Study of protein haptenation by amoxicillin through the use of a biotinylated antibiotic.

Allergic reactions towards β-lactam antibiotics pose an important clinical problem. The ability of small molecules, such as a β-lactams, to bind covalently to proteins, in a process known as haptenation, is considered necessary for induction of a specific immunological response. Identification of the proteins modified by β-lactams and elucidation of the relevance of this process in allergic reactions requires sensitive tools. Here we describe the preparation and characterization of a biotinylated amoxicillin analog (AX-B) as a tool for the study of protein haptenation by amoxicillin (AX). AX-B, obtained by the inclusion of a biotin moiety at the lateral chain of AX, showed a chemical reactivity identical to AX. Covalent modification of proteins by AX-B was reduced by excess AX and vice versa, suggesting competition for binding to the same targets. From an immunological point of view, AX and AX-B behaved similarly in RAST inhibition studies with sera of patients with non-selective allergy towards β-lactams, whereas, as expected, competition by AX-B was poorer with sera of AX-selective patients, which recognize AX lateral chain. Use of AX-B followed by biotin detection allowed the observation of human serum albumin (HSA) modification by concentrations 100-fold lower that when using AX followed by immunological detection. Incubation of human serum with AX-B led to the haptenation of all of the previously identified major AX targets. In addition, some new targets could be detected. Interestingly, AX-B allowed the detection of intracellular protein adducts, which showed a cell type-specific pattern. This opens the possibility of following the formation and fate of AX-B adducts in cells. Thus, AX-B may constitute a valuable tool for the identification of AX targets with high sensitivity as well as for the elucidation of the mechanisms involved in allergy towards β-lactams.

Evaluation of two HbA1c point-of-care analyzers.

BACKGROUND Measurement of HbA1c is the most important parameter to assess glycemic control in diabetic patients. Different point-of-care devices for HbA1c are available. The aim of this study was to evaluate two point-of-care testing (POCT) analyzers (DCA Vantage from Siemens and Afinion from Axis-Shield). We studied the bias and precision as well as interference from carbamylated hemoglobin. METHODS Bias of the POCT analyzers was obtained by measuring 53 blood samples from diabetic patients with a wide range of HbA1c, 4%-14% (20-130 mmol/mol), and comparing the results with those obtained by the laboratory method: HPLC HA 8160 Menarini. Precision was performed by 20 successive determinations of two samples with low 4.2% (22 mmol/mol) and high 9.5% (80 mmol/mol) HbA1c values. The possible interference from carbamylated hemoglobin was studied using 25 samples from patients with chronic renal failure. RESULTS The means of the differences between measurements performed by each POCT analyzer and the laboratory method (95% confidence interval) were: 0.28% (p<0.005) (0.10-0.44) for DCA and 0.27% (p<0.001) (0.19-0.35) for Afinion. Correlation coefficients were: r=0.973 for DCA, and r=0.991 for Afinion. The mean bias observed by using samples from chronic renal failure patients were 0.2 (range -0.4, 0.4) for DCA and 0.2 (-0.2, 0.5) for Afinion. Imprecision results were: CV=3.1% (high HbA1c) and 2.97% (low HbA1c) for DCA, CV=1.95% (high HbA1c) and 2.66% (low HbA1c) for Afinion. CONCLUSIONS Both POCT analyzers for HbA1c show good correlation with the laboratory method and acceptable precision.

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.