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Objective:To evaluate the relationship between hemoglobin(Hb) level and the risk of diabetic retinopathy(DR) in patients with type 2 diabetes mellitus(T2DM).Methods:This study was a prospective cohort study. A total of 1 730 T2DM patients without DR, who received regular management at the Li′s Clinic in Taiwan, China starting from 2002, were selected as the study population. All patients underwent annual dilated fundus examination by professional ophthalmologists. General patient information and laboratory results were collected and analyzed. Based on the occurrence of DR during patient follow-up, patients were divided into the DR group and the non-DR(NDR) group. The impact of Hb levels on DR was explored using a generalized linear mixed model, and the relationship between Hb levels and DR was studied using Cox proportional hazards regression model.Results:After an average follow-up of 9.79 years, 481 patients with DR were detected. Compared with NDR group, DR group displayed a longer course of diabetes, higher rates of cataract, insulin use, and anemia, and higher systolic blood pressure, HbA 1C, and UACR as well as lower Hb. The results of the generalized linear mixed model showed a negative correlation between Hb and the occurrence of DR( β=-0.015, P<0.001). The Cox proportional hazards regression model showed that, after adjusting for confounding variables and based on quartiles of average Hb levels during follow-up, the risk of developing DR increased by 56.9% in the Q1 group(Hb≤127 g/L) compared to the Q4 group(Hb≥142 g/L). The cumulative risk plot showed that, after adjusting for confounding variables, the Q1 group had the highest cumulative risk of developing DR, and the difference was statistically significant( P<0.05). Conclusion:Hb was negatively correlated with DR, and the lower Hb levels were associated with the occurrence of DR, independent of other influencing factors.
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Objective:To analyze the association between HbA 1C variability and the incidence of diabetes retinopathy in patients with type 2 diabetes mellitus(T2DM). Methods:All the patients with type 2 diabetes receiving regular follow-up were enrolled from Lee′s Joint Clinic from 2002 to 2014. Demographic and laboratory data like HbA 1C were collected including fundal examination. According to HbA 1C variability, which was defined as the difference between baseline and last available HbA 1C, participants were divided into three groups, stable group with HbA 1C variability of ±10%, increase group(>10%), and decline group(<-10%). Results:A total of 3 657 T2DM participants were recruited including 662(13.4%) participants with diabetes retinopathy. Blood glucose gradually rose from ideal level [HbA 1C(7.04±1.35)%] and HbA 1C was up to (9.11±1.96)% at the end of follow-up in increase group. HbA 1C gradually fell to (7.27±1.12)% from (10.05±1.99)% of baseline in decline group. HbA 1C of the third group remained relatively stable. Adjusted for age, body mass index, systolic blood pressure, pulse pressure, duration of diabetes, mean HbA 1C of follow-up, glaucoma and so on, logistic regression revealed that participants in stable group( OR=0.800, 95% CI 0.645-0.992) and increase group( OR=0.706, 95% CI 0.548-0.909) had a lower risk of diabetes retinopathy than decline group( P<0.05). Conclusion:HbA 1C variability is an important risk factor of diabetes retinopathy in patients with T2DM. Patients with blood glucose declined had increased risk of diabetes retinopathy as compared to those with rising HbA 1C.
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Objective:To evaluate the effect of urinary albumin creatinine ratio (UACR) on diabetic retinopathy (DR) in patients with type 2 diabetes. Receiver operating characteristic (ROC) curve was applied to find the cut-off value of UACR for diagnosing DR.Methods:A prospective cohort study of 2 490 patients with type 2 diabetes was conducted with a mean follow-up of 7 years ranging from 3 to 10 years. Dilated fundus examination was performed once a year, and patient history and clinical data were collected and analyzed. Patients were divided into three groups according to the UACR: Q1, normal urinary albumin group (UACR<30 mg/g), Q2, microalbuminuria group (30 mg/g≤UACR≤299 mg/g), and Q3, macroalbuminuria group (UACR>300 mg/g), respectively. Cox regression analysis was used to explore the influence of UACR and other factors on DR, and ROC curve was drawn to evaluate the value of UACR in diagnosis of DR.Results:Cox regression analysis showed that UACR was the risk factor of DR( HR=1.108, 95% CI 1.023-1.241, P<0.001). It showed that the patients in Q3 group had the highest risk of proliferative DR ( HR=3.128, 95% CI 2.025-4.831, P<0.001), the patients in Q2 group followed( HR=1.918, 95% CI 1.355-2.714, P<0.001), and the patients in Q1 group were the lowest. ROC curve analysis showed that area under UACR curve was 0.746(95% CI 0.681-0.812, P<0.001), and the cut-off value, sensitivity, and specificity for the diagnosis of PDR were 54.12mg/g, 0.769, and 0.653, respectively. Conclusion:The UACR can predict the progression of PDR in type 2 diabetes patients, therefore it may be used as a preliminary predictor for the progression of DR.
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Objective:To analyze the effects of different blood pressure variables and their variabilities on diabetic nephropathy(DN)in patients with type 2 diabetes.Methods:This prospective cohort study included 3 050 type 2 diabetic patients without DN at baseline from Lee′s clinic in Taiwan, China. The metabolic parameters of patients were regularly checked, and urine albumin creatinine ratio(UACR)were evaluated annually. The average follow-up period was 7 years(3-10 years). The means and standard deviations(SD)of systolic blood pressure(SBP), diastolic blood pressure(DBP), pulse pressure(PP), and mean arterial pressure(MAP)were calculated. According to whether SBP-Mean was higher or lower than 130 mmHg(1 mmHg=0.133 kPa) and SBP-SD was higher or lower than 11.06 mmHg(average SBP-SD), these patients were divided into four groups: Q1(SBP-Mean<130 mmHg, SBP-SD<11.06 mmHg); Q2(SBP-Mean<130 mmHg, SBP-SD≥11.06 mmHg); Q3(SBP-Mean≥130 mmHg, SBP-SD<11.06 mmHg); Q4(SBP-Mean≥130 mmHg, SBP-SD≥11.06 mmHg). In the same way, according to whether PP-Mean was higher or lower than 80 mmHg(average PP-Mean)and PP-SD was higher or lower than 6.48 mmHg(average PP-SD), the patients were divided into Q1-Q4 groups.Results:After adjusting age, sex, and diabetes duration, Cox regression analysis showed that SBP-Mean, SBP-SD, PP-Mean, and PP-SD were the risk factors of DN. After the stratification according to SBP-Mean and SBP-SD, the patients in Q4 group( HR=1.976, P<0.001)had the highest risk while those in Q1 group displayed the lowest risk for DN. Additionally, the patients in Q3 group( HR=1.614, P<0.001)imposed a higher risk than that in Q2 group( HR=1.408, P<0.001). By stratificating the patients based on PP-Mean and PP-SD, the patients in Q4 group revealed the highest risk of DN( HR=1.370, P<0.001)while those in Q1 group had the lowest risk. In addition, the patients in Q3 group( HR=1.266, P<0.001)had a higher risk of DN compared with those in Q2 group( HR=1.212, P<0.001). Conclusion:SBP and PP variabilities are the predictors of DN in patients with type 2 diabetes.
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Objective:To investigate the relationship between HbA 1C variability and diabetic retinopathy(DR) in patients with type 2 diabetes and to explore the influencing factors of HbA 1C variability. Methods:Type 2 diabetic patients who received dilated funduscopic examination annually, were stratified into two groups based on the presence or absence of DR, with a median follow-up period of 4 years(2-5 years). Intrapersonal means and SDs of all recorded HbA 1C measurements were calculated. A 1C-SD represented the measure of HbA 1C variability. In addition, medical history and clinical data of all subjects were collected and analyzed. Subjects were divided into four quartiles based on their A 1C-Mean and A 1C-SD data: Q1(A 1C-Mean<7%, A 1C-SD<0.76%); Q2(A 1C-Mean<7%, A 1C-SD≥0.76%); Q3(A 1C-Mean≥7%, A 1C-SD<0.76%); Q4(A 1C-Mean≥7%, A 1C-SD≥0.76%). Results:Multivariate linear regression showed that exercise, insulin( P<0.01), and smoking( P=0.004) are the influencing factors of HbA 1C variability. Adjusted for age, sex, and diabetes duration, Cox regression analysis revealed that HbA 1C variability was an independent risk factor for DR. Meanwhile, patients in Q4 group had the highest DR prevalence(HR=1.676, P<0.01) while Q1 group had the lowest. In addition, patients in Q2 group(HR=1.437, P=0.005) had a higher risk of DR than those in Q3 group(HR=1.361, P<0.01). Conclusions:HbA 1C variability is an independent predictor of DR in patients with type 2 diabetes. It may play a greater role in DR development than mean HbA 1C does when the mean value of HbA 1C variability index is above 0.75%.