Association of Circulating Very Long-Chain Saturated Fatty Acids With Cardiovascular Mortality in NHANES 2003-2004, 2011-2012

Insights into heart failure metabolite markers through explainable machine learning

Understanding molecular traits through metabolomics offers an avenue to tailor cardiovascular prevention, diagnosis and treatment strategies more effectively. This study focuses on the application of machine learning (ML) and explainable artificial intelligence (XAI) algorithms to detect discriminant molecular signatures in heart failure (HF). We aim to uncover metabolites with significant predictive value by analyzing targeted metabolomics data through ML and XAI algorithms. After quality control, we analyzed 55 metabolites from 124 plasma samples, including 53 HF patients and 71 controls, comparing Ridge Logistic Regression, Support Vector Machine and eXtreme Gradient Boosting models. All achieved high accuracy in predicting group labels: 84.0% [95% CI: 75.3 - 92.7], 85.73 [95% CI: 78.6 - 92.9], and 84.8% [95% CI: 76.1 - 93.5], respectively. Permutation-based variable importance and Local Interpretable Model-agnostic Explanations (LIME) were used for group-level and individual-level explainability, respectively, complemented by H-Friedman statistics for variable interactions, yielding reliable, explainable insights of the ML models. Metabolites well-known for their association with HF, such as glucose and cholesterol, and more recently described, the C18:1 carnitine, were reaffirmed in our analysis. The novel discovery of lignoceric acid (C24:0 fatty acid) as a critical discriminator, was confirmed in a replication cohort, underscoring its potential as a metabolite marker. Furthermore, our study highlights the utility of 2-way variable interaction analysis in unveiling a network of metabolite interactions essential for accurate disease prediction. The results demonstrate our approach's efficacy in identifying key metabolites and their interactions, illustrating the power of ML and XAI in advancing personalized healthcare solutions.

Integration of Fatty Acid-Targeted Metabolome and Transcriptomics Reveals the Mechanism of Chronic Environmental Microcystin-LR-Induced Hepatic Steatosis

Microcystin-LR (MC-LR) is a toxin that causes hepatic steatosis. Our previous study found that exposure to 60 μg/L MC-LR for 9 months resulted in liver lipid accumulation, but the underlying mechanisms remain elusive. Herein, for the first time, fatty acid-targeted metabolome and RNA-seq were combined to probe the effect and mechanism of chronic (12-month) MC-LR treatment on mice lipid metabolism at environmental-related levels (1, 60, and 120 μg/L). It was found that MC-LR dose-dependently raised serum and liver lipid levels. The total cholesterol (TC) levels in the liver were significantly increased following treatment with 1 μg/L MC-LR (equivalent to 0.004 μ/L in human). Treatment with 60 and 120 μg/L MC-LR significantly elevated TC and triglyceride (TG) levels in both serum and liver. Serum fatty acid-targeted metabolome analysis demonstrated that exposure to 1, 60, and 120 μg/L MC-LR caused significant alterations in the fatty acid profile. Chronic 1, 60, and 120 μg/L MC-LR treatment significantly increased serum polyunsaturated fatty acids (PUFAs), including conjugated linoleic acid and eicosapentaenoic acid, which positively correlated with serum or liver TG levels. Chronic exposure to 120 μg/L MC-LR led to a significant decrease in the accumulation of saturated fatty acids, including citramalic acid, pentadecanoic acid, and docosanoic acid, which were negatively correlated with serum or liver lipid levels. These findings suggested that 1 μg/L MC-LR exposure caused mild lipid metabolism disruption, while 60 and 120 μg/L MC-LR treatment resulted in pronounced hepatic steatosis in mice. Transcriptome analysis revealed that chronic environmental MC-LR treatment regulated the expression of genes involved in the phosphatidylinositol 3-kinase (PI3K) complex and fatty acid metabolism. Western blotting and RT-qPCR confirmed that chronic environmental MC-LR exposure activated the PI3K/AKT/mTOR signaling pathway, the downstream of fads3 gene that participates in fatty acid desaturation was upregulated, fatty acid degradation-related genes, including acsl1, acsl4, and ehhadh were inhibited, and lipid transport-related genes, including slc27a4 and apol7a, were promoted. Thus, chronic environmental MC-LR exposure boosts hepatic steatosis. Our work indicated that the limit concentration of 1 μg/L MC-LR in human drinking water for safety needs to be discussed. The study provides the first evidence of the fatty acid profile and gene changes and gains new insights into the mechanisms of chronic environmental MC-LR treatment-induced hepatic steatosis.

Free fatty acids and mortality among adults in the United States: a report from US National Health and Nutrition Examination Survey (NHANES)

BACKGROUND

The relationship between free fatty acids (FFAs) and the risk of mortality remains unclear. There is a scarcity of prospective studies examining the associations between specific FFAs, rather than total concentrations, of their effect on long-term health outcomes.

OBJECTIVE

To evaluate the correlation between different FFAs and all-cause and cardiovascular mortality in a large, diverse, nationally representative sample of adults in the US, and examine how different FFAs may mediate this association.

METHODS

This cohort study included unsaturated fatty acids (USFA) and saturated fatty acids (SFA) groups in the US National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014 and provided blood samples for FFAs levels. Multiple model calibration was performed using Cox regression analysis for known risk factors to explore the associations between FFAs and all-cause and cardiovascular mortality.

RESULTS

In the group of USFA, 3719 people were included, median follow-up, 6.7 years (5.8-7.8 years). In the SFA group, we included 3900 people with a median follow-up, 6.9 years (5.9-8 years). In the USFA group, myristoleic acid (14:1 n-5) (hazard ratio (HR) 1.02 [1.006-1.034]; P = 0.004), palmitoleic acid (16:1 n-7) (HR 1.001 [1.001-1.002]; P < 0.001), cis-vaccenic acid (18:1 n-7) (HR 1.006 [1.003-1.009]; P < 0.001), nervonic acid (24:1 n-9) (HR 1.007 [1.002-1.012]; P = 0.003), eicosatrienoic acid (20:3 n-9) (HR 1.027 [1.009-1.046]; P = 0.003), docosatetraenoic acid (22:4 n-6) (HR 1.024 [1.012-1.036]; P < 0.001), and docosapentaenoic acid (22:5 n-6) (HR 1.019 [1.006-1.032]; P = 0.005) were positively associated with the all-cause mortality, while docosahexaenoic acid (22:6 n-3) had a statistically lower risk of all-cause mortality (HR 0.998 [0.996-0.999]; P = 0.007). Among the SFA group, palmitic acid (16:0) demonstrated a higher risk of all-cause mortality (HR 1.00 [1.00-1.00]; P = 0.022), while tricosanoic acid (23:0) (HR 0.975 [0.959-0.991]; P = 0.002) and lignoceric acid (24:0) (HR 0.992 [0.984-0.999]; P = 0.036) were linked to a lower risk of all-cause mortality. Besides 23:0 and 24:0, the other FFAs mentioned above were linearly associated with the risks of all-cause mortality.

CONCLUSIONS

In this nationally representative cohort of US adults, some different FFAs exhibited significant associations with risk of all-cause mortality. Achieving optimal concentrations of specific FFAs may lower this risk of all-cause mortality, but this benefit was not observed in regards to cardiovascular mortality.

Differential Associations of Erythrocyte Membrane Saturated Fatty Acids with Glycemic and Lipid Metabolic Markers in a Chinese Population: A Cross-Sectional Study

Mounting evidence indicates a complex link between circulating saturated fatty acids (SFAs) and cardiovascular disease (CVD) risk factors, but research on erythrocyte membrane SFA associations with metabolic markers remains limited. Our study sought to investigate the correlations between erythrocyte membrane SFAs and key metabolic markers within glycemic and lipid metabolism in a Chinese population of 798 residents aged 41 to 71 from Guangzhou. Using gas chromatography-mass spectrometry, we assessed the erythrocyte membrane saturated fatty acid profile and performed multiple linear regression to evaluate the relationship between different SFA subtypes and metabolic markers. Our findings revealed that the odd-chain SFA group (C15:0 + C17:0) exhibited negative associations with fasting blood glucose (FBG), homeostatic model assessment for insulin resistance (HOMA-IR), and triglycerides (TG). Conversely, the very-long-chain SFA group (C20:0 + C22:0 + C23:0 + C24:0) exhibited positive associations with fasting insulins (FINS), HOMA-IR, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Furthermore, there was no evidence supporting an association between the even-chain group (C14:0 + C16:0 + C18:0) and metabolic markers. Our findings suggest that different subtypes of SFAs have diverse effects on glycemic and lipid metabolic markers, with odd-chain SFAs associated with a lower metabolic risk. However, the results concerning the correlations between even-chain SFAs and very-long-chain SFAs with markers of glycemic and lipid metabolism pathways are confusing, highlighting the necessity for further exploration and investigation.