changes in cerebral autoregulation and blood biomarkers ...to determine the eﬀect of remote...

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Changes in cerebral autoregulation and bloodbiomarkers after remote ischemicpreconditioningZhen-Ni Guo MD Wei-Tong Guo MD Jia Liu PhD Junlei Chang PhD Hongyin Ma MD Peng Zhang MD

Fu-Liang Zhang MD PhD Ke Han PhD Han-Hwa Hu MD Hang Jin PhD Xin Sun PhD

David Martin Simpson PhD and Yi Yang MD PhD

Neurologyreg 201993e8-e19 doi101212WNL0000000000007732

Correspondence

Dr Yang

doctoryangyi163com

AbstractObjectiveTo determine the effect of remote ischemic preconditioning (RIPC) on dynamic cerebralautoregulation (dCA) and various blood biomarkers in healthy adults

MethodsA self-controlled interventional study was conducted Serial measurements of dCA were per-formed at 7 time points (7 9 and 11 AM 2 5 and 8 PM and 8 AM on the next day) without orwith RIPC carried out at 720 to 8 AM Venous blood samples were collected at baseline (7 AM)and 1 hour after RIPC and blood biomarkers including 5 neuroprotective factors and 25inflammation-related biomarkers were measured with a quantitative protein chip

ResultsFifty participants were enrolled (age 3454 plusmn 1201 years 22 men) Compared with the resultson the day without RIPC dCA was significantly increased at 6 hours after RIPC and theincrease was sustained for at least 24 hours After RIPC 2 neuroprotective factors (glial cell-derived neurotrophic factor and vascular endothelial growth factor-A) and 4 inflammation-related biomarkers (transforming growth factor-β1 leukemia inhibitory factor matrixmetallopeptidase-9 and tissue inhibitor of metalloproteinase-1) were significantly elevatedcompared with their baseline levels Conversely monocyte chemoattractant protein-1 wassignificantly lower compared with its baseline level

ConclusionsRIPC induces a sustained increase of dCA from 6 to at least 24 hours after treatment in healthyadults In addition several neuroprotective and inflammation-related blood biomarkers weredifferentially regulated shortly after RIPC The increased dCA and altered blood biomarkersmay collectively contribute to the beneficial effects of RIPC on cerebrovascular function

ClinicalTrialsgov identifierNCT02965547

RELATED ARTICLE

EditorialRemote ischemicpreconditioning effects onbrain vasculature

5

These authors contributed equally to this work

From the Stroke Center (Z-NG W-TG HM F-LZ HJ XS YY) and Clinical Trial and Research Center for Stroke (Z-NG PZ YY) Department of Neurology First Hospital ofJilin University Changchun Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering (JL) and Center for Antibody DrugInstitute of Biomedicine and Biotechnology (JC) Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen University Town Department of Neurology(KH) Seventh Affiliated Hospital Sun Yat-sen University Shenzhen China Department of Neurology Taipei Medical University-Shaung Ho Hospital (H-HH) and CerebrovascularTreatment and Research Center (H-HH) College of Medicine Taipei Medical University Taiwan and Institute of Sound and Vibration Research (DMS) University of Southampton UK

Go to NeurologyorgN for full disclosures Funding information and disclosures deemed relevant by the authors if any are provided at the end of the article

The Article Processing Charge was funded by the National Key RampD Program of China (2016YFC1301600)

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 40 (CC BY-NC-ND) which permits downloadingand sharing the work provided it is properly cited The work cannot be changed in any way or used commercially without permission from the journal

e8 Copyright copy 2019 The Author(s) Published by Wolters Kluwer Health Inc on behalf of the American Academy of Neurology

Remote ischemic preconditioning (RIPC) defined as brieftransient episodes of ischemiareperfusion applied in distanttissues or organs renders remote tissues and organs resistant toa subsequent prolonged ischemia insult1 Studies of cardio-vascular diseases have repeatedly shown that RIPC could sig-nificantly reduce infarct size after myocardial ischemia in bothanimals and human patients1ndash4 Recently several animal andclinical studies demonstrated a similar beneficial role of RIPCduring cerebral ischemiareperfusion injury and cerebral smallvessel disease5ndash10 It has been shown that RIPC activates bothneuronal signals and humoral factors to confer its protectiveeffects on remote tissues and organs1 but the underlyingmechanisms especially in the brain remain unclear

Dynamic cerebral autoregulation (dCA) is a unique function ofthe cerebrovasculature and is critical to the regulation of cerebralhemodynamics11 dCApredicts the occurrence and prognosis ofcerebrovascular disease in the clinic12 Previous studies showedthat RIPC can regulate several blood biomarkers such asadenosine13 bradykinin1 and nitric oxide or nitrite14 Several ofthese biomarkers are vasoactive so they may affect dCA1516

Nevertheless it remains unknown whether RIPC can regulatedCA in humans Moreover recent studies have shown thatRIPC may have neuroprotective and inflammation regulatoryfunctions in animal models91718 However whether neuro-protective and inflammation-related blood biomarkers are reg-ulated by RIPC in humans is unknown

In the present study we hypothesize that RIPC improves dCAand affects neuroprotective and inflammation-related bloodbiomarkers and we test this using the following approachesFirst we continuously monitored the changes of dCA in healthyadults at 7 time points (baseline and 1 3 6 9 12 and 24 hoursafter RIPC) Second we assessed the effect of RIPC on 30biomarkers in venous blood including 5 neuroprotective factorsand 25 inflammation-related biomarkersWe demonstrated thatRIPC can persistently improve dCA and differentially regulatea series of neuroprotective and inflammation-related biomarkersin the blood

MethodsStandard protocol approvals registrationsand patient consentsThis prospective study was approved by the ethics committeeof the First Hospital of Jilin University Written informed

consent was obtained from all participants The participantshad the right to withdraw at any time point during the pro-cedure This trial is registered at ClinicalTrialsgov(NCT02965547)

ParticipantsFifty healthy adult volunteers (age 18ndash70 years men andwomen Asian) were included in the present study fromJanuary 2017 to July 2017 The exclusion criteria included (1)currently experiencing or having a history of chronic physicalor mental diseases (including generalized anxiety disorderdepression insomnia hypertension diabetes mellitus andchronic heart disease) (2) having an infectious disease in thepast month (3) being pregnant or lactating (women) (4)smoking or heavy drinking (formerly or currently) and (5)being unable to cooperate sufficiently to complete the dCAexamination Each participant received a comprehensivephysical examination by a physician to exclude potential dis-ease before inclusion in the study

Study designEach participant received two 24-hour monitoring sessionsThe first 24-hour session was defined as the control day andthe second 24-hour session was the RIPC day The controlday and the RIPC day were consecutive days (control day day1 and 2 RIPC day day 3 and 4) Serial measurements of dCAwere performed at 7 time points on both the control day andthe RIPC day On the control day the 7 time points were 7 9and 11 AM 2 5 and 8 PM and 8 AM on the next day On theRIPC day the RIPC was carried out at 720 to 8 AM and the 7time points were 7 (baseline) 9 (1 hour after RIPC) and 11(3 hours after RIPC) AM 2 (6 hours after RIPC) 5 (9 hoursafter RIPC) and 8 PM (12 hours after RIPC) and 8 AM on thenext day (24 hours after RIPC figure 1A) Blood sampleswere collected from the cubital vein of each participant beforeand 1 hour after RIPC intervention for further quantitativeprotein chip testing

InterventionThe RIPC was performed by an automatic device (BB-RIC-D1LAPUL Medical Devices Co Ltd China) The wholeintervention process consisted of 4 cycles of extremity is-chemia 5 minutes of blood pressure cuff inflation to 200 mmHg followed by 5 minutes of cuff deflation the whole processtook 40 minutes Tourniquets were applied to 1 upper armand 1 thigh This intervention was undertaken only once ineach participant Measurement of arterial blood pressure

GlossaryABP = arterial blood pressure BDNF = brain-derived neurotrophic factor CBFV = cerebral blood flow velocity dCA =dynamic cerebral autoregulation EOT = eotaxin GDNF = glial cell linendashderived neurotrophic factor IL = interleukin LIF =leukemia inhibitory factor MCP-1 = monocyte chemotactic protein 1 MMP = matrix metalloproteinase PD = phasedifference RIPC = remote ischemic preconditioning TFA = transfer function analysis TGF-β1 = transforming growthfactor-β1TIMP-1 = tissue inhibitor of metalloproteinases-1 TNF-α = tumor necrosis factor-α VEGF-A = vascular endothelialgrowth factor-A

NeurologyorgN Neurology | Volume 93 Number 1 | July 2 2019 e9

(ABP) was performed in the brachial artery by an automaticblood pressure monitor (Omron 711 Tokyo Japan) imme-diately after RIPC

dCA measurement and data analysisAll dCA measurements were performed in a specific quietexamination room with a controlled temperature of 20degC to24degC to minimize confounding stimuli Participants wereasked to adopt a relaxed supine position for 10 minutes andABP was measured at the brachial artery by an automaticblood pressure monitor (Omron 711) Continuous ABP wasmeasured noninvasively with a servo-controlled plethysmo-graph (Finometer model 1 FMS Rotterdam the Nether-lands) on the middle finger Two 2-MHz transcranial Dopplerprobes (MultiDop X2 DWL Sipplingen Germany) wereused to simultaneously measure continuous cerebral bloodflow velocity (CBFV) in the left and right middle cerebralarteries at a depth of 45 to 60 mm The probes were placedover temporal windows and fixed with a customized headframe CBFV and continuous ABP were recorded simulta-neously from each participant in the supine position for 10minutes All data were stored for offline assessment andanalysis

Continuous recordings of ABP and CBFV were processed byMATLAB (MathWorks Inc Natick MA) using scripts de-veloped by the research team The ABP and CBFV signals ineach participant were aligned by a cross-correlation functionThe resultant signals were then down-sampled to 1 Hz after

application of an antialias filter with a cutoff frequency at 05Hz The dynamic relationship between ABP and CBFV wasassessed by transfer function analysis (TFA)19 with an algo-rithm used in previous studies20 TFA was thus calculated inthe frequency domain as the quotient of the cross-spectrum ofthe 2 signals and the autospectrum of ABP Phase difference(PD) gain and coherence function within a low-frequencyrange 006 to 012 Hz were then derived from TFA toevaluate dCA A low value of PD indicated that CBFV fol-lowed the changes of ABP passively whereas a high value ofPD suggested that CBFV was actively regulated to counteractthe fluctuations of ABP Because estimates of TFA are un-reliable when coherence between the signals is low recordingswith low coherence between ABP and CBFV (le040) werenot included in the later statistical analysis

Blood samples and quantitative proteinchip testingBlood samples were collected from the cubital vein of eachparticipant before and 1 hour after RIPC The volume of eachsample was asymp6 mL All blood samples were centrifuged andthe supernatant was stored in separate vials at minus80degC for batchserum analysis

Differential protein screening was conducted with the Ray-Biotech Human Custom Antibody Array (RayBiotech IncNorcross GA catalog No QAH-CUST-H12) which consistsof 16 subarrays in 1 slide and allows the interrogation of 1sample per subarray (8 for standard and 8 for the samples)

Figure 1 Flowcharts and protocols of the study

(A) Flowcharts of the study (B) Study protocols ABP = arterial blood pressure DCA =dynamic cerebral autoregulation HR =heart rate RIPC = remote ischemicpreconditioning

e10 Neurology | Volume 93 Number 1 | July 2 2019 NeurologyorgN

according to the manufacturerrsquos instructions Briefly mono-clonal antibodies against various proteins were printed on theslides as bait to capture the corresponding proteins in serumincubated with a mixture of biotinylated secondary antibodiesand then detected with Cy3-labeled streptavidin Each analytewas assayed in quadruplicate The slides were then scannedwith a Mapix scanner (InnoScan 300 Microarray ScannerInnopsys France) and further processed by the Mapix soft-ware In the array positive control spots composed of stan-dardized amounts of biotinylated immunoglobulin G wereprinted directly onto the array All other variables being equalthe positive control intensities should be the same for each

subarray This allows for normalization of results from dif-ferent subarrays (or samples) The array also included nega-tive control spots consisting of buffer alone (used to diluteantibodies printed on the array)

Because several previous studies showed that RIPC inducedchanges of serum biomarkers rapidly (within 30 minutes to 1hour) after preconditioning21 we compared cubital vein bloodcomponents before and 1 hour after RIPC using the quanti-tative protein chip to identify the biomarkers altered by RIPCAccording to previous studies of RIPC in animal models91718

30 biomarkers were chosen on the basis of their previously

Table 1 Previously reported function of biomarkers chosen for the current study

Biomarkers Previously reported function

Neuroprotectivefactors

VEGF-A Induces vasodilation angiogenesis neuroprotection neurogenesis23

BDNF Directly provides neuroprotection28

GDNF A potential novel candidate of defense against ischemia brain injury28

β-NGF CNTF Mediates sympathetic neurons after mechanical stretch37

Inflammation-relatedbiomarkers

IL-1α IL-1β IL-4 IL-6IL-8 IL-10 IL-18

A group of lymphatic factors involved in maturation activation and proliferation of immunologiccells and immunomodulation and inflammation processes38

IFN-γ A mediator of inflammatory and immune responses in the postischemic brain microvasculature39

MCP-1 Involved in the advanced stage of atherosclerotic cerebrovascular disease40

MIP-1β Might contribute to neuropathologic progression associated with amyloid deposition in Alzheimerdisease41

TNF-α Induced low-level inflammatory response in the CNS neuroprotective or proapoptotic30

TGF-β1 Promoted repair of neurovascular unit regulates immune system function30

CRP Associated with inflammatory response involving chronic and acute inflammation42

GM-CSF Induces spontaneous brain inflammation and neurologic disease43

MMP-2 Possess the ability to activate proinflammatory agents35

MMP-3 Increases intracranial bleeding after ischemic stroke44

MMP-9 Related to brain edema after acute cerebral infarction35

TIMP-1 Protects the blood-brain barrier related to tissue remodeling and inflammation in ischemicstroke34

MMP-9TIMP-1 Related to brain edema after acute cerebral infarction35

EOT EOT-2 EOT-3 Involved in the recruitment of eosinophils and inflammatory responses45

Adiponectin Mediates antiatherogenic responses46

Fas Regulation of apoptosis33

LIF Plays an essential role in endogenous neuroprotective mechanisms triggered by preconditioning-induced stress47

TARC Plays a role in T-cell development in thymus and in trafficking and activation of mature T cells48

Abbreviations BDNF = brain-derived neurotrophic factor β-NGF = beta-nerve growth factor CNTF = ciliary neurotrophic factor CRP = C-reactive protein EOT= eotaxin Fas = tumor necrosis factor receptor superfamilymember 6 GDNF = glial cell linendashderived neurotrophic factor GM-CSF = granulocyte-macrophagecolony-stimulating factor IFN-γ = interferon-γ IL = interleukin LIF = leukemia inhibitory factor MCP-1 = monocyte chemotactic protein-1 MIP-1β = mac-rophage inflammatory protein-1β MMP = matrix metalloproteinase RIPC = remote ischemic preconditioning TARC = thymus and activation-regulatedchemokine TGF-β1 = transforming growth factor-β1 TIMP-1 = tissue inhibitor of metalloproteinases-1 TNF-α = tumor necrosis factor-α VEGF-A = vascularendothelial growth factor A


reported neuroprotective function or their regulation of in-flammatory responses The reasons why each biomarker waschosen are listed in table 1 The 5 neuroprotective factorsincluded brain-derived neurotrophic factor (BDNF) glial cellline-derived neurotrophic factor (GDNF) β-nerve growthfactor ciliary neurotrophic factor and vascular endothelialgrowth factor-A (VEGF-A also a potent vasoactive factor)The 25 inflammation-related biomarkers included interleukin(IL)-1α IL-1β IL-4 IL-6 IL-8 IL-18 IL-10 interferon-γmonocyte chemotactic protein-1 (MCP-1) macrophage in-flammatory protein-1β matrix metalloproteinase (MMP)-2 MMP-3 MMP-9 tissue inhibitor of metalloproteinases-1(TIMP-1) tumor necrosis factor-α (TNF-α) transforminggrowth factor-β1 (TGF-β1) adiponectin C-reactive proteingranulocyte-macrophage colony-stimulating factor eotaxin(EOT) EOT-2 EOT-3 adiponectin tumor necrosis factorreceptor superfamily member 6 leukemia inhibitory factor(LIF) and thymus and activation-regulated chemokine

Statistical analysisThe data were analyzed with the Statistical Program for SocialSciences version 220 (SPSS IBM West Grove PA) Con-tinuous variables were described as mean plusmn SD or median(interquartile range) depending on the distribution of thevariable The Shapiro-Wilk test was used to test the normalityof data A paired t test was used to compare the differencebetween the 2 groups if they were in normal distributionsAlternatively the Wilcoxon signed-rank test was used if thedata distribution was not normal Categorical variables weredescribed as absolute values and percentages To comparePD gain mean arterial pressure and heart rate between RIPCand different time points a mixed linear model for repeatedmeasurements was used Both of the 2 factors (RIPC andtime) that were included in the mixed linear model wereconsidered to be the factor of repeated measurement Mul-tiple biomarkers were compared between baseline and 1 hourafter RIPC so Bonferroni correction for multiple comparisonbetween groups was applied The adjusted p value wasobtained by multiplying the crude p value by the number ofmultiple comparisons (6 times) All tests were 2 tailed andvalues of p lt 005 were considered statistically significant

Data availabilityThe deidentified data generated and analyzed in the currentstudy will be available and shared by request from any qualifiedinvestigator for purposes of replicating procedures and results

ResultsFifty-eight healthy adult volunteers were assessed for eligi-bility and 8 volunteers who did not meet the inclusion criteriaor declined to participate were excluded In the current studywe enrolled 50 healthy adults (age 3454 plusmn 1201 years 22men [44] all Asian) Data from 2 participants were excludeddue to low coherence Thus the study included 48 partic-ipants in total for dCA analysis A summary of the mixed linearmodel for PD gain mean arterial pressure and heart ratemeasurements across intervention and time points is pre-sented in table 2Mean arterial pressure and heart rate of serialmeasurements are presented in table 3 and figure 2

Dynamic cerebral autoregulationThemixed linear model identified the highly significant effectsof intervention (p = 00006) and time points (p = 00024) onPD but did not identify the interaction effect of them (p =04836) (table 2) Compared with the PD values at the sametime points on the control day and RIPC day the PD was notsignificantly altered within 3 hours after RIPC However thePD value significantly increased starting from 6 hours afterRIPC and the increase was sustained for at least 18 hoursuntil 24 hours after RIPC (table 3 and figure 2) The gain didnot differ significantly between the control day and the RIPCday across all study time points

Blood biomarkers

Neuroprotective factorsOne hour after RIPC VEGF-A and GDNF in venous bloodserum increased significantly compared to their baseline levels(figures 3 and 4A) BDNF ciliary neurotrophic factor andβ-nerve growth factor in venous blood serum at 1 hour afterRIPC were not significantly different from their baseline levels(figure 3)

Table 2 Summary of mixed linear model for PD gain mean arterial pressure and heart rate measurements acrossintervention and time points

Indicator (n = 48)

Intervention (RIPC) Time Interaction

F Value p Value F Value p Value F Value p Value

PD degree 136268 00006 38172 00024 08972 04836

Gain 32779 00766 25208 00378 01866 09675

Mean arterial pressure mm Hg 16731 02022 153545 lt00001 75877 lt00001

Heart rate bpm 04627 04997 47146 00004 04034 08361

Abbreviation PD = phase difference RIPC = remote ischemic preconditioning


Table 3 dCA parameter (PD gain) mean arterial pressure and heart rate in participants

PD degree(n = 48)

Gain (n = 48)

Mean arterial pressuremm Hg (n = 48)

Heart rate bpm(n = 48)

Measurement at 7 AM

Control day 49231 plusmn 14545 0948 plusmn 0282 84933 plusmn 10416 67854 plusmn 7492

RIPC day 51128 plusmn 14380 0860 plusmn 0299 85785 plusmn 7755 68062 plusmn 7566

t or z 0763 minus1673 minus1318 minus0751

p Value 04492 01010 01874 04529

Measurement at 8 AM

Control day NA NA NA NA

RIPC day NA NA 85382 plusmn 11746 71396 plusmn 8636

t or z mdash mdash mdash mdash

p Value mdash mdash mdash mdash

Measurement at 9 AM



t or z 0998 minus1354 1267 minus1067

p Value 03284 01758 02115 02916

Measurement at 11 AM



t or z 0676 minus1224 2648 minus0524

p Value 05026 02272 00110 06024

Measurement at 2 PM



t or z 2288 minus1402 3617 minus0655

p Value 00267 01674 00007 05123

Measurement at 5 PM




p Value 00165 01840 02692 08976

Measurement at 8 PM



t or z minus3569 minus0990 minus1128 minus0803

p Value 00004 03272 02648 04260

Measurement at 8 AM the next day



Continued


Inflammation-related biomarkersOne hour after RIPC the levels of TGF-β1 LIF MMP-9 andTIMP-1 were significantly higher than the baseline levels ofthese biomarkers (figures 3 and 4B) In contrast the level ofMCP-1 was significantly lower than the baseline level (figures3 and 4B)

The IL-1α IL-1β IL-4 IL-6 IL-8 IL-10 IL-18 interferon-γmacrophage inflammatory protein-1β MMP-2 MMP-3TNF-α C-reactive protein granulocyte-macrophage colony-stimulating factor adiponectin EOT EOT-2 EOT-3 tumornecrosis factor receptor superfamily member 6 thymus andactivation-regulated chemokine and MMP-9TIMP-1 ratiolevels in venous blood serum at 1 hour after RIPC were notsignificantly different from their baseline levels (figure 3)

DiscussionIn the present study we found that after RIPC dCA improvedfrom 6 to 24 hours after the intervention in healthy adultsRIPC was also associated with changes in some neuro-protective and inflammation-related biomarkers in blood Theincreased dCA and altered blood biomarkers may contributeat least partially to the beneficial effects of RIPC on cere-brovascular function

Previous studies suggested that RIPC can protect the targetorgan or tissue by inducing ischemic tolerance which includesearly ischemic tolerance (from 30 to 60 minutes afterRIPC)21 intermediate tolerance (12 hours after RIPC)22 anddelayed ischemic tolerance (from 24 hours after RIPC andlasts for days)18 In our study we found that dCA was notimmediately modulated by RIPC (no significant changeswithin 3 hours after RIPC) but started to elevate significantlyfrom 6 hours after RIPC implying that the intermediate tol-erance after RIPC may be earlier than previously noted

Several previous studies have reported that RIPC can inducefor example adenosine13 bradykinin1 and nitric oxide ornitrite14 Many of these substances are vasoactive and whencarried to the brain could regulate dCA by changing thediameter of microcerebral arteries16 In the current study wefound that a series of additional blood biomarkers were reg-ulated by RIPC which might also positively regulate the dCAfunction For example we found that the level of circulating

VEGF-A increased significantly 1 hour after RIPC VEGF-Aa potent vasodilator and proangiogenic factor23 has beenreported not only to induce neuroprotection directly in is-chemic disorders but also to improve dCA through hypoxia-inducible transcription factor-1ndashmediated pathways24 Inaddition GDNF can act upstream of VEGF25 and hence mayimprove dCA by enhancing the VEGF signaling pathways26

Further studies are warranted to dissect the relative con-tributions of these biomarkers to improved dCA after RIPC

Neuroprotection is an important function of RIPC in animaland clinical studies9171827 In our study we found that theneurotrophic factor GDNF was significantly elevated afterRIPC This factor can directly provide neuroprotection notonly in cerebrovascular diseases such as stroke and sub-arachnoid hemorrhage28 but also in other neuropathy such asParkinson disease and epilepsy29 These results suggest thatRIPC induces neuroprotective biomarkers in humans andmay be beneficial in the prevention of various neurologicdiseases

Previous studies have reported that RIPC results in the releaseof proinflammatory and anti-inflammatory cytokines andchemokines that orchestrate the neuroinflammatory responseresolution of inflammation and transition to neurologic re-covery and regeneration91730 In our study we found that theTGF-β1 LIF MMP-9 TIMP-1 and MCP-1 levels were sig-nificantly changed compared to their baseline levels Amongthese biomarkers both anti-inflammatory biomarkers (TGF-β1 LIF and TIMP-1) and a proinflammatory factor (MMP-9)underwent significant changes Similar to our study previousstudies have shown differential regulation of inflammation-related factors by RIPC For example a study found that theserum level of macrophage migration inhibitory factor wasincreased whereas no difference was found in IL-6 IL-8 andIL-10 serum levels between the RIPC group and a controlgroup in patients undergoing cardiac surgery31 Another studyreported increased blood levels of TNF-α IL-6 IL-8 and IL-10 in 5 healthy volunteers after RIPC32 TNF-α and IL-6 playmajor roles in initiating and amplifying the postischemic in-flammatory response whereas IL-10 is mainly an anti-inflammatory factor33 Thus these studies and our ownindicate an effect of RIPC on the inflammatory profile al-though there are some differences in biomarkers tested andaffected differences in experimental protocols and measure-ment points may explain some variations in results However

Table 3 dCA parameter (PD gain) mean arterial pressure and heart rate in participants (continued)

PD degree(n = 48)

Gain (n = 48)



t or z 2777 minus1046 0096 minus1302

p Value 00079 02955 09237 01930

Abbreviation dCA = dynamic cerebral autoregulation PD = phase difference RIPC = remote ischemic preconditioning


Figure 2 Autoregulatory parameter and statistical analysis of dCA MAP and HR

(A) Autoregulatory parameter derived from the transfer function analysis (B) Statistical analysis of dynamic cerebral autoregulation (dCA) mean arterialpressure (MAP) and heart rate (HR) by serial measurements p lt 005 for comparison between the control day and remote ischemic preconditioning (RIPC)day


it must be pointed out that whether a factor participates in theproinflammatory function or anti-inflammatory function is celland tissue context dependent The changes of the aforemen-tioned inflammation-related biomarkers in our study couldexplain the effect of RIPC on inflammatory regulation how-ever we do not currently know how these factors regulate theinflammatory system The exact roles andmechanisms of these(or other) factors in the regulation of dCA need further studyFurther evaluation is required to determine whether theoverall effects of these inflammation-related biomarkers arebeneficial to cerebrovascular diseases

In this study we found that RIPC increased the blood level ofTIMP-1 significantly TIMP-1 is an endogenous inhibitor ofMMP-9 which is related to tissue remodeling and in-flammation TIMP-1 has been reported to protect the blood-brain barrier and to play an important role in ischemicstroke34 However we also found an increased level of MMP-9 after RIPC A previous study suggested that MMP-9 levelsand the MMP-9TIMP-1 ratio in serum are related to brainedema after acute cerebral infarction35 However we did notfind a significant change in this ratio after RIPC

Neuronal humoral and immunologic mediators may all playroles in the transduction of protective signals generated fromlimbs to the targeted organs179 A previous review suggestedthat both early ischemic tolerance and delayed ischemic tol-erance induced the attenuation or prevention of ischemicinjury18 RIPC is associated with both local and systemicmechanisms (ie circulating hormones cytokines andgrowth factors) that contribute to improvement in vascularfunction or structure of targeted organs It was rational thatthe biomarkers were produced in the preconditioning loca-tion and then transported by the circulatory system to thebrain and thus affect the function of the cerebrovasculaturedirectly or indirectly (humoral signal transduction)1 It isworth noting that these biomarkers have various half-lives inblood ranging from minutes to several days In addition thetime it takes their biological effects to occur varies sub-stantially For example the vessel genesis effects of VEGF-Awould be quite long (weeks) while the neuroprotectiveeffects of BDNF would be quite limited in time occurringquickly over days Further studies are needed to determine thetemporal profiles of each biomarkers in blood and to clarifyhow these biomarkers contribute to the improved dCA

Figure 3 Heat map of quantitative protein chip of 30 biomarkers

Adip = adiponectin BDNF = brain-derived neurotrophic factor β-NGF = β-nerve growth factor CNTF = ciliary neurotrophic factor CRP = C-reactive proteinEOT = eotaxin Fas = tumor necrosis factor receptor superfamily member 6 GDNF = glial cell linendashderived neurotrophic factor GM-CSF = granulocyte-macrophage colony-stimulating factor IFN-γ= interferon-γ IL = interleukin LIF = leukemia inhibitory factor MCP-1 =monocyte chemotactic protein-1MIP-1β= macrophage inflammatory protein-1β MMP matrix metalloproteinase NX = blood sample number RIPC = remote ischemic preconditioning TARC =thymus and activation-regulated chemokine TGF-β1= transforming growth factor-β1 TIMP-1 = tissue inhibitor of metalloproteinases-1 TNF-α= tumornecrosis factor-α VEGF-A = vascular endothelial growth factor A


Circadian rhythm changes mean arterial pressure over thecourse of a day36 In our study it was interesting to find thatRIPC seemed to have an effect on the circadian rhythm ofmean arterial pressure which was significantly stabilized byRIPC Future studies are needed to explore the underlyingmechanism and potential applications

We found that after RIPC the levels of 2 neuroprotectivefactors and several inflammation-related biomarkers in serumincreased significantly compared to the baseline levels inhealthy participants Thus in the future we can choose morebiomarkers that are related to neuroprotection and in-flammation to study RIPC Furthermore it will be worthinvestigating other biomarkers that are related to these bio-markers identified or factorspathways that act upstream anddownstream of these biomarkers to further elucidate themechanism of RIPC In the current study we collected and

analyzed only serum samples Other biological samples in-cluding brain parenchyma urine and the expression and ge-nomic data in different individuals or in animals wouldfurther elaborate the mechanism of RIPC in dCA improve-ment Besides it would be important in the future to in-vestigate whether these benefits are still apparent at timepoints consistent with other stroke studies including 30 days90 days and 1 year

We acknowledge limitations in this study First we could notcollect blood samples at multiple time points due to diffi-culties of obtaining the consent of participants and approvalof the ethics committee In the present study we chose 1hour after RIPC as the measurement time point of bloodsample collection on the basis of previous studies in whichserum levels of proteins were altered during or rapidly afterRIPC3 Although it is possible that normal circadian

Figure 4 Statistical distributions of 7 biomarkers with significant differences

(A) Statistical distributions of the neuroprotective factors vascular endothelial growth factor A (VEGF-A) and glial cell linendashderived neurotrophic factor (GDNF)at baseline and 1 hour after remote ischemic preconditioning (RIPC) in each group (B) Statistical distributions of inflammation-related biomarkers trans-forming growth factor-β1 (TGF-β1) leukemia inhibitory factor (LIF) matrix metalloproteinase-9 (MMP-9) tissue inhibitor of metalloproteinases-1 (TIMP-1)and monocyte chemotactic protein-1 (MCP-1) at baseline and 1 hour after RIPC in each group Whiskers represent highest and lowest values middle squarerepresents interquartile values and middle line indicates median Adjusted p lt 005 for comparison with the baseline level


fluctuations may account for the differences in serum bio-marker levels we firmly believe that the changes of serumbiomarkers between baseline and 1 hour after RIPC can beattributed to the RIPC on the basis of numerous previousstudies that have demonstrated that RIPC could rapidlyinduce changes in the levels of hundreds of serum proteinsincluding the biomarkers measured in our study3 Furtherresearch into the related biomarker concentrations at longertime points such as 6 and 12 hours and their dynamicchanges is needed The second limitation of this study wasthat the sample size was relatively small and the participantswere healthy adults With the results from this study we cansafely conclude only that the present conclusion is applicableto healthy adults without the conditions described in theMethods More significantly it is necessary to investigatewhether RIPC can also improve dCA in patients with variouscerebrovascular and neurologic diseases such as ischemicstroke depression anxiety disorders and migraine

Overall our results suggested that RIPC improves dCA fromat least 6 to 24 hours after RIPC in healthy adults and thatRIPC plays neuroprotective and inflammation regulatoryroles in humans by altering various blood biomarkers Ourstudy provides evidence of RIPC inducing neuroprotectionand a new approach to improve the cerebrovascular functionin terms of dCA

AcknowledgmentThe authors thank all the volunteers for their contributions tothe study

Study fundingThis article was supported by the National Key RampDProgramof China (2016YFC1301600) and Program for JLU Scienceand Technology Innovative Research Team (2017TD-12) toYi Yang

DisclosureThe authors report no disclosures relevant to the manuscriptGo to NeurologyorgN for full disclosures

Publication historyReceived by Neurology November 7 2018 Accepted in final formFebruary 14 2019

References1 Heusch G Boslashtker HE Przyklenk K Redington A Yellon D Remote ischemic con-

ditioning J Am Coll Cardiol 201565177ndash1952 White SK Frohlich GM Sado DM et al Remote ischemic conditioning reduces

myocardial infarct size and edema in patients with ST-segment elevation myocardialinfarction JACC Cardiovasc Interv 20158178ndash188

Appendix Authors

Name Location Role Contribution

Zhen-NiGuo MD

The First Hospitalof Jilin UniversityChangchun China

Author Study concept anddesign analysisand interpretationof data draftingthe manuscriptand revision forcontent studysupervision

Appendix (continued)


Wei-TongGuo MD


Author Study concept anddesign acquisitionof data analysis andinterpretation ofdata drafting themanuscript revisionthe manuscript forcontent

Jia LiuPhD PhD

ShenzhenInstitutes ofAdvancedTechnologyChinese Academyof Sciences China

Author Analysis andinterpretation ofdata revision ofthe manuscript forcontent

JunleiChangPhD

Shenzhen Institutesof AdvancedTechnologyChinese Academyof Sciences China


HongyinMa MD


Author Acquisition andanalysis of data

PengZhangMD


Author Analysis andinterpretation ofdata

Fu-LiangZhangMD


Author Acquisition of dataand revision of themanuscript forcontent

Ke HanPhD

Sun Yat-senUniversityShenzhen China

Author Supervision ofstatistical analysisand revision of themanuscript forcontent

Han-HwaHu MD

Taipei MedicalUniversity TaipeiTaiwan


Hang JinPhD


Author Acquisition of data

Xin SunPhD



DavidMartinSimpsonPhD

University ofSouthampton UK

Author Supervision ofanalysis andrevision of themanuscript forcontent

Yi YangMD PhD


CorrespondingAuthor

Study concept anddesign criticalrevision ofmanuscript studysupervisionobtaining funding


3 Albrecht M Zitta K Bein B et al Remote ischemic preconditioning regulates HIF-1alpha levels apoptosis and inflammation in heart tissue of cardiosurgical patientsa pilot experimental study Basic Res Cardiol 2013108314

4 Verouhis D Sorensson P Gourine A et al Effect of remote ischemic conditioning oninfarct size in patients with anterior ST-elevation myocardial infarction Am Heart J201618166ndash73

5 Wang Y Meng R Song H et al Remote ischemic conditioning may improve out-comes of patients with cerebral small-vessel disease Stroke 2017483064ndash3072

6 Jensen HA Loukogeorgakis S Yannopoulos F et al Remote ischemic pre-conditioning protects the brain against injury after hypothermic circulatory arrestCirculation 2011123714ndash721

7 Hu S Dong H Zhang H et al Noninvasive limb remote ischemic preconditioningcontributes neuroprotective effects via activation of adenosine A1 receptor and redoxstatus after transient focal cerebral ischemia in rats Brain Res 2012145981ndash90

8 Blauenfeldt RA Hougaard KD Mouridsen K Andersen G High prestroke physicalactivity is associated with reduced infarct growth in acute ischemic stroke patientstreated with intravenous tPA and randomized to remote ischemic perconditioningCerebrovasc Dis (Basel Switzerland) 20174488ndash95

9 Pan J Li X Peng Y Remote ischemic conditioning for acute ischemic stroke dawn inthe darkness Rev Neurosci 201627501ndash510

10 Zhao W Meng R Ma C et al Safety and efficacy of remote ischemic preconditioningin patients with severe carotid artery stenosis before carotid artery stenting a proof-of-concept randomized controlled trial Circulation 20171351325ndash1335

11 Xiong L Liu X Shang T et al Impaired cerebral autoregulation measurement andapplication to stroke J Neurol Neurosurg Psychiatry 201788520ndash531

12 Reinhard M Rutsch S Lambeck J et al Dynamic cerebral autoregulation associateswith infarct size and outcome after ischemic stroke Acta Neurol Scand 2012125156ndash162

13 Randhawa PK Jaggi AS Unraveling the role of adenosine in remote ischemicpreconditioning-induced cardioprotection Life Sci 2016155140ndash146

14 Rassaf T Totzeck M Hendgen-Cotta UB Shiva S Heusch G Kelm M Circulatingnitrite contributes to cardioprotection by remote ischemic preconditioning Circ Res20141141601ndash1610

15 Takada J Ibayashi S Nagao T Ooboshi H Kitazono T Fujishima M Bradykininmediates the acute effect of an angiotensin-converting enzyme inhibitor on cerebralautoregulation in rats Stroke 2001321216ndash1219

16 Guo ZN Shao A Tong LS Sun W Liu J Yang Y The role of nitric oxide andsympathetic control in cerebral autoregulation in the setting of subarachnoid hem-orrhage and traumatic brain injury Mol Neurobiol 2016533606ndash3615

17 Yang J Liu C Du X et al Hypoxia inducible factor 1alpha plays a key role in remoteischemic preconditioning against stroke by modulating inflammatory responses inrats J Am Heart Assoc 20187e007589

18 Durukan A Tatlisumak T Preconditioning-induced ischemic tolerance a win-dow into endogenous gearing for cerebroprotection Exp Transl Stroke Med201022

19 Claassen JA Meel-van den Abeelen AS Simpson DM Panerai RB Transfer functionanalysis of dynamic cerebral autoregulation a white paper from the InternationalCerebral Autoregulation Research Network J Cereb Blood Flow Metab 201636665ndash680

20 Ma H Guo ZN Liu J Xing Y Zhao R Yang Y Temporal course of dynamic cerebralautoregulation in patients with intracerebral hemorrhage Stroke 201647674ndash681

21 Meller R The role of the ubiquitin proteasome system in ischemia and ischemictolerance Neuroscientist 200915243ndash260

22 Ren C Gao X Steinberg GK Zhao H Limb remote-preconditioning protects againstfocal ischemia in rats and contradicts the dogma of therapeutic time windows forpreconditioning Neuroscience 20081511099ndash1103

23 Olsson AK Dimberg A Kreuger J Claesson-Welsh L VEGF receptor signalling incontrol of vascular function Nat Rev Mol Cell Biol 20067359ndash371

24 Sorond FA Tan CO LaRose S et al Deferoxamine cerebrovascular hemodynamicsand vascular aging potential role for hypoxia-inducible transcription factor-1-regulated pathways Stroke 2015462576ndash2583

25 Huang SM Chen TS Chiu CM et al GDNF increases cell motility in human coloncancer through VEGF-VEGFR1 interaction Endocr Relat Cancer 20142173ndash84

26 Yang JP Liu HJ Liu XF VEGF promotes angiogenesis and functional recovery instroke rats J Invest Surg 201023149ndash155

27 England TJ Hedstrom A OrsquoSullivan S et al RECAST (Remote Ischemic Condi-tioning After Stroke Trial) a pilot randomized placebo controlled phase II trial inacute ischemic stroke Stroke 2017481412ndash1415

28 Duarte EP Curcio M Canzoniero LM Duarte CB Neuroprotection by GDNF in theischemic brain Growth Factors 201230242ndash257

29 Morcuende S Muntildeoz-Hernandez R Benıtez-Temintildeo B Pastor AM de la Cruz RRNeuroprotective effects of NGF BDNF NT-3 and GDNF on axotomized extraocularmotoneurons in neonatal rats Neuroscience 201325031ndash48

30 McDonough A Weinstein JR Neuroimmune response in ischemic preconditioningNeurotherapeutics 201613748ndash761

31 Ney J Hoffmann K Meybohm P et al Remote ischemic preconditioning does notaffect the release of humoral factors in propofol-anesthetized cardiac surgery patientsa secondary analysis of the RIPHeart study Int J Mol Sci 201819E1094

32 Shimizu M Saxena P Konstantinov IE et al Remote ischemic preconditioningdecreases adhesion and selectively modifies functional responses of human neu-trophils J Surg Res 2010158155ndash161

33 Iadecola C Anrather J The immunology of stroke from mechanisms to translationNat Med 201117796ndash808

34 Maddahi A Chen Q Edvinsson L Enhanced cerebrovascular expression of matrixmetalloproteinase-9 and tissue inhibitor of metalloproteinase-1 via the MEKERKpathway during cerebral ischemia in the rat BMC Neurosci 20091056

35 Li DD Song JN Huang H et al The roles of MMP-9TIMP-1 in cerebral edemafollowing experimental acute cerebral infarction in rats Neurosci Lett 2013550168ndash172

36 Douma LG Gumz ML Circadian clock-mediated regulation of blood pressure FreeRadic Biol Med 2018119108ndash114

37 Rana OR Schauerte P Hommes D et al Mechanical stretch induces nerve sproutingin rat sympathetic neurocytes Auton Neurosci 201015525ndash32

38 HoGJ Drego R Hakimian EMasliah EMechanisms of cell signaling and inflammationin Alzheimerrsquos disease Curr Drug Targets Inflamm Allergy 20054247ndash256

39 Yilmaz G Arumugam TV Stokes KY Granger DN Role of T lymphocytes andinterferon-gamma in ischemic stroke Circulation 20061132105ndash2112

40 Arakelyan A Petrkova J Hermanova Z Boyajyan A Lukl J Petrek M Serum levels ofthe MCP-1 chemokine in patients with ischemic stroke and myocardial infarctionMediators Inflamm 20052005175ndash179

41 Zhu M Allard JS Zhang Y et al Age-related brain expression and regulation of thechemokine CCL4MIP-1beta in APPPS1 double-transgenic mice J NeuropatholExp Neurol 201473362ndash374

42 Schulz S Ludike H Lierath M et al C-reactive protein levels and genetic variants ofCRP as prognostic markers for combined cardiovascular endpoint (cardiovasculardeath death from stroke myocardial infarction and strokeTIA) Cytokine 20168871ndash76

43 Spath S Komuczki J Hermann M et al Dysregulation of the cytokine GM-CSFinduces spontaneous phagocyte invasion and immunopathology in the central ner-vous system Immunity 201746245ndash260

44 Suzuki Y Nagai N Umemura K Collen D Lijnen HR Stromelysin-1 (MMP-3) iscritical for intracranial bleeding after t-PA treatment of stroke in mice J ThrombHaemost 200751732ndash1739

45 Owczarek W Paplinska M Targowski T et al Analysis of eotaxin 1CCL11 eotaxin2CCL24 and eotaxin 3CCL26 expression in lesional and non-lesional skin ofpatients with atopic dermatitis Cytokine 201050181ndash185

46 Gairolla J Kler R Modi M Khurana D Leptin and adiponectin pathophysiologicalrole and possible therapeutic target of inflammation in ischemic stroke Rev Neurosci201728295ndash306

47 Chollangi S Wang J Martin A Quinn J Ash JD Preconditioning-induced protectionfrom oxidative injury is mediated by leukemia inhibitory factor receptor (LIFR) andits ligands in the retina Neurobiol Dis 200934535ndash544

48 Garlisi CG Xiao H Tian F et al The assignment of chemokine-chemokine receptorpairs TARC and MIP-1 beta are not ligands for human CC-chemokine receptor 8Eur J Immunol 1999293210ndash3215


DOI 101212WNL0000000000007732201993e8-e19 Published Online before print May 29 2019Neurology

Zhen-Ni Guo Wei-Tong Guo Jia Liu et al preconditioning

Changes in cerebral autoregulation and blood biomarkers after remote ischemic

This information is current as of May 29 2019

ServicesUpdated Information amp

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References httpnneurologyorgcontent931e8fullref-list-1

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ISSN 0028-3878 Online ISSN 1526-632XWolters Kluwer Health Inc on behalf of the American Academy of Neurology All rights reserved Print1951 it is now a weekly with 48 issues per year Copyright Copyright copy 2019 The Author(s) Published by

reg is the official journal of the American Academy of Neurology Published continuously sinceNeurology

CORRECTIONS

Genetic variation in PLEKHG1 is associated with white matterhyperintensities (n = 11226)Neurologyreg 201993608 doi101212WNL0000000000007914

In the article ldquoGenetic variation in PLEKHG1 is associated with white matter hyperintensities(n = 11226) by Traylor et al1 first published online January 18 2019 Dr DanutaM Lisiecka-Fordrsquos last name should have appeared hyphenated The editorial office regrets the error

Reference1 Traylor M Tozer DJ Croall ID et al Genetic variation in PLEKHG1 is associated with white matter hyperintensities (n = 11226)

Neurology 2019 92e749ndashe757

Incidence of frontotemporal lobar degeneration in ItalyThe Salento-Brescia Registry study

Neurologyreg 201993608 doi101212WNL0000000000008185

In the article ldquoIncidence of frontotemporal lobar degeneration in Italy The Salento-BresciaRegistry study by Logroscino et al1 first published online April 12 2019 the institutionalaffiliation for Drs Binetti Fostinelli Benussi Ghidoni and Cappa should have been ldquoIRCCSIstituto Centro San Giovanni di Dio Fatebenefratelli Bresciardquo The authors regret the error

Reference1 Logroscino G Piccininni M Binetti G et al Incidence of frontotemporal lobar degeneration in Italy the Salento-Brescia Registry study

Neurology 201992e2355ndashe2363

Changes in cerebral autoregulation and blood biomarkers afterremote ischemic preconditioningNeurologyreg 201993608 doi101212WNL0000000000008351

In the article ldquoChanges in cerebral autoregulation and blood biomarkers after remote ischemicpreconditioning by Guo et al1 first published online May 30 2019 in figure 4A the GDNFmeasurement should have been pgmL It appears correctly in the July 2 2019 issue Theauthors regret the error

Reference1 Guo ZN Guo WT Liu J et al Changes in cerebral autoregulation and blood biomarkers after remote ischemic preconditioning


Iron deposition in periaqueductal gray matter as a potentialbiomarker for chronic migraineNeurologyreg 201993608 doi101212WNL0000000000007921

In the article ldquoIron deposition in periaqueductal graymatter as a potential biomarker for chronicmigraine by Domınguez et al1 first published online February 1 2019 and in print March 52019 in figure 2 there should not be a second row of values under panel B PAG iron volume(microL) The authors regret the error

Reference1 Domınguez C Lopez A Ramos-Cabrer P et al Iron deposition in periaqueductal gray matter as a potential biomarker for chronic

migraine Neurology 201992e1076ndashe1085

608 Copyright copy 2019 American Academy of Neurology

Copyright copy 2019 American Academy of Neurology Unauthorized reproduction of this article is prohibited

Remote ischemic preconditioning (RIPC) defined as brieftransient episodes of ischemiareperfusion applied in distanttissues or organs renders remote tissues and organs resistant toa subsequent prolonged ischemia insult1 Studies of cardio-vascular diseases have repeatedly shown that RIPC could sig-nificantly reduce infarct size after myocardial ischemia in bothanimals and human patients1ndash4 Recently several animal andclinical studies demonstrated a similar beneficial role of RIPCduring cerebral ischemiareperfusion injury and cerebral smallvessel disease5ndash10 It has been shown that RIPC activates bothneuronal signals and humoral factors to confer its protectiveeffects on remote tissues and organs1 but the underlyingmechanisms especially in the brain remain unclear

Dynamic cerebral autoregulation (dCA) is a unique function ofthe cerebrovasculature and is critical to the regulation of cerebralhemodynamics11 dCApredicts the occurrence and prognosis ofcerebrovascular disease in the clinic12 Previous studies showedthat RIPC can regulate several blood biomarkers such asadenosine13 bradykinin1 and nitric oxide or nitrite14 Several ofthese biomarkers are vasoactive so they may affect dCA1516

Nevertheless it remains unknown whether RIPC can regulatedCA in humans Moreover recent studies have shown thatRIPC may have neuroprotective and inflammation regulatoryfunctions in animal models91718 However whether neuro-protective and inflammation-related blood biomarkers are reg-ulated by RIPC in humans is unknown

In the present study we hypothesize that RIPC improves dCAand affects neuroprotective and inflammation-related bloodbiomarkers and we test this using the following approachesFirst we continuously monitored the changes of dCA in healthyadults at 7 time points (baseline and 1 3 6 9 12 and 24 hoursafter RIPC) Second we assessed the effect of RIPC on 30biomarkers in venous blood including 5 neuroprotective factorsand 25 inflammation-related biomarkersWe demonstrated thatRIPC can persistently improve dCA and differentially regulatea series of neuroprotective and inflammation-related biomarkersin the blood

MethodsStandard protocol approvals registrationsand patient consentsThis prospective study was approved by the ethics committeeof the First Hospital of Jilin University Written informed

consent was obtained from all participants The participantshad the right to withdraw at any time point during the pro-cedure This trial is registered at ClinicalTrialsgov(NCT02965547)

ParticipantsFifty healthy adult volunteers (age 18ndash70 years men andwomen Asian) were included in the present study fromJanuary 2017 to July 2017 The exclusion criteria included (1)currently experiencing or having a history of chronic physicalor mental diseases (including generalized anxiety disorderdepression insomnia hypertension diabetes mellitus andchronic heart disease) (2) having an infectious disease in thepast month (3) being pregnant or lactating (women) (4)smoking or heavy drinking (formerly or currently) and (5)being unable to cooperate sufficiently to complete the dCAexamination Each participant received a comprehensivephysical examination by a physician to exclude potential dis-ease before inclusion in the study

Study designEach participant received two 24-hour monitoring sessionsThe first 24-hour session was defined as the control day andthe second 24-hour session was the RIPC day The controlday and the RIPC day were consecutive days (control day day1 and 2 RIPC day day 3 and 4) Serial measurements of dCAwere performed at 7 time points on both the control day andthe RIPC day On the control day the 7 time points were 7 9and 11 AM 2 5 and 8 PM and 8 AM on the next day On theRIPC day the RIPC was carried out at 720 to 8 AM and the 7time points were 7 (baseline) 9 (1 hour after RIPC) and 11(3 hours after RIPC) AM 2 (6 hours after RIPC) 5 (9 hoursafter RIPC) and 8 PM (12 hours after RIPC) and 8 AM on thenext day (24 hours after RIPC figure 1A) Blood sampleswere collected from the cubital vein of each participant beforeand 1 hour after RIPC intervention for further quantitativeprotein chip testing

InterventionThe RIPC was performed by an automatic device (BB-RIC-D1LAPUL Medical Devices Co Ltd China) The wholeintervention process consisted of 4 cycles of extremity is-chemia 5 minutes of blood pressure cuff inflation to 200 mmHg followed by 5 minutes of cuff deflation the whole processtook 40 minutes Tourniquets were applied to 1 upper armand 1 thigh This intervention was undertaken only once ineach participant Measurement of arterial blood pressure

GlossaryABP = arterial blood pressure BDNF = brain-derived neurotrophic factor CBFV = cerebral blood flow velocity dCA =dynamic cerebral autoregulation EOT = eotaxin GDNF = glial cell linendashderived neurotrophic factor IL = interleukin LIF =leukemia inhibitory factor MCP-1 = monocyte chemotactic protein 1 MMP = matrix metalloproteinase PD = phasedifference RIPC = remote ischemic preconditioning TFA = transfer function analysis TGF-β1 = transforming growthfactor-β1TIMP-1 = tissue inhibitor of metalloproteinases-1 TNF-α = tumor necrosis factor-α VEGF-A = vascular endothelialgrowth factor-A

















































Blood biomarkers



Indicator (n = 48)



PD degree 136268 00006 38172 00024 08972 04836

Gain 32779 00766 25208 00378 01866 09675


Heart rate bpm 04627 04997 47146 00004 04034 08361




PD degree(n = 48)

Gain (n = 48)



Measurement at 7 AM




p Value 04492 01010 01874 04529

Measurement at 8 AM





Measurement at 9 AM



t or z 0998 minus1354 1267 minus1067

p Value 03284 01758 02115 02916




t or z 0676 minus1224 2648 minus0524

p Value 05026 02272 00110 06024

Measurement at 2 PM



t or z 2288 minus1402 3617 minus0655

p Value 00267 01674 00007 05123

Measurement at 5 PM




p Value 00165 01840 02692 08976

Measurement at 8 PM




p Value 00004 03272 02648 04260




Continued












PD degree(n = 48)

Gain (n = 48)



t or z 2777 minus1046 0096 minus1302

p Value 00079 02955 09237 01930




























Appendix Authors


Zhen-NiGuo MD





Wei-TongGuo MD



Jia LiuPhD PhD



JunleiChangPhD



HongyinMa MD



PengZhangMD



Fu-LiangZhangMD



Ke HanPhD



Han-HwaHu MD



Hang JinPhD



Xin SunPhD






Yi YangMD PhD


CorrespondingAuthor































































Errata





Reprints




CORRECTIONS



































































Blood biomarkers



Indicator (n = 48)



PD degree 136268 00006 38172 00024 08972 04836

Gain 32779 00766 25208 00378 01866 09675


Heart rate bpm 04627 04997 47146 00004 04034 08361




PD degree(n = 48)

Gain (n = 48)



Measurement at 7 AM




p Value 04492 01010 01874 04529

Measurement at 8 AM





Measurement at 9 AM



t or z 0998 minus1354 1267 minus1067

p Value 03284 01758 02115 02916




t or z 0676 minus1224 2648 minus0524

p Value 05026 02272 00110 06024

Measurement at 2 PM



t or z 2288 minus1402 3617 minus0655

p Value 00267 01674 00007 05123

Measurement at 5 PM




p Value 00165 01840 02692 08976

Measurement at 8 PM




p Value 00004 03272 02648 04260




Continued












PD degree(n = 48)

Gain (n = 48)



t or z 2777 minus1046 0096 minus1302

p Value 00079 02955 09237 01930




























Appendix Authors


Zhen-NiGuo MD





Wei-TongGuo MD



Jia LiuPhD PhD



JunleiChangPhD



HongyinMa MD



PengZhangMD



Fu-LiangZhangMD



Ke HanPhD



Han-HwaHu MD



Hang JinPhD



Xin SunPhD






Yi YangMD PhD


CorrespondingAuthor































































Errata





Reprints




CORRECTIONS





















PD degree(n = 48)

Gain (n = 48)



Measurement at 7 AM




p Value 04492 01010 01874 04529

Measurement at 8 AM





Measurement at 9 AM



t or z 0998 minus1354 1267 minus1067

p Value 03284 01758 02115 02916




t or z 0676 minus1224 2648 minus0524

p Value 05026 02272 00110 06024

Measurement at 2 PM



t or z 2288 minus1402 3617 minus0655

p Value 00267 01674 00007 05123

Measurement at 5 PM




p Value 00165 01840 02692 08976

Measurement at 8 PM




p Value 00004 03272 02648 04260




Continued












PD degree(n = 48)

Gain (n = 48)



t or z 2777 minus1046 0096 minus1302

p Value 00079 02955 09237 01930




























Appendix Authors


Zhen-NiGuo MD





Wei-TongGuo MD



Jia LiuPhD PhD



JunleiChangPhD



HongyinMa MD



PengZhangMD



Fu-LiangZhangMD



Ke HanPhD



Han-HwaHu MD



Hang JinPhD



Xin SunPhD






Yi YangMD PhD


CorrespondingAuthor































































Errata





Reprints




CORRECTIONS






























PD degree(n = 48)

Gain (n = 48)



t or z 2777 minus1046 0096 minus1302

p Value 00079 02955 09237 01930




























Appendix Authors


Zhen-NiGuo MD





Wei-TongGuo MD



Jia LiuPhD PhD



JunleiChangPhD



HongyinMa MD



PengZhangMD



Fu-LiangZhangMD



Ke HanPhD



Han-HwaHu MD



Hang JinPhD



Xin SunPhD






Yi YangMD PhD


CorrespondingAuthor































































Errata





Reprints




CORRECTIONS













































Appendix Authors


Zhen-NiGuo MD





Wei-TongGuo MD



Jia LiuPhD PhD



JunleiChangPhD



HongyinMa MD



PengZhangMD



Fu-LiangZhangMD



Ke HanPhD



Han-HwaHu MD



Hang JinPhD



Xin SunPhD






Yi YangMD PhD


CorrespondingAuthor































































Errata





Reprints




CORRECTIONS
















































































Errata





Reprints




CORRECTIONS




















CORRECTIONS




















changes in cerebral autoregulation and blood biomarkers ...to determine the eﬀect of remote...

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