This research aimed to explore the relationship between pre‐treatment inflammatory markers and other clinical characteristics and the survival of small‐cell lung cancer (SCLC) patients who received first‐line platinum‐based treatment and to construct nomograms for predicting overall survival (OS) and progression‐free survival (PFS).n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow‐up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C‐index) and time‐dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value.A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort  staging for predicting the prognosis of SCLC.The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients. We explored the relationship between pre‐treatment inflammatory markers and other clinical characteristics and the survival of small‐cell lung cancer (SCLC) patients who received first‐line platinum‐based treatment. The nomograms for predicting progression‐free survival and overall survival had good predictability and stability. This research may provide clues and guides for individual treatment of SCLC patients. By collecting indicators that may be associated with SCLC, we developed two nomograms for predicting the OS and PFS of SCLC patients. Inflammatory markers that are clinically feasible and convenient to distinguish were determined to predict the prognosis of SCLC patients. The research may provide clues for individual treatment of SCLC patients.22.1A total of 612 patients pathologically diagnosed with SCLC from the First Hospital of China Medical University between March 2008 and August 2021 were included. Inclusion criteria were as follows: availability of complete patient records; no previous tumor‐related treatment; patients who received at least two cycles of standard first‐line platinum‐based doublet chemotherapy and at least once standardized evaluation of efficacy; no previous history of other primary tumors except for SCLC; Eastern Cooperative Oncology Group (ECOG) performance score 0–1. There were only 22 patients receiving PD‐L1 inhibitors in combination with platinum‐based chemotherapy in the first‐line treatment. The last follow‐up date was May 1, 2022. OS was defined as the interval from the date of initial diagnosis to death. OS time for patients who lost to follow‐up or were alive as of last follow‐up was equal to the date of last follow‐up. PFS was the time interval from original diagnosis to documented tumor progression or death due to any cause.2.2SCLC baseline clinicopathological factors were collected for each subject, such as gender, age, smoking history, US Veterans Lung Cancer Society (VALG) staging, chemotherapy, and thoracic radiotherapy history. Also, pre‐treatment lymphocyte, neutrophils, platelet, hemoglobin, RDW, albumin, globulin, total protein, LDH, carcino‐embryonic antigen (CEA), NSE, CRP, and blood sodium were recorded and evaluated by the hospital's examination criteria. All patients underwent standardized evaluation of the best efficacy of first‐line chemotherapy. NLR, PLR, AGR, LMR, CAR, PNI, and LCR were calculated as followed:2.32.3.1p‐value <0.1 in univariate analysis were included in multivariate Cox regression for independent prognostic analysis. Statistically independent variables (p < 0.05) in multivariate analysis were used to construct nomogram plots of 8‐, 12‐, and 24‐month probability for OS and 6‐, 12‐, and 18‐month probability for PFS.SPSS 25.0 and R 4.0.5 were used to conduct data analysis and caret R package was used to divide the training cohort and validation cohort. Pearson correlation coefficient was tested to ensure that there was no potential collinearity and interaction between each variable. The cut‐off level of NLR, PLR, AGR, CAR, PNI, LCR, and LMR was calculated by the survival and survminer R package. The above continuous variables were transformed into binary variables according to the cut‐off value. Chi‐square test was used to compare the clinicopathological parameters among the training cohort and the validation cohort. Cox's proportional hazards model was used to conduct univariate and multivariate survival analyses of OS and PFS. Factors with a 2.3.2Harrell's concordance index (C‐index) and time‐dependent receiver operating characteristic (ROC) curve were used to verify the model differentiation.33.1n = 459) and an independent internal validation cohort (n = 153), at a 3:1 ratio. Table In accordance with the inclusion criteria of this study, a total of 612 patients were randomly split into two cohorts, a training cohort (3.23.2.1p = 0.001), NLR > 1.28 , AGR < 1.36 , NSE > 16.3 ng/mL , hyponatremia , SD + PD for the best efficacy to first‐line chemotherapy , and extensive stage .As shown in Table 3.2.2p = 0.003), NLR > 1.28 , LDH > 250 U/L , NSE > 16.3 ng/mL , hyponatremia , SD + PD for the best efficacy to first‐line chemotherapy , and extensive stage  .As displayed in Table 3.33.3.1The nomograms predicting OS Figure  and PFS 3.3.2p < 0.001) and VALG staging system ,In the training cohort, the bootstrap C‐index of the nomogram was 0.666 (95% CI: 0.635–0.697) and 0.698 (95% CI: 0.665–0.731) of OS and PFS, respectively. Similar results were shown in the validation set, with C‐index 0.747 (95% CI: 0.708–0.786) and 0.727 (95% CI: 0.664–0.790) of OS and PFS, respectively. The calibration curves of 8, 12, and 24‐month OS and 6, 12, and 18‐month PFS probabilities in both training cohorts Figures  and 3B–DIn the training cohort, time‐dependent ROC curves showed the area under the curve (AUC) value in predicting 8‐, 12‐, and 24‐month OS was 0.780, 0.779, and 0.693, respectively Figure . The AUC4We developed and internally validated two nomograms for predicting OS and PFS of SCLC patients based on inflammatory markers and other clinicopathological variables. To the best of our knowledge, this was a real‐world study of maximum sample size using nomograms, ROC and DCA to ascertain the prognostic factors of Chinese SCLC patients. Also, the two prognostic nomogram models were the first to include multiple inflammatory biomarkers in SCLC and showed good predictive abilities. Recent studies indicated that, except for some traditional clinical prognostic factors, inflammation could also be critical in the initiation and progression of tumors.But the CRP/ALB ratio (CAR), a novel biomarker, can reflect both systemic inflammation and nutritional status, and calculate using the above two continuous variables, which can reduce the possibility of overestimation or underestimation and stratifies patients to more accurately predict survival. CAR has been proved to be a significant and convenient factor in predicting the short survival of patients with hematological malignancies and solid cancer.Immune cells can easily affect the occurrence and development of cancer. NLR and PLR are also markers indicative of inflammation and immune status, and have been reported to be closely related to the efficacy of immune checkpoint inhibitors in non small‐cell lung cancer (NSCLC) patients.p = 0.001, HR = 1.543 (95%CI: 1.181–2.016)) and PFS ) in the SCLC patients with liver metastases. Combining serum LDH levels with other indicators for assessing the risk stratification and prognosis of SCLC is essential.In the last step of the aerobic glycolysis process of malignant tumor cells, LDH is responsible for the catabolism of pyruvate into lactic acid, so the level of LDH symbolizes the activity of tumor cells.Inflammatory markers such as lymphocyte/CRP ratio (LCR) have never been reported in predicting survival in SCLC. Our study showed LCR was only significant in the univariate analysis of OS and PFS. Other factors such as LMR and RDW were not significant prognostic factors in SCLC. Consistent with previous studies, hyponatremia, extensive stage, SD + PD for the best efficacy of first‐line chemotherapy and high NSE level were independently negative prognostic factors both in OS and PFS in our present study. In our study, cut‐off values of the above inflammatory markers were calculated using the survival and survminer packages in R software, while other studies may determine cut‐off values by using X‐tile, ROC curves, or log‐rank statistics, which may lead to different results. In addition, because there will be differences in factors such as region and ethnicity of the included population, and the sample size differs, which may also cause different results between these inflammatory markers.Compared with Xie's and Zhou's studies, we included not only some known prognostic factors of SCLC, such as the level of LDH, NSE, and CEA, but also other inflammatory factors to build more comprehensive nomograms both for OS and PFS. Also, we conducted AUC to determine the discrimination of the nomograms and used DCA to estimate the value of the nomograms with respect to competing benefits and problems. Compared with traditional VALG staging and TNM staging, the nomograms have better prognostic predictive value, which could show better prospects in clinical application. Compared with Qie'sThere are some limitations in our study. First, this was a retrospective study and certain biases were inevitable. A prospective study is warranted to evaluate the nomogram models in the prognostic prediction of the SCLC patients in the future. Also, the nomograms in this study were established and validated internally which should be validated externally on a larger number of patients at multiple institutions. Second, the NCCN Guidelines for SCLC have approved that the PD‐L1 inhibitors combining chemotherapy could be a new standard first‐line treatment for ES‐SCLC patients, but owing to the inaccessibility of drugs or lack of reimbursement by medical insurance companies in China, only 22 SCLC patients received PD‐L1 inhibitors combined with chemotherapy in this study. Whether these prognostic markers are able to predict the efficacy and prognosis of immunotherapeutic patients remains to be further explored. Third, other clinical information such as history of non‐neoplastic diseases and complications were not recorded in our study which may inevitably lead to a certain bias. Finally, our nomograms were mainly built and validated based on the whole 612 SCLC patients. It is well known that there is a difference in survival rate between patients with ES‐SCLC and LS‐SCLC. These predictors may have some bias between SCLC patients of different VALG stages. In spite of these shortcomings, our nomograms were established based on the large sample real‐world evidence of survival of Chinese SCLC patients, and the nomogram models were found to have better stability and accuracy in validation.In conclusion, we constructed and validated nomogram to predict OS and PFS for patients with SCLC. The two nomograms outperform the traditional VALG staging system and provide more accurate prediction for the survival of SCLC patients. Inflammatory markers such as CAR, NLR, and AGR should be used widely to identify SCLC patients who could benefit from immunotherapy and better guide clinical treatment. The two proposed survival prognostic models could be helpful to better guide individualized treatment surveillance and decision.Chang Liu: Writing – original draft (lead). Bo Jin: Data curation ; supervision ; writing – review and editing . Yunpeng Liu: Data curation . Ouyang Juhua: Writing – original draft (supporting). Bowen Bao: Methodology . Bowen Yang: Methodology . Xiuming Liu: Methodology . Ping Yu: Data curation . Ying Luo: Data curation . Shuo Wang: Data curation . Zan Teng: Data curation . Na Song: Data curation . Jinglei Qu: Data curation . Jia Zhao: Data curation . Ying Chen: Data curation . Xiujuan Qu: Data curation ; supervision ; writing – review and editing . Lingyun Zhang: Data curation ; supervision ; writing – review and editing .This work was supported by High‐level Talent Introduction Support Project (Project No. 36003).The authors declare no conflict of interestResearch involving human subjects was reviewed and approved by the Ethics Review Board of the First Hospital of China Medical University (No. [2022] 324). Written informed consent to participate in the study was not required according to national legislation and institutional requirements.


Poor prognosis is linked to peripheral blood levels of preoperative platelet‐lymphocyte ratio (PLR) and neutrophil‐lymphocyte ratio (NLR) in many advanced cancers. Nevertheless, whether the correlation exists in resected early‐stage cases with non‐small cell lung cancer (NSCLC) stays controversial. Consequently, we performed a meta‐analysis to explore the preoperative NLR and PLR's prognostic significance in early‐stage patients with NSCLC undergoing curative surgery.Relevant studies that validated the link between preoperative NLR or PLR and survival results were found via the proceeding databases: PubMed, Embase, Cochrane Library, and Web of Science. The merged 95% confidence interval (CI) and hazard ratio (HR) was employed to validate the link between the NLR or PLR's index and overall survival (OS) and disease‐free survival (DFS) in resected NSCLC cases. We used sensitivity and subgroup analyses to assess the studies' heterogeneity.p < 0.001) and poor OS , respectively. Subgroup analyses were in line with the pooled findings. In aspect of PLR, raised PLR was indicative of inferior DFS  and OS . In the subgroup analyses between PLR and DFS, only subgroups with a sample size <300  and TNM staging of mixed (I‐II)  showed that the link between high PLR and poor DFS was significant.An overall of 21 studies were attributed to the meta‐analysis. The findings indicated that great preoperative NLR was considerably correlated with poor DFS (HR = 1.58, 95% CI: 1.37–1.82, Preoperative elevated NLR and PLR may act as prognostic biomarkers in resected early‐stage NSCLC cases and are therefore valuable for guiding postoperative adjuvant treatment. The meta‐analysis investigated the prognostic significance of preoperative NLR and PLR in early‐stage patients with NSCLC undergoing curative surgery. A total of 21 studies were enrolled in the metaanalysis. Summarizing the available data from the included studies, our results confirmed that high preoperative NLR and PLR were significantly associated with inferior DFS and OS. Therefore, the levels of preoperative NLR and PLR may serve as prognostic biomarkers in the clinic and are valuable measures for guiding postoperative adjuvant treatment. The primary deadline for literature retrieval was March 2022. The search strategy was performed using the following terms:  AND  AND ((“neutrophil lymphocyte ratio” OR “neutrophil‐to‐lymphocyte ratio” OR “NLR”) OR (“platelet lymphocyte ratio” OR “platelet‐to‐lymphocyte ratio” OR “PLR”)). The specific retrieval strategy can be found in Supplementary Text 2.2Studies gathering the following requirements were involved: (a) populations involving early‐stage NSCLC patients; (b) populations of NSCLC patients for whom curative surgical resection was performed; (c) the link between NLR/PLR and prognostic indicators of OS and/or disease‐free survival (DFS) were investigated; (d) the 95% confidence interval (CI) and hazard ratio (HR) could be obtained from the original studies; (e) the literature was written in English.The exclusion criteria were as follows: (a) reviews, case reports, abstracts, letters, and expert opinions; (b) populations of patients with other primary tumors; (c) surgical resection was not performed; (d) the studies had insufficient data to conclude on the HR and 95% CI; (e) literature with Newcastle‐Ottawa Scale (NOS) scores <6; (f) the literature was not published in English.2.3Two reviewers (Weibo Cao and Haochuan Yu) independently extracted relevant data and evaluated the studies' quality. Any disagreements were then resolved through discussion and consensus.The first author's name, the year of publication, study design, country of origin, study design, sample size, pathological category of lung cancer, TNM, median follow‐up (months), NLR/PLR cut‐off values, and survival data (DFS and/or OS) were all extracted from each study. When both multivariate and univariate analyses were run, the multivariate analysis's HRs were extracted first. Our way of determining cut‐off values for subgroup analysis was exploited according to the median value from all of NLR or PLR cut‐off in the studies recruited in this meta‐analysis.We evaluated the literature's quality involved according to the scoring system of the NOS. The NOS contains three aspects: patient selection, comparability, and outcome assessment. Literature with scores of ≥6 was considered of high quality. Studies with lower scores were regarded as low quality and therefore excluded from the analysis.2.42 and Cochran's Q test statistics were utilized to evaluate the studies' heterogeneity. p ≤ 0.10 or I2 ≥ 50% were significant heterogeneity indicative, and in such cases, a random‐effect model was utilized. Conversely, a fixed‐effect model was utilized to evaluate studies that did not present significant heterogeneity. Subgroup analyses dependent on the sample size, TNM, and cut‐off values were also performed to investigate potential factors influencing the NLR/PLR's prognostic significance. Sensitivity analyses were then performed to determine any heterogeneity's source and assess the results' stability. Publication bias assessment was conducted via Funnel plots and Egger's test, in which p < 0.05 was deemed statistically significant. All statistical analyses were done utilizing STATA 15.0 .Higgins I33.1As per the search approach, 1006 studies were initially identified, of which 290 were ruled out owing to duplication. By screening literature titles and abstracts, a further 634 studies were excluded based upon the exclusion criteria, resulting in 82 full‐text studies. Of these, 21 studies met the inclusion criteria and were subsequently involved in the meta‐analysis.Table 3.2p < 0.001). Owing to the great heterogeneity detected , a random‐effect model was utilized to evaluate the studies. From the subgroup analyses, a strong association between raised NLR and poor DFS was noted for all subgroups, indicating the reliability of the meta‐analysis. The findings of this can be found in Table Of the 21 studies, 20 reported the correlation between survival results and NLR in resected early‐stage NSCLC cases. Of these, 13 studies provided information on DFS Figure , which rI2 = 39.60%, p = 0.077), with a pooled HR of 1.65 .From sensitivity analyses Figure , it was 3.3I2 = 68.00%, p < 0.001). Thus, the random‐effect model was again utilized, resulting in a pooled HR of 1.51 . Overall, results from these studies revealed that great NLR was linked with worse OS in cases with NSCLC  after excluding the study of Huang et al. (2018), with a merged HR of 1.46 .Furthermore, sensitivity analyses exploring the potential heterogeneity of OS Figure  suggeste3.4I2 = 58.40%, p = 0.019). The pooled findings revealed that raised PLR was correlated with poorer DFS . As listed in Table p = 0.008) and TNM staging of mixed (I‐II)  demonstrated a significant link between raised PLR and poor DFS.Eight studies reported the correlation between DFS and PLR Figure  and wereI2 = 23.90%, p = 0.247) after ruling out the study of Wang et al. (2019), yet no association was found between preoperative PLR and DFS . Moreover, after excluding the studies of Zhang et al. (2014)  and Wang et al. (2017) , preoperative PLR and DFS remained uncorrelated.In terms of DFS Figure , the het3.5I2 = 59.00%, p = 0.004) was found among these studies. Thus, a meta‐analysis was conducted utilizing the random‐effect model, from which a strong relation was found between high PLR and worse OS . For the subgroup analyses between PLR and OS , with a combined HR of 1.43 , suggesting that raised PLR was still linked with poor OS.As for the sensitivity analyses of OS Figure , after e3.6p = 0.001) and OS (p < 0.001). Subsequently, trim and fill methods were done to explore the publication bias's impact on effect estimates. No statistically significant alterations were noticed in the findings  and OS  showed significant publication bias, accommodated by funnel plots which were not asymmetrical from visual inspection. Moreover, trim and fill methods again indicated no statistically significant changes in the results  indicated a significant correlation between raised PLR and poor DFS. These results suggest that the pooled results between PLR and DFS lack stability, which may be attributed to the limited studies in the meta‐analysis.The underlying mechanisms of the link between great preoperative NLR/PLR and poor survival results in NSCLC cases remain unknown. It has been noticed that tumor‐linked neutrophils can produce IL‐17A, promoting epithelial‐mesenchymal transition (EMT) in gastric cancer via JAK2/STAT3 signaling cascade.As a literature‐dependent meta‐analysis, several restrictions of this meta‐analysis should be noted. First, most of the literature involved were retrospective studies; hence, selection bias is present. More prospective studies are therefore required to affirm our findings. Second, only literature published in English was included in our study, with studies in other languages and unpublished studies not included, which reduced the data available for analysis. It is also possible that unpublished studies contain results considered ‘unfavorable’ or ‘negative’ by the researcher or the publisher, which may otherwise have contributed to this meta‐analysis. Third, characteristics such as sample size, region, and TNM differed throughout studies, which may also be the source of high heterogeneity. Fourth, we cannot rule out the possibility that non‐tumor‐related factors influence the blood markers of patients. Moreover, 19 out of 21 studies did not specify the pathological types of NSCLC and we could not perform subgroup analyses based on pathological type. Finally, there was a lack of standardization of cut‐off values for NLR or PLR in the included studies in the meta‐analysis. The cut‐off values of NLR (range: 1.5–5.0) and PLR (range: 81.07–300) differed throughout the involved studies. These cut‐off values were defined in different ways. For instance, in the study of Yuan et al. (2017), the appropriate cut‐off values for NLR and PLR were termed by X‐tile software.5This meta‐analysis concludes that poor DFS and OS in resected early‐stage cases of NSCLC are strongly correlated with preoperative peripheral blood high NLR or PLR. This suggests that levels of NLR and PLR may serve as prognostic biomarkers in the clinic and are valuable measures for guiding patients' postoperative adjuvant treatment. In the future, more prospective, multi‐center, and large‐sample studies are needed to affirm the findings of this meta‐analysis and promote its clinical application, especially in combination with other prognostic biomarkers.The authors all participated in designing the meta analysis. Weibo Cao, Shuai Zhu, and Ning Zhou participated in the literature search. Weibo Cao and Haochuan Yu independently extrated relevant data and evaluated the quality of the studies. Tong Li, Fan Ren, and Xi Lei performed the statistical analysis. Weibo Cao wrote the first draft. Song Xu and Lingling Zu revised the article. All authors agreed on the final version of the manuscript.The present study was funded by the National Natural Science Foundation of China (82172776), Tianjin Science and Technology Plan Project (19ZXDBSY00060) and (303078100412), Tianjin Key Medical Discipline  Construction Project (TJYXZDXK‐061B), and Diversified Input Project of Tianjin National Natural Science Foundation (21JCYBJC01770).The authors declare no conflict of interest.Ethical approval doesn't apply in the meta‐analysis.Text S1. The specific retrieval strategy in each database.Click here for additional data file.Table S1. Quality assessment by the Newcastle‐Ottawa Scale.Click here for additional data file.Table S2. Sensitivity analysis of the relationship between NLR and DFS.Click here for additional data file.Table S3. Sensitivity analysis of the association between NLR and OS.Click here for additional data file.Table S4. Sensitivity analysis of the link between PLR and DFS.Click here for additional data file.Table S5. Sensitivity analysis of the correlation between PLR and OS.Click here for additional data file.


We conducted a 1‐year retrospective mirror‐image study to investigate the effect of aripiprazole once monthly (AOM) on rehospitalization for bipolar disorder.p < 0.05.Participants were recruited from psychiatric emergency and acute care hospitals in western Japan. We included 39 participants with bipolar disorder who had been administered AOM for at least 1 year with no missing medical records during the observational period. The primary outcomes were rehospitalization rate, number of rehospitalizations, total hospitalization days, and time to rehospitalization in the context of overall psychiatric readmissions. The significance level was set at p = 0.001). The number of rehospitalizations decreased significantly from a mean of 0.85 per person‐year to 0.41 per person‐year (p = 0.048). The total hospitalization days significantly decreased from a mean of 34.9 days to 14.4 days (p = 0.008). AOM significantly prolonged the time to rehospitalization (p < 0.001).AOM significantly reduced the rehospitalization rate from 23/39 (59%) to 7/39 (18%)  on rehospitalization for bipolar disorder. Our study found that AOM significantly reduced rehospitalization rates, number of rehospitalizations, and total hospitalization days and significantly prolonged the time to rehospitalization in the context of overall psychiatric readmissions among patients with bipolar disorder in the real‐world setting. Treatment guidelines in various countries recommend maintenance treatment with mood stabilizers and antipsychotics.Once‐monthly long‐acting injection of aripiprazole, aripiprazole once monthly (AOM) has recently been approved of maintenance treatment of bipolar disorder. AOM prolonged the time to recurrence of any mood episode in a 52‐week, double‐blinded, randomized controlled trial of patients with bipolar disorder type I in the maintenance phase (hazard ratio: 0.45).Previous studies have shown that LAIs effectively reduce rehospitalizations for bipolar disorder in the real‐world setting. In a large Finnish cohort study, compared with identical oral antipsychotics, first‐generation antipsychotic LAIs and second‐generation antipsychotic LAIs (SGA‐LAIs) had an adjusted hazard ratio of 0.70 for psychiatric rehospitalization on bipolar disorder.However, previous studies have not verified whether AOM reduces rehospitalization for bipolar disorder after its use. A large Finnish cohort study included only risperidone LAI and olanzapine LAI but not AOM among SGA‐LAIs.Here, we conducted a 1‐year, retrospective, mirror‐image study to determine whether AOM reduces rehospitalization rates, number of rehospitalizations, and total hospitalization days and prolongs the time to rehospitalization in patients with bipolar disorder after its use. Our study examined the clinical impact of AOM by evaluating various types of readmissions, including overall psychiatric readmissions, readmissions specifically related to manic or depressive episodes, and both voluntary and involuntary readmissions. By analyzing these specific aspects, we aimed to provide a comprehensive understanding of the clinical benefits and effectiveness of AOM.22.1This 1‐year, multicenter, retrospective, mirror‐image study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.2.2The survey period was from January 1, 2023 to March 31, 2023. Six psychiatric emergency and acute care hospitals in Western Japan participated in this study. The information required for the study was retrospectively collected from the medical records of the participating hospitals.2.3We selected participants who (1) had been prescribed AOM between May 1, 2015, and December 31, 2022; (2) were diagnosed with bipolar disorder according to the International Classification of Diseases, 10th Revision (ICD‐10)2.4The following four outcomes were established: rehospitalization rate, number of rehospitalizations, total hospitalization days, and time to rehospitalization. The proportion of patients readmitted at a participating psychiatric hospital at least once during the pre‐ or post‐treatment period was defined as the rehospitalization rate. The total number of psychiatric readmissions during each period was defined as the number of rehospitalizations. The total length of stay in the psychiatric hospital during each period was defined as the total hospitalization days. Finally, the time from the beginning of either the pre‐ or post‐treatment period to the first readmission was defined as the time to rehospitalization.Primary outcomes included the rehospitalization rate, number of rehospitalizations, total hospitalization days, and time to rehospitalization in the context of overall psychiatric readmissions. In contrast, the secondary outcomes specifically examined these same measures in the context of manic or depressive readmissions and voluntary or involuntary rehospitalizations. The aim of the secondary outcomes was to investigate whether the effect of AOM on rehospitalization differed between manic and depressive admissions or between voluntary and involuntary admissions. Trained psychiatrists at each participating hospital collected data from medical records regarding mood episodes and hospitalization type at the time of readmission.2.5Sociodemographic, clinical, and therapeutic data were collected. Sociodemographic variables were age, sex, education, and employment status at the time of AOM introduction. Clinical variables included age at the onset of bipolar disorder, duration of illness, and number of psychiatric hospitalizations from the onset of bipolar disorder to AOM introduction. Therapeutic variables were the annual prescription days of aripiprazole oral formulation during the year before the first AOM administration; maintenance dose of AOM after continuation of its administration for a year; and concurrent use of oral antipsychotics, mood stabilizers, and antidepressants after a year of AOM administration.2.6A common case‐reporting form was used at the participating hospitals to ensure that the information was collected comprehensively as per the uniform definitions in the research protocol. An individual double‐checked the data collected at each participating hospital.2.7We used the R version 4.1.0. software  for statistical analyses. In descriptive statistics, normally distributed continuous variables are expressed as means and standard deviations, whereas non‐normally distributed data are expressed as medians and interquartile ranges. Meanwhile, categorical variables are expressed as numbers and percentages.The following statistical analyses were conducted on the primary and secondary outcomes. McNemar's test was performed on the difference in the rehospitalization rates between the pre‐ and post‐treatment periods, and its effect size was evaluated using Cohen's g. The number of rehospitalizations and total hospitalization days were determined using the Shapiro–Wilk test for non‐normality. The differences between each period were examined using the Wilcoxon signed‐rank test, and non‐parametric effect sizes were evaluated using Cliff's delta. Cohen's g was assessed as follows: <0.05, ≥0.05 and <0.15, ≥ 0.15 and <0.25, and ≥0.25 corresponded to negligible, small, medium, and large, respectively.2.8U test was used to compare continuous variables. The significance level was set at a p‐value of <0.05.We explored the factors associated with rehospitalization after AOM by comparing groups with and without rehospitalization after AOM introduction. We tested for differences between the two groups with respect to sociodemographic, clinical, and therapeutic variables. The Fisher's exact test was used to compare categorical variables, and the Mann–Whitney 33.1n = 62) or had missing medical data (n = 5). Thirty‐nine patients were included in the final analysis  were attributed to mania, 9/33 (27%) to depression, 9/33 (27%) to voluntary admissions, and 24/33 (73%) to involuntary admissions. In contrast, the post‐treatment period saw 16 psychiatric rehospitalizations, with 4/16 (25%) related to mania and 12/16 (75%) to depression. Among these rehospitalizations, 12/16 (75%) were voluntary admissions, while 4/16 (25%) were classified as involuntary.3.4p = 0.001) between the pre‐ and post‐treatment periods, respectively, with a Cohen's g of 0.33 (95% CI: 0.14–0.43) for a large effect size. The number of rehospitalizations decreased significantly from a mean of 0.85 per person‐year to 0.41 per person‐year (p = 0.048) between the pre‐ and post‐treatment periods, with a medium effect size of 0.38 (95% CI: 0.16–0.56) for Cliff's delta. The total hospitalization days fell significantly from a mean of 34.9 days to 14.4 days (p = 0.008) between the pre‐ and post‐treatment periods, with a medium effect size of 0.38 (95% CI: 0.15–0.57) for Cliff's delta. Figure p < 0.001).Table 3.5p < 0.001), whereas no significant improvement was observed for depression (p = 0.53) after AOM administration. Involuntary rehospitalizations demonstrated significant reductions across all measures during the pre‐ and post‐treatment periods, whereas no significant changes were observed for voluntary rehospitalizations (Tables p < 0.001), but no significant effect was noted for voluntary rehospitalizations (p = 0.76) in the pre‐ and post‐treatment period comparisons.Rehospitalizations for mania exhibited significant decreases across all measures between the pre‐ and post‐treatment periods, while rehospitalizations for depression did not show any significant changes Tables . Furthers Tables . Additio3.6p < 0.001).Twenty of 39 (51%) participants were AOM users not concurrently taking any antipsychotic, mood stabilizer, or antidepressant medications. This subgroup showed a significant reduction in rehospitalization across all measures between the pre‐ and post‐treatment periods Table , along w3.7p = 0.02). No significant differences were detected between the two groups for any other variables. Details are shown in Table Seven and 32 patients were in the rehospitalization and non‐rehospitalization groups, respectively, after introducing AOM. Six of seven (86%) patients in the rehospitalization group and nine of 32 (28%) in the non‐rehospitalization group were on mood stabilizers after 1 year of AOM, with a significant difference between the two groups .n = 17).n = 49)The effect of AOM on rehospitalization for bipolar disorder shown in this study is consistent with the results of several previous studies. In the present study, the rehospitalization rate significantly decreased from 59% to 18% between the pre‐ and post‐treatment periods with AOM introduction; likewise, the rehospitalization rate significantly decreased from 57% to 23% in the SGA‐LAI subgroup of the STAR study  of rehospitalizations after AOM were attributed to depression, indicating the concurrent use of mood stabilizers served as a treatment approach for recurrence/relapse of depression following AOM introduction. In cases where the initiation of mood stabilizers was delayed, the worsening of depression may have led to rehospitalization. A network meta‐analysis suggested that the combination of oral aripiprazole with either valproate or lamotrigine was more effective in preventing recurrence/relapse of the depressed phase in bipolar disorder compared to AOM alone.4.5The findings of this study should be interpreted with caution owing to its limitations. First, although the number of AOM users in this study was larger than that in previous studies, it was still lower at 39. Second, because the study participants were recruited from six psychiatric emergency and acute care hospitals, the results of this study are applicable to severely ill patients with bipolar disorder and may not be generalizable. Third, as this study did not include patients with bipolar disorder who discontinued AOM within 1 year, it may have excluded patients who had an unfavorable response to AOM within 1 year, leading to selection bias and overestimation of the treatment effect. Furthermore, although we diagnosed bipolar disorder based on medical record data, we lacked sufficient information to distinguish bipolar disorder types I and II. We determined the mood episodes at readmission (manic or depressive); however, we were unable to assess the severity of these episodes using established rating scales such as the Young Mania Rating Scale or Hamilton Depression Scale. Additionally, our study did not include the observation of recurrence/relapse events not requiring rehospitalization. We were also unable to adjust for risk factors for rehospitalization, such as substance abuse and personality disorders,5Our study found that AOM significantly reduced rehospitalization rates, number of rehospitalizations, and total hospitalization days and significantly prolonged the time to rehospitalization in the context of overall psychiatric readmissions among patients with bipolar disorder by comparing data from 1 year before and after AOM treatment. These findings suggest that AOM may help prevent rehospitalization as a maintenance treatment for bipolar disorder. Future studies should examine the robustness and persistence of the rehospitalization preventive effect of AOM with larger sample sizes and extended observation periods beyond 1 year.Goto J. and Nabeshima Y. conceptualized and designed the study. Goto J., Shono M., Abe Y., Fujita Y., Ueda K., and Yoshida B. were involved in the data acquisition. Goto J., Shono M., and Nabeshima Y. interpreted data for the work. Goto J. prepared the manuscript. Shono M., Abe Y., Fujita Y., Ueda K., Yoshida B., and Nabeshima Y. critically revised the manuscript for important intellectual content. All authors approved the version to be published and agreed to be accountable for all aspects of this work.This study did not receive any specific grants from funding agencies in the public, commercial, or non‐profit sectors.Goto J. received lecture fees from Otsuka Pharmaceutical Co., Ltd., Janssen Pharmaceutical K.K., Sumitomo Pharma Co., Ltd., and Meiji Seika Pharma Co., Ltd.; Shono M. received lecture fees from Otsuka Pharmaceutical Co., Ltd., Sumitomo Pharma Co., Ltd., and Nippon Boehringer Ingelheim Co., Ltd.; Abe Y. received lecture fees from Otsuka Pharmaceutical Co., Ltd., Janssen Pharmaceutical K.K., Sumitomo Pharma Co., Ltd., MSD K.K., Viatris Inc., Meiji Seika Pharma Co., Ltd., Takeda Pharmaceutical Co., Ltd., Yoshitomiyakuhin Corporation, and EA Pharma Co., Ltd.; Fujita Y. received lecture fees from Otsuka Pharmaceutical Co., Ltd., Sumitomo Pharma Co., Ltd., Janssen Pharmaceutical K.K., Meiji Seika Pharma Co., Ltd., Takeda Pharmaceutical Co., Ltd., Viatris Inc., Eisai Co., Ltd., and MSD K.K.; Ueda K. declared no conflicts of interest; Yoshida B. received a lecture fee from Otsuka Pharmaceutical Co., Ltd.; and Nabeshima Y. received lecture fees from Otsuka Pharmaceutical Co., Ltd., Eisai Co., Ltd., Takeda Pharmaceutical Co., Ltd., Sumitomo Pharma Co., Ltd., MSD K.K., Meiji Seika Pharma Co., Ltd., Janssen Pharmaceutical K.K., and Yoshitomiyakuhin Corporation and an advisory fee from Sumitomo Pharma Co., Ltd.Approval of the research protocol by an Institutional Reviewer Board: The protocol for this research project has been approved by the suitably constituted Ethics Committees of Yuge Hospital , and it conforms to the provisions of the Declaration of Helsinki.Informed consent: The opt‐out method of consent was adopted, wherein the study information was posted on institutional websites as a clinical study and patients were provided with an adequate period to opt out.Registry and the registration no. of the study/trial: N/A.Animal Studies: N/A.Tables S1‐S6.Click here for additional data file.


Olanzapine is one of the most frequently used antipsychotic medications during pregnancy, but information about its safety and adverse effects profile during pregnancy is scarce. We herein describe a case of a pregnant woman with several psychiatric disorders who developed water retention, hypertension, and subsequent preterm preeclampsia 3 weeks after initiation of treatment with olanzapine. To the best of our knowledge, this is the first case of olanzapine-associated preeclampsia described in literature. Psychiatric disorders are common among women of reproductive age, even during pregnancy . WorldwiHere, we present a case of a 32-year-old woman with depression and anxiety where introduction of olanzapine treatment during the 26th week of pregnancy was associated with onset of water retention, elevated blood pressure, subsequent preeclampsia, and preterm delivery in gestational week 29.A 32-year-old Caucasian pregnant woman with a history of generalized anxiety disorder, emotionally unstable personality disorder, obsessive-compulsive disorder, posttraumatic stress disorder as well as one previous suicide attempt several years ago, was admitted to the maternity ward in gestational week 24 due to severe hyperemesis gravidarum, suicidal ideation, and increased anxiety. She had no family history of preeclampsia and she had stopped smoking during pregnancy.The patient had been mentally stable for several years on treatment with lamotrigine 150 mg/day and venlafaxine 225 mg/day. A gradual deterioration of her mental health began after the sudden death of her mother 6 months prior to the current event. Around the same time, the patient discovered that she was pregnant. Her primary care physician began tapering her lamotrigine and venlafaxine, after consulting a psychiatrist. Her medications at the time of admission to the maternity ward were lamotrigine 25 mg daily and venlafaxine 187.5 mg daily. She was transferred to the psychiatry ward 6 days after being admitted to the maternity ward. At admission to the psychiatric ward (the 25th week of pregnancy), the patient’s weight was recorded at 85 kg. The patient’s self-recorded weight pre-pregnancy was approximately 73 kg (BMI 25.9). At the time of admission, her vital signs included a blood pressure of 105/59 mm Hg, a heart rate of 88 beats/min, and an oxygen saturation of 97%. Laboratory tests were normal except for lower serum albumin measuring 28–27 g/L and positive urine protein ranging from 1 to 2 + which is equivalent to 0.3–1 g/L protein in the urine. Positive urine protein finding had been present as early as gestational week 10 when the patient sought medical attention for hyperemesis gravidarum. Of note, during her pregnancy, there were intermittent instances of negative urine protein results. Her hypoalbuminemia and proteinuria were not evaluated further during her hospitalization.pro re nata: PRN). The restoration of her sleep quality prompted the discontinuation of quetiapine after a total of 10 days of use. Concurrently, the patient’s clinical picture was dominated by severe anxiety with intermittent suicidal thoughts that led to the introduction of olanzapine, 2.5 mg twice a day (BID), with subsequent gradual increase to 7.5 mg/day and then 5 mg BID during the following 2 weeks. She was treated at the psychiatry ward for a duration of 25 days, during which she received olanzapine for 20 days. She was discharged with scheduled outpatient psychiatric follow-up, including planned monthly concentration measurements of lamotrigine with potential dose increase if required. For a summarized overview of the drug treatment regimen over the course of the disease, see During the patient’s hospitalization, her psychotropic treatment was intensified with the gradual increase of lamotrigine to 150 mg/day over 2 weeks and increase of venlafaxine to 225 mg/day. Following dose increase, plasma levels of venlafaxine and its active metabolite were within the therapeutic range. In response to significant sleep disturbances in the beginning of hospitalization, a nightly regimen of 10 mg zolpidem and 25 mg quetiapine was initiated. She additionally received oxazepam 10 mg and promethazine 25 mg for anxiety as well as ondansetron 4 mg for nausea as needed . Blood sugar, HbA1C, screening for viral infections, and thrombophilia were normal. Chest computer tomography revealed bilateral pleural effusion but showed no signs of pulmonary embolism. Echocardiography revealed signs of fluid overload, with all four chambers slightly dilated, but the heart function was within the normal range. The patient was prescribed labetalol, clonidine, nifedipine, and furosemide for hypertension and oedema as well as magnesium sulfate for seizure prophylaxis. An urgent cesarean section was performed 2 days later, at gestational week 29 + 1, due to preeclampsia. The newborn’s birth weight was 1,200 g, and the Apgar scores at 1, 5, and 10 min were 8, 7, and 7, respectively. No growth restriction or developmental abnormalities were noted at birth. Postpartum microscopic pathological evaluation of the placenta was consistent with preeclampsia, where accelerated maturation and hypoxic changes as well as microinfarctions were present.The symptoms of dyspnoea and peripheral oedema, as well as serum albumin and NT-proBNP started to improve promptly postpartum. Her blood pressure was controlled by daily doses of 600 mg labetalol, 5 mg enalapril, and 40 mg furosemide, and subsequent echocardiography revealed no pathological changes. The patient was mentally stable and was transferred to neonatal intensive care unit 5 days postpartum, for medical care of her newborn. Psychiatric medication remained unchanged at discharge, i.e., lamotrigine 150 mg/day, venlafaxine 225 mg/day, zolpidem 10 mg/day, olanzapine 10 mg/day, oxazepam 10 mg PRN, and promethazine 25 mg PRN.Preeclampsia is a life-threatening complex multisystem disease that can occur during pregnancy, characterized by onset of elevated blood pressure and often accompanied by complications such as proteinuria, organ dysfunction, or placental dysfunction. The pathophysiological mechanisms of preeclampsia are not completely understood but certain risk factors have been suggested to contribute to the condition, such as nulliparity, chronic hypertension, pregestational diabetes, family history of preeclampsia, high BMI, and maternal age .in vitro experiments on human trophoblast cells exposed to olanzapine reported altered trophoblast invasion, a phenomenon that is implicated in adverse pregnancy outcomes, such as preeclampsia  for the publication of any potentially identifiable images or data included in this article.JI: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. DF: Writing – review & editing. PA: Writing – review & editing. DP: Writing – review & editing. JMK: Writing – review & editing. DL: Conceptualization, Data curation, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing.


The intake and homeostasis of iodine, an essential micronutrient that plays a vital role in thyroid physiology, is of particular concern with the increasing popularity of vegetarian (VG) and vegan (VN) diets. Children on these restrictive diets may be at risk of possible adverse effects on growth and development, but there is currently a lack of recent epidemiological studies on this topic.n = 91), VN diet (n = 75), or omnivore diet . Cross-sectional comparison of the groups and linear regression was used. Stratified analyses were performed based on age (according to WHO): 0–5 years and 6–18 years.We gathered clinical, anthropometric, and blood/urine data on iodine status and thyroid function from children aged 0–18 years who followed either a VG diet (p < 0.001). The presence of anti-thyroglobulin antibodies (AhTGc) was notably more common in the VG (18.2%)/VN (35.0%) groups than in the OM group (2.1%) (p < 0.001). Regarding iodine status, the concentration of iodine in spot urine (UIC) was found to be highest in the OM group . Notably, the lowest (5.99 µg/l) and highest (991.80 µg/l) levels were measured in the VN group. Of the participants, 31 VN, 31 VG and 10 OM children met the criteria for iodine deficiency . We found that children with regular iodine supplementation had higher UIC (p < 0.001). Importantly, the median UIC was above 100 µg/l in all three groups, through the recommended intake (RDI) of iodine was rarely met throughout the groups.Our study revealed no significant differences in levels of thyroid-stimulating hormone (TSH), triiodothyronine (fT3), thyroglobulin (TG) or anti-thyroid peroxidase antibody (ATPOc) between the VG, VN, and OM groups. However, thyroxine (fT4) levels were found to be higher in the VN group compared to the OM group  for iodine deficiency in all dietary groups. These results suggest that VN and VG children may be more at higher risk of iodine deficiency, this theory is also supported by higher prevalence of AhTGc positivity. Further research is needed to investigate the long-term impact of these dietary patterns on iodine status and thyroid function in children. Given our findings, it may also be necessary to consider new guidelines for supplementing children following VG and VN diets to ensure their iodine needs are met. The increasing popularity of plant-based diets in recent years has been driven by various motives such as perceived health benefits, environmental concerns, and ethical considerations , 2. ThesIodine, an essential micronutrient, is not evenly distributed in our environment, and insufficient soil contents in many areas of the world increase the risk of low iodine intake. The primary dietary sources of iodine are iodized table salt, dairy, eggs, and seafood . DespiteBetween November 2019 and July 2021, a total of 222 children were recruited for the study. The participants were recruited through a network of general practitioners, social media, and vegan-focused web pages. Of the total participants, 92 were VG, 78 were VN, and 52 were OM who served as controls. Flow diagram of the study enrollment is depicted in Fig. The study was conducted according to the Declaration of Helsinki guidelines and approved by the Ethics Committee of the Third faculty, Charles University, Prague, and Ethics Committee of Faculty Hospital Královské Vinohrady. All examinations were performed with parental written consent, with no financial incentives.Trained clinicians conducted structured medical interviews that focused on dietary habits and iodine intake. The participants were asked to self-identify as VN/VG/OM, and their dietary habits were examined accordingly. Specifically, the use of iodine supplements and their dosages (in µg), frequency of usage , and regularity  were assessed through questionnaires. The parents of the participants were asked to fill in the questionnaires under medical supervision to ensure accuracy. Participants who did not take any supplements containing iodine were classified as “No” in supplement use, while those who did use supplements were classified as “Yes”.Blood samples were collected from participants after an overnight fast through venepuncture, and spot urine samples were collected in the morning after an overnight fast. In cases where participants were infants or toddlers, blood samples were obtained after breastfeeding or a small breakfast. The biomarkers of interest included serum TSH, fT4, fT3, TG, and levels of antibodies, namely ATPOc and AhTGc, and UIC, the analyses were performed immediately in an ISO-certified institutional laboratory using validated routine methods. All parameters  were analyzed using chemiluminescence immunoassay automatically on the Siemens Atellica Solution system. For TG measurements, the electrochemiluminiscent elecsys TG assay on the Roche Cobas e411 analyzer was used. UIC was assessed using high-performance liquid chromatography. The reference values provided by the manufacturer were used for all parameters. Please see Table http://www.nutridatabaze.cz/), which is part of EuroFIR (https://www.eurofir.org/food-information/food-composition-databases/). If a food source that contained iodine was not listed in the Czech database, we searched for iodine content in common foods in the USDA, FDA, and ODS-NIH database for iodine content in common foods Release 2.0 . For products used solely in Europe, such as infant formula, which were not listed in the NutriDatabaze.cz, the dietitian registered the iodine content from the product packaging. We did not include iodine-containing supplements in this analysis of iodine intake. All the dietary data were calculated by a single dietitian. Please refer to Table We used a 3-day weighed dietary record method to evaluate the iodine intake of our VG, VN, and OM participants. Parents were responsible for weighing and documenting all food and beverage items consumed by their children over three consecutive days (two weekdays and one weekend day) using electronic kitchen scales. In instances where accurate weighing was not possible (such as eating out), household measures  and a photo booklet depicting child portion sizes of special VG and VN food were used to facilitate semi-quantitative recording. Any missing data were assessed by the study staff, who requested the necessary information from parents via electronic communication. Breast milk consumption was estimated based on maternal records and calculated using information that provides an estimate of the daily amount of milk a child could consume. The source of this information was the general recommendations for breast milk intake according to WHO and ESPHGAN . We manuThe participants' body height and body weight were measured three times using calibrated scales, and the average was calculated. The length of infants and toddlers was measured using an infantometer, while the heights of older children were measured with a stadiometer. The measurements were then transformed into percentiles using standard percentile graphs that have been validated for use in the Czech Republic  .p values were calculated to quantify differences between dietary and supplementation groups using the Wilcoxon signed-rank tests. Additionally, a simple linear regression model was employed to examine the relationship between log-transformed continuous values and diet type, with adjustments made for age and sex. For categorical variables, the chi-square test was used to calculate p values for differences between groups. To ensure the robustness of the findings, a sensitivity analysis was conducted by excluding participants diagnosed with AIT, as they displayed extreme values for TSH and antibody tests. The statistical analysis was done using RStudio software.Participants were stratified based on self-reporting into three dietary groups . For VG and VN, participants were further classified according to their iodine supplementation habits as supplementing or not supplementing. New categorical variables were created classifying the participants as “deficient” or “not deficient” based on fulfillment of the laboratory criteria for iodine deficiency using various laboratory markers. For descriptive analyses, mean and standard deviations were calculated for numerical variables, with median and IQR calculated for variables with skewed distribution, while proportions and absolute numbers were reported for categorical variables. To evaluate the association of the diets with iodine status, effect sizes and their corresponding 95% confidence intervals, and n = 222 children, i.e., n = 92 VG, n = 78 VN and n = 52 OM. The mean age of participants was 5.4 years (±4.3 years) for VG, 4.4 years (±5.5 years) for VN and 6.7 years (±5.6 years) for OM and ranged from 0.5 to 18.5 years overall. The median age was 4.0 years for VG, 2.0 years for VN and 4.5 years for OM. There were no significant differences in age observed between the VG and VN groups when compared to the OM group. However, a significant difference was found between the VN and OM groups (p < 0.05). Due to this uneven distribution, we made the decision to divide our study group into two subgroups: “pre-schoolers,” encompassing children aged 0.5–6 years, and “older children and adolescents,” consisting of children aged 6–18.5 years. In the pre-schoolers group, there were a total of n = 28 participants in the OM group, n = 54 participants in the VG group, and n = 60 participants overall. As for the older children and adolescents group, there were n = 24 participants in the OM group, n = 38 participants in the VG group, and n = 18 participants in the VN group. There were no significant differences in sex, height percentile, weight percentile between the groups, but we observed a higher number n = 7  of VN children with lower BMI i.e., below the 3rd percentile (p = 0.006). Please refer to Table The final sample consisted of n = 52) of OM children did not take any supplements with iodine content, followed by 83.7% (n = 77) VG and 78.2% (n = 61) VN children. We observed a significant difference in dosage of the supplementation between VN (26.54 µg/day ± 55.95)/VG (16.83 µg/day ± 41.82) group and OM (p < 0.001). There was a significant difference with moderate effect size in mean intake of iodine (without supplements) between VG (59.59 ± 82.18), with the highest intake and VN (33.86 µg/day ± 39.78) with the lowest intake (p < 0.001). When we examined the proportion of children who met the RDI as outlined in Table n = 2 OM (3.90%), n = 6 VG (6.70%) and n = 4 VN (5.2%) met the RDI through their dietary intake alone (p = 0.8). When we have performed the analysis with iodine supplements, the results were: n = 2 OM (3.90%), n = 14 VG (15.2%) and n = 12 VN (15.4%) (p = 0.09). For details on iodine intake, please see Tables According to our data 100% n = 5 of OM chp < 0.001). Lower values of UIC were observed in VN group compared to the VN group and particularly the OM group. The lowest (5.99 µg/l) but also the highest (991.80 µg/l) levels of UIC were measured in VN subjects. We identified n = 31 VN (41.9%), n = 31 VG (34.8%) and n = 10 OM (19.6%) children with iodine deficiency  (p = 0.06), out of which n = 8 VN, n = 11 VG and n = 2 OM with mild iodine deficiency  and n = 5 VN and n = 1 VG with severe iodine deficiency . In total the children with regular iodine supplement use had higher UIC; for children who did not take any supplements the median UIC was n = 75 VG 121 µg/l (75.2–208.50 µg/l) and n = 57 VN 101 µg/l (52.80–169.30 µg/l) compared to children who take the iodine supplements n = 14 VG 177.40 µg/l (149.85–268.73 µg/l) and n = 17 VN 231.90 µg/l (148.80–316.30 µg/l) (p < 0.001). The numbers of children with sufficient iodine concentration in morning spot urine were as follows: n = 43 (58.1%) in VN, n = 58 (65.2%) in VG, and n = 41 (80.4%) in OM (p = 0.03). The median UIC surpassed the World Health Organization (WHO) cut-off of 100 µg/l in all three groups. However, there is a noticeable trend of lower median UIC in the VN group, followed by the VG group, while the OM group exhibited the most favorable results. After stratifying the sample according to the age, a significant difference in urinary iodine concentration (UIC) persisted among the pre-schoolers, but it was not statistically significant among the older children and adolescents. This observation could be attributed to the reduction in sample size, potentially leading to a diminished impact on the results. There was also a significant difference in levels of fT4 with surprisingly highest levels in VN compared to OM (p < 0.001), this finding remains the same also after the division into two study groups only in pre-schoolers but is not significant in older children and adolescents. We found no significant differences in levels of TSH, fT3, or TG across the groups. To analyze the antibodies, we made the decision not to divide the group, as the criteria for determining positivity or negativity remained consistent across all age groups throughout the study duration. While there was no statistically significant difference in ATPOc positivity, the prevalence of AhTGc was significantly higher in the VG (18.2%)/VN (35.0%) groups compared to the OM group (2.1%) (p < 0.001), please refer to Table n = 1 VN, n = 2 VG and n = 1 OM children with AIT, these outliers were omitted in data analysis, for data visualization please refer to Fig. The differences in evaluated biomarkers are reported in table Tables –4. When The knowledge about the necessity of vitamin B12 supplementation is well-known among VN, and the prevalence of vitamin B12 deficiency is low as shown in a previous study  or VechiWe observed that (1) the mean daily intake of iodine are significantly lower among VN compared to OM and VG children from the Czech Republic; (2) these differences were mirrored by a lower median UIC among VN children compared to OM children but all of them were >100 µg/l; (3) there were statistical significant differences in levels of fT4 between the groups, but no differences in TSH, fT3 and TG; (4) the VG/VN group exhibited a higher prevalence of AhTGc positivity, the difference in ATPOc was not statistically significant.We observed the highest iodine intake among the VG group, lowest in the VN group, with intermediate intake levels in the OM control group. This is in partial agreement with other published studies where VN tend to have the lowest , 20 intaThe overconsumption of algae is linked with the risk of exceeding the limits for iodine daily intake which may lead to thyroid dysfunction. This has been described in some case reports , 14, whiEven though iodine intake and status among consumers of plant-based diet are not sufficiently examined, there are several studies among adults to suggest that VN and VG diets are risk factors for developing iodine deficiency. These studies consistently describe a lowest UIC among VN, and the highest among the OM , 25–29. In our study we observe a higher prevalence of positive titers of AhTGc in the VG/VN group; this laboratory parameter is a marker of incipient autoimmune thyroid disease but also described in some studies as a marker of insufficient iodine intake –32. The Diagnosing iodine deficiency has proven to be a complex task, even with an extensive range of laboratory tests, and has been a subject of scientific deliberation for a considerable period. In order to evaluate the iodine status of a specific population for epidemiological purposes, the recommended approach is to calculate the median UIC . Accordin = 7 VN children with BMI values below the 3rd percentile. Recently published studies performed in a population of children consuming plant-based diets showed that VG/VN children are smaller and slimmer [A secondary outcome of our analysis, apart from the main hypotheses on differences in iodine intake and status, is our description of a higher number of  slimmer –39. Some slimmer .Among other limitation of this study is that the sample was a convenience sample, which may introduce selection bias. Of note we observed a low recruitment response rate, particularly among adolescents and parents with alternative lifestyles. Furthermore, observation bias may have influenced our findings as the 3-day records were filled out by parents without any control. However, it is worth noting that previous research has shown that 3-day records can provide more accurate nutrient intake data compared to food frequency questionnaires, namely in VG and VN where FFQs tend to underestimate dietary intakes . The higWe found that Czech VN and VG children are at the potential risk of iodine deficiency when they rely only on natural sources of iodine, namely when consuming a strictly plant-based diet . We observed more children with lower UIC, and trends to lower median UI  in VN group than in VG or OM group. The positivity of AhTGc, an indicator of iodine deficiency, was more prevalent in VG/VN group. Only few children met the RDI via their diet. Regular use of iodine-containing supplements was associated with significantly higher levels of UIC. Apart from our focus on iodine intake and status, a higher number of VN children with lower BMI values, i.e., below the 3rd percentile, was observed. Our observations in a pediatric population concerning iodine saturation are in line with findings from other published studies predominantly carried out among adults [The current study provides valuable insights into the iodine status of Czech children consuming plant-based diets and highlights the need for further research and evidence-based discussion regarding medical guidelines and recommendations for this population in European countries. In light of our findings, we recommend avoiding excessive consumption of algae, using iodized table salt when preparing homemade meals, and considering regular iodine supplementation within the daily recommended dosage range by the WHO, which includes 80 µg/day for children aged 1–3 years, 90 µg/day for children aged 4–6 years, 120 µg/day for children aged 7–9 years, and 150 µg/day for older children. Additionally, the possibility of a wide-reaching iodization of plant-based dairy alternatives may be a topic for further discussion.Supplemental material


Our main outcome measures were hazard ratios (HRs) for incident obesity‐related cancers and relative excess risk due to interaction (RERI).We conducted a population‐based cohort  and with incident HTN . The association among those with incident T2DM was null . There was a positive additive interaction between overweight/obesity and CVD , meaning that the combined association was 0.19 more than the sum of the individual associations. In contrast, a RERI of −0.24  was observed for the combined association between overweight/obesity and T2DM.A total of 38,082 individuals developed obesity‐related cancers after a median of 8 years of follow‐up. The positive association between BMI and obesity‐related cancer risk was similar among individuals free of cardiometabolic conditions  higher relative risk of obesity‐related cancers among “healthy” individuals and a 5% higher relative risk among those with HTN . The HRs for the remaining transitions were as follows: CVD , HTN, T2DM, & CVD , HTN & CVD , T2DM & CVD , HTN & T2DM  and T2DM  (in descending order of effect size). Our secondary aim was to study the joint associations of overweight/obesity and incident cardiometabolic conditions with obesity‐related cancer risk.22.1www.sidiap.org) in Catalonia, Spain. The SIDIAP is a pseudo‐anonymized database of EHRs containing data from 5.8 million people living in Catalonia since 2006. This database covers >75% of the population of Catalonia and is representative of the overall population in terms of age, sex, and geographic distribution.We conducted a population‐based cohort study from January 1, 2010 to December 31, 2018 using prospectively collected primary care records from the Information System for Research in Primary Care . We excluded participants who had been registered in the database for less than one year, who had been diagnosed with any cancer type , HTN, T2DM, and/or CVD prior to index date, and those with less than 12 months follow‐up  from squamous cell carcinoma or gastric cardia (obesity‐related) from non‐cardia cancers. The incidence of the non‐obesity‐related subtypes/subsites of these cancers is higher in Spain.In a previous study comparing cancer diagnoses registered in the SIDIAP including the CMBD to those of provincial population‐based registries of Catalonia, the sensitivity for obesity‐related cancers ranged between 74.6 (70.2–79.0) for thyroid cancer and 90.4 (89.4–91.4) for breast postmenopausal .2.42) was calculated using weight (kg) and height (cm) assessed in a standardized manner by general practitioners or nurses.BMI (continuous in kg/m2) and cardiometabolic conditions, coded as a time‐varying variable with eight categories . All study participants were in the “healthy” category at index date, and during follow‐up, they could change states as shown in Figure For our secondary objective, the exposure was a composite variable of 16 categories combining binary BMI < or ≥ 25 kg/m5 kg/m2 a2.5female, male), age (in years and 5‐year categories) at index date (and updated at a diagnosis of a cardiometabolic condition), geographic region of nationality ,Mortalidad en áreas pequeñas Españolas y Desigualdades Socioeconómicas y Ambientales (MEDEA) deprivation index .never, former, or current smoker) and alcohol intake  (the closest record to the index date within 5 years before or at the index date was selected).The covariates were sex .2 increment of BMI. We evaluated multiplicative interaction between BMI and cardiometabolic conditions by comparing the log‐likelihood of models with and without the interaction term. We checked the proportional hazards assumptions by visual inspection of survival curves. We conducted two supplementary analyses to contextualize our findings: stratification of the results by sex and age groups (aged <65 or ≥65 years) to assess potential effect modification and re‐running the main model analyzing site‐specific cancers (with ≥100 cancer cases) as outcomes. As sensitivity analyses, we re‐run the main model (i) without updating the BMI and age of participants, and without imputed BMI by (ii) including only individuals with a real BMI assessment at baseline (iii) or also during follow‐up. We (iv) added as an adjustment variable the number of visits to primary care centers (year before study entry or upon diagnosis of cardiometabolic condition[s]) to account for potentially different health attitudes of the participants. Results of these sensitivity analyses (i) to (iv) are shown in Figure To investigate if incident HTN, T2DM, or CVD modify the association between BMI and obesity‐related cancer risk, we fitted Cox proportional hazards models with age as the time metric including BMI, cardiometabolic conditions as a time‐varying variable, and an interaction of those with BMI. We fitted two models, a model adjusted by sex and stratified by age at index date (5‐year categories)  and one further adjusted by nationality, MEDEA deprivation index, smoking status, and alcohol intake (fully adjusted model). We used a directed acyclic graph (DAG) to guide our decisions on the control for confounding Figure .23 Mis2) and incident cardiometabolic conditions .RERIRR=RR11−RR10−RR01+1, where 11 denotes being exposed to both factors , 10 to one factor , and 01 to the other one . A RERI of 0 was considered a lack of additive interaction and 95% CIs were calculated as proposed by Hosmer and Lemeshow.2 and the condition of interest) from the expected joint association .For our secondary aim, we assessed the relative excess risk due to interaction (RERI) of obesity‐related cancers between overweight/obesity  and obesity‐related cancers separately . For this analysis, we also explored potential residual confounding by smoking and collider bias (among never smokers and non‐obesity‐related cancers as the outcome).We used R version 4.0.1 for all the analyses. We obtained approval from the Clinical Research Ethics Committee of the IDIAPJGol (project code: 20/237‐P) to perform this study.3n = 3,097,073) was older and presented with more comorbidities and complete information on the covariates of interest than the study population .There were 3,097,073 adults aged ≥40 years at index date eligible to enter the study. We excluded 144,772 individuals due to having less than 1 year of prior clinical history; 1,133,231 due to prevalent cancer, HTN, T2DM, or CVD; and 44,166 due to less than 1 year of follow‐up . However, those without any BMI measurement had a higher representation of males, non‐Spanish individuals, individuals living in the least deprived areas, had fewer comorbidities, and were more frequently transferred out of SIDIAP than those with a BMI assessment at baseline.Of the 1,774,904 study participants, 681,386 (39%) had a BMI assessment at baseline, 589,319 (33%) had at least one BMI assessment during follow‐up and 504,199 (28%) did not have any BMI assessment Table . Age was2 (after multiple imputations), the median age was 51 (44–60) years and 53% were females  kg/mhealthy”) study participants, 38,082 (2.1%) were diagnosed with obesity‐related cancers  in the association with obesity‐related cancers  higher relative risk of obesity‐related cancers among “healthy” individuals and 5% higher among those with HTN  , HTN, T2DM, & CVD , HTN & CVD , and T2DM & CVD  (in descending order of estimates) were positive, they overlapped with 1. We did not find evidence of interaction between these and BMI  suggesting no differences in relative risks as compared to “healthy” individuals. In contrast, the HRs for associations for those with T2DM  and HTN & T2DM  were attenuated . The risk estimates in the minimally adjusted models were similar to those of the fully adjusted models  Figure . Even ths Figure . The ress Figure .3.22 and without a cardiometabolic condition , participants with overweight/obesity (BMI≥25 kg/m2) and with or without a cardiometabolic condition had a consistently higher relative risk of obesity‐related cancer  was 2.87 (95% CI: 2.82–2.92) per 1000 person‐years among those with a BMI < 25 kg/m2 and 3.16 (3.12–3.20) among those with a BMI≥25 kg/m2  and CVD was 0.19 times larger than the sum of the separate associations. This translated into 53 (31 to 71) additional obesity‐related cancer cases per 100,000 person‐years among adults with a BMI≥25 kg/m2 and CVD as compared to those with a BMI≥25 kg/m2 without CVD. If confirmed in subsequent studies, these findings are important to guide public health interventions for overweight/obesity prevention because population subgroups that are jointly exposed to a BMI≥25 kg/m2 and a CVD would obtain a greater risk reduction of obesity‐related cancer than would others. In contrast, among individuals with T2DM, a higher BMI was not associated with an increased risk of obesity‐related cancer. The joint association of overweight/obesity and T2DM was associated with fewer than expected obesity‐related cancer cases . However, the IR among those with T2DM was still more than double than that of those without overweight/obesity and T2DM.In this cohort study of 1,774,904 adults in Catalonia, we found that a higher BMI increased the risk of obesity‐related cancer similarly among individuals free of cardiometabolic conditions and those with healthy” individuals is in line with well‐established evidence.healthy” population did not include individuals with HTN, T2DM, and CVD by definition, our results support the existence of pathways between body fatness and cancer risk independent of these conditions.The positive association between BMI and obesity‐related cancers among “HTN. This observation could be explained by an independent (from HTN) pathway between BMI and cancer risk. Mechanistic pathways that are implicated in the pathogenesis of HTN, T2DM, or CVD—such as chronic inflammation—can also drive cancer progression. However, for HTN and CVD, independent mechanisms of those from obesity have been proposed. For example, hypoxia due to HTN may promote cancer development in the kidneys,Our results revealed that the BMI‐obesity‐related cancer association still remains present among individuals with an incident diagnosis of This study has several strengths. The size of SIDIAP offered the possibility to reliably address our research question, whereas traditional cohort studies may often lack statistical power to detect interactions and investigate the co‐occurrence of incident morbidities longitudinally. Furthermore, individuals included in SIDIAP are representative of the general population living in Catalonia in terms of age, sex, and geographic distribution which supports the external validity of these findings.Our findings should be interpreted in light of some limitations. First, data missingness was an important constraint regarding our exposure assessment given that there was a high proportion of individuals who did not have a BMI assessment at baseline. We pre‐specified in our statistical analysis plan that we would deal with missing data using multiple imputations as this method accounts for the uncertainty associated with missing data. We used information from any recording in the individuals' health records  for the time‐raster multiple imputations; however, 28% of the study participants did not have any BMI assessment which likely introduced high variability in their BMI estimations among the imputed datasets. Further, the decision to assess BMI in primary care could be related to the patient's apparent weight or their health status. Nevertheless, we already reported in the methods section that the distribution of BMI in SIDIAP was similar to population‐based surveys of the Spanish population suggesting that those with BMI information in SIDIAP were not substantially different in terms of BMI to the broader population.In this large Southern European study, we found that a higher BMI increased the risk of obesity‐related cancers similarly among adults free of cardiometabolic conditions and among those with HTN, but not among adults with T2DM. Furthermore, individuals with both overweight/obesity and CVD accounted for the highest number of obesity‐related cancer cases compared to those with other individual or combined cardiometabolic conditions. Our findings reinforce the need for public health strategies focusing on the reduction of overweight and obesity for cancer prevention among the general population, but also among population groups with HTN or CVD.Martina Recalde: Conceptualization ; data curation ; formal analysis ; writing – original draft . Andrea Pistillo: Data curation ; formal analysis (supporting); software ; writing – review and editing (supporting). Vivian Viallon: Formal analysis (supporting); writing – review and editing (supporting). Emma Fontvieille: Investigation (supporting); methodology (supporting); writing – review and editing (supporting). Talita Duarte‐Salles: Conceptualization ; data curation (supporting); funding acquisition ; supervision ; writing – review and editing . Heinz Freisling: Conceptualization ; funding acquisition ; methodology ; supervision ; writing – review and editing .Funding [grant number: IIG_2019_1978] was obtained from World Cancer Research Fund (UK), as part of the World Cancer Research Fund International grant program. MR was also funded by Wereld Kanker Onderzoek Fonds , as part of the international grants from the World Cancer Research Fund. TDS acknowledges receiving financial support from the Instituto de Salud Carlos III . The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.The authors declare no conflicts of interest.We obtained approval from the Clinical Research Ethics Committee of the IDIAPJGol (project code: 20/237‐P) to perform this study.The analytical code used in this study is available upon request.Where authors are identified as personnel of the International Agency for Research on Cancer and World Health Organization, the authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer and World Health Organization.Data S1.Click here for additional data file.


Age‐dependent Ca2+‐mediated changes in intrinsic excitability, synaptic plasticity, and activity have helped identify some of the mechanisms engaged in memory and cognitive decline based on work done mostly at the single‐cell level and in the slice preparation. Recently, our lab identified age‐ and Ca2+‐related neuronal network dysregulation in the cortex of the anesthetized animal. Still, investigations in the awake animal are needed to test the generalizability of the Ca2+ hypothesis of brain aging. Here, we used in vigilo two‐photon imaging in ambulating mice, to image GCaMP8f in the primary somatosensory cortex (S1), during ambulation and at rest. We investigated aging‐ and sex‐related changes in neuronal networks in the C56BL/6J mouse. Following imaging, gait behavior was characterized to test for changes in locomotor stability. During ambulation, in both young adult and aged mice, an increase in network connectivity and synchronicity was noted. An age‐dependent increase in synchronicity was seen in ambulating aged males only. Additionally, females displayed increases in the number of active neurons, Ca2+ transients, and neuronal activity compared to males, particularly during ambulation. These results suggest S1 Ca2+ dynamics and network synchronicity are likely contributors of locomotor stability. We believe this work raises awareness of age‐ and sex‐dependent alterations in S1 neuronal networks, perhaps underlying the increase in falls with age.Over the past 30 years, the calcium  and characterize neuronal networks. During ambulation, in both young adult and aged mice, an increase in network connectivity and synchronicity was noted. An age‐dependent increase in synchronicity was seen in ambulating aged males only. Additionally, females displayed increases in the number of active neurons, Ca2+ transients, and neuronal activity compared to males, particularly during ambulation.Here, we used  Additionally, we investigated all measures in both males and females to elucidate potential sex differences in neuronal network and behavioral phenotypes. In this study, we test the hypothesis that alterations in S1 neuronal networks in ambulating mice impact distinct patterns of gait behavior, and these alterations differ across both age and sex.Here, and for the first time, we used 22.1n = 11, female n = 12) and aged  C57BL/6J mice were received from The Jackson Laboratory . On Week 1, animals underwent adeno‐associated virus (AAV) injection and installation of a chronic cranial window and headplate. During Weeks 2–4, animals were allowed to recover. On Week 5, animals were tested using a grip strength meter to measure forelimb and hind limb grip strengths. On Week 6, animals were habituated to the Neurotar Mobile HomeCage and 2P room and trained to ambulate during head‐fixation. On Week 7, following 6 weeks of GCaMP8f expression, 2P neuronal Ca2+ imaging in S1 cortex was obtained. On Week 8, gait parameters were obtained while animals ambulated on the 3‐plane visualization walking task apparatus.The work presented here strictly adheres to our Institutional Animal Care and Use Committee protocol. On Week 0, young adult  carrying the neuron‐specific Ca2+ indicator GCaMP8f . Briefly, animals were anesthetized (1.5%–3.5% isoflurane) and placed on a warming pad (37°C), then head‐fixed to a stereotaxic frame  using non‐penetrating ear bars. Artificial tears  were placed on each eye and 0.5 mL warm sterile saline was administered subcutaneously. PhysioSuite (Kent Scientific Corporation) was used to monitor physiological outcomes  and to control the warming pad. Hair above the skull was wetted with an alcohol pad, then shaved using a sterile scalpel. After application of alcohol and povidone‐iodine to the shaved skin, a circular piece of tissue was removed around the boundaries of the scalp to expose the skull. A motorized hand drill  was then used to remove the periosteum from the bone, followed by application of acetone to degrease the skull. Bonding agent  was applied to cover the skull and cured by UV light . A 3‐mm diameter hole was then drilled stereotaxically  on either the left or right skull directly above S1 . The space above the brain was irrigated with cold saline following removal of the 3‐mm bone flap. Advancement/retraction speed of the drill was set to 1.0 mm/min. AAV injection (0.25 μL at 0.2 μL/min) was accomplished  using a Hamilton® syringe with a 30° bevel. A 4‐mm circular glass coverslip  and stainless steel headplate  were attached to the bone using light‐curing dental cement . Following this, animals received subcutaneous injections of meloxicam (10 mg/kg) and buprenorphine (0.2 mg/kg) and were temporarily placed in a heated cage for recovery. Once fully awake and ambulating normally, animals were returned to their home cage. Postoperative inflammation was controlled by re‐administration of subcutaneous meloxicam (10 mg/kg) 24 h following surgery. Animals recovered for 3 weeks to allow inflammation reduction and adequate expression of GCaMP8f.After 1 week of acclimation to vivarium conditions, animals received aseptic injections of AAV .To test neuromuscular function between age groups, all animals underwent grip strength testing of forelimb and hindlimbs using a digital grip strength meter . For forelimb measures, the mouse was lowered over a horizontal grid, allowing only its forepaws to attach, then gently pulled back by the tail until a maximum grip strength value was recorded. For hind limb measures, the mouse was lowered over a grid with a 30°C downward tilt, then measuring the maximum force when the animal pushed off to jump from the grid. For all limbs combined, the mouse was lowered over the horizontal grid, allowing both the forepaws and hindpaws to attach, then gently pulled back by the tail until a maximum grip strength value was recorded. All measures were repeated three times per animal, then averaged to yield one value per measurement per animal. We present grip strength measures collected from 36 GCaMP8f‐treated animals . Six days prior to 2P imaging, animals were habituated to the environment in the 2P imaging room for 2 days, then handled to allow habituation to brief head‐fixation (using fingers to grab headbar for periods of 5 s) for an additional 2 days. Animals then underwent acclimation to 20‐min periods of head‐fixation in the Neurotar environment for 2 days prior to imaging, in which they could navigate the donut maze Figure . Animals2.52P imaging was accomplished in layers 2/3 of S1 (~200 μm below the dura) using a Scientifica Hyperscope  equipped with scanning mirrors , a large back aperture objective , and a GaAsP detector mounted inside a multiphoton detection chamber (MDUXL) housing dichroic and infrared blocking filters. All hardware and data acquisition were controlled by ScanImage  running under MATLAB . GCaMP8f was excited at 930 nm using an InSight X3 dual‐wavelength femtosecond‐pulsed laser (Spectra‐Physics). Image acquisition (512 × 512 pixels at 30 Hz) was accomplished during ambulation and rest. Velocity of animal movement was collected simultaneously using a locomotion tracking software . One field of view (FOV) was randomly selected within the boundaries of the hind limb area of S1 , and was stored for further analysis.2.6F/F Ca2+ analysis, regions of interest (ROIs) of individual neurons were selected using adaptive thresholding (sensitivity at 0.35) to binarize potential ROIs followed by size (>60 pixel) and shape (0–0.9 eccentricity) filters to remove any undersized or irregularly shaped ROIs, as these likely represent imaging artifacts or cellular debris. All FOVs were checked for the presence of clear, morphologically distinct single neurons. The thresholded and filtered image was then used to extract raw GCaMP8f signal intensities across time (traces) for all ROIs in the FOV. Across all 2P Ca2+ data, we analyzed a total of 564 neurons from seven young males, 423 neurons from five aged males, 574 neurons from six young females, and 498 neurons from five aged females.Signal processing and data extraction were accomplished using a custom MATLAB pipeline as previously described  for each pair of neurons across time. We initially selected a thresholded CC value of >0.4, allowing us to select for pairs of neurons with 40% coinciding activity (considered an active connection). Connectivity was determined as the number of active connections per mm2 and connection length was defined as the average distance (in μm) between each pair. Network synchronicity was derived by calculating a ratio of the number of events divided by the total number of possible events for each neuron and is provided as a percentage of the time. To calculate the total number of possible events, we used the maximum of each frequency range. For instance, to analyze 1–2 Hz range, we multiply 2 Hz by the length of the period  to yield the number of total possible events per neuron . We report network measures collected from 23 GCaMP8f‐treated animals .For each ROI, the raw trace underwent a continuous wavelet transform (CWT) using a Morse wavelet to extract power across frequencies (0.05–14 Hz). The peak magnitude within discrete frequency bands  of the CWT spectrum was then determined for each frequency band across time, then binarized. An event was identified if the power was >2 times the standard deviation from the mean power, providing a dataset of ones (events) and zeroes (non‐events) across time. Measures of active neurons were calculated by dividing the number of neurons with at least one event during ambulation in each FOV by the FOV's area in mm2.82+ transients were then characterized for measures of neuronal activity (# of events detected per second), area‐under‐curve, rise‐time, and decay‐time constants. Each trace was normalized to resting fluorescence values (%ΔF/F). One young female displayed values on measures of area‐under‐curve .For ΔF/F analysis, the threshold for an event in each ROI was set to >2 times the standard deviation from the mean GCaMP8f intensity during resting . Once identified, Cae Figure  reported2.9n = 8, aged male n = 6, young female n = 10, aged female n = 7).For comparison of gait across age, animals were recorded using a digital camera (3840 × 2160 pixels at 25 Hz) ambulating across a 3‐plane visualization walking task built in‐house  between step‐to‐step paw placement for each paw across four steps; stride time deviation—the standard deviation of time (in seconds) between step‐to‐step paw placements for each paw across four steps.We used previously published methods , suggesting that forelimbs and hindlimbs contribute differentially to overall limb strength. We specifically did not normalize these data to body weight and report the numbers in Newtons as the females  were much smaller  than males , which would have artificially inflated their measures of grip strength compared to male, if normalized to body weight , connectivity , and synchronicity  engaged during movement. Additionally, a main effect of locomotion status was noted with increased measures of connectivity  and synchronicity , showing that these values are increased during ambulation. Interestingly, a significant effect of age was noted for measures of synchronicity , where aged males showed increased synchronicity during ambulation only. From these measures  and frequency  on measures of connectivity, showing that during ambulation, there is an increase in connectivity in the lower frequency domains (0.1–2 Hz). Additionally, a main effect of locomotion , frequency , and age, carried mostly by males , was noted for measures of synchronicity. As previously reported in other brain regions, we show that irrespective of frequency, aged males display greater connection length  in the activated network of S1, compared to young males. This result is in line with the concept that with aging, larger receptive field size as well as reduced place cell specificity are seen  and investigation of the alignment between ambulatory behavior and network activity across these frequencies Figure . Here, wF1,10 = 5.565, p = 0.04), while females appeared to show decreases in synchronicity with age . These effects only appear during periods of ambulation as post hoc analyses revealed that none of the measures presented in Figure We used neuronal network analyses during ambulation across different substrates as a proxy to measures of tactile discrimination and proprioception. Specifically, a flat surface, or plastic mesh with 3‐mm, 4‐mm, or 5‐mm spacing , area‐under‐curve , rise time , and decay time . We also saw a significant main effect of sex for measures of neuronal activity  and area‐under‐curve . The alignment between these two methods of analyses further translated to the directionality of the effect, where females showed an increase in the number of active neurons or events/s  and stride time deviance , highlighting that aged animals ambulate slower and exhibit impaired gait rhythm. Furthermore, a main effect of sex was noted for measures of stride time deviance  and stride length , where females show less deviance and slight increases in stride length compared to males. We detected a main effect of surface on measures of velocity , suggesting this behavioral task is able to identify surfaces that present with greater challenges for the aged animals.Following 2P neuronal Ca42+ dynamics across sex in young (4 months) and aged (22 months) ambulating C57BL/6J mice using two methods (CWT and ΔF/F). The work includes over 2000 active neurons imaged across four groups , and reports on changes in layers 2/3 of S1 while investigating the relationship between Ca2+ dynamics and gait dysregulation. We specifically tested the hypothesis that age‐ and sex‐dependent alterations in S1 neuronal networks are present in the awake, ambulating mouse during behavioral engagement when measured across tens of neurons communicating in local circuits.This study examined neuronal CaOur results align well with previously published work using different methods in awake animals showing cortical signals spontaneously oscillating in the lower delta wave range  compared to quiet wakefulness  across hundreds of individual neurons. Importantly, and in alignment with others, we report on dynamic changes in neuronal network communication during ambulation compared to rest, including increases in connectivity and synchronicity. Of note, recent work has highlighted the importance of thalamic input on regulating cortical excitability , we are able to detect similar results of sex‐ and locomotion‐dependent increases in neuronal activity  were identified as regulators of cellular excitability  and conditions  do not translate well to the in vigilo condition. Clearly, future studies are needed to identify the sources of neuronal Ca2+ dysregulation with age, perhaps with a focus on local pharmacological intervention  in the awake, behaving animal. Together, these future lines of investigation may provide insight on potential therapeutic targets that can help offset gait dysregulation and fall risk in the older adult population.Here, we tested the Cal Figure . Indeed,Authors S.L.C. and O.T. conceptualized and wrote the manuscript. Authors S.L.C. and R‐L.L. acquired all data and analyzed results presented in the figures. Author R.‐L.L provided editorial feedback on the manuscript.The authors declare no competing financial interests.Figures S1–S2Click here for additional data file.