Global Journal of Medical Research, F: Diseases, Volume 22 Issue 4
Time period from onset of diabetes to 3.97 ± 4.3 3.73 ± 4.3 0.81 admission to CCC (years) BMI at baseline (kg/m2) 19.85 ± 5.3 19.05 ± 3.5 0.41 Family history of diabetes [n (%)]b Positive 10 (50) 58 (58) Negative 10 (50) 42 (42) 0.36 Mean HbA1C (%) 9.5 ± 1.7 8.6 ± 1.2 0.004 Fluctuations in HbA1C [n (%)] Present 12 (60) 54 (54) Absent 8 (40) 46 (46) 0.05 Data are means ± SD unless otherwise specified. a [n (%)] indicates the number in each category and (percentage). b Totals do not add up to 120 due to missing data. The mean HbA1C per individual was 8.65 ± 1.3 in the whole sample. As shown in Table 1, mean HbA1C was 9.5 ± 1.7% among those who developed nephropathy compared to a mean of 8.6 ± 1.2% for those who did not develop nephropathy, and was sta- tistically significant between the two groups (p = 0.004). The association between fluctuations in HbA1C and diabetic nephropathy is shown in Table1. Among those who developed nephropathy, 10 of 20(60%) had fluctuations in HbA1C; compared to those who do not develop nephropathy 54 of 100 (54%) had fluctuations in HbA1C (p = 0.05). In order to identify the predictors for diabetic nephropathy, we performed a multivariate analysis, by entering all risk covariates into a multiple logistic regression analysis (Table 2). Results from the full model (referred to as Model 1) revealed that mean HbA1C was the only significant predictive factor; all other variables were not significant. Since our hypothesis is to test whether the presence of fluctuations in HbA1C predicts the development of nephropathy adjusting for the mean HbA1C, we further studied three other models one including the two covariates the mean and the ‘‘fluctuations” in HbA1C (referred to Model 2), another model including only mean HbA1C as a covariate (referred to as Model 3) and the last model including the fluctuations in HbA1C (Model 4). The Model 2 leads to a smaller BIC than Model 3 (BIC dropped from 101.4 to 104.7), indicating positive evidence for a better fit. We also noticed that the odds ratio of the mean HbA1C decreases from 1.76 to 1.56 when the covariate ‘‘fluctuations” is added to the model and becomes closer to 1. Considering Model 4, the odds ratio of the fluctuations in HbA1C is 4.18; however when adjusting for the mean HbA1C (Model 2), the odds ratio dropped to 2.35 and the fluctuations in HbA1c was no more a significant predictor factor. Table 2: Multivariate analysis for the prediction of diabetic nephropathy Parameter Odds ratio Model 1 (95%CI) Model 2 Model 3 Model 4 Average mean of HbA1C 1.67 (1.04; 2.69) 1.56 (1.02; 2.39)* 1.76 (1.19; 2.60)* Fluctuations in HbA1C 1.90(0.43; 8.42) 2.35 (0.57; 9.78) 4.18 (1.14; 15.32)* Gender 0.86(0.28; 2.64) Family history 1.33 (0.43; 4.14) Age at onset 1.07(0.89; 1.27) Time between onset of diabetes till admission to CCC 0.94 (0.81; 1.09) Baseline BMI 0.94 (0.76; 1.15) BIC —123.41 —104.72 —101.41 —104.23 IV. D iscussion WHO multicentric study of vascular disease in diabetes, observed a wide geographic variation in prevalence of nephropathy i.e. 2.4% from Hong Kong, 23% from Delhi to 37% from Oklahoma, USA21. These geographical and population variation in prevalence of diabetic nephropathy could be due to real ethnic variation in the susceptibility to diabetic nephropathy i.e. genetics, poor glycaemic control, hypertension or other socioeconomic, cultural and environmental factors. Several studies indicated that HbA1c may show a glycaemic threshold with micro and macro-vascular complications of diabetes, suggesting it may 5 Year 2022 Global Journal of Medical Research Volume XXII Issue IV Version I ( D ) F © 2022 Global Journals To Evaluate the Role of HbA1C as a Predictor for the Development of Diabetic Nephropathy in Type 1 Diabetic Patients
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