The participants were categorized into two groups based on the urinary albumin-to-creatinine ratio (UACR): patients without albuminuria (UACR < 30 mg/g) and those with albuminuria (UACR ≥ 30 mg/g). Greater concentrations of plasma galectin-3 were associated with lower eGFRcrea-cyst and eGFRcrea levels in patients with and without albuminuria. Plasma galectin-3 concentrations were negatively correlated with eGFRcrea-cyst in patients with normoalbuminuria and albuminuria . Galectin-3 concentrations were significantly associated with eGFRcrea-cyst after adjusting for sex, age, and other confounding factors, including UACR as a categorical or continuous variable in multiple regression analyses . Likewise, when eGFRcrea-cyst was treated in place of eGFRcrea, this result was replicated in the correlation and regression analyses. Galectin-3 concentration was negatively associated with eGFR in patients with T2DM, independent of albuminuria status.This study aimed to investigate the association between galectin-3 concentration and estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes mellitus (T2DM) with and without albuminuria. In this cross-sectional study, we examined 334 patients with T2DM. The eGFR was calculated using a creatinine-based formula (eGFR The prevalence of T2DM is increasing, with a substantial burden on clinical and public health owing to its complications1. Chronic hyperglycemia plays a pivotal role in progressive end-organ damage in patients with T2DM2. Diabetic nephropathy is a common microvascular complication and an important cause of end-stage kidney disease, which may require dialysis or kidney transplantation2. A large body of evidence demonstrates that albuminuria is an important risk factor for progressive kidney failure and cardiovascular disease in patients with T2DM6. In addition, reduced glomerular filtration rate (GFR) has been suggested to be independently linked to the risk of detrimental cardiovascular and kidney outcomes in patients with T2DM7.Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that is characterized by chronic hyperglycemia9. Furthermore, progression from microalbuminuria to macroalbuminuria is assumed to initiate renal function loss9. However, recent evidence suggests that an initial decline in GFR can occur in the setting of normal urinary albumin excretion10, indicating that albuminuria may not be a sensitive marker for early diabetic nephropathy11. Autopsy studies of patients with diabetes suggest that diabetes-related histopathological lesions in the kidney occur before the onset of albuminuria13. Therefore, in a clinical setting, it is crucial to identify the factors with implications for early kidney damage in patients with diabetes.According to the traditional view, the natural history of diabetic nephropathy is characterized by a progressive increase in urinary albumin excretion from normoalbuminuria to microalbuminuria and, subsequently, to macroalbuminuria14. Galectin-3 is ubiquitously expressed in various cells and tissues and secreted into the bloodstream14. Its structural property allows galectin-3 to bind several proteins, thus exerting multiple context-dependent biological functions15. Recently, galectin-3 has been implicated in inflammation and fibrosis15. In addition, galectin-3 is linked to tissue injury at an early stage16. In a preclinical study, galectin-3 was found to be associated with renal injury17. Galectin-3 expression was rapidly up-regulated in models of renal damage18. In community-based studies, elevated concentrations of plasma galectin-3 were associated with an increased risk of chronic kidney disease19. In a renal biopsy-based study, plasma galectin-3 concentrations were positively correlated with renal fibrosis and inversely correlated with eGFR20. In addition, a longitudinal study of patients with T2DM showed that high concentrations of galectin-3 were associated with a doubling of serum creatinine levels and incident macroalbuminuria. However, little is known about the contribution of galectin-3 to early renal function decline in patients with T2DM and normoalbuminuria.Galectin‐3, which belongs to the galectin family, is a β-galactoside‐binding protein mainly composed of a C-terminal carbohydrate-recognition domain and an N-terminal domainTherefore, this study aimed to evaluate the relationship between galectin-3 concentration and eGFR in patients with T2DM with and without albuminuria.crea-cyst and eGFRcrea were significantly lower in patients with albuminuria than in those with normoalbuminuria. In addition, plasma galectin-3 concentrations were significantly higher in patients with albuminuria than those in patients with normoalbuminuria.Table crea-cyst and eGFRcrea.We subdivided patients with T2DM into with and without albuminuria at an overall median galectin-3 level of 8.73 ng/mL  and albuminuria . Likewise, plasma galectin-3 concentrations were negatively correlated with eGFRcrea in patients with normoalbuminuria  and albuminuria .Pearson’s correlation coefficients between plasma galectin-3 concentrations and eGFR are shown in Fig. crea-cyst after adjusting for sex, age, body mass index, smoking, hemoglobin, duration of diabetes, A1C, hyperlipidemia, hypertension, and use of oral hypoglycemic agents (OHAs) and insulin . Furthermore, the significant relationship persisted after adjusting for UACR as a categorical variable  and continuous measure . Likewise, the results were replicated for eGFRcrea in the multiple regression analyses  are important contributors to the development of diabetic nephropathy38. Galectin-3 has a high binding affinity for AGEs39 and has been suggested to participate in the degradation of AGEs19. Studies using knockout mouse models have shown that targeted disruption of the galectin-3 gene results in accelerated diabetic glomerulopathy40. Therefore, further investigations are needed to establish the exact mechanism by which galectin-3 is involved in the pathogenesis of renal function loss in diabetes.Despite a strong association between galectin-3 concentration and renal function loss in diabetes, the exact mechanism underlying the association is controversial. Galectin-3 is a potent activator of fibroblasts and may contribute to renal fibrosisThere are several limitations in our study. First, because of the cross-sectional design, the causal and temporal relationship could not be established in this study. Second, although several potential confounding factors were considered in the regression models, the association between galectin-3 and eGFR could still be affected by unmeasured confounders. Another limitation is that the sample size is relatively small. In spite of these limitations, we believe that our data provide valuable information on the relationship between galectin-3 concentration and eGFR in patients with T2DM with and without albuminuria.In conclusion, galectin-3 concentrations were negatively associated with eGFR in patients with T2DM, independent of albuminuria status. Further large longitudinal investigations are warranted to confirm that galectin-3 predicts an early progressive decline in renal function in patients with T2DM with and without albuminuria.41. Patients taking glucocorticoids; those with an inflammatory disorder, infection, coronary artery disease, heart failure, peripheral artery disease, stroke, kidney disease unrelated to diabetes , chronic liver disease, malignancy, or end-stage kidney disease; those who had undergone kidney transplantation; and those who were on dialysis were excluded. If the patient had a blood pressure ≥ 140/90 mmHg or was taking antihypertensive agents, the patient was considered to have hypertension. If the patient had total cholesterol levels ≥ 6.5 mmol/L or triglyceride levels ≥ 2.3 mmol/L or was taking lipid-lowering agents, the patient was considered to have hyperlipidemia. The study was approved by the ethics committee of Chonnam National University Hospital, and informed consent was obtained from all participants. The study was conducted in accordance with the Helsinki Declaration-based ethical principles for medical research involving human subjects.In this cross-sectional study, we consecutively recruited 334 patients with T2DM who visited the diabetes clinic of our hospital. T2DM was diagnosed according to the expert committee’s report on the diagnosis and classification of diabetes mellitus42. Creatinine level was measured using the Jaffe method. Plasma galectin-3 concentrations were determined using a human galectin-3 Quantikine enzyme-linked immunosorbent assay kit  (inter-assay coefficient of variation < 7% and intra-assay coefficient of variation < 4.0%) following the manufacturer’s instructions. Glycated hemoglobin (A1C) was assayed using ion-exchange liquid chromatography . Urinary albumin excretion was determined using the urinary albumin-to-creatinine ratio (UACR). The mean values of urinary albumin excretion were determined from two spot urine samples obtained on two consecutive mornings. Normoalbuminuria was defined as UACR < 30 mg/g, and albuminuria was ≥ 30 mg/g. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula combining creatinine-cystatin C (eGFRcrea-cyst)43. As an alternative, eGFR was also estimated using the CKD-EPI creatinine formula (eGFRcrea)43.Venous blood samples were collected after overnight fasting. Cystatin C was measured using an assay from Gentian , traceable to the international calibrator ERM-DA471/IFCC44. Using a two-tailed test, the sample size was determined to detect a medium effect size (correlation coefficient) of 0.3 with α of 0.05 and a power of 0.80. The minimum sample size was estimated to be 84.The sample size was estimated using G*Power 3.1.9.245 and/or exhibited significant differences between patients according to median galectin-3 in either normoalbuminuria or albuminuria group. These covariates are as follows: age, body mass index, smoking, duration of diabetes, A1C, hemoglobin, hyperlipidemia, hypertension, and anti-diabetic therapy. Sex was also considered as a covariate. We adjusted for sex and age (Model 1). Model 2 included adjustments for body mass index, smoking, duration of diabetes, A1C, hemoglobin, hyperlipidemia, hypertension, and use of insulin and oral hypoglycemic agents in addition to the parameters in Model 1. In Model 3a, we adjusted for all parameters in Model 2 plus UACR as albuminuria status (yes/no). In Model 3b, we adjusted for all parameters in Model 2 plus UACR as the continuous variable. Multicollinearity was examined using the variance inflation factor. A variance inflation factor > 10 was excluded from the models. All analyses were conducted using the SPSS software version 20.0 . P significance was set at < 0.05.Quantitative variables are presented as the mean ± standard deviation (SD) or median . Qualitative variables are presented as frequencies (percentages). Differences between groups were assessed using Student's t-test and Mann–Whitney U test for parametric and nonparametric continuous variables, respectively. Chi-square test or Fisher’s exact test was used to compare categorical variables. Normal distribution was assessed using both visual inspection of histograms and the Kolmogorov–Smirnov test. Logarithmic transformation for variables with skewed distributions was conducted prior to correlation and regression analyses. Pearson’s correlation analysis was used to assess the association between plasma galectin-3 levels and eGFR. We evaluated the association between galectin-3 concentration and eGFR using a multiple linear regression model. Variables were used as covariates in the regression model if they were previously shown to relate to the risk of renal function declineSupplementary Information.


Coronary artery stenosis (CAS) ≥50% often coexists in patients with ischemic stroke, which leads to a significant increase in the occurrence of major vascular events after stroke. This study aimed to develop a nomogram for diagnosing the presence of ≥50% asymptomatic CAS in patients with ischemic stroke.A primary cohort was established that included 275 non-cardioembolic ischemic stroke patients who were admitted from January 2011 to April 2013 to a teaching hospital in southern China. The preoperative data were used to construct two models by the best subset regression and the forward stepwise regression methods, and a nomogram between these models was established. The assessment of the nomogram was carried out by discrimination and calibration in an internal cohort.Out of the two models, model 1 contained eight clinical-related variables and exhibited the lowest Akaike Information Criterion value (322.26) and highest concordance index 0.716 . The nomogram showed good calibration and significant clinical benefit according to calibration curves and the decision curve analysis.The nomogram, composed of age, sex, NIHSS score on admission, hypertension history, fast glucose level, HDL cholesterol level, LDL cholesterol level, and presence of ≥50% cervicocephalic artery stenosis, can be used for prediction of ≥50% asymptomatic coronary artery disease (CAD). Further studies are needed to validate the effectiveness of this nomogram in other populations. SeveralTherefore, our study aimed to develop an effective nomogram for prediction of asymptomatic CAD in non-cardioembolic stroke patients by using preexisting vascular risk factors, feasible baseline measurements, and adopting advanced statistical analysis.22.1i.e., angiographically confirmed CAD, unstable angina, coronary artery stent, or angioplasty/coronary artery bypass graft); suspected to have non-atherosclerotic arterial stenosis, such as arterial dissection and vasculitis, patients who undergone revascularization procedures, or undergone radiotherapy for nasopharyngeal cancer, patients without both cervicocephalic digital subtract angiography (DSA) and coronary multi-detector computed tomography angiography (MDCTA), were not eligible. The study was approved by the ethics committee of the Second Affiliated Hospital of Guangzhou Medical University (No. 2021-hs-27) and was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. The informed consent of patients was waived because of its retrospective design.This was a single-center, cross-sectional study. Data from patients admitted to the stroke unit of the second affiliated hospital of Guangzhou Medical University from January 2011 to April 2013 were collected for this study if they meet the following selection criteria: at least 18 years old; experienced an ischemic stroke within 14 days after the onset of symptoms; no prior history of CAD  were collected from medical records. Routine blood tests, fast blood glucose, and lipid profile were measured within 48 h after admission. Systolic and diastolic blood pressures were measured in the supine position upon admission. All patients were subjected to brain magnetic resonance imaging with spin-echo diffusion-weighted imaging or computed tomography scan and 12-lead electrocardiography.2.3Imaging of cervical and intracranial arteries consisted of cervical and intracranial DSA in all patients. Cervicocephalic atherosclerosis was assessed using standardized methods . The cer2.4The presence of coronary artery atherosclerosis was assessed using 64-section CT coronary angiography. All 64-section CT coronary angiographies were reviewed by two experienced radiologists blinded to the clinical data and results of cervicocephalic atherosclerosis assessment. The existence of CAS was confirmed when there was stenosis ≥50% in at least one segment at the four main coronary branches, which are the left main, left anterior descending, left circumflex, and right coronary arteries .2.5To construct the nomogram, we used two methods to select the significant predictors of asymptomatic CAD. In order to avoid over-fitting or under-fitting of the model, three advanced statistical methods, best subset regression (BSR) and forward stepwise regression, were adopted to select variables in the primary cohort. The criteria of variable selection for the BSR and the forward stepwise regressions were determined by the Bayesian information criterion (BIC) .2.6CAD-related predictive models were established in the primary cohort based on the selected variables by adopting binary logistic regression. Eventually, we developed two models: (a) model 1 consisted of eight variables according to the BSR, (b) model 2 consisted of six variables according to the forward stepwise regression. The final model was determined by the Akaike Information Criterion (AIC),  the rece2.7The model was internally validated using all data of the training cohort by 10-fold cross-validation. Discriminative performance was measured by C-index. Calibration was tested using a calibration plot with bootstraps of 500 resamples, which described the degree of fit between actual and nomogram-predicted CAD.2.8p < 0.05.The decision curve analysis (DCA) was used to assess the clinical usage of nomogram. Detailed descriptions of the DCA have been previously reported . Results2.9Categorical variables were expressed as numbers (percentage) and continuous variables as medians (quartile). Differences in baseline characteristics between groups for continuous variables were assessed using Mann-Whitney U test, and the Chi-squared test or Fisher’s exact test was used for categorical variables according to their sample size. Proportions were calculated for categorical variables, dividing the number of events by the total number after excluding missing/unknown cases.p < 0.05. SPSS 21.0 , R statistical software , and Free Statistics software version 1.4 was used for statistical analysis.Differences between groups were considered statistically significant at 33.11). A total of 275 patients  were included in the study. The mean age of enrolled subjects was 63.3±10.1 years. A total of 88 acute stroke patients had manifestations of CAS, giving a prevalence rate of 32.0%.The flow chart outlining the patient inclusion process is shown in Fig.  on admission, fast blood glucose level, high-density lipoprotein cholesterol (HDL-C) level, and presence of ≥50% stenosis in the cervicocephalic artery were significantly enriched in the group with CAD compared to the groups without CAD . All risk factors shown in Table 1 were adopted in the variables selection for prediction model development.The risk factors for CAD and the comparisons between groups with and without CAD are shown in Table 3.22  or minimum BIC . As shown in Fig.  level, and CVD. However, only one parameter was selected by minimum BIC: hypertension history. We also used the stepwise regression to choose combinations of six potential predictors, including sex, age, hypertension, fast blood glucose level, HDL level, and CVD (Supplementary Table 1). Because it is inappropriate to use a single variable to develop the prediction model, we excluded the result from minimum BIC for further analysis. The choice of final prediction model was determined by the ROC curve and the C-index, which were also used to examine the efficiency of the two models, with model 1 based on adjusted R2 and model 2 based on stepwise regression .The BSR method showed great benefits in variable selection because all possible combinations of variables were calculated and the final selected combination was considered optimal based on the max adjusted R3). Higher total points based on the sum of the assigned number of points for each predictor in the nomogram were associated with an increased risk of mortality. For example, a male patient aged 70 years with a baseline NIHSS score of 14, hypertension history, fast blood glucose level 8.0mmol/L, HDL-C level 1.0mmol/L, LDL-C level 4.0mmol/L and presence of CVA ≥50% stenosis would have a total of 258 points . The predicted CAD is 70.0% for this patient.The nomogram obtained from the final model was optimal  shows a calibration plot, which compares the nomogram prediction and actual observation of CAD. The calibration plot revealed good predictive accuracy of the nomogram.Prediction potential of the nomogram was measured by calculating the C-index, which was 0.716 , indicating relatively good predictive power. Fig. (4b), the DCA indicated that when the threshold probabilities ranged between 2.1% and 78.0% in the training cohort, the use of the nomogram to predict CAD provided a greater net benefit than the “treat all” or “treat none” strategies, which indicates the clinical usefulness of the nomogram.DCA can estimate the net benefit of a model based on the difference between the number of true- and false-positive results and is widely used in assessing whether the nomogram-assisted decision would improve patient outcomes. As shown in Fig.  -11, 18. Advanced age, male sex, and hypertension history have previously been reported to be associated with the incidence of asymptomatic CAD in acute ischemic stroke patients . ConsistIn addition to nonmodifiable variables  of the individual patient, the levels of fasted glucose, HDL-C, and LDL-C upon admission were also predictors in the nomogram. Hyperglycemia shares many common mechanisms with other atherogenic factors, such as endothelial activation and inflammation, mitochondrial oxidative stress, changes in extracellular matrix components, and disruption of cellular defense systems . In diabThe presence of ≥50% cervicocephalic artery stenosis has been proved to be strongly associated with asymptomatic CAD in nondisabled stroke patients , 9. In oSeveral limitations should be addressed when interpreting the results of this study. First, we only included patients of Asian ethnicity, which may limit the generalizability of the results. Second, this is a retrospective study. The findings of our study need to be corroborated by prospective studies and randomized controlled trials. Third, large-scale and multi-center clinical trials are needed to be performed to validate and modify the model. Despite these limitations, our analysis has a few strengths. We developed a novel and relatively accurate nomogram. In addition, the nomogram was based on variables that can be easily abstracted and used in a real-world setting.In conclusion, approximately one-third of noncardioembolic ischemic stroke patients have ≥50% asymptomatic CAD. In addition, our study developed a nomogram, composed of age, sex, NIHSS score on admission, hypertension history, fast blood glucose level, HDL cholesterol level, LDL cholesterol level, and presence of ≥50% cervicocephalic artery stenosis, which could be used for prediction of ≥50% asymptomatic CAD. Further studies are warranted to validate the effectiveness of this nomogram in other populations.


Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test.P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI . Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function.The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging. Acute kidney injury (AKI) is a common disorder in patients with heart failure (HF), with the reported incidence rate varying from 7% to 38% in cardiology departments -3. A recAlthough a few specific clinical markers  have been adopted to evaluate the renal function of patients with HF such that those at high risk of AKI can be identified, these markers lack the ability to screen de novo AKI patients who had normal renal function at admission ,8. Of noAs recently conducted cardiovascular studies have demonstrated that an unsupervised machine learning approach is able to model correlations among variables that contain prognostic information and cluster cohesive patients into 1 homogeneous phenogroup -11, we h2 as calculated by the serum creatinine [SCr] version of the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] equation [The proposed retrospective study used a real-world data set obtained from the EHR system of the Chinese PLA General Hospital (PLAGH). The data set documented regular medical information in 84,705 hospitalizations of 29,699 patients who were diagnosed with HF in the PLAGH from 1998 to 2018. Adult patients with HF and normal renal function .In this study, 58 variables potentially associated with AKI, including demographics, vital sign measurements, medications, laboratories, operations, and echocardiogram exams, and routinely documented in EHRs at the admission stage of hospitalization were considered as candidates for analysis. To ensure that the most informative variables were selected and the correlation between variables could be diluted, we excluded variables with a missing rate larger than 30% or with a Pearson correlation coefficient >0.6 or that were documented fewer than 100 times in the raw EHR data set. As a result, 39 variables were included in the cohort. All continuous variables were transformed to standard normal distribution for the convenience of the unsupervised machine learning model  standard , with thP value of <.01 was considered statistically significant.Once patients with HF were categorized into separate phenogroups, we measured the differences of variables in different groups. Continuous variables are reported as median and IQR (interquartile range). Categorical variables are reported as the frequencies and counts. Differences between groups were tested using the 1-way analysis of variance, Kruskal-Wallis test, or the chi-square test where appropriate. A P values for all included original variables as well as the phenogroup index on both the whole PLAGH data set and the following subgroups: age (age <65 vs ≥65 years), sex, type of HF (acute vs chronic), diabetes mellitus, stroke, atrial fibrillation, coronary heart disease, anemia, and left ventricular ejection fraction . To assess continuous variables appropriately, we categorized all continuous variables in validation, and the cutoff points for these continuous variables are presented in online supplementary Table S2, We validated whether the phenogroup index generated by K-means clustering correlated with outcomes of interests by carrying out the following 3 experiments. First, Kaplan-Meier estimators with log-rank tests were conducted to analyze the time-to-event characteristics in different phenogroups. Second, we compared the prediction performance on AKI and in-hospital mortality to check whether the inferred phenogroup index was an effective risk predictor for outcomes of interest. Specifically, we selected the top-ranked 10 variables using a forward stepwise strategy with the Akaike information criterion and then developed 5 logistic regression (LR) models to predict the outcomes of interest. Model 1 used the phenogroup index as the univariate predictor. Model 2 used the top-ranked 10 variables as predictors. Model 3 used the top-ranked 10 variables and the phenogroup index. Model 4 used all 39 variables. Model 5 used all 39 variables and the phenogroup index. All models were trained by 70% of the data from the PLAGH data set and tested with the remaining 30% of data. Third, to evaluate whether the phenogroup index could achieve the competitive discriminative performance compared to the original variables with respect to the primary and secondary outcomes, we applied unadjusted Cox proportional hazard regression to examine hazard ratios (HRs), 95% CIs, and We externally validated our model on a well-known open-source database, Medical Information Mart for Intensive Care (MIMIC)-III . After aIn this study, statistical and machine learning analysis was based on sklearn, lifelines, scipy package -21, and After preprocessing, 5075 hospitalizations and 39 variables  were resP<.001) and in-hospital mortality . In addition, patients in phenogroup 2 were generally older than those in phenogroup 1 .P<.001). Moreover, cardiac function of patients in phenogroup 2 was worse than that in phenogroup 1. Specifically, there were statistical differences between patients in phenogroup 1 and phenogroup 2 in terms of left ventricular ejection fraction , diastolic blood pressure , systolic blood pressure , N-terminal pro-brain natriuretic peptide , hemoglobin , atrial fibrillation , diuretic usage , and positive inotropic drug usage . Furthermore, phenogroup 2 had higher troponin T levels , indicating that there were more patients in phenogroup 2 who underwent myocardial damage. Patients in phenogroup 2 had higher values of gamma-glutamyl transferase , total bilirubin , and aspartate aminotransferase , indicating that patients in phenogroup 2 might have worse liver function compared with phenogroup 1. Moreover, although we had excluded patients with renal dysfunction in advance, patients in phenogroup 2 had worse eGFR values  and urea . These findings demonstrated that patients in phenogroup 2 had relatively worse kidney function. Furthermore, patients in phenogroup 2 used less angiotensin-converting enzyme inhibitor/angiotensin receptor blocker , calcium channel blocker , and antiplatelets . It was worth nothing that patients in phenogroup 2 had higher lipid levels (low-density lipoprotein cholesterol and triglyceride) and BMI . Compared to phenogroup 1, phenogroup 2 also received less angiography  and percutaneous coronary intervention . Comprehensive baseline characteristics including all 58 candidate variables are listed in Table S5, As can be seen in As the prevalence of AKI and in-hospital mortality had a significant difference between the generated phenogroups, phenogroup 1 was intuitively labeled as “low-risk” and phenogroup 2 as “high-risk.” We further investigated whether the generated phenogroup index could serve as an essential risk indicator for clinical outcomes of interest.P=.004; MIMIC-III: P=.002). In addition, we found that most AKI events often happened in the first few days of hospitalization in both the PLAGH and MIMIC-III data sets. This finding was in line with the literature [P=.002; MIMIC-III: P=.01). In consideration of the baseline difference between the PLAGH data set and MIMIC-III data set, the results demonstrated that our model was robust in discriminating between high-risk and low-risk patients and easily transferable to different clinical settings.terature ,8. For iWe used unadjusted Cox proportional hazard regression to determine whether the phenogroup index can act as an essential risk stratification indicator in comparison with the original 39 included variables. The top-ranked 10 variables with the highest HR are listed in We explored the potential of using a large volume of EHR data to cluster patients with HF and identify those with normal renal function but susceptible to de novo AKI via an unsupervised machine learning model. The experimental results showed that there was significant difference in AKI and in-hospital mortality occurrence between the 2 phenogroups generated from EHR data. As EHR is a real-world, readily available data source containing rich medical information of thousands of patients, our study demonstrated that it was possible for researchers to answer important clinical and scientific questions effectively by exploiting the huge potential of EHR data via machine learning techniques at a fraction of the resource cost that would have been required using traditional approaches ,23.We demonstrated that HF patients with normal renal function can be naturally separated into a “high-risk phenogroup,” of patients susceptible to de novo AKI and a “low-risk phenogroup” who were not. Patients in high-risk phenogroup were typically older, more susceptible to multi-organ dysfunction and anemia, and had significantly higher in-hospital mortality than did those in the low-risk phenogroup. These findings were in line with recent studies ,24 and wIdentification of patients with HF with normal renal function but at high-risk of de novo AKI is a major challenge in HF treatment management. Clinicians have highlighted the need for more effective methods to perform this important clinical task . In thisOur study has potentially important clinical ramifications. For one, as AKI risk is often underestimated or neglected in patient with HF, especially those with normal renal function , our stuSeveral limitations of this study should be acknowledged. First, this is a single-institution study. Although we have evaluated our model on an external validation data set extracted from MIMIC-III, the methods may perform less well in other situations due to the lack of sufficient external validation samples collected from different medical facilities and in different clinical settings. Second, our study was limited by its retrospective design, and all analyses were purely observational. Although we found that there were distinct variables associated with increased risks of de novo AKI and in-hospital mortality, these nonrandomized comparisons should be interpreted cautiously in this context, and the prognostic ability of our model needs to be supported by validation in prospective studies. Third, considering the sensitivity and the specificity for AKI forecasting, our model was relatively sensitive but not very specific. Despite the influence of false-positive classification being limited in this study, further study will be required to enable machine learning–based analysis to capture the salient features distinguishing high- from low-risk cases, such that the prediction performance of our model can be improved.This study demonstrated that unsupervised machine learning–based EHR analysis is able to separate patients with HF and normal renal function into mutually exclusive phenogroups that correspond to saliently distinct AKI risk levels. Our investigation paves the way for developing an easy-to-use, broadly available model that allows the identification of patients with HF at high-risk of de novo AKI and may help improve outcomes in HF, offering a crucial advantage over traditional techniques for patient phenogrouping and clinical risk stratification.


Conclusions: The TG/HDL-C ratio was a risk factor associated with CKD and was noticeable in monitoring and assessing the risk of progression of CKD.Background: Dyslipidemia is highly prevalent in patients with chronic kidney disease (CKD), and the relationship between dyslipidemia and renal function in these patients remains controversial. Our objectives were to determine the triglycerides/HDL-cholesterol ratio (TG/HDL-C), evaluate the correlation between TG/HDL-C and the urine albumin/creatinine ratio (ACR), and estimate the glomerular filtration rate (eGFR) according to MDRD in CKD patients. Methods: A descriptive cross-sectional study was conducted on 152 patients with CKD at the Endocrine Clinic, the University of Medicine and Pharmacy Hospital, Ho Chi Minh City, Vietnam. Study subjects were medically examined and recorded information on the data collection form. Subjects were tested for total cholesterol, triglycerides, HDL-C, LDL-C, urea, creatinine and albumin, urine creatinine, and eGFR according to the MDRD formula. Data were analyzed using SPSS Statistics version 20.0. Results: The average age was 58.08 ± 15.69 years, and the overweight and obesity rate was 54%. Most patients had comorbidities, among which the most common diseases were hypertension and diabetes mellitus. Among the subjects, 57.3% were CKD stage 3 patients, and ACR was in the range of 30–300 mg/g. According to the classification of CKD using GFR and ACR categories, 40.8% of patients were at very high risk. The average TG/HDL-C ratio was 5.09 ± 4.26. There was a medium negative correlation between TG/HDL-C and eGFR (R = 0.44,  Chronic kidney disease (CKD) is a global health problem associated with high mortality; the principal outcome of CKD is a progressive loss of kidney function, leading to end-stage renal disease (ESRD) ,2,3. TheThe TG/HDL-C ratio was found to have a more robust predictive value for cardiovascular events than TG, LDL-C, or the cholesterol/HDL-C ratio . KurellaA descriptive cross-sectional study was conducted at the Endocrine Clinic, University of Medicine and Pharmacy Hospital, Ho Chi Minh City, from November 2016 to July 2017. We enrolled patients older than 18 years diagnosed with CKD according to the 2012 Kidney Disease Improving Global Outcomes (KDIGO) categories. The exclusion criteria for this study were: patients who (1) had diseases that could affect the results of eGFR or ACR in the urine, such as malnutrition, amputation, muscular dystrophy, muscle atrophy, pregnancy, urinary tract infections, and urinary tumors; and (2) were unwilling to participate in the study. The sample size was estimated by using the formula for a single proportion with an estimated proportion of CKD patients, which was 0.031, as reported in a prior study [2 (with or without kidney damage) for ≥3 months [KDIGO is defined CKD as kidney damage (with or without decreased GFR) with one or more of the following signs: albuminuria , urine sediment abnormalities, electrolyte and other abnormalities due to tubular disorders, abnormalities detected by histology, structural abnormalities detected by imaging and history of kidney transplantation, or GFR below 60 mL/min/1.73 m3 months ,20. Data on eligible CKD patients were extracted from data collection forms, patient medical records, and paraclinical test results. All tests for total cholesterol, triglycerides, HDL-C, LDL-C, urea, blood creatinine, albuminuria, and urine creatinine were performed at the hospital laboratory of the University of Medicine and Pharmacy Hospital according to the standard process. First, patients were asked not to consume anything but water for 12 h before the test and not to eat breakfast on the day of the test; then, 2 mL of blood was and about 10 mL of urine were taken from each patient for testing. Serum urea, serum creatinine, total cholesterol, triglycerides, HDL-C, and LDL-C were quantified by enzymatic colorimetric method, whereby substances participate in color reactions with reagents to form complexes; then the absorbance of the complexes is at the appropriate wavelength . Urine cThe following patient characteristics were collected: age, gender, height, weight, BMI, and medical history . Clinical symptoms included edema, pale mucous membranes, hematuria, and others. Paraclinical characteristics included blood lipids  and parameters to evaluate kidney function  . Classifp < 0.05. Qualitative variables were described by frequency and percentage, and quantitative variables were presented as mean ± standard deviation. The relationship between two qualitative variables was compared by testing (Chi-squared); between two quantitative variables by t-test with 95% confidence. The correlation coefficients of two quantitative variables were determined by the Pearson correlation coefficient if the variables were normally distributed and by the Spearman correlation coefficient if the variables non-normally distributed. The convention proposed by Guilford (1965) was used to interpret the correlation coefficients of the sample . All study participants gave their consent and agreed that their personal information would be kept private. This study did not harmed the patients’ health.2, and the overweight/obesity rate accounted for 54% of subjects. The main clinical symptom was pale mucous membranes; less common symptoms included edema and hematuria. Most patients had comorbidities, the most common of which were hypertension (63%) and diabetes (24%). The group of patients with TG/HDL-C < 4 accounted for 53.3% of subjects—higher than that of TG/HDL-C ≥ 4. A proportion of 57% of subjects were CKD stage 3 patients, and the urine ACR was in the range of 30–300 mg/g. According to the classification of CKD using GFR and ACR categories, 41% of patients were at very high risk . TG/HDL-C < 4 occurred most commonly among patients at moderate risk of kidney disease; TG/HDL-C ≥ 4 was primarily associated with the very high-risk group (p < 0.05). Patients with TG/HDL-C < 4 mostly had stages 1 and 2 CKD; in contrast, TG/HDL-C ≥ 4 was more common in patients with stages 3, 4, and 5 CKD (p < 0.05)  .p < 0.01) and a weak positive correlation between TG/HDL-C and ACR   .2 are associated with CKD [Among 152 patients with CKD, 55% were men, and 45% were women—similar to the sample in a study by Chih-I Ho et al., which included 56.1% men and 43.9% women . TherefoClinical symptoms of CKD included hypertension, pale mucous membranes, edema, and hematuria, among which pale mucous membranes and hypertension conditions accounted for a high rate, as patients often had chronic anemia. Most patients had comorbidities, of which the common diseases were hypertension (63%) and diabetes mellitus 24%). Over time, high blood pressure could damage blood vessels throughout the body, reducing the blood supply to vital organs, such as the kidneys, damaging the kidney’s tiny filtering units. As a result, the kidneys might stop removing waste and extra fluid from the blood; therefore, hypertension is a significant cause of kidney failure, especially CKD. Long-term diabetes also causes atherosclerosis of large blood vessels, including renal arteries, narrowing blood vessels and resulting in high blood pressure and kidney failure. In this study, the mean concentration of total cholesterol, triglycerides, HDL-C, and LDL-C was 192.80 ± 58.42 mg/dL, 208.33 ± 140.92 mg/dL, 47.22 ± 14.94 mg/dL, and 121.0 ± 44.45 mg/dL, respectively. Recent guidelines indicated that triglycerides and HDL-C should be routinely evaluated in patients with type 2 diabetes %. Over t. Increas2, of which stage 3 of CKD was the most common (57%). This was similar to the results of a study by Le Quoc Tuan and Dang Huynh Anh Thu (2017), who recorded the rate of stages 1-5 CKD as 18%, 30%, 36%, 10%, and 6%, respectively [In addition, we found that the average eGFR was 52.50 ± 20.10 mL/min/1.73 mectively . The uriectively . Similarp < 0.05). TG/HDL-C ≥ 4 was found primarily in the very high-risk group (p < 0.05) and was more common in patients with stage 3 and higher CKD (p < 0.05). Research by Chih-I Ho et al. (2015) showed increases from the lowest to the highest quartile of the TG/HDL-C ratio. Men and women in the highest TG/HDL-C ratio quartile (>2.76) had a 1.4-fold and 1.74-fold greater risk of CKD than those in the lowest quartile (≤1.04), respectively, independent of confounding factors. Therefore, A TG/HDL-C ratio ≥2.76 may be helpful in clinical practice to detect subjects with worsened cardiometabolic profiles who require monitoring to prevent CKD [When considering two groups of patients with TG/HDL-C ratios < 4 and ≥ 4, we found a significant difference in BMI, triglycerides, HDL-C, eGFR, and ACR (vent CKD .p < 0.01). Dyslipidemia might decrease the glomerular filtration rate and accelerate the rate of progression of kidney disease by several mechanisms [p < 0.01). Many studies have demonstrated that a high TG/HDL-C ratio is a valuable marker for proteinuria abnormalities [There was a medium negative correlation between TG/HDL-C and eGFR (R = −0.44, chanisms . First, chanisms . Second,chanisms . The loschanisms ,26. Morechanisms . This remalities ,28,29. Amalities ,31.This study is subject to some limitations. First, the observational nature of the cross-sectional research reduced the reliability of establishing a cause-and-effect relationship. Second, the short study period and small sample size might not be sufficient to comprehensively assess the impact of dyslipidemia on renal function. Third, some potential risk factors were not analyzed, such as the history of lipid-lowering drugs and other comorbidities. Therefore, we recommended that further studies be conducted with larger sample sizes in patients with annual physical examinations and simultaneously consider the potential risk factors mentioned above to evaluate the role of TG/HDL-C for CKD in the general population. Otherwise, the MDRD and CKD-EPI equations are the most popular methods for estimating GFR in patients aged 18 years and older. Both of these methods may allow us to observe that CKD is present despite a serum creatinine concentration that appears to fall within or just above the normal reference interval; thus, future studies should determine GFR by CKD-EPI equation and directly compare the impact of these equations on the research results.This study is one of the few studies in Vietnam and globally to show the clinical significance of the ratio of TG/HDL-C for assessment of kidney function progression in Vietnamese patients with CKD. This helped to monitor and evaluate the risk of developing CKD more effectively. In addition, this result also emphasizes the importance of the continued need for medical interventions to treat dyslipidemia in CKD patients.The TG/HDL-C ratio was found to be a risk factor associated with CKD and should be used in early screening, monitoring, and assessment of the risk of CKD progression. During annual health check-ups, if an increase in TG/HDL-C is observed, doctors should to monitor the patient’s kidney function.


This study aimed to compare clinical and prognostic characteristics between recurrent and first-ever ICH. Four thousand twelve patients entered the study, and 64% of them were male. The median age is 62 years . Among them, 3,750 (93.5%) patients had no experience of previous ICH, and 262 (6.5%) patients were considered as recurrent ICH. We compared demographic data, baseline clinical characteristics, imaging information, hematological parameters, and clinical outcomes between recurrent and first-ever ICH. We found that recurrent ICH was significantly associated with older age, more frequent history of ischemic heart disease, ischemic stroke, hypertension, and hyperlipidemia, while patients with recurrent ICH had previously received more antihypertensive therapy, and showed lower admission blood pressure  and higher baseline of National Institute of Health stroke scale (NIHSS) score . We also demonstrated that recurrent ICH was an independent risk factor of 3-month function dependence after adjusting for many potentially competitive risk factors. Intracerebral hemorrhage (ICH) is a more devastating disease than ischemic stroke, features a high mortality rate of 25–50% within a month , and theDespite various studies that have been conducted to search for candidate risk factors of ICH recurrence , the cliTo this end, we conducted a population-based cohort study using a nationwide representative sample from the Chinese cerebral hemorrhage: mechanism and intervention (CHERRY) study, to compare clinical and prognostic characteristics between recurrent and first-ever ICH.We use a representative sample from the CHERRY study. This study included 4,012 patients in 31 medical institutions from December 2018 to March 2021. The ethics of the study is in line with the principles expressed in the Declaration of Helsinki. The local institutional review board approved all aspects of the study .Patients were included according to the following criteria: (1) clinically confirmed spontaneous ICH, which is defined as non-traumatic bleeding into the brain parenchyma confirmed by CT scan ; (2) 18 The following data were collected: (1) demographic data, including age, gender, medical history, medication history (prior use of antithrombotic and antihypertensive agents); (2) admission data, including baseline blood pressure, the modified Rankin Scale (mRS), the National Institute of Health stroke scale (NIHSS), and the Glasgow Coma Score (GCS), imaging data, such as hematoma location, hematoma volume, and intraventricular hemorrhage (IVH); (3) hematological parameters; (4) the structural lesions, medication, amyloid angiopathy, systemic disease, hypertension, and undetermined (SMASH-U) etiology of ICH. Recurrent ICH was defined as any ICH ≥ 24 h after the first incident event . All recThe primary outcome is death and functional dependence referred to the mRS score of 3–6 at 90 days. The second outcomes are 30-day functional dependence, and in-hospital, 30-day and 90-day death.U test for categorical variables and continuous variables, respectively. Non-normally distributed continuous variables were categorized based on clinical and statistical significance in the subgroup analysis. All variables of univariate analysis with a P-value < 0.1 were included in the multivariate regression model. All tests were two-tailed and a P-value < 0.05 was considered significant. Statistical analyses were performed using SPSS software (version 27.0) and R software (version 4.1.2).The continuous or discrete variables were presented as median with interquartile, and categorical variables were presented as percentages. Univariate analysis was analyzed using the χ2 test and Mann–Whitney After exclusion for ineligible patients, a total of 4,012 patients were entered into the study . Among admission laboratory tests, recurrent ICH was found related to lower alanine transaminase (ALT), total cholesterol (TC), total triglycerides (TG), high-density lipoprotein cholesterin (HDL-C), and international normalized ratio (INR). Moreover, ICH etiologies of amyloid angiopathy and hypertension were obsevered more frequently in recurrent ICH than first-ever ICH. However, there was no significant difference in the volume and location of a hematoma between the two groups.When clinical outcomes are compared between recurrent and first-ever ICH, recurrent ICH was associated with more 3-month functional dependence , with thP-values of interaction are greater than 0.05; We further conducted a subgroup analysis on the above logistic regression results. No statistically significant interaction between recurrent ICH and these interesting factors was observed  recurrent ICH was significantly associated with older age, more frequent history of ischemic heart disease, ischemic stroke, hypertension, hyperlipidemia, and higher baseline NIHSS score; (2) patients with recurrent ICH had previously received more antihypertensive therapy and showed lower admission blood pressure; (3) recurrent ICH was independently associated with poor 3-month functional outcomes.Our analysis revealed that age, previous ischemic stroke, hypertension, and hyperlipidemia show significant group differences between recurrent and first-ever ICH patients. These are all the examined risk factors of ICH recurrence . AlthougIntriguingly, patients with recurrent ICH exhibited even lower admission blood pressure, which may be attributable to the previous applications of the antihypertensive agents. Also, it is noteworthy that the admission SBP of patients with recurrent ICH was still as high as 160  mmHg, although it may be affected by an acute hypertensive response . It is uP = 0.04), but ALT was almost within the normal range in both groups. This may be due to the improvement in liver function caused by the patient’s lifestyle optimizations after the cerebral hemorrhage, such as avoiding alcohol and high-fat diets.Moreover, our results revealed that patients with recurrent ICH showed lower total serum cholesterol, which has been considered inversely associated with ICH recurrence in previous studies , althougP < 0.001). Most of the previous articles only studied the recurrence of ICH as one of the clinical outcomes, while few studies specifically investigated the prognosis of recurrent ICH  we conducted a national multi-center and large-sample study; (2) we strictly reviewed previous disease history for decades; (3) numerous confounders were adjusted to determine the association between ICH recurrence and clinical outcomes. After all, the limitations of our study lie in that: (1) lack of continuous monitoring to clinical characteristics, such as blood pressure and plasma glucose; (2) The enrolled patients were all Chinese.The raw data supporting the conclusions of this article will be made available by the corresponding authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YW, HG, and RB conducted the data analysis and wrote the manuscript. BH together with QH designed this study and directed the writing of the manuscript. SC, JS, ML, YX, LZ, ZS, XC, ZC, ZW, DG, JX, and DZ helped with the data collection and literature search. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Ambulophobia is a type of specific phobia that involves a fear of walking. This phobia mainly affects older people, who prefer not to leave their bed or home to avoid walking on uneven surfaces and reduce the risk of falling. The problem seems to be very important in terms of public health and the organization of long-term care, as Poland has one of the highest rates of elderly population growth.The aim of the study was to determine the prevalence of ambulophobia among patients of long-term care facilities in Poland and to identify factors increasing the risk of this specific phobia in the study group.2 test was used to test the significance of differences (p = 0.05).The study was conducted between January and July 2021. Data collected from 379 patients of 16 long-term care facilities located in Poland were analyzed. The study used the diagnostic criteria for specific phobias according to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, and standardized questionnaires such as MMSE, GDS-16, DOS and ADL. The χThe prevalence of ambulophobia in the study group was 30.1%. Ambulophobia significantly more often concerned the female sex (37.7%) and people over 70 years of age . Factors such as depression, Parkinson's disease, orthostatic hypotonia, a history of falling or being a witness to another person falling, and disability of at least a moderate degree increased the likelihood of ambulophobia.Based on the obtained results, it was found that the protective factors in the development of ambulophobia are male sex, younger age, high independence, fewer drugs used per day and no previous falls or seeing another person fall. Poland is among the leading countries in Europe with the fastest aging population . By 2050Falls are the leading cause of death among accident-related deaths in the over-65 age group . Still aAnxiety disorders are the most prevalent mental health problems among older adults . A revieIn addition to the previously mentioned anxiety disorders, anxiety in geriatric patients (less frequently in children) is associated with fear of walking (ambulophobia) and fear of falling (basiphobia) , 14. PeoAmbulophobia is caused by a well-defined factor and therefore constitutes a type of specific phobia. According to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition , the folSevere fear or anxiety about a specific object or situation;The fearful object or phobic situation always produces immediate fear or anxiety;The patient avoids the fearful object or phobic situation, otherwise, there is severe fear or anxiety;The severity of the fear or anxiety is inappropriate to the level of risk posed by the specific object or situation and to the socio-cultural contextThe anxiety, distress and avoidance are sustained and usually last 6 months or longerAnxiety, distress and the need for avoidance cause impairment in social, occupational and other important areas of functioningThe disorder cannot be better explained by the presence of symptoms of another mental disorder.People suffering from phobia often try to defend themselves against anxiety in various ways—mostly they avoid what arouses unpleasant feelings in them and use the escape mechanism. Unfortunately, this leads to a significant reduction in life opportunities .Ambulophobia occurring in geriatric patients, due to the risk of large and irreversible complications, represents a significant clinical and social problem requiring a multidisciplinary approach , 9. It iThe aim of this study was to determine the prevalence of ambulophobia among patients of long-term care facilities in Poland and to identify factors that increase the risk of this specific phobia in the study group.Patients from 16 long-term care facilities  located in Poland were included in the study. The directors/managers of each facility gave their consent for their institution to participate in the study.p = 0.89 and p = 0.73 respectively). Participation in the study was voluntary. Respondents whose cognitive functioning allowed them to understand the purpose of the study gave their independent and informed consent to participate in the study. For patients diagnosed with cognitive impairment, dual consent was required: given by the patient themselves and by their carer or family.The survey was conducted between January and July 2021. Data collected from 379 patients were analyzed. The participating sample was representative in terms of age and gender for the general population of patients in long-term care facilities in Poland  . Nevertheless, all research standards were observed in the study. It complies with the provisions of the Declaration of Helsinki.The main inclusion criteria were: the patient's status in the respective long-term care facility, consent to participate in the study, and maintenance of basic independent mobility skills. Severely ill patients whose condition required prolonged bed rest  were excluded from the study. Considering preserved mobility as a necessary factor for ambulophobia, participants who could not move independently and were in a wheelchair for somatic reasons  were also excluded from the study. Nonetheless, the study analyzed data from 6 patients who used a wheelchair despite the absence of any somatic complaints that would have prevented independent mobility . Those with acute positive symptoms associated with schizophrenia were also excluded from the study.A survey questionnaire was used in this study, which consisted of a metric section  and screening tests. Among the screening tests used were:Mini-Mental State Examination (MMSE)—for screening assessment of cognitive functioning. MMSE was adjusted for age and number of years of education. Scores were interpreted according to the following standard :- Normal score: 30–27 points;- Cognitive impairment without dementia: 26–24 points;- Mild dementia: 23–19 points;- Moderate dementia: 18–11 points;- Severe dementia: 10–0 points.Geriatric Depression Scale, short-form (GDS-15)—The GDS is a screening tool for assessing the severity of depressive symptoms in elderly people. Due to its short form, it is recommended for both healthy and neurologically impaired people. The maximum score that can be obtained is 15 points. Points were interpreted according to the following scale :- 0–5 points—no depression,- 6–10 points—moderate depression,- 11–15 points—severe depression.Delirium Observation Screening Scale (DOS)—a scale shortened to 13 points used to assess clinical symptoms of delirium in patients at risk . Points were interpreted according to the following scale , 18:- 0 points—no presence of symptoms of delirium as assessed by the DOS scale was found,- 1–2 points—a probability of developing symptoms of delirium not meeting the criteria of the DOS scale was found. The current risk of developing delirium in the future.- 3 and more points—the presence of symptoms of delirium fulfilling the criteria of the DOS scale was found.Activities of Daily Living (ADL)—a scale assessing the ability to perform basic life activities by the person subject to the examination. It allows for the assessment of the current functional status of the patient, including the ability to self-care and the need for assistance from others. The scale was interpreted according to the following scoring :- 6–5 points—fully preserved activities;- 4–3 points—moderate impairment;- 2–0 points—severe functional impairment.These screening tests were chosen because they are standardized and commonly used among geriatric patients. Their use allowed the assessment of selected parameters and minimized the risk of error associated with the use of less common, non-standardized tests.The diagnostic criteria for specific phobia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, were used to assess the occurrence of ambulophobia. These criteria are presented in detail in the introduction of this article.2 test was used to test the significance of differences. Non-parametric correlation tests: ϕ and V Cramer were used in the analysis of associations between the examined variables.The characteristics of the study group with particular reference to ambulophobia and including age, polypragmasia, degree of dementia and fear of falling are presented in this paper. The normality of distributions was assessed using the Shapiro Wilk test. The χp < 0.05 were considered statistically significant. Statistical analysis was performed using Statistica 13.3 PL program .Results for which n = 223) were women and 41.2% (n = 156) were men. The mean age was 81.3 ± 8.5 years, with the youngest patient being 65 years old and the oldest 100 years old. With regard to age categories, the largest group was patients between 71–80 years  and 81–90 years , while less numerous groups were: ≤ 70 years  and 91–100 years   .n = 366), with its severe form occurring in 18.5% of patients (n = 70). The prevalence of polypragmasia was similar in men and women (Polypragmasia was common in the study group—it affected 96.6% of patients (nd women .n = 114). Ambulophobia was significantly more common (p < 0.0001) in women  than in men   .n = 174)—moved independently, followed by those with the help of a walker , while the fewest used a wheelchair . Significantly more women  than men  moved with the help of a cane/ orthopedic crutch while moving with the help of a walker was more common among men  than women  (p < 0.0001) (The way the patients moved was varied. The majority of study participants −45.9% ( 0.0001) .n = 126; 33.2%) and mild dementia , while the least number of patients whose MMSE results indicated severe dementia   .n = 205; 54.1%). The risk of depression was significantly higher among women than men—moderate and severe depression was noted in 37.7% (n = 84) and 16.1% (n = 36) of women and 26.9% (n = 42) and 7.7% (n = 12) of men respectively  there were no symptoms of delirium. Symptoms of delirium fulfilling the criteria of the DOS scale were more frequent among men  than women , while the opposite trend was observed for symptoms of delirium not fulfilling the DOS criteria. The differences within sex were statistically significant (p < 0.001) (In 44.6% of the subjects (< 0.001) .n = 78), while moderately unfit patients predominated   .n = 48; 42.11%)  and in the group of people moving with the help of a walker  . Differences (p = 0.04) were found between those with ambulophobia aged 71–80 years and those aged 91–100 years in terms of how they moved—patients with ambulophobia aged 91–100 years were more likely to move independently  than those aged 71–80 years .Based on the statistical analysis, ambulophobia was most common in patients aged 71–80 years  were significantly more likely  than those without ambulophobia . Ambulophobia was not recorded among patients with no polypragmasia.The presence of ambulophobia moderately correlated with the amount of medication taken (V = 0.47). It was observed, that patients with ambulophobia took more medication than those without diagnosed ambulophobia . Among tn = 48; 100%)  and in 74.8% (n = 89) of those with depression   .2 = 51.0; df = 1; p < 0.0001) than those with normal results or those indicating the presence of cognitive impairment without dementia . The greatest differences were noted in the case of severe depression—as many as 87.5% (n = 42) of people with severe depression met the criteria for ambulophobia (The GDS test results confirmed that ambulophobia was significantly more common among people with moderate and severe depression (χlophobia . The GDSn = 84), only 14.3% (n = 12) of the subjects had scores indicating ambulophobia   . A weak n = 12; 15.4%). Furthermore, it was observed that any deterioration in the patient's physical fitness, interpreted on the ADL scale as moderate or severe disability, had a comparable impact on the occurrence of ambulophobia—the percentage of those with ambulophobia among the moderately unfit was 34.3% (n = 60) and for the severely unfit-−33.3% (n = 42). The observed differences were statistically significant   . The ass2 = 276.3; df = 3; p < 0.0001)  . Based on = 24), whereas in patients who had a fall, ambulophobia was almost twice more common, affecting 11.1% of the study population (n = 42) . The strength of the correlation between the patient's own fall and ambulophobia was defined as moderate (ϕ = 0.34). The event of observing another person fall corresponded quite strongly with ambulophobia (ϕ = 0.53)—among patients who had seen a fall, the proportion with ambulophobia was as high as 17.4% (n = 66)   . Closer The rate of aging processes is characterized by multifactorial variation. The older people get, the more they differ from each other in their morphofunctional characteristics . In old Involutionary processes affect on the impairment of the functions of the motor and postural system, on which postural stability depends. Postural stability is understood as the resistance of the posture to endogenous and exogenous disturbances, the source of which can be both environmental variability and the interaction of the organism with the environment . ChangesDespite the significant impact of ambulophobia on the daily functioning of older adults, the literature does not provide information on the epidemiology of that phenomenon and the factors that contribute to its occurrence. Instead, limited studies have investigated the associations between fear of walking and gait and musculoskeletal changes , 23.Some studies , 25 haveGreat importance was also attributed to multimorbidity, which often occurs in older people. Our study showed that ambulophobia was significantly more frequent among patients with orthostatic hypotonia, Parkinson's disease and depression. Orthostatic hypotonia is a condition in which there is a decrease of at least 20 mmHg in systolic and diastolic blood pressure values associated with a change in body position . Rapid hThe study found that all patients who had Parkinson's disease met the diagnostic criteria for ambulophobia. This is a surprising result and there is need for more accurate research. Nevertheless, the characteristic presentation of Parkinson's disease, which includes motor slowing, clumsiness in movements, slowed mental processes, impaired balance or difficulty performing simple tasks such as getting up from a chair or bed, may drastically affect the occurrence of fear of movement and fear of falling . It is aThe co-occurrence of ambulophobia and depression are shown in our study is not surprising. Co-occurrence of depression and anxiety is frequently observed in clinical practice. Similar observations have also been reported in other scientific reports , 12, 27.Atkin et al.  showed tThe emotional state of a human being is a very complex aspect and depends on many factors. One element of emotional state is fear or anxiety. Fear is described as a state of strong emotional tension, appearing in situations of real danger. It is the desired state, connected with primal instincts conditioning survival in surprising and difficult situations. Anxiety, on the other hand, often takes a maladaptive form, disturbing human functioning. When it becomes dominant in behavior and prevents normal functioning, it becomes a pathology requiring treatment . The devFurthermore, the feeling of this anxiety may increase as a result of experiencing an injury from a previous fall or witnessing another person's injury. Our study also showed that subjective feelings of fear of mobility were higher in those who met diagnostic criteria for ambulophobia than in those who did not, demonstrating the need for a standard assessment of mobility anxiety in every patient in a long-term care facility or geriatric ward. We also suggest that it should become standard practice to talk to a psychologist or psychogeriatrician after experiencing one's fall or witnessing another person's fall. Such early specialist consultation could prevent the development of full-blown ambulophobia and act as a preventative measure. It would also allow us to know the extent of the problem and thus increase the awareness of health care professionals and develop appropriate and effective treatments.Research –35 show We consider the main advantage of our publication to be its innovative character and the fact that it is the largest ever, multi-center, population-based, representative study of patients treated in long-term care facilities in Poland. As an advantage, we also point to the identification of many factors, which strongly correlate with ambulophobia, which gives grounds for special observation of these groups of patients and early specialist intervention. The selection of the group (patients of long-term care facilities) in our opinion is a strong point of this study, because in Polish and international publications, this group of patients is often neglected, and taking into account the fact of aging society more and more people will benefit from this form of care. On the other hand, such selection of the group may be a limitation of this study, because its results are not adequate for the whole population of Polish seniors. Therefore, the next stage of knowledge development in this area should be a study conducted among elderly people staying at home and treated on an outpatient basis rather than in the long-term care system. We are also aware of other limitations, which include the lack of analyses between ambulophobia and the level of education or the type of injuries sustained during a fall. It would also be important to research the correlation between ambulophobia and specific medications taken by patients. Unfortunately, given the scale of the study and the number of variables analyzed, this was not possible in this part of the publication.As practitioners, psychogeriatricians, we believe that the topic of fear of movement and fear of falling is an important aspect of our professional work, encountered frequently in our practice, and we encourage further research on this topic. It is worth remembering that falls and accompanying injuries cause not only a health burden but also a socio-economic burden, so prevention of their occurrence seems to be particularly beneficial, not only for individual reasons but also from the point of view of society as a whole. Undiagnosed and untreated anxiety disorders, apart from being a major inconvenience, a sense of constant emotional tension and psychological discomfort, can lead to insomnia, somatic deterioration, addictions and increase the risk of suicide, which translates into a worsening of patients' quality of life.In conclusion, the prevalence of ambulophobia in the study group was 30.1%. Ambulophobia significantly more often concerned women and people in the age group of 71–80 years. Depression, Parkinson's disease, orthostatic hypotonia, a history of falling or seeing another person fall, and moderate or severe disability in activities of daily living increase the likelihood of ambulophobia. This type of specific phobia is also significantly more common among patients with polypragmasia, which supports the need to evaluate the medications used and reduce them. On the other hand, protective factors against the development of ambulophobia include male gender, younger age, fewer medications used per day, and no previous falls or seeing another person fall.The data that support the findings of this study are available from the corresponding author upon reasonable request.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.MGó led the conception of work and analyses, as well as the interpretation of data for publication and the writing of the manuscript. KK, KG, KB, MGr, JF, and BC contributed to the design and the writing of the manuscript, namely preparation and critical revision. MB supervised the analyses. RP was the primary reviewer of the paper and contributed many of the final revisions. All authors have read and approved the final version for publication.Upon acceptance for publication, the publication will be paid for by the Medical University of Silesia in Katowice.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hypertension is highly prevalent worldwide and is the major risk factor for heart failure (HF). More than half of the patients with HF in Asia suffer from hypertension. According to the 2022 American Heart Association/American College of Cardiology/Heart Failure Society of America HF guideline, there are four stages of HF, including at risk for HF (stage A), pre‐HF (stage B), symptomatic HF (stage C), and advanced HF (stage D). Given the high prevalence of hypertension as well as HF and the stronger association between hypertension and cardiovascular diseases in Asians compared to the west, measures to prevent and alleviate the progression to clinical HF, especially controlling the blood pressure (BP), are of priority for Asian populations. After reviewing evidence‐based studies, we propose a BP target of less than 130/80 mmHg for patients at stages A, B, and C. However, relatively higher BP may represent an opportunity to maximize guideline‐directed medical therapy (GDMT), which could potentially result in a better prognosis for patients at stage D. Traditional antihypertensive drugs are the cornerstones for the management of hypertension at stages A and B. Notably, calcium channel blockers (CCBs) are inferior to other drug classes for the preventing of HF, whereas diuretics are superior to others. For patients at stage C, GDMT is essential which also helps the control of BP. In particular, sodium‐glucose cotransporter‐2 (SGLT2) inhibitors are newer therapies recommended for the treatment of HF and presumably even in hypertension to prevent HF. Regarding patients at stage D, GDMT is also recommended if tolerable and measures should be taken to improve hemodynamics. Hypertension causes left ventricular hypertrophy (LVH) and fibrotic changes, ultimately leading to diastolic dysfunction.1 in ECG.The diagnosis of hypertensive HF is based on the classic symptoms and signs of HF in conjunction with long‐standing hypertension and evidence of LVH.1.2There are several classification methods of HF that are based on distinct perspectives, from New York Heart Association (NYHA) functional class to ejection fraction (EF) categories and stages of HF.The ACC/AHA stages of HF were initially introduced in 2001, including four stages  showed that compared with a systolic blood pressure (SBP) target of less than 140 mmHg (1 mmHg = .133kPa), targeting a SBP of less than 120 mmHg significantly reduced the rate of HF by 38%.For patients at stage B, a subgroup analysis of the SPRINT study showed that among participants with baseline LVH, intensive BP treatment could significantly regress their LVH,For patients at stage C, there are no clinical trials that are designed to compare different BP lowering targets in HF populations. Therefore, the evidence of BP target mainly stems from trials that compared BP lowering drugs to placebo or other active antihypertensive treatment. The Candesartan in HF: Assessment of Reduction in Mortality and morbidity study (CHARM)‐preserved program presented that compared with placebo, a lower BP of less than 130/80 mmHg in the candesartan subgroup significantly prevented admissions for chronic HF.For patients at stage D, who always suffer from a loss of myocardial contractility and pump failure, and presumably manifest as reduced cardiac output and systemic congestion.1.4Given the poor prognosis of clinical HF, primary prevention is the most important step that should be realized at stage A and stage B. Measures to prevent the progression of clinical HF, especially BP control, are of important priority at these stages. Considering the high prevalence of hypertension and the potential benefit from antihypertensive treatment in Asian populations, it is imperative to take measures to improve BP control. From the perspective of antihypertensive treatment, it should be emphasized that BP reduction is more important than the choice of specific BP‐lowering regimens.In addition to conventional antihypertensive drugs, angiotensin receptor‐neprilysin inhibitor (ARNI) is a new class of cardiovascular agent that shows a tremendous antihypertensive effect, even towering over ARBs.For patients at stage C, most drugs that are recommended to treat HF can lower BP simultaneously. ACEIs, ARBs, β‐blockers and mineralocorticoid receptor antagonists (MRAs) were recommended as first‐line therapy for patients with HFrEF to reduce mortality and HF hospitalization, whereas diuretics were suggested to relieve the symptoms of congestion.For patients with HFpEF, diuretics are recommended to improve symptoms caused by congestion.For patients at stage D, relatively high BP indicates an opportunity to maximize GDMT. We suggest titrating the doses of ACEIs/ARBs/ARNI, β‐blockers, MRAs, and SGLT2 inhibitors with caution, if tolerable. In addition, measures should be taken to improve hemodynamics. Fluid restriction, inotropic support, mechanical circulatory support, and cardiac transplantation might be considered.2Hypertension is an important risk factor as well as a common comorbidity of HF. Given the high prevalence of hypertension as well as HF in Asia, the management of hypertension is a priority for preventing and alleviating the development of HF. In the early diagnosis of HF with hypertension, the development of AI methods including the integration of more parameters that can be easily obtained by simple diagnostic methods can be helpful. Focusing on stages of HF, we propose the BP target of less than 130/80 mmHg for patients at stage A, B, and C with hypertension. For patients at stage D, relatively higher BP may represent an opportunity to maximize GDMT and indicate a better prognosis. Traditional antihypertensive drugs are still the cornerstones for the management of hypertension at stage A and stage B. For patients at stage C with hypertension, GDMT is essential as it also helps the control of BP. Notably, SGLT2 inhibitors are newer regimens recommended for the treatment of HF and presumably even in hypertension to prevent HF. In terms of patients at stage D, GDMT is also recommended if tolerable.The authors have no competing interests.Huanhuan Miao: Designed the study, carried out the literature search, performed the analysis and wrote the manuscript. Changhong Zou: Designed the study, performed the analysis and revised the manuscript. Shijie Yang: Carried out the literature search, designed the tables and revised the manuscript. Yook‐Chin Chia, Minh Van Huynh, Guru Prasad Sogunuru, and Jam Chin Tay: Revised the manuscript. Tzung‐Dau Wang: Designed the tables and revised the manuscript. Kazuomi Kario: Designed the studyand revised the manuscript. Yuqing Zhang: Designed the study, performed the analysis and revised the manuscript.


Intermittent hypoxia applied at rest or in combination with exercise promotes multiple beneficial adaptations with regard to performance and health in humans. It was hypothesized that replacing normoxia by moderate hyperoxia can increase the adaptive response to the intermittent hypoxic stimulus.Our objective was to systematically review the current state of the literature on the effects of chronic intermittent hypoxia–hyperoxia (IHH) on performance- and health-related outcomes in humans.PubMed, Web of Science™, Scopus, and Cochrane Library databases were searched in accordance with PRISMA guidelines (January 2000 to September 2021) using the following inclusion criteria: (1) original research articles involving humans, (2) investigation of the chronic effect of IHH, (3) inclusion of a control group being not exposed to IHH, and (4) articles published in peer-reviewed journals written in English.n = 1), older patients with cardiovascular (n = 3) and metabolic disease (n = 2) or cognitive impairment (n = 1), and young athletes with overtraining syndrome (n = 1). The included studies confirmed the beneficial effects of chronic exposure to IHH, showing improvements in exercise tolerance, peak oxygen uptake, and global cognitive functions, as well as lowered blood glucose levels. A trend was discernible that chronic exposure to IHH can trigger a reduction in systolic and diastolic blood pressure. The evidence of whether IHH exerts beneficial effects on blood lipid levels and haematological parameters is currently inconclusive. A meta-analysis was not possible because the reviewed studies had a considerable heterogeneity concerning the investigated populations and outcome parameters.Of 1085 articles initially found, eight studies were included. IHH was solely performed at rest in different populations including geriatric patients (https://www.crd.york.ac.uk/prospero/).Based on the published literature, it can be suggested that chronic exposure to IHH might be a promising non-pharmacological intervention strategy for improving peak oxygen consumption, exercise tolerance, and cognitive performance as well as reducing blood glucose levels, and systolic and diastolic blood pressure in older patients with cardiovascular and metabolic diseases or cognitive impairment. However, further randomized controlled trials with adequate sample sizes are needed to confirm and extend the evidence. This systematic review was registered on the international prospective register of systematic reviews (PROSPERO-ID: CRD42021281248) ( With the development and widespread availability of devices inducing a systemic or local hypoxic environment , the “live low-train high” approach has gained considerable popularity as an effective and efficient training modality for a variety of professional athletes –3 as weliO2) via oxygen filtration or nitrogen dilution (normobaric hypoxia)  separated by 5 min of normoxia, 4 times a day, daily for 10 days) [iO2 = 0.15–0.12] separated by 3 min of normoxia, 5–6 times a day, 5 sessions per week for 3 weeks) or total haemoglobin mass (same protocol as . H. HiO2 = tocol as . It can tocol as  and that5–0.145) . Further5–0.145)  stated t studies –109, the studies . Althoug studies  has showThe individual blood lipid profile , blood glucose level, and blood pressure are important indicators concerning the assessment and management of health-related risk factors. Among other factors, their purposeful modification (e.g. due to interventions) may have a great importance for the prevention of metabolic and cardiovascular diseases –145.iO2 = ~ 0.135) resulted in a significant decrease in total cholesterol, low-density lipoprotein cholesterol, and triglycerides, whereas high-density lipoprotein cholesterol was increased in male patients with coronary artery disease. However, the study did not contain a control group that was not exposed to hypoxia. In general, the findings of the studies that have investigated the effect of continuous hypoxic training on blood lipid levels are relatively heterogeneous , interspersed with 1 h of normoxia)  interspersed by 3 min of normoxia) and found a decrease of 22 mmHg in systolic and 16.6 mmHg in diastolic blood pressure. In a more recent study, Muangritdech et al. [pO2 = 90.8 ± 2.31% to 87.7 ± 1.89%] interspersed by 3 min of normoxia combined with continuous treadmill walking at 35–50% of the participants’ individual heart rate reserve). Moreover, Serebrovska et al. [In addition to the effects on blood lipid and glucose concentration, the influences of IHHE on resting systolic and diastolic blood pressure were also investigated , 109. Thh et al.  reporteda et al.  reporteda et al. , 165. Ina et al. . Given tiO2 = 0.09–0.16 and intra-session frequency of 3–15 cycles per session or day to achieve positive effects on multiple structures such as the cardiovascular, respiratory, musculoskeletal, neuronal, and immune systems [In general, the acute and chronic responses to hypoxia are complex and could be either adaptive/beneficial or maladaptive/pathological depending, among other factors, on the hypoxic dose. The hypoxic dose can be adjusted by modulating various variables Table  includin systems . Althoug systems , 168, we systems .Table 4OpO2) and external intensity (e.g. FiO2). Reductions in SpO2 at a fixed FiO2 vary widely within and between individuals due to different compensatory processes especially with increasing hypoxia intensity [pO2 and heart rate). Subsequently, the hypoxia intensity and intra-session density of the IHHE program was individually tailored based on the results of this hypoxia test [pO2 were monitored and the IHHE session was controlled via biofeedback .With regard to the intensity of the hypoxic stimulus, it is crucial to differentiate between internal , the maDespite a somewhat limited number of studies included in our qualitative analysis, the current systematic review provides first hints that IHHE can be a non-pharmacological intervention strategy for improving peak oxygen consumption, exercise tolerance, and cognitive performance as well as reducing cardiometabolic risk factors  in older patients with cardiovascular and metabolic diseases or cognitive impairment. Importantly, although the results appear promising, more high-quality randomized controlled trials with a detailed description of the hypoxia dose and population (i.e. specific disease phenotype) are warranted before robust conclusions for the use of IHHE in therapy or clinical practice can be drawn. The evidence concerning the effects of IHHE on total cholesterol, high- and low-density lipoprotein cholesterol, and triglyceride blood level as well as erythropoiesis and haemoglobin mass is still inconclusive. Moreover, there is no evidence that replacing normoxic periods with hyperoxic periods enhances hypoxia-related adaptations in humans. This is mainly due to the fact that only one study directly compared the effect of IHHE and intermittent hypoxic exposure on blood glucose and lipoprotein cholesterol level in older patients with prediabetes.Given the relatively low number of studies investigating the chronic effects of IHHE on performance- and health-related outcomes, there are some important aspects that should be addressed in future studies. These include the direct comparison of the effectiveness of IHHE or IHHT and hypoxic–normoxic exposure or training on, for example, changes in physical performance (e.g. exercise tolerance), cognitive performance (e.g. working memory), or cardiometabolic risk factors (e.g. systolic and diastolic blood pressure). Moreover, the cellular and molecular changes  driving the adaptations to IHHE or IHHT should be examined. Furthermore, to better individualize IHHE or IHHT interventions, the optimal combination of variables that determine the dose–response relationship needs to be investigated with respect to physiological and structural adaptations as well as their importance for physical and cognitive performance improvements. These variables include the intensity of hypoxia and hyperoxia, the duration of a single hypoxic period, the intra-session frequency (i.e. the number of cycles), the intra-session density (i.e. duration of a single hyperoxic period), the inter-session frequency, the inter-session density, and the duration over which the IHHE or IHHT intervention is carried out (see Table