The impact of dietary fatty acids on human adipose tissue

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.

Adipose tissue fatty acids as biomarkers for metabolic dysfunction in obese females: Implication of menopause and ageing

Fatty acids (FA) are biomarkers of metabolic dysfunction. Adipose tissue is the largest reservoir of FA and acts differently in obese individuals. Menopause by itself significantly alters metabolism, lipid metabolism dysregulation, and adipose tissue distribution. How adipose tissue FA alters an obese individual's metabolism depending on a female's menopausal status is yet poorly understood. Hence, the subcutaneous (scAT) and visceral adipose tissue (vAT) FA profile for 173 obese premenopausal and postmenopausal women was measured and associated with biochemical parameters. scAT and vAT FA profiles were distinct by themselves and in menopause. In total 816 associations were found with biochemical parameters, where only 58 were independent of the menopausal status. The associations found to emphasize the importance of assessing the adipose tissue FA profile and how their behavior changes with menopause. The FA are crucial in metabolic processes and can be helpful biomarkers in the prevention/treatment and follow-up of female obesity.

The Effects of a Low Linoleic Acid/α-Linolenic Acid Ratio on Lipid Metabolism and Endogenous Fatty Acid Distribution in Obese Mice

A reduced risk of obesity and metabolic syndrome has been observed in individuals with a low intake ratio of linoleic acid/α-linolenic acid (LA/ALA). However, the influence of a low ratio of LA/ALA intake on lipid metabolism and endogenous fatty acid distribution in obese patients remains elusive. In this investigation, 8-week-old C57BL/6J mice were randomly assigned to four groups: low-fat diet (LFD) as a control, high-fat diet (HFD), high-fat diet with a low LA/ALA ratio (HFD+H3L6), and high-fat diet with a high LA/ALA ratio (HFD+L3H6) for 16 weeks. Our results show that the HFD+H3L6 diet significantly decreased the liver index of HFD mice by 3.51%, as well as the levels of triacylglycerols (TGs) and low-density lipoprotein cholesterol (LDL-C) by 15.67% and 10.02%, respectively. Moreover, the HFD+H3L6 diet reduced the pro-inflammatory cytokines interleukin-6 (IL-6) level and aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio and elevated the level of superoxide dismutase (SOD) in the liver. The HFD+H3L6 diet also resulted in the downregulation of fatty acid synthetase (FAS) and sterol regulatory element binding proteins-1c (SREBP-1c) expression and the upregulation of peroxisome proliferator-activated receptor-α (PPAR-α) and acyl-CoA oxidase 1 (ACOX1) gene expression in the liver. The low LA/ALA ratio diet led to a notable increase in the levels of ALA and its downstream derivative docosahexaenoic acid (DHA) in the erythrocyte, liver, perienteric fat, epididymal fat, perirenal fat, spleen, brain, heart, and gastrocnemius, with a strong positive correlation. Conversely, the accumulation of LA in abdominal fat was more prominent, and a high LA/ALA ratio diet exacerbated the deposition effect of LA. In conclusion, the low LA/ALA ratio not only regulated endogenous fatty acid levels but also upregulated PPAR-α and ACOX1 and downregulated SREBP-1c and FAS gene expression levels, thus maintaining lipid homeostasis. Optimizing dietary fat intake is important in studying lipid nutrition. These research findings emphasize the significance of understanding and optimizing dietary fat intake.

Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data

In the US, people frequently snack between meals, consuming calorie-dense foods including baked goods (cakes), sweets, and desserts (ice cream) high in lipids, salt, and sugar. Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are reasonably healthy; however, excessive consumption of food high in saturated fatty acid (SFA) has been related to an elevated risk of cardiovascular diseases. The National Health and Nutrition Survey (NHANES) uses a 24 h recall to collect information on people's food habits in the US. The complexity of the NHANES data necessitates using machine learning (ML) methods, a branch of data science that uses algorithms to collect large, unstructured, and structured data sets and identify correlations between the data variables. This study focused on determining the ability of ML regression models including artificial neural networks (ANNs), decision trees (DTs), k-nearest neighbors (KNNs), and support vector machines (SVMs) to assess the variability in total fat content concerning the classes (SFA, MUFA, and PUFA) of US-consumed snacks between 2017 and 2018. KNNs and DTs predicted SFA, MUFA, and PUFA with mean squared error (MSE) of 0.707, 0.489, 0.612, and 1.172, 0.846, 0.738, respectively. SVMs failed to predict the fatty acids accurately; however, ANNs performed satisfactorily. Using ensemble methods, DTs (10.635, 5.120, 7.075) showed higher error values for MSE than linear regression (LiR) (9.086, 3.698, 5.820) for SFA, MUFA, and PUFA prediction, respectively. R2 score ranged between -0.541 to 0.983 and 0.390 to 0.751 for models one and two, respectively. Extreme gradient boost (XGR), Light gradient boost (LightGBM), and random forest (RF) performed better than LiR, with RF having the lowest score for MSE in predicting all the fatty acid classes.

Designing healthier plant-based foods: Fortification, digestion, and bioavailability

Many consumers are incorporating more plant-based foods into their diets as a result of concerns about the environmental, ethical, and health impacts of animal sourced foods like meat, seafood, egg, and dairy products. Foods derived from animals negatively impact the environment by increasing greenhouse gas emissions, land use, water use, pollution, deforestation, and biodiversity loss. The livestock industry confines and slaughters billions of livestock animals each year. There are concerns about the negative impacts of some animal sourced foods, such as red meat and processed meat, on human health. The livestock industry is a major user of antibiotics, which is leading to a rise in the resistance of several pathogenic microorganisms to antibiotics. It is often assumed that a plant-based diet is healthier than one containing more animal sourced foods, but this is not necessarily the case. Eating more fresh fruits, vegetables, nuts, and whole grain cereals has been linked to improved health outcomes but it is unclear whether next-generation plant-based foods, such as meat, seafood, egg, and dairy analogs are healthier than the products they are designed to replace. Many of these new products are highly processed foods that contain high levels of saturated fat, sugar, starch, and salt, and low levels of micronutrients, nutraceuticals, and dietary fibers. Moreover, they are often rapidly digested in the gastrointestinal tract because processing disrupts plant tissues and releases the macronutrients. Consequently, it is important to formulate plant-based foods to reduce the levels of nutrients linked to adverse health effects and increase the levels linked to beneficial health effects. Moreover, it is important to design the food matrix so that the macronutrients are not digested and absorbed too quickly, but the micronutrients are highly bioavailable. In this article, we discuss how next-generation plant-based foods can be made healthier by controlling their nutrient profile, digestibility, and bioavailability.

Chronic docosahexaenoic acid supplementation improves metabolic plasticity in subcutaneous adipose tissue of aged obese female mice

This study aimed to characterize the potential beneficial effects of chronic docosahexaenoic acid (DHA) supplementation on restoring subcutaneous white adipose tissue (scWAT) plasticity in obese aged female mice. Two-month-old female C57BL/6J mice received a control (CT) or a high fat diet (HFD) for 4 months. Then, 6-month-old diet-induced obese (DIO) mice were distributed into the DIO and the DIOMEG group (fed with a DHA-enriched HFD) up to 18 months. In scWAT, the DHA-enriched diet reduced the mean adipocyte size and reversed the upregulation of lipogenic genes induced by the HFD, reaching values even lower than those observed in CT animals. DIO mice exhibited an up-regulation of lipolytic and fatty oxidation gene expressions that was reversed in DHA-supplemented mice except for Cpt1a mRNA levels, which were higher in DIOMEG as compared to CT mice. DHA restored the increase of proinflammatory genes observed in scWAT of DIO mice. While no changes were observed in total macrophage F4/80+/CD11b+ content, the DHA treatment switched scWAT macrophages profile by reducing the M1 marker Cd11c and increasing the M2 marker CD206. These events occurred alongside with a stimulation of beige adipocyte specific genes, the restoration of UCP1 and pAKT/AKT ratio, and a recovery of the HFD-induced Fgf21 upregulation. In summary, DHA supplementation induced a metabolic remodeling of scWAT to a healthier phenotype in aged obese mice by modulating genes controlling lipid accumulation in adipocytes, reducing the inflammatory status, and inducing beige adipocyte markers in obese aged mice.

Dietary lipids from body to brain

Dietary habits have drastically changed over the last decades in Western societies. The Western diet, rich in saturated fatty acids (SFA), trans fatty acids (TFA), omega-6 polyunsaturated fatty acids (n-6 PUFA) and cholesterol, is accepted as an important factor in the development of metabolic disorders, such as obesity and diabetes type 2. Alongside these diseases, nutrition is associated with the prevalence of brain disorders. Although clinical and epidemiological studies revealed that metabolic diseases and brain disorders might be related, the underlying pathology is multifactorial, making it hard to determine causal links. Neuroinflammation can be a result of unhealthy diets that may cause alterations in peripheral metabolism. Especially, dietary fatty acids are of interest, as they act as signalling molecules responsible for inflammatory processes. Diets rich in n-6 PUFA, SFA and TFA increase neuroinflammation, whereas diets rich in monounsaturated fatty acids (MUFA), omega-3 (n-3) PUFA and sphingolipids (SL) can diminish neuroinflammation. Moreover, these pro- and anti-inflammatory diets might indirectly influence neuroinflammation via the adipose tissue, microbiome, intestine and vasculature. Here, we review the impact of nutrition on brain health. In particular, we will discuss the role of dietary lipids in signalling pathways directly applicable to inflammation and neuronal function.

Generation of immune cell containing adipose organoids for in vitro analysis of immune metabolism

Adipose tissue is an organized endocrine organ with important metabolic and immunological functions and immune cell-adipocyte crosstalk is known to drive various disease pathologies. Suitable 3D adipose tissue organoid models often lack resident immune cell populations and therefore require the addition of immune cells isolated from other organs. We have created the first 3D adipose tissue organoid model which could contain and maintain resident immune cell populations of the stromal vascular fraction (SVF) and proved to be effective in studying adipose tissue biology in a convenient manner. Macrophage and mast cell populations were successfully confirmed within our organoid model and were maintained in culture without the addition of growth factors. We demonstrated the suitability of our model for monitoring the lipidome during adipocyte differentiation in vitro and confirmed that this model reflects the physiological lipidome better than standard 2D cultures. In addition, we applied mass spectrometry-based lipidomics to track lipidomic changes in the lipidome upon dietary and immunomodulatory interventions. We conclude that this model represents a valuable tool for immune-metabolic research.

Optimal diet and lifestyle strategies for the management of cardio-metabolic risk

Cardio-metabolic risk (CMR) embodies a clustering of metabolic abnormalities that increase the likelihood of developing CVD in the large arteries of the heart, peripheral tissues and brain. These abnormalities share a common origin of insulin resistance, which manifests typically as excess visceral adipose tissue in the abdominal cavity, and within cells of key metabolic tissues (ectopic fat), including the liver, pancreas, heart and skeletal muscle. As expected, the increased risk of CVD that can be attributed to CMR factors is alarmingly high in overweight and obese populations, but this risk can be reduced by reversing many of the inappropriate diet and lifestyle behaviours that underlie its development. The Nutrition Society's 2018 Winter Meeting at the Royal Society of Medicine addressed the topic of the 'Optimal diet and lifestyle for managing cardio-metabolic risk', with the aim of providing mechanistic insights into the impact of macronutrients, dietary patterns and meal timing in key metabolic tissues. The 2-d programme concluded with a summary of its main outcomes, and an overview of their implications for dietary policy in the UK.