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Selected Papers from the 2nd Conference with International Participation “Basic Research in Endocrinology: A Modern Strategy for the Development and Technologies of Personalized Medicine”

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Basic Research in Endocrinology: A Modern Strategy for the Development and Technologies of Personalized Medicine 2022 Nowosibirsk, Ragino, Yuliya I., Rymar, Oksana D.·2025·MDPI eBooks
DOI 10.3390/books978-3-7258-4017-5
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Abstract

The 2nd all-Russia conference with international participation Basic Research in Endocrinology: A Modern Strategy for the Development and Technologies of Personalized Medicine was held in Novosibirsk on 2425 November 2022. The purpose of this conference was to disseminate the latest basic and clinical findings in the fields of etiology, clinical characteristics, and modern diagnostics and treatments of endocrine disorders among various relevant specialists. The conference was intended for practicing endocrinologists, primary care physicians, medical geneticists, pediatric endocrinologists, pediatricians, and physicianscientists. The conference included plenary sessions, specialty sessions, satellite symposia, an open competition for young scientists, and the 2nd-in-Russia educational course for physicians: Maturity Onset Diabetes of the Young (MODY): Molecular Genetic Determinants and a Personalized Approach to Patient Management.

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  1. Siamashvili, M.; Davis, H.A.; Davis, S.N. Nocturnal hypoglycemia in type 1 and type 2 diabetes: An update on prevalence, prevention, pathophysiology and patient awareness. Expert Rev. Endocrinol. Metab. 2021, 16, 281-293. [CrossRef]
  2. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group. Prolonged nocturnal hypoglycemia is common during 12 months of continuous glucose monitoring in children and adults with type 1 diabetes. Diabetes Care 2010, 33, 1004-1008. [CrossRef]
  3. Henriksen, M.M.; Andersen, H.U.; Thorsteinsson, B.; Pedersen-Bjergaard, U. Asymptomatic hypoglycaemia in Type 1 diabetes: Incidence and risk factors. Diabet. Med. 2019, 36, 62-69. [CrossRef]
  4. Guelho, D.; Paiva, I.; Batista, C.; Barros, L.; Carrilho, F. A1c, glucose variability and hypoglycemia risk in patients with type 1 diabetes. Minerva Endocrinol. 2014, 39, 127-133.
  5. Wilson, D.M.; Calhoun, P.M.; Maahs, D.M.; Chase, H.P.; Messer, L.; Buckingham, B.A.; Aye, T.; Clinton, P.K.; Hramiak, I.; Kollman, C.; et al. In Home Closed Loop Study Group. Factors associated with nocturnal hypoglycemia in at-risk adolescents and young adults with type 1 diabetes. Diabetes Technol. Ther. 2015, 17, 385-391. [CrossRef]
  6. McLachlan, G.J. Cluster analysis and related techniques in medical research. Stat. Methods Med. Res. 1992, 1, 27-48. [CrossRef]
  7. Sarría-Santamera, A.; Orazumbekova, B.; Maulenkul, T.; Gaipov, A.; Atageldiyeva, K. The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review. Int. J. Environ. Res. Public Health 2020, 17, 9523. [CrossRef]
  8. Kahkoska, A.R.; Adair, L.A.; Aiello, A.E.; Burger, K.S.; Buse, J.B.; Crandell, J.; Maahs, D.M.; Nguyen, C.T.; Kosorok, M.R.; Mayer-Davis, E.J. Identification of clinically relevant dysglycemia phenotypes based on continuous glucose monitoring data from youth with type 1 diabetes and elevated hemoglobin A1c. Pediatr. Diabetes 2019, 20, 556-566. [CrossRef]
  9. Schroder, C.; Diez, J.L.; Laguna, A.J.; Bondia, J.; Tarin, C. Classification of postprandial glycemic patterns in type 1 diabetes subjects under closed-loop control: An in silico study. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2019, 2019, 5443-5446. [CrossRef]
  10. Tao, R.; Yu, X.; Lu, J.; Shen, Y.; Lu, W.; Zhu, W.; Bao, Y.; Li, H.; Zhou, J. Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes. BMJ Open Diabetes Res. Care 2021, 9, e001869. [CrossRef]
  11. Li, L.; Sun, J.; Ruan, L.; Song, Q. Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM. J. Clin. Endocrinol. Metab. 2021, 106, 2187-2197. [CrossRef]
  12. Varghese, J.S.; Ho, J.C.; Anjana, R.M.; Pradeepa, R.; Patel, S.A.; Jebarani, S.; Baskar, V.; Narayan, K.M.V.; Mohan, V. Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data. Diabetes Technol. Ther. 2021, 23, 555-564. [CrossRef]
  13. Inayama, Y.; Yamanoi, K.; Shitanaka, S.; Ogura, J.; Ohara, T.; Sakai, M.; Suzuki, H.; Kishimoto, I.; Tsunenari, T.; Suginami, K. A novel classification of glucose profile in pregnancy based on continuous glucose monitoring data. J. Obstet. Gynaecol. Res. 2021, 47, 1281-1291. [CrossRef]
  14. Danne, T.; Nimri, R.; Battelino, T.; Bergenstal, R.M.; Close, K.L.; DeVries, J.H.; Garg, S.; Heinemann, L.; Hirsch, I.; Amiel, S.A.; et al. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care 2017, 40, 1631-1640. [CrossRef] [PubMed]
  15. Cassisi, C.; Montaldo, P.; Aliotta, M.; Cannata, A.; Pulvirenti, A. Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining. In Advances in Data Mining Knowledge Discovery and Applications; Karahoca, A., Ed.; InTech: Rijeka, Croatia, 2012; pp. 71-96. [CrossRef]
  16. Rousseeuw, P. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. J. Comput. Appl. Math. 1987, 20, 53-65. [CrossRef]
  17. Ward, J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 1963, 58, 236-244. [CrossRef]
  18. Kirilyuk, I.L.; Senko, O.V. Assessing the validity of clustering of panel data by Monte Carlo methods (using as example the data of the Russian regional economy). Comput. Res. Model. 2020, 12, e1501-e1513. [CrossRef]
  19. Ono, M.; Katsuki, T.; Makino, M.; Haida, K.; Suzuki, A. Interpretation Method for Continuous Glucose Monitoring with Subsequence Time-Series Clustering. Stud. Health Technol. Inform. 2020, 270, 277-281. [CrossRef]
  20. Klimontov, V.V.; Myakina, N.E. Glucose variability indices predict the episodes of nocturnal hypoglycemia in elderly type 2 diabetic patients treated with insulin. Diabetes Metab. Syndr. 2017, 11, 119-124. [CrossRef]
  21. Owens, D.R.; Bailey, T.S.; Fanelli, C.G.; Yale, J.F.; Bolli, G.B. Clinical relevance of pharmacokinetic and pharmacodynamic profiles of insulin degludec (100, 200 U/mL) and insulin glargine (100, 300 U/mL) -a review of evidence and clinical interpretation. Diabetes Metab. 2019, 45, 330-340. [CrossRef]
  22. Díez-Fernández, A.; Cavero-Redondo, I.; Moreno-Fernández, J.; Pozuelo-Carrascosa, D.P.; Garrido-Miguel, M.; Martínez-Vizcaíno, V. Effectiveness of insulin glargine U-300 versus insulin glargine U-100 on nocturnal hypoglycemia and glycemic control in type 1 and type 2 diabetes: A systematic review and meta-analysis. Acta Diabetol. 2019, 56, 355-364. [CrossRef]
  23. Brøsen, J.M.B.; Agesen, R.M.; Alibegovic, A.C.; Ullits Andersen, H.; Beck-Nielsen, H.; Gustenhoff, P.; Krarup Hansen, T.; Hedetoft, C.G.R.; Jensen, T.J.; Stolberg, C.R.; et al. Continuous Glucose Monitoring-Recorded Hypoglycemia with Insulin Degludec or Insulin Glargine U100 in People with Type 1 Diabetes Prone to Nocturnal Severe Hypoglycemia. Diabetes Technol. Ther. 2022, 24, 643-654. [CrossRef]
  24. Forlenza, G.P.; Li, Z.; Buckingham, B.A.; Pinsker, J.E.; Cengiz, E.; Wadwa, R.P.; Ekhlaspour, L.; Church, M.M.; Weinzimer, S.A.; Jost, E.; et al. Predictive Low-Glucose Suspend Reduces Hypoglycemia in Adults, Adolescents, and Children With Type 1 Diabetes in an At-Home Randomized Crossover Study: Results of the PROLOG Trial. Diabetes Care 2018, 41, 2155-2161. [CrossRef]
  25. Chen, E.; King, F.; Kohn, M.A.; Spanakis, E.K.; Breton, M.; Klonoff, D.C. A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia. Diabetes Technol. Ther. 2019, 21, 602-609. [CrossRef] [PubMed]
  26. Brown, S.A.; Beck, R.W.; Raghinaru, D.; Buckingham, B.A.; Laffel, L.M.; Wadwa, R.P.; Kudva, Y.C.; Levy, C.J.; Pinsker, J.E.; Dassau, E.; et al. iDCL Trial Research Group. Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial. Diabetes Care 2020, 43, 1822-1828. [CrossRef] [PubMedDisclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
  27. Vos, T.; Allen, C.; Arora, M.; Barber, R.M.; Bhutta, Z.A.; Brown, A.; Liang, X.; Kawashima, T.; Coggeshall, M.; et al. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 386, 743-800. [CrossRef] [PubMed]
  28. Dedov, I.I.; Shestakova, M.V.; Suntsov, Y.I.; Peterkova, V.A.; Galstyan, G.R.; Mayorov, A.Y.; Kuraeva, T.L.; Sukhareva, O.Y.E. Federal targeted programme 'Prevention and Management of Socially Significant Diseases (2007-2012)': Results of the 'Diabetes mellitus' sub-programme. Diabetes Mellit. 2013, 16, 1-48. [CrossRef]
  29. Dedov, I.I.; Shestakova, M.V.; Galstyan, G.R. The prevalence of type 2 diabetes mellitus in the adult population of Russia (NA-TION study). Diabetes Mellit. 2016, 19, 104-112. [CrossRef]
  30. Dedov, I.I.; Shestakova, M.V.; Vikulova, O.K.; Zheleznyakova, A.V.; Isakov, M. Epidemiological characteristics of diabetes mellitus in the Russian Federation: Clinical and statistical analysis according to the Federal diabetes register data of 01.01.2021. Diabetes Mellit. 2021, 24, 204-221. [CrossRef]
  31. Dedov, I.I.; Shestakova, M.V.; Vikulova, O.K.; Zheleznyakova, A.V.; Isakov, M.A. Diabetes mellitus in Russian Federation: Prevalence, morbidity, mortality, parameters of glycaemic control and structure of glucose lowering therapy according to the Federal Diabetes Register, status 2017. Diabetes Mellit. 2018, 21, 144-159. [CrossRef]
  32. Al-Sofiani, M.E.; Ganji, S.S.; Kalyani, R.R. Body composition changes in diabetes and aging. J. Diabetes Its Complicat. 2019, 33, 451-459. [CrossRef] [PubMed]
  33. Kelly, T.; Yang, W.; Chen, C.-S.; Reynolds, K.; He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes. 2008, 32, 1431-1437. [CrossRef]
  34. Alberti, K.G.M.M.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.T.; Loria, C.M.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the international diabetes federation task force on epidemiology and prevention; National heart, lung, and blood institute; American heart association; World heart federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640-1645. [CrossRef]
  35. Mach, F.; Baigent, C.; Catapano, A.L.; Koskinas, K.C.; Casula, M.; Badimon, L.; Chapman, M.J.; De Backer, G.G.; Delgado, V.; Ference, B.A.; et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. 2020, 41, 111-188. [CrossRef]
  36. Mustafina, S.V.; Rymar, O.D.; Malyutina, S.K.; Denisova, D.V.; Shcherbakova, L.V.; Voevoda, M.I. Prevalence of diabetes in the adult population of Novosibirsk. Diabetes Mellit. 2017, 20, 329-334. [CrossRef]
  37. Wild, S.; Roglic, G.; Green, A.; Sicree, R.; King, H. Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care. Diabetes Care 2004, 27, 1047-1053. [CrossRef]
  38. Algoblan, A.; Alalfi, M.; Khan, M.Z. Mechanism linking diabetes mellitus and obesity. Diabetes Metab. Syndr. Obes. 2014, 7, 587-591. [CrossRef] [PubMed]
  39. Lu, Y.; Yang, H.; Xu, Z.; Tang, X. Association between Different Obesity Patterns and the Risk of Developing Type 2 Diabetes Mellitus Among Adults in Eastern China: A Cross-Sectional Study. Diabetes Metab. Syndr. Obes. 2021, 14, 2631-2639. [CrossRef] [PubMed]
  40. Hamman, R.F.; Wing, R.R.; Edelstein, S.L.; Lachin, J.M.; Bray, G.A.; Delahanty, L.; Hoskin, M.; Kriska, A.M.; Mayer-Davis, E.J.; Pi-Sunyer, X.; et al. Effect of Weight Loss with Lifestyle Intervention on Risk of Diabetes. Diabetes Care 2006, 29, 2102-2107. [CrossRef]
  41. Lachin, J.M.; Christophi, C.A.; Edelstein, S.L.; Ehrmann, D.A.; Hamman, R.F.; Kahn, S.E.; Knowler, W.C.; Nathan, D.M.; on behalf of the DPP Research Group. Factors Associated with Diabetes Onset during Metformin versus Placebo Therapy in the Diabetes Prevention Program. Diabetes 2007, 56, 1153-1159. [CrossRef]
  42. Schnurr, T.M.; Jakupović, H.; Carrasquilla, G.D.; Ängquist, L.; Grarup, N.; Sørensen, T.I.A.; Tjønneland, A.; Overvad, K.; Pedersen, O.; Hansen, T.; et al. Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: A case-cohort study. Diabetologia 2020, 63, 1324-1332. [CrossRef] [PubMed]
  43. Neeland, I.J.; Turer, A.T.; Ayers, C.R.; Berry, J.D.; Rohatgi, A.; Das, S.R.; Khera, A.; Vega, G.L.; McGuire, D.K.; Grundy, S.M.; et al. Body Fat Distribution and Incident Cardiovascular Disease in Obese Adults. J. Am. Coll. Cardiol. 2015, 65, 2150-2151. [CrossRef]
  44. Hayashi, T.; Boyko, E.J.; McNeely, M.J.; Leonetti, D.L.; Kahn, S.E.; Fujimoto, W.Y. Visceral Adiposity, Not Abdominal Subcutaneous Fat Area, Is Associated with an Increase in Future Insulin Resistance in Japanese Americans. Diabetes 2008, 57, 1269-1275. [CrossRef]
  45. Chen, M.E.; Chandramouli, A.G.; Considine, R.V.; Hannon, T.S.; Mather, K.J. Comparison of β-Cell Function Between Over- weight/Obese Adults and Adolescents Across the Spectrum of Glycemia. Diabetes Care 2017, 41, 318-325. [CrossRef]
  46. Norhammar, A.; Schenck-Gustafsson, K. Type 2 diabetes and cardiovascular disease in women. Diabetologia 2012, 56, 1-9. [CrossRef]
  47. Tirosh, A.; Shai, I.; Bitzur, R.; Kochba, I.; Tekes-Manova, D.; Israeli, E.; Shochat, T.; Rudich, A. Changes in Triglyceride Levels over Time and Risk of Type 2 Diabetes in Young Men. Diabetes Care 2008, 31, 2032-2037. [CrossRef]
  48. Zhao, J.; Zhang, Y.; Wei, F.; Song, J.; Cao, Z.; Chen, C.; Zhang, K.; Feng, S.; Wang, Y.; Li, W.-D. Triglyceride is an independent predictor of type 2 diabetes among middle-aged and older adults: A prospective study with 8-year follow-ups in two cohorts. J. Transl. Med. 2019, 17, 403. [CrossRef] [PubMed]
  49. Peng, J.; Zhao, F.; Yang, X.; Pan, X.; Xin, J.; Wu, M.; Peng, Y.G. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older Chinese adults: A secondary analysis of a nationwide cohort. BMJ Open 2021, 11, e042821. [CrossRef] [PubMed]
  50. Beshara, A.; Cohen, E.; Goldberg, E.; Lilos, P.; Garty, M.; Krause, I. Triglyceride levels and risk of type 2 diabetes mellitus: A longitudinal large study. J. Investig. Med. 2016, 64, 383-387. [CrossRef] [PubMed]
  51. Scherer, P.E. Adipose Tissue: From lipid storage compartment to endocrine organ. Diabetes 2006, 55, 1537-1545. [CrossRef] [PubMed]
  52. Boden, G. Obesity and Free Fatty Acids. Endocrinol. Metab. Clin. N. Am. 2008, 37, 635-646. [CrossRef]
  53. Boden, G.; Chen, X.; Ruiz, J.; White, J.V.; Rossetti, L. Mechanisms of fatty acid-induced inhibition of glucose uptake. J. Clin. Investig. 1994, 93, 2438-2446. [CrossRef] [PubMed]
  54. E Agardh, E.; Sidorchuk, A.; Hallqvist, J.; Ljung, R.; Peterson, S.; Moradi, T.; Allebeck, P. Burden of type 2 diabetes attributed to lower educational levels in Sweden. Popul. Health Metr. 2011, 9, 60. [CrossRef]
  55. Mathisen, J.; Jensen, A.K.G.; Andersen, I.; Andersen, G.S.; Hvidtfeldt, U.A.; Rod, N.H. Education and incident type 2 diabetes: Quantifying the impact of differential exposure and susceptibility to being overweight or obese. Diabetologia 2020, 63, 1764-1774. [CrossRef]
  56. Demakakos, P.; Marmot, M.; Steptoe, A. Socioeconomic position and the incidence of type 2 diabetes: The ELSA study. Eur. J. Epidemiol. 2012, 27, 367-378. [CrossRef]
  57. Sacerdote, C.; Ricceri, F.; Rolandsson, O.; Baldi, I.; Chirlaque, M.D.; Feskens, E.; Bendinelli, B.; Ardanaz, E.; Arriola, L.; Balkau, B.; et al. Lower educational level is a predictor of incident type 2 diabetes in Euro-pean countries: The EPIC-InterAct study. Int. J. Epidemiol. 2012, 41, 1162-1173. [CrossRef] [PubMed]
  58. Caballero, B. Humans against Obesity: Who Will Win? Adv. Nutr. 2019, 10 (Suppl. S1), S4-S9. [CrossRef]
  59. Lin, X.; Li, H. Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front. Endocrinol. 2021, 12, 706978. [CrossRef]
  60. Ling, C.; Rönn, T. Epigenetics in Human Obesity and Type 2 Diabetes. Cell Metab. 2019, 9, 1028-1044. [CrossRef] [PubMed]
  61. Herrera, B.M.; Lindgren, C.M. The genetics of obesity. Curr. Diab. Rep. 2010, 10, 498-505. [CrossRef]
  62. Vettori, A.; Pompucci, G.; Paolini, B.; Del Ciondolo, I.; Bressan, S.; Dundar, M.; Kenano glu, S.; Unfer, V.; Bertelli, M.; Geneob Project. Genetic background, nutrition and obesity: A review. Eur. Rev. Med. Pharmacol. Sci. 2019, 23, 1751-1761. [CrossRef]
  63. Hurtado, A.M.D.; Acosta, A. Precision Medicine and Obesity. Gastroenterol. Clin. N. Am. 2021, 50, 127-139. [CrossRef] [PubMed]
  64. Singh, R.K.; Kumar, P.; Mahalingam, K. Molecular Genetics of Human Obesity: A Comprehensive Review. C R Biol. 2017, 340, 87-108. [CrossRef] [PubMed]
  65. Hebebrand, J.; Hinney, A.; Knoll, N.; Volckmar, A.L.; Scherag, A. Molecular genetic aspects of weight regulation. Dtsch. Arztebl. Int. 2013, 110, 338-344. [CrossRef] [PubMed]
  66. Muñoz, C.; Garcia-Vargas, G.G.; Morales, R.P. Monogenic, Polygenic and Multifactorial Obesity in Children: Genetic and Environmental Factor. Austin J. Nutr. Metab. 2017, 4, 1052.
  67. Littleton, S.H.; Berkowitz, R.I.; Grant, S.F.A. Genetic Determinants of Childhood Obesity. Mol. Diagn. Ther. 2020, 24, 653-663. [CrossRef] [PubMed]
  68. Mahmoud, R.; Kimonis, V.; Butler, M.G. Genetics of Obesity in Humans: A Clinical Review. Int. J. Mol. Sci. 2022, 23, 11005. [CrossRef] [PubMed]
  69. GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 2017, 377, 13-27. [CrossRef] [PubMed]
  70. Loos, R.J.F.; Yeo, G.S.H. The Genetics of Obesity: From Discovery to Biology. Nat. Rev. Genet. 2022, 23, 120-133. [CrossRef] [PubMed]
  71. Heianza, Y.; Qi, L. Gene-Diet Interaction and Precision Nutrition in Obesity. Int. J. Mol. Sci. 2017, 18, 787. [CrossRef]
  72. Litwin, M.; Kułaga, Z. Obesity, metabolic syndrome, and primary hypertension. Pediatr. Nephrol. 2021, 36, 825-837. [CrossRef]
  73. Ahmed, B.; Sultana, R.; Greene, M.W. Adipose tissue and insulin resistance in obese. Biomed. Pharmacother. 2021, 137, 111315. [CrossRef]
  74. Brunner, K.T.; Henneberg, C.J.; Wilechansky, R.M.; Long, M.T. Nonalcoholic Fatty Liver Disease and Obesity Treatment. Curr. Obes. Rep. 2019, 8, 220-228. [CrossRef]
  75. Milić, S.; Lulić, D.; Štimac, D. Non-alcoholic fatty liver disease and obesity: Biochemical, metabolic and clinical presentations. World J. Gastroenterol. 2014, 20, 9330-9337. [CrossRef]
  76. Zhang, T.; Chen, J.; Tang, X.; Luo, Q.; Xu, D.; Yu, B. Interaction between adipocytes and high-density lipoprotein:new insights into the mechanism of obesity-induced dyslipidemia and atherosclerosis. Lipids Health Dis. 2019, 18, 223. [CrossRef]
  77. Yan, B.; Yang, J.; Zhao, B.; Wu, Y.; Bai, L.; Ma, X. Causal Effect of Visceral Adipose Tissue Accumulation on the Human Longevity: A Mendelian Randomization Study. Front Endocrinol. 2021, 1, 722187. [CrossRef]
  78. Cao, H. Adipocytokines in obesity and metabolic disease. J. Endocrinol. 2014, 220, 47-59. [CrossRef] [PubMed]
  79. Maximus, P.S.; Al Achkar, Z.; Hamid, P.F.; Hasnain, S.S.; Peralta, C.A. Adipocytokines: Are they the Theory of Everything? Cytokine 2020, 133, 155144. [CrossRef]
  80. Zorena, K.; Jachimowicz-Duda, O.; Ślęzak, D.; Robakowska, M.; Mrugacz, M. Adipokines and Obesity. Potential Link to Metabolic Disorders and Chronic Complications. Int. J. Mol. Sci. 2020, 21, 3570. [CrossRef] [PubMed]
  81. Wen, W.; Kato, N.; Hwang, J.Y.; Guo, X.; Tabara, Y.; Li, H.; Dorajoo, R.; Yang, X.; Tsai, F.J.; Li, S. Genome-wide association studies in East Asians identify new loci for waist-hip ratio and waist circumference. Sci. Rep. 2016, 20, 17958. [CrossRef] [PubMed]
  82. Dedov, I.I.; Shestakova, M.V.; Melnichenko, G.A.; Mazurina, N.V.; Andreeva, E.N.; Bondarenko, I.Z.; Gusova, Z.R.; Dzgoeva, F.K.; Eliseev, M.S.; Ershova, E.V.; et al. Interdisciplinary Clinical Practice Guidelines "Management of obesity and its comorbidities". Obes. Metab. 2021, 18, 5-99. [CrossRef]
  83. Yumuk, V.; Tsigos, C.; Fried, M.; Schindler, K.; Busetto, L.; Micic, D.; Toplak, H. Obesity Management Task Force of the European Association for the Study of Obesity. European Guidelines for Obesity Management in Adults. Obes. Facts 2015, 8, 402-424. [CrossRef]
  84. Serra-Juhé, C.; Martos-Moreno, G.; De Pieri, F.B.; Flores, R.; Chowen, J.A.; Pérez-Jurado, L.A.; Argente, J. Heterozygous rare genetic variants in non-syndromic early-onset obesity. Int. J. Obes. 2020, 44, 830-841. [CrossRef]
  85. Ghoshal, K.; Chatterjee, T.; Chowdhury, S.; Sengupta, S.; Bhattacharyya, M. Adiponectin Genetic Variant and Expression Coupled with Lipid Peroxidation Reveal New Signatures in Diabetic Dyslipidemia. Biochem. Genet. 2021, 59, 781-798. [CrossRef]
  86. Iwabu, M.; Okada-Iwabu, M.; Yamauchi, T.; Kadowaki, T. Adiponectin/AdipoR Research and Its Implications for Lifestyle-Related Diseases. Front. Cardiovasc. Med. 2019, 6, 116. [CrossRef]
  87. Luo, Z.; Zhang, T.; Wang, S.; He, Y.; Ye, Q.; Cao, W. The Trp64Arg Polymorphism in B3 Adrenergic Receptor (ADRB3) Gene Is Associated with Adipokines and Plasma Lipids: A Systematic Review, Meta-Analysis, and Meta-Regression. Lipids Health Dis. 2020, 19, 99. [CrossRef]
  88. Li, C.; Cheng, H.; Adhikari, B.K.; Wang, S.; Yang, N.; Liu, W.; Sun, J.; Wang, Y. The Role of Apelin-APJ System in Diabetes and Obesity. Front. Endocrinol. 2022, 13, 820002. [CrossRef]
  89. Ignacio, R.M.; Gibbs, C.R.; Lee, E.S.; Son, D.S. Differential Chemokine Signature between Human Preadipocytes and Adipocytes. Immune Netw. 2016, 16, 189-194. [CrossRef] [PubMed]
  90. Claussnitzer, M.; Dankel, S.N.; Kim, K.-H.; Quon, G.; Meuleman, W.; Haugen, C.; Glunk, V.; Sousa, I.S.; Beaudry, J.L.; Puviindran, V.; et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N. Engl. J. Med. 2015, 373, 895-907. [CrossRef] [PubMed]
  91. Zhang, L.; Zhang, M.; Wang, J.J.; Wang, C.J.; Ren, Y.C.; Wang, B.Y.; Zhang, H.Y.; Yang, X.Y.; Zhao, Y.; Han, C.Y.; et al. Association of TCF7L2 and GCG Gene Variants with Insulin Secretion, Insulin Resistance, and Obesity in New-Onset Diabetes. Biomed. Environ. Sci. 2016, 29, 814-817. [CrossRef] [PubMed]
  92. Mayendraraj, A.; Rosenkilde, M.M.; Gasbjerg, L.S. GLP-1 and GIP Receptor Signaling in Beta Cells-A Review of Receptor Interactions and Co-Stimulation. Peptides 2022, 151, 170749. [CrossRef] [PubMed]
  93. Landgren, S.; Simms, J.A.; Thelle, D.S.; Strandhagen, E.; Bartlett, S.E.; Engel, J.A.; Jerlhag, E. The Ghrelin Signalling System Is Involved in the Consumption of Sweets. PLoS ONE 2011, 6, e18170. [CrossRef]
  94. Nauck, M.A.; Quast, D.R.; Wefers, J.; Pfeiffer, A.F.H. The evolving story of incretins (GIP and GLP-1) in metabolic and cardiovas- cular disease: A pathophysiological update. Diabetes Obes. Metab. 2021, 23 (Suppl. S3), 5-29. [CrossRef] [PubMed]
  95. Støy, J.; Edghill, E.L.; Flanagan, S.E.; Ye, H.; Paz, V.P.; Pluzhnikov, A.; Below, J.E.; Hayes, M.G.; Cox, N.J.; Lipkind, G.M.; et al. Insulin gene mutations as a cause of permanent neonatal diabetes. Proc. Natl. Acad. Sci. USA 2007, 104, 15040-15044. [CrossRef]
  96. Wabitsch, M.; Pridzun, L.; Ranke, M.; von Schnurbein, J.; Moss, A.; Brandt, S.; Kohlsdorf, K.; Moepps, B.; Schaab, M.; Funcke, J.B.; et al. Measurement of immunofunctional leptin to detect and monitor patients with functional leptin deficiency. Eur. J. Endocrinol. 2017, 176, 315-322. [CrossRef]
  97. Curat, C.A.; Wegner, V.; Sengenes, C.; Miranville, A.; Tonus, C.; Busse, R.; Bouloumie, A. Macrophages in human visceral adipose tissue: Increased accumulation in obesity and a source of resistin and visfatin. Diabetologia 2006, 49, 744-747. [CrossRef]
  98. Stromsdorfer, K.L.; Yamaguchi, S.; Yoon, M.J.; Moseley, A.C.; Franczyk, M.P.; Kelly, S.C.; Qi, N.; Imai, S.I.; Yoshino, J. NAMPT- Mediated NAD+ Biosynthesis in Adipocytes Regulates Adipose Tissue Function and Multi-Organ Insulin Sensitivity in Mice. Cell Rep. 2016, 16, 1851-1860. [CrossRef] [PubMed]
  99. Campbell, C.D.; Lyon, H.N.; Nemesh, J.; Drake, J.A.; Tuomi, T.; Gaudet, D.; Zhu, X.; Cooper, R.S.; Ardlie, K.G.; Groop, L.C.; et al. Association studies of BMI and type 2 diabetes in the neuropeptide Y pathway: A possible role for NPY2R as a candidate gene for type 2 diabetes in men. Diabetes 2007, 56, 1460-14607. [CrossRef] [PubMed]
  100. Blaner, W.S. Vitamin A signaling and homeostasis in obesity, diabetes, and metabolic disorders. Pharmacol. Ther. 2019, 197, 153-178. [CrossRef] [PubMed]
  101. Qi, Q.; Menzaghi, C.; Smith, S.; Liang, L.; de Rekeneire, N.; Garcia, M.E.; Lohman, K.K.; Miljkovic, I.; Strotmeyer, E.S.; Cummings, S.R.; et al. Genome-wide association analysis identifies TYW3/CRYZ and NDST4 loci associated with circulating resistin levels. Hum. Mol. Genet. 2012, 21, 4774-4780. [CrossRef] [PubMed]
  102. Rathwa, N.; Patel, R.; Palit, S.P.; Ramachandran, A.V.; Begum, R. Genetic variants of resistin and its plasma levels: Association with obesity and dyslipidemia related to type 2 diabetes susceptibility. Genomics 2019, 111, 980-985. [CrossRef]
  103. Schnabl, K.; Li, Y.; Klingenspor, M. The gut hormone secretin triggers a gut-brown fat-brain axis in the control of food intake. Exp. Physiol. 2020, 105, 1206-1213. [CrossRef]
  104. Oliveira, M.S.; Rheinheimer, J.; Moehlecke, M.; Rodrigues, M.; Assmann, T.S.; Leitão, C.B.; Trindade, M.R.M.; Crispim, D.; de Souza, B.M. UCP2, IL18, and miR-133a-3p are dysregulated in subcutaneous adipose tissue of patients with obesity. Mol. Cell. Endocrinol. 2020, 509, 110805. [CrossRef]
  105. Khoramipour, K.; Chamari, K.; Hekmatikar, A.A.; Ziyaiyan, A.; Taherkhani, S.; Elguindy, N.M.; Bragazzi, N.L. Adiponectin: Structure, Physiological Functions, Role in Diseases, and Effects of Nutrition. Nutrients 2021, 13, 1180. [CrossRef]
  106. Pruitt, K.D.; Tatusova, T.; Brown, G.R.; Maglott, D.R. NCBI Reference Sequences (RefSeq): Current Status, New Features and Genome Annotation Policy. Nucleic. Acids Res. 2012, 40, D130-D135. [CrossRef]
  107. Achari, A.E.; Jain, S.K. Adiponectin, a Therapeutic Target for Obesity, Diabetes, and Endothelial Dysfunction. Int. J. Mol. Sci. 2017, 18, 1321. [CrossRef]
  108. Bueno, A.C.; Sun, K.; Martins, C.S.; Elias, J.; Miranda, W.; Tao, C.; Foss-Freitas, M.C.; Barbieri, M.A.; Bettiol, H.; de Castro, M.; et al. A Novel ADIPOQ Mutation (p.M40K) Impairs Assembly of High-Molecular-Weight Adiponectin and Is Associated with Early-Onset Obesity and Metabolic Syndrome. J. Clin. Endocrinol. Metab. 2014, 99, E683-E693. [CrossRef] [PubMed]
  109. Apalasamy, Y.D.; Rampal, S.; Salim, A.; Moy, F.M.; Bulgiba, A.; Mohamed, Z. Association of ADIPOQ Gene with Obesity and Adiponectin Levels in Malaysian Malays. Mol. Biol. Rep. 2014, 41, 2917-2921. [CrossRef] [PubMed]
  110. Olusegun, E.; Ogundele, O.E.; Adekoya, K.O.; Osinubi, A.A.A.; Awofala, A.A.; Oboh, B.O. Association of Adiponectin Gene (ADIPOQ) Polymorphisms with Measures of Obesity in Nigerian Young Adults. Egypt. J. Med. Hum. Genet. 2018, 19, 123-127. [CrossRef]
  111. Siitonen, N.; Pulkkinen, L.; Lindström, J.; Kolehmainen, M.; Eriksson, J.G.; Venojärvi, M.; Ilanne-Parikka, P.; Keinänen- Kiukaanniemi, S.; Tuomilehto, J.; Uusitupa, M. Association of ADIPOQ Gene Variants with Body Weight, Type 2 Diabetes and Serum Adiponectin Concentrations: The Finnish Diabetes Prevention Study. BMC Med. Genet. 2011, 12, 5. [CrossRef] [PubMed]
  112. Palit, S.P.; Patel, R.; Jadeja, S.D.; Rathwa, N.; Mahajan, A.; Ramachandran, A.V.; Dhar, M.K.; Sharma, S.; Begum, R. A Genetic Analysis Identifies a Haplotype at Adiponectin Locus: Association with Obesity and Type 2 Diabetes. Sci. Rep. 2020, 10, 2904. [CrossRef]
  113. Vasseur, F.; Helbecque, N.; Dina, C.; Lobbens, S.; Delannoy, V.; Gaget, S.; Boutin, P.; Vaxillaire, M.; Leprêtre, F.; Dupont, S.; et al. Single-Nucleotide Polymorphism Haplotypes in the Both Proximal Promoter and Exon 3 of the APM1 Gene Modulate Adipocyte-Secreted Adiponectin Hormone Levels and Contribute to the Genetic Risk for Type 2 Diabetes in French Caucasians. Hum. Mol. Genet. 2002, 11, 2607-2614. [CrossRef] [PubMed]
  114. Kondo, H.; Shimomura, L.; Matsukawa, Y.; Kumada, M.; Takahashi, M.; Matsuda, M.; Ouchi, N.; Kihara, S.; Kawamoto, T.; Sumitsuji, S.; et al. Association of Adiponectin Mutation With Type 2 Diabetes A Candidate Gene for the Insulin Resistance Syndrome. Diabetes 2002, 51, 2325-2328. [CrossRef]
  115. Hammad, S.S.; Eck, P.; Sihag, J.; Chen, X.; Connelly, P.W.; Lamarche, B.; Couture, P.; Guay, V.; Maltais-Giguère, J.; West, S.G.; et al. Common Variants in Lipid Metabolism-Related1 Genes Associate with Fat Mass Changes in Response to Dietary Monounsaturated Fatty Acids in Adults with Abdominal Obesity. J. Nutr. 2019, 149, 1749-1756. [CrossRef]
  116. ADRB3 Adrenoceptor Beta 3 [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/155 (accessed on 14 November 2022).
  117. Takenaka, A.; Nakamura, S.; Mitsunaga, F.; Inoue-Murayama, M.; Udono, T.; Suryobroto, B. Human-specific SNP in obesity genes, adrenergic receptor beta2 (ADRB2), Beta3 (ADRB3), and PPAR γ2 (PPARG), during primate evolution. PLoS ONE 2012, 7, e43461. [CrossRef]
  118. Kadowaki, H.; Yasuda, K.; Iwamoto, K.; Otabe, S.; Shimokawa, K.; Silver, K.; Walston, J.; Yoshinaga, H.; Kosaka, K.; Yamada, N.; et al. A Mutation in the B3-Adrenergic Receptor Gene Is Associated with Obesity and Hyperinsulinemia in Japanese Subjects. Biochem. Biophys. Res. Commun 1995, 215, 555-560. [CrossRef]
  119. Xie, C.; Hua, W.; Zhao, Y.; Rui, J.; Feng, J.; Chen, Y.; Liu, Y.; Liu, J.; Yang, X.; Xu, X. The ADRB3 Rs4994 Polymorphism Increases Risk of Childhood and Adolescent Overweight/Obesity for East Asia's Population: An Evidence-Based Meta-Analysis. Adipocyte 2020, 9, 77-86. [CrossRef] [PubMed]
  120. J. Pers. Med. 2023, 13, 460 138.
  121. Lombard, Z.; Crowther, N.J.; van der Merwe, L.; Pitamber, P.; Norris, S.A.; Ramsay, M. Appetite regulation genes are associated with body mass index in black South African adolescents: A genetic association study. BMJ Open. 2012, 2, e000873. [CrossRef] 139. Manju, S.K.; Anilkumar, T.R.; Vysakh, G.; Leena, B.K.; Lekshminarayan, V.; Kumar, P.G.; Shenoy, T.K. A Case-Control Study of the Association of Leptin Gene Polymorphisms with Plasma Leptin Levels and Obesity in the Kerala Population. J. Obes. 2022, 2022, 1040650. [CrossRef] 140.
  122. Ashraf, R.; Khan, M.; Lone, S.; Bhat, M.; Rashid, S.; Majid, S.; Bashir, H. Implication of Leptin and Leptin Receptor Gene Variations in Type 2 Diabetes Mellitus: A Case-Control Study. J. Endocrinol. Metab. 2022, 12, 19-31. [CrossRef] 141.
  123. Enns, J.E.; Taylor, C.G.; Zahradka, P. Variations in Adipokine Genes AdipoQ, Lep, and LepR are Associated with Risk for Obesity-Related Metabolic Disease: The Modulatory Role of Gene-Nutrient Interactions. J. Obes. 2011, 2011, 168659. [CrossRef] [PubMed] 142. Yaghootkar, H.; Zhang, Y.; Spracklen, C.N.; Karaderi, T.; Huang, L.O.; Bradfield, J.; Schurmann, C.; Fine, R.S.; Preuss, M.H.; Kutalik, Z.; et al. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Diabetes 2020, 69, 2806-2818. [CrossRef] [PubMed] 143.
  124. Haglund, E.; Nguyen, L.; Schafer, N.P.; Lammert, H.; Jennings, P.A.; Onuchic, J.N. Uncovering the molecular mechanisms behind disease-associated leptin variants. J. Biol. Chem. 2018, 293, 12919-12933. [CrossRef] [PubMed] 144. NAMPT Nicotinamide Phosphoribosyltransferase [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/ gene/10135 (accessed on 14 November 2022). 145. Fagerberg, L.; Hallstrom, B.M.; Oksvold, P.; Kampf, C.; Djureinovic, D.; Odeberg, J.; Habuka, M.; Tahmasebpoor, S.; Danielsson, A.; Edlund, K.; et al. Analysis of the Human Tissue-Specific Expression by Genome-Wide Integration of Transcriptomics and Antibody-Based Proteomics. Mol. Cell. Proteom. 2014, 13, 397-406. [CrossRef] 146. Böttcher, Y.; Teupser, D.; Enigk, B.; Berndt, J.; Klöting, N.; Schön, M.R.; Thiery, J.; Blüher, M.; Stumvoll, M.; Kovacs, P. Genetic Variation in the Visfatin Gene (PBEF1) and Its Relation to Glucose Metabolism and Fat-Depot-Specific Messenger Ribonucleic Acid Expression in Humans. J. Clin. Endocrinol. Metab. 2006, 91, 2725-2731. [CrossRef] 147. Johansson, L.M.; Johansson, L.E.; Ridderstråle, M. The Visfatin (PBEF1) G-948T Gene Polymorphism Is Associated with Increased High-Density Lipoprotein Cholesterol in Obese Subjects. Metabolism 2008, 57, 1558-1562. [CrossRef] 148.
  125. Blakemore, A.I.F.; Meyre, D.; Delplanque, J.; Vatin, V.; Lecoeur, C.; Marre, M.; Tichet, J.; Balkau, B.; Froguel, P.; Walley, A.J. A Rare Variant in the Visfatin Gene (Nampt/Pbef1) Is Associated with Protection from Obesity. Obesity 2009, 17, 1549-1553. [CrossRef] 149. Tabassum, R.; Mahendran, Y.; Dwivedi, O.P.; Chauhan, G.; Ghosh, S.; Marwaha, R.K.; Tandon, N.; Bharadwaj, D. Common Variants of IL6, LEPR, and PBEF1 Are Associated with Obesity in Indian Children. Diabetes 2012, 61, 626-631. [CrossRef] 150. Rong, J.; Chu, M.; Xing, B.; Zhu, L.; Wang, S.; Tao, T.; Zhao, Y.; Jiang, L. Variations in the PBEF1 Gene Are Associated with Body Mass Index: A Population-Based Study in Northern China. Meta Gene 2015, 6, 65-68. [CrossRef] 151. Zhou, Q.; Chen, B.; Ji, T.; Luo, M.; Luo, J. Association of Genetic Variants in RETN, NAMPT and ADIPOQ Gene with Glycemic, Metabolic Traits and Diabetes Risk in a Chinese Population. Gene 2018, 642, 439-446. [CrossRef] 152. Kim, J.-E.; Kim, J.-S.; Jo, M.-J.; Cho, E.; Ahn, S.-Y.; Kwon, Y.-J.; Ko, G.-J.T.; Roles, A.; Kim, J.-E.; Kim, J.-S.; et al. The Roles and Associated Mechanisms of Adipokines in Development of Metabolic Syndrome. Molecules 2022, 27, 334. [CrossRef] 153. PPY Pancreatic Polypeptide [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/5539 (accessed on 14 November 2022). 154. PYY Peptide YY [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/5697 (accessed on 14 November 2022). 155.
  126. Simpson, K.; Parker, J.; Plumer, J.; Bloom, S. CCK, PYY and PP: The Control of Energy Balance. Handb. Exp. Pharmacol. 2012, 209, 209-230. [CrossRef] 156. Friedlander, Y.; Li, G.; Fornage, M.; Williams, O.D.; Lewis, C.E.; Schreiner, P.; Pletcher, M.J.; Enquobahrie, D.; Williams, M.; Siscovick, D.S. Candidate Molecular Pathway Genes Related to Appetite Regulatory Neural Network, Adipocyte Homeostasis and Obesity: Results from the CARDIA Study. Ann. Hum. Genet. 2010, 74, 387-398. [CrossRef] [PubMed] 157.
  127. Siddiq, A.; Gueorguiev, M.; Samson, C.; Hercberg, S.; Heude, B.; Levy-Marchal, C.; Jouret, B.; Weill, J.; Meyre, D.; Walley, A.; et al. Single Nucleotide Polymorphisms in the Neuropeptide Y2 Receptor (NPY2R) Gene and Association with Severe Obesity in French White Subjects. Diabetologia 2007, 50, 574-584. [CrossRef] 158.
  128. Ahituv, N.; Kavaslar, N.; Schackwitz, W.; Ustaszewska, A.; Martin, J.; Hébert, S.; Doelle, H.; Ersoy, B.; Kryukov, G.; Schmidt, S.; et al. Medical Sequencing at the Extremes of Human Body Mass. Am. J. Hum. Genet. 2007, 80, 779-791. [CrossRef] 159. Perez-Frances, M.; van Gurp, L.; Abate, M.V.; Cigliola, V.; Furuyama, K.; Bru-Tari, E.; Oropeza, D.; Carreaux, T.; Fujitani, Y.; Thorel, F.; et al. Pancreatic Ppy-Expressing γ-Cells Display Mixed Phenotypic Traits and the Adaptive Plasticity to Engage Insulin Production. Nat. Commun. 2021, 12, 4458. [CrossRef] [PubMed] 160.
  129. Kim, H.J.; Lee, S.Y.; Kim, C.M. Association between Gene Polymorphisms and Obesity and Physical Fitness in Korean Children. Biol. Sport 2018, 35, 21-27. [CrossRef] 161. RBP4 Retinol Binding Protein 4 [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/5950 (accessed on 14 November 2022). 162. Yang, Q.; Graham, T.E.; Mody, N.; Preitner, F.; Peroni, O.D.; Zabolotny, J.M.; Kotani, K.; Quadro, L.; Kahn, B.B. Serum Retinol Binding Protein 4 Contributes to Insulin Resistance in Obesity and Type 2 Diabetes. Nature 2005, 436, 356-362. [CrossRef] 163. Craig, R.L.; Chu, W.S.; Elbein, S.C. Retinol Binding Protein 4 as a Candidate Gene for Type 2 Diabetes and Prediabetic Intermediate Traits. Mol. Genet. Metab. 2007, 90, 338-344. [CrossRef] 164. Wu, Y.; Li, H.; Loos, R.J.F.; Qi, Q.; Hu, F.B.; Liu, Y.; Lin, X. RBP4 Variants Are Significantly Associated with Plasma RBP4 Levels and Hypertriglyceridemia Risk in Chinese Hans. J. Lipid. Res. 2009, 50, 1479-1486. [CrossRef] 165. Codoñer-Franch, P.; Carrasco-Luna, J.; Allepuz, P.; Codoñer-Alejos, A.; Guillem, V. Association of RBP4 Genetic Variants with Childhood Obesity and Cardiovascular Risk Factors. Pediatr. Diabetes 2016, 17, 576-583. [CrossRef] 166. Hu, S.; Ma, S.; Li, X.; Tian, Z.; Liang, H.; Yan, J.; Chen, M.; Tan, H. Relationships of SLC2A4, RBP4, PCK1, and PI3K Gene Polymorphisms with Gestational Diabetes Mellitus in a Chinese Population. Biomed. Res. Int. 2019, 2019, 7398063. [CrossRef] 167. RETN Resistin [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/56729 (accessed on 14 November 2022). 168. Tripathi, D.; Kant, S.; Pandey, S.; Ehtesham, N.Z. Resistin in Metabolism, Inflammation, and Disease. FEBS J. 2020, 287, 3141-3149. [CrossRef] [PubMed] 169.
  130. Hishida, A.; Wakai, K.; Okada, R.; Morita, E.; Hamajima, N.; Hosono, S.; Higaki, Y.; Turin, T.C.; Suzuki, S.; Motahareh, K.; et al. Significant Interaction between RETN -420 G/G Genotype and Lower BMI on Decreased Risk of Type 2 Diabetes Mellitus (T2DM) in Japanese-the J-MICC Study. Endocr. J. 2013, 60, 237-243. [CrossRef] [PubMed] 170.
  131. Zayani, N.; Omezzine, A.; Boumaiza, I.; Achour, O.; Rebhi, L.; Rejeb, J.; ben Rejeb, N.; ben Abdelaziz, A.; Bouslama, A. Association of ADIPOQ, Leptin, LEPR, and Resistin Polymorphisms with Obesity Parameters in Hammam Sousse Sahloul Heart Study. J. Clin. Lab. Anal. 2017, 31, e22148. [CrossRef] [PubMed] 171.
  132. Beckers, S.; Zegers, D.; van Camp, J.K.; Boudin, E.; Nielsen, T.L.; Brixen, K.; Andersen, M.; van Hul, W. Resistin Polymorphisms Show Associations with Obesity, but Not with Bone Parameters in Men: Results from the Odense Androgen Study. Mol. Biol. Rep. 2013, 40, 2467-2472. [CrossRef] 172. Chung, C.M.; Lin, T.H.; Chen, J.W.; Leu, H.B.; Yin, W.H.; Ho, H.Y.; Sheu, S.H.; Tsai, W.C.; Chen, J.H.; Lin, S.J.; et al. Common Quantitative Trait Locus Downstream of RETN Gene Identified by Genome-Wide Association Study Is Associated with Risk of Type 2 Diabetes Mellitus in Han Chinese: A Mendelian Randomization Effect. Diabetes Metab. Res. Rev. 2014, 30, 232-240. [CrossRef] 173. Ortega, L.; Navarro, P.; Riestra, P.; Gavela-Pérez, T.; Soriano-Guillén, L.; Garcés, C. Association of Resistin Polymorphisms with Resistin Levels and Lipid Profile in Children. Mol. Biol. Rep. 2014, 41, 7659-7664. [CrossRef] 174. Nakatochi, M.; Ichihara, S.; Yamamoto, K.; Ohnaka, K.; Kato, Y.; Yokota, S.; Hirashiki, A.; Naruse, K.; Asano, H.; Izawa, H.; et al. Epigenome-Wide Association Study Suggests That SNPs in the Promoter Region of RETN Influence Plasma Resistin Level via Effects on DNA Methylation at Neighbouring Sites. Diabetologia 2015, 58, 2781-2790. [CrossRef] 175. Elkhattabi, L.; Morjane, I.; Charoute, H.; Amghar, S.; Bouafi, H.; Elkarhat, Z.; Saile, R.; Rouba, H.; Barakat, A. In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure. J. Diabetes Res. 2019, 2019, 4951627. [CrossRef] 176. SCT Secretin [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/6343 (accessed on 14 November 2022). 177. Laurila, S.; Rebelos, E.; Honka, M.J.; Nuutila, P. Pleiotropic Effects of Secretin: A Potential Drug Candidate in the Treatment of Obesity? Front Endocrinol. 2021, 12, 1259. [CrossRef] 178. Laurila, S.; Sun, L.; Lahesmaa, M.; Schnabl, K.; Laitinen, K.; Klén, R.; Li, Y.; Balaz, M.; Wolfrum, C.; Steiger, K.; et al. Secretin Activates Brown Fat and Induces Satiation. Nat. Metab. 2021, 3, 798-809. [CrossRef] [PubMed] 179. UCP2 Uncoupling Protein 2 [Homo sapiens (Human)]. Available online: https://www.ncbi.nlm.nih.gov/gene/7351 (accessed on 14 November 2022). 180.
  133. Demine, S.; Renard, P.; Arnould, T. Mitochondrial Uncoupling: A Key Controller of Biological Processes in Physiology and Diseases. Cells 2019, 8, 795. [CrossRef] [PubMed] 181. Esterbauer, H.; Schneitler, C.; Oberkofler, H.; Ebenbichler, C.; Paulweber, B.; Sandhofer, F.; Ladurner, G.; Hell, E.; Strosberg, A.D.; Patsch, J.R.; et al. A Common Polymorphism in the Promoter of UCP2 Is Associated with Decreased Risk of Obesity in Middle-Aged Humans. Nat. Genet. 2001, 28, 178-183. [CrossRef] [PubMed] 182.
  134. Bulotta, A.; Ludovico, O.; Coco, A.; di Paola, R.; Quattrone, A.; Carella, M.; Pellegrini, F.; Prudente, S.; Trischitta, V. The Common -866G/A Polymorphism in the Promoter Region of the UCP-2 Gene Is Associated with Reduced Risk of Type 2 Diabetes in Caucasians from Italy. J. Clin. Endocrinol. Metab. 2005, 90, 1176-1180. [CrossRef] 183. Andersen, G.; Dalgaard, L.T.; Justesen, J.M.; Anthonsen, S.; Nielsen, T.; Thørner, L.W.; Witte, D.; Jørgensen, T.; Clausen, J.O.; Lauritzen, T.; et al. The Frequent UCP2-866G>A Polymorphism Protects against Insulin Resistance and Is Associated with Obesity: A Study of Obesity and Related Metabolic Traits among 17 636 Danes. Int. J. Obes. 2013, 37, 175-181. [CrossRef] 184. Salopuro, T.; Pulkkinen, L.; Lindström, J.; Kolehmainen, M.; Tolppanen, A.M.; Eriksson, J.G.; Valle, T.T.; Aunola, S.; Ilanne-Parikka, P.; Keinänen-Kiukaanniemi, S. Variation in the UCP2 and UCP3 genes associates with abdominal obesity and serum lipids: The Finnish Diabetes Prevention Study. BMC Med. Genet. 2009, 10, 94. [CrossRef] 185. Oktavianthi, S.; Trimarsanto, H.; Febinia, C.A.; Suastika, K.; Saraswati, M.R.; Dwipayana, P.; Arindrarto, W.; Sudoyo, H.; Malik, S.G. Uncoupling Protein 2 Gene Polymorphisms Are Associated with Obesity. Cardiovasc. Diabetol. 2012, 11, 41. [CrossRef] 186. Martinez-Hervas, S.; Mansego, M.L.; de Marco, G.; Martinez, F.; Alonso, M.P.; Morcillo, S.; Rojo-Martinez, G.; Real, J.T.; Ascaso, J.F.; Redon, J.; et al. Polymorphisms of the UCP2 Gene Are Associated with Body Fat Distribution and Risk of Abdominal Obesity in Spanish Population. Eur. J. Clin. Invest. 2012, 42, 171-178. [CrossRef] 187. Xu, L.; Chen, S.; Zhan, L. Association of Uncoupling Protein-2 -866G/A and Ala55Val Polymorphisms with Susceptibility to Type 2 Diabetes Mellitus: A Meta-Analysis of Case-Control Studies. Medicine 2021, 100, e24464. [CrossRef] 188.
  135. Fall, T.; Mendelson, M.; Speliotes, E.K. Recent Advances in Human Genetics and Epigenetics of Adiposity: Pathway to Precision Medicine? Gastroenterology 2017, 152, 1695-1706. [CrossRef] [PubMed] 189. Christakoudi, S.; Tsilidis, K.K.; Evangelou, E.; Riboli, E. A Body Shape Index (ABSI), hip index, and risk of cancer in the UK Biobank cohort. Cancer Med. 2021, 10, 5614-5628. [CrossRef] [PubMed] 190. Wu, S.; Hsu, L.-A.; Teng, M.-S.; Chou, H.-H.; Ko, Y.-L. Differential Genetic and Epigenetic Effects of the KLF14 Gene on Body Shape Indices and Metabolic Traits. Int. J. Mol. Sci. 2022, 23, 4165. [CrossRef] [PubMed] 191. Nagayama, D.; Sugiura, T.; Choi, S.Y.; Shirai, K. Various Obesity Indices and Arterial Function Evaluated with CAVI-Is Waist Circumference Adequate to Define Metabolic Syndrome? Vasc. Health Risk. Manag. 2022, 18, 721-733. [CrossRef] [PubMed] 192. Nagayama, D.; Fujishiro, K.; Watanabe, Y.; Yamaguchi, T.; Suzuki, K.; Saiki, A.; Shirai, K. A Body Shape Index (ABSI) as a Variant of Conicity Index Not Affected by the Obesity Paradox: A Cross-Sectional Study Using Arterial Stiffness Parameter. J. Pers. Med. 2022, 12, 2014. [CrossRef] 193. Young, K.L.; Graff, M.; Fernandez-Rhodes, L.; North, K.E. Genetics of Obesity in Diverse Populations. Curr. Diab. Rep. 2018, 18, 145. [CrossRef] [PubMed] 194.
  136. Locke, A.E.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.H.; Day, F.R.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015, 518, 197-206. [CrossRef] 195. Pan, D.Z.; Miao, Z.; Comenho, C.; Rajkumar, S.; Koka, A.; Lee, S.H.T.; Alvarez, M.; Kaminska, D.; Ko, A.; Sinsheimer, J.S.; et al. Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes. Genome Med. 2021, 13, 123. [CrossRef] 196. Larsson, S.C.; Bäck, M.; Rees, J.M.B.; Mason, A.M.; Burgess, S. Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: A Mendelian randomization study. Eur. Heart J. 2020, 41, 221-226. [CrossRef] 197.
  137. Beaney, K.E.; Cooper, J.A.; Shahid, S.U.; Ahmed, W.; Qamar, R.; Drenos, F.; Crockard, M.A.; Humphries, S.E. Clinical Utility of a Coronary Heart Disease Risk Prediction Gene Score in UK Healthy Middle-Aged Men and in the Pakistani Population. PLoS ONE 2015, 10, e0130754. [CrossRef] 198. Iribarren, C.; Lu, M.; Jorgenson, E.; Martínez, M.; Lluis-Ganella, C.; Subirana, I.; Salas, E.; Elosua, R. Weighted Multi-marker Genetic Risk Scores for Incident Coronary Heart Disease among Individuals of African, Latino and East-Asian Ancestry. Sci. Rep. 2018, 8, 6853. [CrossRef] 199. Semaev, S.; Shakhtshneider, E. Genetic Risk Score for Coronary Heart Disease: Review. J. Pers. Med. 2020, 10, 239. [CrossRef] [PubMed] 200. Benbaibeche, H.; Bounihi, A.; Koceir, E.A. Leptin level as a biomarker of uncontrolled eating in obesity and overweight. Ir. J. Med. Sci. 2020, 190, 155-161. [CrossRef] [PubMed] 201.
  138. Poetsch, M.S.; Strano, A.; Guan, K. Role of Leptin in Cardiovascular Diseases. Front. Endocrinol. 2020, 11, 354. [CrossRef] 202. Reyes-Barrera, J.; Sainz-Escárrega, V.H.; Medina-Urritia, A.X.; Jorge-Galarza, E.; Osorio-Alonso, H.; Torres-Tamayo, M.; Leal- Escobar, G.; Posadas-Romero, C.; Torre-Villalvazo, I.; Juárez-Rojas, J.G. Dysfunctional adiposity index as a marker of adipose tissue morpho-functional abnormalities and metabolic disorders in apparently healthy subjects. Adipocyte 2021, 10, 142-152. [CrossRef] [PubMed] 203.
  139. Trinh, T.; Broxmeyer, H.E. Role for Leptin and Leptin Receptors in Stem Cells During Health and Diseases. Stem. Cell Rev. Rep. 2021, 17, 511-522. [CrossRef
  140. Firdous, P.; Nissar, K.; Ali, S.; Ganai, B.A.; Shabir, U.; Hassan, T.; Masoodi, S.R. Genetic Testing of Maturity-Onset Diabetes of the Young Current Status and Future Perspectives. Front. Endocrinol. 2018, 9, 253. [CrossRef] [PubMed]
  141. Mohan, V.; Radha, V. Precision Diabetes Is Slowly Becoming a Reality. Med. Princ. Pract. 2019, 28, 1-9. [CrossRef] [PubMed]
  142. Shields, B.M.; Hicks, S.; Shepherd, M.H.; Colclough, K.; Hattersley, A.T.; Ellard, S. Maturity-onset diabetes of the young (MODY): How many cases are we missing? Diabetologia 2010, 53, 2504-2508. [CrossRef] [PubMed]
  143. Ellard, S.; Bellanné-Chantelot, C.; Hattersley, A.T. European Molecular Genetics Quality Network (EMQN) MODY group. Best practice guidelines for the molecular genetic diagnosis of maturity-onset diabetes of the young. Diabetologia 2008, 51, 546-553. [CrossRef]
  144. Ellard, S.; Flanagan, S.E.; Girard, C.A.; Patch, A.M.; Harries, L.W.; Parrish, A.; Edghill, E.L.; Mackay, D.J.; Proks, P.; Shimomura, K.; et al. Permanent neonatal diabetes caused by dominant, recessive, or compound heterozygous SUR1 mutations with opposite functional effects. Am. J. Hum. Genet. 2007, 81, 375-382. [CrossRef]
  145. Bowman, P.; Flanagan, S.E.; Edghill, E.L.; Damhuis, A.; Shepherd, M.H.; Paisey, R.; Hattersley, A.T.; Ellard, S. Heterozygous ABCC8 mutations are a cause of MODY. Diabetologia 2012, 55, 123-127. [CrossRef]
  146. Nessa, A.; Rahman, S.A.; Hussain, K. Hyperinsulinemic Hypoglycemia-The Molecular Mechanisms. Front. Endocrinol. 2016, 7, 29. [CrossRef]
  147. Shyng, S.; Nichols, C.G. Octameric stoichiometry of the KATP channel complex. J. Gen. Physiol. 1997, 110, 655-664. [CrossRef]
  148. Ashcroft, F.M.; Rorsman, P. Electrophysiology of the pancreatic beta-cell. Prog. Biophys. Mol. Biol. 1989, 54, 87-143. [CrossRef] [PubMed]
  149. Tucker, S.J.; Gribble, F.M.; Zhao, C.; Trapp, S.; Ashcroft, F.M. Truncation of Kir6.2 produces ATP-sensitive K+ channels in the absence of the sulphonylurea receptor. Nature 1997, 387, 179-183. [CrossRef] [PubMed]
  150. Jha, R.M.; Koleck, T.A.; Puccio, A.M.; Okonkwo, D.O.; Park, S.Y.; Zusman, B.E.; Clark, R.S.B.; Shutter, L.A.; Wallisch, J.S.; Empey, P.E.; et al. Regionally clustered ABCC8 polymorphisms in a prospective cohort predict cerebral oedema and outcome in severe traumatic brain injury. J. Neurol. Neurosurg. Psychiatry 2018, 89, 1152-1162. [CrossRef] [PubMed]
  151. Galcheva, S.; Demirbilek, H.; Al-Khawaga, S.; Hussain, K. The Genetic and Molecular Mechanisms of Congenital Hyperinsulinism. Front. Endocrinol. 2019, 10, 111. [CrossRef] [PubMed]
  152. Ashcroft, F.M.; Puljung, M.C.; Vedovato, N. Neonatal Diabetes and the KATP Channel: From Mutation to Therapy. Trends Endocrinol. Metab. 2017, 28, 377-387. [CrossRef] [PubMed]
  153. Patch, A.M.; Flanagan, S.E.; Boustred, C.; Hattersley, A.T.; Ellard, S. Mutations in the ABCC8 gene encoding the SUR1 subunit of the KATP channel cause transient neonatal diabetes, permanent neonatal diabetes or permanent diabetes diagnosed outside the neonatal period. Diabetes Obes. Metab. 2007, 9, 28-39. [CrossRef]
  154. Bonnefond, A.; Boissel, M.; Bolze, A.; Durand, E.; Toussaint, B.; Vaillant, E.; Gaget, S.; Graeve, F.; Dechaume, A.; Allegaert, F.; et al. Pathogenic variants in actionable MODY genes are associated with type 2 diabetes. Nat. Metab. 2020, 2, 1126-1134. [CrossRef]
  155. Koufakis, T.; Sertedaki, A.; Tatsi, E.-B.; Trakatelli, C.-M.; Karras, S.N.; Manthou, E.; Kanaka-Gantenbein, C.; Kotsa, K. First Report of Diabetes Phenotype due to a Loss-of-Function ABCC8 Mutation Previously Known to Cause Congenital Hyperinsulinism. Case Rep. Genet. 2019, 2019, 3654618. [CrossRef]
  156. Männikkö, R.; Flanagan, S.E.; Sim, X.; Segal, D.; Hussain, K.; Ellard, S.; Hattersley, A.T.; Ashcroft, F.M. Mutations of the same conserved glutamate residue in NBD2 of the sulfonylurea receptor 1 subunit of the KATP channel can result in either hyperinsulinism or neonatal diabetes. Diabetes 2011, 60, 1813-1822. [CrossRef]
  157. Işık, E.; Demirbilek, H.; Houghton, J.A.; Ellard, S.; Flanagan, S.E.; Hussain, K. Congenital Hyperinsulinism and Evolution to Sulfonylurearesponsive Diabetes Later in Life due to a Novel Homozygous p.L171F ABCC8 Mutation. J. Clin. Res. Pediatr. Endocrinol. 2019, 11, 82-87. [CrossRef]
  158. Abdulhadi-Atwan, M.; Bushman, J.; Tornovsky-Babaey, S.; Perry, A.; Abu-Libdeh, A.; Glaser, B.; Shyng, S.L.; Zangen, D.H. Novel de novo mutation in sulfonylurea receptor 1 presenting as hyperinsulinism in infancy followed by overt diabetes in early adolescence. Diabetes 2008, 57, 1935-4190. [CrossRef]
  159. Kapoor, R.R.; Flanagan, S.E.; James, C.T.; McKiernan, J.; Thomas, A.M.; Harmer, S.C.; Shield, J.P.; Tinker, A.; Ellard, S.; Hussain, K. Hyperinsulinaemic hypoglycaemia and diabetes mellitus due to dominant ABCC8/KCNJ11 mutations. Diabetologia 2011, 10, 2575-2583. [CrossRef]
  160. Qin, L.J.; Lv, Y.; Huang, Q.Y. Meta-analysis of association of common variants in the KCNJ11-ABCC8 region with type 2 diabetes. Genet. Mol. Res. 2013, 12, 2990-3002. [CrossRef] [PubMed]
  161. Delvecchio, M.; Pastore, C.; Giordano, P. Treatment Options for MODY Patients: A Systematic Review of Literature. Diabetes Ther. 2020, 11, 1667-1685. [CrossRef] [PubMed]
  162. Richards, S.; Aziz, N.; Bale, S.; Bick, D.; Das, S.; Gastier-Foster, J.; Grody, W.W.; Hegde, M.; Lyon, E.; Spector, E.; et al. Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 2015, 17, 405-423. [CrossRef]
  163. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2005, 28, 37-42. [CrossRef] [PubMed]
  164. Stanik, J.; Dusatkova, P.; Cinek, O.; Valentinova, L.; Huckova, M.; Skopkova, M.; Dusatkova, L.; Stanikova, D.; Pura, M.; Klimes, I.; et al. De novo mutations of GCK, HNF1A and HNF4A may be more frequent in MODY than previously assumed. Diabetologia 2014, 57, 480-484. [CrossRef]
  165. Sambrook, J.; Russell, D.W. Purification of nucleic acids by extraction with phenol: Chloroform. Cold Spring Harb. Protoc. 2006, 2006, 4455. [CrossRef] [PubMed]
  166. Karczewski, K.J.; Francioli, L.C.; Tiao, G.; Cummings, B.B.; Alföldi, J.; Wang, Q.; Collins, R.L.; Laricchia, K.M.; Ganna, A.; Birnbaum, D.P.; et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 2020, 581, 434-443. [CrossRef]
  167. Ivanoshchuk, D.E.; Shakhtshneider, E.V.; Rymar, O.D.; Ovsyannikova, A.K.; Mikhailova, S.V.; Fishman, V.S.; Valeev, E.S.; Orlov, P.S.; Voevoda, M.I. The Mutation Spectrum of Maturity Onset Diabetes of the Young (MODY)-Associated Genes among Western Siberia Patients. J. Pers. Med. 2021, 11, 57. [CrossRef]
  168. In Supplimentary Wang, Z.; Diao, C.; Liu, Y.; Li, M.; Zheng, J.; Zhang, Q.; Yu, M.; Zhang, H.; Ping, F.; Li, M.; et al. Identification and functional analysis of GCK gene mutations in 12 Chinese families with hyperglycemia. J. Diabetes Investig. 2019, 10, 963-971. [CrossRef] [PubMed]
  169. Ivanoshchuk, D.E.; Ovsyannikova, A.K.; Mikhailova, S.V.; Shakhtshneider, E.V.; Valeev, E.S.; Rymar, O.D.; Orlov, P.S.; Voevoda, M.I. Variants of the HNF4A and HNF1A genes in patients with impaired glucose metabolism and dyslipidemia. Ateroscleroz 2022, 17, 11-19. [CrossRef]
  170. Madariaga, L.; García-Castaño, A.; Ariceta, G.; Martínez-Salazar, R.; Aguayo, A.; Castaño, L.; Spanish Group for the Study of HNF1B Mutations. Variable phenotype in HNF1B mutations: Extrarenal manifestations distinguish affected individuals from the population with congenital anomalies of the kidney and urinary tract. Clin. Kidney J. 2018, 12, 373-379. [CrossRef] [PubMed]
  171. Urrutia, I.; Martínez, R.; Rica, I.; Martínez de LaPiscina, I.; García-Castaño, A.; Aguayo, A.; Calvo, B.; Castaño, L. Spanish Pediatric Diabetes Collaborative Group. Negative autoimmunity in a Spanish pediatric cohort suspected of type 1 diabetes, could it be monogenic diabetes? PLoS ONE 2019, 14, e02206342019. [CrossRef]
  172. Hohendorff, J.; Kwiatkowska, M.; Pisarczyk-Wiza, D.; Ludwig-Słomczy ńska, A.; Milcarek, M.; Kapusta, P.; Zapała, B.; Kieć-Wilk, B.; Trznadel-Morawska, I.; Szopa, M.; et al. Mutation search within monogenic diabetes genes in Polish patients with long-term type 1 diabetes and preserved kidney function. Pol. Arch. Intern. Med. 2022, 132, 16143. [CrossRef]
  173. Reilly, F.; Sanchez-Lechuga, B.; Clinton, S.; Crowe, G.; Burke, M.; Ng, N.; Colclough, K.; Byrne, M.M. Phenotype, genotype and glycaemic variability in people with activating mutations in the ABCC8 gene: Response to appropriate therapy. Diabet. Med. 2020, 37, 876-884. [CrossRef] [PubMed]
  174. Ovsyannikova, A.K.; Rymar, O.D.; Shakhtshneider, E.V.; Klimontov, V.V.; Koroleva, E.A.; Myakina, N.E.; Voevoda, M.I. ABCC8- Related Maturity-Onset Diabetes of the Young (MODY12): Clinical Features and Treatment Perspective. Diabetes Ther. 2016, 7, 591-600. [CrossRef] [PubMed]
  175. Liu, C.; Lai, Y.; Guan, T.; Zhan, J.; Pei, J.; Wu, D.; Ying, S.; Shen, Y. Associations of ATP-Sensitive Potassium Channel's Gene Polymorphisms With Type 2 Diabetes and Related Cardiovascular Phenotypes. Front. Cardiovasc. Med. 2022, 23, 816847. [CrossRef]
  176. Globa, E.; Zelinska, N.; Mackay, D.J.; Temple, K.I.; Houghton, J.A.; Hattersley, A.T.; Flanagan, S.E.; Ellard, S. Neonatal diabetes in Ukraine: Incidence, genetics, clinical phenotype and treatment. J. Pediatr. Endocrinol. Metab. 2015, 28, 1279-1286. [CrossRef]
  177. Russo, L.; Iafusco, D.; Brescianini, S.; Nocerino, V.; Bizzarri, C.; Toni, S.; Cerutti, F.; Monciotti, C.; Pesavento, R.; Iughetti, L.; et al. Permanent diabetes during the first year of life: Multiple gene screening in 54 patients. Diabetologia 2011, 54, 1693-1701. [CrossRef]
  178. Vaxillaire, M.; Dechaume, A.; Busiah, K.; Cavé, H.; Pereira, S.; Scharfmann, R.; de Nanclares, G.P.; Castano, L.; Froguel, P.; Polak, M.; et al. New ABCC8 mutations in relapsing neonatal diabetes and clinical features. Diabetes 2007, 56, 1737-1741. [CrossRef]
  179. J. Pers. Med. 2023, 13, 172
  180. Rego, S.; Dagan-Rosenfeld, O.; Zhou, W.; Sailani, M.R.; Limcaoco, P.; Colbert, E.; Avina, M.; Wheeler, J.; Craig, C.; Salins, D.; et al. High-frequency actionable pathogenic exome variants in an average-risk cohort. Cold Spring Harb. Mol. Case Stud. 2018, 4, a003178. [CrossRef] [PubMed]
  181. De Franco, E.; Saint-Martin, C.; Brusgaard, K.; Knight Johnson, A.E.; Aguilar-Bryan, L.; Bowman, P.; Arnoux, J.B.; Larsen, A.R.; Sanyoura, M.; Greeley, S.A.W.; et al. Update of variants identified in the pancreatic β-cell KATP channel genes KCNJ11 and ABCC8 in individuals with congenital hyperinsulinism and diabetes. Hum. Mutat. 2020, 41, 884-905. [CrossRef] [PubMed]
  182. Odgerel, Z.; Lee, H.S.; Erdenebileg, N.; Gandbold, S.; Luvsanjamba, M.; Sambuughin, N.; Sonomtseren, S.; Sharavdorj, P.; Jodov, E.; Altaisaikhan, K.; et al. Genetic variants in potassium channels are associated with type 2 diabetes in a Mongolian population. J. Diabetes 2012, 4, 238-242. [CrossRef] [PubMed]
  183. Engwa, G.A.; Nwalo, F.N.; Chikezie, C.C.; Onyia, C.O.; Ojo, O.O.; Mbacham, W.F.; Ubi, B.E. Possible association between ABCC8 C49620T polymorphism and type 2 diabetes in a Nigerian population. BMC Med. Genet. 2018, 19, 78. [CrossRef]
  184. Matharoo, K.; Arora, P.; Bhanwer, A.J. Association of adiponectin (AdipoQ) and sulphonylurea receptor (ABCC8) gene polymor- phisms with Type 2 Diabetes in North Indian population of Punjab. Gene 2013, 527, 228-234. [CrossRef]
  185. Reis, A.F.; Ye, W.Z.; Dubois-Laforgue, D.; Bellanné-Chantelot, C.; Timsit, J.; Velho, G. Association of a variant in exon 31 of the sulfonylurea receptor 1 (SUR1) gene with type 2 diabetes mellitus in French Caucasians. Hum. Genet. 2000, 107, 138-144. [CrossRef]
  186. Flanagan, S.E.; Patch, A.M.; Mackay, D.; Edghill, E.L.; Gloyn, A.L.; Robinson, D.; Shield, J.P.; Temple, K.; Ellard, S.; Hattersley, A.T. Mutations in ATP-sensitive K+ channel genes cause transient neonatal diabetes and permanent diabetes in childhood or adulthood. Diabetes 2007, 56, 1930-1937. [CrossRef]
  187. Zhou, Q.; Garin, I.; Castaño, L.; Argente, J.; Muñoz-Calvo, M.; Perez de Nanclares, G.; Shyng, S.L. Neonatal diabetes caused by mutations in sulfonylurea receptor 1: Interplay between expression and Mg-nucleotide gating defects of ATP-sensitive potassium channels. J. Clin. Endocrinol. Metab. 2010, 95, 473-478. [CrossRef]
  188. Edghill, E.L.; Flanagan, S.E.; Ellard, S. Permanent neonatal diabetes due to activating mutations in ABCC8 and KCNJ11. Rev. Endocr. Metab. Disord. 2010, 11, 193-198. [CrossRef
  189. Meager, A.; Visvalingam, K.; Peterson, P.; Möll, K.; Murumägi, A.; Krohn, K.; Eskelin, P.; Perheentupa, J.; Husebye, E.; Kadota, Y.; et al. Anti-interferon autoantibodies in autoimmune polyendocrinopathy syndrome type 1. PLoS Med. 2006, 3, 1152-1164. [CrossRef]
  190. Zhang, L.; Barker, J.M.; Babu, S.; Su, M.; Stenerson, M.; Cheng, M.; Shum, A.; Zamir, E.; Badolato, R.; Law, A.; et al. A robust immunoassay for anti-interferon autoantibodies that is highly specific for patients with autoimmune polyglandular syndrome type 1. Clin. Immunol. 2007, 125, 131-137. [CrossRef] [PubMed]
  191. Meloni, A.; Furcas, M.; Cetani, F.; Marcocci, C.; Falorni, A.; Perniola, R.; Pura, M.; Bøe Wolff, A.S.; Husebye, E.S.; Lilic, D.; et al. Autoantibodies against type I interferons as an additional diagnostic criterion for autoimmune polyendocrine syndrome type I. J. Clin. Endocrinol. Metab. 2008, 93, 4389-4397. [CrossRef] [PubMed]
  192. J. Pers. Med. 2022, 12, 1948
  193. Meager, A.; Wadhwa, M.; Dilger, P.; Bird, C.; Thorpe, R.; Newsom-Davis, J.; Willcox, N. Anti-cytokine autoantibodies in autoimmunity: Preponderance of neutralizing autoantibodies against interferon-alpha, interferon-omega and interleukin-12 in patients with thymoma and/or myasthenia gravis. Clin. Exp. Immunol. 2003, 132, 128-136. [CrossRef]
  194. Gupta, S.; Tatouli, I.P.; Rosen, L.B.; Hasni, S.; Alevizos, I.; Manna, Z.G.; Rivera, J.; Jiang, C.; Siegel, R.M.; Holland, S.M.; et al. Distinct functions of autoantibodies against interferon in systemic lupus erythematosus: A comprehensive analysis of anticytokine autoantibodies in common rheumatic diseases. Arthritis Rheumatol. 2016, 68, 1677-1687. [CrossRef]
  195. Burbelo, P.D.; Browne, S.; Holland, S.M.; Iadarola, M.J.; Alevizos, I. Clinical features of Sjögren's syndrome patients with autoantibodies against interferons. Clin. Transl. Med. 2019, 8, e1. [CrossRef]
  196. Bastard, P.; Rosen, L.B.; Zhang, Q.; Michailidis, E.; Hoffmann, H.H.; Zhang, Y.; Dorgham, K.; Philippot, Q.; Rosain, J.; Béziat, V.; et al. Autoantibodies against type I IFNs in patients with life-threatening COVID-19. Science 2020, 370. [Cross- Ref] [PubMed]
  197. Bastard, P.; Michailidis, E.; Hoffmann, H.H.; Chbihi, M.; Le Voyer, T.; Rosain, J.; Philippot, Q.; Seeleuthner, Y.; Gervais, A.; Materna, M.; et al. Auto-antibodies to type I IFNs can underlie adverse reactions to yellow fever live attenuated vaccine. J. Exp. Med. 2021, 218, e20202486. [CrossRef]
  198. Bastard, P.; Gervais, A.; Le Voyer, T.; Rosain, J.; Philippot, Q.; Manry, J.; Michailidis, E.; Hoffmann, H.-H.; Eto, S.; Garcia-Prat, M.; et al. Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths. Sci. Immunol. 2021, 6, eabl4340. [CrossRef] [PubMed]
  199. Troya, J.; Bastard, P.; Casanova, J.L.; Abel, L.; Pujol, A. Low lymphocytes and IFN-neutralizing autoantibodies as biomarkers of COVID-19 mortality. J. Clin. Immunol. 2022, 42, 738-741. [CrossRef] [PubMed]
  200. Savvateeva, E.; Filippova, M.; Valuev-Elliston, V.; Nuralieva, N.; Yukina, M.; Troshina, E.; Baklaushev, V.; Ivanov, A.; Gryadunov, D. Microarray-based detection of antibodies against SARS-CoV-2 proteins, common respiratory viruses and type I interferons. Viruses 2021, 13, 2553. [CrossRef]

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Authors
Nowosibirsk, B. R. I. E. A. M. S. F. T. D. A. T. O. P. M. 2., Ragino, Y. I., & Rymar, O. D.
Venue
MDPI eBooks
Publisher
MDPI
Published
2025-05-19
DOI
10.3390/books978-3-7258-4017-5
OpenAlex
W4411834376
Cited by
0
References
0
FWCI
0.00
Type
book
Language
en
Open access
Licence
cc-by
Locations
  • MDPI eBooks (publishedVersion, cc-by)
  • Directory of Open access Books (OAPEN Foundation) (submittedVersion, cc-by)
Funders
Russian Science Foundation (20-15-00057)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Adrenal Hormones and Disorders
Keywords
endocrine disorders, personalized medicine, endocrinology, diabetes mellitus, maturity-onset diabetes of the young, molecular genetics, endocrine diagnostics, endocrine therapy, pediatric endocrinology, medical genetics

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