TY - JOUR
T1 - Targeted Metabolomic Analysis of Serum Fatty Acids for the Prediction of Autoimmune Diseases
AU - Tsoukalas, Dimitris
AU - Fragoulakis, Vassileios
AU - Sarandi, Evangelia
AU - Docea, Anca Oana
AU - Papakonstaninou, Evangelos
AU - Tsilimidos, Gerasimos
AU - Anamaterou, Chrysanthi
AU - Fragkiadaki, Persefoni
AU - Aschner, Michael
AU - Tsatsakis, Aristidis
AU - Drakoulis, Nikolaos
AU - Calina, Daniela
N1 - Funding Information:
This work was encouraged and coordinated by the European Institute of Nutritional Medicine. The authors would like to thank all the administrative, the technical and medical staff of Toxplus, the Metabolomic Medicine clinic, and the Laboratory of Toxicology for their dedicated involvement in this study. MA was supported by National Institute of Health (NIH) R01 ES10563, R01 ES07331 and R01 ES020852.
Publisher Copyright:
© Copyright © 2019 Tsoukalas, Fragoulakis, Sarandi, Docea, Papakonstaninou, Tsilimidos, Anamaterou, Fragkiadaki, Aschner, Tsatsakis, Drakoulis and Calina.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Autoimmune diseases (ADs) are rapidly increasing worldwide and accumulating data support a key role of disrupted metabolism in ADs. This study aimed to identify an improved combination of Total Fatty Acids (TFAs) biomarkers as a predictive factor for the presence of autoimmune diseases. A retrospective nested case-control study was conducted in 403 individuals. In the case group, 240 patients diagnosed with rheumatoid arthritis, thyroid disease, multiple sclerosis, vitiligo, psoriasis, inflammatory bowel disease, and other AD were included and compared to 163 healthy individuals. Targeted metabolomic analysis of serum TFAs was performed using GC-MS, and 28 variables were used as input for the predictive models. The primary analysis identified 12 variables that were statistically significantly different between the two groups, and metabolite-metabolite correlation analysis revealed 653 significant correlation coefficients with 90% level of significance (p < 0.05). Three predictive models were developed, namely (a) a logistic regression based on Principal Component Analysis (PCA), (b) a straightforward logistic regression model and (c) an Artificial Neural Network (ANN) model. PCA and straightforward logistic regression analysis, indicated reasonably well adequacy (74.7 and 78.9%, respectively). For the ANN, a model using two hidden layers and 11 variables was developed, resulting in 76.2% total predictive accuracy. The models identified important biomarkers: lauric acid (C12:0), myristic acid (C14:0), stearic acid (C18:0), lignoceric acid (C24:0), palmitic acid (C16:0) and heptadecanoic acid (C17:0) among saturated fatty acids, Cis-10-pentadecanoic acid (C15:1), Cis-11-eicosenoic acid (C20:1n9), and erucic acid (C22:1n9) among monounsaturated fatty acids and the Gamma-linolenic acid (C18:3n6) polyunsaturated fatty acid. The metabolic pathways of the candidate biomarkers are discussed in relation to ADs. The findings indicate that the metabolic profile of serum TFAs is associated with the presence of ADs and can be an adjunct tool for the early diagnosis of ADs.
AB - Autoimmune diseases (ADs) are rapidly increasing worldwide and accumulating data support a key role of disrupted metabolism in ADs. This study aimed to identify an improved combination of Total Fatty Acids (TFAs) biomarkers as a predictive factor for the presence of autoimmune diseases. A retrospective nested case-control study was conducted in 403 individuals. In the case group, 240 patients diagnosed with rheumatoid arthritis, thyroid disease, multiple sclerosis, vitiligo, psoriasis, inflammatory bowel disease, and other AD were included and compared to 163 healthy individuals. Targeted metabolomic analysis of serum TFAs was performed using GC-MS, and 28 variables were used as input for the predictive models. The primary analysis identified 12 variables that were statistically significantly different between the two groups, and metabolite-metabolite correlation analysis revealed 653 significant correlation coefficients with 90% level of significance (p < 0.05). Three predictive models were developed, namely (a) a logistic regression based on Principal Component Analysis (PCA), (b) a straightforward logistic regression model and (c) an Artificial Neural Network (ANN) model. PCA and straightforward logistic regression analysis, indicated reasonably well adequacy (74.7 and 78.9%, respectively). For the ANN, a model using two hidden layers and 11 variables was developed, resulting in 76.2% total predictive accuracy. The models identified important biomarkers: lauric acid (C12:0), myristic acid (C14:0), stearic acid (C18:0), lignoceric acid (C24:0), palmitic acid (C16:0) and heptadecanoic acid (C17:0) among saturated fatty acids, Cis-10-pentadecanoic acid (C15:1), Cis-11-eicosenoic acid (C20:1n9), and erucic acid (C22:1n9) among monounsaturated fatty acids and the Gamma-linolenic acid (C18:3n6) polyunsaturated fatty acid. The metabolic pathways of the candidate biomarkers are discussed in relation to ADs. The findings indicate that the metabolic profile of serum TFAs is associated with the presence of ADs and can be an adjunct tool for the early diagnosis of ADs.
KW - autoimmune diseases
KW - biomarkers
KW - inflammation
KW - metabolomics
KW - total fatty acids
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U2 - 10.3389/fmolb.2019.00120
DO - 10.3389/fmolb.2019.00120
M3 - Article
AN - SCOPUS:85075366228
SN - 2296-889X
VL - 6
JO - Frontiers in Molecular Biosciences
JF - Frontiers in Molecular Biosciences
M1 - 120
ER -