Gene x dietary pattern interactions in obesity : analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associationswith obesity traits have been identified through genome wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphismswere genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjustedWHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjustedWHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.Peer reviewe

Gene × dietary pattern interactions in obesity: Analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GR

Dietary Patterns : Identification and Health Implications in the Swedish Population

We eat foods not nutrients. What is more, we eat them in combinations. Consequently, capturing our complex food habits is likely an advantage in nutrition research. The overall aim of this doctoral thesis was therefore to investigate dietary patterns in the Swedish population –nutrient intakes, nutritional biomarkers and health aspects. Prostate cancer is the most common cancer among men in the developed world. However, the impact of dietary factors on disease risk is largely unknown. In Study I we investigated the association between a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern and prostate cancer risk, in a cohort of elderly Swedish men. The latter (but not the former) was associated, inversely, with prostate cancer risk when taking validity in food records into account. Diet is one of our main exposure routes to environmental contaminants. Hence, such exposure could act as a mediating factor in the relation between diet and health. In Study II we investigated the association between; a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern, as well as the official dietary recommendations, and circulating levels of environmental contaminants, in an elderly Swedish population. The first two patterns were positively related to levels of both persistent organic pollutants and heavy metals, whilst the dietary recommendations were inversely associated to dioxin and lead. Finally, although dietary patterns are likely to influence health, little is known about current dietary patterns in Sweden. In Study III we used a data-reduction method to identify dietary patterns in a nationwide sample of the Swedish population. Two major patterns were derived; a Healthy pattern of foods generally considered healthy (e.g. vegetables, fruits, fish and vegetable-oils) and a Swedish traditional pattern (with e.g. meats, potatoes, sauces, non-Keyhole milk-products, sweet-bakery products and margarine). Derived patterns were associated to population characteristics and the Healthy dietary pattern was inversely associated to anthropometric variables in Study IV. Dietary characteristics of the patterns were well reflected in correlations to nutrient intake and (to a lesser extent) in nutritional biomarkers. In conclusion dietary patterns for overall health should be considered, as well as other lifestyle-factors, when interpreting results in nutrition epidemiology and establishing dietary recommendations

Dietary Patterns : Identification and Health Implications in the Swedish Population

We eat foods not nutrients. What is more, we eat them in combinations. Consequently, capturing our complex food habits is likely an advantage in nutrition research. The overall aim of this doctoral thesis was therefore to investigate dietary patterns in the Swedish population –nutrient intakes, nutritional biomarkers and health aspects. Prostate cancer is the most common cancer among men in the developed world. However, the impact of dietary factors on disease risk is largely unknown. In Study I we investigated the association between a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern and prostate cancer risk, in a cohort of elderly Swedish men. The latter (but not the former) was associated, inversely, with prostate cancer risk when taking validity in food records into account. Diet is one of our main exposure routes to environmental contaminants. Hence, such exposure could act as a mediating factor in the relation between diet and health. In Study II we investigated the association between; a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern, as well as the official dietary recommendations, and circulating levels of environmental contaminants, in an elderly Swedish population. The first two patterns were positively related to levels of both persistent organic pollutants and heavy metals, whilst the dietary recommendations were inversely associated to dioxin and lead. Finally, although dietary patterns are likely to influence health, little is known about current dietary patterns in Sweden. In Study III we used a data-reduction method to identify dietary patterns in a nationwide sample of the Swedish population. Two major patterns were derived; a Healthy pattern of foods generally considered healthy (e.g. vegetables, fruits, fish and vegetable-oils) and a Swedish traditional pattern (with e.g. meats, potatoes, sauces, non-Keyhole milk-products, sweet-bakery products and margarine). Derived patterns were associated to population characteristics and the Healthy dietary pattern was inversely associated to anthropometric variables in Study IV. Dietary characteristics of the patterns were well reflected in correlations to nutrient intake and (to a lesser extent) in nutritional biomarkers. In conclusion dietary patterns for overall health should be considered, as well as other lifestyle-factors, when interpreting results in nutrition epidemiology and establishing dietary recommendations

Dietary Patterns : Identification and Health Implications in the Swedish Population

We eat foods not nutrients. What is more, we eat them in combinations. Consequently, capturing our complex food habits is likely an advantage in nutrition research. The overall aim of this doctoral thesis was therefore to investigate dietary patterns in the Swedish population –nutrient intakes, nutritional biomarkers and health aspects. Prostate cancer is the most common cancer among men in the developed world. However, the impact of dietary factors on disease risk is largely unknown. In Study I we investigated the association between a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern and prostate cancer risk, in a cohort of elderly Swedish men. The latter (but not the former) was associated, inversely, with prostate cancer risk when taking validity in food records into account. Diet is one of our main exposure routes to environmental contaminants. Hence, such exposure could act as a mediating factor in the relation between diet and health. In Study II we investigated the association between; a Mediterranean- and a Low-carbohydrate-high-protein dietary pattern, as well as the official dietary recommendations, and circulating levels of environmental contaminants, in an elderly Swedish population. The first two patterns were positively related to levels of both persistent organic pollutants and heavy metals, whilst the dietary recommendations were inversely associated to dioxin and lead. Finally, although dietary patterns are likely to influence health, little is known about current dietary patterns in Sweden. In Study III we used a data-reduction method to identify dietary patterns in a nationwide sample of the Swedish population. Two major patterns were derived; a Healthy pattern of foods generally considered healthy (e.g. vegetables, fruits, fish and vegetable-oils) and a Swedish traditional pattern (with e.g. meats, potatoes, sauces, non-Keyhole milk-products, sweet-bakery products and margarine). Derived patterns were associated to population characteristics and the Healthy dietary pattern was inversely associated to anthropometric variables in Study IV. Dietary characteristics of the patterns were well reflected in correlations to nutrient intake and (to a lesser extent) in nutritional biomarkers. In conclusion dietary patterns for overall health should be considered, as well as other lifestyle-factors, when interpreting results in nutrition epidemiology and establishing dietary recommendations

Automatic Detection of Common Signal Quality Issues in MRI Data using Deep Neural Networks

Magnetic resonance imaging (MRI) is a commonly used non-invasive imaging technique that provides high resolution images of soft tissue. One problem with MRI is that it is sensitive to signal quality issues. The issues can arise for various reasons, for example by metal located either inside or outside of the body. Another common signal quality issue is caused by the patient being partly placed outside field of view of the MRI scanner.   This thesis aims to investigate the possibility to automatically detect these signal quality issues using deep neural networks. More specifically, two different 3D CNN network types were studied, a classification-based approach and a reconstruction-based approach. The datasets used consist of MRI volumes from UK Biobank which have been processed and manually annotated by operators at AMRA Medical. For the classification method four different network architectures were explored utilising supervised learning with multi-label classification. The classification method was evaluated using accuracy and label-based evaluation metrics, such as macro-precision, macro-recall and macro-F1. The reconstruction method was based on anomaly detection using an autoencoder which was trained to reconstruct volumes without any artefacts. A mean squared prediction error was calculated for the reconstructed volume and compared against a threshold in order to classify a volume with or without artefacts. The idea was that volumes containing artefacts should be more difficult to reconstruct and thus, result in a higher prediction error. The reconstruction method was evaluated using accuracy, precision, recall and F1-score.  The results show that the classification method has overall higher performance than the reconstruction method. The achieved accuracy for the classification method was 98.0% for metal artefacts and 97.5% for outside field of view artefacts. The best architecture for the classification method proved to be DenseNet201. The reconstruction method worked for metal artefacts with an achieved accuracy of 75.7%. Furthermore, it was concluded that reconstruction method did not work for detection of outside field of view artefacts.    The results from the classification method indicate that there is a possibility to automatically detect artefacts with deep neural networks. However, it is needed to further improve the method in order to completely replace a manual quality control step before using the volumes for calculation of biomarkers.

Automatic Detection of Common Signal Quality Issues in MRI Data using Deep Neural Networks

Magnetic resonance imaging (MRI) is a commonly used non-invasive imaging technique that provides high resolution images of soft tissue. One problem with MRI is that it is sensitive to signal quality issues. The issues can arise for various reasons, for example by metal located either inside or outside of the body. Another common signal quality issue is caused by the patient being partly placed outside field of view of the MRI scanner.   This thesis aims to investigate the possibility to automatically detect these signal quality issues using deep neural networks. More specifically, two different 3D CNN network types were studied, a classification-based approach and a reconstruction-based approach. The datasets used consist of MRI volumes from UK Biobank which have been processed and manually annotated by operators at AMRA Medical. For the classification method four different network architectures were explored utilising supervised learning with multi-label classification. The classification method was evaluated using accuracy and label-based evaluation metrics, such as macro-precision, macro-recall and macro-F1. The reconstruction method was based on anomaly detection using an autoencoder which was trained to reconstruct volumes without any artefacts. A mean squared prediction error was calculated for the reconstructed volume and compared against a threshold in order to classify a volume with or without artefacts. The idea was that volumes containing artefacts should be more difficult to reconstruct and thus, result in a higher prediction error. The reconstruction method was evaluated using accuracy, precision, recall and F1-score.  The results show that the classification method has overall higher performance than the reconstruction method. The achieved accuracy for the classification method was 98.0% for metal artefacts and 97.5% for outside field of view artefacts. The best architecture for the classification method proved to be DenseNet201. The reconstruction method worked for metal artefacts with an achieved accuracy of 75.7%. Furthermore, it was concluded that reconstruction method did not work for detection of outside field of view artefacts.    The results from the classification method indicate that there is a possibility to automatically detect artefacts with deep neural networks. However, it is needed to further improve the method in order to completely replace a manual quality control step before using the volumes for calculation of biomarkers.

Miljöföroreningar i blod och urin och kopplingar till rapporterat matintag i Riksmaten 2010-11 – resultatsammanställning : (Samrapporterat med "Analys av PCB och bromerade flamskyddsmedel i prover insamlade i anslutning till kostenkäten")

Riksmaten 2010-11 är en nationell matvaneundersökning genomförd av Livsmedelsverket mellan maj 2010 och maj 2011. Fem tusen svenskar ombads delta i en fyra dagars kostregistrering samt att fylla i en enkät. En undergrupp på cirka 1000 personer tillfrågades även om att lämna blod och urinprov för undersökning av nutritionsstatus och miljöföroreningar, och av dessa valde 300 individer att delta i biomonitoreringen. Analyser av miljöföroreningar genomfördes under 2011-2012. Exponeringsnivåer för samtliga miljöföroreningar har sammanställts och redovisas i denna rapport. Utöver detta har även samband mellan nivåer av miljöföroreningarna och kost- och livsstilsfaktorer, socioekonomiska variabler och boendemiljö undersökts. Resultaten från de analyserna sammanfattas här kort och redovisas i helhet som separata vetenskapliga publikationer

Miljöföroreningar i blod och urin och kopplingar till rapporterat matintag i Riksmaten 2010-11 – resultatsammanställning : (Samrapporterat med "Analys av PCB och bromerade flamskyddsmedel i prover insamlade i anslutning till kostenkäten")

Riksmaten 2010-11 är en nationell matvaneundersökning genomförd av Livsmedelsverket mellan maj 2010 och maj 2011. Fem tusen svenskar ombads delta i en fyra dagars kostregistrering samt att fylla i en enkät. En undergrupp på cirka 1000 personer tillfrågades även om att lämna blod och urinprov för undersökning av nutritionsstatus och miljöföroreningar, och av dessa valde 300 individer att delta i biomonitoreringen. Analyser av miljöföroreningar genomfördes under 2011-2012. Exponeringsnivåer för samtliga miljöföroreningar har sammanställts och redovisas i denna rapport. Utöver detta har även samband mellan nivåer av miljöföroreningarna och kost- och livsstilsfaktorer, socioekonomiska variabler och boendemiljö undersökts. Resultaten från de analyserna sammanfattas här kort och redovisas i helhet som separata vetenskapliga publikationer