Correlation of omega-3 levels in serum phospholipid from 2053 human blood samples with key fatty acid ratios

<p>Abstract</p> <p>Background</p> <p>This research was conducted to explore the relationships between the levels of omega-3 fatty acids in serum phospholipid and key fatty acid ratios including potential cut-offs for risk factor assessment with respect to coronary heart disease and fatal ischemic heart disease.</p> <p>Methods</p> <p>Blood samples (n = 2053) were obtained from free-living subjects in North America and processed for determining the levels of total fatty acids in serum phospholipid as omega-3 fatty acids including EPA (eicosapentaenoic acid, 20:5 n-3) and DHA (docosahexaenoic acid, 22:6 n-3) by combined thin-layer and gas-liquid chromatographic analyses. The omega-3 levels were correlated with selected omega-6: omega-3 ratios including AA (arachidonic acid, 20:4n-6): EPA and AA:(EPA+DHA). Based on previously-published levels of omega-3 fatty acids considered to be in a 'lower risk' category for heart disease and related fatality, 'lower risk' categories for selected fatty acid ratios were estimated.</p> <p>Results</p> <p>Strong inverse correlations between the summed total of omega-3 fatty acids in serum phospholipid and all four ratios (omega-6:omega-3 (n-6:n-3), AA:EPA, AA:DHA, and AA:(EPA+DHA)) were found with the most potent correlation being with the omega-6:omega-3 ratio (R²= 0.96). The strongest inverse relation for the EPA+DHA levels in serum phospholipid was found with the omega-6: omega-3 ratio (R²= 0.94) followed closely by the AA:(EPA+DHA) ratio at R²= 0.88. It was estimated that 95% of the subjects would be in the 'lower risk' category for coronary heart disease (based on total omega-3 ≥ 7.2%) with omega-6:omega-3 ratios <4.5 and AA:(EPA+DHA) ratios <1.4. The corresponding ratio cut-offs for a 'lower risk' category for fatal ischemic heart disease (EPA+DHA ≥ 4.6%) were estimated at < 5.8 and < 2.1, respectively.</p> <p>Conclusions</p> <p>Strong inverse correlations between the levels of omega-3 fatty acids in serum (or plasma) phospholipid and omega-6: omega-3 ratios are apparent based on this large database of 2053 samples. Certain fatty acid ratios may aid in cardiovascular disease-related risk assessment if/when complete profiles are not available.</p

Consumption of pasteurized human lysozyme transgenic goats’ milk alters serum metabolite profile in young pigs

Nutrition, bacterial composition of the gastrointestinal tract, and general health status can all influence the metabolic profile of an organism. We previously demonstrated that feeding pasteurized transgenic goats’ milk expressing human lysozyme (hLZ) can positively impact intestinal morphology and modulate intestinal microbiota composition in young pigs. The objective of this study was to further examine the effect of consuming hLZ-containing milk on young pigs by profiling serum metabolites. Pigs were placed into two groups and fed a diet of solid food and either control (non-transgenic) goats’ milk or milk from hLZ-transgenic goats for 6 weeks. Serum samples were collected at the end of the feeding period and global metabolite profiling was performed. For a total of 225 metabolites (160 known, 65 unknown) semi-quantitative data was obtained. Levels of 18 known and 4 unknown metabolites differed significantly between the two groups with the direction of change in 13 of the 18 known metabolites being almost entirely congruent with improved health status, particularly in terms of the gastrointestinal tract health and immune response, with the effects of the other five being neutral or unknown. These results further support our hypothesis that consumption of hLZ-containing milk is beneficial to health

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups