Study On The Correlation Between Glycemic Variability Percentage And Bone Metabolism In Hospitalized Male Patients With Type 2 Diabetes

Objective:Hospitalized male patients with type 2 diabetes mellitus（T2DM）were selected as the study objects,and the blood glucose variability（GVP）was calculated through continuous 3d blood glucose monitoring（CGM）system.The correlation between the serum bone metabolism markers 25-hydroxyvitamin D[25（OH）D3],osteocalcin（OC）,β-collagen special sequence（β-CTX）,type I procollagen amino terminal peptide（PINP）and parathyroid hormone（PTH）was analyzed.To study the relationship between different levels of blood glucose changes and bone metabolism,as well as related factors affecting bone metabolism,and further explore the possible mechanism of bone metabolism changes in male T2DM patients,providing theoretical basis for clinical prevention and early diagnosis and treatment of diabetes-related bone metabolic diseases.Methods:1.Male T2DM patients hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Nanchang University from June 2022 to December 2022were selected.After screening according to the inclusion and exclusion criteria,105patients were selected as the study objects.Collect general data of patients:name,age,course of diabetes,medication,weight（kg）,height（m）,and calculate body mass index（BMI）.Biochemical indicators for fasting venous blood collection in the morning of the next day:alanine transaminase（AST）,aspartate transaminase（ALT）,creatinine（Cr）,urea nitrogen（BUN）,uric acid（SUA）,serum albumin（Alb）,calcium（Ca）,phosphorus（P）,triglyceride（TG）,total cholesterol（TC）,low-density lipoprotein cholesterol（LDL-C）,glycosylated hemoglobin（Hb A1c）,thyrotropin（TSH）,free triiodothyronine（FT3）Free tetraiodothyronine（FT4）,fasting C-peptide（F-CP）,fasting blood glucose（FBG）,25-hydroxyvitamin D₃[25（OH）D₃],osteocalcin（OC）β-Collagen specific sequence（β-CTX）,type I collagen amino acid extender peptide（PINP）,parathyroid hormone（PTH）and other indicators.2.Using a glucose monitoring（CGM）system to continuously monitor blood glucose for 3 days,288 blood glucose values were derived based on the data and blood glucose change map exported by the dynamic blood glucose monitor.The blood glucose fluctuation index was calculated using Excel:percentage of blood glucose change（GVP）,and the glucose target within range time（TIR）,glucose above range time（TAR）,and glucose below range time（TBR）were collected.3.According to the percentage of blood glucose variability（GVP）,it is divided into a low GVP group（GVP≤30%）and a high GVP group（30%>GVP）.Compare the differences in various indicators between the two groups,and evaluate the correlation between GVP and bone metabolism indicators using Pearson analysis and multiple linear regression.Results:1.For the comparison of the baseline data between the low GVP group and the high GVP group,there was no significant difference in age,BMI,diabetes course,ALS,AST,TG,TC,LDL-C,Cr,Alb,TSH,FT3,FT4（p>0.05）;There was a statistically significant difference between the F-CP,FBG,and Hb A1c groups（P<0.05）.2.Compared with the blood glucose fluctuation related indicators between the two groups,the TIR of the low GVP group was higher than that of the high GVP group,while the TAR was lower than that of the high GVP group,with a statistically significant difference（P<0.05）.3.Comparison of bone metabolism markers between two groups:low GVP groupβ-CTX was lower than the high GVP group,while PINP was higher than the high GVP group,with statistically significant differences（P<0.05）.There was no statistically significant difference in OC,PTH,25（OH）D₃,Ca,P,and ALP between the two groups（P>0.05）.4.Pearson correlation analysis was conducted on Hb A1c,blood glucose fluctuation related indicators GVP,TIR,TAR,and bone metabolism indicators,and the results showed thatβ-CTX was only positively correlated with GVP（r=0.348,P<0.01）,PINP was negatively correlated with GVP and Hb A1c（r=-0.296 P<0.01;r=-0.316 P<0.01）,PTH was negatively correlated with Hb A1c（r=-0.245 P=0.012）,OC was negatively correlated with Hb A1c and TAR（r=-0.343 P<0.01;r=-0.260P<0.01）,and TIR was positively correlated（r=0.240,P=0.014）.5.Multiple linear regression analysis showed that both Hb A1c and GVP had an impact on PINP（p<0.05）,and Hb A1c had a greater impact on PINP compared to GVP（β’=-0.275,P<0.05）.6.Multiple linear regression analysis of risk factors related to bone metabolism indicators shows that GVP,a blood glucose fluctuation indicator,isβ-CTX is an independent risk factor for PINP,which has statistical significance before and after adjusting for confounding factors（P<0.05）,while it has no statistical significance after adjusting for confounding factors with 25（OH）D₃,OC,and PTH（P>0.05）.Conclusion:1.Blood glucose fluctuation and bone metabolic markers in hospitalized male patients with type 2 diabetesβ-CTX and PINP are closely related,and as GVP levels increase,β-CTX level and the decrease of PINP level are not conducive to bone formation,suggesting that excessive variation of blood glucose aggravates bone metabolism disorder,which may be one of the risk factors leading to bone metabolic disease in type 2 diabetes.2.GVP is a marker of bone metabolism in hospitalized male patients with type 2diabetesβ-CTX is an independent protective factor and an independent risk factor for PINP.