Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension. Hypertension is a major factor of many cardiovascular diseases (CVDs), which are a group of disorders of the heart and blood vessels, including coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, etc. . AlthougFortunately, blood pressure is the most important preventable factor of CVDs. Early prevention and management of hypertension are the major and most effective means of improving people’s health levels worldwide. Healthy lifestyles , early detection, evaluation of blood pressure levels, proper diagnosis, and treatment with low-cost medication are beneficial in the prevention and control of hypertension . The sevClearly, earlier attention and treatment are more effective in preventing hypertension and other CVDs. However, as we know, most hypertension patients have no symptoms in the stage of elevated blood pressure and even in hypertension. Thus, many people miss the best time for treatment and experience some complications. However, some physiological signals change based on blood pressure level ,5, such Many researchers have used the MIMIC database assuming that all simultaneously collected signals were synchronized ,11,12,13The rest of this paper is organized as follows: In this study, the data were collected from the MIMIC database, which is a free-to-use database that contains tens of thousands of Intensive Care Unit (ICU) patients ,8. The rSR), middle (PATb-2R or PATW-1), and end (PATRO) of the PPG waveform. Their study recommended the use of PAT-middle as it is highly correlated with BP, and therefore, in our study we used the PATb-2R to represent the PAT feature.The definition of PAT in the literature is highly inconsistent . On manyAs the referenced blood pressure source of this study, the original ABP signal did not undergo any preprocessing. Systolic blood pressure (SBP) and blood pressure categories were extracted and labeled directly from the original ABP waveform signal . A 0.5–1Time Span (23): The time span features are expressed as their letters with a dash on top : The S, N, D, w-1, a-2, b-2, c-2, etc. features were defined in PPG waveform. They represent the amplitude of the corresponding waveform from the PPG baseline.Features of VPG and APG (20): The a, b, c, d, and e features were defined in the APG waveform, and the w, x, y, and z features were defined in the VPG waveform. They represent the amplitude of the corresponding waveform from the APG baseline and VPG baseline. Other features based on these features were also defined, such as b/a, c/a, d/a, e/a, (b-c-d-e)/a, (b-c-d)/a, and so on.Waveform Area (4): The waveform AC component area features are expressed as their letters with a polyline on top : The power area features are expressed as their letters with a brace on top : The ratio features are expressed directly as their ratio formulae : The slope features are expressed as their letters with a tilde on top . These classifiers represent different classification theories such as regression, decision tree, cluster, and bagged decision tree. From the results, we can see that KNN achieves better classification performance than the others. As we know, KNN is a very common classifier that can be used in many applications and can be easy to realize.In this study, the dataset was divided into a training set (70%) and a testing set (30%). In the training phase, the training adopted 10-fold cross validation to validate the generalization ability of the trained model. The trained model was then used to predict for the testing set. The F1 score was calculated as an evaluation measure, as follows:bd segment.In our past research ,25,26, wThe 10 PPG features and PAT feature were used to classify the different BP categories, which include normotension (46 subjects), prehypertension (41 subjects), and hypertension (34 subjects). Meanwhile, four different classifiers were trained and tested. A comparison was also carried out with our past research. Because of the difference in the PPG datasets of this study (MIMIC database) and the past one (collected by a designed device ), the 10PPG signal is affected by heart activity, vascular wall function, and peripheral arterial status . TherefoSeveral issues have been studied in our past research, such as optimal SQI , optimalPAT and PPG features reflect different physiological information: PAT can indicate the transmission of the arterial wave in the blood vessel, while PPG features can indicate the status change of vascular tissue and blood volume. Therefore, three experimental analyses were implemented to determine the feature differences in the different BP level classifications . Based on our past research, 10 PPG features were used in this study for these experimental classifications. PAT has some limitations as it cannot classify these three categories of blood pressure levels; PPG features showed better performance in classifying hypertension from normotension than the other experiments. Furthermore, the feature set of PAT feature and 10 PPG features obviously improved the classification performance for all three experiments. This indicates that the combination of arterial wave propagation theory and PPG morphological theory can be beneficial in modeling and quantizing the BP formation, which is comprehensive and complex. Various influencing factors work together to determine and affect blood pressure, such as a heart′s cyclical activity, vasomotion, total blood volume, cardiac output, vascular elasticity, peripheral resistance, and so on. Therefore, the blood pressure level is the physiological response of the cardiovascular system, and cardiac function, total blood volume, and vascular elasticity play decisive roles in the formation of blood pressure. Hence, it is feasible to use arterial wave propagation theory to explain blood transmission and to use PPG morphological theory to explain the changes of vascular aging, stiffness, and compliance that generally occur at different BP levels.In our past research, the PPG signal was collected as 1000 Hz sample frequency and 12 bits ADC, and the blood pressure was collected by a commercial BP device: the Omron 7201 BP device . CompariThe accurate identification of feature points is very important, especially based on the PPG morphology method, and the PPG signal quality is the key. Because the sampling frequency is only 125 Hz in the MIMIC database, this could lead to the identification error of each characteristic point. Therefore, this actually limits the database from being extended to blood pressure research, especially based on PPG morphology, to achieve the dynamic monitoring of blood pressure. Moreover, many recordings have ECG, ABP , and PPG (named “PLETH”) in the MIMIC database. However, collecting satisfactory recordings with ECG, ABP, and PPG simultaneously  is not eIn addition, the ABP signal is a continuous invasive blood pressure signal collected using a catheter. Thus, there is a little difference between the dataset in this study and our past research, which collected the blood pressure using an Omron 7201 cuff BP device . Even soNote, it is assumed that the linear relationship between BP and PAT calculated from the MIMIC database are inconsistent from subject to subject. If all signals were synchronized, perhaps the correlation would be more salient. However, there is an overall trend of correlation between BP and PAT in the recordings used from the MIMIC database.The proposed method could play a significant role in the early detection of hypertension in low- and middle-income countries (LMICs). Note that an estimated 1.04 billion people had hypertension in LMICs in 2010 . Having PPG morphological features were shown to achieve better classification performance than PAT using the MIMIC database. PPG signals contain sufficient physiological information about the activity of the heart and arteries. Although they are easily affected by many factors, the 10 evaluated PPG features achieved an acceptable classification performance. This indicates that the PPG signal, which is the status response of the heart and arteries, varies according to the BP levels, such as normotension, prehypertension, and hypertension. Interestingly, adding the PAT feature to the PPG feature set improved the overall classification performance, even though not all ECG and PPG signals in the MIMIC database were synchronized. Our results show that the PAT feature and PPG features have great potential to manage BP levels.


CKD) is an independent risk factor for the development of cardiovascular disease and is characterized by reduced nitric oxide (NO) bioavailability and vascular dysfunction, typically assessed using brachial artery flow‐mediated dilation (FMD). It has been previously reported that passive leg movement (PLM)‐induced hyperemia, an assessment of lower extremity vascular function, is highly dependent on NO, but has not yet been utilized to assess vascular function in patients with CKD. The purpose of this study was to comprehensively assess vascular function in patients with CKD using PLM, in addition to the traditional FMD technique. Assessment of vascular function via PLM and FMD was performed on 12 patients  and 16 age‐matched healthy controls . Blood velocity and artery diameters during PLM and FMD were measured using duplex ultrasound of the femoral and brachial arteries, respectively. Habitual physical activity, assessed by accelerometry, was performed in a subset of each group. CKD patients had reduced peak leg blood flow (LBF)  and change in LBF from baseline to peak (∆peakLBF)  during PLM compared to CON. Additionally, PLM responses were significantly associated with kidney function and physical activity levels. As anticipated, FMD was significantly attenuated in CKD patients . In conclusion, both upper and lower extremity measures of vascular function indicate impairment in CKD patients when compared to controls. PLM appears to be a novel and feasible approach to assessing lower extremity vascular function in CKD.Chronic kidney disease ( Chronic kidney disease (CKD) is a leading public health concern and is characterized by damage to the kidneys and their inability to effectively filter blood. Reduced kidney function leads to systemic disturbances such as altered blood pressure regulation, acid‐base imbalances, altered fluid and electrolyte regulation, and a buildup of uremic waste products, all of which contribute to organ dysfunction. Moreover, kidney disease is an independent risk factor for the development of cardiovascular disease (CVD) , a traditional assessment of vascular function, as a confirmatory indicator of peripheral vascular function. We hypothesized that both PLM and FMD responses would be attenuated in CKD patients when compared to healthy adults, suggestive of reduced endothelial function in both the lower and upper extremities of this population.2  for a minimum of 3 months  participated in this study. Patients with CKD and healthy controls were primarily identified through a database of subjects previously enrolled in studies at the University of Delaware and who had provided written consent to being contacted regarding participation in research studies. Additionally, participants were recruited through advertisements located on the University of Delaware campus and in the surrounding Newark, DE region. All participants provided written informed consent prior to participation. Inclusion criteria for patients included stage 3–5 CKD as defined by an estimated glomerular filtration rate (eGFR) of ≤60 mL/min/1.73 mAll studies were performed in a temperature‐controlled environment (~23°C). Participants were instructed to report to the laboratory fasted for 12 h, without caffeine, alcohol, or exercise for 24 h prior to the visit, and to avoid taking any medications, vitamins, or supplements on the morning of the visit. Upon arrival to the laboratory, participants underwent intravenous blood sampling, urine sampling, assessment of resting vitals, and body anthropometry. Blood samples were assessed clinically for a complete blood count, complete metabolic panel, and lipid panel. Urine sampling was analyzed clinically for a complete urinalysis. Body composition was assessed using bioelectrical impedance analysis technology . Participants then rested supine for a minimum of 20 min prior to vascular function measurements.PLM was performed as previously described  and healthy control (n = 7) participants underwent habitual physical activity assessment through use of wearable accelerometers  for 7 consecutive days. Participants were fitted with accelerometers during the study visit and instructed to wear them for all waking hours over the 7‐day period. Physical activity assessments were completed within 2 weeks of participants’ vascular function assessments. Accelerometer data were analyzed using ActiLife software version 6.11.9. Wear time was validated using the Troiano algorithm  values [angle‐corrected and intensity‐weighted area under the curve (AUC)] were then automatically calculated using commercially available software. Using arterial diameter and Vmean, the blood flow in the femoral and brachial arteries were mathematically calculated as follows: blood flow = Vmeanπ (vessel diameter/2)2 × 60, where blood flow is in milliliters per minute.Measurements of arterial blood flow velocity and blood vessel diameter were performed in both the PLM and FMD assessments using a Logic e ultrasound system . This system was equipped with a linear array transducer operating at an imaging frequency of 12 MHz. Blood vessel diameters were determined at a perpendicular angle along the central axis of the scanned area. Blood velocities were obtained using the same transducers with a Doppler frequency of 5 MHz. All blood velocity measurements were obtained with the probe appropriately positioned to maintain an insonation angle of 60° or less. The sample volumes were maximized according to vessel size and were centered within the vessel based on real‐time ultrasound visualization. Femoral and brachial arterial diameters were measured and mean velocity (yi(xi+1)( − xi) + (1/2)(yi+1)(  − yi)(xi+1)( − xi)).Femoral artery blood flow was calculated through offline analysis of anterograde and retrograde blood flow velocities achieved during PLM using continuous ultrasound Doppler imaging. Baseline leg blood flow (LBF) was calculated via 12‐sec averages of anterograde and retrograde blood flow velocities, while second‐by‐second analysis of anterograde and retrograde blood flow velocities were used to determine LBF during the movement phase of PLM, using the blood flow equation previously described. Peak LBF was calculated as the maximal value achieved during the first 30 sec of PLM. The change in LBF from baseline flow to peak flow (∆peakLBF) was calculated as peak LBF – baseline LBF. Cumulative area under the curve (AUC) for values of blood flow were determined and interpreted to indicate the overall increase in blood volume achieved during movement. AUC was calculated as the sum of LBF above baseline for each second during the 60‐sec movement phase of PLM, according to the trapezoidal rule and using the equation as follows: Σ R‐wave gated images were collected from the video output of the Logiq e for offline analysis of brachial artery vasodilation using automated edge‐detection software . %FMD was quantified as the maximal percentage in brachial artery diameter change following cuff release. Shear rate was calculated as: shear rate = 8g for 10 min at 4°C. Serum from each sample was extracted and frozen at −80°C until analysis. Lipid oxidation, a marker of oxidative stress, was assessed by quantifying concentrations of plasma malondialdehyde (MDA) via an ELISA . Asymmetric dimethylarginine (ADMA), a metabolic byproduct known to inhibit NO synthesis and elevate oxidative stress, was also assessed by an ELISA .Blood samples were obtained through the antecubital vein at the time of the study visit. A portion of each sample was centrifuged at 1734 t‐tests. Additionally, relationships between participant characteristics and vascular function variables were analyzed using Pearson's r correlations. All analyses were performed using the Statistical Package for the Social Sciences . Statistical significance was set at α ≤ 0.05. All data are presented as mean ± standard error of the mean (SEM).Group differences in participant characteristics and vascular function measurements were analyzed using independent samples P < 0.05), however, body fat percentage values were not different between the groups . Although within the normal range, CKD patients reported significantly lower high‐density lipoprotein levels compared to CON (P < 0.05). Additional hematology and biochemical parameters were within expected ranges for CKD patients and healthy controls  in the CKD patients when compared to controls. Demographics for both groups were representative of the state of Delaware population .Second‐by‐second leg blood flow (LBF) responses to PLM for both CKD patients and healthy controls are illustrated in Figure r = 0.41, P < 0.05) and ∆peakLBF  achieved during PLM. Results were similar when analyses were performed on only patients with CKD, where there was a trend between eGFR and peak LBF  and a significant association between eGFR and ∆peakLBF  , therefore we did not normalize our FMD values for shear stress. Additionally, a significant relationship between kidney function as assessed by eGFR and FMD was observed for all participants for both %FMD  and ∆FMD .Brachial artery diameters at baseline were not different in patients with CKD compared to controls . Upper limb peripheral vascular function as measured by the percent change in brachial artery diameter from baseline to maximal dilation (%FMD) was significantly reduced in CKD patients  and ∆peakLBF  achieved during PLM for all participants  and ∆peakLBF   Fig. C and D. μmol/L, respectively; P < 0.05) . MDA values also tended to be higher in patients with CKD when compared to controls  . Further investigation is necessary to identify the role that ADMA might play in the blunted PLM responses identified in patients with CKD.NO production within endothelial cells relies on synthesis of L‐arginine, however, production of L‐arginine is reduced in CKD due to a loss of functional renal tissue Baylis . In addiWe acknowledge several limitations which should be considered when interpreting the results of this study. First, while brachial artery FMD was utilized to help validate our PLM findings, we recognize that a lower extremity FMD would be more appropriate for validation of localized lower‐limb vascular dysfunction. Nonetheless, brachial artery FMD still provides important information which can be interpreted in combination with the results of PLM. From a mechanistic perspective, FMD provides information about localized, large conduit artery function. Alternatively, the hyperemic response during PLM is likely influenced substantially by the responsiveness of the microvasculature, providing us with additional information on downstream function. In addition, our goal was to characterize potential limb‐specific impairments in vascular function and using measures of both upper extremity and lower extremity vascular measures allowed us to highlight this. Second, there are known limitations to using bioelectrical impedance analysis (BIA) in renal populations due to potential fluid shifts and elevated extracellular volume. These fluid disturbances that may be associated with inaccurate BIA results are often reported in dialysis patients (Kushner et al. Through our use of peripheral vascular assessments in both the upper and lower extremities, we provide evidence of widespread vascular dysfunction in patients with CKD. This is likely mediated, at least in part, by a reduction in NO bioavailability which has been previously documented in this population. This study uses PLM as a novel method in patients with CKD and provides evidence of dysfunction of the vasculature which perfuses the locomotive muscles. It is possible that the vascular dysfunction is contributing to exercise intolerance and reduced cardiorespiratory fitness levels characteristic of patients with CKD, which is associated with a higher CVD burden in this population (Howden et al. None declared.


Hypertension constitutes one of the most widespread pathological conditions in developed and developing countries. Currently, more than 1 billion people worldwide are affected by the condition, either as frank hypertension or as prehypertension, raising the risk for major long-term complications and life-threatening pathologies. The costs in terms of health care services, medications for the treatment of hypertension and its complications, and associated loss in productivity represent a major economic burden for the various countries. The necessity of developing treatments that are economically more sustainable and with better compliance has been increasing alongside the incidence of the pathology. Along these lines, attention has been paid to the implementation of affordable but nutritious diets that deliver appropriate levels of macro- and micronutrients as integral part of the diets themselves or as supplements. In particular, experimental and clinical evidence suggests that an appropriate intake of dietary magnesium can be beneficial in controlling blood pressure. Additional advantages of a more diffuse therapeutic and/or preventive utilization of magnesium supplements are the virtual absence of side-effects and their affordable costs. The present review will attempt to frame our knowledge of how magnesium exerts its beneficial effects on blood pressure maintenance, which may lead to the development of more effective treatments of hypertension and its main complications. Hypertension represents one of the most common human pathological conditions diagnosed. According to data published in 2013 by the World Health Organization, approximately 1.2 billion people worldwide are affected by the pathology .2+ on vascular tone and reactivity, and consequently on blood pressure, is in line with the clinical observation that Mg2+ infusion decreases peripheral vascular resistance and blood pressure and can induce hypotension through vasodilatation i necessary for smooth muscle cells contraction and endocrine-regulated increase in vascular tone. Alternatively, owing to the involvement of extra- and intracellular Mg2+ in modulating a variety of signaling pathways [2+ concentrations can have major opposite implications for smooth muscle cells contraction and relaxation, whereby influencing vascular tone and blood pressure.A modulating effect of Mglatation , 17. Inctriction , 19. Contriction , 21. The blocker , it is cpathways –8, it is2+ are not limited to vascular tone but influence vascular endothelial functions as well [2 [2+ and endothelin-1 has been reported [2+ can modulate vascular tone and blood pressure is through its antioxidant and anti-inflammatory effects [2+ would reduce ROS formation and antagonize the vasoconstriction effect. How exactly Mg2+ exerts this effect is not fully elucidated. Recently, Kolisek and collaborators [2+ levels is regulated through PARK7/DJ-1. This protein has antioxidant properties and tightly regulates cellular redox homeostasis. In addition, through androgen receptor activation, it would modulate the expression of SLC41A1, the main exchange mechanism responsible for Mg2+ extrusion in mammalian cells (see section on Magnesium: Cellular Homeostasis and Transport).The effects of Mg as well . The vas well [2 . Magnesi well [2 –25. In areported , further effects , 28. Pro effects . Conversborators  have hyp2+ levels and circulating renin concentrations [Epidemiologic studies indicate that the consumption of “hard water” with high levels of magnesium is cardioprotective whereas consumption of “soft water”  is associated with hypertension and overall higher incidence of cardiovascular diseases. This inverse association was first observed by Joffres et al. in their Honolulu Heart study  and substrations  and stiftrations .2+ levels and the detected concentrations of endothelin-1, PGI2, ROS, NO, or renin, or systolic and diastolic blood pressure values, however, leaves unanswered the question as to whether magnesium supplementation can be therapeutic in restoring physiological and age-appropriate pressure values. In other words, are magnesium levels prophylactic, or they can also be therapeutic? The attempts to address this question have resulted in contradicting results, and only more recent studies appear to confirm beneficial therapeutic responses following administration of Mg2+ salts. The meta-analysis conducted by Dickinson and collaborators [2+ ranging from 120 to ~970 mg/d [The presence of an inverse relationship between circulating Mgborators  assessedborators . Becauseborators . More re970 mg/d . The con970 mg/d . Similar970 mg/d . For the970 mg/d .Based on these more recent studies , 38, it Cerebral insults (strokes) represent one of the most common, and feared, complications of hypertension, as they can result in paralysis, inability to speak, and difficulty in swallowing  when they do not cause the immediate death of the patients. Strokes result from severe, not-controlled hypertension, affect ~800,000 new patients every year, and are responsible for ~140,000 deaths/year in the US alone . BecauseThe incidence of metabolic syndrome in the human population shows a trend similar to that of hypertension, affecting more than 500 million people worldwide . Haller 2+ accumulation within cells [2+ levels in the context of insulin resistance are to be expected. According to the CARDIA study, a magnesium enriched diet or magnesium supplementation appear effective in reducing the risk of metabolic syndrome and its progression towards diabetes and cardiovascular complications [As mentioned above, an increase in systolic and diastolic blood pressure values represents one of the diagnostic criteria for this condition. Hypomagnesemia  and intrin cells , 6, reduications .The term preeclampsia refers to a clinical disorder during pregnancy characterized by hypertension after 20 weeks of gestation, with marked proteinuria . The disMagnesium sulfate constitutes the treatment of choice for the prevention of preeclampsia related convulsions. The first report of its effectiveness was by Pritchard in 1955 . As its 2+ entry), vascular responsiveness to endothelin-1, renin, and ROS production, and overall vascular function, including increased nitric oxide and PGI2 production, which promote vasodilation and inhibit platelet aggregation, respectively [2+ and therefore are more exposed to the vascular irregularities and hypertone prevented by Mg2+. Some studies have indeed reported decreased serum and intracellular Mg2+ levels [Treatment of preeclampsia with magnesium sulfate has been reported to improve endothelial function. This improvement can be due to the previously described beneficial effects of magnesium on vascular tone  add to the “appeal” of a routine use of this mineral as a dietary supplement, especially in areas where health care costs, availability of more expensive drugs, and ultimately compliance of the patients to a given therapeutic protocol represent critical factors hampering the effectiveness of medical intervention.


Spirometry, whole-body plethysmography, impulse oscillometry and SF6-MBW were prospectively performed. MBW parameters reflecting global , acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from three consecutive wash-outs. LCI was calculated for the traditional 2.5% and an earlier 5% stopping point that has the potential to reduce wash-out times. 91 asthmatics (66%) and 47 non-asthmatic controls (34%) were included in final analysis. LCI2.5 and LCI5 were higher in asthmatics (p < 0.001). Likewise, Sacin and Scond were elevated (p < 0.001 and p < 0.01). Coefficient of variation was 3.4% for LCI2.5 and 3.5% for LCI5 in asthmatics. Forty-one asthmatic patients had normal spirometry. ROC analysis revealed an AUC of 0.906 for the differentiation from non-asthmatic controls exceeding diagnostic performance of individual and conventional parameters . SF6-MBW is feasible and reproducible in adult asthmatics. Ventilation heterogeneity is increased as compared to non-asthmatic controls persisting in asthmatic patients with normal spirometry. Diagnostic performance is not affected using an earlier LCI stopping point while reducing wash-out duration considerably.Ventilation heterogeneity is frequent in bronchial asthma and can be assessed using multiple breath wash-out testing (MBW). Most data is available in paediatric patients and using nitrogen as a tracer gas. We aimed to evaluate sulphur hexafluoride (SF Regrettably, up to half of the patients are poorly controlled3. Despite therapeutic advances, numbers remained fairly unaltered during the last decade.Disease control is the primary goal of asthma therapy being linked to absence of symptoms and exacerbations3 and symptoms4. Lung function is also impaired in severe disease indicated by a lower forced expiratory volume in one second (FEV1) and lower forced vital capacity (FVC)3. Both parameters represent rather central sites of obstruction. However, involvement of peripheral airways is common in the majority of asthmatic patients5. This holds true across the whole spectrum of severity6 and may be a consequence of several influencing factors. These include inflammation, wall thickening, smooth muscle hypertrophy, and mucus10. However, changes in the lung periphery are still often missed by commonly used techniques such as spirometry.The clinical and pathophysiological explanations associated with poor disease control are heterogenous. In general, more severe disease is related to more frequent exacerbations, health care contacts11, the related characteristics of disease control13 and response to acute bronchodilator therapy14. Small airway dysfunction was shown to impair quality of life in smokers15. This even applies in absence of obstructive lung disease.Impulse oscillometry (IOS) is an inexpensive non-invasive technique to measure airway resistance. It was shown to identify small airway obstruction16. Eosinophilic airway inflammation is common in asthma and associated with increased corticosteroid responsiveness. Blood eosinophilia is already a widely used parameter in patients with severe disease planned for targeted antibody therapy in personalized medicine17. Single surrogate markers such as blood eosinophil count, immunoglobulin E, and fractional exhaled nitric oxide (FeNO) have overall moderate diagnostic accuracy18. However, it was recently demonstrated that FeNO predicts response to ICS in patients with non-specific respiratory symptoms19. These may represent a distinct phenotype or stage of asthmatic disease not detected using current diagnostic work-up. Ventilation heterogeneity is also frequent in obstructive lung disease and can be assessed using multiple breath wash-out testing (MBW). The technique was shown to be feasible in adult patients with chronic obstructive pulmonary disease (COPD)22 and pulmonary hypertension23. Asthmatic patients were found to have increased ventilation heterogeneity as compared to controls25 that could be improved by therapy27 and is worse during exacerbations28. Moreover, previous investigations also suggest a link to poorer asthma control29. An association of ventilation heterogeneity with airway responsiveness was seen in asthma but not in COPD30.Other techniques aiming at an early diagnosis or phenotyping have also been investigated. Most recently, Sugawara and co-workers could identify three subtypes of cough variant asthma. They were able to demonstrate a differential therapeutic effect of inhaled corticosteroids (ICS)2) as tracer gas. The latter potentially suffers from technical issues such as N2-back diffusion, measurement inaccuracies and vulnerability to leak flows34. Mass spectrometry using open wash-in MBW sulphur hexafluoride (SF6) is considered the gold standard35. However, this has been associated with high costs and effort. Introduction of a photo-magneto-acoustic multi-gas analyser allowed the direct measurement of SF6 concentrations with high accuracy36. Recently, this led to construction of a closed circuit SF6-MBW setup considerably facilitating application and reducing costs37. We therefore set out to evaluate the feasibility of SF6-MBW in adult patients with bronchial asthma and derive potential clinical applications.Hence, MBW has great potential to improve phenotyping as well as differential diagnostics. Nevertheless, several metrological issues have still to be overcome. Most MBW data in bronchial asthma has been acquired in paediatric patients using nitrogen , compliant with the Declaration of Helsinki and registered at clinicaltrials.gov (NCT03820427). Written informed consent was obtained from all participants and/or the respective legal guardian prior to inclusion.We prospectively evaluated patients with known or first diagnosis of bronchial asthma as well as non-asthmatic controls. Classification criteria are given in detail in the 1/FVC < 70% of predicted), hyperinflation  > 40%), and/or respective flow-volume-curves or flow-pressure curves. In addition to these parameters, maximum expiratory flow (MEF) at 75, 50 and 25% of FVC as well as functional residual capacity (FRC) were determined. Reference values were derived from the revised 1993 version of the European Community for Steel and Coal equations (ECSC)38.All subjects underwent pre-bronchodilator conventional lung function testing including spirometry as well as whole-body plethysmography (MasterScreen Body) and were in stable clinical condition. Existing maintenance therapy was not withheld. Reversibility testing was then performed with doses of 40 µg ipratropium bromide and 100 µg fenoterol hydrobromide administered via soft-mist haler . Indications include presence of either obstruction . The difference was defined as ΔTLCO.37. The device consists of a 3-liter rebreathing bag filled with a mixture of room air and test gas  from an on-board gas cylinder. Functional residual capacity (FRC), acinar (Sacin) and conductive (Scond) ventilation heterogeneity were derived from three consecutive wash-outs using proprietary software provided by the manufacturer (software version 8.0 beta 1). LCI was calculated for the 2.5% (LCI2.5) and 5% (LCI5) stopping points, respectively. Subjects were breathing tidally and the test was stopped when end tidal SF6 had fallen below 1/40 of the starting concentration for three consecutive breaths. Wash-out was always performed until the later LCI2.5 stopping point. To show the time saving potential of an earlier LCI5 stopping point, wash-out times were calculated separately. Only patients with at least two technically acceptable LCI measurements based on slightly modified ATS/ERS criteria (online supplement) were included in final analysis. All subjects underwent three consecutive pre-bronchodilator MBW tests in upright position.A commercially available closed-circuit system  was used for MBW measurements as previously described in detailres, reactance area Ax), respectively. In patients undergoing reversibility testing according to above mentioned indications, three consecutive post-bronchodilator measurements were additionally performed.IOS measurements were performed during tidal breathing . We conducted three consecutive pre-bronchodilator measurements and derived parameters of airway resistance (frequency dependence D5–20) and reactance  was used for all data analysis40. A subgroup of asthma patients with normal lung function in conventional testing was prespecified and defined as having normal FEV1 (>80% of predicted) and no obstruction in spirometry (FEV1/FVC > 70%). All lung function parameters differing between asthma patients with normal lung function and non-asthmatic controls in univariate analysis (p ≤ 0.05) were included in an elastic-net generalized linear model corrected for age. L1 regularization was used for variable selection based on a k-fold cross validation approach to find optimal hyper-parameters. Diagnostic performance was evaluated using receiver operating curve (ROC) analysis and calculation of area under the curve (AUC).Mean values are given ± standard deviation (SD) unless stated otherwise. Differences between groups were assessed by Student’s t-test for continuous variables or Chi-squared test for categorical variables. The coefficient of variation (CV) was calculated as SD/mean from two or three valid MBW measurements, respectively. Sample size calculation was based on previously published data1 and Ax, 21% for RV and 29% for D5–20, respectively.Final analysis was performed in 91 patients with bronchial asthma (66%) and 47 non-asthmatic controls (34%) with valid data sets. Only two valid MBW measurements could be obtained in 11 non-asthmatic controls (23%) and 32 asthmatics . Five asthmatic patients (3.4%) had to be excluded due to the prespecified acceptability criteria for MBW testing whereas in another 4 patients (2.7%) only one valid MBW measurement could be obtained. Baseline characteristics are given in Table pleth) and ΔTLCO  and 3.2% for LCI5 (p = 0.37) in non-asthmatic controls. Parameters of local ventilation heterogeneity showed considerably larger variation. CVs were 42% for Sacin and 82% for Scond in asthma as compared to 39% (p = 0.74) and 127% (p = 0.17) in non-asthmatic controls, respectively. ΔFRCpleth-MBW was 0.04 ± 0.52 L in non-asthmatic controls as compared to 0.61 ± 0.75 L in asthmatic patients .All lung function parameters differed between asthmatic patients and non-asthmatic controls except for total lung capacity (TLC), functional residual capacity as determined by whole-body plethysmography , LCI2.5, FRCMBW, area under the reactance curve (Ax), resonance frequency (Fres), TLCO/VA and ΔFRCpleth-MBW remained as predictors for asthma after adjustment for age and smoking status. ROC analysis revealed an AUC of 0.906 for the differentiation from non-asthmatic controls that exceeded diagnostic performance of the individual parameters  and LCI2.5  when used as distinct parameters. Sacin and Scond had lower diagnostic accuracy (AUC = 0.615 and 0.593).In the regularized generalized linear model used for variable selection, FEVers Fig. . Diagnosers Fig. . A modelers Fig. . No diff2.5. When using the earlier LCI5 stopping point, wash-out times were significantly reduced by 24 s (p<<0.001) and 28 s (p<<0.001), respectively.Mean total wash-out time was 102 ± 37 s in non-asthmatic controls and 102 ± 39 s in patients with asthma for LCIWe were able to demonstrate that MBW measurements are feasible in patients with bronchial asthma. Global and local ventilation heterogeneity is increased as compared to non-asthmatic controls. These differences persist when comparing asthmatic patients with normal conventional lung function parameters to non-asthmatic controls. MBW parameters therefore may contain additional information that can be used to identify asthmatic patients with abnormal ventilation missed by current tests. Including parameters of ventilation heterogeneity and small airway disease significantly improves diagnostic accuracy of our prediction model. Using an earlier stopping point does not affect diagnostic performance of LCI while considerably reducing wash-out duration.1. We also found significant differences between adults with asthma and non-asthmatic in acinar but not conductive ventilation heterogeneity. In contrast, parameters of regional inhomogeneity did not differ significantly between asthmatic and healthy children25. LCI was moderately, but statistically significantly elevated in a smaller cohort of asthmatic children while effect of acute bronchodilator therapy with salbutamol was detected. However, a very small subgroup of four patients with large improvement of airway obstruction were identified to show a decreased ventilation heterogeneity41. MBW indices were found not to be ideal for assessing bronchodilator reversibility in younger children with stable wheeze42. Although measurements using our setup themselves were demonstrated to be rather time effective, an additional interval should have been introduced after the forced breathing manoeuvres required during spirometry and plethysmography. We therefore decided to focus on the pre-bronchodilator in our investigation acknowledging the potential value of additional post-bronchodilator measurements to be further evaluated in adults. Abnormalities identified visually using magnetic resonance imaging (MRI) were shown to significantly correspond to poor asthma control43. In adult respiratory care, application of MBW comprises numerous potential scenarios. Kjellberg and co-workers were able to identify a “small airway subtype” in asthma patients. It was linked to reduced FEV1, smoking and blood eosinophilia and may potentially improve phenotyping. IOS and N2-MBW were found to give complementary information about peripheral airway dysfunction. These are attributable to different domains of small airway disease addressed by either test as recently demonstrated5. Altered values of D5–20 and LCI were found in 23% and 45% of asthmatic patients with normal FEV144. This favourably corresponds to our findings at hand in asthma and also with previous reports in COPD using IOS and MBW, respectively. Small airway disease was detected with IOS even in early stages and linked to typical symptoms such as dyspnoea45. In early stages, IOS parameters outperformed conventional spirometry when differentiating heavy smokers with normal lung function as well as early-stage COPD46. Likewise, LCI was shown to be elevated in COPD in general and increases with GOLD stage20. Even in absence of spirometric obstruction, LCI was already pathologically altered22. Differences in FRC as measured by plethysmography and MBW were described previously in patients with obstructive lung disease48. When using an exogenous tracer gas such as SF6, these can be easily explained as only ventilated areas will contribute to FRC determination. In contrast, all compressible gas volumes are measured in body plethysmography49. Correspondingly, we found an increased ΔFRCpleth-MBW in asthmatic patients even in absence of spirometric changes. Inclusion of this MBW parameter increases diagnostic accuracy already considerably. A model based on all objectively selected parameters including MBW and IOS further improves diagnostic accuracy. This outperforms both conventional tests and as well as distinct parameters from novel tests alone. Therefore, ΔFRCpleth-MBW itself may yield diagnostic information due to tracer gas not reaching non-ventilated areas when obstruction is present. Taken together, evidence exists that novel parameters of small airway disease improve diagnostic accuracy and have therapeutic implications.Our findings are in accordance with previously published data in asthmatic children. Residual global ventilation heterogeneity could be identified despite well-controlled disease and normal FEV39 and preschool children50. Intra-test repeatability is not affected while total test time is reduced significantly. These findings could be replicated in pre-school children suffering from cystic fibrosis using an N2-based setup51 with all three studies opposing current general recommendations35. Although we were able to acquire at least two successful runs in at least 90% of asthmatic patients and all non-asthmatic controls, using an earlier stopping point may eventually contribute to even higher success rates. In accordance with our findings, LCI5 was shown to be reliable in childhood and adolescence52 with equal diagnostic performance to the traditional stopping point53. However, this approach requires separate normal values that may be overcome using predictive modelling. An algorithm was demonstrated to reliably extrapolate LCI2.5 from earlier stopping points with reasonable accuracy. Additionally, this has the potential to further reduce wash-out time by up to 41% when using a 10% cut-off for prediction54. A similar 43% reduction in wash-out time can be potentially achieved when estimating LCI using Bayesian modelling55. Meaningful differences in MBW outcome measures depending on the test gases have been described56. In general, nitrogen-based systems reproducibly yield higher absolute LCI readings than SF6-MBW58. N2 has a higher diffusion rate and smaller molar mass as compared to SF6 resulting in a more proximal diffusion-convection front35. While SF6 may not reach very poorly ventilated regions during wash-in, endogenous N2 may prolong wash-out from these regions resulting in higher LCI values. Hence, attention has to be paid to test gas choice when interpreting MBW results59. Despite the above-mentioned potential advantages of SF6 -MBW, it should be noted that tracer gas still is associated with considerable costs. The system we used is commercially available and has regulatory approval (FDA and CE mark). Nevertheless, SF6 is not allowed for medical use in several countries. While no solid reference values are available for closed-circuit SF6-MBW in adults to date, age was identified the major factor influencing factor for nitrogen-based LCI measurements in adults and therefore prespecified as a control variable in our model60. The influence of height seems to be negligible in patients older than six years while a nonlinear decrease pattern was described when using SF6 as tracer gas61. Single breath N2-wash-out can be seen as a potentially time efficient alternative to MBW testing. However, sensitivity was found to be lower in adult asthmatic patients as compared to MBW and may therefore be a marker of more severe disease62. Within-test repeatability found in our study is comparable to previously published data in stable adults showing CVs between 2.5 and 4.5% for LCI using SF6 as tracer gas64. When paediatric populations are investigated, CV tends to be slightly higher ranging from 3.2 to 8.5%65. Likewise, considerably larger CVs have been shown for Scond and Sacin with more variability in non-asthmatic controls than in COPD ranging from 17 to 73%66. This corresponds favourably to our findings. We found similar CV ranges in both groups except for Scond in non-asthmatic controls showing a higher repeatability of 127%. We can reasonably exclude poor quality control as all lung function tests were independently assessed by two experienced investigators. Rather this may be attributable to the overall lower values of Scond making it more vulnerable to variation in non-asthmatic controls. The fact that variation was largest in non-asthmatic controls lets us conclude that this does not negatively affect diagnostic performance in disease. In summary, our findings add well to previous reports in paediatric cohorts and different test gases and may further facilitate the routine use of MBW. Results obtained with different tracer gases should not be compared directly.In clinical and scientific routine, several metrological aspects should be considered. Recently, it was demonstrated that duplicate LCI measurements are sufficient in adults1. Although this clinical information would have been valuable, we do not consider this a major drawback of our study as we primarily set out for feasibility evaluation. The same holds for biomarkers such as laboratory parameters or FeNO. The latter did not show differences between asthmatic children and controls whereas LCI was significantly elevated25. Second, our cross-sectional design does not allow to provide estimates of the minimal clinical important difference for SF6-MBW. Due to the high intra-test repeatability, it should be possible to detect small longitudinal changes while further research is required. Nevertheless, our findings contribute to closing another knowledge gap in adult MBW testing. Personalized medicine cannot be reduced to a single biomarker or functional test. Assessment of ventilation heterogeneity using MBW may be one brick in the wall for improving phenotyping of asthmatic patients that warrants further investigation. Third, reference values were derived from the revised 1993 version of European Community for Steel and Coal (ECSC) equations38. This was mainly as the current Global Lung Function Initiative (GLI) reference values are restricted to spirometry. Requiring separate reference calculations for whole-body plethysmography, we aimed to avoid any mixing of the two statistically differing concepts.When interpreting our results, several points should be taken into consideration. First, we did not obtain structured information on asthma control, symptoms or medication. Moreover, diagnosis relied on a respiratory specialist evaluation according to Global Initiative for Asthma (GINA) criteria6-MBW measurements are feasible in adult patients with bronchial asthma. Indices of global (LCI) and local ventilation heterogeneity  both are elevated as compared to non-asthmatic controls. Differences persist in asthmatic patients without obstruction in spirometry. MBW parameters therefore may contain additional information that can be used to identify asthmatic patients with abnormal ventilation missed by current tests. For LCI as the most important MBW outcome measure, using an earlier stopping point does not affect diagnostic performance while considerably reducing the duration of the wash-out procedure. Reproducibility was high for both LCI stopping points. Sacin and Scond both showed larger variability.SFSupplementary Information.


The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential. Since the amount of optical absorption or reflection depends on the amount of blood that is present in the optical path, the PPG signal is responsive to changes in the volume of the blood, rather than the pressure of the blood vessels. In other words, PPG detects the change of blood volume by the photoelectric technique, whether transmissive or reflective, to record the volume of blood in the sensor coverage area to form a PPG signal. Indeed, the sensor coverage area includes both veins and arteries, and numerous capillaries. Thus, the PPG signal is a complex mixture of the blood flow in veins and arteries of the cardiovascular circulatory system. A raw PPG signal generally includes pulsatile and non-pulsatile blood volume.16 In clinical practice, PPG is routinely used to monitor cardiac-induced blood volume changes in microvascular beds at peripheral body sites, such as the finger, forehead, earlobe, and toe.17 Since the maximum pulsatile component of reflected light occurs approximately in the range between 510 and 590 nm,18 the green (565 nm) or yellow (590 nm) light is generally used for reflective PPG sensors.19 However, the red (680 nm) or near-infrared (810 nm) light is generally used for transmissive PPG devices, with the infrared light having the deepest penetration.21 Given that the optical absorption of hemoglobin is a function of oxygenation and optical wavelength, the use of PPG at multiple wavelengths is also routinely used in pulse oximetry.The pulsatile component of a PPG signal is related to changes in blood volume inside the arteries and is synchronous with the heartbeat, whereas the non-pulsating component is a function of the basic blood volume, respiration, the sympathetic nervous system, and thermoregulation.22 while green light can penetrate less than 1 mm22 into tissue. Hence, the detection of blood pressure, atherosclerosis, blood sugar, and other physiological parameters uses the infrared light (deeper light penetration compared with the green light) to obtain PPG signals.Green and red infrared light are often used to obtain PPG signals because of the difference in the wavelength; each light penetrates human tissue differently. Infrared light has the deepest penetration ability, and it can reflect the blood pulse from deep tissue. Therefore, it is used more. Red and infrared light can penetrate about 2.5 mm,23 that can be applied to various aspects of cardiovascular monitoring, including the detection of blood oxygen saturation, heart rate, BP, cardiac output, respiration, arterial aging, endothelial function, microvascular blood flow, and autonomic function.8 Several different types of PPG waveforms have been observed and found to correlate with age and cardiovascular pathology.25 Since the volume and distension of the arteries can be related to the pressure in the arteries, the PPG signal produces pulse waveforms that are very similar to pressure waveforms generated by tonometry; however, PPG offers the added advantage that it can be measured continuously using miniature, inexpensive, and wearable optical electronics.PPG technology thus represents a convenient and low-cost technology26 found a single feature that is correlated with BP, called the slope transit time (STT) that requires only a single PPG signal. The STT reflects the steep trend of rising pulse wave. It is a slope parameter calculated from the foot to peak of the systolic waveform, as shown in Fig. 27 found that the bd area, shown in Fig. In 2016, Addison28 however, these propagation times are defined differently. As shown in Fig. 29The pulse arrival time (PAT) and pulse transition time (PTT) parameters are often used interchangeably;30 For measurement of PAT, the commonly utilized fiducial points are the R-wave of the ECG and systolic peak of the PPG waveform, which is measured at a distal site, such as the fingertip. For measurement of PTT, generally two arterial sites are used, such as the PPG proximal systolic peak waveform as measured from the upper arm, and the distal systolic peak of the PPG signal as measured from the fingertip.28 Interestingly, using different PPG fiducial points differentially impacts the accuracy of BP calculations.27Although within the literature, there exists some inconsistencies with regards to the fiducial points that are used to define the start and end points for the PTT and PAT intervals, we can also find some general conventions.30 determined the PTT using PPG signals measured from the toe and finger simultaneously. Note that the PTT duration was calculated from a distal site (figure) to a distal site (toe). They hypothesized that PTT measurement using PPG signals from different sites (finger and toe) would be more accurate than using PPG in combination with ECG signals. They reported that the PTT measured from the PPGtoe and PPGfinger, as shown in Fig. toe; however, the PTT calculated using ECG and PPGtoe was found to have a better correlation with SBP than the PTT calculated from PPGtoe and PPGfinger.In order to avoid the variable PEP time in the estimation of blood pressure, some recent attempts have been made to determine PTT directly from multiple PPG signals. For example, Nitzan et al.31 Each BCG beat consists mainly of five waves: H, I, J, K, and L waves,32 which were clearly identified to represent heart activity information, as shown in Fig. 33 found that the time difference (TD) between predetermined BCG indicia  and predetermined PPG indicia  correlated with systolic and diastolic blood pressure. Moreover, they used the TD to predict the subject’s blood pressure.Ballistocardiography (BCG) is a long-established technique for evaluating the cardiovascular health of patients that records the vibrations produced in the body during the cardiac cycle. Unlike the measurement of ECG signals, BCG does not require the skin contact and can easily estimate heartbeat information from patients, which makes it suitable for long-term monitoring and measurement.34 In addition to the two main heart sounds, designated as S1 and S2, the waveform of the PCG signal also contains useful diagnostic information that can reveal abnormalities in the movement of the heart wall, closure of the valves, or leakage of blood flow.35 For the purpose of estimating BP, the PCG signal is often used together with the PPG signal from a distal arterial site to calculate another propagation time known as the vascular transit time (VTT).36 The VTT is derived from the first heart sound (known as S1) of a PCG and the systolic peak of the corresponding PPG, as shown in Fig. Heart sounds associated with valve movement is recorded using the phonocardiograph (PCG) instrument. Collected sounds can provide information about the mechanical cardiac function and blood flow.37 At present, commercial mobile and wearable devices can measure a variety of physiological parameters, including heart rate, body temperature, skin conductance, and physical activity. Adding the estimation of SBP and DBP is logical and expected.38 A list of wearable BP estimation devices, as well as descriptions of the devices and their functions, is presented in Table With the goal of achieving long-term continuous BP estimation, many companies and academic research groups have explored various ways to measure BP with mobile phones or wearable sensors. Many challenges exist in achieving this goal; however, the clinical benefits of such technology still require further development.40 wristband,42 armband,43 chest belt44, and vest.45 Bluetooth and ZigBee are the most utilized transmission mode.As shown in Table Before the emergence of smartphones, a variety of wearable devices were developed that used PPG sensors and PPG signal analysis to estimate blood pressure. One important consideration is that the PPG signal amplitude critically depends on the applied external pressure as well as the hydrostatic pressure, which is determined by the relative height of the PPG measurement site with respect to the heart. Several research groups have explored this relationship in order to estimate BP using a method analogous to the oscillometric method used in BP cuffs.46 to perform an oscillometric measurement using PPG by varying the hydrostatic pressure. This method was primarily demonstrated using a device in the form of a wearable ring, which measured PPG on the user’s finger, and contained a 3-axis accelerometer used to measure the orientation of the hand and arm. By raising and lowering the arm, it was possible to vary the hydrostatic pressure over enough range to be able to vary the PPG amplitude and thus estimate the mean arterial pressure. While such methods may be difficult to implement in practice, this research demonstrated that it is possible to create oscillometric methods to measure BP which do not require a cuff or significant electric power.Instead of varying the externally applied pressure to perform an oscillometric BP measurement, a clever method was demonstrated a few years earlier in 2007 by Shaltis et al.47 Since 2011, several companies, such as Azumio Inc.  began to release mobile apps that make use of this technique to measure heart rate and heart rate variability, with more recent efforts using the PPG modality to target-specific heart diseases, such as atrial fibrillation.48 Using the PPG signal derived from the phone camera, oscillometric methods have also been attempted. However, when a pressure cuff is not used, the main technical challenge is determining the exact pressure applied by a finger on the phone camera.Shortly after the emergence of the conventional smartphone in 2007 (iPhone) and 2008 (Android), it was soon discovered that the smartphone camera could be used as a photoplethysmographic sensor to obtain a PPG waveform.http://www.icarefit.com/), makes use of the smartphone camera to acquire a finger PPG signal and estimate the BP. Although iCareFit does not disclose its exact method for deriving BP from the PPG signal, the mobile app instructions require the user to press one finger on the mobile phone camera, and simultaneously press the opposing thumb on the front touch screen of the phone, which balances the force of the finger pressing against the back-side camera. Using the touch screen sensor to estimate the finger pressure, it is possible to implement the oscillometric measurement method for BP.The iCare Health Monitor, released in 2016 by iCareFit Studio .A true oscillometric method was recently developed by Chandrasekhar,29 demonstrated a wearable device to measure PTT, which consisted of a PPG wristband combined with a PPG ring. By measuring the PTT from the two sites on the hand, it was possible to estimate blood pressure using an adaptive algorithm that makes use of the Moens–Kortweg equation and a nonlinear mechanical model of the arterial walls, as follows:K1 and K2 are calibration constants.Wearable and smartphone devices have also used wave propagation methods to estimate BP using PPG signals. For example, in 2007, McCombie et al.The estimated BP values were compared with the continuous BP values as measured by a commercial Finapres device from the same hand, and the results had good qualitative although not quantitative agreement.49 demonstrated the measurement of BP using the PPG signal collected using the phone camera, in conjunction with the heart sound waveform collected using the mobile phone microphone. Using these two measurements, it was possible to calculate the VTT , and subsequently use this calculation to estimate BP. The following year, AuraLife  released the first commercial mobile app, named Instant Blood Pressure (IBP), which used this method to measure BP. While this approach seems promising, the specific implementation in the IBP app demonstrated poor performance when clinically tested.50 While Aura Labs never published any validation study for their app, an independent investigation was performed by Plante et al.50 The findings of this clinical study showed unreliable BP estimations and weak correlation between the estimated BP using the app and the cuff-based BP readings. Significant concerns were raised from the clinical community that individuals may use these apps to assess their BP, and the app was subsequently removed from the Apple App store.In 2013, Chandrasekaran et al.51  released a custom case and mobile app for the iPhone, called Wello, which implemented estimation of BP using the wave propagation method. The raw signals were provided by electronic sensors embedded in the custom phone case, which included an ECG sensor as well as two PPG sensors. By holding the phone with both hands, the mobile app was able to capture two measurements of PAT and use that to estimate BP. Up to our knowledge, there is no clinical study validates the use of this product.Also in 2014, Azoi Inc.52 demonstrated a similar mobile app which uses the PTT measurement derived from the seismic waveform of the user’s heart as measured by the phone’s internal accelerometer. Blood pressure estimates were computed using the familiar Moens–Kortweg simplification using PTT, as described in Eq. , makes use of three miniature PPG sensors that sample the pulse waveform at three different locations: the angular artery near the bridge of the nose, the temporal artery on the side of the head, and the occipital artery behind the ear. Relative blood pressure was calculated by measurement of PTT by measuring the pulse time from the angular artery to one of the other two locations, with the temporal artery yielding slightly better results. PPG data were collected from only four participants over a period of 5 days (12 + h per day) along with three blood pressure measurements per hour using an oscillometric BP cuff device, and the data were fit to the linear BP estimation equation using PTT  PPG signal, the ChebyshevII filter is more efficient for making the dicrotic notch more salient, compared with the Butterworth filter, as shown in Fig. It is worth noting that designing an efficient filter plays an important role in processing PPG signals. In addition, filtering algorithms can also produce time-shifts in the position of time series features. In much of the literature, filtering of the PPG time series data is common practice; however, the choice of filter is not often discussed. Butterworth filters are particularly common for filtering PPG data.58 demonstrated how PPG parameters could be fit to a parametric Windkessel model to estimate blood pressure. The Windkessel model describes the arterial blood flow as an electric circuit that contains two elements: resistance and capacity.59 Choudhury et al. used linear regression to derive the resistive and capacitive elements for a 2-element Windkessel model, and was able to achieve moderate agreement, using PPG and BP data collected from resting hospital patients. Banerjee et al.60 subsequently demonstrated the use of multi-layer neural networks to derive the resistive and capacitive element values for the 2-element Windkessel model using the PPG data collected from a smartphone. The authors claimed the resulting mobile app, named HeartSense, produced results within ±10% of sphygmomanometer readings; however, due to insufficient clinical validation, as of 2018, the mobile app is no longer available in the Google Play store, and no additional information is available.New types of data analysis methods have also emerged over the past decade, and this has also been applied to the problem of BP estimation derived from the raw PPG signals. In 2014, Choudhury et al.61 By including PPG features (not explicitly stated the features used), they were able to predict BP with an error less than 8 mmHg. We note that the published study was conducted with only three subjects, and larger published studies are not readily available.With the increasing popularity of machine-learning methods, other research has continued to explore methods of estimating blood pressure using features of the PPG signal alone without the aid of any additional sensors. In 2016, Sola et al. demonstrated the estimation of BP from the finger PPG signal using standard machine-learning and linear regression.41 This neural net approach was able to achieve an error of ±9.82 mmHg and ±3.88 mmHg for SBP and DBP, respectively, with Pearson correlation coefficients of 0.68 and 0.74, respectively.Following a similar approach, in 2018, Radha et al. demonstrated the use of machine-learning neural nets to estimate BP using a variety of PPG signal features, including time-domain, frequency-domain, and entropy-based features.4 which used the GoogLeNet pretrained convolutional neural network, provided similar or slightly better accuracy for hypertension stratification, compared with the traditional feature extraction approach over the same data set. Thus, the use of deep learning in hypertension assessment using PPG signals is promising and may evolve over time. However, it does not yet provide clinical insights as can be seen with the traditional feature extraction followed by classical machine-learning algorithms.A recent PPG-based machine-learning study,62 Samsung has not disclosed the exact method used to calculate BP; however, several of the methods mentioned in this review are possible candidates. A recent review63 of the BP measurement app for the Samsung S9 and S9 + demonstrated good agreement with a commercial BP cuff device when the user is at rest. However, some discrepancy in the readings appeared when measured shortly after exercise.The use of PPG to estimate blood pressure has been demonstrated primarily by research groups or small start-up companies. However, these methods are gaining wider acceptance and mobile phone manufacturers, such as Samsung, have begun to integrate BP measurement capability into the stock mobile phone software. In 2018, in partnership with UC San Francisco, Samsung released “My BP Lab”, which is a mobile app that measures changes in blood pressure using the finger PPG signal collected from the mobile phone camera.Based on the reported sample sizes and limited validation attempts seen in publications listed in Table 64 and it contains PPG signals collected along with BP readings from patients admitted to the Guilin People’s Hospital in Guilin, China. It includes data collected from 219 subjects, aged 21–86 years, with a median age of 58 years, covering several diseases including hypertension, diabetes, cerebral infarction, and insufficient brain blood supply. The second database is the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database,65 which contains thousands of recordings of multiple physiologic signals such as arterial blood pressure (ABP), PPG signals, and respiration, with additional waveforms simultaneously collected.To the best of our knowledge, there are two main publicly available databases. The first database, called PPG-BP Database, was recently published68 have used the MMIC database to assess pulse transit times, it should be noted that the PPG and ECG signal data are not perfectly synchronized. Although multi-parameter hospital monitors collect various physiological parameters “simultaneously”, the electronic hardware and software filtering on the measured signals produce additional time delays (up to 500 milliseconds), which can have a significant effect on the calculation of PAT and other propagation delays.69 Therefore, it is not recommended to use the MIMIC database to calculate PTT or PAT.While several papers23 As demonstrated by recent research, analysis of the PPG waveform can assist in understanding the underlying status of peripheral blood vessels under the influence of blood propulsion and the blood recycling process. For this reason, many parameters extracted directly from waveforms can be used to accurately evaluate vascular status. The parameters mentioned often in the literature include pulse width, augmentation index, large artery stiffness index, crest time, etc. As described previously, the waveform’s shape is influenced by the blood circulatory system. Therefore, based on the waveform propagation and waveform morphology theories, two different research directions have been formed and developed to continuously estimate BP through cuffless methods.The present review aimed to provide an overview of the current methods of measuring BP that are cuffless and support continuous BP estimation. In the body of literature related to pulse morphology and pulse wave analysis, there is increasing interest in using the PPG waveform to understand the formation of blood pressure. It is generally accepted that the physiological status of peripheral blood vessels, such as aging, stiffness, and compliance, can be partially expressed in terms of peripheral signal waveforms.71 from PPG waveforms, and using these features to create new machine-learning models, will continue to be an obvious approach to the problem of BP estimation.Based on the current literature, there is clear evidence that the fluctuations in BP are reflected in the PPG signals. Given that the exact relationships between PPG waveforms and BP are not yet clear, BP estimates are difficult to achieve by the simple fitting of models or equations. Fortunately, continued advances in machine-learning technology will continue to provide new insights into the exact relationship between the BP and PPG waveforms. Many features, such as amplitude, time span, area, ratio, spectral information, and spectral entropy information, continue to be explored. Certainly, extracting more featuresIn the body of literature relating to wave propagation theory, physical models can now explain the process of blood pressure propagation through the human body, and this process is presented and widely recognized in the form of pressure waves and ECG, BCG, PCG, and PPG signals. However, several aspects require special attention in determining the merits of BP estimation results, such as the transmission distance, the starting and ending points of the transmission, PTT, etc. Numerous problems still exist with respect to accurate positioning of sensors, calculation of propagation distances, and the impact of the variable PEP time on the pulse wave velocity timing. While there exists a definite formulaic relationship between BP and PTT, as described above, these practical challenges have resulted in a wide variation in systolic blood pressure estimates across different studies.Naturally, researchers in the field have conducted a series of studies in order to address these issues. To avoid the problems associated with the PEP, different cardiac signals have been utilized in studies to obtain more accurate heart valve opening times. Many studies have shown that PCG and BCG signals could be more accurate in extracting the heart valve opening time than ECG signals. Some researchers have also conducted comparative studies on the performance of BP estimation using different ending points in the PPG signal. The literature has also evidenced the application of distinct linear and nonlinear mathematical formulas to calculate BP and to obtain better approximations. The need for individual short-term or long-term calibration has also been confirmed by a body of literature as a means of improving the accuracy of BP estimation. However, various limitations in accurate BP measurement, such as repeated calibration, inconvenient measurement locations, etc., often greatly limit the practical application of theory. In addition, based on the analysis of waveform propagation theory, most of the BP estimations are focused on SBP, and DBP has no definite theoretical relationship with any parameter derived from this theory. For this reason, many experimental studies have experienced difficulties in obtaining satisfactory DBP prediction results.65 and included PPG and ECG signals, as well as the ABP which is considered the gold standard. It can be seen that there is an inverse relationship between the blood pressure and the PAT duration, the higher the blood pressure, the smaller the PAT duration, and vice versa. However, more precise correlation is not possible given that the data in MIMIC- II do not contain proper time synchronization across signals, and is thus not designed for PAT/PTT analysis.Figure Based on this literature review, it appears that Pearson’s correlation coefficient has been mostly adopted by researchers and is used similarly here as well. There have been two approaches for correlating PAT with blood pressure: the first approach is to use the PAT as a single feature or combined with other physiological features to classify normotensive and hypertensive subjects. The second approach is to use the PAT duration itself or combined with other features to estimate (or predict) the actual blood pressure.72 the ME difference between the estimated BP values and the mercury standard must be ≤5 mmHg or the STD must be ≤ 8 mmHg.Regarding the second approach, the calculation of the correlation coefficient is not enough to validate the estimated BP values. The mean error (ME) and standard deviation (STD) between predicted BP and reference BP are also needed. The estimation of blood pressure values has been standardized by the AAMI. To meet the AAMI criteria,73 A least square algorithm is usually implemented to determine the unknown coefficients β0 and β1, which is considered as the calibration process. If more than one feature to be used in the estimation of BP using linear regression than more coefficients need to be considered; however, there are other mathematical models can be used, as shown in Table At present, some studies have shown good predictive performance for continuous BP estimation techniques based on machine-learning technology, as shown in Table entclass1pt{minimaIt is clear from Table 13 and the World Health Organization is now leading the Global Pulse Oximetry Project, which aims to make the pulse oximeter component available in every operating room in the world.74 This widespread use of pulse oximetry represents another possible opportunity to employ the ubiquitous pulse oximeter as a device for estimating BP.While BP estimation from the PPG signal is relatively new, the use of PPG in the clinical setting is widespread. In the developed world, the use of the pulse oximeter for anesthesia monitoring during surgery has been the standard of care for more than 20 years,Although the emerging algorithms for BP estimation and health monitoring are increasingly complex, the processing power of wearable and mobile technologies, as well as cloud computing have also increased dramatically over the past decade. The ability of wearable health devices to monitor BP and cardiovascular health status is becoming increasingly feasible.At present, a series of health parameters, such as body temperature, heart rate, ECG, and PPG, can be monitored by commercial wearable products. Soon, wearable BP products will also likely be an acceptable form of health monitoring utilized by most people, and their precision, wear, ease of use, and other characteristics will continue to improve significantly.77 In the future, the increasing adoption of wearable PPG devices will also likely play an essential role in patient care.The use of PPG signals as a replacement for cuff-based or invasive BP measurement is relatively new, and most PPG analysis methods are still relatively simple. A variety of estimation methods continue to be explored, including those that utilize PPG signals exclusively, and those which combine PPG with other physiological signals. As can be seen from the tables, the estimation of BP from PTT continues to be perhaps the most common approach, but generally only with very small sample sizes. With the advent of wearable devices, the real-time analysis of PPG waveforms also enables the possibility to monitor BP on a nearly continuous basis.Whenever possible, use additional physiological cardiovascular measurements  to PPG, in order to increase accuracy;When multiple cardiovascular signals are used, care must be taken to ensure time synchronization across all sensors;Standard and consistent use of use measurement terminology needs to be encouraged in order to avoid confusing or misleading research results ;Standards such as AAMI need be adopted for use in estimating BP using PAT, PTT, or other propagation times;For the purpose of validating and labeling data, a proper FDA-approved BP measurement device needs to be used ;n > 100 subjects, with a mixture of both normotensive and hypertensive subjects;Additional research is required using sample sizes of Widespread use of BP estimation algorithms will also require additional studies that include socioeconomic diversity ;The robustness of BP estimation algorithms for ambulatory devices needs to be tested under a variety of movement conditions, not just sedentary;78 to examine correlation with BP;The research community would benefit if published studies also included additional clinical PPG features64 and MIMIC-II65 that contain time-synchronized physiological signals for the purpose of calculating PTT and PAT; andThere is a need for more publicly available physiological databases, in addition to PPG-BPIncreased collaboration between engineering and clinical researchers would help enrich the validation and protocol process, in addition to helping with access to patients, and improving the quality and availability of research data.Our recommendations, based on a review of BP estimation using PPG signals, are as follows:Given the widespread use of blood pressure in medicine and health care, we have provided a review of photoplethysmography as a tool for cuffless estimation of BP, examining the use of PPG independently, as well as in combination with other cardiovascular measurements. The increasing demand for the PPG-based wearable devices also provides an interesting direction for continuous ambulatory measurement of BP.Currently, most PPG-based BP estimation is mainly divided into two research directions based on waveform morphology theory and waveform propagation theory. Research based on the waveform morphology theory, also known as pulse wave analysis, has typically relied on parameters extracted from a single PPG waveform. Some of these explorations have led to excellent research findings that have enabled the implementation of wearable designs, but many problems are present, such as the need for a large amount of data and a certain period of pre- training and calibration. Research based on the waveform propagation theory, utilizing the pulse propagation delays between multiple pulse signals, has also shown promising results. Even so, significant problems remain, relating to the acquisition of multiple signals, the location of sensors on the body, and individual factors, which can directly affect the accuracy of BP estimations. The existence of such practical problems and the difficulty in overcoming them has made the applications of this theory difficult.The development of noninvasive, cuffless, and continuous BP estimation is a promising yet challenging field. That the trend is toward wearable BP technology is evident. In future studies, a more comprehensive understanding of PPG information can hopefully enable researchers to solve the abovementioned problems and to successfully develop technologies for BP estimation using mobile and wearable devices.


Although left ventricular hypertrophy (LVH) has been established as a predictor of cardiovascular events in chronic kidney disease (CKD), the relationship between the prevalence of LVH and CKD stage during the pre-dialysis period has not been fully examined.We measured left ventricular mass index (LVMI) in a cross-sectional cohort of participants in the Chronic Kidney Disease Japan Cohort (CKD-JAC) study to identify factors that are associated with increased LVMI in patients with stage 3–5 CKD.P = 0.0004], body mass index , systolic blood pressure , urinary albumin , and serum total cholesterol level .We analyzed the baseline characteristics in 1088 participants . Diabetes mellitus was the underlying disease in 41.7% of the patients, and mean age was 61.8 ± 11.1 years. LVH was detected in 23.4% of the patients at baseline. By multivariate logistic analysis, independent risk factors for LVH were past history of cardiovascular disease [odds ratio (OR) 2.364; 95% confidence interval ([CI) 1.463–3.822; The cross-sectional baseline data from the CKD-JAC study shed light on the association between LVH and risk factors in patients with decreased renal function. Further longitudinal analyses of the CKD-JAC cohort are needed to evaluate the prognostic value of LVH in CKD patients. Chronic kidney disease (CKD) is the leading risk factor for cardiovascular disease (CVD), a great threat to health and an economic burden . In Japa2. A cohort of CKD patients closely observed by the physicians is required to accurately analyze renal and CV events. However, few studies have been conducted on the prevalence of LVH in a pre-dialysis population [Renal dysfunction and albuminuria in CKD patients have been established as a risk factor for cardiovascular (CV) events independent of conventional CV risk factors –9. Populpulation –13.The aim of the present study was to clarify whether there is a close correlation between the prevalence of LVH and the stage of CKD classified according to eGFR and to identify factors related to LVH among the participants in the Chronic Kidney Disease Japan Cohort (CKD-JAC) .2. eGFR was calculated using a modified three-variable equation for eGFR in Japanese patients [− 0.287 × sCr− 1.094 , where sCr = serum creatinine.Baseline characteristics of CKD-JAC are described elsewhere . The folpatients : eGFR = All patients were classified on the basis of CKD stage as described in our previous paper . The folInformation on past medical history, including hypertension, acute myocardial infarction, angina pectoris, congestive heart failure, peripheral arterial disease, cerebrovascular disease, and prescription of antihypertensive agents, including angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), diuretics, and b-blockers, statins, and antiplatelet agents, was collected from the medical records at each institution.2 in men and > 110 g/m2 in women [Blood pressure (BP) was measured in outpatient clinics with an automated sphygmomanometer after a 5-min rest. BP in the right arm was measured three times at intervals of 1 min, and the mean values were used for analyses. A mercury sphygmomanometer was used to measure the BP of patients who had frequent premature contractions, atrial fibrillation, or atrial flutter. Pulse pressure was calculated by subtracting diastolic BP from systolic BP. A two-dimensional guided M-mode echocardiographic study was performed in 1178 patients at each institution. Of them, the cases whose left ventricular end-diastolic diameter was not measured nor body surface area could not be calculated were excluded. Finally, this study included 1088 cases. Measurements included the diastolic thickness of the interventricular septum (IVST) and left ventricular posterior wall (PWT), and the internal diameter of the left ventricle at the end of diastole (LVDd) and the end of systole (LVDs). The modified Penn cube formula was used to calculate LV mass [in women . RelativHypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg or taking an antihypertensive agent. Diabetes mellitus (DM) was defined as HbA1C ≥ 6.5% or taking an antidiabetic agent. Diabetic patients were identified as those with diabetic nephropathy as the primary cause of CKD. Dyslipidemia was defined as serum triglyceride level > 150 mg/dl, or serum high-density lipoprotein (HDL) cholesterol level < 40 mg/dl in men and < 50 mg/dl in women.Whole blood, serum, and urine samples were collected for measurement of serum Cr, HbA1c, intact parathyroid hormone (iPTH), and urinary albumin and Cr levels at a central laboratory. Urinary albumin excretion was expressed as the albumin to Cr ratio (ACR). HbA1c was measured by the JDS method, and the value was converted to the A1C value measured by the NGSP method by adding 0.4% as determined by the Japanese Diabetes Society. Each clinical center measured serum Cr at each visit. A 24-h urine specimen was collected from each patient once a year to measure the amount of proteinuria.t test or one-way analysis of variance (ANOVA) were used to detect between-group differences. ACR values had a skewed distribution and were log-transformed to achieve a normal distribution. Logistic linear regression was used to investigate the relation of LVMI to eGFR, BMI, and log ACR. Univariate logistic regression analyses were performed in an attempt to identify factors related to LVH. Multivariate logistic regression analyses were used to identify independent variables related to LVH. We considered some variables that had a P value < 0.10 in univariate logistic regression analyses as independent variables for multivariate logistic regression analyses. The model included the variables as follows: sex, smoking status, complications of DM, dyslipidemia and hypertension, past history of congestive heart failure (CHF), angina pectoris, myocardial infarction (MI), stroke and peripheral artery disease (PAD), systolic and diastolic BPs, pulse pressure, BMI, eGFR, uric acid, ACR, A1C, iPTH, HDL cholesterol, triglyceride, calcium, phosphorus, and prescription of antihypertensive agents. The two-sided 95% confidence interval (CI) and odds ratio (OR) were calculated by estimation. A two-sided probability level of 5% was considered significant. All statistical analyses were performed using the SAS software program for Windows ver. 9.4 .All variables are reported as mean ± SD and frequency. Descriptive statistics of baseline characteristics were calculated by CKD stage, sex, and the presence or absence of LVH. CKD stages were defined according to the patient’s eGFR. Chi-squared test and Student’s N = 1088) who were examined by echocardiography (UCG), consisted of 694 Japanese men (63.8%) and 394 Japanese women (36.2%), 454 (41.7%) and 807 (74.2%) of whom had DM and dyslipidemia, respectively. Most of the subjects had hypertension (88.7%) and were being treated with an antihypertensive agent (92.5%), most of them with ACE inhibitors (25.8%)/ARBs (75.9%), as shown in Table We studied 2966 participants. Eleven participants  were excluded from 2977 of baseline participants . The basCKD was stage 3a in 128 patients (11.8%), stage 3b in 349 patients (32.1%), stage 4 in 427 patients (39.2%), and stage 5 in 184 patients (16.9%) . The prevalence of eccentric and concentric LVH was higher among patients with more advanced CKD stages . The prevalence of LVH was 255 of 1088 (23.4%) of the study population  of LVH in the present study was much lower than (30% <) that reported in previous studies in the pre-dialysis CKD population –21. The n = 1006, 92.5%), including ACE inhibitors  and/or ARBs .The prevalence rates of pre-existing CVD, i.e., MI (6.5%), angina (10.4%), CHF (5.5%), peripheral artery disease (3.7%), and stroke (12.3%), were higher than in the general Japanese population [More than 90% of the subjects were being treated with antihypertensive agents  of the 1088 study subjects, whereas LVMI tended to increase with the progression of CKD. In addition, the prevalence of eccentric and concentric LVH was higher among patients with more advanced CKD stages. CKD patients have a high prevalence of LVH, ranging from 34 to 74% in different studies, and its prevalence increases as renal function declines , 25, 26.Elevated systolic BP has a continuous, graded, and independent association with risk of coronary heart disease, stroke, and ESKD . LVH migIn accordance with the theory of non-hemodynamic LVH-promoting factors in our CKD patients, BMI was found to be a factor that was independently associated with LVH. Obesity is thought to be a risk factor independent of LVH, and heart disorders in obesity include structural adaptation with LVH and functional abnormalities . Kotsis Currently, information about sex differences in renal abnormalities and CVD in healthy individuals is limited and conflicting. In the Prevention of Renal and Vascular End-Stage Disease (PREVEND) study, the prevalence of microalbuminuria in men was almost double that observed in women, and for a higher value of age and BMI was greater in men than in women . In addiThe results of the present study have shown that albuminuria is an independent predictor of LVH in CKD patients. A recent study reported that higher ACR was associated with LV mass, size, systolic function, and diastolic function in CKD patients . This fiVarious abnormalities of mineral–bone metabolism are common in CKD patients, and mineral metabolism disorders such as hypocalcemia, hyperphosphatemia, and vitamin D deficiency have been found to be closely associated with CVD in CKD patients . The meaP = 0.55). Fourth, the definition of LVH in Japanese patients may be different from those in the USA. The LVMI value is dependent on body height and body weight. The cutoff values of LVMI were 125 g/m2 for men and 110 g/m2 for women in this study. These values were considered as 75 percentile of LVMI in this study. However, it is difficult to determine if the cutoff values of LVMI are suitable for use with the Japanese population. By contrast, the strengths of this study include its multicenter nature, the standard nephrology care, and the evaluation of patients with a wide range of eGFR.Some limitations have to be acknowledged in this study. First, selection bias cannot be ruled out, because patients were mostly enrolled at large-sized hospitals that can provide nephrology care . TherefoIn conclusion, the results of this study showed that the prevalence of LVH was low in stage 3–5 CKD patients treated by nephrologists in Japan. The cross-sectional baseline data from the CKD-JAC study shed light on the association between LVH and risk factors in patients with decreased renal function. Differences in the presence of previous CVD, blood pressure control, and metabolic state may lead to different outcomes of CVD in a longitudinal study. Future analysis of the CKD-JAC cohort will clarify whether the incidence of LVH varies with the causative disease during further follow-up.