Abstract
Overweight and obesity are associated with altered stress reactivity and increased inflammation. However, it is not known whether stress-induced changes in brain function scale with BMI and ...if such associations are driven by peripheral cytokines. Here, we investigate multimodal stress responses in a large transdiagnostic sample using predictive modeling based on spatio-temporal profiles of stress-induced changes in activation and functional connectivity. BMI is associated with increased brain responses as well as greater negative affect after stress and individual response profiles are associated with BMI in females (
p
perm
< 0.001), but not males. Although stress-induced changes reflecting BMI are associated with baseline cortisol, there is no robust association with peripheral cytokines. To conclude, alterations in body weight and energy metabolism might scale acute brain responses to stress more strongly in females compared to males, echoing observational studies. Our findings highlight sex-dependent associations of stress with differences in endocrine markers, largely independent of peripheral inflammation.
Peripheral immune profiling studies have consistently identified inflammatory subgroups within primary psychiatric disorders such as major depressive disorder. However, clinical trials that have ...rationally selected immunomodulatory therapeutic agents have had mixed results, and few have stratified patients by immune biomarker status. Therefore, there are critical gaps in our knowledge about how peripheral inflammatory markers affect central immune status and which immune biomarkers can be used to inform clinical trial design.
Accordingly, we conducted a narrative review of immune biomarkers in psychiatric illness: circulating immune markers including C-reactive protein (CRP), non-human primate and human experimental studies of central immune responses to administration of peripheral immune molecules, inflammatory biomarker measurements in clinical trials, the utility of Mendelian randomization for ascribing causality to inflammatory biomarkers in depression, and the relationship between peripheral-central immune gene expression.
Immune biomarker studies suggest that circulating immune markers can identify patients who would benefit from anti-inflammatory treatment and can inform selection of antidepressants. Although elevated CRP levels have been linked to poor response to SSRIs, other immune biomarkers may serve as better stratification indices for treatment trials. Detailed results from our findings will be discussed during the NSAS workshop (June, 2023) and presented at ISPNE.
We will synthesize our findings to identify: which immune biomarkers may be useful for patient stratification in clinical trials of depression and other psychiatric illnesses, unappreciated rational immune targets based on existing literature, and gaps in our understanding of immune biomarkers that should inform future experimental research studies.
As the availability of omics data has increased in the last few years, more multi‐omics data have been generated, that is, high‐dimensional molecular data consisting of several types such as genomic, ...transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block‐wise missing multi‐omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi‐omics data sets, we compare the predictive performances of several of these approaches for different block‐wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.
This article is categorized under:
Applications of Computational Statistics > Genomics/Proteomics/Genetics
Applications of Computational Statistics > Health and Medical Data/Informatics
Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data
Multi‐omics data are high‐dimensional molecular data for which several types of omics data (e.g., RNA data) are available for the same patients. A common issue in prediction using multi‐omics data is block‐wise missingness, meaning that some of the omics data types are missing for subsets of the patients.
Abstract
Summary
Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To ...address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization.
Availability and implementation
The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.
As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, ...transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.
