Dataset Information

Development of a spirometry T-score in the general population.

ABSTRACT: Spirometry values may be expressed as T-scores in standard deviation units relative to a reference in a young, normal population as an analogy to the T-score for bone mineral density. This study was performed to develop the spirometry T-score.T-scores were calculated from lambda-mu-sigma-derived Z-scores using a young, normal age reference. Three outcomes of all-cause death, respiratory death, and COPD death were evaluated in 9,101 US subjects followed for 10 years; an outcome of COPD-related health care utilization (COPD utilization) was evaluated in 1,894 Korean subjects followed for 4 years.The probability of all-cause death appeared to remain nearly zero until -1 of forced expiratory volume in 1 second (FEV1) T-score but increased steeply where FEV1 T-score reached below -2.5. Survival curves for all-cause death, respiratory death, COPD death, and COPD utilization differed significantly among the groups when stratified by FEV1 T-score (P<0.001). The adjusted hazard ratios of the FEV1 T-score for the four outcomes were 0.54 (95% confidence interval, 0.48-0.60), 0.43 (95% CI: 0.37-0.50), 0.30 (95% CI: 0.24-0.37), and 0.69 (95% CI: 0.59-0.81), respectively, adjusting for covariates (P<0.001).The spirometry T-score could predict all-cause death, respiratory death, COPD death, and COPD utilization.

SUBMITTER: Lee SW

PROVIDER: S-EPMC4770011 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Development of a spirometry T-score in the general population.

Lee Sei Won SW Kim Hyun Kuk HK Baek Seunghee S Jung Ji-Ye JY Kim Young Sam YS Lee Jae Seung JS Lee Sang-Do SD Mannino David M DM Oh Yeon-Mok YM

International journal of chronic obstructive pulmonary disease 20160223

<h4>Background and objective</h4>Spirometry values may be expressed as T-scores in standard deviation units relative to a reference in a young, normal population as an analogy to the T-score for bone mineral density. This study was performed to develop the spirometry T-score.<h4>Methods</h4>T-scores were calculated from lambda-mu-sigma-derived Z-scores using a young, normal age reference. Three outcomes of all-cause death, respiratory death, and COPD death were evaluated in 9,101 US subjects fol ...[more]

PMID: 26966359

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Project description:Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case-control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.

Project description:BACKGROUND:Recent genome-wide association studies have identified multiple genetic loci that increase the risk of chronic kidney disease (CKD) in the general population. We hypothesized that knowledge of these loci might permit improved CKD risk prediction beyond that provided by traditional phenotypic risk factors. STUDY DESIGN:Observational cohort study. SETTING & PARTICIPANTS:Participants who attended the 15th (1977-1979) and 24th (1995-1998) examination cycles of the original cohort or the 6th (1995-1998) and 8th cycles (2005-2008) of the offspring cohort of the Framingham Heart Study (n = 2,489). PREDICTORS:Single-nucleotide polymorphisms at 16 stage 3 CKD loci were genotyped and used to construct a genetic risk score. Standard clinical predictors of incident stage 3 CKD also were used. OUTCOMES & MEASUREMENTS:Incident stage 3 CKD was defined as estimated glomerular filtration rate <60 mL/min/1.73 m(2) at follow-up. Participants with baseline stage 3 CKD were excluded. Logistic regression was used to generate C statistics, which measured the power of the genetic risk score to discriminate risk of incident CKD stage 3 with and without traditional risk factors. RESULTS:There were 270 new stage 3 CKD cases during an average of 10.8 years of follow-up. Mean genetic risk score was 17.5 ± 2.8 (SD) for those who developed stage 3 CKD and 17.3 ± 2.6 for those who did not (P for genotype score difference = 0.2). The OR for stage 3 CKD was 1.06 (95% CI, 1.01-1.11; P = 0.03) per additional risk allele, adjusting for age and sex. In the age- and sex-adjusted model, the C statistic was 0.748 without the genotype score and 0.751 with the score (P difference = 0.3). The risk score was not statistically significant in a multivariable model adjusted for standard stage 3 CKD risk factors (P = 0.07). LIMITATIONS:All participants were of European ancestry; the genotype score may not be valid in different ancestral groups. CONCLUSIONS:A genetic score generated from 16 known CKD risk alleles did not predict new cases of stage 3 CKD in the community beyond knowledge of common clinical risk factors alone.

Dataset Information

Development of a spirometry T-score in the general population.

Publications

Development of a spirometry T-score in the general population.

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