Pitfalls in complement analysis : A systematic literature review of assessing complement activation

Background: The complement system is an essential component of our innate defense and plays a vital role in the pathogenesis of many diseases. Assessment of complement activation is critical in monitoring both disease progression and response to therapy. Complement analysis requires accurate and standardized sampling and assay procedures, which has proven to be challenging. Objective: We performed a systematic analysis of the current methods used to assess complement components and reviewed whether the identified studies performed their complement measurements according to the recommended practice regarding pre-analytical sample handling and assay technique. Results are supplemented with own data regarding the assessment of key complement biomarkers to illustrate the importance of accurate sampling and measuring of complement components. Methods: A literature search using the Pubmed/MEDLINE database was performed focusing on studies measuring the key complement components C3, C5 and/or their split products and/or the soluble variant of the terminal C5b-9 complement complex (sTCC) in human blood samples that were published between February 2017 and February 2022. The identified studies were reviewed whether they had used the correct sample type and techniques for their analyses. Results: A total of 92 out of 376 studies were selected for full-text analysis. Forty-five studies (49%) were identified as using the correct sample type and techniques for their complement analyses, while 25 studies (27%) did not use the correct sample type or technique. For 22 studies (24%), it was not specified which sample type was used. Conclusion: A substantial part of the reviewed studies did not use the appropriate sample type for assessing complement activation or did not mention which sample type was used. This deviation from the standardized procedure can lead to misinterpretation of complement biomarker levels and hampers proper comparison of complement measurements between studies. Therefore, this study underlines the necessity of general guidelines for accurate and standardized complement analysi

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1,024 OCD patients and 1,028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC=0.702) than unmedicated (AUC=0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level