Glycated albumin (GA) is highly specific but may not be sensitive enough to screen for diabetes in adults with obesity, according to researchers (J Appl Lab Med 2023; doi: 10.1093/jalm/jfad004).

Most American adults who develop diabetes are obese. Several studies have shown an inverse association between body mass index and GA, suggesting GA may have a role as a biomarker of hyperglycemia, or high blood sugar, as GA is a short-term measure of glycemic control. In 2017, the Food and Drug Administration approved GA for clinical use in managing diabetes. However, no guidelines provide advice on using GA to diagnose prediabetes and diabetes. And no related diagnostic cut points have been established.

To assess GA as a potential biomarker of glycemia, the researchers measured GA in adults who participated in the 1999–2004 National Health and Nutrition Examination Survey. In separate groups of adults with and without diabetes, the researchers assessed the association beyween GA and adiposity measures in sex-stratified multivariable regression models. The adiposity measures included body mass index (BMI), waist circumference, trunk fat, total body fat, and fat mass index. The researchers compared GA sensitivity and specificity to identify elevated hemoglobin AIC (HbA1C) by obesity status.

Of the 10,835 participants with samples, 1,085 self-reported a diabetes diagnosis, and 9,750 did not. In covariate-adjusted regression models, all adiposity measures were inversely associated with GA in adults with and without diabetes. Among GA in adults with diabetes, β equaled −1.73 to −0.92%-point GA per 1 standard deviation (SD). Among GA in adults without diabetes, β equaled −0.48 to −0.22%-point GA per 1 SD adiposity measure.

Among participants with diabetes, GA identified above-target glycemia (HbA1c ≥ 7%) with specificity of more than 80% overall. GA had sensitivity of 81% in adults with obesity, versus 93% in those without obesity. Comparing adults with versus without obesity, GA had lower sensitivity (43% vs 54%) to detect undiagnosed diabetes (HbA1c ≥ 6.5%).

The researchers called for future studies of how albumin turnover, circulation, and glycation kinetics are altered in the context of obesity to clarify the mechanism by which adiposity affects GA.


Bisecting N-acetylglucosamine in combination with tau is a valuable blood biomarker for predicting Alzheimer’s disease (AD), recent research concludes (Alzheimers Dement 2023; doi: 10.1002/alz.13024).

A previous study by the same researchers showed that levels of the bisecting N-acetylglucosamine glycan epitope was elevated in cerebrospinal fluid in AD. However, its diagnostic value in blood is unknown.

To determine its value as an AD blood biomarker, the researchers analyzed blood levels of bisecting N-acetylglucosamine and total tau in a retrospective cohort of 233 individuals. They compared progression to AD between the groups using Cox regression and determined the biomarker’s predictive power by logistic regression.

The researchers observed a more than twofold increased risk of developing AD in the group with intermediate tau/bisecting GlcNAc ratio, compared with the groups with high or low tau/bisecting GlcNAc ratio. Individuals in the high or low tau/bisecting GlcNAc ratio groups had either lower bisecting GlcNAc levels or lower t-tau levels than average. Because both bisecting GlcNAc levels and t-tau are elevated in AD patients’ cerebrospinal fluid, the researchers believe that lower bisecting GlcNAc or lower t-tau levels in blood have protective effects on AD development.

Although neither tau nor bisecting GlcNAc on its own predicted AD, the tau/bisecting GlcNAc ratio significantly predicted progress to AD in individuals with intermediate tau/GlcNAc ratio. Combining tau/bisecting GlcNAc ratio with APOE ε4 allele status further improved the prediction of AD. Combining tau/bisecting N-acetylglucosamine ratio, apolipoprotein E (APOE) ε4 status, and Mini-Mental State Examination score produced a strong predictive model, the researchers wrote (area under the curve=0.81, 95% CI: 0.68–0.93).

They suggested further research on the tau/bisecting GlcNAc with an eye toward validating it as a blood biomarker for early prediction of AD.


Researchers have developed a reliable prediction model for kidney function decline and estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and for early-to-moderate chronic kidney disease (CKD) (JAMA Network Open 2023; doi:10.1001/jamanetworkopen. 2023.1870).

Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools for clinical practice are lacking. In response, researchers aimed to externally validate a model to predict future trajectories in eGFR among adults with type 2 diabetes and CKD using data from three multinational, European cohorts.

The current study included a total of 4,637 adult participants age 18–75 years with type 2 diabetes and mildly to moderately impaired kidney function, defined as a baseline eGFR of at least 30 mL/min/1.73 m2. Of these subjects, 3,323 from the German Chronic Kidney Disease (GCKD) and Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers (PROVALID) were in the model development cohort. The current study’s validation cohort included 1,314 subjects from the Diabetes Cohort (DIACORE).

The researchers examined variables from routine clinical care visits. They included age; taking blood pressure, glucose-lowering, or lipid-lowering medications; body mass index; hemoglobin; HbA1C; mean arterial pressure; serum cholesterol levels; sex; smoking status; and urinary albumin-creatinine ratio. Investigators used a linear mixed-effect model for eGFR measurements at baseline and multiple visits up to 5 years after the baseline.

Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance. Improved predictive performance was particularly evident in the visual inspection

of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04–1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77–0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63–0.76) at year 1 to 0.58 (95% CI, 0.53–0.63) at year 5.

These findings reveal the potential of a publicly available online tool that can be used by patients, caregivers, and primary health care professionals to predict individual eGFR trajectories and disease progression up to 5 years after baseline, the researchers wrote.

An accompanying commentary noted that some of the required 13 variables may not be routinely measured, such as total cholesterol in some patients on statins. The editorial called for validation in non-white populations.