Development and Validation of an Enzymatic Method for Total Cholesterol Analysis Using Whole Blood Spot

Corso, Gaetano; Papagni, F; Gelzo, Monica; Gallo, Monica; Barone, Rosalba; Graf, Maria; N, Scarpato; Russo, Antonio Dello

doi:10.1002/jcla.21890

Cited by 16 publications

(10 citation statements)

References 31 publications

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“…For example Lakshmy et al was able to achieve a correlation of r = 0.797 between concentrations of total cholesterol measured in DBS and serum samples (Lakshmy et al 2012 ). This is broadly in keeping with the other results reported in the literature with Samuelsson et al reporting r = 0.480 (Samuelsson et al 2015 ), Corso et al achieving r = 0.611 (Corso et al 2016 ) and Lacher et al an R 2 of 0.340 (Lacher et al 2013 ) between concentrations of total cholesterol measured in DBS and serum samples. This concordance between studies is not just seen for total cholesterol with Quraishi et al obtaining a correlation of r = 0.970 between triglyceride concentration measured in DBS and plasma samples (Quraishi et al 2006 ) with subsequent studies achieving correlations of r = 0.838 and r = 944 (Lakshmy et al 2012 , 2010 ).…”

Section: Introductionsupporting

confidence: 91%

“…There is a significant body of literature looking at how to analyse a wide range of drug analytes in these samples (Wilhelm et al 2014 ; Elst et al 2013 ; Manicke et al 2011 ), with DBS samples being standardly used pharmokinetic studies in neonates and young children (Abu-Rabie and Spooner 2009 ). An array of endogenous compounds have also been measured from dried blood spots including HbA1c (Crimmins et al 2014 ; Lacher et al 2013 ), total cholesterol (Abhijit et al 2016 ; Miller et al 2015 ; Corso et al 2016 ), HDL (Lacher et al 2013 ; Miller et al 2015 ; Samuelsson et al 2015 ), triglyceride (Lakshmy et al 2012 , 2010 ; Quraishi et al 2006 ) and CRP (Elst et al 2013 ) among others. A detailed discussion of this literature is beyond the scope of this manuscript and there are several excellent reviews on this subject (Spooner et al 2019 ; Zakaria et al 2016 ; Chance et al 2014 ).…”

Section: Introductionmentioning

confidence: 99%

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Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

et al. 2020

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Introduction Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profile data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identified in plasma. Objectives To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict 'clinical lipid' concentration from lipid profile data. Methods Lipid profiles were generated from plasma (n = 777) and DBS (n = 835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four 'clinical lipids'. Results In plasma samples panels of lipid markers were identified that could predict the concentration of the 'clinical lipids' with correlations between estimated and measured triglyceride, HDL, LDL and total cholesterol of 0.920, 0.743, 0.580 and 0.424 respectively. When translated into DBS samples, correlations of 0.836, 0.591, 0.561 and 0.569 were achieved for triglyceride, HDL, LDL and total cholesterol. Conclusion DBSs represent an alternative to venous blood, however further work is required to improve the combined lipidomics and machine learning approach to develop it for use in health monitoring.

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Section: Introductionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

et al. 2020

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“…The total serum protein and serum globulin were obtained, following the procedure detailed in Rezende et al [36]. The serum cholesterol was determined by enzymatic colorimetric method [37].…”

Section: Hematological and Serum Biochemical Analysismentioning

confidence: 99%

Saccharomyces cerevisiae, bentonite, and kaolin as adsorbents for reducing the adverse impacts of mycotoxin contaminated feed on broiler histopathology and hemato-biochemical changes

et al. 2021

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Background and Aim: Saccharomyces cerevisiae, bentonite and kaolin were used to reduce the adverse effects of mold-contaminated diet on broilers. The aim of the study was to evaluate the impact of S. cerevisiae, bentonite, and kaolin in reducing the adverse effects of mold (fungal) contaminated diet on broilers. Specifically, we investigated the histopathological, hematological, and serum biochemical changes associated with broilers fed mold-contaminated diets supplemented with these three adsorbents. We also isolated and identified the common fungal contaminants in the poultry feeds as well as the mycotoxins they produced. Materials and Methods: Hundred broilers (3-weeks-old) were randomly grouped into five dietary treatments, basal feed (negative control), feed contaminated with mold, mold-contaminated feed+S. cerevisiae, mold-contaminated feed+bentonite, and mold-contaminated feed+kaolin. The fungal contaminants in the feeds were isolated and molecularly identified while the mycotoxins in the feed where analyzed using high-performance liquid chromatography. Blood samples of birds from each group were analyzed for hematology and serum biochemistry. The liver, spleen, kidney, and bursa of Fabricius of the birds were excised and analyzed for histopathological changes. Results: The most common fungal contaminants in the feeds were Penicillium (33.3%) species, followed by Aspergillus species (22.2%). The mold-contaminated feed had the highest number of fungal contaminants, 55.6%, while the negative control (basal feed group) had none. Total aflatoxin and deoxynivalenol were high in the mold-contaminated feed (53.272 μg/kg and 634.5 μg kg, respectively), but these were reduced by the addition of adsorbents to the feed. The birds fed mold-contaminated feed had significantly (p<0.05) reduced red blood cell count counts, packed cell volume, and hemoglobin but increased white blood cell count compared to the negative control. Liver enzyme activity (alanine transaminase, aspartate aminotransferase, and alkaline phosphatase) and cholesterol concentration increased significantly (p<0.05) in the group fed mold-contaminated feed while the serum albumin and total protein decreased significantly (p<0.05) in comparison with the negative control. Adverse histopathological changes were observed in the liver, kidney, spleen, and bursa of Fabricius in the group fed mold-contaminated feed. Addition of S. cerevisiae, bentonite or kaolin in the mold-contaminated feed ameliorated these toxic effects. Conclusion: The observed histopathological lesions were consistent with mycotoxicosis in birds and were mild in the adsorbent treated groups. Kaolin had a higher protective effect against mycotoxicosis than the two other adsorbents.

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“…Glucose, lipid profile and creatinine estimations were carried out in Cobas 6000 auto analyzer using the kits supplied by Roche Diagnostics (Mannheim, Germany) as per the manufacturer's instructions. (11)(12)(13)(14)(15)(16) ApoB (apoB1) was measured by nephelometric method (17) using kits from Agappe Diagnostics. While, microalbumin was determined by the immune-turbidimetric method.…”

Section: Measurement Of Biochemical Parametersmentioning

confidence: 99%

Assessment of sdLDL-C by Three Different Formula and Its Correlation with Clinical Variables Among Diabetes Individuals with and without Nephropathy

et al. 2021

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BACKGROUND: Diabetes mellitus is a common disease worldwide which affects renal function. Cardiovascular morbidity and mortality in diabetes patients can be accelerated by dyslipidemia. Small dense lowdensity lipoprotein-cholesterol (sdLDL-C) is atherogenic and its predominance has been known as a cardiovascular risk factor. The study aimed to assess the validity of calculated sdLDL-C using three different formulae and its association with other clinical variables in diabetic patients with and without nephropathy, and also to determine the best suited formula to measure sdLDL-C.METHODS: The study subjects were divided into two groups based on the amount of albumin excreted in the urine. Group I or the control group consisted of diabetic subjects without microalbuminuria, while group II consisted of diabetic subjects with microalbuminuria. Blood glucose, glycated haemoglobin (HbA1c), creatinine, total cholesterol (TC), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), LDL-C and apoB were estimated. Three formulae used for the validation of calculated sdLDL-C were TG/HDL, sdLDL (mg/dL) = 0.580 (nonHDL) + 0.407 (direct-LDL-C) – 0.719 (calculated-LDL-C) – 12.05, and LDL-C/LDL apoB.RESULTS: There was no significant difference in sdLDL-C levels of diabetic subjects with and without nephropathy. The sdLDL-C had strong correlation with TC, TG, LDL-C, very-low-density lipoprotein (VLDL), non-HDL and apoB in both study groups. ROC curve showed that LDL-C/LDL apoB derived sdLDL-C had better sensitivity (85%) and specificity (69%) compared to other two measures.CONCLUSION: Though the calculated sdLDL-C do not predict the occurrence of nephropathy in diabetes subjects, it may still be used in conjunction with the traditional markers since it is cost effective. The LDL-C/LDL apoB formula is the best predictor of sdLDL-C among the three equations.KEYWORDS: HDL-C, LDL-C, Microalbuminuria, sdLDL-C, T2DM

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Development and Validation of an Enzymatic Method for Total Cholesterol Analysis Using Whole Blood Spot

Abstract: The results show that this enzymatic method is suitable to analyze cholesterol in DBS and it could be automated and used for population screening of total blood cholesterol.

Cited by 16 publications

References 31 publications

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots

Saccharomyces cerevisiae, bentonite, and kaolin as adsorbents for reducing the adverse impacts of mycotoxin contaminated feed on broiler histopathology and hemato-biochemical changes

Assessment of sdLDL-C by Three Different Formula and Its Correlation with Clinical Variables Among Diabetes Individuals with and without Nephropathy

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