Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Modeling of atrophy size trajectories: variable transformation, prediction and age-of-onset estimation.

ABSTRACT:

Background

To model the progression of geographic atrophy (GA) in patients with age-related macular degeneration (AMD) by building a suitable statistical regression model for GA size measurements obtained from fundus autofluorescence imaging.

Methods

Based on theoretical considerations, we develop a linear mixed-effects model for GA size progression that incorporates covariable-dependent enlargement rates as well as correlations between longitudinally collected GA size measurements. To capture nonlinear progression in a flexible way, we systematically assess Box-Cox transformations with different transformation parameters λ. Model evaluation is performed on data collected for two longitudinal, prospective multi-center cohort studies on GA size progression.

Results

A transformation parameter of λ=0.45 yielded the best model fit regarding the Akaike information criterion (AIC). When hypertension and hypercholesterolemia were included as risk factors in the model, they showed an association with progression of GA size. The mean estimated age-of-onset in this model was 67.21±6.49 years.

Conclusions

We provide a comprehensive framework for modeling the course of uni- or bilateral GA size progression in longitudinal observational studies. Specifically, the model allows for age-of-onset estimation, identification of risk factors and prediction of future GA size. A square-root transformation of atrophy size is recommended before model fitting.

SUBMITTER: Behning C

PROVIDER: S-EPMC8369742 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Distinct brain volume changes correlating with clinical stage, disease progression rate, mutation size, and age at onset prediction as early biomarkers of brain atrophy in Huntington's disease.

Project description:Searching brain and peripheral biomarkers is a requisite to cure Huntington's disease (HD). To search for markers indicating the rate of brain neurodegenerative changes in the various disease stages, we quantified changes in brain atrophy in subjects with HD. We analyzed the cross-sectional and longitudinal rate of brain atrophy, quantitatively measured by fully-automated multiparametric magnetic resonance imaging, as fractional gray matter (GM, determining brain cortex volume), white matter (WM, measuring the volume of axonal fibers), and corresponding cerebral spinal fluid (CSF, a measure of global brain atrophy), in 94 gene-positive subjects with presymptomatic to advanced HD, and age-matched healthy controls. Each of the three brain compartments we studied (WM, GM, and CSF) had a diverse role and their time courses differed in the development of HD. GM volume decreased early in life. Its decrease was associated with decreased serum brain-derived-neurotrophic-factor and started even many years before onset symptoms, then decreased slowly in a nonlinear manner during the various symptomatic HD stages. WM volume loss also began in the presymptomatic stage of HD a few years before manifest symptoms appear, rapidly decreasing near to the zone-of-onset. Finally, the CSF volume increase began many years before age at onset. Its volume measured in presymptomatic subjects contributed to improve the CAG-based model of age at onset prediction. The progressive CSF increase depended on CAG mutation size and continued linearly until the last stages of HD, perhaps representing the best marker of progression rate and severity in HD (R(2)= 0.25, P < 0.0001).

| S-EPMC6494025 | biostudies-literature

Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide.

Project description:Current geographic spread of documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections shows heterogeneity. This study explores the role of age in potentially driving differentials in infection spread, epidemic potential, and rates of disease severity and mortality across countries. An age-stratified deterministic mathematical model that describes SARS-CoV-2 transmission dynamics was applied to 159 countries and territories with a population ≥1 million. Assuming worst-case scenario for the pandemic, the results indicate that there could be stark regional differences in epidemic trajectories driven by differences in the distribution of the population by age. In the African Region (median age: 18.9 years), the median R0 was 1.05 versus 2.05 in the European Region (median age: 41.7 years), and the median (per 100 persons) for the final cumulative infection incidence was 22.5 (versus 69.0), for severe and/or critical disease cases rate was 3.3 (versus 13.0), and for death rate was 0.5 (versus 3.9). Age could be a driver of variable SARS-CoV-2 epidemic trajectories worldwide. Countries with sizable adult and/or elderly populations and smaller children populations may experience large and rapid epidemics in absence of interventions. Meanwhile, countries with predominantly younger age cohorts may experience smaller and slower epidemics. These predictions, however, should not lead to complacency, as the pandemic could still have a heavy toll nearly everywhere.

| S-EPMC7444586 | biostudies-literature

Biomarkers selection and mathematical modeling in biological age estimation.

Project description:Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

| S-EPMC10314900 | biostudies-literature

Ranked prediction of p53 targets using hidden variable dynamic modeling.

Project description:Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively.

| S-EPMC1557743 | biostudies-literature

Clinical syndromes associated with posterior atrophy: early age at onset AD spectrum.

Project description:ObjectivePosterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are clinical syndromes associated with posterior brain atrophy. We compared PCA and LPA to each other and to an age-matched group of patients with early age at onset of Alzheimer disease (EO-AD). We hypothesized that these 3 syndromes are part of a single clinical and biologic continuum.MethodsVoxel-based morphometry (VBM) was used to assess atrophy in 14 PCA, 10 LPA, and 16 EO-AD patients compared to 65 healthy controls. Genetic analysis for APOE was conducted in 30 patients and 44 controls. Four patients came to autopsy. An additional 14 were studied with the beta-amyloid specific PET with tracer (11)C-labeled Pittsburgh Compound-B (PIB).ResultsVBM results demonstrated that, compared to controls, each patient group showed a large area of overlapping atrophy in bilateral parietal, occipital, precuneus, posterior cingulate, posterior temporal, and hippocampal regions. Surrounding this common area, group-specific atrophy was found in small, symptom-specific regions for each group: the right ventral-occipital and superior parietal regions in PCA, the left middle and superior temporal gyri in LPA, and the prefrontal cortex in EO-AD. APOE epsilon4 frequency was higher in all patient groups compared to controls. Four PCA, 5 LPA, and 8 EO-AD patients showed evidence of cortical amyloid at pathology (n = 3) or on PIB-PET (n = 14).ConclusionsLogopenic progressive aphasia and posterior cortical atrophy showed largely overlapping anatomic and biologic features with early age at onset of Alzheimer disease, suggesting that these clinical syndromes represent the spectrum of clinical manifestation of the nontypical form of Alzheimer disease that presents at an early age.

| S-EPMC2777069 | biostudies-literature

Sample size for binary logistic prediction models: Beyond events per variable criteria.

Project description:Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictors that can be examined. We present an extensive simulation study in which we studied the influence of EPV, events fraction, number of candidate predictors, the correlations and distributions of candidate predictor variables, area under the ROC curve, and predictor effects on out-of-sample predictive performance of prediction models. The out-of-sample performance (calibration, discrimination and probability prediction error) of developed prediction models was studied before and after regression shrinkage and variable selection. The results indicate that EPV does not have a strong relation with metrics of predictive performance, and is not an appropriate criterion for (binary) prediction model development studies. We show that out-of-sample predictive performance can better be approximated by considering the number of predictors, the total sample size and the events fraction. We propose that the development of new sample size criteria for prediction models should be based on these three parameters, and provide suggestions for improving sample size determination.

| S-EPMC6710621 | biostudies-literature

Prediction of brain age suggests accelerated atrophy after traumatic brain injury.

Project description:ObjectiveThe long-term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals' differences in chronological and predicted structural "brain age," and test whether TBI produces progressive atrophy and how this relates to cognitive function.MethodsA predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging-derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age.ResultsThe initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be "older," with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase.InterpretationTBI patients' brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age-associated conditions, motivating further research into the age-like effects of brain injury and other neurological diseases.

| S-EPMC4403966 | biostudies-literature

Inflection Points in Blood Pressure Trajectories Preceding Hypertension Onset in Different Age Groups.

Project description:Background Understanding the natural history of elevated blood pressure (BP) is important to determine the window for primary prevention of hypertension. The authors aimed to investigate the natural history of elevated BP and examine whether there were inflection points in BP trajectories preceding hypertension onset in Chinese adults. Methods and Results A total of 8688 participants with an average of 5 BP measurements were included from the CHNS (China Health and Nutrition Survey). In each wave, triplicate measurements on the same arm were taken, and the mean systolic BP (SBP) and diastolic BP (DBP) were used in the analysis. Hypertension onset was defined as SBP ≥140 mm Hg or DBP ≥90 mm Hg or diagnosed by physician or currently under antihypertensive treatment. The median follow-up time was 13.0 years. Overall, BP elevation with age prior to the onset of hypertension showed a nonlinear trajectory. The increased rates in both SBP and DBP were obviously faster after the inflection point than before. According to hypertension onset at age 30 to 39, 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years, at the inflection point, patients were ≈29, 38, 48, 57, and 67 years, SBP levels were 112.6, 114.8, 116.8, 117.4, and 118.0 mm Hg, and DBP levels were 73.4, 75.7, 76.9, 76.2, and 73.8 mm Hg, respectively. Conclusions There was a nonlinear trajectory of BP elevation preceding hypertension onset. The inflection points for SBP and DBP were in the range of 112 to 118 mm Hg and 73 to 77 mm Hg, respectively. Once BP levels exceeded the changing points, the level of SBP and DBP increased more rapidly.

| S-EPMC9939055 | biostudies-literature

Age of onset and effect size in genome-wide association studies.

Project description:Genome-wide association studies (GWAS) have identified many susceptibility loci for complex traits, but have not identified the majority of the genetic contribution to common diseases. We explored whether the magnitude of associations detected in GWAS and, therefore, the likelihood of detecting a significant association for a given sample size, is generally greater for childhood-onset traits (e.g., birth defects) than for traits with onset in adulthood.Data were obtained from the National Human Genome Research Institute Catalog of Published GWAS. Traits were categorized as having an average age of onset in childhood (<18 years, n = 15 traits), early adulthood (18-54 years, n = 32 traits), or late adulthood (?55 years, n = 31 traits). The relationship between age of onset category and the magnitude of significant associations detected by GWAS was assessed using logistic regression.Associations characterized by an odds ratio (OR) ? 1.5 were significantly more common for GWAS of childhood traits than for late adulthood-onset traits after adjustment for several covariates (adjusted OR, 2.55; 95% confidence interval, 1.37-4.73). Results in subgroup analyses using more stringent inclusion criteria (based on sample size, effect size, p value threshold for inclusion, and novel variant-trait associations) were similar.These findings suggest that, on average, marker-trait associations detected in GWAS for traits with young onset may have a larger magnitude of effect than those for traits with adult onset. Therefore, GWAS for young-onset traits, such as birth defects, may be more likely than those for adult-onset traits to identify major genetic risk factors. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.

| S-EPMC4219508 | biostudies-literature

A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer's disease.

Project description:The ?4 allele of the apolipoprotein E (APOE) gene is currently the strongest and most highly replicated genetic factor for risk and age of onset of late-onset Alzheimer's disease (LOAD). Using phylogenetic analysis, we have identified a polymorphic poly-T variant, rs10524523, in the translocase of outer mitochondrial membrane 40 homolog (TOMM40) gene that provides greatly increased precision in the estimation of age of LOAD onset for APOE ?3 carriers. In two independent clinical cohorts, longer lengths of rs10524523 are associated with a higher risk for LOAD. For APOE ?3/4 patients who developed LOAD after 60 years of age, individuals with long poly-T repeats linked to APOE ?3 develop LOAD on an average of 7 years earlier than individuals with shorter poly-T repeats linked to APOE ?3 (70.5 ± 1.2 years versus 77.6 ± 2.1 years, P=0.02, n=34). Independent mutation events at rs10524523 that occurred during Caucasian evolution have given rise to multiple categories of poly-T length variants at this locus. On replication, these results will have clinical utility for predictive risk estimates for LOAD and for enabling clinical disease prevention studies. In addition, these results show the effective use of a phylogenetic approach for analysis of haplotypes of polymorphisms, including structural polymorphisms, which contribute to complex diseases.

| S-EPMC2946560 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data