Rapid deterioration of HRQOL was defined a change in the HRQOL value greater than the median. Among 970 patients, 360 (37.1%) were in the rapid kidney function decline group. In 720 patients who were 1:1 propensity score-matched, the baseline eGFR was not significantly different between the non-rapid and rapid kidney function decline groups. Compared with the baseline PCS score, the 5-year PCS score decreased in the non-rapid and rapid kidney function decline groups. The 5-year MCS score significantly decreased in the rapid kidney function decline group alone. Rapid decline in kidney function was significantly associated with rapid deterioration of the PCS  and MCS  scores. Rapid decline in kidney function was associated with rapid deterioration of HRQOL in patients with CKD.This study aimed to evaluate changes in health-related quality of life (HRQOL) in patients with chronic kidney disease (CKD) according to decline in kidney function. HRQOL was assessed using the Short Form-36 questionnaire composed of a physical component summary (PCS) and mental component summary (MCS). Rapid decline in kidney function was defined as a decline in the estimated glomerular filtration rate (eGFR) of > 3 mL/min/1.73 m HRQOL scores are significantly lower in patients with CKD of all stages, including dialysis, than in the general population2. According to the results of the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD), which comprised a South Korean CKD cohort, the HRQOL scores are lower in patients with advanced CKD compared to early CKD3.Quality of life (QOL) is the degree of well-being or happiness at which an individual is comfortable, healthy, and able to participate in or enjoy life events. Health-related quality of life (HRQOL) represents a patient’s comprehensive perception of mental and physical health and is regarded as a health outcome. Renal and extrarenal conditions can lead to poor HRQOL in patients with chronic kidney disease (CKD). Patients with advanced CKD have various complex comorbid conditions that can lead to poor HRQOL4. Moreover, cardiovascular outcomes and mortality are associated with low HRQOL in patients with predialysis CKD6. As CKD progresses, HRQOL can decrease, which may result in poor patient outcomes; therefore, monitoring HRQOL is important.Low HRQOL is associated with poor patient outcomes. A previous study performed on African Americans with hypertensive CKD showed that low physical or mental HRQOL is associated with an increased risk of cardiovascular events/cardiovascular death and CKD progression or death7. HRQOL could decrease according to kidney function decline; however, little is known about how a rapid decline in kidney function affects HRQOL changes. Therefore, this study aimed to evaluate changes in HRQOL in patients with CKD according to rapid kidney function decline using data obtained from a large Korean CKD cohort.In patients with CKD, kidney function declines over time; some patients have a rapid decline in kidney function, while others do not. Lack of energy, drowsiness, and fatigue are common symptoms that result in poor HRQOL in patients with predialysis CKD2. Among all patients, 37.1% (360 patients) were in the rapid decline group. Patients with diabetes mellitus (DM) (P = 0.017) and hypertension (HTN) (P = 0.007) were more likely to experience a rapid decline in kidney function. The baseline eGFR value (P < 0.001) was lower in the rapid kidney function decline group.In 2,238 patients enrolled in the KNOW-CKD cohort between 2011 and 2016, 970 participants had an estimated glomerular filtration rate (eGFR) of more than three times during the follow-up period and underwent HRQOL evaluation at enrollment and at 5 years . In the propensity score-matched patients, the mean age (P = 0.174) and body mass index (BMI) (P = 0.104) were similar between the non-rapid and rapid kidney function decline groups. The number of patients with DM (P = 0.118) and HTN (P = 0.0.152) was also similar in both the groups. The mean eGFR slope was −4.01  mL/min/1.73 m2/year in the rapid kidney function decline group, whereas it was −1.52  mL/min/1.73 m2/year in the non-rapid kidney function group. In the baseline HRQOL values of propensity score-matched patients, there was no significant difference in the physical component summary  or mental component summary  score between the non-rapid and rapid kidney function decline groups.As there was a significant difference in baseline kidney function between the two groups, we performed propensity score matching (PSM) to minimize this difference. In propensity score-matched patients, the baseline eGFR was not significantly different between the non-rapid and rapid kidney function decline groups (non-rapid vs. rapid decline groups: 49.5 ± 26.7 vs. 49.9 ± 26.7 mL/min/1.73 mP = 0.001) and 5-year-MCS  scores were significantly decreased compared with their respective baseline scores  and rapid (P < 0.001) kidney function decline groups  decreased significantly at 5 years in the entire patient cohort, as well as in the non-rapid and rapid kidney function decline groups Fig. A. The scP = 0.004) and MCS  scores (Table P = 0.018) and MCS  scores in model 3. In addition, rapid HRQOL deterioration was not significantly associated with the CKD causes  and underlying cardiovascular disease. Using angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and calcium-based phosphate binders did not affect the rapid deterioration in HRQOL.In an unadjusted binary logistic regression model, rapid kidney function decline was a significant factor related to rapid HRQOL deterioration in terms of both the PCS 11. Another study also showed that HRQOL dimensions—physical health and bodily pain—remain almost stable over the course of CKD12. However, the perception of general health presents a declining trend to an important extent. Therefore, it is necessary to pay attention to patients’ health awareness and to educate them on improving their HRQOL. Since a patient's symptom burden can reduce their HRQOL, clarifying these symptoms and approaching treatment will help improve HRQOL. HRQOL is one of the many patient-centered outcomes that can be assessed using patient-reported outcome measures. Incorporating patient-reported outcomes into clinical care is crucial for providing patient-centered care and improving the patients’ QOL13. Therefore, efforts are being made to focus on patient-reported results in patients with CKD, and studies in which symptom burden was relieved have also been reported15.General health, which represents overall general health awareness, did not decrease significantly during the follow-up period in this study. However, the general health score was much lower at baseline than those for the other PCS components. The Heart and Soul Study showed that self-assessed overall health was more reduced in earlier stages of renal function than mental health outcomes or QOL16. Another study showed that physical health is affected in patients with moderate CKD; however, mental health outcomes remain stable11. Therefore, physicians should be concerned with and supportive of patients’ mental health.The MCS score was significantly decreased in the rapid kidney function decline group but not in the non-rapid kidney function decline group. The MCS component scores also significantly decreased in the rapid kidney function decline group alone. That is, when patients did not have rapidly decreasing kidney function, the PCS score decreased, although the MCS score did not decrease significantly. This suggests that the mental health composition of patients with CKD was better tolerated in the absence of rapid kidney function decline. Although not evaluated in patients with rapid decline in kidney function, a previous study showed that CKD stage 2-3b had no significant effect on mental HRQOL2), rapid decline in kidney function was a significant risk factor for rapid deterioration of both the PCS and MCS scores. This suggests that not only is HRQOL lower in advanced CKD, but the rate of deterioration is also faster. Although the criteria for GFR for decreased HRQOL varies between studies , decreased renal function is a risk factor for decreased HRQOL16. In advanced CKD, more attention is required for the deterioration of HRQOL, especially in patients with a rapid decline in kidney function.In advanced CKD . In addition, the universal standard methods for assessing HRQOL are limited. We cannot rule out potential residual confounders, although we considered various confounding factors in the multivariable analysis. Finally, the study participants belonged to a single ethnic group of Koreans; thus, caution is required when generalizing our findings to other ethnicities.In conclusion, rapid decline in kidney function was associated with rapid deterioration of HRQOL in patients with predialyis CKD. Attention should be paid to the deterioration of HRQOL in patients with a rapid decline in kidney function. Furthermore, early assessment of HRQOL deterioration in high-risk patients and attempting to modify them may help improve HRQOL in patients with CKD.17. Of the patients enrolled in the KNOW-CKD cohort, 970 participants had an eGFR of more than three times during the follow-up period and underwent HRQOL evaluation at enrollment and at 5-year follow-up. The study protocol was approved in 2011 by the ethical committee of each participating clinical center and by the institutional review boards of Seoul National University Hospital (1104-089-359), Yonsei University Severance Hospital (4-2011-0163), Seoul St. Mary’s Hospital (KC11OIMI0441), Seoul National University Bundang Hospital (B-1106/129-008), Kangbuk Samsung Medical Center (2011-01-076), Gil Hospital (GIRBA2553), Eulji General Hospital (201105-01), Chonnam National University Hospital (CNUH-2011-092), and Pusan Paik Hospital (11–091). This study was performed in accordance with the principles of the Declaration of Helsinki, and all study participants provided written informed consent.We reviewed the baseline data and follow-up results from the KNOW-CKD, a nationwide multicenter prospective cohort study that includes predialysis patients with CKD stages 1–5. The detailed study design and methods of the KNOW-CKD have been described previouslyhttp://www.phactax.org) with the help of the Division of Data Management at the Seoul National University Medical Research Collaborating Center. Patients with DM were defined as those with a history of DM, those with a fasting serum glucose level of ≥ 126 mg/dL, or those on anti-diabetic medication. Patients with HTN were defined as those with a history of HTN, those with a systolic blood pressure of ≥ 140 mmHg or a diastolic blood pressure of ≥ 90 mmHg, or those on antihypertensive drugs. The following laboratory values were measured using a ≥ 8-h fasting blood sample at each participating center: hemoglobin, uric acid, albumin, total cholesterol, and C-reactive protein levels. Serum creatinine level was measured at a central laboratory  using an isotope dilution mass spectrometry-traceable method18. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation19. CKD stages were defined according to the Kidney Disease: Improving Global Outcomes guidelines20. Second-voided or random urine samples were immediately sent to a central laboratory to measure urine creatinine and protein levels. Urinary protein excretion was quantified using the random urine protein-to-creatinine ratio (g/g). Low-income status was defined as a monthly family income of less than ₩1,500,000 (approximately 1500 US dollars). A low education level was defined as an academic background lower than high school graduation because high school education is compulsory in Korea. The rate of kidney function decline per year was determined using the slope of the eGFR, which was calculated using a generalized linear mixed model with a random slope and random intercept. The random slope model assumes an explanatory variable (repeated serum creatinine measurements) to be a different effect for each group with a different eGFR slope, which allows each group line to have a different slope. A random intercept model estimates a separate intercept for each individual in each group, but when the slope for each group is 0, the intercept for each individual in each group is 0, making an intercept of 0 in the entire group22. In our study protocol, all CKD patients are required to have their serum creatinine levels measured every 6 months for 1 year after enrollment and every 1 year from the first year onward. In the mixed model, we used the individual's eGFR measured at least three times as the outcome variable and an individual and repeated serum creatinine measurements as an explanatory variable. The rapid decline in kidney function was defined as a decline in eGFR of > 3 mL/min/1.73 m2/year. Patients were categorized into non-rapid and rapid kidney function decline groups.Data on baseline demographic characteristics, such as age, sex, comorbidities, smoking status, and laboratory parameters at enrollment, were extracted from an electronic data management system , which measures eight domains of HRQOL comprising a PCS and an MCS. The simple SF-36 questionnaire used in this study are presented in Table HRQOL was evaluated using the Korean version 1.3 of the KDQOL-SFt-test or Mann–Whitney test. Categorical variables are presented as frequencies and percentages and were analyzed using the Chi-square test or Fisher’s exact test, as appropriate. A log transformation was used to normalize variables with a skewed distribution. We conducted PSM to minimize differences in baseline kidney function between the two groups. The propensity scores were calculated from the logistic regression models and denoted the probability of being allocated to either the non-rapid or rapid kidney function decline group. The following covariates were used to perform the logistic regression: age, sex, and eGFR. Patients were matched using the nearest neighbor method with no replacement, a 0.2 caliper width, and 1:1 matching. The non-rapid and rapid kidney function decline groups after PSM showed similar distributions of propensity scores, indicating that the differences in covariates between the two groups were minimal. The characteristics of the two groups were compared before and after PSM.Continuous variables are presented as means ± standard deviations or as medians (interquartile ranges) and were compared using the P < 0.05. SPSS statistical software  was used for all descriptive and outcome analyses, and R software version 4.1.3  was used for PSM.Multivariable binary logistic regression analysis was performed after adjusting for multiple confounders to identify factors related to the rapid deterioration of HRQOL. The outcomes of patients with a rapid decline in kidney function were analyzed. In the subgroup analysis using a fully adjusted multivariable binary logistic regression model, we categorized patients according to their age, sex, presence of DM, and eGFR. Statistical significance was set at Supplementary Information.


Obstructive sleep apnea (OSA) is associated with hypertension; however, the associations between cardinal features of OSA, such as intermittent hypoxia (IH) and sleep fragmentation (SF), and blood pressure remain unclear. We performed this study to address this issue.p-values for linear trends of blood pressure across oxygen-desaturation index (ODI)/microarousal index (MAI) quartiles. Logistic regressions were used to determine the risk factors for abnormal blood pressure and to detect the multiplicative interaction between ODI and MAI with blood pressure.We investigated 335 subjects with the polysomnography (PSG) tests. Data, including basic characteristics, PSG parameters, and blood pressure, were collected. We calculated After adjusting for multiple variables, compared with subjects with lower ODI quartiles, those with higher ODI quartiles had significant higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) . And compared with subjects with lower ODI quartiles, those with higher ODI quartiles were also more likely to have abnormal DBP and hypertension after adjusting for multiple variables. Similarly, compared with subjects with lower MAI quartiles, those with higher MAI quartiles had significant higher SBP and DBP, and were more likely to have abnormal DBP and hypertension. No significant multiplicative interactions between ODI and MAI with blood pressure were detected.Subjects with more severe IH/SF had significant higher blood pressure and were more likely to have abnormal DBP and hypertension than those with less severe IH/SF. No interaction between IH and SF on the relationship with blood pressure was shown. Obstructive sleep apnea (OSA) is a common sleep disorder and its prevalence increases with age and obesity . A recenObstructive sleep apnea is characterized by two cardinal pathophysiological features, including intermittent hypoxia (IH) and sleep fragmentation (SF). IH has long been recognized as one of the mechanisms in the development of OSA-related hypertension, and rodent studies have demonstrated a robust association between IH and hypertension . It has We performed this study to address these gaps. We aimed to deeply assess the independent associations of the two cardinal features of OSA, IH and SF, with blood pressure, and the effects of the interaction between IH and SF.n = 65); known diabetes mellitus previously (n = 12); previous OSA treatment (n = 15); serious systemic diseases  (n = 2); and missing data (n = 16). A total of 335 patients with complete data were included (Adult inpatients with suspected OSA who underwent polysomnography (PSG) between January 2014 and January 2021 at the First Affiliated Hospital of Nanchang University were included. The following criteria were used to exclude 110 subjects: known hypertension previously (included .2). Blood tests were also performed, including fasting glucose levels.Comprehensive medical histories and blood tests were obtained from the subjects. The medical history included gender, age, hypertension, diabetes, other diseases, and smoking and alcohol consumption. Weights and heights were recorded and BMI was calculated as weight (kg)/height . Electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG), nasal and oral airflow, thoracic and abdominal respiratory effort, pulse oximetry, posture, and snoring data were obtained. Sleep stages, respiratory events, and microarousals were scored automatically using a computer software and were subsequently checked manually by a skilled technician, following the American Academic Sleep Medicine (AASM) criteria. The apnea–hypopnea index (AHI) was defined as the number of apnea and hypopnea events per hour during sleep. The oxygen-desaturation index (ODI) was defined as the number of times per hour of sleep that the blood oxygen level dropped by ≥ 4% from the baseline. The microarousal index (MAI) was defined as the number of arousals per hour of sleep, and an arousal was defined as an abrupt shift in the EEG frequency, including alpha, theta and/or frequencies > 16Hz (but not spindles) that lasted at least 3 s, with at least 10 s of stable sleep preceding the change. The lowest oxygen saturation value during sleep was referred to as the LSpO2 . As AHI Three daytime blood pressure measurements were recorded after at least 5 min of rest in a sitting position in accordance with the American Society of Hypertension guidelines using a mercury sphygmomanometer, and their mean was calculated. The blood pressure threshold for hypertension was 140/90 mmHg. According to the Guidelines for Prevention and Treatment of Hypertension in China, hypertension was defined as abnormal SBP (≥ 140 mmHg) or DBP (≥ 90 mmHg) or both .p-values for trends of basic characteristic, PSG parameters and blood pressure across ODI quartiles or MAI quartiles were calculated using the polynomial linear trend test for continuous variables and the linear-by-linear association test for dichotomous variables. Binary logistic regression analyses were used to determine the risk factors for abnormal SBP, abnormal DBP, and hypertension, along with the linear trends of the risk of abnormal blood pressure across ODI quartiles or MAI quartiles. Multiplicative interactions were detected with logistic regression. All statistical analyses were performed using SPSS Statistics 21.0 software . P < 0.05 was taken to indicate statistical significance.Continuous data with a normal distribution were presented as means ± standard deviation, while those with skewed distribution were presented as medians (with interquartile range). Categorical data were presented as numbers (%). The 2 levels)      and MAI  quartiles. Most subjects with a high ODI were obese males, and this group had a higher percentage of drinkers, higher glucose levels, and more severe OSA  . The per< 0.001) . Similar < 0.05) . The per< 0.001) .p for trend = 0.010 and 0.018, respectively) (p for trend = 0.047). Although no significant linear trend for hypertension with increasing ODI quartiles was seen (p for trend = 0.087), compared with subjects with the lowest ODI quartile, those with higher ODI quartiles were more likely to have hypertension   . Logistip = for trend 0.008 and 0.011, respectively) (p for trend = 0.002). Although no significant linear trend for hypertension with increasing MAI quartiles was seen (p for trend = 0.082), compared with subjects with the lowest MAI quartile, those with the highest MAI quartile were more likely to have hypertension   . LogistiWe investigated the effect of a multiplicative interaction between MAI and ODI on blood pressure. After adjusting for multiple variables, there were no statistically significant interactions, with ORs (95%CI) of 1.121 , 0.768 , and 1.098  for abnormal SBP, abnormal DBP, and hypertension, respectively .We found that after adjusting for multiple variables, compared with subjects with lower ODI quartiles, those with higher ODI quartiles had significant higher SBP and DBP. Logistic regression analyses showed that compared with subjects with lower ODI quartiles, those with higher ODI quartiles were more likely to have abnormal DBP and hypertension after adjusting for multiple variables. Similarly, compared with subjects with lower MAI quartiles, those with higher MAI quartiles had significant higher SBP and DBP, and were more likely to have abnormal DBP and hypertension. Furthermore, no significant multiplicative interaction of ODI and MAI on blood pressure was detected.In this study, we found that those with more severe IH (represented by ODI) had significant higher SBP and DBP, and were more likely to have abnormal DBP and hypertension than those with less severe IH. An increasing number of studies have demonstrated that IH can induce hypertension. In human experimental models, hypoxia has been shown to lead to measurable elevations in blood pressure and sympathetic activation –28. IH cResults from previous clinical studies about the relationship between SF and blood pressure have been controversial. The PROOF-SYNAPSE study of 780 healthy older subjects found that repetitive sympathetic arousals during sleep were associated with elevated SBP and a higher risk of hypertension . 18) fo fo18) foIn addition to the independent relationships between IH/SF and blood pressure, we also explored the multiplicative effects of IH and SF on blood pressure, and found no evidence of an interaction. This suggests that these two factors contribute independently to abnormal blood pressure.Meanwhile, we explored the relationships between BMI and hypertension. We found those with higher BMI quartiles were more likely to have abnormal SBP and hypertension than those with lower BMI quartiles, which indicated the important role of obesity in hypertension . We alsoThis study had some limitations. First, it was a retrospective study, so causality cannot be inferred. Second, the statistical power was relatively low due to the small sample size. Third, although we adjusted for several common confounding factors, other more complex factors, such as lifestyle and exercise, were not taken into consideration. Despite these limitations, the sleep data based on standard PSG and objective measurements increased the credibility of our results.In conclusion, we found that subjects with more severe IH or SF had significant higher SBP and DBP, and were more likely to have abnormal DBP and hypertension than those with less severe IH or SF. There was no multiplicative interaction between IH and SF regarding the effects on blood pressure. Therefore, it is important to consider the roles of both SF and IH while treating hypertensive patients with OSA.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This study was approved by the Internal Review Board of the Institutional Ethics Committee of the First Affiliated Hospital of Nanchang University , and the study was conducted in accordance with all relevant tenets of the Declaration of Helsinki. This was a retrospective study. The ethics committee waived the requirement of written informed consent for participation.YYX and YPX designed the study and wrote and manuscript. KY and YYX collected the data. KY performed the analysis. 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.


To review the recent large-scale randomised evidence on pharmacologic reduction in blood pressure for the primary and secondary prevention of cardiovascular disease.P for interaction = 1.0). When these patient groups were further stratified by their baseline systolic blood pressure in increments of 10 mmHg from < 120 to ≥ 170 mmHg, there was no significant heterogeneity in the relative risk reduction across these categories in people with or without previous cardiovascular disease .Based on findings of the meta-analysis of individual participant-level data from 48 randomised clinical trials and involving 344,716 participants with mean age of 65 years, the relative reduction in the risk of developing major cardiovascular events was proportional to the magnitude of achieved reduction in blood pressure. For each 5-mmHg reduction in systolic blood pressure, the risk of developing cardiovascular events fell by 10% . When participants were stratified by their history of cardiovascular disease, the HRs (95% CI) in those with and without previous cardiovascular disease were 0.89 (0.86 to 0.92) and 0.91 (0.89 to 0.94), respectively, with no significant heterogeneity in these effects (adjusted Pharmacologic lowering of blood pressure was effective in preventing major cardiovascular disease events both in people with or without previous cardiovascular disease, which was not modified by their baseline blood pressure level. Treatment effects were shown to be proportional to the intensity of blood pressure reduction, but even modest blood pressure reduction, on average, can lead to meaningful gains in the prevention of incident or recurrent cardiovascular disease. Raised blood pressure is the world’s leading cause of premature death , 2. OverIn this paper, we explore some of the earlier findings that have established the importance of raised blood pressure as a risk factor of CVD and describe observations that gave rise to uncertainties in the management of raised blood pressure. We will show reports demonstrating the efficacy of blood pressure–lowering drug treatment, and highlight findings from recent studies that examined the efficacy across strata of baseline blood pressure in people with and without prior CVD. We will then discuss how recent evidence addressed uncertainties in blood pressure–lowering treatment strategies. Finally, we will discuss and show future directions that can help inform clinical management of people with raised blood pressure.Observations from prospectively followed cohorts of community-dwelling persons, who were usually free of known cardiovascular disease, have informed the importance of elevated blood pressure as a risk factor of vascular conditions. Investigations from the Framingham Study, which originally recruited 5209 men and women aged 30 to 62 years in 1948 who were then prospectively followed, have helped demonstrate the relationship between elevated blood pressure and the risks of incident coronary heart disease and stroke •, 19, 20In contrast, observations of the association between blood pressure and CVD seem to be different in people who have had a prior vascular disease , 25. ForThe contrasting observations between population-based and patient cohorts are difficult to resolve given that observational studies remain prone to residual confounding. In addition, the risk associated with high blood pressure levels seen in observational studies does not necessarily establish raised blood pressure as a modifiable risk factor. Randomised trials investigating the effects of pharmacological agents to lower blood pressure provide evidence for the unconfounded effects of blood pressure–lowering on CVD outcomes and can demonstrate that such treatment can modify the risk associated with elevated blood pressure. In a meta-analysis based on published data from these trials, the effect of lowering systolic blood pressure by 10 mmHg resulted in a proportional reduction in the risk of major cardiovascular events by 20%  , 27. Thuwww.bplttc.org). The current phase of the BPLTTC has focused on investigating the stratified effects of blood pressure–lowering treatment on major cardiovascular events, other less commonly reported vascular outcomes, and safety outcomes [The Blood Pressure–Lowering Treatment Trialists’ Collaboration (BPLTTC), established in 1995, is a global collaboration of principal investigators and research scientists investigating the effects of blood pressure–lowering drug treatment on CVD and mortality through a series of individual participant-level data (IPD) meta-analyses  than in the former group (146/84 mmHg). Over a median follow-up of 4.2 years, 42,324 participants (12.3%) developed a major cardiovascular event. The achieved blood pressure reduction differed across trials, largely depending on the study design. Since the relative risk reduction was proportional to the magnitude of achieved blood pressure reduction . Similar patterns of the results were seen for the effects of treatment in lowering diastolic blood pressure. When expressing treatment effects on absolute disease rates during follow-up, the absolute risk reduction between those with and without previous CVD differed due to a higher risk of future CVD events in those with prior CVD .Overall, a 5-mmHg reduction in systolic blood pressure lowered the risk of developing the composite outcome of major cardiovascular events by 10% . Similar effects were seen when the components of the composite outcome were examined separately. When the analyses were stratified according to history of CVD, blood pressure–lowering treatment was equally effective in both groups , 2.6% had < 120 mmHg systolic and 5.1% with < 70 mmHg diastolic blood pressure; among those with previous CVD , the proportions were 7.5% and 8.8%, respectively. There was no significant heterogeneity in the proportional risk reduction of major cardiovascular events across categories of baseline blood pressure in people with or without prior CVD .There was a widespread in the baseline blood pressure of trial participants. The systolic blood pressure ranged from < 120 to ≥ 170 mmHg and diastolic blood pressure from < 70 to ≥ 110 mmHg. Among participants without previous CVD (P for interaction = 0.050) [P for interaction = 1.00). While the effect of treatment to reduce CVD was weak in older ages, there was no adverse impact on deaths due to any cause even when their baseline blood pressure was not abnormally elevated.The BPLTTC has also conducted other IPD meta-analyses, and recently investigated stratified effects of blood pressure–lowering drug treatment by age ••. As pr= 0.050) ••. AlthoP for difference between these groups = 0.9). There was also no impact on all-cause mortality for either subgroups.In a separate study, the BPLTTC also examined any potential heterogeneity in treatment effects according to previous atrial fibrillation . In thisMany of the prevailing questions surrounding the clinical management of raised blood pressure relate to the blood pressure threshold at which to initiate pharmacotherapy, and, in determining treatment goals, how low should the blood pressure be reduced to.Pharmacotherapy is generally considered when patients have raised blood pressure, such as in hypertension. Such an approach is typically reflected in clinical guidelines when setting the blood pressure threshold of  ≥ 140/90 mmHg to consider pharmacologic intervention to lower blood pressure. However, evidence from this current study has shown that the beneficial effects of drug treatment are demonstrable even when baseline blood pressure is within the ‘normal’ or ‘high normal’ range. Moreover, the absolute risk reduction did not vary across the blood pressure levels considered in the study, suggesting that baseline level of blood pressure, as a single risk factor, could not discriminate very well the level of CVD risk in the study population. Put differently, there is a substantial proportion of the population whose background risk of CVD is high yet whose blood pressure may not reach the threshold by which pharmacologic treatment could be initiated , 37. ThuOn the other hand, considering drug treatment for all has cost implications. Depending on the health care system and service provision, there would be questions around affordability to support such a policy . MoreoveAs mentioned earlier, one of the uncertainties relating to blood pressure–lowering therapy concerns the J-shaped association between blood pressure level and vascular disease risk. If this is true, then there might be a blood pressure threshold associated with the least harm, and reducing blood pressure below this level could potentially cause more harm. However, randomised evidence does not support this notion. As has been described, the benefits of pharmacologic lowering of blood pressure were seen whether or not individuals have had previous CVD and regardless of their baseline blood pressure (and within the blood pressure range commonly observed in the population). Rather than setting a blood pressure target for the treatment to achieve, given that the absolute treatment benefit relates to the magnitude of blood pressure reduction, it may seem more reasonable to ask how much reduction in blood pressure should the treatment strategy aim for.Physiologically, substantive blood pressure reductions can be achieved in well-controlled studies involving short-term interventions of several weeks or months and relatively compliant participants . HoweverRather than focusing on modifying the risk factor, the aim of blood pressure–lowering pharmacotherapy should be to improve important health outcomes. By taking this perspective, making treatment decisions primarily based on blood pressure thresholds would be avoided, and individuals whose blood pressure levels may be within the ‘normal’ range, yet whose background risk of CVD is elevated, could still be potentially considered for blood pressure–lowering pharmacotherapy. The wider effects of blood pressure–lowering therapy beyond major vascular outcomes should be considered. Observational studies have reported associations between blood pressure and several vascular conditions which are not frequently included as part of primary outcomes •. There Another important consideration in determining treatment strategies is the adverse events linked to the use of blood pressure–lowering medications, particularly when treatment regimens involve intense lowering of blood pressure. These largely non-serious adverse events, such as syncope, hypotension, and falls, were generally time-limited and reversible •• and unRecent studies being led by the BPLTTC have addressed several uncertainties surrounding pharmacotherapy to lower blood pressure, in particular, examining stratified treatment effects according to different patient characteristics. To date, the BPLTTC has reported stratified effects according to a wide range of baseline blood pressure in people with and without previous cardiovascular disease, in middle-aged adults and older individuals, and in those with and without previous atrial fibrillation. Future investigations from the BPLTTC will consider other patient characteristics, such as history of diabetes and renal disease. There is a need for further work to understand treatment effects on other important vascular and non-vascular health outcomes, such as peripheral vascular disease, cognitive function impairment, and bone fractures. Understanding the wide-ranging benefits of blood pressure–lowering treatment may help provide informed discussions on therapeutic options and preferences between patients and clinicians.Risk-based approaches for determining treatment strategies need further exploration. Some of the multivariable models to estimate an individual’s risk typically use information on personal characteristics, lifestyle, and clinical factors, such as age, sex, smoking status, blood pressure, lipids, and body size, to quantify the probability of developing an event, for example, in the next 10 years. Among the commonly used equations to estimate a patient’s absolute CVD risk at baseline include the Framingham Risk Scores , Pooled There is also a need to further explore the use of combinations of different drug classes and even new types of drugs to lower blood pressure. A once-daily single pill with low doses of different classes of blood pressure–lowering drugs could be a promising pharmacotherapeutic strategy if it could optimally lower blood pressure as well as cause minimal adverse events . MoreovePharmacologic lowering of raised blood pressure is effective in the primary and secondary prevention of CVD. These effects are seen across a wide range of baseline blood pressure levels and not limited to those with high values only. However, clinical decision-making to treat raised blood pressure should include several considerations, including the level of blood pressure reduction that is feasible to achieve given the current evidence on the treatment effects of trialled drugs and therapeutic regimens. Conventional approaches that use blood pressure levels to assess the need for pharmacologic interventions to manage raised blood pressure may limit access to such treatment for those whose blood pressure levels do not reach the threshold for ‘abnormally high’ levels yet are at high risk of developing CVD. Alternative strategies to guide treatment decisions could be based on thresholds based on background CVD risk rather than on blood pressure levels. While there are several existing ways to assess background CVD risk, data-driven models, as applied to large-scale health data, may provide novel approaches to estimate the predicted absolute CVD risk at baseline. Future work should further compare and evaluate these risk-modifying approaches to therapy against standard strategies based on blood pressure thresholds in different populations and health care settings. Whichever strategy is used, discussions between patients and clinicians should include treatment effects on other important but less commonly reported vascular and non-vascular disease outcomes, as well as safety outcomes. Finally, future investigations should aim to develop novel pharmacological and non-pharmacological interventions that can achieve greater effects in blood pressure lowering but with minimal, or even less, adverse effects, than can be achieved with existing therapies.


P = 0.0001). Cutoff point of NT-proBNP that distinguished patients with HF and without it was 750 pmol/L . Using multivariate comparative analysis we established that concentrations of irisin < 10.4 ng/mL  and NT-proBNP > 750 pmol/mL , left atrial volume index (LAVI) > 34 mL/m2  independently predicted HF. Irisin being added to NT-proBNP improved predictive modality for HF, whereas combination of NT-proBNP and LAVI > 34 mL/m2 did not. In conclusion, we established that irisin had independent predicted potency for HF in patients with established T2DM.We hypothesize that serum irisin can have additional discriminative potency for heart failure (HF) in individuals with type 2 diabetes mellitus (T2DM). The study group comprised 226 consecutive T2DM patients (153 patients with any HF phenotypes and 30 patients without HF) aged 41 to 65 years. The plasma levels N-terminal brain natriuretic pro-peptide (NT-proBNP) and irisin were detected by ELISA at the baseline of the study. We found that the most appropriate cut-off value of irisin (HF versus non-HF) were 10.4 ng/mL (area under curve [AUC] = 0.96, sensitivity = 81.0%, specificity = 88.0%;  Type 2 diabetes mellitus (T2DM) remains to be a powerful risk factor for newly heart failure (HF) . MoreoveIncidence and prognosis of HF in T2DM patients seem to be potentially predictable factors, which are suggested to be attributes of pathophysiological mechanisms playing pivotal role in various maladaptive responses, such as adverse cardiac remodeling, systemic and microvascular inflammation, endothelial and adipose tissue dysfunctions, oxidative stress, hyperglycemia, lipotoxicity, metabolic memory and altered repair system –8. BeingIrisin is a multifunctional myokine secreted by adipocytes, cardiac myocytes, and skeletal muscle, possibly mediating a wide range of metabolic processes including 'browning' of white adipose tissue, thermogenesis, insulin resistance, muscle endurance, endothelial function, inflammatory and immune reactions and bone osteoblast activity , 15. In The study patient population composed of mainly male with average age of 51 years and numerous co-morbidities and cardiovascular (CV) risk factors, which included dyslipidemia (83.1%), hypertension (86.3%), stable coronary artery disease (29.5%), smoking (43.2%), and obesity (45.9%) (Table st-to-3rd grades) and having smoking habits than non-HF cohorts . Therefore, LVEDV, LVESV, LVMMI, LAVI and E/e` were found to be sufficiently higher and LVEF was lower in HF patients than in non-HF individuals. In addition to that, we did not find any difference in HOMA-IR and biochemical parameters between cohorts, apart from NT-proBNP and irisin, the levels of which exerted multidirectional changes. The HF individuals demonstrated significantly higher NT-proBNP and lower irisin than those without HF.We did not notice significant differences between cohorts in demographic and anthropomorphic parameters, presentation of several CV risk factors, and blood pressure. Along with it, HF cohorts composed of more amount of patients with chronic coronary artery disease, LV hypertrophy, chronic kidney disease      for further analysis. The last limitation was a lack of serial measures of circulating biomarkers in a protocol of the study. However, we believe that these limitations are not conclusive and allow extrapolating the results of the study on other populations.A total of 226 T2DM patients who were under investigation in the Private Multidisciplinary Hospital Vita-Centre LTD  were consecutively included in the study from October 2020 to December 2021. The following criteria such as age > 18 years, established T2DM regardless of HF, and the levels of HbAc1 less 6.9% were used as inclusion ones. The study design, inclusion / exclusion criteria and reasons to exclusion at the prescreening are reported Fig. We evaluated CV risk factors and co-morbidities according to current guidelines of the European Society of Cardiology (ESC) –46 so asWe evaluated conventional anthropometric parameters including body mass index (BMI), height, weight, and hip-to-waist ratio (WHR) .Conventional B-mode echocardiography and impulse Doppler examination on Vivid T8  were performed with the aim of elucidating hemodynamic features  in compliance with current guidelines . Left veT2DM patients received diet together with personally adjusted dose of metformin and sodium-glucose cotransporter-2 (SGLT2) inhibitor optionally. HF patients were treated in line with conventional clinical recommendation with beta-blockers, antagonists of renin–angiotensin–aldosterone system, loop diuretics, while possible use of SGLT2 inhibitions or i/f blocker ivabradine were also recommended as adjunctive therapies. Lipid-lowering agents were prescribed in subjects with known dyslipidemia, T2DM or established CVD without conventional contraindications. Antiplatelet drugs were added to the therapy when needed.Blood samples were collected for biochemical analysis after overnight fasting. After centrifugation  polled serum aliquots were stored at ≤ –70ºC until analysis. Concentrations of irisin and NT-proBNP were measured with commercial ELISA kits . All ELISA data were evaluated in step with the quality curve and every sample was measured twice because the norm was finally analyzed. Conventional biochemistry parameters were routinely measured in local laboratory using Roche P800 analyzer . We used CKD-EPI formula to estimate glomerular filtration rate (GFR) . InsulinP < 0.05) included in multivariate logistic regression. Predictive value of things for HF were confirmed using estimation of integrated discrimination indices (IDI) and net-reclassification improvement (NRI). All the tests performed were 2 sided, and p value < 0.05 was considered statistically significant.We used v. 23 SPSS  software and v. 9 GraphPad Prism  software for statistical analysis. The Kolmogorov–Smirnov test was accustomed evaluate distribution of variables. Baseline characteristics were expressed as counts with percentages or mean ± variance (SD) or median (interquartile range – IQR) for continuous variables and as proportions for categorical variables. For the normally distributed continuous variables, Student’s t-test was used. Otherwise, the Mann–Whitney U-test was chosen instead. We used Spearman coefficient of correlation to represent the connection between variables. With the aim of obtaining a receiver operating characteristic (ROC)-determined biomarker cut-points Youden method was performed. For the optimal cut-of value, the realm under the curve (AUC), 95% confidence Intervals (95% CI), also as a sensitivity and specificity were estimated. Univariate logistic regression was accustomed elucidate plausible predictive factors for HF. The potential candidates . This finding seem to have promising importance in discovering new predictive models for HF in these patient populations.


Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective . Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872. Chronic kidney disease (CKD), defined as abnormalities in kidney damage or glomerular filtration rate (GFR) below 60 mL/min/1.73 The risk of developing CVD is 5 to 10 times higher in CKD than in healthy subjects, and the probability of CV events becomes significantly higher in patients even in the early stages of CKD compared to the general population. Interestingly, CKD patients are much more likely to experience CV death than reach the stage of end-stage renal disease (ESRD)  eGFR = 186 × [creatinine concentration in mg/dL] − 1.154 × [age] − 0.203 .The CKD group was selected from the patients of the Nephrology Outpatient Clinic, Peritoneal Dialysis Clinic, and the Dialysis Center at the Clinical Hospital. H. Święcicki in Poznań. Here, four subgroups were formed.The hemodialyzed patients’ group, named HD (n = 71), included patients treated with hemodialysis (HD). HD procedures were performed on each patient three times a week via an arteriovenous fistula from their own or artificial vessels. The duration of HD was at least 10 h/week using standard bicarbonate dialysis fluids and polysulfone low-flux dialyzers. The blood flow during HD was 200–350 mL/min, with an average dialysis fluid flow of 500 mL/min.The peritoneal dialyzed patients’ group, named PD (n = 35), included patients treated with peritoneal dialysis with standard lactate and glucose-based fluids. In this group, initially, due to the treatment technique, two subgroups were separated: (a) a group (n = 15) treated with the automatic peritoneal dialysis (ADO) technique in which the cycler performed an automatic fluid exchange at night in the peritoneal cavity, the mean exchange time was 10 h, and in some patients, an additional 7.5% icodextrin solution was used during the day; and (b) a group of patients (n = 20) using the technique of continuous cycling peritoneal dialysis (CAPD), performing 3–5 daily dialysis fluid changes in the peritoneal cavity, volume 1.5–2.5 liters. In some patients, an additional 7.5% icodextrin solution was used in a single, long exchange. When the subgroups were analyzed separately, no statistically significant differences between the two techniques were shown, possibly due to the small size of the groups; hence, the group of peritoneal dialysis patients was presented as a whole, i.e., the PD group.The pre-dialyzed patients’ group, named PRE (n = 48), included patients in the pre-dialysis period (stage G3b-G4 CKD) with a moderate or severe decrease in eGFR (eGFR 44–29 mL/min/1.73 Patients at the early stages of CKD formed a group named CKD1-2 (n = 29), which included patients at the stages G1-G2 CKD with a mild decrease in eGFR , alpha-blockers, calcium binders , and vitamin D and its derivatives . Some patients also received drugs stimulating erythropoiesis by subcutaneous injection (patients in the PRE and DO) or intravenously (patients in the HD).Cardiology patients’ group, named CARD (n = 37), including patients with a history of at least one CV event, admitted to the hospital  for elective angiography, in whom both laboratory and clinical signs of impaired kidney function were revealed.Healthy volunteers’ group, named CONTROL (n = 32), was composed of healthy people, with no evidence of impairment of renal and cardiovascular functions in their history and at the time of enrollment in the study. This group included people recruited from among those reporting to the laboratory for routine checkups.Apart from the CKD group, a non-CKD group was formed. It was created from two subgroups:The exclusion criteria for all studied groups were as follows: active acute infection, immunosuppressive therapy, kidney transplant, abnormal liver function, malignant tumors within the last five years, and alcohol abuse within the last five years. The structure of the groups is shown in For non-invasive cardiological examinations, the following devices based on advanced algorithms for analyzing pulse waveforms and EKG curves were used: the Portapres TM , Enschede, The Netherlands), Pulse Trace 2000 TM , the SphygmoCor tonometer , and the Colin blood pressure monitor (BPM)-7000 TM device .The analyses were performed in the morning hours. After prior consultation with the attending physician, the persons participating in the study did not take their morning dose of medications. Initially, the examined person, previously connected to the monitoring equipment, rested for 15 min in a warm, air-conditioned, and dark room. Then, for 15 min, the waveform of the right hand’s pulse in the middle finger was continuously recorded using the PortapresTM device using the phenomenon of photoplethysmography. This device consists of a cuff that fits over the finger and a transducer placed on the test subject’s wrist. The cuff contains a diode that emits infrared light and a photocell that registers the amount of light that has not been absorbed. As it passes through the finger tissue, the emitted light is absorbed by the heme contained in the hemoglobin. The entire measurement is performed non-invasively, continuously, from beat to beat. This produces a pulse wave curve at finger level. Then, using a Pulse Trace 2000TM device, the change in pulse wave contour was analyzed based on the changes in blood volume in the fingertip. The Puls TraceTM device shows the shape of a typical pulse wave on the monitor, based on which the RI and SI indicators are calculated.Central (aortic) systolic blood pressure was examined using the SphygmoCor Mx system . For this purpose, the applanation tonometry method was used with the Colin BMP 7000 device (Japan). The Colin device is placed on the wrist, which enables continuous recording of the pulse wave at the level of the radial artery. The resulting analog signal is then sent in real time to the SphygmoCor Mx device. This makes it possible to analyze the central (aortic) pulse wave shape.-Heart rate (HR) [bpm],-Ejection duration (ED) [ms],-Peripheral systolic blood pressure (pSP) [mmHg],-Peripheral diastolic blood pressure (pDP) [mmHg],-Peripheral mean pressure  [mmHg],-Time to first peak  [ms],-Time to second peak  [ms],-Peripheral pressure at first peak (pP1) [ms],-Peripheral pressure at second peak ,-Peripheral T2/ED% (pT2ED),-Peripheral augmentation index (pAI) [%],-Peripheral end systolic pressure (pESP) [mmHg],-Central diastolic pressure (cDP) [mmHg],-Central mean pressure  [mmHg],-Central pulse pressure (cPP) [mmHg],-Time to first peak-Aortic (cT1) [ms],-Time to second peak-Aortic (cT2) [ms],-cT1R (time of the start of the reflected wave) [ms],-Central pressure at first peak (cP1) [mmHg],-Central pressure at second peak (cP2) [mmHg],-T1/ED% control panels (cT1ED),-Central T2/ED% (cT2ED),-Central augmentation index (cAI) [%],-Central end systolic pressure (cESP) [mmHg],-Central augmented pressure (cAP) [mmHg],-Central mean pressure of systole (cMPS) [mmHg],-Central mean pressure of diastole (cMPD) [mmHg],-Central tension time index (cTTI) [mmHg · s],-Central diastolic pressure-time index (cDTI) [%],-cSVI (SEVR) Buckberg Sub-Endocardial Viability Ratio [%],-Central pulse period (cPERIOD) [ms],-Central diastolic duration (cDD) [mmHg],-Central pulse high (cPH) [mmHg],-Central systolic pulse pressure (cPP) [mmHg],-Central diastolic pressure (cDP) [mmHg],-Time from systolic inflection point (if present) or systolic peak to diastolic inflection point (PPT).The evaluation of the variables using the non-invasive devices was based on advanced algorithms for analyzing pulse waveforms and EKG curves. The SphygmoCor tonometer used derived the central aortic pressure waveform from cuff pulsations recorded at the brachial artery. Analysis of the waveforms provided the values of the key parameters, including:-Reflection index (RI) [%],-Vascular stiffness index (SI) [m/s],-Peripheral pulse pressure (pPP) [mmHg],-Peripheral pulse pressure/central pulse pressure (pPP/cPP ratio) [%].Moreover, pulse trace TM enabled the assessment of the following variables:https://atcormedical.com/technology/sphygmocor/ (accessed on 30 May 2022).A more detailed explanation of the methods of determining the selected variables can be found at The study was conducted following the Helsinki Declaration of the World Medical Society and approved by the Bioethics Committee of the Poznan University of Medical Sciences . All enrolled study participants met the criteria and completed the study. They were fully informed about the study, and all gave written informed consent before enrollment.The following techniques were used to evaluate the potential of tonometry measurements for patient classification based on group profiles: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. General information about the groups is presented in Analysis of variance was performed with the Kruskal–Wallis H test see . Differep < 0.01). Since tonometric variables are connected in many ways, the network correlation structure of the data was explored. Spearman rank correlation for every variable was calculated and compared between groups. All group-specific correlations are reported in To filter out redundant markers, a multinomial logit model was used. This step was able to limit the number of analyzed tonometric parameters, which was important due to a high number of strong correlations present in the data. To find a satisfactory subset of significant variables able to differentiate the groups of patients, step-wise selection based on Akaike information criterion (AIC) was executed. The final model results described by the Wald test can be found in The performance of six parameter arrangements, the five selected by previously presented methods plus the whole original set were evaluated in terms of Kernel Fisher Discrimination Analysis performance. Lastly, Kernel Fisher Discrimination Analysis (KFDA) ,12 was eAlmost every part of the data analysis was performed in Python 3.8. Kruskal–Wallis H tests,  (1.3.3) . Correla (1.3.3) . A multi (1.3.3) , tidyver (1.3.3) . Discrim (1.3.3)  that imp (1.3.3) . The fol (1.3.3) , pandas  (1.3.3) ,20, matp (1.3.3) ,22, seab (1.3.3)  and sci- (1.3.3) .p = 0.1386). Sex ratio in each group was similar (p = 0.2491) although some variation for percent of tobacco users is present  and percent of overweight patients is presented in p = 0.0007). This could be a result of treatment accessibility, assignment, and popularity. The highest mortality is seen in group HD reaching almost 30%; a similarly high percentage, close to 25%, is observed for PD and CARD groups (see p = 0.7194) though it was more likely that older patients would die during 2 years of follow-up .Crucial information about mortality and duration of dialysis treatment is illustrated in oups see B. No deaTo detect and analyze differences for each cardiovascular parameter, Kruskal–Wallis tests were performed and are summarized in As best illustrated by Group differences are well depicted by the simplified z-score plot in Tonometric parameters are expected to be correlated. Since most of the analyzed variables are signal-related features or stay in a direct relationship or dependence from each other, it is reasonable to anticipate a high number of strong correlations. To demonstrate how codependence of parameters is influenced by the presence of cardiovascular distortions, an effort was made to create correlation graph profiles for each group analyzed in this study.Spearman correlations for each pair of variables were performed separately for each group. Detailed correlation heatmaps are presented in Summarized information about correlation graphs has been placed in Although differences in correlations may seem interesting, parameters that have little or no associations with others can turn out to be the most significant to the profiles. In fact, such parameters may hold unique information about patients’ conditions that in case of dense correlation could be easily overlooked.Assuming that tonometric cardiovascular parameters are capable of good characterization of the groups analyzed in this study, an effort was made to extract the simplest possible profiles for each group. To do this, multistep analysis was performed.At first, the best model was chosen to filter out some less significant parameters, i.e., a multinomial logistic regression model was developed in R based on an AIC step-wise approach. The results of this analysis indicated that 23 out of 39 parameters are important for the model. The results are summarized in The multinomial model was later treated as a feature selection method along with mean and variance analysis and correlation graph analysis. Six sets of parameters were compared as input for the discrimination analysis . However, it is difficult to capture general patterns for many parameters simultaneously, and radar plots appear to be surprisingly clear in the presentation of high-level profiles. For example, plots in In this study, multiple approaches were used to analyze the internal structure and vascular profiles based solely on tonometric data collected from patients with cardiovascular and kidney disorders. It can be concluded that there is no single tonometric parameter able to sufficiently separate each of the six analyzed groups . Moreover, proportionally large subsets of parameters were highly correlated in each group, although exact correlations seem to be different within groups. It should be pointed out that the healthy subjects seem to have the smallest number of high correlations between parameters. The graphs with correlations created for each group may have good potential for differentiation between the groups.Some light is shed by the results of the multinomial regression model, where more than a half (23/39) of parameters were included in the best model by the step-wise method (many similar models may be created due to the high number of correlations) that was used in this study as a feature selection technique. It could be suggested that either the model takes advantage of the noisy variance in the data or the search for linear models to separate the groups is fruitless and the key information about the unique traits of each group cannot be attributed to parameters themselves but to the relations between them.Kernel Fisher Discrimination Analysis results seem to support the second scenario. Nonetheless, at this moment , it should be pointed out that deriving conclusions from the data must be carefully considered, and more research is needed to further support this interpretation of the results. KFDA is a nonlinear kernel-based method for class separation . Since iFe-fusion and dimensionality reduction methods are still being developed, and some new techniques are worth mentioning as they have great potential for future use. For example, discrimination power analysis is a promising technique that can create precise individual profiles for identification purposes . Other pCreating machine learning models based on tonometry results was completed several times. Kublanov et al.  comparedIn fact, group sizes in this study are not sufficient to create a useful machine learning model either see . That waAnother example of the use of machine learning on hemodynamics data was provided by Jones et al. . The autGarcia-Carretero and others found that arterial stiffness evaluated by pulse wave velocity features turns out to be a good predictor of cardiovascular disease development . The autNabeel et al. used an artificial neural network as a method for continuous blood pressure estimation . To builCKD has impact on arterial stiffness . The tonIt is worth mentioning that tonometers may become easily accessible devices, including smartphones that may have some of their functions. In fact, Wu et al.  developeIt should be noted that arterial stiffness is one of the earliest symptoms of cardiovascular dysfunction in CKD. It can be detected even before an overt impairment of the ejection fraction or diastolic filling. The gold standard for measuring aortic stiffness is invasive aortic catheterization; however, it is an expensive, risky, and clinically impractical study. Hence, earlier and improved quantification of arterial stiffness in patients may improve disease risk stratification and is an attractive imaging biomarker for use in clinical trials. Hence, the use of non-invasive methods of aortic stiffness assessment in patients with CKD, ESRD, and CVD has great potential.The importance of screening and prevention in medicine has always been highlighted. Non-invasive vascular tonometry is a typical examination that helps in everyday diagnostics of CVD patients. This study shows that it is possible to assess kidney condition along typical tonometry measurements. Although the set of data analyzed in this study is too small to create an accurate machine learning model that could be validated and applied in clinical reality, clear patterns and distinct profiles of CKD patients are revealed.In recent years progress in machine learning and statistical tools made extraction and validation of complex features easier. Thanks to advancements in methods and software, it was possible to perform multiple analyses  on tonometric data and create profiles for each of the examined groups. The advanced data analysis points out that deploying a model that would differentiate between groups is possible. Such a model could be used as a CKD screening tool during aregular vascular tonometric assessment. Although it is probably possible to create a complex feature reflecting kidney condition, the true relationships within the data are likely nonlinear and may be difficult to model by simple mathematical expressions. It is also worth noting that more advanced examinations such as 3D vessel reconstruction  could shTonometry may disclose a detailed vascular profile which is particularly important for CKD patients, as arterial hypertension and CKD are often strongly connected. It is well-known that the condition of kidneys depends on cardiovascular health and vice versa. Since CKD is a global problem that affects 10% of the worldwide population , faster


The benefit of controlling cardiovascular risk factors in slowing the progression of small abdominal aortic aneurysm (AAA) is controversial. This study investigated the association of optimal blood pressure control at entry with the growth of small AAA.p = 0.65). The results were similar in sensitivity analyses excluding outliers or focused on systolic or diastolic blood pressure alone.A total of 1,293 patients with initial AAA diameter <50 mm were followed by a median 5  ultrasound scans for a median of 3.6 years . Optimal blood pressure control was defined as blood pressure ≤140/90 mmHg at recruitment. The association of optimal blood pressure control at entry with AAA growth was assessed using linear mixed effects models adjusted for established risk factors of AAA growth and factors which were unequally distributed among the blood pressure groups. Optimal blood pressure control at entry was not significantly associated with AAA growth. In the risk factor adjusted model the mean difference in AAA growth between blood pressure groups was 0.04 mm/year (95% CI −0.20, 0.13; This observational study suggests that optimal blood pressure control at entry is not associated with slower AAA growth. Abdominal aortic aneurysm (AAA) is an important cause of mortality in older people . In manyHypertension is an established risk factor for AAA diagnosis, however, there is lack of evidence on whether controlling blood pressure can reduce AAA growth. Hemodynamic factors, such as peak wall stress, which have been associated with AAA growth and rupture, are dependent on systemic blood pressure, suggesting that the blood pressure could be an important target to limit AAA progression , 6, 7. PSeveral observational studies and meta-analyses have evaluated whether blood pressure or hypertension is associated with AAA growth –22. SomeCurrent guidelines recommend maintaining blood pressure less or equal to 140/90 mmHg in people diagnosed with AAA in order to reduce the risk of cardiovascular events , 24. TheThis investigation was designed as part of an ongoing prospective cohort study that aims to identify risk factors associated with AAA diagnosis and outcome. The study commenced in 2002 and remains ongoing. Patients were recruited from vascular services in Australia, including Townsville University Hospital, the Mater Hospital Townsville, Gosford Vascular Services and The Royal Brisbane and Women's Hospital. The patients were initially identified through outpatient and surveillance programs at the respective hospitals. For inclusion in the current study, patients had to have infrarenal AAA diagnosed by a vascular specialist; at least two ultrasound scans to monitor AAA growth; an initial AAA diameter <50 mm; minimum follow up of 6 months; and the assessment of blood pressure at recruitment. These inclusion criteria were selected in order to enable testing of our hypothesis that optimal blood pressure control was associated with faster AAA growth. This study included data for a follow up period of up to 6 years. Patients with a AAA diameter ≥50 mm, symptomatic AAA patients and patients who had a AAA repair were all excluded from the study. The study was performed in accordance with the Declaration of Helsinki, and ethical approval was granted from the respective institutional Ethics Committees. Written informed consent was obtained from all participants.Risk factors, and medications of participants were recorded at study entry by clinical interview and physical examination. Smoking status was classified as ever (including current and former smoker) or never smoker –27. HypeThis was measured at recruitment using a digital blood pressure monitor - Omron Intellisense (HEM – 907) according to current clinical guidelines . RestingMaximum anterior to posterior and transverse infrarenal aortic diameters were measured by an experienced sonographer using ultrasound machines employed in the vascular laboratories at each center ; Philips HDI 5,000 ; GE LOGIQ 9 ; Siemens Acuson Antares™ ; Philips IU22  and a standard protocol, as described previously –32. Aorthttp://www.r-project.org), as described previously  and optimal diastolic blood pressure ( ≤90 mmHg) at entry with AAA growth. This blood pressure target was chosen in line with current guidelines , 34. Dateviously . P-valuen = 304; 23.5%) had a repeat blood pressure measurement a median (IQR) of 2.0  years after recruitment. Participants with sub-optimal blood pressure at recruitment had significantly higher median (IQR) systolic blood pressure when assessed during follow-up than those with optimal blood pressure at recruitment .Out of the 1,293 participants, 475 (36.7%) had optimum blood pressure control and 818 (63.3%) had sub-optimal blood pressure control at recruitment. Participants with optimal blood pressure control had a significantly higher prevalence of diabetes, IHD, past smoking and past stroke and they were also significantly more likely to be prescribed aspirin, frusemide, BB, ACEI, diuretics, statins and drugs to treat diabetes . They alp < 0.001   ultrasound scans for a median of 3.6 years . The unadjusted analysis suggested that participants with sub-optimal blood pressure control had significantly slower AAA growth. The mean difference in AAA growth between blood pressure groups was 1.75 mm/year   < 0.001 . This as = 0.65) . A sensi = 0.65) .p <0.001; p = 1.00; Model 2: mean difference in AAA growth was −0.01, 95% CI −0.17, 0.15, p = 0.90). A sensitivity analysis excluding potential outliers did not alter the findings  participants had systolic blood pressure ≤140 mm Hg and 790 (61.6%) had systolic blood pressure >140 mmHg . The assfindings . Furtherfindings .p = <0.001, p = 0.07). Removal of potentially influential outliers did not change these results  participants had a diastolic blood pressure ≤90 mmHg and 376 (27.6%) had a diastolic blood pressure >90 mmHg. The association of diastolic blood pressure groups with risk factors is shown in  results . Additio results .The findings of the current study suggest that guideline recommended control of blood pressure at entry is not associated with reduced AAA growth in patients that have small AAA. Findings were consistent in sensitivity analyses. The unadjusted analysis suggested that patients with suboptimal blood pressure at entry had reduced AAA growth. This association was however not maintained in analyses adjusted for importance confounding factors. Larger initial AAA diameter was the major predictor of faster AAA growth. However, no interaction was observed between the initial diameter and AAA growth, suggesting that blood pressure levels does not affect AAA growth depending on the initial diameter.Several observational studies and meta-analyses have evaluated whether blood pressure or hypertension is associated with AAA growth –22. OverA number of small randomized control trials have assessed the effect of blood pressure lowering medications, including propranolol, amlodipine, perindopril and telmisartan, on AAA growth –11. NoneDue to the small size of these prior randomized trials and the limited range of anti-hypertensive drugs tested, it remains uncertain whether blood pressure lowering reduces AAA growth. Observational studies may provide some evidence whether high blood pressure is a treatment target to limit AAA growth, although in previous such studies findings have been inconsistent , 20–22. The current study has a number of strengths and limitations. In comparison to the other observational studies, the current study had a large sample size in which data were collected according to standardized protocols. This allowed for well-powered analyses. Also, a range of sensitivity and adjusted analyses were performed. The main limitation of the study was that the participants only had one blood pressure measurement performed at study entry. Only patients with clinical requirement had repeat blood pressure measurements and a separate analysis could not be performed due to the small sample size. Medical management changes were also not recorded during the study. Blood pressure was only measured in the right arm which could have resulted in underestimation of values in the presence of subclavian artery stenosis or occlusion. Even though the study included a large number of patients, there was a difference in sample sizes of the two blood pressure groups which may have influenced the power of the analyses. The current study was also not a randomized control trial; hence it remains possible that factors we did not measure including chronic airways disease and therefore could not adjust for led to residual confounding. AAA diameter was measured with ultrasound rather than computed tomography but we have previously reported excellent reproducibility of ultrasound measured AAA diameter –33.In conclusion this study suggests that optimal control of blood pressure at entry is unlikely to limit growth of small AAA. This does not outweigh the benefits of blood pressure management in AAA patients to reduce cardiovascular events and other AAA complications. Further well-designed studies including patients with multiple blood pressure measurements are required for conclusive evidence.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Townsville Hospital and Health Services Ethics Committee. The patients/participants provided their written informed consent to participate in this study.DT was involved in project administration, data curation, conceptualization, formal analysis, investigation, methodology, validation, visualization, writing, and editing. JG was involved in project administration, conceptualization, funding acquisition, data collection, investigation, methodology, supervision, writing, and editing. JM was involved in conceptualization, formal analysis, investigation, methodology, supervision, validation, and editing. SK was involved in supervision and editing. FQ, MB, and BB were involved in data collection and data curation and editing. RJ was involved in formal analysis, methodology, and editing. All authors contributed to the article and approved the submitted version.This study was supported by funding from the National Health and Medical Research Council (1063476 and 1180736) and the Queensland Government . JG holds a Practitioner Fellowship from the National Health and Medical Research Council (1117061) and a Senior Clinical Research Fellowship from the Queensland Government. JM was supported by an Advance Queensland Fellowship from the Queensland Government. DT was supported by a JCU (James Cook University) Postgraduate Research Scholarship and a JCU College of Medicine and Dentistry Scholarship.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.