Measuring of Alpha-1 Antitrypsin Concentration by Nephelometry or Turbidimetry

Most clinical laboratories quantify alpha-1 antitrypsin using either nephelometry or turbidimetry techniques because they are commercially available, amenable to automation, and precise. Both methods are based on light scatter. The foundation of both techniques is based on incubation of the specimen with anti-AAT polyclonal antibody solution, a polymer matrix between endogenous AAT and the reagent antibodies forms, leading to production of light-scattering large particles. Although these two terms are sometimes used synonymously, technically speaking they are not.Nephelometry measures the amount of turbidity or cloudiness of a solution by directly quantifying the intensity of the light scattered by insoluble particles in the sample. Therefore, this technique measures the light that passes through the sample, with the detector being placed at an angle from the sample. Turbidimetry is the process of measuring the loss of intensity of the light transmitted linearly through a sample caused by the scattering effect of insoluble particles. The decrease in light transmission is measured compared to a reference, and the absorbed light is quantified.Beyond specific technical differences between both techniques, there are two major differences between the two procedures that may influence the results. First, the concentration of the sample and the resulting intensity of scattered light relative to the intensity of the light source is one major factor. Second, the size of the scattering particles is also a key differentiating factor. This chapter describes the technical requirements, the different protocols, and the clinical applicability of these two techniques in the diagnosis of alpha-1 antitrypsin deficiency.

Metabolic phenotypes of obesity influence triglyceride and inflammation homoeostasis

BACKGROUND

We examined the degree of postprandial triglyceride (TG) response over the day, representing a highly dynamic state, with continuous metabolic adaptations, among normal-weight, overweight and obese patients, according to their metabolically healthy or abnormal status.

MATERIALS AND METHODS

A total of 1002 patients from the CORDIOPREV clinical trial (NCT00924937) were submitted to an oral fat load test meal with 0·7 g fat/kg body weight (12% saturated fatty acids (SFA), 10% polyunsaturated fatty acids (PUFA), 43% monounsaturated fatty acids (MUFA), 10% protein and 25% carbohydrates). Serial blood test analysing lipid fractions and inflammation markers (high-sensitivity C-reactive protein (hs-CRP)) were drawn at 0, 1, 2, 3 and 4 h during postprandial state. We explored the dynamic response according to six body size phenotypes: (i) normal weight, metabolically healthy; (ii) normal weight, metabolically abnormal; (iii) overweight, metabolically healthy; (iv) overweight, metabolically abnormal; (v) obese, metabolically healthy; and (vi) obese, metabolically abnormal.

RESULTS

Metabolically healthy patients displayed lower postprandial response of plasma TG and large triacylglycerol-rich lipoproteins (TRLs)-TG, compared with those metabolically abnormal, independently whether or not they were obese (P < 0·001 and P < 0·001, respectively). Moreover, the area under the curve (AUC) of TG and AUC of large TRLs-TG were greater in the group of metabolically abnormal compared with the group of metabolically healthy (P < 0·001 and P < 0·001, respectively). Interestingly, metabolically abnormal subjects displayed higher postprandial response of plasma hs-CRP than did the subgroup of normal, overweight and obese, metabolically healthy patients (P < 0·001).

CONCLUSIONS

Our findings showed that certain types of the metabolic phenotypes of obesity are more favourable modulating phenotypic flexibility after a dynamic fat load test, through TG metabolism and inflammation homoeostasis. To identify, these phenotypes may be the best strategy for personalized treatment of obesity.