PROTOCOL AND EXPERIMENTAL DESIGN OF THE LEOGRA (LAST EVIDENCES OF GENETIC RISK FACTORS IN THE AGED) STUDY, A POPULATION-BASED GENETIC APPROACH TO CARDIOVASCULAR RISK

Aims There are several non-genetic risk factors for new-onset atrial fibrillation, including age, sex, obesity, hypertension, diabetes, and alcohol consumption. However, whether these non-genetic risk factors have equal significance among different age groups is not known. We performed a nationwide population-based analysis to compare the clinical significance of non-genetic risk factors for new-onset atrial fibrillation in various age groups. Methods and results A total of 9,797,409 people without a prior diagnosis of atrial fibrillation who underwent a national health check-up in 2009 were included. During 80,130,090 person-years of follow-up, a total of 196,136 people were diagnosed with new-onset atrial fibrillation. The impact of non-genetic risk factors on new-onset atrial fibrillation was examined in different age groups. Obesity, male sex, heavy alcohol consumption, smoking, hypertension, diabetes and chronic kidney disease were associated with an increased risk of new-onset atrial fibrillation. With minor variations, these risk factors were consistently associated with the risk of new-onset atrial fibrillation among various age groups. Using these risk factors, we created a scoring system to predict future risk of new-onset atrial fibrillation in different age groups. In receiver operating characteristic curve analysis, the predictive value of these risk factors ranged between 0.556 and 0.603, and no significant trends were observed. Conclusions Non-genetic risk factors for new-onset atrial fibrillation may have a similar impact on different age groups. Except for sex, these non-genetic risk factors can be modifiable. Therefore, efforts to control non-genetic risk factors might have relevance for both the young and old.

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Shared and specific genetic risk factors for lifetime major depression, depressive symptoms and neuroticism in three population-based twin samples

Psychological Medicine ◽

10.1017/s003329171800377x ◽

2018 ◽

Vol 49 (16) ◽

pp. 2745-2753 ◽

Cited By ~ 10

Author(s):

Kenneth S. Kendler ◽

Charles O. Gardner ◽

Michael C. Neale ◽

Steve Aggen ◽

Andrew Heath ◽

...

Keyword(s):

Risk Factors ◽

Depressive Symptoms ◽

Major Depression ◽

Genetic Risk ◽

Structural Equation ◽

Common Factor ◽

Genetic Risk Factors ◽

Population Based ◽

Self Report ◽

Equation Modeling

AbstractBackgroundVulnerability to depression can be measured in different ways. We here examine how genetic risk factors are inter-related for lifetime major depression (MD), self-report current depressive symptoms and the personality trait Neuroticism.MethodWe obtained data from three population-based adult twin samples (Virginia n = 4672, Australia #1 n = 3598 and Australia #2 n = 1878) to which we fitted a common factor model where risk for ‘broadly defined depression’ was indexed by (i) lifetime MD assessed at personal interview, (ii) depressive symptoms, and (iii) neuroticism. We examined the proportion of genetic risk for MD deriving from the common factor v. specific to MD in each sample and then analyzed them jointly. Structural equation modeling was conducted in Mx.ResultsThe best fit models in all samples included additive genetic and unique environmental effects. The proportion of genetic effects unique to lifetime MD and not shared with the broad depression common factor in the three samples were estimated as 77, 61, and 65%, respectively. A cross-sample mega-analysis model fit well and estimated that 65% of the genetic risk for MD was unique.ConclusionA large proportion of genetic risk factors for lifetime MD was not, in the samples studied, captured by a common factor for broadly defined depression utilizing MD and self-report measures of current depressive symptoms and Neuroticism. The genetic substrate for MD may reflect neurobiological processes underlying the episodic nature of its cognitive, motor and neurovegetative manifestations, which are not well indexed by current depressive symptom and neuroticism.

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Clinical and Genetic Risk Factors for Perianal Crohnʼs Disease in a Population-Based Cohort

The American Journal of Gastroenterology ◽

10.1038/ajg.2011.437 ◽

2012 ◽

Vol 107 (4) ◽

pp. 589-596 ◽

Cited By ~ 45

Author(s):

T W Eglinton ◽

R Roberts ◽

J Pearson ◽

M Barclay ◽

T R Merriman ◽

...

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Population Based

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Genetic risk factors in recurrent venous thromboembolism: A multilocus, population-based, prospective approach

Clinica Chimica Acta ◽

10.1016/j.cca.2009.01.011 ◽

2009 ◽

Vol 402 (1-2) ◽

pp. 189-192 ◽

Cited By ~ 10

Author(s):

Robert Y.L. Zee ◽

Vadim Bubes ◽

Sanjay Shrivastava ◽

Paul M Ridker ◽

Robert J. Glynn

Keyword(s):

Risk Factors ◽

Venous Thromboembolism ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Population Based ◽

Recurrent Venous Thromboembolism

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Genetic Approach to Thrombophilia

Thrombosis and Haemostasis ◽

10.1055/s-0037-1616205 ◽

2001 ◽

Vol 86 (07) ◽

pp. 92-103 ◽

Cited By ~ 39

Author(s):

Rogier Bertina

Keyword(s):

Risk Factors ◽

Venous Thrombosis ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Small Subset ◽

Multifactorial Disease ◽

Genetic Approach ◽

Thrombotic Risk ◽

Genetic And Environmental Factors ◽

Multiple Interactions

SummaryVenous thrombosis is a multifactorial disease. Multiple interactions between genetic and environmental factors contribute to the development of the disease. Presently, we know of six or seven genetic risk factors for venous thrombosis. However, together these defects can explain the clustering of thrombotic events in only a small subset of families with thrombophilia. As to the identification of new genetic risk factors for thrombosis, we seem to have arrived at the end of a practicable road with the classical approach of thrombophilia, which usually starts with the study of the association of hemostatic phenotypes and thrombotic risk. At the same time we have undertaken various genetic approaches aiming at identifying polymorphisms/ mutations causing thrombotic risk. This review summarizes what we have learnt so far, what to do and what not to do. The odds for finding remaining common genetic risk factors for venous thrombosis during the next ten years may be predicted to be fairly high.

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General population screening for childhood type 1 diabetes: is it time for a UK strategy?

Archives of Disease in Childhood ◽

10.1136/archdischild-2021-321864 ◽

2021 ◽

pp. archdischild-2021-321864

Author(s):

Rachel Elizabeth Jane Besser ◽

Sze May Ng ◽

John W Gregory ◽

Colin M Dayan ◽

Tabitha Randell ◽

...

Keyword(s):

Risk Factors ◽

Type 1 Diabetes ◽

Genetic Risk ◽

Genetic Risk Factors ◽

Population Based ◽

Screening Strategy ◽

Smooth Transition ◽

Islet Autoantibodies ◽

Awareness Campaigns

Type 1 diabetes (T1D) is a chronic autoimmune disease of childhood affecting 1:500 children aged under 15 years, with around 25% presenting with life-threatening diabetic ketoacidosis (DKA). While first-degree relatives have the highest risk of T1D, more than 85% of children who develop T1D do not have a family history. Despite public health awareness campaigns, DKA rates have not fallen over the last decade. T1D has a long prodrome, and it is now possible to identify children who go on to develop T1D with a high degree of certainty. The reasons for identifying children presymptomatically include prevention of DKA and related morbidities and mortality, reducing the need for hospitalisation, time to provide emotional support and education to ensure a smooth transition to insulin treatment, and opportunities for new treatments to prevent or delay progression. Research studies of population-based screening strategies include using islet autoantibodies alone or in combination with genetic risk factors, both of which can be measured from a capillary sample. If found during screening, the presence of two or more islet autoantibodies has a high positive predictive value for future T1D in childhood (under 18 years), offering an opportunity for DKA prevention. However, a single time-point test will not identify all children who go on to develop T1D, and so combining with genetic risk factors for T1D may be an alternative approach. Here we discuss the pros and cons of T1D screening in the UK, the different strategies available, the knowledge gaps and why a T1D screening strategy is needed.

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