The optimal intraoperative blood pressure range and crystalloid administration protocol for the prevention of acute kidney injury (AKI) after elective noncardiac surgery remain unknown.This single-center retrospective cohort study included 6296 patients aged ≥ 50 years who had undergone elective noncardiac surgery under general anesthesia. We evaluated the relationship between duration of intraoperative hypotension and AKI. To assess whether the effects of crystalloid administration differed according to baseline estimated glomerular filtration rate (eGFR), we examined the interaction between intraoperative crystalloid administration and eGFR. We calculated univariable and multivariable adjusted odds ratios (ORs) and their 95% confidence intervals (95% CIs) for the prevalence of AKI.−1 1.73m−2 who were managed with restrictive or liberal crystalloid administration  as opposed to those with eGFR >45 ml min−1 1.73m−2 who were managed with moderately restrictive crystalloid administration.AKI occurred in 431 (6.8%) patients and was associated with intraoperative hypotension. Effects of intraoperative crystalloid administration differed significantly according to baseline eGFR. Increased risk of AKI was noted in patients with eGFR ≤45 ml minOur findings suggest that anesthesiologists should avoid intraoperative hypotension as well as either restrictive or liberal (as opposed to moderately restrictive) crystalloid administration in patients with decreased eGFR. Intraoperative blood pressure and crystalloid administration protocol are major modifiable factors that must be optimized to prevent postoperative AKI.The online version contains supplementary material available at 10.1186/s40981-021-00429-9. Postoperative acute kidney injury (AKI) has a detrimental effect on patient outcome: it has been associated with higher risk of chronic kidney disease (CKD) –3 and wiAppropriate management of intraoperative blood pressure is an important part of preventing postoperative AKI –9. A sysIdentifying an optimal approach to crystalloid administration is another important step in preventing postoperative AKI . A meta-The aims of the present study were (i) to investigate the effects of IOH (defined based on either an absolute or a relative threshold) on AKI after elective non-cardiac surgery and (ii) to investigate whether the effects of crystalloid administration differ according to baseline renal function. As far as we know, this is the first study to examine the effects of IOH and intraoperative crystalloid administration on AKI in Japan.This study was approved by the institutional review boards (IRB) of Jichi Medical University Saitama Medical Center, Saitama, Japan,  and Teikyo University, Tokyo, Japan, , and the requirement for written informed consent was waived by the IRBs.We conducted a retrospective cohort study in compliance with the applicable Strengthening the Reporting of Observational Studies in Epidemiology statement. We screened data on patients who had undergone elective noncardiac surgery under general anesthesia at Jichi Medical University Saitama Medical Center, a 628-bed tertiary care center in Saitama, Japan, that performs around 6500 operations a year, and who satisfied our selection criteria. Data from April 1, 2009, through March 31, 2018, were used in this study.−1 1.73m−2; (5) American Society of Anesthesiologists Physical Status (ASAPS)=1.We included patients who met all of the following criteria: (1) patients aged ≥ 50 years who had undergone elective noncardiac surgery under general anesthesia between April 1, 2009, and March 31, 2018; (2) duration of anesthesia was >60 min. We limited our study to patients aged ≥ 50 years because elderly patients are at higher risk of postoperative AKI. We excluded patients who met one of the following criteria: (1) urological surgery; (2) for patients with multiple surgeries during the study period, we included the first surgery only; (3) missing baseline or postoperative creatinine values; (4) eGFR <15 ml minThe data used in this study were extracted from two databases, namely, the electronic medical systems software Cosmos  and the anesthesia record system ORSYS . Preoperative creatinine, postoperative creatinine, preoperative systolic blood pressure (SBP), and diastolic blood pressure (DBP) were extracted from Cosmos using SQL language. Preoperative blood pressure was measured with the patient at rest in the ward the day before surgery with a Terumo electronic sphygmomanometer ES-H55 . Intraop−1 within 48 h and/or (ii) increase in serum creatinine to ≥1.5 times baseline within 7 days [The primary endpoint was the development of postoperative AKI. Based on the definition proposed in the Kidney Disease: Improving Global Outcome guidelines, AKI was defined as the presence of one or both of the following: (i) increase in serum creatinine by ≥0.3 mg dln 7 days .2 of body surface area. Coexisting medical conditions included information on congestive heart failure, hypertension, coronary artery disease, valvular heart disease, diabetes mellitus, dyslipidemia, cerebrovascular disease, spinal cord injury, chronic obstructive pulmonary disease, interstitial pneumonia, mixed obstructive and restrictive lung disease, respiratory failure, cirrhosis, coagulopathy, hyperthyroidism, neuromuscular diseases, Parkinson’s disease, depression, schizophrenia, dementia, and ileus. Intraoperative factors included anesthesia time, hemorrhage, crystalloid administration (ml kg−1 h−1), urine , type of surgical procedure, and the use or non-use of combined epidural anesthesia, invasive arterial catheter, 0.9% saline infusion, synthetic colloid infusion, and transfusion of red blood cells (RBC) and/or fresh frozen plasma (FFP). In previous observational studies investigating the effects of IOH, the volume of intraoperative crystalloid administration was not adjusted for as a confounding factor. However, a recent RCT showed that restrictive crystalloid administration was associated with postoperative AKI [As potential confounding factors, we identified variables related to patients’ baseline characteristics, coexisting medical conditions, and intraoperative factors. Baseline patient characteristics included sex, age, body mass index (BMI), body weight, ASAPS, and cardiac medication histories. Cardiac medication histories included the use of angiotensin-converting enzyme inhibitors (ACE inhibitors), angiotensin receptor blockers (ARBs), beta blockers, calcium channel blockers (CCBs), and diuretics. We also collected data on preoperative SBP and eGFR as defined by Cockcroft-Gault and adjusted for body surface area by normalizing the output per 1.73 mtive AKI . Administive AKI . In thisAs for the type of surgical procedure, the data originally identified 535 distinct procedure names. We combined them into the following 12 broader categories: major abdominal surgery, laparoscopic major abdominal surgery, gynecological surgery, thoracic surgery, major vascular surgery, peripheral vascular surgery, neurosurgery, spinal surgery, orthopedic surgery, head and neck surgery, other high-risk surgeries, and other low-risk surgeries , 20. ThePatients’ baseline characteristics, coexisting medical conditions, intraoperative factors, and surgical procedure types were summarized as means and standard deviations (SD) for continuous variables and as frequencies and proportions for nominal variables, according to the presence or absence of postoperative AKI.Our analysis consisted of two steps. In step 1, we prepared the data and evaluated the effects of duration of IOH (defined according to absolute or relative threshold) on AKI using univariable and multivariable logistic regression models. In step 2, we investigated whether the effects of crystalloid administration differed according to baseline renal function. The details of each step are presented below.SBP ≥300 mmHg or ≤35 mmHgDBP ≥225 mmHg or ≤5 mmHgDifference between SBP and DBP <10 mmHgSBP rises or falls by ≥110 mmHg within 1 minBlood pressure was measured non-invasively at 1- to 5-min intervals and invasively at 1-min intervals. When both non-invasive and invasive blood pressure measurements were available for the same time point, we used invasive measurements. We removed out-of-range blood pressure data as artifactual according to the following exclusion criteria .SBP ≥30After artifact removal, blood pressure values between measurements were linearly interpolated according to 1-min interval data.We examined the relationship between IOH and postoperative AKI using univariable and multivariable logistic regression models for both absolute and relative thresholds. We tested five different absolute thresholds of SBP: 70 mmHg, 75 mmHg, 80 mmHg, 85 mmHg, and 90 mmHg. The ORs and the 95% CIs of postoperative AKI were calculated according to the logistic regression model for each of four duration categories: 0 min (reference), 1 to 5 min, 5 to 20 min, and >20 min. We chose these four categories in keeping with previous studies to assess the effects of both short and long periods of IOH. Modeling duration as a continuous variable, we tested for trends in how the OR increased. As relative SBP thresholds, we tested 65%, 70%, 75%, 80%, and 85% of baseline. The same analysis used to assess the absolute values was also conducted for these relative thresholds. The covariates included in the final model were determined by a stepwise selection method with inclusion and exclusion criteria of 0.2. All of the covariates included in step 1 were also included in step 2.−1 1.73m−2 (reference is 45 ml min−1 1.73m−2≤ eGFR) and eGFR <60 ml min−1 1.73m−2 (reference is 60 ml min−1 1.73m−2≤ eGFR) according to K/DOQI practice guidelines [−1 1.73m−2 in this model because the sample size for this population was too small. We also classified patients into three categories according to the volume of crystalloid administration: liberal (>24 ml kg−1 h−1), moderately restrictive , and restrictive (≤7 ml kg−1 h−1) [We determined whether the effects of crystalloid administration differed according to baseline renal function by including an interaction term between crystalloid administration and baseline renal function into the model used in step 2. In this model, we used an absolute threshold for blood pressure. We classified baseline renal function into two categories: eGFR <45 ml minidelines . We did g−1 h−1) .As a sensitivity analysis, we conducted the same analyses described in step 1 using mean arterial pressure (MAP) instead of SBP.p-value of 0.05 was considered to indicate statistical significance in all analyses. Statistical analyses were performed using SAS Ver. 9.4.We assumed a postoperative AKI incidence of 5% and defined a difference of 2–5% associated with the presence or absence of IOH as clinically important. With a two-sided α error of 0.05 and a power of 0.8, 435 to 2213 patients were required for each arm of the study. A two-tailed p<0.05 in all cases). As for intraoperative factors, patients with postoperative AKI were likely to have longer anesthesia time and greater hemorrhage volume, and were more likely to receive 0.9% saline administration, synthetic colloid administration, RBC and FFP transfusion, combined epidural anesthesia, and arterial catheter . As for surgical procedures, patients with postoperative AKI were more likely to have undergone major abdominal surgery, major vascular surgery, or other high-risk surgeries, and less likely to have undergone laparoscopic major abdominal surgery, thoracic surgery, neurosurgery, spinal surgery, and other low-risk surgeries.Figure −1 1.73m−2; longer anesthesia time; greater hemorrhage volume; and RBC transfusion were independently associated with postoperative AKI as indicated by multivariable logistic regression . As for surgical procedures, laparoscopic major abdominal surgery, gynecological surgery, thoracic surgery, peripheral vascular surgery, neurosurgery, spinal surgery, orthopedic surgery, and other low-risk surgeries were associated with decreased risk of postoperative AKI compared with major abdominal surgery .The results of univariable logistic regression are shown for variables other than blood pressure . Postoperative AKI was more likely in patients with eGFR <45 ml min−1 1.73m−2 and liberal crystalloid administration than in patients with eGFR ≥45 ml min−1 1.73m−2 and with moderately restrictive crystalloid administration, with an OR of 6.42 (95% CI 2.23–16.01)  or 2.98 (6.42/2.15) times as likely, respectively, to develop postoperative AKI compared with those with eGFR ≥45 ml min−1 1.73m−2 who were treated with moderately restrictive crystalloid administration. Similar results were seen in patients with eGFR <60 ml min−1 1.73m−2 and with eGFR <30 ml min−1 1.73m−2 (results not shown). These differences were not only statistically but also clinically significant. We presume that the kidney becomes more susceptible to inappropriate crystalloid administration as the eGFR decreases. As far as we know, this is the first study to show that the effect of crystalloid administration differs significantly according to baseline renal function.Our analysis in step 2 suggests that the effect of crystalloid administration differs significantly according to baseline renal function. Restrictive and liberal crystalloid administration lead to markedly worse effects on patients with impaired renal function than on those without. Patients with eGFR <45 ml minThe effects of age and 0.9% saline were significant in univariable logistic regression but not significant in multivariable logistic regression. These variables were omitted from the final logistic regression model according to the stepwise method.Our study has certain strengths. First, we excluded patients who had undergone emergency surgery because emergency surgery was a possible cause of reverse causality. Patients whose pre-operative conditions were severe enough to lead to postoperative AKI are more likely to experience low intraoperative blood pressure due to factors other than IOH such as sepsis. Septic patients are particularly susceptible to IOH, and a relatively high blood pressure should be maintained. Second, we adjusted for intraoperative crystalloid administration as a confounding factor although previous retrospective studies investigating blood pressure thresholds did not , 9, 25.Our study also has some weaknesses and limitations. First, it is subject to the inherent limitations associated with its single-center, retrospective, and observational design, such as limited generalizability, inability to prove causality, and unmeasured confounding factors. In particular, we did not have data on history of malignancy, which is a possible cause of postoperative AKI. We believe, however, that adjusting for surgical procedure type partially eliminates confounding by malignancy. Second, we used SBP as the main target of the analysis while using MAP for the sensitivity analysis. MAP is the most appropriate means of assessing tissue perfusion, but it is not always available in clinical practice, whereas SBP is almost always available. In fact, the Terumo ES-H55 electronic sphygmomanometer displays SBP but not MAP. Third, removing iatrogenic blood pressure fluctuations as artifacts may have caused us to underestimate the effect of IOH. However, there is no universal protocol for the removal of artifacts, and we excluded blood pressure data that were unlikely to occur in real patients. Fourth, we linearly interpolated non-invasive measurements into 1-min interval data. This may have caused us to overlook some short periods of IOH, but, given that abrupt changes of blood pressure are most common during induction of general anesthesia, which is normally a brief process, we believe it is unlikely that short-term measurements would noticeably change the effects of IOH. Fifth, we included both invasive and non-invasive blood pressure measurements. There is sometimes a gap between these measurements, particularly in critically ill patients whose systemic vascular resistance is abnormally high or low. However, our inclusion of elective surgeries only should minimize this blood pressure gap. Sixth, the minimum duration of hypotension that can trigger harm is unclear from this study. However, a graded relationship between the duration of hypotension and postoperative AKI has previously been assumed and is statistically supported by our study. Seventh, we used only creatinine instead of urine output to diagnose postoperative AKI due to the retrospective study design. This may have caused us to underestimate the incidence of postoperative AKI. Eighth, the appropriate volume of intraoperative crystalloid administration is not clear from this study. It should be determined for each case in light of the surgical procedure type and coexisting medical conditions such as baseline renal function. Ninth, appropriate organ perfusion was accomplished by optimizing intraoperative blood pressure, crystalloid administration, and catecholamine use, which are not available from this study. Despite these limitations, this study provides important evidence that anesthesiologists should maintain an absolute SBP >80 mmHg and avoid either restrictive or liberal crystalloid administration in patients with decreased eGFR. Intraoperative blood pressure and crystalloid administration are the major modifiable factors that must be optimized to prevent postoperative AKI. Future randomized trials should be conducted to determine the appropriate volume of intraoperative crystalloid administration based on each patient’s renal function and surgical procedure type.The results of our study strongly suggest that even patients undergoing elective surgery are at increased risk of postoperative AKI after being exposed to SBP <80 mmHg. Furthermore, the effects of restrictive and liberal crystalloid administration differed significantly according to baseline renal function. Our results highlight the importance of maintaining SBP >80 mmHg while administering an appropriate volume of crystalloid to prevent postoperative AKI.Additional file 1: Supplemental Digital Content. Supplemental Digital Content 1, Table 1S. The types of surgical procedures included in the “other high-risk surgeries” and “other low-risk surgeries” categories. Supplemental Digital Content 2, Table 2S. Univariable logistic regression analysis for acute kidney injury (N=6296). Supplemental Digital Content 3, Table 3S. All variables included in the multivariable logistic regression model for systolic blood pressure as a trend . Supplemental Digital Content 4, Table 4S. All variables included in the multivariable logistic regression model for systolic blood pressure as a trend . Supplemental Digital Content 5, Table 5S. Univariable and multivariable associations between MAP absolute thresholds and acute kidney injury (N=6296).


The heart may be a mirror of the soul, but the human mind is more than its heart rate variability (HRV). Many techniques to quantify HRV promise to give a view of what is going on in the body or even the psyche of the subject under study. This “Hypothesis” paper gives, on the one hand, a critical view on the field of HRV-analysis and, on the other hand, points out a possible direction of future applications. In view of the inherent variability of HRV and the underlying processes, as lined out here, the best use may be found in serial analysis in a subject/patient, to find changes over time that may help in early discovery of developing pathology. Not every future possibility is bright and shining, though, as demonstrated in a fictional diary excerpt from a future subject, living in a society geared toward preventive medicine. Here implanted biochips watch over the health of the population and artificial intelligence (AI) analyses the massive data flow to support the diagnostic process. Probably I was tossing and turning very much while asleep. Would that explain my headaches, sleepiness and higher than normal blood pressure during the day? This biochip-thing is getting on my nerves. At the time it seemed like a good idea to have it implanted, my health insurance company insisted on it, or else my premium would almost double. So much for freedom of choice in the era of preventive medicine.This morning I downloaded the data from my biochip to iCAIre, my home health system. It took longer than normal, which worried me, as always when the chip obviously had decided to do more measurements. A few days ago, iCAIre already made an appointment for me to see an eGP later today, we will have a videocall at 17:30 CET . The eGPs have their office in the United States, they came with the system that I chose. I could have checked in my preferences to have them contact my own GP directly, but hey, it is my health. I have looked at the files that were transmitted, but they did not make much sense to me: heart rate excursions, very large while I was asleep, same for blood pressure, oxygen- and COThat was not a nice videocall at all. The woman on the other side was rather blunt: I am overweight, my blood glucose is not OK, and, worst of all, I have developed obstructive sleep apnea, that is causing all this trouble during the night- and daytime. What now to do? See a dietician, I have to lose at least 20 kilos of my present 109. I always lived under the impression that my height of 1.85 m would allow me some more mass than average, but maybe she is right. In the meantime, I must make an appointment with my local GP whom they will contact to have me equipped with a device to prevent these apneic periods during sleep. I was reprimanded, because I should have checked in earlier, when the system started to bleep after every upload. In a way it was a lucky choice, this US system. It was more expensive than the regular ones, but it promised more privacy; the more common Asian systems are automatically coupled to the national registrar for preventive health care, my data are kept private as long as they ascertain that I am in the green zone. Which I, obviously, am not, at least not always. Now I risk to be exposed anyway. ICAIre will follow the developments closely, the operator said, not so much for my sake, but in order not to lose their license to sell and contract in the EU.Horizon 2030: innovative applications of Heart Rate Variability pushes the authors to, somehow, rise above their field and imagine a not-too-distant future where heart rate variability (HRV) might feature in a new role. The Frontiers journal is usually about science, but this invites now to venture the border between science and science fiction. Therefore, the present contribution is more a personal view rather than a systematic review of HRV interpretation. The paper’s title is to stress this fact: looking through a keyhole one can only observe part of what is going on behind that door and much of the testimony of what happened there is interpretation, maybe helped by knowledge of the players and what the room looks like, but not an actual eyewitness report.The title of the present Research Topic: The use of HRV has expanded into many areas of biomedical research and even into medical apps on smartphones that are supposed to help a person relax. Still, in accepted medical practice HRV is at best a side note in a Holter monitoring report . Why the discrepancy? Might this change in the coming decade? What do we understand about HRV, and how and when can that knowledge be of help in medical practice?In theory, the origin of HRV is not elusive: generally speaking, fast, beat-to-beat variations in heart rate are parasympathetic, vagally mediated. The slower changes, extending over many beats, are mainly due to sympathetic nervous influences. Heart rate changes over long periods (tens of seconds to minutes and hours) are due to humoral factors like adrenaline, vasopressin and angiotensin .Recognition of the underlying autonomic neural activities has awakened hope to understand the  nervous condition from heart rate variability. Initially, this was restricted mainly to measurement of respiratory variations , in search for a measure of vagal outflow. By maximal deep in- and expiration at a low frequency, it was held that vagal outflow was switched on and off, thus the peak-to-trough values of RSA would represent total vagal outflow. Decreases beyond those due to aging were (and are still) considered indicative of disease processes like diabetic neuropathy, as reviewed in Closer inspection of heart rate variations, aided by computer spectral analysis, had demonstrated the existence of slower than respiratory heart rate variations, in particular with around 10 s periodicity. These are also (mainly) due to vagal effects, but riding on slow sympathetically driven blood pressure waves . Since tTM. Of the around 30,000 core publications mentioning HRV up to now, only some 1,500 (5%) have “heart failure” as keyword as well. Much of the work is not so much devoted to practical medical applications as it is to the development of analysis techniques to squeeze information out of the HRV-signal. In particular, the easy availability of digitized long heart rate recordings has inspired the mathematics- and physics-savvy community to devise more and more intricate techniques of non-linear analysis to discover hidden features in HRV beyond those detectable by simple statistics of the signal in the time domain or by frequency domain analysis.In 1987 Kleiger and the multicenter post-infarction research group showed that decreased HRV in particular in the low- and very low-frequency area as calculated from 24 h ECG’s could predict mortality in the years after a myocardial infarction . A few yThe first attempts were exploiting the fractal properties of the heart rate signal: the “jumpiness” that repeats itself on any scale, from seconds to minutes to hours . A true The next push came from application of information theory: a purely regular sequence of heart intervals has, information-wise, low entropy. From that insight various descriptors for the predictability, or rather unpredictability, have been proposed. The first application came by Pincus, who coined the term “Approximate Entropy” (ApEn) to describe the irregularity in a series of biological events like successive heart periods . TogetheThe calculation of information entropy, applied to heart period series, has opened new doors to quantify the visual impression that one may have when looking at a beat-to-beat heart rate signal. It must be stressed, though, that there are thus far few practical applications. Moorman et al. devised a tool that has shown promise in neonatal intensive care units . CompariHeart rate should not be judged separately from the prevalent blood pressure (BP). The invention of a reliable method for non-invasive measurement of continuous BP by the Czech physiologist Jan Peňáz, further developed together with the Dutch biomedical engineer Karel Wesseling and his team , has ope1 of blood pressure or 24 h continuous beat-to-beat estimation by the same principle, starting from one calibration with a traditional arm cuff. The issue has been picked up by the Institute of Electrical and Electronics Engineers (IEEE) that has developed its P1708-2014 – IEEE Standard for Wearable Cuffless Blood Pressure Measuring Devices. Even though that may sound like a precise way of testing some new device, the center piece remains the arm cuff, requiring two independent, trained observers who read the same sphygmomanometer using a Y-piece for their stethoscopes. In many hospitals and medical practices, doctors and nurses will prefer an automatic upper arm blood pressure measuring device. This works by the oscillometric principle and gives exact looking numbers for heart rate and systolic, diastolic (sometimes mean) to the nearest mmHg pressure value. The technique of accurate measurement by the hand-inflated arm cuff and stethoscope seems like a dying art.Presently, cuffless blood pressure estimation is under way to fill that gap: by correlating upper arm BP, or some other calibrated pressure, to pulse wave velocity measurement. Pulse wave velocity in a vessel is related to its stiffness via the Moens–Korteweg equation: PWV = √(h.E/ρ.d), where h is wall thickness, d its diameter, E Young’s modulus (quantifying stiffness) and ρ the density of the fluid. Stiffness is related to wall composition, to the level of blood pressure and (strongly) to age. The method calculates BP by applying the estimated biophysical properties of the vascular wall in that particular subject to the arrival time of the pressure wave at e.g. the wrist after, for instance, the R-wave of the ECG . A review of the method and various algorithms and calibration methods used is given in In blood pressure measurement we seem to be stuck to a one century-old technique  expressing the pressure in a unit that no longer makes sense (mmHg) since mercury has long been banished from use due to its toxicity when spills are entering the environment. The various hypertension societies have successfully blocked the introduction of the International SI-unit of pressure, the Pascal , arguing that comparison to older literature and patient data will be hampered, which is a good point, but it postpones the shift forever and it makes for instance the understanding of flow resistance units so much more difficult.The combined analysis of heart rate and continuous blood pressure adds another sought-after key figure to the non-invasive diagnostic and research arsenal, i.e., baroreflex sensitivity (BRS), the amount of heart period lengthening divided by the amount of causative blood pressure rise (ms/mmHg). BRS measurement, originally, was developed by imposing a fast blood pressure rise by the injection into the blood stream of a strongly vasoactive substance. At first this was mainly angiotensin , later pBaroreflex sensitivity is the overall effect of a multi-step transduction process; at every step its effectiveness may be altered by factors external or internal to the reflex. It starts at the transformation of blood pressure changes to vessel wall extension, then to receptor activity to afferent nerve activity to nucleus tractus solitarii activity to efferent cardiac vagus nerve activity (and inhibition of sympathetic outflow) to sinoatrial node depolarization to atrial and then ventricular activation to arterial pressure pulse generation. Details of the various steps will be discussed below under the appropriate headings. This reflex loop is, of course, not only active when phenylephrine is injected, but continuously, from beat to beat. As a consequence, one may relate the changes in pressure to concomitant changes in heart period and expect to find a number related to the phenylephrine BRS. This, indeed proves to be the case: the correlation of the slower systolic pressure variations around 0.1 Hz to those in heart period gives numbers in the right order of magnitude . The fasThe variability of BRS comes to light by a technique that follows the baroreflex, as it were, from moment to moment while comparing sliding windows of 10 s blood pressure values to ensuing heart periods (xBRS). This gives very variable numbers over short periods, but the geometric averages correlate favorably to the BRS determined by vasoactive drug injections in the same experimental subjects . The samConsidering all incoming nervous activity to the CNS, ultimately any afferent nerve may have an effect on HR. Not all nerves will have an equally strong or immediate effect, but, if the stimulus is sufficiently strong, this corollary will hold true. Importantly, visceral afferents from thoracic or abdominal organs can elicit large HR-effects, mainly by their ability to modulate outgoing vagal activity to the heart. This occurs at a fairly basal level, just above the spinal cord, in the medulla oblongata . There, via the cranial nerves, is not special in this respect: this, too, may give rise to heart rate effects when activated, for this a descending pathway down the brain stem to the medullary level exists. Some specific afferent activity may even trigger early evolutionary mechanisms, in particular the diving response can be mentioned here in response to cold wetting of the face , dictated by higher centers enforcing a perception of the present condition: flight or fight, stress or relaxation or something in between. In this personal review I will not go into the origins and specifics of the “polyvagal theory”  but see Final output from the CNS to the heart comes from the nucleus ambiguous in the medulla oblongata as far as the vagal activity is concerned and from the sympathetic command neurons in the rostroventrolateral medulla. The axons of the latter descend to the sympathetic motor neurons in the intermediolateral columns at the lowest cervical and upper thoracic segments of the spinal cord. However, the simple pictures from the textbooks as to how the final, peripheral postganglionic fibers reach their target require an update. Close to the heart the autonomic fibers form intricate plexuses, together nicknamed the heart’s “little brain” , like thNervous activity in this interwoven network of sympathetic and parasympathetic fibers will, finally, lead to heart rate variability, or rather to liberation of autonomic transmitters at the sinus node. Its effect is not exactly predetermined by the type (adrenergic or cholinergic) and amount, but dependent on the moment of arrival in the cycle of the sinus node and on the previous history of autonomic stimuli. In an earlier study . I showeApart from heart rate variations due to some form of cardiac disease, which have been left out of the equation altogether in this review, one should maybe better ask “what does HRV not tell?” In the literature a wide range of conditions, healthy and diseased, has been explored, where HRV in some way would be different, at the group level, from a reference group. As I tried to demonstrate in the above, so many different circuits are involved in the stream of information reaching the motor neurons in the medulla and finally the heart, that it is next to impossible to retrace at any given moment what may have caused this particular output. Even quasi simple effects, like respiratory sinus arrhythmia, may turn out very differently in the same subject under slightly different circumstances.Therefore, as hinted in the beginning, tracking a person’s HRV over time may better tell a story where pathology is developing. We have demonstrated something along those lines when we looked at HRV in pregnant women who developed pre-eclampsia . In yet The author is the sole creator of this manuscript. All views expressed here are entirely his own responsibility.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


I begin this text highlighting the importance of multidisciplinarity in the development of knowledge.In 1842, Johann Christian Doppler, an Austrian physicist, defined that the frequency of the star’s light changed with its motion, as it moved closer or away. One and a half century later, such “useless curiosity” started to be applied for the diagnosis of congenital heart diseases. And for a long time, we do know when the train is coming or going.1Needless to highlight the similarities among the areas of biological sciences, with emphasis on animal experiments. Affinities with Cardiology are described in this publication.Felypressin is an analogue of vasopressin , a hormone produced in the neurohypophysis. In Dentistry, felypressin is of special interest because it acts as a vasoconstrictor that prolongs the anesthetic effect.In an artificially induced hypertensive model , the effect of atenolol and felypressin on blood pressure was assessed in artificially hypertensive rats submitted to anesthesia, exposed to cigarette smoke. The results indicated that smoking can reduce epinephrine-induced vasodilation and increase the hypertensive response compared with felypressin.2 however, concluded that more studies are needed to increase the strength of the evidence.These data add to the literature on the cardiac effects of vasoconstrictors. A recent review,3 suggesting that the dose of anesthetic was not sufficient to prevent the increase in sympathetic activity in the operated rats.It is interesting to note the elevated blood pressure of the anesthetized rats. Systemic arterial hypertension is not a common response to ketamine and xylazine,4 concluded that “felypressin increased the diastolic blood pressure of hypertensive patients with controlled blood pressure. Patients with high trait anxiety presented increases in systolic blood pressure upon some procedures”. It is worth pointing out that the arterial catheter was placed after occlusion of the left carotid artery, which may have activated baroreceptors located in the carotid sinus, resulting in arterial hypertension. Maybe femoral artery catheterization would have been better.A recent study with awake humans, published in Arquivos Brasileiros de Cardiologia,5Also, the time of cigarette smoke exposure was short (10 minutes/day), different from other studies in which the animals are exposed for longer periods.The publication of this study in the Arquivos Brasileiros de Cardiologia is timely; many heart disease patients want to know about the risks involved in their dental treatment, and physicians should be prepared for it.


Plasma aldosterone-to-renin ratio (ARR) is popularly used for screening primary aldosteronism (PA). Some medications, including diuretics, are known to have an effect on ARR and cause false-negative and false-positive results in PA screening. Currently, there are no studies on the effects of sodium–glucose cotransporter-2 (SGLT2) inhibitors, which are known to have diuretic effects, on ARR. We aimed to investigate the effects of SGLT2 inhibitors on ARR.We employed a retrospective design; the study was conducted from April 2016 to December 2018 and carried out in three hospitals. Forty patients with diabetes and hypertension were administered SGLT2 inhibitors. ARR was evaluated before 2 to 6 months after the administration of SGLT2 inhibitors to determine their effects on ARR.No significant changes in the levels of ARR (90.9 ± 51.6 vs. 81.4 ± 62.9) were found. Body mass index, diastolic blood pressure, heart rate, fasting plasma glucose, and hemoglobin A1c were significantly decreased by SGLT2 inhibitors. Serum creatinine was significantly increased.SGLT2 inhibitor administration yielded minimal effects on ARR and did not increase false-negative results in PA screening in patients with diabetes and hypertension more than 2 months after administration.The online version contains supplementary material available at 10.1186/s12902-020-00656-8. Primary aldosteronism (PA) is the most common form of secondary hypertension. It is characterized by the inappropriate production of aldosterone and accounts for 5–10% of all patients with hypertension . PA is cAldosterone-to-renin ratio (ARR) is commonly used to screen for PA. The national guidelines of the Japan Endocrine Society and the Endocrine Society recommend screening for PA using ARR , 12. HowSodium–glucose cotransporter-2 (SGLT2) inhibitors are a new class of diabetes medications that have diuretic effects. SGLT links one glucose to one sodium ion for transportation into the proximal tubule cell of the kidney. SGLT2 inhibitors provide an insulin-independent reduction in hemoglobin A1c (HbA1c) levels, with potential additional benefits, such as body weight loss, natriuresis, and osmotic diuresis , 17. In We adopted a retrospective design, and the study was conducted in patients with diabetes and hypertension in either of the following three hospitals: Kanazawa University Hospital, Fukui Prefectural Hospital, and Houju Memorial Hospital from April 2016 to December 2018. The study was approved by the ethics committees of the Kanazawa University Hospital (No. 2015185), Fukui Prefectural Hospital (No. 17–85), and Houju Memorial Hospital (No. 16–19) with a waiver of consent being obtained from all the ethics committees. All procedures were performed in accordance with the 1964 Helsinki Declaration and its later amendments. The inclusion criteria were as follows: (1) patients with type 2 diabetes and hypertension aged more than 20 years and who was administrated SGLT2 inhibitor, (2) plasma renin activity (PRA) and plasma aldosterone concentration (PAC) was evaluated before (within 3 months before SGLT2 administration) and after (2 to 6 months after SGLT2 administration) administration of SGLT2 inhibitor, and (3) patients whose anti-hypertensive and anti-diabetic agents were not changed between the period of 3 months before the first evaluation and second evaluation points of PRA and PAC. The exclusion criteria were as follows: (1) secondary hypertension including PA, and (2) patients taking mineralocorticoid receptor antagonists (MRA), such as spironolactone and eplerenone. Data were collected routinely within the setting of clinical practice according to standard procedures. Informed consent was obtained in the form of opt-out on the web site. The datasets during the current study are available from the corresponding author on reasonable request.Systemic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body weight (BW), body mass index (BMI), PRA, PAC, fasting plasma glucose (FPG), HbA1c, hematocrit (Ht), serum creatinine (s-Cr), estimated glomerular filtration rate (eGFR), and serum potassium (s-K) were collected as data. The examination of these parameters was performed using the standard methods. The plasma samples for PRA and PAC evaluation were collected into EDTA tubes between 0900 and 1000 h in the spine position for 30 min. PRA and PAC were measured using a radioimmunoassay . Blood wP values < 0.05 were considered to indicate statistical significance. Variables determined before and after the administration of SGLT2 inhibitors were compared using a pairwise t-test. We calculated that sample size of 35 was necessary to provide up to 80% power to detect a difference in mean ARR level between two groups, assuming a mean difference of 40 and standard deviation of 100 according to previous study with significance of 0.05 and power of 80%.Data are expressed as mean ± SD and were analyzed using commercially available statistical software . The associations between each parameters and % changes in ARR were examined via Pearson correlation coefficient analysis. For variables with significant correlations, a multiple linear regression analysis was performed to identify independent factors, adjusting for sex, age, and BMI..Patients were divided into two groups based on changes in ARR before and after SGLT2 inhibitor treatment. In the first group (elevated group), ARR was elevated after the administration of SGLT2 inhibitors, whereas in the second group (decreased group), ARR decreased after the administration of SGLT2 inhibitors. R Statistical Package (version 3.5.0) was used to perform random forest classifier . A randoListwise deletion was used to construct the sample for multiple linear regression analysis and random forest classifier, because all types of missing were missing completely at random.Forty-eight patients met the inclusion criteria. In total, 40 patients remained in the analysis after the exclusion of two patients with PA and six patients taking MRA. The clinical characteristics  are summarized in Table The Change in PRA, PAC, and ARR after treatment with SGLT2 inhibitors are summarized in Table p = 0.003) and HR  were significant predictors of change in ARR after adjusting for sex, age, and BMI at enrollment with multiple regression analysis; however, no significant relationship was found between the change in ARR and other markers.The correlation among PRA, PAC, and ARR before and after treatment is shown in Fig. Table We analyzed the relationship between the change of ARR and duration after the administration of SGLT2 inhibitors. There was no correlation between the change of ARR and the duration after the administration of SGLT2 inhibitors .For exclusion of the effects of ARBs/ACEIs, diuretics, and beta-blockers, and MRA, we analyzed 15 patients without these medications. The Change in PRA, PAC, and ARR after treatment with SGLT2 inhibitors in these group are summarized in Two to six months after administering the SGLT2 inhibitors, no significant changes were found in the levels of ARR. The change in ARR had a positive correlation with HR pretreatment and change in DBP.Several clinical studies have evaluated the effects of SGLT2 inhibitors on systemic and intrarenal RAAS . It was 14C]-glucopyranoside in renal proximal epithelial cells. In addition, the mRNA and protein expression of SGLT2 and GLUT2 are high in these patients [Renal glucose reabsorption is increased in patients with diabetes . In factpatients . SGLT2 ipatients . With thpatients , 37.+-Cl− symporter and decreasing extracellular fluid volume [Treatment with 10 mg dapagliflozin per day decreased body plasma volume to approximately 5.4% at 1 week after administration; however, this reduction was attenuated after 12 weeks . Thiazidd volume . Schork d volume . AlthougSeveral studies have indicated that SGLT2 inhibitors decrease blood pressure without causing an elevation in HR. Matsubayashi et al. reported that SGLT2 inhibitors decrease HR and this change in HR correlates with the baseline HR levels in patients with type 2 diabetes . There aOur study had the following limitations:1) We adopted a retrospective design and therefore, data for the control groups were not obtained, the sample size was also small.2) The impact of various classes of antihypertensive medications on ARR might have interfered to some degree with the interpretation of our results. However, previous studies proved that ARR is an effective screening tool even in patients receiving anti-hypertensive treatment , 43.3) ARRs were evaluated 2 to 6 months after the administration of SGLT2 inhibitor in our study. The change of ARR had correlation with SGLT2 inhibitor induced diuretic effect and sympathoinhibition effect. The sympathoinhibition by SGLT2 inhibitor was reported from early phase of SGLT2 inhibitor and continue to delayed phase of SGLT2 inhibitors . Convers4) The target patients in our study had poor diabetic control. Nevertheless, improving blood glucose level, diabetic state, and anti-diabetic agents might have some effects on the RAAS. In fact, pioglitazone, a thiazolidinedione, was reported to suppress the production of aldosterone , 46. LirWe revealed that ARR did not significantly change after treatment with SGLT2 inhibitors. SGLT2 inhibitors neither significantly reduced ARR nor increased the risk of false-negative results during PA screening.Additional file 1: Supplemental Table S1. Medication for subjects with hypertension or diabetes. Abbreviations: ARB, angiotensin II receptor blocker; ACEI, angiotensin converting enzyme inhibitor. Data are n (%).Additional file 2: Supplemental Table S2. Effect of sodium–glucose cotransporter-2 inhibitor administration in patients without ARBs/ACEIs, diuretics, and beta-blockers, and MRA. See Table Additional file 3: Supplemental Fig. S1. Feature importance ranking to categorize elevated ARR group and decreased ARR group. The explanatory variables were BW, body weight; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; Ht, hematocrit; s-Cr, serum creatinine; eGFR, estimate glomerular filtration rate; s-K, serum potassium level; age; sex.Additional file 4: Supplemental Fig. S2. Correlation between the change in ARR and the duration after the administration of SGLT2 inhibitor. Abbreviations: ARR, aldosterone-to-renin ratio.


The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests.In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients.We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups. COVID-19 is a severe respiratory illness caused by the virus SARS-CoV-2. The scientific community has focused on this disease with near unprecedented intensity. However, the majority of primary studies published on COVID-19 suffered from small sample sizes , 4. TherWe believe that the ability of predicting COVID-19 patients based on clinical variables and using an easily accessible computational model would be extremely useful to address the widespread lack of testing capabilities for COVID-19 worldwide. Because many countries and hospitals are not able to allocate sufficient testing resources, healthcare systems are deprived of one of their most effective tools for containing a pandemic: identification of case hotspots and targeted action towards regions and specific individuals with the disease . The scaA number of meta-analyses have been done on COVID-19, but almost none of them comprehensively included data from all published studies. Three different meta-analyses, published in February, March, and April of 2020, included data from 10, 8, and 31 articles, respectively –8. We inhttps://github.com/yoshihiko1218/COVID19ML/projects.Patient clinical data were manually curated from a PubMed search with the keyword “COVID-19.” A total of 1439 publications, dating from January 17, 2020 to March 23, 2020, were reviewed. All publications with no primary clinical data, including reviews, meta-analyses, and editorials, were excluded from our analysis. After manual review, we found 151 studies with individual-level data, encompassing data from 413 patients. All individual patient data with 2 more clinical variables reported per patient were included. Clinical variables sought for included demographics, signs and symptoms, laboratory test results, imaging results, and COVID-19 diagnosis. The compiled dataset with clinical variables for each patient, along with a reference to the source study for each patient, can be found in Table For our machine learning classification task to discriminate COVID-19 patients from influenza patients, we used clinical variables for 21 influenza patients from a study by Cheng et al. and 1050 patients from the Influenza Research Database [p-value is 0.05 or below.We sought to uncover correlations that could yield critical insights into the clinical characteristics of COVID-19 by correlating every variable to each other. For two continuous variables, the Spearman correlation test was applied. For one continuous variable and one categorical variable, the Kruskal-Wallis test was applied. For two different categorical variables, the chi-squared test was applied. All statistical tests were considered significant if the A self-organizing map (SOM) is an artificial neural network that constructs a two-dimensional, discretized depiction (map) of the training set. We used the SOM algorithm to cluster our patients based on similar patterns of clinical variables. The SOMbrero package in R was used . BecauseN = 398 patients and P = 48 variables. From this, we selected 27 clinical variables with very high significativity (p < 0.001) after running an ANOVA test across all neurons and ran another iteration of trainSOM with these variables. We generated SOMs from the 3 × 3 neuron grid to 20 × 20 neuron grid and selected the 9 × 9 SOM with 81 neurons as our final model based on minimal topographic error. We then aggregated the neurons into super-clusters using the superClass method in SOMbrero.The trainSOM function was used to implement numeric SOM on our data set, which is inputted as an N x P matrix, with Data were preprocessed by combining data from COVID-19 cases and influenza cases into a single matrix, followed by removal of any clinical variables that were not present in both the COVID-19 dataset and the influenza dataset. Nineteen clinical variables were included as machine learning input. The variables include age, sex, serum levels of neutrophil , serum levels of leukocytes , serum levels of lymphocytes , result of CT scans, result of chest X-rays, reported symptoms , body temperature, and underlying risk factors . Categorical data were converted to dummy variables using the get_dummies function in Pandas because non-numerical data are not allowed in our machine learning algorithm.The eXtreme Gradient Boosting algorithm (XGBoost), an ensemble machine learning method widely known for its superior performance over other machine learning methods, was selected for our study . We firsXGBoost results were evaluated by plotting a receiver operating characteristic (ROC) curve and a precision recall (PR) curve. The area under the curve (AUC) was also calculated for both curves. We also performed classification using three other machine learning models, LASSO, RIDGE, and random forest, and compared results obtained with that obtained by XGBoost. AUC of the ROC curve was compared across the different models.9 cells/L and a variance of 12.63 × 109 cells/L. Certain variables, including all counts of blood cell populations, have both ordinal and continuous components. The continuous component describes the raw count of these populations, while the ordinal component describes whether these counts are within normal range, below normal range, or above normal range. A summary for all data is shown in Table After compiling information from 151 published studies, we present a total of 42 different clinical variables, including 21 categorical and 21 continuous variables, that are reported in more than 1 study. Discrete variables include nominal categorical variables like gender, which is 49.49% (194 patients) male and 50.51% (198 patients) female, and ordinal categorical variables like lymphocytes level, of which 86 patients (48.86%) have low levels, 73 patients (41.47%) have normal levels, and 17 patients (9.65%) have high levels. Continuous variables include age, which has a mean of 38.91 years and variance 21.86 years, and serum neutrophil levels, which has a mean of 6.85 × 10To evaluate heterogeneity of patient data between the different studies, we performed principle component analysis (PCA) using all clinical variables as input Figure . Visualip < 0.05). Among 143 Spearman correlation tests, 27 show significant correlation, with 9 of these involving age, corroborating reports that age plays a critical role in the development of COVID-19 [We performed correlation between all possible pairs of clinical variables to uncover potentially important associations Table . If bothCOVID-19 . We obsep < 0.05). Among 319 Kruskal-Wallis comparisons, 36 correlations were significant. Some of the significant pairs overlapped with correlations between two continuous variables for variables that have both ordinal and continuous components. Such correlations are not displayed twice in Fig. For pairs of one continuous and one discrete variable, the Kruskal-Wallis test is applied   Table . We thenWe then examined the defining features of patients assigned to the same neurons. We investigated four neurons associated with the largest number of patients and identified the four variables with the smallest nonzero standard deviations for each patient cluster. In the largest cluster, with 39 patients, the four smallest nonzero standard deviations were for the variables region of infection, sore throat, RT-PCR results, and coughing. In the second largest cluster, with 37 patients, the variables were baby death if pregnant, lymphocyte levels, fever, and coughing. In the next largest cluster, with 21 patients, the variables were sore throat, duration of illness in days, RT-PCR results, and coughing. In the fourth largest cluster, with 20 patients, the variables were sore throat, fever, coughing, and age.We next used the function superClass to compute the relative Euclidean distances between the 81 patient clusters and form superclusters. The relative distances between the individual clusters are shown in Fig. p < 0.05)  Table . We disc5) Table g, cluste5) Table h, and 3 Because it can be difficult to distinguish influenza from COVID-19, we downloaded clinical data collected for influenza from a study by Cheng et al. and from the Influenza Research Database . MachineThe datasets from non-COVID patients and COVID-19 patients were merged and then split into training and testing patient sets, with 80% and 20% of the patients, respectively. Categorical variables were encoded as dummy variables. We then tuned the model using the Bayesian optimization method for hyperparameter search. We found the best hyperparameters to be gamma = 0.0933, learning rate = 0.4068, max depth = 6.558, and n_estimators = 107.242.From the ROC curve of prediction results, we obtained an AUC of 0.990 Fig. a. HoweveWe applied the XGBoost model to our 4 SOM superclusters to investigate whether classification results are better for specific subtypes of patients. The ROC AUC is over 0.9 for all 4 superclusters, with the best classification performance seen for clusters 3 and 4 Figure . The higSince XGBoost could be prone to overfitting, which is an inherent disadvantage of boosting models, we have also fitted other machine learning classification models to our data. Using RIDGE regression, random forest, and LASSO regression, we have obtained an AUC of 96.6%, 95.3%, and 96.3%, respectively, when trying to distinguish between influenza and COVID-19 patients , middle age (40–65 years old), and old (> 65 years old). We discovered that XGBoost performs the best in all classification tasks out of all our models, except for when the model was used for old patients , is creating difficulties for countries to test large numbers of suspected patients . TestingIn this study, we aim to mine published clinical data of COVID-19 patients to generate a new diagnostic framework. We hypothesize that novel or complex associations between clinical variables could be exploited for diagnosis with the aid of machine learning. Not only may underlying relationships between clinical variables in COVID-19 be useful for the development of a computational diagnostic test based on signs, symptoms, and laboratory results, these correlations can also yield critical insights into the biological mechanisms of COVID-19 transmission and infection.Using correlational tests, we corroborated previous findings and expected results for COVID-19 patients but also uncovered novel relationships between clinical variables. We found that age is correlated with CRP level, an indicator of inflammation, and decreased platelet levels. It is known that as age increases, the proinflammatory response becomes stronger, leading to increasing CRP and decreasing platelet levels . HoweverWe also classified COVID-19 patients into different clusters using the SOM machine learning algorithm. Two of the clusters are defined by low vs. high levels of immunological parameters, including immune cell counts and CRP levels. A third cluster is defined by a tendency for fewer reported symptoms, including sore throat, fever, and shortness of breath, and is predominantly female.Finally, using the machine learning algorithm XGBoost, we constructed a computational model that successfully classified influenza patients from COVID-19 patients with high sensitivity and specificity. We believe that our model demonstrated the feasibility of using data mining and machine learning to inform diagnostic decisions for COVID-19. Such a model could be extremely useful for more effective identification of COVID-19 cases and hotspots, which could allow health officials to act before testing shortages could be addressed.Interestingly, we have found age to be the most significant determinant of the accuracy of our model. We hypothesize that this could be due to the respective incidence rates of COVID-19 and influenza for each age group. While COVID-19 disproportionally affects the elderly population, influenza affects the younger population much more. According to CDC data, people aged 65 or above the only constitute 3.9% of all influenza cases from 2010 to 2016 . In contDespite promising results, several limitations exist for our study, all of which stem from the lack of large-scale clinical data. First, our sample size is severely limited because most clinical reports published do not publish individual-level patient data. Second, data on influenza signs and symptoms are equally inaccessible. We were only able to locate data for patients with H1N1 influenza A, which is not one of the active strains in the current influenza season. Third, many of our data sources are case studies that focused on specific cohorts of COVID-19 patients. This increases the chance of us capturing a patient population that is not representative of the general population, although this is an inherent risk of sampling. We anticipate that as more data are made openly available in the weeks and months to come, we will be able to build a more robust computational model. Therefore, we intend to provide the model we constructed as a computational framework for computation-aided diagnosis of COVID-19 data rather than a ready-to-use model. We also encourage researchers around the world to release de-identified patient data to aid in data mining and machine learning efforts against COVID-19.In conclusion, we demonstrated the use of machine learning models to predict COVID-19 presence using only commonly recorded clinical variables. Specifically, we successfully differentiated COVID-19 patients from influenza A patients using these clinical variables alone. Our machine learning models were constructed using publicly available data in published literature. We also conducted correlational analyses on this dataset and determined that males with COVID-19 have higher serum neutrophil and leukocyte levels than females with COVID-19. Finally, we used this dataset to cluster COVID-19 patients into 3 clinically relevant subtypes.Additional file 1 : Table S1. Correlation Results between All Pairs of Variables.Additional file 2 : Table S2. Significativity of ANOVA tests for SOM.Additional file 3 : Table S3.p-values of Kruskal-Wallis Tests across Superclusters.Additional file 4 : Table S4. (TXT 104 kb)Additional file 5: Figure S1.Additional file 6: Figure S2.


Hypertension in older adults is related to adverse cardiovascular outcomes, such as heart failure, stroke, myocardial infarction, and death. The global burden of hypertension is increasing due to an aging population and increasing prevalence of obesity, and is estimated to affect one third of the world's population by 2025. Adverse outcomes in older adults are compounded by mechanical hemodynamic changes, arterial stiffness, neurohormonal and autonomic dysregulation, and declining renal function. This review highlights the current evidence and summarizes recent guidelines on hypertension, pertaining to older adults. Management strategies for hypertension in older adults must consider the degree of frailty, increasingly complex medical comorbidities, and psycho‐social factors, and must therefore be individualized. Non‐pharmacological lifestyle interventions should be encouraged to mitigate the risk of developing hypertension, and as an adjunctive therapy to reduce the need for medications. Pharmacological therapy with diuretics, renin‐angiotensin system blockers, and calcium channel blockers have all shown benefit on cardiovascular outcomes in older patients. Given the economic and public health burden of hypertension in the United States and globally, it is critical to address lifestyle modifications in younger generations to prevent hypertension with age. Treating elevated BP in older adults has, therefore, remained controversial. However, recent data demonstrates CVD benefits in treating HTN in older adults. This emanates from several key studies, including the UK Prospective Diabetes Study (UKPDS),2There are specific underlying mechanisms of HTN in older persons, including mechanical hemodynamic changes, arterial stiffness, neurohormonal and autonomic dysregulation, and the aging kidney.Furthermore, there are hemodynamic mechanical changes that alter wave reflection causing a reduction in the aortic elasticity, as well as loss of recoil during diastole. There is also increase in pulse pressure and pulse‐wave velocity. The change in arterial structure causes an increase in the reflected pressure waves added to the forward pressure waves in the ascending aorta that further augments the central SBP.Over time, endothelial dysfunction occurs, inducing an elevation in endothelin‐1 and decreasing bioavailability of nitric oxide, which affects arterial dilation.Reduced baroreflex sensitivity with age and loss of artery compliance causes orthostatic hypotension, defined as a reduction in SBP by at least 20 mmHg or DBP by at least 10 mmHg within 3 minutes of standing.Postprandial hypotension in geriatric patients is an under‐recognized cause of syncope.The aging changes in the kidney are increased salt sensitivity due to a decline in the activity of the sodium/potassium and calcium adenosine triphosphate pumps, which prompts vasoconstriction and vascular resistance.2.1The diagnosis of hypertension requires measurement of BP in the proper environment under optimum conditions.3Treatment of ISH in older adults was somewhat unclear until randomized studies such as the Systolic Hypertension in the Elderly Program (SHEP),While the SPRINT and HYVET studies support blood pressure control even in frail individuals, analysis of NHANES data by Odden et al suggest that impaired walking speed (a surrogate for frailty) may assist in risk stratifying frail seniors. In slower walkers (<0.8 m/s), elevated SBP and DBP (≥140/90) were not associated with mortality.A meta‐analysisUntil SPRINT,In the INVEST sub‐study, the adjusted hazard ratio for primary outcomes showed a J‐shaped relationship between each age group with on‐treatment SBP and DBP. The SBP at the hazard ratio nadir increased with aging, highest for the elderly (140 mmHg). Nevertheless, DBP at the hazard ratio nadir was slightly decreased for the very old (70 mmHg).This evidence has been synthesized into recent guidelines and expert consensus recommendations. The Seventh Joint National Committee (JNC 7)The European Society of Cardiology (ESC)/European Society of Hypertension,The ACC/AHA guidelines emphasize individualized CV risk assessment with the use of the ASCVD risk calculator, which uses data from a pooled cohort to create personalized risk for each patient. Therefore, an individual with an ASCVD risk calculator score of >10% 10‐year risk will receive more aggressive treatment. While the ESC/ESH guidelines uses instead the SCORE  system coupled with risk modifiers and assessment of HTN mediated organ damage, and a patient with >10% 10‐year CV risk will require more aggressive therapy. The SCORE system estimates the risk based on age, sex, tobacco use, total cholesterol and systolic BP and uses European cohort datasets. One of the benefits of using the ESC/ESH guidelines in patients older than 65 years is that the SCORE system was adapted for this group.The consensus documents looked at the same evidence and came up with different recommendations. When reviewing the data from the “minority view” paper in Annals there is a statistical type II misinterpretation of the data.4The challenge in geriatric cardiology is making decisions not on age alone, but on the overall medical, physical, social, and mental characteristics of the patient. There is definitely a place for personalized medicine. The physician will need to alter current practice to altered pharmacokinetics and pharmacodynamics in aging and be mindful of issues such as cognitive impairment,55.1Non‐pharmacologic lifestyle interventions should be encouraged as preventive care for the development of HTN and as adjunctive therapy for established HTN. Current recommendations advocate for regular physical activity, weight control, smoking cessation, stress reduction, and avoidance of excessive alcohol intake.Reduction is sodium intake (approximately 1000 mg per day) and weight loss constitute effective and safe ways to improve BP, as seen in the Trial of Nonpharmacologic Interventions in the Elderly (TONE).The recommended physical activity recommended are aerobic exercise (90‐150 minutes per week with achievement of 65% to 75% of heart rate reserve), dynamic resistance (90‐150 minutes per week), or isometric resistance .Dolan et al5.2When medications are needed to manage older adults with uncontrolled HTN, factors to consider prior to selecting a medication include comorbidities, frailty of the patient, ability to follow instructions, complexity of the current regimen, supporting care  and lastly electrolytes and renal function.In patients with isolated HTN, the ESH/ESC guidelines recommends a calcium antagonist or diuretic in the elderly patients.Initiation of any medication should be done with assessment of orthostatic hypotension and gradual titration according to tolerance. Renal function should be assessed to detect possible increases in serum creatinine and reductions in GFR as a result of BP‐related reductions in renal perfusion. Hypokalemia is also an important side effects of diuretics which needs to be monitored. The medical team needs to be cognizant of treatment related side effects which may occur more frequently than reported in clinical trials.6The key component of managing the elderly is to address lifestyle modifications in the younger populations to prevent HTN with aging. Although there is a component just related to aging of the tissues, there is also a critical behavioral component that we can adjust by reducing obesity, incidence of diabetes mellitus, tobacco use, and alcohol consumption.When a physician sees younger patients, it is important to emphasize the importance of prevention of HTN with lifestyle modification in order to reduce their risk of cognitive impairment, stroke, myocardial infarction, heart failure, renal failure, and other complications. Newer clinical trials addressing polypill could be a solution to increase adherence as it would minimize number tablets. The use of telemedicine, text messages to trend BP medicines and the HoME study will provide answers regarding the use of technology to manage patients from home.7As Americans are living longer, there will be an increasing burden of HTN in the growing population of elderly patients. While lifestyle changes have the potential to mitigate this, adoption of healthy practices have been lagging in the US population. Education and awareness programs in community centers, place of worship , and businesses, such as the barber shop initiatives will continue to have impact. Screening for blood pressure elevation, improving access to care, and adoption of guideline driven management are keys to reducing the impact of HTN in our elderly patients. Further research is needed in this population, since most clinical trials include a small proportion of persons over age 85 years old. Such trial can help solidify the current recommendations to use >130/80 after lifestyle intervention as the threshold for initiation of therapy and <130/80 at the target of therapy.The authors declare no potential conflict of interests.


Background: Primary open-angle glaucoma (POAG) is considered one of the main causes of blindness. Detection of POAG at early stages and classification into evolutionary stages is crucial to blindness prevention. Methods: 1001 patients were enrolled, of whom 766 were healthy subjects and 235 were ocular hypertensive or glaucomatous patients in different stages of the disease. Spectral domain optical coherence tomography (SD-OCT) was used to determine Bruch’s membrane opening-minimum rim width (BMO-MRW) and the thicknesses of peripapillary retinal nerve fibre layer (RNFL) rings with diameters of 3.0, 4.1 and 4.7 mm centred on the optic nerve. The BMO-MRW rim and RNFL rings were divided into seven sectors (G-T-TS-TI-N-NS-NI). The k-means algorithm and linear discriminant analysis were used to classify patients into disease stages. Results: We defined four glaucoma stages and provided a new model for classifying eyes into these stages, with an overall accuracy greater than 92% . An online application was also implemented to predict the probability of glaucoma stage for any given eye. Conclusions: We propose a new objective algorithm for classifying POAG into clinical-evolutionary stages using SD-OCT. A correct classification is based on the data to which, of a set of categories, a new observation belongs. The main difficulty encountered in medical science is identifying a criterion that can frame all forms of a disease such as glaucoma in a coherent system to offer direct therapeutic suggestions . The objGlaucoma is considered one of the main causes of blindness, and based on this relationship, the WHO specifically recommends early diagnosis and treatment for blindness prevention .The currently accepted classification in Europe was proposed by the European Glaucoma Society in 2017  provided informed consent according to the data protection law currently in force. The study adhered to the conditions of the Declaration of Helsinki  and was approved by the ethics committees of both participating centres .The inclusion criteria were: (A) cognitive ability to accept and understand the proposed procedures; (B) a clinical record of intraocular pressure (IOP) < 23 with the absence of perimetric damage, normal optic disc appearance in the fundus examination and all sector values assessed by SD-OCT (BMO-MRW and peripapillary measurements) over the 5th percentile; (C) a clinical record of intraocular pressure IOP > 23 mmHg indicative of ocular hypertension (OH). In the absence of perimetric damage, normal optic disc appearance and all sector values assessed by SD-OCT over the 5th percentile; (D) POAG with historical clinical records of a IOP > 23 mmHg at least once, perimetric damage or glaucomatous optic disc appearance; such patients should have received anti-hypertensive treatment. (E) All the patients had to have a minimum of two clinical records: VF < 20% considering false-negative and false-positive responses; <20% fixation losses, as well as glaucomatous defects, according to the Hodapp classification. VF tests were performed using the standard Swedish 64 interactive threshold algorithm (SITA) target and the VF target strategy with a Humphrey II field analyser 65 ; (F) Caucasian race; (G) open-angle verification by gonioscopy and the absence 68 of signs of pseudoexfoliation and pigmentary glaucoma.The exclusion criteria: Ocular hypertensive patients or POAG patients were excluded from the study if they (A) did not agree to participate or did not meet the inclusion criteria, (B) had a direct family history of cognitive neurodegenerative diseases or exhibited suspicious signs, (C) had diseases affecting the retina and optic nerve before or at the time of recruitment, (D) had undergone intraocular surgeries in the previous 6 months, except for successful cataract removal or surgeries related to POAG, (E) exhibited non-transparent media at any level, (F) had severe systemic diseases, (G) had refractive errors above the spherical equivalent of three dioptres, (H) had SD-OCT tests with signal strengths below 20, or (I) were initially considered hypertensive and did not have high IOP values after 3 months of anti-hypertensive treatment and discontinuation of this treatment.A complete ophthalmological examination was performed during the inclusion session, which included best-corrected visual acuity, cycloplegic refractions (Tropicamide 1%), visual acuity with best correction, measurement of central corneal thickness by ultrasound pachymetry, slit-lamp examination and gonioscopy, and a retinal and optic nerve funduscopy with a 78-dioptric hand lens.SD-OCT was performed on all patients on one side to avoid problems related to a correlation between the eyes of the same patient. OCT was performed with the Heidelberg Spectralis device .The glaucoma program provided by the manufacturer was used, which has a patented anatomical positioning system (APS) with a series of patterns for scanning of the optic nerve head, the RNFL and the macular ganglion cell layer. The program compares the eyes of patients with normalised baseline values for normal eyes. All the participants underwent an examination of the thicknesses of peripapillary RNFL rings with diameters of 3.0, 4.1, and 4.7 mm centred on the optic nerve and BMO-MRW.No segmentations were performed manually for the RNFL values. Only small corrections were made by the same experienced operator (A.P.B.) to readjust Bruchs membrane endings during the BMO-MRW examination.i in variable x.x.x.i.x on age.i.x over the area of the BMO-MRW.i in variable x.The absolute values of healthy, glaucomatous and hypertensive eyes prior to normalisation indicated by Heidelberg Engineering and obtained from the seven sectors of the BMO-MRW and the three peripapillary rings (G-TS-T-TI-NS-N-NI), were exported to a Microsoft Excel  table. These absolute values were adjusted for age and the area of the papillary cup, and were normalised following the Heidelberg Engineering indications using the following formula provided by Heidelberg Engineering:Data processing and analysis were performed with programmes R (version 3.5), RKWard (version 7.0) and rk.Teaching (package) (version 1.3) .A descriptive summary of the main variables in the study was given by groups , including the mean and standard deviation.The normality of RNFL variables was proven with the Kolmogorov–Smirnov test, and the homogeneity of variances was tested with Box’s M test using an Pearson’s correlation coefficient was used for the correlation analysis of the variables. A strong correlation was considered for (r > 0.75) and a very strong correlation for (r > 0.9). The principal components analysis (PCA) was performed to reduce data dimensionality and to determine which combination of variables explained most of the variability of data. The PCA is a statistical method used to reduce data dimensionality by transforming the original variables  into a new set of linearly uncorrelated variables. The first principal component is a linear combination of variables that explain the widest variability in the data (maximum variance). The second principal component is a linear combination of variables with maximum variance, which is orthogonal to the first component .k clusters or classes according to the Euclidean distance from the eye to the cluster centroids in the n-dimensional space of the variables. Every eye is assigned to the cluster with the closest centroid. The initial choice of cluster centroids was random. The number of clusters k was decided according to elbow criteria [The k-means method was followed to cluster eyes into glaucoma stages . This alcriteria  by obserk-means method. LDA is a statistical method used to identify a set of discriminant functions, all of which are uncorrelated linear combinations of the variables, that maximizes the difference between classes and separates the classes the best [A linear discriminant analysis (LDA)  was usedthe best . The numhttps://github.com/asalber/glaucoma-staging and http://aprendeconalf.es/glaucoma-staging/.The data and all the results of this study are publicly available at For the study sample, 1001 individuals were selected . To avoid a natural correlation between the eyes of the same individuals, only left eyes were considered. For each eye, the main variables measured through OCT were the thickness of the BMO-MRW at the temporal inferior (BMO-MRW.TI), temporal (BMO-MRW.T), temporal superior (BMO-MRW.TS), nasal superior (BMO-MRW.NS), nasal (BMO-MRW.N), nasal inferior (BMO-MRW.NI) and general (BMO-MRW.G) sectors, the thickness of the 3.5 ring of the RNFL at the temporal inferior (RNFL3.5.TI), temporal (RNFL3.5.T), temporal superior (RNFL3.5.TS), nasal superior (RNFL3.5.NS), nasal (RNFL3.5.N), nasal inferior (RNFL3.5.NI) and general (RNFL3.5.G) sectors, the thickness of the 4.1 ring of the RNFL at the temporal inferior (RNFL4.1.TI), temporal (RNFL4.1.T), temporal superior (RNFL4.1.TS), nasal superior (RNFL4.1.NS), nasal (RNFL4.1.N), nasal inferior (RNFL4.1.NI) and general (RNFL4.1.G) sectors, and the thickness of the 4.7 ring of the RNFL at the temporal inferior (RNFL4.7.TI), temporal (RNFL4.7.T), temporal superior (RNFL4.7.TS), nasal superior (RNFL4.7.NS), nasal (RNFL4.7.N), nasal inferior (RNFL4.7.NI) and general (RNFL4.7.G) sectors. The individuals’ ages and BMO-MRW areas were also documented for standardization. The correlation analysis  showed aAs most of the variables were highly correlated, a PCA was performed to reduce the dimensionality and to determine which combination of variables explained most of the variability of the data.Plotting the subjects’ eyes on the first two principal components  revealedAccording to To define the severity glaucoma stages, the k-means cluster algorithm was applied to the BMO-MRW and RNFL variables of eyes with glaucoma. Based on x-axis of the first principal component.x-axis , overlapping the eyes of cluster I and partially overlapping the eyes of cluster II. This finding is logical as cluster I contained eyes in the early glaucoma stages, which do not differ that much from healthy eyes. Clusters III and IV are located at the other end of the x-axis far from the healthy eyes, which correspond to eyes with severer glaucoma. We can see that cluster IV do not overlap with healthy eyes and the overlap of cluster III is minimal, which comes over more clearly in To determine whether the detected classes were related to glaucoma severity, that is, whether a gradient of severity existed from cluster I to cluster IV, we added healthy eyes to the previous plot. In After defining clusters, we applied LDA to determine whether the four defined stages can be easily predicted. The LDA was applied to the principal components of the BMO-MRW rims and RNFL sectors as the original variables were highly correlated. Previously, we assessed the normality and homogeneity of variances of the principal components.As shown in We repeated the same analysis including healthy eyes. As expected, the classification power by LDA decreased, although this decrease was slight as we were still able to correctly classify 89% of eyes. After defining the glaucoma classes and assessing whether these classes could be predicted fairly well, we examined whether these classes could be defined without using all the BMO-MRW rims and RNFL sectors, but using only the most important variables because we observed a strong correlation among most of them. So we repeated the LDA for different combinations of the BMO-MRW rims and RNFL sectors.As the potential number of variable combinations was computationally impracticable, we focused on the variables that more contributed to the first principal component, which was the direction along which the separation of classes was clearer. These variables were sectors G and TI of BMO-MRW and RNFL, as shown in The model with the highest overall accuracy was the model with rims G and TI of the BMO-MRW and all the RNFL sector variables . However, the model that employed only the G and TI rims of BMO-MRW and the RNFL 3.5 sector achieved almost the same overall accuracy both with and without including healthy eyes. Regarding the sensitivity, specificity, and overall accuracy of this model, Thus, we decided to use only the G and TI rims of the BMO-MRW and RNFL 3.5 sectors to classify eyes into glaucoma stages. https://asalberapps.shinyapps.io/Glaucoma-Staging-System/.Finally, in order to render this staging system operational, we developed an online application (app) that implemented this classification model. Even after OCT emergence, one constant in the attempt made to classify POAG was the VF. Our study did not consider the VF to be a classification element given its subjective nature because of arbitrary limits of exclusion according to fixed losses, false-positives, cognitive impairment and difficult collaborations. VF-based classifications have been proposed, and many reviews of these classifications have been published ,17,18. AOther proposals for the diagnosis and classification of POAG have recently been presented based on OCT angiography ,21,22,23The diagnostic accuracy of the sectoral and total analysis of RNFL and/or BMO-MRW has also been determined in glaucoma. Danthurebandara et al.  used an Fan et al.  comparedp = 0.006), and glaucoma (mean VF DM: −9.36 ± 8.31 dB) , with the same specificity (96.1%). We belive that below the 5th percentile, the best definitions are of little significance when more specific sectors like G and TI are not studied.Authors like Zheng et al.  have alsIn our study, we relied on artificial intelligence (AI) for its potential for detect, diagnose and classify glaucoma through the automated processing of large data sets and the early detection of new patterns of diseases . We consIn a recent study, Brusini  proposedOur study provides a more practical and simpler proposal and incorporates healthy, OHT and glaucomatous patients. We noted some substantial differences with he study by Brusini. Firstly, we included more variables that may suffer the effect of glaucoma (28 variables of BMO-MRW and RNFL sectors) than those Brusini included (only two RNFL sectors). Brusini did not consider BMO-MRW assessment. We corroborated the importance of BMO-MRW for determining glaucoma severity as two of its variables (BMO-MRW.G and BMO-MRW.TI) appear in the simplified classification model .Brusini considered the mean thicknesses of the upper and lower quadrants of RNFL, but ignored other variables because they are not reliable for determining structural damage, although no justification for excluding these variables was provided. We propose considering all the the RNFL and BMO-MRW sectors , as well as a global measure (G) that averages 728 points in each rim. We selected those sectors with greater discriminatory power in the final classification model.k-means algorithm. A bigger number of classes does not substantially reduce the total intra-group variability, as shown in Brusini proposed six groups of increasing severity to classify POAG arbitraryly, but it does not obey objective or statistical criteria. Our work established four classes because this number is optimal for reducing intra-group variability according to the The groups that Brusini established are separated on the Cartesian plane in the direction of the two variables considered by taking intervals of equal length in the x- and y-axes. However, these axes do not correspond to the directions of the maximum variability of the data, which would be the directions of the principal components.k-means algorithm, the resulting groups were not same size, but emerge more naturally by agglutination around the centroids of each group. As for the validation of the classification model, Brusini’s only studied sensitivity (0.85–0.95) and specificity (0.92–0.98) to distinguish between patients with perimetric damage and healthy patients, but not for making the distinction between different stages. Our results, which include ocular hypertensive patients (without 315 perimetric damage), validate the system with similar balanced accuracies  shows low sensitivity and specificity, significant values are reached in more advanced stages (III and IV), thus enabling diagnosis with a mean global precision of 0.88. When we considered the robustness of our study due to the verification and evaluation of 1001 patients, 235 of whom had glaucoma at various disease stages, we can accept that only a single tomographic scan was considered for the study (the last scan performed). This test was considered the examination that would define the current process state.This study has some limitations. As it is a cross-sectional study, its conclusions cannot be strictly considered to POAG progression in time and follow-up. More studies have to be done in this sense. Moreover, this work was performed with Caucasian participants, so its results should not be extended to other races. In adition, individuals with refractive error above the spherical equivalent of three dioptres were excluded from this study in order to avoid artifactual results of OCT. Hence this system cannot be applied to eyes with moderate to severe refractive errors, and external validity of the current study may be restricted according to ethnic or refractive factors. Finally, we only considered VF results as the inclusion/exclusion criteria in this work. Thus, we did not compare our proposed OCT clinical-evolutionary staging system with VF parameters. The reasons for this were the subjective nature and poor reliability of VF, as explained above. However, we consider that this issue should be addressed in further studies.In conclusion, we present a new objective method to classify glaucoma patients according to both BMO-MRW and RNFL measurements that could be useful in clinical practice.


Compared to never consumers, the odds ratio (95% CI) of hypertension was 0.78 (0.73–0.84), 0.84 (0.77–0.91), 0.91 (0.88–0.94), and 0.97 (0.95–0.99) for daily, 4–6 days/week, 1–3 days/week, and monthly groups, P trend < 0.001. Our cross-sectional study in a nationwide sample of Chinese adults suggests that higher coarse grain intake was associated with lower blood pressure and lower hypertension risk.Whole grain intake was associated with better blood pressure control, but evidence is lacking in non-Western populations with different grain intake patterns. We aimed to determine the associations between coarse grain intake, usually considered as the best proxy of whole grain intake for Chinese diets, with blood pressure and undiagnosed hypertension using baseline data from the China Kadoorie Biobank study. After excluding participants with clinically diagnosed hypertension or use of antihypertensive dugs, 435,907 participants were included in our analysis. A self-reported questionnaire was used to measure coarse grain intake frequency. Overall, 12.8% and 29.2% of the participants reported daily consumption and never consumption, respectively. With multivariable adjustments including BMI, outdoor temperature, and physical activity, higher frequency of coarse grain intake was associated with lower systolic and diastolic blood pressure in those older than 40 years,  Globally, elevated blood pressure was identified as a major modifiable risk factor for non-communicable diseases, such as stroke and ischemic heart disease . A 25% rFrom recent studies, increased intake of whole grains was associated with reduced blood pressure ,8,9,10. In the present study, we aim to determine the association between coarse grain intake frequency with blood pressure, and undiagnosed hypertension using cross-sectional data from a nationwide large-scale cohort in China.We utilized the baseline data of the China Kadoorie Biobank (CKB). The detailed study design has been described elsewhere ,18. In b2). After at least five minutes’ rest, blood pressure was measured on the right upper arm at least twice in a seated position using a digital sphygmomanometer  in a quiet and warm room. All devices were calibrated and measurements were done by trained staff according to a standard protocol to ensure measurement consistency. Normally, the cuff of the sphygmomanometer for adult size was used, while an alternative sphygmomanometer cuff size was used if arm circumference was outside the normal range, with a proper elasticity. The center of the cuff was approximately flush with the subject’s heart position. The average of the two measurements was used in the analysis. Because blood pressure is strongly associated with outdoor temperature, we also collected monthly mean outdoor temperature (MMOT) of each study area during baseline survey from the website of the China Meteorological Administration [At the study clinics, information on sociodemographic status, lifestyle factors , and medical history were collected via face-to-face interviews by trained health workers, using laptop-based questionnaires. With all participants wearing light clothes and no shoes, weight was measured using a bioelectrical impedance analysis instrument . Standing height was measured to the nearest 0.1 cm using a stadiometer and BMI was calculated as weight in kilograms divided by the square of height in meters  was used in plotting the figures. Two-sided P < 0.05 was considered as statistically significant.Mean (SD) or number (percentage) of population characteristics were presented by coarse grain intake level. General linear model was used in comparing between-group differences for continuous variables, and chi-square test was used for categorical variables. Adjusted means of SBP and DBP were generated using general linear model by adjusting for age, sex, study area, education level , smoking , alcohol drinking , physical activity level /day), BMI, and MMOT where appropriate. Odds ratios (OR) of undiagnosed hypertension by coarse grain intake frequency were evaluated using logistic regression. Age, sex, and study area were included as covariates in model 1; education level, smoking, alcohol drinking, physical activity, BMI, and MMOT were additionally adjusted in model 2. SAS 9.3  was used in data analyses, and R Software  reported never or rarely consume coarse grains, while 55,997 (12.8%) reported daily consumption . Daily cThe adjusted means of SBP and DBP by coarse grain intake level and age group are presented in the p for trend <0.001. The association was not substantially changed by further adjusting for education, smoking, alcohol drinking, physical activity level, BMI, and MMOT . The associations were similar in different age groups, but tended to be stronger among men and those living in urban areas, with ORs (95% CI) for daily consumption of 0.76 (0.70–0.823) and 0.74 (0.66–0.81), respectively,  , the preIntriguingly in stratified analysis by gender, the association between coarse grain intake with blood pressure and undiagnosed hypertension was stronger in men than in women. In the previous studies on the association between whole grain intake with blood pressure and hypertension, no gender-specific results were reported ,8,9,10. The exact mechanisms underlying the association between coarse grain intake frequency and blood pressure are not clear to us, although a few intervention studies suggested that dietary fiber supplementation may effectively reduce blood pressure in patients with hypertension ,33. MeanOur study has several limitations. Firstly, with a cross-sectional design, temporal association could not be established; despite people with a known status of hypertension having been excluded from analyses, the main results were not substantially changed in a sensitivity analysis by further excluding those diagnosed with diabetes and cardiovascular disease, or those in the Henan area, where 99% of participants reported daily consumption ; secondlIn summary, with data from more than 0.4 million adult Chinese free of diagnosed hypertension, inverse independent associations were identified of coarse grain intake frequency with blood pressure and risk of undiagnosed hypertension. Our results may provide insights into further interventional and mechanism studies addressing the beneficial effects of coarse grain consumption on vascular health, especially among Chinese populations without a diagnosis of hypertension, and in regions where coarse grains are well-available and popular. Furthermore, to promote public awareness of potential cardiometabolic benefits of coarse grain intake, new interventional approaches  deserve further verification.