A genetic risk score is differentially associated with migraine with and without aura

Pisanu, Claudia; Preisig, Martin; Castelao, Enrique; Glaus, Jennifer; Pistis, Giorgio; Squassina, Alessio; Zompo, Maria Del; Merikangas, Kathleen R.; Waeber, Gérard; Vollenweider, Péter; Mwinyi, Jessica; Schiöth, Helgi B.

doi:10.1007/s00439-017-1816-5

Cited by 22 publications

(16 citation statements)

References 36 publications

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“…Migraines with and without aura are believed to have different, though somewhat overlapping, etiologies Studies with twins indicate that genetics plays a greater role in migraine with aura than without . Recently, a genetic risk score that combined multiple genetic risk variants confirmed that these the two main forms of migraine have a different genetic susceptibility background …”

Section: Epidemiologymentioning

confidence: 99%

Migraine Burden of Disease: From the Patient's Experience to a Socio‐Economic View

2018

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Migraine is one of the most common diseases worldwide and, importantly, a major cause of disability. This translates into a huge clinical and economic burden to individuals and society. Despite existing data on how migraine affects populations, the disease continues to be underdiagnosed and therefore undertreated, so the scale of the public health problem may be underestimated. The impact on the daily lives of patients and their families has also been underappreciated. Clinical and regulatory guidelines are encouraging the use of patient-reported outcome tools (PROs), as these may help in disease management and facilitate physician-patient interactions. In clinical trials in migraine, PRO data are key to evaluating the effects of new therapies on patients, such as disability assessment, productivity, quality of life, and emotional and physical functioning. Digital technologies have enabled the development of ePRO tools, which may play a growing role in the collection of PRO data in the future. Here, we will consider the current view of the burden imposed by migraine on patients and society, illustrate this with two patient case studies, and examine the initiatives underway to use our knowledge to improve our management approaches for the disease.

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Section: Epidemiologymentioning

confidence: 99%

Migraine Burden of Disease: From the Patient's Experience to a Socio‐Economic View

2018

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“…The genotype of each variant was coded based on the amount of risk alleles, 0 for no risk alleles, 1 for heterozygous variant carriers and 2 for individuals carrying two risk alleles, respectively. Genetic variants with mean decrease accuracy (MDA) >1 by random forest algorithm were considered to have positive effects on the risk of sepsis and were chosen for construction of wGRS [21]. The wGRS was constructed on the base of the β coefficients obtained from the logistic regression analysis in the additive model, and the equation was as follows: wGRS=β1×SNP1+β2×SNP2+…+βi×SNPi.…”

Section: Discussionmentioning

confidence: 99%

Polygenic risk score for early prediction of sepsis risk in the polytrauma screening cohort

Wen

Sun

et al. 2020

Preprint

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Background: Increasing genetic variants associated with sepsis have been identified by candidate-gene and genome-wide association studies, but single variant conferred minimal alterations in risk prediction. Our aimed to evaluate whether a weighted genetic risk score (wGRS) that aggregate information from multiple variants could improve risk discrimination of traumatic sepsis . Methods: 64 genetic variants potential relating to sepsis were genotyped in Chinese trauma cohort. Genetic variants with mean decrease accuracy (MDA)>1.0 by random forest algorithms were selected to construct the multilocus wGRS. Area under curve (AUC) and Net reclassification improvement (NRI) were used to evaluate the discriminatory and reclassification ability of wGRS. Results: Seventeen variants were extracted to construct the wGRS in 883 trauma patients. The wGRS was significantly associated with traumatic sepsis (OR=2.19, 95%CI=1.53-3.15, P=2.01×10 -5 ) after adjusted by age, sex, and ISS. Patients with higher wGRS have an increasing incidence of traumatic sepsis (P trend =6.81×10 -8 ), higher SOFA (P trend =5.00×10 -3 ) and APACHEII score (P trend =1.00×10 -3 ). The AUC of risk prediction model incorporating wGRS into the clinical variables was 0.768 (95%CI=0.739-0.796), with an increase of 3.40% (P=8.00×10 -4 ) versus clinical factors-only model. Furthermore, the NRI increased 25.18% (95%CI=17.84-32.51%) (P=6.00×10 -5 ). Conclusions: Our finding indicated that genetic predictors could improve the predictive ability of risk model for sepsis and highlighted the application among trauma populations, indicating that the sepsis risk assessment model will be a promising tool for high risk population screening and prediction.

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“…We de ned a linear weight of 0, 1, and 2 to different genotypes containing 0, 1, and 2 risk alleles, respectively. Genetic variants with mean decrease accuracy (MDA) >1 by random forest algorithm were considered to have positive effects on the risk of sepsis and were chosen for construction of wGRS [21]. The wGRS was constructed on the base of the β coe cients obtained from the logistic regression analysis in the additive model, and the equation was as follows:…”

Section: Discussionmentioning

confidence: 99%

Polygenic risk score for early prediction of sepsis risk in the polytrauma screening cohort

Wen

Sun

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Increasing genetic variants associated with sepsis have been identified by candidate-gene and genome-wide association studies, but single variant conferred minimal alterations in risk prediction. Our aimed to evaluate whether a weighted genetic risk score (wGRS) that aggregate information from multiple variants could improve risk discrimination of traumatic sepsis.Methods: 64 genetic variants potential relating to sepsis were genotyped in Chinese trauma cohort. Genetic variants with mean decrease accuracy (MDA) > 1.0 by random forest algorithms were selected to construct the multilocus wGRS. Area under curve (AUC) and Net reclassification improvement (NRI) were adopted to evaluate the discriminatory and reclassification ability of wGRS.Results: Seventeen variants were extracted to construct the wGRS in 883 trauma patients. The wGRS was significantly associated with sepsis after trauma (OR = 2.19, 95%CI = 1.53–3.15, P = 2.01 × 10− 5) after adjusted by age, sex, and ISS. Patients with higher wGRS have an increasing incidence of traumatic sepsis (Ptrend=6.81 × 10− 8), higher SOFA (Ptrend=5.00 × 10− 3) and APACHE II score (Ptrend=1.00 × 10− 3). The AUC of risk prediction model incorporating wGRS into the clinical variables was 0.768 (95%CI = 0.739–0.796), with an increase of 3.40% (P = 8.00 × 10− 4) versus clinical factors-only model. Furthermore, the NRI increased 25.18% (95%CI = 17.84–32.51%) (P = 6.00 × 10− 5).Conclusions: Our finding indicated that genetic variants could enhance the predictive power of risk model for sepsis and highlighted the application among trauma patients, suggesting that the sepsis risk assessment model will be a promising screening and prediction tool for high risk population.

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A genetic risk score is differentially associated with migraine with and without aura

Cited by 22 publications

References 36 publications

Migraine Burden of Disease: From the Patient's Experience to a Socio‐Economic View

Migraine Burden of Disease: From the Patient's Experience to a Socio‐Economic View

Polygenic risk score for early prediction of sepsis risk in the polytrauma screening cohort

Polygenic risk score for early prediction of sepsis risk in the polytrauma screening cohort

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