This study aims to systematically review the diagnostic accuracy of a digital blood pressure measurement device compared to the gold standard mercury sphygmomanometer in published studies. Searches were conducted in PubMed, Cochrane, EBSCO, EMBASE and Google Scholar host databases using the specific search strategy and filters from 1st January 2000 to 3rd April 2021. We included studies reporting data on the sensitivity or specificity of blood pressure measured by digital devices and mercury sphygmomanometer used as the reference standard. Studies conducted among children, special populations, and specific disease groups were excluded. We considered published manuscripts in the English language only. The risk of bias and applicability concerns were assessed based on the author’s judgment using the QUADAS2 manual measurement evaluation tool. Based on the screening, four studies were included in the final analysis. Sensitivity, specificity, diagnostic odds ratio (DOR), and 95% confidence interval were estimated. The digital blood pressure monitoring has a moderate level of accuracy and the device can correctly distinguish hypertension with a pooled estimate sensitivity of 65.7% and specificity of 95.9%. After removing one study, which had very low sensitivity and very high specificity, the pooled sensitivity estimate was 79%, and the specificity was 91%. The meta-analysis of DOR suggests that the digital blood pressure monitor had moderate accuracy with a mercury sphygmomanometer. This will provide the clinician and patients with accurate information on blood pressure with which diagnostic and treatment decisions could be made. A mercury sphygmomanometer is a standard instrument that is used manually to monitor blood pressure in health care facilities. This method is considered the gold standard and has been used in practice for more than a hundred years2. However, mercury-based devices have limitations due to environmental contamination and observer bias in measurement. To address these limitations, wearable and portable digital devices that could monitor blood pressure were introduced and used by health care professionals and patients4. Multiple guidelines are available to appropriately use digital devices for self-monitoring blood pressure by patients at their convenience and in health facilities6. However, the convenience and flexibility of these automated digital devices should be considered with caution about their measurement accuracy5.Hypertension is one of the major chronic diseases that leads to higher mortality worldwide. Early diagnosis and treatment of hypertension could significantly reduce the risk of mortality7. Multiple validation studies have been undertaken to assess the importance of the diagnostic accuracy of mercury-based and digital devices for blood pressure monitoring. However, these validation studies need to be considered with caution concerning the quality of evidence and standard of method that they have used. At present, there is a lack of systematic reviews on the diagnostic accuracy of digital blood pressure measurement devices. In this background, we undertook a systematic review to generate evidence on the diagnostic accuracy of a digital blood pressure measurement device compared to the gold standard mercury sphygmomanometer8.The benefits and efficiency of blood pressure screening methods require an evidence base about measurement accuracy and validity9 9 . The proA comprehensive literature search was undertaken in search engines that included PubMed, Cochrane, EBSCO, EMBASE and Google Scholar.The search terms used to retrieve published information specifically for each database are provided in Supplementary Table The PICO criteria for the systematic review are given in Supplementary Table Studies done in more than 18 years of age group.Digital blood pressure monitoring devices.A mercury sphygmomanometer was used manually.The sensitivity and specificity of digital and mercury sphygmomanometer-based blood pressure measurements.Cross-sectional and observational cohort studies assessing the diagnostic accuracy of blood pressure measured by mercury sphygmomanometer and digital devices.Journal articles published in English between 1st January 2000 and 3rd April 2021 with full-text accessibility were included.A mercury sphygmomanometer was used as the reference standard.Diagnostic accuracy studies that considered digital blood pressure monitoring devices using a standard nonmercury sphygmomanometer as a comparator, and studies conducted among children, special populations and specific disease groups were excluded.The studies were screened individually by two reviewers, and any discrepancies raised in the selection process were resolved with the third reviewer. Duplicates were removed from the studies shortlisted after the title and abstract screening. Full-paper screening was performed for the shortlisted studies. Studies that did not meet the selection criteria did not have relevant information for inclusion and that required data were excluded. The remaining eligible studies were included in the review.10, a standard tool used for quality assessment of diagnostic accuracy studies. This questionnaire measures the risk of bias and applicability concerns.The quality of the included studies was assessed using the QUADAS-2 questionnaire  and 95% confidence interval were estimated. For the quantitative meta-analysis, RevMan software (version 5.4) and R studio were used. A forest plot was used to depict the pooled estimates for DOR, sensitivity, and specificity. The heterogeneity of the included studies was also assessed by the The PRISMA flowchart describes the study selection process Fig. . Overall11, while the other two were conducted in Colombia and the USA. Of these studies, two studies were conducted among the general population, one at the community health center and one among nursing college students. The cohort study design was adapted in three studies, while one study was cross-sectional in design. All studies used a mercury sphygmomanometer as the reference standard with systolic and diastolic blood pressure cut-offs at 140 and 90 respectively. The sample size ranged from 108 to 1084, with the total study population accounting for 1919. The average age of the study population in these studies ranged from 40 to 54 years. The average systolic blood pressure reported in the included studies varied from 107.10 to 172 mmHg. Similarly, the average diastolic blood pressure reported varied from 67.98 to 96.3 mmHg.The general characteristics of the included studies are presented in Table The risk of bias assessment was done by two reviewers and applicability concerns were assessed based on the author’s judgment using the QUADAS2 evaluation tool  Fig. . The speI2 values were considerably higher than 50 in all the parameters assessed. The receiver operating characteristic (ROC) curve points represent different studies, and the fitted summary ROC curves depict trade-offs between sensitivity and specificity that arise because of differences between the studies  using a digital blood pressure monitor ranged from 8.39 to 547.69 with a pooled estimate of 50.84 (95% CI 7.24–356.87) (Fig. 13. We reemphasize the fact that for correct estimation of blood pressure, mercury sphygmomanometers are considered the gold standard, with the comparability of measurement accuracy, found that digital blood pressure monitoring is almost as accurate as mercury sphygmomanometer. Digital monitoring can be substituted with the traditional mercury sphygmomanometer.This meta-analysis evaluated the studies concerning the accuracy of a digital blood pressure measurement device compared to the gold standard mercury sphygmomanometer to diagnose hypertension. Rapid early diagnosis is essential to control hypertension and cardiovascular diseases. In the current meta-analysis, the diagnostic reference standard used was similar in all included studies. The meta-analysis results of DOR suggest that digital blood pressure monitoring has a moderate level of accuracy and that the device can correctly distinguish hypertension with a pooled estimated sensitivity of 65.7% and specificity of 95.9%. After removing one study, which had very low sensitivity and very high specificity, the pooled estimates of sensitivity were 79% and specificity of 91%, respectively. Our analysis suggests that digital blood pressure monitoring had equal accuracy with mercury sphygmomanometer. Our findings corroborate the studies that reported that digital blood pressure monitoring should be used for proper and better management of hypertension14. It is also useful in continuous monitoring of measurements of blood pressure from the mass population during daily life15. The use of mercury devices for blood pressure measurement continues to diminish their role and the preferable modality of blood pressure measurement has changed in health systems. Mercury devices have placed new interest in alternative methods, of which digital devices are the leading nominees. However, the error reported on the accuracy of digital devices ranged from 1 to 44%17. Validation studies that compared mercury versus digital devices in the setting of a large clinical trial found it to be accurate18.High blood pressure is a major cardiovascular risk factor that is treatable; however, knowledge, attitudes and practices remain low. Digital blood pressure measurement tools could improve the management of hypertension by enabling individual monitoring of blood pressure anytime and anywhere20. In this respect, there is a paucity of information in the Indian context. Intensive blood pressure control has been found to significantly reduce cardiovascular morbidity and all-cause mortality associated with hypertension21. Regular and frequent measurement of blood pressure was recommended for a patient with hypertension to prevent cardiovascular morbidity23. The technology revolution and development of new technologies allow people to use and would enhance autonomy while providing physicians with a more complete picture of their blood pressure profile, leading to blood pressure control and better long-term clinical outcomes24.The effect of potential mercury toxicity and the problems associated with the disposal of mercury has led to widely reduced usage of mercury instruments across the world. For this reason, the European Union recently directed phasing out of mercury instruments. The advantages of digital blood pressure monitoring are that it is easy to use, more affordable, portable, and has no adverse environmental impacts. A large study on the accuracy of digital instruments reported that it was almost as accurate as mercury instruments25. Considering all this, there is a need to utilize more feasible and inexpensive instruments without frequent replacements, particularly for low-income countries.There is a need to consider the disadvantages associated with digital blood pleasure monitoring devices in terms of lifespan and quality standards26. The digital blood pleasure monitor is a simple, accurate and affordable device that can be used under field conditions in the management of hypertension. In the advanced digital health information technology era, there is a paradigm shift from traditional methods to modern methods. These methods are expected to change the quality of detection of hypertension and management. This new approach contributes to early diagnosis and prevention, which refers to identifying risk and predicting the onset of cardiovascular events and allowing interventions to reduce risk. India is home to an estimated 234 million adults who have hypertension27. Like diabetes patients who have glucometers at home, asthma patients who have a peak flow meter at home, pyrexia patients who have a thermometer at home and COVID patients who have a pulse oximeter at home, we may also find blood pressure monitoring in every patient’s home.The current DOR estimated in studies conducted at health facilities was lower than that estimated in studies conducted in the community. This underscores that the device with high specificity was found to be better performing in community-based studies than in a health facility setup. This may be due to the better performance device in a large population. Control of blood pressure begins with an accurate measurement, leading to appropriate diagnosis and treatmentWe are interpreting our meta-analysis results with few limitations. First, potential heterogeneity was explored in the included studies in our meta-analysis. The second was that number of studies is limited. The present study identified only four studies which met our inclusion criteria. Of this two studies were conducted in general population and thus more generalizable, but other two studies were conducted in institutional setting which might be different from general population. One study was used cross sectional design and rest were cohort design. While this could be a limitation still these studies were meeting the inclusion criteria based on intervention and comparator.The evidence from this review suggests that digital blood pressure measures were able to correctly rule out hypertension due to their high specificity but have moderate accuracy in identifying true positive cases. It can be utilized for screening a larger population in an efficient and timebound way.Supplementary Information.


Background: Previous studies had reported increased circulating concentrations of growth differentiation factor-15 (GDF-15) in chronic heart failure (CHF), suggesting the potential prognostic significance of GDF-15 in this setting. To verify the relationship between the circulating GDF-15 levels and prognosis of CHF patients, we conducted an updated evidence-based meta-analysis.Methods: A comprehensive literature retrieval of PubMed, EMBASE, and Cochrane library was performed to collect the qualified studies that analyzed the prognostic value of GDF-15 in CHF from the inception of these online databases to September 25, 2021. The hazard ratio (HR) calculated for logGDF-15 of all-cause death and the related 95% confidence interval (CI) in multivariate analysis were used to measure the effect size. Additionally, subgroup analyses stratified by characteristics of the study participants were conducted for incremental evidence of GDF-15 in CHF with different clinical status.Results: A total of ten eligible studies involving 6,244 CHF patients were finally taken into the quantitative analysis. Results in the random-effects model indicated that there was an increased risk of 6% in all-cause mortality with a per 1LnU increase in baseline GDF-15 concentration . In stratified analyses, the association of GDF-15 with risk of all-cause mortality was found among chronic ischemic HF patients , while the association was not found among chronic nonischemic HF patients .Conclusion: The elevated GDF-15 is associated with an increased risk of all-cause mortality in CHF, especially, among CHF patients with ischemic etiology. The circulating GDF-15 might be a prognostic indicator in CHF patients.Registration Number:https://www.crd.york.ac.uk/PROSPERO; CRD42020210796. Heart failure (HF), the terminal stage of multiple cardiovascular diseases (CVD), is a complex clinical syndrome with high morbidity and mortality . In spitGrowth differentiation factor-15 (GDF-15), a stress-responsive cytokine, is a member of the transforming growth factor-β (TGF-β) cytokine superfamily. It is expressed in response to several pathologic processes including inflammatory reaction, oxidative stress and myocardial ischemia –8. CliniHowever, there are conflicting data regarding the association between GDF-15 with the risk of adverse outcomes in CHF patients . PreviouThis meta-analysis was reported in accordance with the PRISMA  guidelines .At the beginning of this meta-analysis, two independent reviewers (JW Luo and YQ Yu) conducted a systematic, computer-based literature search of PubMed, EMBASE, and Cochrane Library from the inception of the databases to October 1, 2020. On September 25, 2021, we conducted a second literature retrieval to supplement related studies. The following Medical Subject Headings were used to identify relevant studies: “growth differentiation factor 15”, “GDF-15”, “heart failure”, “cardiac failure”, and “cardiac dysfunction”. A manual search of references for related researches was also performed. The literature retrieval was restricted to human studies and papers written in English. A detailed searching strategy is presented in The inclusion criteria were: (1) prospective or retrospective follow-up studies which examined the association between GDF-15 and risk of CHF outcomes; (2) Patients diagnosed with CHF with either reduced or preserved left ventricular ejection fraction (LVEF), without age, sexual, and racial limitations; (3) Available data of hazard ratio (HR) and the 95% confidence interval (CI) for all-cause mortality in multivariate analysis. Studies with enrollment of end-stage HF patients were excluded, since up to 75% of patients with end-stage HF had a life expectancy of less than 1 year . End-staInvestigators then reviewed the obtained 957 records to identify the eligible studies according to a standard protocol. Where disagreements occurred, discussions were done among the researchers or a third investigator (WH Duan), a specialist in the field, was consulted.The Cochrane risk-of-bias assessment tool was not employed due to the study type of non-randomized trials. The Newcastle-Ottawa-Scale (NOS), designed for case-control study and cohort study, was used to assess the methodological quality of each selected study. The NOS is mainly comprised of three dimensions: selection of participants, comparability among groups, and outcome assessment, ranging from 0 to 9 stars. Articles with seven stars and above were rated as high-level quality.The following data were extracted from each included study: first author's name, publication year, study design, location, number of participants, characteristics of patients at baseline , follow-up period, HR, and 95% CI for all-cause mortality in multivariate-adjusted model. In instances where there was insufficient information, we contacted the corresponding author.I2 index were used to measure the heterogeneity among all studies  existed, while the fixed-effects model was applied in all other instances.This study used HR and 95% CI as a measure of the predictive value of GDF-15 in CHF. In each included study, the HR for logGDF-15 was calculated. Therefore, the recorded HR represented the risk of a per 1LnU increase in baseline circulating concentration of GDF-15. Both Cochran's Q test and  studies . The ranpost-hoc analyses of randomized clinical trials [RCTs]), year of publication (≤ 2015 or > 2015), sample size (≤ 500 or >500) and follow-up duration (≤ 36 or > 36 months).In addition, we performed subgroup analyses stratified by clinical status (NYHA class or LVEF), ischemic etiology (percentage of subjects with ischemic disease < 60% or ≥60%), baseline concentration of GDF-15 (< 2000ng/L or ≥2000ng/L), age (< 65 or ≥65), study design . The results in the present study showed that the risk of all-cause mortality in CHF was increased by 6% with a per 1LnU increase in baseline concentration of GDF-15   .P < 0.001) compared to those with non-reduced ejection fraction . When we split the NYHA class in low class (NYHA I or II) and high class (NYHA III or IV), no significant heterogeneity was found between the two groups (P = 0.56 for interaction). When we stratified the studies by the percentage of CHF patients with ischemic etiology (<60% or ≥60%), we found a close relationship between the elevated concentrations of GDF-15 and the increased all-cause mortality among CHF patients with ischemic etiology (≥60%) . However, this relationship was not shown in CHF patients without ischemic etiology (<60%) . When we stratified the studies by the baseline levels of GDF-15 (< 2000ng/L or ≥ 2000ng/L), the HR for all-cause mortality among patients with lower GDF-15 concentration was 1.69 , while the HR among patients with higher GDF-15 was 1.01 . When we stratified the studies by the type of study design, the relationship between GDF-15 and all-cause mortality of CHF was detected in the subgroup of cohort study , whereas that relationship was not identified in the subgroup of post-hoc analyses . In other subgroups stratified by age, publication year, and duration of follow-up, no significant difference was presented.As summarized in P = 0.107), year of publication (P = 0.8), age (P = 0.288), follow-up duration (P = 0.571), proportion of male (P = 0.142), NT-proBNP (P = 0.833), and body mass index (BMI) (P = 0.301). The sensitivity analysis showed that although Anand et al. . The distribution became symmetrical after correction with five additional points in the Begg's funnel plot (P = 0.017), proving the robustness of our assessment.As shown in the nel plot , suggestThis meta-analysis indicates that the elevated circulating GDF-15 concentration is associated with the increased risk of all-cause mortality in CHF patients, especially, among those with ischemic etiology (e.g. coronary atherosclerosis). Furthermore, the association becomes stronger after removing the study from Fernandez et al.  conducteConsistent with earlier studies , 16, ourpost-hoc analysis of Valsartan Heart Failure Trial  and provided an argued evidence that GDF-15 was a biomarker in prediction of all-cause death in CHF with non-ischemic etiology. These conflicting results might be attributed to the study design. To further elucidate the prognostic value of GDF-15 in CHF patients with or without ischemic etiology, chronic ischemic HF population and chronic non-ischemic HF population should be studied, respectively, in future.Ischemic heart disease is one of the major causes of CHF. Myocardial ischemia induced by coronary atherosclerosis or microcirculation dysfunction is tightly associated with heart structure and function. Several observational studies showed that GDF-15 concentration was continuously associated with the risk of coronary heart disease , 34. OurP = 0.23 for interaction), so GDF-15 might be served as an independent risk indicator in CHF patients with different age. There were several evidences that the circulating GDF-15 concentration was positively correlated with the NYHA class among CHF patients (P = 0.56 for interaction). It was noteworthy that of the 10 studies, only three were conducted in NYHA class III/IV patients. The predictive role of GDF-15 in CHF with different NYHA functional classification need further assessments.Certain studies have reported that the concentrations of GDF-15 increased with age and GDF-15 was an independent predictor of mortality in older individuals without history of CVD , 36. Howpatients , 16, suget al's data. Most of the studies included in our meta-analysis were done in Europe including Spain, Czech, and Germany, only the study from Liu et al. , extracellular signal regulated kinase (ERK), and the transcription factor R-SMAD1  signaling pathways; whereas, Xu et al. . However, we did not stratified studies by age of 50 or 75 since the median age of the study population included in this meta-analysis ranged from 61 to 75 years. Thus, larger-scale prospective studies should be conducted to validate the effect of age on the predictive ability of GDF-15. Furthermore, the effect of comorbidities such as chronic kidney diseases and atrial fibrillation on the prognostic value of GDF-15 in CHF should be considered.Firstly, this meta-analysis extensively collected eligible studies conducted in different countries. Secondly, the subgroup analyses provided evidence of GDF-15 as a potential biomarker in CHF with different clinical status. Finally, each individual study included in our analysis had a good quality; the modification for publication bias using the “trim-and-fill” method did not change the overall estimation of HR, proving the robustness of our assessment. Nevertheless, there are several limitations should be noted. Obvious heterogeneity across all the included studies was detected and the meta–regression analyses and subgroup analyses could not distinguish the sources of heterogeneity. Some inherent differences including different assay methods of GDF-15, different medications, and other comorbidities may contribute to the heterogeneity. Thus, one should be cautious to interpret the results in our study. Furthermore, we failed to assess the relationship between GDF-15 and HF rehospitalization due to the limited data.This meta-analysis indicates that the elevated level of GDF-15 is associated with the increased risk of all-cause mortality in CHF patients and the predictive power of GDF-15 is more significant among ischemic CHF patients than non-ischemic CHF patients. The circulating GDF-15 could be a predictive factor in CHF patients.The original contributions presented in the study are included in the article/J-WL, W-HD, and D-ZS conceived the idea of this research. J-WL developed a protocol with the assistance of D-ZS and W-HD. J-WL and Y-QY performed the data analyses independently. J-WL and LS participated in the data interpretation and drafting of manuscript. D-ZS and W-HD critically reviewed the manuscript. All authors had browsed and approved the final version of the manuscript.This research is supported by the project of TCM and Western Medicine clinical cooperation for major and intractable diseases, angina after coronary revascularization.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.


Furthermore, regulatory T cells positively correlate with body-mass index and weight. Machine learning analysis of baseline immunome or microbiome data predicts sustained systolic blood pressure response within the fasting group, identifying CD8+ effector T cells, Th17 cells and regulatory T cells or Desulfovibrionaceae, Hydrogenoanaerobacterium, Akkermansia, and Ruminococcaceae as important contributors to the model. Here we report that the high-resolution multi-omics data highlight fasting as a promising non-pharmacological intervention for the treatment of high blood pressure in metabolic syndrome patients.Periods of fasting and refeeding may reduce cardiometabolic risk elevated by Western diet. Here we show in the substudy of NCT02099968, investigating the clinical parameters, the immunome and gut microbiome exploratory endpoints, that in hypertensive metabolic syndrome patients, a 5-day fast followed by a modified Dietary Approach to Stop Hypertension diet reduces systolic blood pressure, need for antihypertensive medications, body-mass index at three months post intervention compared to a modified Dietary Approach to Stop Hypertension diet alone. Fasting alters the gut microbiome, impacting bacterial taxa and gene modules associated with short-chain fatty acid production. Cross-system analyses reveal a positive correlation of circulating mucosa-associated invariant T cells, non-classical monocytes and CD4 Nutritional modification including fasting has been shown to reduce cardiometabolic risk linked to western diet. Here the authors show implementation of fasting resulted in alterations to the intestinal microbiota, and circulating immune cells, improving blood pressure and body weight in patients with metabolic syndrome. In contrast, today’s Western diet promotes cardiometabolic disease (CMD)2. How diet affects the gut microbiota, immune system and subsequently host (patho)physiology is not fully understood, and information is lacking on how periodic fasting affects the gut microbiome in patients with metabolic syndrome (MetS). To reduce CMD risk, exercise and a healthy diet are often prescribed. Shifting from a “Western diet” to a healthier “Mediterranean-like” DASH diet3 to achieve optimal nutrition and negative energy balance is recommended, although compliance is a major hurdle. Our study is the first of its kind to investigate the effects of a lifestyle modification in combination with fasting therapy in patients with MetS using a multi-omics approach by combining gut microbiome analysis and deep immunophenotyping. The “Western diet” is known to induce metabolic inflammation, accelerating CMD4. The gut microbiota is a delicate ecosystem that plays a pivotal role in health and disease. Dysbiosis has been observed as a characteristic of several inflammatory, cardiovascular, and metabolic disorders (e.g. obesity)5, including hypertension7. The “healthy” gut microbiome is relatively stable, although various factors such as antibiotics, intestinal infections, and profound dietary or lifestyle changes, such as moving on or off a “Western diet”, can induce transient or persistent changes to this ecosystem. Traditionally, fasting plays an important role in different cultural and religious practices. Dramatic caloric restriction not only affects host health and physiology, but also has an impact on the microbiome10. Here, we studied the role of fasting in cardiovascular risk patients with MetS  differences in microbial composition within individuals during fasting, reflecting a characteristic intervention-induced shift, which later partially reverted following a 3-month refeeding period on a DASH diet  changes in host immune cell composition during the intervention, revealing a fasting-specific signature, which likewise largely reversed during refeeding  and TCRγ/δ+ T cells. However, these changes were reversed upon refeeding  and a lower percentage of the naïve phenotype , while memory T cells were not affected , as well as TNFα- and IFNγ-producing Th1 cells  modules for abundance shifts during fasting or refeeding, as well as persistent shifts across the 3-month study period, controlling for age, sex and any changes in medication  changes during the study yielded a better fit of the data than when it was modeled as a function of the fasting intervention. Bacteroidaceae showed the opposite pattern. At the end of the refeeding period, a persistent depletion could be seen in Enterobacteriaceae, especially Escherichia coli. These shifts were accompanied by vast changes in microbial metabolic capacity  assessed from shotgun sequencing data available for the fasting arm. Certain fasting- or refeeding-associated functional gene modules from our data were found to overlap with signatures of metformin usage or MetS, though there was little concordance on a taxonomic composition level, in line with previously described higher functional than taxonomic concordance between microbiomes. Of note, when comparing the metformin signal to the MetS signal, it is clear that these two effects are functionally distinct and often oppose one another. In contrast, the inferred gut functional signature of metformin treatment shared some features with that of our fasting intervention  affect 24 h ambulatory SBP, the gold standard of clinical BP measurements   reduce their intake of antihypertensive medication in 43% of cases, compared to only 17% of the cases on DASH alone, while their BP remained under control  had a median SBP decrease of 8.0 mmHg, irrespective of the high reduction in medications amongst these patients, while the decrease in the non-responder group (n = 10) was significantly lower  showed no decrease in median SBP  reduced BMI and body weight even 3 months post-fasting  and Prevotellaceae NK3B31 group  modules les Fig. . Some moles Fig. . For exa+-producing Th and γδT cells) changed in abundance significantly only when tested in the responder group, indicating relevant differences between responders and non-responders. Upon fasting, the frequency of both pro- and anti-inflammatory adaptive immune cells showed a stronger decrease in responders, indicating a stronger anti-inflammatory effect of fasting in responders.Changes to the immunome of responders are similar to those seen in the unstratified fasting group and differ from those in the DASH arm Fig. , 7, 8. IP < 0.05 in a post-hoc test accounting for the presence of the same subjects at all three time points , which positively correlated with 24 h ambulatory SBP -producing CD4+ cells significantly correlated with SBP. These immune clusters showed significant interconnection to a remarkable number of microbial SCFA producers  and Dorea longicatena (ref mOTU v2 4203), and the acetate producer Hungatella hathewayi (ref mOTU v2 0882) negatively correlated with the abundance of the GM-CSF and IL-2-producing CD4+ T cells, and with the absolute number of IFNγ+ and TNFα-producing MAITs, respectively.In addition, abundance of IL-2ers Fig. , though Alistipes spp. (shahii and inops) , and of two ps) Fig. . While fps) Fig. .18, was negatively associated with IFNγ+ and TNFα+ MAIT cells , reported to facilitate the uptake of nutrients mostly found in red meat19. We found in our study that the abundance of MAIT cells did not correlate with BMI, weight, waist circumference, waist-hip ratio, or body fat percentage , a cell type previously linked to morbid obesity in human subjects20.The association between MAIT cells and BMI is still a matter of debate21. Interestingly in our study, circulating non-classical monocytes were enriched upon fasting and then depleted again upon refeeding to remain below baseline levels 3 months after fasting , alongside lower abundances of CD24+ memory CD8+ T cells and IL-17+TNFα+MAIT cells in responders relative to non-responders  was achieved using a leave-subject-out cross-validation for whether or not a future patient would respond favorably to fasting with regards to BP Fig. . Within ers Fig. . Regardiers Fig. . Notablyers Fig. . In cont17, and were moreover depleted of propionate biosynthesis genes  was available, using the same software pipeline as for our own samples. We compared the results to ours, collapsing species/OTU fasting/refeeding/long-term follow-up signals in either dataset at the genus level for clarity , we observe substantial agreement between the two datasets; dynamics of Bifidobacterium, Roseburia, Bacteroides, Coprococcus and Intestinimonas are comparable  healthWe have carried out the first high-resolution multi-omics characterization of (periodic) fasting in patients with MetS, including detailed clinical and immunophenotyping along with gut microbiome sequencing. Our major finding is that periodic fasting followed by 3 months of a modified DASH diet induces concerted and distinct microbiome and immunome changes that are specific to fasting itself, leading to a sustained BP benefit Fig. , which wFasting followed by modified DASH also led to a significant long-term reduction in body weight. However, neither the change in BP nor global changes to the microbial composition or immunome appeared to be mediated by this BMI decrease  are likely beneficial to the host. A recent study profiling the hypertensive microbiome showed that during disease, patients experienced an enrichment of Actinomyces, and a depletion of F. prausnitzii, Bacteroides and Firmicutes7. Moreover, Guevara-Cruz et al. recently showed in a Mexican cohort involving 146 MetS patients, that a 75 day long, 500 kcal/day, low-saturated fat dietary intervention improved the clinical phenotype, significantly decreased gut dysbiosis and increased the abundance of Akkermansia muciniphila and SCFA producer F. parusnitzii27  to the respective baseline values of the study subjects. This intraindividual analysis allowed us to identify BP responder-specific changes in spite of the reduced power in such a sub-stratified analysis. The responder-specific microbiome changes in our fasting arm post-intervention . In addition, the relatively low patient number could be regarded as a limitation. Although our present study is large enough to allow inference of significance for the strongest contributors to the observed effect, our results are likely not complete, and follow-up in additional and larger studies will be needed for a comprehensive view of subtle fasting-associated host and microbiome features. Our study design did not allow for the blinding of participants regarding their intervention. To maximally reduce the bias, the scientific staff were blinded during the course of processing, measurement, and analysis of collected samples. Further, the present study cannot infer how frequently fasting cycles should be repeated to control BP in at-risk patients, nor whether it is as effective without a concomitant DASH intervention. Despite the low number of participants of the study, machine-learning algorithms were able to predict BP responsiveness based on the immunome and 16S data. Only the latter could be confirmed in an independent dataset, as no equivalent immunome profiling in a fasting dataset has been published to date. Confirmation of the predictive capability of the immunome data and testing further hypothesis raised above (e.g. the interaction between SCFA availability and BP responsiveness) require future prospective clinical studies. The favorable impact of fasting followed by a DASH diet during refeeding phase shown here highlights this intervention as a promising non-pharmacological intervention for the treatment of high BP in MetS patients.It is important to recognize that our study represents patients with hypertension and MetS solely from a Caucasian-European background. This selection criterion introduces a selection bias in our study design. Additional research is necessary to elucidate whether the results presented here could be applicable in a more heterogeneous patient population. Further, our recruitment procedure could already have introduced a selection bias toward patients who were interested in fasting/dietary studies and therefore are sensitive about their cardiometabolic health. Since the participants were especially interested in the fasting procedure, the allocated DASH participants were offered a cost-free fasting cycle after successful completion of the study. However, we cannot exclude that this led to an increased long-term motivation compared to the participants who started with the fasting protocol. Furthermore, the study design did not allow us to investigate the long-term effects of a fasting intervention without a subsequent DASH diet on the BP, microbiome, or immunome. In our cohort, fasting was required on top of DASH to achieve the observed outcomes, but we cannot conclude (and do not expect) fasting without a subsequent dietary change to do so either. We can only claim fasting was required prior to the DASH diet to achieve the effects observed in our cohort. DASH, which is rich in fibers, might furthermore “fuel” the beneficial microbiome, thus further contributing to cardiovascular health, and may play a part in maintaining this microbiotal state longer. However, some effects are replicated in the similar dataset from healthy males (without MetS and without DASH intervention) in the Mesnage datasetThe study was planned as part of a randomized-controlled bi-centric trial conducted by the outpatient center of the department of Internal and Integrative Medicine at Charité-Universitätsmedizin. The study was approved by the ethics committees of the Charité-Universitätsmedizin Berlin  and registered at ClinicalTrials.gov (registration number: NCT02099968).Participants were recruited from the existing patients at study centers and through local newspaper announcements. Patients were first screened over the phone by a research assistant to assess eligibility. Eligible patients were invited for an assessment by a physician, where they were examined and provided detailed written information describing the study. If patients met all inclusion criteria and did not meet any exclusion criteria, informed consent was obtained and they were included in the study.Male and female patients with MetS according to National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria were included. MetS was defined as the presence of at least three out of five risk factors: (i) increased waist circumference (>94 cm in men and >80 cm in women), (ii) hypertriglyceridemia (>150 mg/dl (1.7 mmol/l) or lipid-lowering medication), (iii) low levels of high-density lipoprotein cholesterol  in men and <50 mg/dl (1.3 mmol/l) in women or use of HDL-increasing medication (niacin or fibrate), (iv) elevated blood pressure (≥130/85 mm Hg or use of antihypertensive medication), and (v) elevated fasting plasma glucose (≥110 mg/dl or treatment for diabetes mellitus). Beyond NCEP ATP III criteria, patients were required to have been diagnosed with systolic hypertension (either being on antihypertensive medication or untreated). Further inclusion criteria included basic mobility and the ability to provide informed consent.Exclusion criteria included (i) diabetes mellitus type 1 or insulin bolus therapy (c-peptide < 1.2 ng/ml), (ii) manifest treated coronary artery disease, myocardial infarction, pulmonary embolism, or stroke within the past 3 months, (iii) heart failure ≥ stage I NYHA, (iv) peripheral artery disease ≥ stage 2, (v) chronic kidney disease > stage 2 (GFR < 60 ml/min), (vi) manifest eating disorder, vii) dementia or manifest psychosis, or viii) other severe internal diseases.40. The dietary education included counseling, comprehensive lectures and cooking classes.The interventions in both groups were delivered as an intensive group-based behavioral intervention. The educational concept incorporated aspects of the mind–body program designed by the Benson–Henry Mind/Body Medical Institute of Harvard Medical School41. The program entailed 10 h of group sessions for the initial periodic fasting and 50 h of nutritional education, which included lectures and cooking lessons. Similar to protocols from previous trials on periodic fasting in rheumatoid arthritis and diabetes mellitus type 243 patients were instructed to follow a modified DASH diet after the fasting period, with additional emphasis on plant-based and Mediterranean diet to optimize refeeding46.Intervention within the fasting arm Fig.  started 48. The intervention was similarly delivered as an the fasting group-based behavioral intervention with aspects of the mind–body program of the Benson–Henry Mind/Body Medical Institute, Harvard Medical School40. Overall, the program consisted of 50 h of group sessions over a period of 10 weeks and also included comprehensive lectures and cooking lessons.The DASH group Fig.  was trai49. The list was password-secured and only the biometrician was able to access it. On this basis, sealed, sequentially numbered opaque envelopes containing the treatment assignments were prepared.Patients were randomly allocated to Fasting or DASH by block-randomization with randomly varying block lengths, stratified by a) study center, and b) the intake/non-intake of antihypertensive medication. The randomization list was created by a biometrician not involved in patient recruitment or assessment using the Random Allocation SoftwareOutcomes were assessed at baseline and at 1 and 12 weeks after randomization by a blinded outcome assessor who was not involved in patient recruitment, allocation, or treatment. Two primary outcome measures were defined: 24 h ambulatory systolic blood pressure at week 12 and the Homeostasis Model Assessment (HOMA)-index at week 12.® PWA, I.E.M., Stolberg, Germany)50. Baseline ABPM measurements were performed within one week before the starting of the intervention, those at week 12 within a week after the end of the intervention. ABPM was initiated at the same time of day for each successive visit. The monitoring software automatically removed incorrect measurements using built-in algorithms. Blood pressure and heart rate values were further categorized as day or night values using each patient’s reported awake and sleep times. Office blood pressure was measured in the hospital by a sphygmomanometer, using the average of three consecutive measurements after 5 min rest while sitting in a quiet room. Office blood pressure was measured at each time point, ambulatory blood pressure only at baseline and week 12.Twenty-four-hour ambulatory blood pressure monitoring (ABPM) and pulse pressure recording were performed using a digital blood pressure monitor , uric acid, blood creatinine level, estimated glomerular filtration rate (eGFR), C-reactive protein (CRP), insulin-like growth factor 1 (IGF-1), and interleukin-6 (IL-6), triglyceride, fasting glucose level54. Samples were destroyed after the analysis and were not further stored.Blood samples were collected from the antecubital vein into vacutainer tubes and analyzed using the Modular P analyzer . Metabolic parameters included plasma and blood glucose levels, blood insulin levels, HbA1C, and HbA1C IFCC and were analyzed using standard procedures. HOMA index was calculated as blood insulin level (µU/ml) × blood glucose level (mmol/l)/22.5All adverse events occurring during the study period were recorded. Patients experiencing adverse events were asked to see the study physician to assess their status and initiate any necessary response. The most common symptoms during the fasting period were mild weakness, headaches, and mild perception of hunger. No serious adverse effects were reported. During the normocaloric diet periods no adverse effects were reported.56.All analyses were conducted on an intention-to-treat basis, including all participants being randomized, regardless of whether or not they gave a full set of data or adhered to the study protocol. Missing data were multiply imputed by Markov chain Monte Carlo methods+ cells were selected (Miltenyi CD4+ Selection Kit). Cells (106) from CD4+ and CD4− fractions were placed onto U-bottom plates and re-stimulated for 4 h at 37°C and 5% CO2 in a humidified incubator in a final volume of 200 µl RPMI 1640 (Sigma) supplemented with 10% FBS (Merck), 100U/ml penicillin (Sigma), 100 mg/ml streptomycin (Sigma), 50 ng/ml phorbol 12-myristate 13-acetate , 250 ng/ml ionomycin (Sigma) and 1.3 µl/ml Golgistop (BD). After re-stimulation, cells were labeled with Life/Dead Fixable Aqua Dead Cell Stain Kit, for 405 nm excitation (Invitrogen), followed by labeling with surface antigen-specific fluorophore-conjugated monoclonal antibodies. Cells were then fixated and permeabilized by FoxP3/Transcription Factor Staining Kit (eBioscience), and subsequently labeled with intracellular-antigen-specific fluorophore-conjugated monoclonal antibodies. Antibodies are listed in Table Whole blood staining was performed using antibodies against major leukocyte lineages. Quantitative measurement was performed using a high throughput sampler (BD) and a BD FACS CantoII (BD). Peripheral venous blood was obtained and mononuclear cells were isolated within 24 h of collection by density gradient centrifugation using Biocoll and cryopreserved until further processing. Thawed cell aliquots were either labeled for extracellular antigens using fluorophore-conjugated monoclonal antibodies or CD457 algorithm, an R58 bio-conductor package that uses self-organizing maps for dimensional reduction and visualization of flow cytometry data. All data were scaled and log-transformed on import. Cells were assigned to a Self-Organizing Map (SOM) with a 10 × 10 grid, grouping similar cells into 100 nodes. Each node in the FlowSOM tree gets a score indicating its correspondence with this requested cell profile. To visualize similar nodes in branches, a minimal spanning tree (MST) was constructed and cell counts were log scaled. To visualize the differences between the two-time points, the mean percentage per sample group was computed in each cluster and then the statistical difference was performed by applying MWU test on every node within metaclusters. P values were two-sided and analysis was performed using RStudio (version 3.4.4). The FlowSOM algorithm was run 3 times to ensure reproducibility of the results and P < 0.05 was considered to be statistically significant.Data were manually gated on single live cells and exported as FCS files in FCS Express V6.02 (De Novo Software). The automated analysis of FCS files was done by the FlowSOMAntihypertensive drugs were normalized in order to track changes during intervention. In a first step, antihypertensives (according to the WHO ATC classification system), diuretics, beta-blocking agents, calcium channel blockers, and agents acting on the renin-angiotensin system as well as the given dosage were identified at V1 and at follow-up visit after 3 months (V3).Secondly, drug dosage was normalized to the lowest drug dosage per patient and drug. The lowest drug dosage at baseline was set to one, while corresponding drug dosages at other time points where either zero if the medication was discontinued, one if there was no change in drug dosage between time points, smaller than one if the drug dosage was decreased or greater than one if the drug dosage was increased at a certain time point. The sum of the agents taken was calculated at each time point.59. Briefly, samples were treated with 500 µl of extraction buffer , 200 µl of 20% SDS, 500 µl of phenol:chloroform:isoamyl alcohol (24:24:1) and 100 µl of zirconia/silica beads (0.1 mm diameter). Samples were homogenized twice with a bead beater (BioSpec) for 2 min. After precipitation of DNA, crude DNA extracts were resuspended in TE Buffer with 100 µg/ml RNase I and column purified to remove PCR inhibitors.For DNA-based 16S rRNA gene and metagenomics sequencing, fecal samples were collected into RNALater containing tubes, shipped at room temperature and stored at −80 °C until processing. The DNA isolation protocol has been previously described61. Briefly, for DNA-based amplicon sequencing 25 ng of DNA was used per PCR reaction in a final volume 30 µl. The PCR conditions consisted of initial denaturation for 30 s at 98 °C, followed by 25 cycles . Each sample was amplified in triplicates and subsequently pooled. After normalization, PCR amplicons were sequenced on MiSeq PE300 platform (Illumina) at the Helmholtz Centre for Infection Research, Braunschweig, Germany.Amplification of the V4 region (F515/R806) of the 16S rRNA gene was performed according to previously described protocolsSixty microliters of total DNA was used for shearing by sonication (Covaris). Fragmentation was performed as follows; processing time: 150 s, fragment size: 200 bp, intensity: 5, duty cycle: 10. Library preparation for Illumina sequencing was performed using the NEBNext Ultra DNA library prep Kit (New England Biolabs). The library preparation was performed according to the manufacturer’s instructions. An input of 500 ng of sheared DNA was used and the size selection was performed using AMPure XP beads with the following parameters. First bead selection: 55 µl, and second: 25 µl. Adaptor enrichment was performed using seven cycles of PCR using NEBNext Multiplex oligonucleotides for Illumina . Sequencing was performed on NovaSeq PE1000 platform (Illumina) at the Helmholtz Centre for Infection Research, Braunschweig, Germany.62. The pipeline includes sequence quality filtering63, read merging64, adapter and primer removal, chimera removal65, clustering66, and taxonomic classification67 based on the SILVA (v138)68 database. The validation dataset22 was reprocessed using the exact same settings.Reads retrieved from 16S amplicon sequencing were analyzed using the LotuS (1.62) pipeline69. Reads were quality filtered by a minimum read length of 45 bp and a minimum Phred quality score of 25. Sequences passing that filter were mapped to the human genome  and filtered. Sequences identified as non-human were mapped with bwa70 to a) the IGC gene catalog (0.5)70 with a minimum match size of 45 bp and a minimum identity of 95%, b) 40 reference marker genes described in Ciccarelli et al.71 and Sorek et al.72 with a minimum match size of 45 bp and a minimum identity of 97%. Reads mapping to the marker genes were extracted and further mapped to marker gene-based OTUs73. Mapping statistics can be found in Supplementary Data Metagenomic shotgun sequences were processed within the NGLess framework (0.10)71 were rarefied using the RTK (0.93.1)74 with default settings . Reads were mapped to the mOTUv2 (2.1) taxonomic marker genes73 were likewise rarefied (5838 reads/sample). Reads mapped to 16S OTUs (27813 reads), to ensure sample compatibility regardless of sampling depth. For functional microbiome analysis, IGC genes were binned to KEGG KOs75 based on the annotations in MOCAT2 (2.0.1)75, then binned by averaging over KOs to KEGG modules and to Gut Microbial Modules (GMMs)76. 16S and mOTUv2 (2.1) OTUs were binned at more rootwards taxonomic levels using the taxonomies provided with LotuS (1.62)62 and the mOTUv2 (2.1) tool73 respectively.Reads mapped to the IGC microbial gene catalog (0.5)74. Unpaired MWU tests failed to reach significance (P > 0.05) for all comparisons of subsets of samples: each time point versus each other time point, in each arm separately and pooled, and between the arms within each time separately and pooled. Subsequently, we assessed within-individual changes in alpha diversity for both the DASH and the fasting+DASH arm, analogously to analysis of microbial taxa, functional modules, clinical phenotypes, and immune cell population counts, controlling for medication changes in the same manner. Supplementary Data We assessed several metrics for gut alpha diversity using the 16S species data , namely species richness, Shannon diversity, community evenness, Simpson’s and the Inverse Simpson’s metric, and the Chao1 index, calculated using the RTK (0.93.1)tool77 R package, stratified for patient ID.Mutlivariate analysis was carried out using Principal Coordinates Analysis (PcoA) as per the vegan (2.5-5) R package, with the same distance metrics as noted above. Where described, delta metrics for the first two dimensions of unconstrained ordination were computed. PERMANOVA tests for multivariate effect were done using the adonis function in the vegan (2.5-5)78 R package, and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg (BH) procedure within each measurement space. In the second step, features with FDR < 0.1 were retained for a second phase of post-hoc tests using Mann–Whitney U comparisons between values at each pair of time points, BH FDR-adjusted between time point comparisons (n = 3) and requiring FDR < 0.05 to retain the result as significant. Standardized non-parametric effect sizes were taken using the (signed) Cliff’s delta metric as implemented in the orddom (3.1)79 R package. The same methods were used to analyze the validation dataset, with the exception no drugs were adjusted for as subjects were unmedicated22.For all univariate analysis of clinical, immunome, or microbiome features, medication changes during the course of the study were accounted for as possible confounders using the following two-step procedure. The first step was a nested model comparison of a linear model for each feature, involving as predictors age, patient ID, sex, and normalized dosage of each salient medication tracked at each time point, with the same model but additionally containing time point V1-V3 as a predictor. Models were compared using a likelihood ratio test as implemented in the lmtest (0.9-37)Body weight and blood pressure change differences between Responders and Non-Responders were compared with two-sided Mann–Whitney U test using GraphPad Prism (6.01).15 on the genus-level abundance table.Enterotypes of the samples in the fasting arm were performed by implementing the R package DirichletMultinomial (1.32.0.)P-values from this were FDR-adjusted with the BH method for each comparison of two data spaces, requiring FDR < 0.05 for significance. Second, a post-hoc test was done to account for dependency between same-donor samples: for each of two correlated features, a mixed-effects model was fitted of the rank-transformed variable using the rank of the other as predictor, with patient ID as a random effect. This model was compared to a simpler model containing only the random effect under a likelihood ratio test as implemented in the lmtest (0.9–37)78 R package. The highest P-value for the two possible such models was taken, and P < 0.05 was additionally required to retain the correlation as robust. Correlation was visualized by the R packages circilize80 and pheatmap81.To assess possible interactions between immune cells, taxa, and quantitative phenotypes, another two-step test was used: first a Spearman correlation test using samples pooled across time points, and with Spearman’s rho used as standardized signed effect estimate. 14 and Forslund et al.11 were previously mapped to the IGC gene catalog (0.5) and the mOTU marker genes; these abundances . The Kushugulova samples were tested for significantly differential abundances between MetS cases and controls using the Mann–Whitney U test, then controlling that a MetS status predictor still significantly improves fit (using the R lmtest ((0.9–37)78 package) of the rank-transformed abundances when added to a linear model already incorporating metformin status as a predictor, thereby controlling for confounding influence of metformin treatment status. Analogously, the Forslund samples were tested for significantly differential abundances between metformin-treated and untreated patients using the Mann–Whitney U test, then controlling that a metformin status predictor still significantly improves fit (using the R lmtest (0.9–37)78 package) of the rank-transformed abundances when added to a linear model already incorporating MetS status as a predictor, thereby controlling for confounding influence of MetS status. The validation dataset22 was analyzed exactly as the main study dataset, as described above.Samples from Kushugulova et al.82. To this end, the set of n participants was iteratively split into n − 1 participants as training set, and the untouched data from the hold-out participant as the test set. All input variables were z-scored by centering to zero mean and unit-scaling to a variance of one83. In each of n cross-validation folds, the logistic-regression algorithm was a natural choice of method for binary classification . Given that the number of variables was >10× times larger than the number of participants, dimensionality reduction was necessary for a preliminary selection of a set of ten most promising input variables that could be relevant for outcome prediction. Forward-stepwise selection is an established means84 to screen the relevance of several hundred quantitative measures. The first step identifies the single input variable among the p candidates, with the best p-value having a statistically significant association with the blood-pressure outcome. After adding this first variable to the empty null model, the second most significant  was searched based on the remaining p − 1 input variables. Based on a two-variable model, the third most significant variable was searched based on p − 2 remaining variables, and so forth. This successive identification of the ten most promising among the p overall input dimensions did not bias the subsequently performed prediction performance estimate, because the entire variable reduction scheme was exclusively carried out on the n − 1 participants of the current cross-validation fold. Based on the top 10 variables, the logistic-regression algorithm could be more robustly fit to these subselected ten input dimensions only. The ensuing predictive model was then explicitly validated by computing whether or not the obtained model parameters allowed for accurate derivation of the relevant blood-pressure response for the independent, unseen participant. In this way, the omics data of each patient in our dataset served as test observation once. Averaging these yes-no results over all n predicted, versus observed clinical responses, yielded an estimate of the expected forecasting accuracy of the predictive model in participants that we would observe in other or later acquired datasets.To estimate how well the omics data enables forecasting of the blood-pressure response in future patients, we performed a leave-one-patient-out cross-validation procedure. This approach represents the gold standard in the machine-learning community to carry out an acid-test that empirically evaluates the practical value of a predictive modelFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Reporting summary


K: Minimum Evoked Current of K), and evaluated its accuracy in predicting sympathetic response to nociceptive stimuli under constant opioid administration. Thirty patients undergoing open surgery under general anesthesia were included. After anesthetic induction, remifentanil was administered at a constant concentration of 2 ng/ml at the effect site followed by tetanus stimulation. MECK was defined as the minimal current needed to produce a change in K. MECK significantly (P < 0.001) correlated with the rate of change of systolic blood pressure during skin incision (ROCBP). Bland–Altman plot analysis using the predicted ROCBP calculated from MECK and the measured ROCBP showed that the prediction equation for ROCBP was highly accurate. This study showed the potential of MECK to predict blood pressure change during surgical incision under opioid analgesia.Opioid inhibition of nociceptive stimuli varies in individuals and is difficult to titrate. We have reported the vascular stiffness value (K) as a standard monitor to quantify sympathetic response with high accuracy. On the contrary, among individuals, a considerable variation in the rate of change in K for constant pain has been observed. In this study, we proposed a new index, the minimum stimulus intensity value that evoked the response on K (MECClinical trial registration Registry: University hospital medical information network; Registration number: UMIN000041816; Principal investigator's name: Satoshi Kamiya; Date of registration: July 9th, 2019. The International Association for the Study of Pain defines pain as an aversive sensory and emotional experience. Patients under general anesthesia are unconscious and do not have an aversive experience. However, anesthesiologists commonly use analgesics in addition to sedatives because nociceptive stimuli can cause noxious autonomic reflexes. Opioids are the most commonly used analgesics during general anesthesia due to their lack of a ceiling effect. Opioids exert their analgesic effects mainly by inhibiting sensory nerve transmission in the spinal cord and by inhibiting the excitation of pain conduction pathways in the brain. When opioids are administered during general anesthesia, noxious autonomic reflexes are suppressed and multiple parameters such as heart rate, blood pressure, electrocardiogram, and respiratory rate are affected. During surgery, anesthesiologists rely on these parameters to estimate pain levels and adjust the opioid dosage. However, this nociceptive stimuli-induced sympathetic response varies among individuals, and therefore, so do individual anesthetic requirements. Adverse events, either as an unexpected increase in blood pressure due to underdosing or delayed arousal due to overdosing, are common. Thus, if the individual's opioid requirement can be accurately quantified in advance, more stable general anesthesia can be performed.4. However, PPG measures blood flow at the measurement site and does not, in principle, directly indicate the degree of sympathetic response. Therefore, we proposed a method to extract only the degree of vasoconstriction from sympathetic cutaneous blood flow responses to PPG and quantify it as vascular stiffness value (K)5. We reported that K could be used to quantify pain and reflect the activity of the sympathetic nervous system7. Moreover, we reported that K value reflects changes in pain caused by the administration of opioids during general anesthesia and that the response of K to pain was attenuated with increasing doses of opioids10.Nociceptive stimuli input to the central nervous system is output to effector organs such as the heart and blood vessels via the sympathetic nervous system. Opioids inhibit the input of nociceptive stimuli to the central nervous system. Therefore, the administration of opioids blunts the sympathetic response to nociceptive stimuli. In other words, by accurately quantifying the sympathetic response to a given nociceptive stimulus under opioid administration, we can determine the relationship between the opioid dose and the response to the nociceptive stimulus and quantify opioid sensitivity. Photoplethysmography (PPG), an increasingly popular tool, has recently begun to be used to measure sympathetic cutaneous blood flow responses for quantifying sympathetic nerve activity in peripheral vasculature10. We speculated that this may have been due to the fact that the pathway of nociceptive stimuli, which travel from the peripheral nerves through the central nervous system to effector organs such as peripheral blood vessels, is strongly influenced by autonomic changes due to aging and coexisting diseases. While opioids inhibit the afferent pathway of pain perception, the rate of change of K represents the intensity of the sympathetic response or the efferent pathway. In other words, the rate of change in K includes information from both efferent and afferent pathways, so the measurement results may vary depending on the sensitivity of the effector. Therefore, we hypothesized that the “intensity of nociceptive stimuli" at which sympathetic responses appear is a better indicator of opioid sensitivity than the "intensity of sympathetic responses" to nociceptive stimuli  because it exclusively extracts only information regarding afferent pathways.On the contrary, among individuals, a considerable variation in the rate of change in K for constant pain has been observedBP by MEC for each parameter, and the secondary objective was to compare the prediction accuracy of ROCBP between MECK and KR80.In this study, we proposed a new index, the minimum stimulus intensity value that evoked the response (MEC: Minimum Evoked Current ) on each parameter, and evaluated its accuracy in predicting sympathetic response to nociceptive stimuli under constant opioid administration. The primary objective of this study was to compare the prediction accuracy of ROCPrior to the study, we received approval from the Ethics Committee of Hiroshima University  and registered the clinical trial . This study was conducted in accordance with the Declaration of Helsinki and STROBE statement. The study population consisted of patients over 20 years of age who underwent open surgery under general anesthesia from July 2019 to October 2019. A total of 30 patients gave their written informed consent before the study. All procedures were conducted at the Hiroshima University Hospital. We excluded patients with irregular R-R via electrocardiogram (ECG), inability to perform invasive arterial pressure measurements in the radial artery, significant hemodynamic or neurological impairment in the upper extremity, and severe stenosis or occlusive lesions in the coronary arteries or cerebral vessels. If, for a patient, the mean blood pressure remained below 50 mmHg for more than 3 min during the study time, the study protocol for that patient was discontinued.11 was used to calculate predicted effect-site concentrations. Administration of remifentanil was initiated as per the dosing plan, and propofol 3 μg/ml was used to induce anesthesia using a target-controlled infusion (TCI) pump with built-in ‘Diprifusor’ . After the patient was unconscious, 50 mg of rocuronium was administered and a 22 G catheter was secured in the left radial artery for measuring arterial blood pressure (ABP). Data from ECG, ABP, and PPG were output to a personal computer from a bedside patient monitor  and were used to calculate K values in real-time.Before induction of anesthesia, a photoplethysmography probe  on the middle finger of the left hand, ECG on the chest, electroencephalogram  on the anterior forehead, neuromuscular blockade monitoring device  on the ulnar side of the forearm of the right hand, and a non-invasive blood pressure cuff on the right upper arm were placed. In all patients, a preoperative dosing plan was developed to achieve a predicted effect-site concentration of 2 ng/ml remifentanil. Minto's pharmacokinetic modelAfter the predicted effect-site concentration of remifentanil reached a steady state at 2 ng/ml, tetanus stimuli at 50 Hz for 5 s were delivered through a two-pole body surface electrode on the ulnar side of the right hand using the INNERVATOR 252 . The current value was initially 10 mA and increased in increments of 10 mA until 80 mA, the maximum output of the INNERVATOR 252, was reached. Thus, a total of eight tetanus stimulation sessions were performed. Thirty seconds after the tetanus stimulation, and after at least another 10 s of steady-state, the next stimulation was performed. Subsequently, tracheal intubation was performed using a Macintosh laryngoscope or McGRATH MAC video laryngoscope . All endotracheal intubations were successfully executed in a single attempt. Thereafter, the same amount of propofol TCI target blood levels and rate of remifentanil administration were maintained until the skin incision was performed.8. In brief, we first detected the heartbeats with the R wave of the ECG and cut out the ABP and PPG waveforms for each heartbeat. All wave height data associated with one heartbeat were provided in the following equations:In this study, the motion of the vessel wall was predicted using a model in which springs and dampers were aligned in parallel to the diameter direction of the vessel. The arterial wall, moved by a force (blood pressure) applied in the direction of the diameter of the vessel, was dampened by springs and dampers that represent the stiffness and viscosity of the vessel wall itself. The behavior of the vessel wall after damping was observed as a change in the PPG waveform Fig. . We usedt0, ABP wave height at time t was Pb(t), and PPG wave height at time t was represented as Pl(t). Ṗl(t) is equal to the first derivative of the PPG wave height. The coefficients for vascular stiffness and vascular viscosity values, K and B, respectively, were determined as one value per heartbeat by performing a least-squares fit of the equation to all data acquired in each heartbeat. K determined in this way was actually displayed automatically at the bedside in real-time, every heartbeat. If the coefficient of determination was less than 0.95, or if K and/or B were negative, the data were removed from the analysis. Only K was analyzed in the present study because previous studies have estimated that K was a promising candidate as a measure of nociceptive stimulus intensity8.The time at which the change was first observed was considered K, MECHR, MECBP, and MECPPG, respectively. The rate of change in K before and after 80 mA tetanus stimulation (KR80) was calculated as a measure of the rate of change for constant intensity nociceptive stimuli. The median systolic blood pressure of 10 s before the scalpel skin incision was defined as pre-BP, and the maximum systolic blood pressure between the skin incision and the start of electrocautery use was defined as post-BP. The ROCBP before and after skin incision was calculated by dividing post-BP by pre-BP. A first-order regression was performed to confirm the relationship between MEC and ROCBP. Specifically, a first-order regression equation was created between cases where MEC and ROCBP could be measured. A Smirnov–Grubbs test was performed using this regression formula, and a measurement in which P was greater than or equal to 0.05 was removed as an outlier. The regression equation was remade for each of the removed outliers. The regression equation at the time outliers were removed was adopted as the final correlation line. Pearson's correlation coefficients were calculated using the measurements at the time when the outliers were eliminated. Adjusting for multiplicity, p values of Pearson's correlation coefficients were corrected using the Bonferroni correction 4 times. The significance level was set at 0.05.Figure BP using MECK and KR80. Specifically, the prediction equation was based on the first-order approximate equation previously developed, and the predicted ROCBP was calculated from MECK. For cases in which MECK was outside the measured range, we fitted the mean value of ROCBP in out-of-range cases to the approximate equation and substituted the estimated value. A similar first-order approximation and outlier treatment was performed for KR80 and ROCBP. From the final approximate equation, the predicted ROCBP was calculated. A Bland–Altman analysis was performed for both measured and predicted ROCBP, and fixed bias and limit of agreement range were calculated for each.Further, we attempted to predict the ROCK, and therefore, the prior number of cases cannot be determined. Power analysis showed that 26 patients were needed to show Pearson’s correlation coefficient of 0.5 between the K and BP under the conditions α = 0.05 and 1-β = 0.8. The number of patients was set to 30 to account for the fact that some data measurements were incomplete. Moreover, the effect size was calculated to ensure a sufficient sample size. In Pearson's correlation analysis, the r-value, which is the correlation coefficient, was used as a measure of the effect size. All the r-values of the main results of this study indicate moderate-to-high effect sizes.No prior studies are available on MECBP was 18.3 ± 11.8% (mean ± SD).A total of 30 patients male, 15) were included in the study and the mean age of participants was 62 years. No patients who regularly used opioids were included. Thirteen patients underwent an upper abdominal incision, and 17 underwent a lower abdominal incision. None of the patients met the exclusion criteria. A summary of patient characteristics is shown in Table 5 were inFigure R80, the rate of change in K at the maximum current value (80 mA), was calculated. A scatter plot of each MEC and KR80 with ROCBP, respectively, is depicted in Fig. BP is − 0.723 (MECK), − 0.067 (MECHR), − 0.565 (MECBP), − 0.711 (MECPPG) . MECK, MECBP, and MECPPG were shown to be significantly correlated with ROCBP. Three cases for MECK, 9 cases for MECBP, and 5 cases for MECPPG were classified as out of the measurement range. The ROCBP of these out-of-range cases were 2.6% (− 3.7 to 3.3), 14.4% (2.6–23.3), and 3.3% (− 3.7 to 15.4), respectively.Then, in addition to the MEC of each parameter determined above, KBP of MECK and MECPPG measurement range groups. However, out-of-range cases of MECPPG had a large degree of variability and included cases with large ROCBP. Therefore, we determined that MECK was the index with the highest degree of correlation with ROCBP among MECK, MECHR, MECBP, and MECPPG.Among the out-of-measurement range groups, there was no statistically significant difference in the ROCK and KR80 to predict ROCBP. First, for MECK, a first-order regression equation was applied after outlier treatment in the scatter plot in Fig. BP in cases outside the MECK measurement range was 0.7%. This rate of change was substituted into the temporal regression equation, which yielded a value of 107 (mA). This value was used as a provisional MECK value for cases outside the MECK measurement range. Next, for KR80, the first-order regression equation after outlier treatment in the scatter plot in Fig. We then used MECBP. The results of the Bland–Altman plot of measured and predicted values are shown in Fig. BP is shown as the vertical axis and the mean as the horizontal axis. For MECK, fixed bias was small (− 0.17%), and the Pearson correlation coefficient in the scatter plot was R = 0.322 (P = 0.088), and thus, no significant proportional bias was found. In contrast, for KR80, the fixed bias was small (− 0.01%); however, the Pearson correlation coefficient of the scatter plot contained significant proportional bias .The above prediction equation was used to calculate the predicted ROCK as an indicator was able to predict ROCBP with high accuracy.Although opioids are essential drugs for general anesthesia, inappropriate administration may lead to unpleasant effects on patients. Estimation of appropriate dosage is difficult because of large individual differences in opioid sensitivity. In this study, a prediction formula using MECK is the threshold for the appearance of a sympathetic response when blood concentrations of opioids are 2 ng/ml and can be regarded as an intrinsic value that indicates individual opioid sensitivity. In other words, for patients with a low MECK, increasing the dose of opioids prior to skin incision can reduce circulatory fluctuations. The relationship between the MECK and ROCBP at different opioid concentrations remains to be determined; however, the present results indicate that the MECK may be a good indicator of opioid sensitivity.MECK correlated better with ROCBP than KR80 , and ROCBP variability of MECPPG was also higher. This may be because PPG amplitude reflects changes in blood flow and is influenced by multiple parameters such as cardiac output and vascular tone. The PPG amplitude was reduced, probably by vasoconstriction with nociceptive stimuli; however, it may have been increased despite nociceptive stimuli as a consequence of an augmentation factor that was a result of increased cardiac output that occurred at the same time12.MECK was the most accurate measure of MEC.In contrast, K was not affected by blood flow changes due to changes in cardiac output because it measured vascular compliance. This means that in cases where PPG cannot detect or shows excessive changes, K can accurately detect the presence or absence of a sympathetic response. For these reasons, we believe that the MEC13. It is possible that the short stimulation time increased the number of out-of-range groups, especially in MECHR. However, when nociceptive stimulation is given under opioid analgesia, the possibility may arise that sympathetic responses do not occur in patients with high opioid sensitivity. Considering that more than 80% of BP and more than 60% of PPG measurements could measure MEC by under 80 mA tetanus stimulation and that a significant correlation was observed between MECBP, MECPPG and ROCBP, we regarded this as an appropriate tetanus stimulation time for BP and PPG.In this study, we used a tetanus stimulation of 50 Hz for 5 s. This is shorter than a stimulation interval of 30 s reported in a previous study13. In this study, the tetanus stimulus was highly specific to Aδ and C fibers in the inhibition of muscle contraction due to the administration of muscle relaxants. However, it is not ruled out that repeated tetanus stimulation may cause neural sensitization, which may modulate the measurement results at the next stimulation. KR80, which indicates the intensity of the sympathetic response, may be affected by neurosensitization. In contrast, it is unlikely that MEC was affected by neurosensitization because stimuli weaker than MEC did not affect each parameter.Sympathetic response also occurs in muscle contraction induced by tetanus stimulation. Therefore, tetanus stimulation is not considered to be a pure Aδ or C fiber stimulus. However, Funcke et al. stated that the sympathetic responses to tetanus and intradermal electrical stimulation might be considered as a similar stimulusK, additional studies designed to adjust the remifentanil dose using MECK as an indicator are needed. In addition, since the results of this study strictly apply only to propofol anesthesia, it is also necessary to verify whether the results are applicable when anesthetics other than propofol are used or when anesthesia is not performed.To generalize the opioid sensitivity index based on MECBP at other concentrations are unknown. However, at 2 ng/ml, MEC varied appropriately between 10 and 80 mA. In a previous study by our research group, 6 ng/ml resulted in many cases with very poor sympathetic responses9. In contrast, using a remifentanil concentration lower than 2 ng/ml may result in too high ROCBP, which seems not to be appropriate for patient safety. These facts suggest that the concentration of 2 ng/ml used in this study was reasonable regarding the amount of opioids needed to inhibit tetanus stimulation.Since the remifentanil concentration was studied only at 2 ng/ml, the changes in MEC and ROC13, and the median value of 10 s before stimulation was set as the pre-stimulus value. Therefore, it is possible that return to the steady-state was not reliably judged. However, prolonging the study time under general anesthesia increases the possibility of external factors such as changes in body temperature and insensible perspiration, etc. Since measurements including K can be taken in real-time for each heartbeat, we consider that the effect of the short interval between stimuli may be more limited than that reported in the past literature.In the present study, the interval between stimuli (30 s) was shorter than that reported by Funke et al.In the present study, the peak value for 20 s after the stimulus was adopted as the post-stimulus value. This is shorter than the value reported in previous studies. The peak values of blood pressure and heart rate caused by the increase in blood catecholamine concentration due to nociception may occur later than 20 s. However, the time from tetanus stimulation to the peak value in the cases where we were able to set MEC was 9.8 ± 1.9 s, 7.3 ± 1.4 s, 10.0 ± 2.0 s, and 9.9 ± 1.9 s (mean ± standard deviation) for K, HR, sBP, and PPG, respectively. In other words, the neurogenic response peaked within 20 s for all parameters. Therefore, we believe that ending the measurement at 20 sseconds has a limited impact on MEC, which is the essence of this study.Since the measurement of arterial pressure requires an invasive blood pressure measurement method and takes time, it is generally difficult to accept this research model as-is for clinical use. In the future, it will be necessary to improve the protocol by reducing the number of stimulation steps at low currents, where responses are less likely to occur.R80, MECHR, MECBP, and MECPPG, MECK correlated best with blood pressure changes during skin incisions. The MECK-indexed prediction equation adequately predicted the increase in blood pressure that occurred during skin incision at a remifentanil effect-site concentration of 2 ng/ml. MECK was shown to be a potentially more accurate indicator of opioid sensitivity than the rate of change in K.Compared with K


Orthostatic changes in diastolic, but not in systolic, BP were linearly related to the death risk (HR (1 mmHg reduction): 1.04, 95% CI 1.01–1.07, p = 0.006), and this was also true for CV death . The strength of this association was not affected by further data adjustment (p ≤ 0.05). These findings suggest that independent of the formal diagnosis of orthostatic hypotension, even minor orthostatic reductions in diastolic BP bear an excess death risk in this population.Blood pressure changes upon standing reflect a hemodynamic response, which depends on the baroreflex system and euvolemia. Dysautonomia and fluctuations in blood volume are hallmarks in kidney failure requiring replacement therapy. Orthostatic hypotension has been associated with mortality in hemodialysis patients, but neither this relationship nor the impact of changes in blood pressure has been tested in patients on peritoneal dialysis. We investigated both these relationships in a cohort of 137 PD patients. The response to orthostasis was assessed according to a standardized protocol. Twenty-five patients (18%) had systolic orthostatic hypotension, and 17 patients (12%) had diastolic hypotension. The magnitude of systolic and diastolic BP changes was inversely related to the value of the corresponding supine BP component ( Blood pressure (BP) modulation is a complex mechanism which involves the cardiovascular, nervous, renal, and endocrine systems . While pThe shift from the supine to the upright positions translates into a rapid decrease in central blood volume, causing, through a cascade of events, a fall in BP . This faDysautonomia ,12 and fOH has been associated with syncope , cardiovThe aim of this retrospective study is to investigate the relationship between OH and orthostatic blood pressure changes with all-cause and cardiovascular (CV) mortality in a cohort of PD patients.n = 137). Data included in this study were retrospectively collected using clinical records, according to the current ethical rules. In order to avoid selection bias, all patients on regular PD, either on 4 standard exchanges per day or on continuous cycling peritoneal dialysis, with response to orthostasis assessed and available at clinical chart review, were included. The study flow is described in The study population represents the prevalent and incident PD patients followed at our center from 1 January 2000 to 23 April 2014 , C-Reactive Protein (CRP), fibrinogen, and hemoglobin measurements were performed using standard methods in the routine clinical laboratory.In this paper, the association between OH and orthostatic SBP and DBP changes with overall and cardiovascular (CV) mortality was tested. Patients were followed-up from baseline (day of the measurement of the response to orthostasis) until death or censoring. Patients who underwent kidney transplantation or shifted from PD to hemodialysis (HD) were censored. Cardiovascular events were centrally adjudicated and classified as follows: stroke (ischemic or hemorrhagic) documented by computed tomography, magnetic resonance imaging, and/or clinical and neurological evaluation; transient ischemic attacks (TIA); myocardial infarction confirmed by serial changes in ECG and cardiac biomarkers; ECG-documented angina episodes; ECG-documented arrhythmia; unexpected, sudden death highly suspected as of cardiac origin. De novo chronic heart failure (CHF) was defined as CHF in a patient without CHF at baseline. To be classified as having CHF, patients had to show mild or more severe dyspnea during ordinary activities (NYHA class II or higher), plus evidence of anatomical/functional LV disease on echocardiography. Each cause of death was assessed by 3 independent physicians. In doubtful cases, diagnosis was attributed by consensus. During the review process, the investigator used all available medical information, including hospitalization forms and medical records. In the case of death occurring at home, family members and/or general practitioners were contacted to retrieve the cause of death.Considering the retrospective design of our study, no sample size calculation was made. However, in order to assess if data collection would be adequate to perform the planned survival analyses, we carefully checked the database before the statistical assessment. Among 137 patients, 69 manifested the event of interest  during the follow-up, allowing us to build survival models including up to 7 variables. This made our cohort satisfactory for our analysis.t-Test, Mann–Whitney U, or Chi-square test, as appropriate. Pearson’s correlation analyses were performed to investigate the correlates of magnitude of SBP and DBP changes. Survival analyses were performed to investigate the association between OH, systolic and diastolic changes, and the considered outcomes. Bivariate Cox regression models included SBP and DBP changes, together with the corresponding supine measurement (SBP or DBP). In multivariate models, age, gender, diabetes, and cardiovascular comorbidities were also included. The association between antihypertensive drugs and changes in blood pressure was tested, building a multiple linear regression model adjusted for all antihypertensive drugs classes . To overcome the issue of model overfitting when CV death was included in survival models as an outcome variable, we repeated multivariate analyses using the van Houwelingen and le Cessie heuristic shrinkage estimate [Data were expressed as mean ± standard deviation , median and inter-quartile range , or as percent frequency . Comparisons among groups were made by Student’s estimate . As the The main demographic, somatometric, clinical, and biochemical characteristics of the study population are detailed in r = −0.16, p = 0.056 (systolic) and r = −0.25, p = 0.003 (diastolic), respectively) /−1 ± 8 mmHg (diastolic). Twenty-five patients (18%) had systolic, and 17 patients (12%) had diastolic OH. The magnitude of SBP and DBP changes were inversely related to the value of the corresponding supine BP component (ctively) . One hunp = 0.26). Among them, 45% died due to CV causes. The IR of CV death was 8.94 in patients with OH, and 5.27 in patients without OH . Forty-four patients (9 with OH and 35 without OH) shifted to HD, and 17 underwent kidney transplantation (4 with OH and 13 without OH). Univariate Cox regression models showed no association between OH and all-cause/CV mortality  (During a median follow-up of 37 months (interquartile range: 22–62 months), 69 patients died (19/34 (56%) with OH and 50/103 (49%) without OH). The corresponding Incidence Rates (IR) were 16.99/100 PY and 12.55/100 PY, with an IRR of 1.45 ( = 0.16) .p = 0.006), and this was also true for CV death . Further data adjustment for other potential confounders  did not materially affect the strength of the association between diastolic BP changes and the risk of all-cause  and CV death . Considering the low number of CV death, in order to overcome the issue of model overfitting we repeated multivariate analyses using the shrinkage approach. After shrinkage, the results remained roughly the same (p = 0.001). On the basis of this analysis, we introduced beta blockers into the multiple Cox models of all-cause and CV mortality, and the links between DBP changes and these two endpoints remained substantially unchanged . Of note, in these survival models, the use of B-blockers failed to be related .However, in Bivariate Cox regression models, orthostatic changes in DBP, adjusted for supine DBP, were linearly related to the death risk , the atrial volume, and the number of antihypertensive drugs are significantly higher in PD patients . A peculOur study has limitations—first of all, the small sample size. However, considering the length of the follow-up and the fact that half of the patients experienced the event of interest, our cohort can be considered satisfactory. Secondly, the patients enrolled were followed up in a single dialysis center, thus our results cannot be generalized. Third, despite more than half of our patients experiencing the outcome of interest during the follow-up, the number of the events collected was relatively small, allowing us to adjust only for a limited set of confounders. Furthermore, smoking habit was available only in a limited number of subjects. In addition, OH was diagnosed using only baseline BP measurements, as longitudinal data were not available. However, BP measurements were evaluated according to a standardized protocol of ESC/ESH  and are In spite of these limitations, our study is the first showing an association between orthostatic BP fall and mortality in PD patients. As OH is a more robust predictor of cardiovascular events than night-time reverse dipping in the elderly , and, unTherapeutic strategies range from patients’ educational program to drugs therapy. For example, patient training focused on recognizing OH and avoiding any conditions that can aggravate it  is extremely helpful. Another approach consists of educating patients to prevent BP decreases using countermeasures, such as muscle tension and squatting . In seleIn conclusion, orthostatic SBP and DBP changes (but not OH) are associated with adverse outcomes in PD patients. Further studies, specifically designed for the purpose, are needed to investigate this phenomenon.