Increased body fat mass reduces the association between fructosamine and glycated hemoglobin in obese type 2 diabetes patients

Inverse Associations between Measures of Adiposity and Glycated Albumin in US Adults, NHANES 1999-2004

BACKGROUND

Glycated albumin (GA) is a short-term measure of glycemic control. Several studies have demonstrated an inverse association between body mass index (BMI) and GA, which may affect its performance as a biomarker of hyperglycemia. We investigated cross-sectional associations between GA and multiple measures of adiposity, and compared its performance as a glycemic biomarker by obesity status, in a nationally representative sample of US adults.

METHODS

We measured GA in adults from the 1999-2004 National Health and Nutrition Examination Survey. Separately in adults with and without diabetes, we assessed associations of GA with adiposity measures (BMI, waist circumference, trunk fat, total body fat, and fat mass index) in sex-stratified multivariable regression models. We compared sensitivity and specificity of GA to identify elevated hemoglobin A1c (HbA1c), by obesity status.

RESULTS

In covariate-adjusted regression models, all adiposity measures were inversely associated with GA in adults without diabetes (β=-0.48 to -0.22%-point GA per 1 SD adiposity measure; n = 9750) and with diabetes (β=-1.73 to -0.92%-point GA per SD). Comparing adults with vs without obesity, GA exhibited lower sensitivity (43% vs 54%) with equivalent specificity (99%) to detect undiagnosed diabetes (HbA1c ≥ 6.5%). Among adults with diagnosed diabetes (n = 1085), GA performed well to identify above-target glycemia (HbA1c ≥ 7.0%), with high specificity (>80%) overall but lower sensitivity in those with vs without obesity (81% vs 93%).

CONCLUSIONS

Inverse associations between GA and adiposity were present in people with and without diabetes. GA is highly specific but may not be sufficiently sensitive for diabetes screening in adults with obesity.

Trimester-Specific Serum Fructosamine in Association with Abdominal Adiposity, Insulin Resistance, and Inflammation in Healthy Pregnant Individuals

This study aimed to (1) characterize the variations in serum fructosamine across trimesters and according to pre-pregnancy BMI (ppBMI), and (2) examine associations between fructosamine and adiposity/metabolic markers (ppBMI, first-trimester adiposity, leptin, glucose homeostasis, and inflammation measurements) during pregnancy. Serum fructosamine, albumin, fasting glucose and insulin, leptin, adiponectin, interleukin-6 (IL-6), and C-reactive protein (CRP) concentrations were measured at each trimester. In the first trimester, subcutaneous (SAT) and visceral (VAT) adipose tissue thicknesses were estimated by ultrasound. In the 101 healthy pregnant individuals included (age: 32.2 ± 3.5 y.o.; ppBMI: 25.5 ± 5.5 kg/m2), fructosamine concentrations decreased during pregnancy whereas albumin-corrected fructosamine concentrations increased (p < 0.0001 for both). Notably, fructosamine concentrations were inversely associated with ppBMI, first-trimester SAT, VAT, and leptin (r = −0.55, r = −0.61, r = −0.48, r = −0.47, respectively; p < 0.0001 for all), first-trimester fasting insulin and HOMA-IR (r = −0.46, r = −0.46; p < 0.0001 for both), and first-trimester IL-6 (r = −0.38, p < 0.01). However, once corrected for albumin, most of the correlations lost strength. Once adjusted for ppBMI, fructosamine concentrations were positively associated with third-trimester fasting glucose and CRP (r = 0.24, r = 0.27; p < 0.05 for both). In conclusion, serum fructosamine is inversely associated with adiposity before and during pregnancy, with markers of glucose homeostasis and inflammation, but the latter associations are partially influenced by albumin concentrations and ppBMI.

Diabetes management in spinal surgery

Diabetes mellitus can lead to long-standing complications in multiple arenas. An area that is often overlooked is implications for major surgery. Spinal decompression and fusions have unique challenges in the diabetic patient. In this review, we briefly highlight the pathophysiology of diabetes mellitus prior to examining implications for spinal surgery. We focus on the wound healing process, surgical infection risk, and delayed fusion. The paper then transitions to a focus on early diagnostics as well as pre-operative glucose control. Finally, we highlight important management strategies post operatively, continued necessity of monitoring, and emerging treatment and diagnostic approaches. This paper will serve as a key clinical guide that clinicians can utilize for diagnostic, management, and follow-up planning.

Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes

Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.