Biology subjects

Goeman, J. J.

Publications and source records attributed to Goeman, J. J..

5 recordsLinked to original sources

bioRxiv2023

The AccelerAge framework: A new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individuals survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individuals aging status.

molecular biology↗

bioRxiv2023

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors

There is variability in the rate of aging among people of the same chronological age. The concept of biological age is postulated to capture this variability, and hence to better represent an individuals true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of ones chronological age-independent rate of aging {Delta}. We show that the most popular cross-sectional biological age predictors--based on multiple linear regression, the Klemera-Doubal method or principal component analysis--rely on the same strong underlying assumption, namely that a candidate marker of agings association with chronological age is directly informative of its association with the aging rate {Delta}. We call this the identical-association assumption and prove that it is untestable in a cross-sectional setting. Using synthetic data, we illustrate the consequences if the assumption does not hold: in such scenarios, there is no guarantee that the weights that a cross-sectional method assigns to candidate markers are informative of the underlying truth. Using real data we illustrate that the extent to which the identical-association assumption holds is of direct practical relevance for anyone interested in developing or interpreting cross-sectional biological age predictors.

molecular biology↗

bioRxiv2019

Search for early pancreatic cancer blood biomarkers in five European prospective population biobanks using metabolomics

Background and aimMost patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. MethodsThe low incidence rate complicates prospective biomarker studies. Here, we took advantage of the availability of biobanked samples from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian biobank, Rotterdam Study) and identified prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between one month and seventeen years after blood sampling, and compared these with age- and gender-matched controls from the same cohorts. We applied 1H-NMR-based metabolomics on the Nightingale platform on these samples and applied logistic regression to assess the predictive value of individual metabolite concentrations, with gender, age, body mass index, smoking status, type 2 diabetes mellitus status, fasting status, and cohort as covariates. ResultsAfter quality assessment, we retained 356 cases and 887 controls. We identified two interesting hits, glutamine (p=0.011) and histidine (p=0.012), and obtained Westfall-Young family-wise error rate adjusted p-values of 0.43 for both. Stratification in quintiles showed a 1.5x elevated risk for the lowest 20% of glutamine and a 2.2x increased risk for the lowest 20% of histidine. Stratification by time to diagnosis (<2 years, 2-5 years, >5 years) suggested glutamine to be involved in an earlier process, tapering out closer to onset, and histidine in a process closer to the actual onset. Lasso-penalized logistic regression showed a slight improvement of the area under the Receiver Operator Curves when including glutamine and histidine in the model. Finally, our data did not support the earlier identified branched-chain amino acids as potential biomarkers for pancreatic cancer in several American cohorts. ConclusionWhile identifying glutamine and histidine as early biomarkers of potential biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.

epidemiology↗

bioRxiv2018

Post-mortem T2*- weighted MRI imaging of cortical iron reflects severity of Alzheimer’s Disease

The value of iron-based MRI changes for the diagnosis and staging of Alzheimers disease (AD) depends on an association between cortical iron accumulation and AD pathology. Therefore, this study determined the cortical distribution pattern of MRI contrast changes in cortical regions selected based on the known distribution pattern of tau pathology and investigated whether MRI contrast changes reflect the underlying AD pathology in the different lobes.[Formula]-weighted MRI was performed on post-mortem cortical tissue of controls, late-onset AD, and early-onset AD followed by histology and correlation analyses. Combining ex-vivo high-resolution MRI and histopathology revealed that: LOAD and EOAD have a different distribution pattern of AD pathological hallmarks and MRI contrast changes over the cortex, with EOAD showing more severe MRI changes; (2) per lobe, severity of AD pathological hallmarks correlates with iron accumulation, and hence with MRI. Therefore, iron-sensitive MRI sequences allow detection of the cortical distribution pattern of AD pathology ex-vivo.\n\nAbbreviations

neuroscience↗

bioRxiv2017

All-Resolutions Inference for Brain Imaging

The most prevalent approach to activation localization in neuroimaging is to identify brain regions as contiguous supra-threshold clusters, check their significance using random field theory, and correct for the multiple clusters being tested. Besides recent criticism on the validity of the random field assumption, a spatial specificity paradox remains: the larger the detected cluster, the less we know about the location of activation within that cluster. This is because cluster inference implies \"there exists at least one voxel with an evoked response in the cluster\", and not that \"all the voxels in the cluster have an evoked response\". Inference on voxels within selected clusters is considered bad practice, due to the voxel-wise false positive rate inflation associated with this circular inference. Here, we propose a remedy to the spatial specificity paradox. By applying recent results from the multiple testing statistical literature, we are able to quantify the proportion of truly active voxels within selected clusters, an approach we call All-Resolutions Inference (ARI). If this proportion is high, the paradox vanishes. If it is low, we can further \"drill down\" from the cluster level to sub-regions, and even to individual voxels, in order to pinpoint the origin of the activation. In fact, ARI allows inference on the proportion of activation in all voxel sets, no matter how large or small, however these have been selected, all from the same data. We use two fMRI datasets to demonstrate the non-triviality of the spatial specificity paradox, and its resolution using ARI. One of these datasets is large enough for us to split it and validate the ARI estimates. The conservatism of ARI inference permits circularity without losing error guarantees, while still returning informative estimates.

neuroscience↗