Associations of Glycated Albumin and HbA1c with Chronic Kidney Disease in US Adults

Glycated Albumin and Adverse Clinical Outcomes in Patients With CKD: A Prospective Cohort Study

RATIONALE & OBJECTIVE

Hemoglobin A1c (HbA1c) is widely used to estimate glycemia, yet it is less reliable in patients with chronic kidney disease (CKD). There is growing interest in the complementary use of glycated albumin (GA) to improve glycemic monitoring and risk stratification. However, whether GA associates with clinical outcomes in a non-dialysis-dependent CKD population remains unknown.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

3,110 participants with CKD from the Chronic Renal Insufficiency Cohort study.

EXPOSURE

Baseline GA levels.

OUTCOME

Incident end-stage kidney disease (ESKD), cardiovascular disease (CVD) events, and all-cause mortality.

ANALYTICAL APPROACH

Cox proportional hazards regression.

RESULTS

Participant characteristics included mean age 59.0±10.8 SD years; 1,357 (43.6%) female; and 1,550 (49.8%) with diabetes. The median GA was 18.7% (IQR, 15.8%-23.3%). During an average 7.9-year follow-up, there were 980 ESKD events, 968 CVD events, and 1,084 deaths. Higher GA levels were associated with greater risks of all outcomes, regardless of diabetes status: hazard ratios for ESKD, CVD, and death among participants with the highest quartile compared with quartile 2 (reference) were 1.42 (95% CI, 1.19-1.69), 1.67 (95% CI, 1.39-2.01), and 1.63 (95% CI, 1.37-1.94), respectively. The associations with CVD and death appeared J-shaped, with increased risk also seen at the lowest GA levels. Among patients with coexisting CKD and diabetes, the associations of GA with outcomes remained significant even after adjusting for HbA1c. For each outcome, we observed a significant increase in the fraction of new prognostic information when both GA and HbA1c were added to models.

LIMITATIONS

Lack of longitudinal GA measurements; and HbA1c measurements were largely unavailable in participants without diabetes.

CONCLUSIONS

Among patients with CKD, GA levels were independently associated with risks of ESKD, CVD, and mortality, regardless of diabetes status. GA added prognostic value to HbA1c among patients with coexisting CKD and diabetes.

PLAIN-LANGUAGE SUMMARY

Hemoglobin A1c (HbA1c) is widely used to estimate glycemia, yet it is less reliable in patients with chronic kidney disease (CKD). There is growing interest in the complementary use of glycated albumin (GA) to improve glycemic monitoring and risk stratification. However, whether GA associates with clinical outcomes in a non-dialysis-dependent CKD population remains unknown. In this cohort study of 3,110 individuals with non-dialysis-dependent CKD, GA levels were independently associated with risks of end-stage kidney disease, cardiovascular disease (CVD), and mortality. The associations with CVD and mortality appeared to be J-shaped. Among patients with coexisting CKD and diabetes, GA added prognostic value to HbA1c. Thus, GA may be a valuable complementary test to HbA1c in patients with CKD.

Identification of risk factors and development of a predictive model for chronic kidney disease in patients with obesity: a four-year cohort study

OBJECTIVE

The sneaky onset and dismal prognosis of chronic kidney disease (CKD) make it an important public health issue. Obesity-related kidney illness has garnered more attention in recent times. Establishing and validating a risk prediction model for chronic renal illness in overweight or obese adults was the goal of this investigation.

METHODS

Data from the China Health and Retirement Longitudinal Study were used for analysis. The definition of CKD was reduced renal function (eGFR < 60 mL/min/1.73 m²), while overweight and obesity were characterized through a body mass index exceeding 24 kg/m². The dataset was divided into derivation and validation cohorts using a 7:3 ratio. With respect to the derivation cohort, we constructed a prediction model using LASSO analysis and multivariate logistic regression. The model's performance was evaluated using Hosmer-Lemeshow tests, calibration curves, decision curve analysis, and receiver operating characteristic (ROC) curves. The validation cohort's model was subjected to additional assessment.

RESULTS

The study was based on survey data from 2011 to 2015 and comprised 3246 individuals who were overweight or obese, with 2274 being part of the derivation cohort and 972 being part of the validation cohort. The research constructed a prediction model that included age, sex, fasting blood glucose, glycated hemoglobin, triglyceride, hypertension, and BMI. The validation cohort's area under the ROC curve was 0.812 (95% CI = 0.763, 0.859) while the derivation cohort's was 0.789 (95% CI = 0.754, 0.831). Hosmer-Lemeshow tests were utilized to evaluate the model's accuracy in the validation and derivation cohorts (P = 0.681 and 0.547, respectively). The calibration curve showed a high level of consistency between the actual observations and the projected outcomes. According to decision curve analysis, the model offered significant net advantages.

CONCLUSIONS

The forecasting model established in this research has predictive value for CKD in patients with overweight or obesity. These findings could help doctors conduct early detection and intervention in clinical practice and further improve patient prognosis.