Dysbiosis of the gut microbiota is considered the most important risk factor for diarrhea. Prebiotics have shown efficacy in treating diarrhea by regulating the balance of the gut microbiota in vitro fermentation system to prevent the interference of host-gut microbe interactions during in vivo examination and investigated the effect of fructo-oligosaccharides (FOS) on gut microbiota composition and metabolism in 39 pediatric patients with functional diarrhea.In this study, we used an p < 0.05). This improvement manifested as a significant increase  in probiotic bacteria  and a significant inhibition  of harmful bacteria . Notably, the analysis of bacterial metabolites after FOS treatment showed that the decrease in isobutyric acid, isovaleric acid, NH3, and H2S levels was positively correlated with the relative abundance of Lachnoclostridium. This decrease also showed the greatest negative correlation with the abundance of Streptococcus. Random forest analysis and ROC curve validation demonstrated that gut microbiota composition and metabolites were distinct between the FOS treatment and control groups (area under the curve [AUC] > 0.8). Functional prediction using PICRUSt 2 revealed that the FOS-induced alteration of gut microbiota was most likely mediated by effects on starch and sucrose metabolism.16S rRNA sequencing revealed that FOS significantly improved α- and β-diversity in volunteers with pediatric diarrhea (This study is the first to evince that FOS can modulate gut microbial disorders in children with functional diarrhea. Our findings provide a framework for the application of FOS to alleviate functional diarrhea in children and reduce the use of antibiotics for managing functional diarrhea-induced disturbances in the gut microbiota. Diarrhea is one of the most common diseases among children worldwide . PatientMany studies have also documented changes in the gut microbiota before, during and after diarrheal episodes . MoreoveProbiotics are non-pathogenic live microbes that can reverse gut dysbiosis by improving the microbial balance in the gut. Since probiotics are typically non-pathogenic bacteria that already exist in the human gut, they are often considered harmless. Probiotics have been shown to be effective in treating diarrhea caused by multiple factors, including traveler’s diarrhea, acute rotavirus diarrhea, diarrhea due to radiation therapy, and diarrhea due to lactose intolerance . Studiesin vivo studies on prebiotics can be confounded by host–microbe interactions in the gut. Therefore, in this study, we adopted an in vitro fermentation model to study the direct effect of prebiotics on the gut microbiota in children with functional diarrhea.Prebiotics are recognized as “substrates that are selectively used by probiotics and produce beneficial effects” . The int2PO4, K2HPO4, MgSO4, CaCl2, metaphosphoric acid, and crotonic acid were purchased from Sangon Biotech (Shanghai) Co., Ltd., China.FOS (purity ≥95% and moisture ≤5%) was purchased from Shandong Baoling Bao Biological Co. Ltd., China. Tryptone, bile salt, yeast extract, L-cysteine, and heme were purchased from Sigma Company, USA. Phosphate-buffered saline (PBS), NaCl, KHThe study included 39 child participants (aged 6–15 years) with functional diarrhea diagnosed based on the Rome IV Criteria who had been living in Hangzhou, Zhejiang Province, China. All participants had experienced recurrent discomfort or abdominal pain associated with no less than two of the following features for at least 1 day per week in the last 3 months: (1) pain related to defecation; (2) onset related to a change in the frequency of feces; and (3) the immunocompetent patient with “watery” diarrhea of at least 4 weeks duration. Further details about inclusion criteria were as described previously . ChildreThree 1.5 mL sterile centrifuge tubes were each filled with 0.2 g of fresh feces and stored in a refrigerator at −80°C as backup original fecal samples. Subsequently, 8 mL of sterile PBS and 0.8 g of fresh fecal samples were added to 10 mL sterile centrifuge tubes. The tubes were sealed with tape and placed on a shaker for thorough mixing. The supernatant was collected after filtration to obtain a 10% gut microbiota extraction solution.2, 0.2 g/L CaCl2, 0.4 g/L KH2PO4, 1.0 mL Tween 80, 1.0 mL resazurin, and 2.0 mL of a trace element solution. After sufficient dissolution and boiling, 4.5 mL of the YCFA medium was injected into a nitrogen-filled cilium vial. Finally, the vials were fixed with caps and sterilized using high-pressure steam. FOS were added to the YCFA medium (0.8 g/100 mL) to obtain the FOS treatment group for diarrhea (FOSD), as the mod. Pure YCFA medium was used to obtain the control treatment group for diarrhea (CtrlD).The isolated gut microbes were inoculated into a simulated gut fermentation system and incubated at 37°C for 24 h under anaerobic and airtight conditions, as described previously . The YeaGenomic DNA was extracted from the gut microbiota using the FastDNA® Spin Kit for Soil  based on the manufacturer’s instruction. Using a thermocycler PCR system , the V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with the primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR conditions were as described previously . PurifieAmplicon sequence variants (ASVs) were identified by denoising the streamlined sequences after quality control splicing using the DADA2 plug-in in QIIME2 (version 2020.2). The Naive Bayes agreement taxonomy classifier in QIIME2 and the SILVA 16S rRNA database (v138) were used to analyze the taxonomic profiles of the ASVs. Protocol sequence data for all original fecal samples and all fermentation samples were submitted to the National Center for Biotechnology Information Short Read Archive under the serial no. PRJNA978990.in vitro fermentation, the composition and content of SCFAs were analyzed. The contents of six SCFAs — including acetic acid (Ace), propionic acid (Pro), isobutyric acid (Isob), butyric acid (But), isovaleric acid (Isov), and valeric acid (Pen) — were determined. Significant age-related differences in total SCFAs and in each type of SCFA were analyzed. Heat maps and Spearman correlation coefficients were used to analyze the correlation between SCFAs showing significant variation and the relative abundance of bacterial genera.After 24 h of To identify SCFAs, 2.5 g of metaphosphoric acid was dissolved in 100 mL of deionized water, and 0.6464 g of crotonic acid was added to obtain a crotonic acid/metaphosphoric acid solution. The fermentation solution (500 μL) was mixed well with the crotonic acid/metaphosphoric acid solution (100 μL), placed at −40°C for 24 h, and then centrifuged at 4°C and 13,000 r/min for 3 min. Finally, the supernatant was filtered using a 0.22-μm microporous membrane. A gas chromatograph was loaded with the sample. Subsequently, the aging strategy was adopted. The gas chromatography conditions were as described previously . The acq4, H2S, NH3, CO2, and H2. The concentration of the five gases increased and then decreased over time, with the highest value representing the volumetric concentration of the gas produced by gut microbiota fermentation. After the values of all five gases had dropped to zero, the next sample bottle was tested.Fermentation-completed bottles were removed and cooled to 25°C. Subsequently, gas detection was performed using a gut microbial gas detector according to a previously described method . After tp ≤ 0.05; **0.001 < p ≤ 0.01; and ***p ≤ 0.001.Microbial data were analyzed on the online Majorbio Cloud Platform.in vitro fermentation, various gut microbes were examined using 16S rRNA sequencing. p = 0.008715), Chao (p = 0.001438), and Shannon (p = 0.001438) indices differed significantly between the two groups (p < 0.05), indicating a significant difference in the community-level diversity between the FOSD and CtrlD groups. p = 0.001) and NMDS analysis (p = 0.001) revealed a significant difference in the genus-level structure of the bacterial community between the FOSD and CtrlD groups during in vitro fermentation , Bifidobacterium (18.13% vs. 1.80%), Streptococcus (16.48% vs. 8.44%), Bacteroides (4.19% vs. 8.96%), and Lactobacillus (12.66% vs. 1.04%), respectively. To further determine whether specific bacterial taxa were differentially enriched in the FOSD group versus the CtrlD group, we applied the LEfSe analysis method, which is based on the LDA binding effect size. As shown in Bifidobacterium, Streptococcus, Lactobacillus, and Erysipelatoclostridium was identified in the FOSD group. Meanwhile, Escherichia-Shigella, Bacteroides, Lachnoclostridium, and Fusobacterium were significantly more abundant in fecal samples from the CtrlD group. We further evaluated the value of the gut microbiota as a biomarker using random forest model analysis. The AUC-Random Forest algorithm was used to identify an optimal random forest model that maximizes the AUC value of the ROC curve. In the validation queues of the FOSD and CtrlD groups, the first 18 genera were selected in order of feature importance to distinguish the FOSD group from the CtrlD group, as shown in The relative abundance of different genera differed among the 79 fecal samples, as shown by the bar plots in in vitro fermentation. Six SCFAs, including Ace, Pro, Isob, But, Isov, and Pen, were evaluated (p ≤ 0.001) and Isov (p ≤ 0.01)  . Random  ≤ 0.01) . ROC cur ≤ 0.01) . Isob anin vitro fermentation. H2S (p ≤ 0.001) and NH3 (p ≤ 0.001) levels were significantly lower in the FOSD group than in the CtrlD group , and pathways with a proportion of average relative abundance <1% were screened out (p = 0.006762) and nucleotide metabolism (p = 1.845e−6). Moreover, six metabolic pathways were significantly more enriched in the CtrlD group than in the FOSD group, including energy metabolism (p = 1.683e−9) and metabolism of cofactors and vitamins (p = 0.0001327). At Level 3, 12 metabolic pathways were found to be significantly different (p = 4.548e−6) and biosynthesis of amino acids ribosome (p = 3.3e−5), while four were significantly more enriched in the CtrlD group, including metabolic pathways (p = 0.0003577) and microbial metabolism in diverse environments (p = 2.9e−6).In order to predict and compare gut microbiota function between the FOSD and CtrlD groups, PICRUSt analysis for functional prediction was performed based on the KEGG database. Further, STAMP software was used to analyze the prediction results  exhibited increased microbial α- and β-diversity than those of the CtrlD group . Diversity of the gut microbiota is essential for maintaining stable gut function  in the gut can also improve diarrhea symptoms  were the most affected by FOS. Undigested starch and sucrose promote bacterial fermentation and gas production, with water diffusion, causing abdominal bloating, pain, and diarrhea. Probiotics such as Bifidobacterium and Lactobacillus can significantly relieve diarrhea by increasing starch and sucrose metabolism  in this study revealed that FOS alters bacterial metabolic pathways in childhood functional diarrhea. Starch and sucrose metabolism  can be found below: The studies involving humans were approved by the Ethical Committee of Hangzhou Centers for Disease Control and Prevention (No. 202047). The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from The human samples used in this study were acquired from fresh fecal. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.ZD: funding acquisition, data curation, formal analysis, and writing – original draft. JL: data curation, formal analysis, and methodology. WL and HF: data curation and formal analysis. JD, GR, and LZ: sample collection. XP: methodology, supervision, project administration, and review. XY: methodology, supervision, project administration, and review. All authors contributed to the article and approved the submitted version.This research was supported by the Hangzhou Agricultural and Society Development Project (202004A20) and the Natural Science Foundation of Zhejiang Province (LYY22H280002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The prognostic factors for diffuse large B‐cell lymphoma (DLBCL) have been fully explored, but prognostic information for bulky mass DLBCL patients is limited. This study aimed to analyze the prognostic value of MYC protein expression and other biological parameters in bulky mass DLBCL patients.We defined a bulky mass as a maximum tumor diameter ≥7.5 cm and studied 227 patients with de novo bulky mass DLBCL.n = 140) showed significantly worse overall survival  and progression‐free survival  than the non‐MYC overexpression group (n = 87). Subgroup analyses demonstrated that the MYC overexpression group was associated with inferior OS and PFS in the subgroups with the International Prognostic Index score of 3–5 , Ann Arbor stage 3–4  and GCB subtype . Consolidation radiotherapy improved OS and PFS in patients with bulky mass DLBCL  as well as in those with MYC overexpression . The prognostic value of MYC overexpression was maintained in a multivariate model adjusted for the International Prognostic Index.In all patients with bulky mass DLBCL, the 1‐year and 3‐year OS rates were 72.7% and 57.1%, respectively, and the 1‐year and 3‐year PFS rates were 52.0% and 42.5%, respectively. The MYC overexpression group (MYC overexpression is a poor predictor for bulky mass DLBCL patients. Consolidation radiotherapy for residual disease after induction therapy may improve outcomes for patients with bulky mass DLBCL. Our study showed that MYC overexpression but not MYC/BCL2 double expression is a poor predictor for bulky mass DLBCL patients. Consolidation radiotherapy for residual disease after induction therapy may improve outcomes for patients with bulky mass DLBCL. Rituximab combined with cyclophosphamide, hydroxydaunorubicin, vincristine, and prednisone (R‐CHOP) is recognized as the standard therapy. However, up to 40% of DLBCL patients exhibit refractory disease or disease relapse. Therefore, it is crucial to identify DLBCL subgroups with a poor prognosis. The International Prognostic Index (IPI) is commonly used to stratify the risk prognosis of DLBCL, but it does not capture biological parameters, which may lead to inaccurate risk stratification. An increasing number of studies have demonstrated the prognostic effect of MYC and BCL2 gene or protein levels. The MYC gene is located on chromosome 8q24 and has been reported to regulate up to 10% of genes within the human genome.Moreover, DLBCL patients with bulky mass are recognized as a distinct group with inferior survival outcomes.22.1We reviewed formalin‐fixed, paraffin‐embedded (FFPF) biopsies from 227 patients with de novo bulky mass DLBCL at the First Affiliated Hospital of Zhengzhou University between February 2018 and December 2021. The diagnosis of DLBCL was based on the 2008 WHO classification criteria.2.2All tissues were fixed in 3.7% neutral formaldehyde, routinely dehydrated, paraffin‐embedded, and serially sectioned to 2–3 μm thick for H&E and immunohistochemical staining. The EnVision two‐step method was used for immunohistochemistry. Monoclonal antibodies against MYC , BCL2 , BCL6 (ZSGB), and Ki‐67  were used. The cutoff scores defined as protein overexpression were 40% for MYC Figure , 50% forFISH analysis for MYC, BCL2, and BCL6 was performed based on a tissue microarray (TMA) using dual‐colour break‐apart probes . The signals from 100 interphase nuclei were analyzed. The cases with break‐apart signals >10% of nuclei were considered positive for the presence of a translocation.2.32 test or Fisher's exact test was used to assess differences in categorical variables. Univariate and multivariate analyses were performed using the Cox proportional hazards regression model. Statistical analysis was performed using SPSS version 23.0. A p value of <0.05 was considered statistically significant.Overall survival (OS) was measured from the date of diagnosis until death of any cause or the last follow‐up. Progression‐free disease (PFS) was measured from the date of diagnosis until the date of disease progression, relapse, or death of any cause. Response assessment was defined as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD).33.1The baseline characteristics of patients with bulky mass DLBCL are presented in Table n = 227) received immunochemotherapy as the first‐line induction treatment: 180 (79.3%) patients received R‐CHOP, and 47 (20.7%) patients received R‐EPOCH. Of these 227 patients, 14 (6.2%) patients underwent surgery before induction treatment, 37 (16.3%) patients underwent consolidation radiotherapy (RT) for the residual disease after induction treatment, and 10 (4.4%) received autologous hematopoietic stem cell transplantation after CR was achieved. The baseline characteristics were similar between the non‐MYC overexpression (n = 87) group and MYC overexpression (n = 140) group.All patients (3.2n = 227) had MYC status confirmed by immunohistochemistry at the time of initial diagnosis. A total of 140 of 227 (61.7%) patients had MYC overexpression. The MYC overexpression group had a significantly worse OS than the non‐MYC overexpression group, with 3‐year OS rates of 51.1% and 67.3%, respectively . All patients . As of the date of follow‐up, a greater proportion of patients in the MYC overexpression group compared with the non‐MYC overexpression group experienced disease relapse and progression .Moreover, compared with the non‐MYC overexpression group, patients with MYC overexpression exhibited a worse ORR to first‐line induction treatment  but not in the subgroups with IPI score of 0–2, Ann Arbor stage 1–2, and non‐GCB subtype (p > 0.05).Further survival analysis was investigated according to the MYC overexpression status based on the IPI, stage, and COO subtype subgroups. MYC overexpression status was significantly associated with inferior OS and PFS in the subgroup with IPI score of 3–5  and PFS (p = 0.004) in all patients with bulky mass DLBCL. Of pivotal importance, we identified that RT was associated with a preferable OS (p = 0.001) and PFS (p = 0.001) in patients with MYC overexpression. However, RT showed no significant differences in OS (p = 0.704) and PFS (p = 0.523) within the non‐MYC overexpression group.With respect to the impact of various treatment strategies on survival among bulky mass DLBCL patients, we did not observe a significant difference in survival outcomes between patients who did or did not receive dose‐intensive immunohistochemistry treatment such as R‐EPOCH, surgery, and autologous transplantation. By contrast, consolidation RT was associated with a better OS (3.5p = 0.318) and PFS (p = 0.114). The MYC, BCL2, and BCL6 cytogenetic statuses were confirmed by FISH in 175, 174, and 174 patients, respectively. MYC rearrangement was detected in 11 of 175 (6.3%) patients, BCL2 rearrangement in nine of 174 (5.2%) and BCL6 rearrangement in 44 of 174 (19.4%). MYC/BCL2 or MYC/BCL6 double hit (DH) was present in two of 173 (1.1%) patients. MYC rearrangement, BCL2 rearrangement, BCL6 rearrangement, and DH status did not correlate with a significantly worse OS and PFS (p > 0.05).BCL2 overexpression was detected in 151 of 194 (77.8%) patients, BCL6 in 112 of 146 (76.7%) patients, and MYC/BCL2 DE in 106 of 194 (54.6%) patients. By contrast to the results of previous studies, compared with non‐MYC/BCL2 DE status, MYC/BCL2 DE status did not correlate with poorer OS  and PFS  after adjusting for the IPI score ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Donglin Liu: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Xudong Zhang: Conceptualization ; funding acquisition ; supervision . Mingzhi Zhang: Conceptualization ; funding acquisition ; supervision . Shenglei Li: Data curation ; methodology ; validation . Xiaoyan Feng: Data curation ; methodology ; validation . Meng Dong: Data curation ; methodology ; validation . Shanshan Ma: Data curation . Siyu Qian: Data curation . Zeyuan Wang: Data curation . Yue Zhang: Data curation . Pengyuan Wang: Data curation . Shuhao Mei: Data curation . Qingjiang Chen: Conceptualization ; funding acquisition ; resources ; supervision .The authors declare no conflict of interest.Informed consent for the collection of medical information was obtained from all patients. The present study was conducted according to the Helsinki declaration and was approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University .


This study used the event-related potential (ERP) technique to investigate whether active inhibition exists in retro-cue Effect (RCE) in visual working memory using modified retro-cue tasks. In this modified task, the participants were first asked to memorize six color blocks and then presented with directed remembering or directed forgetting cues; finally, their working memory performance was tested. For behavioral results, due to the extension of the memory interval, this study did not find RCE in accuracy but reflected it in the total reaction time. For ERP results, the frontal late positive potential (LPP) followed by the directed forgetting condition was larger than that followed by directed remembering and baseline conditions, and there was no significant difference between directed remembering and baseline conditions. There was no significant difference in parietal P3 followed by both the directed remembering and directed forgetting conditions, which were significantly larger than the baseline condition. This result reveals that active inhibition plays an important role in directed forgetting RCE. There was a correlation between parietal P3 and frontal LPP with the same time window but different scalp regions in the directed forgetting condition, indicating a potential relationship between active inhibition and retelling in directed forgetting RCE. Visual working memory is responsible for the temporary storage and processing of visual information and is an important part of the human cognitive system . HoweverAs retro-cues are important for improving visual working memory task performance, researchers have examined the factors that influence RCE . For example, The mechanism of RCE can be explained by the continuous resource model and the boost and bounce model. The former posits that visual working memory resources are limited and that internal attention can selectively allocate them to each representation in visual working memory . TherefoThe boost and bounce model proposes that working memory is a workspace in which task-relevant information is enhanced due to excitatory manipulation, but task-irrelevant information is weakened due to inhibitory operations . This moIt is impossible to report which model is more practical by behavior research alone because different cognitive processes may produce similar behavior results. Event-related potential (ERP) technology can help us understand the neural activity pattern behind the behavior. Previous studies used contralateral delayed activity (CDA), a neural indicator that reflects visual working memory load, and found that retro-cues reduced visual working memory load through selective attention to improve task performance . The CDAThe P3 component is a positive wave appearing in the 400–600 ms, representing the rehearsal of information and the degree of cognitive resources occupied thereby . TherefoTo investigate the active inhibition process induced by retro-cues more accurately, this study modified the retro-cue task of visual working memory and recorded ERP data during the experiment. In the modified paradigm, the interval between memory and cues was set as 800–1200 ms random time to avoid the preparation state caused by participants’ familiarity with the memory stage and cue stage intervals. To directly compare the neural activities of the two cues in the time course, the two types of cues were set as a within-subject variable. This is different from the research by This study mainly focused on examining whether active inhibition exists in RCE using a modified visual working memory retro-cue task, that is, whether the weakening of task-irrelevant information is due to active inhibition or passive abatement. If the weakening of task-irrelevant information is due to active inhibition, cues should also induce significantly enhanced frontal LPP in addition to the P3 component reflecting task-relevant information enhancement. By contrast, if the weakening of task-irrelevant information is due to passive subtraction, the cue does not induce significantly enhanced frontal LPP.Twenty-five participants participated in this experiment and their mean age was 21.80 ± 2.062 years. All participants had normal or corrected vision, without color blindness or color weakness. They were also right-handed with no psychiatric history or brain injuries. With the consent of the Academic Ethics Committee of Liaoning Normal University, the participants gave informed consent and received payment after the experiment.In this study, seven different color blocks were used as memory materials: red , purple , green , black , yellow , and white , with a size of 1.25° × 1.25°. The fixation point divided the field of vision into left and right sections. Each visual field included three color blocks. The minimum distance between color blocks was 0.55°. The fixation point was 1.8° from the memory material in both visual fields. The directed remembering cue was a solid English word, “right/left,” while the directed forgetting cue was a hollow English word, “right/left,” with a size of 1° × 1.4°. All materials were presented on a gray background . All stimuli were presented using a Dell 27-inch 60 Hz display. The experimental program was written and run using E-Prime 2.0. The experiment was conducted in a dark, soundproof room, and the participants were placed approximately 60 cm from the display screen.The experiment was divided into three conditions: directed remembering, directed forgetting, and baseline conditions.In the directed remembering condition, the participants were first presented with a 500 ms fixation point and then presented with a 500 ms memory stage. After the 800–1200 ms memory interval, the participants were presented with a cue for 1000 ms, followed by another 1000 ms memory interval, and finally presented with a 5000 ms test stage (disappeared after pressing the key). If the cue was “right,” there were three items in the right field of vision during the test phase. Participants were required to judge whether the items in the right visual field were the same as those in the memory phase and the F key for “Yes” or J for “No.”In the directed forgetting condition, the cues presented to participants were directed forgetting cues. For example, if the cues were “left,” the test stage presented three items of the right visual field, and the participants were to judge whether the items in the right visual field were the same as that in the memory stage. The other settings were the same as the directed remembering condition.In the baseline condition, the cue was replaced by fixation, and the test phase randomly tested one side of the visual field.Before each trial, participants were required to perform a voice inhibition task for 500 ms. Specifically, two numbers were randomly presented from 1 to 9 on both sides of the central fixation point (4° apart). The participants were required to repeat these two numbers verbally until the test stage was completed. Each condition had 120 trials and was randomly presented, with 360 trials divided into five blocks. Before the formal experiment, participants performed 12 practice experiments to familiarize themselves with the experiment. An example is shown in The EEG was recorded from 64 Ag/AgCl electrodes mounted in elastic caps, and the reference electrode was placed at the FCz site . The electrode position was based on the extended 10–20 system, using a vertical facial electrode (under the left eye) and a horizontal facial electrode (to the right of the right eye) to record an electrooculogram (EOG). The EEG data were sampled at 500 Hz/channel with a 0.01–100 Hz band-pass, and before the recording, all electrode impedances were maintained below 5 kΩ. EEGLAB and ERPLAB were used for preprocessing. First, the EEG data were re-referenced as the average values at the left and right mastoids. After ocular correction , the data were filtered with a 0.1 Hz IIR, 50-Hz notch, and 30-Hz FIR filters. Trials contaminated with electrooculogram artifacts or those with artifacts due to amplifier clipping, bursts of electromyographic activity, or peak-to-peak deflections exceeding ±80 μV were excluded from averaging. The EEGs were averaged separately for each participant, and only trials with correct responses were included in the ERP averages. The range of valid trials for the directed remembering, directed forgetting, and baseline conditions were 62–104, 59–99, and 66–105, respectively. The voltage of the EOG electrode did not exceed 3 μV: specifically, for the directed remembering condition: −0.33 ± 0.42 μV, for the directed forgetting condition: 1.05 ± 0.52 μV, and for the baseline condition: −0.18 ± 0.50 μV.The frontal lobe was divided into three scalp positions: left-frontal (F3 and FC3), middle-frontal (Fz and FCz), and right-frontal (F4 and FC4). The parietal lobe was also divided into left-parietal (CP3 and P3), middle-parietal (CPz and Pz), and right-parietal (CP4 and P4) scalp regions to reflect their EEG activities. According to previous studies, the N1 component reflects the early alert state of attention and is related to the response induced by the stimuli's visual and physical characteristics . This st01 index was used for the Bayesian analysis of the critical p-value.SPSS was used for statistical tests, and the Greenhouse-Geisser correction was conducted for the effect of nonconformance with the spherical test. In addition, Bonferroni correction was performed for the main effect, interaction, postanalysis, and simple effect tests. The BFF = 2.431, p = .099.The percentage of correct responses was taken as the accuracy index and was analyzed by one-way ANOVA at three levels . The results showed that the accuracy of the three different conditions, directed remembering, directed forgetting, and baseline, were 78%, 75%, and 76%, respectively. The main effect of the task type was not significant, F = 24.772, p < .001, pη2 = .508. The results of the simple effect test showed that the RT of the directed remembering condition was significantly lower than that of the directed forgetting and baseline conditions (ps < .006). The RT of the directed forgetting condition was significantly lower than that of the baseline condition (p = .001).The average reaction time of the correct trial was considered the indicator of the total reaction time (RT), and a one-way ANOVA at three levels  was performed. The results showed that the RT of directed remembering, directed forgetting, and baseline were 1082, 1126, and 1265 ms, respectively. The main effect of task type was significant, 2(2) = .750, p = .687  were combined into the balance integration score (BIS), a standardized performance index , 2022. Tp = .687 .A 3  × 3  repeated-measures ANOVA was performed for N1 (100–180 ms), P2 (200–300 ms), and frontal LPP (400–750 ms).F = 5.180, p = .009, pη2 = .178. The post-hoc paired test results showed that the N1 amplitude of the directed remembering condition was significantly larger than that of the baseline condition (p = .033). There was no significant difference between the directed remembering and directed forgetting conditions (p = .718), as with the directed forgetting and baseline conditions (p = .145). The main effect of the frontal scalp region was not significant, F = .698, p = .502. Similarly, the task type × frontal scalp region interaction was not significant, F = 1.228, p = .304.During the N1 (100–180 ms) time window, the mean amplitudes of directed remembering, directed forgetting, and baseline conditions were −.796, −.568, and −0.21 μV, respectively. The main effect of task type was significant, F = 48.529, p < .001, pη2 = .669, and the main effect of the frontal scalp region was significant, F = 12.243, p < .001, pη2 = .338. The interaction of task type × frontal scalp region was significant, F = 9.800, p < .001, pη2 = .290. A simple effect test showed that in the left and right frontal regions, the P2 amplitude of the directed remembering condition was significantly smaller than that of the directed forgetting condition (ps ≤ .034); however, there was no significant difference between the two conditions in the middle frontal region, p = .099. In the three regions of the frontal scalp, the amplitude of the directed forgetting condition was significantly larger than that of the baseline condition, ps ≤ .001. Similarly, the amplitude of the directed remembering condition was also significantly larger than that of the baseline condition in the three regions of the frontal scalp (ps ≤ .001). In the directed remembering condition, the amplitude of the middle frontal region was significantly larger than that of the left and right frontal regions (ps ≤ .001), but there was no significant difference between the left and right frontal regions (p = 1.000). In the directed forgetting condition, we also found that the middle frontal lobe was significantly larger than the left and right frontal regions (ps ≤ .001), but the amplitude difference between the left and right frontal regions was not significant (p = .1000). At baseline, there was no significant difference between the three scalp regions (ps = 1.000).During the P2 (200–300 ms) time window, the mean amplitude of directed remembering, directed forgetting, and baseline conditions were 4.286, 4.940, and −.173 μV, respectively. The main effect of task type was significant, F = 8.636, p = .001, pη2 = .265, and the main effect of the frontal scalp region was significant, F = 9.318, p < .001, pη2 = .280. The task type × frontal scalp region interaction was significant, F = 4.699, p = .002, pη2 = .164. A simple effect test showed that the amplitude of the frontal LPP in the directed remembering condition was smaller than that in the directed forgetting condition (ps ≤ .011), regardless of the left-frontal, middle-frontal, or right-frontal regions. The directed forgetting condition of the three frontal scalp regions was significantly larger than that of the baseline condition (ps ≤ .018). A significant difference between the directed remembering and baseline conditions was found only in the right frontal region (p = .041) but not in the middle frontal and left frontal regions (ps ≥ .142). Both the directed remembering and directed forgetting conditions showed that the amplitude of the left frontal region was lower than that of the middle frontal and right frontal regions (ps ≤ .021), and there was no significant difference between the middle frontal and the right frontal regions (ps = 1.000), indicating that the frontal LPP was sourced from the right frontal region. There was no significant difference among the three scalp regions in the baseline condition (ps ≥ .715).For the frontal LPP time window (400–750 ms), the mean amplitudes of directed remembering, directed forgetting, and baseline condition were .768, 1.696, and −.245 μV, respectively. The main effect of task type was significant, F = 42.989, p < .001, pη2 = .642, and the main effect of parietal lobe region was significant, F = 10.422, p < .001, pη2 = .303. The interaction between task type and parietal scalp region was significant, F = 12.301, p < .001, pη2 = .339. The simple effect test showed no significant difference in P3 amplitude between the directed remembering and directed forgetting conditions in all parietal regions, ps ≥ .069. The P3 amplitude of the directed remembering condition was significantly larger than that of the baseline condition (ps ≤ .001), and the P3 of directed forgetting was also significantly larger than that of the baseline condition (ps ≤ .001). For the directed remembering condition, the P3 amplitude of the left parietal region was significantly lower than that of the middle and right parietal regions (ps ≥ .008), and there was no significant difference between the right and middle parietal regions (p = 1.000). For the directed forgetting condition, the P3 amplitudes of the left and right parietal regions were significantly lower than those of the middle parietal region (ps ≤ .049), and the amplitude of the left parietal region was significantly lower than that of the right parietal region (p = .050). For the baseline condition, there were no significant differences among the three parietal regions (ps = 1.000).For the P3 time window (400–750 ms), the mean amplitudes of directed remembering, directed forgetting, and baseline conditions were 2.165, 2.543, and −.129 μV, respectively. A 3  × 3  repeated-measures ANOVA was performed for P3. The main effect of task type was significant, To determine that frontal LPP and parietal P3 are two different ERP components in this study, the source localization was performed during 400–750 ms using the Brainstorm  source ar = . 290, p = .169). However, a positive marginal correlation was observed for the directed forgetting condition between the P3 amplitude and frontal LPP amplitude . For the baseline condition, the correlation was not significant   .ps ≤ .002). Therefore, we conducted Granger causality tests for LPP and P3 in the directed remembering and directed forgetting conditions, respectively, with the lag set to 4. For the directed remembering condition, the Granger causality for both frontal LPP to parietal P3 and parietal P3 to frontal LPP were not significant (ps ≥ .222). For the directed forgetting condition, the Granger causality for parietal P3 to frontal LPP was not significant (p = .236), but frontal LPP to parietal P3 was significant (p = .005).To clarify the potential causality of the correlation between the EEG components of two different brain regions, we performed a Granger causality test. Eviews 11 was used for the Granger causality test. The unit root (stationary) for this study was tested by utilizing the augmented Dickey-Fuller (ADF). The ADF was performed for the LPP and P3 in the directed remembering and directed forgetting condition, respectively, and the results revealed that they were both significant  adoptedThis study found no significant difference in P3 amplitude induced by directed remembering and directed forgetting cues. P3 is closely related to memory encoding . AccordiBased on the high temporal resolution of ERP, we observed that different cues induced different cognitive activities. Specifically, when the cue was directed toward remembering, the participants deployed attention resources to task-relevant information and intensively retold them. However, when the cue was directed toward forgetting, the participants needed to recruit more attentional resources. On the one hand, they need to strengthen the retelling of task-relevant information. On the other hand, they need to inhibit task-irrelevant information. It should be noted that compared with the directed remembering condition, in which participants only needed to enhance task-irrelevant information, participants in the directed forgetting condition needed to perform two different cognitive operations (inhibition and enhancement), which may lead to the prolongation of participants’ behavioral reaction time.The boost and bounce theory proposed by After controlling for the P2 amplitude, we found no significant correlation between P3 and frontal LPP for directed remembering and baseline conditions, which may be due to the active inhibition of task-irrelevant information that was not induced in these two conditions. However, for the directed forgetting condition, P3 was positively correlated with LPP in the frontal lobe, and Bayesian analysis tended to be significantly correlated, which may indicate that there is a potential relationship between active inhibition and enhancement. Furthermore, the Granger cause test results indicated that the frontal LPP is Granger causal to the parietal P3 in the directed forgetting condition. It has been found that visual information is processed in the occipital lobe , while tA limitation of this study is that it only solves the problem of whether active inhibition components are present in RCE and does not discuss how active inhibition changes with load. There is an active inhibitory component in the RCE of visual working memory that manifests as a frontal LPP component induced by a directed forgetting cue. There is a potential correlation between the enhancement of task-relevant information and the weakening of task-irrelevant information in the directed forgetting condition.


There was no change in plasma selenium concentration in the placebo‐treated group. There was no significant effect of selenium supplementation on OC , PINP , CTX  or BALP . The relatively replete baseline selenium status in the study participants may explain this lack of effect. Testing in more deficient populations may provide further insights into the impact of selenium supplementation on bone health. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).Higher selenium status has been associated with lower bone turnover markers (BTM) in epidemiological studies. However, the long‐term impact of selenium supplementation on BTMs has not been studied. We investigated the effects of selenium supplementation on BTMs including osteocalcin (OC), procollagen type I N‐terminal propeptide (PINP), collagen type I cross‐linked C‐telopeptide (CTX), and bone alkaline phosphatase (BALP) in the short (6 months) and long term (5 years). A total of 481 Danish men and women (60–74 years) were randomized to receive placebo‐yeast versus 100, 200, or 300 μg selenium as selenium‐enriched yeast daily for 5 years. Plasma selenium concentration was measured using inductively coupled plasma mass spectrometry, and BTMs were measured in nonfasted samples at baseline, 6 months, and 5 years. Data were analyzed by ANCOVA to investigate the shape of the dose‐response relationships. Covariates included age, body mass index, baseline selenium status, baseline BTM, smoking, alcohol, supplement use, and medication. Plasma selenium concentration (mean 86.5 μg/d at baseline) increased significantly with increasing selenium supplementation to 152.6, 209.1, and 253.7 μg/L after 6 months and remained elevated at 5 years (158.4, 222.4, and 275.9 μg/L for 100, 200, and 300 μg supplemental selenium/d, respectively ( Selenium intakes vary greatly; residents of Europe, New Zealand, central Africa, and some parts of China have insufficient selenium intake due to low soil selenium. Among Europeans, selenium intakes declined from 60–63 μg/d to 29–39 μg/d from 1970 to 2000. In Denmark, selenium intake fell from 51 to 42 μg/d during 1990–95, and plasma selenium concentration dropped from 42 to 37 μg/L between 1995–2000 and 2002. However, among older adults, aged 65 to 75 years, intakes increased from the study period by approximately 25 μg/d between 1995–2001 and 2011–2013.Globally, 0.5 to 1 billion individuals are selenium deficient with blood concentrations below 70 μg/L. Inadequate selenium status in humans impairs the expression of the consortium of selenoproteins that are the biologically active selenium‐containing molecules.1 Sele Compared with selenium‐supplemented mice, selenium‐deficient mice had higher concentrations of inflammatory markers and bone resorption markers with poorer bone microarchitecture. Similarly, abnormal skeletal growth and poor bone health was observed in selenium‐deficient rats, whereas selenium supplementation improved bone microarchitecture.In humans, selenium deficiency is associated with muscle fatigue, pain, weakness, and increased serum concentration of creatine kinase.8, 9 Multiple studies have shown an inverse relationship between selenium status and inflammatory molecules, such as interleukin‐6 (IL‐6) and tumor necrosis factor‐α (TNF‐α). Both ROS and pro‐inflammatory molecules  can initiate bone resorption. Because selenoproteins are expressed within osteoblasts and osteoclasts, the bone formation and resorption cells, respectively, they may regulate bone resorption by moderating oxidative stress through ROS reduction. Higher ROS concentrations increase bone loss through the RANK pathway. Participants with lower selenium status have higher concentrations of IL‐6 and supplementation with 200 μg selenium daily for 12 weeks reduced IL‐6 concentration. Concentrations of IL‐6 are higher in osteoporotic individuals. In addition, higher selenium concentrations are associated with higher bone mineral density (BMD) and lower bone turnover markers (BTM). NHANES data suggested that higher selenium status (mean 131.1 μg/L) in US individuals, especially postmenopausal women, was positively associated with femur BMD. Consequently, improving selenium status could be an effective and inexpensive approach to reducing the age‐related decline in bone health. A recent randomized controlled trial (RCT) in 120 postmenopausal women showed that supplementation with sodium selenite for 6 months did not affect bone turnover or BMD. Our study aims to extend the findings from this investigation by Walsh and colleagues by using both men and women, a larger sample size, and a longer study duration.When incorporated into selenoproteins, selenium is important for musculoskeletal function. Most selenoproteins are involved in redox reactions, reducing concentrations of reactive oxygen species (ROS), such as hydrogen peroxide.16 MulThis study tested the hypothesis that long‐term supplementation with selenium improves bone health in older adults. We investigated the long‐term effects of selenium supplementation on biomarkers of bone turnover through secondary analysis of data from a 5‐year trial of adults in Denmark who were randomized to supplements providing 100, 200, or 300 μg selenium/d or to a placebo.ClinicalTrials.gov ID: NCT01819649) and other outcomes from the study have been published. The regional Data Protection Agency and Scientific Ethical Committees of Vejle and Funen counties approved the study . The present study is a secondary analysis to determine the effect of selenium supplementation on biomarkers of bone turnover.Data was obtained from the Danish Prevention of Cancer by Intervention with Selenium (PRECISE): A Pilot Study. The study began in 1998 and ended in 2004 and was organized by the Selenium Centre, Odense University Hospital, Denmark. It aimed to assess the viability for a full randomized controlled trial and hypothesized that selenium supplementation would reduce cancer risk in healthy adults. The trial was registered (ublished.32 The) were assessed. After this, 491 participants met the inclusion and adherence criteria for continuation. All participants provided written informed consent.Using a random sample from the Danish Civil Registration, invitation letters were sent to 2897 men and women aged 60 to 74 years from the County of Funen between November 1998 and June 1999. Of these, 630 accepted the invitation and were screened for inclusion  wen = 126), 100 μg selenium/d (n = 124), 200 μg selenium/d (n = 122), or 300 μg selenium/d (n = 119). There were 482 participants wtih BTM measurements. One participant was removed ; therefore, participants with BTMs at baseline were n = 124, 122, 118, and 117 for placebo, 100, 200, and 300 μg selenium/d, respectively  contained 54% to 60% of total selenium as selenomethionine (SeMet) with unknown seleno compounds providing the remainder. The placebo was identical to the supplementation tablets and consisted of inactive, spray‐dried baker's yeast . Smell and taste were matched by coating all tablets in titanium oxide, and tablets were packaged as 28‐tablet blister packs.The selenium was provided as selenium‐enriched yeast (in tablet form) in 100, 200, and 300 μg doses. These doses were suggested to be safe, as the tolerable upper intake level for adversity, set by the Institute of Medicine, is 400 μg/d.33 TheParticipant characteristics were determined at baseline. Further evaluations were performed at 6, 12, 18, 24, 36, and 60 months, which included assessment of medical status, tablet count, records of side effects, and the provision of new tablets, as previously described.31Nonfasting blood samples were collected at baseline, 6 months, and 5 years. Plasma was prepared and stored at −80°C.). The selenium concentration for the certified reference standard BCR‐637 was 78.3 (SD 2.7) μg/L (16 independent replicates), indicating good accuracy of the method. High‐selenium concentrations had an intra‐assay coefficient of variation (CV) of 0.5%, whereas low‐selenium concentrations had an intra‐assay CV of 3%. The interassay CV was 3.4%.Total selenium in plasma (μg/L) was measured at baseline, 6 months, and 5 years by LGC Limited using inductively coupled plasma mass spectrometry with external calibration (described in Cold and colleagues31). T PINP and CTX were selected because they are the two reference markers recommended by the International Osteoporosis Foundation (IOF) and the International Federation of Clinical Chemistry for inclusion of all studies using bone turnover markers. Additionally, they have been associated with selenium and selenoprotein P in the OPUS study,  where OC was more closely associated with selenoprotein P than PINP. Studies suggest BALP can help identify changes in bone mineralization such as osteomalacia and Paget's disease.The rationale for choice of the selected BTMs is described in Supplemental Table S35 PIN Serum was analyzed from the baseline, 6‐month, and 5‐year time points for N‐MID osteocalcin , procollagen type I N‐terminal propeptide , collagen type I cross‐linked C‐telopeptide (CTX), and bone alkaline phosphatase (BALP) at the Bone Biochemistry Laboratory, Department of Oncology and Metabolism, University of Sheffield (England). OC, PINP, CTX, and BALP were measured using the IDS‐iSYS automated immunoassays . The interassay CVs were 5.0%, 7.2%, 6.5%, and 3.5% for OC, PINP, CTX, and BALP, respectively.The BTMs were analyzed in 2017, with study recruitment from 1998–1999 until 2004. Thus, the oldest study samples were 19 years old, and the most recent ones were 13 years old. Studies have suggested that BTMs are stable when stored at −80°C for longer periods of time.39 Ser2 height (continuous). Participants were classified into education status using surveys based on time spent in education after public school . Living status was determined as living alone . Smoking status was determined at baseline . Alcohol intake was reported as standard drinks per week (continuous). Medication usage  was classified as a binary variable depending on the medication , with thyroid medication having three categories . Supplementation usage  was classified as a binary variable .Data on covariates were collected during visits with trained research nurses. Body mass index (BMI) was calculated as kg weight/mp value < 0.05 was considered statistically significant. To determine the normality of the variables, quantile–quantile (QQ) plots were used. Log 10 transformation was applied to all BTM measurements to normalize the data. Participant baseline characteristics are presented according to the supplementation dose. Differences in characteristics across supplementation doses were assessed using chi‐square test  or Kruskal–Wallis . Data were analyzed using intention‐to‐treat.Data were analyzed using the IBM  statistical software package version 24.0 (SPSS). A  Outcomes are reported as estimated marginal means with upper and lower 95% confidence intervals after back transformation.For the main analyses, the shape of the dose‐response relationship between selenium supplementation and each BTM , at each time , was investigated separately using an ANCOVA with orthogonal polynomials. Covariates included age (continuous), BMI (continuous), baseline selenium status (continuous), baseline BTM (continuous), smoking (binary), alcohol (binary), supplement use , and medication . These covariates were selected based on previous literature showing their effect on bone health.40 Outn = 4) because of their potential to influence BTMs. A second analysis excluded systemic GC, inhaled GC, antiresorptive, and thyroid medication users (n = 53). Although some research suggests inhaled GC have minimal effects on bone, a recent article suggested they increased the risk of osteoporosis. Thyroid medication has been shown to have a detrimental effect on bone health. Another sensitivity analysis excluded those using HRT (n = 75). A further sensitivity analysis removed supplement users (n = 215) because calcium, vitamin D, and multivitamins can influence bone metabolism. A final sensitivity analysis removed those using antiresorptives at baseline, 6 months, and 5 years, as well as those who had fractures, as a proxy to estimate those with osteoporosis (n = 14). The analyses were also repeated after categorizing baseline plasma selenium concentration into a binary variable, above and below 70 μg/L, based on evidence that this concentration is required to optimize glutathione peroxidase 3 (GPx) activity. Sensitivity analyses are reported in Supplemental Tables One participant of the 482 with BTMs was removed from the analyses. This participant's BTM concentrations for OC, PINP, CTX, and BALP were 6‐, 5‐, 7‐, and 2‐fold higher than mean concentrations. The removal of this participant had no significant effect on the main findings or baseline descriptives (data not shown). Sensitivity analyses were undertaken after excluding those participants receiving systemic GC . Least significant changes were set to 20% for OC, 21% for PINP, and 30% for CTX and BALP. These calculations showed that we had >90% power to detect least significant changes in OC, PINP, and BALP at 6 months and 5 years, but only 5% and 16% for CTX at 6 months and 5 years, respectively.This was a secondary analysis, sample size was not determined for this particular study. The initial pilot study proposed a sample size of 500 participants.31 Thep = 0.476, respectively) (Table p = 0.047), calcium supplementation (p = 0.028), and vitamin D supplementation (p = 0.020), but, otherwise, the supplementation groups were well matched  or reasons for dropout . However, participants who dropped out were more likely to have lower selenium status at 6 months (p = 0.009) but not at baseline. There were no other significant differences in baseline characteristics between participants who dropped out and those who did not  plasma selenium concentration in the placebo group remained unchanged  participants with BTM measurements died and 57 (11.9%) withdrew due to nonfatal adverse events and reactions .Adverse events for the full study have been reported and consisted of grooved nails, hair loss, and skin reactions.32 Durd and 57 1.9% withIn this RCT of selenium supplementation in older Danish adults, plasma selenium concentration did not have a significant effect on BTMs. That study recruited 120 osteoporotic and osteopenic postmenopausal women (aged 55 to 83 years) with a baseline plasma selenium concentration (79.4 μg/L) similar to our present study (86.5 μg/L) and found no effect of selenium supplementation on any of the measured BTMs.These results are consistent with findings from a RCT of selenium supplementation for 6 months in older women in the UK.30 Tha) is also adequate for bone, then it is likely that only a small minority of PRECISE study participants (12.1%) had potential to benefit from selenium supplementation. Studies that have observed responses to selenium, such as cancer prevalence or BMD, are usually in populations with suboptimal intakes or status, whereas replete populations are less responsive or nonresponsive. The relatively high baseline selenium concentration (≥70 μg/L) in the present study may explain the lack of response in BTMs to selenium supplementation.The plasma selenium concentration for optimal bone health is not known with certainty. However, if it is assumed that the concentration that is adequate for wider aspects of health ≥70 μg/L47) is47 is.Specific forms of selenium that have been used for supplementation studies differ in their bioavailability.54 For and correlates well with recent intakes of organic selenium. The use of BTMs can help determine metabolic imbalances within bone, fracture risk, and detect nonresponders to treatment. Using a range of biomarkers allowed us to overcome some of the individual limitations of each BTM to provide a more representative finding, as well as using markers suggested by the IOF.Among the strengths of our study, to our knowledge, this is the first, long‐term (5 years), large‐scale RCT exploring the effects of selenium supplementation on BTMs in older men and women. There was a large dose‐dependent increase in plasma selenium concentration with supplementation, suggesting compliance was good. This was gauged by the linear increase in selenium concentration with an increasing daily dose at 6 months, which was maintained at 5 years. In contrast, the plasma selenium concentration remained similar to baseline in the placebo group. Plasma selenium concentration is a robust indicator of selenium status59 and Circadian rhythm can also affect BTMs, especially markers of bone resorption, for which concentrations are highest in the morning. Consequently, the use of nonfasted samples may increase the variance in BTM measurements, but this is likely to be similar for all supplementation groups. We have no information on dietary intakes of other nutrients that influence bone health, such as calcium and vitamin D, although we were able to adjust for supplementation with calcium, vitamin D, and multivitamins. We do not have data on BMI change through the study, but mean values at baseline were similar for all supplement groups, so this is unlikely to have been a confounder. Our participants were aged 60 to 74 years at baseline and so we are unable to generalize our results to older populations among whom osteoporosis and micronutrient deficiencies are more likely. Future studies exploring the effects of selenium supplementation on bone health should consider including older people with lower baseline selenium status and/or those with inflammatory conditions who are at greater risk of osteoporosis and who may be more responsive to selenium supplementation.A limitation of our study was the use of nonfasted blood samples as feeding can decrease BTMs.61 CirThis was the first long‐term (5 years) RCT exploring the effects of selenium supplementation on BTMs in healthy older adults. Selenium supplementation did not have any significant effect on BTMs. We cannot rule out the potential of selenium supplementation to improve bone health in adults with lower selenium status and/or poorer bone health at baseline.RE received consultancy funding from IDS, Sandoz, Samsung, Haoma Medica, CL Bio, Biocon, Coherus, and Takeda; meeting presentations for Pharmacosmos, Alexion, and Amgen; and grant funding from Roche, Pharmacosmos, and Alexion. JSW received speaker's honoraria from Eli Lilly and Sandoz, grant funding from Alexion, donation of drug from Eli Lilly for clinical studies, and consulting fees from Mereo Biopharma. All other authors state that they have no conflicts of interest.Giorgia Perri: Formal analysis; writing – original draft; writing – review and editing. Tom R Hill: Supervision; writing – review and editing. John C Mathers: Supervision; writing – review and editing. Jennifer S Walsh: Conceptualization; data curation; funding acquisition; writing – review and editing. Fatma Gossiel: Conceptualization; data curation; investigation; writing – review and editing. Kristian Winther: Conceptualization; data curation; investigation; supervision; writing – review and editing. Jacob Frølich: Conceptualization; data curation; writing – review and editing. Lars Folkestad: Conceptualization; data curation; writing – review and editing. Søren Cold: Investigation; writing – review and editing. Richard Eastell: Conceptualization; data curation; funding acquisition; writing – review and editing.https://publons.com/publon/10.1002/jbmr.4703.The peer review history for this article is available at Appendix S1. Supplementary Information.Click here for additional data file.


It has been proven that tumor necrosis is associated with poor prognoses in various solid malignant tumors. However, the prognostic effect of tumor necrosis in hepato‐biliary‐pancreatic cancers is still unclear. Therefore, this study was performed to evaluate the associations of tumor necrosis with survival outcomes and clinicopathological features in patients with hepato‐biliary‐pancreatic cancers.Based on the PRISMA statement, eligible studies were identified from PubMed, Embase, Cochrane Library, and Web of Science from inception until January 2023. The pooled hazard ratios (HRs) and 95% confidence intervals (95%CIs) were calculated to assess the connection between tumor necrosis and hepato‐biliary‐pancreatic cancers. We then choose which effects model to use to generate pooled HRs and 95% CIs, depending on data heterogeneity.p < 0.001) and recurrence‐free survival . In addition, tumor necrosis was correlated with larger tumor size, a higher frequency of lymph node metastasis, poorer histologic differentiation, and higher recurrence and metastasis rates.In total, 6497 articles were identified, 10 of which were included in this meta‐analysis. Our results suggested that the presence of tumor necrosis predicted a poorer outcome for overall survival (HR = 1.54, 95% CI = 1.35–1.77, Our meta‐analysis suggests that hepato‐biliary‐pancreatic cancer patients with tumor necrosis have dismal survival outcomes, and that their tumors have aggressive biological behaviors. Tumor necrosis has the potential to be a promising biomarker for forecasting poor prognosis in these patients. Hepato‐biliarypancreatic cancer patients with tumor necrosis have dismal survival outcomes, and their tumors have aggressive biological behaviors. Tumor necrosis has the potential to be a promising biomarker for forecasting poor prognosis in these patients. A recent study reported that approximately 1.5 million new cases of HBP cancers occurred, and 1.4 million patients died of HBP cancers in 2020 worldwide, which accounted for 8% of all new cancer cases and 14% of cancer deaths.Tumor necrosis is a pathological phenomenon often discovered in solid tumors. The degree of hypoxia within the tumor can reveal the level of necrosis. Increased tumor necrosis may contribute to disease progression, treatment resistance, and poor outcomes.Several recent studies have suggested that tumor necrosis is likely to affect the outcomes of patients in liver, bile tract, and pancreatic cancer. However, there currently existed no studies systematically clarifying the overall prognostic value of tumor necrosis in HBP cancers. Therefore, this systematic review and meta‐analysis was conducted to assess whether the presence of tumor necrosis affects the prognoses of HBP cancer patients. Our study was performed according to the PRISMA guidelines for the reporting of meta‐analyses.22.1Literature was systematically reviewed by searching PubMed, Embase, Cochrane Library, and Web of Science for studies published from inception up until January 2023. The Medical Subject Heading (MeSH) terms and main keywords accepted were as follows: “tumor necrosis”, “necrosis”, “biliary tract neoplasms”, “biliary cancer”, “cholangiocarcinoma”, “extrahepatic cholangiocarcinoma”, “intrahepatic cholangiocarcinoma”, “liver neoplasms”, “liver cancer”, “hepatocellular carcinoma”, “pancreatic neoplasms”, “pancreatic cancer”, “prognosis”, “survival”, “mortality”, “survival analysis”, and their combinations. The detailed retrieval strategies are provided in the Supplementary material. Additional relevant references were identified by manually searching the reference lists of the selected research articles.2.2Studies meeting the following criteria were considered eligible: (1) patients with HBP cancers underwent surgical resection without receiving any previous therapy (neoadjuvant radiotherapy or chemotherapy); (2) tumor necrosis was confirmed histopathologically; (3) studies that reported the prognostic role of tumor necrosis in HBP cancers including overall survival (OS), disease‐specific survival (DSS), and recurrence‐free survival (RFS); (4) the eligible studies provided hazard ratios (HRs) and 95% confidence intervals (CIs). Otherwise, the studies were asked to provide related data to estimate the HR; (5) A score of ≥6 points on the Newcastle–Ottawa Scale (NOS). The exclusion criteria were as follows: (1) patients who received local treatment, neoadjuvant radiotherapy or chemotherapy before surgery; (2) tumor necrosis was not identified pathologically; (3) the associations between tumor necrosis and clinical outcomes were not reported, and the HRs/95%CIs were unavailable; (4) review articles, letters, commentaries, case reports, and conference abstracts; (5) articles not written in English.2.3The investigators (author 1–6) independently recorded related information with a standardized form. Information included author, study year, country, study design, sample size, age of patients, sex ratio, cancer type, histology, and survival end point. The quality assessment of cohort researches was assessed by the NOS2.4P heterogeneity of <0.10 or I2 > 50% suggested significant heterogeneity. Sensitivity analysis was used to discover the sources of heterogeneity. When significant heterogeneity was observed, the random effects model was used. Otherwise, the fixed effects model was adopted. In order to assess the heterogeneity and the effects of cancer type and geographic region more precisely, we performed subgroup analyses. All analyses were conducted using Stata 14.0 software (Stata Corp).Study characteristics and outcome parameters of all included studies were extracted and entered into a Microsoft Excel spreadsheet. The primary outcome was OS. Crude or adjusted HRs with 95% CIs were extracted for survival directly from each included study. If the HR and 95% CI were provided in the form of a Kaplan–Meier survival curve, the HR was estimated with the curve data digitized by the Engauge Digitizer software . The heterogeneity of the enrolled studies was evaluated using the Cochran's Q test and the Higgins I‐squared statistic. A 33.1In total, 6392 potentially relevant articles were identified. After excluding 1818 duplicate studies, the titles and abstracts of the 4574 reports were scanned to exclude irrelevant research. In the end, 59 studies were pulled for full‐text evaluation and 10 matching the inclusion criteria were finally included in the meta‐analysis3.2The Newcastle–Ottawa score of all included studies ranged from 6–8, indicating that all studies were of relatively high quality Table .3.3p < 0.001, I2 = 32.8%, Pheterogeneity = 0.166; fixed‐effects model; Figure p < 0.001, I2 = 56.2%, Pheterogeneity = 0.131; random‐effects model; Figure p < 0.001, I2 = 65.1%, Pheterogeneity = 0.003; random‐effects model; Figure A total of eight articles reported OS, and two studies provided DSS. The meta‐analysis indicated that tumor necrosis in HBP cancers predicted a poor outcome for OS , presence of lymph node metastases , higher frequencies of vascular  and neural invasion , higher tumor status , and poorer histologic differentiation . Patients with tumor necrosis were more susceptible to recurrence  and metastases  , DSS , and RFS . There was no heterogeneity found in the analyses of the OS model and only slight heterogeneity in the analyses of the DSS and RFS models. We also found that, compared with moderate tumor necrosis, extensive tumor necrosis was a marker for predicting worse OS  and RFS . Subgroup analysis indicated that PC patients and Asian patients presenting with tumor necrosis had poorer OS  and RFS , which suggested that tumor necrosis might play a better prognostic role in PC and Asian HBP cancer patients compared to other HBP cancers and in patients from other regions. Moreover, we found that the presence of tumor necrosis may be correlated with larger tumors, more frequent lymph node metastases, poorer histologic differentiation and pathologic tumor status, a higher recurrence and distant metastases rate, and a higher rate of vascular and neural invasion.Our meta‐analysis enrolled 3630 patients with HBP cancers from 10 studies of which 46.0% of the patients presented with tumor necrosis. Our study showed that necrosis was correlated with worse OS ; investigation ; writing – original draft . Haijie Hu: Conceptualization ; funding acquisition ; writing – original draft ; writing – review and editing . Yafei Hu: Data curation (supporting). Yushi Dai: Data curation (supporting). Ruiqi Zou: Data curation (supporting); visualization (supporting). Tianrun Lv: Data curation (supporting). Fuyu Li: Conceptualization ; funding acquisition (lead); project administration ; writing – review and editing .This work was supported by 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC21046); 1.3.5 project for disciplines of excellence‐Clinical Research Incubation Project, West China Hospital, Sichuan University (2021HXFH001); Natural Science Foundation of Sichuan Province (2022NSFSC0806); National Natural Science Foundation of China for Young Scientists Fund (82203782); Sichuan Science and Technology Program (2021YFS0100); The fellowship of China Postdoctoral Science Foundation (2021M692277); Sichuan University‐Zigong School‐local Cooperation project (2021CDZG‐23); Science and Technology project of the Health planning committee of Sichuan (21PJ046); Post‐Doctor Research Project, West China Hospital, Sichuan University (2020HXBH127).The authors have no conflicts of interest to disclose.Not applicable.Not applicable.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Loss of muscle mass is linked with impaired quality of life and an increased risk of morbidity and premature mortality. Iron is essential for cellular processes such as energy metabolism, nucleotide synthesis and numerous enzymatic reactions. As the effects of iron deficiency (ID) on muscle mass and function are largely unknown, we aimed to assess the relation between ID and muscle mass in a large population‐based cohort, and subsequently studied effects of ID on cultured skeletal myoblasts and differentiated myocytes.In a population‐based cohort of 8592 adults, iron status was assessed by plasma ferritin and transferrin saturation, and muscle mass was estimated using 24‐h urinary creatinine excretion rate (CER). The relationships of ferritin and transferrin saturation with CER were assessed by multivariable logistic regression. Furthermore, mouse C2C12 skeletal myoblasts and differentiated myocytes were subjected to deferoxamine with or without ferric citrate. Myoblast proliferation was measured with a colorimetric 5‐bromo‐2′‐deoxy‐uridine ELISA assay. Myocyte differentiation was assessed using Myh7‐stainings. Myocyte energy metabolism, oxygen consumption rate and extracellular acidification rate were assessed using Seahorse mitochondrial flux analysis, and apoptosis rate with fluorescence‐activated cell sorting. RNA sequencing (RNAseq) was used to identify ID‐related gene and pathway enrichment in myoblasts and myocytes.P < 0.001) or transferrin saturation  had a significantly higher risk of being in the lowest age‐ and sex‐specific quintile of CER, independent of body mass index, estimated GFR, haemoglobin, hs‐CRP, urinary urea excretion, alcohol consumption and smoking status. In C2C12 myoblasts, deferoxamine‐induced ID reduced myoblast proliferation rate (P‐trend <0.001) but did not affect differentiation. In myocytes, deferoxamine reduced myoglobin protein expression  and tended to reduce mitochondrial oxygen consumption capacity . Deferoxamine induced gene expression of cellular atrophy markers Trim63  and Fbxo32 , which was reversed by ferric citrate . RNAseq indicated that both in myoblasts and myocytes, ID predominantly affected genes involved in glycolytic energy metabolism, cell cycle regulation and apoptosis; co‐treatment with ferric citrate reversed these effects.Participants in the lowest age‐ and sex‐specific quintile of plasma ferritin (OR vs middle quintile 1.62, 95% CI 1.25–2.10, In population‐dwelling individuals, ID is related to lower muscle mass, independent of haemoglobin levels and potential confounders. ID impaired myoblast proliferation and aerobic glycolytic capacity, and induced markers of myocyte atrophy and apoptosis. These findings suggest that ID contributes to loss of muscle mass. Skeletal muscle mass is a key determinant of physical condition and mobility.ID is among the most common mineral deficiencies. The largest amount of iron in the body is used for haemoglobin production in the bone marrow and ID is a common cause of anaemia. In recent years, there has been increasing interest in implications of ID beyond erythropoiesis. Iron plays a role in deoxyribonucleic acid (DNA) synthesis and is essential to the function of numerous enzymes. Moreover, iron is a key component of myoglobin, responsible for oxygen storage in muscle cells. Finally, iron is crucial for mitochondrial function and aerobic metabolism, being involved in the citric acid cycle as well as the electron transport chain.The relationship between ID and muscle cell function has been studied in cardiomyocytes and skeletal muscle cells. In cardiomyocytes, ID impairs oxidative phosphorylation and contractile function.Therefore, in the current study, we addressed whether ID is related to reduced muscle mass in the general population. Moreover, we investigated whether ID influences proliferation and differentiation rate, aerobic metabolism and cell viability in cultured mouse C2C12 myoblasts and differentiated myocytes.We used cross‐sectional data from the Prevention of Renal and Vascular End‐stage Disease (PREVEND) study, a prospective, population‐based cohort of Dutch community‐dwelling individuals aged 25 to 75 years. The study was approved by the local ethics committee and complied with the principles of the Declaration of Helsinki. A full description of the methods, including extensive description of the statistical methods, is provided in the supplemental material. Written informed consent was obtained from all participants before enrolment. In this study, 8592 participants were enrolled at baseline. For the current analysis, we excluded participants with missing data on urinary creatinine excretion rate (CER) or ferritin Figure . All par2 (Thermo Scientific Forma Series II Water Jacket CO2 incubator) and 100% humidity. Sub‐culturing and refreshing medium was performed three times per week. For sub‐culturing, cells were washed with phosphate buffered saline  before trypsinization with 0.05% Trypsin–EDTA . Collected cells were then diluted for sub‐culturing or counted for seeding on plate. Counting of living cells was done using a trypan blue staining and a Bürker cell counting chamber (Marienfeld‐superior Cat. No. 0640211). For the differentiation from myoblasts to myocytes, cell culture was grown to a confluence of >70% in cell culture plates before switching from culture medium to differentiation medium , 1% ps). After seven days, this resulted in the gene expression of differentiation markers  were cultured in Dulbecco's Modified Eagle Medium  supplemented with 10% fetal bovine serum  and 1% penicillin–streptomycin  using T25 flasks (Thermo Scientific Cat. No. 156367). Cells were incubated at 37 °C, 5% COTo induce ID during cell culture, deferoxamine  was added to the medium. Unless stated otherwise, a DFO concentration of 7.5 μM was used. Repletion of iron to the cells was achieved by supplementing medium containing DFO with ferric citrate . Unless stated otherwise, a FC concentration of 10 μM was used. The cells were typically incubated with or without DFO and FC for three days, except for production of quantitative polymerase chain reaction (qPCR) samples for which they were incubated one day.Experimental methods are described in brief below. A more extensive description is provided in the Supplemental material.Proliferation rate of C2C12 myoblasts was determined using a colorimetric 5‐bromo‐2′‐deoxy‐uridine (BrdU) cell proliferation ELISA kit .Differentiation rate of C2C12 myoblasts was determined using staining techniques. The fusion index, or the percentage of nuclei being in a differentiated cell, was counted.basal − OCRoligomycin). To assess respiratory reserve of the cells, the increase in oxygen consumption after injection of FCCP compared with baseline was calculated .Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of skeletal myocytes were measured using a Seahorse Mito Stress test. During the assay, three baseline measurements were taken, after which ATP synthase inhibitor Oligomycin A was injected into the wells to inhibit complex I of the oxidative phosphorylation process. Then, carbonyl cyanide 4‐(trifluoromethoxy)‐phenylhydrazone (FCCP) was injected to uncouple the oxidative phosphorylation and induce rapid oxidation of energy substrates. All data were acquired using a Seahorse XF24 or XF96 analyser and normalized for total protein in the well. To assess ATP‐linked respiration, the decrease in oxygen consumption rate after the injection of oligomycin A was calculated  protein assay . Using normalized protein amounts, ferritin heavy chain I (Fth) and myoglobin bands were visualized and analysed after normalization for β‐actin.Fbxo32, Trim63, Becn1, TfR, Slc39a14, Slc40a1, Myh7, Myod, Myog, Bax, Bcl2, Casp9 and Rplp0  and for ribonucleic acid (RNA) sequencing, total RNA was isolated from C2C12 myoblasts and myocytes. Quantitative PCR (qPCR) technology was then performed by mixing collected cDNA with primers for target genes p0 Table . For RNATo measure apoptosis rate in differentiated C2C12 myocytes, fluorescence‐activated cell sorting (FACS) was used. Cells were stained with 5 μL FITC Annexin‐V and 5 μL propidium ionide staining solution to distinguish early apoptotic cells and late apoptotic cells from living cells.t‐test in case of a normal distribution or a two‐sided Mann–Whitney U‐test in case of a skewed distribution. Trends in concentration series were analysed with the ANOVA test followed by a Tukey's multiple comparisons test in case of a normal distribution or a Kruskal–Wallis test followed by Dunn's multiple comparisons test in case of a non‐normal distribution. A P‐value of <0.05 was considered statistically significant. In column scatter plots, unless stated otherwise, the median and individual values are presented.Data analyses were performed with Prism 8.4.2 (GraphPad Software). For all assessments, experimental groups consisted of at least three biological replicates and all analyses were performed in multiplo. Data are presented as mean ± standard error of the mean. To test for normality of the distribution, a Shapiro–Wilk test was used. Differences between two groups were analysed by an independent samples 2, plasma Hb 13.7 ± 1.2 g/dL and plasma ferritin 97 (48–173) μg/L). Further participant characteristics are provided in Table P = 0.004), and upon further adjustment for plasma haemoglobin . Results were similar in secondary analyses where CER was indexed for length  or length2  years, eGFR 92 ± 17 mL/min/1.73 mP = 0.03, Figure P = 0.03), and slightly weaker, but with a similar trend for CER indexed for length2 , involved in cellular uptake of transferrin‐bound iron, increased with 28% by 7.5 μM DFO treatment of myocytes for one day (P = 0.002) and decreased by 78% upon co‐treatment with 10 μM FC , Myod (encoding myoblast determination protein 1) and Myog (encoding myogenin)  in ATP‐synthase linked respiration reflected by the difference between basal respiration rate and remaining respiration rate after oligomycin injection , which was not restored by co‐treatment with 10 μM FC . Myocytes treated with DFO tended to have a lower maximal mitochondrial capacity, compared with untreated myocytes ; iron repletion with FC did not restore myoglobin levels , and Trim63, encoding MuRF‐1 , both markers of protein degradation and muscle atrophy. Additional treatment with FC significantly decreased expression of Fbxo32  and Trim63 . There was a trend towards reduced Becn1 expression after additional treatment with FC  and tended to increase Bax/Bcl2 ratio (+20%), and to increase the percentage of late apoptotic cells  and Bax/Bcl2 ratio , while it did not significantly alter the total percentage of early apoptotic or necrotic myocytes  in myoblasts compared with 270 DEGs in myocytes; 65 DEGs overlapped between the cell types. In myoblasts, expression of 96 genes was significantly up‐regulated after treatment with DFO and restored after co‐treatment with FC, while 14 genes showed the opposite pattern. In myocytes, 104 genes were up‐regulated after treatment with DFO and restored after additional treatment with FC, and 145 genes showed the opposite pattern Figure . Table 2Next, we assessed whether alterations in genes related to energy metabolism could explain reduced mitochondrial function in iron‐deficient myocytes. Expression of genes encoding all major enzymes involved in the anaerobic steps of glucose metabolism, glycolysis and lactate production was up‐regulated in myocytes after treatment with DFO Figure . On the Fbxo32, Trim63, Becn1 or Bax, we did observe up‐regulated expression of pro‐apoptotic genes Ddit4, Bnip3, Bnip3L, Fam162a and Ero1L in iron‐deficient myoblasts and myocytes. Pathway enrichment analysis showed that in myoblasts, ID most strongly affected energy metabolism, nucleotide metabolism and hypoxic responses  is the transcription factor involved in the expression of the largest cluster of DEGs related to ID. HIF1 is stabilized in response to hypoxia, promoting erythropoiesis and iron uptake from the gut, but may also be up‐regulated by ID, independent of oxygen availability.in vitro studies to investigate underlying mechanisms. Our study also has a number of limitations. First, we were not able to assess the relationship of ID with skeletal muscle strength in humans or with contractile strength in myocytes. Second, we did not assess morphological effects of ID on myocytes and therefore we could only provide circumstantial evidence that ID may induce atrophy and programmed cell death. Third, because we used a cell model, we could not assess any effects of ID on tissue vascularization, which might be increased by the HIF1a signalling cascade. Finally, the in vitro setting in C2C12 cells and the use of DFO to induce ID might not accurately reflect the impact of ID in vivo, and therefore the observations in C2C12 cells cannot be directly translated to the human in vivo setting. Nevertheless, our findings provide mechanistic insights that are supportive of the observations in humans.Our study adds important new insights in the effects of ID on skeletal muscle. Its main strength is the availability of data from a large and extensively phenotyped cohort of community‐dwelling individuals, combined with results of In conclusion, ID was consistently associated with lower muscle mass in community‐dwelling individuals, independent of haemoglobin and other factors. In skeletal muscle cells, ID reduced proliferation and resulted in increased expression of genes related to atrophy, apoptosis and autophagy, while inducing a switch from aerobic to anaerobic energy metabolism. Future studies should address whether correction of ID improves muscle mass.JSJ Vinke has received speaker fees from Vifor Pharma (paid to employer). MF Eisenga has declared receiving consultant fees from Vifor Pharma; serving on the Advisory Board for Cablon Medical; and receiving speaker's bureaus from Vifor Pharma, Cablon Medical, and Astellas . P vd Meer received grant support and/or consultancy fees from: Novartis, Pharma Nord, Pfizer, Ionis, Astra Zeneca, Vifor Pharma, Pharmacosmos, BridgeBio, NovoNordisk. MH de Borst has consultancy agreements with Amgen, Astellas, Astra Zeneca, Bayer, Kyowa Kirin, Vifor Fresenius Medical Care Renal Pharma, and Sanofi Genzyme, and received grant support from Sanofi Genzyme and Vifor Pharma . AR Gorter, WA Dam, J vd Born, SJL Bakker and MF Hoes declared that they have no conflict of interest.This work has been supported by the Dutch Kidney Foundation (grant no 17OKG18).Table S1. Mouse primers for RT‐PCR.Table S2. Top ten transcription factors known, based on literature, to be involved in expression of the largest number of differentially expressed genes after induction of iron deficiency.Click here for additional data file.Figure S1. Flowchart of inclusion.Click here for additional data file.Figure S2. Differentiation of C2C12 myoblasts to myocytes after addition of differentiation medium. Gene expression of differentiation markers Myh7 (A), Myod (B) and Myog (C) before and after seven days of culturing with differentiation medium. Measurements are normalized for housekeeping gene expression and for untreated controls. (D) Fusion index before and after seven days of culturing with differentiation medium. Data are based on three separate experiments each.Click here for additional data file.Figure S3. Association between iron status as reflected by ferritin levels  or TSAT  and CER indexed for length  or CER indexed for length squared  in community‐dwelling individuals. Odds ratios and corresponding 95% confidence intervals are provided for the risk of being in the lowest age‐ and sex‐specific quintile of 24‐hour CER in a crude model (Model 1), a multivariable model, adjusted for BMI, eGFR, hs‐CRP, urinary urea excretion, alcohol consumption and smoking status (Model 2) and with additional adjustment for haemoglobin (Model 3).Abbreviations: CER, creatinine excretion rate; TSAT, transferrin saturation; eGFR, estimated glomerular filtration rate; BMI, body mass index; hs‐CRP, high sensitive C‐reactive protein; OR, odds ratio.Click here for additional data file.Figure S4. Association between iron status as reflected by ferritin levels (A) or TSAT (B) and CER in community‐dwelling individuals, after excluding the 5% most extreme outliers of ferritin (A) or TSAT (B). Odds ratios and corresponding 95% confidence intervals are provided for the risk of being in the lowest age‐ and sex‐specific quintile of 24‐hour CER in a crude model (Model 1), a multivariable model, adjusted for BMI, eGFR, hs‐CRP, urinary urea excretion, alcohol consumption and smoking status (Model 2) and with additional adjustment for haemoglobin (Model 3).Abbreviations: CER, creatinine excretion rate; TSAT, transferrin saturation; eGFR, estimated glomerular filtration rate; BMI, body mass index; hs‐CRP, high sensitive C‐reactive protein; OR, odds ratio.Click here for additional data file.Figure S5. Induction of intracellular ID by DFO is reversed by FC in C2C12 myoblasts. Protein quantification of Fth in myoblasts assessed with Western Blot analysis after incubation with or without DFO without (A) or with (B) FC for three days. All measurements are normalized for total protein content, for β‐actin content and for untreated controls. Experiments performed in myoblasts are depicted as closed circles. Data are based on three separate experiments each. Experiments performed without DFO or FC are depicted as black symbols, experiments with DFO as red symbols and experiments with DFO and FC as blue symbols.Click here for additional data file.Figure S6. Treatment of C2C12 myoblasts with DFO leads to impaired proliferation rate. Proliferation rate was assessed with a BrdU cell proliferation ELISA assay under increasing concentrations of DFO. Data are based on three separate experiments each. Experiments performed without DFO or FC are depicted as black symbols, experiments with DFO as red symbols and experiments with DFO and FC as blue symbols.Click here for additional data file.Figure S7. Treatment with 7.5 μM deferoxamine (DFO) may induce apoptosis. Percentage of apoptotic or necrotic cells assessed with flow cytometry techniques. Data are based on three separate experiments each. Experiments performed without DFO or FC are depicted as black symbols, experiments with DFO as red symbols and experiments with DFO and FC as blue symbols.Click here for additional data file.Figure S8. Revigo TreeMap of Gene Ontology (GO) terms reflecting biological processes in which the differentially expressed genes under deferoxamine treatment in myoblasts (A) and myocytes (B) are involved. Each rectangle represents a GO term and related terms are combined into clusters with the same colour. The size of the rectangles represents the frequency of the GO term related to the differentially expressed genes as well as the P‐value.Click here for additional data file.