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

Tao, Xinmiao; Liu, Lin; Ma, Pingnan; Hu, Jinxia; Ming, Zhu; Dang, Keke; Zhang, Yuntao; Li, Ying

doi:10.1210/clinem/dgad561

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Our study found positive associations between the very-long-chain SFAs and atherogenic lipid markers, such as TC and LDL-C, consistent with prior studies on lipid marker levels [7,33]. C20:0 and C22:0 exhibited an inverse association with TG, consistent with the observed direction in CVD and T2D risks [34][35][36][37]. Moreover, elevated levels of lipid markers (TC and LDL-C) were linked with high levels of C22:0 and C24:0, in alignment with the findings of the EPIC cross-sectional study [7], and C23:0 and C24:0 exhibited positive associations with insulin resistance markers (FINS and HOMA-IR).…”

Section: Discussionsupporting

confidence: 91%

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

Wu,

Luo,

Zhong

et al. 2024

Nutrients

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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.

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Section: Discussionsupporting

confidence: 91%

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

Wu,

Luo,

Zhong

et al. 2024

Nutrients

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“…Additionally, it is essential to consider the impact of meat and mealworm larvae consumption on healthcare costs. Excessive consumption of meat, rich in saturated fatty acids, poses a risk to cardiovascular health and contributes to the onset of chronic diseases [10][11][12]. Treating these diseases imposes significant costs on the state in terms of healthcare [13].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Farm and Industrial Feed Waste on the Safety Parameters of Tenebrio molitor Larvae

Jankauskienė,

Aleknavičius,

Antanaitis

et al. 2023

Processes

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The rising global demand for animal-based food has an increasingly detrimental ecological impact, exacerbated by significant food waste (approximately one-third of all food). This research aimed to analyze the possibility of changing the usually balanced feed with sustainable alternatives that remain as a by-product of the production of farms, grain processing, and breweries, thus promoting the sustainability of agriculture. The mealworm larvae were reared on different substrates: (1) agar-agar gels, wheat bran, and brewer’s yeast, (2) carrots, wheat bran, and brewer’s yeast, (3) sprouted potatoes, wheat bran, and brewer’s yeast, and (4) carrots, brewers’ spent grain and brewer’s yeast. For analysis, the frozen larvae were lyophilized and tested for chemical safety in three accredited laboratories. The results have shown that all tested samples had lower levels of pesticides than the detection limit. In scientific literature, we didn’t find studies on polycyclic aromatic hydrocarbons (PAH). In our study, we found PAH in the substrate and these toxins, as our study shows, can also enter the larvae, but no significant accumulation was observed (sum of benzo(a)pyrene, benz(a)anthracene, benzo(b)fluoranthene and chrysene was 0.0007 mg/kg). Furthermore, the total content of PAH, benzo(a)pyrene and histamine did not exceed recommended levels. We have noticed that the highest concentration of heavy metals (e.g., chromium—1.45 ± 0.02) was found in the sample with the brewer’s by-products. While numerous studies utilize plant-derived by-products, the accumulation of glycoalkaloids has not been explored. Among the all glycoalkaloids (tomatidin, tomatine, α-solanine, α-chaconine and solanidin), amounts of α-solanine and α-chaconine were the highest, detected in the sample with sprouted potatoes (175.12 ± 0.21 and 139.32 ± 0.32 mg/kg, respectively). The amount of total putrescine, tyramine, spermine, and spermidine in mealworm larvae was statistically higher compared to the amount detected in the substrate, and histamine level-on the contrary, was statistically significantly lower compared to the amount detected in the substrate. Considering the amount of toxic substances found in the substrate from the by-products, we can assume that mealworms did not accumulate high levels of toxins, which would violate regulations.

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Insights into Heart Failure Metabolite Markers through Explainable Machine Learning

Baron,

Mehanna,

Daneault

et al. 2024

Preprint

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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). In this study, we aim to uncover metabolites with significant predictive value by analyzing targeted metabolomics data through ML models and XAI methodologies. After robust quality control procedures, we analyzed 55 metabolites from 124 plasma samples, including 53 HF patients and 71 controls, comparing Logistic Regression (Logit) models with Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGB), all achieving high accuracy in predicting group labels: 84.20% (sigma=5.46), 85.73% (sigma=6.25), and 84.80% (sigma=7.84), 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 heart failure, such as glucose and cholesterol, but also more recently described association such C18:1 carnitine, were reaffirmed in our analysis. The novel discovery of lignoceric acid (C24:0) 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.

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

Cited by 5 publications

References 38 publications

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

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

The Impact of Farm and Industrial Feed Waste on the Safety Parameters of Tenebrio molitor Larvae

Insights into Heart Failure Metabolite Markers through Explainable Machine Learning

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