Diagnostic Utility of Serum Glycated Albumin for Diabetes Mellitus and Its Correlation With Hyperlipidemia

Baseline glycated albumin level and risk of type 2 diabetes mellitus in Healthy individuals: a retrospective longitudinal observation in Korea

Glycated albumin (GA) reflects glycemic status for the past three weeks. GA level demonstrates a strong correlation with HbA1c level and is used as an adjunctive biomarker for diagnosis and monitoring of type 2 diabetes mellitus (T2DM). In this study, we validated the predictive performance of baseline GA for development of T2DM in healthy individuals in Korea. From August 2013 to September 2014, the medical records of 3,771 healthy Koreans were retrospectively reviewed. Each participant was categorized into tertiles based on initial GA level. During the follow-up period through May 2020, study participants were evaluated for T2DM using HbA1c, fasting glucose level, and a self-reported diagnosis history. Baseline GA level by tertile (T1 to T3) was 10.4 ± 0.8% (mean ± SD), 12.1 ± 0.3%, and 13.7 ± 0.9%, respectively. The median follow-up was 5.97 years, during which 4.9% (186 of 3,771) of the participants developed T2DM. After adjusting for confounding factors, the hazard ratio for the development of T2DM in the highest GA level group (T3) compared to the reference group (T1) was 2.46 (95% CI, 1.7 to 3.58, p < 0.001 for trend) with a Harrell's C index of 0.80 (95% CI, 0.76 to 0.83). Also, within highest group of baseline HbA1c and FG levels, higher GA levels were associated with an increased HRs for T2DM. In conclusion, Our study confirms that the risk of T2DM increases with baseline GA level. Additional follow-up of the cohort is warranted to investigate the correlations between GA and other clinical indicators including diabetic complications.

Impaired lipophagy induced-microglial lipid droplets accumulation contributes to the buildup of TREM1 in diabetes-associated cognitive impairment

Neuroinflammation caused by microglial activation and consequent neurological impairment are prominent features of diabetes-associated cognitive impairment (DACI). Microglial lipophagy, a significant fraction of autophagy contributing to lipid homeostasis and inflammation, had mostly been ignored in DACI. Microglial lipid droplets (LDs) accumulation is a characteristic of aging, however, little is known about the pathological role of microglial lipophagy and LDs in DACI. Therefore, we hypothesized that microglial lipophagy could be an Achilles's heel exploitable to develop effective strategies for DACI therapy. Here, starting with characterization of microglial accumulation of LDs in leptin receptor-deficient (db/db) mice and in high-fat diet and STZ (HFD/STZ) induced T2DM mice, as well as in high-glucose (HG)-treated mice BV2, human HMC3 and primary mice microglia, we revealed that HG-dampened lipophagy was responsible for LDs accumulation in microglia. Mechanistically, accumulated LDs colocalized with the microglial specific inflammatory amplifier TREM1 (triggering receptor expressed on myeloid cells 1), resulting in the buildup of microglial TREM1, which in turn aggravates HG-induced lipophagy damage and subsequently promoted HG-induced neuroinflammatory cascades via NLRP3 (NLR family pyrin domain containing 3) inflammasome. Moreover, pharmacological blockade of TREM1 with LP17 in db/db mice and HFD/STZ mice inhibited accumulation of LDs and TREM1, reduced hippocampal neuronal inflammatory damage, and consequently improved cognitive functions. Taken together, these findings uncover a previously unappreciated mechanism of impaired lipophagy-induced TREM1 accumulation in microglia and neuroinflammation in DACI, suggesting its translational potential as an attractive therapeutic target for delaying diabetes-associated cognitive decline.Abbreviations: ACTB: beta actin; AIF1/IBA1: allograft inflammatory factor 1; ALB: albumin; ARG1: arginase 1; ATG3: autophagy related 3; Baf: bafilomycin A1; BECN1: beclin 1, autophagy related; BW: body weight; CNS: central nervous system; Co-IP: co-immunoprecipitation; DACI: diabetes-associated cognitive impairment; DAPI: 4',6-diamidino-2-phenylindole; DGs: dentate gyrus; DLG4/PSD95: discs large MAGUK scaffold protein 4; DMEM: Dulbecco's modified Eagle's medium; DSST: digit symbol substitution test; EDTA: ethylenedinitrilotetraacetic acid; ELISA: enzyme linked immunosorbent assay; GFAP: glial fibrillary acidic protein; HFD: high-fat diet; HG: high glucose; IFNG/IFN-γ: interferon gamma; IL1B/IL-1β: interleukin 1 beta; IL4: interleukin 4; IL6: interleukin 6; IL10: interleukin 10; LDs: lipid droplets; LPS: lipopolysaccharide; MAP2: microtubule associated protein 2; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MWM: morris water maze; NFKB/NF-κB: nuclear factor of kappa light polypeptide gene enhancer in B cells; NLRP3: NLR family pyrin domain containing 3; NOS2/iNOS: nitric oxide synthase 2, inducible; NOR: novel object recognition; OA: oleic acid; PA: palmitic acid; PBS: phosphate-buffered saline; PFA: paraformaldehyde; PLIN2: perilipin 2; PLIN3: perilipin 3; PS: penicillin-streptomycin solution; RAPA: rapamycin; RBFOX3/NeuN: RNA binding protein, fox-1 homolog (C. elegans) 3; RELA/p65: RELA proto-oncogene, NF-kB subunit; ROS: reactive oxygen species; RT: room temperature; RT-qPCR: Reverse transcription quantitative real-time polymerase chain reaction; STZ: streptozotocin; SQSTM1/p62: sequestosome 1; SYK: spleen asociated tyrosine kinase; SYP: synaptophysin; T2DM: type 2 diabetes mellitus; TNF/TNF-α: tumor necrosis factor; TREM1: triggering receptor expressed on myeloid cells 1; TUNEL: terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling.

Glycated serum proteins and albumin but not glycated albumin show negative correlation with BMI in an overweight/obese, diabetic population from the United States

BACKGROUND AND AIMS

Multiple previously published studies have shown a weak to medium, negative correlation between BMI and glycated albumin (GA). However, many of these studies were in populations with a narrow range of BMI. It is unknown whether this trend exists if a wider BMI range is used. This is an important question for proper interpretation of GA levels in obese populations.

MATERIALS AND METHODS

A retrospective analysis of clinical trial data (NCT02519309) was performed. After appropriate exclusions, 334 subjects remained. These included 73.7% with type 2 diabetes (T2D) diagnosis and 26.3% with prediabetes. BMI ranged from 24.8-86.9 kg/m2. Laboratory data were measured in a CLIA-certified laboratory using commercially available, automated methods.

RESULTS

No significant, negative correlation was seen between GA and BMI. However, individual components (glycated serum proteins and albumin) as well as the GA/HbA1c ratio show a weak, negative correlation with BMI for all subjects and those with T2D. The strongest negative correlation was with albumin. Examination by traditional BMI subgroups also showed statistically significant differences for those with T2D, but not for the prediabetic cohort. Correlations between BMI and C-reactive protein were similar in those with diabetes and prediabetes; however, correlation between BMI and insulin was stronger in those with diabetes.

CONCLUSION

Negative correlations between BMI and albumin or BMI and glycated serum proteins persist in diabetic populations that are obese and overweight, even when a statistically significant negative correlation is not observed between BMI and GA. Inflammation or insulin-mediated changes in protein synthesis could be contributors to these negative correlations, but BMI-related changes to the glomerulus could also affect clearance of albumin or glycated proteins and should be examined.

Factors associated with glycated albumin in adults without diabetes

Background and aims

Glycated albumin is a glycemic marker useful in short-term monitoring and in situations when a glycated hemoglobin test is not reliable. This study aims to evaluate glycated albumin levels and its associated factors in normoglycemic adults from Southern Brazil.

Method

136 individuals, without diabetes or pre-diabetes, were included in this cross-sectional study. Levels of glycated albumin, glycated hemoglobin, and other biochemical markers were measured.

Results

Glycated albumin levels ranged from 11.1% to 17.5% (2.5^th and 97.5^th percentiles). Glycated albumin/glycated hemoglobin ratio was 2.8±0.2. Glycated albumin did not differ according to gender and age groups. However, in overweight individuals, levels of glycated albumin and glycated albumin/glycated hemoglobin ratio were lower and weakly and negatively correlated with body mass index.

Conclusions

Glycated albumin levels in Brazilians were similar to those previously described in other populations. Glycated albumin seems to be irrespective of gender or age, but weakly correlated with weight. These aspects should be taken into account in the interpretation of glycated albumin results.

Recent Updates and Advances in the Use of Glycated Albumin for the Diagnosis and Monitoring of Diabetes and Renal, Cerebro- and Cardio-Metabolic Diseases

Diabetes mellitus is a heterogeneous and dysmetabolic chronic disease in which the laboratory plays a fundamental role, from diagnosis to monitoring therapy and studying complications. Early diagnosis and good glycemic control should start as early as possible to delay and prevent metabolic and cardio-vascular complications secondary to this disease. Glycated hemoglobin is currently used as the reference parameter. The accuracy of the glycated hemoglobin dosage may be compromised in subjects suffering from chronic renal failure and terminal nephropathy, affected by the reduction in the survival of erythrocytes, with consequent decrease in the time available for glucose to attach to the hemoglobin. In the presence of these renal comorbidities as well as hemoglobinopathies and pregnancy, glycated hemoglobin is not reliable. In such conditions, dosage of glycated albumin can help. Glycated albumin is not only useful for short-term diagnosis and monitoring but predicts the risk of diabetes, even in the presence of euglycemia. This protein is modified in subjects who do not yet have a glycemic alteration but, as a predictive factor, heralds the risk of diabetic disease. This review summarizes the importance of glycated albumin as a biomarker for predicting and stratifying the cardiovascular risk linked to multiorgan metabolic alterations.

Glycated Serum Protein Genetics and Pleiotropy with Cardiometabolic Risk Factors

Measurements of fasting glucose (FG) or glycated hemoglobin A1c (HbA1c) are two clinically approved approaches commonly used to determine glycemia, both of which are influenced by genetic factors. Obtaining accurate measurements of FG or HbA1c is not without its challenges, though. Measuring glycated serum protein (GSP) offers an alternative approach for assessing glycemia. The aim of this study was to estimate the heritability of GSP and GSP expressed as a percentage of total serum albumin (%GA) using a variance component approach and localize genomic regions (QTLs) that harbor genes likely to influence GSP and %GA trait variation in a large extended multigenerational pedigree from Jiri, Nepal (n = 1,800). We also performed quantitative bivariate analyses to assess the relationship between GSP or %GA and several cardiometabolic traits. Additive genetic effects significantly influence variation in GSP and %GA levels (p values: 1.15 × 10^-5 and 3.39 × 10^-5, respectively). We localized a significant (LOD score = 3.18) and novel GSP QTL on chromosome 11q, which has been previously linked to type 2 diabetes. Two common (MAF > 0.4) SNPs within the chromosome 11 QTL were associated with GSP (adjusted pvalue < 5.87 × 10^-5): an intronic variant (rs10790184) in the DSCAML1 gene and a 3'UTR variant (rs8258) in the CEP164 gene. Significant positive correlations were observed between GSP or %GA and blood pressure, and lipid traits (p values: 0.0062 to 1.78 × 10^-9). A significant negative correlation was observed between %GA and HDL cholesterol (p = 1.12 × 10^-5). GSP is influenced by genetic factors and can be used to assess glycemia and diabetes risk. Thus, GSP measurements can facilitate glycemic studies when accurate FG and/or HbA1c measurements are difficult to obtain. GSP can also be measured from frozen blood (serum) samples, which allows the prospect of retrospective glycemic studies using archived samples.