Comparing the Predictive Validity of Trait Affect and Average Daily Affect for the Prospective Prediction of Health Outcomes.
Ontology highlight
ABSTRACT:
SUBMITTER: Willroth EC
PROVIDER: S-EPMC7326312 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Comparing the Predictive Validity of Trait Affect and Average Daily Affect for the Prospective Prediction of Health Outcomes.
Willroth Emily C EC Graham Eileen K EK Mroczek Daniel K DK
Journal of research in personality 20200516
In preregistered secondary data analyses, we compared the predictive utility of trait affect and average daily affect for predicting three health outcomes across nine years (N = 1,376). Trait positive and negative affect were assessed using a 25-item dispositional questionnaire. Average daily affect was assessed as the mean of eight daily diary reports of the same items. Trait affect and average daily affect both had medium associations with self-reported general health and chronic health condit ...[more]
Similar Datasets
Project description:PurposeThe aim of this study was to identify patient-related characteristics that affect the predictive validity of the FCE assessment ELA.MethodsA prospective multicenter study was conducted on 303 patients with musculoskeletal disorders (MSD) recruited from eleven rehabilitation centers. The ELA-based estimation of the participants' ability to cope with physical work demands was considered valid if RTW was paired with a positive ELA outcome (≥ moderate) as well as if non-RTW was accompanied by a negative ELA outcome (rather or very poor). In the remaining cases, the ELA result was judged as non-valid. To reduce the risk of false conclusions, the rating was performed inversely in participants that (1) reported severe limitations regarding their productivity at work, (2) attributed RTW to a change in job resp. a reduction of their physical work demands and in those that (3) attributed non-RTW to non-physical reintegration barriers only. Using questionnaires, 28 patient-related characteristics were collected. Logistic regression models were calculated to identify characteristics that affected the predictive validity of ELA.ResultsELA was considered valid in 208 of 303 (69%) participants. A moderate and strong pain-related disability at work were associated with a 0.15-fold (95% confidence interval (95% CI) 0.05-0.46), respectively, 0.19-fold (95% CI 0.05-0.72) chance for a valid outcome. In addition, a negative influence was found in participants that reported psychosocial distress (odds ratio (OR) 0.35; 95% CI 0.15-0.82), a native language different from the national language (OR 0.16; 95% CI 0.05-0.56) as well as in those that expected to return to work, but not within one month (OR 0.17; 95% CI 0.06-0.46). Further variables-including age, employment status, fear-avoidance beliefs and the level of physical work demands-did not affect the predictive validity of ELA.ConclusionsThe results suggest that the predictive validity of ELA is primarily limited by patients that report a moderate or strong pain-related disability at work, psychosocial distress as well as the expectation to return to work, but not within one month. Furthermore, a negative influence can be assumed for language barriers.
| S-EPMC8551349 | biostudies-literature
Project description:ObjectivesRecent conceptualizations of adaptive emotion regulation is predicated on the ability to flexibly use emotion regulation strategies to meet changing contextual demands. Although trait mindfulness has been linked to enhanced emotional well-being and use of adaptive emotion regulation strategies, there is a dearth of literature examining associations between trait mindfulness and emotion regulation flexibility. Further, despite a rich literature suggesting that emotion regulation processes change with age, no study to date has assessed whether the role of trait mindfulness on emotion regulation responsiveness to negative emotions-a component of emotion regulation flexibility-differs between young and older adults.MethodsThe current study recruited 130 young adults and 130 older adults to assess trait mindfulness, emotion regulation strategy use, and emotion regulation responsiveness of six distinct strategies in daily life.ResultsAcross the full sample, trait mindfulness was related to reduced distraction (β = -0.11, t(238.09) = -3.02, p = .003) and expressive suppression (β = -0.15, t(237.70) = -4.62, p < .001) strategy use. Age moderation analyses revealed that trait mindfulness was associated with reduced expressive suppression responsiveness (β = 0.12, t(247) = 2.31, p = .022) in young adults and increased detached reappraisal responsiveness among older adults (β = 0.15, t(247) = -2.95, p = .003).ConclusionsThe current findings highlight the importance of understanding how trait mindfulness is associated with strategy use and responsiveness to negative affect changes in daily life as well as how these patterns may shift across the lifespan.Manuscript pre-registrationOpen Science Framework, registration number: z5g8v.
| S-EPMC10948115 | biostudies-literature
Project description:Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increased beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection (GS) could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic-estimated breeding values (GEBV) for average daily weight gain (ADG) in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications [Bayesian GBLUP (BGBLUP), BayesA, and BayesCπ] were performed with four genotype panels [Illumina BovineHD BeadChip, TagSNPs, and GeneSeek High- and Low-density indicus (HDi and LDi, respectively)]. Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesCπ model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44) and sample size (568 animals in the training population). Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement GS at lower costs.
| S-EPMC5473763 | biostudies-literature
Project description:Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
| S-EPMC11185554 | biostudies-literature
Project description:Accurate prediction of complex traits is an important task in quantitative genetics. Genotypes have been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regression methods, dimension reduction methods, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were tested in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance and startle response from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effects-ranging from models that assume that every gene affects the trait to more sparse models-and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as a source of biological information to inform prediction models. The results show that differences in prediction accuracy exist. For example, methods performing variable selection achieved higher prediction accuracy for starvation resistance in females, while they generally had lower accuracy for startle response in both sexes. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes for starvation resistance. For startle response, crumbs (crb) and imaginal disc growth factor 2 (Idgf2) were found for females and males, respectively. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
| S-EPMC11813155 | biostudies-literature
Project description:AimsHaemorrhagic complications are strongly linked with adverse outcomes in acute coronary syndrome (ACS) patients. Various risk scores (RS) are available to predict bleeding risk in these patients. We compared the performance of three contemporary bleeding RS in ACS.MethodsWe studied 4500 consecutive patients with ACS. We calculated the ACTION, CRUSADE, and Mehran et al. (2010) bleeding RS, and evaluated their performance for predicting their own major bleeding events and TIMI serious (major or minor) bleeding episodes, in patients with either non-ST-elevation ACS (NSTEACS) or ST-elevation myocardial infarction (STEMI). Calibration (Hosmer-Lemeshow test, HL) and discrimination (c-statistic) for the three RS were computed and compared.ResultsFor RS-specific major bleeding, ACTION and CRUSADE showed the best prognostic discrimination in STEMI (c=0.734 and 0.791, respectively; p=0.04), and in NSTEACS (c=0.791 and 0.810; p=0.4); being CRUSADE significantly superior to Mehran et al. in both ACS types (p<0.05). All RS performed well in patients undergoing coronary arteriography using either a radial or femoral approach (all c≥0.718); however, their discriminative capacity was modest in patients not undergoing coronary arteriography and in those previously on oral anticoagulant (all c<0.70). For TIMI serious bleeding, ACTION and CRUSADE displayed the highest c-index values in both STEMI (0.724 and 0.703, respectively; p=0.3) and NSTEACS (c=0.733 and 0.744, respectively; p=0.6); however, calibration of ACTION was poor in both ACS types (HL p<0.05).ConclusionsOf contemporary bleeding RS, the CRUSADE score was found to be the most accurate quantitative tool for NSTEACS and STEMI patients undergoing coronary arteriography.
| S-EPMC3760544 | biostudies-literature
Project description:Adaptive laboratory evolution is highly effective for improving desired traits through natural selection. However, its applicability is inherently constrained to growth-correlated traits precluding traits of interest that incur a fitness cost, such as metabolite secretion. Here, we introduce the concept of tacking trait enabling natural selection of fitness-costly metabolic traits. The concept is inspired from the tacking maneuver used in sailing for traversing upwind. We use first-principle metabolic models to design an evolution niche wherein the tacking trait and fitness become correlated. Adaptive evolution in this niche, when followed by the reversal to the original niche, manifests in the improvement of the desired trait due to biochemical coupling between the tacking and the desired trait. We experimentally demonstrate this strategy, termed EvolveX, by evolving wine yeasts for increased aroma production. Our results pave the way for precision laboratory evolution for biotechnological and ecological applications.
2022-07-12 | PXD023171 | Pride
Project description:ImportanceThe average health outcomes in the US are not as good as the average health outcomes in other developed countries. However, whether high-income US citizens have better health outcomes than average individuals in other developed countries is unknown.ObjectiveTo assess whether the health outcomes of White US citizens living in the 1% and 5% richest counties (hereafter referred to as privileged White US citizens) are better than the health outcomes of average residents in other developed countries.Design, setting, and participantsThis comparative effectiveness study, conducted from January 1, 2013, to December 31, 2015, identified White US citizens living in the 1% (n = 32) and 5% (n = 157) highest-income counties in the US and measured the following 6 health outcomes associated with health care interventions: infant and maternal mortality, colon and breast cancer, childhood acute lymphocytic leukemia, and acute myocardial infarction. The study used Organisation for Economic Co-operation and Development data, CONCORD-3 cancer data, and Medicare data to compare their outcomes with all residents in 12 other developed countries: Australia, Austria, Canada, Denmark, Finland, France, Germany, Japan, the Netherlands, Norway, Sweden, and Switzerland. Statistical analysis took place from July 25, 2017, to August 29, 2020.Main outcomes and measuresInfant mortality; maternal mortality; 5-year survival of patients with colon cancer, breast cancer, or childhood acute lymphocytic leukemia; and 30-day age-standardized case fatality after acute myocardial infarction.ResultsThe infant mortality rate among White US citizens in the 5% highest-income counties was 4.01 per 1000, and the maternal mortality rate among White US citizens in the 5% highest-income counties was 10.85 per 100 000, both higher than the mean rates for any of the 12 comparison countries. (The infant mortality rate for the top 1% counties was 3.54 per 1000, and the maternal mortality rate was 10.05 per 100 000.) The 5-year survival rate for White US citizens in the 5% highest-income counties was 67.2% (95% CI, 66.7%-67.7%) for colon cancer, higher than that of average US citizens (64.9% [95% CI, 64.7%-65.1%]) and average citizens in 6 countries, comparable with that of average citizens in 4 countries, and lower than that of average citizens for 2 countries. The 5-year survival rate for breast cancer among White US women in the 5% highest-income US counties was 92.0% (95% CI, 91.6%-92.4%), higher than in all 12 comparison countries. The 5-year survival rate for White children with acute lymphocytic leukemia in the 5% highest-income US counties was 92.6% (95% CI, 90.7%-94.2%), exceeding the mean survival rate for only 1 country and comparable with the mean survival rates in 11 countries. The adjusted 30-day acute myocardial infarction case-fatality rate for White US citizens in the 5% highest-income US counties was 8% below the rate for all US citizens and was 5% below the rate for all US citizens in the 1% highest-income US counties; these estimates were similar to the median outcome of other high-income countries.Conclusions and relevanceThis study suggests that privileged White US citizens have better health outcomes than average US citizens for 6 health outcomes but often fare worse than the mean measure of health outcomes of 12 other developed countries. These findings imply that even if all US citizens experienced the same health outcomes enjoyed by privileged White US citizens, US health indicators would still lag behind those in many other countries.
| S-EPMC7770612 | biostudies-literature
Project description:The rate of preterm birth is a public health concern worldwide because it is increasing and efforts to prevent it have failed. We report a Clinically Relevant Complex Systematic Review (CSCSR) designed to identify and evaluate the best available evidence in support of the association between periodontal status in women and pregnancy outcome of preterm low birth weight. We hypothesize that the traditional limits of research synthesis must be expanded to incorporate a translational component. As a proof-of-concept model, we propose that this CSCSR can yield greater validity of efficacy and effectiveness through supplementing its recommendations with data of the proteomic signature of periodontal disease in pregnancy, which can contribute to addressing specifically the predictive validity for adverse outcomes. For this CRCSR, systematic reviews were identified through The National Library of MedicinePubmed, The Cochrane library, CINAHL, Google Scholar, Web of Science, and the American Dental Association web library. Independent reviewers quantified the relevance and quality of this literature with R-AMSTAR. Homogeneity and inter-rater reliability testing were supplemented with acceptable sampling analysis. Research synthesis outcomes were analyzed qualitatively toward a Bayesian inference, and converge to demonstrate a definite association between maternal periodontal disease and pregnancy outcome. This CRCSR limits heterogeneity in terms of periodontal disease, outcome measure, selection bias, uncontrolled confounders and effect modifiers. Taken together, the translational CRCSR model we propose suggests that further research is advocated to explore the fundamental mechanisms underlying this association, from a molecular and proteomic perspective.
| S-EPMC3043352 | biostudies-literature
Project description:We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.
| S-EPMC4476450 | biostudies-literature