Cardiovascular disease is the leading cause of death in end-stage renal disease and is strongly associated with vascular calcification. Both kidney transplantation and phosphate binders may lower the risk of vascular calcification. Vascular calcification is actively inhibited by vitamin-K-dependent matrix γ-carboxyglutamic acid protein (MGP). Whether kidney transplantation or phosphate binders affect vitamin K status is unknown. Therefore, we studied the influence of kidney transplantation and phosphate binder use on vitamin K status.(n = 82), peritoneal dialysis (n = 31) or who recently received a kidney transplantation (n = 36). By medication inventory, we assessed phosphate binder use. With linear regression, we assessed the influence of kidney transplantation and phosphate binder use on natural-log-transformed dp-ucMGP, adjusting for potential confounders.We measured plasma desphospho-uncarboxylated MGP (dp-ucMGP), a marker reflecting low vitamin K status, in a cross-sectional study of patients on hemodialysis Mean age of patients was 52±13 years; 102 (68%) were male. Dp-ucMGP levels were significantly lower in kidney transplant recipients (median 689 pmol/L) compared to patients on dialysis . Eighty-nine patients on dialysis used phosphate binders. Using any phosphate binder was not associated with dp-ucMGP levels  compared to no phosphate binders (median 1142 pmol/L). Twenty-six patients used sevelamer monotherapy, which was associated with higher dp-ucMGP levels  after adjusting for age, sex and vitamin K antagonist use.Recent kidney transplantation is associated with lower dp-ucMGP levels suggesting improved vitamin K status after transplantation. Sevelamer monotherapy is associated with higher dp-ucMGP levels suggesting worsening of vitamin K status. Both findings warrant more attention to vitamin K status in patients on dialysis, as vitamin K is necessary for protection against vascular calcification. Cardiovascular disease accounts for over 50% of deaths in end-stage renal disease (ESRD), and is Kidney transplantation is the preferred treatment for ESRD, and rapidly normalizes serum phosphate. PhosphaPatients on dialysis are routinely prescribed phosphate binders to lower serum phosphate levels. Despite their widespread use, definite proof for beneficial outcomes is lacking. Remarkain-vitro studies indicate that various phosphate binders may not only bind phosphate, but also fat-soluble vitamins such as vitamin K[Recent vitamin K–20. PhosIt is unclear whether kidney transplantation improves low vitamin K status in patients on dialysis. MoreoveWe analyzed a cross-sectional cohort from the ongoing NOCTx study (NCT00950573), a prospective cohort study that included prevalent patients on hemodialysis and peritoneal dialysis with a minimum dialysis vintage of 2 months, and patients who received a kidney transplant 2–3 months before inclusion. Patients were eligible when aged between 18 and 75 years, and were candidates for transplantation when on dialysis. All study participants gave written informed consent. NOCTx excluded patients with a life expectancy <3 months, pre-emptive transplantation, non-adherence to dialysis regimens, drug abuse, and pregnancy. NOCTx has been approved by the Medical Ethics Committee of the University Medical Centre Utrecht and is conducted according to the Declaration of Helsinki. None of the transplant donors were from a vulnerable population and all donors or next of kin provided written informed consent that was freely given.Between December 2009 and February 2016, NOCTx included 181 patients who were referred for study participation to the University Medical Centre of Utrecht, the Netherlands, by 8 Dutch dialysis centers. Patients were treated according to guidelines by the attending nephrologists. For kidney transplant recipients, standard immunosuppressant regimens consisted of a calcineurin inhibitor (tacrolimus), mycophenolate mofetil, and prednisone in tapering doses.(n = 28) or with missing data for medication prescriptions (n = 4), leaving a final sample of 149 patients, 113 of which were on dialysis.Blood samples were collected in 4.5 mL potassium-ethylenediaminetetraacetic acid vacutainers , immediately centrifuged and stored in aliquots at -80°C without thawing. For the present analyses, we excluded patients whose blood samples were not available 1 and K2, which may fluctuate substantially as a result of dietary intake[We determined vitamin K status by measuring dp-ucMGP. Dp-ucMGP is a sensitive marker of vitamin K status, as opposed to circulating vitamin Kry intake and degrry intake. Plasma ry intake. All assStudy personnel recorded phosphate binder use with lists of prescribed medication at time of blood sample collection. Similarly, vitamin K antagonist (VKA) and vitamin D analog use were recorded. We categorized patients according to phosphate binder use: no binders or any binder, subcategorized as exclusively non-calcium containing binders or exclusively calcium containing binders. Additionally, we subcategorized patients according to monotherapy with a single binder .At time of sampling, study personnel recorded demographic and clinical parameters . Total calcium, albumin, phosphate, parathyroid hormone, C-reactive protein and total cholesterol were routinely measured at local treatment facilities . Smoking status and height were self-reported. Study personnel evaluated residual urine production with a 24h-urine collection. We assessed history of kidney disease, current dialysis schedule and presence of comorbidities by chart review. We defined diabetes mellitus as the necessity for oral diabetes medication or insulin therapy, and cardiovascular disease as any history of angina, myocardial infarction, percutaneous coronary intervention, coronary artery bypass grafting, aortic aneurysm ≥5 cm, stroke, intermittent claudication, peripheral artery angioplasty or bypass grafting. We defined dialysis vintage as the time since the first day of dialysis, minus the time with a functioning kidney transplant. For kidney transplant recipients, we estimated glomerular filtration rate with the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation.We present results as mean (± standard deviation) for normally distributed variables, as median  for non-normally distributed variables, or as number (percentage) for categorical data. We tabulated baseline characteristics and medication use by renal replacement therapy. We compared dialysis vintage and dp-ucMGP between groups with Mann-Whitney-U tests.The distribution of dp-ucMGP levels was right-skewed and we therefore natural-log-transformed dp-ucMGP levels. With boxplots and linear regression we examined the associations of both renal replacement therapy and phosphate binder use with log-transformed dp-ucMGP. We compared log-transformed dp-ucMGP between kidney transplantation and dialysis , and between hemodialysis and peritoneal dialysis. We compared log-transformed dp-ucMGP between phosphate binder categories in patients on dialysis only, as none of the kidney transplant recipients used phosphate binders. We compared patients using any phosphate binder, exclusively non-calcium containing phosphate binders, exclusively calcium containing phosphate binders and sevelamer monotherapy to non-phosphate binder users. We adjusted stepwise for potential confounders age (years), sex , VKA use (yes/no), intensive hemodialysis regimens (≥18h/week or <18h/week) and residual urine production (≥100mL/24h or absent). Additionally, we analyzed non-VKA users only. In the final models of both renal replacement therapy and phosphate binder use, we adjusted for age (years), sex  and VKA use (yes/no).We reported regression coefficients with 95% confidence intervals (95% CI). Regression coefficients should be interpreted multiplicatively, i.e. as a ratio, after exponentiation. We considered P-values of ≤ 0.05 (two-tailed) statistically significant and performed all analyses with R 3.3.3 .(n = 149) was 52 ±13 years, 102 patients (68%) were male. Dialysis vintage  was 23 months (IQR 11–49 months), 21 patients (14%) had diabetes mellitus and 16 patients (11%) used VKAs . Eighty-two patients were treated with hemodialysis, 31 with peritoneal dialysis and 36 were kidney transplant recipients (The mean age of the study population cipients .(n = 32 without samples or medication lists) was 52±13 years, 19 (59%) were male, dialysis vintage was 32 months  and 5 patients (16%) had diabetes mellitus. Nineteen of these patients were treated with hemodialysis, 7 with peritoneal dialysis and 6 were kidney transplant recipients.The mean age of patients not included in the evaluation sample (n = 22). We therefore excluded kidney transplant recipients from the phosphate binder analyses.Twenty-four of the 113 patients on dialysis (21%) did not use phosphate binders, while 89 (79%) used any phosphate binder. Fifty-three of these patients used exclusively non-calcium containing phosphate binders, whereas 10 used exclusively calcium containing phosphate binders . Thirty-Overall, sevelamer was the most frequently prescribed phosphate binder , followed by lanthanum carbonate , calcium carbonate , calcium acetate/magnesium carbonate  and calcium acetate . Sevelamer was prescribed as monotherapy in 26 patients (23%). Monotherapy with lanthanum carbonate, calcium carbonate, calcium acetate/magnesium carbonate and calcium acetate occurred too infrequently  and was therefore not studied separately.Median plasma levels of dp-ucMGP in all 149 patients with ESRD were 1302 (IQR 739–1838) pmol/L. Dp-ucMGP levels were highest in VKA users . Dp-ucMGP levels were right-skewed and therefore natural-log-transformed for regression analyses.In kidney transplant recipients, median dp-ucMGP levels were 689 (IQR 489–1078) pmol/L. In patients on hemodialysis, median dp-ucMGP levels were 1605 (IQR 993–2390) pmol/L, and in patients on peritoneal dialysis 1195 (IQR 921–1807) pmol/L . In crud(n = 24), median dp-ucMGP levels were 1142 (IQR 841–1642) pmol/L, while in those on any phosphate binder (n = 89) median dp-ucMGP levels were 1637 (IQR 994–2307) pmol/L. This difference was non-significant, and remained numerically similar when adjusted for age, sex and VKA use ((n = 53) and exclusively calcium containing phosphate binders (n = 10), median dp-ucMGP levels were 1615 (IQR 1068–2061) and 2330 (IQR 1014–3476) pmol/L, respectively. Compared to patients on dialysis without phosphate binders, these dp-ucMGP levels were not significantly different, and the differences remained numerically similar in the adjusted model. In patients on dialysis on sevelamer monotherapy (n = 26), median dp-ucMGP levels were 1740 (IQR 1363–2267) pmol/L. These dp-ucMGP levels were significantly higher compared to patients on dialysis without phosphate binders in the adjusted model  with a similar age to our population . Interein vitro studies have investigated the impact of phosphate binders on vitamin K, demonstrating that most classes of binders can bind fat-soluble vitamins including vitamin K[Recent vitamin K, 19. Thivitamin K. Howevervitamin K, unlike Our study shows an association between sevelamer monotherapy and higher dp-ucMGP levels after adjusting for age, sex and VKA use, but remarkably no association between phosphate binder use in general and dp-ucMGP levels, although we did observe a trend towards higher dp-ucMGP levels. This discrepancy could be explained by confounding by better dietary intake, including vitamin K intake, by patients using multiple phosphate binders. These patients had higher phosphate levels (1.74±0.30 mmol/L) compared to patients using a single phosphate binder (1.58±0.39 mmol/L), which might suggest better dietary intake. On the other hand, the supposed vitamin K-lowering effect of phosphate binders could have been masked by medication non-adherence in patients using multiple phosphate binders.Our findings may be clinically relevant: the better vitamin K status after kidney transplantation may in part explain the lower risk of cardiovascular disease after transplantation, given t2 versus 40.3 ±3.4 g/L and 25.4 ±4.8 in kg/m2 in phosphate binder users). A second limitation is that the aim of the main study was about dialysis modality and vascular calcification, and hence dietary intake and medication adherence were not measured. On the one hand, dosages of phosphate binders were therefore not taken into account. On the other hand, medication non-adherence could have protected patients from iatrogenic vitamin K deficiency by phosphate binders, masking the supposed adverse effect. Third, monotherapy with phosphate binders other than sevelamer occurred too infrequently to be analyzed separately. Finally, we did not measure vitamin D status, which might also be affected by phosphate binder use[Our study has some limitations. First, there may have been indication bias for no phosphate binder use. Using no phosphate binders may have been related to malnutrition, and hence a lower production of various proteins, including MGP and thereby dp-ucMGP. Conversely, malnutrition could also have increased dp-ucMGP levels by poor vitamin K intake. Importantly, both albumin levels and BMI did not suggest malnutrition in non-phosphate binder users Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.


Current reference ranges for blood pressure and heart rate throughout pregnancy have a poor evidence base.This is a systematic review and meta-analysis. We included studies measuring blood pressure or heart rate from healthy pregnant women within defined gestational periods of 16 weeks or less. We analysed systolic blood pressure, diastolic blood pressure and heart rate by gestational age. We assessed effects of measurement year and method.I2 statistic > 50%).We included 39 studies undertaken in 1967–2017, containing 124,349 systolic measurements from 36,239 women, 124,291 diastolic measurements from 36,181 women and 10,948 heart rate measurements from 8317 women. Mean (95% CI) systolic blood pressure was lowest at 10 weeks gestation, 110.4  mmHg, rising to 116.0  mmHg at 40 weeks, mean (95% CI) change 5.6  mmHg. Mean (95% CI) diastolic blood pressure was lowest at 21 weeks gestation, 65.9  mmHg; rising to 72.8  mmHg at 40 weeks, mean (95% CI) change 6.9  mmHg. Mean (95% CI) heart rate rose from 79.3  beats/min at 10 weeks to 86.9  beats/min at 40 weeks gestation, mean (95% CI) change 7.6  beats/min. Studies using manual measurement reported higher diastolic blood pressures than studies using automated measurement, mean (95 CI) difference 4.9  mmHg. Diastolic blood pressure increased by 0.26 (95% CI 0.10–0.43) mmHg/year. Including only higher-quality studies had little effect on findings, with heterogeneity remaining high  contains supplementary material, which is available to authorized users. Heart rate and blood pressure are key vital signs for the assessment of pregnant women , 2. Our This systematic review follows the Meta-analysis Of Observational Studies in Epidemiology (MOOSE)  and PrefWe included cross-sectional, case-control or longitudinal studies containing at least fifty participants and where measurements were taken by a healthcare professional.We included studies containing blood pressure or heart rate measurements from pregnant women recruited as “healthy”. We defined “healthy” as women not known to have conditions likely to affect blood pressure or heart rate at the point of first measurement, according to inclusion and exclusion criteria described in Table We set out to include measurements of all six vital signs recommended for clinical assessment , made antepartum, intrapartum or postpartum . We haveWith a trained librarian, we searched MEDLINE, Embase and CINAHL, from inception until February 2018. We also searched reference lists and contacted field experts. Where required data were not presented or were presented in a form we could not extract, we contacted the original authors by e-mail. We used both MeSH and free-text terms. We did not restrict the year or language of publication. The complete search strategy has been described previously . AdditioTwo reviewers (LL and RP) independently screened retrieved titles and abstracts to exclude studies that clearly fell outside the scope of the review, such as foetal or animal studies. Two reviewers (PW and LM) independently screened the remaining studies by title, then by abstract and finally by full text against the inclusion and exclusion criteria Table . Where rTwo reviewers (LL and RP) independently extracted study data onto a piloted spreadsheet (Microsoft Excel). Disagreements were automatically highlighted and resolved by recourse to the original papers or in consultation with a medical statistician (JB), intensive care specialist (PW) or obstetric physician (LM), as required.https://sourceforge.net/projects/digitizer/works (open source software) to extract data from graphs if the underlying data were not presented and we could not obtain it from the authors.For each study, we extracted information about the study , measurements  and participants , ethnicity, reason for measurements, parity, number of gestations, pregnancy dating method). For each period of pregnancy defined in a paper, we extracted information about the sample size and minimum and maximum gestational age, together with reported summary statistics for blood pressure and heart rate. We used Engauge Digitizer, Two reviewers (LL and RP) independently undertook quality assessment in line with QUADAS -2 , adaptedPrincipal summary statistics were mean and standard deviation for each measure of heart rate and blood pressure, along with sample size. Where mean and standard deviation for blood pressure and heart rate were not reported, we approximated these assuming a normal distribution (with no skewness), in line with previous descriptions of vital sign measurement distributions . Where mWhere the gestational age associated with each data point was reported as a mean or median gestational age, we used this for the analysis. Where the gestational age associated with each data point was reported as a range, we used the mid-point of the range. Gestational age measured using ultrasound measurement of the foetal crown-rump length before 14 weeks gestation was our preferred assessment method .Where the sample size associated with each data point was reported, we used this for the analysis. For studies where the sample size was reported only at baseline, we took the sample size associated with each data point to be the baseline sample size, assuming no dropouts or loss-to-follow-up.Where characteristics of women  were reported at multiple time points , we used the value closest to recruitment. Where the mean values of the characteristics of the sample of women associated with each data point were reported, we used this for analysis. For studies where the characteristics of women were only reported for the entire sample of women recruited, and not for the sample at each time point, we used entire sample values, assuming no dropouts or loss to follow-up. Where only median values were reported, we used these instead of the mean values, assuming normal distributions of characteristics (with no skewness).The study year was taken to be the last year of recruitment to the study or the year of publication where this was not reported.To analyse trajectories of changes in blood pressure and heart rate, we adapted the method for meta-analysis of longitudinal studies proposed by Ishak et al. , 24 IshaWe undertook pre-specified secondary analyses to investigate the effects of method of measurement , year of data collection and parity on systolic and diastolic blood pressure. We added these covariates to the model as fixed effects. We added method of measurement and year of measurement simultaneously as we expected them to be related. To assess the effect of parity, where possible, we separated data into nulliparous and multiparous groups. Otherwise, we entered parity into the model as the proportion of multiparous mothers. We did not undertake secondary analyses of heart rate, as the limited study numbers would have resulted in imprecise results.+ 0 to 12+ 6 weeks; trimester 2 from 13+ 0 to 25+ 6 weeks; trimester 3 from 26+ 0 to 36+ 6 weeks and full term from 37+ 0 weeks to delivery [Additionally, we pooled data for each trimester from each included study in a random effects analysis to generate Forest plots for each vital sign. Trimesters were defined as trimester 1 from 0delivery . Where d2) of the study mean and the between-study variance. To estimate the spread of vital sign data in an individual study setting, we computed 95% prediction intervals from the random effects analysis, according to the methodology presented by Riley et al. [I2 statistic (range 0–100%).We estimated the pooled weighted mean and 95% confidence intervals where the weight of an individual study was inversely proportional to the sum of the variance . We also performed an additional sensitivity analysis excluding studies scoring less than 50% on quality assessment.As the studies included in the review did not have a comparative design, we could not assess publication bias. However, selective reporting was assessed as part of the quality assessment.Statistical analyses were undertaken using StataCorp. 2015  or SAS software .Database screening identified 1897 publications. We identified a further 14 publications from reference lists or expert knowledge. We included 39 studies from 20 countries meeting our pre-specified criteria, Fig. The included studies are summarised in Additional file We included 394 summary measurements of blood pressure, representing 124,349 systolic measurements from 36,239 women and 124,291 diastolic measurements from 36,181 women. The 23 longitudinal studies reported measurements of blood pressure from 33 separate groups of women –54. LongWe included 34 summary measurements of heart rate, representing 10,948 heart rate measurements from 8317 women. The five longitudinal studies reported measurements from seven separate groups of women , 66. SumQuality assessment scores ranged from 5.6 to 84.2% . Additional file p < 0.001). Mean (95% CI) diastolic blood pressure was 67.1  mmHg at 10 weeks gestation. Mean (95% CI) diastolic blood pressure was lowest at 21 weeks 65.9  mmHg, rising to 72.8  mmHg at 40 weeks; a change of 6.9  mmHg from 21 to 40 weeks (p < 0.001). Mean (95% CI) heart rate rose from 79.3  beats/min at 10 weeks to 86.9  beats/min at 40 weeks; a change of 7.6  beats/min from 10 to 40 weeks (p = 0.014), Fig. Both systolic and diastolic blood pressure changed significantly across pregnancy  when modelled using our longitudinal model Fig. . Systoliwas 67.1 .4, 68.7 Forest plots of systolic, diastolic and heart rate data (one plot per trimester) are shown . Mean manual systolic blood pressure measurements did not differ from those in studies that used an automated measurement, difference (95% CI) in mean intercept of 2.8 , p = 0.274  in mean intercept of 4.9  mmHg p = 0.00. Mean mp = 0.003. Systolic blood pressure rose by 0.12  mmHg per annum, but the rise was not statistically significant (p = 0.256). Differences were not affected by gestational age.Studies of automated measures commenced in 2002 becoming more common over time. We therefore analysed the effect of time on blood pressure for manual measurements only. Each year from 1969 to 2017, diastolic blood pressure increased by 0.26 (95% CI 0.10–0.43) mmHg, p = 0.927, or diastolic blood pressure, mean (95% CI) difference 1.30  mmHg, p = 0.671,  difference − 0.32  mmHg, We could not adjust for maternal characteristics, such as age and weight, as such data were often missing or reported using different summary measures at different gestational ages.Excluding outlying studies  reduced the number of studies by a maximum of five studies for studies of blood pressure. As all the outlying studies were “high outliers”, the mean estimates of systolic and diastolic blood pressure were decreased by about 1 mmHg, but the pattern across gestational ages remained the same , or heart rate, and measures of heterogeneity remained above 95%  mmHg between 10 and 40 weeks gestation . Unlike systolic blood pressure, diastolic blood pressure is lowest at 21 weeks gestation, rising by 6.9  mmHg by 40 weeks. Mean diastolic blood pressures were on average 4.9  mmHg lower when measured using automated techniques than when using manual methods. There is no difference in either systolic or diastolic blood pressure between nulliparous and parous women. Over 48 years, average diastolic blood pressures rose by 0.26 (95% CI 0.10–0.43) mmHg per year. Heart rate rises by 7.6 (95% CI 1.8–13.4) beats per minute through pregnancy.Our synthesis brings together more blood pressure and heart rate data from more pregnant women than has previously been done. This increases our understanding of the trends that occur during pregnancy. It also shows the large between-study heterogeneity, emphasising the importance of not simply taking results from a single study. Consequently, our mean estimates were imprecise (95% CI width was around 5 mmHg for blood pressure and 9 beats per minute for heart rate). We did not meta-analyse other centile estimates for blood pressure and heart rate (for example 5th or 95th). As most studies did not report centiles of the distributions, approximations would need to be made, using assumptions about data distributions. The precision would likely have been lower than at the means, resulting in wide confidence intervals, with little clinical value. Such an analysis would have been imprecise and potentially misleading.As with many meta-analyses, combining these data is complex. Included studies were of mixed methodological quality. We identified three aspects of particular concern: Firstly, a lack of accuracy in measuring and reporting of gestational age. Secondly, only a minority of studies reported using automated blood pressure measurement devices ratified for use in pregnancy. This means that we cannot ascertain whether the differences between blood pressures measured with manual sphygmomanometers and automatic methods would have been present if automatic methods ratified for use in pregnancy had been used. Thirdly, many studies insufficiently defined the population studied. These methodological weaknesses may have contributed to the range of possible mean blood pressures for any gestational age in our analyses. They may also partly account for the heterogeneity we found. Sensitivity analyses to account for quality and outlying studies had little effect in reducing heterogeneity. Further exploration of the sources of the heterogeneity was prevented by a lack of consistent reporting of the additional variables that would be required. The substantial between-study difference in blood pressure and heart rates at all gestational ages creates uncertainty around our mean values, but is less of a problem when analysing trends across pregnancy and time and when considering differences between measurement methods.We investigated the effect of methodological quality in two ways, by undertaking a pre-specified analysis excluding outlying studies and by excluding studies scoring less than 50% on quality assessment. As all the outlying studies were “high outliers”, the mean estimates of systolic and diastolic blood pressure were decreased by about 1 mmHg, but the pattern across gestational ages remained the same , increased plasma volume and decreased systemic vascular resistance that occur in pregnancy . HoweverOur finding of increasing diastolic blood pressure over the 48 years in which these studies have been undertaken is previously unknown. We did not find a study with data over a sufficient period to add to this finding. Increasing average maternal age –76 and/oThe mean diastolic blood pressure was lower when measured using automated methods than when measured manually. Previously, several automated monitors have been found to underestimate diastolic blood pressure in pre-eclampsia –80. AlthFuture studies should define gestational age precisely, recruit well-defined populations and for blood pressure only use devices ratified for use in pregnancy. For blood pressure, the differences between automated and manual methods across pregnancy need exploring with automated methods approved for use in pregnancy. The changes in diastolic blood pressure across five decades suggest normal ranges should be regularly revisited. Finally, the impact of using gestation-specific centiles to allow earlier detection of the unwell mother requires investigation.Gestational changes in blood pressure and heart rate should be taken into account when assessing pregnant women, but heterogeneity between studies prevents the production of gestation-specific evidence-based normal ranges. Assessment of blood pressure need not differ between nulliparous and parous women. Automatically measured diastolic blood pressures are lower than those measured manually. A consistent measurement system should be used through pregnancy. Decreases in blood pressure mid-pregnancy and increases in heart rate through pregnancy are smaller than previously thought. Taken with other work, these findings should contribute to more reliable assessment of pregnant women. Increases in diastolic blood pressures recorded over the last half century show contemporary data is required to define normal ranges for current practice.Additional file 1:PRISMA checklist. (DOCX 30 kb)Additional file 2:Exclusion diagnoses and reasons, search strategy, quality assessment criteria and results, summary of included studies, additional analyses. (DOCX 512 kb)


The current classification of traumatic brain injury (TBI) into “mild”, “moderate”, or “severe” does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify “sub-groups” of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.Data from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.Amongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.Sub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient’s sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI. The current structure for classifying patients with traumatic brain injury (TBI) includes three main sub-groups defined by Glasgow Coma Scale (GCS) score: mild, moderate, and severe. –2 ClassiThe objective of this study was to identify sub-classes or “clusters” within a group of patients all currently defined as having mTBI. The sub-classification was determined by considering several heterogeneous characteristics that are typically available when the patient with mTBI presents for initial evaluation and that might relate to patient outcomes: GCS scores, injury characteristics, medical history, substance use, neuroimaging results, vital signs, and basic blood test results. To address the high-dimensionality challenge that is present when using a large number of variables, we used a data-driven approach that automatically selected available clinical features to sub-classify patients and then compared the resulting mTBI sub-groups for their multidimensional 90- and 180-day patient outcomes. In contrast with the majority of existing work that focuses on building predictive models that use clinical variables to predict a single outcome variable of interest, our study has a different perspective in that it allows for the simultaneous consideration of multiple baseline variables and their association with multiple different outcome measures.The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot dataset was downloaded from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System after obtaining appropriate approvals for accessing the data. (tracktbi.ucsf.edu) (fibtir.nih.gov) TRACK-TBI Pilot was conducted between July 2007 and Februray 2011.The TRACK-TBI Pilot dataset includes multiple Excel files and each file contains different variables of patient characteristics at baseline and patient outcomes at follow-up. A global unique identifier (GUID) is used to track different variables of the same patient across the multiple files. The information used in this study was found in 12 different TRACK-TBI Pilot files, including: Abbreviated Injury Score (AIS), Assessment, CT scan results, injury history, medical history, subject demographics, Glasgow Outcome Score Extended (GOSE) , the briIn the dataset for this study, each variable was either extracted from one variable in a FITBIR file or was a combination of multiple variables. Specifically, based on domain knowledge we combined the descriptions of injury mechanisms for each patient into 8 categories, including: bike, pedestrian, motorcycle, motor, other person, fall, striking, and other types; IV fluid was a combination of Saline and Crystalloid, i.e. IV fluid = yes if Saline = yes or Crystalloid = yes; vital signs including diastolic and systolic blood pressure, heart rate, and respiratory rate measured at ED arrival and ED discharge were each categorized into three levels, i.e. low, normal, and high, according to clinically-relevant thresholds. O2 saturation was categorized into two levels, i.e. low and normal.For these analyses, we selected patients from the TRACK-TBI Pilot dataset who had a GCS score of 13 to 15 at arrival to the Emergency Department and a closed head injury. Of the 599 patients in the dataset, 485 patients met these inclusion criteria.After careful review of the variables available within the TRACK-TBI Pilot dataset, consideration of our own clinical knowledge, and knowledge gained from previously published studies, 53 clinical variables were included as potentially relevant for mTBI sub-classification. –19 TheseWe included 10 patient outcome measures: the Glasgow Outcome Score Extended (GOSE)  at 90 anBecause the sub-classification was performed on clinical variables, we only imputed missing data for clinical variables, but did not impute data for missing outcome variables. We only included clinical variables for which fewer than 10% of patients had missing values, except for several TBI-related variables that we hypothesized to be exceptionally important for sub-classifying patients: post-traumatic amnesia (PTA) duration, loss of consciousness (LOC) duration, previous TBI, and pupil reaction at ED arrival. As a classic imputation method, Multivariate Imputation by Chained Equations (MICE) implemented in the R package “mice”  was usedHierarchical Clustering (HC) is a conventional clustering algorithm to build a hierarchy of subgroups by producing a dendrogram that represents a nested set of subgroups. HC starts from the bottom of the dendrogram where each subject is in its own subgroup and the pairwise distance between the subgroups is measured. In the next upper level of the dendrogram, the pair of subgroups with the closest distance is merged into a bigger subgroup and the dendrogram is thus iteratively built. However, when the number of variables used in HC is large, like in this study, Sparse Hierarchical Clustering (SHC)  is more U. Instead of U, SHC builds a dendrogram based on a weighted distance matrix D, where each clinical variable is associated with a weight. An L1-penalty [w and the weighted distance matrix D:Conventional HC is based on an overall distance matrix between each pair of samples, i.e. -penalty  is imposui,i′,j is the distance between samples i and i′ on the j-th clinical variable. wj is the j-th element in the weight vector w, i.e., the weight for the j-th clinical variable. q is the number of clinical variables. ‖w‖1 is an L1-penalty defined as a summation of the absolute weights. The constraint on ‖w‖1 is to impose sparsity. ‖w‖2 is the L2-norm, which is used together with other constraints in (1) to facilitate efficiency of the optimization solution. Di,i′ is an element in D representing the weighted distance between samples i and i′. s is a tuning parameter. Different values for the tuning parameter (s) result in different numbers of clinical variables with non-zero weights. To determine the optimal number of clinical variables, a gap statistic is computed, which measures the strength of the clustering obtained on the real data relative to the clustering obtained on permuted data that does not contain subgroups [Here, ubgroups . The optc is defined as:Class (i) is the cluster membership of sample i; iN is i’s nearest neighbor who can be found from the estimated weighted distance matrix D* using SHC. We determined the cut of the dendrogram  that produced a high overall IGP across all the resulting clusters.Furthermore, depending on where the dendrogram is cut, SHC can produce different numbers of subgroups. To determine the optimal number of subgroups, we adopted a cluster quality measure proposed by Kapp and Tibshirani  called “c0 and c1, the goal is to find the subset of outcome variables on which c0 and c1, significantly differ. Conventional methods would perform a hypothesis testing on each outcome variable and use FDR to control the overall type I error. However, FDR is known to be too conservative. [p outcome variables. Let yi = 1 or 0 representing bad or good recovery for the i-th outcome variable, i = 1,…,p. Let x = 1 or 0 representing subgroups c1 and c0. A logistic regression model can be used to link the subgroup membership of each patient with the log-odds of bad recovery in terms of the i-th outcome variable, i.e.,αi is the log-odds of bad recovery for subgroup c0 and βi represents the increase of log-odds for subgroup c1 compared with c0. If βi = 0, it means that the two subgroups do not differ on the i-th outcome. To simultaneously identify the subset of outcomes on which the two subgroups differ, we imposed an L1-penalty on the joint log-likelihood function over all the outcomes, i.e.,lj is the log-likelihood function corresponding to the i-th outcome based on the model in (3). β =  and α = . The L1-penalty on β, i.e., ‖β‖1, has the effect of shrinking the βi’s with a small magnitude to be exactly zero and the remaining non-zero βi’s compose the subset of outcomes on which the two subgroups significantly differ. λ1 is a tuning parameter that can be selected by minimizing the cross-validated deviance.After the subgroups are found, they are compared with each other in terms of multi-dimensional outcome variables. For a pair of subgroups, e.g., rvative.  Thus, spIn this section, we first introduce the summary statistics to describe the clinical variables from TRACK-TBI Pilot dataset used in our study. Then, we present sub-classification based on clinical variables, followed by characterization of each subgroup with clinical variables and multi-dimensional outcomes. To compare clinical variables and outcomes between each pair of clusters, a Chi-square test for proportions is used, from which a p-value can be provided to indicate the significance of the test result.Summary statistics for the variables included in this analysis and the percentage of missing data for each variable are shown in Inclusion of 12 clinical variables maximized the gap statistic in SHC . These vWe used a “pie chart array” to visualize the distributions of the 12 clinical variables within each cluster, as shown in The most obvious difference between the five clusters is the proportion of patients with brain CT abnormality. Clearly, almost all the patients with an abnormal CT (i.e. “complicated mTBI”) fall into clusters A and E, while almost all the patients with a normal CT fall into cluster B, C, and D. Please note although B and C are the same in terms of both having 100% patients with a normal CT, they differ significantly in some other variables. Specifically, B has higher percentages of tobacco use (p<0.001), neurologic disease history (p<0.001), and psychiatric disease history (p = 0.004) than C.Similarly, we compare D vs B, and D vs C. D is similar to B and C in terms of having 92.5% patients with a normal CT. However, D has lower percentages of abnormal diastolic & systolic blood pressure at ED  and neurologic disease history (p<0.001), compared with B. If comparing with C, D has higher percentages of alcohol use (p<0.001), tobacco use (p<0.001), neurologic disease history (p<0.001), and psychiatric disease history (p<0.001), a higher percentage of unmarried people, and lower percentages of abnormal diastolic & systolic blood pressure at ED .Furthermore, although A and E are similar in terms of CT scans, A has a lower percentage of unemployment (p = 0.001), a higher percentage of unmarried people (p = 0.001), and higher percentages of abnormal diastolic & systolic blood pressure at ED , fluid use (p = 0.002), and alcohol and tobacco use .The SOS approach was used to compare outcomes in sub-groups B, C and D . It was found that B and C differ on GOSE180, BSI, RPQ Somatic, and WAIS. Chi-square tests for proportions on these four outcome measures confirmed that B has significantly worse recovery across the four outcome measures . We contend that this is because although all the patients in B and C have a normal CT, B is worse than C in terms of more tobacco use and neurologic/psychiatric disease history. This seems to affect the recovery on specific domains like BSI, RPQ somatic, and WAIS, and interestingly also the overall recovery measured by GOSE at 180 days. Comparing the outcome measures in D and C, SOS found that D and C differ on GOSE180 and RPQ Somatic. Chi-square tests for proportions on these two outcome measures confirmed that D has significantly worse recovery across the two outcome measures . Recall that D has higher percentages of alcohol use (p<0.001), tobacco use (p<0.001), neurologic disease history (p<0.001), and psychiatric disease history (p<0.001), which seem to affect the recovery on both RPQ somatic and GOSE 180. This is in line with the previous finding on B and C. SOS did not find any outcome measure that significantly differs between D and B, implying that patient difference observed at the time of their clinical assessment did not lead to outcome differences.Comparing the outcome measures in A and E, SOS found that A and E differ only on GOSE180, with A having significantly worse overall recovery (p = 0.05). After removing the 13.3% patients with a normal CT from E, we applied SOS again on A and E_sub . SOS, again, only found GOSE 180 differed between groups, but with an even smaller p value of 0.04. This indicates that for two clusters with 100% abnormal CT , patients’ overall recovery tends to be worse for the cluster whose patients are “unhealthy” in terms of other metrics such as alcohol and tobacco use, diastolic & systolic blood pressure, and use of IV fluids in the ED . Also, A has a significantly lower percentage of unemployment and a higher percentage of unmarried people. This implies demographics may also play a role in overall recovery.Finally, we compared the outcome measures between the CT normal vs abnormal patients. This is similar to combining A and E into one cluster and B, C, D into another cluster. Since we know that A and E have substantial heterogeneity in terms of clinical variables other than CT , we wanted to interrogate what would happen if we ignored this heterogeneity. Interestingly, GOSE score measured at 90 days post-TBI (but not GOSE score measured at 180 days post-TBI) was found to be significantly different between the CT normal and abnormal groups, with the CT normal group having a significantly better recovery than the CT abnormal group (p<0.001). Note that within the 10 outcomes we focused on in this paper, only GOSE was measured at 90 and 180 days, while others were only measured at 180 days. This means that CT abnormality seems to have a more substantial impact on near-term outcomes , while delineation of the heterogeneity beyond CT helps predict longer-term outcomes and on more dimensions.The main objective of this study was to identify sub-groups of patients within a population of individuals who presented with mild TBI (GCS scores of 13–15). Identifying sub-groups based upon clinical data that are typically available at the time a patient with TBI is initially evaluated could allow for recognition of baseline clinical variables that associate with “good” or “bad” patient outcomes. The main finding of this study is that 5 sub-groups of patients were identified within the larger patient population. The twelve clinical variables that contributed to the clustering structure included: gender, employment status, marital status, injury mechanism, head CT findings, systolic blood pressure, diastolic blood pressure, receiving IV fluids while in the ED, having a history of alcohol use, a history of tobacco use, a history of psychiatric disease, and a history of neurologic disease. Patients in each of the five clusters have different outcomes in regards to global post-TBI outcomes (e.g. GOSE), psychological health (e.g. BSI), cognition (e.g. WAIS), and post-TBI related symptoms (RPQ). This study helps to identify patient variables that should be further investigated when developing and validating prognostic models for TBI and when identifying more precise sub-categories of mTBI that correlate with patient outcomes. Predictive outcome models consisting of data that are easily and routinely collected during the initial evaluation of patients with mTBI would assist the clinician with determining how aggressively to manage the patient and with providing prognoses to the patients.158 allele is associated with a lower incidence of PTSD and higher GOSE score at 180 days. A rs6277 T-allele in the dopamine D2 receptor was also found to be associated with better verbal learning and recall on California Verbal Learning Test (CVLT-II) but not with non-verbal processing speed (WAIS-PSI) or cognitive activity measured by the TMT. [There are several published studies that have performed univariate analysis on TRACK-TBI Pilot data that utilize clinical characteristics as predictors for patient outcomes. Connor et al.  studied the TMT. Furthermore, there are several studies aiming to find multivariate predictors in order to explore the joint value of multiple predictors on outcomes. Lingsma et al.  studied Consistent with the previous studies, in our study, CT results, employment status, neurologic disease history and psychiatric disease history were also identified as significant clinical variables contributing to TRACK-TBI Pilot patient sub-classification. Additional factors contributing to sub-classification in our study included: gender, marital status, TBI due to falling, post-TBI blood pressure, use of IV fluids in the emergency department, alcohol use and tobacco use. Furthermore, all of the aforementioned studies performed “supervised” learning of the data, i.e., they assessed how well each clinical variable or combinations of variables can predict an outcome of interest. Our study, on the other hand, performed “unsupervised” learning, i.e., we aimed to find natural subgroups of TBI patients who are relatively homogenous in clinical characteristics within each subgroup and have greater contrast between subgroups. The subgroup membership was then correlated with multidimensional outcome variables. This unsupervised learning approach has advantages over supervised learning in that: 1) The method naturally facilitates multi-domain outcome assessments  of each patient subgroup, and thus eventually could help to determine overall management strategies for patients with mTBI instead of just treating one particular outcome. 2) This method can facilitate genetic and biological discovery of the underlying basis leading to within-subgroup homogeneity and between-subgroup heterogeneity.There are several limitations of this study including: 1) We did not have access to specific brain CT results, beyond “normal” vs. “abnormal”. It is possible that the type and severity of brain CT abnormality would contribute to mTBI sub-classification and it is likely that these more specific CT data would correlate with patient outcomes. 2) We excluded clinical variables for which more than 10% of patients had missing values, except for several TBI-related variables that we hypothesized to be exceptionally important for sub-classifying patients. It is likely that there are other clinical variables contributing to mTBI sub-classification not included in our analysis. 3) GOSE scores of 7 (lower good recovery) and 8 (upper good recovery) were combined into one group for analyses. A recent publication provides evidence that patients within each of these two categories might have substantially different multidimensional outcomes.  Future aWe performed a clustering analysis and identified five sub-groups of mTBI based upon differences in patient socio-demographics, neurologic and psychiatric history, history of alcohol and tobacco use, receiving IV fluids in the ED, blood pressure, injury mechanism, and brain CT findings. Multi-dimensional patient outcomes differed amongst these sub-groups at 90 days and 180 days post-TBI. We plan to test the clustering structure identified in this study using a unique subject sample obtained from FITBIR and to use the knowledge gained from this study to build predictive models for TBI outcomes based upon data available at the time of initial evaluation of the patient with TBI.


Increased ROS did not originate mainly from mitochondria. Plasma of severe asthmatics significantly increased healthy PBMC mitochondrial dioxygen consumption . In conclusion, such asthma endotype, characterized by increased PMBCs mitochondrial oxidative capacity and ROS production likely related to a plasma constituent, may reflect activation of the immune system. Further studies are needed to determine whether increased PBMC mitochondrial respiration might have protective effects, opening thus new therapeutic approaches.Asthma is a chronic inflammatory lung syndrome with an increasing prevalence and a rare but significant risk of death. Its pathophysiology is complex, and therefore we investigated at the systemic level a potential implication of oxidative stress and of peripheral blood mononuclear cells’ (PBMC) mitochondrial function. Twenty severe asthmatic patients with severe exacerbation (GINA 4–5) and 20 healthy volunteers participated at the study. Mitochondrial respiratory chain complexes activities using different substrates and reactive oxygen species (ROS) production were determined in both groups by high-resolution respirometry and electronic paramagnetic resonance, respectively. Healthy PBMC were also incubated with a pool of plasma of severe asthmatics or healthy controls. Mitochondrial respiratory chain complexes activity (+52.45%,  Asthma is a common disease that affects 300 million people worldwide (1 in 10 children and 1 in 12 adults) resulting in a substantial morbidity and an important annual healthcare expenditure. The disease is characterized by chronic inflammation of the conducting airway resulting in bronchial obstruction, mucus overproduction, airway remodeling and bronchial hyper-responsiveness [Different mechanistic pathways are involved in such complex pathology. Locally, generation of oxidative stress with increased reactive oxygen species (ROS) production and mitochondrial dysfunction have been observed  in murine models of ovalbumin-induced asthma and in bronchial epithelial cell cultures ,5,6,7,8.In turn, immune cells of asthmatic patients exposed to an allergen, generate ROS, such as hydroxyl radicals, superoxide, peroxydes, peroxynitrite and nitric oxide . This ROBesides this local characterization of the asthma mechanism, there are relatively few data focused on a potential involvement of mitochondrial function of inflammatory circulatory cells. However, dysfunction of the mitochondrial respiratory chain is likely to play a role in the initiation and progression of other inflammatory pathology such as cardiovascular diseases and anaphylactic choc ,18,19,20In the present study, therefore, we tested the hypothesis that peripheral blood mononuclear cell might demonstrate impaired mitochondrial function together with increased ROS production in severe asthmatic patients with severe exacerbation, as compared to healthy subjects.Twenty healthy volunteers and 20 severe asthmatic patients experiencing severe exacerbation were enrolled in a prospective and controlled study. Severe asthma was defined as a stage 4 or 5 of GINA classification and severe exacerbation as a need for hospitalization or for systemic steroids (or its increase) for more than 3 days [The study design included the clinical characterization of patients and, peripheral blood mononuclear cells mitochondrial respiratory chain complexes’ activities and ROS production in both control subjects and asthmatic patients. Further, after determining an eventual contribution of mitochondria to ROS production, we analyzed the effects of plasma on healthy PBMC. Thus, PBMC from healthy volunteers were placed in contact with heterologous platelet poor plasma of healthy volunteers or severe asthmatics experiencing severe exacerbation.The study was approved by the ethics committee of the Strasbourg University Hospital (number 2016-69) and informed consent was obtained from each patient.Thirty mL of venous blood was sampled in Sodium Heparinate tubes for each patient. 1mL was kept (on ice) for the study of ROS production. The remaining part of the sample was used to separate peripheral blood mononuclear cells (PBMC) and plasma. Briefly, blood was placed on a ficoll density gradient  and centrifuged . PBMC (lymphocytes and monocytes) were sampled, washed in a DPBS solution  and centrifuged . Finally, PBMC were counted by flow cytometry .6 PBMC /mL were introduced in the Oxygraph-2k’s chamber with continuous stirring. The dioxygen consumption was analyzed using the DatLab software 4.3 . At the beginning, cell membranes were permeabilized with saponine (125 µg/mL), and complex I was activated with glutamate (5 mM), and malate (2 mM), this step is the basal dioxygen consumption.The study of the mitochondrial respiratory chain was performed with a high resolution oxygraph  at 37 °C. 2.5 × 106cell.After reaching a steady state for V0, different substrates and inhibitors were introduced in the oxygraph’s chamber. ADP (2 mM) induced the activation of ATP synthase  and allowed the study of mitochondrial complexes I, III, IV, V. Then, succinate (25 mM) was introduced, an activator of the mitochondrial Complex II, for the study of mitochondrial Complexes I, II, III, IV, V. The addition of rotenone (0.5 μM) allowed analysis of mitochondrial Complexes II, III, IV, V by inhibiting the Complex I. TMPD/ascorbate (0.5 mM/0.5 mM) was then added to give electrons to the Complex IV, so allowed preferentially the complex IV. The result was expressed in pmol/s/10g = 3477,452; sweep width 60 G; microwave power 21.85 mW; modulation amplitude 2.4 G; time constant 40.96 ms; conversion time 10.24 ms; number of lag curve points 6. The EPR signal is proportional to the unpaired electron numbers. The result was expressed in µmol/min.ROS production in venous blood was assessed by exploring the superoxide anions production, using electron paramagnetic resonance (EPR),  at 37 °C, as previously described . Briefly6 PBMC diluted in DPBS, for 10 min at 37 °C. Then, cells were washed and centrifuged . Excitation was set at 510 nm and emission at 580 nm A positive control group of mitochondrial ROS production was elaborated with Antimycin. All the experiments were achieved in a light-protected environment. The result was expressed in AU of fluorescence.We determined mitochondrial PBMC ROS production using a fluorescent probe MitoSOX, a red mitochondrial superoxide indicator for live cell imaging  . MitoSOX6 cells/mL were placed into each oxygraph’s chamber with a pooled heterologous platelet poor plasma. In one chamber, plasma came from a pool of 6 healthy volunteers, and in the other one, plasma came from a pool of 6 severe asthmatics experiencing severe exacerbation.PBMC from 7 healthy volunteers were extracted following the same technique as presented above. Then 2 × 10Before introducing the PBMC, the basal respiration of both pooled plasmas was recorded in the oxygraph’s chambers  at 37 °C.6cell.After 6 h exposure to heterologous plasma, PBMC basal consumption of dioxygen (V0) was measured and different substrates and inhibitors were added in order to study the mitochondrial respiratory chain complexes’ activities. Using plasma as the respiratory solution, the uncoupling protocol was more adapted than the classic one. Therefore, an ATP-synthase inhibitor (oligomycine 1.25 µmol/L) was added, to induce a dioxygen consumption mainly due to the leakage of protons through the inner mitochondrial membrane . Then, t2 to ROS in the mitochondrial respiratory chain  instead of reaching cytochrome oxidase to reduce O2 to water [2 to H2O2, whereas four electrons are transferred in the reduction of one mole of O2 to water, the FRL (percent) was calculated as the rate of H2O2 production divided by twice the rate of O2 consumption, and the result was multiplied by 100 [Simultaneously, ROS production was measured by the evolution of using fluorescent probe Amplex Red (20 µM) and Horseradish peroxidase . We also calculated the free radical leak (FRL) corresponding to the fraction of electrons which reduces Oto water . Becaused by 100 .p < 0.05.All values are presented as mean ± standard error of the mean (SEM). Qualitative variables were described as numbers and percentages. When values followed the normality curve, Student’s test was used. But, if values did not follow the normality curve, a non-parametric Mann–Whitney test was used to compare the control and the asthmatics groups. Concerning data obtained with PBMC exposed to pooled heterogenous control plasma or pooled heterogenous asthmatic plasma, a non-parametric paired test was realized (Wilcoxon test). All analysis were performed with GraphPad Prism 5 . Statistical significance was determined as Twenty patients  were admitted for exacerbation of severe asthma at the Strasbourg University Hospital with a need for hospitalization or systemic steroids for more than 3 days. The control group included 20 healthy volunteers  with no medical history of pulmonary or atopic diseases.2 versus 23.0 ± 0.72 kg/m2 in the control group (p = 0.0040). Severe asthmatic patients had mainly an uncontrolled allergic asthma. Fourteen patients presented with atopic and eosinophilic asthma whereas 2 patients had a non-atopic and eosinophilic asthma and 4 patients had non-Th2 mediated asthma. 14 patients had an associated allergic rhinitis, 8 had a basal FEV1 < 80% of the maximal theoretical value. The main cause of exacerbation was bronchial and pulmonary infection and 9 patients were treated by steroids for more than 24 h at the time of inclusion and blood sampling.Subjects’ characteristics are summarized in The maximal mitochondrial respiratory chain activity was significantly increased in patients with severe asthma experiencing severe exacerbation in comparison with the control group . Data us6cell respectively, +67.1%, p = 0.005).V0 was increased in severe asthmatic patients in comparison with the control group .V6cell, +64.97%, p = 0.007).Vsucc was also significantly increased in the severe asthma group compared to the control group .Rotenone injection showed no significant difference between both groups but the combined activity of the Complexes II + III + IV + V tended to be increase in the severe asthmatic patients .V0 corresponds to the basal O2 consumption, with glutamate, and malate as substrates. VADP corresponds to the ADP-stimulated respiration, with glutamate and malate as substrates. Vsucc represents the activation of all complexes . VROT represents the complexes II, III, IV, V activities. VTMPD corresponds to the complex IV contribution. Results are expressed as means ± SEM. n = 20 per group.V2 . VROT represents the complexes II, III, IV, V activities. VTMPD corresponds to the complex IV contribution. Results are expressed as means ± SEM. n = 11 without and 9 with corticoid, respectively.V2 as compared to patients with a BMI < 30 kg/m2, this increase failed to reach statistical significance .Moreover, although mitochondrial respiration tended to increase in severe asthmatics patients with a BMI ≥ 30 kg/m0 corresponds to the basal O2 consumption, with glutamate, and malate as substrates. VADP corresponds to the ADP-stimulated respiration, with glutamate and malate as substrates. Vsucc represents the activation of all complexes . VROT represents the complexes II, III, IV, V activities. VTMPD corresponds to the complex IV contribution. Results are expressed as means ± SEM. n = 16 without and 4 with obesity, respectively.Vp = 0.02, n = 16 per group, ROS were significantly increased in the venous blood of severe asthmatic patients in comparison with the control group .Thus, basal dioxygen consumption Vo was significantly increased in severe asthmatic plasma .After ATP synthase inhibition mitochondrial respiration was activated by an uncoupler (FCCP). The Maximal mitochondrial respiratory chain activity was significantly increased in healthy PBMC in contact with pooled plasma from severe asthmatic patients in comparison with healthy PBMC in contact with pooled control plasma .Similarly, when the complex I was inhibited by rotenone injection mitochondrial activity was significantly increased in healthy PBMC in contact with pooled plasma from severe asthmatic patients in comparison with healthy PBMC Finally, when the complex IV was inhibited by antimycin injection, mitochondrial activity tended to increase in healthy PBMC in contact with pooled plasma from severe asthmatic patients as compared to the control group (1.34 ± 0.45 and 0.80 ± 0.26 pmol/s/10The free radical leak was not statistically increased in healthy PBMC in contact with pooled plasma from severe asthmatic patients, in comparison with the control one. These results are presented in The main result of this study is to demonstrate that mitochondrial respiratory chain complexes’ activities of PBMC are stimulated in severe asthmatic patients with severe exacerbation, and that such enhancement is associated with increased reactive oxygen species. Furthermore, the plasma of asthmatic patients—but not of healthy subjects—appears responsible for PBMC mitochondrial oxidative capacity enhancement.Asthma is a frequent and severe disease affecting a large proportion of the human population, including children and young people and implying a tremendous socioeconomic burden. The pathophysiology of asthma and the balance between oxidant/antioxidant is the subject of intense ongoing research and some studies have explored the local role of mitochondria in atopic diseases ,9,16,29.Interestingly, our study shows that severe asthmatic patients with severe exacerbation had a significant increase in their PBMC mitochondrial respiratory chain activity, in comparison with the control group. These data contrast with a previous report showing that acute nasal allergen challenge induces mitochondrial dysfunction of peripheral blood mononuclear cells in allergic rhinitis  and with2 molecules used for the production of ROS in the mitochondria between the two groups. Thus, as previously reported in asthma, reactive oxygen species could have been produced by NADPH-oxidase and/or by xanthine oxidase rather than by the mitochondrion [The underlying mechanisms still deserve further study but oxidative stress might play a key role. In our study, severe asthmatic patients with severe exacerbation had a significant elevation of ROS in their blood. These ROS did not seem to be mainly produced by mitochondria since the Mitosox analysis was similar in both groups and since the FRL showed no statistically significant difference in the number of Ohondrion ,32.2. Although not statistically significant and focused only on several respiratory chain complexes, this result supports further studies on a potential relationship between obesity per se and the mitochondrial respiration of circulating cells.Obesity might also have played a role since there is a link between mitochondrial dysfunction, obesity and asthma or metabolic syndrome ,34. AccoSimilarly, corticosteroids might modulate PBMC mitochondrial respiration. Indeed, steroids mediated apoptotic signals in human eosinophils through a mitochondrial pathway . However3 respectively, presented with exacerbated PBMC mitochondrial respiration as compared to asthmatic patients without eosinophilia. No difference was observed between both groups, suggesting the eosinophilia might not be a key factor associated with an enhanced PBMC mitochondrial respiration.Finally, we tested whether patients with eosinophilia and hyper-eosinophilia, defined by a blood count of ≥300 and ≥500/mmThe clinical significance of such enhanced mitochondrial respiratory chain complexes’ activities in severe asthmatic patients in exacerbation remains to be further investigated. However, recent studies suggest that during the activation of the adaptive immune response, the shift of dendritic cell’s mitochondrial respiration toward either a glycolytic or an oxidative metabolism might be related to a pro-inflammatory or to an anti-inflammatory differentiation of T cells, respectively. Thus, a reduced mitochondrial respiration seemed to be associated with pro-inflammatory effects whereas an increased dendritic cell’s mitochondrial respiration might antagonize TLR effects ,37,38. TAccordingly, besides their well-known deleterious effects corresponding to oxidative stress, ROS signaling might also allow protective effects through mitochondrial biogenesis and anti-oxidant system enhancements . In asthInterestingly, recent data support a link between antigen-specific IgE receptor expression on PBMC and atopic asthma within school-aged children . FurtherAlthough 20 subjects in each group allowed us to discover interesting characteristics of asthmatic patients, studies with a greater number of subjects will be useful to further investigate the underlying mechanisms. In this context, a study specifically directed toward obese patients and a longer follow-up in order to characterize the patients after the exacerbation will be very interesting for better defining the clinical significance of these data.This study further supports the concept of crosstalk between the mitochondrion and the immune system in asthma. The enhanced mitochondrial respiration observed in the PBMC of patients with severe asthma in exacerbation, likely related to a plasmatic factor potentially including ROS, might participate in the patient’s defense mechanisms. However, this issue still has to be investigated and further studies are needed to determine whether circulating cells’ mitochondrial function might be used as a biomarker in asthma and whether maintaining healthy mitochondria in asthmatic patients’ PBMC might be an interesting therapeutic avenue, as proposed in lung injury ,45. Thus


Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in childhood, related to prematurity, and the most common cause of pulmonary hypertension (PH) secondary to pulmonary disease in children. Moderate and severe BPD have a worse outcome and relate more frequently with PH. The prediction of moderate or severe BPD development in extremely premature newborns is vital to implement preventive strategies. Starting with the hypothesis that molecular biomarkers were better than clinical and echocardiographic factors, this study aims to explore the ability of clinical, echocardiographic and analytical variables to predict moderate or severe BPD in a cohort of extremely preterm infants.We designed a prospective longitudinal study, in which we followed a cohort of preterm newborns . In these newborns we recorded weekly clinical and echocardiographic variables as well as blood and tracheal aspirate samples, to analyze molecular biomarkers . Variables and samples were collected since birth up to week 36 , time-point at which the diagnosis of BPD is established.We included 50 patients with a median gestational age of 26 weeks (IQR 25–27) and weight of 871 g  (range 590-1200g). Three patients were excluded due to an early death. Thirty-five patients (74.5%) developed BPD . We performed a logistic regression in order to identify risk factors for moderate or severe BPD. We compared two predictive models, one with two variables , and another-one with an additional molecular biomarker (ET-1).The combination of clinical and echocardiographic variables is a valuable tool for determining the risk of BPD. We find the two variable model  more practical for clinical and research purposes. Future research on BPD prediction should be oriented to explore the potential role of ET-1. Bronchopulmonary dysplasia (BPD) is a severe chronic lung disease in childhood and one of the most frequent sequel of prematurity . The preBPD pathogenesis has evolved in the past decades from a lung lesion secondary to mechanical ventilation and elevated oxygen concentrations to a multi-factorial disease in which prenatal and postnatal factors are involved , 5. The BPD diagnosis is established at 36 weeks postmenstrual age (PMA) , which iDetermining the risk of BPD provides prognostic information, identifies infants who could benefit from preventive strategies, and helps stratify infants for clinical trial enrollment. Diverse clinical risk factors , ventilator settings, etc.) and molecular biomarkers (inflammatory pathways and vascular growth factors) have been studied in preterm infants as potential risk factors for BPD development and many prediction models have been developed –16. DespStarting with the hypothesis that molecular biomarkers were better than clinical and echocardiographic factors, this study aims to explore the ability of clinical, echocardiographic and analytical variables to predict moderate or severe BPD in a cohort of extremely preterm infants.The study was approved by the ethical committee of all participating hospitals , Hospital de Getafe, Hospital Puerta de Hierro and Complejo Hospitalario de Coruña (2015/001)). The hospital responsible was La Paz with the following approval number PI-1774. Written consent was obtained from all participants´ parents.We designed a prospective multi-center cohort study that took place in the previously mentioned five Spanish hospitals. Extremely low gestational age newborns (ELGAN) of less than 28 weeks of gestation and birth weight under 1250 g, admitted to the participating hospitals during the study period were consecutively and prospectively recruited and followed from birth until 36 weeks of PMA, a time-point at which the diagnosis and severity of BPD was determined. Exclusion criteria were major congenital malformation, neurological lesion and mother with HIV.www.pulmescell.org).Clinical, echocardiographic and analytical variables were registered at different time points  and entered into an electronic online database , which aims to explore safety of umbilical cord-derived mesenchymal stem cells (UC-MSC) in ELGAN. The aims of this preliminary biomarkers study were to find a combination of clinical, echocardiographic, and molecular biomarkers capable of selecting ELGAN at high risk of developing moderate or severe BPD. This information could be used in the design of trials testing preventive strategies, such as UC-MSC. We also aimed to explore the profile of the selected molecular biomarkers in a cohort of ELGAN, treated with standard therapies that could afterwards be compared to the biomarkers profile of the patients participating in the clinical trial, treated with UC-MSC.A patient was considered to have BPD if he/she required supplemental oxygen for at least 28 days. Severity was established at 36 weeks PMA, according to the definition by Jobe and Bancalari  10]. We. We10]. A complete echocardiography was performed to all patients at inclusion (day 1), to rule out congenital heart disease. At days 3, 7, 14, 21 and 28, we performed a complete echocardiographic study searching for PDA, atrial septal defect, pulmonary vein stenosis and PH. PH was defined in three grades , if the Blood samples were collected at days 1, 3, 7, 14, 21 and 28. Tracheal aspirate fluid was also collected at the same time-points, but only in intubated patients. The levels of IL-6, IL-1B, IP-10, CCL5 and endothelin-1 (ET-1) were analyzed in plasma, and IL-6, IL-1B, IP-10, CCL5 and uric acid were analyzed in tracheal aspirate, by commercial enzyme-linked immunosorbent assays , and uric acid levels were determined by the QuantiChromTM Uric Acid Assay Kit colorimetric assay (Bioassay Systems: DIUA-250). IL-6 and IL-1B have been previously described in association with BPD development , 17. In Considering that we aimed to identify high risk patients (moderate or severe BPD), all patients were classified into two groups: 1) No BPD group: absence of BPD and mild BPD; 2) BPD group: moderate/severe BPD and death. A comparative analysis of the characteristics of both groups was performed.Although we obtained biomarkers data at 6 different time-points, for early BPD prediction we analyzed only data on day 7, this will be discussed in detail later.The results are presented as mean and standard deviation if normally distributed or as median and interquartile range if not normally distributed. Differences in discrete variables were tested with Chi-squared or Fisher test. To compare means, Student’s t-test or ANOVA were used for normally distributed variables, and non-parametric tests if variables were not normally distributed. Correlations between quantitative variables were tested with Pearson or Spearman tests. A multivariate logistic regression, forward method, was performed to identify risk factors for BPD development by determining the odds ratio of both groups, no-BPD versus BPD, in relation to clinical, echocardiographic and analytic factors. All clinically relevant variables and those with a p-value <0.2 in the univariate model were included in the multivariate model. The sensitivity and specificity of the resulting models were evaluated by the AUC. A value of p<0.05 was considered significant. Stata version 14 was used for the statistical analysis.Fifty ELGAN were included between November 2014 and November 2016 . There were no patients with a gestational age under 24 weeks. In Spain, given the recommendations of the Spanish Neonatal Society, active resuscitation is performed over 24 weeks . Under 2Mean birth weight was of 871 g (SD 161.0). All additional demographic characteristics, maternal and perinatal history, and patient outcomes are described in The global mortality in the study was of 20% (10 patients): 3 patients, died in the first 4 days; four after 28 days of life; and the other 3 patients died at 12, 15 and 25 days of life. These three patients had been on mechanical ventilation since birth, with persistent parenchymal lung disease and respiratory failure that could not be attributable to other neonatal morbidities, and therefore were considered to have high grade BPD (III (A)), a term recently proposed by Higgins et al .The percentage of patients requiring mechanical ventilation (MV) for more than 7 days was significantly higher in the moderate/severe BPD group. Being on MV on day 7 was associated to the need of MV for a longer time (22.1 days (SD 9.95) versus 3.8 days (SD 5.26) (p<0.001)). Most notably, 81.8% of the patients who developed moderate or severe BPD were intubated at day 7, compared with 36% of the patients without BPD (p = 0.002).When comparing analytical biomarkers between both groups, we observed a significant increase in the levels of IL-1B, IL-6, IP-10 and uric acid in tracheal aspirate of moderate and severe BPD patients; whereas no significant differences were observed among plasma biomarkers . ET-1 waAfter performing a univariate analysis , we inclWhen analyzing echocardiographic variables, in the univariate analysis, we observed that patients with PDA or with PH at day 7 had a greater risk of moderate or severe BPD. In the multivariate analysis only interventricular septum (IVS) flattening, as indirect sign of PH, was significant and therefore included in the model. All other echocardiographic variables such as atrial septum defect, right ventricle dilatation or left ventricle ejection fraction, were not related with moderate or severe BPD outcome.Among these results  we obserOur study shows that a very simple predictive model composed of 2 variables (MV and echocardiographic signs of PH), is able to predict moderate or severe BPD, with a reasonable sensibility and specificity. We also found that the addition of plasma ET-1 (a pathway not yet well explored in BPD), to that model, improved the predictive value.Early BPD diagnosis has been a recurrent research goal for the past decades. Numerous studies have analyzed clinical and analytical biomarkers to identify the optimal BPD prediction strategy , 23, 24.Another issue under discussion is the optimal time-point for risk determination of BPD. Some models have tried to stratify the risk in the first week of life or even at birth, considering mostly prenatal factors . We consAfter exploring clinical, echocardiographic and analytical variables, alone and in combination, we establish that the combination of MV dependency; echocardiographic signs of PH (IVS flattening); and the elevation of certain biomarkers on day 7 of life can predict the risk of developing moderate or severe BPD.MV is a harmful factor responsible for lung injury . AlthougAn echocardiographic study on day 7 of life established that patients with PDA or with PH at that time-point had a greater risk of moderate or severe BPD. Therefore, to increase the predictive power of MV as a risk factor, we selected the echocardiographic parameter of IVS flattening . PatientWe also analyzed plasma and tracheal aspirate levels of biomarkers associated with the development of MV-induced lung injury, BPD and/or PH. Inflammatory biomarkers, like IL-1B and IL-6, are associated with a worst outcome in patients with PH or adult respiratory distress syndrome , 29. CirET-1 is a peptide with vasoconstriction properties secreted by endothelial cells. In animal models, ET-1 is increased in PH and plays an important role in the progression to lung fibrosis . Kuo et When comparing the two models (MV+IVS with MV+IVS+ET-1), we achieved a small increase in sensitivity and specificity with the addition of ET-1 to the model. Although the low levels of ET-1 in high risk moderate/severe BPD patients is one of the most interesting findings of this research we consider the use of the two-variable model (MV + IVS) more feasible in the clinical setting. The role of ET-1 in BPD is not completely understood and further research should be performed before using it in the clinical practice. Also the determination of ET-1 still requires special laboratory determination techniques, which makes it hard to use ET-1 on a daily bases.Early BPD diagnosis is one of the main goals of neonatologists. Knowing which patients are at greater risk of developing BPD in the first week of life can make a difference in the outcome of moderate or severe BPD, by applying preventive strategies. Therefore, for the neonatologist it is very important to detect, at the patient’s bedside, high risk patients. With the model we propose, if the patient is on mechanical ventilation on day 7 of life, and he/she has indirect signs of pulmonary hypertension , this paThe findings of this study can be used to guide the design of clinical trials testing preventive strategies for BPD, such as regenerative therapies. For this goal it is important to identify only patients at risk of moderate and severe BPD, who have the worst outcomes and can benefit from preventive strategies.The strengths of this study are its prospective nature in a population of babies at the highest risk of developing BPD (ELGAN) and the possibility it offers of determining the risk of BPD at a very early stage and at the patient’s bedside by a simple method: assessing the need for MV on day 7 and performing a routine echocardiographic evaluation on that day. In addition, this study is the first to explore the role of plasma ET-1 levels as a biomarker for predicting BPD in ELGAN.The main limitation of the study is the small number of patients, which allows us to explore these novel molecular biomarkers but not to achieve conclusions on the role of ET-1 in BPD. Another limitation is the decreased number of tracheal aspirate samples, considering that tracheal aspirates were obtained only from intubated patients.In summary, the combination of clinical and echocardiographic variables is a valuable tool for determining the risk of moderate or severe BPD. Future research on BPD prediction should be oriented to explore the potential role of ET-1, and also of miRNA determinations, which could be useful not only for BPD prediction, but also to elucidate pathogenic mechanisms and potential therapeutic targets in BPD.