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The aim of this study was to examine the association between CONUT score and all-cause mortality as well as cancer mortality in adults with T2D. For this study, we analyzed a total of 3763 adult patients with T2D who were part of the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. Mortality outcomes were determined by linking to the National Death Index records as of December 31, 2019. Cox proportional risk models were used to estimate risk ratios (HRs) and 95% confidence intervals (CIs) for all-cause and cancer deaths. During the mean follow-up of 8.17 years, there were 823 deaths from all causes and 155 deaths from cancer. After adjusting for multiple variables, the risk of all-cause mortality was higher in patients with a Mild (CONUT score ≥ 2), compared with patients with a Normal (CONUT score of 0–1). All-cause mortality risk was 39% higher, and cancer mortality risk was 45% higher. Consistent results were observed when stratified by age, sex, race, BMI, smoking status, and glycated hemoglobin levels. In a nationally representative sample of American adults with T2D, we found an association between CONUT score and all-cause mortality and cancer mortality.",{"EN":163,"VI":164},"Association of the controlling nutritional status score with all-cause mortality and cancer mortality risk in patients with type 2 diabetes: NHANES 1999–2018","Mối liên quan giữa điểm kiểm soát tình trạng dinh dưỡng với nguy cơ tử vong do mọi nguyên nhân và tử vong do ung thư ở bệnh nhân đái tháo đường type 2: NHANES 1999–2018",{"VOID":166},"GBD 2019 Diabetes Mortality Collaborators. Diabetes mortality and trends before 25 years of age: an analysis of the global burden of Disease Study 2019. Lancet Diabetes Endocrinol. 2022;10(3):177–92. 3.\nWan Z, Guo J, Pan A, Chen C, Liu L. Gang Liu; Association of serum 25-Hydroxyvitamin D concentrations with all-cause and cause-specific mortality among individuals with diabetes. Diabetes Care. 2021;02(2):350–7.\nKonecka M, Schneider-Matyka D, Kamińska M, Bikowska M, Ustianowski P, Grochans E. Analysis of the laboratory results of the patients enrolled in the Nutritional Therapy Program. Eur Rev Med Pharmacol Sci. 2022;26(14):5144–53. https:\u002F\u002Fdoi.org\u002F10.26355\u002Feurrev_202207_29303.\nThaxton GE, Melby PC, Manary MJ, Preidis GA. New Insights into the pathogenesis and treatment of Malnutrition. Gastroenterol Clin North Am. 2018;47(4):813–27. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.gtc.2018.07.007.\nJones KD, Berkley JA. Severe acute malnutrition and infection. Paediatr Int Child Health. 2014;34(Suppl 1):1–S29. https:\u002F\u002Fdoi.org\u002F10.1179\u002F2046904714Z.000000000218.\nTriarico S, Rinninella E, Cintoni M, et al. Impact of malnutrition on survival and infections among pediatric patients with cancer: a retrospective study. Eur Rev Med Pharmacol Sci. 2019;23(3):1165–75. https:\u002F\u002Fdoi.org\u002F10.26355\u002Feurrev_201901_17009.\nKatona P, Katona-Apte J. The interaction between nutrition and infection. Clin Infect Dis. 2008;46(10):1582–8. https:\u002F\u002Fdoi.org\u002F10.1086\u002F587658.\nStratton RJ. Should food or supplements be used in the community for the treatment of disease-related malnutrition? Proc Nutr Soc. 2005;64(3):325–33. https:\u002F\u002Fdoi.org\u002F10.1079\u002Fpns2005439.\nThomson K, Rice S, Arisa O, et al. Oral nutritional interventions in frail older people who are malnourished or at risk of malnutrition: a systematic review. Health Technol Assess. 2022;26(51):1–112. https:\u002F\u002Fdoi.org\u002F10.3310\u002FCCQF1608.\nVisser J, McLachlan MH, Maayan N, Garner P. Community-based supplementary feeding for food insecure, vulnerable and malnourished populations - an overview of systematic reviews. Cochrane Database Syst Rev. 2018;11(11):CD010578. https:\u002F\u002Fdoi.org\u002F10.1002\u002F14651858.CD010578.pub2. Published 2018 Nov 9.\nBouillanne O, Morineau G, Dupont C, et al. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–83. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fajcn\u002F82.4.777.\nShirakabe A, Hata N, Kobayashi N, et al. The prognostic impact of malnutrition in patients with severely decompensated acute heart failure, as assessed using the Prognostic Nutritional Index (PNI) and Controlling Nutritional Status (CONUT) score. Heart Vessels. 2018;33(2):134–44. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00380-017-1034-z.\nYuan K, Zhu S, Wang H, et al. Association between malnutrition and long-term mortality in older adults with ischemic stroke. Clin Nutr. 2021;40(5):2535–42. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.clnu.2021.04.018.\nIgnacio de Ulíbarri J, González-Madroño A, de Villar NGP, González P, González B, Mancha A, Rodríguez F. G Fernández; CONUT: a tool for controlling nutritional status. First validation in a hospital population.Nutricion hospitalaria 2005 Jan-Feb;20(1):38–45.\nYujie Liu T, Geng Z, Wan Q, Lu X, Zhang Z, Qiu L, Li K, Zhu L, Liu A, Pan. Gang Liu; Associations of Serum Folate and Vitamin B12 Levels With Cardiovascular Disease Mortality Among Patients With Type 2 Diabetes.JAMA network open 2022 01 04;5(1):e2146124 doi:https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjamanetworkopen.2021.46124.\nAndy Menke S, Casagrande L, Geiss, Catherine C. Cowie; Prevalence of and Trends in diabetes among adults in the United States, 1988–2012.JAMA 2015;314(10):1021–9 doi:https:\u002F\u002Fdoi.org\u002F10.1001\u002Fjama.2015.10029.\n.Cannon MJ, Masalovich S, Ng BP, et al. Retention among participants in the National Diabetes Prevention Program Lifestyle Change Program, 2012–2017. Diabetes Care. 2020;43(9):2042–9. https:\u002F\u002Fdoi.org\u002F10.2337\u002Fdc19-2366.\nOjo O. Dietary Intake and Type 2 Diabetes. Nutrients. 2019;11(9):2177. Published 2019 Sep 11. doi:https:\u002F\u002Fdoi.org\u002F10.3390\u002Fnu11092177.\nTamura Y, Omura T, Toyoshima K, Araki A. Nutrition Management in Older Adults with Diabetes: A Review on the Importance of Shifting Prevention Strategies from Metabolic Syndrome to Frailty. Nutrients. 2020;12(11):3367. Published 2020 Nov 1. doi:https:\u002F\u002Fdoi.org\u002F10.3390\u002Fnu12113367.\nDavies MJ, Aroda VR, Collins BS, et al. Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the american Diabetes Association (ADA) and the European Association for the study of diabetes (EASD). Diabetologia. 2022;65(12):1925–66. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00125-022-05787-2.\nClemente G, Gallo M, Giorgini M. AMD – Associazione Medici Diabetologi “Diabetes and Cancer” working group. Modalities for assessing the nutritional status in patients with diabetes and cancer. Diabetes Res Clin Pract. 2018;142:162–72. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.diabres.2018.05.039.\nKuroda D, Sawayama H, Kurashige J, et al. Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection. Gastric Cancer. 2018;21(2):204–12. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10120-017-0744-3.\nYamaura T, Arizumi F, Maruo K, et al. The impact of Controlling Nutritional Status (CONUT) score on functional prognosis in hospitalized elderly patients with acute osteoporotic vertebral fractures. BMC Geriatr. 2022;22(1):1002. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12877-022-03708-x. Published 2022 Dec 28.\nArero G, Arero AG, Mohammed SH, Vasheghani-Farahani A. Prognostic potential of the Controlling Nutritional Status (CONUT) score in Predicting all-cause mortality and major adverse Cardiovascular events in patients with coronary artery disease: a Meta-analysis. Front Nutr. 2022;9:850641. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffnut.2022.850641. Published 2022 May 9.\nLontchi-Yimagou E, Sobngwi E, Matsha TE, Kengne AP. Diabetes mellitus and inflammation. Curr Diab Rep. 2013;13(3):435–44. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11892-013-0375-y.\nRohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55(1):31–55. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.immuni.2021.12.013.\nBerridge MJ, Vitamin D. A custodian of cell signalling stability in health and disease. Biochem Soc Trans. 2015;43(3):349–58. https:\u002F\u002Fdoi.org\u002F10.1042\u002FBST20140279.\nHaussler MR, Whitfield GK, Kaneko I, et al. Molecular mechanisms of vitamin D action. Calcif Tissue Int. 2013;92(2):77–98. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00223-012-9619-0.\nWimalawansa SJ. Vitamin D deficiency is a surrogate marker for visceral fat content, metabolic syndrome, type 2 diabetes and future metabolic complications. J Diabetes Metab Disord Control. 2016;3(1):6–13. https:\u002F\u002Fdoi.org\u002F10.15406\u002Fjdmdc.2016.03.00059.\nBlack M, Bowman M. Nutrition and healthy aging. Clin Geriatr Med. 2020;36(4):655–69. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cger.2020.06.008.\nWHO. Global Health Estimates. 2020: Deaths by Cause, Age, Sex, by CONUTry and by Region, 2000–2019[Z\u002FOL]. [2021-02-20]. https:\u002F\u002Fwww.who.int\u002Fdata\u002Fgho\u002Fdata\u002Fthemes\u002Fmortality-andglobal-health-estimates\u002Fghe-leading-causes-of-death.\nHarwell TS, Vanderwood KK, Hall TO, Butcher MK, Helgerson SD, Montana Cardiovascular Disease and Diabetes Prevention Workgroup. Factors associated with achieving a weight loss goal among participants in an adapted diabetes Prevention Program. Prim Care Diabetes. 2011;5(2):125–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.pcd.2010.12.001.\nLean ME, Leslie WS, Barnes AC, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. 2018;391(10120):541–51. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0140-6736(17)33102-1.\nLiu H, Wu S, Li Y, et al. Body mass index and mortality in patients with type 2 diabetes mellitus: a prospective cohort study of 11,449 participants. J Diabetes Complications. 2017;31(2):328–33. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jdiacomp.2016.10.015.\nJackson CL, Yeh HC, Szklo M, et al. Body-Mass Index and all-cause mortality in US adults with and without diabetes. J Gen Intern Med. 2014;29(1):25–33. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11606-013-2553-7.\nElagizi A, Kachur S, Lavie CJ, et al. An overview and update on obesity and the obesity Paradox in Cardiovascular Diseases. Prog Cardiovasc Dis. 2018;61(2):142–50. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.pcad.2018.07.003.\nTobias DK, Manson JE. The obesity Paradox in Type 2 diabetes and mortality. Am J Lifestyle Med. 2016;12(3):244–51. https:\u002F\u002Fdoi.org\u002F10.1177\u002F1559827616650415. Published 2016 May 19.\nLevitsky LL, Drews KL, Haymond M, et al. The obesity paradox: retinopathy, obesity, and circulating risk markers in youth with type 2 diabetes in the TODAY Study. J Diabetes Complications. 2022;36(11):108259. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jdiacomp.2022.108259.\nSingla R, Murthy M, Singla S, Gupta Y. Friendly Fat Theory - explaining the Paradox of diabetes and obesity. Eur Endocrinol. 2019;15(1):25–8. https:\u002F\u002Fdoi.org\u002F10.17925\u002FEE.2019.15.1.25.\nKhor PY, Vearing RM, Charlton KE. The effectiveness of nutrition interventions in improving frailty and its associated constructs related to malnutrition and functional decline among community-dwelling older adults: a systematic review. J Hum Nutr Diet. 2022;35(3):566–82. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjhn.12943.",{"VOID":168},"10.1186\u002Fs13098-023-01138-2","PUBLICATION","VERIFIED","2025-01-17T13:14:34.480+00:00","Auto Verify",[174],"VI","https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-023-01138-2",[177,193,207,221,237,251,265],{"id":178,"sortIndex":19,"researcher":18,"roles":179,"affiliations":181,"properties":190,"displayName":192,"givenName":18,"familyName":18},"7f926c5c-2096-47c4-a465-ab74d1ad6b77",[180],"AUTHOR",[182],{"id":183,"sortIndex":19,"affiliation":184,"properties":18},"6a21d877-dcfc-4196-b222-6c2c677092cb",{"id":183,"createTime":18,"updateTime":18,"relativeEntities":185,"slug":18,"properties":186,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":189,"statistic":18},[],{"title":187},{"VI":188},"Xuanwu Hospital Capital Medical University, Beijing, 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therapy regimens for people with type 1 diabetes (PWT1D) should mimic the physiological insulin secretion that occurs in individuals without diabetes. Intensive insulin therapy, whether by multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII), constitutes the fundamental therapy from the initial stages of type 1 diabetes (T1D), at all ages. This review is an authorized literal translation of part of the Brazilian Diabetes Society (SBD) Guidelines 2021–2022. This evidence-based guideline supplies guidance on insulin therapy in T1D. The methods were published elsewhere in earlier SBD guidelines and was approved by the Internal Institutional Steering Committee for publication. Briefly, the Brazilian Diabetes Society indicated fourteen experts to constitute the Central Committee, designed to regulate the method review of the manuscripts, and judge the degrees of recommendations and levels of evidence. SBD Type 1 Diabetes Department drafted the manuscript selecting key clinical questions to do a narrative review using MEDLINE via PubMed, with the best evidence available, including high-quality clinical trials, metanalysis, and large observational studies related to insulin therapy in T1D, by using the Mesh terms [type 1 diabetes] and [insulin]. Based on extensive literature review the Central Committee defined ten recommendations. Three levels of evidence were considered: A. Data from more than one randomised clinical trial (RCT) or one metanalysis of RCTs with low heterogeneity (I2 \u003C 40%). B. Data from metanalysis, including large observational studies, a single RCT, or a pre-specified subgroup analysis. C: Data from small or non-randomised studies, exploratory analysis, or consensus of expert opinion. The degree of recommendation was obtained based on a poll sent to the panellists, using the following criteria: Grade I: when more than 90% of agreement; Grade IIa if 75–89% of agreement; IIb if 50–74% of agreement, and III, when most of the panellist recommends against a defined treatment. In PWT1D, it is recommended to start insulin treatment immediately after clinical diagnosis, to prevent metabolic decompensation and diabetic ketoacidosis. Insulin therapy regimens should mimic insulin secretion with the aim to achieve glycemic control goals established for the age group. Intensive treatment with basal-bolus insulin therapy through MDI or CSII is recommended, and insulin analogues offers some advantages in PWT1D, when compared to human insulin. Periodic reassessment of insulin doses should be performed to avoid clinical inertia in treatment.",{"EN":345,"VI":346},"The 2021–2022 position of Brazilian Diabetes Society on insulin therapy in type 1 diabetes: an evidence-based guideline to clinical practice","Quan điểm giai đoạn 2021–2022 của Hội Đái tháo đường Brazil về liệu pháp insulin trong đái tháo đường type 1: hướng dẫn dựa trên bằng chứng cho thực hành lâm sàng",{"VOID":348},"Beigelman PM. Severe diabetic ketoacidosis (diabetic “coma”). 482 episodes in 257 patients; experience of three years. Diabetes. 1971;20(7):490–500.\nDanne T, Phillip M, Buckingham BA, Jarosz-Chobot P, Saboo B, Urakami T, et al. ISPAD clinical practice consensus guidelines 2018: Insulin treatment in children and adolescents with diabetes. Pediatr Diabetes. 2018;19(27):115–35.\nAmerican Diabetes Association. 9 Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes-2020. Diabetes Care. 2020;43(1):S98–110.\nCobry E, McFann K, Messer L, Gage V, VanderWel B, Horton L, et al. Timing of meal insulin boluses to achieve optimal postprandial glycemic control in patients with type 1 diabetes. Diabetes Technol Ther. 2010;12(3):173–7.\nBuse JB, Carlson AL, Komatsu M, Mosenzon O, Rose L, Liang B, et al. Fast-acting insulin aspart versus insulin aspart in the setting of insulin degludec-treated type 1 diabetes: efficacy and safety from a randomized double-blind trial. Diabetes Obes Metab. 2018;20(12):2885–93.\nNathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–86.\nLachin JM, Genuth S, Cleary P, Davis MD, Nathan DM. Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N Engl J Med. 2000;342(6):381–9.\nCleary PA, Orchard TJ, Genuth S, Wong ND, Detrano R, Backlund J-YC, et al. The effect of intensive glycemic treatment on coronary artery calcification in type 1 diabetic participants of the diabetes control and complications trial\u002Fepidemiology of diabetes interventions and complications (DCCT\u002FEDIC) study. Diabetes. 2006;55(12):3556–65.\nNathan DM, Cleary PA, Backlund J-YC, Genuth SM, Lachin JM, Orchard TJ, et al. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353(25):2643–53.\nDiabetes Control and Complications Trial (DCCT)\u002FEpidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group. Mortality in type 1 diabetes in the DCCT\u002FEDIC versus the general population. Diabetes Care. 2016;39(8):1378–83.\nBolli GB, Songini M, Trovati M, Del Prato S, Ghirlanda G, Cordera R, et al. Lower fasting blood glucose, glucose variability and nocturnal hypoglycaemia with glargine vs NPH basal insulin in subjects with type 1 diabetes. Nutr Metab Cardiovasc Dis. 2009;19(8):571–9.\nHeise T, Nosek L, Rønn BB, Endahl L, Heinemann L, Kapitza C, et al. Lower within-subject variability of insulin detemir in comparison to NPH insulin and insulin glargine in people with type 1 diabetes. Diabetes. 2004;53(6):1614–20.\nPorcellati F, Rossetti P, Ricci NB, Pampanelli S, Torlone E, Campos SH, et al. Pharmacokinetics and pharmacodynamics of the long-acting insulin analog glargine after 1 week of use compared with its first administration in subjects with type 1 diabetes. Diabetes Care. 2007;30(5):1261–3.\nPlank J, Bodenlenz M, Sinner F, Magnes C, Görzer E, Regittnig W, et al. A double-blind, randomized, dose-response study investigating the pharmacodynamic and pharmacokinetic properties of the long-acting insulin analog detemir. Diabetes Care. 2005;28(5):1107–12.\nHershon KS, Blevins TC, Mayo CA, Rosskamp R. Once-daily insulin glargine compared with twice-daily NPH insulin in patients with type 1 diabetes. Endocr Pract. 2004;10(1):10–7.\nDe Leeuw I, Vague P, Selam JL, Skeie S, Lang H, Draeger E, et al. Insulin detemir used in basal-bolus therapy in people with type 1 diabetes is associated with a lower risk of nocturnal hypoglycaemia and less weight gain over 12 months in comparison to NPH insulin. Diabetes Obes Metab. 2005;7(1):73–82.\nSchober E, Schoenle E, Van Dyk J, Wernicke-Panten K, Pediatric Study Group of Insulin Glargine. Comparative trial between insulin glargine and NPH insulin in children and adolescents with type 1 diabetes mellitus. J Pediatr Endocrinol Metab. 2002;15(4):369–76.\nPieber TR, Treichel HC, Hompesch B, Philotheou A, Mordhorst L, Gall MA, et al. Comparison of insulin detemir and insulin glargine in subjects with type 1 diabetes using intensive insulin therapy. Diabet Med. 2007;24(6):635–42.\nHome PD, Bergenstal RM, Bolli GB, Ziemen M, Rojeski M, Espinasse M, et al. New insulin glargine 300 units\u002FmL versus glargine 100 units\u002FmL in people with type 1 diabetes: a randomized, phase 3a, open-label clinical trial (EDITION 4). Diabetes Care. 2015;38(12):2217–25.\nHeller S, Buse J, Fisher M, Garg S, Marre M, Merker L, et al. Insulin degludec, an ultra-long acting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 1 diabetes (BEGIN basal-bolus type 1): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1489–97.\nLane W, Bailey TS, Gerety G, Gumprecht J, Philis-Tsimikas A, Hansen CT, et al. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 1 diabetes: the SWITCH 1 randomized clinical trial. JAMA. 2017;318(1):33–44.\nMathieu C, Hollander P, Miranda-Palma B, Cooper J, Franek E, Russell-Jones D, et al. Efficacy and safety of insulin degludec in a flexible dosing regimen vs insulin glargine in patients with type 1 diabetes (BEGIN: Flex T1): a 26-week randomized, treat-to-target trial with a 26-week extension. J Clin Endocrinol Metab. 2013;98(3):1154–62.\nRelatório de Recomendação nº 245 da Conitec - Comissão Nacional de Incorporação de Tenologias no SUS - fevereiro de 2017. Available in https:\u002F\u002Fwww.gov.br\u002Fconitec\u002Fptbr\u002Fmidias\u002Frelatorios\u002F2017\u002Frelatorio_insulinas_diabetestipo1_final.pdf. Accessed 27 Oct 2022.\nSiebenhofer A, Plank J, Berghold A, Jeitler K, Horvath K, Narath M, et al. Short acting insulin analogues versus regular human insulin in patients with diabetes mellitus. Cochrane Database Syst Rev. 2006;2:CD003287.\nFord-Adams ME, Murphy NP, Moore EJ, Edge JA, Ong KL, Watts AP, et al. Insulin lispro: a potential role in preventing nocturnal hypoglycaemia in young children with diabetes mellitus. Diabet Med. 2003;20(8):656–60.\nSlattery D, Amiel SA, Choudhary P. Optimal prandial timing of bolus insulin in diabetes management: a review. Diabet Med. 2018;35(3):306–16.\nDanne T, Aman J, Schober E, Deiss D, Jacobsen JL, Friberg HH, et al. A comparison of postprandial and preprandial administration of insulin aspart in children and adolescents with type 1 diabetes. Diabetes Care. 2003;26(8):2359–64.\nRussell-Jones D, Bode BW, De Block C, Franek E, Heller SR, Mathieu C, et al. Fast-acting insulin aspart improves glycemic control in basal-bolus treatment for type 1 diabetes: results of a 26-week multicenter, active-controlled, treat-to-target, randomized, parallel-group rrial (onset 1). Diabetes Care. 2017;40(7):943–50.\nKlonoff DC, Evans ML, Lane W, Kempe HP, Renard E, DeVries JH, et al. A randomized, multicentre trial evaluating the efficacy and safety of fast-acting insulin aspart in continuous subcutaneous insulin infusion in adults with type 1 diabetes (onset 5). Diabetes Obes Metab. 2019;21(4):961–7.\nPickup JC. Insulin-pump therapy for type 1 diabetes mellitus. N Engl J Med. 2012;366(17):1616–24.\nŠoupal J, Petruželková L, Grunberger G, Hásková A, Flekač M, Matoulek M, et al. Glycemic outcomes in adults with T1D are impacted more by continuous glucose monitoring than by insulin delivery method: 3 years of follow-up from the COMISAIR Study. Diabetes Care. 2020;43(1):37–43.\nBell KJ, Barclay AW, Petocz P, Colagiuri S, Brand-Miller JC. Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2014;2(2):133–40.\nBattelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593–603.\nHanas R, Adolfsson P. Bolus calculator settings in well-controlled prepubertal children using insulin pumps are characterized by low insulin to carbohydrate ratios and short duration of insulin action time. J Diabetes Sci Technol. 2017;11(2):247–52.\nBrazilian Diabetes Society. Therapeutic approach to type 1 diabetes mellitus: An official position statement of the Brazilian Diabetes Society. São Paulo: Europa Press; 2020. 31 p. https:\u002F\u002Fprofissional.diabetes.org.br\u002Fwp-content\u002Fuploads\u002F2021\u002F06\u002FPosicionamento_Oficial_Sbd_N012020v6_brLC.pdf. Accessed Oct 27 2022.\nDetemir [package insert on the Internet; approved by ANVISA on 12\u002F11\u002F2018]. Araucária: Novo Nordisk A\u002FS; 2018. https:\u002F\u002Fwww.novonordisk.com.br\u002Fcontent\u002Fdam\u002Fbrazil\u002Faffiliate\u002Fwww-novonordisk-br\u002FProfissionais_da_Saude\u002FBulas-profissionais-de-saude\u002FLevemir%20FlexPen_Profissional.pdf. Accessed Oct 27 2022.\nAspart [package insert on the Internet; approved by ANVISA on 09\u002F30\u002F2019]. Araucária: Novo Nordisk A\u002FS; 2019. https:\u002F\u002Fwww.novonordisk.com.br\u002Fcontent\u002Fdam\u002Fbrazil\u002Faffiliate\u002Fwww-novonordisk-br\u002FBulas\u002F2019-12-19\u002FBula%20profissional_Fiasp_Vial.pdf. 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Bertoluci",{"url":353,"publisher":468,"properties":513},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":469,"slug":10,"properties":470,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":473,"manageAffiliations":482,"indexDatabases":493,"url":84,"thumbnailPath":18,"statistic":508,"gsStatistic":18,"type":148,"analyzePriority":18},[],{"issn":471,"title":472},{"VOID":13},{"EN":15},[474,478],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":475,"label":476,"description":477,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":479,"label":480,"description":481,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[483,488],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":484,"slug":18,"properties":485,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":487,"statistic":18},[],{"title":486},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":489,"slug":18,"properties":490,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":492,"statistic":18},[],{"title":491},{"EN":46},[],[494,501],{"id":50,"indexDatabase":495,"url":61,"indexYears":62,"academicFieldIds":500,"indexDatabaseRanking":66},{"id":52,"createTime":18,"updateTime":18,"relativeEntities":496,"label":497,"description":498,"key":58,"publicationTags":499,"standard":18},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64,65],{"id":68,"indexDatabase":502,"url":81,"indexYears":18,"academicFieldIds":507,"indexDatabaseRanking":18},{"id":70,"createTime":18,"updateTime":18,"relativeEntities":503,"label":504,"description":505,"key":77,"publicationTags":506,"standard":18},[],{"EN":73,"VI":73},{"EN":75,"VI":76},[79,80],[83],{"impactFactor":19,"impactFactorByYear":509,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":510,"totalCitation":117,"totalCitationByYear":511,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":512,"hindexLast5Year":98,"hindex":98},{"2012":87,"2013":88,"2014":89,"2015":90,"2016":91,"2017":92,"2018":88,"2019":93,"2020":94,"2021":95,"2022":96,"2023":90},{"2009":101,"2010":102,"2011":103,"2012":104,"2013":105,"2014":106,"2015":107,"2016":108,"2017":109,"2018":110,"2019":111,"2020":112,"2021":113,"2022":114,"2023":115,"2024":116},{"2009":119,"2010":120,"2011":121,"2012":122,"2013":123,"2014":124,"2015":125,"2016":126,"2017":127,"2018":128,"2019":125,"2020":129,"2021":130,"2022":131},{"2009":134,"2010":135,"2011":136,"2012":137,"2013":138,"2014":139,"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147},{"pages":514,"volume":515},{"VOID":328},{"VOID":516},"14","2022-12-12",2022,[66,79],{"id":521,"createTime":522,"updateTime":523,"relativeEntities":524,"slug":525,"properties":526,"entityType":169,"verifyStatus":170,"verifyTime":536,"verifyNote":172,"languages":18,"translateLanguages":537,"viewCount":19,"primaryUrl":538,"fullTextUrl":18,"authors":539,"publicationType":279,"publisherRelationship":681,"citationCount":18,"citationInfo":18,"publishDate":732,"publishYear":733,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":734,"openAccess":18,"references":18,"isForceReanalyzing":334},"705f9c1a-13f8-44b9-9a64-0c6369f3d3d5","2024-01-21T02:02:49.985+00:00","2026-09-07T06:14:03.604+00:00",[],"Effect-of-supplementation-with-vitamins-D3-and-K2-on-undercarboxylated-osteocalcin-and-insulin-serum-levels-in-patients-with-type-2-diabetes-mellitus-a-randomized-double-blind-clinical-trial",{"abstract":527,"title":529,"references":532,"doi":534},{"EN":528},"Patients with type 2 diabetes mellitus (T2DM) are characterized by chronic hyperglycemia as a consequence of decreased insulin sensitivity, which contributes to bone demineralization and could also be related to changes in serum levels of osteocalcin and insulin, particularly when coupled with a deficiency in the daily consumption of vitamins D3 and K2. The objective of this study was to evaluate the effect of vitamin D3 and vitamin K2 supplements alone or in combination on osteocalcin levels and metabolic parameters in patients with T2DM. A double-blind, randomized clinical trial was carried out in 40 patients aged between 30 and 70 years old for 3 months. Clinical and laboratory assessment was carried out at the beginning and at the end of the treatment. The patients were divided into three groups: (a) 1000 IU vitamin D3 + a calcinated magnesium placebo (n = 16), (b) 100 µg of Vitamin K2 + a calcinated magnesium placebo (n = 12), and (c) 1000 IU vitamin D3 + 100 µg vitamin K2 (n = 12). After treatment in the total studied population, a significant decrease in glycemia (p = 0.001), HOMA-IR (Homeostatic model assessment-insulin resistance) (p = 0.040), percentage of pancreatic beta cells (p \u003C 0.001), uOC\u002FcOC index and diastolic blood pressure (p = 0.030) were observed; in vitamin D3 group, differences in serum undercarboxylated osteocalcin (p = 0.026), undercarboxylated to carboxylated osteocalcin index (uOC\u002FcOC) (p = 0.039) glucose (p \u003C 0.001) and  % of functional pancreatic beta cells (p \u003C 0.001) were demonstrated. In vitamin K2 group a significant decrease in glycemia (p = 0.002), HOMA-IR (p = 0.041), percentage of pancreatic beta cells (p = 0.002), and in cOC (p = 0.041) were observed, conversely cOC concentration was found high. Finally, in the vitamins D3 + K2 a significant decrease in glycemia (p = 0.002), percentage of pancreatic beta cells (p = 0.004), and in the uOC\u002FcOC index (p = 0.023) were observed. Individual or combined supplementation with vitamins D3 and K2 significantly decreases the glucose levels and  % of functional pancreatic beta cells, while D3 and D3 + K2 treatments also induced a reduction in the uOC\u002FcOC index. Only in the group with vitamin D3 supplementation, it was observed a reduction in undercarboxylated osteocalcin while vitamin K2 increased the carboxylated osteocalcin levels. Trial registration NCT04041492",{"EN":530,"VI":531},"Effect of supplementation with vitamins D3 and K2 on undercarboxylated osteocalcin and insulin serum levels in patients with type 2 diabetes mellitus: a randomized, double-blind, clinical trial","Hiệu quả bổ sung vitamin D3 và K2 lên nồng độ osteocalcin chưa carboxyl hóa và insulin huyết thanh ở bệnh nhân đái tháo đường típ 2: thử nghiệm lâm sàng ngẫu nhiên, mù đôi",{"VOID":533},"American Diabetes A. Standards of medical care in diabetes–2012. Diabetes Care. 2012;35(Suppl 1):S11–63.\nBerends LM, Ozanne SE. Early determinants of type-2 diabetes. Best Pract Res Clin Endocrinol Metab. 2012;26(5):569–80.\nShaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14.\nSimo R, Hernandez C. Treatment of diabetes mellitus: general goals, and clinical practice management. Rev Esp Cardiol. 2002;55(8):845–60.\nRishaug U, Birkeland KI, Falch JA, Vaaler S. Bone mass in non-insulin-dependent diabetes mellitus. Scand J Clin Lab Invest. 1995;55(3):257–62.\nLevin ME, Boisseau VC, Avioli LV. Effects of diabetes mellitus on bone mass in juvenile and adult-onset diabetes. N Engl J Med. 1976;294(5):241–5.\nGarcia-Martin A, Reyes-Garcia R, Garcia-Castro JM, Munoz-Torres M. Diabetes, and osteoporosis: action of gastrointestinal hormones on the bone. Rev Clin Esp. 2013;213(6):293–7.\nYokomoto-Umakoshi M, Kanazawa I, Kondo S, Sugimoto T. Association between the risk of falls and osteoporotic fractures in patients with type 2 diabetes mellitus. Endocr J. 2017;64(7):727–34.\nDusso AS, Brown AJ, Slatopolsky E. Vitamin D. Am J Physiol Renal Physiol. 2005;289(1):F8–28.\nTakeda S, Saito M, Sakai S, Yogo K, Marumo K, Endo K. Eldecalcitol, an active vitamin D3 derivative, prevents trabecular bone loss and bone fragility in type I diabetic model rats. Calcif Tissue Int. 2017;101(4):433–44.\nSotelo W, Calvo A. Vitamin D levels in postmenopausal women with primary osteoporosis. Heredian Medical Journal. 2011;22:10–4.\nKawana K, Takahashi M, Hoshino H, Kushida K. Circulating levels of vitamin K1, menaquinone-4, and menaquinone-7 in healthy elderly Japanese women and patients with vertebral fractures and patients with hip fractures. Endocr Res. 2001;27(3):337–43.\nHalder M, Petsophonsakul P, Akbulut A, Pavlic A, Bohan F, Anderson E, Maresz K, Kramann R, Schurgers L. Vitamin K: double bonds beyond coagulation insights into differences between vitamin K1 and K2 in health and disease. Int J Mol Sci. 2019;20:896.\nAdams JS, Hewison M. Update in vitamin D. J Clin Endocrinol Metab. 2010;95(2):471–8.\nDucy P, Desbois C, Boyce B, Pinero G, Story B, Dunstan C, et al. Increased bone formation in osteocalcin-deficient mice. Nature. 1996;382(6590):448–52.\nFerron M, Hinoi E, Karsenty G, Ducy P. Osteocalcin differentially regulates beta cell and adipocyte gene expression and affects the development of metabolic diseases in wild-type mice. Proc Natl Acad Sci USA. 2008;105(13):5266–70.\nOnishi Y, Hayashi T, Sato KK, Ogihara T, Kuzuya N, Anai M, Tsukuda K, Boyko EJ, Fujimoto WY, Kikuchi M. Fasting tests of insulin secretion and sensitivity predict future prediabetes in japanese with normal glucose tolerance: fasting tests predict future diabetes. J Diabetes Investig. 2010;1(5):191–5.\nRojas-Martínez R, Basto-Abreu A, Aguilar-Salinas C, Zárate-Rojas E, Villalpando S, Barrientos-Gutiérrez T. Prevalence of diabetes by prior medical diagnosis in Mexico. Salud Publica Mex. 2018;60:224–32.\nLin X, Brennan-Speranza TC, Levinger I, Yeap BB. Undercarboxylated osteocalcin: experimental and human evidence for a role in glucose homeostasis and muscle regulation of insulin sensitivity. Nutrients. 2018;10(7):847.\nLi J, Zhang H, Yang C, Li Y, Dai Z. An overview of osteocalcin progress. J Bone Miner Metab. 2016;34(4):367–79.\nBourron Olivier, Phan Franck. Vitamin K: a nutrient which plays a little-known role in glucose metabolism. Curr Opin Clin Nutr Metab Care. 2019;22(2):174–81.\nRazny U, Fedak D, Kiec-Wilk B, Goralska J, Gruca A, Zdzienicka A, Kiec-Klimczak M, Solnica B, Hubalewska-Dydejczyk A, Malczewska-Malec M. Carboxylated and undercarboxylated osteocalcin in metabolic complications of human obesity and prediabetes: osteocalcin in obese and prediabetic patients. Diabetes\u002FMetab Res Rev. 2017;33(3):e2862.\nVillafán-Bernal JR, Llamas-Covarrubias MA, Muñoz-Valle JF, Rivera-León EA, González-Hita ME, Bastidas-Ramírez BE, Gurrola-Díaz CM, Armendáriz-Borunda JS, Sánchez-Enríquez S. A cut-point value of uncarboxylated to carboxylated index is associated with glycemic status markers in type 2 diabetes. J Investig Med. 2014;62(1):33–6.\nLiu Yihui, Liu X, Lewis JR, Brock K, Brennan-Speranza TC, Teixeira-Pinto A. Relationship between serum osteocalcin\u002Fundercarboxylated osteocalcin and type 2 diabetes: a systematic review\u002Fmeta-analysis study protocol. BMJ Open. 2019;9(3):e023918.\nJR G-C. Vitamina D y diabetes mellitus tipo 2. Rev Endocrinol Nutr 2010;18(4):186-93.\nvan Ballegooijen AJ, Pilz S, Tomaschitz A, Grübler Martin R, Verheyen Nicolas. The synergistic interplay between Vitamins D and K for bone and cardiovascular health: a narrative review. Int J Endocrinol. 2017;2017:1–12.\nBaez-Duarte BG, Sánchez-Guillén Mdel C, Perez-Fuentes R, Zamora-Ginez I, Leon-Chavez BA, Revilla-Monsalve C, Islas-Andrade. S. Beta-cell function is associated with metabolic syndrome in Mexican subjects. Diabetes Metab Syndr Obes Targets Ther. 2010;3:301.\nSalgado Ana Lúcia, de Azevedo Farias, de Carvalho Luciana, Oliveira Ana Claudia, Nascimento Virgínia, dos Santos Jose, Vieira Gilberto, Parise Edison Roberto. Insulin resistance index (HOMA-IR) in the differentiation of patients with non-alcoholic fatty liver disease and healthy individuals. Arq Gastroenterol. 2010;47(2):165–9.\nTang Qi, Li Xueqin, Song Peipei, Lingzhong Xu. Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: developments in research and prospects for the future. Drug Discov Ther. 2015;9(6):380–5.\nOrtega Anta RM, González-Rodríguez LG, Navia Lombán B, López-Sobaler AM. Adequacy of vitamin K intake in a representative sample of Spanish adults: dietary conditions. Hosp Nutr. 2014;29:187–95.\nPoomthavorn P, Nantarakchaikul P, Mahachoklertwattana P, Chailurkit LO, Khlairit P. Effects of correction of vitamin D insufficiency on serum osteocalcin and glucose metabolism in obese children. Clin Endocrinol. 2014;80(4):516–23.\nBaldock Paul A, Thomas Gethin P, Hodge Jason M, Baker Sara UK, Uwe Dressel, O’Loughlin Peter D, Nicholson Geoffrey C, Briffa Kathy H, Eisman John A, Gardiner EM. Vitamin D action and regulation of bone remodeling: suppression of osteoclastogenesis by the mature osteoblast. J Bone Miner Res. 2006;21(10):1618–26.\nBikle DD. Vitamin D and bone. Curr Osteoporos Res. 2012;10(2):151–9.\nEisman John A, Bouillon Roger. Vitamin D: direct effects of vitamin D metabolites on bone: lessons from genetically modified mice. Bone Key Rep. 2014. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fbonekey.2013.233.\nDíaz CM. Vitamin K action on bone health. J Osteoporos Miner Metab. 2015;7:33–8.\nLopez Zubizarreta M, Hernandez Mezquita MA, Miralles Garcia JM, Barrueco Ferrero M. Tobacco and diabetes: clinical relevance and approach to smoking cessation in diabetic smokers. Endocrinol Diabetes Nutr. 2017;64(4):221–31.\nCastro Torres Y, Fleites Pérez A, Carmona Puerta R, Vega Valdez M, Santiestebán Castillo I. Vitamin D deficiency and arterial hypertension evidence in favor. Colomb J Cardiol. 2016;23(1):42–8.\nSolís Torres A, Alonso Castillo MM, López García KS. Prevalence of alcohol consumption in persons with a diagnosis of type 2 diabetes mellitus. Electr J Ment Health Alcohol Drugs. 2009;5(2):1–13.\nKanazawa I. Interaction between bone and glucose metabolism review. Endocr J. 2017;64(11):1043–53.\nLeiva AM, Martínez MA, Cristi-Montero C, Salas C, Ramírez-Campillo R, Díaz Martínez X, et al. Sedentary lifestyle is associated with an increase in cardiovascular and metabolic risk factors independent of physical activity levels. Med Mag Chile. 2017;145:458–67.\nBonneau J, Ferland G, Karelis AD, Doucet É, Faraj M, Rabasa-Lhoret R, Ferron M. Association between osteocalcin gamma-carboxylation and insulin resistance in overweight and obese postmenopausal women. J Diabetes Complicat. 2017;31(6):1027–34.",{"VOID":535},"10.1186\u002Fs13098-020-00580-w","2025-01-12T23:01:34.986+00:00",[174],"https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-020-00580-w",[540,555,570,583,598,613,626,639,653,667],{"id":541,"sortIndex":19,"researcher":18,"roles":542,"affiliations":543,"properties":552,"displayName":554,"givenName":18,"familyName":18},"430e04b7-be0b-4d9b-820c-d31afe320bda",[180],[544],{"id":545,"sortIndex":19,"affiliation":546,"properties":18},"943d54fa-20f9-447e-a301-62754fe2d64f",{"id":545,"createTime":18,"updateTime":18,"relativeEntities":547,"slug":18,"properties":548,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":551,"statistic":18},[],{"title":549},{"VI":550},"Pharmacology, Health Sciences University Center (CUCS), Universidad de Guadalajara (UdeG), Guadalajara, Mexico",[],{"title":553},{"VI":554},"J. I. 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Llamas-Covarrubias",{"id":654,"sortIndex":655,"researcher":18,"roles":656,"affiliations":657,"properties":664,"displayName":666,"givenName":18,"familyName":18},"e54e62c1-66fc-4ad1-aa66-66ccc1e6f4e5",8,[180],[658],{"id":560,"sortIndex":19,"affiliation":659,"properties":18},{"id":560,"createTime":18,"updateTime":18,"relativeEntities":660,"slug":18,"properties":661,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":663,"statistic":18},[],{"title":662},{"VI":565},[],{"title":665},{"VI":666},"J. 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Rivera-Leon",{"url":538,"publisher":682,"properties":727},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":683,"slug":10,"properties":684,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":687,"manageAffiliations":696,"indexDatabases":707,"url":84,"thumbnailPath":18,"statistic":722,"gsStatistic":18,"type":148,"analyzePriority":18},[],{"issn":685,"title":686},{"VOID":13},{"EN":15},[688,692],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":689,"label":690,"description":691,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":693,"label":694,"description":695,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[697,702],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":698,"slug":18,"properties":699,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":701,"statistic":18},[],{"title":700},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":703,"slug":18,"properties":704,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":706,"statistic":18},[],{"title":705},{"EN":46},[],[708,715],{"id":50,"indexDatabase":709,"url":61,"indexYears":62,"academicFieldIds":714,"indexDatabaseRanking":66},{"id":52,"createTime":18,"updateTime":18,"relativeEntities":710,"label":711,"description":712,"key":58,"publicationTags":713,"standard":18},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64,65],{"id":68,"indexDatabase":716,"url":81,"indexYears":18,"academicFieldIds":721,"indexDatabaseRanking":18},{"id":70,"createTime":18,"updateTime":18,"relativeEntities":717,"label":718,"description":719,"key":77,"publicationTags":720,"standard":18},[],{"EN":73,"VI":73},{"EN":75,"VI":76},[79,80],[83],{"impactFactor":19,"impactFactorByYear":723,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":724,"totalCitation":117,"totalCitationByYear":725,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":726,"hindexLast5Year":98,"hindex":98},{"2012":87,"2013":88,"2014":89,"2015":90,"2016":91,"2017":92,"2018":88,"2019":93,"2020":94,"2021":95,"2022":96,"2023":90},{"2009":101,"2010":102,"2011":103,"2012":104,"2013":105,"2014":106,"2015":107,"2016":108,"2017":109,"2018":110,"2019":111,"2020":112,"2021":113,"2022":114,"2023":115,"2024":116},{"2009":119,"2010":120,"2011":121,"2012":122,"2013":123,"2014":124,"2015":125,"2016":126,"2017":127,"2018":128,"2019":125,"2020":129,"2021":130,"2022":131},{"2009":134,"2010":135,"2011":136,"2012":137,"2013":138,"2014":139,"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147},{"pages":728,"volume":730},{"VOID":729},"1-10",{"VOID":731},"12","2020-08-18",2020,[66,79],{"id":736,"createTime":737,"updateTime":738,"relativeEntities":739,"slug":740,"properties":741,"entityType":169,"verifyStatus":170,"verifyTime":751,"verifyNote":172,"languages":18,"translateLanguages":752,"viewCount":19,"primaryUrl":753,"fullTextUrl":18,"authors":754,"publicationType":279,"publisherRelationship":841,"citationCount":18,"citationInfo":18,"publishDate":891,"publishYear":332,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":892,"openAccess":18,"references":18,"isForceReanalyzing":334},"60b2db42-edc1-4819-a0c5-3f5f15481b6b","2024-01-19T12:49:52.701+00:00","2026-09-06T06:14:02.013+00:00",[],"Age-and-sex-specific-differences-in-the-association-of-serum-osteocalcin-and-cardiometabolic-risk-factors-in-type-2-diabetes",{"abstract":742,"title":744,"references":747,"doi":749},{"EN":743},"Serum osteocalcin levels are closely related to metabolic syndrome and cardiovascular disease. This study aimed to investigate the relationship between serum osteocalcin levels and cardiometabolic risk factors in patients with type 2 diabetes (T2D) according to age and sex. This cross-sectional study included 1500 patients with T2D (991 men and 509 women) aged ≥ 18 years old. The age- and sex-specific disparities in glycemic and lipid control, as well as cardiometabolic risk factors were evaluated. The levels of serum osteocalcin were significantly higher in women aged > 50 years compared with women aged ≤ 50 years (15.6 ± 6.5 ng\u002FmL vs. 11.3 ± 4.5 ng\u002FmL, p \u003C 0.0001). However, this was lower in men aged > 50 years than men aged ≤ 50 years (12.2 ± 4.2 ng\u002FmL vs. 12.9 ± 4.3 ng\u002FmL, p = 0.0081). We performed correlation analyses of serum osteocalcin and cardiometabolic parameters. Serum osteocalcin concentrations were negative associated with FBG and HbA1c levels in women and men ≤ 50 years old, but not in men aged > 50 years old. Serum osteocalcin were negatively correlated with TG and positively correlated with HDL-C and LDL-C only in men aged ≤ 50 years. In binary logistic regression analysis, serum osteocalcin levels were associated with multiple cardiovascular risk factors, as follows: overweight\u002Fobese (odds ratio [OR], 0.944; 95% confidence interval [CI], 0.9–0.991, p = 0.02) in men aged > 50 years; high HbA1C and high FBG in women and men aged ≤ 50 years, but not in men aged > 50 years; after adjustment for confounding factors, high TG (OR, 0.905; 95% CI 0.865–0.947, p \u003C 0.0001), metabolic syndrome (OR, 0.914; 95% CI 0.874–0.956, p \u003C 0.0001), and low high-density lipoprotein cholesterol (OR, 0.933; 95% CI, 0.893–0.975, p = 0.002) were seen in men aged ≤ 50 years only. Serum osteocalcin level has significant relationships with cardiometabolic risk factors and several age- and sex-related differences in patients with T2D. Decreased serum osteocalcin levels are associated with a worse cardiometabolic risk profile.",{"EN":745,"VI":746},"Age- and sex-specific differences in the association of serum osteocalcin and cardiometabolic risk factors in type 2 diabetes","Khác biệt theo độ tuổi và giới tính trong mối liên quan giữa osteocalcin huyết thanh và các yếu tố nguy cơ tim mạch - chuyển hóa ở bệnh đái tháo đường type 2",{"VOID":748},"Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol. 2018;17(1):83.\nInternational Hypoglycaemia Study G. Hypoglycaemia cardiovascular disease, and mortality in diabetes: epidemiology, pathogenesis, and management. Lancet Diabetes Endocrinol. 2019;7(5):385–96.\nStrain WD, Paldanius PM. Diabetes, cardiovascular disease and the microcirculation. Cardiovasc Diabetol. 2018;17(1):57.\nSattar N, Gill JMR, Alazawi W. Improving prevention strategies for cardiometabolic disease. Nat Med. 2020;26(3):320–5.\nSeverinsen MCK, Pedersen BK. Muscle-organ crosstalk: the emerging roles of myokines. Endocr Rev. 2020. https:\u002F\u002Fdoi.org\u002F10.1210\u002Fendrev\u002Fbnaa016.\nLin X, Onda DA, Yang CH, Lewis JR, Levinger I, Loh K. Roles of bone-derived hormones in type 2 diabetes and cardiovascular pathophysiology. Mol Metab. 2020;40: 101040.\nDirckx N, Moorer MC, Clemens TL, Riddle RC. The role of osteoblasts in energy homeostasis. Nat Rev Endocrinol. 2019;15(11):651–65.\nLiu X, Yeap BB, Brock KE, Levinger I, Golledge J, Flicker L, et al. Associations of osteocalcin forms with metabolic syndrome and its individual components in older men: the health in men study. J Clin Endocrinol Metab. 2021;106(9):e3506–18.\nKang JH. Association of serum osteocalcin with insulin resistance and coronary atherosclerosis. J Bone Metab. 2016;23(4):183–90.\nLevinger I, Brennan-Speranza TC, Zulli A, Parker L, Lin X, Lewis JR, et al. Multifaceted interaction of bone, muscle, lifestyle interventions and metabolic and cardiovascular disease: role of osteocalcin. Osteoporos Int. 2017;28(8):2265–73.\nBador KM, Wee LD, Halim SA, Fadi MF, Santhiran P, Rosli NF, et al. Serum osteocalcin in subjects with metabolic syndrome and central obesity. Diabetes Metab Syndr. 2016;10(1 Suppl 1):S42–5.\nHwang YC, Kang M, Cho IJ, Jeong IK, Ahn KJ, Chung HY, et al. Association between the circulating total osteocalcin level and the development of cardiovascular disease in middle-aged men: a mean 8.7-year longitudinal follow-up study. J Atheroscler Thromb. 2015;22(2):136–43.\nKord-Varkaneh H, Djafarian K, Khorshidi M, Shab-Bidar S. Association between serum osteocalcin and body mass index: a systematic review and meta-analysis. Endocrine. 2017;58(1):24–32.\nYeap BB, Chubb SA, Flicker L, McCaul KA, Ebeling PR, Hankey GJ, et al. Associations of total osteocalcin with all-cause and cardiovascular mortality in older men. The health in men study. Osteoporos Int. 2012;23(2):599–606.\nLuo Y, Ma X, Hao Y, Xiong Q, Xu Y, Pan X, et al. Relationship between serum osteocalcin level and carotid intima-media thickness in a metabolically healthy Chinese population. Cardiovasc Diabetol. 2015;14:82.\nEastell R, Szulc P. Use of bone turnover markers in postmenopausal osteoporosis. Lancet Diabetes Endocrinol. 2017;5(11):908–23.\nJung KY, Kim KM, Ku EJ, Kim YJ, Lee DH, Choi SH, et al. Age- and sex-specific association of circulating osteocalcin with dynamic measures of glucose homeostasis. Osteoporos Int. 2016;27(3):1021–9.\nGoossens GH, Jocken JWE, Blaak EE. Sexual dimorphism in cardiometabolic health: the role of adipose tissue, muscle and liver. Nat Rev Endocrinol. 2021;17(1):47–66.\nKautzky-Willer A, Stich K, Hintersteiner J, Kautzky A, Kamyar MR, Saukel J, et al. Sex-specific-differences in cardiometabolic risk in type 1 diabetes: a cross-sectional study. Cardiovasc Diabetol. 2013;12:78.\nIsasi CR, Parrinello CM, Ayala GX, Delamater AM, Perreira KM, Daviglus ML, et al. Sex differences in cardiometabolic risk factors among Hispanic\u002FLatino youth. J Pediatr. 2016;176: 121–7 e1.\nLin L, Zhang J, Jiang L, Du R, Hu C, Lu J, et al. Transition of metabolic phenotypes and risk of subclinical atherosclerosis according to BMI: a prospective study. Diabetologia. 2020;63(7):1312–23.\nDiemar SS, Mollehave LT, Quardon N, Lylloff L, Thuesen BH, Linneberg A, et al. Effects of age and sex on osteocalcin and bone-specific alkaline phosphatase-reference intervals and confounders for two bone formation markers. Arch Osteoporos. 2020;15(1):26.\nSheng L, Cao W, Cha B, Chen Z, Wang F, Liu J. Serum osteocalcin level and its association with carotid atherosclerosis in patients with type 2 diabetes. Cardiovasc Diabetol. 2013;12:22.\nCipriani C, Colangelo L, Santori R, Renella M, Mastrantonio M, Minisola S, et al. The interplay between bone and glucose metabolism. Front Endocrinol (Lausanne). 2020;11:122.\nNeumann T, Lodes S, Kastner B, Franke S, Kiehntopf M, Lehmann T, et al. Osteocalcin, adipokines and their associations with glucose metabolism in type 1 diabetes. Bone. 2016;82:50–5.\nYeap BB, Davis WA, Peters K, Hamilton EJ, Rakic V, Paul Chubb SA, et al. Circulating osteocalcin is unrelated to glucose homoeostasis in adults with type 1 diabetes. J Diabetes Complications. 2017;31(6):948–51.\nLu C, Ivaska KK, Alen M, Wang Q, Tormakangas T, Xu L, et al. Serum osteocalcin is not associated with glucose but is inversely associated with leptin across generations of nondiabetic women. J Clin Endocrinol Metab. 2012;97(11):4106–14.\nChen L, Li Q, Yang Z, Ye Z, Huang Y, He M, et al. Osteocalcin, glucose metabolism, lipid profile and chronic low-grade inflammation in middle-aged and elderly Chinese. Diabet Med. 2013;30(3):309–17.\nXifra G, Moreno-Navarrete JM, Moreno M, Ricart W, Fernandez-Real JM. Obesity status influences the relationship among serum osteocalcin, iron stores and insulin sensitivity. Clin Nutr. 2018;37(6 Pt A):2091–6.\nEastell R, O’Neill TW, Hofbauer LC, Langdahl B, Reid IR, Gold DT, et al. Postmenopausal osteoporosis. Nat Rev Dis Primers. 2016;2:16069.\nNaylor K, Eastell R. Bone turnover markers: use in osteoporosis. Nat Rev Rheumatol. 2012;8(7):379–89.\nMa XY, Chen FQ, Hong H, Lv XJ, Dong M, Wang QY. The relationship between serum osteocalcin concentration and glucose and lipid metabolism in patients with type 2 diabetes mellitus—the role of osteocalcin in energy metabolism. Ann Nutr Metab. 2015;66(2–3):110–6.\nZhang XL, Wang YN, Ma LY, Liu ZS, Ye F, Yang JH. Uncarboxylated osteocalcin ameliorates hepatic glucose and lipid metabolism in KKAy mice via activating insulin signaling pathway. Acta Pharmacol Sin. 2020;41(3):383–93.\nZhou H, Seibel MJ. Bone: Osteoblasts and global energy metabolism—beyond osteocalcin. Nat Rev Rheumatol. 2017;13(5):261–2.\nHu WW, Ke YH, He JW, Fu WZ, Liu YJ, Chen D, et al. Serum osteocalcin levels are inversely associated with plasma glucose and body mass index in healthy Chinese women. Acta Pharmacol Sin. 2014;35(12):1521–6.\nWei J, Ferron M, Clarke CJ, Hannun YA, Jiang H, Blaner WS, et al. Bone-specific insulin resistance disrupts whole-body glucose homeostasis via decreased osteocalcin activation. J Clin Invest. 2014;124(4):1–13.\nHan Y, You X, Xing W, Zhang Z, Zou W. Paracrine and endocrine actions of bone-the functions of secretory proteins from osteoblasts, osteocytes, and osteoclasts. Bone Res. 2018;6:16.",{"VOID":750},"10.1186\u002Fs13098-023-01021-0","2025-01-23T16:10:34.594+00:00",[174],"https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-023-01021-0",[755,770,785,800,815,828],{"id":756,"sortIndex":19,"researcher":18,"roles":757,"affiliations":758,"properties":767,"displayName":769,"givenName":18,"familyName":18},"e496a85e-6d54-4cba-a134-8a49c964c4f3",[180],[759],{"id":760,"sortIndex":19,"affiliation":761,"properties":18},"1713473f-fb08-4483-8ab9-a6b10f9310f0",{"id":760,"createTime":18,"updateTime":18,"relativeEntities":762,"slug":18,"properties":763,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":766,"statistic":18},[],{"title":764},{"VI":765},"Department of Rheumatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China",[],{"title":768},{"VI":769},"Wei Li",{"id":771,"sortIndex":195,"researcher":18,"roles":772,"affiliations":773,"properties":782,"displayName":784,"givenName":18,"familyName":18},"a8dec943-4f18-4eaa-9d99-a6027376a510",[180],[774],{"id":775,"sortIndex":19,"affiliation":776,"properties":18},"a1f2da2c-807e-473f-a2d6-383565546d28",{"id":775,"createTime":18,"updateTime":18,"relativeEntities":777,"slug":18,"properties":778,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":781,"statistic":18},[],{"title":779},{"VI":780},"Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China",[],{"title":783},{"VI":784},"Yan 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               \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>To assess the association of pregnancy loss history with an elevated risk of Gestational diabetes mellitus (GDM) and to investigate whether this association was mediated by high-sensitivity C-reactive protein (hs-CRP).\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>We prospectively collected venous blood and pregnancy loss history information from 4873 pregnant women at 16–23 weeks of gestation from March 2018 to April 2022. Hs-CRP concentrations were measured from collected blood samples. A 75 g fasting glucose test was performed at 24 to 28 weeks of gestation for the diagnosis of GDM, with data obtained from medical records. Multivariate linear or logistic regression models and mediation analysis were used to examine the relationships between pregnancy loss history, hs-CRP, and GDM.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>A multivariable-adjusted logistic regression analysis revealed that compared with pregnant women with no induced abortion history, subjects with 1 and ≥ 2 induced abortions had a higher risk for GDM (RR = 1.47, 95% CI = 1.19–1.81; RR = 1.63, 95% CI = 1.28–2.09). Additionally, the mediation analysis indicated this association was mediated by an increased hs-CRP level with a 20.4% of indirect effect ratio. However, no significant association between a history of miscarriage and the prevalence of GDM was observed.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>A history of induced abortion was significantly associated with an increased risk of GDM, and this association occurred in a dose-response effect. Hs-CRP may be accounted for a mediation effect in the pathways linking induced abortion history with GDM.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":905,"VI":906},"The mediating role of inflammation in the association between pregnancy loss history and gestational diabetes mellitus","Vai trò trung gian của phản ứng viêm trong mối liên quan giữa tiền sử mất thai và bệnh đái tháo đường thai kỳ",{"VOID":908},"37340501",{"VOID":910},"10.1186\u002Fs13098-023-01106-w","2024-12-09T04:36:30.307+00:00",[913],"EN",[174],"https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-023-01106-w",[917,936,953,970,987,1002,1019,1036,1055],{"id":918,"sortIndex":19,"researcher":18,"roles":919,"affiliations":920,"properties":929,"displayName":933,"givenName":18,"familyName":18},"a632f0a1-54ba-4f88-afa4-f9e8092756e4",[],[921],{"id":922,"sortIndex":19,"affiliation":923,"properties":18},"4dfb81b4-1097-4c6a-a38d-bdaaeec952f6",{"id":922,"createTime":18,"updateTime":18,"relativeEntities":924,"slug":18,"properties":925,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":928,"statistic":18},[],{"title":926},{"VI":927},"Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei, 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L, Linnenkamp U, Beagley J, Whiting DR, Cho NH. Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract. 2014;103(2):176–85.",{"doi":1129},"10.1016\u002Fj.diabres.2013.11.003",{"id":18,"text":1131,"url":18,"identifiers":1132},"Ferrara A. Increasing prevalence of gestational diabetes mellitus: a public health perspective. Diabetes Care. 2007;30(Suppl 2):141–S146.",{"doi":1133},"10.2337\u002Fdc07-s206",{"id":18,"text":1135,"url":18,"identifiers":1136},"Hedderson MM, Xu F, Darbinian JA, Quesenberry CP, Sridhar S, Kim C, et al. Prepregnancy SHBG concentrations and risk for subsequently developing gestational diabetes mellitus. Diabetes Care. 2014;37(5):1296–303.",{"doi":1137},"10.2337\u002Fdc13-1965",{"id":18,"text":1139,"url":18,"identifiers":1140},"Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019;62(6):905–14.",{"doi":1141},"10.1007\u002Fs00125-019-4840-2",{"id":18,"text":1143,"url":18,"identifiers":1144},"Tam WH, Ma RCW, Ozaki R, Li AM, Chan MHM, Yuen LY, et al. In Utero exposure to maternal hyperglycemia increases Childhood Cardiometabolic risk in offspring. Diabetes Care. 2017;40(5):679–86.",{"doi":1145},"10.2337\u002Fdc16-2397",{"id":18,"text":1147,"url":18,"identifiers":1148},"Brand JS, West J, Tuffnell D, Bird PK, Wright J, Tilling K, et al. Gestational diabetes and ultrasound-assessed fetal growth in south asian and white european women: findings from a prospective pregnancy cohort. BMC Med. 2018;16(1):203.",{"doi":1149},"10.1186\u002Fs12916-018-1191-7",{"id":18,"text":1151,"url":18,"identifiers":1152},"Chen S, Zhao S, Dalman C, Karlsson H, Gardner R. Association of maternal diabetes with neurodevelopmental disorders: autism spectrum disorders, attention-deficit\u002Fhyperactivity disorder and intellectual disability. Int J Epidemiol. 2021;50(2):459–74.",{"doi":1153},"10.1093\u002Fije\u002Fdyaa212",{"id":18,"text":1155,"url":18,"identifiers":1156},"Quenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. Lancet. 2021;397(10285):1658–67.",{"doi":1157},"10.1016\u002FS0140-6736(21)00682-6",{"id":18,"text":1159,"url":18,"identifiers":1160},"Okoth K, Chandan JS, Marshall T, Thangaratinam S, Thomas GN, Nirantharakumar K, et al. Association between the reproductive health of young women and cardiovascular disease in later life: umbrella review. BMJ. 2020;371:m3502.",{"doi":1161},"10.1136\u002Fbmj.m3502",{"id":18,"text":1163,"url":18,"identifiers":1164},"Lee HJ, Norwitz E, Lee B. Relationship between threatened miscarriage and gestational diabetes mellitus. BMC Pregnancy Childbirth. 2018;18(1):318.",{"doi":1165},"10.1186\u002Fs12884-018-1955-2",{"id":18,"text":1167,"url":18,"identifiers":1168},"Horn J, Tanz LJ, Stuart JJ, Markovitz AR, Skurnik G, Rimm EB, et al. Early or late pregnancy loss and development of clinical cardiovascular disease risk factors: a prospective cohort study. BJOG: an International Journal of Obstetrics and Gynaecology. 2019;126(1):33–42.",{"doi":1169},"10.1111\u002F1471-0528.15452",{"id":18,"text":1171,"url":18,"identifiers":1172},"Sedgh G, Bearak J, Singh S, Bankole A, Popinchalk A, Ganatra B, et al. Abortion incidence between 1990 and 2014: global, regional, and subregional levels and trends. Lancet. 2016;388(10041):258–67.",{"doi":1173},"10.1016\u002FS0140-6736(16)30380-4",{"id":18,"text":1175,"url":18,"identifiers":1176},"Xu B, Zhang J, Xu Y, Lu J, Xu M, Chen Y, et al. Association between history of abortion and metabolic syndrome in middle-aged and elderly chinese women. Front Med. 2013;7(1):132–7.",{"doi":1177},"10.1007\u002Fs11684-013-0250-x",{"id":18,"text":1179,"url":18,"identifiers":1180},"Peters SAE, Yang L, Guo Y, Chen Y, Bian Z, Sun H, et al. Pregnancy, pregnancy loss and the risk of diabetes in chinese women: findings from the China Kadoorie Biobank. Eur J Epidemiol. 2020;35(3):295–303.",{"doi":1181},"10.1007\u002Fs10654-019-00582-7",{"id":18,"text":1183,"url":18,"identifiers":1184},"Arck PC, Hecher K. Fetomaternal immune cross-talk and its consequences for maternal and offspring’s health. Nat Med. 2013;19(5):548–56.",{"doi":1185},"10.1038\u002Fnm.3160",{"id":18,"text":1187,"url":18,"identifiers":1188},"Eschenbach DA. Treating spontaneous and induced septic abortions. Obstet Gynecol. 2015;125(5):1042–8.",{"doi":1189},"10.1097\u002FAOG.0000000000000795",{"id":18,"text":1191,"url":18,"identifiers":1192},"Amirian A, Rahnemaei FA, Abdi F. Role of C-reactive protein(CRP) or high-sensitivity CRP in predicting gestational diabetes Mellitus:systematic review. Diabetes & Metabolic Syndrome. 2020;14(3):229–36.",{"doi":1193},"10.1016\u002Fj.dsx.2020.02.004",{"id":18,"text":1195,"url":18,"identifiers":1196},"Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82.",{"doi":1197},"10.2337\u002Fdc09-1848",{"id":18,"text":1199,"url":18,"identifiers":1200},"Liu A-X, He W-H, Yin L-J, Lv P-P, Zhang Y, Sheng J-Z, et al. Sustained endoplasmic reticulum stress as a cofactor of oxidative stress in decidual cells from patients with early pregnancy loss. J Clin Endocrinol Metab. 2011;96(3):E493–7.",{"doi":1201},"10.1210\u002Fjc.2010-2192",{"id":18,"text":1203,"url":18,"identifiers":1204},"Azenabor AA, Kennedy P, Balistreri S. Chlamydia trachomatis infection of human trophoblast alters estrogen and progesterone biosynthesis: an insight into role of infection in pregnancy sequelae. Int J Med Sci. 2007;4(4):223–31.",{"doi":1205},"10.7150\u002Fijms.4.223",{"id":18,"text":1207,"url":18,"identifiers":1208},"Ma X, Xu LJ, Wang J, Xian MM, Liu M. Association of IL-1β and IL-6 gene polymorphisms with recurrent spontaneous abortion in a chinese Han population. Int J Immunogenet. 2012;39(1):15–9.",{"doi":1209},"10.1111\u002Fj.1744-313X.2011.01049.x",{"id":18,"text":1211,"url":18,"identifiers":1212},"Sedgh G, Finer LB, Bankole A, Eilers MA, Singh S. Adolescent pregnancy, birth, and abortion rates across countries: levels and recent trends. J Adolesc Health. 2015;56(2):223–30.",{"doi":1213},"10.1016\u002Fj.jadohealth.2014.09.007",{"id":18,"text":1215,"url":18,"identifiers":1216},"Löwy I. Dias Villela Corrêa M C. The “Abortion Pill” Misoprostol in Brazil: Women’s empowerment in a conservative and repressive political environment. Am J Public Health. 2020;110(5):677–84.",{"doi":1217},"10.2105\u002FAJPH.2019.305562",{"id":18,"text":1219,"url":18,"identifiers":1220},"Mor G, Aldo P, Alvero AB. The unique immunological and microbial aspects of pregnancy. Nat Rev Immunol. 2017;17(8):469–82.",{"doi":1221},"10.1038\u002Fnri.2017.64",{"id":18,"text":1223,"url":18,"identifiers":1224},"Yang H, Qiu L, Chen G, Ye Z, Lü C, Lin Q. Proportional change of CD4 + CD25 + regulatory T cells in decidua and peripheral blood in unexplained recurrent spontaneous abortion patients. Fertil Steril. 2008;89(3):656–61.",{"doi":1225},"10.1016\u002Fj.fertnstert.2007.03.037",{"id":18,"text":1227,"url":18,"identifiers":1228},"Fu B, Tian Z, Wei H. TH17 cells in human recurrent pregnancy loss and pre-eclampsia. Cell Mol Immunol. 2014;11(6):564–70.",{"doi":1229},"10.1038\u002Fcmi.2014.54",{"id":18,"text":1231,"url":18,"identifiers":1232},"Wagner MM, Jukema JW, Hermes W, le Cessie S, de Groot CJM, Bakker JA, et al. Assessment of novel cardiovascular biomarkers in women with a history of recurrent miscarriage. Pregnancy Hypertens. 2018;11:129–35.",{"doi":1233},"10.1016\u002Fj.preghy.2017.10.012",{"id":18,"text":1235,"url":18,"identifiers":1236},"Lorenzo PI, Martín-Montalvo A, Cobo Vuilleumier N, Gauthier BR. Molecular Modelling of Islet β-Cell adaptation to inflammation in pregnancy and gestational diabetes Mellitus. Int J Mol Sci. 2019; 20(24).",{"doi":1237},"10.3390\u002Fijms20246171",{"id":18,"text":1239,"url":18,"identifiers":1240},"Lekva T, Norwitz ER, Aukrust P, Ueland T. Impact of systemic inflammation on the progression of gestational diabetes Mellitus. Curr Diab Rep. 2016;16(4):26.",{"doi":1241},"10.1007\u002Fs11892-016-0715-9",{"id":1243,"createTime":1244,"updateTime":1245,"relativeEntities":1246,"slug":1247,"properties":1248,"entityType":169,"verifyStatus":170,"verifyTime":1260,"verifyNote":172,"languages":1261,"translateLanguages":1262,"viewCount":19,"primaryUrl":1263,"fullTextUrl":18,"authors":1264,"publicationType":279,"publisherRelationship":1371,"citationCount":253,"citationInfo":1420,"publishDate":18,"publishYear":18,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1422,"openAccess":18,"references":1423,"isForceReanalyzing":334},"224bb922-7da2-4be5-afa9-34b83fe9ffcb","2024-04-19T12:38:58.071+00:00","2026-09-05T02:12:39.299+00:00",[],"Adiponectin-reduces-apoptosis-of-diabetic-cardiomyocytes-by-regulating-miR-711-TLR4-axis",{"openalex":1249,"abstract":1251,"title":1253,"pm":1256,"doi":1258},{"VOID":1250},"W4296050052",{"EN":1252},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:sec>\n                \u003Cjats:title>Objective\u003C\u002Fjats:title>\n                \u003Cjats:p>To investigate the regulation of adiponectin\u002FmiR-711 on TLR4\u002FNF-κB-mediated inflammatory response and diabetic cardiomyocyte apoptosis.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>Diabetes models were established using rats and H9c2 cardiomyocytes. qRT-PCR was used to detect adiponectin, miR-711, and TLR4. MTT, β-galactosidase staining, and flow cytometry were utilized to assess cell viability, senescence, and apoptosis, respectively. The colorimetric method was used to measure caspase-3 activity, DCFH-DA probes to detect ROS, and western blotting to determine the protein levels of Bax, Bcl-2, TLR4, and p-NF-κB p65. ELISA was performed to measure the levels of adiponectin, ICAM-1, MCP-1, and IL-1β. Dual-luciferase reporter system examined the targeting relationship between miR-711 and TLR4. H&E and TUNEL staining revealed myocardial structure and apoptosis, respectively.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>Adiponectin and miR-711 were underexpressed and TLR4\u002FNF-κB signaling pathway was activated in high glucose-treated H9c2 cells. High glucose treatment reduced viability, provoked inflammatory response, and accelerated senescence and apoptosis in H9c2 cells. miR-711 could bind TLR4 mRNA and inactivate TLR4\u002FNF-κB signaling. Adiponectin treatment increased miR-711 expression and blocked TLR4\u002FNF-κB signaling. Adiponectin\u002FmiR-711 reduced myocardial inflammation and apoptosis in diabetic rats.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusion\u003C\u002Fjats:title>\n                \u003Cjats:p>Adiponectin inhibits inflammation and alleviates high glucose-induced cardiomyocyte apoptosis by blocking TLR4\u002FNF-κB signaling pathway through miR-711.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":1254,"VI":1255},"Adiponectin reduces apoptosis of diabetic cardiomyocytes by regulating miR-711\u002FTLR4 axis","Adiponectin làm giảm apoptosis của tế bào cơ tim đái tháo đường thông qua điều hòa trục 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China",[],{"orcid":1278,"title":1280,"openalex":1282},{"VOID":1279},"https:\u002F\u002Forcid.org\u002F0000-0001-6721-7755",{"EN":1281},"Yu Zuo",{"VOID":1283},"A5052325298",{"id":1285,"sortIndex":195,"researcher":18,"roles":1286,"affiliations":1287,"properties":1296,"displayName":1300,"givenName":18,"familyName":18},"c4065243-230e-478a-b96b-de13fb82aa88",[],[1288],{"id":1289,"sortIndex":19,"affiliation":1290,"properties":18},"445bd579-5551-4e14-9aa7-a688ed290292",{"id":1289,"createTime":18,"updateTime":18,"relativeEntities":1291,"slug":18,"properties":1292,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1295,"statistic":18},[],{"title":1293},{"EN":1294},"Nursing Department, The Third Xiangya Hospital of Central South University, No. 138, Tongzipo Road, Yuelu District, Changsha, Hunan, 410013, People’s Republic of 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A, Muller-Wieland D, Muller UA, Landgraf R, Nauck M, Freckmann G, Heinemann L, Schleicher E. Definition, classification and diagnosis of diabetes mellitus. Exp Clin Endocrinol Diabetes. 2019;127:S1–7.",{"doi":1427},"10.1055\u002Fa-1018-9078",{"id":18,"text":1429,"url":18,"identifiers":1430},"Schmidt AM. Highlighting diabetes mellitus: the epidemic continues. Arterioscler Thromb Vasc Biol. 2018;38:e1–8.",{"doi":1431},"10.1161\u002FATVBAHA.117.310221",{"id":18,"text":1433,"url":18,"identifiers":1434},"Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14:88–98.",{"doi":1435},"10.1038\u002Fnrendo.2017.151",{"id":18,"text":1437,"url":18,"identifiers":1438},"Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16:377–90.",{"doi":1439},"10.1038\u002Fs41581-020-0278-5",{"id":18,"text":1441,"url":18,"identifiers":1442},"Kenny HC, Abel ED. Heart failure in type 2 diabetes mellitus. Circ Res. 2019;124:121–41.",{"doi":1443},"10.1161\u002FCIRCRESAHA.118.311371",{"id":18,"text":1445,"url":18,"identifiers":1446},"Fang H, Judd RL. Adiponectin regulation and function. Compr Physiol. 2018;8:1031–63.",{"doi":1447},"10.1002\u002Fcphy.c170046",{"id":18,"text":1449,"url":18,"identifiers":1450},"Achari AE, Jain SK. Adiponectin, a therapeutic target for obesity, diabetes, and endothelial dysfunction. Int J Mol Sci. 2017. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms18061321.",{"doi":1451},"10.3390\u002Fijms18061321",{"id":18,"text":1453,"url":18,"identifiers":1454},"Parida S, Siddharth S, Sharma D. Adiponectin, obesity, and cancer: clash of the Bigwigs in health and disease. Int J Mol Sci. 2019. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms20102519.",{"doi":1455},"10.3390\u002Fijms20102519",{"id":18,"text":1457,"url":18,"identifiers":1458},"Woodward L, Akoumianakis I, Antoniades C. Unravelling the adiponectin paradox: novel roles of adiponectin in the regulation of cardiovascular disease. Br J Pharmacol. 2017;174:4007–20.",{"doi":1459},"10.1111\u002Fbph.13619",{"id":18,"text":1461,"url":18,"identifiers":1462},"Lu TX, Rothenberg ME. MicroRNA. J Allergy Clin Immunol. 2018;141:1202–7.",{"doi":1463},"10.1016\u002Fj.jaci.2017.08.034",{"id":18,"text":1465,"url":18,"identifiers":1466},"Saliminejad K, KhorramKhorshid HR, SoleymaniFard S, Ghaffari SH. An overview of microRNAs: biology, functions, therapeutics, and analysis methods. J Cell Physiol. 2019;234:5451–65.",{"doi":1467},"10.1002\u002Fjcp.27486",{"id":18,"text":1469,"url":18,"identifiers":1470},"Li H, Fan J, Zhao Y, Zhang X, Dai B, Zhan J, Yin Z, Nie X, Fu XD, Chen C, Wang DW. Nuclear miR-320 mediates diabetes-induced cardiac dysfunction by activating transcription of fatty acid metabolic genes to cause Lipotoxicity in the heart. 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Circ Res. 2019;124:1160–2.",{"doi":1518},"10.1161\u002FCIRCRESAHA.118.314665",{"id":18,"text":1520,"url":18,"identifiers":1521},"Sivasankar D, George M, Sriram DK. Novel approaches in the treatment of diabetic cardiomyopathy. Biomed Pharmacother. 2018;106:1039–45.",{"doi":1522},"10.1016\u002Fj.biopha.2018.07.051",{"id":18,"text":1524,"url":18,"identifiers":1525},"Zhang W, Xu W, Feng Y, Zhou X. Non-coding RNA involvement in the pathogenesis of diabetic cardiomyopathy. J Cell Mol Med. 2019;23:5859–67.",{"doi":1526},"10.1111\u002Fjcmm.14510",{"id":18,"text":1528,"url":18,"identifiers":1529},"Chen L, Shan Y, Zhang H, Wang H, Chen Y. Up-Regulation of Hsa_circ_0008792 inhibits osteosarcoma cell invasion and migration and promotes apoptosis by regulating Hsa-miR-711\u002FZFP1. Onco Targets Ther. 2020;13:2173–81.",{"doi":1530},"10.2147\u002FOTT.S239256",{"id":18,"text":1532,"url":18,"identifiers":1533},"Sabirzhanov B, Makarevich O, Barrett JP, Jackson IL, Glaser EP, Faden AI, Stoica BA. Irradiation-induced upregulation of miR-711 inhibits DNA repair and promotes neurodegeneration pathways. Int J Mol Sci. 2020. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms21155239.",{"doi":1534},"10.3390\u002Fijms21155239",{"id":18,"text":1536,"url":18,"identifiers":1537},"Sabirzhanov B, Stoica BA, Zhao Z, Loane DJ, Wu J, Dorsey SG, Faden AI. miR-711 upregulation induces neuronal cell death after traumatic brain injury. Cell Death Differ. 2016;23:654–68.",{"doi":1538},"10.1038\u002Fcdd.2015.132",{"id":18,"text":1540,"url":18,"identifiers":1541},"Xiao WS, Li DF, Tang YP, Chen YZ, Deng WB, Chen J, Zhou WW, Liao AJ. Inhibition of epithelialmesenchymal transition in gastric cancer cells by miR711mediated downregulation of CD44 expression. Oncol Rep. 2018;40:2844–53.",{},{"id":18,"text":1543,"url":18,"identifiers":1544},"Zhang Y, Xu C, Nan Y, Nan S. Microglia-derived extracellular vesicles carrying miR-711 alleviate neurodegeneration in a murine alzheimer’s disease model by binding to Itpkb. Front Cell Dev Biol. 2020;8: 566530.",{"doi":1545},"10.3389\u002Ffcell.2020.566530",{"id":18,"text":1547,"url":18,"identifiers":1548},"Zhu YX, Zhou JH, Li GW, Zhou WY, Ou SS, Xiao XY. Dexmedetomidine protects liver cell line L-02 from oxygen-glucose deprivation-induced injury by down-regulation of microRNA-711. Eur Rev Med Pharmacol Sci. 2018;22:6507–16.",{},{"id":18,"text":1550,"url":18,"identifiers":1551},"Zhao D, Zheng H, Greasley A, Ling F, Zhou Q, Wang B, Ni T, Topiwala I, Zhu C, Mele T, Liu K, Zheng X. The role of miR-711 in cardiac cells in response to oxidative stress and its biogenesis: a study on H9C2 cells. Cell Mol Biol Lett. 2020;25:26.",{"doi":1552},"10.1186\u002Fs11658-020-00206-z",{"id":18,"text":1554,"url":18,"identifiers":1555},"Zhao N, Mi L, Zhang X, Xu M, Yu H, Liu Z, Liu X, Guan G, Gao W, Wang J. Enhanced MiR-711 transcription by PPARgamma induces endoplasmic reticulum stress-mediated apoptosis targeting calnexin in rat cardiomyocytes after myocardial infarction. J Mol Cell Cardiol. 2018;118:36–45.",{"doi":1556},"10.1016\u002Fj.yjmcc.2018.03.006",{"id":18,"text":1558,"url":18,"identifiers":1559},"Zhang JR, Yu HL. Effect of NF-kappaB signaling pathway mediated by miR-711 on the apoptosis of H9c2 cardiomyocytes in myocardial ischemia reperfusion. Eur Rev Med Pharmacol Sci. 2017;21:5781–8.",{},{"id":18,"text":1561,"url":18,"identifiers":1562},"Ciesielska A, Matyjek M, Kwiatkowska K. TLR4 and CD14 trafficking and its influence on LPS-induced pro-inflammatory signaling. Cell Mol Life Sci. 2021;78:1233–61.",{"doi":1563},"10.1007\u002Fs00018-020-03656-y",{"id":18,"text":1565,"url":18,"identifiers":1566},"Mitchell JP, Carmody RJ. NF-kappaB and the Transcriptional Control of Inflammation. Int Rev Cell Mol Biol. 2018;335:41–84.",{"doi":1567},"10.1016\u002Fbs.ircmb.2017.07.007",{"id":18,"text":1569,"url":18,"identifiers":1570},"Avlas O, Fallach R, Shainberg A, Porat E, Hochhauser E. Toll-like receptor 4 stimulation initiates an inflammatory response that decreases cardiomyocyte contractility. Antioxid Redox Signal. 2011;15:1895–909.",{"doi":1571},"10.1089\u002Fars.2010.3728",{"id":18,"text":1573,"url":18,"identifiers":1574},"Boyd JH, Mathur S, Wang Y, Bateman RM, Walley KR. Toll-like receptor stimulation in cardiomyoctes decreases contractility and initiates an NF-kappaB dependent inflammatory response. Cardiovasc Res. 2006;72:384–93.",{"doi":1575},"10.1016\u002Fj.cardiores.2006.09.011",{"id":18,"text":1577,"url":18,"identifiers":1578},"Reddy VS, Pandarinath S, Archana M, Reddy GB. Impact of chronic hyperglycemia on small heat shock proteins in diabetic rat brain. Arch Biochem Biophys. 2021;701: 108816.",{"doi":1579},"10.1016\u002Fj.abb.2021.108816",{"id":18,"text":1581,"url":18,"identifiers":1582},"Wang J, Wang R, Li J, Yao Z. Rutin alleviates cardiomyocyte injury induced by high glucose through inhibiting apoptosis and endoplasmic reticulum stress. Exp Ther Med. 2021;22:944.",{"doi":1583},"10.3892\u002Fetm.2021.10376",{"id":18,"text":1585,"url":18,"identifiers":1586},"Bai Y, Li Z, Liu W, Gao D, Liu M, Zhang P. Biochanin A attenuates myocardial ischemia\u002Freperfusion injury through the TLR4\u002FNF-kappaB\u002FNLRP3 signaling pathway. Acta Cir Bras. 2019;34: e201901104.",{"doi":1587},"10.1590\u002Fs0102-865020190110000004",{"id":18,"text":1589,"url":18,"identifiers":1590},"Chu J, Zhou X, Peng M, Lu Y, Farman A, Peng L, Gao H, Li Q, Chen X, Xie L, Chen Y, Shen A, Peng J. Huoxin pill attenuates cardiac inflammation by suppression of TLR4\u002FNF-kappaB in acute myocardial ischemia injury rats. Evid Based Complement Alternat Med. 2020;2020:7905902.",{},{"id":18,"text":1592,"url":18,"identifiers":1593},"Luo M, Yan D, Sun Q, Tao J, Xu L, Sun H, Zhao H. Ginsenoside Rg1 attenuates cardiomyocyte apoptosis and inflammation via the TLR4\u002FNF-kB\u002FNLRP3 pathway. J Cell Biochem. 2020;121:2994–3004.",{"doi":1594},"10.1002\u002Fjcb.29556",{"id":18,"text":1596,"url":18,"identifiers":1597},"Pan YQ, Li J, Li XW, Li YC, Li J, Lin JF. Effect of miR-21\u002FTLR4\u002FNF-kappaB pathway on myocardial apoptosis in rats with myocardial ischemia-reperfusion. Eur Rev Med Pharmacol Sci. 2018;22:7928–37.",{},{"id":18,"text":1599,"url":18,"identifiers":1600},"Yao J, Li Y, Jin Y, Chen Y, Tian L, He W. Synergistic cardioptotection by tilianin and syringin in diabetic cardiomyopathy involves interaction of TLR4\u002FNF-kappaB\u002FNLRP3 and PGC1a\u002FSIRT3 pathways. Int Immunopharmacol. 2021;96: 107728.",{"doi":1601},"10.1016\u002Fj.intimp.2021.107728",{"id":18,"text":1603,"url":18,"identifiers":1604},"Youssef ME, Abdelrazek HM, Moustafa YM. Cardioprotective role of GTS-21 by attenuating the TLR4\u002FNF-kappaB pathway in streptozotocin-induced diabetic cardiomyopathy in rats. Naunyn Schmiedebergs Arch Pharmacol. 2021;394:11–31.",{"doi":1605},"10.1007\u002Fs00210-020-01957-4",{"id":18,"text":1607,"url":18,"identifiers":1608},"Choi HM, Doss HM, Kim KS. Multifaceted physiological roles of adiponectin in inflammation and diseases. Int J Mol Sci. 2020. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms21041219.",{"doi":1609},"10.3390\u002Fijms21041219",{"id":18,"text":1611,"url":18,"identifiers":1612},"Jeon YH, He M, Austin J, Shin H, Pfleger J, Abdellatif M. Adiponectin enhances the bioenergetics of cardiac myocytes via an AMPK- and succinate dehydrogenase-dependent mechanism. Cell Signal. 2021;78: 109866.",{"doi":1613},"10.1016\u002Fj.cellsig.2020.109866",{"id":18,"text":1615,"url":18,"identifiers":1616},"Mori D, Miyagawa S, Matsuura R, Sougawa N, Fukushima S, Ueno T, Toda K, Kuratani T, Tomita K, Maeda N, Shimomura I, Sawa Y. Pioglitazone strengthen therapeutic effect of adipose-derived regenerative cells against ischemic cardiomyopathy through enhanced expression of adiponectin and modulation of macrophage phenotype. Cardiovasc Diabetol. 2019;18:39.",{"doi":1617},"10.1186\u002Fs12933-019-0829-x",{"id":18,"text":1619,"url":18,"identifiers":1620},"Zhao D, Xue C, Li J, Feng K, Zeng P, Chen Y, Duan Y, Zhang S, Li X, Han J, Yang X. Adiponectin agonist ADP355 ameliorates doxorubicin-induced cardiotoxicity by decreasing cardiomyocyte apoptosis and oxidative stress. Biochem Biophys Res Commun. 2020;533:304–12.",{"doi":1621},"10.1016\u002Fj.bbrc.2020.09.035",{"id":18,"text":1623,"url":18,"identifiers":1624},"Liu H, Wu X, Luo J, Zhao L, Li X, Guo H, Bai H, Cui W, Guo W, Feng D, Qu Y. Adiponectin peptide alleviates oxidative stress and NLRP3 inflammasome activation after cerebral ischemia-reperfusion injury by regulating AMPK\u002FGSK-3beta. Exp Neurol. 2020;329: 113302.",{"doi":1625},"10.1016\u002Fj.expneurol.2020.113302",{"id":18,"text":1627,"url":18,"identifiers":1628},"Raut PK, Park PH. Globular adiponectin antagonizes leptin-induced growth of cancer cells by modulating inflammasomes activation: critical role of HO-1 signaling. Biochem Pharmacol. 2020;180: 114186.",{"doi":1629},"10.1016\u002Fj.bcp.2020.114186",{"id":18,"text":1631,"url":18,"identifiers":1632},"Xu X, Huang X, Zhang L, Huang X, Qin Z, Hua F. Adiponectin protects obesity-related glomerulopathy by inhibiting ROS\u002FNF-kappaB\u002FNLRP3 inflammation pathway. BMC Nephrol. 2021;22:218.",{"doi":1633},"10.1186\u002Fs12882-021-02391-1",{"id":18,"text":1635,"url":18,"identifiers":1636},"Boursereau R, Abou-Samra M, Lecompte S, Noel L, Brichard SM. Downregulation of the NLRP3 inflammasome by adiponectin rescues Duchenne muscular dystrophy. BMC Biol. 2018;16:33.",{"doi":1637},"10.1186\u002Fs12915-018-0501-z",{"id":18,"text":1639,"url":18,"identifiers":1640},"TavakoliDargani Z, Singla DK. Embryonic stem cell-derived exosomes inhibit doxorubicin-induced TLR4-NLRP3-mediated cell death-pyroptosis. Am J Physiol Heart Circ Physiol. 2019;317:H460–71.",{"doi":1641},"10.1152\u002Fajpheart.00056.2019",{"id":1643,"createTime":1644,"updateTime":1645,"relativeEntities":1646,"slug":1647,"properties":1648,"entityType":169,"verifyStatus":170,"verifyTime":1659,"verifyNote":172,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1660,"fullTextUrl":18,"authors":1661,"publicationType":279,"publisherRelationship":1717,"citationCount":19,"citationInfo":1768,"publishDate":1771,"publishYear":1769,"citationAnalyzeStatus":1772,"lastCitationAnalyze":1645,"indexDatabases":1773,"openAccess":18,"references":18,"isForceReanalyzing":334},"80d3b0f6-43e1-44c2-8137-dc38bfc89ffe","2024-02-12T16:33:29.764+00:00","2026-08-15T14:20:48.562+00:00",[],"HbA1C-variability-among-type-2-diabetic-patients-a-retrospective-cohort-study",{"abstract":1649,"title":1651,"gsPaper":1653,"references":1655,"doi":1657},{"EN":1650},"Studies have found that HbA1C variability is an independent risk factor for diabetic complications in type 2 diabetic patients. This study aims to find factors contributing to higher HbA1C variability in the community. The study was conducted in the southern district of Israel, in Clalit Health Services (CHS). The study population was type 2 diabetic individuals aged 40–70 years in 2005, with a follow-up period of 11 years, until 2015. The definition of HbA1C variability was done by the standard deviation from the average HbA1C value of the entire study period, which was calculated for each participant. The study population was divided into two groups, “variability group” with HbA1C SD > 1.2, and “comparison group” of participants with HbA1C SD ≤ 1.2. In the univariate analysis we used X2 or Fisher test for categorical variables and independent t-test for numeric continuous variables. In the multivariate analysis we used logistic regression as well as assessing for possible interactions. Statistical analysis was ascribed for p \u003C 0.05. All the data was drawn from the computerized medical system used by all primary care physicians and nurses in CHS working in the community. The study population included 2866 participants, the average age was 58.6 years, 43.3% men and 56.7% women. Each participant had an average of 20.9 HbA1C measures in their computerized medical record during the 11 years of follow up. The mean HbA1C value was 7.8%. We found 632 patients (22%) with a high variability, whereas 2234 (78%) had a low variability of HbA1C. In the “variability group” there was a higher percentage of smokers, BMI ≥ 30 and a higher rate of visits to diabetic clinics compared to the “no variability” group. In the “variability group” we found a much higher use of insulin and ACE inhibitors. The highest frequency of variability was between HbA1c values of 8.1–8.5. The multivariate analysis showed that HbA1C variability was associated with insulin use (OR = 4.1, p \u003C 0.001), with age (OR = 0.939, p \u003C 0.001), and Ischemic heart disease (OR = 1.258, p = 0.03). BMI ≥ 30 was almost statistically significant (OR = 1.206, p = 0.063). Gender was statistically insignificant. In conclusion, HbA1C variability might be used as an additional marker in Diabetes Mellitus type 2, reflecting the disease complexity characteristics and the patient’s lifestyle profile.",{"EN":1652},"HbA1C variability among type 2 diabetic patients: a retrospective cohort study",{"VOID":1654},"[\"1505599401784399890\"]",{"VOID":1656},"Dorajoo SR, Ng JSL, Goh JHF, et al. HbA1c variability in type 2 diabetes is associated with the occurrence of new-onset albuminuria within three years. Diabetes Res Clin Pract. 2017;128:32–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.diabres.2017.02.007.\nLee S, Liu T, Zhou J, Zhang Q, Wong WT, Tse G. Predictions of diabetes complications and mortality using hba1c variability: a 10-year observational cohort study. Acta Diabetol. 2020. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00592-020-01605-6.\nLi S, Nemeth I, Donnelly L, Hapca S, Zhou K, Pearson ER. Visit-to-visit HbA1c Variability is associated with cardiovascular disease and microvascular complications in patients with newly diagnosed type 2 diabetes. Diabetes Care. 2020;43(2):426–32. https:\u002F\u002Fdoi.org\u002F10.2337\u002Fdc19-0823.\nTakao T, Matsuyama Y, Yanagisawa H, Kikuchi M, Kawazu S. Association between HbA1c variability and mortality in patients with type 2 diabetes. J Diabetes Complicat. 2014;28(4):494–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jdiacomp.2014.02.006.\nLee MY, Hsiao PJ, Huang YT, et al. Greater HbA1c variability is associated with increased cardiovascular events in type 2 diabetes patients with preserved renal function, but not in moderate to advanced chronic kidney disease. PLoS ONE. 2017;12(6):e0178319. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0178319.\nCardoso CRL, Leite NC, Moram CBM, Salles GF. Long-term visit-to-visit glycemic variability as predictor of micro- and macrovascular complications in patients with type 2 diabetes: the Rio de Janeiro type 2 diabetes cohort study. Cardiovasc Diabetol. 2018;17(1):33. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12933-018-0677-0.\nStratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321:405–12.\nRetnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR, UKPDS Study Group. Risk factors for renal dysfunction in type 2 diabetes: U.K. prospective diabetes study 74. Diabetes. 2006;55:1832–9.\nWei M, Gaskill SP, Haffner SM, Stern MP. Effects of diabetes and level of glycaemia on all-cause and cardiovascular mortality. Diabetes Care. 1998;21:1167–72.\nGorst C, Kwok CS, Aslam S, et al. Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis. Diabetes Care. 2015;38(12):2354–69. https:\u002F\u002Fdoi.org\u002F10.2337\u002Fdc15-1188.\nSu JB, Zhao LH, Zhang XL, et al. HbA1c variability and diabetic peripheral neuropathy in type 2 diabetic patients. Cardiovasc Diabetol. 2018;17(1):47. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12933-018-0693-0.\nWan EY, Fung CS, Fong DY, Lam CL. Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in chinese patients with type 2 diabetes mellitus—a retrospective population-based cohort study. J Diabetes Complicat. 2016;30(7):1240–7. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jdiacomp.2016.05.024.\nPrentice JC, Pizer SD, Conlin PR. Identifying the independent effect of HbA1c variability on adverse health outcomes in patients with type 2 diabetes. Diabet Med. 2016;33(12):1640–8. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fdme.13166.\nLow S, Lim SC, Yeoh LY, et al. Effect of long-term glycemic variability on estimated glomerular filtration rate decline among patients with type 2 diabetes mellitus: insights from the diabetic nephropathy cohort in Singapore. J Diabetes. 2017;9(10):908–19. https:\u002F\u002Fdoi.org\u002F10.1111\u002F1753-0407.12512.\nOrsi E, Solini A, Bonora E, Renal insufficiency and cardiovascular events (RIACE) study group, et al. Haemoglobin A1c variability is a strong, independent predictor of all-cause mortality in patients with type 2 diabetes. Diabetes Obes Metab. 2018;20(8):1885–93. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fdom.13306.",{"VOID":1658},"10.1186\u002Fs13098-021-00717-5","2024-06-24T15:37:27.349+00:00","https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-021-00717-5",[1662,1677,1692],{"id":1663,"sortIndex":19,"researcher":18,"roles":1664,"affiliations":1665,"properties":1674,"displayName":1676,"givenName":18,"familyName":18},"aa20ec51-7f6c-4c7e-862b-029d62a65113",[180],[1666],{"id":1667,"sortIndex":19,"affiliation":1668,"properties":18},"93f2e974-7d50-4894-8885-6dc82365fcbe",{"id":1667,"createTime":18,"updateTime":18,"relativeEntities":1669,"slug":18,"properties":1670,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1673,"statistic":18},[],{"title":1671},{"VI":1672},"Internal Medicine Department, Soroka University Medical Centre, Beersheba, Israel",[],{"title":1675},{"VI":1676},"Dikla Akselrod",{"id":1678,"sortIndex":195,"researcher":18,"roles":1679,"affiliations":1680,"properties":1689,"displayName":1691,"givenName":18,"familyName":18},"82548556-1402-400d-9573-84f7c239833a",[180],[1681],{"id":1682,"sortIndex":19,"affiliation":1683,"properties":18},"e001fed6-1be4-44fc-a691-b9c553127064",{"id":1682,"createTime":18,"updateTime":18,"relativeEntities":1684,"slug":18,"properties":1685,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1688,"statistic":18},[],{"title":1686},{"VI":1687},"Department of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beersheba, Israel",[],{"title":1690},{"VI":1691},"Michael Friger",{"id":1693,"sortIndex":209,"researcher":18,"roles":1694,"affiliations":1695,"properties":1712,"displayName":1714,"givenName":18,"familyName":18},"19dc90ae-a8bc-4b09-a5b8-68e086bc0f95",[180],[1696,1704],{"id":1697,"sortIndex":19,"affiliation":1698,"properties":18},"21c0d8c2-2e1c-4140-9b71-6d44d8f4f083",{"id":1697,"createTime":18,"updateTime":18,"relativeEntities":1699,"slug":18,"properties":1700,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1703,"statistic":18},[],{"title":1701},{"VI":1702},"Department of Family Medicine and Siaal Center for Community Research, Division of Community Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beersheba, Israel",[],{"id":1705,"sortIndex":195,"affiliation":1706,"properties":18},"79e7bdf5-7487-4d0d-a0f4-1355391c976b",{"id":1705,"createTime":18,"updateTime":18,"relativeEntities":1707,"slug":18,"properties":1708,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1711,"statistic":18},[],{"title":1709},{"VI":1710},"Clalit Health Services, Beersheba, Israel",[],{"title":1713,"gsAuthor":1715},{"VI":1714},"Aya Biderman",{"VOID":1716},"[\"lQJbwdEAAAAJ\"]",{"url":1660,"publisher":1718,"properties":1763},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1719,"slug":10,"properties":1720,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1723,"manageAffiliations":1732,"indexDatabases":1743,"url":84,"thumbnailPath":18,"statistic":1758,"gsStatistic":18,"type":148,"analyzePriority":18},[],{"issn":1721,"title":1722},{"VOID":13},{"EN":15},[1724,1728],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1725,"label":1726,"description":1727,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1729,"label":1730,"description":1731,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[1733,1738],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":1734,"slug":18,"properties":1735,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1737,"statistic":18},[],{"title":1736},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":1739,"slug":18,"properties":1740,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1742,"statistic":18},[],{"title":1741},{"EN":46},[],[1744,1751],{"id":50,"indexDatabase":1745,"url":61,"indexYears":62,"academicFieldIds":1750,"indexDatabaseRanking":66},{"id":52,"createTime":18,"updateTime":18,"relativeEntities":1746,"label":1747,"description":1748,"key":58,"publicationTags":1749,"standard":18},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64,65],{"id":68,"indexDatabase":1752,"url":81,"indexYears":18,"academicFieldIds":1757,"indexDatabaseRanking":18},{"id":70,"createTime":18,"updateTime":18,"relativeEntities":1753,"label":1754,"description":1755,"key":77,"publicationTags":1756,"standard":18},[],{"EN":73,"VI":73},{"EN":75,"VI":76},[79,80],[83],{"impactFactor":19,"impactFactorByYear":1759,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":1760,"totalCitation":117,"totalCitationByYear":1761,"totalCitationPerPublication":132,"totalCitationPerPublicationByYear":1762,"hindexLast5Year":98,"hindex":98},{"2012":87,"2013":88,"2014":89,"2015":90,"2016":91,"2017":92,"2018":88,"2019":93,"2020":94,"2021":95,"2022":96,"2023":90},{"2009":101,"2010":102,"2011":103,"2012":104,"2013":105,"2014":106,"2015":107,"2016":108,"2017":109,"2018":110,"2019":111,"2020":112,"2021":113,"2022":114,"2023":115,"2024":116},{"2009":119,"2010":120,"2011":121,"2012":122,"2013":123,"2014":124,"2015":125,"2016":126,"2017":127,"2018":128,"2019":125,"2020":129,"2021":130,"2022":131},{"2009":134,"2010":135,"2011":136,"2012":137,"2013":138,"2014":139,"2015":140,"2016":141,"2017":142,"2018":143,"2019":144,"2020":145,"2021":146,"2022":147},{"pages":1764,"volume":1766},{"VOID":1765},"1-7",{"VOID":1767},"13",{"total":19,"publishYear":1769,"statisticByYear":1770},2021,{},"2021-09-18","ERROR_IN_ANALYZE_CITATION",[66,79],{"id":1775,"createTime":1776,"updateTime":1777,"relativeEntities":1778,"slug":1779,"properties":1780,"entityType":169,"verifyStatus":170,"verifyTime":1791,"verifyNote":172,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1792,"fullTextUrl":18,"authors":1793,"publicationType":279,"publisherRelationship":1955,"citationCount":19,"citationInfo":2005,"publishDate":2007,"publishYear":733,"citationAnalyzeStatus":2008,"lastCitationAnalyze":1777,"indexDatabases":2009,"openAccess":18,"references":18,"isForceReanalyzing":334},"eb275edc-2534-453b-8106-c19aee6663fc","2024-02-18T15:40:19.782+00:00","2026-07-28T10:26:08.907+00:00",[],"Severity-and-mortality-of-COVID-19-in-patients-with-diabetes-hypertension-and-cardiovascular-disease-a-meta-analysis",{"abstract":1781,"title":1783,"gsPaper":1785,"references":1787,"doi":1789},{"EN":1782},"The aim of this study is to evaluate the impact of diabetes, hypertension, cardiovascular disease and the use of angiotensin converting enzyme inhibitors\u002Fangiotensin II receptor blockers (ACEI\u002FARB) with severity (invasive mechanical ventilation or intensive care unit admission or O2 saturation \u003C 90%) and mortality of COVID-19 cases. Systematic review of the PubMed, Cochrane Library and SciELO databases was performed to identify relevant articles published from December 2019 to 6th May 2020. Forty articles were included involving 18.012 COVID-19 patients. The random-effect meta-analysis showed that diabetes mellitus and hypertension were moderately associated respectively with severity and mortality for COVID-19: Diabetes [OR 2.35 95% CI 1.80–3.06 and OR 2.50 95% CI 1.74–3.59] Hypertension: [OR 2.98 95% CI 2.37–3.75 and OR 2.88 (2.22–3.74)]. Cardiovascular disease was strongly associated with both severity and mortality, respectively [OR 4.02 (2.76–5.86) and OR 6.34 (3.71–10.84)]. On the contrary, the use of ACEI\u002FARB, was not associate with severity of COVID-19. In conclusion, diabetes, hypertension and especially cardiovascular disease, are important risk factors for severity and mortality in COVID-19 infected people and are targets that must be intensively addressed in the management of this infection.",{"EN":1784},"Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: a meta-analysis",{"VOID":1786},"[\"15357589600428738681\"]",{"VOID":1788},"https:\u002F\u002Fcovid.saude.gov.br\u002F. Accessed 29 May 2020.\nNovel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41(2):145–51.\nChen N, Zhou M, Dong X, Qu J, Gong F, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. 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Hypertension. 2020;76(1):51–8. https:\u002F\u002Fdoi.org\u002F10.1161\u002FHYPERTENSIONAHA.120.15143.\nRico-Mesa JS, White A, Anderson AS. Outcomes in patients with COVID-19 infection taking ACEI\u002FARB. Curr Cardiol Rep. 2020;22(5):31. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11886-020-01291-4.\nZhang X, Yu J, Pan LY, Jiang HY. ACEI\u002FARB use and risk of infection or severity or mortality of COVID-19: a systematic review and meta-analysis. Pharmacol Res. 2020;158:104927. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.phrs.2020.104927.\nCai Q, Chen F, Wang T, Luo F, Wu Q, He Q. Obesity and COVID-19 Severity in a Designated Hospital in Shenzhen. China. Diabetes Care. 2020. https:\u002F\u002Fdoi.org\u002F10.2337\u002Fdc20-0576.",{"VOID":1790},"10.1186\u002Fs13098-020-00586-4","2024-05-16T12:42:50.490+00:00","https:\u002F\u002Fdmsjournal.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs13098-020-00586-4",[1794,1827,1856,1879,1892,1905,1925],{"id":1795,"sortIndex":19,"researcher":18,"roles":1796,"affiliations":1797,"properties":1822,"displayName":1824,"givenName":18,"familyName":18},"677758e0-6733-4114-b66d-c56da8e1c4b4",[180],[1798,1806,1814],{"id":1799,"sortIndex":19,"affiliation":1800,"properties":18},"d498ba16-66e4-4a68-9b57-ce708501ed3f",{"id":1799,"createTime":18,"updateTime":18,"relativeEntities":1801,"slug":18,"properties":1802,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1805,"statistic":18},[],{"title":1803},{"VI":1804},"Departamento de Medicina Preventiva, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil",[],{"id":1807,"sortIndex":195,"affiliation":1808,"properties":18},"5551c217-fa08-49c9-a348-c2d48a2c9776",{"id":1807,"createTime":18,"updateTime":18,"relativeEntities":1809,"slug":18,"properties":1810,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1813,"statistic":18},[],{"title":1811},{"VI":1812},"Sociedade Brasileira de Diabetes–SBD, São Paulo, Brazil",[],{"id":1815,"sortIndex":209,"affiliation":1816,"properties":18},"78d1c4c4-9509-4c64-a2e4-0f2d82f6da0a",{"id":1815,"createTime":18,"updateTime":18,"relativeEntities":1817,"slug":18,"properties":1818,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1821,"statistic":18},[],{"title":1819},{"VI":1820},"Programa de Pós-Graduação Em Endocrinologia E Metabologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil",[],{"title":1823,"gsAuthor":1825},{"VI":1824},"Bianca de Almeida-Pititto",{"VOID":1826},"[\"XPRp3gkAAAAJ\"]",{"id":1828,"sortIndex":195,"researcher":18,"roles":1829,"affiliations":1830,"properties":1853,"displayName":1855,"givenName":18,"familyName":18},"88e45962-ced5-45f6-ac4e-7831062d076d",[180],[1831,1837,1844],{"id":1807,"sortIndex":19,"affiliation":1832,"properties":18},{"id":1807,"createTime":18,"updateTime":18,"relativeEntities":1833,"slug":18,"properties":1834,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1836,"statistic":18},[],{"title":1835},{"VI":1812},[],{"id":1815,"sortIndex":195,"affiliation":1838,"properties":1843},{"id":1815,"createTime":18,"updateTime":18,"relativeEntities":1839,"slug":18,"properties":1840,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1842,"statistic":18},[],{"title":1841},{"VI":1820},[],{},{"id":1845,"sortIndex":209,"affiliation":1846,"properties":1852},"f3885099-7cbf-42ff-beeb-604b7ad77b29",{"id":1845,"createTime":18,"updateTime":18,"relativeEntities":1847,"slug":18,"properties":1848,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1851,"statistic":18},[],{"title":1849},{"VI":1850},"Departamento de Medicina, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil",[],{},{"title":1854},{"VI":1855},"Patrícia M. 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attributable fractions (PAFs) are frequently used to quantify the proportion of Type 2 diabetes cases due to single risk factors, an approach which may result in an overestimation of their individual contributions. This study aimed to examine Type 2 diabetes incidence associated with multiple risk factor combinations, including the metabolic syndrome, behavioural factors, and specifically, depression and anxiety. Using data from the population-based HUNT cohort, we examined incident diabetes in 36,161 Norwegian adults from 1995 to 2008. PAFs were calculated using Miettinen’s case-based formula, using relative risks estimated from multivariate regression models. Overall, the studied risk factors accounted for 50.5% of new diabetes cases (78.2% in men and 47.0% in women). Individuals exposed to both behavioural and metabolic factors were at highest risk of diabetes onset (PAF = 22.9%). Baseline anxiety and depression contributed a further 13.6% of new cases to this combination. Men appeared to be particularly vulnerable to the interaction between metabolic, behavioural and psychological risk factors. This study highlights the importance of risk factor clustering in diabetes onset, and is the first that we know of to quantify the excess fraction of incident diabetes associated with psychological risk factor interactions.",{"EN":2020},"Population attributable fractions for Type 2 diabetes: an examination of multiple risk factors including symptoms of depression and anxiety",{"VOID":2022},"[\"4245885231201051061\"]",{"VOID":2024},"International Diabetes Federation. IDF Diabetes Atlas. 7th ed. Brussels: International Diabetes Federation; 2015.\nMoulton CD, Pickup JC, Ismail K. The link between depression and diabetes: the search for shared mechanisms. Lancet Diabetes Endocrinol. 2015;3(6):461–71.\nWillis T. Diabetes: a medical odyssey. New York: Tuckahoe; 1971.\nLuppino FS, De Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BWJH, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry. 2010;67:220–9.\nRoshanaei-Moghaddam B, Katon WJ, Russo J. The longitudinal effects of depression on physical activity. Gen Hosp Psychiatry. 2009;31(4):306–15.\nKhambaty T, Stewart JC, Muldoon MF, Kamarck TW. Depressive symptom clusters as predictors of 6-year increases in insulin resistance: data from the Pittsburgh Healthy Heart Project. Psychosom Med. 2014;76(5):363–9.\nDemakakos P, Zaninotto P, Nouwen A. Is the association between depressive symptoms and glucose metabolism bidirectional? Evidence from the English Longitudinal Study of Ageing. Psychosom Med. 2014;76(7):555–61.\nYoung EA, Abelson JL, Cameron OG. Effect of comorbid anxiety disorders on the Hypothalamic-Pituitary-Adrenal axis response to a social stressor in major depression. Biol Psychiatry. 2004;56(2):113–20.\nHou R, Baldwin DS. A neuroimmunological perspective on anxiety disorders. Hum Psychopharmacol Clin Exp. 2012;27(1):6–14.\nAmerican Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, DC: American Psychiatric Association; 2013.\nHildrum B, Mykletun A, Stordal E, Bjelland I, Dahl AA, Holmen J. Association of low blood pressure with anxiety and depression: the Nord-Trøndelag Health Study. J Epidemiol Community Health. 2007;61(1):53–8.\nHirschfeld RMA. The comorbidity of major depression and anxiety disorders: recognition and management in primary care. Prim Care Companion J Clin Psychiatry. 2001;33(244):244–54.\nCoventry P, Lovell K, Dickens C, Bower P, Chew-Graham C, McElvenny D, et al. Integrated primary care for patients with mental and physical multimorbidity: cluster randomised controlled trial of collaborative care for patients with depression comorbid with diabetes or cardiovascular disease. BMJ. 2015;16(350):h638.\nMarkowitz SM, Gonzalez JS, Wilkinson JL, Safren SA. A review of treating depression in diabetes: emerging findings. Psychosomatics. 2011;52(1):1–18.\nCosgrove MP, Sargeant LA, Griffin SJ. Does depression increase the risk of developing type 2 diabetes? Occup Med (Chic Ill). 2008;58(1):7–14.\nAl Tunaiji H, Davis JC, Mackey DC, Khan KM. Population attributable fraction of type 2 diabetes due to physical inactivity in adults: a systematic review. BMC Public Health. 2014;14(1):469.\nWang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005;81(3):555–63.\nImamura F, O’Connor L, Ye Z, Mursu J, Hayashino Y, Bhupathiraju SN, et al. Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. BMJ. 2015;351:h3576.\nFord ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome. Diabetes Care. 2005;28(7):1769–78.\nSandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A, et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet. 2007;39(8):951–3.\nHulley SB. Risk factors for coronary heart disease selected recent epidemiological advances. Drugs. 1988;36(Supplement 3):1–4.\nGenest J, Cohn JS. Clustering of cardiovascular risk factors: targeting high-risk individuals. Am J Cardiol. 1995;76(1):8A–20A.\nJousilahti P, Toumilehto J, Vartiainen E, Korhonen HJ, Pitkäniemi J, Nissinen A, et al. Importance of risk factor clustering in coronary heart disease mortality and incidence in eastern Finland. J Cardiovasc Risk. 1995;2(1):63–70.\nKannel WB, McGee D, Gordon T. A general cardiovascular risk profile: the Framingham Study. Am J Cardiol. 1976;38(1):46–51.\nChang M, Hahn RA, Teutsch SM, Hutwagner LC. Multiple risk factors and population attributable risk for ischemic heart disease mortality in the United States, 1971–1992. J Clin Epidemiol. 2001;54(6):634–44.\nLaaksonen MA, Knekt P, Rissanen H, Härkänen T, Virtala E, Marniemi J, et al. The relative importance of modifiable potential risk factors of type 2 diabetes: a meta-analysis of two cohorts. Eur J Epidemiol. 2010;25(2):115–24.\nKrokstad S, Langhammer A, Hveem K, Holmen TL, Midthjell K, Stene TR, et al. Cohort Profile: the HUNT Study, Norway. Int J Epidemiol. 2013;42(4):968–77.\nAlberti KGMM, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469–80.\nCheng AYY, Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Canadian Diabetes Association 2013 clinical practice guidelines for the prevention and management of diabetes in Canada. Can J Diabetes. 2013;37:S1–3.\nWorld Health Organization. Global recommendations on physical activity for health. Geneva: World Health Organization; 2010. p. 58.\nWannamethee SG, Shaper AG, Perry IJ. Smoking as a modifiable risk factor for type 2 diabetes in middle-aged men. Diabetes Care. 1978;24(9):1590–5.\nSøgaard A, Bjelland I, Tell G, Røysamb E. A comparison of the CONOR Mental Health Index to the HSCL-10 and HADS. Nor Epidemiol. 2003;13:279–84.\nHarrell FE. General aspects of fitting regression models. Regression modeling strategies. Cham: Springer; 2015. p. 13–44.\nRoyston P. Multiple imputation of missing values. Stata J. 2004;4(3):227–41.\nHanley JA. A heuristic approach to the formulas for population attributable fraction. J Epidemiol Community Health. 2001;55(7):508–14.\nLevin ML. The occurrence of lung cancer in man. Acta Unio Int Contra Cancrum. 1953;9(3):531–41.\nMezuk B, Eaton W, Albrecht S, Golden S. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care. 2008;31:2383–90.\nGoodpaster BH, Krishnaswami S, Harris TB, Katsiaras A, Kritchevsky SB, Simonsick EM, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med. 2005;165(7):777–83.\nWill JC, Galuska DA, Ford ES, Mokdad A, Calle EE. Cigarette smoking and diabetes mellitus: evidence of a positive association from a large prospective cohort study. Int J Epidemiol. 2001;30(3):540–6.\nCherrington A, Wallston KA, Rothman RL. Exploring the relationship between diabetes self-efficacy, depressive symptoms, and glycemic control among men and women with type 2 diabetes. J Behav Med. 2010;33(1):81–9.\nVogelzangs N, Beekman ATF, de Jonge P, Penninx BWJH. Anxiety disorders and inflammation in a large adult cohort. Transl Psychiatry. 2013;3:e249.\nNaicker K, Johnson JA, Skogen JC, Manuel D, Øverland S, Sivertsen B, et al. Type 2 diabetes and comorbid symptoms of depression and anxiety: longitudinal associations with mortality risk. Diabetes Care. 2017;40(3):352–8.\nRubin RR, Peyrot M. Was Willis right? Thoughts on the interaction of depression and diabetes. Diabetes Metab Res Rev. 2002;18(3):173–5.\nFlegal KM, Panagiotou OA, Graubard BI. Estimating population attributable fractions to quantify the health burden of obesity. Ann Epidemiol. 2015;25(3):201–7.\nHernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:S8–14.\nLanghammer A, Krokstad S, Romundstad P, Heggland J, Holmen J, Galea S, et al. The HUNT study: participation is associated with survival and depends on socioeconomic status, diseases and symptoms. 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L, et al. 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