Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low.There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer.+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values.Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small‐molecule inhibitors, immune checkpoint inhibitors, and platinum‐based chemotherapy drugs. The expression and survival information of 352 tumor samples from TCGA‐OV dataset, which included no normal samples, was used for subsequent analyses. Because the control group for a specific comparative analysis is different, the control and experimental groups in each comparative analysis are described in detail in the part “Result” or figures. For example, in Section The prediction model was verified using the GSE132342 dataset from the Gene Expression Omnibus (GEO) database. The expression data of this dataset were determined using the GPL26748 platform.Validation cohort data were processed as follows: (1) deletion of no‐load probe; (2) deletion of probes corresponding to multiple genes; (3) calculation of median expression values for multiple probes corresponding to the same gene.Homo sapiens were selected from the Kyoto Encyclopedia of Genes and Genomes  website. In total, 160 necroptosis‐associated genes were extracted after ENTREZID transformation.Map0421 necroptosis pathways in 2.2GSEA was used to calculate whether selected gene sets differed significantly between the experimental and control groups. The enrichment of necroptosis‐associated genes in patients with different clinical features was analyzed using the R “clusterProfiler” package. CNVs are copy number amplifications and deletions in genomic regions larger than 1 Kilobase (kb). The frequency of CNV refers to the proportion of the number of samples in which CNV occurs to the total sample. For example, the frequency of GAIN for H2AC18 gene was calculated as follow: 76 (samples with copy number amplification in genomic regions larger than 1 kb)/258  = 0.2946. The frequency of LOSS for H2AC18 gene was calculated as follow: 8 (samples with copy number deletion in genomic regions larger than 1 kb)/258 = 0.031. A CNV was considered common if its population frequency is >1%. The “maftools” package was utilized to analyze and visualize mutations in necroptosis‐associated genes in TCGA‐OV samples.2.3The interactions among necroptosis‐associated genes were investigated in the STRING database. A PPI network was generated, and a network diagram was generated using Cytoscape (v.3.9.1).2.4p < .05. TCGA‐OV tumor samples were classified based on these core genes. These samples were subjected to consistent clustering analysis using the “ConsensusClusterPlus” package with Euclidean distance and the pam clustering method . The Kaplan–Meier (KM) method was used to generate the prognostic survival curves for different ovarian cancer subtypes. The log‐rank test was utilized to evaluate the significance of differences between these curves.Univariate Cox regression analysis was performed on the 160 necroptosis‐associated genes using the R “survival” package. Core genes were screened using a threshold of p < .05 were considered statistically significant.Gene set variation analysis (GSVA) for different ovarian cancer subtypes was performed using the “msigdb.v7.4. symbols.gmt” gene set, which was extracted from the MSigDB database. Results with 2.5p < 0.05. Specially, log2(FC) > 0.585 means up‐regulation, while log2(FC) < −0.585 suggests down‐regulation. The “ClusterProfiler” package was used to conduct KEGG and GO analyses of the DEGs with the following parameters: pvalueCutoff = 0.05, pAdjustMethod = “BH,” and qvalueCutoff = 0.5. The top 10 pathways were visualized.Differential patterns of expression between the two ovarian cancer subtypes were analyzed using the R “limma” package. DEGs were screened according to these parameters: log2 > 0.585 (or < −0.585) and 2.6TCGA‐OV samples were re‐clustered based on the identified DEGs using the R “ConsensusClusterPlus” package with Euclidean distances and the k‐means clustering algorithm . The R “survival” package was used to analyze survival in the re‐clustered subtypes, and “ggplot2” was used to draw box plots of differences in the expression of necroptosis‐related genes.2.7p < .01. Next, the NSS was constructed using principal component analysis (PCA) based on the prognosis‐related genes. Principal components 1 and 2 were feature scores. NSS was developed using the genes with the highest correlation (or anti‐correlation) in the gene set, thus reducing the contribution weight of genes with other unrelated members of the gene set. The following formula was used:i represents individual tumor samples.To quantify necroptosis for individual tumors, we constructed a scoring system named “NSS.” First, univariate Cox regression analysis was performed, aiming to discover the hazard ratio (HR) and prognostic significance of DEGs. Prognosis‐related genes were defined as those with Ovarian cancer samples were grouped into different groups based on the median values by NSS, and the survival status of patients was analyzed. Univariate and multivariate Cox regression analyses were performed to explore the independent prognostic value of the NSS. The same formula was used to calculate the NSS in the validation dataset.2.8The CIBERSORT algorithm of the “IOBR” package was utilized to calculate immune cell fractions in the tumor microenvironment, with perm = 200. The matrix score, immune score, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) score, and tumor purity were calculated using the R “ESTIMATE” package.2.950 value of a drug in different NSS groups, which can be used to evaluate the sensitivity of an antitumor drug. Spearman's correlation was calculated between the NSS values and the IC50 of drugs.The genomics study of cancer drug sensitivity (GDSC) database data were analyzed using the R‐encapsulated oncoPredict to assess the IC2.10p > .05, * = p ≤ .05, ** = p ≤ .01, *** = p ≤ .001, and **** = p ≤ .0001. Survival curves were generated using the KM method. The significance of differences was evaluated using the log‐rank test.All statistical analyses were performed using R (v.4.1.2). To determine the significance of differences in expression levels, infiltration ratios, and various eigenvalues, the Wilcoxon rank‐sum test was used to compare two groups of samples, and the Kruskal–Wallis test was used to compare multiple groups of samples. In all figures, significant differences are indicated as follows: ns = 33.1JAK3, NLRP3, DNM1L, RNF31, PARP4, PLA2G4C, PYGL, and so on  was performed to analyze changes in HALLMARK pathways in the TCGA‐OV cohort. Forty‐two of the fifty pathways showed significant differences between groups with high or low NSS values Figure . The NSS3.750 values for various drugs in the TCGA‐OV samples. Spearman's correlation analysis between the NSS and log2(IC50) of drugs suggested that drugs, such as sepatronium bromide and selumetinib, had a negative correlation with the NSS, whereas drugs, such as nilotinib and oxaliplatin, showed a positive correlation with the NSS  value , we combined transcriptomic data with predicted ICS Figure . These dS Figure . We thenS Figure . No signS Figure ; howeverS Figure . Besidesb Figure , and same Figure . In the e Figure . Therefo4Ovarian cancer is a common gynecological cancer that is difficult to treat. The overall survival rate remains low as there is currently a lack of effective biomarkers for predicting the prognosis or outcomes of patients. In this study, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer.STAT1, CASP8, and IFNB1, and provided a preliminary analysis of their interactions. GSEA demonstrated that some essential pathways are involved in necroptosis in ovarian cancer, such as DNA packaging, protein‐DNA complex assembly, chromatin remodeling and organization, functional changes in organelles, cellular adhesion, the recruitment of immune cells, and oxidative phosphorylation. These abnormally activated pathways may therefore be promising targets for drug intervention research.Previous evidence has suggested that necroptosis is essential in tumorigenesis, tumor progression, and tumor immunity; however, the overall effect of necroptosis in cancer has not yet been determined and may depend on the cancer type and disease stage. For instance, necroptosis was detected in areas of tumor necrosis and is thought to play a critical role in tumor growth and metastasis.UBD, ISG20, CXCL11, HLA‐DOB, and ATP1A3) was constructed using least absolute shrinkage and selection operator regression and could predict the survival and prognosis of patients with ovarian cancer, while also revealing the immune microenvironment to a certain extent.Analyzing the expression of necroptotic genes in large samples to establish prognosis‐related gene signatures is important for the identification of regulatory patterns, molecular typing of tumors, guiding therapeutic options, and predicting therapeutic effects. For example, a novel necroptosis‐related gene signature established in gastric cancer was able to predict early diagnosis, prognosis, and the response to immunotherapy.+ T cell cross‐priming, dendritic cell maturation, cytotoxic T cell cross‐priming, and IFN‐γ production in response to tumor antigen stimulation, which play essential roles in antitumor immunity.The close interaction between necroptosis and immune regulation has made necroptosis‐mediated anti‐tumor immune response the focus of research in recent years, although its specific regulatory mechanisms remain unclear. Necroptosis can induce CD8Despite these notable findings, our study has some limitations. First, we were unable to validate the key necroptosis‐related genes in clinical samples of ovarian cancer. Second, the specific biological functions associated with necroptosis require further exploration and verification using in vivo and in vitro experiments. Third, the sample size of the cohort used to judge the efficacy of immunotherapy and sensitivity of PARP inhibitors was small. Therefore, it is necessary to conduct validation studies using a larger sample size to verify the utility of the NSS for selecting novel therapies or combination therapies.Conceptualization: Shuqin Chen and Huiling Lai; Methodology: Guofen Yang; Software: Yunyun Guo and Linxiang Wu.; Validation: Aligu Yusufu; Formal analysis: Qiyu Zhong, Zhouzhou Liao and Jianyu Ma; Writing—original draft preparation, Huiling Lai and Wen Shi; Writing—review and editing: Huiling Lai and Shuqin Chen. All authors have read and agreed to the published version of the manuscript.The authors declare no conflicts of interest.This research does not include any human or animal studies or studies that require the approval of an institutional review board.FIGURE S1. ROC curves for NSS in the training and validation sets.Click here for additional data file.FIGURE S2. Comparison of immune cell infiltration scores in the two subtypes.Click here for additional data file.FIGURE S3. NSS differences for different clinical characteristics.Click here for additional data file.FIGURE S4. Correlation between NSS and different HALLMARK pathways.Click here for additional data file.


DiabetesWise is an unbranded, data‐driven online resource that tailors device recommendations based on preferences and priorities of people with insulin‐requiring diabetes. The objective of this study is to examine whether DiabetesWise increases uptake of diabetes devices, which are empirically supported to improve glycemic and psychosocial outcomes.age = 37.1, SD = 9.73; 66% female; 81% type 1 diabetes) with insulin‐requiring diabetes and minimal diabetes device use at enrollment. Participants used DiabetesWise and completed online surveys. Chi‐square and t tests evaluated requests for a device prescription, receiving a prescription, and starting a new device at 1 and 3 months post use. Baseline predictors of these variables and past use of continuous glucose monitors (CGMs) and changes in diabetes distress post use were also examined.The sample included 458 participants (Mt(343) = −3.13, p = .002) was the only factor associated with asking for a prescription. Diabetes distress decreased after interacting with DiabetesWise within 1 month (t(193) = 3.51, p < .001) and 3 months (t(180) = 5.23, p < .001).Within the first month of interacting with DiabetesWise 19% of participants asked for a prescription for a diabetes device. This rate rose to 31% in the first 3 months. These requests resulted in 16% of the sample starting a new device within the first 3 months. Whereas several factors were associated with prior CGM use, receiving a prescription, and starting a new device, more diabetes distress (Within 3 months of interacting with DiabetesWise, one in three participants had requested a prescription for a new diabetes device and average distress levels were reduced, indicating benefit from this low‐intensity online platform. HighlightsAbout a third of participants requested diabetes device prescriptions with 3 months of being invited to interact with DiabetesWise.Many factors predicted prior CGM use but diabetes distress was the only predictor of requesting a prescription after interacting with DiabetesWise.Diabetes distress decreased over time after interacting with DiabetesWise. It was The current study sought to evaluate whether DiabetesWise promotes steps toward diabetes technology uptake among its users. To this end, we examined (1) whether interacting with DiabetesWise led to action toward device uptake, (2) factors associated with device uptake after interacting with DiabetesWise, and (3) changes in diabetes distress following engagement with DiabetesWise. By understanding these factors and processes, we hope to evaluate the potential effects of interacting with DiabetesWise and expand our empirical understanding of personal and contextual barriers and facilitators to diabetes device uptake.2Potential participants were recruited through online postings on social media. Inclusion criteria were at least 18 years of age, diagnosis of type 1 or type 2 diabetes, using insulin, and US resident. Exclusion criteria were current consistent CGM use (at least 5 days per week) and/or current closed loop or automated insulin delivery system use at the time of enrollment. If eligible, participants were directed to online documents to review and provide consent prior to study enrollment. Once enrolled, participants completed an online baseline survey, completed at‐home blood collection to measure HbA1c, and received instructions to access DiabetesWise. For this study, the public facing DiabetesWise website was cloned and study access required an initial login and password, which were provided by the study coordinator. Only study participants could access the cloned website, which permitted tracking of activity on the website. Follow‐up surveys were administered monthly for 3 months. As previously described, the platform provided a Check Up  they asked a provider for a prescription for a diabetes device/system; (2) they received a new prescription for a diabetes device/system from their provider, and (3) they started using a new diabetes device/system. They also reported on the type of new device/system  they had started using, if any. These variables were captured through surveys administered monthly after interacting with DiabetesWise.Diabetes distress was measured by the 28‐item Type 1 Diabetes Distress Scale (T1‐DDS),2.2t tests evaluated categorical and continuous factors, respectively associated with past CGM use and requests for and receipts of prescriptions as well as new device uptake. Paired t tests were used to compare diabetes distress at follow‐ups with baseline levels of distress. All analyses were conducted in SPSS version 27 using pairwise deletion to handle missing data. Power analyses revealed that the current sample was statistically powered to detect even a small (d = 0.33) effect size.Chi‐square and 3The current sample included 458 participants Table . All par3.1χ2 (1)=6.03, p = .014), younger , p < .001), have type 1 diabetes =23.54, p < .001), and receive care in a large/academic medical center =36.03, p < .001) compared to people who reported no prior CGM use.Less than 2% of the sample was using CGM (< 5 days per week) at the start of the study and 39% had a history of CGM use. People who reported prior CGM use were more likely to be female  participants asked their diabetes care provider for a prescription for a new diabetes device Figure . Within 3.3A total of 41 participants (11%) reported receiving new prescriptions within the first month. Of those, 88% of the prescriptions were for CGM. An additional 32% requested prescriptions for insulin pumps and 2% for smart systems.A cumulative 85 participants (20%) received a new prescription across the 3‐month study period and 81% of those prescriptions were for CGM. Another 46% received prescriptions for an insulin pump and 3% for smart systems.3.4t(386) = 2.01, p = .045).Twenty (5%) participants began new devices in the first month. All of these participants who started a new device within the first month started a new CGM and 5% also started a new insulin pump. Device uptake within the first month was significantly associated with younger age. Participants who started a new device had an average age of 32.9 (SD = 10.23) years compared to 37.4 (SD = 9.64) years among those who did not start a new device (Sixty‐nine (16%) participants began new devices across the 3‐month study period. Of these new users, 83% started a new CGM only, 7% started a new CGM and insulin pump, 7% started a new insulin pump only, and 3% started a new CGM and smart system.3.5t(343) = −3.13, p = .002) and first 3 months  = −3.53, p < .001) compared to participants who did not request a prescription following DiabetesWise engagement. In addition, diabetes distress was correlated only with requests, not with receiving a prescription or starting a new device.Diabetes distress was the only baseline factor associated with whether participants asked for a new device prescription. Higher baseline diabetes distress was reported among participants who requested a prescription for a diabetes device within the first month =6.15, p = .013).Whether a participant received a prescription from a provider varied by diabetes type. Specifically, 23% of participants with type 1 diabetes received a prescription in the first 3 months compared to 11% of participants with type 2 diabetes (χ2 (1)=14.0, p < .001). In the first 3 months, those who received a prescription =23.1, p < .001) and started a new device =6.1, p = .013) also more commonly received care through a specialty clinic than those who did not receive a prescription or start a new device, respectively.Medical setting was correlated with receiving a prescription and new device uptake. A total of 90% of the participants who received a prescription for a diabetes device in the first month after interacting with DiabetesWise received care from a specialty clinic, compared to 61% of participants who did not receive a prescription (Insurance type and race or ethnicity were not significantly related to any of these processes.3.6t(193) = 3.51, p < .001) and 3‐month  = 5.23, p < .001) follow‐ups. Similarly, the proportion of participants who met or exceeded the clinical cutoff (score ≥2) reduced from baseline to 1‐month =69.08, p < .001) and 3‐month =50.98, p < .001) follow‐ups.Diabetes distress was found to decrease over time following engagement with DiabetesWise. Reductions in distress severity were observed from baseline to 1‐month . Several possible barriers outside of CDCES access could explain this pattern. Compared to primary care providers, diabetes practices likely have more comprehensive understanding and familiarity with diabetes devices and greater experience writing and processing prescriptions for such devices. Given that the majority of adults living with diabetes receive their diabetes care through primary care rather than endocrinology,Although DiabetesWise seems to encourage self‐advocacy at the person level, equitable access cannot be achieved without provider‐ and system‐level interventions that address the barriers between device requests and device uptake. One type of inequity observed in the current study was that participants who received diabetes care through a specialty diabetes practice were more likely to receive a prescription and more likely to start a new device than those followed by primary care. Although prior literature has shown access to a certified diabetes care and education specialist (CDCES),The current study strengths include large sample size, longitudinal study design, and strong retention. Entirely remote study procedures allowed for a broad study sample that included people from geographically diverse regions, with varying income levels, and receiving diabetes care outside of specialty practices. Study limitations include lack of comparison group, relatively homogenous sample based on race or ethnicity, diabetes type, and insurance type, potential sample bias based on online recruitment methods, as well as reliance on self‐report measures of requesting or receiving prescriptions as well as device uptake. Another limitation is that we did not assess for the out‐of‐pocket cost for devices among participants, which likely contributed toward device requests and uptake. Although these limitations prevent any strong causal conclusions from being drawn, the current findings still serve to expand our understanding of diabetes device uptake in general and specifically following interaction with DiabetesWise. Further research on DiabetesWise use activity patterns, potential differences between people with type 1 and type 2 diabetes, feedback from users on if/how DiabetesWise influenced their diabetes management decisions, as well as potential effects of the provider component of DiabetesWise would greatly contribute to this literature. Exploring ways to ensure that people with limited literacy and/or internet access can also benefit from DiabetesWise is another next step that can further promote equity.5Diabetes devices are life‐changing tools. As an unbranded and person‐centered platform, DiabetesWise appears to promote diabetes device awareness and education and to encourage efforts to obtain a diabetes device, especially among those with elevated distress. Nevertheless, further steps must be taken to ensure that every person living with diabetes can make informed and personalized decisions about their management that are reflected in the care and resources that are provided to them.No potential conflicts of interest.


Mycobacterium marinum is a ubiquitous water‐borne non‐tuberculous mycobacterial (NTM) pathogen. In humans, M. marinum infections are acquired through direct inoculation of skin wounds and are almost exclusively localized to skin and soft tissues. Pulmonary infection with M. marinum is extremely rare, and to our knowledge, invasive endobronchial disease has not been reported. Here, we present a case of a 71‐year‐old immunocompetent male surfer with invasive endotracheal M. marinum granulomatous disease. The patient was successfully cured with a regimen of azithromycin 250 mg daily, ethambutol 900 mg (15 mg/kg) daily and rifampicin 600 mg daily for 12 months following culture conversion. This case highlights several important concepts: Firstly, M. marinum infection, including invasive endobronchial infection, should be considered a rare cause of NTM pulmonary disease. Secondly, endotracheal infection can be successfully eradicated with this selected therapeutic regimen. Finally, the absence of M. marinum skin or soft‐tissue infection in this patient, raises the possibility that human disease might also be acquired via inhalation of M. marinum contaminated water in rare circumstances. Mycobacterium marinum infections are acquired through direct inoculation of skin wounds and are almost exclusively localized to skin and soft tissues. Pulmonary infection with M. marinum is extremely rare, and to our knowledge, invasive endobronchial disease has not been reported previously. Here, we present a case of a 71‐year‐old apparently immunocompetent male surfer with invasive endotracheal M. marinum granulomatous disease. The patient was successfully cured with a 12‐month regimen of azithromycin 250 mg daily, ethambutol 900 mg daily and rifampicin 600 mg daily.In humans,  Mycobacterium marinum is a water‐borne non‐tuberculous mycobacteria (NTM) ubiquitously distributed in aquatic environments.M. marinum causes skin and soft tissue disease, resulting in chronic granulomatous ulcers, nodules and nodular lymphangitis. Infection occurs via direct inoculation, when contaminated water is exposed to skin wounds or abrasions. While contemporary chlorination practices have reduced M. marinum outbreaks in public swimming pools,M. marinum continues to be an important cause of human skin infection in individuals who engage in natural outdoor marine activities or cleaning and maintenance of fish tanks.M. marinum infections are nearly always localized to skin and soft tissues, in part due to the portal of inoculation and the growth characteristics of the organism; which demonstrates optimal growth at 30°C and suboptimal replication at 37°C., M. marinum infection results in locally invasive disease into deeper soft‐tissue structures, including tenosynovitis, osteomyelitis, arthritis, and bursitis.M. marinum is extremely rare; with only five cases in apparently ‘immunocompetent’ individuals reported in the literature to date.M. marinum infection has never been reported to our knowledge.In humans, M. marinum had cultured from an expectorated sputum sample.A 71‐year‐old Caucasian male never a smoker was referred to our service for investigation and management of suspected NTM pulmonary disease. The patient had a 24‐month history of productive cough with tenacious purulent sputum, mild left upper lobe nodular infiltrates, cylindrical bronchiectasis and an endoluminal soft tissue irregularity of the distal trachea on thoracic Computed Tomography Figure . Acid‐fa1 1.57 L, 55.1% predicted), allergic rhinosinusitis, varicella zoster virus reactivation, gastroesophageal reflux disease and keratoacanthomas. Medications were inhaled fluticasone propionate/salmeterol xinafoate (200/6 mcg) 2 puffs daily, pantoprazole 40 mg daily, intranasal mometasone 50 mcg spray daily. He was prescribed 1–2 short courses of oral prednisone (25 mg for 5–7 days) most winters in recent years for poorly controlled asthma.Medical history included childhood‐onset allergic asthma with moderately‐severe fixed expiratory airflow limitation  and rifampicin 600 mg daily, for a total of 12 months after bronchoscopic culture conversion. S. marcescens and B. gladioli coinfections were also treated with a 14‐day course of trimethoprim‐sulfamethoxazole at treatment outset. At 3‐month follow‐up, there was a complete resolution of clinical symptoms and the patient returned to surfing in the ocean. Clinical improvement was also associated with complete resolution of bronchial secretions with smear and culture conversion from bronchoscopy washings. After 12‐months of treatment, white‐light bronchoscopy demonstrated near‐complete resolution in macroscopic endotracheal abnormalities; with resolution of the erythematous mucosal protuberance affecting the lateral wall of the distal trachea  pulmonary disease.S. marcescens and B. gladioli coinfection in this clinical presentation; the histological finding of abundant mycobacteria associated with caseating granulomas suggests M. marinum as the causative pathogen.It should be recognized that although this patient did not have a detectable immunodeficiency syndrome; asthma, inhaled corticosteroids use, intermittent oral steroids and gastroesophageal reflux disease are known host risk factors non‐tuberculous mycobacterial infection.M. marinum demonstrates optimal growth at 30°C and more limited replication at 37°C, we cultured the pathogen on liquid culture media at 36°C after 17 days, demonstrating the ability of the isolate to replicate at temperature ranges present at the distal tracheal mucosal interface.M. marinum infection has been underestimated as incubation at the optimal growth temperature of 30°C is not usually utilized for respiratory samples .Although M. marinum infection to include invasive endobronchial granulomatous disease which is treatable using a three‐drug combination antibiotic regimen.This case expands the spectrum of human All authors contributed to drafting and editing of the manuscript.None declared.The authors declare that appropriate written informed consent was obtained for the publication of this manuscript and accompanying images.


Aged male patients are more vulnerable to severe or critical symptoms of COVID‐19, but the underlying mechanism remains elusive. In this study, we analyzed previously published scRNA‐seq data from a large cohort of COVID‐19 patients, castrated and regenerated mice, and bulk RNA‐seq of a RNAi library of 400 genes, and revealed that both immunity and OXPHOS displayed cell‐type‐, sex‐, and age‐related variation in the severe or critical COVID‐19 patients during disease progression, with a more prominent increase in immunity and decrease in OXPHOS in myeloid cells in the males relative to the females (60–69 years old). Male severe or critical patients above 70 years old were an exception in that the compromised negative correlation between OXPHOS and immunity in these patients was associated with its disordered transcriptional regulation. Finally, the expression levels of OXPHOS and androgens were revealed to be positively correlated, and the responses of macrophages to android fluctuation were more striking than other types of detected immune cells in the castrated mice model. Therefore, the interplay of OXPHOS and immunity displayed a cell‐type‐specific, age‐related, and sex‐biased pattern, and the underlying potential regulatory role of the hormonal milieu should not be neglected. The immunity and energy metabolism displayed cell‐type‐dependent, age‐related, and sex‐biased characteristics in severe or critical COVID‐19 patients. The immunity of 60‐ to 69‐year‐old male patients was higher than that of the females in several types of immune cells, but it was on the contrary for patients above 70 years old. The underlying correlations of OXPHOS with immunity or androgen were also revealed. For example, the two energetic pathways OXPHOS and glycolysis are reported to be involved in anti‐inflammatory and proinflammatory process, respectively, in macrophages (macro).To decipher the contributing factors of the sex dimorphism in COVID‐19 patients, we compared the immune and metabolic activities of various immune cell populations from severe or critical COVID‐19 patients of both genders in several age groups. To better understand the role of androgens in different populations of immune cells, we analyzed published single‐cell RNA‐sequencing (scRNA‐seq) data for castrated mice to check for the responses of distinct immune cell populations’ immune and metabolic activities. Via comprehensive receiver operating characteristics (ROC) analysis, we were able to obtain the information of cell‐type‐specific, age‐related, and sex‐biased immunity and energy metabolism in severe or critical patients during disease progression (prog) or convalescence (conv). The interplay of mitochondrial OXPHOS and immunity were also illustrated across different immune cell types between both genders, that is, from various mitochondrial metabolic activities, we discovered the OXPHOS subunits may take important roles in responding androgen signals to regulate immune activities in many immune cells, especially in myeloid cells. Additionally, the disorder of the reciprocal correlation of OXPHOS and immunity was revealed in aged male severe or critical COVID‐19 patients (≥70 years old).22.1p‐value = 6.528e‐07) (Supporting Information Table + T cells (CD4.T), and CD8+ T cells (CD8.T) were much larger than dendritic cells (DC), macrophages (macro), natural killer (NK) cells, and neutrophils (neu) in the four age groups and were, thus, paid more attention in regard to the cell numbers for valid statistical analysis in subgroups divided by disease stages (prog and conv) and genders  values of 49 immune pathways, gene sets, signatures in mono and two types of adaptive immune cells (CD4.T and CD8.T) were calculated across four age groups in both genders during disease prog and conv, respectively Figure . The difThe overall sex‐biased changes of immune activities in four comparing age groups between both genders indicated that Mono.prog changed the most among the three types of immune cells (mono > CD8.T ≈ CD4.T) Figure . While MCollectively, the results strongly suggested that sex‐dependent, cell‐type‐specific, and age‐related variations in immune activities existed in severe or critical COVID‐19 patients.2.2To obtain detailed information in age‐related sex bias in immunity from severe or critical patients, we profiled the AUC ranks in the three types of immune cells via ROC analysis in 49 immune pathways between both genders in each age group , especially in age group 7.2.3Next, we checked whether energy metabolism was involved in the cell‐type‐dependent, age‐related, and sex‐associated variation in immunity in severe or critical COVID‐19 patients. A total of 149 mitochondrial pathways were monitored via ROC analysis. The top 20 AUC values between both genders across four age groups in three types of immune cells displayed that the mitochondrial activities in mono changed more strikingly than those in CD4.T and CD8.T (mono > CD4.T ≈ CD8.T) in severe or critical COVID‐19 patients (prog and conv) Figure , which wNotably, the KEGG oxidative phosphorylation pathway and the four OXPHOS subunits  displayed stronger negative correlations with all 12 immune signaling pathways, gene sets, or signatures in Mono.prog cells than those in CD4.T.prog cells from a mixture of male and female severe or critical patients Figure  and S7. Additionally, the expression profiles of KEGG oxidative phosphorylation and GOBP ATP metabolic processes along with age also displayed sex‐bias in CD8.T and mono (prog), especially in age groups 6 and 7  but also in innate immunity (mono) Figure . To deciNotably, in line with the lower immunity, OXPHOS subunits CIII, CIV, and CV were all elevated in Mono.M7.prog in comparing to Mono.F7.prog Figure .Next, we calculated the number of TFs correlated with the immune signature of interferon‐stimulated genes in Mono.M6, Mono.M7, and Mono.F7  mouse models.In summary, the lower immunity in Mono.M7.prog than that in Mono.F7.prog or Mono.M6.prog was highly related to the dysregulated TFs, which were usually involved in improving immune activities and lowering OXPHOS. Additionally, in myeloid cells, genes as IRF7 and KIT, were also needed in the coupling of downregulation of OXPHOS with the increase in immunity.2.6A lower level of serum testosterone is common in aging men.Even more, androgen deprivation led to inhibition of mitochondrial activities in T cells and macro Figure . The lev2.7According to the ranks of the top 20 AUC values in the nine comparing pairs, we revealed that T03 (on day 14 postcastration) had the most striking fluctuation in immune activities in the four types of immune cells Figure . More imAccording to the correlation patterns among immune responses, OXPHOS, and androgen in immune cells, strong negative correlation was revealed in macro, which was also the same in mature neu Figure . CI, CII3The metabolic program in immune cells is well known to be changed along with pro‐ or anti‐inflammatory responses, a process also known as immunometabolism.Both of the age‐related increase of immune activity and the cell‐type‐ or age‐related decline of OXPHOS in male severe or critical COVID‐19 patients were similar to those in castrated animals. For example, compared to age group 3, age groups 5 and 6 of CD8.T and Mono showed higher immune activities in the male severe or critical COVID‐19 patients (prog) Figure . This isAmong the underlying mechanisms accounting for the sex dimorphism in immune activities, the distinct roles of androgens and estrogens at physiological concentrations are important.As to the mechanism behind the biased effect of androgens on innate and adaptive immune cells, there is still no clear answer. In this report, our finding of the suppressive role of androgens on oxidative phosphorylation in mitochondria was striking and is similar to the well‐known suppressive effect of inflammation on oxidative phosphorylation in immune cells.In summary, sex bias in immunity and energy metabolism were important features in shaping the adaptive and innate immunity in severe or critical COVID‐19 patients, which usually displayed cell‐type‐dependent and age‐related characteristics between both genders. In advanced age, the sex bias was extremely prominent. Therefore, sex and age factors should be given particular attention in samples involved in immunity and metabolism.44.1The scRNA‐seq data for intact prostate, castration prostate, and regeneration prostate were derived from the NCBI GEO database: GSE146811. The deposited raw count data were processed, and all cell clusters were annotated according to an original report.The scRNA‐seq data of mature neu from bone marrow were derived from the NCBI database accession number GEO: GSE198175.The scRNA‐seq data of COVID‐19 patients were derived from the NCBI GEO database: GSE158055. The raw count data and meta.data annotating the cell clusters were used according to the original report.4.2A total of 400 siRNAs data in HUVEC cells were from GEO database with accession code GSE27869.Bulk RNA‐seq data of IRF7KO and WT were obtained from GEO database with accession code GSE178560.4.3Gene sets and pathways were copied from Hallmark gene sets of MSigDB collections, the KEGG pathway database, Reactome pathway database, and the WikiPathways pathway database, and some ontology terms were derived from the Gene Ontology resource. OXPHOS gene signatures were from MitoCarta3.0.TFs and TF cofactors were obtained from the database of AnimalTFDB 3.0.4.4All statistical analyses were performed with unpaired Wilcoxon rank‐sum test (two‐sided or less) with Bonferroni correction via the rstatix package or ggpubr package.p < 0.05).Pearson correlation coefficient was calculated with the linkET package  in Seurat 4.3. DEGs between Macro.M7.prog and Macro.F7.prog were performed with . Upregulated or down‐regulated genes were then enriched in nonredundant pathways via WebGestalt and filtered with FDR < 0.05.Conceptualization, YL, RL, FS, SJ; methodology, YL, LL, GW; software, GX, GW; validation, YL, SJ, LY, JC, SL; formal analysis, YL; resources, GX, LL, JZ, YH, JC, FS, SL, RL; visualization, YL, LL, SJ, GX; writing—original draft, YL, SJ; writing—review & editing, RL, FS, SJ, LY; supervision, SJ, FS, RL; project administration, FS, RL; funding acquisition, RL, FS, YL. All authors have read and approved the final manuscript.The authors have no conflict of interest to declare.Not applicable.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Ulcerative colitis (UC) is a chronic inflammatory bowel disease mainly involving the colon and rectum, which features recurrent mucosal inflammation. The excessive production of reactive oxygen species (ROS) is a trigger for pathological changes such as cell apoptosis and disordered immune microenvironments, which are crucial for the progression of UC and can be a promising therapeutic target. Nowadays, the development of targeted therapeutic strategies for UC is still in its infancy. Thus, developing effective therapies based on ROS scavenging and elucidating their molecular pathways are urgently needed. Herein, a biomimetic nanoformulation (Pd@M) with cubic palladium (Pd) as the core and macrophage‐derived extracellular vesicles (MEVs) as the shell is synthesized for the treatment of UC. These Pd@M nanoformulations exhibit multienzyme‐like activities for effective ROS scavenging, excellent targeting ability as well as good biocompatibility. It is verified that Pd@M can regulate the polarization state of macrophages by inhibiting glycolysis, and decrease neutrophil infiltration and recruitment. In this way, the colonic inflammatory and immune microenvironment is remodeled, and apoptosis is prevented, ultimately improving colonic mucosal barrier function and alleviating colitis in the mouse model. This finding provides a promising alternative option for the treatment of UC patients. This study reports a novel macrophage‐derived extracellular vesicle‐coated palladium (Pd@M) nanoformulation for the targeted treatment of ulcerative colitis (UC). Pd@M can potently scavenge ROS, remodel the colonic inflammatory and immune microenvironments in vivo, and ultimately improve mucosal barrier function and induce the remission of UC, providing a promising alternative option for clinical application. According to consensus‐based guidelines, systemic treatment relies heavily on the induction and maintenance of remission. Sulfasalazine and 5‐aminosalicylates are the first‐line treatments for UC with an expected remission rate of ≈50%, while glucocorticoids or immunosuppressive agents are indicated for patients who are refractory to 5‐aminosalicylates. However, the results of clinical pharmacotherapy are unsatisfactory, as reflected by the high recurrence rate and a variety of complications. Hence, exploiting more powerful and appropriate treatments for UC is imperative.As the most common inflammatory bowel disease worldwide, ulcerative colitis (UC) is characterized by mucus or mucus‐free bloody diarrhea. In particular, UC has a markedly disturbed inflammatory microenvironment including invasive pathogens, damaged tissues, and infiltrated immune cells during the active or remission stage, which is accompanied by overproduced interleukin (IL)−1β, IL‐6, tumor necrosis factor (TNF)‐α and other proinflammatory cytokines.1, 2 Under the pathological conditions of UC, ROS oxidize intracellular proteins and lipid components, and damage deoxyribonucleic acid. It has been shown that ROS induces the transcription and translation of hypoxia‐inducible factor‐1α (HIF‐1α) through numerous regulators including the mammalian target of rapamycin 1 (mTOR1). By far, a number of nanoparticles (NPs), engineered bacteria, polymers, biomolecules, and organic molecules have been developed as potent mimic enzymes that exhibit catalase (CAT) or superoxide dismutase (SOD) activity, which can deplete excessive ROS. In the biomedical field, nanomaterials can overcome the disadvantages of natural antioxidants such as short cycle time, increased toxic side effects from multiple dosing, and low absorption rate and bioavailability. Among them, noble metal NPs have attracted great interest due to their high stability, mass production, and tunable catalytic properties. Previously, palladium (Pd) has been reported to have intrinsic superior SOD and CAT‐like activities. However, existing studies have mainly focused on ROS scavenging and final manifestation, and it is still unclear exactly how Pd NPs further play a regulatory role beyond ROS. As a result, it is essential to uncover how Pd nanomedicine further modulates UC‐associated alternations in inflammatory and immune responses based on the scavenging capacity of ROS.Reactive oxygen species (ROS) possess powerful oxidative capability and are detrimental to cells at high concentrations, but display complex signaling functions at low concentrations.7 Unde Macrophages (Mφs), long‐lived cells of the innate immune system, play an important role in adaptive immunity, representing the first defense line of the body against pathogenic responses, and also get involved in antigen presentation and cytokine production. Furthermore, Mφs are essential for tissue homeostasis, which are roughly classified into two groups on the basis of their activation and microenvironment: M1 and M2. Both phenotypes are not stereotypical but change dynamically with the metabolism of Mφs, which thus leads to different roles in host defense against different pathogens, inflammation resolution, and wound healing. Previous studies have demonstrated that the mTOR1 and HIF‐1α are central regulators of the glucose metabolic switch in Mφs and the classical M1 polarization program is mainly up to glycolysis. Specifically, mTOR1 senses cellular nutrients and regulates energy metabolism, which can modulate the function of innate and adaptive immune cell populations. In lipopolysaccharide (LPS)‐stimulated inflammatory Mφs, the activation of the mTOR1 pathway drives glycolytic metabolism by inducing HIF‐1α transcription. HIF‐1α binds to hypoxia‐responsive elements in promoters of target genes, like glycolytic enzymes and glucose transporter‐1 (GLUT1), to facilitate this glycolytic metabolic switch, which thereby regulates the polarization state of Mφs. Then, activated M1 Mφs produce multiple cytokines, including TNF‐α, IL‐1β, IL‐6, inducible nitric oxide synthase (iNOS), etc., for the purpose of aggravating inflammation. Therefore, decreasing the polarization of glycolysis‐mediated M1 Mφs is a promising strategy for maintaining colonic homeostasis. However, a compromise between high catalyst activity, immune homeostasis, as well as targeting ability remains a challenging process in UC treatment.In addition to ROS, mucosal immune dysfunction is involved in the development of UC and the homeostasis of the intestinal microenvironment requires a counterbalance between innate and adaptive immune responses.18 Mac Specially, with the premise of ensuring optimal biocompatibility and targeting ability, macrophage‐derived extracellular vesicles (MEVs), have become a reliable alternative to macrophage membranes and can be used to develop biomimetic drug delivery systems targeting inflammatory regions because of their property to adhere to the inflamed endothelium of vascular tissue via particular ligands. Here, MEVs‐coated cubic Pd (Pd@M), a biomimetic nanomedicine, was synthesized as an efficient ROS scavenger for the treatment of UC , we first designed biomimetic nanoformulations that combine ROS scavenging and targeting properties. The palladium (Pd) nanocubes were optimally synthesized, whose morphology was described by transmission electron microscopy (TEM). As shown in standard TEM images Figure 1A,B, theFigure 32 Subn Figure . P‐selece Figure . Overall2−), or hydrogen peroxide (H2O2), reactive oxygen species (ROS) plays a critical role in intracellular functions and cellular signaling processes. The excessive production of ROS disrupts the cellular redox balance and contributes to inflammatory conditions. The clearance of excess ROS and the restoration of a normal microenvironment are essential to alleviate inflammation. Therefore, the effects of Pd@M on typical ROS models  were investigated. First, electron spin resonance (ESR) was utilized to assess the ability of Pd@M to remove •OH radicals. Fenton reaction was performed between Fe2+ and H2O2 to generate •OH radicals. The characteristic ESR spectrum for •OH/5‐dimethyl‐1‐pyrroline‐Noxide (DMPO) had a relative intensity of 1:2:2:1, and the peak intensity showed a markedly concentration‐dependent decrease with the addition of Pd@M , superoxide radicals •O2−, or hydM Figure , which iO Figure . SimilarO Figure . Nitro bn Figure . UV−vis n Figure . Meanwhi2.2 Therefore, the uptake of Pd@M was investigated using RAW264.7 cells. RAW264.7 cells were used to incubate with fluorescein isothiocyanate (FITC)‐labeled Pd@M at different time periods , whose nuclei were stained by 2‐(4‐amidinophenyl)−6‐indolecarbamidine dihydrochloride (DAPI). Confocal images . Flow cytometry analysis  induced the inflammation of RAW264.7 cells36 thas Figure  showed as Figure  showed s−1) of Pd@M for 24 and 48 h and exhibited no visible reduction in viability compared to the control group, which illustrated no apparent cytotoxicity to the cell line  assay to evaluate the therapeutic effect of Pd@M. Cells were first incubated with various concentrations  or facultative diffusion through glucose transporters (GLUTs) located in the membrane of diverse cells. Once entering the cells, glucose is oxidized via a stepwise mechanism. The first phase is glycolysis , and the tricarboxylic acid (TCA) cycle generally contribute to the generation of adequate ATP levels to meet the bioenergetic requirements of cells, tissues, and organisms to maintain steady‐state homeostasis. In the activated immune system, however, glycolysis can be rapidly induced despite being an inefficient means of producing ATP compared with O2‐dependent mitochondrial oxidative phosphorylation (OXPHOS), which offers rapid energy to meet the metabolic demands of activated immune cells.The glycolytic system not only functions as the first step in carbohydrate metabolism to produce adenosine triphosphate (ATP) that directly supplies the body with energy but also exerts a regulatory effect on the function of the immune system.26 In  Glycolytic metabolism distinctly affects the function of immune cells and has a profound influence on the immune response. For example, Mφ activation may manifest as a classical/M1 phenotype or an alternative/M2 one, which displays crucial differences in metabolism. M1 polarization is featured by enhanced glycolysis along with reduced mitochondrial OXPHOS, while M2 features efficient OXPHOS. Glycolysis is essential to maintaining the infiltration capacity and microbicidal effect of M1 Mφs and enables them to initiate an immune response and produce ROS, NO, and pro‐inflammatory cytokines. By comparison, M2 Mφs upregulate arginase‐1 (Arg‐1), IL‐10, and other anti‐inflammatory cytokines, and release polyamines and growth factors, which thus promote angiogenesis and wound repair. Therefore, whether Pd@M could regulate the glycolysis of Mφs and affect Mφ polarization in an inflammatory state was assessed first. Seahorse technology was used to analyze metabolic flux. Extracellular acidification rate (ECAR), which indicates glycolytic rate, showed marked changes between the LPS‐stimulated group and the LPS+Pd@M‐treated group  ligands. The activation of the mTOR1 pathway drives glycolytic metabolism by activating the transcription and translation of HIF‐1α. HIF‐1α, a heterodimeric protein comprising an oxygen‐sensitive α subunit, is usually stable under anaerobic/hypoxic conditions and is responsible for the regulation of glycolysis in hypoxia. It can accumulate within the cytoplasm and subsequently translocate to the nucleus to form HIF heterodimers. Upon binding to hypoxia‐responsive elements (HREs) within the promoter/enhancer regions of target gene, these heterodimers initiate gene transcription and stimulate an array of adaptive responses to hypoxia, including the reprogramming of HIF‐1α‐dependent glucose metabolism to reduce dependence on O2‐dependent energy production. To be specific, superfluous HIF‐1α upregulates the expression of genes encoding GLUTs (like GLUT1 and GLUT3) and glycolytic pathway enzymes, including HK‐2, PFKM, PKM2, and LDHA, and suppresses OXPHOS to achieve an adaptive increase in glycolytic flux. Notably, the mTORC1 (Raptor) and HIF‐1α protein levels in the Pd@M‐treated group showed a significant decrease compared with those in the LPS group, consistent with the trend of proteins related to the glycolytic pathway  and hypoxia‐inducible factor‐1α (HIF‐1α) with a wide range of downstream targets to facilitate this process.25 The2.4 Therefore, the inflammation targeting ability and in vivo biodistribution of biomimetic Pd@M nanocubes were examined after the intravenous injection of free Cy5.5, Cy5.5‐labeled Pd, or Cy5.5‐labeled Pd@M into mice with colitis. Major organs and colons were collected for ex vivo imaging at 3 and 6 h after injection. It was observed that Cy5.5 in all formulations accumulated mainly in the liver, probably because of its non‐specific uptake by the mononuclear phagocyte system of the liver. Of note, Cy5.5‐labeled nanocubes were significantly different only in colons rather than in other organs  was used for assessing whether Pd@M has therapeutic effects in vivo  and HIF‐1α proteins, as well as glycolytic enzymes  was significantly decreased in the DSS+Pd@M group compared with the DSS group ‐positive neutrophils were markedly recruited in the DSS group as expected, but decreased sequentially after Pd and Pd@M treatments  staining showed that goblet cells in colon tissues displayed the function of secreting mucin to protect the intestinal mucosa. It turned out that Pd@M vigorously reversed the DSS‐induced decrease in colonic goblet cells , extensive epithelial cell apoptosis, and the reduced complexity of tight junctions (TJs) between epithelial cells, which ultimately impairs the function of the epithelial barrier and destroys tissues.57 Bass Figure . Taken t2.8−1) intravenously to wild‐type mice. On days 1, 7, and 30 after injection, no distinguishable hemorrhage, necrosis, inflammatory lesions, or tissue injuries were observed in the major organs of the mice that received Pd@M management  to catalyze the cascade reactions of ROS scavenging. Mac‐1 and CD44 of the MEVs shell have high affinity for ICAM‐1 and P‐selectin, which are highly expressed on UC inflammation‐impaired endothelial cells, endowing Pd@M with targeting ability. Therefore, these Pd@M NPs could be guaranteed to accumulate and exert therapeutic effects in the lesion of UC. In vitro and 4Animal care and experimental procedures were approved by the Institutional Animal Care and Use Committee of Shanghai Rat and Mouse Biotech Co. (No. 202210[13]).p < 0.05 was considered statistically significant. Graphs were plotted using GraphPad Prism  and OriginPro .Data analysis was conducted using SPSS software version 25.0 . Data normality was determined using the Shapiro‐Wilk test. One‐way analysis of variance (ANOVA) testing followed by Tukey's post‐hoc test was used to compare the differences in groups. Data were presented as mean ± standard deviation (SD) from at least three independent repeats. The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.Recombinant human parathyroid hormone (1‐84), rhPTH(1‐84), is an approved adjunctive treatment to oral calcium and active vitamin D for adult patients with hypoparathyroidism; however, there is limited information on the effect of twice daily (BID) dosing of rhPTH(1‐84). This was a phase I, open‐label, randomized, crossover, multicenter study conducted in adult patients with chronic hypoparathyroidism. The primary objective was to assess the pharmacokinetic profile and pharmacodynamic effects of 1 day of treatment with rhPTH(1‐84) administered subcutaneously at 25 μg BID, 50 μg BID, and 100 μg once daily (QD) with or without supplemental oral calcium. Safety and tolerability were evaluated as secondary objectives. In total, 33 patients with chronic hypoparathyroidism completed the study. Treatment with rhPTH(1‐84), both BID and QD, over the short‐term maintained serum calcium, lowered serum phosphorus, decreased urinary calcium excretion, and increased urinary phosphorus excretion. The decrease in urinary calcium excretion was numerically greater for BID than QD. Generally, baseline‐adjusted pharmacokinetic parameters including area under the curve and maximum observed concentration increased with increasing rhPTH(1‐84) dose, although this effect was not dose proportional. No new safety findings were observed. Our study revealed no differences thought to be clinically meaningful in pharmacokinetic or pharmacodynamic parameters with BID versus QD rhPTH(1‐84) dosing. Future long‐term studies are warranted to further elucidate the effects of alternative dosing strategies. © 2023 Takeda Development Center Americas, Inc and The Authors.  Hypocalcemia associated with hypoparathyroidism is often managed via conventional therapy with oral calcium and active vitamin D; however, this therapy does not replace other physiologic functions of PTH. Compared with the general population, patients with chronic hypoparathyroidism whose condition is managed with calcium and active vitamin D alone are at increased risk of renal complications , cardiovascular conditions, urinary tract infection, immune system impairment, and hospitalization due to infection. In addition, patients report high rates of neurologic and neuromuscular symptoms, including fatigue, cramping, paresthesia, mental lethargy, inability to concentrate, and forgetfulness.Lack of parathyroid hormone (PTH) in hypoparathyroidism leads to reduced bone turnover, reduced calcium mobilization from bone, diminished renal production of active vitamin D, and loss of calcium‐conserving and phosphaturic effects on renal tubules, resulting in hypocalcemia, hyperphosphatemia, and hypercalciuria.2, 3Recombinant human parathyroid hormone (1‐84), rhPTH(1‐84), is an approved adjunctive treatment to oral calcium and active vitamin D for adult patients with hypoparathyroidism.15 In REPLACE, 53% of patients in the rhPTH(1‐84) arm, compared with 2% of patients in the placebo arm, achieved the primary study endpoint . Urinary calcium excretion did not differ between the rhPTH(1‐84) and placebo arms, decreasing by −73.6 mg/24 hours and −83.8 mg/24 hours, respectively (p = 0.57). The efficacy of rhPTH(1‐84) QD was maintained during a 5‐year open‐label extension of REPLACE; in addition, mean urinary calcium excretion declined to within the reference range for both men and women by the end of the analysis period.The pivotal phase III, placebo‐controlled REPLACE study established the efficacy and safety of once daily (QD) administration of rhPTH(1‐84) at 50, 75, or 100 μg over 24 weeks in adults with chronic hypoparathyroidism.16 In  Given the relatively short plasma half‐life of PTH after a single SC dose, investigators evaluated more frequent administration schedules.A phase I, dose‐escalating study evaluating the pharmacokinetics (PK) and pharmacodynamics (PD) of QD rhPTH(1‐84) at 50 or 100 μg administered subcutaneously (SC), in patients with chronic hypoparathyroidism, found that the half‐life of PTH was approximately 2.5 to 3 hours with either dose; plasma PTH levels peaked at 10 to 15 minutes and then again at 1 to 3 hours after injection, and then declined to baseline by 12 to 24 hours posttreatment.18 Giv showed that 50 μg twice daily (BID) was associated with greater reductions in 24‐hour urinary calcium excretion than 100 μg QD dosing; both regimens maintained serum calcium within the reference range.Using a quantitative systems pharmacology modeling approach to simulate outcomes with varying rhPTH(1‐84) doses, Khurana and colleagues19 shoThese previous studies motivated the investigation of BID versus QD dosing to compare these two dosing regimens for SC rhPTH(1‐84) administration. As a condition of the approval of rhPTH(1‐84) for hypoparathyroidism, the US Food and Drug Administration mandated the execution of a postmarketing trial to assess the PK and PD effects of rhPTH(1‐84) dose and dosing regimen on the control of serum and urinary calcium. The present study reports the effects of one day of treatment with rhPTH(1‐84) administered as 25 μg BID, 50 μg BID, and 100 μg QD, with and without supplemental calcium, on PK and PD outcomes in adults with chronic hypoparathyroidism.Clinicaltrials.gov identifier, NCT02781844; PARALLAX). The study was conducted at multiple centers across North America and Europe from May 2016 to May 2019. The study compared QD and BID dosing regimens of rhPTH(1‐84) without (cohorts 1 and 2) and with (cohorts 3 and 4) supplemental oral calcium and in the absence of administration of active vitamin D  over a single day of treatment  were excluded from the study. Additional key inclusion and exclusion criteria are provided in Table The study enrolled patients aged ≥18 years with a diagnosis of chronic hypoparathyroidism of ≥12 months' duration who required conventional therapy of ≥1000 mg/day oral calcium and ≥0.25 μg/day active vitamin D . Included patients had serum calcium within the normal reference range , urinary calcium excretion ≥200 mg (5 mmol)/24 hours, normal serum magnesium levels, and normal serum thyroid function tests before the initiation of study procedures. Patients with hypoparathyroidism resulting from an activation mutation in the Enrollment in the study was staggered such that each treatment cohort was filled sequentially. After completion of treatment within each of cohorts 1, 2, and 3, safety data were reviewed before enrollment began in the subsequent cohort. Within each cohort, patients were randomly assigned 1:1 to treatment sequence order: QD followed by BID, or BID followed by QD. Randomization was achieved via allocation of a computer‐generated randomization number and assignment to a cohort via interactive response technology. Patients received a single day of treatment in each of the two treatment periods, separated by a washout period of 5 to 30 days. The QD dose or first BID dose of rhPTH(1‐84) was administered SC in the thigh in the morning after an overnight fast of ≥8 hours. For patients who received rhPTH(1‐84) BID, the second dose was administered in the opposite thigh 12 hours after the first dose. The day before rhPTH(1‐84) treatment was defined as day −1, the day of rhPTH(1‐84) treatment was defined as day 1, and the day after rhPTH(1‐84) treatment was defined as day 2. Patients continued to receive their routine supplemental calcium and active vitamin D through day −1 in each of the two treatment periods. On day 1, supplemental calcium was withheld for cohorts 1 and 2 only, and active vitamin D was withheld for all cohorts. Supplemental calcium and active vitamin D were resumed after the completion of all study procedures on day 2 . All patients had a follow‐up visit which took place 30 ± 2 days after the last dose of rhPTH(1‐84) was administered.Plasma PTH concentration was measured in blood samples collected predose; at 10, 20, 30, 60, and 90 minutes postdose; and at 2, 4, 8, 12, 16, and 24 hours postdose. Samples were analyzed for intact PTH by Celerion , using a previously validated second‐generation immunoradiometric assay with a quantification range of 10 to 2000 pg/mL .100 × . The serum concentration values used for the calculation of fractional excretion values were those at, or closest to, the middle of the urine collection period. Blood samples for PD endpoints were scheduled at 24, 22.5, 20, 18, 16, 14, 12, 10.5, and 8 hours before the first dose; predose on day 1; 1.5, 4, 6, 8, 10, 12, 13.5, 16, 20, and 24 hours postdose on day 1; and continuing through day 2 for BID dosing. Urine for 24‐hour urinary calcium excretion analysis was collected continuously on day −1 and on treatment day 1 through day 2.PD endpoints included albumin‐corrected serum calcium, serum phosphorus, and serum creatinine measurements; serum calcium‐phosphate product was calculated based on albumin‐corrected serum calcium and serum phosphate concentrations. Total urinary calcium, phosphorus, sodium, magnesium, citrate, and cyclic adenosine monophosphate (cAMP) excreted over 24 hours postdose  were also calculated and were assayed using the most current validated bioanalytical methods according to the relevant standard operating procedures in place at PPD Central Laboratory , and at inVentiv Health Clinique Inc. . Total 24‐hour urine assessments were calculated by summing the individual measurements from each component collection period . Fractional excretion of calcium, phosphorus, and magnesium were evaluated in each collection period and calculated as follows: Treatment‐emergent adverse events (TEAEs) were recorded continuously from the time of administration of the first rhPTH(1‐84) dose through study completion. TEAEs were defined as any unfavorable and unintended sign , symptom, or disease temporally associated with use of the study drug, regardless of whether the event was considered by the clinical study investigator to be related to the study drug. Serious TEAEs were defined as any adverse event that required hospitalization or prolonged an ongoing hospitalization, caused persistent or significant disability, was considered by the investigator to be an important medical event, was life‐threatening, or resulted in death.max), and the t½ . PK parameters were determined from plasma concentration‐time data by noncompartmental analysis using WinNonlin Phoenix version 8.1 . PD parameters, including AUC from time 0 to 24 hours after the first rhPTH(1‐84) dose (AUC0–24), were calculated from raw serum concentrations using WinNonlin Phoenix version 8.1.The safety analysis set included all patients who received one or more doses of rhPTH(1‐84). The PK analysis set comprised patients who received one or more doses of rhPTH(1‐84) and had one or more evaluable postdose PK concentrations available for one or more dose regimens. The PD analysis set comprised patients who received one or more doses of rhPTH(1‐84) and had one or more evaluable postdose PD measurements available for one or more dose regimens. PK parameters included area under the curve (AUC), the maximum observed concentration reported  administration on urinary calcium excretion. Urinary calcium and urinary calcium relative to creatinine change from baseline as well as percentage change from baseline were calculated for 50 μg BID and 100 μg QD for day 1 to 2 and compared between 50 μg BID and 100 μg QD using a two‐sample t test, for patients in cohorts 2  and 4 .In total, eight patients were required to complete each treatment in each cohort. The sample size was determined based on a similar previous PK/PD study.18 TheThe study protocol and all‐subject consent form were reviewed and approved by the institutional review board/independent ethics committee and regulatory agency, as appropriate, before study initiation. The study was conducted in accordance with the International Conference on Harmonization of Good Clinical Practice guideline, the Declaration of Helsinki, and other applicable local requirements. All patients provided written informed consent before any study procedure was performed.Out of 78 screened patients, a total of 34 patients were enrolled Fig. , of whom0–24 and Cmax increased as the rhPTH(1‐84) dose increased (Table ½ of PTH was comparable across dose regimens and ranged from 1.5 hours for rhPTH(1‐84) 25 μg BID with calcium to 2.2 hours for 100 μg QD with calcium.A double PTH peak concentration‐time profile was observed for all dose regimens, with an initial peak occurring at 5 to 30 minutes after injection and a second peak approximately 1 to 2 hours later. Following achievement of peak levels, mean concentrations of PTH declined steadily and became undetectable at 12 to 24 hours postdose  without supplemental calcium, within‐cohort mean serum calcium concentrations were similar on day 1 and day −1  dosing regimens with and without oral calcium decreased 24‐hour urinary calcium excretion relative to creatinine Fig. A and re dosing rAdministration of rhPTH1‐84) with or without calcium supplementation under all dosing regimens markedly increased total 24‐hour urinary phosphorus compared with day −1 (Table  with or n = 1) and fatigue (n = 1), resolved by study completion. Among the reported TEAEs, 26 reported in 10 patients (29.4%) were considered by the investigator as related to treatment with rhPTH(1‐84). The most common TEAEs considered related to treatment were dizziness , nausea , and headache .Overall, 58 TEAEs were reported in 18 patients  injection. In the previous phase I study by Clarke et al., the initial PTH peak was observed a median of 10 to 15 minutes after injection and a second peak 1 to 3 hours later, with an estimated t½ of approximately 2.5 to 3 hours for both the 50 and 100 μg doses, which is similar to observations of the present study. A dose‐proportional increase in baseline‐adjusted AUC of PTH was observed between the 50 μg QD and 100 μg QD dose regimens in the previous phase I study, but was not clearly observed in the current study. Lack of PK dose proportionality in the current study was likely due to high variability in the PTH concentrations across cohorts, as evidenced by the high geometric mean coefficient of variation values for baseline‐adjusted PTH AUCs (ranging from 23% to 449%). In addition, some rhPTH(1‐84) dosing errors were suspected, which may have resulted in the observed lack of dose proportionality. Two patients receiving rhPTH(1‐84) 50 μg BID with calcium had unexpectedly low AUC0–24 values (<300 pg∙h/mL), a likely indicator of partial dosing.This study found no differences considered clinically meaningful in PD parameters in adult patients with chronic hypoparathyroidism treated with BID versus QD rhPTH(1‐84). The PK and PD characteristics of rhPTH(1‐84) have been assessed previously in an open‐label, phase I study that evaluated a single SC dose of rhPTH(1‐84) at 50 μg followed by 100 μg in patients with chronic hypoparathyroidism who were receiving calcium and vitamin D, using a design similar to that of the current study.18 OveIn this study, albumin‐corrected serum calcium concentrations over 24 hours remained within levels recommended by treatment guidelines.21n of 29 for a superiority study with calcium assuming the same treatment difference of 20% and 80% power. In this study, rhPTH(1‐84) administration caused a rapid reduction of urinary calcium excretion, but no inference regarding the effect of long‐term treatment is possible. It is anticipated that long‐term treatment may be necessary to normalize urinary calcium. After 24 weeks of treatment in the phase III REPLACE study, urinary calcium excretion had declined from baseline in both the rhPTH(1‐84) and placebo arms but remained above the reference range for women in the rhPTH(1‐84) arm at the end of the study. No significant differences in 24‐hour urinary calcium excretion were observed between the two arms at the end of the study. However, in two long‐term, single‐arm, open‐label clinical trials of rhPTH(1‐84) treatment, mean urinary calcium excretion decreased from above the reference range at baseline to within the reference range with 5 to 8 years of treatment.Administration of rhPTH(1‐84), whether QD or BID, also resulted in a reduction of urinary calcium excretion. Coadministration of adjunctive calcium diminished the effect of rhPTH(1‐84) on urinary calcium decline, likely because of increased renal filtered load of calcium. Indeed, cohorts that received supplemental calcium showed a trend for higher levels of serum calcium after rhPTH(1‐84) injection compared with cohorts that received no supplemental calcium. For this study, the sample size was not based on statistical power considerations because the statistical analyses were primarily descriptive, and no hypothesis testing was prespecified in the study protocol. This study was not designed to show a significant effect of 50 μg BID versus 100 μg QD rhPTH(1‐84), and none of the post hoc analyses suggested a significant effect of a 50 μg BID rhPTH(1‐84) dose to reduce urinary calcium excretion compared with a 100 μg QD dose, although there appeared to be a further numerically incremental reduction in urinary calcium excretion with BID administration compared with QD. As an estimate only, a post hoc power analysis suggested that, with the current sample size and observed treatment difference in this study , the power to detect a statistically significant difference without calcium was 18% and with calcium 2.7%. That same analysis suggested that, to detect a statistically significant difference at an assumed treatment difference of 20%, with a superiority design without calcium, a sample size of 15 to complete would have been required, greater than the sample size within the study. Similarly, a sample size estimation yields an he study.22 How and vascular calcification, which are independent predictors of cardiovascular disease in the general population. In this study, administration of rhPTH(1‐84) increased urinary phosphorus excretion in all cohorts and lowered serum phosphorus concentrations. These effects were independent of oral calcium supplementation. Larger and longer studies are required to confirm these findings and to understand the clinical significance of these differences between dosing frequencies.In patients with chronic hypoparathyroidism, the lack of phosphaturic PTH effects can result in hyperphosphatemia. Increased serum phosphorus has been reported to be associated with vascular stiffness24 and showing a significant decrease from baseline in urinary citrate after 6 months of synthetic PTH(1‐34) administration, an effect that persisted through 5 years of treatment. In Gafni and colleagues, at the last measure after treatment with human PTH 1–34 (up to 5 years), urinary pH was significantly increased and blood bicarbonate concentration was significantly decreased. These findings suggest a mild metabolic acidosis, which is a key determinant of urinary citrate excretion. In the present study, which was shorter than the study by Gafni and colleagues and evaluated rhPTH(1‐84), there was no meaningful change in serum bicarbonate and a modest increase in urinary pH. There was a numerical reduction in 24‐hour urinary magnesium excretion in all but one treatment regimen in this study , but time course data revealed a more nuanced pattern. Fractional excretion of magnesium tended to decrease during the first 6 hours after rhPTH(1‐84) injection but then began to increase back to or beyond baseline levels.With respect to other urinary PD parameters, a numerical increase in urinary cAMP excretion was observed with rhPTH(1‐84) administration, under all treatment regimens. Conversely, inconsistent results were seen for urinary sodium and urinary citrate across rhPTH(1‐84) treatments. The urinary citrate results with short‐term rhPTH(1‐84) reported here are in contrast to data from Gafni and colleagues30 shoNo new safety findings were observed with rhPTH(1‐84), whether administered QD or BID, in patients with chronic hypoparathyroidism. In addition, although dosing regimens that included adjunctive calcium generally resulted in an increased albumin‐corrected serum calcium concentration when compared with regimens without adjunctive calcium, albumin‐corrected serum calcium concentrations remained in the lower half or slightly below the reference range for all treatment groups in this study. In addition, sequential enrollment of study cohorts, potentially resulting in PTH assay variability over the duration of the study, likely contributed to observed PK variability. There were also differences in BMI across cohorts that may have affected PK and PD parameters. Furthermore, PD effects should be interpreted with caution because of the complexity of factors, such as bone turnover and kidney function, that contribute to calcium homeostasis. Finally, although active vitamin D intake was paused on the day of rhPTH(1‐84) dosing, some biological activity likely persisted. A study including longer treatment durations would be needed to confirm the results reported here.This study has several limitations. Data were generated for a single day of QD or BID dosing, and results cannot be generalized to long‐term treatment strategies. Because of the large blood volume needed for the PK evaluations, only two rhPTH(1‐84) treatments for PK and PD assessment within a month could be performed in any given participant. Consequently, rhPTH(1‐84) dose regimens 25, 50, and 75 μg QD, and 75 and 100 μg BID were not investigated. However, patients in all cohorts received the 100 μg QD treatment and either a 25 or 50 μg BID treatment. The study was also limited by small cohort sample sizes, which have the potential for type II errors in statistical analysis.33 In  Short‐term treatment with rhPTH(1‐84) in this study maintained serum calcium, lowered serum phosphorus, decreased urinary calcium excretion, and increased urinary phosphorus excretion. Overall, no substantial differences in PD parameters were observed with BID versus QD rhPTH(1‐84) dosing. Although these observations were similar both with and without calcium supplementation, the data on urinary calcium excretion suggest that administration of rhPTH(1‐84) without calcium supplementation may be optimal; however, these results pertain to one day of treatment only, thus, data on long‐term treatment should also be considered when evaluating the most effective treatment regimen. No new safety findings were observed with rhPTH(1‐84), whether administered QD or BID, in patients with chronic hypoparathyroidism. Further studies would be needed to better understand the effects of alternative rhPTH(1‐84) dosing strategies on serum and urine biochemistry in patients with chronic hypoparathyroidism.Our study population, although modest, is generally representative of patients with hypoparathyroidism, with the majority of patients included being women above the age of 45 years.34 ShoSteven W. Ing: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Richard D. Finkelman: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Ping He: Formal analysis; methodology; writing – original draft; writing – review and editing. Aliya A. Khan: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Michael Mannstadt: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Lars Rejnmark: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Ivy Song: Conceptualization; formal analysis; methodology; writing – original draft; writing – review and editing. István Takács: Conceptualization; investigation; methodology; writing – original draft; writing – review and editing. Yuna Wu: Formal analysis; methodology; writing – original draft; writing – review and editing.SWI has served as an advisory board member and research investigator for Takeda Pharmaceuticals USA, Inc., and as a research investigator for Amgen Inc., Chugai Pharmaceutical Co., Ltd, Radius Health Inc., and Ultragenyx Pharmaceutical. R.D.F. was an employee of Takeda Pharmaceuticals USA, Inc. at the time the research was conducted and is now a consultant for Takeda Pharmaceuticals USA, Inc. with stock options. I.S. and Y.W. are employees of and hold stock/stock options at Takeda Pharmaceuticals USA, Inc. P.H. was an employee of Takeda Pharmaceuticals USA, Inc., at the time the research was conducted and is now an employee of Travere Therapeutics, San Diego, CA, USA. A.A.K. has served as a research investigator for Takeda Pharmaceuticals USA, Inc. M.M. has served as an advisory board member, consultant, and research investigator for Takeda Pharmaceuticals USA, Inc., as a consultant and research investigator for Calcilytix and Chugai Pharmaceutical Co., Ltd, and as a consultant for Amolyt Pharma. L.R. has served as an advisory board member, research investigator, and speaker for and received research support from Takeda Pharmaceuticals USA, Inc.; L.R. has also served as an advisory board member for Amolyt Pharma and a research investigator for Ascendis Pharma. I.T. has served as a research investigator and received research support from Takeda Pharmaceuticals USA, Inc.https://www.webofscience.com/api/gateway/wos/peer‐review/10.1002/jbm4.10758.The peer review history for this article is available at


The efficacy of adjuvant therapy for patients with cervical cancer with intermediate risk (CC‐IR) remains controversial. We examined the impact of adjuvant therapy on survival outcomes in patients with CC‐IR and evaluated the heterogeneous treatment effects (HTEs) of adjuvant therapies based on clinicopathologic characteristics.We retrospectively analyzed a previous Japanese nationwide cohort of 6192 patients with stage IB–IIB cervical cancer who underwent radical hysterectomy. We created two pairs of propensity score‐matched treatment/control groups to investigate the treatment effects of adjuvant therapies: (1) adjuvant therapy versus non‐adjuvant therapy; (2) chemotherapy versus radiotherapy conditional on adjuvant therapy. Multivariate analyses with treatment interactions were performed to evaluate the HTEs.p = 0.199). Of the patients in the treatment group, 654 and 340 received radiotherapy and chemotherapy, respectively. Patients who received chemotherapy had better PFS than those who received radiotherapy . Tumor size was a significant factor that affected the treatment effects of chemotherapy; patients with large tumors gained better therapeutic effects from chemotherapy than those with small tumors.Among the 1613 patients with CC‐IR, 619 and 994 were in the non‐treatment and treatment groups, respectively. Survival outcomes did not differ between the two groups: 3‐year progression‐free survival (PFS) rates were 88.1% and 90.3% in the non‐treatment and treatment groups, respectively (Adjuvant therapy is optional for some patients with CC‐IR; however, chemotherapy can be recommended as adjuvant therapy, particularly for patients with large tumors. In Japan, 10,879 women were newly diagnosed with cervical cancer in 2019, and 2887 patients died of the disease in 2020.Due to the wide variety of patient and tumor characteristics observed in CC‐IR, the effectiveness of adjuvant therapy might vary among patients. Therefore, we focused on the heterogeneous treatment effects (HTEs) of adjuvant therapies in patients with CC‐IR. HTE analysis focuses on investigating different treatment effects in individuals or subgroups in a population.22.1This study was a secondary analysis of the Japanese Gynecologic Oncology Group (JGOG) dataset (JGOG1072S) generated by a previous study investigating the prognosis of cervical cancer patients who received radical hysterectomy.From the JGOG1072S database, we extracted patients with CC‐IR based on the Japanese Society of Gynecologic Oncology guidelines as follows.2.2The data collected included age, institution, pathology, tumor size, the presence or absence of LNM, parametrial tumor involvement, stromal invasion to the outer half, LVSI, uterine corpus invasion, vaginal invasion, PFS and OS rates, and the context of adjuvant therapy. Age was categorized as <40, 40 to <50, 50 to <60, 60 to <70, and ≥70 years; tumor size was categorized as <2 cm, 2 to <4 cm, and ≥4 cm. The pathological information consisted of histological evaluation (squamous cell carcinoma [SCC] or non‐SCC). Institutions were categorized into two groups: high‐volume and non‐high‐volume centers. High‐volume centers were defined as the top 10 institutions in terms of registered cases since the number of enrollments at these institutions accounted for one fourth of the total enrollment.Patients with CC‐IR were categorized into subgroups according to the presence or absence of adjuvant therapy, namely, treatment and non‐treatment (control) Figure . Patient2.3The Kaplan–Meier method was used in each subgroup to estimate PFS and OS following propensity score matching. Propensity scores, including the probability of receiving adjuvant treatment among patients with CC‐IR and the probability of receiving chemotherapy among the treatment groups, were estimated using logistic regression based on all clinicopathologic characteristics previously presented. Matching was performed using the “Matching” package in R .HTEs were estimated using Cox proportional hazard regression models that considered interactions between treatment indicators and each covariate, as previously reported.First, PFS and OS were compared between the treatment and non‐treatment groups using a propensity score‐matched cohort (treatment vs. non‐treatment) Figure . Second,p < 0.05. All analyses were conducted using R (version 4.2.0).PFS and OS were compared between the chemotherapy and radiotherapy groups to evaluate therapeutic effects according to the type of adjuvant therapy Figure . Subsequ33.1Among the 6192 patients in the original cohort, 1613 were enrolled in this analysis Figure . Among tp = 0.199, Figure p = 0.627, Figure Kaplan–Meier curves based on adjuvant treatment after propensity score matching are shown in Figure Subsequently, we conducted a HTE analysis to identify subgroups that could have a better prognosis with adjuvant treatment Table . “BaseliNext, we compared the rates and types of adjuvant therapy based on the type of institution. No difference was found between high‐volume and non‐high‐volume centers in the rates or types of adjuvant therapies Figure . Among tAs previous studies have demonstrated that the prognosis of patients with CC‐IR worsens according to the number of recurrence risk factors, we subsequently analyzed the frequency of adjuvant therapy based on the number of recurrence risk factors. The frequency of adjuvant therapy increased with the number of recurrence risk factors: 43.5%, 74.8%, and 86.2% in those with one, two, and three risk factors, respectively Figure . In the 3.2Because most patients with more than two recurrent risk factors received adjuvant therapy, we next focused on the effect of chemotherapy on CC‐IR compared with radiotherapy‐based treatment. Of the 994 patients with CC‐IR in the treatment group, 654 and 340 received radiotherapy and chemotherapy, respectively. More than half of the regimens were taxane–platinum combinations, such as paclitaxel plus carboplatin or cisplatin Table . PatientOf the 994 patients in the treatment group, 311 patients in the chemotherapy group were matched with 311 patients in the radiotherapy group Figure . For allp = 0.010), and 5‐year OS rates were 95.5% and 90.3%, respectively (p = 0.041).Kaplan–Meier curves of PFS and OS of patients who received chemotherapy and radiotherapy are shown in Figure Subsequently, we conducted an HTE analysis to identify subgroups that could have a better prognosis with adjuvant chemotherapy than with adjuvant radiotherapy‐based treatments Table . “Baselip = 0.003, Figure p = 0.049, Figure p = 0.441, Figure p = 0.945, Figure Subgroup analysis was conducted after propensity score matching in each subgroup to validate the treatment effect of chemotherapy compared with that of radiotherapy Figure . The bac4In this study, we analyzed the significance of adjuvant therapies for patients with CC‐IR using a nationwide cohort database. Due to adjuvant therapy's selection bias, it has been difficult to perform unbiased analyses using single‐center data. However, because of the differences in treatment selection (for both indications and types of adjuvant therapy) between facilities, using a nationwide cohort database decreases the bias.In the first analysis, we compared survival outcomes between patients with CC‐IR with and without adjuvant treatment and revealed that adjuvant treatment had no significant treatment effects on survival. The treatment and non‐treatment groups had similar 3‐year PFS rates , which are similar to the rates reported previously in the treatment group.The indications and the type of adjuvant therapy depend on the institutions. Therefore, we could compare the treatment effects between the radiotherapy and chemotherapy groups. Our results indicated that patients with CC‐IR who underwent adjuvant chemotherapy had a better prognosis than those who received adjuvant radiotherapy, with 3‐year PFS rates of 90.9% and 82.9%, respectively, which is better than those of previous reports.Notably, adjuvant chemotherapy prolonged OS and improved PFS in patients with cervical cancer. Only definitive radiotherapy‐ and surgical‐based therapies are recognized as definitive therapeutics for cervical cancer,This study had some limitations. First, even when using propensity score matching, we were uncertain about the effect of unobserved factors on disease recurrence or OS. Particularly, information regarding patients' general condition that could affect both the indication for adjuvant therapy and survival, including their performance status and complications, may have improved comparability between the groups. Therefore, a prospective randomized controlled study is warranted to validate the results of this study. Second, because only approximately one fifth of patients with two or more recurrent risk factors were in the non‐treatment group, most of the patients in the matched analysis had only one risk factor, and a limited number of those with two or more risk factors were included. Therefore, indications for adjuvant therapy should be carefully determined for patients with multiple risk factors. However, this study is still important because it confirmed that adjuvant therapy has no treatment effects on PFS and OS in patients with CC‐IR  who are treated at Japanese hospitals. Third, this was a retrospective study with missing data. Some survival events could have been underestimated because of insufficient prognostic follow‐up and short observational periods.In conclusion, adjuvant therapy had no therapeutic effect for some patients; therefore, adjuvant therapy is optional for patients with CC‐IR. However, these results should be applied with caution to patients with multiple risk factors. Regarding the type of adjuvant therapy, chemotherapy may be recommended for patients with CC‐IR, particularly those with large tumor sizes. Further prospective randomized controlled studies are warranted to confirm these results.Ayumi Taguchi: Conceptualization (lead); data curation ; investigation ; visualization ; writing – original draft (lead). Kosuke Kato: Data curation ; formal analysis (lead); investigation ; visualization . Konan Hara: Conceptualization ; methodology (lead); supervision ; writing – review and editing . Akiko Furusawa: Project administration ; writing – review and editing . Yujiro Nakajima: Data curation (supporting); formal analysis (supporting). Chihiro Ishizawa: Writing – review and editing . Michihiro Tanikawa: Writing – review and editing . Kenbun Sone: Writing – review and editing . Mayuyo Mori: Supervision (supporting); writing – review and editing . Muneaki Shimada: Data curation ; supervision ; writing – review and editing . Aikou Okamoto: Supervision . Munetaka Takekuma: Supervision ; writing – review and editing .No funding.The authors declare that they have no conflict of interest.Institutional review board approval for this study was obtained from the University of Tokyo  and Tokyo Metropolitan Komagome Hospital .The institutional review board granted an opt‐out recruitment approach and waived the need for obtaining written informed consent. We adhered to the Declaration of Helsinki.Figures S1–S7.Click here for additional data file.Tables S1–S4.Click here for additional data file.