Dietary fat: From foe to friend?

Ludwig, David S.; Willett, Walter C.; Volek, Jeff S.; Neuhouser, Marian L.

doi:10.1126/science.aau2096

Cited by 225 publications

(146 citation statements)

References 98 publications

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“…[51][52][53] The bNMF clustering algorithm enabled us to dissect dietary intake genetic heterogeneity and identify three main domains of genetic variants that may have distinct impacts on obesity and T2D. Our findings, in line with previous evidence, 54 may suggest that increasing proportion of fat intake in place of protein and carbohydrate associates with lower BMI. In addition, the combination of increased proportion of fat and protein intake associates with lower BMI and lower T2D risk.…”

Section: Several Genes Insupporting

confidence: 84%

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Merino

Sarnowski

et al. 2019

Preprint

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Dietary intake, a major contributor to the global obesity epidemic 1-5 , is a complex phenotype partially affected by innate physiological processes. [6][7][8][9][10][11] However, previous genome-wide association studies (GWAS) have only implicated a few loci in variability of dietary composition. 12-14 Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium, and identify 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in b1tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brainspecific epigenomic annotations identify 15 additional loci. Clustering of genomewide significant variants yields three main genetic clusters with distinct associations with obesity and type 2 diabetes (T2D). Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.As dietary components are strongly correlated, we conducted a multi-trait genomewide association meta-analysis of overall variation in dietary intake among 283,119European-ancestry participants from the UK Biobank 15 and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium 14 (Methods; Supplementary Table 1). First, we conducted single-trait GWAS for the proportion of total energy intake from carbohydrate, fat, and protein in UK Biobank (n=192,005).Next, single-trait GWAS from the UK Biobank and CHARGE Consortium (n=91,114) were meta-analyzed and combined into a multi-trait genome-wide association metaanalysis (Methods). An analysis overview is presented in Supplementary Fig. 1.We evaluated dietary intake using 24-hour web-based diet recall in the UK Biobank 16,17 and validated food frequency questionnaires, diet history and diet records in the CHARGE Consortium. 14 We observed strong genome-wide genetic correlations for nutrient estimates between the UK Biobank and CHARGE datasets (r g >0.6 for all; P <0.001; Supplementary Table 2). The quantile-quantile plots of single-trait and multi-trait meta-analyses showed moderate inflation (l GC ranging from 1.12 to 1.17) with a linkage disequilibrium (LD) score intercept 18 of ~1 (standard error (s.e.) = 0.01), indicating that most inflation could be explained by polygenic signal ( Supplementary Fig. 2, Supplementary Table 3). In single-trait meta-analyses, genome-wide SNP-based heritability 19 was estimated at 3.9% (s.e.=0.01), 2.8% (s.e.=0.01), and 3.0% (s.e.=0.01) for carbohydrate, fat, and protein, respectively ( Supplementary Table 3), in line with previous GWAS findings 12,14 and other behavioral phenotypes such as tobacco or alcohol use. 20

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Section: Several Genes Insupporting

confidence: 84%

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Merino

Sarnowski

et al. 2019

Preprint

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“…One objective of this study was to identify loci associated with variation in 18:3 content, 515 as these omega-3 fatty acids are a large determinant of oil quality. Increased consumption of 516 omega-3 fatty acids is associated with improved cardiovascular health, although there is 517 uncertainty regarding the benefits of consuming omega-3 fatty acids relative to omega-6 fatty 518 acids, such as 18:2 (Ludwig et al 2018). In oat seeds, the very low abundance of 18:3 relative to 519 18:2 in germplasm ( Figure 2A) renders enhanced 18:3 content an unlikely breeding target for 520 improving the healthfulness of oats.…”

Section: Integration Of Gwas Results 405mentioning

confidence: 99%

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Carlson

Montilla-Bascón

Hoekenga

et al. 2019

Preprint

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34Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful 35 unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to 36 identify loci associated with variation in fatty acid composition, defined as the types and quantities 37 of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and 38 measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. 39Measurements of individual fatty acids were highly correlated across samples, consistent with fatty 40 acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations 41 in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we 42 fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for 43 population structure and relatedness among cultivars. In the second, we performed a univariate 44 association test for each principal component (PC) derived from a singular value decomposition 45 of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we 46 also conducted univariate association tests for each trait. The multivariate mixed model approach 47 revealed the numerous beneficial effects of oat consumption on human health, from reduction in 58 cardiovascular diseases risk (Grundy et al. 2018) to cancer prevention (Meydani 2009). These 59 positive health effects are likely due to oat's unique nutritional profile, which differs markedly 60 from that of other cereals, notably in the complement of essential amino acids, fatty acids, β-61 glucan, and phenolic compounds (Butt et al. 2008). In particular, lipids account for as much as 62 18% of the oat grain (Halima et al. 2015). Moreover, these lipids are predominantly composed of 63 unsaturated fatty acids, rendering oats a healthful energy source in human and animal diets. In 64 response to the growing awareness of oat's health-promoting properties, nutritional quality has 65 become a key target for oat breeders. Maintaining and/or optimizing lipid composition is an 66 important component of these efforts (Valentine et al. 2011). 67 To date, researchers have primarily investigated the genetic basis of variation in fatty acid 68 composition by mapping quantitative trait loci (QTL) in biparental populations (Hizbai et al. 69 2012). However, the use of such QTL in marker-assisted selection (MAS) is likely only effective 70 if the parents in the mapping population exhibit trait variation and are closely related to lines in 71 the relevant breeding populations (Snowdon and Friedt 2004). A genome-wide association study 72 (GWAS) can mediate this limitation if the population in which the GWAS is conducted captures 73 the genetic variation present in the target breeding population(s) (Lipka et al. 2015). Indeed, a 74 GWAS can identify allelic diversity associated with trait variation in complex plant pedigrees 75 when both genotypic and phenoty...

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“…The systemic metabolic state of the organism is influenced by the diet macronutrient quantity, quality, and ratio. The metabolic state can be viewed as a component of the general physiologic state, and it is well established that different dietary regimes induce metabolic states that influence health [26]. The interaction between diet and health is at least in part mediated by the activation of defense mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Crossroads between immune responses and physiological regulation: Metabolic control of resistance versus tolerance against disease

Pucillo

Vitale

2020

Eur J Immunol

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If a threat cannot be avoided, the organism has two defense options: it can try to eliminate the threatening agent or boost physiological mechanisms to tolerate the challenge and its consequences. Both strategies can be (and usually are) used at the same time. Fighting an infection, for instance, requires mounting immune responses to control pathogen burden as well as physiologic adaptations to tolerate stress and damage. Thus, the two strategies are connected and interdependent. We are starting to understand how the regulation of host metabolic physiology during disease impacts both the ability to resist pathogens’ burden and tolerate parenchymal tissue functional damage. Here, we review a number of recent publications that have begun to shed light on the physiological and immunological mechanisms that coordinate host defense and metabolic processes. In particular, we will cover the areas of energetic control, substrates utilization, and the regulatory signals that promote infectious disease tolerance.

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Dietary fat: From foe to friend?

Cited by 225 publications

References 98 publications

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Crossroads between immune responses and physiological regulation: Metabolic control of resistance versus tolerance against disease

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