastases in papillary thyroid carcinoma based on tumor histologic type and focality. Arch Otolaryngol Head Neck Surg. 2010;136(7):692–6.

    Article  PubMed  Google Scholar 

  86. Ferrari SM, Fallahi P, La Motta C, Elia G, Ragusa F, Ruffilli I, et al. Recent advances in precision medicine for the treatment of anaplastic thyroid cancer. Expert Rev Precis Med Drug Dev. 2019;4(1):37–49.

    Article  Google Scholar 

  87. Lloyd R, Osamura RY, Klöppel G, Rosai J. WHO classification of tumours of endocrine organs. Lyon: IARC Press; 2017.

    Google Scholar 

  88. Prete A, Borges de Souza P, Censi S, Muzza M, Nucci N, Sponziello M. Update on fundamental mechanisms of thyroid cancer. Front Endocrinol (Lausanne). 2020;11:102.

    Article  Google Scholar 

  89. Antonelli A, Ferrari SM, Fallahi P, Berti P, Materazzi G, Minuto M, et al. Thiazolidinediones and antiblastics in primary human anaplastic thyroid cancer cells. Clin Endocrinol. 2009;70(6):946–53.

    Article  CAS  Google Scholar 

  90. Ferrari SM, Ruffilli I, Centanni M, Virili C, Materazzi G, Alexopoulou M, et al. Lenvatinib in the therapy of aggressive thyroid cancer: state of the art and new perspectives with patents recently applied. R Recent Pat Anticancer Drug Discov. 2018;13(2):201–8.

    Article  CAS  Google Scholar 

  91. Miccoli P, Antonelli A, Spinelli C, Ferdeghini M, Fallahi P, Baschieri L. Completion total thyroidectomy in children with thyroid cancer secondary to the Chernobyl accident. Arch Surg. 1998;133(1):89–93.

    Article  CAS  PubMed  Google Scholar 

  92. Asa SL. The current histologic classification of thyroid cancer. Endocrinol Metab Clin N Am. 2019;48(1):1–22.

    Article  Google Scholar 

  93. Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer. 2013;13(3):184–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell. 2014;159(3):676–90.

    Article  Google Scholar 

  95. Xing M. Genetic-guided risk assessment and management of thyroid cancer. Endocrinol Metab Clin N Am. 2019;48(1):109–24.

    Article  Google Scholar 

  96. Mazzaferri EL. An overview of the management of papillary and follicular thyroid carcinoma. Thyroid. 1999;9(5):421–7.

    Article  CAS  PubMed  Google Scholar 

  97. Landa I, Ibrahimpasic T, Boucai L, Sinha R, Knauf JA, Shah RH, et al. Genomic and transcriptomic hallmarks of poorly differentiated and anaplastic thyroid cancers. J Clin Invest. 2016;126(3):1052–66.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Sheng Z, Sun W, Smith E, Cohen C, Sheng Z, Xu X-XJO. Restoration of positioning control following Disabled-2 expression in ovarian and breast tumor cells. Oncogene. 2000;19(42):4847–54.

    Article  CAS  PubMed  Google Scholar 

  99. Romei C, Ciampi R, Elisei RJNRE. A comprehensive overview of the role of the RET proto-oncogene in thyroid carcinoma. Nat Rev Endocrinol. 2016;12(4):192–202.

    Article  CAS  PubMed  Google Scholar 

  100. Aksoy A, Ally A, Arachchi H, Asa S, Auman J, Balasundaram MJC. Integrated genomic characterization of papillary thyroid. Cell. 2014;159:676–90.

    Article  PubMed Central  Google Scholar 

  101. Chua EL, Wu WM, Tran KT, McCarthy SW, Lauer CS, Dubourdieu D, et al. Prevalence and distribution of ret/ptc 1, 2, and 3 in papillary thyroid carcinoma in New Caledonia and Australia. J Clin Endocrinol Metab. 2000;85(8):2733–9.

    CAS  PubMed  Google Scholar 

  102. Adeniran AJ, Zhu Z, Gandhi M, Steward DL, Fidler JP, Giordano TJ, et al. Correlation between genetic alterations and microscopic features, clinical manifestations, and prognostic characteristics of thyroid papillary carcinomas. Am J Surg Pathol. 2006;30(2):216–22.

    Article  PubMed  Google Scholar 

  103. Khan MS, Qadri Q, Makhdoomi MJ, Wani MA, Malik AA, Niyaz M, et al. RET/PTC gene rearrangements in thyroid carcinogenesis: assessment and clinico-pathological correlations. Pathol Oncol Res. 2020;26(1):507–13.

    Article  CAS  PubMed  Google Scholar 

  104. Paulson VA, Rudzinski ER, Hawkins DSJG. Thyroid cancer in the pediatric population. Genes (Basel). 2019;10(9):723.

    Article  CAS  Google Scholar 

  105. Vuong HG, Altibi AM, Duong UN, Hassell L. Prognostic implication of BRAF and TERT promoter mutation combination in papillary thyroid carcinoma—a meta-analysis. Clin Endocrinol. 2017;87(5):411–7.

    Article  CAS  Google Scholar 

  106. Yuan X, Liu T, Xu D. Telomerase reverse transcriptase promoter mutations in thyroid carcinomas: implications in precision oncology—a narrative review. Ann Transl Med. 2020;8(19):1244.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Boos LA, Dettmer M, Schmitt A, Rudolph T, Steinert H, Moch H, et al. Diagnostic and prognostic implications of the PAX 8–PPAR γ translocation in thyroid carcinomas—a TMA-based study of 226 cases. Histopathology. 2013;63(2):234–41.

    Article  PubMed  Google Scholar 

  108. Nikiforova MN, Biddinger PW, Caudill CM, Kroll TG, Nikiforov YE. PAX8-PPARγ rearrangement in thyroid tumors: RT-PCR and immunohistochemical analyses. Am J Surg Pathol. 2002;26(8):1016–23.

    Article  PubMed  Google Scholar 

  109. Yang J, Gong Y, Yan S, Chen H, Qin S, Gong R. Association between TERT promoter mutations and clinical behaviors in differentiated thyroid carcinoma: a systematic review and meta-analysis. Endocrine. 2020;67(1):44–57.

    Article  CAS  PubMed  Google Scholar 

  110. Romei C, Tacito A, Molinaro E, Piaggi P, Cappagli V, Pieruzzi L, et al. Clinical, pathological and genetic features of anaplastic and poorly differentiated thyroid cancer: a single institute experience. Oncol Lett. 2018;15(6):9174–82.

    PubMed  PubMed Central  Google Scholar 

  111. Smith N, Nucera C. Personalized therapy in patients with anaplastic thyroid cancer: targeting genetic and epigenetic alterations. J Clin Endocrinol Metab. 2015;100(1):35–42.

    Article  CAS  PubMed  Google Scholar 

  112. Quiros RM, Ding HG, Gattuso P, Prinz RA, Xu X. Evidence that one subset of anaplastic thyroid carcinomas are derived from papillary carcinomas due to BRAF and p53 mutations. Cancer. 2005;103(11):2261–8.

    Article  CAS  PubMed  Google Scholar 

  113. Fagin JA, Matsuo K, Karmakar A, Chen DL, Tang S-H, Koeffler HP. High prevalence of mutations of the p53 gene in poorly differentiated human thyroid carcinomas. J Clin Invest. 1993;91(1):179–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Pozdeyev N, Gay LM, Sokol ES, Hartmaier R, Deaver KE, Davis S, et al. Genetic analysis of 779 advanced differentiated and anaplastic thyroid cancers. Clin Cancer Res. 2018;24(13):3059–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Elisei R, Tacito A, Ramone T, Ciampi R, Bottici V, Cappagli V, et al. Twenty-five years experience on RET genetic screening on hereditary MTC: an update on the prevalence of germline RET mutations. Genes. 2019;10(9):698.

    Article  CAS  PubMed Central  Google Scholar 

  116. Tate John G, Sally B, Jubb Harry C, Zbyslaw S, Beare David M, Nidhi B, Ray S, Thompson Sam L, Shicai W, Sari W, Campbell Peter J, Forbes Simon A, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2018;47(D1):D941–7.

    Article  PubMed Central  Google Scholar 

  117. The Lancet. 20 years of precision medicine in oncology. Lancet (London, England). 2021;397(10287):1781.

    Article  CAS  Google Scholar 

  118. National Cancer Institute (NIH). Precision Medicine. https://www.cancergov/publications/dictionaries/cancer-terms/def/precision-medicine.

  119. Xiao H, Liu R, Yu S. Towards precision medicine in thyroid cancer. Ann Transl Med. 2020;8(19):1212.

    Article  PubMed  PubMed Central  Google Scholar 

  120. Khatami F, Larijani B, Nikfar S, Hasanzad M, Fendereski K, Tavangar SM. Personalized treatment options for thyroid cancer: current perspectives. Pharmgenomics Pers Med. 2019;12:235–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Miccoli P, Materazzi G, Antonelli A, Panicucci E, Frustaci G, Berti P. New trends in the treatment of undifferentiated carcinomas of the thyroid. Langenbeck’s Arch Surg. 2007;392(4):397–404.

    Article  Google Scholar 

  122. Smallridge RC, Ain KB, Asa SL, Bible KC, Brierley JD, Burman KD, et al. American Thyroid Association guidelines for management of patients with anaplastic thyroid cancer. Thyroid. 2012;22(11):1104–39.

    Article  PubMed  Google Scholar 

  123. Pinto N, Black M, Patel K, Yoo J, Mymryk JS, Barrett JW, et al. Genomically driven precision medicine to improve outcomes in anaplastic thyroid cancer. J Oncol. 2014;2014:936285.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Samimi H, Fallah P, Sohi AN, Tavakoli R, Naderi M, Soleimani M, et al. Precision medicine approach to anaplastic thyroid cancer: advances in targeted drug therapy based on specific signaling pathways. Acta Med Iran. 2017;55:200–8.

    PubMed  Google Scholar 

  125. Collaborators GO. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.

    Article  Google Scholar 

  126. Hruby A, Hu FB. The epidemiology of obesity: a big picture. PharmacoEconomics. 2015;33(7):673–89.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016;8(1):1–11.

    Article  Google Scholar 

  128. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Cifuentes L, Eckel-Passow J, Acosta A. Precision medicine for Obesity. Dig Dis Interv. 2021;5:239–48.

    Google Scholar 

  130. Heart N, Lung, Institute B, Diabetes NIo, Digestive, Diseases K. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report. National Heart, Lung, and Blood Institute; 1998.

    Google Scholar 

  131. Blüher M. The distinction of metabolically ‘healthy’from ‘unhealthy’obese individuals. Curr Opin Lipidol. 2010;21(1):38–43.

    Article  PubMed  Google Scholar 

  132. Goodarzi MO. Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications. Lancet Diabetes Endocrinol. 2018;6(3):223–36.

    Article  CAS  PubMed  Google Scholar 

  133. Chami N, Preuss M, Walker RW, Moscati A, Loos RJ. The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK biobank population. PLoS Med. 2020;17(7):e1003196.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Loos RJ, Yeo GS. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23(2):120–33.

    Article  CAS  PubMed  Google Scholar 

  135. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):1–15.

    Article  Google Scholar 

  137. Aleksandrova K, Rodrigues CE, Floegel A, Ahrens W. Omics biomarkers in obesity: novel etiological insights and targets for precision prevention. Curr Obes Rep. 2020;9(3):219–30.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Bouchard C. Genetics of obesity: what we have learned over decades of research. Obesity. 2021;29(5):802–20.

    Article  PubMed  Google Scholar 

  139. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40(6):768–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, Froguel P, et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet. 2008;40(6):716–8.

    Article  CAS  PubMed  Google Scholar 

  141. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41(1):18–24.

    Article  CAS  PubMed  Google Scholar 

  142. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Kilpeläinen TO, Carli JFM, Skowronski AA, Sun Q, Kriebel J, Feitosa MF, et al. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels. Nat Commun. 2016;7(1):1–14.

    Article  Google Scholar 

  144. Yaghootkar H, Zhang Y, Spracklen CN, Karaderi T, Huang LO, Bradfield J, et al. Genetic studies of leptin concentrations implicate leptin in the regulation of early adiposity. Diabetes. 2020;69(12):2806–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Kichaev G, Bhatia G, Loh P-R, Gazal S, Burch K, Freund MK, et al. Leveraging polygenic functional enrichment to improve GWAS power. Am J Hum Genet. 2019;104(1):65–75.

    Article  CAS  PubMed  Google Scholar 

  146. Sun Q, Cornelis MC, Kraft P, Qi L, van Dam RM, Girman CJ, et al. Genome-wide association study identifies polymorphisms in LEPR as determinants of plasma soluble leptin receptor levels. Hum Mol Genet. 2010;19(9):1846–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet. 2019;28(1):166–74.

    Article  CAS  PubMed  Google Scholar 

  148. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41(1):25–34.

    Article  CAS  PubMed  Google Scholar 

  149. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet. 2017;49(10):1458–67.

    Article  CAS  PubMed  Google Scholar 

  150. Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50(1):26–41.

    Article  CAS  PubMed  Google Scholar 

  151. Farooqi IS, Matarese G, Lord GM, Keogh JM, Lawrence E, Agwu C, et al. Beneficial effects of leptin on obesity, T cell hyporesponsiveness, and neuroendocrine/metabolic dysfunction of human congenital leptin deficiency. J Clin Invest. 2002;110(8):1093–103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Collet T-H, Dubern B, Mokrosinski J, Connors H, Keogh JM, de Oliveira EM, et al. Evaluation of a melanocortin-4 receptor (MC4R) agonist (Setmelanotide) in MC4R deficiency. Mol Metab. 2017;6(10):1321–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Roth JD, Roland BL, Cole RL, Trevaskis JL, Weyer C, Koda JE, et al. Leptin responsiveness restored by amylin agonism in diet-induced obesity: evidence from nonclinical and clinical studies. Proc Natl Acad Sci U S A. 2008;105(20):7257–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Brunkwall L, Chen Y, Hindy G, Rukh G, Ericson U, Barroso I, et al. Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 Swedish cohorts. Am J Clin Nutr. 2016;104(3):809–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Marigorta UM, Gibson G. A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects. Front Genetics. 2014;5:225.

    Article  Google Scholar 

  156. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Hasanzad M, Sarhangi N, Ehsani Chimeh S, Ayati N, Afzali M, Khatami F, et al. Precision medicine journey through omics approach. J Diabetes Metab Disord. 2021;21:881–8.

    Google Scholar 

  158. Amri E-Z, Scheideler M. Small non coding RNAs in adipocyte biology and obesity. Mol Cell Endocrinol. 2017;456:87–94.

    Article  CAS  PubMed  Google Scholar 

  159. Deutsch A, Feng D, Pessin JE, Shinoda K. The impact of single-cell genomics on adipose tissue research. Int J Mol Sci. 2020;21(13):4773.

    Article  CAS  PubMed Central  Google Scholar 

  160. González-Plaza JJ, Gutiérrez-Repiso C, García-Serrano S, Rodriguez-Pacheco F, Garrido-Sánchez L, Santiago-Fernández C, et al. Effect of Roux-en-Y gastric bypass-induced weight loss on the transcriptomic profiling of subcutaneous adipose tissue. Surg Obes Relat Dis. 2016;12(2):257–63.

    Article  PubMed  Google Scholar 

  161. Armenise C, Lefebvre G, Carayol JM, Bonnel S, Bolton J, Di Cara A, et al. Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects. Am J Clin Nutr. 2017;106(3):736–46.

    CAS  PubMed  Google Scholar 

  162. Rangel-Huerta OD, Pastor-Villaescusa B, Gil A. Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies. Metabolomics. 2019;15(6):1–31.

    Article  Google Scholar 

  163. Suhre K, Shin S-Y, Petersen A-K, Mohney RP, Meredith D, Wägele B, et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature. 2011;477(7362):54–60.

    Article  CAS  PubMed  Google Scholar 

  164. Bakar MHA, Sarmidi MR, Cheng K-K, Khan AA, Suan CL, Huri HZ, et al. Metabolomics–the complementary field in systems biology: a review on obesity and type 2 diabetes. Mol BioSyst. 2015;11(7):1742–74.

    Article  PubMed  Google Scholar 

  165. Xie B, Waters MJ, Schirra HJ. Investigating potential mechanisms of obesity by metabolomics. J Biomed Biotechnol. 2012;2012:805683.

    Article  PubMed  PubMed Central  Google Scholar 

  166. Cirulli ET, Guo L, Swisher CL, Shah N, Huang L, Napier LA, et al. Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metab. 2019;29(2):488–500.e2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Geidenstam N, Magnusson M, Danielsson AP, Gerszten RE, Wang TJ, Reinius LE, et al. Amino acid signatures to evaluate the beneficial effects of weight loss. Int J Endocrinol. 2017;2017:6490473.

    Article  PubMed  PubMed Central  Google Scholar 

  168. Zhao L. The gut microbiota and obesity: from correlation to causality. Nat Rev Microbiol. 2013;11(9):639–47.

    Article  CAS  PubMed  Google Scholar 

  169. John GK, Mullin GE. The gut microbiome and obesity. Curr Oncol Rep. 2016;18(7):45.

    Article  PubMed  Google Scholar 

  170. Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell. 2006;124(4):837–48.

    Article  CAS  PubMed  Google Scholar 

  171. Rad SS, Nikkhah A, Orvatinia M, Ejtahed H-S, Sarhangi N, Jamaldini SH, et al. Gut microbiota: a perspective of precision medicine in endocrine disorders. J Diabetes Metab Disord. 2020;19:1827–34.

    Google Scholar 

  172. Bäckhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, et al. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 2004;101(44):15718–23.

    Article  PubMed  PubMed Central  Google Scholar 

  173. Bauer PV, Hamr SC, Duca FA. Regulation of energy balance by a gut–brain axis and involvement of the gut microbiota. Cell Mol Life Sci. 2016;73(4):737–55.

    Article  CAS  PubMed  Google Scholar 

  174. Ejtahed H-S, Soroush A-R, Angoorani P, Larijani B, Hasani-Ranjbar S. Gut microbiota as a target in the pathogenesis of metabolic disorders: a new approach to novel therapeutic agents. Horm Metab Res. 2016;48(06):349–58.

    Article  CAS  PubMed  Google Scholar 

  175. Solas M, Milagro FI, Martínez-Urbistondo D, Ramirez MJ, Martínez JA. Precision obesity treatments including pharmacogenetic and nutrigenetic approaches. Trends Pharmacol Sci. 2016;37(7):575–93.

    Article  CAS  PubMed  Google Scholar 

  176. Goni L, Milagro FI, Cuervo M, Martínez JA. Single-nucleotide polymorphisms and DNA methylation markers associated with central obesity and regulation of body weight. Nutr Rev. 2014;72(11):673–90.

    Article  PubMed  Google Scholar 

  177. Guzman A, Ding M, Xie Y, Martin K. Pharmacogenetics of obesity drug therapy. Curr Mol Med. 2014;14(7):891–908.

    Article  CAS  PubMed  Google Scholar 

  178. O’Connor A, Swick AG. Interface between pharmacotherapy and genes in human obesity. Hum Hered. 2013;75(2–4):116–26.

    Article  PubMed  Google Scholar 

  179. Li QS, Lenhard JM, Zhan Y, Konvicka K, Athanasiou MC, Strauss RS, et al. A candidate-gene association study of topiramate-induced weight loss in obese patients with and without type 2 diabetes mellitus. Pharmacogenet Genomics. 2016;26(2):53–65.

    Article  CAS  PubMed  Google Scholar 

  180. de Luis DA, Soto GD, Izaola O, Romero E. Evaluation of weight loss and metabolic changes in diabetic patients treated with liraglutide, effect of RS 6923761 gene variant of glucagon-like peptide 1 receptor. J Diabetes Complicat. 2015;29(4):595–8.

    Article  Google Scholar 

  181. Zhang J-P, Lencz T, Zhang RX, Nitta M, Maayan L, John M, et al. Pharmacogenetic associations of antipsychotic drug-related weight gain: a systematic review and meta-analysis. Schizophr Bull. 2016;42(6):1418–37.

    Article  PubMed  PubMed Central  Google Scholar 

  182. Doo M, Kim Y. Obesity: interactions of genome and nutrients intake. Prev Nutr Food Sci. 2015;20(1):1–7.

    Article  PubMed  PubMed Central  Google Scholar 

  183. Rathmann W, Bongaerts B. Pharmacogenetics of novel glucose-lowering drugs. Diabetologia. 2021;64(6):1201–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Hasanzad M, Aghaei Meybodi HR, Sarhangi N, Larijani B. Artificial intelligence perspective in the future of endocrine diseases. J Diabetes Metab Disord. 2022;21(1):971–78.

    Google Scholar 

  185. Hasanzad M, Sarhangi N, Naghavi A, Ghavimehr E, Khatami F, Ehsani Chimeh S, et al. Genomic medicine on the frontier of precision medicine. J Diabetes Metab Disord. 2021;21(1):853–61.

    Google Scholar 

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Aghaei Meybodi, H.R., Hasanzad, M., Sarhangi, N., Larijani, B. (2022). Precision Medicine in Endocrinology Practice. In: Hasanzad, M. (eds) Precision Medicine in Clinical Practice. Springer, Singapore. https://doi.org/10.1007/978-981-19-5082-7_5

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