Interaction between Common Genetic Variants and Total Fat Intake on Low-Density Lipoprotein Peak Particle Diameter: A Genome-Wide Association Study

Rudkowska, Iwona; Pérusse, Louis; Bellis, Claire; Blangero, John; Després, Jean; Bouchard, Claude; Vohl, Marie‐Claude

doi:10.1159/000431151

Cited by 25 publications

(13 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most frequently found, unsurprisingly, were genes associated by GWAS to diabetes or diabetes-related cardio-metabolic traits (cluster 3: MYO3B , cluster 4: DAPK1 , cluster 5: LPIN2 , cluster 7: SAMD4A and FHIT , cluster 8: ERG and PLCB1 , cluster 12: MYT1L , cluster 15: UBE2WP1 , cluster 16: ADARB2, CDKAL1, and CLIP1 , cluster 17: C8orf37-AS1 , cluster 21: FHOD3 and MCF2L, cluster 24: MTCL1 , cluster 26: NTM , cluster 31: PCDH15 , CDH4 , and DCTD , cluster 31: KLF12 , cluster 39: FHOD3 , cluster 45: IGF1R , BCAS3, and TENM4 , cluster 46: NRXN3 ). Cluster eight is characterized by cardiovascular complications, and three of the top ranking genes for this cluster have been associated with LDL peak particle diameter ( THBS4 ; Rudkowska et al, 2015), abdominal aortic aneurysm ( ERG ; Jones et al, 2017), pulse pressure ( ERG ; Warren et al, 2017), and diastolic blood pressure ( PLCB1 ; Warren et al, 2017). Cluster 21 is enriched for the ICD-10 diagnosis foot ulcer (L97), and MCF2L , one of the top ranking genes for cluster, has been associated with both end-stage coagulation (Williams et al, 2013) and prothrombin time (Tang et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Kirk

Simon

Banasik

et al. 2019

eLife

View full text Add to dashboard Cite

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.

show abstract

Section: Resultsmentioning

confidence: 99%

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Kirk

Simon

Banasik

et al. 2019

eLife

View full text Add to dashboard Cite

show abstract

“…The QFS cohort involves participants from 222 French-Canadian families from Quebec City, making up a largely homogeneous ancestry population. Family relatedness was handled by using generalized linear mixed models, a statistical method successfully applied in the past when testing genetic associations in samples with family or cryptic relatedness among individuals (Choquette et al, 2012; Plourde et al, 2013; Rudkowska et al, 2015; Chen et al, 2016). A recent study comparing distributions of polygenic scores of type 2 diabetes and cardiovascular disease within populations with different ancestries has shown that the risk level estimated for one population can considerably differ from the level in another (Reisberg et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Family-based associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS231 was constructed. Generalized linear mixed effects models were used to test the association of PRS231 with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS231 with obesity in the QFS discovery sample (ORtrend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10-16). We also found that the obesity prevalence was significantly greater in the higher PRS231 quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (ORtrend = 1.16 [95% CI, 1.07-1.26]; P = 6.7x10-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (ORtrend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10-2). PRS231 explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it. Further studies are encouraged on larger populations.

show abstract

“…Following the completion of the mapping of the Human Genome, a cumulative number of association studies have been performed in order to identify the genetic factors that may explain the inter-individual variability of the metabolic response to specific diets. In this sense, while numerous genes and polymorphisms have been already identified as relevant factors in this heterogeneous response to nutrient intake [ 2 , 3 , 4 , 5 , 6 , 7 ], clinical evidence supporting these statistical relationships is currently too weak to establish a comprehensive framework for personalized nutritional interventions in most cases [ 8 ]. Thus, although most of findings on this topic are still relatively far from giving their fully expected potential in terms of translation and application of this knowledge to precision nutrition [ 9 ], some of them have been successfully developed in both the public and the private sectors.…”

Section: Precision Nutritionmentioning

confidence: 99%

Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome

et al. 2017

Self Cite

View full text Add to dashboard Cite

The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition analyzing most relevant aspects affecting an individual response to lifestyle/nutritional interventions. Latest advances in the analysis and monitoring of dietary habits, food behaviors, physical activity/exercise and deep phenotyping will be discussed, as well as the relevance of novel applications of nutrigenomics, metabolomics and microbiota profiling. Recent findings in the development of precision nutrition are highlighted. Finally, results from published studies providing examples of new avenues to successfully implement innovative precision nutrition approaches will be reviewed.

show abstract

Interaction between Common Genetic Variants and Total Fat Intake on Low-Density Lipoprotein Peak Particle Diameter: A Genome-Wide Association Study

Cited by 25 publications

References 47 publications

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study

Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome

Contact Info

Product

Resources

About