Severe coronavirus disease 2019 (COVID-19) infection can lead to extensive lung infiltrate, a significant increase in the respiratory rate, and respiratory failure, which can affect the acid–base balance. No research in the Middle East has previously examined acid–base imbalance in COVID-19 patients. The present study aimed to describe the acid–base imbalance in hospitalized COVID-19 patients, determine its causes, and assess its impact on mortality in a Jordanian hospital.2 of 35–45 mmHg, and HCO3− of 21–27 mEq/L. Other patients were divided into 10 additional groups: mixed acidosis and alkalosis, respiratory and metabolic acidosis with or without compensation, and respiratory and metabolic alkalosis with or without compensation. This is the first study to categorize patients in this way.The study divided patients into 11 groups based on arterial blood gas data. Patients in normal group were defined as having a pH of 7.35–7.45, PaCOP<0.0001). Mixed acidosis nearly quadruples the risk of death when compared with those with normal levels . Furthermore, the risk of death was twice as high (OR = 2) for metabolic acidosis with respiratory compensation (P=0.002), respiratory alkalosis with metabolic compensation (P=0.002), or respiratory acidosis with no compensation (P=0.002).The results showed that acid–base imbalance was a significant risk factor for mortality (In conclusion, acid–base abnormalities, particularly mixed metabolic and respiratory acidosis, were associated with increased mortality in hospitalized COVID-19 patients. Clinicians should be aware of the significance of these abnormalities and address their underlying causes. The new strain of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease, which is known as COVID-19, spreads through sneezing and coughing droplets . COVID-1The clinical range of the disease manifests as mild, moderate, or severe illness ,8. IntenSeveral predictors of COVID-19 severity and mortality have been identified . In a re2) tension and the bicarbonate ions (HCO3−) levels in physiological fluids [A normal acid–base balance is necessary to ensure appropriate physiology and cell activity. The occurrence of any acid–base abnormalities increases the probability of experiencing a negative consequence ,15. Acidl fluids ,17. Acidl fluids . An earl2, and HCO3− as coordinates can help to identify compensation and mixed acid–base disorders [As a common acid–base disorder in COVID-19 patients, respiratory alkalosis was linked to an increased risk of severe events [isorders ,20. In aisorders . Anotherisorders .Although there have been some studies about acid–base imbalance in COVID-19 patients, there have been none from the Middle East. Consequently, the present study’s objectives were to describe the acid–base imbalance in hospitalized COVID-19 patients, identify the risk factors for this imbalance, and assess how it affected in-hospital mortality for COVID-19 patients in Jordan. In addition, the present study aimed to describe the clinical characteristics of patients with and without acid–base imbalances and examine the association between distinct acid–base imbalances and the outcome of adult COVID-19 patients.This retrospective study was carried out at King Abdullah University Hospital (KAUH), a tertiary hospital and one of the largest medical structures in Jordan. Patients with COVID-19 who were admitted to KAUH between September 20, 2020, and August 8, 2021, whose arterial blood gas (ABG) measurements upon admission were reported, and whose SARS-CoV-2 nasopharyngeal swab polymerase chain reaction (PCR) confirmed positivity, were included in the study.Patients under the age of 18 and those with missing ABG parameters on admission or who were asymptomatic were excluded from the study. KAUH is a university teaching hospital, so all data are consented to be used in cohort studies once the patient agreed to be treated in the hospital and the IRB committee approved the study.2 (m2) and categorized according to World Health Organization (WHO) guidelines [2) of 94% or more on room air at sea level were classified as having ‘moderate illness’. Patients were classified as having ‘severe illness’ when they exhibited characteristics such as a SpO2 of less than 94%, a PaO2/FiO2 of less than 300 mmHg, a respiratory rate of more than 30 breaths per minute, or lung infiltrates of more than 50%. Patients with respiratory failure, septic shock, and/or multiple organ dysfunction were considered ‘critically ill’.Electronic hospital records were used to identify patients’ clinical data, such as vital signs, comorbidities, and hospitalization course and outcomes. Laboratory results were also included in the study alongside age, gender, smoking status, height, and weight. The body mass index (BMI) was computed using the formula BMI = weight (kg)/heightidelines . Comorbiidelines , the sev2 was between 35 and 45 mmHg, and HCO3− level was between 21 and 27 mEq/L. Patients with respiratory acidosis had a high CO2 concentration (>45) and a pH less than 7.4, whereas patients with respiratory alkalosis had a low CO2 concentration (<35) and a pH greater than 7.4. On the other hand, metabolic alkalosis is considered when HCO3− exceeds 27 mEq/L and pH exceeds 7.4, whereas metabolic acidosis occurs when pH falls below 7.4 and HCO3− falls below 21 mEq/L. The compensatory mechanisms from the respiratory or metabolic sides were also considered in categorizing the patients, as illustrated in ABG values at admission were used to categorize patients. 2 tests. Furthermore, mortality rates, mechanical breathing requirements, and disease severity were assessed using χ2 tests to assess statistical differences across acid–base-categorized groups. In addition to ratio testing, we utilized a nominal logistic regression model to calculate the odds ratio and statistical significance for each of the investigated components and the acid-base status. A two-sided P ≤ 0.05 was regarded as statistically significant.The analysis began with categorizing the patients based on their ABG values on admission. Then, a distributional study of patient features was done across all acid–base classification groups. Summary tables were generated to investigate the proportion of COVID-19 inpatients by age group, gender, and clinical features within each acid–base group. To investigate statistical differences in the frequencies of the categorical groupings, the acid–base status linked with each attribute was studied using χ2, and HCO3− levels, we classified the patients into 11 groups.A total of 1233 patients admitted to KAUH with confirmed COVID-19 met the inclusion criteria and were included in the present study during the study period. Based on their arterial blood pH, PaCOThe distribution of acid–base status among patients hospitalized due to COVID-19 is shown in n=169), males (n=201), obese (n=141), and nonsmokers (n=259). Most patients among all different variable groups (highest percent in a row), according to the parameters analyzed, are shown to have normal acid–base values, with the exception of those who were active smokers, had asthma, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), end-stage renal disease (ESRD), or have low potassium levels. The bulk of the patients who were underweight were not in the normal category either.Normal-parameter patients were predominantly elderly . The highest percent of patients with low potassium levels had RAlkNC (26.7%) and RAlkC (13.3%). Not surprisingly, the highest percentages of patients with normal potassium KFT also have normal ABG values. In descending order, the highest precents of patients with increased potassium levels had normal ABG levels (21.7%), MAC (20.6%), and metabolic acidosis with no compensation (MANC) (19.6%).Further, hypertension, diabetes, dyslipidemia, asthma, COPD, CKD, and ESRD comorbidities, as well as potassium levels, were the only factors found to have a significant impact on acid–base status . Among tP=0.3913). The majority of patients  were classified as critical cases, while the minority  were classified as mild. Critical cases were found in 62.9% of RAlkNC, 61.1% of mixed acidosis as well as RAlkC, 60.3% of respiratory acidosis with compensation (RAC), 58.9% of RANC, and 58.3% of metabolic alkalosis with compensation (MAlkC) cases.Then, we investigated the impact of acid–base status on illness severity. The severity of the patients is shown in P<000.1). Among the acid–base categories, the normal group had the lowest percentage of those who needed mechanical ventilation. Mixed acidosis was the only category that had a larger proportion of patients who needed mechanical ventilation (83.3%) than those who did not.We also investigated the requirement for an invasive mechanical ventilator . In totaP<0.0001). The metabolic alkalosis with no compensation (MAlkNC) (66.0%) and normal (64.2%) groups had the highest survival percentages among the acid–base classed groups. Among the acid–base classed groups, MAC (60%), MANC (53.1%), mixed acidosis (77.8%), mixed alkalosis (66.7%), RAlkC (53.5%), and MANC (53.1%) were more common in deceased patients than in survivors.Then, we explored the impact of the patients’ acid–base status on their survival . About 5P<0.05).Since demographics, comorbidities, and laboratory tests influenced acid-base status and could be a risk factor for mortality, the effect of acid–base status on the death could be attributable to the other investigated factors. To confirm the link between acid–base status and mortality, a regression model that included all analyzed covariates  along with acid–base status was utilized. As numbers show in P=0.049). Furthermore, MAC, RAlkC, or RANC doubles the mortality risk when compared with normal.Even so, the acid–base status remains a substantial risk factor. When compared with those with normal levels, mixed acidosis increases the fatality risk almost four times , CKD , high LDH , low potassium , and high AST  were associated with higher odds of acquiring an acid–base imbalance.Finally, a nominal logistic regression analysis was performed to determine factors associated with the occurrence of acid–base imbalance . Variabln=335, 27.2%). A separate study discovered that just 25% of COVID-19 patients admitted to the ICU had normal acid–base balance, whereas 61% had alkalosis. However, the categorization process used in our investigation and the previously cited study differed [2 = 70.1 ± 32.9 mmHg). Moreover, patients with respiratory failure with hypercapnia (type 2 respiratory failure) were found to experience respiratory acidosis (PCO2 = 62.2 ± 13.4 mmHg) [The present study is the first comprehensive report of the range of acid–base changes as measured by ABG levels among hospitalized COVID-19 patients at a Jordanian hospital. COVID-19 is a highly contagious virus that causes pneumonia , and pnediffered . The mosdiffered . This stdiffered ,22. One .4 mmHg) .In the present study, hypertension, diabetes mellitus, dyslipidemia, asthma, COPD, CKD, and ESRD comorbidities, as well as potassium levels, were shown in the results to have a significant impact on acid-base status. In a study by Alfano et al., it was demonstrated that the presence of CKD and the levels of potassium have a significant effect on the acid-base status of hospitalized COVID-19 patients, similar to our findings . In the The kidneys are responsible for maintaining the body’s acid–base balance by reabsorbing bicarbonate that has been filtered by the glomeruli and excreting titratable acids and ammonia through the urine. Acid retention and metabolic acidosis occur in CKD when renal function declines . The higDespite the fact that only 18 patients had mixed acidosis, 14 of those patients died. Furthermore, when compared with normal, mixed acidosis quadruples the fatality risk. The most frequently reported acid–base derangement in non-COVID-19 critically ill patients is severe mixed acidemia. This was observed in 6% of critically ill patients and was associated with a 57% ICU mortality rate . Our res2 < 35 mmHg) on admission, and those patients have an increased chance of developing severe cases [2 levels <35 mm Hg died [RAlkC is found in 14.0% of patients and has been shown to be a key factor in mortality. When compared with normal patients, it doubled the death risk. In a Chinese study of 230 adult COVID-19 patients, 66 patients (28.7%) showed respiratory alkalosis (pH > 7.45, PaCO Hg died . HoweverWhen compared to patients with normal ABG readings, RANC and MAC doubled the risk of fatality. Lung-protective ventilation in COVID-19 respiratory failure may result in severe respiratory acidosis without considerable hypoxemia . The higThe significant association between the presence of acid–base derangements and increased mortality among hospitalized patients with severe COVID-19 infection should alert treating clinicians to the importance of addressing these abnormalities. Understanding the underlying etiology for acid–base disorders and early intervention for metabolic and respiratory acidosis may help to improve the outcome of these patients. Moreover, early identification of major acid–base disorders like mixed respiratory and metabolic acidosis may help to identify patients with poor prognoses.The present study has a few limitations. It utilized inpatient record data from a single tertiary hospital in the north of Jordan, and data collection was retrospective. In addition, our study was limited to the acid–base status at the time of admission. Future prospective research on the dynamic acid–base status may be required.Acid–base imbalance in hospitalized patients with COVID-19 is a risk factor for mortality, especially mixed respiratory and metabolic acidosis. Rigorous acid–base monitoring during COVID-19 hospitalization should be required in order to identify patients at increased risk of death. Early detection of such derangements may help to prevent future clinical derangements and hence improve survival.Click here for additional data file.


To assess the clinical characteristics and the risk factors related to the unfavorable prognosis of endometrioid ovarian carcinoma (EOVC) based on data from the Surveillance, Epidemiology, and End Results (SEER) database and two clinical centers in China.Data were extracted from the SEER database and two clinical centers in China (2010 ~ 2021), 884 cases and 87 patients with EOVC were selected, respectively. Overall survival (OS) and progression-free survival (PFS) were compared among the different groups using Kaplan–Meier analysis. The Cox proportional-hazards model was used to identify independent prognostic factors related to EOVC. A nomogram was constructed based on the risk factors of the SEER database affecting prognosis and the discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.2 was an independent risk factor relating to OS and PFS. Additionally, C-index of internal and external verification for the nomogram were 0.812 and 0.754 respectively, revealing good accuracy and clinical applicability.The average age at diagnosis of patients with EOVC in the SEER database and two centers in China was 55.77 ± 12.40 years and 47.14 ± 11.50 years, 84.7% and 66.6% of them were diagnosed at FIGO stage I ~ II, respectively. In the SEER database, age over 70 years, advanced FIGO stage, tumor grade 3, only unilateral salpingo-oophorectomy were independent risk factors of unfavorable prognosis. In two clinical centers in China, 27.6% of EOVC patients were diagnosed with synchronous endometriosis. Advanced FIGO stage, HE4 > 179 pmol/L and bilateral ovarian involvement significantly correlated with poor OS and PFS in Kaplan–Meier analysis. Body mass index (BMI) < 19.34 kg/mMost patients were diagnosed at early stage, low grade and had better prognosis. Asian/Pacific Islander and Chinese diagnosed with EOVC were more likely to be younger than whites and blacks. Age, tumor grade and FIGO stage (SEER database) and BMI (two centers) are independent prognostic factors. HE4 appears to be more valuable in prognostic assessment compared with CA125. The nomogram had good discrimination and calibration for predicting prognosis, providing a convenient and reliable tool for clinical decision-making for patients with EOVC.The online version contains supplementary material available at 10.1186/s12885-023-11048-1. Endometrioid ovarian cancer (EOVC) accounts for approximately 10% ~ 13% of epithelial ovarian cancer (EOC) and has clinical and biological difference compared with other pathological subtypes . DomestiMost patients with EOVC are diagnosed at an early stage, and the age of onset tends to be younger, with a better prognosis than that of high-grade serous cancer (HGSOC) and clear cell carcinoma (OCCC) . CA125 aThe Surveillance, Epidemiology, and End Results (SEER) database is currently the largest publicly available cancer statistics database that includes approximately 30% of the United States population . In thisIn this retrospective study, we obtained permission to access the SEER database and extracted data of 884 EOVC patients from Incidence-SEER Research Data, 9 Registries, Nov 2020 Sub (1975 ~ 2018) using the SEER*Stat software, version 8.3.8. Inclusion criteria are as follows: (1) the primary site of malignant tumor is restricted as "ovary"; (2) the pathological subtype is endometrioid carcinoma (ICD-O-3: 8380/3 Endometrioid carcinoma); (3) complete clinical, surgical, pathological, and follow-up data. Exclusion criteria are as follows: (1) the origin of the tumor is uncertain; (2) the patients who have not received surgical treatment; (3) a large number of cases with incomplete data indicators. Variables collected from the SEER database included age at diagnosis, race, origin recode, pathologic grade, SEER summary stage 2000, American Joint Committee on Cancer (AJCC) 7th staging, types of surgery, laterality, the number of lymph nodes (LN) resected, regional LN status, CS mets -brain, CS mets-liver, CS mets-lung, tumor size, CA125 status, follow-up status and survival time. The International Federation of Gynecology and Obstetrics (FIGO) staging is commonly used internationally for EOC, therefore we converted AJCC 7th staging into FIGO staging according to the National Comprehensive Cancer Network (NCCN) guidelines  the patients with histologically confirmed EOVC by pathologists who had undergone comprehensive staging surgery or primary cytoreductive surgery with adjuvant chemotherapy; (2) complete clinical information and pathological information; (3) exempt informed consent. Exclusion criteria are as follows: (1) the patients with synchronous ovary and endometrium carcinoma cannot identify the origin of the tumor; (2) the patients with incomplete clinical data; (3) the patients receiving adjuvant chemotherapy and radiotherapy before surgery; (4) the patients who in other hospital with unknown treatment conditions. Clinical data extracted includes demographic and pathologic characteristics, pre-operative biomarkers, surgery, chemotherapy regimen and course, survival time, and status at last follow-up. All clinical information was anonymized before analysis and this study was approved by the medical research ethics committees of the First Affiliated Hospital of University of Science and Technology of China . Detailed screening of all the cases included in this retrospective study is shown in Figure SEight hundred and eighty-four patients with EOVC in the SEER database received initial surgical treatment. The types of surgery including unilateral salpingo-oophorectomy ± hysterectomy, bilateral salpingo-oophorectomy ± hysterectomy, salpingo-oophorectomy & omentectomy ± hysterectomy, and cytoreductive surgery. No chemotherapy information was obtained from the SEER database.Eighty-seven patients from two centers in China received comprehensive staging surgery or primary debulking surgery. Only 14 patients with FIGO stage I were followed up after surgery, and the remaining patients received paclitaxel combined with platinum-based chemotherapy for 4 ~ 6 cycles after surgery. After treatment, all patients underwent pelvic examination and evaluation of tumor markers at each visit. If necessary, ultrasound, CT, or MRI were performed. Recurrence was defined as histologic evidence by tumor biopsy or fine-needle biopsy and/or the appearance of new lesions on imaging. Overall survival (OS) was defined as the time from initial diagnosis to the time of death by any cause or the last follow-up date, progression-free survival (PFS) was defined as the length of time during and after the treatment that a patient lives with the disease without it getting worse.P-value of less than 0.05 was considered statistically significant and P-value of less than 0.01 was considered highly significant. The nomogram model was constructed, and C-index and calibration curves were calculated using the R Statistical Software, version 4.1.1.All statistical analyses were performed using IBM SPSS, version 26.0 and GraphPad Prism, version 6.0. Baseline characteristics were summarized into means or medians, or counts, or percentages, as appropriate. In the analysis using Kaplan–Meier curves, some variables were classified by cut-off value and the differences between subgroups were evaluated by log-rank test. The chi-square test was used to assess associations between categorical variables. Hazard ratios (HR) were determined using univariate and multivariate Cox proportional-hazards models. The mean age at diagnosis of 884 patients with EOVC is 55.77 ± 12.40 years . The distribution of age category was 35.2%, 31.9%, 19.9% and 13.0%, for those aged 50 years or younger, 51 to 60 years, 61 to 70 years, and those older than 70 years, respectively. Only 72 (8.1%) patients were diagnosed with advanced-stage cancer (FIGO IIIC ~ IV) and 802 patients (90.7%) had early-stage (FIGO IA ~ IIIB) cancer. There were 50 (5.7%) patients diagnosed with regional LN positive and 539 (61.0%) patients showed abnormal CA125 status. And 17 patients had distant metastases to the brain (1 case), lung (4 cases) or liver (12 cases). Finally, 142 out of 884 patients died and 107 of these deaths were attributed to the disease.P < 0.001), FIGO stage (P < 0.001), grade (P < 0.001), SEER summary stage 2000 (P < 0.001), the number of LN resected (P < 0.001) and regional LN status (P < 0.001) were highly significantly associated with OS. Factors with P < 0.01 in univariate Cox analysis were further included in multivariate analysis, showing that age (> 70y), tumor grade (G3) and FIGO stage (III ~ IV) and types of surgery  were significant independent risk factor . Majority of patients  were diagnosed with early-stage disease (FIGO IA ~ IIIB). The CA125 assay was performed in 83 patients and the median level was 235.8 U/mL  and of these, only 9 (10.3%) patients had a negative CA125 level. The median level of HE4 was 178.8 pmol/L (range 39.48 ~ 9999 pmol/L) and the proportions of negative expression were 21.8%. Of the 87 patients, 24 had co-existing endometriosis (27.6%).There were 65 (74.7%) patients and 22 (25.3%) patients who received complete staging surgery and cytoreductive surgery, respectively (Table SP = 0.044), there is no statistical significance of other indicators between the two groups.In the group of 24 patients with co-existing endometriosis, the mean age at diagnosis was 47.25 ± 11.13 years. A comparison of the clinical characteristics of patients with and without co-existing endometriosis is presented in Table STwelve patients eventually relapsed after receiving at least six cycles of taxanes plus platinum following staging or primary cytoreductive surgery. After recurrence, one patient underwent re-cytoreductive surgery and chemotherapy and seven patients only underwent chemotherapy. Eventually, ten patients died of the disease Table S.P = 0.028), HE4 level (P = 0.039), laterality (P = 0.025) and Body mass index (BMI) (P = 0.036) were certain associated with OS in patients with EOVC; age (P = 0.025), FIGO stage (P = 0.003), HE4 level (P = 0.008) and laterality (P = 0.003) were certain associated with PFS in patients with EOVC. Factors with P < 0.1 in univariate Cox analysis of OS were further included in multivariate analysis, showing that BMI (P = 0.020), laterality (P = 0.028), and advanced-stage tumor (P = 0.032) had statistical significance. Similarly, BMI (P = 0.018) had statistical significance in the multivariate bootstrap analysis of PFS  patients were followed-up continuously. Among these, 50 were followed up for more than one year, 29 for more than three years, and only eight for more than five years. Kaplan–Meier survival curves  and 51 to 60 years old (32%), respectively. However, the incidence of EOVC in our centers is significantly higher in people between 31 and 40 years of age than in Asian/Pacific Islander. Although the mean age at diagnosis was younger in our centers, the proportion of patients with FIGO III ~ IV stage was significantly higher than that of SEER database and Asian/Pacific Islander . If the role of endometriosis in the pathogenesis of EOVC is further elucidated, a proper risk model for patients with endometriosis can be developed to prevent the occurrence of EOVC.Endometriosis is thought to play a role in the pathogenesis of ovarian cancer, it’s estimated that patients with endometriosis have a fourfold and 2.32-fold increased risk of ovarian cancer and EOVC, respectively , 17. In Surgical combined with chemotherapy is the most-common strategy used to treatment EOVC. The NCCN guidelines recommend patients with high grade should undergo adjuvant chemotherapy after surgery regardless of FIGO staging. A study assessing the effect of surgical staging and adjuvant chemotherapy on survival in stage I, low grade EOVC patients found that patients with G2 had a significantly high recurrence rate, but adjuvant chemotherapy and staging lymphadenectomy didn’t improve survival . In contP = 0.887).LN are a common site of metastasis in patients with EOC and the therapeutic value of lymphadenectomy during debulking surgery is still under debate . As lateWe found 12 patients diagnosed with endometrioid carcinoma of the ovary and uterus. However, these patients were excluded from our study because of the uncertainty about tumor origin. Synchronous diagnosis of endometrioid ovarian carcinoma and endometrioid endometrial carcinoma (SEO-EEC) is largely documented. Patients with SEO-EEC can be classified into three groups: endometrial cancer with metastasis to the ovary, ovarian cancer with metastasis to the endometrium, or synchronous primary cancers , 34. WomThe study has a few limitations that should be noted. Firstly, the satisfaction of surgery and the sensitivity of adjuvant chemotherapy after surgery affect the overall therapeutic effect of EOVC patients to a large extent. Regrettably, the SEER database didn’t contain detailed postoperative chemotherapy information. The nomogram model constructed based on SEER database may have certain deviation in prognostic prediction on account of the lack of important adjuvant chemotherapy indicators, which can be further improved in subsequent prospective multi-center studies and include more effective indicators in order to build a prognostic prediction model for Chinese patients with EOVC. Secondly, the SEER database provided no information on BMI, HE4, CA19-9, cancer recurrence, and history of endometriosis, it wasn’t possible to compare more detailed differences in clinical characteristics between the two cohorts. Thirdly, the tolerance and severity of adverse reactions of patients in our centers to chemotherapy will affect the cycle and dose of chemotherapy and the therapeutic effect. Due to the limitation of the retrospective study, we did not include more factors affecting chemotherapy for prognostic evaluation, which may cause some offset in the results of the final analysis. We expect to conduct more clinical studies in subsequent studies on patients with ovarian cancer who are not sensitive to advanced chemotherapy. In addition, BMI was an independent prognostic factor in the analysis of data from the two centers in China. Owing to the nature of our study, we did not have information on the hip circumference or the waist-to-hip ratio, which might to be better predictors of all-cause mortality. CA125 and HE4 are important indictors for diagnosis and prognosis evaluation of EOC, it was found that different from HGSOC, HE4 may have greater predictive value for EOVC. Of course, this conclusion was only based on small sample size data, which lacks representation of the whole population, and needs to be verified by multi-center and large-sample clinical studies.2 may be unfavorable risk factors for prognosis. In addition, HE4 and CA125, as prognostic indicator, has certain guiding significance. A prognostic nomogram was developed and validated to assist clinicians in evaluating prognosis of EOVC patients. In the future, more studies are needed to further explore the molecular pathogenesis and treatment strategies of EOVC, a special pathological type, so as to provide new directions and ideas for early screening and early diagnosis of EOVC and improvement of advanced poor prognosis.In conclusion, most EOVCs were diagnosed at an early stage, low grade and had better prognosis. The age of onset for EOVC may be younger in Asian/Pacific Islander and Chinese. The elder, high grade, advanced FIGO stage, only unilateral salpingo-oophorectomy, and BMI < 19.34 kg/mAdditional file 1: Table S1. FIGO and AJCC staging system for epithelial ovarian carcinoma. Table S2. Surgical procedures and clinical outcomes of 87 patients with EOVC. Table S3. Comparison of EOVC patients with/without endometriosis in the two clinical centers in China. Table S4. Treatment and survival outcomes of the 12 patients with recurrence.Additional file 2:Figure S1. Flowchart of the selection for patients with endometrioid ovarian carcinoma. Figure S2. The calibration curves of one-year, three-year and five-year overall survival of the nomogram. Figure S3. Distribution of age, FIGO stage and grade of EOVC patients of the SEER database (N=884) and our centers (N=87).


Hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are the most frequent cardiomyopathies that cause acute heart failure and sudden cardiac death. Previous genetic reports have shown that pathogenic variants of genes encoding Z-disc components such as telethonin protein (TCAP) are the primary cause of DCM and HCM.TCAP gene was analyzed in 40 unrelated patients  who were clinically diagnosed with HCM and DCM. In addition, we conducted a thorough review of all published articles and the databases that were the first to report novel pathogenic or likely pathogenic variants the in TCAP gene.This study was the first investigation on the TCAP gene among the Iranian cardiomyopathies population wherein the TCAP gene in the literature where a majority of mutations were found to be missense. Pathogenic mutations in TCAP may cause diseases including limb-girdle muscular dystrophy 2G (LGMD-2G), DCM, HCM, intestinal pseudo-obstruction, and telethonin deficiency. However, a large number of affected patients were clinically diagnosed with limb-girdle 2G compared to other presenting phenotypes.In the cohort of this study, we identified only one intronic variant c.111-42G > A in one of the HCM patients that were predicted as polymorphism by in-silico analysis. Moreover, a total of 44 variants were reported for the TCAP gene pathogenic mutations might not be a common cause of cardiomyopathies among Iranian patients. These gene disease-causing mutations may cause various manifestations, but it has a high prevalence among LGMD-2G, HCM, and DCM patients.These findings suggest that the  These disorders can be categorized into primary cardiomyopathies which are due to genetics, acquired or mixed factors and solely affect the heart, and secondary cardiomyopathies which result from a systemic illness and impact several parts of the body causing different manifestations including hypertrophic (HCM), dilated (DCM) and restrictive (RCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC) [Cardiomyopathies are defined as a heterogeneous group of pathological conditions stemming from the myocardium's electrical and/or mechanical dysfunction . These dy (ARVC) , 2. Hypey (ARVC) . Dilatedy (ARVC) , 3. Howey (ARVC) .MYH7) gene and the cardiac alpha-actin (ACTC1) gene in developing HCM and DCM, respectively, over 100 genes have been reported to cause cardiomyopathies [CSRP3 (MLP protein), TTN (titin protein), and TCAP (telethonin protein) encode several Z-disc proteins of the cardiac sarcomere can lead to several cardiac dysfunctions [Some patients harbored pathogenic mutations of MLP/TCAP-HCM that phenotypically resemble myofilament-HCM and experience more severe conditions than the subset of patients who remain without a disease-causing mutation [After the identification of pathogenic variants in the myosin heavy chain 7  and dilated cardiomyopathy (DCM) patients referring to our tertiary center in Tehran. The disease-causing effects and functional consequences of the variants in the M-domain of the Titin protein were also determined using prediction analysis.A total number of 74 patients diagnosed with hypertrophic (HCM) and dilated (DCM) cardiomyopathies were registered in the cohort of this study, these patients were referred to our center between 1959 and 2018. Informed consent was obtained from all of the patients. Clinical data were documented from the medical records and provided questionnaires. The given questionnaires thoroughly assembled the information regarding the clinical presentations, other diseases, syndromes, and environmental factors of each patient. The local ethics committee of the Cardiovascular, Medical Research Center approved the research protocol by protocol number (no IR.IUMS.REC.1399.157).Medical evaluations were performed and family histories were recorded for further analysis. From the cohort of this study, 40 patients were selected for genetic testing from whom 20 individuals were diagnosed with HCM , and the other 20 patients were diagnosed with DCM . Clinical presentations in most cases include hypertension, palpitation, dyspnea, syncope, fatigue, and high blood lipid, even though a handful of patients presented cardiac and abdominal edema, ischemia, kidney, and chest pain, muscular complications, and liver disorder. The diagnosis of HCM and DCM in all the patients of this study were confirmed by specialized physicians and the findings of medical testing were Echocardiography (ECG), Magnetic Resonance Imaging (MRI), two-dimensional echocardiography, and necessary clinical laboratory tests.5 ml of peripheral blood sample was obtained from each patient for genetic analysis. Genomic DNA was extracted based on the salting-out procedure. Primers were designed for coding regions which include exon and exon-intronic boundaries of the TCAP gene (NM_003673) Table , and theThen, PCR was performed in a volume of 50 μL reagents on the following condition: 200 ng DNA, 1.5–2 mmol/L MgCl2, 10.5 μL primers (Forward and Reverse primers), 200 mmol/L dNTP, and 1 U of Taq DNA polymerase. The PCR thermal program was initial denaturation for 5 min at 95 °C and 30 cycles for denaturation at 95 °C (30 s), annealing at 58 °C (30 s), extension at 72 °C (30 s), and final extension at 72 °C (10 min). Direct sequencing was implemented with the BigDye Terminator DNA sequencing kit and Genetic Analyzer . Segregation analysis was performed for the families who had a novel variant.The cohort was examined only for TCAP gene variants, since it had never been investigated in the Iranian population. All the mutations identified in the 40 patients of this study’s cohort are shown in Tables TCAP gene variants were analyzed using bioinformatic predicting software, such as MutationTaster, Provean, SIFT, and CADD [Pathogenicity and clinical significance of and CADD –15. NuclFor extracting the necessary information associated with each article, a checklist was used to cover the mutation, mutation type, article title, first author's name, year of publication, ethnicity, sample size , and the rate of consanguineous marriages.https://mutationtaster.org/) and Varsome (https://varsome.com/), and if required, they were corrected. In addition, variants that were reported as pathogenic or likely pathogenic in ClinVar, but were not available in the literature were included in this study. Thereafter, the reported variants were divided into six types including missense, nonsense, deletion, insertion, intronic, and duplication.The variant position in protein was determined based on UniProtKB/SwissProt—O15273 and also using NM_003673. The Human Gene Mutation Database (HGMD) and reference SNP ID (rsID) for the variants were examined using the databases MutationTaster  and the other half with DCM . According to the statistical findings, the prevalence of DCM in this population is higher among males than females. The cohort is classified into three main age groups; children and youngsters < 20), adults (40–20), middle-aged, and elderly (> 40). Medical history revealed that the symptoms started to manifest mostly in adulthood (44%) and middle-aged (35%) and a minority in childhood and youth (21%). On clinical examination, 7.69% of the patients with familial cardiomyopathy were shown to be asymptomatic whereas other patients had the most common presentations of hypertrophic or dilated cardiomyopathy, including hypertension, dyspnea, fatigue, chest pain, and palpitations  showed cardiomyopathy transmission through up to three consecutive generations.Following cardiac disorders, the second and third most commonly reported diseases among the family members of these patients were cancer (20%) and stroke (14%).TCAP gene in the studied cohort revealed one novel intronic variant of c.111-42G > A in intron 1 of an HCM patient (No. 13). In silico analysis tools including MutationTaster, and CADD predicted this variant to be considered as polymorphism. In addition, Varsome considered this variant to have uncertain significance. The electropherogram (sequencing result) of the polymorphism variant and the pedigree of the patient are shown in Fig. Our genetic analysis of the TCAP gene depicted in Tables As for other detected variants, 80% of DCM patients (accounted 18 out of 20) were found to possess previously reported variants of cDNA Level: NM_003673.3(TCAP): c.453A > C, gDNA Level: g.39666058A > C, Protein Level: (p.Ala151 =)(A151 =) whereas no mutation was detected in the remaining patients (20%). Furthermore, 70% of HCM patients (accounted 14 out of 20) were identified to have c.453A > C as well as two other patients with reported mutations including cDNA level: NM_003673.4(TCAP):c.316C > T, gDNA level: g.39665921C > T, protein level: (p.Arg106Cys)(R106C) and cDNA level: NM_003673.4(TCAP):c.110 + 48C > T gDNA level: g.39665517C > T. All the variants detected in this study’s cohort are shown on the TCAP gene in HGMD and Clinvar were extracted that have been identified thus far. A total of 44 reported mutations were included in Table Variants associated with the , contractures, and distal and proximal limb weakness (Table The affected patients presented with diseases including limb girdle muscular dystrophy 2G (LGMD-2G), dilated and hypertrophic cardiomyopathy (DCM and HCM), intestinal pseudo-obstruction, and telethonin deficiency Table . Howeverss Table . A greatTCAP gene in the Iranian hypertrophic cardiomyopathies (HCM) and dilated cardiomyopathies (DCM) populations. The cohort consisted of 17 females and 23 males who were clinically diagnosed with HCM and DCM. Their medical records were documented and their blood samples were genetically analyzed, wherein we detected one novel intronic variant c.111-42G > A in intron 1 of the TCAP gene in one of our patients. This novel HCM-associated variant was predicted to be polymorphism and have uncertain significance by in-silico analysis.This paper was the first to study the MYH7, TTN, MLP, and TCAP. Pathogenic variants of these genes may disturb the structures of the protein or other proteins that bind them and result in functional alterations of the Z-disc complex. Previous studies on the TCAP gene (telethonin protein) suggested that this gene may be a rare cause of cardiomyopathies among the other involving genes, even though dysfunctional telethonin interferes organizing of the structure of sarcomere assembly and regulates the sarcomere length. Therefore, genetic testing is required for the patients to identify their disease-causing variants and apply efficient treatment for alleviating their symptoms and also detect other susceptible family members before worsening their manifestation.Dilated and hypertrophic cardiomyopathies are the most frequent cardiac diseases in the affected patients. These disorders impair the myocardium function and lead to severe complications and sudden cardiac death. Various genes were reported to play pivotal roles in presenting cardiomyopathies, such as scapular winging, and contractures to fewer common ones, such as intestinal complications. LGMD-2G and HCM were the top two common diseases among the patients and a great number of them were found to have nonsense and missense variants, respectively.Hitherto, 44 mutations have been detected in the TCAP that can cause various phenotypes ranging from the most common symptoms of hypertrophy, TCAP gene mutations in the Iranian population, we investigated the TCAP gene in our patients for the presence of HCM and DCM-susceptibility mutations. However, we identified no disease-causing variants in the gene among our cohort suggesting the TCAP gene may not be a common cause of heart failure among Iranian patients.Mainly due to the lack of studies on the TCAP gene mutations in Iranian patients with HCM and DCM.Since our cohort was limited, further analysis is needed to reach a conclusive result regarding the role of


Achieving complete response (CR) after first-line chemotherapy in gastric DLBCL patients often results in longer disease-free survival. We explored whether a model based on imaging features combined with clinicopathological factors could assess the CR to chemotherapy in patients with gastric DLBCL.P < 0.10) and multivariate (P < 0.05) analyses were used to identify factors associated with a CR to treatment. As a result, a system was developed to evaluate whether gastric DLBCL patients had a CR to chemotherapy. Evidence was found to support the model's ability to predict outcomes and demonstrate clinical value.Univariate (P > 0.05).We retrospectively analysed 108 people who had been diagnosed gastric DLBCL; 53 were in CR. Patients were divided at random into a 5:4 training/testing dataset split. β2 microglobulin before and after chemotherapy and lesion length after chemotherapy were independent predictors of the CR of gastric DLBCL patients after chemotherapy. These factors were used in the predictive model construction. In the training dataset, the area under the curve (AUC) of the model was 0.929, the specificity was 0.806, and the sensitivity was 0.862. In the testing dataset, the model had an AUC of 0.957, specificity of 0.792, and sensitivity of 0.958. The AUC did not differ significantly between the training and testing dates (A model constructed using imaging features combined with clinicopathological factors could effectively evaluate the CR to chemotherapy in gastric DLBCL patients. The predictive model can facilitate the monitoring of patients and be used to adjust individualised treatment plans. DLBCL is the most prevalent kind of non-Hodgkin lymphoma (NHL), which is a category of diseases with high heterogeneity. DLBCL accounts for approximately 30–40% of NHL cases  were included. The inclusion criteria were as follows: (1) histopathologically diagnosed gastric DLBCL for the first time; (2) complete clinical and pathological information and auxiliary examination results; and (3) acceptance of a complete treatment cycle in our hospital. The exclusion criteria were (1) poor image quality owing to various reasons and (2) local or systemic antitumour therapy before chemotherapy.We used a random 5:4 split to separate the patients into our training and testing datasets. The model was developed using the training dataset, and its accuracy was checked using the testing dataset. Our institution's ethics committee green-lighted this retrospective study without requiring participants to give their permission.All patients with gastric DLBCL received standard treatment within 1 week after CT, and the treatment plan was six cycles of R-CHOP alone. The response to treatment was determined based on post-treatment physical condition and information gathered from the PET–CT scan results. The patients' electronic medical record (EMR) was mined for information on their clinical and pathological details Table  using thP < 0.10) and then used multivariate logistic regression analysis to determine the independent predictors related to CR after treatment in gastric DLBCL patients (P < 0.05) for model development.Clinical variables relevant to the CR after treatment in gastric DLBCL patients include sex, pre-chemotherapy β2 microglobulin, post-chemotherapy β2 microglobulin, pre-chemotherapy LDH, post-chemotherapy LDH, pre-chemotherapy lesion length, post-chemotherapy lesion length, pre-chemotherapy longest lymph-node diameter, post-chemotherapy longest lymph-node diameter, tumour location, and stage. We first used univariate logistic regression analysis to identify candidate clinical factors related to gastric DLBCL response after treatment . The data were analysed statistically by use of SPSS . Statspackage (version 4.1.1) in R software was used for single and multi-factor logistic regression analysis. pROCpackage (version 1.18.0) was used for ROC curve. ModelGood (version 1.0.9) was used to draw calibration curve. Plot decision curves were plotted using ggDCA (version 1.2).For this retrospective study, we employed the Kruskal–Wallis test for continuous and ordinal variables and either the chi-squared or Fisher's exact test for categorical data. The model's discrimination, sensitivity, and specificity for the occurrence of events were measured using the ROC and AUC curves. A significance level of P > 0.05). However, β2 microglobulin before and after chemotherapy, LDH after chemotherapy, lesion length before and after chemotherapy, longest diameter of the lymph node before and after chemotherapy, and stage differed significantly between the two groups (P < 0.05). There were no significant differences (P < 0.05) in clinical and pathological features between the testing and training datasets  showed that β2 microglobulin before and after chemotherapy, LDH after chemotherapy, lesion length after chemotherapy, longest diameter of the lymph node before and after chemotherapy, and stage were significantly associated with treatment response after chemotherapy in patients with gastric DLBCL. All seven significant variables were included in the multivariate analysis, and β2 microglobulin before chemotherapy and lesion length after chemotherapy were statistically significant , indicating that the prediction model has a high discriminative ability. The calibration curve . Decision curve analysis of the testing dataset (Fig. Based on these three independent prognostic factors, a model and nomogram for evaluating the CR after treatment in gastric DLBCL patients were constructed Fig. . The prorve Fig. a showed set Fig. b showed Following chemotherapy in gastric DLBCL, treatment responses vary widely, ranging from no tumour regression to complete remission (Yin et al. Univariate and multivariate analyses revealed that β2 microglobulin levels before chemotherapy and lesion length after chemotherapy were independent predictors of CR in patients with gastric DLBCL. Wilder et al.  demonstrKim et al.  used absThis study had several limitations. First, a small number of quantitative features were extracted from the PET–CT images, and the model only considered the length of the lesion. Therefore, selection bias may have occurred in our analysis. Second, the number of patients we included is small, and most of them are concentrated in phase I and phase II. In future studies, we will further expand the number of patients; third, this was a single-centre study, and prospective multicentre experimental studies are needed to validate the model experimentally. In the future, we will consider using radiomics to evaluate the response of patients with gastric DLBCL after chemotherapy and explore more clinical risk factors related to the prognosis of these patients, such as lesion volume and metabolic tumour burden.A model constructed using indirect imaging features combined with clinicopathological factors could effectively evaluate the response of patients with gastric DLBCL after chemotherapy. Our findings are of great significance as the model could be used to facilitate careful and regular patient monitoring, accurately evaluate the treatment response, and adjust the individualised treatment plans.


Cardiovascular and renal diseases are closely related. Brain natriuretic peptide (BNP) and urinary albumin are established predictors for cardiac and renal morbidities, respectively. To date, no reports have investigated the combined predictive value of BNP and urinary albumin for long-term cardiovascular–renal events in patients with chronic kidney disease (CKD). The aim of this study was to investigate this theme.Four hundred eighty-three patients with CKD were enrolled into this study and followed-up for 10 years. The endpoint was cardiovascular–renal events.During the median follow-up period of 109 months, 221 patients developed cardiovascular–renal events. Log-transformed BNP and urinary albumin were identified as independent predictors for cardiovascular–renal events, with a hazard ratio of 2.59  and 2.27  for BNP and urinary albumin, respectively. For the combined variables, the group with high BNP and urinary albumin had a markedly higher risk  of cardiovascular–renal events compared with that of the group with low BNP and urinary albumin. Adding both variables to a predictive model with basic risk factors improved the C-index , net reclassification improvement , and integrated discrimination improvement  more than each of them alone.This is the first report to demonstrate that the combination of BNP and urinary albumin can stratify and improve the predictability of long-term cardiovascular–renal events in CKD patients. The aim of this study is to investigate this theme.Biomarkers are helpful in assessing these conditions. Urinary albumin, a representative urinary biomarker, is related to cardiovascular disease (CVD) and death as well as to renal prognosis.8 between February 2009 and September 2010. The inclusion criteria were as follows: (1) urinary albumin >30 mg/g Cr or estimated GFR (eGFR) <60 mL/min/1.73 m2; (2) eGFR >15 mL/min/1.73 m2; (3) a follow-up period of at least 1 year; and (4) BNP and urinary albumin were measured at enrollment.Patients were selected from the cohorts that were enrolled in our previous studies9 was used. Stroke was defined as the presence of clinical signs of focal or global disturbances in cerebral function caused by cerebrovascular damage. Progression of renal dysfunction was defined as the doubling of serum creatinine (Cr) or the initiation of renal replacement therapy.The study participants were followed-up clinically until April 2020. The primary endpoint was the composite of the occurrence of cardiovascular events and progression of renal dysfunction. Cardiovascular events were defined as HF requiring hospitalization, myocardial infarction, angina pectoris requiring a therapeutic intervention, stroke, aortic dissection, or aortic aneurysm rupture. The Framingham criteria for HFAll procedures that involved human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee at the Fujita Health University School of Medicine (authorized number: HM20-059). Informed consent was waived because of the retrospective nature of the study.10 Urinary albumin levels were divided into normal to mild (30 mg/g Cr), moderate (30–300 mg/g Cr), and high categories (>300 mg/g Cr) in accordance with the Kidney Disease Improving Global Outcomes (KDIGO) CKD guidelines.2 Hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular–renal events, cardiovascular events, or renal events were calculated for each factor using the Cox proportional hazards analysis after adjusting for age, sex, systolic blood pressure, hemoglobin, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, eGFR, history of CVD, and diabetes. To assess whether the accuracy of predicting endpoints would improve after combining BNP and urinary albumin in a baseline model using the already known risk13 score, we calculated the C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The C-index, which is defined as the area under the receiver operating characteristic curve between the individual predictive probabilities for endpoints and the incidence of endpoints, was compared using the baseline model.14 The NRI is a relative indicator of how many patients showed an improvement in the predicted probability for the endpoints. The IDI indicates the average improvement in the predicted probability for the endpoints after adding variables to the baseline model.15 A p-value <0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 25.0 .Normally distributed variables are expressed as the mean±standard deviation, whereas nonparametric data are presented as the median and interquartile range. Intergroup differences were evaluated using a one-way analysis of variance, and the Kruskal–Wallis and Chi-square tests were used for continuous and categorical variables, respectively. Cumulative incidence rates of cardiovascular–renal events were estimated using the Kaplan–Meier method, and intergroup differences were compared using a log-rank test. BNP levels were divided into low (<40 pg/mL), moderate (40–100 pg/mL), and high (>100 pg/mL) categories, in accordance with the JCS 2017/JHFS 2017 Guideline on Diagnosis and Treatment of Acute and Chronic Heart Failure.2. Clinical characteristics on the basis of the BNP category are shown in Patient selection is shown in During the follow-up period of 109 (56–120) months, 221 patients (45.8%) developed cardiovascular and renal events; these included doubling of serum Cr levels in 144 patients (29.8%), induction of dialysis in 101 patients (20.9%), HF in 42 patients (8.9%), angina pectoris/myocardial infarction in 41 patients (8.5%), cerebral stroke in 48 patients (9.9%), and aortic dissection/aneurysm rupture in four patients (0.8%). On Cox multivariable analysis, log BNP  and log urinary albumin  were identified as independent predictors for cardiovascular–renal events after adjusting for sex, age, systolic blood pressure, hemoglobin, HDL-cholesterol, LDL-cholesterol, eGFR, history of CVD, and diabetes . For carP<0.0001; When patients were divided into high, moderate, and low groups on the basis of BNP and urinary albumin levels, the high BNP group had a risk of cardiovascular–renal events that was 2.41-times  higher than that of the low BNP group. A similar result was obtained on the basis of urinary albumin ; NRI, 0.497 (p<0.0001); and IDI, 0.071 (p<0.0001). When BNP and urinary albumin are compared individually, the combination of BNP and urinary albumin significantly improved the NRI  and IDI  better than each of the variables alone. Thus, these results might increase the knowledge about biomarkers for cardio-renal syndrome.16 and it is routinely used in clinical practice to diagnose or to exclude HF. BNP was classified as a Class 1 recommendation for diagnosis, severity, and prognostic evaluation in the 2017 guideline for diagnosis and treatment of acute and chronic HF.10BNP is released by the left ventricle in response to left ventricular end-diastolic wall stress,18 The following three fates are proposed for secreted BNP: (i) capture by its receptors for signal transduction or clearance; (ii) inactivation by neutral endopeptidase; or (iii) excretion from the kidney in its active form.19 In the highest urinary albumin category (300 mg/g Cr) in this study, the aHRs of cardiovascular–renal events between the BNP <40 pg/mL and 40<BNP<100 pg/mL categories were comparable. These results might be attributed to decreased excretion from the kidney that is caused by reduced renal function.Patients with CKD have higher baseline BNP levels compared with those of matched patients with normal renal function because of impaired renal clearance, chronic pressure/volume overload, and CKD-associated cardiomyopathy.20 In the consensus conference on CRS that was sponsored by the Acute Dialysis Quality Initiative (ADQI), CRS was classified into five subtypes.3 In CRS type 2, chronic HF results in CKD onset or progression. Examples of CRS type 2 include progressive CKD resulting from chronic HF in congenital or acquired heart disease or from repeated bouts of acute decompensated HF. Extended periods of chronic HF result in altered renal hemodynamics and progressive renal pathology related to albuminuria. The correlation of BNP with renal events in the present study may reflect this pathophysiology.BNP levels are also significantly elevated in patients with CRS.3 The common traditional risk factors for CKD and CVD are older age, smoking, hypertension, diabetes, and dyslipidemia.21 Other factors associated with CKD, such as anemia, albuminuria, CKD–mineral and bone disorder, inflammation, increased renin–angiotensin–aldosterone system activation, sympathetic nervous system activation, oxidative stress, and insulin resistance, are also associated with the development of CVD.13 The correlation of urinary albumin with BNP also reflects these common backgrounds.However, in CRS type 4, CKD promotes the progression of chronic HF, ventricular hypertrophy, and diastolic dysfunction and increased the risk of adverse cardiovascular events.13 which comprised age, sex, systolic blood pressure, hemoglobin, HDL-cholesterol, LDL-cholesterol, eGFR, history of CVD, and diabetes, and found that combining BNP and urinary albumin, rather than each factor alone, significantly improved the prediction of cardiovascular and renal events at 109 months better than the basic risk factors, as shown by the increase in the C-index, NRI, and IDI. Thus, measurement of both BNP and urinary albumin might improve the quality of risk management in clinical practice.We assessed the effect of adding BNP and urinary albumin to the basic risk factors,22 A drug-induced reduction in urinary albumin was also found to be renoprotective.23 On the basis of the indications for drugs that have been shown to inhibit both cardiovascular and renal events, evaluating BNP combined with albuminuria might help to improve patients’ prognosis. Monitoring the change in BNP and albuminuria will also help to evaluate the effect of drugs such as renin–angiotensin–aldosterone system inhibitors24 and sodium–glucose cotransporter-2 inhibitors.25A meta-analysis showed a decrease in the hospitalization rate due to HF, CVD, or all-cause mortality when patients underwent BNP-guided therapy.26 researched the association between plasma BNP level and renal function among Japanese patients with type 2 diabetes mellitus. In their report, the highest BNP level (≥39.2 ng/mL) was independently and positively associated with microalbuminuria and macroalbuminuria. Naganuma et al.27 investigated BNP and urinary albumin as markers for cardiovascular evaluation of kidney donors. They found that plasma BNP concentrations in kidney donors were significantly higher than those in the healthy volunteers, and among kidney donors, 16% had microalbuminuria and 4% had overt proteinuria. Wang et al.28 evaluated the incremental usefulness of ten multiple biomarkers for predicting the risk of cardiovascular events in Framingham Heart Study participants. They showed that BNP and the urinary albumin-to-creatinine ratio were the best predicted major cardiovascular events. However, there have been no reports on the significance of the combination of BNP and urinary albumin as a predictor of cardiovascular–renal events over a long period in CKD patients. Thus, the present study is novel.Several reports have been published about the relationship between BNP and urinary albumin. Furukawa et al.This study had some limitations. First, this was a single-center cohort study. Second, serum Cr levels were affected by both the eGFR and muscle mass. Third, we did not assess the serial changes in BNP and albuminuria over time.In conclusion, BNP and urinary albumin are closely associated with long-term cardiovascular–renal events in CKD patients. The present report is the first to demonstrate that the combination of BNP and urinary albumin can stratify and improve the predictability of long-term cardiovascular–renal events in CKD patients.


ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5–11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants’ biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case–control study of 499 autistic and 209 typically developing (TYP) children, ages 18–48 months, enrolled in the Children’s Autism Metabolome Project (CAMP;  As autiClinicalTrials.gov Identifier: NCT02548442) to recruit a large number of children, ages 18–48 months, and used metabolomics-specific protocols to identify biomarkers and metabolic phenotypes associated with autism.A range of biomarker modalities for the screening of autism have been investigated including genomic, transcriptomic, proteomic, neuroimaging, EEG, eye tracking and metabolic markers . There hMetabolic phenotypes are biochemical signatures that reflect an individual’s unique metabolism and result from the interplay of one’s genetic background, environment, microbiome, co-occurring conditions, and diet . Due to The current study further explores the hypothesis that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements can identify metabolic subpopulations of autistic individuals , 9, 10. 2.2.1.ClinicalTrials.gov Identifier: NCT02548442) study enrolled 1,102 children, ages 18–48 months, across 8 clinical sites from August, 2015 through January, 2018. We selected this age range because a consensus has emerged that a professional diagnosis of autism can be carried out accurately as early at 18 months of age. The centers included: The Children’s Hospital of Philadelphia; Cincinnati Children’s Hospital; The Lurie Center at Massachusetts General Hospital; The Melmed Center; The MIND Institute, University of California – Davis; Nationwide Children’s Hospital; The University of Arkansas for Medical Sciences; and Vanderbilt University Medical Center. Written informed consent from a parent or legal guardian was obtained and monetary compensation was provided to each participant. The study protocol was approved and monitored by Institutional Review Boards at each of the clinical centers.The Children’s Autism Metabolome Project  assessment  was perf2.1.3.Enrollment was limited to one child per household to minimize genetic or family environmental effects. Children participating in other clinical studies could not have used any investigational agent within 30 days of participation. Children were excluded from the study if they were previously diagnosed with a genetic condition such as Fragile X syndrome, Rett syndrome, Down syndrome, tuberous sclerosis, or inborn errors of metabolism. Participants with fetal alcohol syndrome, serious neurological disorders, metabolic, psychiatric, cardiovascular, or endocrine system disorders were also excluded. Participants exhibiting acute signs of illness within 2 weeks of enrollment such as vomiting, diarrhea, fever, cough, or ear infection were rescheduled.2.2.Blood was collected from participants who had not eaten for at least 12 h by venipuncture into 6 mL sodium heparin tubes placed on wet ice , 26. Pla2.3.Three quantitative LC–MS/MS methods measuring 54 small molecule metabolites were performed in a CLIA-certified laboratory. The methods were analytically validated in compliance with FDA and CLSI guidance for bioanalytical method validation , 33. Qua2.4.CAMP enrolled 1,102 participants and 916 met the inclusion and exclusion criteria described above . Of thes2.5.We measured the concentrations of 54 metabolites and also evaluated the ratios of these metabolites . MetabolThe metabolite ratios were generated from all unique combinations of the metabolites except 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), 4-ethylphenylsulfate (4-EPS) and dodecanedioic acid where 90% of the measurements were below the LLOQ. To create uniform visualization of metabolite ratios, the numerator and denominator were selected to yield a ratio with values that are greater than the biomarker threshold . The concentrations of each metabolite or ratio of metabolites values were log base 2 transformed to reduce skewness and standardized  by subtracting the mean and dividing by the standard deviation prior to analyses. Participants’ metabolite measurements with missing data were omitted from analysis reducing the number of samples analyzed for a test statistic or imputed with the median value when missing values are not allowed by a test statistic. Analyses were conducted using R version 4.1.0 .2.6.p-value <0.1 (Receiver operator curve (ROC) analysis was used to select a biomarker value threshold  that maxlue <0.1 , 26.The final model thresholds were set using the entire study set of CAMP autism and TYP participants. These thresholds were used to generate participant biomarker outcome profiles by scoring a participant positive or biomarker negative for each biomarker. CAMP participant biomarker profiles were used to cluster the biomarkers, determine the prevalence of biomarker clusters in CAMP, and comparisons based on biomarker positive and negative populations. An overview of the methods used for biomarker selection and creating participant biomarker profiles is presented in 2.7.Clustering was performed to reduce the complexity of the biomarkers by aggregating related biomarkers or biomarker clusters into groups. Hierarchical complete-linkage clustering of the participant biomarker outcome profiles was performed using the Jaccard distance based on scoring a biomarker outcome as 0 = negative and 1 = positive. The optimal number of biomarker clusters was estimated using the maximum value of the average silhouette width cluster validation index over a range of 5 to 50 clusters. The biomarker clusters were further evaluated by clustering the fold changes of metabolites by hierarchical complete-linkage clustering using a distance matrix based on the Pearson correlation coefficients (|1-r|) of the metabolite fold changes. The biomarker clusters dendrogram was cut based on similar patterns of fold changes for a subset of metabolites (see Results). Clustering was performed using the R packages ComplexHeatmap  and NbCl2.8.CAMP participant phenotypic and demographic information were based on physical and neurological examinations and behavioral testing performed by clinicians as well as a parental questionnaire. CAMP information from autistic and/or TYP children related to demographic information, diet, medications, behavioral assessments, or co-occurring conditions were selected for association analysis. The selected information was filtered to remove questions missing responses in more than 10% of autistic participants or that had an identical response in >98% of participants. The percent of ideal body weight (IBW) was based on the method of Traub and Johnson .2.9.post hoc tests for the Kruskal-Wallis tests. Association analysis between biomarker positive and negative populations and CAMP metadata was performed using Fisher exact tests for categorical variables and Welch t-tests for continuous metadata variables. Post hoc tests for Fisher exact tests of categorical metadata variables were performed by creating a dichotomous response variable for each response of the categorical variable followed by a response wise Fisher exact test. Effect sizes were reported using Cohen’s d for Welch t-tests, r statistic for Wilcoxon independent two sample tests, bias corrected Crammer’s V or odds ratios for Fisher’s Exact tests, and rank eta squared (η2) for Kruskal-Wallis tests. False discovery rate corrections  were performed to control for multiple comparison testing  with age as a covariate were used when a demographic or phenotypic variable was continuous and a Kruskal-Wallis test was used when the metadata variable was categorical. Response wise Wilcoxon rank sum tests were used as  testing . Partial testing .3.3.1.p-value <0.05, Plasma samples from 708 CAMP participants corresponding to 499 autistic and 209 typically developing children were utilized in this study. The autistic population was 2.5 months older than the TYP population  but low sensitivities (5–11%). The high specificity of these tests allows for a ‘stacking approach’ whereby a subset of the 153 biomarker-based tests can be combined into a battery of tests that increases overall test sensitivity for a likelihood of autism. Using a test optimization process to maintain a specificity of at least 90% , a subse3.6.The levels of biomarkers corresponding to metabolite concentrations and ratios of metabolite values from autistic participants were evaluated for associations with behavioral test scores, demographic factors, co-occurring conditions, diets, and medications.3.6.1.ρ = −0.26 to 0.15, adj. p-values <0.1) with 50 biomarkers primarily consisting of metabolite ratios containing lactate, pyruvate, a TCA intermediate, xanthine or hypoxanthine  and Alanine/2-Ketoglutarate   and Methionine/Carnitine  were increased in children with preferred diet (food selectivity)  and Lactate/Tyrosine  were increased in participants on special diets  was increased in females compared to males  and Glycine/Methionine  exhibited lower levels in Asians compared to White participants (The age of the participants was correlated (xanthine . BMI was = 0.04) . The carctivity) . The leval diets . The ornto males ; no otheicipants . Associa3.6.2.ρ = 0.17, adj. p-value = 0.04)  and Lactate/Threonine . MSEL DQ values are negatively correlated with increased lactate  and 8 lactate-containing ratios  and these ratios are also negatively correlated with one or more scores in MSEL domains (The biomarker Arginine/Tyrosine was associated with increased ADOS-2 CSS ( = 0.04) . Increas domains . Lactate domains .3.6.3.η2 = 0.02, adj. p-value = 0.02) was increased with delayed attainment of walking  with increased levels of lactate and metabolite ratios containing lactate . The number of biomarker clusters was also correlated  with more severe SA CSS. The number of clusters was not associated with RRB CSS . That the number of clusters have an association with SA CSS and lack of association with RBB CSS suggests that the increased autism severity associated with increased number of clusters is related to deficits in social communication.The autistic participants were evaluated for associations with autism severity based on the ADOS-2 test scores  and the p-value = 0.008). Decreased DQ was associated with a negative correlation of expressive  and receptive language , fine motor  and visual reception  with increasing numbers of clusters.The autistic participants with increasing numbers of biomarker clusters exhibited more severe developmental delays as measured by lower MSEL test scores . The DQ p-value > 0.1 or Spearman partial correlation test adjusted p-value>0.1) with age, race, BMI, diet, medications, sleep problems, gastrointestinal issues, food selectivity, or delayed attainment of early language or motor milestones . We requested information about special diets but some relevant dietary information may not have been obtained. Full laboratory and genomic information were not available for study participants, thereby limiting other clinical correlations and additional understanding of underlying pathophysiology. The metabolic observations lack longitudinal measurements in the study participants which would be required to evaluate the stability of the biomarkers over time.This study reveals numerous quantitative biomarkers that identify metabolic subpopulations of autistic individuals. These biomarkers have low sensitivities and high specificities that are individually of limited diagnostic value but, in aggregate, have associations with autism-related behaviors and metabolism. These biomarkers can be leveraged as an adjunctive tool for early screening of children considered likely to attain a diagnosis of autism . BiomarkThe original contributions presented in the study are included in the article/The studies involving humans were approved by the Western Institutional Review Board and the IRBs of all 8 participating clinical centers. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.AS conducted bioinformatic analyses related to biomarker, clustering, and association analyses. AS, RB, and MN performed research for the manuscript. AS, ED, DA, RB, and MN wrote the manuscript. AS, ED, DN, DA, RB, and MN edited the manuscript. AS, RB, ED, and DA were involved in the design and execution of the Children’s Autism Metabolome Project. All authors contributed to the article and approved the submitted version.


Little is known about nursing home (NH) residents' family characteristics despite the important role families play at end-of-life (EOL).n = 43,405).To describe the size and composition of first-degree families (FDFs) of Utah NH residents who died 1998–2016  and compared sociodemographic and death characteristics of those with and without FDF members (n = 9424).Using the Utah Population Caregiving Database, we linked NH decedents to their FDF (p < 0.001). Despite similar levels of disease burden (Charlson Comorbidity score 3 + 37.7% vs. 38.0%), those without FDF were more likely to die from cancer (14.2% vs. 12.4%), Chronic Obstructive Pulmonary Disease (COPD) (6.0% vs. 4.0%), and dementia , and were less likely to have 2+ hospitalizations at EOL .Compared to NH decedents with FDF (78.3%), those without (21.7%) were more likely to be female (64.7% vs. 57.1%), non-White/Hispanic (11.2% vs. 4.2%), less educated , and die in a rural/frontier NH (25.3% vs. 24.0%, all Among NH decedents, those with and without FDF have different sociodemographic and death characteristics—factors that may impact care at EOL. Understanding the nature of FDF relationship type on NH resident EOL care trajectories and outcomes is an important next step in clarifying the role of families of persons living and dying in NHs. Many NH residents, however, do not have any family to advocate for their care, which may impact their EOL care.12 Despite the influential role of families at EOL, little is known about the characteristics of families of those who die in a NH. The purpose of this brief report is to describe the sociodemographic and death characteristics of NH decedents with and without first-degree family (FDF) members.Families often play a critical role in person-centered care for NH residents, promoting quality of life and making EOL care decisions.14 who died in a Utah NH of natural causes between 1998 and 2016. The Utah C-PopS cohort is derived from the Utah Population Database15–18 which is a statewide population health research database that links individual-level administrative and health data to spousal and FDF members' data through a population pedigree  structure. FDF include children, siblings, and parents. Key variables and data sources used to measure decedent sociodemographic, EOL care, and death characteristics are described elsewhere.13 Descriptive statistics, chi-square tests, and t-tests were performed to describe the NH decedent cohort and their FDF members, as well as to compare sociodemographic, socioeconomic, and death characteristics of NH decedents with and without FDF. All descriptive statistics analyses were performed using R statistical software . This study was approved by the University of Utah's Resource for Genetic and Epidemiologic Research and Institutional Review Board.This retrospective cohort study focused on 43,405 individuals in the Utah Caregiving Population Science (Utah C-PopS) cohortn = 9424) did not have any FDF members and 78%  were linked to ≥1 adult FDF . The most common primary causes of death were heart disease (21.1%), dementia (16.7%), and cancer (12.8%).Nearly 40% had moderate levels of comorbidity (Charlson Comorbidity Index [CCI] 3+),p < 0.001) and a spouse .The median number of FDF members for NH decedents was two (IQR = 0–26). Male decedents were more likely to have at least one documented FDF compared to females . The median age of FDF members at time of decedent death was 63.4 (IQR 20.01–108.85). The majority of FDF members were female (52.6%), White/non-Hispanic (95.0%), married (60.9%), with high school/some college education (53.6%), and lived in an urban setting (75.2%).p < 0.001, p < 0.001), compared to NH decedents with FDF. While NH residents with and without FDF members had similar levels of disease burden (CCI score 3 + 37.7% vs. 38.0%), NH residents without any FDF members were less likely to have two or more hospitalizations at EOL .Compared to NH decedents with FDF members, those without were more likely to be older (median age 85.3 vs. 84.6), female (64.7% vs. 57.1%), non-White/Hispanic (11.2% vs. 4.2%), less educated , have Medicaid only (4.0% vs. 2.3%), and die in a rural/frontier NH , those without FDF were less likely to have multiple hospitalizations at the EOL compared to those with FDF. Such a finding aligns with our prior mixed-methods studies, as well as other studies in the literature7–13 suggesting that families may advocate for more aggressive care at the EOL. While fewer hospitalizations at EOL are generally perceived as a desirable outcome, it is not clear whether this finding reflects underlying social determinants of health that impact access to care and contribute to EOL health disparities,22 or the significant role family may play in advocating for more aggressive care at the EOL, as other studies have found.23–27 Future research should examine patterns of EOL health care utilization, including NH length of stay and use of palliative and hospice care to better understand the role families play at EOL.Findings highlight gender, race, ethnicity, health insurance, socioeconomic, and geographic disparities in NH decedents without FDF—factors that may impact EOL care for an already vulnerable population.29 Our study found that nearly 25% of all decedents died in a rural/frontier NH. Despite national policy initiatives to promote home- and community-based services over institutional care,30 these efforts have been slow to be realized for individuals living in rural areas who have limited access to NH alternatives such as formal paid care, assisted living facilities, and adult day care centers.32 Future research is needed to better understand the impact of family, or lack thereof, on rural NH resident EOL care trajectories.While 90.6% of the population in Utah lives in urban settings, ∼20% of Utah NHs are located in rural counties. Rural NHs are an important focus for policymakers given the lack of available postacute and long-term care services in these areas.34 Second, this study is our first analysis of this NH cohort, and we only examined those who died in a NH and the binary association of FDF on EOL hospitalizations. Future work will link CMS Minimum Data Set, electronic health record, administrative claims, and other data for both NH residents and their FDF to examine the effect of types of family on NH resident EOL care outcomes, and how caring for a NH resident impacts family health. Third, not all family members serve as caregivers, or they may live out of state yet still provide substantial long-distance caregiving—factors not accounted for in this study. Other studies, suggest, however, that family geographic proximity, as well as their employment status and/or own health status may impact the role they want, or can, play in the care of NH residents.9 Despite possible limitations, this is the first study to find that among NH decedents, those with and without FDF have different sociodemographic and death characteristics—factors that may impact care at EOL. It further sets the stage for future research understanding the critical role that family plays in EOL care experiences of NH residents.We acknowledge several study limitations. First, this study utilized data from one state; therefore, findings may not generalize to the national NH population. Nevertheless, study NH population demographics mirror national estimates that NH residents are predominately female, White, with at least a high school education.36 Understanding the nature of FDF relationship type on NH resident EOL care trajectories and outcomes is an important next step in clarifying the role of families of persons living and dying in NHs.This study begins to illuminate vulnerable populations of NH residents  for whom greater support may be needed to reduce health disparities and improve EOL care. These findings align with previous research suggesting NH families may need more anticipatory guidance and palliative care support to reduce potentially aggressive care at the EOL.


Protein-energy wasting (PEW) is a common and serious co-morbidity in haemodialysis (HD) patients. Its importance as a prognostic factor has been increasingly recognised during the past decades. Much effort has been invested in the improvement of nutritional status and amelioration of consequences through different therapeutic approaches, either intradialytic parenteral nutrition or more commonly oral nutritional supplementation. In the article, we present the results of a prospective study in HD patients after 12 months of therapeutic intervention with oral nutritional supplements (ONS).A total of 92 HD adult patients were enrolled in the study after 3 months of wash-out period. Baseline nutritional status was assessed using composite scores, laboratory markers, bioelectrical impedance analysis, and hand-grip strength test. Patients recognised as undernourished or at high risk for undernutrition received renal-specific commercially available ONS on HD day in addition to their regular diet. After 12 months, the effect of ONS on surrogate markers of undernutrition, serum albumin level, phase angle, and hand-grip strength was analysed in 71 surviving patients.After 12 months, data for 71 patients, 39 (54.9%) men, 62.4 ± 12.9 years, and median haemodialysis vintage 53.3 (IQR 27.5–92.8) months, were available. Patients were divided into three groups: group A patients were with normal nutritional status at baseline not necessitating ONS; group B patients received ONS; and group C patients were entitled to receive but refused to take ONS. The baseline results showed statistically significant differences between the groups in serum albumin levels and phase angle but not hand-grip strength. Differences between the groups remained statistically significant at month 12; we did not find any statistically significant positive changes within the groups, indicating no positive effect of intervention with ONS.In a prospectively designed interventional single-centre study, we did not find a statistically significant change in surrogate markers of PEW in our cohort of HD patients, receiving ONS for 12 months. Since PEW is an independent risk factor influencing the survival of HD patients, efforts should be directed towards a timely and comprehensive nutritional approach, including intensive, personalised dietary counselling, increase in protein and energy intake and advocating tight control of nutritional status during HD treatment, possibly providing psychological support and motivation. Protein-energy wasting (PEW) is highly prevalent in patients with chronic kidney failure, especially in patients undergoing maintenance haemodialysis (HD) . PEW is This study aimed to assess the effect of long-term (12 months) ONS on surrogate markers of PEW in a real-life, single-centre cohort, prospective interventional study in HD patients.This was a prospective, longitudinal interventional study with repeated measure design on a well-defined cohort of HD patients at the Department of Dialysis, University Medical Centre Maribor, Slovenia.We assessed 140 consecutive patients for study eligibility. All patients on HD were considered. Patients younger than 18 years and undergoing dialysis for less than 6 months were excluded. Thirty-eight patients refused to participate. Finally, 92 patients were included in the study. 2) = body weight (kg)/body height (m)2 and MUAMC (cm) = MAC (cm) − 0.314 × TSFT (mm)). Additionally, body composition using BIA and HGS tests was measured at inclusion, during follow-up and after 12 months. BIA were performed after regular HD sessions with the Bodystat® Quadscan 4000 device , with the patient resting in a supine position with pairs of electrodes placed on the dorsum of the hand and foot on the side of the body opposite to the arteriovenous (AV) fistula. The software provided by the manufacturer calculated PhA, fat-free mass index (FFMI), and dry lean mass (DLM). After the same HD session, HGS on the arm without AV fistula was measured in kilogrammes with the patient in a sitting position, their elbow at 90°, and their forearm in a neutral position using a Jamar® handgrip hydraulic dynamometer . The calculated average HGS of three consecutive measurements was used for analysis. Data on the dietary caloric and protein intake of patients were gathered via a 72-h recall questionnaire and assessed using PRODI® software . In the present study, no sub-report of dietary questionnaires was analysed.We collected data on the medical history along with prescribed medications, HD vintage, atherosclerosis risk factors, and co-morbidities . To perform laboratory tests, blood was drawn from a peripheral vein prior to and after HD. Patients were categorised into three groups after baseline NUS was assessed  using composite tools including SGA, malnutrition-inflammation score (MIS), and anthropometric and biochemical parameters. Laboratory parameters regularly used to describe malnutrition in HD patients, e.g., serum albumin, total cholesterol, triglycerides, TIBC, inflammation marker CRP, and safety markers potassium and phosphate, were recorded. Anthropometric measurements were made, e.g., body weight after the HD procedure and body height were measured with a standard medical scale; waist circumference (WC) and MAC were measured in centimetres using a tape; TSFT was measured in millimetres using Jamar® skinfold calliper ; BMI and MUAMC were calculated using standard formulas  and patients with a high risk for undernutrition (group 2) were prescribed ONS for 12 months. In addition to their regular diet, patients received two bottles of commercially available disease-specific sip-drink , one to drink after every HD procedure and one to take and drink at home until the next HD day. They were asked to return empty bottles of ONS as proof of adherence. All patients received nutritional counselling from the treating physician and principal investigator, but regular clinical dietitian follow-up was not available. All patients were equally followed according to their HD schedule, and the duration of the intervention was 12 months.The primary outcome was defined as the change in surrogate markers of NUS, e.g., serum albumin, PhA, and HGS, from baseline to month 12. Secondary outcomes included changes in another laboratory , anthropometric , and BIA (FFMI and DLM) measurements and two safety parameters, such as serum potassium and phosphate concentrations.post-hoc test for paired comparison. For categorical variables, Fisher’s exact test was used. Changes in observed parameters at baseline and after 12 months within groups were compared using paired samples t-test or non-parametric Wilcoxon signed-rank test depending on data distribution. SPSS programme v. 28.0 was used for intention-to-treat (ITT) and per protocol (PP) analyses, and analysis of covariance (ANCOVA) was run to determine the effect of the group on the post-intervention results after controlling baseline measurements. A p value of <0.05 was considered statistically significant.Statistical calculations were conducted using the R programming language . Numericn = 25) patients were with normal NUS at baseline, group B (n = 37) patients received ONS, and group C (n = 9) patients were prescribed ONS but refused to take them. Patients who received less than half prescribed ONS for less than 3 months were classified as group C. In group B, patients received, on average, 1531.49 kcal and 61.38 g of protein per week, in addition to their regular diet. The baseline characteristics of patients in these three groups are presented in Data for 71 patients, 39 (54.9%) men, 62.4 ± 12.9 years, and median HD vintage 53.3 (IQR 27.5–92.8) months, were available for analysis after 12 months. The most prevalent co-morbidities were arterial hypertension, diabetes mellitus, and cardiovascular diseases, contributing to the second leading cause of death in our cohort, in accordance with other authors confirming atherosclerosis as one of the most significant causes of morbidity and mortality in the developed world . In totap = 0.040 and p = 0.015, respectively). Patients in group B were undergoing maintenance HD for the longest period of time; however, the difference among groups was not significant . Patients in group B had a significantly higher prevalence of malignant disease . All other characteristics, e.g., gender, other co-morbidities, and dietary intake of calories and protein, were not significantly different between the groups in ANOVA analysis, but in ITT and PP analyses, dietary intake of calories was significantly lower in group A  and PhA (p < 0.001) when comparing group A with group B and group A with group C . There were no statistically significant differences in HGS between the groups at baseline . Serum albumin and PhA stayed significantly different between the groups but showed no statistically significant improvement during follow-up after 12 months within groups, including group B receiving ONS in ITT or in PP analysis, although PhA was close to reaching significance in ITT analysis. According to ANOVA .According to ITT and PP analyses, changes in surrogate markers of NUS at the baseline and after 12 months within each group and between groups are presented in to ANOVA , there wp = 0.022 for both comparisons), MAC (p = 0.03 and p = 0.037), FFMI (p = 0.004 and p = 0.001), and DLM (p = 0.011 and p = 0.004) were significantly higher in group A when comparing it with group B and group C. MUAMC was significantly higher in group A than in group C (p = 0.01), but there was no statistically significant difference between groups A and B. There were no statistically significant differences in values of total cholesterol, tryglicerides, and CRP between groups at the baseline, and barely significant difference of WC and TIBC between groups A and C.According to ANOVA, changes in other measured parameters at baseline and after 12 months within each group and between groups are shown in p = 0.039), but there are also negative changes in MAC (p = 0.003), MUAMC (p = 0.001), and DLM (p = 0.016) within group A and negative changes in MAC (p = 0.01), MUAMC (p < 0.001), and DLM (p < 0.001) within group B during follow-up. We believe that this indicates progressive muscle mass loss in HD patients, supplemented with ONS. In group C, there was no statistically significant change in any observed parameters, probably due to the small number of patients. We observed the worst outcome in group C since 16 out of 25 included patients died during 12 months of follow-up . The results are presented in After 12 months, we observed a slight significantly positive change in FFMI  approved 11/01/2016. Informed consent was obtained from each patient at inclusion. All methods were performed in accordance with the relevant guidelines and regulations.AO, PP, and SB wrote the manuscript and analysed and interpreted the statistical results. AO was a major contributor to writing the manuscript. PP performed the statistical analysis. AO and PP created tables and figures. All authors contributed to the article and approved the submitted version.