Associations between sheep meat intake frequency and blood plasma levels of metabolites and lipoproteins in healthy Uzbek adults

Abstract

Introduction: Uzbekistan is one of the countries with the highest number of diet-related chronic diseases, which is believed to be associated with high animal fat intake. Sheep meat is high in fats (~ 5% in muscle), including saturated and monounsaturated fatty acids, and it contains nearly twice the higher amounts of n-3 polyunsaturated fatty acids and conjugated linoleic acids compared to beef. Nevertheless, sheep meat is considered health promoting by the locals in Uzbekistan and it accounts for around 1/3 of red meat intake in the country.

Objectives: The aim of this study was to apply a metabolomics approach to investigate if sheep meat intake frequency (SMIF) is associated with alterations in fasting blood plasma metabolites and lipoproteins in healthy Uzbek adults.

Methods: The study included 263 subjects, 149 females and 114 males. For each subject a food intake questionnaire, including SMIF, was recorded and fasting blood plasma samples were collected for metabolomics. Blood plasma metabolites and lipoprotein concentrations were determined using ¹H NMR spectroscopy.

Results and conclusion: The results showed that SMIF was confounded by nationality, sex, body mass index (BMI), age, intake frequency of total meat and fish in ascending order (p < 0.01). Multivariate and univariate data analyses showed differences in the levels of plasma metabolites and lipoproteins with respect to SMIF. The effect of SMIF after statistical adjustment by nationality, sex, BMI, age, intake frequency of total meat and fish decreased but remained significant. Pyruvic acid, phenylalanine, ornithine, and acetic acid remained significantly lower in the high SMIF group, whereas choline, asparagine, and dimethylglycine showed an increasing trend. Levels of cholesterol, apolipoprotein A1, as well as low- and high-density lipoprotein subfractions all displayed a decreasing trend with increased SMIF although the difference were not significant after FDR correction.

Keywords: Choline; Lipoproteins; Meat intake; Metabolomics; NMR; Sheep meat.

Fig. 1

Study Design and workflow. A total of 263 healthy Uzbek adults were recruited, which were divided into three groups based on their sheep meat intake frequency (SMIF): high SMIF (> 10 times per month), moderate (1–10 times per month) and zero (reporting less than 1 time per month) SMIF. Fasting plasma of all individuals were collected and subjected to proton nuclear magnetic resonance (¹H NMR)-based metabolomics. Blood plasma metabolome profile (60 metabolites) and 65 lipoproteins were quantified from ¹H NMR spectra by SigMa software. To reveal the possible effects of SMIF, statistical analyses methods including principal component analysis (PCA), ANOVA-simultaneous component analysis analysis (ASCA), one-way analysis of variance (ANOVA), linear model and partial least squares discriminant analysis (PLS-DA) were applied on the metabolome and lipoprotein datasets separately

Fig. 2

Blood plasma metabolites found to be associated to the sheep meat intake frequency (SMIF) in healthy Uzbek adults. A Scores and B loadings plots of the principal component analysis (PCA) model developed on blood plasma metabolomics data. C Box plots of the discriminant metabolites pyruvic acid, phenylalanine, ornithine, acetic acid, dimethylglycine, asparagine, and choline. The horizontal lines and dots inside the boxes represent median and mean values, respectively. p-values and effect sizes (%) are calculated using ANOVA. Multiple linear regression model (MLR) p-values represents false discovery rate (FDR) corrected p-values from multiple linear regression analysis adjusted for sex, age and BMI

Fig. 3

Classification performances of partial least squares-discriminant analysis (PLS-DA) models developed to classify high and zero sheep meat intake frequency (SMIF, more than 10 times/month, 1–10 times/month, less than 1 time/month) subjects using the NMR based blood plasma metabolomics data (top panel) and lipoprotein data (bottom panel). Left graphs show the area under the curve (AUC) of receiver operating characteristic (ROC) curve of the PLS-DA models. AUC and ERROR (misclassification rate) are shown both for the test set prediction, using independent subjects, and training models. The graphs to the right show selectivity ratios of metabolites and lipoproteins identified to be strong discriminants for classifying the high SMIF and zero SMIF subjects using a PLS-DA variable selection approach. The metabolite and lipoprotein variables showed in the plots were listed in Table S1 and S2, respectively. Both PLS-DA models show a moderate classification power with misclassification rates of 17 and 30% and AUC of 0.75–0.89 for training and test set models, respectively. *LV latent variables, VIP variable importance projection, LDL low-density lipoprotein, Chol cholesterol, HDL high-density lipoprotein, Phosl phospholipid, Mainfrac main fraction, Subfrac subfraction, ApoA1 apolipoprotein A1, ApoB apolipoprotein B, chole cholesterol ester

Fig. 4

Human blood plasma lipoproteins associated with SMIF. (A) Scores and (B) loadings plot of the PCA model developed on the autoscaled concentrations of 65 plasma lipoprotein variables quantified in the three groups: high (more than 10 times/month, n = 80), moderate (1–10 times/month, n = 112) and zero (less than 1 time/month, n = 71) SMIF. (C) Box plots of selected plasma lipoproteins that are found to be different in the three SMIF groups. FDR corrected p-values and effect sizes (%) are calculated from ANOVA. The horizontal lines and dots inside the boxes represent median and mean values, respectively. Multiple linear regression (MLR) p-value represents false discovery rate (FDR) corrected p-value from multiple linear regression analysis adjusted by sex, BMI, and age