Several modalities are available for the diagnosis of rectal cancer, including conventional gold standard rigid endoscopy and recent flexible endoscopy and magnetic resonance imaging (MRI). Each modality affects the management of these patients.To compare the accuracy of flexible endoscopy and MRI in the measurement of tumor height in patients with rectal cancer.p ˂ .001). The accordance between the two modalities was not affected by sex, age, body mass index, histopathology, or metastasis.This study included 174 patients with rectal cancer who underwent flexible endoscopy and MRI for the measurement of tumor height. Data on patient demographics, comorbidities, treatment, and histopathology were identified and collected. We evaluate intraclass correlation coefficient (ICC) and Bland–Altman plot to test the agreement between the measurements. ICC were excellent with an ICC of 89% (95%CI 48%–99%). The mean ± standard deviation of the distance from the anal verge to the distal part of the tumor was 7.73 ± .47 for flexible endoscopy and 6.21 ± 0.39 for MRI, with mean difference of 1.52 (Excellent agreement between flexible endoscopy and MRI was noted, and no factor was found to affect such concordance. The inclusion criteria were a diagnosis of rectal cancer of any stage from 2017 to 2020, a flexible endoscopy report, an MRI report, age >18 years, and rectal cancer height ≤18 cm from the anal verge. The exclusion criterion was sigmoid cancer height >18 cm. This study primarily aimed to compare between flexible endoscopy and MRI in the diagnosis of rectal cancer by measuring tumor height. The tumor height was measured from the anal verge to the distal part of the tumor. Agreement between the measurements was tested using intraclass correlation coefficient (ICC), and ICC values ≥0.75 were considered as excellent agreement, 0.4–0.75 as fair to good agreement, and <0.4 as poor agreement.2.2Using Google forms, two consultant surgeons collected and reviewed the patient data, including patients' demographics, BMI, and comorbidities such as DM, HTN, dyslipidemia, and hypothyroidism, as well as rectal cancer staging, management plan , patient mortality, last follow‐up, MRI report, endoscopy report, and histopathology.MRI images were obtained on a 1.5 Tesla Skyra Siemens MR magnet. The protocol includes multiplanar T1 and T2 weighted images of the pelvis as large field of views with pre and post contrast images as well as small field of view double oblique images of the rectal mass . Distances from the anal verge were obtained on the sagittal T2 weighted images or post contrast T1 weighted images (whichever showed the margins of the tumor the best). An expertized radiologist (co‐author AOB) obtained MRI images and measurement methods are shown in Figure 2.3t‐test were used to test the difference between MRI and endoscopy. p‐values <.05 were considered statistically significant. Ethical approval was obtained from the Research Ethics Committee Unit of King Abdulaziz University, Faculty of Medicine (ref no. 602‐20).Data were analyzed using SPSS version 26. We used one‐way random‐effects model to calculate the ICC. We used Python 3.2 to run Bland–Altman analysis and construct plots. We used mean and SD to summarized continues variables and frequency and percentage to summarized categorical variables. Two‐way 3Patient demographics, comorbidities, type of surgery, histopathology, chemotherapy, and radiotherapy are shown in Table p ˂ .001). In Table p = .63), age (p = .89), BMI (p = .86), histopathology (p = .61), and metastasis (p = .79). Figure A comparison between the use of flexible endoscopy and MRI is shown in Table 4In this study, the accuracy of flexible endoscopy and MRI in the measurement of the tumor height in patients with rectal cancer was compared and excellent agreement was found between the two modalities. Rectal tumor height is the distance between the distal end of the tumor and the anal verge.p ˂ .001). In addition, the two modalities were significantly affected by and varied between the male sex, metastasis, radiotherapy, histopathology, chemotherapy, and death. However, excellent agreement was noted between the two modalities, which was not affected by any factor, such as sex, age, BMI, histopathology, and metastasis. Although both modalities varied significantly in some variables, the agreement was not affected by any of the variables, and we assessed the variables showing excellent agreement.In the current study, the patients' demographics, comorbidities, histopathology, and tumor data were investigated. The mean height was determined using both flexible endoscopy and MRI; a significant difference in the mean height between the two modalities was observed, although a uniform definition of the height was used. A significant difference in the distance measured by the two modalities was found ; methodology ; supervision ; writing – original draft ; writing – review and editing . Mohammed A. Ezzat: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; visualization ; writing – original draft ; writing – review and editing . Aseel H. Khayyat: Conceptualization ; methodology ; writing – original draft ; writing – review and editing . Eyad Saleh A. Alamri: Conceptualization ; methodology ; writing – original draft ; writing – review and editing . Turki A. Madani: Conceptualization ; methodology ; writing – original draft ; writing – review and editing . Rana Y. Bokhary: Data curation ; project administration ; resources ; supervision . Arwa O. Badeeb: Conceptualization ; data curation ; investigation ; project administration ; writing – review and editing . Hussam A. Hijazi: Conceptualization ; methodology ; supervision ; writing – original draft ; writing – review and editing .The authors have stated explicitly that there are no conflicts of interest in connection with this article.Ethical approval was obtained from the Research Ethics Committee Unit of King Abdulaziz University, Faculty of Medicine (ref no. 602‐20).


Results are validated in an independent cohort at functional level  and in a public dataset. AMI serum exposure results in an overt AMI signature, enriched in debris cleaning, mitosis, and immune pathways. Moreover, gene networks associated with AMI and with poor clinical prognosis in AMI are identified. Network‐guided drug screening on the latter unveils prostaglandin E2 (PGE2) signaling as target for clinical intervention in detrimental macrophage imprinting during AMI trauma healing. The results demonstrate pronounced context‐induced macrophage reprogramming by the AMI systemic environment, to a degree decisive for patient prognosis. This offers new opportunities for targeted intervention and optimized cardiovascular disease risk management.Acute myocardial infarction (AMI) is accompanied by a systemic trauma response that impacts the whole body, including blood. This study addresses whether macrophages, key players in trauma repair, sense and respond to these changes. For this, healthy human monocyte‐derived macrophages are exposed to 20% human AMI ( A heart infarct induces profound changes to blood composition, which may impact key cells in repair of the heart after infarction: the macrophages. Here, this work demonstrates that macrophages are reprogrammed by their exposure to blood from infarcted patients. This has consequences for the patients’ future cardiovascular health but also offers new opportunities for therapeutic intervention. Recovery of heart function after infarct is controlled by the cardiac macrophage pool. During the first 24 h, cardiac‐resident macrophages in the infarct zone will be supplemented by recruited, mainly spleen‐derived monocytes, to assist in debris clearance, reperfusion damage control, and tissue repair. Simultaneously, this first inflammatory phase is characterized by considerable vascular leakage and myocardial edema. Considering that macrophages are highly plastic cells that adopt a wide variety of effector phenotypes in response to environmental challenges, we hypothesized that the altered AMI patient environment will impact on macrophage functionality and thereby on recovery of cardiac function and cardiovascular health in the aftermath of AMI. Here, we present evidence that the AMI systemic environment transcriptionally and functionally reprograms human macrophages for improved clearance, with potentially profound impact on disease outcome. We validated this imprinting in an independent patient cohort and, at transcriptional level, in an open access cohort. Moreover, we identified a gene program that associated with poor patient prognosis and identified candidate drugs able to rescue adverse macrophage programming.Acute myocardial infarction (AMI) elicits an immediate trauma response with major repercussions for blood composition, including leukocytes and plasma proteome.1 Whil22.1Figure + monocyte‐derived macrophages from at least six different healthy volunteers were pooled to account for interdonor variability. Macrophages were verified to be bona fide M0 macrophages which could be polarized with interferon γ (IFNγ) or interleukin 4 (IL‐4) to express signature M1 and M2 markers , while dendritic cell ) or monocyte markers  were barely or un‐detectable. Initial data exploration immediately revealed transcriptional differences between AMI serum exposed and control serum exposed macrophages  essentially confirmed AMI‐induced functional changes, except for macrophage actin stress fiber density and granularity  secretion and, to a lesser extent enhanced lipid uptake and inflammasome activity Figure 2A,B. Par2.2Figure To identify coherent pathways driving the functional changes observed in AMI‐mac, weighted gene co‐expression network analysis (WGCNA) was carried out on the discovery cohort RNA sequencing dataset. Gene modules M5 , M7 , and M14  correlated strongly with AMI serum exposure, as well as with macrophage phagocytosis capacity and IL‐6 secretion Figure . Indeed, No significant overlap was found between the total of differentially expressed genes (DEGs) in our dataset and GSE59867 6 h AMI‐ctrl DEGs. However, upon constricting our dataset to DEGs belonging to AMI‐associating modules, they showed 30% overlap (p = 1.62 × 10−26) with DEGs in PBMCs from ST‐elevation myocardial infarction (STEMI) patients (n = 111) versus control PBMC samples (n = 46)  from an AMI cohort sampled 6 h postinfarct (GSE59867).9 No s) Figure .2.3n = 25), while the other 25 patients suffered larger infarcts, with left ventricular reduced ejection fractions (EF) and higher end‐diastolic volumes (EDV), associated with post‐MI ventricular dysfunction  and one protecting modules (M14: stress fiber and granule formation) to be significantly associated with EF and EDV  of the infarcted heart . The AMI arm of the discovery cohort was designed to contain 25 patients with a small infarct size  signaling, concordant with their reported role in adverse cardiac remodeling. Of note, RAR binding partner peroxisome proliferator‐activated receptor γ (PPARγ) and c‐Rel were also identified as key drivers, pointing to alternative macrophage activation in AMI patients with poor prognosis. Identifying the key regulators of the AMI‐serum induced imprinting offers unique opportunities for therapeutic intervention.Based on gene expression data from AMI‐mac and Ctrl‐mac we constructed a regulatory network and identified 27 regulators of the significantly POSTEMI‐Large correlating modules  Figure . POSTEMImodeling.11 Of  Within this repository, THP‐1 human monocyte‐derived cells most closely relate to the primary macrophages used in our study. Applying in silico drug screening we selected leads able to neutralize detrimental gene programs induced by the AMI circulatory environment. Top candidates with cytostatic or broad‐spectrum activity profiles, such as several PI3kinase family inhibitors , chemokine (C motif) ligand (XCL1), and NF‐kappa‐B essential modulator (NEMO).1b In  Confirming the immune activation, we observed consistent increases in macrophage IL‐6 secretion after 24 h exposure to AMI serum. Of note, IL‐6 was also increased directly in the AMI serum compared to controls in the POSTEMI cohort. In accordance with our data, we interrogated a recently published AMI serum proteomics open access dataset by gene ontology overrepresentation analysis and found IL‐6 production to be one of the key terms most strongly associating with the AMI serum specific proteome.At the transcriptional level, AMI serum exposure affected significant changes pointing to increased proteasome activity, cell cycle regulation, and immune activation. The induction of proteasome and suppression of cell cycle gene programs as well as significant metabolic changes in AMI‐sera exposed macrophages, were supportive of the reported debris‐clearing, immune activating role of macrophages in the infarcted heart.18 Con applications generally rely on assessment of a single functional parameter. The applied multiparameter screening strategy, especially when integrated with transcriptional analysis, provides a much more granular and unbiased insight into the macrophage's phenotypic changes. Deploying advanced machine learning approaches, we identified key features of AMI‐serum challenged human macrophages for two independent AMI patient cohorts: increased phagocytosis and inflammation (IL‐6 and TNFα), resonating the AMI serum associated transcriptional changes. Inhibition of IL‐6 and TNFα to improve cardiac healing has yielded variable results in clinical trials. Thus, IL‐6 and TNFα production in AMI serum exposed macrophages should be interpreted within the full framework of AMI‐induced reprogramming, offering new options for therapeutic design. Slightly surprisingly, efferocytosis turned out to be unaffected by AMI serum exposure. Efferocytosis is however an extremely conserved feature of macrophages, and we postulate here that its machinery is fully able to effectively process apoptotic cells in the early phase of AMI, regardless of modulating factors contained in AMI or control serum. Conceivably, the observed phenotypic changes upon AMI serum exposure mirror effects of cardiac ischemia on cardiac macrophages in situ, as blood‐born and heart‐resident macrophages in the inflamed edematous infarct zone will both be exposed to plasma components. Of note, functional changes of serum exposure could be corroborated with plasma from the same AMI patient cohort, excluding those changes are attributable to coagulation. Inflammatory monocytes are a main source of monocyte‐derived macrophages accumulating at the infarct site, as shown repeatedly. The fact that we could partly validate this AMI signature in a cohort of PBMCs taken 6 h after infarct pleads for this notion. Thus, the AMI‐mac signature is mirroring changes seen in circulating PBMCs in AMI patients, especially considering the heterogenous nature of this cell preparation. Nevertheless, it remains to be firmly established whether (resident and monocyte‐derived) cardiac macrophages will undergo similar reprogramming in AMI in situ.To comprehensively map environment‐induced changes in macrophage functionality, we employed high content screening (HCS). While HCS has been used successfully for drug or RNA interference screening purposes,20 appIn addition to characterizing the response of macrophages upon contact with the AMI systemic environment, we explored differences between patients having suffered larger infarcts associated with significant post‐MI ventricular dysfunction (POSTEMI‐Large) and patients having suffered smaller infarcts . Five AMI sera‐exposed macrophage gene modules correlated with clinical features such as left ventricular EF. Interestingly, genes from those modules showed significant overlap with DEGs in PBMCs (6 h after infarct) from patients with larger versus smaller infarcts in the GSE59867 cohort. In addition, we have found enrichment of our POSTEMI‐Large and Small gene signatures in specific populations of cardiac macrophages in human infarcted hearts. This data suggests that context‐induced gene programs, operational in monocytes and macrophages immediately after AMI, could influence patient prognosis. and haemorrhage. While this may be partly ascribed to post‐AMI monocytosis, the prevalent procoagulant state, and hemodynamic factors, our results also suggest a role for AMI‐induced reprogramming of blood‐born or plaque‐resident macrophages, rendering them more phagocytic, immune‐activated, and metabolically challenged.Next to implications of AMI associated macrophage imprinting for infarct healing, myocardial infarction (MI) is also known to predispose to cardiovascular events in the immediate aftermath, by inducing plaque growth, destabilization,2a and This led us to speculate that the AMI‐associated macrophage imprint may resonate with plaque inflammation. The notion that organ‐resident macrophages adapt to systemic environmental changes with potential impact on future health increasingly gains momentum, but in a human CVD setting, evidence has so far been lacking. Regrettably, scRNA‐seq or bulk gene expression studies on isolated human plaque macrophages from infarct patients are not yet available, hampering direct assessment of our ex vivo AMI signature in plaque macrophages. Nevertheless, context imprinting in AMI trauma response, as here described, offers a unique opportunity for therapeutic steering of macrophage responses toward a more favorable direction. Our network‐guided in silico drug screening method identified PGE2 receptor agonist 16,16‐dimethylprostaglandin‐e2 as a possible drug candidate. Indeed, prostaglandin E2 receptor (EP 2) signalling in macrophages is critical for cardiac repair and functional recovery in infarcted mice, underpinning the therapeutic potential for short‐term PGE2 treatment after AMI. Our findings warrant future studies on PGE2's potential to reverse detrimental reprogramming of monocyte‐derived macrophages by AMI.Indeed, macrophage subsets enriched in plaque of symptomatic carotid endarterectomy patients shared characteristics with our AMI‐induced “unfavourable” phenotype, i.e., enhanced phagocytosis and PPAR‐dependent alternative macrophage activation.25 ThiImportantly, our study is limited by its rather small cohort sizes, which do not represent the diversity of the whole population. Nevertheless, the robust replication of our findings in an independent patient cohort suggests that our results have general validity. As we aimed to study effects of the circulatory environment on cells and not vice versa, our patient cohorts were blood‐based, whereas our readout cells were derived from healthy donors. Future studies should ideally complement this work by investigating the AMI‐related imprinting in monocytes and macrophages isolated from patients and control individuals at different timepoints after AMI. Still, our work clearly identifies the systemic circulation as an undervalued actor in macrophage behavior in post‐AMI repair.In conclusion, our study is the first to demonstrate the transcriptional and functional rewiring of macrophages upon exposure to the AMI circulatory environment, to improved debris clearance and healing responses. Moreover, we identified gene programs and their regulatory circuitry, associating with poor patient prognosis, which could be targeted by PGE2 receptor agonists, offering an entirely new therapeutic approach for prevention of adverse postinfarct remodeling.4Human primary monocyte‐derived macrophages from healthy volunteers were exposed for 24 h to a 1:5 dilution of human patient serum, and changes in transcription and function were analyzed. Patient sera (discovery cohort) were obtained either from control patients or patients suffering an AMI. Four months after initial AMI event, patients with regained and worsened cardiac function showing signs of heart failure were scored to allow linkage of the AMI‐serum induced macrophage effects to clinical prognosis. Sera‐exposed macrophages were analyzed by RNA sequencing and a multiparameter functional screening method based on both high content imaging and multiplex ELISA (Macroscreen). Functional screening data was validated using an independent patient cohort . The RNA sequencing dataset was computationally analyzed and integrated with functional data and patient characteristics to identify meaningful gene programs relating to AMI serum exposure. Finally, a drug repurposing screen identified a potential therapeutic with the ability to reverse unfavorable clinical effects. An overview of all cohorts mentioned in this manuscript, as well as how they are used, is presented in Table  The trial was approved by the Regional Committee for Medical Research Ethics, South‐East Norway on July 30, 2008, and registered (NCT00922675). The study was conducted in accordance with the Declaration of Helsinki, and all patients gave written informed consent. More information on patient inclusion and exclusion criteria is provided in the Supporting Information. This work aimed to (1) compare serum from AMI patients with controls and (2) compare serum from patients with large MI with serum from patients with small MI. To enable the second comparison, we designed the group of 50 AMI patients to contain 25 randomly selected patients from the highest quartile of MI and 25 patients from the lowest quartile, according to infarct size measured by cardiac magnetic resonance imaging  at 4 months follow‐up  and matched for age, BMI, smoking status, and total plasma cholesterol levels at admission were added to these 50 POSTEMI patient samples to obtain the full discovery cohort (Table −1) and the presence of carotid atherosclerosis (assessed by ultrasound) were considered exclusion criteria. Inclusion of control samples was approved by the Oslo University Hospital local ethical committee . Serum samples from this discovery cohort were used to stimulate human primary macrophages which were analyzed by RNA sequencing and Macroscreen functional assays  and the presence of carotid atherosclerosis (assessed by ultrasound) were considered exclusion criteria. Validation cohort buildup was approved by the Oslo University Hospital local ethical committee . Also for the validation cohort, serum samples were used to stimulate human primary macrophages, which were functionally profiled by the Macroscreen assay platform  and 23 patients with STEMI. AMI diagnosis was defined as a typical rise and fall of the cardiac biomarker troponin T with at least one value above the 99th percentile of the upper reference limit in patients presenting with symptoms of ischemia. STEMI patients adhered to the additional ECG criteria as described above. Control samples consisted of 25 age‐matched healthy volunteers (Mean age ± SEM: 63.9 ± 1.9 for AMI versus 62.5 ± 1.3 for controls). Cardiovascular disease history, elevated hsCRP . Blood samples were collected into endotoxin‐free tubes without additives and centrifuged within 1 h after collection at 2500 g for 10 min after full coagulation at room temperature. Immediately following centrifugation, serum was collected and stored at −80 °C in multiple aliquots until analyses. Samples were thawed only once before use.+ monocytes were positively selected using CD14 MicroBeads (Miltenyi) according to manufacturer's protocol. Monocytes were pooled from 6 to 8 donors per assay, plated in Falcon 96‐well Black Imaging Microplates at a density of 75 000 cells per well in RPMI1640 (Thermofisher) supplemented with FCS  and PenStrep , and cultured in a controlled environment . Monocytes were differentiated to macrophages using rh‐MCSF  for 7 or 8 days with one medium change.Buffy coats were collected from healthy volunteers of the Blood Bank . PBMCs were isolated using Ficoll–Paque gradient (Sigma). CD14γ), followed by Macroscreen functional analysis and RNA sequencing. Details about stimulation, RNA isolation, and sequencing analyses can be found in Supporting Information.Macrophages were stimulated by patient sera (denoted as AMI‐mac and Ctrl‐mac) and by PGE2, IL‐4 or Interferon gamma (IFN) for cell shape assessment. For all image analyses, Hoechst‐based nuclear identification served as input for primary object segmentation. Primary objects were enlarged using assay‐dependent analysis pipelines and fluorescence intensity was measured within each object. Attovision results were further processed using the DIVA software (BD Bioscience), enabling efficient gating of fluorophore‐positive cells. Threshold for positivity was determined by means of the technical control wells. Details on the methodology of functional and cytokine assays and Macroscreen statistics and preprocessing can be found in Supporting Information.Functional assays were performed using a high content analyzer BD Pathway 855 (BD Bioscience). Each 96‐well stimulated macrophage plate contained at least six control wells of nonstimulated macrophages, as well as technical control wells where fluorophores were omitted. All functional assays were performed at least in duplicate, exposing macrophages to serum samples from the complete discovery cohort (50 AMI patients and 20 control serum samples) or validation cohort  foRNA‐seq analysis was performed on the discovery cohort to evaluate the changes between AMI‐ or control sera‐stimulated macrophages at the gene level. Details about the RNA isolation, sequencing analysis, data preprocessing, quality control, as well as computational analyses including PCA and differential gene expression analysis (Tables ), 100 genes were sequentially selected using R package mRMRe (v2.1.0). With the maximal information coefficient‐based approach (MIC), top 100 genes most associating with the AMI‐ctrl phenotype were calculated by the R package minerva (v1.5.8). Top 100 AMI‐mac versus Ctrl‐mac DEGs were identified based on the ranking of adjusted p‐values from low to high. Genes shared between the top 100 feature selected genes from mRMR, MIC and DEGs were intersected, resulting in a final list of 36 feature selected genes , 129, 1[PLS‐DA, a supervised linear classification model, provided in R package mixOmics (v6.8.1), was performed on Macroscreen functional data . Samples were plotted on the first two components. SVM , RF, and XGB were applied on the RNA sequencing dataset of the discovery cohort and the Macroscreen functional data  using the scikit‐learn (v0.23.2) and xgboost (v1.3.0) packages (Python). A radial‐based function kernel was used in SVM. Other hyperparameters were set as default. Accuracy and area under the receiver operating characteristics curve (ROC AUC) were measured under stratified fivefold cross‐validation with 10 repeats. SHAP values of each Macroscreen measurement were calculated by fitting an RF classifier on the training data followed by prediction on the testing samples . The training/testing split was realized by stratified fivefold cross‐validation (10 repeats). Results from each run were aggregated to represent the feature contribution  method, a game theoretic approach to explain the output of a model.31 SHAn Figure .R2 used in the function scaleFreeFitIndex in the WGCNA package: 0.91 and 0.89, soft threshold set to 8 and 6, respectively). Other parameters were set as default, resulting in 22 modules in the full dataset (module M22 contains genes that are not coexpressed), and 24 modules in the POSTEMI subcohort data (module M24 contains genes that are not coexpressed). For the full AMI/control dataset, Pearson correlation coefficients  between the eigengene of each module and Macroscreen functional measurements as well as sample identity “AMI” was calculated. For the POSTEMI subcohort analysis, Pearson correlation coefficients  between the eigengene of each module and CMR parameters infarct size, EF, and EDV were calculated.WGCNA (v1.68) was performed two times independently: 1) on the complete transcriptomics RNA sequencing dataset of the discovery cohort, including 47 AMI‐mac and 20 Ctrl‐mac , a plug‐in software provided in Cytoscape (v3.7.1). The FDR was set to 0.05 to filter out less significantly enriched GO terms. The Cytoscape plug‐in Enrichment Map (v3.2.0) was used to visualize the GO network. Modules correlating positively  and negatively (M14 and M17) with the AMI phenotype were separately analyzed and visualized by red and blue colors. Similarities between GO terms were calculated by the Jaccard Index and were further represented by weighted edges. The edge cut‐off was set to 0.25, and the node cut‐off q‐value was set to 0.1. GO clusters with similar biological functions were manually annotated.R package clusterProfiler (v3.12.0) was applied for enrichment analysis based on gene ontology resources. Enriched GO terms with log‐transformed  All 67 samples  of the discovery cohort transcriptomics dataset were included. A list of 1639 (1018 contained in our data) transcription factors (TFs) retrieved from Lambert et al. was provided as candidate TFs, and all 12341 genes in our dataset were considered as targets. The ARACNe regulatory network was constructed using a bootstrapping strategy with p‐value as 1 × 10−7, data processing inequality (DPI) as 0.1, and resampling as 100. The GRN achieved scale‐free property, with a scale‐free fitting index of 0.839. To identify those regulators associated with adverse cardiac remodeling, a subnetwork was created by only preserving the members of POSTEMI Small–Large modules M7, M11, M14, M22, and M23 as regulating targets, thus containing 1018 candidate TFs and 2990 targets. Regulatory scores (RS) were defined as the total of all edge weights multiplied by its targets’ absolute log2 fold changes in expression between the POSTEMI‐Small and POSTEMI‐Large sample groups (see Table The gene regulatory network (GRN) was constructed using ARACNe , an information theoretic‐based network inference framework.32 All DEGs between the 6 h time point after AMI and control samples were analyzed (absolute log2 fold change threshold > 0.15) and nonrandomness in gene overlap was evaluated using hypergeometric testing. For validation of the AMI‐specific WGCNA network, only the GSE59867 6 h PBMC samples from those AMI patients marked “heart failure”  and “non‐heart failure”  were included. Briefly, 6 months after the initial myocardial infarction, these patients had the highest and lowest, respectively, level of plasma NT‐proBNP in combination with the lowest and highest, respectively, levels of LVEF (left ventricular EF). Differential expression levels of modules M7, M11, M14, M22, and M23, AMI‐specific WGCNA modules significantly correlating with clinical traits, were visualized between the HF and the non‐HF group. Within‐group expressions were averaged, and between‐group contrasts were highlighted by deducing the average expression in all patient groups.Differential expression of WGCNA module genes (full discovery cohort dataset) was validated in the GSE59867 dataset .9 DEGs Only myeloid cells were selected for further analysis. The single‐sample GSEA (ssGSEA) was run to test the DEGs between POSTEMI‐Large versus POSTEMI‐Small in the heart scRNA‐seq dataset. For each cell and each DEG list, we obtained an enrichment score, and these values were further averaged per cell type to present the overall enrichment of a DEG list in the corresponding cell type. The ssGSEA was executed using the R package GSVA (v1.42.0).Counts data from the human infarcted hearts scRNA‐seq dataset were retrieved from the Kuppe study data deposition.10 Onl was used for network‐guided drug screening. The original LINCS L1000 database contains >1 million mRNA expression signatures for 350 cell lines and >30 000 drug and gene perturbations, offering a huge repository of gene expression profiles, which can be browsed and compared to other data. We included DEGs from AMI‐specific WGCNA modules M7, M11, M14, M22, and M23, resulting in 268 upregulated and 125 downregulated genes useful for drug repurposing. Connectivity scores between changes of reference gene expression induced by drugs in THP1 cells and gene expression changes regarding the POSTEMI Large–Small comparison were calculated as described. A score close to −1 indicates that the drug is likely able to reverse the gene expression signature, hence a promising candidate lead for therapeutic intervention. A full list of all candidates with connectivity scores between −1 and −0.60 as well as 0.60 and 1 and target information is available in Table The THP1 cell perturbation database from LINCS L100013 wast‐test or the nonparametric Mann—Whitney U test as an alternative for not normally distributed data. Significant differences in binary data were analyzed with a Chi‐square test. For applications using computational methods, all Macroscreen datapoints were transformed to z‐scores, calculated separately for each functional read‐out parameter.Unless otherwise stated, data are presented as median ± 95% confidence interval (CI). For all Macroscreen functional data, each dot corresponds to the average of sample duplicates (discovery cohort) or triplicates . Prism v5.03 (GraphPad Software) was used for all statistical analyses. Normal distribution of data was tested with the D'Agostino–Pearson omnibus K2 test. Statistically significant differences were evaluated using Student's The authors declare no conflict of interest.M.A.C.F., H.J., L.T., and E.A.L.B. contributed equally to this work. L.T., M.F., and E.B. designed the study. M.F. and L.T. performed the experiments. H.J. performed all computational analysis. M.G., N.A., and D.M. shared expert knowledge on high content screening. M.R. assisted with setting up of high content screening assays. E.W. performed RNA isolations and M.S. performed RNA sequencing experiments. L.S. and C.R. provided Annexin probes and expertise. C.S. provided multiplex ELISA equipment and expertise. L.G. and B.H. collected clinical samples for the validation cohort. G.A. and J.E. designed and collected samples for the POSTEMI cohort. P.H. did the CMR analyses for the POSTEMI cohort. M.S. and B.H. collected all control samples for discovery and validation cohort. P.A. and B.H. designed samples for the validation cohort and the matched control samples for the POSTEMI cohort. J.K. and E.S. gave critical advice on machine learning. D.M. and B.H. provided critical input to the manuscript. L.T., M.F., and H.J. wrote the manuscript. E.B. funded the study and critically reviewed the manuscript.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


The progression of carotid intima‐media thickness (cIMT) and plaques are associated with cardiovascular health, especially for high‐risk population of cardiovascular disease (CVD).Risk factors for atherosclerosis may vary by sex. This study aimed to investigate the sex‐specific risk factors of cIMT and plaque progression.We selected subjects who were identified as high‐risk population of CVD, and collected their carotid ultrasound data and baseline characteristics. Linear regression and logistic regression analyses were used to identify risk factors for cIMT and plaque progression. Sex‐specific risk factors were identified respectively.p ˂ .05 for all cases). The association of baseline cIMT and smoking with annual cIMT change rate and increased total cholesterol with new plaque presence were significantly differentiated between both sexes (p for interaction ˂ .05).A total of 7908 participants were included. The mean age was 57.75 ± 9.45 years and 61.51% were female. During mean follow‐up of 1.92 ± 0.89 years, the median annual cIMT change rate was −7.25 μm/year. Seven hundred and fifteen subjects free from plaques at baseline developed plaque. Age, smoking, hypertension, and diabetes were common risk factors for carotid atherosclerosis progression in all participants. Smoking and alcohol drinking were significantly associated with increased cIMT change in women, while hypertension and antihypertensive medication were significant in men. Increased total cholesterol and diabetes were significantly associated with new plaque presence in women, while smoking, increased triglyceride, and dyslipidemia were significant in men (The risk factors for cIMT and plaque progression differed by sex. After excluding 10,935 subjects missing follow‐up carotid ultrasound data, 161 subjects without complete baseline demographic and clinical data, and 37 subjects with invalid carotid ultrasound data, 7908 participants were enrolled in formal analysis  with a 3–12 MHz phased‐array probe and linear array transducer. The examiner scanned longitudinally through the carotid arteries and bulbs of the participants. Meanwhile, the bilateral values of proximal, middle, and distal thickest cIMT would be recorded as measured from the media‐adventitia interface to the intima lumen interface. The mean thickness of bilateral cIMT was calculated as the average of these values. If the protuberant intima‐media was found, the observer will adjust the direction of the probe to observe the shape and size from multiple angles. The thickness of the plaque was measured through the largest section that can be observed. All original images of measured values are saved. The definition of increased cIMT was a thickness > 0.9 mm,2.32). Overweight and obesity were defined as BMI 24–27.9 kg/m2 and ≥28 kg/m2, respectively.We applied standardized questionnaires to assess socio‐demographic information , lifestyle behaviors , combined disease history  and medication history  of the participants. CVD was defined as having coronary heart disease or having a history of myocardial infarction or stroke. Body mass index (BMI) was calculated by body mass (kg) divided by the square of height  and significant variables that in the crude model . Univariate and multivariate logistic regression analyses with backward stepwise method were applied to identify independent risk factors for new carotid plaque formation in participants with no plaque at baseline. The results in adjusted model of linear regression and multivariate logistic regression varied by sex were evaluated, considering the interaction terms. Statistical analyses were conducted using SPSS (version 27.0), R Software and EmpowerStats (X&Y Solutions). p‐value < .05 indicated statistic significant.Continuous variables were presented as mean ± standard deviation or median  as appropriate. Categorical variables were presented as frequency with percentages. The baseline characteristics and carotid ultrasound data of all participants were stratified into two groups by sex. The categorical variables were compared using 33.1A total of 7908 participants were included. The mean age was 57.75 ± 9.45 years and 4864 61.51%) of individuals were females. Characteristics of all participants and stratified by sex were presented in Table .51% of i3.2β = 6.708, 95% CI: 0.274,13.142). Diabetes was negatively related with cIMT change rate in the crude model, but became positively related after adjustment . Baseline mean bilateral cIMT and TG was inversely associated with cIMT change. Per 0.1 mm cIMT increment in baseline was associated with a cIMT change rate of −28.894 μm/year . Compared to men, women had significant lower cIMT change rate .As shown in Table 3.3A total of 4085 participants who have no plaque presence at baseline were enrolled in the logistic regression analysis Table . Accordi3.3.1p for interaction =  0.001). Smoking , alcohol drinking  and SBP  were significant risk factors in women, while hypertension  and antihypertensive medication  were significant in men. Compared with men, smoking significantly accelerated cIMT progression in women (p for interaction = 0.004). Other included variables were not significantly associated with cIMT change in both sexes.According to Table p for interaction = 0.042).For new plaque presence Table , age and4In this study, we have identified risk factors for the progression of cIMT and carotid plaque and found different risk factors between men and women. Age, smoking, hypertension and diabetes were common risk factors for both cIMT progression and new plaque formation in all participants. For women, smoking and alcohol drinking were significantly related to cIMT progression, while lipids metabolism, hypertension and diabetes were related to plaque presence. For men, hypertension was significant risk factor for cIMT progression, while lipids metabolism, hypertension and smoking affected the formation of plaques.The previous studies which explored the sex‐specific risk factors for carotid atherosclerosis usually focused on a specific category of indicators,Smoking is universally known as a strong factor for ASCVD. This study has revealed the significant association of smoking with both cIMT progression and plaque formation, the impact of smoking on cIMT progression is much greater in women. A prospective study based on UK Biobank participants also found that smoking was related to higher risk for myocardial infarction in women.The association of dyslipidemia with carotid atherosclerosis progression is stronger in men. For lipid profiles, TC was a better predictor for new plaque formation in women, while TG predicted better in men. This result may contradict previous findings. Iso et al. have considered the effect of estrogen which could reduce the variation of TG.The limitations of this study should be noticed. First, we have a relatively short observation period. Second, the physical and laboratory examination data was only collected completely at baseline, we could not assess the changes of these data during the follow‐up. It is unclear how these changes of risk factors may affect the results. Third, the islet beta cell function and relevant autoimmune markers were not tested, so we could not further classify diabetes as type 1 and type 2. Moreover, for the analysis of new plaque formation, because the specific number of plaques is not collected, we treated plaque presence as a binary variable, which reduced accuracy. Due to the technical limitations, we could not assess more parameters like the total plaque area, which could be a more precise method for evaluation of carotid plaques.5In this study, we have identified risk factors for cIMT progression and plaque formation, sex‐specific risk factors were also revealed. For all participants, age, smoking, hypertension and diabetes were risk factors for both cIMT and plaque progression. But the risk factors differed by sex. Sex‐specific prevention and management strategies for carotid atherosclerosis are needed.The authors declare no conflict of interest.1The data used in this study can be obtained from the corresponding author upon reasonable request.Figurementary Figure 1 The research flow chart.Click here for additional data file.Supporting information.Click here for additional data file.


Kandelia obovata seedlings were cultured in pots in which lead (Pb), zinc (Zn) and copper (Cu) were added separately and in combinations. The results showed that heavy metal stress improved the rate of root oxygen leakage, enhanced root activity, and reduced root porosity. The root under single heavy metal stress was impacted by the addition of other heavy metals, demonstrating antagonistic or synergistic effects. Iron plaque (IP) formation was improved under single Zn or Cu stress, and inhibited in binary stress of Pb + Cu. The adsorptions of IP on heavy metals in combined stress  were higher than that in single stress . K. obovata accumulated higher levels of heavy metals in root , compared with IP , stem , and leaf . Correlation analysis showed that single and binary stresses affected K. obovata, with more significant effect of trinary stress. Regression path analysis showed that multiple heavy metal stress firstly affected root, then indirectly contributed to IP formation, as well as heavy metal in IP and root; at last, heavy metal in IP directly contributed to heavy metal bioaccumulations in root.Multiple heavy metal pollution in mangrove wetlands is serious.  To overcome the harsh hypoxia intertidal environment, aerenchyma (root porosity) is developed in mangrove plant to transfer oxygen from aboveground part to root, and release oxygen into the rhizosphere environment, being known as radial oxygen loss (ROL). ROL can affect nutrient uptake and heavy metal tolerance, alter microbial activity and chemical process in the rhizosphere environment, changing the bioavailability of heavy metal through iron plaque (IP) formation on the root surface4. IP formation acts as “barrier” or “reservoir” to regulate the transfers of heavy metals in plants, being attributed to the status of IP formation6. On the other hand, IP formation would be affected by heavy metal stress through the following ways: (1) causing oxidative stress and production of peroxide free radical which oxidizes Fe2+ to Fe3+7; (2) altering the ROL and anatomical structure of root 8; (3) affecting sediment physic-chemical properties and microbial community to alter the availability of Fe2+ in IP formation9.Mangrove is important for maintaining ecological balance and biodiversity in coastal zone, being mainly featured with low tide current velocity, high organic matter deposition, and reducibility in sediment12. In actual field environment, several heavy metals coexist normally, and single heavy metal could not really reflect the true occurrence characteristics and biological toxicity of multiple heavy metals due to their complex interactions13. Furthermore, combined pollution of heavy metals and other substances could impact the iron plaque formation and heavy metal absorption in plants  explore the characteristics of root responses, IP formation and heavy metal bioaccumulation under multiple heavy metals; (2) clarify the interrelations among single, binary, and trinary stresses, as well as the inner links among root responses, IP formation and heavy metal bioaccumulation.The responses of root and IP formation in terms of single heavy metal stress have been widely reportedts Table . Owing tROL did not change significantly in single stress compared to the control . While, RROL reduced significantly in trinary stress compared to binary stresses of Pb + Cu and Zn + Cu (P < 0.05). In Fig. P > 0.05), with improved root activity under binary stresses (P < 0.05). However, root activity reduced significantly in trinary stress compared to binary stresses of Pb + Cu and Zn + Cu (P < 0.05). In Fig. P < 0.05). Binary stresses of Pb + Zn and Pb + Cu reduced root porosity compared to single Pb stress (P < 0.05), with no significant reduce for single Zn or Cu stress (P > 0.05). Moreover, no significant change of root porosity was detected in trinary stress compared to binary stresses (P > 0.05).In this study, RP < 0.05, Fig. P < 0.05). Moreover, binary stresses of Pb + Cu and Pb + Zn significantly reduced Fe concentration in single Cu and Zn stress, respectively (P < 0.05). Trinary stress significantly improved Fe concentration compared to binary Pb + Cu stress (P < 0.05). In Fig. P < 0.05); No significant change of heavy metal in IP was detected among single and binary stresses (P > 0.05); Trinary stress significantly improved the immobilizations of Pb and Cu in IP (P < 0.05). In combined stress, heavy metals on IP  were higher than that in single stress .IP increased significantly under single Zn or Cu stress (P < 0.05). K. obovata mainly accumulated heavy metals in root , compared with IP , stem , and leaf . As for Pb in root, Zn reduced Pb concentration in single Pb stress (P < 0.05); while, significant improvements were detected in trinary stress compared to binary stresses of Pb + Zn and Pb + Cu (P < 0.05). As for Zn in roots, Pb or Cu reduced Zn concentration in single Zn stress (P < 0.05), and trinary stress improved Zn concentration in binary Zn + Cu stress (P < 0.05). As for Cu in roots, no significant difference was detected among multiple heavy metal stresses (P > 0.05). Thus, single heavy metal in K. obovata root was affected by the presence of a second or third heavy metal, leading to the inhibited or increased accumulation of heavy metals.As shown in Fig. root) and from root to leaf (TFleaf) indicated that the interactions of heavy metals on TFroot levels were limited , the presence of Cu in binary and trinary stresses improved TFleaf levels of Zn compared to single Zn stress (P < 0.05).Furthermore, the transfers of heavy metals from IP to root  and RROL, indicating their indirect even reverse interaction relationships. While, positive correlation was detected among IP formation and root porosity though not significant. Root activity was positively and negatively correlated with root ROL  and porosity , respectively.In order to explore interactions of plant responses, pearson correlation analysis was performed Table . In thisP < 0.01). The effects of Pb + Zn, Zn + Cu, and Pb + Cu on heavy metals in IP, root, stem and leaf were significant (P < 0.01), except for heavy metal in root under Pb + Cu treatment. There were interactive effects of Pb + Zn + Cu on root porosity, root activity, root ROL, IP formation, root biomass, and heavy metals in IP, root, and stem (P < 0.01).Furthermore, three-way ANOVA analysis indicated that single heavy metal stress affected plant response, except for root ROL, and stem biomass Table . Root acP < 0.01). Root porosity and root oxygen leakage did not have direct impact on IP formation, which also did not significantly affect heavy metal in IP and root (P > 0.05). Heavy metal in IP significantly affected heavy metals in root (P < 0.05), which negatively affected heavy metals in stem (P < 0.05).As shown in Fig. K. obovata under heavy metal stress. In the present study, no toxicity symptom in the plant (such as necrosis) was found, indicating higher tolerance of K. obovata in responding to multiple heavy metals. Furthermore, the growths of root and stem were not significantly reduced by heavy metals, with leaf to be improved , leading to higher root/shoot under heavy metal stress  was regulated by various stimuli, including ethylene and hypoxia24. Under heavy metal stress, root porosity of mangrove plants would reduce regardless of single or combined heavy metals25. In Fig. Bruguiera gymnorrhiza, and higher level of root porosity facilitated the loss of oxygen from the root to the rhizosphere environment17. However, in this study, root porosity of K. obovata did not have a similar trend with that of root ROL, and reduced under heavy metal stress, especially for the lowest level in Pb + Cu treatment  to adapt to the harsh tidal environmentAvicennia marina, Aegiceras corniculatum, and B. gymnorrhiza, the combined stress of Pb, Zn and Cu inhibited seedling growths and reduced root ROL26. Furthermore, under combined stress of Pb, Zn and Cu, the reduced root ROL in B. gymnorrhiza coexisted with metal-induced inhibition of growth and root aeration, which may be, at least partly, related to the damage to root tissues such as aerenchyma17. However, the improved levels of root ROL were detected under binary stresses of Pb + Cu and Zn + Cu  or negative  correlations  also found that the highest level of root ROL and lowest thickness of IP coexisted in Veronica serpyllifolia30. Thus, it was expected that the roles of root ROL, root activity, and root porosity on IP formation of K. obovata were limited, and some other factors should be investigated further. In fact, apart from root ROL, there were also some biological and abiotic factors affecting IP formation, including microorganism, root exudate, Fe2+ activity, and soil moisture32.Generally speaking, IP formation is an adaptive behavior of plants to stressful environment Cu Fig. , which w33. This study showed that IP formation was only improved under single Zn or Cu stress, the heavy metals immobilized in IP were all improved, and trinary stress improved the immobilizations of Pb and Cu (not Zn) compared to binary stress , and Zn mainly accumulated in IP with lower TFroot levels (< 1). Thus, the interactions of heavy metals on allocation strategies between IP and root were limited.IP can combine with heavy metals and nutrients by adsorption and co-precipitation, affecting their distribution and accumulation in plantsZn Table . In factots Fig. , binary ots Fig. , Cu and 42. In this study, multiple heavy metal stress firstly affected root responses, which indirectly contributed to IP formation on root, as well as heavy metal accumulations in IP and root; while heavy metals in IP directly contributed to their bioaccumulations in root, reducing their transfers to aboveground parts.In order to fully explore the interactive effects of single, binary, and trinary heavy metals, pearson correlation analysis was performed Table . The resKandelia obovata. The results showed that the root ROL, root activity, root porosity, and IP formation under heavy metal stress were affected with the presence of other metals. Furthermore, the changes of root ROL and root activity were not inhibited under the presence of heavy metals, being different from the reducing root porosity. Thus, heavy metal stress would change root ROL through affecting root activity of K. obovata. The adsorption of IP on heavy metals in combined stress was higher than that in single stress. Most heavy metals were stabilized in belowground parts , with limited interactions of heavy metals on their allocations between root and IP. Furthermore, the phytoextraction of Zn in leaf was higher than that of Pb and Cu, which was improved with the presence of Cu in binary and trinary stresses. Single and binary heavy metal stresses affected plant responses, with more significant effect to be detected in trinary stress. Overall, multiple heavy metals affected root responses, indirectly impacted IP formation, and heavy metals in IP and root, and directly impacted on heavy metal in plant, especially for root.This study mainly explored the interactions of Pb, Zn, and Cu on root growth, IP formation, and heavy metal bioaccumulation in K. obovata and sediment for plant culture were collected from mangrove wetlands in Futian National Nature Reserve, Shenzhen, China . In details, propagules  were planted into the seedbed  filled with clean sand. The seedbed was irrigated with 1/2-strength Hoagland’s nutrient solution . During the experiment, the deionized water was provided to ensure the moist every morning as previous reports20. After 2 months, the seedlings were prepared for pot experiment with combined heavy metal stresses.The propagules of –1; Pb, 88.55 μg g−1; Zn, 216.03 μg g−1; Cu, 65.53 μg g−120. PbCl2, ZnCl2, and CuCl2 were applied into sediments based on the actual heavy metal pollution status in mangrove wetlands43. Chemical reagents  were firstly dissolved in deionized water, and then homogenized with the sediment matrix. The concentrations of Pb, Zn and Cu in different treated sediment matrix were presented in Table 20. After 2 months, the sediment matrix was used for pot experiments. A total of 32 pots were used . In each pot, air-dried sediment (3 kg) was filled in and a nylon net  was installed. Three uniform seedlings of K. obovata were transplanted into the nylon net installed in the pot. The nylon net could restrict root growth and help the roots to be sampled from sediment44.The sediment matrix was mixed fully and separated into 8 groups Table , includi20. The plant culture and experimental implementation were shown in Figure All the experimental pots were placed outdoors randomly and were protected from the rain by a transparent canopy. The temperatures in the summer and autumn were 26–32 °C and 22–31 °C, respectively. The pots were irrigated with deionized water to compensate for evaporation loss of water every morningK. obovata were collected, which were firstly separated  and then dried at 70 °C for two days to obtain the constant weight. Some fresh roots were used to determine root ROL, root activity, root porosity, and IP formation. The root activity was determined by triphenyl tetrazolium chloride (TTC), which was commonly used in studies on plant stress45.After 5 months, the seedlings of 46, which can be expressed as follows:ROL) is the rate of radial oxygen loss (μmol O2 kg−1 root d.w. h−1); c is the initial volume of Ti3+-citrate added to each tube (L); y is the concentration of Ti3+-citrate solution of control (without plants) (μmol Ti3+ L−1); z is the concentration of Ti3+-citrate solution (μmol Ti3+ L−1); g is root dry weight after drying at 70 °C for 72 h (in kg).ROL was determined colorimetrically48, which can be expressed as follows:FA is the mass of pycnometer with water and vacuumed roots; FB is the mass of pycnometer with water and fresh roots in g; FW is the mass of water-filled pycnometer in g; TW is the mass of fresh roots in g.Root porosity (% gas volume/root volume) was measured for the entire lateral roots by a pycnometer method49. The dry roots, stems, and leaves were grounded and digested with HNO3/HClO4 50. IP was determined using an atomic absorption spectrophotometer , with Pb, Zn, and Cu immobilized in IP, plants, and sediments to be determined using inductively coupled plasma-atomic emission spectrometry . IP was expressed as 0.1591 × [Fen+] in the extraction/root dry weight (mg g−1 root d‧wt). The detection limits for Fe, Pb, Zn, and Cu were 0.002, 0.001, 0.003, and 0.003 μg mL−1, respectively. The reagents with no sample addition were regarded as blank. Furthermore, internal standard method was used to test the recovery of heavy metals in samples. In this study, the recoveries of heavy metals ranged from 94.45% to 107.21%. The translocation factors of heavy metals in plant were expressed as: TFroot = Croot/CDCB, TFleaf = Cleaf/Croot. Croot, Cleaf, and CDCB are heavy metal concentrations in root, leaf, and DCB extract, respectively.The cold dithionite–citrate–bicarbonate (DCB) solution was used to extract IP from the root surface within 24 h after harvestingThe data were shown as means ± standard deviation (S.D.) with triplicates. One-way ANOVA and Duncan test were conducted to determine the significant difference among different groups. A three-way ANOVA was conducted to assess interactive effects of different heavy metal stress on root growth, iron plaque formation, and metal bioaccumulation. Pearson correlation analysis was performed to explore the relationships among different growth parameters. Regression path analysis (RPA) was employed to identify relationships among root response, IP formation, and heavy metal bioaccumulations. In this study, SPSS Version 20.0  was used to perform all statistical analysis.Experimental research on plants complies with relevant institutional, national, and international guidelines and legislation. Appropriate permission was obtained as plant samples were collected in this study.Supplementary Information.


CD8A, CD8B, TCF7, and LEF1) to provide a prognostic immunotherapy biomarker for different cancers.Immune checkpoint blockade (ICB) therapy has demonstrated favorable clinical efficacy, particularly for advanced or difficult‐to‐treat cancer types. However, this therapy is ineffective for many patients displaying lack of immune response or resistance to ICB. This study aimed to establish a novel four‐gene signature (Transcriptome profiles and clinical data were obtained from The Cancer Genome Atlas database. Multivariate Cox regression analysis was used to establish a four‐gene signature. The R package estimate was used to obtain the immune score for every patient.Risk scores of the novel four‐gene signature could effectively divided all patients into high‐ and low‐risk groups, with distinct outcomes. The immune score calculated via the estimate package demonstrated that the four‐gene signature was significantly associated with the immune infiltration level. Furthermore, the four‐gene signature could predict the response to atezolizumab immunotherapy in patients with metastatic urothelial cancer.The novel four‐gene signature developed in this study is a good prognostic biomarker, as it could identify many kinds of patients with cancer who are likely to respond to and benefit from immunotherapy. Multivariate Cox regression analysis was used to establish a novel four‐gene signature, which was significantly associated with the immune infiltration level. The four‐genesignature could predict the response to atezolizumab immunotherapy in patients withmetastatic urothelial cancer. The novel four‐gene signature developed in this study isa good prognostic biomarker, as it could identify many kinds of patients with cancerwho are likely to respond to and benefit from immunotherapy. Nevertheless, many patients with cancer fail to respond to immunotherapy or achieve durable remission. The objective response rate of ICB is maintained at 13%–27.3% in many tumors, such as non‐small‐cell lung cancer (NSCLC),+ T lymphocytes is often a predictor of patient survival and response to immunotherapy,+ T‐cell expansion. However, not all CD8+ T‐cell subsets exhibit this behavior.Previous studies have reported that PD‐L1 expression in tumor cells can be used to predict clinical responses to ICB therapy.+ T cells in human lymph nodes are highly similar to a subset of mouse CD8+ T cells, identified in chronic infection models, that respond to checkpoint blockade immunotherapy. These cells exhibit a distinct transcriptional signature, including the expression of lymphoid enhancer binding factor 1 (LEF1) and T‐cell‐specific transcription factor 7 (TCF7).+ T cells displaying hallmarks of exhausted cells and central memory cells was identified; notably, TCF7 expression was required for the generation of this CD8+ T‐cell subset, which exhibited a proliferative burst after PD1 blockade.+ tumor‐infiltrating lymphocytes that persisted long term and retained polyfunctionality. Moreover, melanoma patients displaying a higher percentage of progenitor exhausted cells experienced more durable response to checkpoint blockade therapy.+ T cells in human tumors have been confirmed to be stem‐like CD8+ T cells expressing TCF7, which divide into terminally differentiated cells that express effector molecules. Additionally, initiation of effector differentiation is critical for the infiltration of many T cells into the tumor.Some memory CD8TCF7 and LEF1, belonging to the high‐mobility group (HMG) family owing to their conserved HMG DNA‐binding domains, are well‐known stem cell‐associated transcription factors that are usually expressed in CD8+ T cells,+ T‐cell identity through their intrinsic histone deacetylase activity.+ T‐cell function and differentiation.CD8A and CD8B, respectively. Granzyme A (GZMA) and granzyme B (GZMB) are T cell‐ and natural killer cell‐specific serine proteases, which may function as common components necessary for the lysis of target cells by cytotoxic T lymphocytes (CTLs) and natural killer cells. Perforin 1 (PRF1) forms membrane pores that allow the release of granzymes and the subsequent cytolysis of target cells. Previous studies have shown that the average expression levels of CD8A, CD8B, GZMA, GZMB, and PRF1 can be used to estimate CTL levels in a tumor.CD8A, CD8B, TCF7, and LEF1 expression may reveal the presence of stem‐like CD8+ T cells and may be thus used as a reliable immunotherapy biomarker to predict ICB outcomes. In the current study, we established an immune‐related four‐gene signature  based on multivariable Cox regression analysis of transcriptome profiles downloaded from The Cancer Genome Atlas (TCGA) database, and assessed whether the four‐gene signature could predict the clinical responses and treatment benefits of patients with various types of cancer before the beginning of immunotherapy.The CD8 antigen, acting as a co‐receptor of T lymphocytes, is composed of the isoforms CD8 alpha chain and CD8 beta chain, which are encoded by 22.1https://cancergenome.nih.gov/). Reads per kilobase per million (RPKM) indicates the gene expression levels in different cancer patients. Ten types of cancers were included in this study: Breast cancer (BRCA), skin cutaneous melanoma (SKCM), lower‐grade glioma (LGG), kidney renal papillary cell carcinoma (KIRP), rectum adenocarcinoma (READ), kidney renal clear cell carcinoma (KIRC), THCA, LIHC, ACC, and UVM (Table Transcriptome profiles (RNA‐seq profiles) and clinical information were obtained from the TCGA database .2.3CD8A, CD8B, TCF7, and LEF1 between tumor and normal tissues was performed using edge R (version 3.30.3).Differential expression analysis for the four genes 2.4CD8A, CD8B, TCF7, and LEF1. The terms were sorted by their p value.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package clusterProfiler (version 3.16.0) to investigate the functional and pathway enrichment associated to the four genes 2.5CD8A, CD8B, TCF7, and LEF1) was created as a prognostic model that was used to calculate the risk score of each patient based on multivariable Cox regression analysis. The mean risk score value was used as a threshold, according to which the patients were divided into high‐ and low‐risk groups. The C‐index was used to evaluate the validation of the four‐gene signature prognostic model. Time‐dependent receiver operating characteristic (ROC) curves were used to assess the sensitivity and specificity of the four‐gene signature. Multivariate Cox regression analysis was performed using the R package survival (version 3.2–3).The four‐gene signature  curves, and statistical significance was assessed using the log‐rank test. Receiver operating characteristic (ROC) curve analysis was performed using the R package survival ROC (version 1.0.3). Statistical significance was set at 2.7Immune scores, which represent the immune infiltration levels of patients, were calculated with the R package estimate (version 1.0.13)33.1CD8A, CD8B, TCF7, and LEF1), multivariate analysis was performed using Cox proportional hazard regression for patients with BRCA. The resulting KM survival curves and log‐rank tests pointed at significant differences in both clinical survival outcomes , confirming the robustness of the four‐gene signature predictive capacity. The analysis of time‐dependent ROC curves revealed that the 3‐year overall survival (OS) and RFS of the area under the curve (AUC) were 0.658 and 0.612, respectively.To assess the predictive ability of the four‐gene signature , the four‐gene signature was used to predict the survival outcomes of patients with cancer at different stages, namely stages 1, 2, 3, and 4. KM curves and log‐rank tests indicated that the four‐gene signature might be more robust for patients with BRCA at stages 2, 3, and 4 than for those at stage 1. The ROC for the 5‐year OS of stage 2 patients was 0.656, while that for the 5‐year OS of stage 3 and 4 patients was 0.807 , age , and tumor stage  were significantly associated with OS in patients with BRCA. Multivariate Cox regression analysis confirmed that the risk score  was independent of age  and tumor stage  ‐positive BRCA, progesterone receptor (PR)‐positive BRCA, human epidermal growth factor receptor 2 (HER2)‐positive BRCA, and TNBC. Compared with low‐risk patients with BRCA, high‐risk patients have poor outcomes, suggesting that the predictive capacity of the four‐gene signature is independent of the pathological subtype of BRCA, the time‐dependent ROC curves for the 3‐year OS of patients with ER‐positive, PR‐positive, HER2‐positive, and TNBC BRCA were 0.748, 0.803, 0.85, and 0.651, respectively Figure .Given th7 Figure . Both un3.2p = 0.053)  Table . Further3) Table . However3) Table . Owing t3.3TCF7 was expressed in the range of 1.28–22.27 RPKM, LEF1 in the range of 0.31–33.53 RPKM, CD8A in the range of 0.32–9.68 RPKM, and CD8B in the range of 0.10–3.84 RPKM. Therefore, the expression profiles of the four genes were consistent in the different cancers.We further examined the expression patterns of the four signature genes in the 10 types of cancers under investigation based on RPKM analysis. The 25th, 50th, and 75th percentiles are shown in Figure . Taking TCF7, LEF1, CD8A, and CD8B in different cancers, we investigated the transcriptomic profiles retrieved from the TCGA database. Compared with that in normal tissues, TCF7 expression in READ, KIRC, and LIHC tissues was higher, while being lower in BRCA tissues; in addition, LEF1 was highly expressed in BRCA, READ, KIRC, and LIHC tissues, but was expressed at lower levels in KIRP tissues; finally, both CD8A and CD8B were expressed at higher levels in BRCA, KIRC, and KIRP tissues, while CD8A was expressed at lower levels in THCA and READ tissues (Table To detect the logarithm fold change (logFC) in the expression of the four genes 3.4TCF7, LEF1, CD8A, and CD8B, GO and KEGG enrichment analyses were performed. GO enrichment analysis showed that CD8A, CD8B, TCF7, and LEF1 are involved in T‐cell activation, T‐cell differentiation, lymphocyte differentiation, V(D) J recombination, MHC class I protein binding, MHC protein binding, and coreceptor activity  or partial disease subgroups  . Collectively, our results suggest that the four‐gene signature can be used to predict the efficacy of immunotherapy and identify patients that could benefit from immunotherapy.To investigate the immune‐related risk stratification of the four‐gene signature, the immune score, which represents the immune infiltration level, was obtained using the R package estimate. When comparing high‐ to low‐risk groups of cancer patients, the four‐gene signature was found to be significantly associated with the immune infiltration level in patients with 10 types of cancer Figure . For exa) Figure . The fou4+ T lymphocytes can be used to predict patient survival and response to immunotherapy.+ T cells, is a positive prognostic marker in multiple solid tumors, but these cells fail to effectively eliminate cancer cells. This is because not all CD8+ T‐cell subsets expand following ICB.+ T cells, a subpopulation of CD8+ T cells that can express effector molecules, play critical roles in maintaining CD8+ T‐cell responses in human cancers.+ T cells after ICB. Moreover, melanoma patients with a higher percentage of progenitor exhausted cells benefit from a more durable response to ICB therapy.CD8A, CD8B, TCF7, and LEF1), which serves as a biomarker of stem‐like CD8+ T cells and predicts the immune responses and clinical outcomes of patients with metastatic cancer prior to treatment.In many cancers, the presence of tumor‐infiltrating CD8First, KM survival curves and log‐rank tests were used to examine the predictive capacity of the four‐gene signature for patients with BRCA. The results revealed significant differences in both clinical survival outcomes and RFS outcomes between high‐risk and low‐risk patients, indicating that the four‐gene signature has a robust predictive capacity for breast cancers. The cross‐tumor predictive value of the four‐gene signature was assessed in other nine cancers, namely THCA, LIHC, SKCM, LGG, KIRP, READ, ACC, KIRC, and UVM. Both the KM curves and log‐rank tests proved that the four‐gene signature has prognostic value for these cancers. Therefore, the four‐gene signature can be used to predict clinical immune responses in a wide variety of cancers.Both univariate and multivariate Cox regression analyses were performed to determine whether the four‐gene prognostic signature could serve as an independent prognostic factor. The results demonstrated that the four‐gene signature has predictive value for different ages, stage events, or sexes, which could serve as independent prognostic factors for patients with BRCA, LGG, LIHC, or SKCM. However, the four‐gene signature could not serve as an independent prognostic factor in patients with KIRC, KIRP, THCA, or READ in the current study. Regarding KIRC, KIRP, THCA, and READ, previous studies have shown that age and sex are closely associated with OS.CD8A, CD8B, TCF7, and LEF1 cannot serve as therapeutic targets for these cancers because of their different expression profiles. GO enrichment analysis showed that CD8A, CD8B, TCF7, and LEF1 are involved in T‐cell activation, T‐cell differentiation, lymphocyte differentiation, V(D) J recombination, MHC class I protein binding, MHC protein binding, and coreceptor activation. Furthermore, KEGG enrichment analysis indicated that the four genes were mainly associated with the following KEGG terms: T‐cell receptor signaling pathway, primary immunodeficiency, melanogenesis, hematopoietic cell lineage, arrhythmogenic right ventricular cardiomyopathy, adherent junction, acute myeloid leukemia, antigen processing, and presentation. In fact, CD8A and CD8B might be involved in directing the cell fate of immature double‐positive (CD4+CD8+) thymocytes towards two subsets of T cells: MHC class II‐restricted CD4+ helper T cells and MHC class I‐restricted CD8+ cytotoxic T cells.+ T‐cell identity, as they are directly involved in genetic and epigenetic regulation resulting in an appropriate gene expression pattern for CD8+ T cells. Such regulatory functions mainly depend on their intrinsic histone deacetylase activity.+ T‐cell maturation.Interestingly, We further investigated whether the immune‐related risk stratification of the four‐gene signature correlated with the immune infiltration level in patients with 10 types of cancer, namely THCA, LIHC, SKCM, LGG, KIRP, READ, ACC, KIRC, BRCA, and mUC. The results revealed significant differences in immune infiltration levels between the high‐ and low‐risk groups of patients. Based on the efficacy and safety data of atezolizumab in mUC patients from the phase II IMvigor210 study, KM survival curve analysis and log‐rank tests were performed; the results demonstrated that the four‐gene signature had prognostic value for survival outcomes in mUC patients. Compared with those of the CRs groups, there were significant differences in the four‐gene signature risk scores of the SDs and PDs groups. These results demonstrated that the four‐gene signature could serve as a prognostic immunotherapy biomarker to predict the immune response in patients with cancer.CD8A, CD8B, TCF7, and LEF1) could serve as a predictive biomarker of the immune responses to ICB and clinical outcomes of patients with different cancers, including BRCA, THCA, LIHC, SKCM, LGG, KIRP, READ, ACC, KIRC, and UVM. The four‐gene signature could be widely used to optimize biomarkers of ICB responses, guide ICB therapy, and identify new immunotherapy targets for restoring immune responses. However, in the current study, the four‐gene signature was established based only on transcriptome analysis, and its combination with other biomarkers may yield a more promising tool for the prediction of immune responses to checkpoint blockades in multiple cancers in the future.In summary, the novel four‐gene signature ((I) Conception and design: HZ, SZ, and YL; (II) Administrative support: HZ and SZ; (III) Provision of study materials or patients: YL, MN, and LL; (IV) Collection and assembly of data: MN, LL, JW, and ZT; (V) Data analysis and interpretation: YL, MN, SZ, and HZ; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.The authors declare that they have no competing interest.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Table S1Click here for additional data file.


Objectives This study evaluated the surface microhardness of composite, affected by surface coating with different dental adhesive systems.Materials and Methods A total of 100 composite discs were divided into five groups. Group 1 was uncoated (control group C), and groups 2 to 5 were coated with different adhesive systems . The Vickers microhardness (VHN) was measured without and with 500 thermocycles.Statistical Analysis The data were analyzed using two-way ANOVA and Tukey's posthoc test at the 95% confidence level.Results At 24 hours, the VHN of C (59.96 ± 3.68) and FL (59.83 ± 4.54) were significantly higher than SOLO (51.73 ± 4.63) and AIO (51.45 ± 4.11). The VHN of XTR (54.96 ± 3.68) was not significant compared with that of C and all other groups. After thermocycling, VHN were significantly decreased in all groups. However, there were no significant differences among all groups.Conclusions At 24 hours, composite coated with different adhesive systems have different effects to VHN. Thermocycling all adhesive resin systems coated on composite surface significantly decreased the VHN. Dental composite was widely used in restorative dentistry and gained increasing popularity due to its pleasing aesthetic, minimal invasive of cavity preparation, remarkable mechanical properties' improvement, and decline in amalgam use, which was a cause for concern on account of mercury toxicity.Recently, Bisco Modeling Resin , Ultradent composite wetting resin , Brush and Sculpt resin , and GC modeling liquid , G-Coat Plus (GC Corp.), Composite Primer (GC Corp.), and Modeling Resin  were marketed specifically to use as sculpting composite or wetting agents, in order to make composite less sticky.n = 20). Group 1 was control, whereas groups 2 to 5 were coated with one layer of different types of dental adhesive used as surface coating.A total of 100 specimens of disc-shaped composite  were prepared by placing in single increment, using stainless steel split mold . The mold was placed on polyester matrix strip over a glass slab. A total of 100 specimens were divided into five groups , Kerr, Orange, CA, United States).Group SOLO—Coated specimens with adhesive resin of two-step etch and rinse (OptiBond SOLO Plus (primer and bonding bottle), Kerr, Orange, CA, United States).Group XTR—Coated specimens with adhesive resin of two-step self-etch  andGroup AIO—Coated specimens with adhesive resin of one-step self-etch The name and compositions of composite and adhesive agents used in this experiment were presented inFor control, after filling the composite into the mold, a polyester strip was covered the specimen; then, a glass slab was pressed over the matrix strip to obtain a flat surface.For groups 2 to 5, after filling the composite into the mold, the surface was immediately coated with different adhesive systems by a single operator as follows:1) The designated adhesive resin was dispensed into a clean dispenser; then, the instrument  was dipped into the adhesive resin for a second. The excessive adhesive was removed by wiping at the edge of well dispenser for 3 seconds on each side of the instrument.2) The surface of specimen was coated with designated adhesive resin using CVIPC to smoothen and flatten the surface.3) A polyester matrix strip was covered the specimen; then, a glass slab was pressed over the matrix strip to obtain flat surface.All of the specimens were polymerized from the top surface for 40 seconds with LED curing unit , and then gently removed from the split mold. The curing side of each specimen was polished using new aluminum oxide abrasive discs  from medium to extrafine grit for 15 seconds in each step with water cooling every 2 seconds by the same operator. Each specimen was examined for flaws or voids under stereomicroscope(X40) . If any flaws or voids were presented, that sample must be excluded. Also, the final thickness after polishing were controlled to be 2 ± 0.5 mm. Then, all of the polished specimens were immersed in distilled water at 37 °C for 24 hours.n = 10): without and with thermocycling. In thermocycling group, the specimens were thermocycled in a thermocycle machine , according to ISO: 11405(2015) protocol of 500 cycles between temperatures of 5 and 55 °C with a dwell time of 20 seconds and a transfer time of 10 seconds.The specimens in each group were randomly divided into two subgroups (The surface Vickers microhardness (VHN) was measured on the polished surface of each specimen using a microhardness tester  with a 300 g load for 15 seconds. Three indentations per specimen were measured. The first indentation was located at the center of the specimen. The second and third indentations were located 1 mm apart from the first indentation at the right and left, according to ISO 6507–12018)18The Vp > 0.05). The VHN average values were compared using two-way ANOVA and Tukey's posthoc test, considering two factors  and their interaction. The statistical significance level was set atp < 0.05.The data were analyzed using IBM SPSS version 24.0 . The normality of VHN was examined using the Shapiro–Wilk test. The Shapiro–Wilk test indicated normal distribution of VHN in all groups (p < 0.01).The mean VHN values and standard deviations (SD) for the composites were shown inAt 24 hours, without thermocycling, ranking of VHN were divided into two levels as shown inp > 0.05) in VHN among adhesive types.In contrast, after thermocycling, the VHN of all adhesive types were significantly lower than that of control. There was no statistical difference (p > 0.05).Thermocycling significantly decreased the VHN of composite in both control and all type of adhesives  have no negative influence on surface hardness of composite resin at 24 hours. After thermocycling, all of adhesive resin systems coated on composite surface significantly decreased the VHN.


Aging in men is accompanied by a broad range of symptoms, including sexual dysfunction, cognitive and musculoskeletal decline, obesity, type 2 diabetes, cardiovascular disease and hypertension, organ degeneration/failure, and increasing neoplasia, some of which are associated with declining levels of Leydig cell‐produced testosterone. High natural biological variance, together with multiple factors that can modulate circulating testosterone concentration, may influence its interpretation and clinical implications. Insulin‐like peptide 3 is a biomarker of Leydig cell function that might provide complementary information on testicular health and its downstream outcomes.To characterize insulin‐like peptide 3 as a biomarker to assess gonadal status in aging men.A large European multicenter  cohort of community‐dwelling men was analyzed to determine how insulin‐like peptide 3 relates to a range of hormonal, anthropometric, and lifestyle parameters.Insulin‐like peptide 3 declines cross‐sectionally and longitudinally within individuals at approximately 15% per decade from age 40 years, unlike testosterone (1.9% per decade), which is partly compensated by increasing pituitary luteinizing hormone production. Importantly, lower insulin‐like peptide 3 in younger men appears to persist with aging. Multiple regression analysis shows that, unlike testosterone, insulin‐like peptide 3 is negatively dependent on luteinizing hormone and sex hormone‐binding globulin and positively dependent on follicle‐stimulating hormone, suggesting a different mechanism of gonadotropic regulation. Circulating insulin‐like peptide 3 is negatively associated with increased body mass index or waist circumference and with smoking, and unlike testosterone, it is not affected by weight loss in obese individuals. Geographic variation in mean insulin‐like peptide 3 within Europe appears to be largely explained by differences in these parameters. The results allowed the establishment of a European‐wide reference range for insulin‐like peptide 3  adjusted for increasing age.Insulin‐like peptide 3 is a constitutive biomarker of Leydig cell functional capacity and is a robust, reliably measurable peptide not subject to gonadotropin‐dependent short‐term regulation and within‐individual variation in testosterone. The best known of these hormones is testosterone (T), whose reduced concentrations have been linked to sexual dysfunction, obesity, metabolic syndrome, cardiovascular disease, cognitive decline, and physical frailty. However, there is considerable variability in the concentration of T, its biologically relevant proportion, and the responsiveness of various organ systems. Consequently, only clearly reduced levels of T, together with significantly increased symptoms of hypogonadism, are considered sufficient to constitute a clinical hypoandrogenemia  warranting treatment.LOH, thus defined, represents only 2.1% of the community‐dwelling adult male population.Although anatomical evidence indicates some loss of mature Leydig cells with age,If we could better understand the multiple interrelationships impacting the age‐related decline in circulating T and its relationship to increasing age‐dependent morbidity, then appropriate intervention at an earlier age and applied to a larger proportion of men might be able to redress the functional decline with aging and its consequent socioeconomic as well as health burden. A specific biomarker of Leydig cell functional status, in addition to T, that is independent of other (extra‐testicular) modifying factors could contribute to a more objective assessment of the age‐related decline in male gonadal function.INSL3 is a peptide hormone secreted into the systemic circulation by the steroidogenic Leydig cells of the testes.Following an initial cross‐sectional study of community‐living men in Australia,22.1The first phase of the EMAS study recruited 3369 community‐dwelling men aged 40–79 years between 2003 and 2005, approximately equally distributed between eight European centers in northern, western, eastern, and southern Europe.2. Waist circumference  was measured three times for each subject, and the average was taken.For the present study, 2283 serum samples (83.4%) from the second phase were available for the measurement of INSL3, approximately equally distributed between all eight centers. From the first phase, only 1252 serum samples (37.2%) from four centers were available. Consequently, most analyses presented here represent the EMAS second phase, and samples from the first phase are only used for longitudinal comparison. Descriptive statistics for the parameters used for the second‐phase subjects analyzed here are given in Table 2.2Smoking habits were obtained by postal questionnaires, which recorded whether the participant was a current, past or non‐smoker. For this analysis, current smokers were compared with pooled past smokers and non‐smokers. Alcohol consumption was provided in terms of approximately weekly intake, whereby for multivariate analysis, 0–1 alcoholic drinks per week were allocated “low” (0), 2–4 drinks per week “moderate” (1), and >4 drinks per week “high” (2).2.3am, and separated sera were stored at ‐80°C. Total T was measured by gas chromatography–mass spectrometry (MS)Blood samples were collected before 10.00 2.4R, with INSL3, T, and cFT . To adjust for age, the INSL3 concentration (mean and 95% CI) at a given age of 65 years  was calculated by interpolation from the center‐specific regressions of INSL3 against age .3.3n = 961) for which data from both phases were available shows that the within‐individual change in INSL3 concentration with age is minimal . The slope (0.935) implies a mean 6.5% reduction in INSL3 over 4.3 years, or 15.1% per decade.The regression of the phases 1 and 2 data for the four centers  than INSL3 Figure . cFT als3.4R = ‐0.110, p < 0.001, n = 1874), as well as to alcohol consumption .Simple unadjusted correlation analysis Table  indicateβ) indicate that for INSL3, where the R2 shows that 30% of the variance is accounted for by the model, T contributes most, followed by SHBG, LH (negatively), age, smoking (negatively), and FSH (positively). For T, where the R2 indicates that almost 50% of variance is accounted for by the model, SHBG contributes most, followed by LH (positively), FSH (negatively), INSL3, BMI, and age. The sequential, stepwise analysis means that only those parameters that significantly contribute to the variance of the dependent variable (INSL3 or T) are included in the models, with non‐significant parameters excluded  in either BMI ±2 units) or WC ±4 cm) over the 4.3 years (average) between the two phases of the study, this had no significant impact on circulating INSL3 levels  population of Leydig cells in the testes.The present study confirmed the cross‐sectional age‐dependent decline in INSL3, T, and cFT between ages 40 and 80 years, previously identified.In a recent study of a much more homogeneous cohort of military conscripts, all healthy and aged approximately 18 years, there was no significant correlation between INSL3 and the T/LH ratio.R2 = 0.55 vs. 0.53, respectively, not shown), the cross‐sectional correlation of T with age is substantially weaker than for INSL3 , although not all, of the center‐specific variation in these older men. Other influences could include reduced general health status and genetic or environmental factors. Such geographic variation could be considered in the establishment of clinical reference ranges for INSL3. Based on the total EMAS data for all centers, INSL3 reference ranges (95% CI; Figure Leuven and Manchester had significantly higher mean circulating INSL3 concentrations than the total cohort average, and Lodz and Tartu had significantly lower concentrations. In contrast, the T concentration indicates only small between‐center differences Table . Inspect5Together, these findings show that in normal subjects, Leydig cell functional capacity as reflected in circulating insulin‐like peptide 3 is highly consistent within an individual over long periods and that insulin‐like peptide 3 is a powerful long‐term index of Leydig cell status.The authors declare that they have no conflicts of interest.Ravinder Anand‐Ivell supervised the INSL3 measurement and its validation, and contributed to the conception, analysis, and writing of this study. Kee Heng was responsible for INSL3 measurement. Katie Severn advised and supported the statistical analysis. Ilpo T. Huhtaniemi and Frederick C.W. Wu were responsible for the provision and coordination of EMAS samples and data and contributed to the conception and writing of this study. Leen Antonio, Gyorgy Bartfai, Felipe F. Casanueva, Aleksander Giwercman, Mario Maggi, Terence W. O'Neill, Margus Punab, Giulia Rastrelli, Jolanta Slowikowska‐Hilczer, Jos Tournoy, and Dirk Vanderschueren helped with data collection, reviewing, commenting on and approving the final manuscript. Richard Ivell was responsible for the overall conception, analysis, and writing of the study.Supplementary materialClick here for additional data file.Supplementary materialClick here for additional data file.