Next Article in Journal
Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice
Previous Article in Journal
[18F]FDG PET/CT in the Evaluation of Melanoma Patients Treated with Immunotherapy
Previous Article in Special Issue
The Triglyceride/High-Density Lipoprotein Cholesterol (TG/HDL-C) Ratio as a Risk Marker for Metabolic Syndrome and Cardiovascular Disease
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 5
10.3390/diagnostics13050979
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Fatty Acid Profile and Genetic Variants of Proteins Involved in Fatty Acid Metabolism Could Be Considered as Disease Predictor

by
Raja Chaaba
1,2,*,
Aicha Bouaziz
2,3,
Asma Ben Amor
2,4,
Wissem Mnif
5,*,
Mohamed Hammami
1 and
Sounira Mehri
1
1
Lab-NAFS “Nutrition-Functional Food & Health”, Faculty of Medicine, University of Monastir, Avicene Street, Monastir 5000, Tunisia
2
Higher School of Health Sciences and Techniques, Sousse, University of Sousse, Sousse 4054, Tunisia
3
Bio-Resources, Integrative Biology & Valorization (BIOLIVAL, LR14ES06), Higher Institute of Biotechnology of Monastir, University of Monastir, Monastir 5000, Tunisia
4
Faculty of Medicine, “Ibn El Jazzar” University of Sousse, Sousse 4054, Tunisia
5
Department of Chemistry, Faculty of Sciences, University of Bisha, P.O. Box 199, Bisha 61922, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Diagnostics 2023, 13(5), 979; https://doi.org/10.3390/diagnostics13050979
Submission received: 22 November 2022 / Revised: 22 February 2023 / Accepted: 2 March 2023 / Published: 4 March 2023
(This article belongs to the Special Issue Recent Advances in the Diagnosis of Metabolic Disorders)
Browse Figures
Versions Notes

Abstract

:
Circulating fatty acids (FA) have an endogenous or exogenous origin and are metabolized under the effect of many enzymes. They play crucial roles in many mechanisms: cell signaling, modulation of gene expression, etc., which leads to the hypothesis that their perturbation could be the cause of disease development. FA in erythrocytes and plasma rather than dietary FA could be used as a biomarker for many diseases. Cardiovascular disease was associated with elevated trans FA and decreased DHA and EPA. Increased arachidonic acid and decreased Docosahexaenoic Acids (DHA) were associated with Alzheimer’s disease. Low Arachidonic acid and DHA are associated with neonatal morbidities and mortality. Decreased saturated fatty acids (SFA), increased monounsaturated FA (MUFA) and polyunsaturated FA (PUFA) (C18:2 n-6 and C20:3 n-6) are associated with cancer. Additionally, genetic polymorphisms in genes coding for enzymes implicated in FA metabolism are associated with disease development. FA desaturase (FADS1 and FADS2) polymorphisms are associated with Alzheimer’s disease, Acute Coronary Syndrome, Autism spectrum disorder and obesity. Polymorphisms in FA elongase (ELOVL2) are associated with Alzheimer’s disease, Autism spectrum disorder and obesity. FA-binding protein polymorphism is associated with dyslipidemia, type 2 diabetes, metabolic syndrome, obesity, hypertension, non-alcoholic fatty liver disease, peripheral atherosclerosis combined with type 2 diabetes and polycystic ovary syndrome. Acetyl-coenzyme A carboxylase polymorphisms are associated with diabetes, obesity and diabetic nephropathy. FA profile and genetic variants of proteins implicated in FA metabolism could be considered as disease biomarkers and may help with the prevention and management of diseases.

1. Introduction

Fatty acids (FA) belong to the lipid class. They can be free or associated with alcohols to provide triglycerides, phospholipids, cerids or sterides. They can be saturated (SFA) or unsaturated (UFA) depending on the presence or absence of a double bound in their structure. Based on the number of double bounds, we can distinguish between monounsaturated FA (MUFA with only one double bound) or polyunsaturated FA (PUFA with two or more double bounds). According to the carbon chain length, four groups of FA are identified: short-chain FA with 4 to 6 carbon atoms, medium-chain FA with 8 to 12 carbon atoms, long-chain FA with 14 to 20 carbon atoms, and very long-chain FA with 22 or more carbon atoms. Depending on the configuration, a distinction is made between trans FA and cis FA. FA differ from each other in the number of carbon atoms, unsaturations and amount of configuration. They are present in all cells and tissues of the body. SFA and MUFA are synthesized by all organisms. However, not all PUFA are synthesized by mammals, including humans, because they do not have the enzymes (Δ12- and Δ15 desaturases) necessary for their synthesis. These are therefore called essential FA. These FA are represented by alpha-linolenic acid (ALA, C18: 3 n-3) [1] and linoleic acid (LA, C18: 2 n-6) [2]. They will play the role of precursors of other FA with longer chains and of bioactive mediators in the form of oxygenated molecules (eicosanoids, docosanoids, etc.) [3].

1.1. Different Fatty Acids in Plasma and Erythrocytes

Lipids are characterized by a high proportion of phospholipids, while reserve lipids mainly take the form of triglycerides. Phospholipids and triglycerides are both made up of FA.
Fatty acids are multiple and variable. Their profile is not constant in all species. UFAs are more common in plants, while SFAs are more common in animals. Additionally, FA differ between individuals in the same species. These differences can be explained by variation in dietary habits or by genetic differences that may affect the proteins involved in FA metabolism [4,5]. However, according to different studies, in all people, palmitic acid is the most frequent SFA, oleic acid is the most frequent MUFA and LA is the most frequent PUFA (Table 1) [6,7,8,9].
Fatty acid analyses are often conducted on samples of whole blood or plasma or erythrocytes (RBC). In fact, plasma analyses reflect recent FA intake obtained through the diet, while analyses of RBC and adipose tissue reflect dietary fatty acids intake over the long term [10]. The results of selected three studies in three different populations (Tunisia, Chile and the USA) exploring both profiles of plasma and erythrocyte clearly show how these profiles differ, comparing the percentage abundances of individual FA. Total saturated FA mainly stearic acid are more frequent in RBC, whereas linoleic acid is more frequent in plasma.
Indeed, the FA profile is not the result of dietary intake alone; several other factors can intervene, such as genetic factors [5].

1.2. Functions of Fatty Acids

FA are highly energetic substrates. In the mitochondria, they provide a large amount of energy through beta oxidation. This quantity depends on the number of carbons that make up the FA, but it is always greater than that provided by a molecule of glucose (for FA with more than four carbons).
FA are the main constituent of cell membranes. They influence membrane fluidity [11]. Incorporated into membrane phospholipids, PUFA increase membrane fluidity and can affect cell function. Moreover, the FA composition of the membrane influences cell signaling. Increasing the concentration of docosahexaenoic acid (DHA, C22:6 n-3) in the excitable membranes of the brain and retina acts on cell signaling by altering lipid rafts at this level [12]. Impaired insulin signaling by free FA leads to endothelial dysfunction [13]. MUFA affect the Intracellular Signaling in Cancer [14].
FA can be precursors of oxygenated molecules. Dihomo-gamma-linolenic (C20:3 n-6), arachidonic (AA, C20:4 n-6), and eicosapentaenoic (EPA, C20:5 n-3) acids enter the enzymatic pathways of oxygenation, hydroxylation and of peroxidation to form eicosanoids (powerful mediators). Membrane FA released under the effect of phospholipase A2 can enter two different metabolic pathways: (i) the cyclo-oxygenase (COX) pathway which will give rise to prostaglandins, prostacyclins and thromboxanes and (ii) the lipoxygenase (LOX) pathway, which produces leukotrienes and hydroperoxidized FA [3]. In addition, peroxidation of 20-carbon PUFAs through the cytochrome P450 pathway can yield epoxyeicotrienoic acids. AA and DHA can yield isoprostanes and neuroprostanes through non-enzymatic peroxidation [3].
PUFAs have the ability to bind directly or through their derivatives (eicosanoids) to transcription factors, which modulate the expression of certain genes involved in various metabolic pathways [15]. The most important transcription factors are peroxisome proliferator-activated receptors (PPARs), sterol regulatory element binding protein (SREBP-1c for sterol regulatory element binding protein-1c), hepatic nuclear factors (HNF4 for hepatic nuclear factor 4), retinoid receptor (RXRa for retinoid X receptor) and the hepatic X receptor (LXRa for liver X receptor).
Given the important roles played by FA, it can be assumed that an abnormal lipid profile will be associated with disease development or prevention in different ways. In fact, FA can influence inflammation, oxidation and immunity [16]. For example, LA is a precursor of AA, which is a precursor of certain factors involved in inflammation, such as prostaglandins and leukotrienes. This leads us to assume that a high level of these FA will be accompanied by significant inflammation. However, this hypothesis should be rejected [17]. However, these FA can intervene in different ways in inflammation: (i) acting (inhibiting or activating) on the Toll-like receptor4 signaling pathways [18]; (ii) acting on the expression of microRNAs [19]. Moreover, FA are implicated in oxidation in different manners (i) they are involved in membrane lipid peroxidation, which induces Ferroptosis [20]; (ii) they are associated with LDL oxidation [21]; (iii) they have an impact on redox status [22].

2. Materials and Methods

PubMed database searches were performed to search for articles related to this subject. For the paragraphs about fatty acid characteristics in many diseases, we introduced fatty acids and disease prediction as key words and 10 years as the filter. For the association between gene polymorphisms and disease, the keywords used to query the database were desaturase polymorphism, elongase polymorphism, Fatty Acid Binding Protein polymorphism, and Acetyl-coenzyme A carboxylase polymorphism. The number of items found was very large. The preliminary selection of articles used in this review was based on the content of the abstracts. The abstracts needed to be consistent with the objective of the present work: the fatty acids had to be analyzed in blood and not in other tissues, nor in food or food supplements. After this step, reading the articles allowed us to select the articles that took the greatest number of fatty acids into consideration. Articles based on the study of a single fatty acid were excluded. In all articles, FA were measured in total lipids and not in lipid subfraction (phospholipids, ceramide, cholesterol ester, etc.).

3. Results and Discussion

3.1. Fatty Acids and Diseases

Dietary fat intake is associated with the development of many diseases. Since 1970, reduced saturated fat consumption has been recommended in the US [23]. A diet with high PUFA and MUFA and with low SFA and trans FA prevents cardiovascular disease [24,25]. In fact, FA in plasma, RBC and adipocytes reflect dietary FA. However, this review will not focus on the relationship between dietary FA and diseases, but will focus on fatty acid assays in humans (in plasma, RBC) in order to answer the following question: can we consider individual FA as biomarkers of many diseases?

3.1.1. Fatty Acids and Cardiovascular Disease (CVD)

FA play a crucial role in CVD. They exert their effect by acting on lipoprotein metabolism [26] and endothelial function [27]. FA-mediated dysregulation of nitric acid and cytokine production, inflammation, oxidative stress, apoptosis, and activation of the renin–angiotensin system, which causes endothelial dysfunction, therefore increases CVD risk [27]. Many FA are associated, whereas others are inversely associated with CVD (Table 2). A strong inverse correlation was shown between RBC oleic acid and CVD [28]. Another study showed a protective effect of plasma very long-chain SFA (arachidic acid (20:0), behenic acid (22:0) and lignoceric acid (24:0)) in heart failure [29]. However, Hadj Ahmed et al. showed an increased level of RBC C26:0, C24:0, C22:0, EPA and AA in patients with coronary artery disease compared to the normal population and consider them biomarkers of coronary artery disease [9]. The same team showed an association between RBC and plasma trans FA and coronary artery disease severity [30]. Many other studies showed an association between RBC trans FA and coronary heart disease [26]. This association is mediated by increased low-density lipoprotein cholesterol and decreased high-density lipoprotein cholesterol [26]. Acute myocardial infarction is associated with decreased short-chain FA and increased long-chain FA levels [31]. Acute coronary syndrome is inversely associated with DHA/AA ratio [32]. Early onset coronary atherosclerosis is associated with decreased EPA and DHA levels [33] and risk of atherosclerotic plaque rupture is associated with high AA/DHA ratio [34]. The results are different for different studies. For example, cardiovascular disease is associated with elevated n-6 PUFA [35] or elevated SFA and trans FA [36] or increased MUFA [37] and the same is true for coronary artery diseases [9,38]. Such differences could be explained by genetic, lifestyle and dietary differences between the studied populations. One study showed that the association between FA and cardiometabolic risk is modulated by concurrent physical activity [39]. The main conclusion is that the FA profile is modified between healthy subjects and patients. The most consistent association is for increased trans FA and decreased DHA and EPA.
Moreover, the FA profile could predict cardiovascular disease development in unhealthy subjects such as diabetic patients [40] and patients with renal failure [41]. Additionally, it predicts psychiatric disorder at 6 months after acute coronary syndrome [42]. Moreover, it predicts mortality among patients with acute cardiovascular disease [43], coronary artery disease [44] and with myocardial infarction [45].

3.1.2. Fatty Acids and Diabetes (Table 2)

Many studies have highlighted the association between elevated total plasma free FA and diabetes [46,47,48], whereas other studies examined individual FA. RBC n-3 PUFA was negatively associated with the risk of type 2 diabetes [49]. Serum C22:0 and plasma n-6/n-3 were associated with diabetes [50,51]. Many studies have agreed on elevated plasma palmitic acid, stearic acid and oleic acid in diabetic patients [47,48,52,53,54,55]. In fact, palmitic acid slows down insulin signal transduction [56]. Meanwhile, the association between increased oleic acid concentration and the development of diabetes is not well understood, as studies show that oleic acid may play a role in the protection and treatment of diabetes [55]. Elevated RBC-linolenic acid was associated with high type 2 diabetes incidence [57]. Patel et al. showed that plasma FAs appear to be more strongly associated with diabetes incidence compared to RBC FA [58]. Serum EPA, C22:5 n-3 and DHA were associated with lower incidence of type 2 diabetes and higher insulin sensitivity [59]. Plasma trans FA are associated with diabetes through their association with fasting glucose, insulin, HbA1c and insulin index [60]. Moreover, the association between plasma trans FA (industrial and ruminant) and diabetes showed that ruminant trans FA (18:1 n-7 t and t10c12-CLA) are inversely associated with diabetes; however, c9t11-CLA is positively associated with diabetes [61]. In fact, the strength of the association between plasma FA and diabetes varied after adjustment for BMI, age and triglycerides [62]. Moreover, the n-6 to n-3 ratio was inversely related to diabetic retinopathy [63] and C22:0 was directly associated with a rapid decline in kidney function in T1D [64].

3.1.3. Fatty Acids and Cancer (Table 2)

A study in patients with any type of cancer (except head and neck cancer) showed that cancer is associated with decreased SFA (C16:0 and C18:0) and increased MUFA (C18:1) and PUFA (LA and C20:3 n-6) [65]. Hepatocellular carcinoma is strongly associated with low levels of very long-chain SFA and n-3 PUFA [66]. Oral cancer is associated with decreased EPA and DHA [67]. Pancreatic cancer is associated with decreased n-3 PUFA and MUFA and increased arachidonic acid [68], whereas the association between FA and breast cancer depends on many variables, such as menopause [69] and BMI [70]. In breast cancer, FA (omega 3 and omega 6) modulate the cancer immune response and the fatty acid endogenous synthesis [71]. The concentration of oleic acid is lower in cancer patients, increasing their exposure to the disease [72]. In addition to their low level, an in vitro study showed that this fatty acid could be linked to cancer through the expression of miRNA [73]. Additionally, low RBC n-3/n-6 PUFA ratio was associated with multiple myeloma [74].

3.1.4. Fatty Acids and Other Diseases (Table 2)

Serum γ-linolenic acid and C9:0 and C19:0 were associated with obesity [75,76] and dihomo-gamma-linolenic acid and palmitoleic acid were able to predict the future development of metabolic syndrome (MS) in obese subjects [77]. Zarrouk et al. identified hexacosanoic (C26:0) as blood (RBC and plasma) lipid biomarkers of dementia [7], whereas Yamagishi et al. showed that Serum ALA was inversely associated with the risk of disabling dementia [78]. Hammouda et al. showed that AA increased in plasma and RBC and DHA decreased only in plasma of Alzheimer patients [79]. Additionally, Sala-vila et al. found that DHA was inversely associated with Alzheimer’s disease [80]. Meanwhile, a study by Tomata et al. did not support the association between PUFA and Alzheimer’s disease risks [81]. Oresic et al. showed that decanoic and octanoic acids are associated with severe traumatic brain injury [82]. Zhao et al. found that PUFA are associated with liver injury [83] and Mutsuda et al. showed that Dihomo-γ-linolenic acid is associated with hepatic steatosis [84]. Two studies are in favor of the association between FA profile and autoimmune diseases [85,86]. Mikkelsen et al. showed that C22:5 n-3 and C18:1 n-9 can predict allergies in children [87]. Fares et al. mentioned that neonatal morbidities and mortality is associated with decreased AA and DHA [88].
Table
Table 2. Fatty acid characteristics in many diseases.
Table 2. Fatty acid characteristics in many diseases.
DiseaseSampleSpecific Fatty AcidsReferences
Cardiovascular diseasesBloodDecreased n-6 PUFA[35]
Cardiovascular diseaseBloodIncreased SFA and trans FA[36]
Cardiovascular riskSerumIncreased MUFA
Decreased n-6 PUFA and C22:6 n-3 (DHA)		[37]
Coronary artery diseaseRed blood cellsIncreased C26:0, C24:0, C22:0, C20:5 n-3 (EPA), C20:4 n-6 (AA)
Decreased DHA, EPA/AA		[9]
Coronary artery diseasePlasmaLow levels of ALA, EPA, eicosatetraenoic (C20:4 n-3) and DHA[38]
Coronary heart diseaseRed blood cellsIncreased Trans fatty acids[26]
Coronary artery disease severityRed Blood Cells and plasmaIncreased Trans fatty acids[30]
Heart FailurePlasmaDecreased very long-chain saturated fatty acids arachidic acid (20:0), behenic acid (22:0) and lignoceric acid (24:0) [29]
Acute myocardial infarctionPlasmaDecreased short-chain FA
Increased long-chain FA		[31]
Acute coronary syndromeSerumLow DHA/AA[32]
Early onset coronary atherosclerosisRBCLow EPA and DHA[33]
Risk of atherosclerotic plaque ruptureserumIncreased AA/DHA[34]
Type 2 diabetesRBCElevated linolenic acid[57]
Type 2 diabetesPlasmaIncreased palmitic, stearic and oleic acid[55]
Type 2 diabetesPlasmaIncreased c9t11-CLA[61]
Type 2 diabetesSerumIncreased C22:6[50]
Type 2 diabetesPlasmaIncreased n-6/n-3[51]
Type 2 diabetesSerumEPA, C22:5 n-3 and DHA
(inverse)		[59]
CancerRBCDecreased SFA (C16:0 et C18:0)
Increased MUFA (C18:1)
Increased PUFA (LA and C20:3 n-6)		[65]
Oral cancerRBCDecreased EPA, DHA[67]
Breast cancerPlasmaDecreased linoleic acid (C18:2 n-6)[72]
Pancreatic cancerPlasmaDecreased n-3 PUFA and MUFA
Increased AA		[68]
Hepatocellular carcinomaPlasmaVery long-chain SFAs and very long-chain n-3 PUFAs (inverse)[66]
Multiple myelomaRed blood cellLow n-3/n-6 PUFA ratio[74]
DementiaRed blood cells and plasmaIncreased Hexacosanoic Acid (C26:0)[7]
DementiaSerumALA (inverse)[78]
Alzheimer’s diseasePlasma and erythrocyteIncreased AA[79]
PlasmaDecreased DHA
The indexes AA/Dihomo-gamma-linolenic acid and C24:4 n-6/Adrenic acid (AdA) were both higher
Alzheimer’s diseaseRed blood cellDHA (inverse)[80]
ObesitySerumIncreased γ-linolenic acid[75]
ObesitySerumIncreased C9:0 and C19:0[76]
Drug-induced liver injurySerumAltered PUFA[83]
Hepatic steatosisSerumIncreased dihomo-γ-linolenic acid[84]
Severe traumatic brain injuryPlasmaIncreased decanoic and octanoic acids[82]
Neonatal morbidities and mortalityRed blood cellLow AA and DHA[88]
Autoimmune diseasesSerumdecreased n-6/n-3 ratio[85]
AutoimmunityRed blood cellDecreased levels of n-3 fatty acid and docosapentaenoic acid[86]
Allergy in childrenBloodIncreased C22:5 n-3 and decreased C18:1 n-9[87]

3.2. Polymorphisms of Gene Implicated in Fatty Acid Metabolism and Diseases

Many studies showed that the fatty acid in biological tissues is an indicator of fatty acid intake [89,90]. However, this is not always the case. Amézaga et al. showed that the variation in erythrocyte FA is not linked to dietary intake [65]. FA have an exogenous and endogenous origin. They can be provided by food or synthesized, mainly in hepatocytes. Several proteins are involved in the metabolism of FA (Figure 1). Any perturbation in the activity or quantity of those enzymes could be a cause of a fatty acid profile change, then disease development [5]. Enzyme activity and expression are under genetic and environmental control. In this paper, we try to summarize the contributions of some genetic polymorphisms of many proteins involved in fatty acid metabolism (FADS, ELOVL, Fatty acid binding protein and acetyl-coenzyme A carboxylase) to the development of certain diseases (Table 3).
Delta 9 MUFAs (mainly oleic acid) are synthesized under the control of many enzymes. The key enzyme of desaturation is stearoyl Co A desaturase (SCD: SCD1 and SCD5). This enzyme plays a key role in the development of many diseases. Thus, inhibiting it will have many beneficial effects. SCD1 enzyme was a potential target for cancer [91], non-alcoholic fatty liver disease [92], diabetes, obesity and hepatic steatosis [93]. The activity of SCD1 is usually calculated as C18:1/C18:0 ratio. Single Nucleotide Polymorphisms (SNP) in SCD1 gene (located on chromosome 10q24.31) are associated with many diseases. The rs41290540 SNP in the 3’-untranslated region is associated with decreased risk of coronary artery disease [94]. Liu explained this association based on the fact that this SNP increases SCD1 expression (in vitro study), and then the concentration of MUFA will increase and reduce total cholesterol concentration. Another SNP located in the 3’-untranslated region, the rs 1393492, is associated with metabolic syndrome in dialysis patients [95]. Guo et al. showed that AA genotype is associated with a large waist circumference, high blood pressure and high blood glucose. Another polymorphism (rs1393491) in SCD1 gene is associated with Grove’s ophthalmology [96].
The association between SCD1 gene polymorphism and disease development depends on many factors, such as diet: oil intake in case of obesity [97] and PUFA intake in case of cancer death [98]. The relationship between SCD1 and disease development could be explained by the variation in SCD1 expression according to gene variant, which influences the fatty acid profile (unsaturated: saturated fatty acid ratio) [99]. Despite the great attention attributed to SCD1 isoform, few studies on SCD5 isoform have been conducted. The rs3811792 SNP in SCD5 promoter is associated with type 1 and type 2 diabetes [100]. This polymorphism decreases SCD5 promoter activity. Zambo explained the association between the polymorphism and type 2 diabetes based on the fact that SCD5 regulates the distribution of fats and accumulation in viscus, which represents a diabetes risk factor. However, the association with type 1 diabetes is due to the overexpression of SCD5 in the pancreas compared to other tissues. Many other polymorphisms in SCD5 gene are described: rs6840, rs1065403, rs3821974 in 3′-UTR and rs4693472, rs6535374 in intron. All of them are associated with hepatocellular carcinoma [101].
PUFA synthesis requires other enzymes. Deasaturase and elongase enzymes involved in fatty acid synthesis from ALA and LA are summarized in Figure 2. Two types of desaturases exist: delta-6-desaturase (FA desaturase 2, FADS2) and delta5-desaturase (FA desaturase 1, FADS1). FADS2 converts substrates ALA and LA, respectively at C18:4 n-3 and C18:3 n-6. This enzyme is also involved in a second step leading to the desaturation of the C24:5 n-3 and C24:4 n-6 substrates to C24:6 n-3 and C24:5 n-6, respectively. FADS1 desaturated the FA C20:4 n-3 and C20:3 n-6, respectively into EPA and AA. The activity of elongases (ELOVL) on eighteen- and twenty-carbon FA and n-3 long-chain PUFA is the result of ELOVL5 gene expression. On the other hand, ELOVL2 is the gene allowing the elongation of PUFAs with twenty and twenty-two carbon atoms. The genes coding for desaturases and elongases are the most studied to explore the genetic effect of FA on the development of diseases [79,102,103,104]. Hammouda et al. showed that rs174556 TT genotype of FADS1 is associated with a higher AA level and AA/DGLA (Dihomo-gamma-linolenic acid) index and increased risk of Alzheimer’s disease. Additionally, rs3756963 TT genotype of ELOVL2 is associated with increased AA and AA/DGLA index and increased risk of Alzheimer’s disease. However, rs174617 of FADS2 is not associated with either the fatty acid profile or Alzheimer’s disease. The combination of the two variants increases further the susceptibility to Alzheimer’s disease.
This study [79] suggested that FADS1 and ELOVL2 variants could likely influence the AA biosynthesis, then the inflammation. Khamlaoui et al. studied the association between gene polymorphisms and obesity [103]. They showed that rs174556 C allele of FADS1, rs174617 of FADS2 and rs3756963 of ELOVL2 are risk factors of obesity. The two polymorphisms rs174556 of FADS1 and rs174617 of FADS2 are, respectively, associated with low DHA and High EPA. However, the association between ELOVL2 and FA was unclear. The above two studies suggest that the association between gene polymorphism and fatty acid profile could, in part, explain the association between those polymorphisms and disease development. Song et al. showed an association between rs174556 of FADS1 and Acute Coronary Syndrome and no association for FADS2 and ELOVL2 polymorphisms [102]. Sun et al. showed an association between rs526126 of FADS2, rs10498676, rs17606561, rs3756963 and rs9468304 of ELOVL2 and Autism spectrum disorder [104]. However, in the two cited studies, no analysis of fatty acid was performed. Sun et al. explained that FADS polymorphisms may cause irreversible functional and structural changes in neurons by acting on FA, which can lead to Autism spectrum disorder.
The fatty acid-binding protein (FABP) family includes several FABPs that are abundantly expressed in tissues with active fatty acid metabolism. FABPs would have several roles, including the intracellular transport of fatty acid chains and other lipophilic substances, such as eicosanoids and retinoids, from plasma membranes to sites of metabolism. Members of this family include FABP1 in liver, FABP2 in intestine, FABP3 in muscle and heart and FABP4 in adipocytes. Valizadeh et al. showed that the rs2241883 CC genotype of FABP1 gene is associated with dyslipidemia [105] and Xue et al. found that rs2197076 and rs2241883 of the same protein are associated with polycystic ovary syndrome [106]. According to Xue et al., rs2197076 seemed to have a more important role in the mechanism of polycystic ovary syndrome than rs2241883 because it is closely related to some important clinical features of polycystic ovary syndrome. Moreover, rs2197076 FABP1 is associated with type 2 diabetes [107], rs2241883 is associated with metabolic syndrome [108] and rs2241883 with rs1545224 are associated with non-alcoholic fatty liver [109]. The most FABP-studied polymorphism is located in FABP2 gene. It is an amino acid substitution (Ala54 to Thr54). This polymorphism is associated with diabetes, metabolic syndrome, obesity and non-alcoholic liver disease [110,111,112,113]. In diabetic patients, the polymorphism is associated with peripheral atherosclerosis, retinopathy and renal disease in diabetic patients [114,115,116]. Another polymorphism in FABP2 is associated with essential hypertension [117] and diabetes [118].
Acetyl-coenzyme A carboxylase (ACACB) catalyzes the synthesis of malonyl-CoA, a metabolite that plays an essential role in the synthesis and oxidation of FA. A meta-analysis showed that rs2268388 C allele in ACACB was inversely associated with susceptibility risk of diabetic nephropathy among Caucasian patients [119], Chinese patients [120] and Asian Indian patients [121]. However, Chan et al. did not find any association between ACACB gene polymorphism and cardiovascular risk susceptibility in type 2 diabetic patients [122]. In Spanish postmenopausal women, the rs2268388 T allele was associated with obesity and diabetes and the rs2239607 C allele was associated with diabetes [123]. Additionally, another polymorphism (rs47665887) in ACACB gene influence metabolic syndrome, but this association is modulated by dietary fat [124].
Genetics alone cannot confirm the presence of a disease, except in certain cases in which polymorphisms are mutations that directly affect the presence or function of the enzyme. However, genetic studies make it possible to predict diseases and help to manage them. In fact, diseases are usually multifactorial. One disease can be the result of many protein disturbances. Additionally, environmental factors such as diet, temperature and stress can modulate the effect of genetic factors.

4. Conclusions

FA and the genes involved in their metabolism can be considered predictors of certain disease development and progression. However, in many cases, the results are inconsistent. Additional studies should be considered to better highlight the factors that may influence these associations in order to finally define powerful predictors.

Author Contributions

Conceptualization, R.C.; writing—original draft preparation, R.C., A.B., A.B.A. and S.M.; writing—review and editing, M.H. and W.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Deanship of Scientific Research at University of Bisha-Saudi Arabia, through the project number UB-GRP-66-1444.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at University of Bisha for funding this research through the general research project under grant number (UB-GRP-66-1444).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Holman, R.T.; Johnson, S.B.; Hatch, T.F. A case of human linolenic acid deficiency involving neurological abnormalities. Am. J. Clin. Nutr. 1982, 35, 617–623. [Google Scholar] [CrossRef]
  2. Hansen, A.E.; Haggard, M.E.; Boelsche, A.N.; Adam, D.J.; Wiese, H.F. Essential fatty acids in infant nutrition: III. Clinical manifestations of linoleic acid deficiency. J. Nutr. 1958, 66, 565–576. [Google Scholar] [CrossRef]
  3. Guesnet, P.; Alessandri, J.-M.; Astorg, P.; Pifferi, F.; Lavialle, M. Les rôles physiologiques majeurs exercés par les acides gras polyinsaturés (AGPI). Oléagineux Corps Gras Lipides 2005, 12, 333–343. [Google Scholar] [CrossRef] [Green Version]
  4. Parsons, H.G.; Hill, R.; Pencharz, P.; Kuksis, A. Modulation of human erythrocyte shape and fatty acids by diet. Biochim. Et Biophys. Acta (BBA)—Biomembr. 1986, 860, 420–427. [Google Scholar] [CrossRef]
  5. Lankinen, M.; Uusitupa, M.; Schwab, U. Genes and Dietary Fatty Acids in Regulation of Fatty Acid Composition of Plasma and Erythrocyte Membranes. Nutrients 2018, 10, 1785. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Chamorro, R.; Gonzalez, M.F.; Aliaga, R.; Gengler, V.; Balladares, C.; Barrera, C.; Bascuñan, K.A.; Bazinet, R.P.; Valenzuela, R. Diet, Plasma, Erythrocytes, and Spermatozoa Fatty Acid Composition Changes in Young Vegan Men. Lipids 2020, 55, 639–648. [Google Scholar] [CrossRef]
  7. Zarrouk, A.; Riedinger, J.-M.; Ahmed, S.H.; Hammami, S.; Chaabane, W.; Debbabi, M.; Ben Ammou, S.; Rouaud, O.; Frih, M.; Lizard, G.; et al. Fatty Acid Profiles in Demented Patients: Identification of Hexacosanoic Acid (C26:0) as a Blood Lipid Biomarker of Dementia. J. Alzheimer’s Dis. 2015, 44, 1349–1359. [Google Scholar] [CrossRef] [PubMed]
  8. Sun, Q.; Ma, J.; Campos, H.; Hankinson, S.E.; Hu, F.B. Comparison between plasma and erythrocyte fatty acid content as biomarkers of fatty acid intake in US women. Am. J. Clin. Nutr. 2007, 86, 74–81. [Google Scholar] [CrossRef] [Green Version]
  9. Ahmed, S.H.; Koubaa, N.; Kharroubi, W.; Zarrouk, A.; Mnari, A.; Batbout, F.; Gamra, H.; Hammami, S.; Lizard, G.; Hammami, M. Identification of long and very long chain fatty acids, plasmalogen-C16: 0 and phytanic acid as new lipid biomarkers in Tunisian coronary artery disease patients. Prostaglandins Other Lipid Mediat. 2017, 131, 49–58. [Google Scholar] [CrossRef] [PubMed]
  10. Hodson, L.; Skeaff, C.M.; Fielding, B.A. Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Prog. Lipid Res. 2008, 47, 348–380. [Google Scholar] [CrossRef]
  11. Maulucci, G.; Cohen, O.; Daniel, B.; Sansone, A.; Petropoulou, P.I.; Filou, S.; Spyridonidis, A.; Pani, G.; De Spirito, M.; Chatgilialoglu, C.; et al. Fatty acid-related modulations of membrane fluidity in cells: Detection and implications. Free. Radic. Res. 2016, 50, S40–S50. [Google Scholar] [CrossRef] [Green Version]
  12. Stillwell, W.; Shaikh, S.R.; Zerouga, M.; Siddiqui, R.; Wassall, S.R. Docosahexaenoic acid affects cell signaling by altering lipid rafts. Reprod. Nutr. Dev. 2005, 45, 559–579. [Google Scholar] [CrossRef] [Green Version]
  13. Ghosh, A.; Gao, L.; Thakur, A.; Siu, P.M.; Lai, C.W.K. Role of free fatty acids in endothelial dysfunction. J. Biomed. Sci. 2017, 24, 50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Scott, J.S.; Nassar, Z.D.; Swinnen, J.V.; Butler, L.M. Monounsaturated Fatty Acids: Key Regulators of Cell Viability and Intracellular Signaling in Cancer. Mol. Cancer Res. 2022, 20, 1354–1364. [Google Scholar] [CrossRef] [PubMed]
  15. Wahle, K.W.J.; Rotondo, D.; Heys, S.D. Polyunsaturated fatty acids and gene expression in mammalian systems. Proc. Nutr. Soc. 2003, 62, 349–360. [Google Scholar] [CrossRef] [Green Version]
  16. Miles, E.; Childs, C.; Calder, P. Long-Chain Polyunsaturated Fatty Acids (LCPUFAs) and the Developing Immune System: A Narrative Review. Nutrients 2021, 13, 247. [Google Scholar] [CrossRef] [PubMed]
  17. Innes, J.K.; Calder, P.C. Omega-6 fatty acids and inflammation. Prostaglandins Leukot. Essent. Fat. Acids 2018, 132, 41–48. [Google Scholar] [CrossRef] [Green Version]
  18. Rogero, M.M.; Calder, P.C. Obesity, Inflammation, Toll-Like Receptor 4 and Fatty Acids. Nutrients 2018, 10, 432. [Google Scholar] [CrossRef] [Green Version]
  19. MacDonald-Ramos, K.; Martínez-Ibarra, A.; Monroy, A.; Miranda-Ríos, J.; Cerbón, M. Effect of Dietary Fatty Acids on MicroRNA Expression Related to Metabolic Disorders and Inflammation in Human and Animal Trials. Nutrients 2021, 13, 1830. [Google Scholar] [CrossRef] [PubMed]
  20. Krümmel, B.; von Hanstein, A.-S.; Plötz, T.; Lenzen, S.; Mehmeti, I. Differential effects of saturated and unsaturated free fatty acids on ferroptosis in rat β-cells. J. Nutr. Biochem. 2022, 106, 109013. [Google Scholar] [CrossRef] [PubMed]
  21. Sherratt, S.C.; Juliano, R.A.; Mason, R.P. Eicosapentaenoic acid (EPA) has optimal chain length and degree of unsaturation to inhibit oxidation of small dense LDL and membrane cholesterol domains as compared to related fatty acids in vitro. Biochim. et Biophys. Acta (BBA)—Biomembr. 2020, 1862, 183254. [Google Scholar] [CrossRef] [PubMed]
  22. Mitrovic, M.; Turnic, T.N.; Zivkovic, V.; Pavic, Z.; Vranic, A.; Srejovic, I.; Sretenovic, J.; Bolevich, S.; Jakovljevic, V.L. High-protein diet and omega-3 fatty acids improve redox status in olanzapine-treated rats. Mol. Cell. Biochem. 2020, 468, 143–152. [Google Scholar] [CrossRef]
  23. Mozaffarian, D.; Rosenberg, I.; Uauy, R. History of modern nutrition science—Implications for current research, dietary guidelines, and food policy. BMJ 2018, 361, k2392. [Google Scholar] [CrossRef] [Green Version]
  24. Estruch, R.; Ros, E.; Salas-Salvadó, J.; Covas, M.-I.; Corella, D.; Arós, F.; Gómez-Gracia, E.; Ruiz-Gutiérrez, V.; Fiol, M.; Lapetra, J.; et al. Primary Prevention of Cardiovascular Disease with a Mediterranean Diet. N. Engl. J. Med. 2013, 368, 1279–1290. [Google Scholar] [CrossRef] [Green Version]
  25. Wilczek, M.M.; Olszewski, R.; Krupienicz, A. Trans-Fatty Acids and Cardiovascular Disease: Urgent Need for Legislation. Cardiology 2017, 138, 254–258. [Google Scholar] [CrossRef]
  26. Sun, Q.; Ma, J.; Campos, H.; Hankinson, S.E.; Manson, J.E.; Stampfer, M.J.; Rexrode, K.M.; Willett, W.C.; Hu, F.B. A Prospective Study of Trans Fatty Acids in Erythrocytes and Risk of Coronary Heart Disease. Circulation 2007, 115, 1858–1865. [Google Scholar] [CrossRef] [Green Version]
  27. Mallick, R.; Duttaroy, A.K. Modulation of endothelium function by fatty acids. Mol. Cell. Biochem. 2021, 477, 15–38. [Google Scholar] [CrossRef] [PubMed]
  28. Fan, W.X.; Parker, R.; Parpia, B.; Qu, Y.S.; Cassano, P.; Crawford, M.; Leyton, J.; Tian, J.; Li, J.Y.; Chen, J.S. Erythrocyte fatty acids, plasma lipids, and cardiovascular disease in rural China. Am. J. Clin. Nutr. 1990, 52, 1027–1036. [Google Scholar] [CrossRef]
  29. Lemaitre, R.N.; McKnight, B.; Sotoodehnia, N.; Fretts, A.M.; Qureshi, W.T.; Song, X.; King, I.B.; Sitlani, C.M.; Siscovick, D.S.; Psaty, B.M.; et al. Circulating Very Long-Chain Saturated Fatty Acids and Heart Failure: The Cardiovascular Health Study. J. Am. Heart Assoc. 2018, 7, e010019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Ahmed, S.H.; Kharroubi, W.; Kaoubaa, N.; Zarrouk, A.; Batbout, F.; Gamra, H.; Najjar, M.F.; Lizard, G.; Hininger-Favier, I.; Hammami, M. Correlation of trans fatty acids with the severity of coronary artery disease lesions. Lipids Health Dis. 2018, 17, 52. [Google Scholar] [CrossRef] [Green Version]
  31. Guo, M.; Fan, X.; Tuerhongjiang, G.; Wang, C.; Wu, H.; Lou, B.; Wu, Y.; Yuan, Z.; She, J. Targeted metabolomic analysis of plasma fatty acids in acute myocardial infarction in young adults. Nutr. Metab. Cardiovasc. Dis. 2021, 31, 3131–3141. [Google Scholar] [CrossRef]
  32. Nishizaki, Y.; Shimada, K.; Tani, S.; Ogawa, T.; Ando, J.; Takahashi, M.; Yamamoto, M.; Shinozaki, T.; Miyazaki, T.; Miyauchi, K.; et al. Association between the ratio of serum n-3 to n-6 polyunsaturated fatty acids and acute coronary syndrome in non-obese patients with coronary risk factor: A multicenter cross-sectional study. BMC Cardiovasc. Disord. 2020, 20, 160–167. [Google Scholar] [CrossRef] [PubMed]
  33. Bittner, D.O.; Goeller, M.; Zopf, Y.; Achenbach, S.; Marwan, M. Early-onset coronary atherosclerosis in patients with low levels of omega-3 fatty acids. Eur. J. Clin. Nutr. 2020, 74, 651–656. [Google Scholar] [CrossRef]
  34. Bazan, H.A.; Lu, Y.; Jun, B.; Fang, Z.; Woods, T.C.; Hong, S. Circulating inflammation-resolving lipid mediators RvD1 and DHA are decreased in patients with acutely symptomatic carotid disease. Prostaglandins Leukot. Essent. Fat. Acids 2017, 125, 43–47. [Google Scholar] [CrossRef]
  35. Yang, W.-S.; Chen, Y.-Y.; Chen, P.-C.; Hsu, H.-C.; Su, T.-C.; Lin, H.-J.; Chen, M.-F.; Lee, Y.-T.; Chien, K.-L. Association between Plasma N-6 Polyunsaturated Fatty Acids Levels and the Risk of Cardiovascular Disease in a Community-based Cohort Study. Sci. Rep. 2019, 9, 19298. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Chien, K.-L.; Lin, H.-J.; Hsu, H.-C.; Chen, P.-C.; Su, T.-C.; Chen, M.-F.; Lee, Y.-T. Comparison of predictive performance of various fatty acids for the risk of cardiovascular disease events and all-cause deaths in a community-based cohort. Atherosclerosis 2013, 230, 140–147. [Google Scholar] [CrossRef]
  37. Würtz, P.; Havulinna, A.S.; Soininen, P.; Tynkkynen, T.; Prieto-Merino, D.; Tillin, T.; Ghorbani, A.; Artati, A.; Wang, Q.; Tiainen, M.; et al. Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts. Circulation 2015, 131, 774–785. [Google Scholar] [CrossRef] [Green Version]
  38. Dozio, E.; Vianello, E.; Grossi, E.; Menicanti, L.; Schmitz, G.; Romanelli, M.M.C. Plasma fatty acid profile as biomarker of coronary artery disease: A pilot study using fourth generation artificial neural networks. J. Biol. Regul. Homeost. Agents 2018, 32, 1007–1013. [Google Scholar]
  39. Muldoon, M.F.; Erickson, K.I.; Goodpaster, B.H.; Jakicic, J.M.; Conklin, S.M.; Sekikawa, A.; Yao, J.K.; Manuck, S.B. Concurrent Physical Activity Modifies the Association between n3 Long-Chain Fatty Acids and Cardiometabolic Risk in Midlife Adults. J. Nutr. 2013, 143, 1414–1420. [Google Scholar] [CrossRef] [Green Version]
  40. Harris, K.; Oshima, M.; Sattar, N.; Würtz, P.; Jun, M.; Welsh, P.; Hamet, P.; Harrap, S.; Poulter, N.; Chalmers, J.; et al. Plasma fatty acids and the risk of vascular disease and mortality outcomes in individuals with type 2 diabetes: Results from the ADVANCE study. Diabetologia 2020, 63, 1637–1647. [Google Scholar] [CrossRef]
  41. Shoji, T.; Kakiya, R.; Hayashi, T.; Tsujimoto, Y.; Sonoda, M.; Shima, H.; Mori, K.; Fukumoto, S.; Tahara, H.; Shioi, A.; et al. Serum n-3 and n-6 Polyunsaturated Fatty Acid Profile as an Independent Predictor of Cardiovascular Events in Hemodialysis Patients. Am. J. Kidney Dis. 2013, 62, 568–576. [Google Scholar] [CrossRef] [PubMed]
  42. Noguchi, H.; Okubo, R.; Hamazaki, K.; Yamashita, A.; Narisawa, T.; Matsuoka, Y.J. Serum polyunsaturated fatty acids and risk of psychiatric disorder at 6 months after acute coronary syndrome: A prospective cohort study. Prostaglandins Leukot. Essent. Fat. Acids 2019, 149, 18–23. [Google Scholar] [CrossRef]
  43. Ouchi, S.; Miyazaki, T.; Shimada, K.; Sugita, Y.; Shimizu, M.; Murata, A.; Kato, T.; Aikawa, T.; Suda, S.; Shiozawa, T.; et al. Decreased circulating dihomo-gamma-linolenic acid levels are associated with total mortality in patients with acute cardiovascular disease and acute decompensated heart failure. Lipids Health Dis. 2017, 16, 150. [Google Scholar] [CrossRef] [Green Version]
  44. Li, Z.; Zhang, Y.; Su, D.; Lv, X.; Wang, M.; Ding, D.; Ma, J.; Xia, M.; Wang, D.; Yang, Y.; et al. The opposite associations of long-chain versus very long-chain monounsaturated fatty acids with mortality among patients with coronary artery disease. Heart 2014, 100, 1597–1605. [Google Scholar] [CrossRef]
  45. Harris, W.S.; Kennedy, K.F.; O’Keefe, J.H.; Spertus, J.A. Red blood cell fatty acid levels improve GRACE score prediction of 2-yr mortality in patients with myocardial infarction. Int. J. Cardiol. 2013, 168, 53–59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Salgin, B.; Ong, K.K.; Thankamony, A.; Emmett, P.; Wareham, N.J.; Dunger, D.B. Higher Fasting Plasma Free Fatty Acid Levels Are Associated with Lower Insulin Secretion in Children and Adults and a Higher Incidence of Type 2 Diabetes. J. Clin. Endocrinol. Metab. 2012, 97, 3302–3309. [Google Scholar] [CrossRef] [Green Version]
  47. Clore, J.N.; Allred, J.; White, D.; Li, J.; Stillman, J. The role of plasma fatty acid composition in endogenous glucose production in patients with type 2 diabetes mellitus. Metabolism 2002, 51, 1471–1477. [Google Scholar] [CrossRef]
  48. Liu, L.; Li, Y.; Guan, C.; Li, K.; Wang, C.; Feng, R.; Sun, C. Free fatty acid metabolic profile and biomarkers of isolated post-challenge diabetes and type 2 diabetes mellitus based on GC–MS and multivariate statistical analysis. J. Chromatogr. B 2010, 878, 2817–2825. [Google Scholar] [CrossRef]
  49. Jo, S.; An, W.-S.; Park, Y. Erythrocyte n-3 Polyunsaturated Fatty Acids and the Risk of Type 2 Diabetes in Koreans: A Case-Control Study. Ann. Nutr. Metab. 2013, 63, 283–290. [Google Scholar] [CrossRef]
  50. Ma, Y.; Xiong, J.; Zhang, X.; Qiu, T.; Pang, H.; Li, X.; Zhu, J.; Wang, J.; Pan, C.; Yang, X.; et al. Potential biomarker in serum for predicting susceptibility to type 2 diabetes mellitus: Free fatty acid 22:6. J. Diabetes Investig. 2020, 12, 950–962. [Google Scholar] [CrossRef] [PubMed]
  51. Shetty, S.S.; N., S.K.; Shetty, P.K. ω-6/ω-3 fatty acid ratio as an essential predictive biomarker in the management of type 2 diabetes mellitus. Nutrition 2020, 79-80, 110968. [Google Scholar] [CrossRef] [PubMed]
  52. Lu, Y.; Wang, Y.; Ong, C.-N.; Subramaniam, T.; Choi, H.W.; Yuan, J.-M.; Koh, W.-P.; Pan, A. Metabolic signatures and risk of type 2 diabetes in a Chinese population: An untargeted metabolomics study using both LC-MS and GC-MS. Diabetologia 2016, 59, 2349–2359. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Grapov, D.; Adams, S.H.; Pedersen, T.L.; Garvey, W.T.; Newman, J.W. Type 2 Diabetes Associated Changes in the Plasma Non-Esterified Fatty Acids, Oxylipins and Endocannabinoids. PLoS ONE 2012, 7, e48852. [Google Scholar] [CrossRef] [Green Version]
  54. Yi, L.; He, J.; Liang, Y.; Yuan, D.; Gao, H.; Zhou, H. Simultaneously quantitative measurement of comprehensive profiles of esterified and non-esterified fatty acid in plasma of type 2 diabetic patients. Chem. Phys. Lipids 2007, 150, 204–216. [Google Scholar] [CrossRef]
  55. Sobczak, A.I.S.; Blindauer, C.A.; Stewart, A.J. Changes in Plasma Free Fatty Acids Associated with Type-2 Diabetes. Nutrients 2019, 11, 2022. [Google Scholar] [CrossRef] [Green Version]
  56. Ruddock, M.W.; Stein, A.; Landaker, E.; Park, J.; Cooksey, R.C.; McClain, D.; Patti, M.-E. Saturated Fatty Acids Inhibit Hepatic Insulin Action by Modulating Insulin Receptor Expression and Post-receptor Signalling. J. Biochem. 2008, 144, 599–607. [Google Scholar] [CrossRef]
  57. Miao, Z.; Lin, J.-S.; Mao, Y.; Chen, G.-D.; Zeng, F.-F.; Dong, H.-L.; Jiang, Z.; Wang, J.; Xiao, C.; Shuai, M.; et al. Erythrocyte n-6 Polyunsaturated Fatty Acids, Gut Microbiota, and Incident Type 2 Diabetes: A Prospective Cohort Study. Diabetes Care 2020, 43, 2435–2443. [Google Scholar] [CrossRef] [PubMed]
  58. Patel, P.S.; Sharp, S.J.; Jansen, E.; Luben, R.N.; Khaw, K.-T.; Wareham, N.J.; Forouhi, N.G. Fatty acids measured in plasma and erythrocyte-membrane phospholipids and derived by food-frequency questionnaire and the risk of new-onset type 2 diabetes: A pilot study in the European Prospective Investigation into Cancer and Nutrition (EPIC)—Norfolk cohort. Am. J. Clin. Nutr. 2010, 92, 1214–1222. [Google Scholar] [CrossRef] [Green Version]
  59. Takkunen, M.J.; the DPS Study Group; Schwab, U.S.; de Mello, V.D.; Eriksson, J.G.; Lindstroöm, J.; Tuomilehto, J.; Uusitupa, M.I.J. Longitudinal associations of serum fatty acid composition with type 2 diabetes risk and markers of insulin secretion and sensitivity in the Finnish Diabetes Prevention Study. Eur. J. Nutr. 2015, 55, 967–979. [Google Scholar] [CrossRef] [PubMed]
  60. Liu, B.; Sun, Y.; Snetselaar, L.G.; Sun, Q.; Yang, Q.; Zhang, Z.; Liu, L.; Hu, F.B.; Bao, W. Association between plasma trans-fatty acid concentrations and diabetes in a nationally representative sample of US adults. J. Diabetes 2018, 10, 653–664. [Google Scholar] [CrossRef]
  61. Prada, M.; Wittenbecher, C.; Eichelmann, F.; Wernitz, A.; Kuxhaus, O.; Kröger, J.; Weikert, C.; Schulze, M.B. Plasma Industrial and Ruminant Trans Fatty Acids and Incident Type 2 Diabetes in the EPIC-Potsdam Cohort. Diabetes Care 2022, 45, 845–853. [Google Scholar] [CrossRef]
  62. Luo, C.; Liu, H.; Wang, X.; Xia, L.; Huang, H.; Peng, X.; Xia, C.; Liu, L. The associations between individual plasma SFAs, serine palmitoyl-transferase long-chain base subunit 3 gene rs680379 polymorphism, and type 2 diabetes among Chinese adults. Am. J. Clin. Nutr. 2021, 114, 704–712. [Google Scholar] [CrossRef] [PubMed]
  63. Zhao, S.; Jin, D.; Wang, S.; Xu, Y.; Li, H.; Chang, Y.; Ma, Y.; Xu, Y.; Guo, C.; Peng, F.; et al. Serum ω-6/ω-3 polyunsaturated fatty acids ratio and diabetic retinopathy: A propensity score matching based case-control study in China. eClinicalMedicine 2021, 39, 101089. [Google Scholar] [CrossRef]
  64. Afshinnia, F.; Rajendiran, T.M.; He, C.; Byun, J.; Montemayor, D.; Darshi, M.; Tumova, J.; Kim, J.; Limonte, C.P.; Miller, R.G.; et al. Circulating Free Fatty Acid and Phospholipid Signature Predicts Early Rapid Kidney Function Decline in Patients with Type 1 Diabetes. Diabetes Care 2021, 44, 2098–2106. [Google Scholar] [CrossRef]
  65. Amézaga, J.; Arranz, S.; Urruticoechea, A.; Ugartemendia, G.; Larraioz, A.; Louka, M.; Uriarte, M.; Ferreri, C.; Tueros, I. Altered Red Blood Cell Membrane Fatty Acid Profile in Cancer Patients. Nutrients 2018, 10, 1853. [Google Scholar] [CrossRef] [Green Version]
  66. Jiao, J.; Kwan, S.-Y.; Sabotta, C.M.; Tanaka, H.; Veillon, L.; Warmoes, M.O.; Lorenzi, P.L.; Wang, Y.; Wei, P.; Hawk, E.T.; et al. Circulating Fatty Acids Associated with Advanced Liver Fibrosis and Hepatocellular Carcinoma in South Texas Hispanics. Cancer Epidemiol. Biomark. Prev. 2021, 30, 1643–1651. [Google Scholar] [CrossRef]
  67. Chen, Q.; Wang, J.; Lin, J.; Chen, L.; Lin, L.-S.; Pan, L.-Z.; Shi, B.; Qiu, Y.; Zheng, X.-Y.; Chen, F.; et al. Erythrocyte ω-3 polyunsaturated fatty acids are inversely associated with the risk of oral cancer: A case-control study. Nutr. Diabetes 2020, 10, 35. [Google Scholar] [CrossRef]
  68. Shishavan, N.G.; Mohamadkhani, A.; Sepanlou, S.G.; Masoudi, S.; Sharafkhah, M.; Poustchi, H.; Hekmatdoost, A.; Pourshams, A. Circulating plasma fatty acids and risk of pancreatic cancer: Results from the Golestan Cohort Study. Clin. Nutr. 2020, 40, 1897–1904. [Google Scholar] [CrossRef] [PubMed]
  69. Zaridze, D.G.; Chevchenko, V.E.; Levtshuk, A.A.; Lifanova, Y.E.; Maximovitch, D.M. Fatty acid composition of phospholipids in erythrocyte membranes and risk of breast cancer. Int. J. Cancer 1990, 45, 807–810. [Google Scholar] [CrossRef]
  70. Hirko, K.A.; Chai, B.; Spiegelman, D.; Campos, H.; Farvid, M.S.; Hankinson, S.E.; Willett, W.C.; Eliassen, A.H. Erythrocyte membrane fatty acids and breast cancer risk: A prospective analysis in the nurses’ health study II. Int. J. Cancer 2017, 142, 1116–1129. [Google Scholar] [CrossRef] [PubMed]
  71. McGee, E.E.; Kim, C.H.; Wang, M.; Spiegelman, D.; Stover, D.G.; Heng, Y.J.; Collins, L.C.; Baker, G.M.; Farvid, M.S.; Schedin, P.; et al. Erythrocyte membrane fatty acids and breast cancer risk by tumor tissue expression of immuno-inflammatory markers and fatty acid synthase: A nested case-control study. Breast Cancer Res. 2020, 22, 78. [Google Scholar] [CrossRef] [PubMed]
  72. Ruan, X.; Wang, Y.; Zhou, L.; Zheng, Q.; Hao, H.; He, D. Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer. Front. Pharmacol. 2022, 13, 894099. [Google Scholar] [CrossRef]
  73. Komi, D.E.A.; Shekari, N.; Soofian-Kordkandi, P.; Javadian, M.; Shanehbandi, D.; Baradaran, B.; Kazemi, T. Docosahexaenoic acid (DHA) and linoleic acid (LA) modulate the expression of breast cancer involved miRNAs in MDA-MB-231 cell line. Clin. Nutr. ESPEN 2021, 46, 477–483. [Google Scholar] [CrossRef] [PubMed]
  74. Jurczyszyn, A.; Czepiel, J.; Gdula-Argasińska, J.; Czapkiewicz, A.; Biesiada, G.; Dróżdż, M.; Perucki, W.; Castillo, J.J. Erythrocyte membrane fatty acids in multiple myeloma patients. Leuk. Res. 2014, 38, 1260–1265. [Google Scholar] [CrossRef]
  75. Kaikkonen, J.E.; Jula, A.; Viikari, J.S.A.; Juonala, M.; Hutri-Kähönen, N.; Kähönen, M.; Lehtimäki, T.; Raitakari, O.T. Associations of Serum Fatty Acid Proportions with Obesity, Insulin Resistance, Blood Pressure, and Fatty Liver: The Cardiovascular Risk in Young Finns Study. J. Nutr. 2021, 151, 970–978. [Google Scholar] [CrossRef]
  76. Ma, Y.; Qiu, T.; Zhu, J.; Wang, J.; Li, X.; Deng, Y.; Zhang, X.; Feng, J.; Chen, K.; Wang, C.; et al. Serum FFAs profile analysis of Normal weight and obesity individuals of Han and Uygur nationalities in China. Lipids Health Dis. 2020, 19, 13. [Google Scholar] [CrossRef] [Green Version]
  77. Ni, Y.; Zhao, L.; Yu, H.; Ma, X.; Bao, Y.; Rajani, C.; Loo, L.W.; Shvetsov, Y.B.; Yu, H.; Chen, T.; et al. Circulating Unsaturated Fatty Acids Delineate the Metabolic Status of Obese Individuals. eBioMedicine 2015, 2, 1513–1522. [Google Scholar] [CrossRef] [Green Version]
  78. Yamagishi, K.; Ikeda, A.; Chei, C.-L.; Noda, H.; Umesawa, M.; Cui, R.; Muraki, I.; Ohira, T.; Imano, H.; Sankai, T.; et al. Serum α-linolenic and other ω-3 fatty acids, and risk of disabling dementia: Community-based nested case–control study. Clin. Nutr. 2016, 36, 793–797. [Google Scholar] [CrossRef] [PubMed]
  79. Hammouda, S.; Ghzaiel, I.; Khamlaoui, W.; Hammami, S.; Mhenni, S.Y.; Samet, S.; Hammami, M.; Zarrouk, A. Genetic variants in FADS1 and ELOVL2 increase level of arachidonic acid and the risk of Alzheimer’s disease in the Tunisian population. Prostaglandins Leukot. Essent. Fat. Acids 2020, 160, 102159. [Google Scholar] [CrossRef]
  80. Sala-Vila, A.; Satizabal, C.L.; Tintle, N.; van Lent, D.M.; Vasan, R.S.; Beiser, A.S.; Seshadri, S.; Harris, W.S. Red Blood Cell DHA Is Inversely Associated with Risk of Incident Alzheimer’s Disease and All-Cause Dementia: Framingham Offspring Study. Nutrients 2022, 14, 2408. [Google Scholar] [CrossRef]
  81. Tomata, Y.; Larsson, S.C.; Hägg, S. Polyunsaturated fatty acids and risk of Alzheimer’s disease: A Mendelian randomization study. Eur. J. Nutr. 2019, 59, 1763–1766. [Google Scholar] [CrossRef] [Green Version]
  82. Orešič, M.; Posti, J.; Kamstrup-Nielsen, M.H.; Takala, R.S.; Lingsma, H.F.; Mattila, I.; Jäntti, S.; Katila, A.J.; Carpenter, K.; Ala-Seppälä, H.; et al. Human Serum Metabolites Associate With Severity and Patient Outcomes in Traumatic Brain Injury. eBioMedicine 2016, 12, 118–126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Zhao, S.; Fu, H.; Zhou, T.; Cai, M.; Huang, Y.; Gan, Q.; Zhang, C.; Qian, C.; Wang, J.; Zhang, Z.; et al. Alteration of Bile Acids and Omega-6 PUFAs Are Correlated With the Progression and Prognosis of Drug-Induced Liver Injury. Front. Immunol. 2022, 13, 772368. [Google Scholar] [CrossRef] [PubMed]
  84. Matsuda, M.; Kawamoto, T.; Tamura, R. Predictive value of serum dihomo-γ-linolenic acid level and estimated Δ-5 desaturase activity in patients with hepatic steatosis. Obes. Res. Clin. Pract. 2017, 11, 34–43. [Google Scholar] [CrossRef]
  85. Tsoukalas, D.; Fragoulakis, V.; Sarandi, E.; Docea, A.O.; Papakonstaninou, E.; Tsilimidos, G.; Anamaterou, C.; Fragkiadaki, P.; Aschner, M.; Tsatsakis, A.; et al. Targeted Metabolomic Analysis of Serum Fatty Acids for the Prediction of Autoimmune Diseases. Front. Mol. Biosci. 2019, 6, 120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Norris, J.M.; Kroehl, M.; Fingerlin, T.E.; Frederiksen, B.N.; Seifert, J.; Wong, R.; Clare-Salzler, M.; Rewers, M. Erythrocyte membrane docosapentaenoic acid levels are associated with islet autoimmunity: The Diabetes Autoimmunity Study in the Young. Diabetologia 2013, 57, 295–304. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Mikkelsen, A.; Galli, C.; Eiben, G.; Ahrens, W.; Iacoviello, L.; Molnár, D.; Pala, V.; Risé, P.; Rodriguez, G.; Russo, P.; et al. Blood fatty acid composition in relation to allergy in children aged 2–9 years: Results from the European IDEFICS study. Eur. J. Clin. Nutr. 2016, 71, 39–44. [Google Scholar] [CrossRef] [PubMed]
  88. Fares, S.; Sethom, M.; Hammami, M.; Cheour, M.; Feki, M.; Hadj-Taieb, S.; Kacem, S. Postnatal RBC arachidonic and docosahexaenoic acids deficiencies are associated with higher risk of neonatal morbidities and mortality in preterm infants. Prostaglandins Leukot. Essent. Fat. Acids 2017, 126, 112–116. [Google Scholar] [CrossRef]
  89. Jaček, M.; Hrnčířová, D.; Rambousková, J.; Dlouhý, P.; Tůma, P. Effect of Food with Low Enrichment of N-3 Fatty Acids in a Two-Month Diet on the Fatty Acid Content in the Plasma and Erythrocytes and on Cardiovascular Risk Markers in Healthy Young Men. Nutrients 2020, 12, 2207. [Google Scholar] [CrossRef]
  90. Seethaler, B.; Basrai, M.; Vetter, W.; Lehnert, K.; Engel, C.; Siniatchkin, M.; Halle, M.; Kiechle, M.; Bischoff, S.C. Fatty acid profiles in erythrocyte membranes following the Mediterranean diet—Data from a multicenter lifestyle intervention study in women with hereditary breast cancer (LIBRE). Clin. Nutr. 2019, 39, 2389–2398. [Google Scholar] [CrossRef]
  91. Raeisi, M.; Hassanbeigi, L.; Khalili, F.; Kharrati-Shishavan, H.; Yousefi, M.; Mehdizadeh, A. Stearoyl-CoA desaturase 1 as a therapeutic target for cancer: A focus on hepatocellular carcinoma. Mol. Biol. Rep. 2022, 49, 8871–8882. [Google Scholar] [CrossRef]
  92. Stearoyl-CoA desaturase 1: A potential target for non-alcoholic fatty liver disease?-perspective on emerging experimental evidence. World J. Hepatol. 2022, 14, 168–179. [CrossRef] [PubMed]
  93. Iida, T.; Ubukata, M.; Mitani, I.; Nakagawa, Y.; Maeda, K.; Imai, H.; Ogoshi, Y.; Hotta, T.; Sakata, S.; Sano, R.; et al. Discovery of potent liver-selective stearoyl-CoA desaturase-1 (SCD1) inhibitors, thiazole-4-acetic acid derivatives, for the treatment of diabetes, hepatic steatosis, and obesity. Eur. J. Med. Chem. 2018, 158, 832–852. [Google Scholar] [CrossRef]
  94. Liu, Z.; Yin, X.; Mai, H.; Li, G.; Lin, Z.; Jie, W.; Li, K.; Zhou, H.; Wei, S.; Hu, L.; et al. SCD rs41290540 single-nucleotide polymorphism modifies miR-498 binding and is associated with a decreased risk of coronary artery disease. Mol. Genet. Genom. Med. 2020, 8, e1136. [Google Scholar] [CrossRef] [Green Version]
  95. Guo, Y.; Xiong, Z.; Su, M.; Huang, L.; Liao, J.; Xiao, H.; Huang, X.; Xiong, Z. Positive association of SCD1 genetic variation and metabolic syndrome in dialysis patients in China. Pers. Med. 2020, 17, 111–119. [Google Scholar] [CrossRef]
  96. Planck, T.; Shahida, B.; Sjögren, M.; Groop, L.; Hallengren, B.; Lantz, M. Association of BTG2, CYR61, ZFP36, and SCD Gene Polymorphisms with Graves’ Disease and Ophthalmopathy. Thyroid 2014, 24, 1156–1161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Martín-Núñez, G.M.; Cabrera-Mulero, R.; Rojo-Martínez, G.; Gómez-Zumaquero, J.M.; Chaves, F.J.; de Marco, G.; Soriguer, F.; Castaño, L.; Morcillo, S. Polymorphisms in the SCD1 gene are associated with indices of stearoyl CoA desaturase activity and obesity: A prospective study. Mol. Nutr. Food Res. 2013, 57, 2177–2184. [Google Scholar] [CrossRef] [PubMed]
  98. Byberg, L.; Kilander, L.; Lemming, E.W.; Michaëlsson, K.; Vessby, B. Cancer death is related to high palmitoleic acid in serum and to polymorphisms in the SCD-1 gene in healthy Swedish men. Am. J. Clin. Nutr. 2014, 99, 551–558. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  99. Tibori, K.; Orosz, G.; Zámbó, V.; Szelényi, P.; Sarnyai, F.; Tamási, V.; Rónai, Z.; Mátyási, J.; Tóth, B.; Csala, M.; et al. Molecular Mechanisms Underlying the Elevated Expression of a Potentially Type 2 Diabetes Mellitus Associated SCD1 Variant. Int. J. Mol. Sci. 2022, 23, 6221. [Google Scholar] [CrossRef]
  100. Zámbó, V.; Orosz, G.; Szabó, L.; Tibori, K.; Sipeki, S.; Molnár, K.; Csala, M.; Kereszturi, É. A Single Nucleotide Polymorphism (rs3811792) Affecting Human SCD5 Promoter Activity Is Associated with Diabetes Mellitus. Genes 2022, 13, 1784. [Google Scholar] [CrossRef]
  101. Yu, G.I.; Mun, K.H.; Yang, S.H.; Shin, D.H.; Hwang, J.S. Polymorphisms in the 3′-UTR of SCD5 gene are associated with hepatocellular carcinoma in Korean population. Mol. Biol. Rep. 2018, 45, 1705–1714. [Google Scholar] [CrossRef] [PubMed]
  102. Song, Z.; Cao, H.; Qin, L.; Jiang, Y. A Case-Control Study between Gene Polymorphisms of Polyunsaturated Fatty Acid Metabolic Rate-Limiting Enzymes and Acute Coronary Syndrome in Chinese Han Population. BioMed Res. Int. 2013, 2013, 928178. [Google Scholar] [CrossRef]
  103. Khamlaoui, W.; Mehri, S.; Hammami, S.; Hammouda, S.; Chraeif, I.; Elosua, R.; Hammami, M. Association Between Genetic Variants in FADS1-FADS2 and ELOVL2 and Obesity, Lipid Traits, and Fatty Acids in Tunisian Population. Clin. Appl. Thromb. 2020, 26. [Google Scholar] [CrossRef]
  104. Sun, C.; Zou, M.; Wang, X.; Xia, W.; Ma, Y.; Liang, S.; Hao, Y.; Wu, L.; Fu, S. FADS1-FADS2 and ELOVL2 gene polymorphisms in susceptibility to autism spectrum disorders in Chinese children. BMC Psychiatry 2018, 18, 283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Valizadeh, M.; Aghasizadeh, M.; Nemati, M.; Hashemi, M.; Aghaee-Bakhtiari, S.H.; Zare-Feyzabadi, R.; Esmaily, H.; Ghazizdaeh, H.; Sahebi, R.; Ahangari, N.; et al. The association between a Fatty Acid Binding Protein 1 (FABP1) gene polymorphism and serum lipid abnormalities in the MASHAD cohort study. Prostaglandins Leukot. Essent. Fat. Acids 2021, 172, 102324. [Google Scholar] [CrossRef] [PubMed]
  106. Xue, H.; Zhao, H.; Liu, X.; Zhao, Y.-R.; Chen, Z.-J.; Ma, J. Association of single-nucleotide polymorphisms rs2197076 and rs2241883 of FABP1 gene with polycystic ovary syndrome. J. Assist. Reprod. Genet. 2015, 33, 75–83. [Google Scholar] [CrossRef] [Green Version]
  107. Mansego, M.L.; Martínez, F.; Martínez-Larrad, M.T.; Zabena, C.; Rojo, G.; Morcillo, S.; Soriguer, F.; Martín-Escudero, J.C.; Serrano-Ríos, M.; Redon, J.; et al. Common Variants of the Liver Fatty Acid Binding Protein Gene Influence the Risk of Type 2 Diabetes and Insulin Resistance in Spanish Population. PLoS ONE 2012, 7, e31853. [Google Scholar] [CrossRef]
  108. Mozaffari, M.; Ghayour-Mobarhan, M.; Zare-Feyzabadi, R.; Valizadeh, M. FABP1 gene variant associated with risk of metabolic syndrome. Comb. Chem. High Throughput Screen. 2022, 25, 1355–1360. [Google Scholar] [CrossRef] [PubMed]
  109. Peng, X.-E.; Wu, Y.-L.; Lu, Q.-Q.; Hu, Z.-J.; Lin, X. Two genetic variants in FABP1 and susceptibility to non-alcohol fatty liver disease in a Chinese population. Gene 2012, 500, 54–58. [Google Scholar] [CrossRef]
  110. Fisher, E.; Li, Y.; Burwinkel, B.; Kühr, V.; Hoffmann, K.; Möhlig, M.; Spranger, J.; Pfeiffer, A.; Boeing, H.; Schrezenmeir, J.; et al. Preliminary Evidence of FABP2 A54T Polymorphism Associated with Reduced Risk of Type 2 Diabetes and Obesity in Women from a German Cohort. Horm. Metab. Res. 2006, 38, 341–345. [Google Scholar] [CrossRef]
  111. Shabana; Hasnain, S. The fatty acid binding protein 2 (FABP2) polymorphism Ala54Thr and obesity in Pakistan: A population based study and a systematic meta-analysis. Gene 2015, 574, 106–111. [Google Scholar] [CrossRef] [PubMed]
  112. Liu, Y.; Wu, G.; Han, L.; Zhao, K.; Qu, Y.; Xu, A.; Huang, Q. Association of the FABP2 Ala54Thr polymorphism with type 2 diabetes, obesity, and metabolic syndrome: A population-based case-control study and a systematic meta-analysis. Genet. Mol. Res. 2015, 14, 1155–1168. [Google Scholar] [CrossRef]
  113. Peng, X.; Zhang, L.; Wang, Q.; Cui, X. Study on the relationship between FABP2 Ala54Thr polymorphism and the risk of non-alcoholic fatty liver diseases. Wei Sheng Yan Jiu 2009, 38, 401–404. [Google Scholar]
  114. Khattab, S.A.; Abo-Elmatty, D.; Ghattas, M.H.; Mesbah, N.; Mehanna, E.T. Intestinal fatty acid binding protein Ala54Thr polymorphism is associated with peripheral atherosclerosis combined with type 2 diabetes mellitus. J. Diabetes 2016, 9, 821–826. [Google Scholar] [CrossRef] [Green Version]
  115. Li, Z.; Ni, C.-L.; Niu, W.-Y.; Chang, B.-C.; Chen, L.-M. The intestinal fatty acid binding protein-2 Ala54Thr polymorphism is associated with diabetic retinopathy in Chinese population. Diabetol. Metab. Syndr. 2015, 7, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Canani, L.H.; Capp, C.; Ng, D.P.; Choo, S.G.; Maia, A.L.; Nabinger, G.B.; Santos, K.; Crispim, D.; Roisemberg, I.; Krolewski, A.S.; et al. The Fatty Acid–Binding Protein-2 A54T Polymorphism Is Associated With Renal Disease in Patients With Type 2 Diabetes. Diabetes 2005, 54, 3326–3330. [Google Scholar] [CrossRef] [Green Version]
  117. Abbas, S.; Raza, S.T.; Chandra, A.; Rizvi, S.; Ahmed, F.; Eba, A.; Mahdi, F. Association of ACE, FABP2 and GST genes polymorphism with essential hypertension risk among a North Indian population. Ann. Hum. Biol. 2014, 42, 461–469. [Google Scholar] [CrossRef]
  118. Sikhayeva, N.; Iskakova, A.; Saigi-Morgui, N.; Zholdybaeva, E.; Eap, C.-B.; Ramanculov, E. Association between 28 single nucleotide polymorphisms and type 2 diabetes mellitus in the Kazakh population: A case-control study. BMC Med. Genet. 2017, 18, 76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. An, L.; Jiang, H.; Tang, R.-N. The ACACB gene rs2268388 polymorphism is associated with nephropathy in Caucasian patients with diabetes: A meta-analysis. Ren. Fail. 2015, 37, 925–928. [Google Scholar] [CrossRef] [Green Version]
  120. Tang, S.C.W.; Leung, V.T.M.; Chan, L.Y.Y.; Wong, S.S.H.; Chu, D.W.S.; Leung, J.C.K.; Ho, Y.W.; Lai, K.N.; Ma, L.; Elbein, S.C.; et al. The acetyl-coenzyme A carboxylase beta (ACACB) gene is associated with nephropathy in Chinese patients with type 2 diabetes. Nephrol. Dial. Transplant. 2010, 25, 3931–3934. [Google Scholar] [CrossRef] [Green Version]
  121. Shah, V.N.; Cheema, B.S.; Sharma, R.; Khullar, M.; Kohli, H.S.; Ahluwalia, T.S.; Mohan, V.; Bhansali, A. ACACβ gene (rs2268388) and AGTR1 gene (rs5186) polymorphism and the risk of nephropathy in Asian Indian patients with type 2 diabetes. Mol. Cell. Biochem. 2012, 372, 191–198. [Google Scholar] [CrossRef] [PubMed]
  122. Chan, G.C.W.; Zhi, H.; Hicks, P.J.; Freedman, B.I.; Tang, S.C.W. Acetyl-coenzyme A carboxylase beta gene polymorphism does not predict cardiovascular risk susceptibility in Chinese type 2 diabetic individuals. Nephrology 2022, 27, 404–409. [Google Scholar] [CrossRef] [PubMed]
  123. Riancho, J.; Vázquez, L.; García-Pérez, M.; Sainz, J.; Olmos, J.; Hernández, J.; Pérez-López, J.; Amado, J.; Zarrabeitia, M.; Cano, A.; et al. Association of ACACB polymorphisms with obesity and diabetes. Mol. Genet. Metab. 2011, 104, 670–676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Phillips, C.M.; Goumidi, L.; Bertrais, S.; Field, M.R.; Cupples, L.A.; Ordovas, J.M.; McMonagle, J.; Defoort, C.; Lovegrove, J.A.; Drevon, C.A.; et al. ACC2 gene polymorphisms, metabolic syndrome, and gene-nutrient interactions with dietary fat. J. Lipid Res. 2010, 51, 3500–3507. [Google Scholar] [CrossRef] [Green Version]
Diagnostics 13 00979 g001 550
Figure 1. Polymorphisms of genes for proteins involved in fatty acid metabolism. ACACB: Acetyl-coenzyme A carboxylase, ELOVL: Elongase, FADS: Fatty Acid Desaturase, FABP: Fatty Acid Binding Protein, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, SCD: stearoyl Co A desaturase.
Figure 1. Polymorphisms of genes for proteins involved in fatty acid metabolism. ACACB: Acetyl-coenzyme A carboxylase, ELOVL: Elongase, FADS: Fatty Acid Desaturase, FABP: Fatty Acid Binding Protein, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, SCD: stearoyl Co A desaturase.
Diagnostics 13 00979 g001
Diagnostics 13 00979 g002 550
Figure 2. Desaturase and elongase metabolic pathway in long-chain fatty acid synthesis.
Figure 2. Desaturase and elongase metabolic pathway in long-chain fatty acid synthesis.
Diagnostics 13 00979 g002
Table
Table 1. Fatty acid profile in three different studies.
Table 1. Fatty acid profile in three different studies.
Study 1 [6]Study 2 [7]Study 3 [8]
SamplesPlasmaRBCPlasmaRBCPlasmaRBC
Total SFA29.9 ± 2.342.1 ± 4.634.22 ± 3.2650.56 ± 6.8228.71 ± 2.3834.37 ± 1.60
Lauric acid, 12:00.09 ± 0.02---0.02 ± 0.030.002 ±0.01
Myristic acid, 14:00.95 ± 0.30.73 ± 0.2--0.58 ± 0.280.19 ± 0.10
Palmitic acid, 16:021.1 ± 1.925.9 ± 3.026.24 ± 2.7530.05 ± 4.7119.31 ± 2.4118.65 ± 1.86
Stearic acid, 18:07.08 ± 0.813.2 ± 1.27.16 ± 1.5216.51 ± 3.417.29 ± 0.7813.14 ± 1.00
Arachidic acid, 20:00.06 ± 0.050.14 ± 0.10.25 ± 0.350.62 ± 0.27--
Behenic acid, 22:00.19 ± 0.070.50 ± 0.20.30 ± 0.121.71 ± 0.62--
Lignoceric acid, 24:00.17 ± 0.081.71 ± 0.70.17 ± 0.050.79 ± 0.29--
Cerotic acid, 26:0--0.1 ± 0.050.88 ± 0.06--
Total MUFA25.0 ± 4.320.8 ± 2.928.2 ± 3.4830.71 ± 5.1322.86 ± 3.0118.64 ± 1.24
Myristoleic acid, 14:1 n-50.01 ± 0.010.03 ± 0.11.32 ± 1.211.52 ± 1.23--
Palmitoleic acid, 16:1 n-5--2.53 ± 0.91.53 ± 0.97--
Oleic acid, 18:1 n-922.7 ± 3.418.4 ± 2.421.01 ± 3.5622.01 ± 4.6218.60 ± 2.4313.26 ± 1.17
cis-Vaccenic acid, 18:1 n-7--1.93 ± 0.482.41 ± 0.68--
Eicosenoic acid, 20:1 n-90.25 ± 0.10.90 ± 0.20.64 ± 0.130.49 ± 0.29--
Erucic acid, 22:1 n-9--0.43 ± 0.161.33 ± 1.05--
Nervonic acid, 24:1 n-9--0.34 ± 0.021.42 ± 1.08--
Total PUFA45.1 ± 4.937.1 ± 6.737.58 ± 4.6718.73 ± 4.9744.87 ± 4.8143.48 ± 1.77
Alpha-linolenic acid, 18:3 n-30.69 ± 0.10.12 ± 0.010.71 ± 0.230.47 ± 0.470.50 ± 0.150.18 ± 0.05
Eicosatrienoic acid, 20:3 n-3--1.75 ± 0.481.71 ± 0.98--
Eicosapentaenoic acid, 20:5 n-30.50 ± 0.20.32 ± 0.20.30 ± 0.310.61 ± 0.410.49 ± 0.211.15 ± 0.91
Docosahexaenoic acid, 22:6 n-3--1.43 ± 0.270.73 ± 0.071.56 ± 0.603.71 ± 1.09
Linoleic acid, 18:2 n-633.7 ± 4.514.8 ± 2.227.78 ± 5.269.49 ± 3.3130.58 ± 4.3313.66 ± 1.80
linolenic acid, 18:3 n-60.36 ± 0.10.40 ± 0.20.59 ± 0.300.39 ± 0.45--
Dihomo-c-linolenic acid, 20:3 n-61.29 ± 0.31.50 ± 0.40.57 ± 0.372.01 ± 0.85--
Arachidonic acid, 20:4 n-66.27 ± 1.64.82 ± 1.524.45 ± 1.243.32 ± 2.067.80 ± 1.6214.63 ± 1.29
Table
Table 3. Association between genetic polymorphisms, fatty acid characteristics and diseases.
Table 3. Association between genetic polymorphisms, fatty acid characteristics and diseases.
GenesPolymorphismsDiseasesReferences
SCD1rs41290540Decreased risk of coronary artery disease[62]
rs1393492Metabolic syndrome[63]
rs1393491Graves’ Ophthalmopathy[64]
SCD5rs3811792Diabetes[68]
rs6840, rs1065403, rs3821974, rs4693472, rs6535374Hepatocellular carcinoma[69]
FADS1rs174556Increase the risk of Alzheimer’s disease[53]
Acute Coronary Syndrome[70]
Obesity[71]
FADS2rs174617No effect on the risk of Alzheimer’s disease[53]
obesity[71]
rs526126Autism spectrum disorder[72]
ELOVL2rs3756963Increase the risk of Alzheimer’s disease[53]
obesity[71]
rs10498676, rs17606561, rs3756963 and rs9468304Autism spectrum disorder[72]
FABP1rs2241883Dyslipidemia[73]
rs2197076 and rs2241883Polycystic ovary syndrome[74]
rs2241883 and rs1545224Non-alcoholic fatty liver[77]
rs2197076Type 2 diabetes[75]
rs2241883Metabolic syndrome[76]
FABP2Ala54ThrDiabetes, metabolic syndrome and obesity[78]
Peripheral atherosclerosis combined with T2DM[82]
Obesity[79]
Diabetic retinopathy[83]
Type 2 diabetes and metabolic syndrome[80]
Renal disease in type 2 diabetic[84]
Non-alcoholic fatty liver[81]
rs1799883Essential hypertension[85]
Diabetes[86]
ACACBrs2268388Diabetes related nephropathy[87,89]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chaaba, R.; Bouaziz, A.; Ben Amor, A.; Mnif, W.; Hammami, M.; Mehri, S. Fatty Acid Profile and Genetic Variants of Proteins Involved in Fatty Acid Metabolism Could Be Considered as Disease Predictor. Diagnostics 2023, 13, 979. https://doi.org/10.3390/diagnostics13050979

AMA Style

Chaaba R, Bouaziz A, Ben Amor A, Mnif W, Hammami M, Mehri S. Fatty Acid Profile and Genetic Variants of Proteins Involved in Fatty Acid Metabolism Could Be Considered as Disease Predictor. Diagnostics. 2023; 13(5):979. https://doi.org/10.3390/diagnostics13050979

Chicago/Turabian Style

Chaaba, Raja, Aicha Bouaziz, Asma Ben Amor, Wissem Mnif, Mohamed Hammami, and Sounira Mehri. 2023. "Fatty Acid Profile and Genetic Variants of Proteins Involved in Fatty Acid Metabolism Could Be Considered as Disease Predictor" Diagnostics 13, no. 5: 979. https://doi.org/10.3390/diagnostics13050979

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop