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- Fukaya, Eri, et al.
(författare)
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Clinical and Genetic Determinants of Varicose Veins Prospective, Community-Based Study of approximate to 500 000 Individuals
- 2018
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Ingår i: Circulation. - 0009-7322 .- 1524-4539. ; 138:25, s. 2869-2880
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Varicose veins are a common problem with no approved medical therapies. Although it is believed that varicose vein pathogenesis is multifactorial, there is limited understanding of the genetic and environmental factors that contribute to their formation. Large-scale studies of risk factors for varicose veins may highlight important aspects of pathophysiology and identify groups at increased risk for disease. METHODS: We applied machine learning to agnostically search for risk factors of varicose veins in 493 519 individuals in the UK Biobank. Predictors were further studied with univariable and multivariable Cox regression analyses (2441 incident events). A genome-wide association study of varicose veins was also performed among 337 536 unrelated individuals (9577 cases) of white British descent, followed by expression quantitative loci and pathway analyses. Because height emerged as a new candidate risk factor, we performed mendelian randomization analyses to assess a potential causal role for height in varicose vein development. RESULTS: Machine learning confirmed several known (age, sex, obesity, pregnancy, history of deep vein thrombosis) and identified several new risk factors for varicose vein disease, including height. After adjustment for traditional risk factors in Cox regression, greater height remained independently associated with varicose veins (hazard ratio for upper versus lower quartile, 1.74; 95% Cl, 1.51-2.01; P<0.0001). A genomewide association study identified 30 new genome-wide significant loci, identifying pathways involved in vascular development and skeletal/ limb biology. Mendelian randomization analysis provided evidence that increased height is causally related to varicose veins (inverse -variance weighted: odds ratio, 1.26; P=2.07x10(-16)). CONCLUSIONS: Using data from nearly a half -million individuals, we present a comprehensive genetic and epidemiological study of varicose veins. We identified novel clinical and genetic risk factors that provide pathophysiological insights and could help future improvements of treatment of varicose vein disease.
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- Lindholm, Daniel, 1982-, et al.
(författare)
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Bioimpedance and New-Onset Heart Failure : A Longitudinal Study of >500 000 Individuals From the General Population.
- 2018
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Ingår i: Journal of the American Heart Association. - 2047-9980. ; 7:13
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Heart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially could enable early diagnosis and preemptive treatment.METHODS AND RESULTS: We applied machine learning in the UK Biobank in an agnostic search of risk factors for heart failure in 500 451 individuals, excluding individuals with prior heart failure. Novel factors were then subjected to several in-depth analyses, including multivariable Cox models of incident heart failure, and assessment of discrimination and calibration. Machine learning confirmed many known and putative risk factors for heart failure and identified several novel candidates. Mean reticulocyte volume appeared as one novel factor and leg bioimpedance another, the latter appearing as the most important new marker. Leg bioimpedance was lower in those who developed heart failure during an up to 9.8-year follow-up. When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95% confidence interval], 0.60 [0.48-0.73] and 0.75 [0.59-0.94], in age- and sex-adjusted and fully adjusted models, respectively, comparing the upper versus lower quartile). A model including leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good discrimination for future heart failure hospitalization (Concordance index [C-index]=0.82) and good calibration.CONCLUSIONS: Leg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively noninvasive measures, combining leg bioimpedance with history of myocardial infarction, age, and sex provides accurate predictive capacity.
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