We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers.–1·1.73 m–2 for ≥ 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets.Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL·min2 = 0.404) variable among the 8 modified values. Ten variables , and urinary protein) were included in the final risk prediction model for CKD stage 3 (R2 = 0.330). Ten variables , and urinary protein) were included in the final risk prediction model for CKD stage 4 (R2 = 0.386). Four variables  and urinary protein) were included in the final risk prediction model for CKD stage 5 (R2 = 0.321).We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory  and Harrel’s C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT. The incidences of chronic kidney disease (CKD) and end-stage renal disease (ESRD) have been increasing rapidly . The oveTherefore, physicians have difficulty in deciding which patients will ultimately progress to kidney failure and when they will require RRT. Identifying patients at risk of CKD progression may facilitate more optimal nephrology care. In the present study, we aimed to develop a model of CKD progression for predicting the probability and time to progression from CKD to RRT, using 6 months of clinical data variables routinely measured at healthcare centers. This developed model would provide more precise predictions than the commonly used Kidney Disease: Improving Global Outcomes (KDIGO) CKD stages, based eGFR and albuminuria.The data were derived from the electronic medical record (EMR) database at Ajou University Hospital, Suwon, South Korea, from October 1997 to September 2012. This database contains information on patients and medical records, and includes data from all medical departments in the hospital. We extracted the data without personal identification to ensure patient confidentiality. The study was approved by the institutional review board of Ajou University Hospital.–1·1.73 m–2 for ≥ 3 months [–1·1.73 m–2) was satisfactory. We excluded patients who were < 19 years old and those who had undergone RRT within 6 months of the study.We included patients who were diagnosed with CKD and followed up for at least 6 months. The diagnostic criterion for CKD is estimated glomerular filtration rate (eGFR) < 60 mL·minMDRD equationCKD-EPI equationWe randomly divided the final study population into a training set and a test set for the verification process .–1·1.73 m–2. RRT included hemodialysis, peritoneal dialysis, and renal transplantation. The initial RRT point was defined as the first day of hemodialysis, day of catheter insertion for peritoneal dialysis, or the day of surgery for renal transplantation. If we could not identify the renal replacement event, we regarded the last follow-up date as the last observation day. The study period refers to the 180 days from the initial day of observation.The observation period was defined as interval from the initial day of observation to the day of initiation of RRT or the day of censoring. The initial day is the first day on which the eGFR decreased to < 60 mL·minThe variables were as follows: demographic variables, including age and sex; comorbid conditions, including diabetes mellitus (DM), hypertension (HTN), glomerular nephritis (GN), systemic lupus erythematosus (SLE), and polycystic kidney disease (PKD); laboratory variables, including levels of blood urea nitrogen (BUN), hemoglobin, serum creatinine, serum calcium, serum phosphate, serum albumin, serum bicarbonate, urinary creatinine, urinary protein, and urinary blood, eGFR by the MDRD, and eGFR by the CKD-EPI. We excluded urine albumin, urine hemoglobin, and urine creatinine levels as variables because they were not measured in more than 50% of the patients.Data regarding laboratory examination and comorbidity variables were collected throughout the study period. For missing values, we included data for 30 days before the initial day of observation and 30 days after completion of the study period. Urinary protein by dipstick was reported semi-quantitively as trace, 1+, 2+, 3+, or 4+ corresponding to albumin levels of 10, 30, 100, 300, or 1000mg/dl albumin respectively. Urinary protein level was coded as 5 dummy variables on the basis of negative values . Criteria for the 5 comorbidities are described as follows.DM: ICD-10 (E10–E14) code, serum HbA1c > 6.5%, or use of hypoglycemic medicationHTN: ICD-10 (I10–I15) code or use of antihypertensive medicationGN: ICD-10 (N01–N08) codeSLE: ICD-10 (M32) codePCKD: ICD-10 (Q61) codeWe developed 8 “modified values” that were potentially associated with CKD for 6 months and chose the “representative value” that demonstrated the greatest efficiency in a multivariate Cox proportional hazards regression model.The modified values were: value at baseline, value at the end of the study period, minimum value, maximum value, ratio of the minimum to maximum values, slope of the minimum to maximum vales, integral means, and slope of initial to integral means(details as follows).The value at baseline: the value obtained closest to the initial day of observation (± 30 days)The value at the end of the study period: the value obtained closest to the end of the study period (± 30 days)The minimum value: the minimum value during the study periodThe maximum value: the maximum value during the study periodThe ratio of the minimum to maximum values: the maximum value/minimum valueThe slope of the minimum to maximum valuesThe integral meansThe slope of initial to integral meansValues were excluded if 50% of the cases had missing data. Urinary protein was only available at baseline.t (years) is as follows [Multivariate Cox proportional hazards regression was used for model development. We created a prediction model according to CKD stages . The pro follows .t: Followup timeβi: Regression coefficientXi: Level of risk factor i of a patientS0(t): Underlying probability of survivingp < 0.05).To evaluate the expected prediction error of the training model, we calculated the Brier score (BS)  and HarrHarrell’s C statistic is a common and well-validated measure to assess the discrimination. The higher the C-statistic, the better the model can discriminate between subjects who experience the outcome of interest and subjects who do not. C-statistics provide overall measures of predictive accuracy.We collected EMR data from Microsoft SQL Server 2012, and used PASW statistics (18.0.0)  for selecting representative values. The multivariate Cox proportional hazards regression model, BS, and Harrel’s C statistics were analysed using R package (3.4.3).We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after the exclusion of patients with missing values, the training and test sets included 1,625 and 1,618 patients, respectively .–1·1.73 m–2 vs. 41.1 mL·min–1·1.73 m–2 and 39.4 mL·min–1·1.73 m–2 vs. 40.6 mL·min–1·1.73 m–2, p < 0.05) and RRT events were higher  in the training set than in the test set.Patients in the training and test sets were similar with regard to demographics, comorbidities, laboratory values, and outcomes, with the exception of eGFR (MDRD), eGFR (CKD-EPI), and RRT events. The eGFR (MDRD) and eGFR (CKD-EPI) were lower (39.9 mL·min2 = 0.404), except for the end value (R2 = 0.546)  . We exclVariable selection process underwent through the multivariate Cox proportional hazards regression model using the backward elimination method. We made 3 models for three separate analysis for CKD stage 3–5. The final model for CKD stage 3 that included 10 selected variables  had risk predictive power of approximately 33% in the Cox proportional hazards regression model . The risThe risk index (RI) of CKD stage 3 patients can be defined as follows.The RI of CKD stage 4 patients can be defined as follows.The RI of CKD stage 5 patients can be defined as follows.t, years) is as follows.By using RI, the formula for the probability of a patient not undergoing RRT at some point (S(t): probability of not undergoing renal replacement therapyS0(t): underlying probabilityS0(1) = 0.973,S0(3) = 0876,S0(5) = 0.756,S0(7) = 0.756,S0(10) = 0.423) at CKD stage 3, 0.80 (0.76–0.84) at CKD stage 4, 0.84 (0.78–0.90), respectively.We analysed 2 cases in which the observation period was approximately 5 years, using the risk prediction model in the test set. CKD is asymptomatic in the early stages, but symptoms appear in the later stages, accompanied by complications such as cardiovascular disease, anemia, infection, cognitive impairment, and impaired physical function –11. The We considered many variables cited in previous articles that could affect renal function, including age, sex, laboratory findings, and comorbidities, to develop a risk prediction model. These included variables such as young age, male sex, African-American ethnicity, DM, HTN, obesity, urine protein, serum albumin, anemia, lipidemia, smoking, and cardiovascular disease . In the We identified variables that were associated with RRT through the clinical guidance and backward elimination  methods. From a clinical point of view, models bsased on referral eGFR are more useful than an overall model. Predictions for patient with an eGFR of 60 would probably only be interesting to the patient, while predictions for a patient with an eGFR 15 are critical for dialysis preparation. So we underwent separate analysis according to CKD stages: 1) CKD stage 3: age, sex, DM, PKD, levels of serum albumin, serum hemoglobin, serum phosphate, and serum potassium, eGFR, and urinary protein. 2) CKD stage 4: age, sex, DM, GN, PKD, levels of serum hemoglobin, serum BUN, serum calcium, eGFR, and urinary protein. 3) CKD stage 5: level of serum hemoglobin, serum BUN, eGFR, and urinary protein. The results were similar to those of previous studies. First, one study reported that the risk of progression to ESRD was decreased among older patients with CKD stage 3  . Third, 2, but the number of patients was inadequate to evaluate the model. We will compare the integral mean and end value in a larger dataset in a further study.To identify representative values that show renal function change over 6 months, we considered 8 modified values and developed a multivariate Cox proportional hazards regression model. The integral mean contains the time and the value in order to obtain sufficient power to explain the change in data over 6 months. The end value had the highest RFinally, we developed the renal prediction model with several variables using integral means from continuous variables. To evaluate prediction error, we calculated the BS and the Harrel’s C statistics. From the results of the BS and Harrel’s C statistics, we consider that our model has sufficient explanatory power to predict renal progression.The strength of our analysis is that we divided patients into 2 groups: the training set and the test set. Thus, we calculated the BS and Harrel’s C statistics in order to confirm the accuracy of the model. Second, the prediction equation must include variables that are very routinely available in the nephrology clinic for convenience of use. Local healthcare facilities can collect laboratory data easily and integrate the risk prediction tool into decision-making for patients who require further evaluation or in preparation for RRT.The limitations of our analysis are that the study was performed retrospectively, and therefore, the data obtained are insufficient including blood pressure measurements of the patients, which was important predictor in previous studies , 20. We Many studies have identified a wide range of risk factors for the progression of CKD. Although many studies have identified similar risk factors, there has not been sufficient research performed on the risk prediction models for RRT. To develop accurate and easy-to-use models, further large prospective studies are required. Our predictive model for CKD may have sufficient power to predict RRT, as shown in 2 cases in the present study. However, there are also cases that did not fit the model. If data were collected from a greater number of patients with greater accuracy, a more precise model could be developed. The development of the representative value seems very complicated for a prediction tool in a clinical setting. To simplify this model could be achieved by cooperation among nephrologists and statisticians.In summary, a model was developed and validated to predict the risk for ESRD. This model uses commonly available clinical variables and may -provide more precise predictions than the commonly used KDIGO CKD stages, based on eGFR and albuminuria.S1 Fig(EMF)Click here for additional data file.S2 Fig(EMF)Click here for additional data file.S3 Fig(EMF)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.


Is adding preoperative and intraoperative data associated with improved risk stratification of patients undergoing noncardiac surgery for postoperative acute kidney injury?In this prognostic study of 42 615 patients who underwent noncardiac surgery, the addition of preoperative to prehospitalization data improved model performance (area under the curve increased from 0.71 to 0.80) as did adding preoperative plus intraoperative data (area under the curve further increased to 0.82).Although electronic health record data may be used to accurately stratify patients at risk of postoperative acute kidney injury, there appears to be only modest improvement in performance when adding intraoperative data to risk stratification models. Acute kidney injury (AKI) is one of the most common complications after noncardiac surgery. Yet current postoperative AKI risk stratification models have substantial limitations, such as limited use of perioperative data.To examine whether adding preoperative and intraoperative data is associated with improved prediction of noncardiac postoperative AKI.A prognostic study using logistic regression with elastic net selection, gradient boosting machine (GBM), and random forest approaches was conducted at 4 tertiary academic hospitals in the United States. A total of 42 615 hospitalized adults with serum creatinine measurements who underwent major noncardiac surgery between January 1, 2014, and April 30, 2018, were included in the study. Serum creatinine measurements from 365 days before and 7 days after surgery were used in this study.Postoperative AKI  was the primary outcome. The area under the receiver operating characteristic curve (AUC) was used to assess discrimination.P < .001 for AUC comparison). The AUC further increased to 0.817  when adding intraoperative variables (P < .001 for comparison vs model using preoperative variables). However, the statistically significant improvements in discrimination did not appear to be clinically significant. In particular, the AKI rate among patients classified as high risk improved from 29.1% to 30.0%, a net of 15 patients were appropriately reclassified as high risk, and an additional 15 patients were appropriately reclassified as low risk.Among 42 615 patients who underwent noncardiac surgery, the mean (SD) age was 57.9 (15.7) years, 23 943 (56.2%) were women, 27 857 (65.4%) were white, and the most frequent surgery types were orthopedic (15 718 [36.9%]), general (8808 [20.7%]), and neurologic (6564 [15.4%]). The rate of postoperative AKI was 10.1% (n = 4318). The progressive addition of clinical data improved model performance across all modeling approaches, with GBM providing the highest discrimination by AUC. In GBM models, the AUC increased from 0.712  using prehospitalization variables to 0.804  using preoperative variables  (The findings of the study suggest that electronic health record data may be used to accurately stratify patients at risk of perioperative AKI, but the modest improvements from adding intraoperative data should be weighed against challenges in using intraoperative data. This prognostic study evaluates the addition of preoperative and intraoperative data to prehospitalization data to identify patients at risk for postoperative acute kidney injury after undergoing major noncardiac surgery. Improving risk stratification may be helpful for preoperative and perioperative management in the setting of noncardiac surgery.Acute kidney injury (AKI) is a common postoperative complication, occurring in 12% of patients undergoing surgical procedures,6 levels of accuracy,10 although they have not used consistent definitions of the AKI outcome, have used a mix of statistical and machine learning approaches, and have not uniformly focused on noncardiac surgery. For example, large studies of AKI after general or other noncardiac surgery demonstrated moderate predictive accuracy , but predated current consensus standards on AKI definition.12 The lack of common definitions and methods underscores the need to compare performance across these various approaches. Furthermore, while some studies have used data from the electronic health record (EHR), they have not incorporated detailed physiological and clinical data  collected intraoperatively. Because adding such data improves risk stratification for other postoperative complications,13 these data may also yield improvements in risk stratification for AKI.Existing models to predict AKI provide moderateIn this study, we examined whether adding intraoperative data was associated with improved prediction of noncardiac postoperative AKI compared with models using administrative and preoperative clinical information alone. Furthermore, we compared performance across multiple statistical and machine learning approaches and definitions of AKI.14 Cohort data were randomly split by patient into derivation (60%), validation (20%), and test (20%) sets.15Electronic health record data were collected on adult patients undergoing noncardiac surgery during an inpatient admission between January 1, 2014, and April 30, 2018, at the University of Pennsylvania Health System. We used code developed by the Multicenter Perioperative Outcomes Group that was run on University of Pennsylvania Health System Epic Clarity databases to standardize intraoperative and postoperative data and combined the data with administrative and preoperative data.TRIPOD) reporting guideline.16The University of Pennsylvania Institutional Review Board approved the study design and granted a waiver of informed consent from study participants for secondary use of electronic health records. This study follows the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis 17 and restricted to major therapeutic procedures using Agency for Healthcare Research Quality Healthcare Cost Utilization Project Surgery Flag Software.18 We focused on noncardiac surgery because the association between preoperative and intraoperative variables and AKI likely differ for cardiac surgery owing to the use of cardiopulmonary bypass.Patients 18 years or older across 4 academic medical centers in University of Pennsylvania Health System during the study period were included if they underwent major noncardiac surgery. We identified noncardiac surgery using primary 9 or if they met criteria for AKI within the 7 days before surgery  to avoid overlap between preoperative and postoperative periods. In addition, patients were excluded if they did not have at least 1 preoperative and postoperative serum creatinine measurement , had end-stage renal disease and underwent dialysis within the past year, had an elevated baseline serum creatinine level greater than or equal to 4.5 mg/dL ,19 We excluded the urine output criteria owing to concerns for poor specificity for AKI classification20 and the lack of reliable data in our data set. If discharge occurred earlier than 7 days after surgery and there was no evidence of AKI to date, an outcome of no AKI was assigned. Secondary outcomes included use of inpatient dialysis, a postsurgical length of stay of 7 or more days , and in-hospital mortality . Data for this category were collected between the start and end of the surgical procedure using timestamps in the EHR  to 98.6%   separately. We used 3 modeling approaches: logistic regression with elastic net selection, random forest, and gradient boosting machines (GBMs), which we applied to each definition of AKI. For random forest and GBM models, we used a randomized grid search using 3-folds across 30 iterations on our derivation data set for selecting optimal model parameters. For GBMs, we used decision trees as the weak learner with logistic regression for the loss function. Validation sets were used to evaluate, verify, and finalize our model parameters. Final model results are reported for the test sets of data only.2 tests and continuous variables were compared using Mann-Whitney tests. Model performance was assessed using the AUC,27 which we calculated by comparing the AKI estimated from the models with observed AKI. We calculated 95% CIs using the method of DeLong et al28 with 1000 bootstrapping samples to test for significance between models. We compared model performance within each of the 3 modeling approaches for each of the 3 groups of variables (reflecting the progressive addition of data), as well as across the 3 modeling approaches when using the same group of data elements.We compared differences between the development, validation, and test data sets and reported results of model performance using the test data sets (20% of sample). Categorical variables were compared using χ29 with the remaining 80% of patients stratified into a low-risk category. Risk stratification was conducted on prehospitalization, preoperative, and perioperative data sets, examined for primary and secondary outcomes, and examined by patient encounters with and without events.To illustrate implications for clinical utility, we stratified patients as high and low risk for Kidney Disease Improving Global Outcomes AKI and compared incidence rates of our primary and secondary outcomes associated with AKI. Patients were stratified into a high-risk category if their predicted risk for AKI was in the top 20% of the test data set population (n = 8494),32 Given the lack of an evidence-based definition of a high-risk probability value for AKI, the top 20% was arbitrarily selected and so we examined sensitivity to cutoff by using top 10% and top 30%.We tested the sensitivity of our results to several data and modeling decisions, including using a super learner algorithm, classifying outlier data values as missing, by surgical type , and alternative definitions of AKI (eMethods in the P < .05.Logistic regression with elastic net selection (PROC GLMSELECT) was implemented using SAS software, version 9.4 (SAS Institute Inc). Super Learner was implemented using the R, version 3.4.3 SuperLearner Package (R Foundation). All other code and predictive models  were conducted in Python, version 3.6 (Python Software Foundation), with Pandas 0.23.3 and Scikit-learn 0.19.1 libraries. Two-tailed tests were considered statistically significant at 24 A total of 3859 patients (9.1%) had multiple operations during the study period. Of the study sample, 4318 patients (10.1%) experienced AKI , and 0.790  with perioperative variables that included intraoperative variables (P = .02 for AUC comparison vs model using preoperative variables only). The random forest models resulted in an AUC of 0.710  with prehospitalization variables, a higher AUC of 0.787  with preoperative variables , and the highest AUC of 0.808  using perioperative variables (P < .001 for AUC comparison vs model using preoperative variables only). The GBM models generated the highest AUCs across all models with an AUC of 0.712  using the prehospitalization variables, a higher AUC of 0.804  with preoperative variables , and the highest AUC of 0.817  when using perioperative variables . Full model performance across data sets, calibration curves, and variable coefficients and importance can be found in eTables 3-7 and the eFigure in the Among the 8494 patients in the test set, 845 patients (9.9%) experienced Kidney Disease Improving Global Outcomes AKI . Use of A total of 1699 of the 8494 patients (20.0%) were classified as high risk and 6795 patients (80.0%) were classified as low risk, using the GBM model . We applThe small improvements were concordant across primary and secondary outcomes . Rates oThe results of several sensitivity analyses were consistent with our main results  in previous studies.The study has several limitations. First, this was a single-institution study and the availability of EHR data as well as practice patterns may vary at other institutions. However, we used data from multiple hospitals within a health system with different surgery and anesthesia groups and clinicians. Furthermore, the intraoperative data that we used are likely captured as part of routine monitoring of patients while in surgery. Third, our follow-up period was limited to the hospital setting and there may have been limited documentation of other important clinical outcomes. We did not capture longitudinal outcomes, which may affect the ability to risk stratify for other important, longer-term outcomes. Fourth, we did not have reliable data on urine output, which could have led to incomplete identification of AKI.The findings of this study suggest that EHR data can be used to accurately stratify patients at risk of perioperative AKI. However, the modest improvements in performance from adding intraoperative data should be weighed against clinical utility and examination of whether particular subgroups may benefit from the addition requires further research.


People with Phenylketonuria (PKU) who respond to tetrahydrobiopterin (BH4) often decrease dependence on medical food (MF) following increased phenylalanine (phe) tolerance. Responders to BH4 may experience a reduction in certain nutrients if not compensated through intact foods or supplements. This study investigated B6, B12, folate, and iron status based on blood levels and dietary intake in patients with PKU responsive to BH4 over 1 year.Fifty-eight patients with PKU, ages 4–50 years were recruited and initiated on BH4 therapy. Patients were monitored for BH4 response, and nutritional status was recorded at regular intervals over 12 months. The analysis included 33 patients with known BH4 response status and complete nutritional data. Nutrient intake was determined by National Data System for Research (NDSR) analysis of self reported 3 day diet records and compared to Dietary Reference Intakes (DRIs). Blood biomarkers were analyzed by Quest Diagnostics and compared to laboratory reference ranges. Patient laboratory values were compared to controls from the National Health and Examination Survey (NHANES). Differences in nutrient intakes across time points were examined, stratified by age, using nonparametric methods. Statistical analyses were completed with SAS 9.4, with significance set at α = 0.05.p < 0.01) and adult (p = 0.06) BH4 responders over 1 year. Among those < 18 years of age, mean percent of calories obtained from MF declined from 21.3 to 4.7%. In adults, percent calories from MF dropped from 19.5 to 4.0%. Though maintaining laboratory and dietary values within reference ranges, responders < 18 years experienced a significant decline in serum B12 (p = 0.01), dietary folate (p = 0.006), and dietary iron (p = 0.004) over the study.Medical food intake declined among pediatric  contains supplementary material, which is available to authorized users. Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism identified in infancy through newborn screening. This disorder can lead to severe neurological or psychomotor complications if left untreated . PKU is While sapropterin is an effective adjunct therapy for many PKU patients, the consequent diet liberalization often results in decreased requirement for MF consumption , 8, 9. TRecent studies have revealed the potential for macro- and micronutrient deficiencies in BH4-responsive patients , 11, 12.Given prior evidence of B-vitamin deficiencies among patients with PKU on liberalized diets, and the biologic mechanisms which link B-vitamins and iron to erythroblast production and anemia risk , we evalPrior studies evaluating micronutrient status in patients with PKU responsive to BH4 have been limited by reliance on diet records alone , 16, havThe study protocol and schematic has been described elsewhere in detail , 19. BriEach patient was followed for up to 1 year. Data were collected at baseline and at three consecutive study visits spaced evenly throughout the year, with final data collection completed at 1 year. Three day diet records were completed at each study visit and analyzed by a registered dietitian. Average energy, phe (mg), protein (g), amino acids (g), fatty acids, and macro- and micronutrient intake were determined using the Nutrition Data System for Research  diet analysis software, and reported as units per day. A blood draw was also performed at each visit for laboratory analysis of plasma amino acids  and micronutrient biomarkers (Quest Diagnostics). Table To compare laboratory nutrient values in our sample with a standard population, two age and gender matched controls were selected for each patient from the most recent NHANES collection with available B12, B6, folate, and iron blood measurements (NHANES 2005–2006). NHANES serum and plasma based laboratory measurements were performed following Center for Disease Control and Prevention (CDC) guidelines .n = 7). Participants with missing diet or laboratory information were excluded from analysis. Provisional responders were also removed from the analysis to allow clear comparison of diet-liberalized responders on BH4 to non-responders.All statistical procedures were completed using SAS software v.9.4.1 . Because dietary recommended intakes (DRI) differ based on age, analyses were stratified by age group (< 18 years and ≥ 18 years). Baseline and final visit time points were used in assessment. Where final visit dietary data was not recorded, and where participants had stabilized diets by the previous visit (month 8), data from month 8 was used to represent the final visit  for each age group. The Kruskal-Wallis test was conducted to compare baseline and end point values in each response group with NHANES controls.The percentage of nutrients obtained from MF and intact food was calculated from diet record data at baseline and final visit. MF intake included primary and secondary formula, if prescribed. Differences in the percentage of micronutrients obtained from intact foods versus MF were tested using the Wilcoxon-Mann-Whitney and Signed Rank tests.In total, 48 of the 58 recruited patients completed the one-year study. The final sample for analysis included 33 subjects, 15 non-responsive to sapropterin therapy and 18 confirmed to be responsive, henceforth referred to simply as “responders”. Exclusions are described in Fig. In the final analysis cohort, the mean age was 18.8 years, with 19 (57.5%) < 18 years of age and 14 42.4%) ≥18 years of age. The majority of participants were female (51.5%)  . Overall declines in micronutrients may be attributed to the lowered MF intake, especially given comparison with non-responders and older responders who did not see significant declines in percentage MF intake or laboratory and dietary measured micronutrients over time. AV sum [Responders in both age groups experienced a decline in proportion of caloric and nutrient intake derived from MF over the one-year period Table . This de. AV sum  was alsoMedian micronutrient values remained above DRI and laboratory reference ranges among responders despite noted declines over the study period. In addition, the number of responders who did not meet DRI values increased from start to end of study for all micronutrients Table . For insp = 0.02), folate (p = 0.004), and B6 (p = 0.04). Laboratory assessed micronutrients appeared similar between both groups at individual time points


In this review we summarized the actual clinical data for a cardioprotective therapeutic role of melatonin, listed melatonin and its agonists in different stages of development, and evaluated the melatonin cardiovascular target tractability and prediction using machine learning on ChEMBL. To date, most clinical trials investigating a cardioprotective therapeutic role of melatonin are in phase 2a. Selective melatonin receptor agonists Tasimelteon, Ramelteon, and combined melatonergic-serotonin Agomelatine, and other agonists with registered structures in CHEMBL were not yet investigated as cardioprotective or cardiovascular drugs. As drug-able for these therapeutic targets, melatonin receptor agonists have the benefit over melatonin of well-characterized pharmacologic profiles and extensive safety data. Recent reports of the X-ray crystal structures of MT1 and MT2 receptors shall lead to the development of highly selective melatonin receptor agonists. Predictive models using machine learning could help to identify cardiovascular targets for melatonin. Selecting ChEMBL scores > 4.5 in cardiovascular assays, and melatonin scores > 4, we obtained 284 records from 162 cardiovascular assays carried out with 80 molecules with predicted or measured melatonin activity. Melatonin activities (agonistic or antagonistic) found in these experimental cardiovascular assays and models include arrhythmias, coronary and large vessel contractility, and hypertension. Preclinical proof-of-concept and early clinical studies (phase 2a) suggest a cardioprotective benefit from melatonin in various heart diseases. However, larger phase 3 randomized interventional studies are necessary to establish melatonin and its agonists’ actions as cardioprotective therapeutic agents. N-acetyl-5-methoxytryptamine) is an indolamine with a molecular mass of 232.283 g/mol that presents two basic properties: (1) It is amphiphilic (partition coefficient of 1.2), being almost equally diffusible in aqueous and in lipid medium so that it can be found in all compartments of the organism, and (2) it is a potent antioxidant molecule, efficiently scavenging almost any oxygen and nitrogen reactive species. Its origin, and supposedly its primary function, seems to be linked to this antioxidant potency that is well preserved throughout the phylogenetic tree. The other melatonin cellular and systemic functions and also different ways of action were acquired during evolution, according to the complexity of the organism and the site of melatonin production [Melatonin  and MT2 (MTRN1B). They are protein-coupled receptors linked to Gi/Go or Gq/G11, and signaling, therefore, happens either through the inhibition of cAMP or cGMP synthesis or activation of phospholipase C, depending on the central or peripheral localization of the target. These melatonin receptors are ubiquitously present in peripheral and central organs, including the cardiovascular system. MTNR1B mutations are associated with increased blood sugar levels and increased risk for type 2 diabetes, while variants in MTNR1A and B have been linked to autism spectrum disorders ,4. Due tMelatonin controls cardiovascular function (blood pressure and heart rate) acting either peripherally (heart and blood vessels) or centrally , in addition to regulating the renin-angiotensin system  and mitoA large preclinical portfolio suggests a cardioprotective role and other cardiovascular benefits of melatonin. Evidence for cardiovascular benefits of dietary melatonin has been recently reviewed . Due to In this review, we summarized the actual clinical data for a cardioprotective therapeutic role of melatonin, listed melatonin and its agonists in different stages of development, and evaluated the melatonin cardiovascular target tractability and prediction using machine learning on ChEMBL.Acute coronary syndrome (ACS) contains three types of coronary artery disease: ST-segment elevation myocardial infarction (STEMI), non–ST-segment elevation myocardial infarction (NSTEMI), and unstable angina. Reasons for the use of melatonin in acute coronary syndrome have been reviewed by Dominguez-Rodriguez et al. ,10. StipAlthough intravenous and intracoronary melatonin during primary percutaneous coronary intervention (PPCI) in patients with STEMI was not associated with a reduction in infarct size  , a signiResults are awaited from the following completed investigational randomized trials:Completed trial on prophylactic melatonin treatment for preventive effect on depression, depressive and anxiety symptoms, sleep, and circadian disturbances following acute coronary syndrome  .Completed trial on intracoronary and systemic melatonin effects on the Myocardial Salvage Index, high-sensitivity troponin, creatine kinase myocardial band, and clinical events have been investigated in patients with acute myocardial infarction  [An ongoing study aims at researching the cardioprotective effects of intravenous melatonin administered prior to reperfusion and continued after restoration of coronary blood flow in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention .Melatonin 5 mg in nondippers with coronary artery disease decreased nocturnal blood pressure but also caused a daytime increase . StipulaA clinical intervention trial is underway to evaluate whether melatonin decreases the occurrence of atrial fibrillation after cardiac surgery , based on its reported putative antiarrhythmic effects preclinical experimental models ,24,25. APreclinical studies have documented the beneficial effects of melatonin in reducing myocardial tissue injury in models of experimental ischemia-reperfusion ,30. FurtAn ongoing trial studies the effect of melatonin 10 mg on cardiovascular and muscle mass and function in patients with heart failure .Melatonin treatment in the perioperative period of major surgery  decreased clinical cardiac morbidity and the occurrence of myocardial ischemia after abdominal aortic aneurism repair  .The first indications for melatonin involvement in blood pressure regulations date back in early 70 s when pinealectomy-induced hypertension that could be prevented with melatonin was described in rats ,32. MechDecrease in systolic blood pressure throughout the 24 h period by melatonin  .Increase in 24-h mean blood pressure by melatonin  in hypertensive patients treated by nifedipine .Decrease in nocturnal blood pressure in patients with untreated essential hypertension by repeated (3 weeks) but not single-dose melatonin (2.5 mg) .Decrease in nocturnal blood pressure in normotensive and treated hypertensive women by melatonin sustained-release (1 mg rapidly and 2 mg slowly) .Decrease in nocturnal systolic blood pressure in patients with nocturnal hypertension by melatonin sustained-release (2 mg for 4 weeks) Improvement in sleep quality in hypertensive patients treated with beta-blockers by melatonin (2.5 mg for 3 weeks)  [No significant effect on nocturnal blood pressure in African Americans with essential hypertension by melatonin 24 mg sustained-release was found  .A meta-analysis of melatonin effects on nocturnal blood pressure concluded that controlled-release melatonin is effective and safe in ameliorating nocturnal hypertension . Though,The conclusions from these trials are awaiting to be reinforced with further phase 2b studies to confirm the clinical efficacy of melatonin and determine the therapeutic dose range. Different study populations and time-dependent covariates like sleep apnea should be considered. Further phase 3 randomized controlled trials are necessary to confirm and expand on safety and effectiveness the results from phase 2 trials.Pharmacokinetic parameters of melatonin in humans have been reviewed by Harpsøe NG et al. . No linkTwelve melatonin receptor small molecules agonists were identified in the ChEMBL bioactivity database, which has > 2 million compound records created by mining and extracting data from medicinal chemistry literature  Table 2Table 2.Tasimelteon, a selective dual MT1 and MT2 melatonin receptor agonist, was not investigated in heart or cardiovascular disorders. Tasimelteon is indicated for the treatment of non-24-h sleep-wake disorder (N24HSWD) [N24HSWD) ,45, and N24HSWD) . TasimelRamelteon, a selective dual MT1 and MT2 melatonin receptor agonist, was cardioprotective through postconditioning in a preclinical heart ischemia-reperfusion injury model proof-of-concept study [pt study . The carpt study . To datept study . Rameltept study .Agomelatine, (CHEMBL10878) is a melatonergic agonist (at both MT1 and MT2 receptors) and serotonin 2C (5-HT2C) receptor antagonist indicated for the treatment of major depression [pression .Melatonin analogs CHEMBL3230568, CHEMBL3230569, CHEMBL34730, CHEMBL33700 , melatonTwo recent papers elucidated the crystal structures and characterized the structural basis of ligand recognition for the human MT1  and MT2 The fundamental step in drug discovery is the generation of a working hypothesis to justify a new lead compound for a therapeutic target. Machine learning models as virtual assays are widely used for compound target prediction and quantitative structure-activity relationship predictions . The ChEEvaluation of predictive models.The validation set was split in an 80/20 ratio, i.e., training and test sets, respectively . The traMelatonin, a multifunctional indoleamine, has attributed cardioprotective role through several mechanisms, and apparently has a safe pharmacological profile. Despite the vast preclinical evidence for a cardioprotective and cardiovascular protective melatonin, a better understanding of the pharmacokinetics and pharmacodynamics of melatonin and its associated pharmacophores shall help to translate the basic research findings into a clinical setting.Melatonin is marketed as a dietary food supplement and commonly prescribed for insomnia . It has Melatonin receptor agonists have the benefit over melatonin of well-characterized pharmacologic profiles and selective action . Unravel


The non-invasive biomarkers of migraine can help to develop the personalized medication of this disorder. In testing of the antimigraine drugs the capsaicin-induced skin redness with activated TRPV1 receptors in sensory neurons associated with the release of the migraine mediator CGRP has already been widely used.Fourteen migraine patients (mean age 34.6 ± 10.2 years) and 14 healthy volunteers (mean age 29.9 ± 9.7 years) participated in the experiment. A new arrangement of imaging photoplethysmography recently developed by us was used here to discover novel sensitive parameters of dermal blood flow during capsaicin applications in migraine patients.Blood pulsation amplitude (BPA) observed as optical-intensity waveform varying synchronously with heartbeat was used for detailed exploration of microcirculatory perfusion induced by capsicum patch application. The BPA signals, once having appeared after certain latent period, were progressively rising until being saturated. Capsaicin-induced high BPA areas were distributed unevenly under the patch, forming “hot spots.” Interestingly the hot spots were much more variable in migraine patients than in the control group. In contrast to BPA, a slow component of waveforms related to the skin redness changed significantly less than BPA highlighting the latter parameter as the potential sensitive biomarker of capsaicin-induced activation of the blood flow. Thus, in migraine patients, there is a non-uniform (both in space and in time) reaction to capsaicin, resulting in highly variable openings of skin capillaries.BPA dynamics measured by imaging photoplethysmography could serve as a novel sensitive non-invasive biomarker of migraine-associated changes in microcirculation. One of the main trends in migraine studies is to find out the most sensitive and preferably non-invasive biomarkers serving for the diagnostic and personalized treatments of this often-intractable disorder. Application of capsaicin to the skin is widely used to monitor the reactiveness of local blood flow following activation of capsaicin-sensitive TRPV1 receptors . The undA recent study has shown that migraine patients reported higher level of capsaicin-induced feeling of pain and larger areas of flare . In mostLaser Doppler imaging systems are commonly used for assessment of the cutaneous microcirculation , 12. HowThe study was conducted in St. Petersburg and Kazan in accordance with ethical standards presented in the 2013 Declaration of Helsinki. The Ethics Committees of the Pavlov First Saint-Petersburg State Medical University and Kazan Federal University prior the research approved the protocol of this study. The study involved 14 patients with migraine and 14 healthy volunteers. Both groups were comparable in age, main constitutional, and hemodynamic parameters  . PatientParameters of the cutaneous blood flow were measured at the upper arm of each subject by using imaging photoplethysmography (IPPG) method . Figure Before measurements, each subject was asked to relax in the laboratory during 15 min. After relaxation, his/her blood pressure was measured. Thereafter, subject took the recumbent position for video recording of an area of the upper arm during 20 s by means of the IPPG system  containing 8% capsaicin  was applied to the upper-arm area recorded by the IPPG system in the previous steps. Application of the Quatenza patch lasted from 15 to 25 min during which the subject was asked to keep the recumbent position and refrain from movements. Video images of the area with Quatenza patch were recorded during 20 s repeating the recording about every minute. Since the Quatenza patch is transparent for the green light, we were able to estimate parameters of blood circulation and their evolution under the patch.Then the patch . (ii) in every ROI we calculated frame-by-frame evolution of average pixel values to evaluate a PPG waveform. Typical waveform consisted of alternative component (AC) modulated at the heartbeat frequency, which was superimposed with the slowly varying component (DC). After calculation of AC-to-DC ratio, deducing the unity, and inverting the sign, we obtained a PPG waveform. Examples of the waveforms are shown in Fig. Each series of recorded video frames was processed off-line by using custom-made software implemented in the MATLAB platform. First, we calculated the spatial distribution of the blood pulsations amplitude (BPA) by using the algorithm described in details in our previous paper . BrieflyThe typical PPG waveform before Quatenza patch application is shown in Fig. p < 0.05.Statistical analysis was performed using the STATISTICA 10 software. All continuous variables were expressed as the mean ± standard deviation of the mean (SD). The results of the comparative analysis are graphically represented as the mean, error of the mean, and SD. Non-parametric Mann - Whitney U-test was used to assess the reliability of the differences. One-variance dispersion analysis (ANOVA) with selection of the least number of significant indicators was used to assess the coupled variability of blood flow characteristics in the main and control groups. The diagnostic significance of blood flow parameters in response to capsaicin application was assessed through discriminant analysis using the lowest number of indicators. Significance was assumed when The baseline of blood perfusion (before lidocaine application) in the capillary bed of the upper arm was at low level for majority of subjects (BPA was less than 0.3%). According to the protocol of our experiment, the lidocaine patch was applied for one hour and after its removal, the 8%-capsaicin patch was applied to the same area of the upper arm. At the beginning of capsaicin patch application the noise-like PPG waveforms  of blood pulsations. These hot spots were clearly corresponded to the locally increased blood flow in the capillary bed. Examples of the spatial BPA distribution and its variation during capsaicin application are shown in Fig. The transparency of the capsaicin patch for green light allowed us to measure spatial distribution of BPA an its evolution 2 approximately fitting the size of “hot spots”  are shown in Fig. ts” Fig. . Notablyn = 14 versus 0.09 ± 0.03 for control, n = 14, p = 0.10. For quantitative estimation of the “hot spots” migration we used the coefficient of variation (CV) which is the ratio of the BPA-growth speed averaged over all big ROIs in the time interval between the start of growth and signal saturation  was different for different subjects varying from 0.04 to 0.19% per min. However, this parameter did not differ significantly between migraine and control groups: 0.11 ± 0.04 for migraine, р = 0.049, Fig. p = 0.018 with the Fisher’s criterion F = 6.37 for CV, and p = 0.049, F = 4.27 for DT. Distribution of CV and DT in both groups is shown in Fig. p < 0.005). Furthermore, a joint analysis of variance mean speed and STD also allowed detecting significant differences between patients with migraine and control groups: p = 0.026 with the Fisher’s criterion F = 4.23.Moreover, we found that migraine patients, compared to control group, have a smaller DT of BPA growth: 6.3 ± 2.6 min versus 8.9 ± 3.9 min, p > 0.05).A typical reaction of the human skin to capsaicin application is progressive skin redness , which bFigure Based on innovative IPPG technique, we present here the novel method for evaluation of capsaicin induced changes in peripheral blood flow that is more sensitive than the commonly used skin redness analysis. The main finding of our study is the remarkable variability (both in space and time) in skin microcirculation after capsaicin application in migraine patients. We suggest this novel phenomenon can serve as a quantitative biomarker of vascular changes in this disorder and probably as a predictive parameter for efficiency of anti-migraine CGRP-based drugs.Despite the high prevalence of migraine, the pathogenesis of migraine pain remains unclear. However, recent animal and human studies suggested the significant role of TRPV1 receptors expressed in meningeal nerve fibers in migraine pathology , 5. Acculocal asynchronous’ reactions in certain skin areas triggered by application of capsaicin which were higher presented in migraine. One of the main findings of our study is highly heterogeneous (in time and in space) reaction of migraine patients to stimulation of TRPV1 receptors. It became possible to obtain this novel finding through the use of the advanced IPPG technique allowing us to register the pulsatile blood flow with simultaneous ECG recordings, resulting in enhanced sensitivity of this approach [Capsaicin-induced increase in DBF presented as the skin redness is a simple test to evaluate the release of CGRP in peripheral tissues and to test the efficiency of anti-migraine medicines . Measuriapproach . Anotherapproach , 32. It approach  which poInterestingly, the phenomenon of hot spots leading to asynchronous high intensity activation of local parts of the skin can unexpectedly slow down the global growth of the BPA masking thus the reactivity of the current individual to capsaicin. This can explain why we did not find significant changes in the speed of BPA growth in migraine patients in our small sample. Nevertheless, high variability of BPA in capsaicin test can predict the sensitivity of CGRP-specific treatments such as triptans and CGRP neutralizing antibodies.Left-right side asymmetry (asynchronous reactions) in the sympathetic skin responses was also found in the headache-free period in unilateral migraine patients . One of The main limitation of the study is that it was performed in a small cohort and the data should be confirmed in a larger population. However, even in a limited number of patients we found the novel criterion for evaluation of the peripheral blood flow such as the high variability of zones in the skin with increased BPA. In addition, the trend to a shorter latency in BPA increase should be revisited in a larger population. We also used lidocaine as the obligatory component of the pre-treatment that may have partially masked the intensity of capsaicin effects. The genetic reason for asynchronous activation of microcirculation remains unclear. As TRPV1 gene polymorphism determines sensitivity to capsaicin [In conclusion, in this pilot study, we suggest the novel non-invasive biomarkers of migraine such as the BPA-growth-speed variations observed during mild activation of skin TRPV1 receptors with capsaicin. This simple test performed with transparent capsaicin patch could serve for the diagnostic purposes and for the prediction of the personalized treatments of migraine patients.


Cerebral autoregulation is crucial for maintaining cerebral blood flow and perfusion. In recent years, the importance of cerebral oxygenation in syncope and orthostatic intolerance (OI) has received increased attention. Cerebral tissue oxygenation can be measured by using near-infrared spectroscopy (NIRS), which determines the ratio of oxygenated hemoglobin to total hemoglobin in cerebral tissue. NIRS is non-invasive technology using near-infrared light, which displays real-time cerebral tissue oxygenation. Normal values of cerebral tissue oxygenation in healthy subjects are 60 to 80%. Head-up tilt test (HUT) offers the opportunity to observe the haemodynamic changes precipitating syncope and is, today, the standard method for the evaluation of syncope and orthostatic intolerance syndromes. In previous studies where NIRS was applied during HUT, a significant decrease in cerebral tissue oxygenation both prior to and during loss-of-consciousness in vasovagal syncope (VVS) has been observed. Interestingly, cerebral tissue oxygenation appears to decrease even before haemodynamic changes can be observed. Apart from VVS, cerebral tissue oxygenation decreases during orthostatic provocation in patients with orthostatic hypotension (OH) and postural orthostatic tachycardia syndrome (POTS), in the latter even in the absence of hypotension. Importantly, decline of cerebral tissue oxygenation in VVS and POTS during HUT may not correlate with hemodynamic changes. In this mini review, we summarize the current knowledge of the application of cerebral oximetry in syncope and orthostatic intolerance syndromes, discuss its likely value as a clinical diagnostic tool and also emphasize its potential in the understanding of the relevant pathophysiology. Syncope and orthostatic intolerance (OI) involve a variety of clinical syndromes on the basis of cardiovascular disease or autonomic dysfunction . These c2. Because oxygenated Hb and deoxygenated Hb have different absorption spectra, their proportion can be calculated  has gained increased attention since it offers the opportunity to observe absolute values of cerebral oxygenation . NIRS melculated . The prilculated .NIRS technology combined with so called diffuse correlation spectroscopy permits direct monitoring of cerebral blood flow in addition to oxygenation . In shorHead-up tilt test (HUT) is an established method for the evaluation of unexplained syncope and orthostatic intolerance by its opportunity to observe the haemodynamic changes during orthostatic provocation . By applWhen standing up, venous blood is pooled in the lower extremities and splanchnic area which leads to decreased venous return and reduced cardiac output. In turn, decreased cardiac output initiates various autonomic responses that if insufficient or impaired, ultimately may result in decreased cerebral perfusion and syncope . The impAnother study using diffuse correlation spectroscopy flow-oximetry found that a threshold of ~50% cerebral blood flow decrease during HUT can be applied in order to separate pre-syncopal patients from controls. Potentially, continuous monitoring of cerebral tissue oxygenation and blood flow may provide predictive information for impending VVS during HUT, possibly both shortening the test and avoiding the need for induction of syncope , 21.Cerebral oximetry may also offer the possibility to analyse the syncope mechanism in potentially overlapping diagnoses such as VVS, postural orthostatic tachycardia syndrome (POTS), or OH by providing characteristic signal patterns during HUT , 11 Fig. DiffereAn issue with HUT is the apparent high false-positive and false-negative rates in adults  althoughKim et al.  using ceCerebral tissue oxygenation declines during orthostasis in patients with POTS , 15 Fig. POTS isNIRS and diffuse correlation spectroscopy flow-oximetry have definite potential for practical measurements of cerebral perfusion. They are both safe, non-invasive, applicable at the bedside, and suitable for quantitative measurements and continuous monitoring with little operator-dependency. Wireless NIRS devices have recently been validated in animals  and are The limitations of cerebral oximetry are mainly technical. NIRS does not measure cerebral blood flow directly, unless some form of flow tracer is used, e.g., a short breath of 100% oxygen or an injection of a contrast agent . As prevFurther, NIRS displays a great variety in the human anatomy of vessels and non-vascular tissue, where baseline values vary by ~10% between individuals. Thus, cerebral oximetry is more suitable for detecting intra-individual rather than inter-individual changes . Also, tFinally, cerebral oximetry with NIRS measures oxygen saturation levels in the regions where the probes are located and does not provide information on cerebral saturation in remote parts of the brain. A significant redistribution of the cerebral blood flow values during HUT has previously been found , with a As described in this review, cerebral oximetry has been increasingly used in various experimental settings in the recent years. However, cerebral oximetry is yet to find its role as an established method in the clinical settings of syncope and orthostatic intolerance. The future applications of cerebral oximetry can be broadly divided into two partly overlapping major goals: First, cerebral oximetry may aid in the understanding of pathophysiological mechanisms underlying syncope and orthostatic intolerance, the relationship between haemodynamic changes and cerebral autoregulation, and also serving as a complement to the current methods of cerebral circulation assessment. The cerebral oximetry may be applied for better monitoring of coupling between haemodynamic changes and characteristic symptoms observed in both VVS , 11, 20 As an example, monitoring of cerebral tissue oxygenation may provide predictive information for impending VVS during HUT, avoiding the need for induction of syncope , 21. TheCerebral oximetry with near infrared spectroscopy is a harmless, non-invasive technique which provides the opportunity to measure real-time regional cerebral tissue oxygenation. With the addition of diffuse correlation spectroscopy, simultaneous estimation of cerebral blood flow can be made. These techniques are able to demonstrate different patterns of cerebral deoxygenation during orthostatic provocation in patients with various types of syncope and orthostatic intolerance, before any haemodynamic changes can be observed. Furthermore, the addition of cerebral oximetry to the established head up tilt test allows a more sensitive detection of orthostatic intolerance and may reflect disrupted homeostasis of cerebral oxygenation in POTS. With increasing use in research, cerebral oximetry may be able to provide information about factors related to cerebral hypoperfusion as well as clues to hitherto unknown factors behind syndromes associated with recurrent syncope and orthostatic intolerance.Concept and design, drafting the article, critical revision of the article, and approval of the article: IK, HH, EB, FR, RS, AF, and VH. Funding secured by: AF and VH. Other: IK and AF.AF reports personal fees from Medtronic Inc. and Cardiome Corp., and patent royalties from Thermo Fisher Scientific outside the submitted work. RS reports consultancy for Medtronic Inc., Member of Speakers' Bureau Abbott Labs and Stockholder in Edwards LifeSciences, Boston Scientific Inc. and AstraZeneca PLC. VH reports previous educational congress grant from Boston Scientific Inc. outside the submitted work and Stockholder in Swedish Orphan Biovitrum AB (publ). EB has been employed by AstraZeneca after completion of this study. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


N = 667, aged 18–88). We found that cardiovascular health and age made approximately similar contributions to white matter health and explained up to 56% of variance therein. Lower diastolic blood pressure, higher systolic blood pressure, and higher heart rate were each strongly, and independently, associated with white matter abnormalities on all indices. Body mass and exercise were associated with white matter health, both directly and indirectly via cardiovascular health. These results highlight the importance of cardiovascular risk factors for white matter health across the adult lifespan and suggest that systolic blood pressure, diastolic blood pressure, and heart rate affect white matter health via separate mechanisms.Cardiovascular health declines with age, increasing the risk of hypertension and elevated heart rate in middle and old age. Here, we used multivariate techniques to investigate the associations between cardiovascular health  and white matter macrostructure (lesion volume and number) and microstructure (as measured by diffusion-weighted imaging) in the cross-sectional, population-based Cam-CAN cohort ( •Cardiovascular health is related to white matter lesion burden and diffusivity.•Low diastolic pressure, high systolic pressure, and higher heart rate contribute independently.•Cardiovascular health and age explain up to 56% of variance in white matter health.•The uncinate fasciculus, inferior fronto-occipital fasciculus, and forceps minor show most sensitivity.•Lower BMI and more exercise may have protective effects. SEM comWe hypothesized that (1) cardiovascular health is associated with white matter lesion burden and microstructure ; (2) sys22.1N = 667 cognitively healthy adults . See https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/, and the covariance matrix and analyses scripts can be downloaded from https://osf.io/zwk5p/.A healthy, population-based, adult lifespan sample was collected as part of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study, as described in detail elsewhere . Exclusi2.2Heart rate and systolic and diastolic blood pressure were measured using the A&D Medical Digital Blood Pressure Monitor (UA-774), an automated sphygmomanometer. Measurements were carried out while participants were seated, and the arm resting comfortably. Blood pressure and heart rate are known to undergo fluctuation in response to stress and movement . We addr2.3www.statistical-modelling.de/lst.html  white matter tractography atlas averaged over the hemispheres . See Sup2.52. Height and weight were measured using portable scales (Seca 875).We modeled exercise and BMI as potential protective factors for cardiovascular and white matter health in an exploratory analysis. We assessed exercise using the European Physical Activity Questionnaire . Four me2.62 test, the Root Mean Square Error of Approximation (RMSEA) and its confidence interval, the Comparative Fit Index (CFI), and the Standardized Root Mean Square Residual (SRMR). We report the scaled test statistics. Good fit was defined as approximately RMSEA < 0.05, CFI > 0.97, and SRMR < 0.05, acceptable fit as approximately RMSEA = 0.05–0.08, CFI = 0.95–0.97, and SRMR = 0.05–0.10  = 984.28, p < 0.001; RMSEA = 0.247 [0.240 0.255]; CFI = 0.474; SRMR = 0.218. We found that a three-factor model with heart rate and diastolic and systolic blood pressure as separate factors showed adequate fit (χ2(3) = 181.71, p < 0.001). We therefore used the three-factor measurement model in all subsequent analyses. The first measurement for both diastolic and systolic blood pressure showed a lower factor loading than the latter 2 measurements. This is consistent with the notion that the first measurement was less reliable, potentially due to movement or adaptation to the testing setting, and highlights the value of latent variables for reducing measurement error.To establish the relationship between cardiovascular and white matter health, we first used Confirmatory Factor Analysis to specify a measurement model for our cardiovascular health measures . Estimatuate fit  and fit χ2(35) = 418.66, p < 0.001; RMSEA = 0.130 [0.120–0.140]; CFI = 0.879; SRMR = 0.062), MD (χ2(35) = 1825.69, p < 0.001; RMSEA = 0.281 [0.271–0.292]; CFI = 0.766; SRMR = 0.090), or MK (χ2(35) = 283.51, p < 0.001; RMSEA = 0.105 [0.097–0.112]; CFI = 0.840; SRMR = 0.054). This indicates that white matter microstructure cannot be adequately captured by a single factor in our cohort. We therefore modeled each of the 10 white matter tracts as a separate indicator.We also assessed fit of a single-factor measurement model of white matter microstructure, by estimating a single latent variable from white matter microstructure in 10 white matter tracts. We found that this single-factor model did not fit well for FA , making a single-factor model just identified. Just-identified models do not yield meaningful fit statistics. We therefore modeled each of the lesion burden measures as separate indicators in all subsequent analyses.3.2We examined the relationship between cardiovascular health and white matter lesion burden using structural equation models in which diastolic blood pressure, systolic blood pressure, and heart rate were modeled as predictors for total lesion volume and number. To account for potential confounding with age, this variable was additionally included as a covariate. The full model showed good fit . All regΔχ2(4) = 31.49, p < 0.001). This indicates that diastolic blood pressure, systolic blood pressure, and heart rate differed in their relationship to white matter lesion burden, with diastolic and systolic blood pressure showing greater effects than heart rate  = 2.40, p = 0.494). This indicates that lesion volume and number showed a similar sensitivity to cardiovascular health. The total effect size of cardiovascular health and age on white matter lesion burden was considerable, with an R2 = 0.51 for lesion number and R2 = 0.30 for volume.We took the same approach to test whether lesion volume and lesion number showed different sensitivity to cardiovascular health. We found that a model in which parameter estimates for paths from each cardiovascular health measure to total lesion volume and number  were constrained to be equal did not differ significantly in fit from a model in which these parameters were freely estimated  = 49.53, p < 0.001; MD: Δχ2(20) = 118.66, p < 0.001; and MK: Δχ2(20) = 53.16, p < 0.001). This highlights that diastolic blood pressure, systolic blood pressure, and heart rate differed in their relationship to white matter microstructure.To probe the specificity of the association between cardiovascular health and white matter microstructure, we again compared a series of models with different equality constraints. For all measures of white matter microstructure, the full model fit better than a model in which the parameter estimates of all cardiovascular factors were constrained to be equal (FA: Δχ2(27) = 54.80, p = 0.001; MD: Δχ2(27) = 44.59, p = 0.018; and MK: Δχ2(27) = 30.54, p = 0.290), indicating that for FA and MD, white matter tracts differed in their sensitivity to cardiovascular health. The tracts most affected across our DWI measures were the uncinate fasciculus, inferior fronto-occipital fasciculus, and forceps minor  models fit better than models in which white matter tracts were constrained to be equally vulnerable  = 47.65, p < 0.001; BMI: Δχ2(3) = 128.67, p < 0.001). This indicates that exercise and BMI were associated with white matter microstructure both directly and indirectly, via cardiovascular health.In an exploratory analysis, we examined whether 2 potential protective factors for cardiovascular health and white matter health—BMI and self-reported exercise—affected cardiovascular and white matter health. We modeled exercise and BMI in separate models and assessed their direct and indirect  effects on MD, the indicator of white matter health in our analyses, which was most sensitive to cardiovascular health . For bot3.4.1χ2(117) = 577.52, p < 0.001; RMSEA = 0.077 [0.071 0.083]; CFI = 0.941; SRMR = 0.163). Exercise at home and during leisure showed no clear association with either MD or cardiovascular health (p = 0.001), systolic blood pressure , and heart rate , indicating that more energy spent while commuting was associated with better cardiovascular health overall. Exercise at work showed no clear relationship to cardiovascular health but was the most significant predictor of MD. There was a weak, negative correlation between exercise and MD in 7 tracts with standardized coefficients ranging from −0.04 to −0.12  = 516.44, p < 0.001; RMSEA = 0.080 [0.073 0.086]; CFI = 0.938; SRMR = 0.201). Higher BMI was associated with higher diastolic blood pressure , systolic blood pressure , and heart rate , thus predicting reduced cardiovascular health overall. Higher BMI was weakly correlated with lower MD in 3 tracts , and when controlling for social class, education levels, alcohol consumption, and smoking.4.1lower diastolic blood pressure had a similarly detrimental effect as higher systolic blood pressure. This negative correlation was not evident in a single-indicator model including diastolic blood pressure alone, showing that low diastolic blood pressure is likely to be particularly detrimental at moderate to high levels of systolic blood pressure. This conditional effect may capture the difference between systolic and diastolic blood pressure, a measure known as pulse pressure (We found that systolic hypertension predicted poorer white matter macrostructure and microstructure on all indices, in line with previous studies showing bivariate links between high systolic blood pressure and lower FA, higher MD, and greater white matter lesion volume and number . Systolipressure . Pulse ppressure , increaspressure . Explorapressure .These findings also highlight the importance of assessing and modeling systolic and diastolic blood pressure, rather than just hypertensive status. The use of multivariate models reveals the existence of conditional effects, which can remain undetected when using univariate techniques. This pattern of results is also relevant to clinical practice. Current UK treatment guidelines include only upper limits for blood pressure , but our4.2Elevated heart rate was associated with poorer white matter health, independent of age and blood pressure. Previous research showed that high heart rate predicts cardiovascular problems independent of blood pressure, physical activity, and comorbidities . Nightti4.3Body mass and exercise were related to white matter health, both directly and indirectly via cardiovascular health. Higher BMI was associated with poorer cardiovascular health and higher MD, indicating reduced white matter integrity, although effect sizes were relatively small. This finding replicates previous studies showing a negative association of BMI and white matter health  and indi4.4more healthy than the average population. Although this limits generalizability, it suggests that the negative effects of poor cardiovascular health are observed even in healthy, nonclinical samples.Despite strengths of this study, such as the large sample size, multivariate methodology, and a broad set of cardiovascular and neural measures, several methodological limitations should be noted. First, given the inclusion criteria for Cam-CAN imaging see , our samFurthermore, blood pressure and heart rate are known to undergo fluctuation in response to stress and movement . AlthougSimilarly, our findings of the effects of exercise depend on limitations of questionnaire measures in accurately assessing levels of exercise . Future Finally, although previous longitudinal research in humans  and expe4.5We found that the uncinate fasciculus, forceps minor, and inferior fronto-occipito fasciculus were the 3 white matter tracts most affected by cardiovascular health. Although we did not explicitly model cognitive function in the present study, we note that these 3 tracts have been consistently linked to cognitive aging in previous research. The uncinate fasciculus is thought to play a major role in the formation of mnemonic associations and episodic memory  and has 5Our multivariate analyses show that lower diastolic blood pressure, higher systolic blood pressure, and higher heart rate were each strongly associated with poorer white matter macrostructure and microstructure in a large sample of healthy, community-dwelling adults. These adverse effects were largely additive—diastolic blood pressure, systolic blood pressure, and heart rate each made partially independent contributions to white matter health. These findings highlight the value of concurrently assessing multiple indicators for cardiovascular health and indicate that systolic blood pressure, diastolic blood pressure, and heart rate may each affect neurological health via separate disease mechanisms.The authors declare no competing financial interests.


Fasting during the lunar month of Ramadan is mandatory to all healthy adult Muslims. Renal transplant recipients are often worried about the impact of fluid and electrolyte deprivation during fasting on the function of their allograft. We aimed to examine the effect of fasting Ramadan on the graft function in renal transplant recipients. This retrospective cohort study included patients who underwent kidney transplantation in our tertiary referral center. Baseline pre-Ramadan estimated glomerular filtration rate (eGFR), mean arterial pressure (MAP), and urinary protein excretion were compared to those during and after Ramadan within and between the fasting and non-fasting groups.P = 0.53; and 72.6 ± 23.7 versus 72 ± 23.2 mL/min/1.73 m2, P = 0.14, respectively). Compared to baseline, there were no significant differences between the fasting and the non-fasting groups in terms of mean percent changes in eGFR, MAP, and urinary protein excretion. The study population included 280 kidney transplant recipients who chose to fast during the Ramadan month (June-July 2014) and 285 recipients who did not fast. In the fasting group, baseline eGFR did not change from that during or post-Ramadan (72.6 ± 23.7 versus 72.3 ± 24.5 mL/min/1.73 m2,  Fasting during the month of Ramadan did not have significant adverse effects on renal allograft function. Ramadan, the ninth lunar month of Islamic (Hijri) calendar, is a sacred month for Muslims all around the world. Fasting during the month of Ramadan is obligatory to all healthy adult Muslims .During this month, Muslims refrain from eating, drinking, and taking medications from dawn to sunset each day. As the Islamic calendar is lunar, the beginning of the Islamic year moves ahead 11 days each year compared with the solar (Gregorian) year; thus, the month of Ramadan happens at various times of the seasonal year over a 33-year cycle , 3. ThisPatients for whom fasting may be detrimental to health are exempted from fasting . HoweverThe restoration of renal function and the enhancement of survival allow renal transplant recipients to share normal life activities including fasting. The question about the safety of fasting on renal allograft can be challenging for the healthcare professionals who need to be familiar with this spiritual commitment as well as its impact on health . The majThere have been a few studies that have previously assessed the effect of Ramadan fasting on patients with renal transplantation, with normal or moderately impaired renal function –15. AlthHere, we report on one of the largest cohort studies conducted to examine the potential effect of fasting during the month of Ramadan, on renal function in kidney transplant recipients with normal or impaired graft function.An observational retrospective cohort study was conducted in a tertiary referral centre in Riyadh, Saudi Arabia, by reviewing patients' records from the Renal Transplant Clinic.α (type I) error of 5%, 25% proportion of outcome among exposed (fasting), risk ratio (RR) of 1.66 with 0.5 standard deviation (SD), and lost to follow-up (missing data) rate of 5%. Based on the above assumptions, the total sample size included 602 patients with exposed (fasting) to non-exposed (non-fasting) ratio of 1 : 1. Non-probability consecutive sampling method was used to sample individuals into the two groups (fasting and nonfasting) from the eligible patients in the records. Patients with incomplete baseline and/or outcome data were excluded. The final cohort included 565 patients with complete data for analysis. The data of patients who had voluntarily chose to observe fast during the Ramadan month were analyzed and compared to those of patients who had not engaged in fasting. The study followed the STROBE reporting guidelines [We recruited our patients through a written questionnaire (linked with patient record review), given to patients who presented for follow-up at the Renal Transplant Clinic from July 1, 2015, to December 30, 2015. The questionnaire included whether they observed the fast or not, the advice given by the health educator prior to fasting concerning fluids and medications intake, and the development of any complications during fasting. The source population included all kidney transplant recipients, aged >18 years, living in Riyadh , who had stable renal graft function for at least 6 months and transplant duration for more than one year prior to the month of Ramadan, corresponding to June/July 2014. We excluded patients who had been living outside the region of Riyadh to exclude the climate discrepancy bias. The sample size was calculated using the STATCALC module of Epi-Info v.7 software. Estimation was based on assumptions with a power of 80%, idelines .A standard data collecting checklist was prepared based on routine data registration using standardized care and follow-up forms employed by the Renal Transplant Clinic in our institution. Data were extracted from patients' files and electronic medical records by eight adult kidney transplant staff physicians using the prepared data collecting checklist. Extracted demographic and clinical data of transplant recipients included age, gender, body mass index (BMI), primary kidney disease, type of donor , immunosuppressive protocols (drugs and doses), time since transplant, and presence or absence of diabetes mellitus and hypertension. The clinical and biochemical data of eligible patients were examined in three occasions: baseline pre-Ramadan (the last visit within 3 months before the month of Ramadan), during (after mid-Ramadan), and post-Ramadan (the first visit within 3 months after the month of Ramadan). The following parameters were analyzed: BMI; systolic, diastolic, and mean blood pressure; serum creatinine, eGFR, timed trough plasma levels of cyclosporine and tacrolimus; and urine protein excretion. The glomerular filtration rate (GFR) was estimated using a four-variable modification of diet in renal disease (MDRD) formula .During the month of Ramadan, patients were advised by our health educators to regularly take the usual dose of their twice-daily medications (including immunosuppressive drugs) immediately at the time of breaking the fast at sunset, and just before re-starting their fast at dawn. The changes in estimated GFR and other parameters, from baseline to during and after the month of Ramadan, were calculated and compared for the fasting and non-fasting groups.The primary outcome of this study was change in graft function (sCr and eGFR) from baseline pre-Ramadan to during and post-Ramadan values. The secondary outcomes were acute rejection rates and graft loss.The current study protocol was approved by our research ethics committee and institutional review board (IRB). The study was carried out in accordance with the ethical standards of the Declaration of Helsinki and the declaration of Istanbul. A written informed consent was obtained from each participant before questionnaire completion (for enrollment) and retrospective examination of their medical records (for data extraction).t-test was used to assess the differences between the fasting and the non-fasting participants in terms of baseline characteristics and differences in eGFR. Chi-square (χ2) and Fisher exact test were used to find the association between two independent variables. The paired t-test was used to assess the differences between the pre- and post-Ramadan eGFR among the fasting participants. Analysis of variance (ANOVA) was used to compare means of variables in more than two groups (eGFR subgroups). We presented a forest plot to summarize the standardized mean differences (SMD) in eGFR of fasting versus non-fasting patients in subgroups including age, post-transplant time frames, presence of DM or HTN, baseline eGFR, and proteinuria. Multivariate linear regression analysis models were built to determine the independent variables significant as predictors of outcomes after adjustment for possible confounders. Results were considered significant when the two-sided P value was lower than 0.05.Data were analyzed using the statistical packages for social sciences (SPSS) version 20.0.0  for Microsoft Windows. Categorical variables were expressed as number and percentage, while continuous variables were expressed as mean ± standard deviation (SD). The A total of 1011 kidney transplant recipients completed the questionnaire. After patient record review and applying inclusion/exclusion criteria, 602 participants were left eligible. We further excluded 37 subjects with missing data (listwise deletion) on covariates or outcome data results . Thus, the final cohort included 565 participants with complete data. These were further subdivided into 2 groups, the fasting group (280 patients) and the non-fasting group (285 patients) During the month of Ramadan (corresponding to June/July 2014), the daily fasting hours ranged from 12 to 14 hours, the average temperature was 36°C, and the highest temperature during the day was 44°C , while tThe two groups were nearly comparable at baseline , yet we The characteristics of both groups are shown in P = 0.001)  . The fas= 0.001) .P = 0.013) while the mean time interval from the end of Ramadan to the first SCr/eGFR after Ramadan was similar , as well as the time range interval (months) between the baseline pre-Ramadan and the follow-up post-Ramadan readings .The mean time interval (months) from the baseline SCr/eGFR value to the start of Ramadan was different in both the fasting and the non-fasting groups , as were the mean eGFRs . In the fasting group, the mean serum creatinine levels before and during Ramadan  as well as before and after Ramadan  were similar, as were the eGFR values before and during Ramadan , and before and after Ramadan   or eGFR values   . Similarctively) .P = 0.7), MAP (P = 0.7), or proteinuria (P = 0.08)  .P < 0.0001) and baseline proteinuria (P = 0.032) with change in eGFR  measure of effect summarized exploratory subgroup analyses for change in eGFR in fasting versus non-fasting patients . The subP value > 0.05) , moderate (45–75 mL/min/1.73 m2), and high GFR (>75 mL/min/1.73 m2). Statistical analysis revealed no significant differences in the eGFR after Ramadan compared to that at baseline, within or between the subgroups ( > 0.05) .P = 0.53); urinary tract infection was detected in 8 (2.9%) versus 8 (2.8%) patients, respectively (P = 0.97); and other infections  in 3 (1%) versus 6 (2.1%) patients, respectively (P = 0.33). No rejection episodes or renal function deterioration were noted after Ramadan. There were no graft or patient losses in any of the groups.The frequency of acute adverse events during the month of Ramadan in both fasting and non-fasting groups was not statistically different. CNI toxicity was detected in 6 (2%) versus 8 (2.8%) patients, respectively  of the world's population  and inhaDaytime dehydration due to water deprivation during the intermittent fast of Ramadan will induce a degree of stress on the concentrating ability of the kidneys . The majThere have been a few studies that have previously assessed the effect of Ramadan fasting on renal transplant patients with normal or impaired renal function –15. ThesP value > 0.05) , moderate (45–75 mL/min/1.73 m2), and high GFR (>75 mL/min/1.73 m2), we found no significant differences in the eGFR after Ramadan from that at baseline, within or between the subgroups ( > 0.05) . These fIn our study, we found no significant difference in the frequency of acute complications during Ramadan in both the fasting and the non-fasting groups. No deterioration in renal function or rejection episodes were observed during or after Ramadan. There were no graft or patient losses in any of the groups. These findings agree with the results of other reports –15. limitations need to be considered. The study cohort is representative for a Western Asian, primarily Arab, population and results might thus not be generalizable to populations in other regions of the world or with different ethnic backgrounds. Despite multivariate adjustment to reduce bias, our findings might still be affected by residual confounders, either unknown or unmeasured , which cannot be ruled out in any observational study [When interpreting our study, someal study . Further strengths, first and foremost, the availability of outcome data of high quality in a well-maintained electronic medical record. Furthermore, we have a large sample size of several hundred transplant recipients with a sufficiently substantial number of patients to conduct adequate analyses, while the existing studies collectively included fewer than 400 participants.Our study has severalBased on the results of this study, we believe that fasting during Ramadan has no adverse effects on renal function in kidney transplant recipients with normal as well as impaired renal allograft function. We think fasting can be allowed when the renal graft function has been stable for at least 6 months and the transplanted kidney has been in place for at least one year. It is recommended, however, that all renal transplant patients, who choose to fast, to do so under medical supervision and to pay strict attention to fluid intake, daily physical activity, drug regimen adjustment, and renal function monitoring. The patients should be advised to drink liberal amounts (at least 2.5 L) of fluids after breaking their fast (immediately after sunset), till just before the start of the fast (predawn) and to avoid excessive exposure to heat or overexertion to avoid dehydration.Despite the fact that Ramadan fasting proved safe across all eGFR levels in our study, the definition of a specific allograft function values (GFR level) as safe for Ramadan fasting still needs longer follow-up analyses.With the currently available data, there is no evidence of substantial graft injury associated with fasting; however, the possibility of a long-term adverse renal outcome cannot be excluded. These observations and hypotheses need to be explored further in analyses that incorporate more clinical outcomes and longer follow-up.


From the start of the test, migraineurs showed a greater relative increase in right-side %Δ[HHb] than controls, who showed varying reactions, and right-side increase in %Δ[tHb] was also greater in migraineurs . With multivariate regression model, migraine predicted the magnitude of maximal blood pressure increases, associated in migraineurs with mood scores and an intensity of both headache and painful signs of temporomandibular disorders (pTMD). Although changes in circulatory parameters predicted maximal NIRS responses, the between-group differences in the right-side NIRS findings remained significant after adjusting them for systolic blood pressure and heart rate. A family history of migraine, reported by all migraineurs and four controls, also predicted maximal increases in both %Δ[HHb] and %Δ[tHb]. Presence of pTMD, revealed in clinical oral examination in eight migraineurs and eight controls, was related to maximal %Δ[HHb] increase only in controls. To conclude, the greater prefrontal right-side increases in cerebral %Δ[HHb] and %Δ[tHb] may reflect disturbance of the tooth clenching-related cerebral (de)oxygenation based on impaired reactivity and abnormal microcirculation processes in migraineurs. This finding may have an impact in migraine pathophysiology and help to explain the deleterious effect of masticatory dysfunctions in migraine patients. However, the role of tooth clenching as a migraine trigger calls for further studies.Prevalence of masticatory parafunctions, such as tooth clenching and grinding, is higher among migraineurs than non-migraineurs, and masticatory dysfunctions may aggravate migraine. Migraine predisposes to cerebrovascular disturbances, possibly due to impaired autonomic vasoregulation, and sensitization of the trigeminovascular system. The relationships between clenching, migraine, and cerebral circulation are poorly understood. We used Near-Infrared Spectroscopy to investigate bilateral relative oxy- (%Δ[O Migraine is a common disabling neurological disorder with the prevalence of 18.2 % in females and 6.5 % in males . IntericMasticatory apparatus is a pain-sensitive structure innervated by the trigeminal nerve. Pericranial muscles and tendon insertions in the orofacial area have been considered as potential sources of migraine-triggering stimuli . MasticaDysfunction of autonomic nervous system, described in migraineurs , involve2 delivery and utilization, has been applied to monitor cerebral hemodynamics (2Hb]) after sumatriptan injection has been reported during a migraine attack (2Hb] increase during voluntary tooth clenching associated with the task intensity (Near-Infrared Spectroscopy (NIRS), evaluating a regional balance between microvascular Odynamics . Using Ne attack . Also, Ne attack . In healntensity . In addintensity . To our In the present study, we hypothesized that divergences in cerebrovascular responses to tooth clenching in migraineurs may elucidate the role of tooth clenching as a headache trigger and aggravating factor for migraine. For this purpose, bilateral changes in local (de)oxygenation and blood volume in prefrontal cerebral cortex induced by maximal tooth clenching (MTC) were measured with NIRS in migraineurs and controls.This study was carried out in accordance with the recommendations of Research Ethics Committee of the Hospital District of Northern Savo. The protocol was approved by the Research Ethics Committee of the Hospital District of Northern Savo, Finland. All subjects gave written informed consent in accordance with the Declaration of Helsinki.The study groups consisted of twelve subjects (ten women) suffering from migraine, diagnosed by a physician, and 14 control subjects (ten women). The main characteristics of the study subjects are presented in Table In all participants, clinical oral examinations were carried out by one specialist in temporomandibular disorders (TMD) using the Diagnostic Research Criteria for TMD . Pain inThe experiments were performed in an air-conditioned laboratory and were preceded by abstinence from eating and drinking for 2 h and significant physical activity for 1 d.2Hb]) and deoxyhemoglobin (Δ[HHb]), whereas local prefrontal blood volume was assessed by total hemoglobin (Δ[tHb] = Δ[O2Hb] + Δ[HHb]) (∧0.814) according to the manufacturer's guidelines, and a sampling frequency of 10 Hz was used for collecting the data. The obtained data were averaged to give values in 1-s intervals. The obtained NIRS responses were normalized , so that 100% represents a maximum-to-minimum amplitude of changes during MTC.The protocol of MTC included two series of 5 s clenching + 5 s break, followed by one maximal clenching until volitional exhaustion [Figure  Δ[HHb]) . The difThe systolic (SBP) and diastolic (DBP) blood pressures were measured from the 2nd and 3rd left fingertips using a Portapres device . Electrocardiogram was recorded using a modified chest lead 5 (V5).Muscular activity from both masseter muscles was controlled with surface electromyography with ME6000 biosignal monitor .For SBP, DBP, heart rate (HR), and NIRS data, the average values were calculated for ten time points: two points with 10-s intervals, then during maximal clenching , and first, second and third minutes after clenching  was used for the statistical analyses. Changes in blood pressure, HR and normalized NIRS data were compared between the study groups with a Linear mixed model, adjusted for multiple comparisons (Bonferroni). A stepwise linear regression analysis was used to identify the predictors of the measured changes. Fisher's exact test, p < 0.005, Table p < 0.001 for all) but there were no time-group interactions in the dynamics of blood pressure or HR . In addition, %Δ[HHb] values were associated with sex . The greater values of %Δ[HHb] were related to the right side of measurement (p < 0.05) and, in migraineurs, there was such a tendency for %Δ[tHb] (p < 0.1 after Bonferroni correction). However, there were no time-group interactions in the dynamics of side-to-side differences in NIRS findings.We entered explanatory variables  one at a time into the model to check their associations with the measured changes. There were no relationships between the blood pressure or HR and explanatory variables, but %Δ[HHb] and %Δ[tHb] values were associated with SBP, DBP, and HR  and %Δ[tHb] , but there were no significant differences in dynamics of %Δ[O2Hb] between the groups. The most representative curves of the relative changes in oxygenation parameters are presented in Figure Right-side %Δ[HHb] increase occurred in all migraineurs, however, with varying magnitude and in some of them, small %Δ[HHb] increase was followed by decrease. Controls showed varying reactions, %Δ[HHb] decreased in four, fluctuated in six and increased in four subjects. MTC induced also an initial right-side relative decrease of %ΔOHb in ninp < 0.05 for both, Mann-Whitney U-test, Table p < 0.05 for all, Table The maximal registered changes were also analyzed. Maximal increases in SBP and DBP, compared to baseline levels, were greater in migraineurs than in controls , neural activation may lead to more rapid increase in local fractional O2 extraction resulting in increased %Δ[HHb]. Similar dynamics of [HHb] has been shown in patients with occlusal dysesthesia and suggested similarly to indicate reduced blood flow to the frontal pole cortex oxygenation in individuals with migraine. Mechanisms behind the findings remain unclear but may include impaired stress reactivity together with sensitization of the trigeminovascular system and impaired regulation of the CBF. Although this finding may help to explain the worsening effect of masticatory dysfunctions on migraine, the role of tooth clenching as a migraine trigger calls for further studies involving larger sample sizes.NZ, A-PR, JL, and MN: Conceived and designed the experiments; NZ, JL, A-PR, SR, and AV: Performed the experiments; NZ, JL, and A-PR: Analyzed the data; NZ and A-PR: Wrote the paper; SS, JL, SR, AV, PK, and MN: Revised the paper; PK and MN: Supervised the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.