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Diagnostic markers based on a computational model of lipoprotein metabolism.

ABSTRACT: BACKGROUND: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. RESULTS: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. CONCLUSIONS: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible.

SUBMITTER: van Schalkwijk DB

PROVIDER: S-EPMC3305892 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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Diagnostic markers based on a computational model of lipoprotein metabolism.

van Schalkwijk Daniël B DB van Ommen Ben B Freidig Andreas P AP van der Greef Jan J de Graaf Albert A AA

Journal of clinical bioinformatics 20111026 1

<h4>Background</h4>Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we f ...[more]

PMID: 22029862

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Project description:A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10-5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10-5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66-0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64-0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75-0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

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Project description:ObjectiveThe aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning.MethodThe pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets.ResultsNine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result.ConclusionsThe XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

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Diagnostic markers based on a computational model of lipoprotein metabolism.

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Diagnostic markers based on a computational model of lipoprotein metabolism.

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