| 11. |
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| 12. |
- Ganna, Andrea, et al.
(författare)
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5 year mortality predictors in 498 103 UK Biobank participants : a prospective population-based study
- 2015
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Ingår i: The Lancet. - 0140-6736 .- 1474-547X. ; 386:9993, s. 533-540
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Tidskriftsartikel (refereegranskat)abstract
- Background To our knowledge, a systematic comparison of predictors of mortality in middle-aged to elderly individuals has not yet been done. We investigated predictors of mortality in UK Biobank participants during a 5 year period. We aimed to investigate the associations between most of the available measurements and 5 year all-cause and cause-specific mortality, and to develop and validate a prediction score for 5 year mortality using only self-reported information. Methods Participants were enrolled in the UK Biobank from April, 2007, to July, 2010, from 21 assessment centres across England, Wales, and Scotland with standardised procedures. In this prospective population-based study, we assessed sex-specific associations of 655 measurements of demographics, health, and lifestyle with all-cause mortality and six cause-specific mortality categories in UK Biobank participants using the Cox proportional hazard model. We excluded variables that were missing in more than 80% of the participants and all cardiorespiratory fitness test measurements because summary data were not available. Validation of the prediction score was done in participants enrolled at the Scottish centres. UK life tables and census information were used to calibrate the score to the overall UK population. Findings About 500 000 participants were included in the UK Biobank. We excluded participants with more than 80% variables missing (n=746). Of 498 103 UK Biobank participants included (54% of whom were women) aged 37-73 years, 8532 (39% of whom were women) died during a median follow-up of 4.9 years (IQR 4.33-5.22). Self-reported health (C-index including age 0.74 [95% CI 0.73-0.75]) was the strongest predictor of all-cause mortality in men and a previous cancer diagnosis (0.73 [0.72-0.74]) was the strongest predictor of all-cause mortality in women. When excluding individuals with major diseases or disorders (Charlson comorbidity index >0; n=355 043), measures of smoking habits were the strongest predictors of all-cause mortality. The prognostic score including 13 self-reported predictors for men and 11 for women achieved good discrimination (0.80 [0.77-0.83] for men and 0.79 [0.76-0.83] for women) and significantly outperformed the Charlson comorbidity index (p<0.0001 in men and p=0.0007 in women). A dedicated website allows the interactive exploration of all results along with calculation of individual risk through an online questionnaire. Interpretation Measures that can simply be obtained by questionnaires and without physical examination were the strongest predictors of all-cause mortality in the UK Biobank population. The prediction score we have developed accurately predicts 5 year all-cause mortality and can be used by individuals to improve health awareness, and by health professionals and organisations to identify high-risk individuals and guide public policy.
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| 13. |
- Ganna, Andrea, 1985-, et al.
(författare)
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Genetic determinants of mortality : Can findings from genome-wide association studies explain variation in human mortality?
- 2013
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Ingår i: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 132:5, s. 553-561
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Tidskriftsartikel (refereegranskat)abstract
- Twin studies have estimated the heritability of longevity to be approximately 20-30 %. Genome-wide association studies (GWAS) have revealed a large number of determinants of morbidity, but so far, no new polymorphisms have been discovered to be associated with longevity per se in GWAS. We aim to determine whether the genetic architecture of mortality can be explained by single nucleotide polymorphisms (SNPs) associated with common traits and diseases related to mortality. By extensive quality control of published GWAS we created a genetic score from 707 common SNPs associated with 125 diseases or risk factors related with overall mortality. We prospectively studied the association of the genetic score with: (1) time-to-death; (2) incidence of the first of nine major diseases (coronary heart disease, stroke, heart failure, diabetes, dementia, lung, breast, colon and prostate cancers) in two population-based cohorts of Dutch and Swedish individuals (N = 15,039; age range 47-99 years). During a median follow-up of 6.3 years (max 22.2 years), we observed 4,318 deaths and 2,132 incident disease events. The genetic score was significantly associated with time-to-death [hazard ratio (HR) per added risk allele = 1.003, P value = 0.006; HR 4th vs. 1st quartile = 1.103]. The association between the genetic score and incidence of major diseases was stronger (HR per added risk allele = 1.004, P value = 0.002; HR 4th vs. 1st quartile = 1.160). Associations were stronger for individuals dying at older ages. Our findings are compatible with the view of mortality as a complex and highly polygenetic trait, not easily explainable by common genetic variants related to diseases and physiological traits.
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| 14. |
- Ganna, Andrea, et al.
(författare)
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Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease
- 2014
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Ingår i: PLOS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 10:12, s. e1004801-
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Tidskriftsartikel (refereegranskat)abstract
- Analyses of circulating metabolites in large prospective epidemiological studies could lead to improved prediction and better biological understanding of coronary heart disease (CHD). We performed a mass spectrometry-based non-targeted metabolomics study for association with incident CHD events in 1,028 individuals (131 events; 10 y. median follow-up) with validation in 1,670 individuals (282 events; 3.9 y. median follow-up). Four metabolites were replicated and independent of main cardiovascular risk factors [lysophosphatidylcholine 18∶1 (hazard ratio [HR] per standard deviation [SD] increment = 0.77, P-value<0.001), lysophosphatidylcholine 18∶2 (HR = 0.81, P-value<0.001), monoglyceride 18∶2 (MG 18∶2; HR = 1.18, P-value = 0.011) and sphingomyelin 28∶1 (HR = 0.85, P-value = 0.015)]. Together they contributed to moderate improvements in discrimination and re-classification in addition to traditional risk factors (C-statistic: 0.76 vs. 0.75; NRI: 9.2%). MG 18∶2 was associated with CHD independently of triglycerides. Lysophosphatidylcholines were negatively associated with body mass index, C-reactive protein and with less evidence of subclinical cardiovascular disease in additional 970 participants; a reverse pattern was observed for MG 18∶2. MG 18∶2 showed an enrichment (P-value = 0.002) of significant associations with CHD-associated SNPs (P-value = 1.2×10-7 for association with rs964184 in the ZNF259/APOA5 region) and a weak, but positive causal effect (odds ratio = 1.05 per SD increment in MG 18∶2, P-value = 0.05) on CHD, as suggested by Mendelian randomization analysis. In conclusion, we identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role in CHD development.
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| 15. |
- Ganna, Andrea, et al.
(författare)
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Large-scale non-targeted metabolomic profiling in three human population-based studies
- 2016
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Non-targeted metabolomic profiling is used to simultaneously assess a large part of the metabolome in a biological sample. Here, we describe both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. At present, more than 200 metabolites have been manually annotated in more than 3600 participants using an in-house library of standards and publically available spectral databases. Data available at the metabolights repository include individual raw unprocessed data, processed data, basic demographic variables and spectra of annotated metabolites. Additional phenotypical and genetic data is available upon request to cohort steering committees. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases.
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| 16. |
- Ganna, Andrea, et al.
(författare)
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Multilocus Genetic Risk Scores for Coronary Heart Disease Prediction
- 2013
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Ingår i: Arteriosclerosis, Thrombosis and Vascular Biology. - : Lippincott Williams & Wilkins. - 1079-5642 .- 1524-4636. ; 33:9, s. 2267-2272
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Tidskriftsartikel (refereegranskat)abstract
- Objective-Current guidelines do not support the use of genetic profiles in risk assessment of coronary heart disease (CHD). However, new single nucleotide polymorphisms associated with CHD and intermediate cardiovascular traits have recently been discovered. We aimed to compare several multilocus genetic risk score (MGRS) in terms of association with CHD and to evaluate clinical use. Approach and Results-We investigated 6 Swedish prospective cohort studies with 10 612 participants free of CHD at baseline. We developed 1 overall MGRS based on 395 single nucleotide polymorphisms reported as being associated with cardiovascular traits, 1 CHD-specific MGRS, including 46 single nucleotide polymorphisms, and 6 trait-specific MGRS for each established CHD risk factors. Both the overall and the CHD-specific MGRS were significantly associated with CHD risk (781 incident events; hazard ratios for fourth versus first quartile, 1.54 and 1.52; P<0.001) and improved risk classification beyond established risk factors (net reclassification improvement, 4.2% and 4.9%; P=0.006 and 0.017). Discrimination improvement was modest (C-index improvement, 0.004). A polygene MGRS performed worse than the CHD-specific MGRS. We estimate that 1 additional CHD event for every 318 people screened at intermediate risk could be saved by measuring the CHD-specific genetic score in addition to the established risk factors. Conclusions-Our results indicate that genetic information could be of some clinical value for prediction of CHD, although further studies are needed to address aspects, such as feasibility, ethics, and cost efficiency of genetic profiling in the primary prevention setting.
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| 17. |
- Ganna, Andrea, et al.
(författare)
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Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies
- 2015
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 16:4, s. 563-575
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Tidskriftsartikel (refereegranskat)abstract
- It is common and advised practice in biomedical research to validate experimental or observational findings in a population different from the one where the findings were initially assessed. This practice increases the generalizability of the results and decreases the likelihood of reporting false-positive findings. Validation becomes critical when dealing with high-throughput experiments, where the large number of tests increases the chance to observe false-positive results. In this article, we review common approaches to determine statistical thresholds for validation and describe the factors influencing the proportion of significant findings from a 'training' sample that are replicated in a 'validation' sample. We refer to this proportion as rediscovery rate (RDR). In high-throughput studies, the RDR is a function of false-positive rate and power in both the training and validation samples. We illustrate the application of the RDR using simulated data and real data examples from metabolomics experiments. We further describe an online tool to calculate the RDR using t-statistics. We foresee two main applications. First, if the validation study has not yet been collected, the RDR can be used to decide the optimal combination between the proportion of findings taken to validation and the size of the validation study. Secondly, if a validation study has already been done, the RDR estimated using the training data can be compared with the observed RDR from the validation data; hence, the success of the validation study can be assessed.
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| 18. |
- Ganna, Andrea, 1985-, et al.
(författare)
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Risk prediction measures for case-cohort and nested case-control designs : an application to cardiovascular disease.
- 2012
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 175:7, s. 715-724
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Tidskriftsartikel (refereegranskat)abstract
- Case-cohort and nested case-control designs are often used to select an appropriate subsample of individuals from prospective cohort studies. Despite the great attention that has been given to the calculation of association estimators, no formal methods have been described for estimating risk prediction measures from these 2 sampling designs. Using real data from the Swedish Twin Registry (2004-2009), the authors sampled unstratified and stratified (matched) case-cohort and nested case-control subsamples and compared them with the full cohort (as "gold standard"). The real biomarker (high density lipoprotein cholesterol) and simulated biomarkers (BIO1 and BIO2) were studied in terms of association with cardiovascular disease, individual risk of cardiovascular disease at 3 years, and main prediction metrics. Overall, stratification improved efficiency, with stratified case-cohort designs being comparable to matched nested case-control designs. Individual risks and prediction measures calculated by using case-cohort and nested case-control designs after appropriate reweighting could be assessed with good efficiency, except for the finely matched nested case-control design, where matching variables could not be included in the individual risk estimation. In conclusion, the authors have shown that case-cohort and nested case-control designs can be used in settings where the research aim is to evaluate the prediction ability of new markers and that matching strategies for nested case-control designs may lead to biased prediction measures.
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| 19. |
- Ganna, Andrea
(författare)
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Risk prediction models for cardiovascular disease and overall mortality
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Prediction or prognostication is at the core of modern evidence-based medicine. Prediction of overall mortality and cardiovascular disease can be improved by a systematic evaluation of measurements from large-scale epidemiological studies or by using nested sampling designs to discover new markers from omics technologies. In study I, we investigated if prediction measures such as calibration, discrimination and reclassification could be calculated within traditional sampling designs and which of these designs were the most efficient. We found that is possible to calculate prediction measures by using a proper weighting system and that a stratified casecohort design is a reasonable choice both in terms of efficiency and simplicity. In study II, we investigated the clinical utility of several genetic scores for incident coronary heart disease. We found that genetic information could be of clinical value in improving the allocation of patients to correct risk strata and that the assessment of a genetic risk score among intermediate risk subjects could help to prevent about one coronary heart disease event every 318 people screened. In study III, we explored the association between circulating metabolites and incident coronary heart disease. We found four new metabolites associated with coronary heart disease independently of established cardiovascular risk factors and with evidence of clinical utility. By using genetic information we determined a potential causal effect on coronary heart disease of one of these novel metabolites. In study IV, we compared a large number of demographics, health and lifestyle measurements for association with all-cause and cause-specific mortality. By ranking measurements in terms of their predictive abilities we could provide new insights about their relative importance, as well as reveal some unexpected associations. Moreover we developed and validated a prediction score for five-year mortality with good discrimination ability and calibrated it for the entire UK population. In conclusion, we applied a translational approach spanning from the discovery of novel biomarkers to their evaluation in terms of clinical utility. We combined this effort with methodological improvements aimed to expand prediction measures in settings that were not previously explored. We identified promising novel metabolomics markers for cardiovascular disease and supported the potential clinical utility of a genetic score in primary prevention. Our results might fuel future studies aimed to implement these findings in clinical practice.
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| 20. |
- Ganna, Andrea, et al.
(författare)
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Utilizing Twins as Controls for Non-Twin Case-Materials in Genome Wide Association Studies
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:12, s. e83101-
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Tidskriftsartikel (refereegranskat)abstract
- Twin registries around the globe have collected DNA samples from large numbers of monozygotic and dizygotic twins. The twin sample collections are frequently used as controls in disease-specific studies together with non-twins. This approach is unbiased under the hypothesis that twins and singletons are comparable in terms of allele frequencies; i.e. there are no genetic variants associated with being a twin per se. To test this hypothesis we performed a genome-wide association study comparing the allele frequency of 572,352 single nucleotide polymorphisms (SNPs) in 1,413 monozygotic (MZ) and 5,451 dizygotic (DZ) twins with 3,720 healthy singletons. Twins and singletons have been genotyped using the same platform. SNPs showing association with being a twin at P-value < 1 × 10-5 were selected for replication analysis in 1,492 twins (463 MZ and 1,029 DZ) and 1,880 singletons from Finland. No SNPs reached genome-wide significance (P-value < 5 × 10-8) in the main analysis combining MZ and DZ twins. In a secondary analysis including only DZ twins two SNPs (rs2033541 close to ADAMTSL1 and rs4149283 close to ABCA1) were genome-wide significant after meta-analysis with the Finnish population. The estimated proportion of variance on the liability scale explained by all SNPs was 0.08 (P-value=0.003) when MZ and DZ were considered together and smaller for MZ (0.06, P-value=0.10) compared to DZ (0.09, P-value=0.003) when analyzed separately. In conclusion, twins and singletons can be used in genetic studies together with general population samples without introducing large bias. Further research is needed to explore genetic variances associated with DZ twinning.
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