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Acids, Omega-6 Fatty

Acids, Omega-6 Fatty: A class of polyunsaturated fatty acids that have a final carbon-carbon double bond in the n-6 position, counting from the methyl end of the molecule.
They are essential fatty acids that must be obtained through diet and play crucial roles in human health, including regulating inflammation and supporting cardiovascular function.
Important omega-6 fatty acids include linoleic acid and arachidonic acid.
Optimizing research on omega-6 fatty acids can be enhanced through the use of PubCompare.ai, an AI-powered platform for identifying the best protocols, products, and methodologies from the literature to advance your studies with confidence and reproducibility.

Most cited protocols related to «Acids, Omega-6 Fatty»

1
Dietary Inflammatory Index (DII) Improvements
The original DII was the first attempt to quantify the overall effect of diet on inflammatory potential(8 (link)). At that time 2700 articles published through 2007 were screened, and 929 were read and scored in formulating the index(10 (link)). In the original DII, literature review-based scores were multiplied by individuals’ actual intakes of food parameters, with no attempt to relate to any external standard of intake. While on the face appearing to be assumption-free, this approach is sensitive to the units of measurement. For example, μg and mg differ by three orders of magnitude and some parameters, such as vitamin A and β-carotene, had to be divided by 100 and others, such as n-3 and n-6 fatty acids, multiplied by 10 in order to place them in a ‘reasonable’ range so as not to over- or underestimate their influence on the overall score.
The new DII is improved in a number of ways. First, an improved scoring system has been applied to the forty-five ‘food parameters’, consisting of whole foods, nutrients and other bioactive compounds derived from a much larger literature review. Second, eleven food consumption data sets from around the world were identified that represent a range of human dietary intakes that serve as the ‘referent’ population database to provide comparative consumption data for these forty-five food parameters(11 –23 ). Third, a percentile scoring system was devised that serves as the actual values against which individuals’ intakes are multiplied in order to derive each individual's DII score.
Shivappa N., Steck S.E., Hurley T.G., Hussey J.R, & Hébert J.R. (2013). Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutrition, 17(8), 1689-1696.
Publication 2013
Acids, Omega-6 Fatty Carotene Diet Eating Face Food Homo sapiens Inflammation Nutrients Vitamin A
2
Genome-wide Association Study of Fatty Acids
Genotyping was done in each cohort separately using high-density SNP marker platforms (ARIC, CARDIA and MESA - Affymetrix 6.0, CHS - Illumina 370, InCHIANTI - Illumina 550). Samples with call rates below 95% (ARIC, CARDIA, MESA), or 97% (CHS, InCHIANTI) at genotyped markers were excluded. Genotypes were imputed to ~2.5 million HapMap SNPs using MACH31 (link) (ARIC, InCHIANTI), BIMBAM32 (link) (CHS), BEAGLE33 (link) (CARDIA) or IMPUTE2.1.034 (link) (MESA). SNPs for which testing Hardy Weinberg equilibrium resulted in p<10−5 were excluded from imputation. SNPs with minor allele frequency (MAF) < 1% or imputation quality score (estimated r2) < 0.3 were excluded from the meta-analyses. Additional details on genotyping and imputation per cohort are provided in Supplementary Table 1.
The main analysis was linear regression of each fatty acid on single-SNP allele dosage from imputation, including covariates to account for age, sex, site of recruitment when appropriate (InCHIANTI, CARDIA, CHS and MESA), as well as the top 2 (MESA) or top 10 (CARDIA, CHS) principal components to adjust for potential population structure. To reduce the complexity of analysis by each cohort, we chose a conservative model without adjusting for diet and other lifestyle variables. In all cohorts, we used a robust Huber-White sandwich variance estimator which provides protection against miss-specified mean models, as well as non-constant variance (heteroskedasticity)35 -37 . The association results in each cohort were corrected by genomic control method38 (link), which provides additional protection against spurious findings due to population stratification, the results were then combined using inverse-variance weighted meta-analysis in METAL (www.sph.umich.edu/csg/abecasis/metal). Cochran's Q-test was used to assess potential heterogeneity among results from multiple cohorts39 . As the Cochran Q-test p-value for each meta-analysis in our study was ≥0.05, we chose the fixed effect meta-analysis to pool results across the cohorts. We declared a fatty acid-SNP association “genome-wide significant” if the nominal p-value for the SNP was < 5 × 10−8. For the significantly associated SNPs, we calculated the “proportion of variation explained” by a particular variant in each cohort using an approximation: (β2*2*MAF*(1-MAF))/Var(Y), where β is the regression coefficient for one copy of the allele, MAF is the minor allele frequency and Var(Y) is the variance of the fatty acid in the corresponding cohort.
To explore additional independent susceptibility variants at the loci identified in the main analysis, we repeated the GWAS and meta-analysis conditioning on the most significant SNPs in each loci, specifically rs10740118 (chromosome 10), rs174547 (chromosome 11), and rs16966952 (chromosome 16).
We also performed GWAS and meta-analysis in which each SNP was tested for association with n6 fatty acid levels, adjusting for levels of the preceding fatty acid in the biological pathway (Figure 1). For example, to identify additional SNPs associated with GLA (18:3n6), we conducted a GWAS of GLA with adjustment for LA (18:2n6).
Guan W., Steffen B.T., Lemaitre R.N., Wu J.H., Tanaka T., Manichaikul A., Foy M., Rich S.S., Wang L., Nettleton J.A., Tang W., Gu X., Bandinelli S., King I.B., McKnight B., Psaty B.M., Siscovick D., Djousse L., Chen Y.D., Ferrucci L., Fornage M., Mozafarrian D., Tsai M.Y, & Steffen L.M. (2014). Genome-Wide Association Study of Plasma N6 Polyunsaturated Fatty Acids within the CHARGE Consortium. Circulation. Cardiovascular genetics, 7(3), 321-331.
Publication 2014
Acids, Omega-6 Fatty Alleles Biopharmaceuticals Cardia Chromosomes, Human, Pair 10 Chromosomes, Human, Pair 11 Chromosomes, Human, Pair 16 Diet Fatty Acids Genetic Heterogeneity Genome Genome-Wide Association Study Genotype HapMap Metals Single Nucleotide Polymorphism Susceptibility, Disease
3
Dietary Inflammatory Index Calculation
Participants were asked to complete a semi-quantitative FFQ that included questions on their habitual daily consumption of twenty-five food items during the past year(28 (link)). This FFQ was based on an existing FFQ used in this population and on a short FFQ (i.e. sixty items) developed by Willett(29 (link),30 ). Participants were asked to indicate how often they consumed each item in a list of frequencies (every day; 5–6 d/week; 2–4 d/week; 1 d/week; 1–3 times/month; never or less than once a month), and to indicate approximate portion size.
FFQ-derived dietary information was used to calculate DII scores for all of the subjects, as described in detail elsewhere(24 (link),25 (link)). Briefly, dietary data for each study participant were first linked to a regionally representative global database that provided a robust estimate of means and standard deviations for each of the food parameters considered (i.e. foods, nutrients and other food components such as flavonoids)(24 (link)). A z-score was derived by subtracting the ‘standard global mean’ from the amount reported, and then this value was divided by the standard deviation. To minimise the effect of ‘right skewing’ (a common occurrence with dietary data), this value was then converted to a centred percentile score, which was then multiplied by the respective inflammatory effect score of the food parameters (derived from a literature review and scoring of 1943 ‘qualified’ articles) to obtain the subject’s food parameter-specific DII score. All of the food parameter-specific DII scores were then summed to create the overall DII score for each subject in the study. For the current FFQ, data were available for a total of seventeen food parameters (carbohydrate, protein, total fat, fibre, cholesterol, saturated fat, monounsaturated fat, polyunsaturated fat, n-6 fatty acid, thiamin, riboflavin, vitamin B12, Fe, Mg, Zn, vitamin A and vitamin C). A description of the validation work of the DII score, based on both dietary recalls and a structured questionnaire, the 7 d dietary recall that is similar to an FFQ, is available elsewhere(26 (link)). Thus far, the DII has been found to be associated with inflammatory cytokines, including CRP and IL-6(26 (link),31 (link),32 (link)), the glucose intolerance component of the metabolic syndrome, the increased odds of asthma and FEV1 (reduced forced expiratory volume in 1 min), inflammatory markers in shift workers, and colorectal, prostate and pancreatic cancers(31 (link)–38 ).
Shivappa N., Hébert J.R., Rietzschel E.R., De Buyzere M.L., Langlois M., Debruyne E., Marcos A, & Huybrechts I. (2015). Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study. The British journal of nutrition, 113(4), 665-671.
Publication 2015
Acids, Omega-6 Fatty Ascorbic Acid Asthma Carbohydrates Cholesterol Cobalamins Cytokine Diet Fibrosis Flavonoids Food Inflammation Intolerances, Glucose Mental Recall Nutrients Pancreatic Cancer Prostate Proteins Riboflavin Saturated Fatty Acid Thiamine Vitamin A Volumes, Forced Expiratory Workers
4
Dietary Inflammatory Index Protocol
Questionnaires at baseline obtained information on demographics, lifestyle factors, health history, diet, ethnic identity (Multigroup Ethnic Identity Measure [MEIM]), and several psychosocial measures including social approval and desirability, which have previously been shown to bias dietary and physical activity self-reporting (20 (link)–22 (link)). The 144-item food frequency questionnaire (FFQ) obtained information on frequency and serving size of commonly consumed foods and beverages which were used to estimate nutrient intake.
The DII is grounded in peer-reviewed research (i.e., 1,943 articles) examining the relationship between dietary components (termed food parameters) and inflammation to create inflammatory effect scores for each food parameter. At the same time, actual intake of each food parameter is standardized to a “world” database consisting of mean (and standard deviation) of the intake of that dietary component from 11 populations around the world (i.e., Australia, Bahrain, Denmark, India, Japan, Mexico, New Zealand, South Korea, Taiwan, the United Kingdom and the United States). A z-score was created by subtracting the “world” means from actual intake and dividing this by the standard deviation. In order to dampen the effect of [right] skewness, these z-scores were then converted to percentile values and centered on zero by doubling the percentile and subtracting 1. These values were multiplied by the literature derived inflammatory effect score and summed across food parameters. DII scores were calculated per 1,000 calories consumed to account for varying energy intake between people. DII information can be found elsewhere (3 (link)). These are the 31 DII food parameters available through HEALS: carbohydrates; protein; total, saturated, monounsaturated, polyunsaturated, and trans fat; alcohol; fiber; cholesterol; omega 3 and omega 6 fatty acids; niacin; thiamin; riboflavin; vitamins A, B6, B12, C, D, and E; iron; magnesium; zinc; selenium; folate; beta carotene; isoflavones; onion; garlic; and tea.
Wirth M.D., Shivappa N., Davis L., Hurley T.G., Ortaglia A., Drayton R., Blair S.N, & Hébert J.R. (2017). Construct Validation of the Dietary Inflammatory Index among African Americans. The journal of nutrition, health & aging, 21(5), 487-491.
Publication 2017
Acids, Omega-6 Fatty Allium cepa beta Carotene Beverages Carbohydrates Cholesterol Diet Eating Ethanol Fibrosis Folate Food Garlic Inflammation Iron Isoflavones Magnesium Niacin Nutrient Intake Omega-3 Fatty Acids PER1 protein, human Population Group Proteins Riboflavin Selenium Thiamine Vitamins Wound Healing Zinc
5
Adolescent Dietary Assessment Using HELENA-DIAT
Dietary intake data were obtained using a self-administered, computerized 24HR, named HELENA-DIAT, which was based on the Young Adolescents’ Nutrition Assessment on Computer (YANA-C) (40 (link), 41 (link)), a tool validated in Flemish adolescents. The basic version was improved by adding dishes representative of cultural/culinary differences observed in European nations participating in HELENA (42 (link)). The collection of dietary data is organized in six meal occasions, i.e. breakfast, morning snack, lunch, afternoon snack, evening meal and evening snack. The participants can select from about 400 predefined food items and are free to add non-listed foods manually. Special techniques are used to allow a detailed description and quantification of foods; e.g., pictures of portion sizes and dishes. Amounts eaten could be reported as grams or using common household measures. After a short introduction by a trained researcher, the adolescents completed the HELENA-DIAT 24-HR during school time while a research staff member was present in the classroom to assist the adolescents if necessary. They completed the HELENA-DIAT twice on non-consecutive days within a time span of 2 weeks, to achieve information closer to habitual food intake than assessing food intake on consecutive days. The two 24HR thus comprised weekdays and weekend days, but not necessarily a weekday and weekend day for each individual. To calculate energy and nutrient intake, data from the HELENA-DIAT were linked to the German Food Code and Nutrient Database BLS (Bundeslebensmittelschlu¨ssel) version II.3.1, 2005) (43 (link)). For this purpose, culture-specific composite dishes were disaggregated into their basic food components, all of which were available in the German database (44 (link)). Two 24HR were collected in order to allow corrections for within-person variability. The multiple source method (MSM) (45 –47 (link)), a statistical modelling technique, was used to estimate the usual dietary intake of nutrients and foods.
24HR-derived dietary information was used to calculate DII scores for all subjects, as described in detail elsewhere (27 , 28 (link)). Briefly, the dietary data for each study participant were first linked to the regionally representative global database that provided a robust estimate of a mean and standard deviation for each of the food parameters (i.e., foods, nutrients, and other food components such as flavonoids) considered (27 ). A z-score was derived by subtracting the “standard global mean” from the amount reported and then dividing this value by the standard deviation. To minimize the effect of “right skewing” (a common occurrence with dietary data), this value was then converted to a centered percentile score, which was then multiplied by the respective food parameter inflammatory effect score (derived from a literature review and scoring of 1943 “qualified” articles) to obtain the subject’s food parameter-specific DII score. All of the food parameter-specific DII scores were then summed to create the overall DII score for every subject in the study. For the current study, data were available for a total of 25 nutrients (carbohydrate, protein, total fat, alcohol, fibre, cholesterol, saturated fat, mono unsaturated fat, poly unsaturated fat, omega-3, omega-6 fatty acid, niacin, thiamin, riboflavin, vitamin B12, vitamin B6, iron, magnesium, zinc, vitamin A, vitamin C, vitamin D, vitamin E, folic acid and betacarotene). A description of validation work of the DII score, based on both dietary recalls and the 7-day dietary record, a structured questionnaire similar in terms of its layout to an FFQ, is available elsewhere (48 ). The details of the steps are described in figure 1.
Shivappa N., Hebert J.R., Marcos A., Diaz L.E., Gomez S., Nova E., Michels N., Arouca A., González-Gil E., Frederic G., González-Gross M., Castillo M.J., Manios Y., Kersting M., Gunter M.J., De Henauw S., Antonios K., Widhalm K., Molnar D., Moreno L, & Huybrechts I. (2017). Association between dietary inflammatory index and inflammatory markers in the HELENA study. Molecular nutrition & food research, 61(6), 10.1002/mnfr.201600707.
Publication 2017
Acids, Omega-6 Fatty Adolescent Adolescent Nutritional Physiological Phenomena Ascorbic Acid beta Carotene Carbohydrates Cholesterol Cobalamins Cultural Evolution Diet Eating Ergocalciferol Ethanol Europeans Fats, Unsaturated Fibrosis Flavonoids Folic Acid Food Households Hyperostosis, Diffuse Idiopathic Skeletal Inflammation Iron Magnesium Mental Recall Niacin Nutrient Intake Nutrients Omega-3 Fatty Acids Poly A Proteins Riboflavin Saturated Fatty Acid Snacks Thiamine Vitamin A Vitamin B6 Vitamin E Zinc

Most recents protocols related to «Acids, Omega-6 Fatty»

1
Genome-Wide Genetic Instruments for Fatty Acids
Instruments on circulating levels of n-3 fatty acids, n-6 fatty acids as well as other fatty acids are acquired from a genome-wide association studies (GWASs) (N = 114,999) including 6 types of fatty acids in UK Biobank (16 (link), 17 (link)).
We primarily selected Single nucleotide polymorphisms (SNPs) with significant evidence of association (p ≤ 5e-8), and then we excluded the SNPs whose minor allele frequency ≤ 0.01. TwoSampleMR R package was used to remove instrumental variants with linkage disequilibrium (LD) (R2 > 0.001) (18 (link)). Finally, we extracted 48 independent SNPs for n-3 fatty acids, 51 independent SNPs for n-6 fatty acids, 53 independent SNPs for PUFAs, 47 independent SNPs for SFAs and 56 independent SNPs for MUFAs (Figure 1), which were then used to as instruments of fatty acids levels, respectively (see below).
Lin J.Y., Ma L.J., Yuan J.P., Yu P, & Bai B.X. (2023). Causal effects of fatty acids on atopic dermatitis: A Mendelian randomization study. Frontiers in Nutrition, 10, 1083455.
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Publication 2023
Acids Acids, Omega-6 Fatty Fatty Acids Genome-Wide Association Study Polyunsaturated Fatty Acids Single Nucleotide Polymorphism
2
Fatty Acid Profiling of Grazing Sheep Muscle
After intramuscular fat was extracted from longissimus dorsi (LD) muscle and herbage by using a chloroform/methanol mixture, FAs in intramuscular fat were quantified using an Agilent 6890 gas chromatograph coupled with a mass spectrometer (GC/MS, Agilent Technologies Inc., Santa Clara, CA, USA). More details can be found in Guo’s research [6 (link)]. We selected the 16 lambs that underwent rumen and liver metabolism measurements, and then obtained the FAs content in their LD muscle, which are as follows: C18:2n6 (2.517 ± 0.147), C18:3n3 (0.217 ± 0.022), C20:3n6 (0.081 ± 0.005), C20:4n6 (1.054 ± 0.069), C20:5n3 (0.078 ± 0.009), C22:6n3 (0.039 ± 0.005), total FA (29.818 ± 1.654), n-3 polyunsaturated fatty acids (PUFAs) (0.334 ± 0.035), n-6 PUFAs (3.652 ± 0.208), n-6/n-3 PUFAs (12.889 ± 1.326). FA content was expressed as an mg/100 g of fresh meat. Based on the fatty acid content in the herbage and the estimated forage intake, we calculated the daily fatty acid intake of grazing sheep as follows: C12:0 (50.14 mg), C14:0 (99.48 mg), C16:0 (1589.48 mg), C16:1 (51.64 mg), C17:0 (33.36 mg), C18:0 (277.71 mg), C18:1n9c (1160.59 mg), C18:2n6 (4331.75 mg), C18:3n3 (1248.74 mg), C20:0 (196.46 mg), C20:1 (28.61 mg), C21:0 (98.35 mg), C20:2 (28.57 mg), C22:0 (292.76 mg), C22:1n9 (26.62 mg), C23:0 (119.74 mg), C24:0 (212.77 mg), total FAs (9846.78 mg).
Li Z., Zhao X., Jian L., Wang B, & Luo H. (2023). Rumen microbial-driven metabolite from grazing lambs potentially regulates body fatty acid metabolism by lipid-related genes in liver. Journal of Animal Science and Biotechnology, 14, 39.
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Publication 2023
Acids, Omega-6 Fatty Chloroform Fatty Acids Gas Chromatography Gas Chromatography-Mass Spectrometry Liver Meat Metabolism Methanol Muscle Tissue Omega-3 Fatty Acids Rumen Sheep
3
Dietary Inflammatory Index Calculation
This study analyzed 28 of the 45 food components from the original DII: carbohydrates, protein, total fat, alcohol, fiber, cholesterol, saturated fat, MUFA, PUFA, n-3 fatty acids, n-6 fatty acids, niacin, vitamin A, thiamin (vitamin B1), riboflavin (vitamin B2), vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, Fe, Mg, zinc, selenium, folic acid, beta-carotene, caffeine, and energy. There is evidence that DII is still useful for predicting overall inflammation when only information on fewer food components is available (Shivappa et al., 2014a (link)). DII calculations were based on a 24-h dietary recall interview or food record of the participant or their guardian (Shivappa et al., 2014b (link); Wirth et al., 2017 (link)). There are standard reference values for each food parameter in the world database. The 24-h dietary recall data were multiplied by standard food parameters from the world database to obtain individual dietary inflammation composite cognitive function scores (Z-scores) relative to the standard global average. We transformed this value into a percentile to reduce bias. Each percentile was doubled, and then 1 was subtracted from it. The percentage values for each food parameter were then multiplied by their respective “overall food parameter-specific inflammatory effect scores” to obtain individual food-specific DII scores. Finally, the DII scores for all individual food components were summed to obtain the “overall DII score” for each person (Shivappa et al., 2014a (link)).
Li W., Li S., Shang Y., Zhuang W., Yan G., Chen Z, & Lyu J. (2023). Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans. Frontiers in Neuroscience, 17, 1117056.
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Publication 2023
Acids, Omega-6 Fatty Ascorbic Acid beta Carotene Caffeine Carbohydrates Cholesterol Cobalamins Cognition Diet Ergocalciferol Ethanol Fibrosis Folic Acid Food Inflammation Legal Guardians Mental Recall Niacin Omega-3 Fatty Acids Polyunsaturated Fatty Acids Proteins Riboflavin Saturated Fatty Acid Selenium Thiamine Vitamin A Vitamin B6 Vitamin E Zinc
4
Formulation and Characterization of Experimental Rodent Diets
Four defined diet formulations were prepared as described in Supplementary Table 3. All formulations used purified American Institute of Nutrition (AIN)-93G diet (70 g/kg fat) as a base to provide optimal nutrition to experimental rodents (62 (link)). All diets contained 10 g/kg corn oil as a source of essential ω-6 fatty acids. The basal diet for Study 1 and control (CON) diet for Study 2 contained 60 g/kg high-oleic safflower oil (Hain Pure Food, Boulder, CO). For DHA-enriched diets, human equivalent caloric consumption of 5 g DHA per day was achieved by adding 25 g/kg microalgal oil containing 40% DHA (DHASCO; DSM Nutritional Products, Columbia, MD) in place of high-oleic safflower oil, resulting in 10 g DHA/kg diet (63 (link)). For TPPU-amended diets, 22.5 mg TPPU (95% purity based on H-NMR analysis), synthesized and purified as described previously (34 (link)), was added to 1 kg of CON or DHA diet, resulting in the TPPU and TPPU+DHA diets. Fatty acid (Table 1) and TPPU (Supplementary Table 4) concentrations in each diet were confirmed as described below.
Favor O.K., Chauhan P.S., Pourmand E., Edwards A.M., Wagner J.G., Lewandowski R.P., Heine L.K., Harkema J.R., Lee K.S, & Pestka J.J. (2023). Lipidome modulation by dietary omega-3 polyunsaturated fatty acid supplementation or selective soluble epoxide hydrolase inhibition suppresses rough LPS-accelerated glomerulonephritis in lupus-prone mice. Frontiers in Immunology, 14, 1124910.
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Publication 2023
Acids, Omega-6 Fatty Corn oil DHA-10 Diet Dietary Formulations Fatty Acids Food Homo sapiens Microalgae Rodent Safflower oil
5
Dietary Inflammatory Index Calculation Protocol
The FFQ-derived dietary data was used to calculate the energy-adjusted DII (E-DII) scores for each of the enrolled subjects. The comprehensive description of development [12 (link)] and construct validation [42 (link)] of the DII are available elsewhere. Briefly, 1943 research articles published through 2010 that reported the link between 45 food parameters (consisting of whole foods, nutrients, and flavonoids) and six inflammatory biomarkers [IL-1β, IL-4, IL-6, IL-10, TNF-α, and C-reactive protein (CRP)] were reviewed and scored. Regionally representative datasets based on diet surveys from 11 countries were collectively used as comparative standards for each of the 45 parameters. To calculate the E-DII scores for the subjects of this study, the intake scores of the noted datasets were applied. Food and nutrient intake from the FFQ were first adjusted for total energy intake (density method = nutrient/total energy intake × 1000 kcal) and then standardised by creating a z-score for each food component using mean and standard deviation (SD) values from a global energy-adjusted database. The energy-adjusted standardised dietary intake was then multiplied by the respective literature-derived inflammatory effect score to obtain a food parameter-specific E-DII score for an individual, and summed across all components to obtain the overall E-DII score for each study subject.
The resulting E-DII score increases with the increased inflammatory potential of the diet, with a higher or more positive value of the E-DII score indicating a more pro-inflammatory diet, whereas a lower or more negative value of the E-DII score represents a more anti-inflammatory diet. For the current study, 29 food parameters were included to calculate the E-DII score: energy, carbohydrates, proteins, total fat, saturated fatty acids (SFA), trans fat, monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), alcohol, vitamin A, C, D, E, B6, B12, β-carotene, caffeine, cholesterol, dietary fibre, folic acid, iron, magnesium, niacin, omega-3 fatty acids, omega-6 fatty acids, riboflavin, selenium, thiamin, and zinc. For analytical purposes, E-DII scores were categorised into four groups (quartiles) to investigate the relationship with the different variables. The lowest and highest E-DII scores were found in the first and fourth quartiles, respectively.
Shafiee N.H., Razalli N.H., Shahril M.R., Muhammad Nawawi K.N., Mohd Mokhtar N., Abd Rashid A.A., Ashari L.S., Jan Mohamed H.J, & Raja Ali R.A. (2023). Dietary Inflammatory Index, Obesity, and the Incidence of Colorectal Cancer: Findings from a Hospital-Based Case-Control Study in Malaysia. Nutrients, 15(4), 982.
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Publication 2023
Acids, Omega-6 Fatty Anti-Inflammatory Agents Biological Markers Caffeine Carbohydrates Carotene Cholesterol C Reactive Protein Diet Dietary Fiber Diet Surveys Ethanol Fatty Acids, Monounsaturated Flavonoids Folic Acid Food IL10 protein, human Inflammation Interleukin-1 beta Iron Magnesium Niacin Nutrient Intake Nutrients Omega-3 Fatty Acids Polyunsaturated Fatty Acids Proteins Riboflavin Saturated Fatty Acid Selenium Thiamine Tumor Necrosis Factor-alpha Vitamin A Zinc

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Db 23 by Agilent Technologies
Sourced in United States, Italy, Japan
The DB-23 is a gas chromatography (GC) column designed for the separation and analysis of fatty acid methyl esters (FAMEs). It features a 60-meter length, 0.25-millimeter internal diameter, and 0.25-micrometer film thickness. The DB-23 column is optimized for the separation of cis and trans isomers of FAMEs.
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Trace tr fame by Thermo Fisher Scientific
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The Trace TR-FAME is a laboratory instrument designed for the analysis of fatty acid methyl esters (FAMEs) in various sample types. It performs high-resolution gas chromatography (GC) separations and provides sensitive detection of FAMEs.
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More about "Acids, Omega-6 Fatty"

Omega-6 fatty acids, also known as n-6 fatty acids, are a class of polyunsaturated fatty acids (PUFAs) that play a crucial role in human health.
These essential fatty acids must be obtained through diet, as the body cannot synthesize them.
Key omega-6 fatty acids include linoleic acid (LA) and arachidonic acid (AA).
Omega-6 fatty acids are involved in regulating inflammation, supporting cardiovascular function, and other important physiological processes.
Optimizing research on these fatty acids can be enhanced through the use of tools like PubCompare.ai, an AI-powered platform that helps researchers identify the best protocols, products, and methodologies from the literature.
When studying omega-6 fatty acids, researchers may utilize analytical techniques such as gas chromatography (e.g., using a DB-23 capillary column) and mass spectrometry (e.g., Trace TR-FAME) to accurately quantify and identify the fatty acid profiles in biological samples.
Sample preparation steps may include lipid extraction using a Sep-Pak C18 cartridge and derivatization to fatty acid methyl esters (FAMEs) using a Sonifier.
Computational tools like MATLAB R2020b can be employed to analyze the data and uncover insights.
The Beckman LX20 Pro analyser is another useful instrument for measuring various lipid parameters.
By leveraging these techniques and technologies, researchers can advance their understanding of the role of omega-6 fatty acids in human health and disease, ultimately leading to improved Clarus 500 and DB-23 based research outcomes.