| 1. |
- Ankarfeldt, Mikkel Z., et al.
(författare)
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Body characteristics, dietary protein and body weight regulation. Reconciling conflicting results from intervention and observational studies?
- 2014
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Ingår i: PLOS ONE. - : Public library science. - 1932-6203. ; 9:7, s. e101134-
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Tidskriftsartikel (refereegranskat)abstract
- Background/Objectives: Physiological evidence indicates that high-protein diets reduce caloric intake and increase thermogenic response, which may prevent weight gain and regain after weight loss. Clinical trials have shown such effects, whereas observational cohort studies suggest an association between greater protein intake and weight gain. In both types of studies the results are based on average weight changes, and show considerable diversity in both directions. This study investigates whether the discrepancy in the evidence could be due to recruitment of overweight and obese individuals into clinical trials. Subjects/Methods: Data were available from the European Diet, Obesity and Genes (DiOGenes) post-weight-loss weight-maintenance trial and the Danish Diet, Cancer and Health (DCH) cohort. Participants of the DCH cohort were matched with participants from the DiOGenes trial on gender, diet, and body characteristics. Different subsets of the DCH-participants, comparable with the trial participants, were analyzed for weight maintenance according to the randomization status (high or low protein) of the matched trial participants. Results: Trial participants were generally heavier, had larger waist circumference and larger fat mass than the participants in the entire DCH cohort. A better weight maintenance in the high-protein group compared to the low protein group was observed in the subgroups of the DCH cohort matching body characteristics of the trial participants. Conclusion: This modified observational study, minimized the differences between the RCT and observational data with regard to dietary intake, participant characteristics and statistical analysis. Compared with low protein diet the high protein diet was associated with better weight maintenance when individuals with greater body mass index and waist circumference were analyzed. Selecting subsets of large-scale observational cohort studies with similar characteristics as participants in clinical trials may reconcile the otherwise conflicting results.
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| 2. |
- Stocks, Tanja, et al.
(författare)
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TFAP2B-dietary protein and glycemic index interactions and weight maintenance after weight loss in the DiOGenes trial
- 2013
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Ingår i: Human Heredity. - : S. Karger AG. - 0001-5652 .- 1423-0062. ; 75:2-4, s. 213-219
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Tidskriftsartikel (refereegranskat)abstract
- Background: TFAP2B rs987237 is associated with obesity and has shown interaction with the dietary fat-to-carbohydrate ratio, which has an effect on weight loss. We investigated interactions between rs987237 and protein-to-carbohydrate ratio or glycemic index (GI) in relation to weight maintenance after weight loss.Methods: This study included 742 obese individuals from 8 European countries who participated in the Diet, Obesity, and Genes (DiOGenes) trial, lost >= 8% of their initial body weight during an 8-week low-calorie diet and were randomized to one of 5 ad libitum diets with a fixed energy percentage from fat: either low-protein/low-GI, low-protein/high-GI, high-protein/low-GI, or high-protein/high-GI diets, or a control diet for a 6-month weight maintenance period. Using linear regression analyses and additive genetic models, we investigated main and dietary interaction effects of TFAP2B rs987237 in relation to weight maintenance.Results: In total, 468 completers of the trial were genotyped for rs987237. High-protein diets were beneficial for weight maintenance in the AA genotype group (67% of participants), but in the AG and GG groups no differences were observed for low- or high-protein diets. On the high-protein diet, carriers of the obesity risk allele (G allele) regained 1.84 kg (95% CI: 0.02; 3.67, p = 0.047) more body weight per risk allele than individuals on a low-protein diet. There was no interaction effect between rs987237 and GI on weight maintenance.Conclusion: TFAP2B rs987237 and dietary protein/carbohydrate interacted to modify weight maintenance. Considering the carbohydrate proportion of the diet, the interaction was different from the previously reported rs987237-fat-to-carbohydrate ratio interaction for weight loss. Thus, TFAP2B-macronutrient interactions might diverge depending on the nutritional state.(C) 2013 S. Karger AG, Basel
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| 3. |
- Stocks, Tanja, et al.
(författare)
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TFAP2B influences the effect of dietary fat on weight loss under energy restriction
- 2012
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:8, s. e43212-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Numerous gene loci are related to single measures of body weight and shape. We investigated if 55 SNPs previously associated with BMI or waist measures, modify the effects of fat intake on weight loss and waist reduction under energy restriction.Methods and Findings: Randomized controlled trial of 771 obese adults. (Registration: ISRCTN25867281.) One SNP was selected for replication in another weight loss intervention study of 934 obese adults. The original trial was a 10-week 600 kcal/d energy-deficient diet with energy percentage from fat (fat%) in range of 20-25 or 40-45. The replication study used an 8-weeks diet of 880 kcal/d and 20 fat%; change in fat% intake was used for estimation of interaction effects. The main outcomes were intervention weight loss and waist reduction. In the trial, mean change in fat% intake was -12/+4 in the low/high-fat groups. In the replication study, it was -23/-12 among those reducing fat% more/less than the median. TFAP2B-rs987237 genotype AA was associated with 1.0 kg (95% CI, 0.4; 1.6) greater weight loss on the low-fat, and GG genotype with 2.6 kg (1.1; 4.1) greater weight loss on the high-fat (interaction p-value; p=0.00007). The replication study showed a similar (non-significant) interaction pattern. Waist reduction results generally were similar. Study-strengths include (i) the discovery study randomised trial design combined with the replication opportunity (ii) the strict dietary intake control in both studies (iii) the large sample sizes of both studies. Limitations are (i) the low minor allele frequency of the TFAP2B polymorphism, making it hard to investigate non-additive genetic effects (ii) the different interventions preventing identical replication-discovery study designs (iii) some missing data for non-completers and dietary intake. No adverse effects/outcomes or side-effects were observed.Conclusions: Under energy restriction, TFAP2B may modify the effect of dietary fat intake on weight loss and waist reduction.
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| 4. |
- Vanfretti, Luigi, et al.
(författare)
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SmarTS Lab - A Laboratory for Developing Applications for WAMPAC Systems
- 2012
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Ingår i: Power and Energy Society General Meeting, 2012 IEEE. - : IEEE. - 9781467327299 ; , s. 6344839-
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Konferensbidrag (refereegranskat)abstract
- At the core of the development of “Smart Transmission Grids” is the design, implementation, and testing of synchronized phasor measurement data applications that can supplement Wide-Area Monitoring, Protection, and Control Systems(WAMPAC). Nevertheless, the development of new PMU data-based WAMPAC applications has been relatively slow. The great potential of WAMPAC systems is being limited by this, and efforts are needed so that new applications can be developed. The slow rate of development of these applications is strongly related to, among other factors, the application development approach used. This article starts by discussing the needs and approaches for developing WAMPAC applications that exploit synchronized phasor measurements, and illustrates how one of these approaches has been achieved. A preliminary work carried out to develop and implement a Smart Transmission System Laboratory (SmarTS Lab), a hardware and softwarebased system for developing and analyzing “Smart Transmission Grids” paradigms and applications for WAMPAC systems, are described. The laboratory’s conceptual architecture and hardware and software implementation are presented, and some of its components are described. Finally, the article illustrates proofof-concept examples of how PMU data-based applications can be developed.
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| 5. |
- Ängquist, Lars, et al.
(författare)
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Improving the calculation of statistical significance in genome-wide scans
- 2005
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Ingår i: Biostatistics. - : Oxford University Press (OUP). - 1468-4357 .- 1465-4644. ; 6:4, s. 520-538
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Tidskriftsartikel (refereegranskat)abstract
- Calculations of the significance of results from linkage analysis can be performed by simulation or by theoretical approximation, with or without the assumption of perfect marker information. Here we concentrate on theoretical approximation. Our starting point is the asymptotic approximation formula presented by Lander and Kruglyak (1995, Nature Genetics, 11, 241-247), incorporating the effect of finite marker spacing as suggested by Feingold et al. (1993, American Journal of Human Genetics, 53, 234-251). We consider two distinct ways in which this formula can be improved. Firstly, we present a formula for calculating the crossover rate rho for a pedigree of general structure. For a pedigree set, these values may then be weighted into an overall crossover rate which can be used as input to the original approximation formula. Secondly, the unadjusted p-value formula is based on the assumption of a Normally distributed nonparametric linkage (NPL) score. This leads to conservative or anticonservative p-values of varying magnitude depending on the pedigree set structure. We adjust for non-Normality by calculating the marginal distribution of the NPL score under the null hypothesis of no linkage with an arbitrarily small error. The NPL score is then transformed to have a marginal standard Normal distribution and the transformed maximal NPL score, together with a slightly corrected value of the overall crossover rate, is inserted into the original formula in order to calculate the p-value. We use pedigrees of seven different structures to compare the performance of our suggested approximation formula to the original approximation formula, with and without skewness correction, and to results found by simulation. We also apply the suggested formula to two real pedigree set structure examples. Our method generally seems to provide improved behavior, especially for pedigree sets which show clear departure from Normality, in relation to the competing approximations.
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| 6. |
- Ängquist, Lars
(författare)
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Pointwise and Genomewide Significance Calculations in Gene Mapping through Nonparametric Linkage Analysis: Theory, Algorithms and Applications
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In linkage analysis or, in a wider sense, gene mapping one searches for disease loci along a genome. This is done by observing so called marker genotypes (alleles) and phenotypes (affecteds/unaffecteds) of a pedigree set, i.e. a set of multigenerational families, in order to locate the loci corresponding to the underlying disease genes or, at least, to narrow down the interesting genome regions. In this context the key concept is the genetic inheritance of alleles with respect to the phenotype outcomes. A significant deviation from what is expected under random inheritance is taken as statistical evidence of existing genetic components suggested to be located at the loci giving significant results. In the thesis introduction we begin by outlining the needed genetical foundation of statistical genetics as well as some basic concepts, for instance, the process of allelic inheritance, the genetic disease model, the pedigree set, the inheritance vector and various types of genetic information. Next, we give an introduction to one-locus nonparametric linkage analysis focusing on significance calculations of nonparametric linkage (NPL) scores and, moreover, make some comments on the generalizations to two-locus procedures and the, related but contrasting, approach of parametric linkage analysis. In the third section we very briefly discuss some competing and complementary subfields within the context of statistical genetics and finally we put the papers included in this thesis into context by summarizing their content. Performing gene mapping-studies through whole, or substantial parts of, the genome gives rise to interpretational problems according to multiple testing. The theme of the thesis is how to calculate significance levels and powers in several contexts of such kind. In the first two papers one-locus NPL analysis, i.e. where one searches for one disease gene at a time, is considered. In Paper A existing analytical approximations of significance levels are improved and extended. The suggested formula is based on extreme-value theory for stochastic processes and a general link function between a continuous version of an arbitrary distribution function and the standard normal distribution function. In Paper B, in order to calculate significance levels, a new variant of weighted simulation for stochastic processes is developed. The method can handle complete as well as incomplete marker data and is very fast in relation to traditional methods of performing such simulations using Monte Carlo-based algorithms. The last two papers are directed towards two-locus NPL analysis, i.e. where one is interested in diseases with genetic components based on two distinct (nonsyntenic) disease genes. In Paper C significance levels and powers using unconditional two-locus analysis, i.e. where one simultaneously searches for two disease genes, are derived and discussed for homogeneous pedigree sets based on units of affected sib-pairs. Finally, in Paper D, a general approach for calculation of significance levels and powers in conditional two-locus analysis is developed. The conditional approach might be seen as a hybrid of one-locus and two-locus NPL analysis. Of central importance to this paper is the concept of noncentrality parameters, which basically is the expected value of the test statistic of interest, i.e. the NPL score, under a corresponding instance of the alternative hypotheses.
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| 7. |
- Ängquist, Lars, et al.
(författare)
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Strategies for Conditional Two-Locus Nonparametric Linkage Analysis.
- 2008
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Ingår i: Human Heredity. - : S. Karger AG. - 1423-0062 .- 0001-5652. ; 66:3, s. 138-156
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Tidskriftsartikel (refereegranskat)abstract
- In this article we deal with two-locus nonparametric linkage (NPL) analysis, mainly in the context of conditional analysis. This means that one incorporates single-locus analysis information through conditioning when performing a two-locus analysis. Here we describe different strategies for using this approach. Cox et al. [Nat Genet 1999;21:213-215] implemented this as follows: (i) Calculate the one-locus NPL process over the included genome region(s). (ii) Weight the individual pedigree NPL scores using a weighting function depending on the NPL scores for the corresponding pedigrees at speci fi c conditioning loci. We generalize this by conditioning with respect to the inheritance vector rather than the NPL score and by separating between the case of known (prede fi ned) and unknown (estimated) conditioning loci. In the latter case we choose conditioning locus, or loci, according to prede fi ned criteria. The most general approach results in a random number of selected loci, depending on the results from the previous one-locus analysis. Major topics in this article include discussions on optimal score functions with respect to the noncentrality parameter (NCP), and how to calculate adequate p values and perform power calculations. We also discuss issues related to multiple tests which arise from the two-step procedure with several conditioning loci as well as from the genome-wide tests. Copyright (c) 2008 S. Karger AG, Basel.
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| 8. |
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