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      Prediction of Blood Lipid Phenotypes Using Obesity-Related Genetic Polymorphisms and Lifestyle Data in Subjects with Excessive Body Weight

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          Abstract

          Background and Aim

          Individual lipid phenotypes including circulating total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), and triglycerides (TG) determinations are influenced by gene-environment interactions. The aim of this study was to predict blood lipid level (TC, LDL-c, HDL-c, and TG) variability using genetic and lifestyle data in subjects with excessive body weight-for-height.

          Methods

          This cross-sectional study enrolled 304 unrelated overweight/obese adults of self-reported European ancestry. A total of 95 single nucleotide polymorphisms (SNPs) related to obesity and weight loss were analyzed by a targeted next-generation sequencing system. Relevant genotypes of each SNP were coded as 0 (nonrisk) and 1 (risk). Four genetic risk scores (GRS) for each lipid phenotype were calculated by adding the risk genotypes. Information concerning lifestyle (diet, physical activity, alcohol drinking, and smoking) was obtained using validated questionnaires. Total body fat (TFAT) and visceral fat (VFAT) were determined by dual-energy X-ray absorptiometry.

          Results

          Overall, 45 obesity-related genetic variants were associated with some of the studied blood lipids. In addition to conventional factors (age, sex, dietary intakes, and alcohol consumption), the calculated GRS significantly contributed to explain their corresponding plasma lipid trait. Thus, HDL-c, TG, TC, and LDL-c serum concentrations were predicted by approximately 28% (optimism-corrected adj. R 2 = 0.28), 25% (optimism-corrected adj. R 2 = 0.25), 24% (optimism-corrected adj. R 2 = 0.24), and 21% (optimism-corrected adj. R 2=0.21), respectively. Interestingly, GRS were the greatest contributors to TC (squared partial correlation (PC 2) = 0.18) and LDL-c (PC 2 = 0.18) features. Likewise, VFAT and GRS had a higher impact on HDL-c (PC 2 = 0.09 and PC 2 = 0.06, respectively) and TG levels (PC 2 = 0.20 and PC 2 = 0.07, respectively) than the rest of variables.

          Conclusions

          Besides known lifestyle influences, some obesity-related genetic variants could help to predict blood lipid phenotypes.

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          Most cited references48

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          Bootstrap Methods for Developing Predictive Models

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            Reproducibility of an FFQ validated in Spain.

            To evaluate the reproducibility of a semi-quantitative FFQ used in the Seguimiento Universidad de Navarra (SUN) project. The data that were analysed were collected from an FFQ answered twice by a 326-participant subsample of the SUN project (115 men, 35.3 %; 211 women, 64.7 %), with either less than 1 year or more than 1 year between responses. The questionnaire included 136 items. Pearson correlation coefficients (r) were calculated to evaluate the magnitude of the association between both measures after energy adjustment and correcting for within-person variability. We also evaluated misclassification by quintiles distribution. The highest corrected correlations among participants who answered before 1 year were found for PUFA (r = 0.99). Among participants who answered after 1 year between both questionnaires, olive oil had the highest corrected correlation (r = 0.99). The highest percentage of gross misclassification, lowest quintile in FFQ1 and highest quintile in FFQ2 or highest quintile in FFQ1 and lowest quintile in FFQ2 was for cereals, fish or seafood, and n-3 fatty acids (7.6 %). Alcoholic drinks had the highest percentage of reasonable classification, same or adjacent quintile, in FFQ1 and FFQ2 (86.4 %). Our study suggests that FFQ reproducibility is acceptable for participants who answered the same questionnaire twice less than 1 year apart. Participants who answered FFQ more than 1 year apart showed worse values on reproducibility. We consider this Spanish FFQ as an important, valid and reproducible tool in nutritional epidemiology.
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              Perilipin controls lipolysis by regulating the interactions of AB-hydrolase containing 5 (Abhd5) and adipose triglyceride lipase (Atgl).

              The mobilization of stored lipid by hormones is a fundamental function of fat cells, and there is strong evidence that perilipin (Plin), a lipid droplet scaffold, and adipose tissue triglyceride lipase (Atgl), a triglyceride-specific lipase, play critical roles. Previous work suggested that Abhd5, a protein activator of Atgl, coordinates with Plin in controlling basal and stimulated lipolysis; however, the underlying mechanism is controversial. The present experiments investigated protein trafficking and interactions among Plin, Atgl, and Abhd5 in live cells. The results demonstrate that Plin binds Abhd5 with high affinity and thereby suppresses the interaction of Abhd5 with Atgl. Sequestration of Abhd5 appears to a major mechanism by which Plin reduces basal lipolysis. Phosphorylation of Plin on serine 492 or serine 517 rapidly releases Abhd5 from Plin, allowing Abhd5 to directly interact with Atgl. Imaging experiments demonstrated that the Plin-dependent interaction of Abhd5 and Atgl occurs mainly, but not exclusively, on lipid droplets that contain Plin.
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                Author and article information

                Contributors
                Journal
                Int J Genomics
                Int J Genomics
                IJG
                International Journal of Genomics
                Hindawi
                2314-436X
                2314-4378
                2018
                19 November 2018
                : 2018
                : 4283078
                Affiliations
                1Department of Nutrition, Food Science and Physiology, and Center for Nutrition Research, University of Navarra, Pamplona, Spain
                2Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
                3CIBERobn, Fisiopatología de la Obesidad y la Nutrición, Carlos III Health Institute, Madrid, Spain
                4Madrid Institute of Advanced Studies (IMDEA Food), Madrid, Spain
                Author notes

                Academic Editor: Sang Hong Lee

                Author information
                http://orcid.org/0000-0002-2505-1555
                http://orcid.org/0000-0002-3228-9916
                http://orcid.org/0000-0001-5218-6941
                Article
                10.1155/2018/4283078
                6276413
                56af8048-d956-4f28-999f-b970c798560e
                Copyright © 2018 Omar Ramos-Lopez et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 June 2018
                : 1 September 2018
                : 20 September 2018
                Funding
                Funded by: Government of Navarra
                Award ID: PT024
                Funded by: CIBERobn
                Award ID: CB12/03/30002
                Funded by: NUTRIGENIO
                Award ID: AGL2013–45554-R
                Categories
                Research Article

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