Increased occurrence of migraine aura and susceptibility to spreading depolarizations at altitude

Increased occurrence of migraine aura and susceptibility to spreading depolarizations at altitude | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Increased occurrence of migraine aura and susceptibility to spreading depolarizations at altitude Katelyn M. Reinhart , Melissa M. Cortez , Cecilia Martindale , Leon S. Moskatel , J. Gary Urry , Zubair A. Ahmed , Anna Newman , Kendra Pham , Jared Bartell , Todd Schwedt , Sheena Aurora , Kathleen B. Digre , Susan K. Baggaley , View ORCID Profile K.C. Brennan doi: https://doi.org/10.1101/2025.08.09.25333153 Katelyn M. Reinhart 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa M. Cortez 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cecilia Martindale 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT 2 Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, WA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leon S. Moskatel 3 Department of Neurology, Stanford University , Palo Alto CA Find this author on Google Scholar Find this author on PubMed Search for this author on this site J. Gary Urry 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zubair A. Ahmed 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT 4 Apex Medical Research , Cleveland OH Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Newman 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kendra Pham 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jared Bartell 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Todd Schwedt 5 Mayo Clinic , Scottsdale AZ Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sheena Aurora 6 Swedish Headache Center , Seattle WA 7 C2N Diagnostics , Seattle WA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen B. Digre 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan K. Baggaley 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site K.C. Brennan 1 Department of Neurology, University of Utah School of Medicine , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K.C. Brennan For correspondence: k.c.brennan{at}hsc.utah.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Headache is a common consequence of ascent to high altitudes, and acute mountain sickness shares many features with migraine. Evidence also suggests that the prevalence of migraine is increased in people living at both high and moderate elevations. Here we identify an increased occurrence of migraine aura with increasing elevation. Our findings are supported by multiple lines of clinical evidence including a chart review characterizing the phenotype, a systematic prospective cohort comparison between moderate and low elevation, cross-sectional data from a multicenter headache clinic registry, and a nationwide electronic medical record-based dataset. In data from mice housed in altitude chambers, we show that susceptibility to spreading depolarization is increased with simulated elevation, providing a candidate mechanism for the altitude phenotype. The increased susceptibility to aura and SD with elevation may provide a unique opportunity to develop targeted strategies for mitigation relevant to the nearly 1 billion people worldwide living at higher elevations worldwide. Introduction Migraine is a common neurovascular disorder that affects 14-15% of the general population, 1 – 5 including an estimated 17.5% of women and 8.9% of men globally. 4 Migraine is the second leading cause of disability among young adults, accounting for nearly 5% of all years lived with disability in the global population. 3 , 6 Approximately 30% of individuals with migraine experience aura - most often manifesting as transient visual disturbances. 7 – 10 Though not all migraine sufferers experience aura related to headache attacks, aura has a significant impact on quality of life relative to other migraine symptoms, and is associated with greater migraine-related disability. 7 , 11 For those with migraine aura, the aura is typically the first element of the migraine attack. The underlying mechanism of aura, spreading depolarization (SD; also known as cortical spreading depression) is physiologically measurable in both animal models and humans. As a result, we have a growing understanding of how migraine with aura can lead to head pain. 12 – 19 However, the precise mechanism(s) by which a massive depolarization like SD can be triggered in an apparently uninjured brain remain unclear. 12 – 14 Thus, it is particularly important to identify conditions that predispose individuals to developing aura. Our original objective was to determine whether an anecdotal clinical insight, namely that there appeared to be a greater prevalence of migraine aura in a headache clinic at moderate (1400m) elevation, was correct. As we gathered more data we realized that our findings might be more generalizable, and set out to test the hypothesis that aura is more common with increasing elevation. Here we report an increased occurrence of migraine aura at moderate to high elevations, drawing from multiple clinical data sources. Additionally, we demonstrate an increased susceptibility to SD in mice exposed to simulated altitudes above sea level. These findings may be of interest for clinical reasons, as an estimated 700 million to 1 billion people worldwide live at or above moderate elevations. 20 , 21 Studying migraine in this setting may also help identify candidate mechanisms by isolating responses that may mediate the relationship between elevation and aura. Materials and methods Retrospective chart review We reviewed the electronic medical records of new, consecutive patient visits presenting to headache specialist clinic visits over a 10-month period. Clinic sites spanned moderate and low elevations: Salt Lake City (SLC), Utah (University of Utah Headache Clinic, elevation 1400m; 3 providers, 272 patients) and Seattle, Washington (Swedish Headache Center, elevation 57m; 1 provider, 199 patients). Migraine with and without aura were classified according to the International Classification of Headache Disorders (ICHD) criteria in effect during the study period (January - October 2011; ICHD-II). Additionally, clinical and demographic data (gender, age, body mass index [BMI]) were collected. Data on race/ethnicity, caffeine intake, depression, and migraine disability level were not captured in the review; data on religion and hematocrit were included in the original review, but excluded from the current analysis due to >40% missingness. 22 , 23 The study protocol was approved by the Institutional Review Boards from both the University of Utah and the Swedish Headache Center. We compared migraine with aura diagnosis between the moderate and low elevation groups (Pearson’s chi-squared test for association), as well as other clinical variables (two-sample t-test or chi-squared as appropriate). We then tested the association between location and aura status adjusting for age and sex using logistic regression. Analyses of the retrospective clinical data were performed using R version 4.3.3 and RStudio Version 1.4.1717 (Posit; Boston, MA, USA). Prospective cohort study We prospectively evaluated 104 consecutive patients presenting to the University of Utah Headache Clinic and 104 consecutive patients presenting to the Cleveland Clinic Headache Clinic (199m elevation) from August 2016 - February 2017. The visual aura rating scale (VARS) was used to classify participants as having migraine with or without aura. 24 The VARS is a well-validated instrument that quantifies the severity of aura-related symptoms in migraine patients, with a sensitivity and specificity of 96.4% and 79.5%, respectively, for the diagnosis of migraine-associated aura. 25 VARS criteria include aura duration between 5-60 minutes, presence of scotoma, zigzag lines, unilateral visual field involvement, and visual symptoms developing gradually between 5-60 minutes. A cut-off score of 5 or more was used to confirm the aura diagnosis for the purposes of this study. 24 Inclusion criteria were age 18 years old or greater with a (Headache Specialist) clinician-assigned, chart-documented diagnosis of migraine, including both migraine with and without aura. Exclusion criteria were the presence of other, non-migraine neurological conditions, disorders associated with hypercoagulability, and/or inability to provide informed consent. A single provider/rater [ZA] collected data for each site including VARS to determine aura (which was coded as a dichotomous variable, yes/no, for the presence of aura), as well as gender, age, ethnicity, caffeine intake, and BMI; data on religion, income, Migraine Disability Assessment (MIDAS), Patient Health Questionnaire (PHQ-9), and hematocrit were included in the original study, but excluded from the current analysis due to >40% missingness. 22 , 23 To determine elevation of residence, we derived ZIP code level mean elevation from Shuttle Radar Topography Mission satellite image estimates, which were available via ecolo-zip. 26 This approach enabled us to approximate participant residential elevations based on their ZIP code rather than relying on the elevation of their clinical sites (since our study sites primarily functioned as referral centers, many participants traveled long distances for care). We compared aura rates across the two sites (Pearson’s chi-squared test for association), as well as other clinical variables (Kruskal-Wallis tests or chi-squared as appropriate). We then compared aura percentage (within the site cohorts) between SLC and Cleveland, adjusting for the effects of age and sex as potential confounders using a multivariable logistic regression model. Results from the logistic regression are presented as odds ratios, calculated as ( e ^β). Analyses of the prospective clinical data were performed using R version 4.3.3 and RStudio Version 1.4.1717. Cross-sectional database study We examined aura and elevation data available within a national database of headache specialty clinics, the American Registry for Migraine Research (ARMR). The initial ARMR study enrolled migraine patients from 2016 to 2020 at a total of eight participating locations (University of Utah, University of Colorado, Mayo Clinic Arizona, Dartmouth, Dent Neurologic Institute, Georgetown, University of Texas, and Thomas Jefferson University) and collected comprehensive patient-reported data on headaches experienced by participants, as well as provider-assigned ICHD diagnoses based on clinical assessment. 27 This data is included in the ARMR database, and described in detail elsewhere. 27 Inclusion criteria for ARMR included age over 18 years old, fluency in English, access to a computer or other device with internet connection, ability to provide informed consent, and presence of a primary or secondary headache disorder as determined by ICHD criteria in effect during the study period (2016-2020). 27 We also implemented additional inclusion criteria, which required available ZIP code data and headache features data using the patient-reported headache feature survey in ARMR according to previously published methods. 27 , 28 In addition to ZIP code of residence reported at baseline during enrollment, we also extracted age, sex, race (white or non-white), and additional clinical variables of depression score (PHQ-4), MIDAS, VARS, and headache days per month. Data on income, religion, caffeine intake, hemoglobin, and hematocrit were not available within the available ARMR dataset; data on BMI were included in the original study, but excluded from the current analysis due to >40% missingness. 22 , 23 The presence and severity of aura were assessed using the VARS to code participant aura grouping (yes/no aura), following the same criteria outlined for the prospective cohort study reported above. Of the eight participating ARMR sites, three provided ZIP code information, allowing for geocoding for elevation of residence as documented in the ARMR database. To determine elevation of residence, ZIP code level mean elevation was derived via satellite estimates as described above. 26 For reference of the approximate sample, included study site elevations ranged from 216m - 1400m, though the actual range of elevation of residence was expected to vary outside these clinic site based elevations: Dartmouth Medical Center (clinic elevation 216m), Mayo Clinic Scottsdale (clinic elevation 501m), and SLC (clinic elevation 1400m). We compared aura rates across the included ARMR sites (Pearson’s chi-squared test for association), as well as other clinical variables (Kruskal-Wallis tests or chi-squared as appropriate). We assessed the association of elevation of individual-participant residence with VARS-defined aura rates using binary logistic regression, with elevation as the primary predictor and aura (dichotomous, yes/no) as the response variable, adjusted for age and sex. Finally, to better understand how elevation was associated with aura-specific factors, we examined relationships between elevation and specific VARS metrics -- 1) aura onset, 2) duration, 3) scotoma, 4) zigzag lines, and 5) unilateral location -- using logistic regression and adjusting for age and sex. All results of logistic regression are presented as odds ratios, calculated as ( e ^β). Analyses of the ARMR-based data were performed using R version 4.3.3 and RStudio Version 1.4.1717. Cross-sectional, nationwide electronic medical record (EMR) analysis To further evaluate the relationship of migraine aura to elevation of residence more broadly, without the site-level biases of our prior work, we utilized the Epic Cosmos Research Platform (Epic System Corporation, Verona, WI) to evaluate a nationwide sample. The Epic Cosmos Research Platform is an aggregated, de-identified database of electronic health records (EHR) from health systems across the United States, specifically designed for research purposes, and consists of over 300 million patient records from over 1,744 hospitals and 40,700 clinics from all 50 states, Lebanon, and Saudi Arabia. 29 Of note, Epic Cosmos data is known to generally reflect the United States population’s demographics. 30 We queried the Cosmos database for patients in the United States with an International Classification of Diseases, Tenth Edition (ICD-10) code of G43 “Migraine,” or higher resolution, over the five-year period of January 2020 - December 2024. As individual-level ZIP code data was not available with our access to the database, we aimed to estimate elevation of residence via county-level designations. To accomplish this, Epic Cosmos-based ICD diagnoses were aggregated to the county level, based on patient residence, to identify the subset of patients who had been diagnosed with an ICD-10 code of G43.1, indicating ‘Migraine with aura,’ or a more specific diagnosis during this time frame. We then calculated the percentage of patients with a migraine diagnosis who also had codes for migraine with aura within each county. Alaska was excluded for insufficient patients and the District of Columbia was not included as it is a single county. Counties without Epic Coverage could not be included. Counties with fewer than 10 patients with migraine with aura during the queried time period were excluded as Epic Cosmos does not provide exact numbers of patients for data elements fewer than 10 patients. Elevation was then determined using the standard digital elevation model from the United States Geological Survey (USGS) elevation dataset. 31 This database provides latitude and longitude coordinates for each county seat and returns the corresponding elevation value for any location within the United States. To ensure accuracy, county seat elevation data was verified using Google Earth elevation data. We then performed ordinary least squares-based linear regression with the percentage of patients with migraine who have migraine with aura at the county level as the dependent variable and the county seat altitude (in feet) as the independent variable, adjusted for age, sex, and state-effects. State-effects were controlled for through clustering at the state level to account for repeat measurements in each state. The result of the linear regression is presented as a coefficient measuring the percentage change in migraine with aura at the county level for each increase in elevation. Analysis of the Epic Cosmos data was performed in Stata 14 (StataCorp LLC, College Station, TX, USA, 2016). Mouse studies All procedures were conducted in accordance with protocols approved by the University of Utah Health Sciences Center Institutional Animal Care and Use Committee. Experiments used adult male mice (1.5 – 4 months old, 22.6 ± 0.2g), housed in a temperature and humidity-controlled vivarium with a 12-hour light/dark cycle and free access to food and water. C57Bl/6J mice were obtained from The Jackson Laboratory (Bar Harbor, ME, USA) and acclimated to local conditions for at least seven days before altitude chamber habituation. A total of 145 mice were used across all experiments, including 30 for pilot studies, 72 for SD thresholding, and 43 for SD counting. Of the 72 mice used in SD thresholding studies, 6 mice ( n = 2 per altitude) were excluded before analysis and unblinding. The most common exclusion reasons were due to inadvertent SD triggering (capillary action or breakage of the KCl pipette tip during placement, 5 mice); whereas the one mouse was excluded due to bleeding during surgical preparation of the rostral SD induction site. In SD counting studies, 43 mice were tested, with two exclusions: one from 0m (only one SD detected over the two hour recording period) and one from 1400m (camera failure prevented accurate SD frequency assessment). Altitude exposure We employed custom-made altitude chambers to test the effect of high (4500m, 14,760ft), moderate (1400m, 4594ft; lab altitude in SLC), and low (0m) altitudes on SD threshold in mice. Aluminum vacuum chambers (5 gallon, 1931N18 McMaster-Carr, Robbinsville, NJ, USA) were fitted with adjustable pressure-relief valves and connected to vacuum (high altitude) or pressure (low altitude) pumps to maintain the desired atmospheric pressure within the chamber (101kPa for 0m, 86kPa for 1400m, and 58kPa for 4500m 32 ). Pre-calibrated NeuLog sensors (NeuLog, Rochester, NY, USA) were used to continuously monitor absolute pressure (in kPa; NUL-210 sensor) and effective oxygen concentration (in %; NUL-205 Clark-type sensor) within chambers to confirm oxygen levels corresponded to those predicted by our pressure manipulations. Following 2–5 days of habituation in altitude chambers (4 – 5 mice/chamber)-during which mice were maintained at local altitude pressures but acclimated to the noise of vacuum and pressure pump operation - pressure-relief valves were incrementally adjusted at a rate of ≤1 kPa/min. Once target altitude pressures were reached, as verified by atmospheric pressure sensor readings, chamber conditions were maintained for 10 days. On day ten, mice were returned to local lab conditions (≤1 kPa/min), and experiments were conducted that same day by an experimenter blinded to the altitude group being tested. To ensure rigor, an independent researcher, who was not involved in SD thresholding and counting experiments (see below), monitored all chambers and interleaved altitude groups to maintain blinding. Surgical preparation Experiments were conducted in each mouse under isoflurane anesthesia. Following anesthesia induction (3 – 4% isoflurane), mice were positioned in a stereotaxic frame assembly and respiration rate was maintained at 75-85 breaths/min throughout the experiment (1.1-1.8% isoflurane). Body temperature was monitored at 15-minute intervals throughout the experiment (Acorn Series thermometer, Oakton Instruments) and maintained at 36.5 – 37.5°C using a heating pad warmed via a circulating water bath (TE-8D, Techne). After head-fixation in the stereotaxic frame, the skull was exposed and small diameter craniotomies/burr holes were carefully drilled using a pneumatic dental drill (Henry Schein Inc., Melville, NY, USA). For SD thresholding experiments, two burr holes (0.5mm diameter) were made in the right hemisphere for SD initiation (AP: +3.0mm ML: +1.0mm) and detection (AP: - 3.0mm, ML: +3.0mm). For SD counting experiments, we followed our previously published methods with minor surgical modifications. 33 – 35 Briefly, a 1.5mm diameter craniotomy was created in the right barrel cortex (AP: -1.5mm ML: +2.5mm) to expose the brain for SD initiation (see SD Methods below). Electrophysiology Extracellular recordings were used to detect SD in thresholding experiments. A borosilicate glass micropipette (1-3.5 MΩ) containing a Ag/AgCl wire was filled with normal saline (0.9% NaCl) and carefully placed in the cortex through the caudal burr hole (200μm depth from the skull surface) for recording of local field potentials (LFP). Recordings were referenced to a Ag/AgCl ground wire carefully inserted into the contralateral cortex via an additional burr hole created in the left hemisphere. Signals were amplified at 10kHz using a Multiclamp 700B amplifier, A/D-converted using a Digidata 1332 digitizer and displayed in Clampex 9.2 software (Molecular Devices, LLC., San Jose, CA, USA). After electrode insertion, a 30 – 45-minute baseline was recorded to ensure stable anesthesia (based on respiratory rate and cortical LFP activity 36 , 37 ). LFP recordings then served to confirm SD in real-time via the emergence of the large characteristic direct current shift (DC shift) in the extracellular slow potential (<1Hz) and subsequent suppression of spontaneous cortical activity (1-70Hz 38 ). SD thresholding SD threshold was evaluated using our previously published methods, with minor modifications. 33 Briefly, focal 1M KCl was applied into the rostral burr hole via a large diameter (22-24μm) borosilicate glass pipette (Sutter Instruments) connected to a pneumatic pico-pump. KCl volume was stepwise increased every five minutes using 20ms square-wave pressure pulses (increasing by 2.5 psi up to 15 psi) until SD was detected via the emergence of the DC shift at the distant LFP electrode. Each pressure ejection was visually monitored using a dissection microscope. The KCl pipette was promptly removed from the burr hole immediately after the delivery of KCl and throughout the 5-minute interval to prevent excessive KCl application due to capillary action. SD counting SD counting procedures were performed using our previously published methods, with minor modifications. 34 , 35 Briefly, the entire dorsal surface of the skull was illuminated using a white light-emitting diode and intrinsic optical signals (IOS) were collected (0.5Hz) using a CCD Camera (Mightex). Mineral oil was utilized to enhance skull translucency. A small barrier composed of cyanoacrylate glue was created around the craniotomy to prevent mineral oil from encountering the exposed cortex. This barrier also served to restrict the application of perfusion solutions to the site designated for SD induction. After surgical procedures, saline (0.9%) was slowly (0.016mL/min) perfused onto the cortex via a syringe pump connected to a blunt 34-gauge MicroFil flexible needle (World Precision Instruments). During this 15-minute baseline period, no SDs were detected in any mice. After baseline imaging, saline was replaced with a 1M KCl solution that was perfused for a total of 2 hours (0.016mL/min). Off-line analysis (prior to unblinding) included plotting the z-axis profile of image sequences in Fiji software 39 to count SD totals over the 2-hour period. Hematocrit determination After SD experiments, blood was collected from the tail of each mouse into 60mm heparinized microhematocrit tubes (VWR International, Randor, PA, USA). Capillary tubes were centrifuged at 13,000 x□ g for 3 min and the Hct value (%) was measured according to the DIN 58933 practical standard reading graph. 40 Statistics for mouse studies Sample size determination and power analyses were calculated using G*Power 3.1 software. 41 An initial pilot study in which the experimenter was not blinded to altitude groups was conducted in male C57Bl/6J mice ( n = 30; 10.4±0.4 weeks of age; 10/group) and was used to establish the timeline of altitude exposures and to determine the sample size required for the SD thresholding studies. Using each group’s mean SD threshold (0m: 7.0; 1400m: 6.8; 4500m: 4.3 in psi; see SD thresholding methods) and the median StDev across groups (±2.7psi), the effect size was f = 0.45 through the post hoc F-test ANOVA (fixed effects, omnibus, one-way). Using f = 0.45 and a significance level α = 0.05, a total sample size of n = 66 mice was calculated in order to achieve ≥ 90% power (actual power = 90.8%); 22 mice/group was thus the target sample size for SD thresholding experiments. Sample sizes for SD counting experiments were calculated based on published work from our lab where we found a significant difference in SD count in mice using this method (t-test between two independent means: Cohen’s d = 1.52; post hoc calculated power = 0.801; data shown in Figure 6 ). 35 Sample size calculations for the current work used d = 1.52 and a significance level of α = 0.05, with the assumption that the largest effect would most likely be observed in the high-altitude group (i.e., 0m vs. 4500m); as such, a sample size of 11 mice/group was calculated to achieve 90% power. For SD thresholding and counting experiments, each animal served as an individual experimental unit. After completion of all experiments and data analyses, the experimenter was unblinded to altitude groups. A z-score normalization approach was used to generate a composite ‘SD susceptibility’ score from all SD thresholding and SD counting experiments. Because the two datasets differed in scale and units (SD thresholding measured in psi and SD counting in frequency) z-scoring was used to standardize each dataset independently, transforming raw values into unitless measures of deviation from the mean. For SD thresholding data, the reciprocal of the observed SD threshold (in psi) was computed prior to z-scoring, ensuring that higher z-scores reflected greater SD susceptibility across both experimental techniques. Z-scores were then calculated for both datasets using the formula: Where x is the data value, µ is the mean, and σ is the standard deviation of that dataset. Data were plotted and statistical analyses were conducted using Graphpad Prism 10.1.1 (GraphPad Software, LLC, San Diego, CA). Grubbs’ (α = 0.05) and D’Agostino and Pearson tests were used to identify outliers and determine if data were normally distributed, respectively. Statistical significance for normally distributed data (Hct data and SD counting data) was evaluated using Ordinary one-way ANOVA with Bonferroni’s multiple comparisons test. For nonparametric data (SD thresholding data), the Kruskal-Wallis test with Dunn’s multiple comparisons test was used to evaluate statistical significance. SD susceptibility scores (described above) were plotted according to their respective altitude and a simple linear regression was used to estimate the relationship between SD susceptibility and altitude. Reporting of Data For descriptive statistics, categorical variables are presented as count with percent, normally distributed continuous variables are presented as mean with standard deviation (StDev), and skewed continuous variables are presented as median with interquartile range. Descriptive statistics are presented in aggregate and stratified by group. Data Availability The data that support the findings of this study are available from the corresponding author, upon reasonable request. Results Retrospective chart review Of the total sample of 471 participants, 74.3% were female ( n = 350), with a median age of 42.0 years (range 18-91; IQR = 20.0). There were no significant differences between sites for age, sex, or BMI. Demographic data and aura prevalence are summarized in Table 1 . View this table: View inline View popup Download powerpoint Table 1 demographic features and aura prevalence by site, retrospective cohort study. The proportion of participants with migraine with aura was significantly higher in those participants residing at moderate elevation in SLC (1400m) compared to those in Seattle (57 m), with rates of 53.1% and 17.6%, respectively ( P < 0.0001). Location was significantly associated with aura status: participants in Seattle were 0.19 times as likely to have aura compared to participants in Salt Lake City (95% CI: 0.12, 0.29). When adjusting for age and sex, findings were similar, where participants in Seattle were 0.18 times as likely to have aura compared to participants in Salt Lake City (95% CI: 0.12, 0.28). Thus, participants in SLC were 5.6 times as likely to have aura as those in Seattle. Prospective cohort study Of the 208 participants studied, 86.1% were female ( n = 179), with a median age of 40.0 years (range 18-76 years; IQR = 21.0). There were no significant differences between sites for sex, or caffeine intake, whereas age was significantly higher in SLC compared to Cleveland, participants were more likely to be white in SLC, and BMI was higher in Cleveland. Demographic data and aura prevalence are summarized in Table 2 . View this table: View inline View popup Download powerpoint Table 2 demographic features and aura prevalence by site, prospective cohort study. The percentage of migraine with aura within the SLC-based cohort (moderate elevation) was significantly higher than in the Cleveland-based cohort (51.9% in SLC 1400m vs 33.7% in Cleveland 199m respectively; P = 0.008). Binary logistic regression found that a difference of 100m elevation was associated with 4.7% increase in proportion of aura across cohorts (95%CI: 0.9, 8.7). After adjusting for age and sex, binary logistic regression analysis found that a difference of 100m elevation was associated with 3.7% increase in proportion of aura across cohorts (95%CI: 0.0, 7.8). Cross-sectional database analysis Our initial sample was composed of 1016 participants; 71 were excluded due to lack of ZIP code data; 36 were excluded because they held a different primary ICHD diagnosis than migraine or no diagnosis assignment was included in the ARMR database; and 25 were excluded due to incomplete headache features data needed to calculate aura scores. Thus, our final sample included 884 migraine patients for analysis. Analyses were completed with 884 participants that met inclusion criteria. Figure 1 is a schematic of participant exclusions. Download figure Open in new tab Figure 1. Participant exclusions in ARMR cohort. Of the initial 1016 participants in the ARMR cohort, 71 participants were excluded due to lack of ZIP code data used to obtain elevation, 36 were excluded for lack of clinical migraine diagnosis, and 25 were excluded due to incomplete headache features. Analyses were completed with 884 participants that met inclusion criteria. Out of the 884 participants included in our analysis, 86.9% were female ( n = 768), with a median age of 47 (range 18-86; IQR = 21.0). There were no significant differences between sites for sex, ethnicity, nor headache days/month, whereas age, PHQ-4, and MIDAS differed across sites. Participant characteristics are summarized in Table 3 . The mean elevation across the entire sample was 673m (range 0.305m – 3,220m). Site-specific mean elevations of residence for participants at each site were: 351m at Dartmouth (26.6 – 883m), 561m at Mayo Clinic (0.305 – 3220m), and 1490m at University of Utah (1170 – 2980m). Note that the range of elevation of residence was greater than the local altitude range around each clinical site, likely reflecting that patients sometimes traveled long distances to these tertiary referral centers. Thus, as in our prospective cohort, our analysis focused on elevation of residence. View this table: View inline View popup Download powerpoint Table 3 demographic features and aura rates by site, ARMR cohort. Binary logistic regression analysis of elevation of residence and aura status (as a dichotomous yes/no) across the full ARMR sample provided an odds ratio of 1.029 per 100m of elevation (95%CI: 1.003, 1.056; P = 0.030), which was robust to adjusting for sex and age (OR: 1.029; 95%CI: 1.003, 1.056). Thus, for each incremental increase of 100m (328ft), the odds of aura were 2.9% higher (95%CI: 0.3, 5.6%). For example, when comparing individuals with migraine at 1000m of elevation to sea level, this model estimates a 29% greater aura prevalence. Figure 2 plots aura prevalence by elevation for the ARMR cohort. Download figure Open in new tab Figure 2. Aura rate comparison against elevation in ARMR cohort. We compared aura rate by elevation in increments of 100m (328ft). Each point represents the prevalence of migraine aura among ARMR participants at that elevation. Logistic regression, examining the relationship of elevation and specific aura features via VARS (aura onset, duration, scotoma, zigzag lines, and unilateral location) found a significant relationship between scotoma and elevation ( P = 0.0002), where an increase in elevation of 100m is associated with 4.8% higher odds of scotoma (95%CI: 2.1, 7.7%). When adjusting for age and sex, an increase in elevation of 100m is associated with 5.2% higher odds of scotoma (95%CI: 2.4, 8.0%). In contrast, the other VARS-based aura features of onset, duration, zigzags, and location were not significantly associated with elevation. Nationwide EMR analysis The initial query of the Epic Cosmos database yielded county-level data for 3109 counties across 49 included states. A total of 2,685/3109 (86.4%) of these counties had at least 10 patients with migraine with aura, as well as average age and percentage of female patients available, and were included in the analysis. These counties had a median elevation of 259m [range -2 to 3221m; IQR 134, 410] and an average migraine aura prevalence of 27.2±5.4% in this single EMR-based sample. Figure 3 is a schematic of participant exclusions. Download figure Open in new tab Figure 3. Participant exclusions in Epic Cosmos cohort. Of the 3143 counties or county equivalents in the Epic Cosmos cohort, 30 were excluded from Alaska and District of Columbia, 4 were excluded as they did not have Epic Coverage, and 424 were excluded as they had fewer than 10 patients with migraine with aura. Analyses were completed with 2685 counties or county equivalents. Linear regression with ordinary least squares demonstrated a statistically significant association between migraine aura prevalence and elevation. With each 100m increase in elevation, we observed a 0.299% increase in migraine aura prevalence (95% CI: 0.258, 0.339%; P <0.001). This association remained robust when adjusting for age, sex, and potential state-effect (which was controlled for with clustering at the state level) with a 0.129% increase in migraine aura prevalence per 100m increase in elevation (95% CI: 0.014, 0.245%; P =0.029). This suggests that patients at an elevation of 1,000m would have a 1.29 to 2.99 percentage point higher prevalence of migraine aura relative to those living at sea level. ( Figure 4 ). Download figure Open in new tab Figure 4. Aura rate comparison against elevation in Epic Cosmos cohort. We compared aura rate by elevation for each county included in the Epic Cosmos cohort. Each point represents county-specific elevation and rate of migraine aura. Mouse Study SD is recognized as the physiological mechanism underlying migraine aura. 12 – 14 , 42 , 43 The increased prevalence of aura observed in patients living above sea level may be due to a greater susceptibility to SD. To test this, we exposed mice to high (4500m), moderate (1400m, local lab altitude), and low (0m, sea level) altitudes for 10 days and then used two methods to evaluate SD susceptibility ( Figure 5A ) . Throughout the experiment, atmospheric pressure (in kPa) and effective oxygen percentage were continuously monitored in each chamber and remained stable ( Figure 5B ). Download figure Open in new tab Figure 5. Experimental timeline and altitude exposure. A. Experimental timeline of study. Mice were transferred from standard mouse cages into custom-made altitude chambers and habituated (dashed line) for 2-5 days prior to altitude manipulations (day 0). On the morning of day 10, mice were gradually returned to local lab conditions for experiments assessing SD threshold and susceptibility. B. Top schematic shows the design of chambers and color legend indicating the groups tested. Below, left panel shows average atmospheric pressure sensor data from 0m (101.6 ± 0.1 kPa, n = 32 chambers), 1400m (88.5 ± 0.4 kPa, n = 32), and 4500m (58.6 ± 0.2, n = 34) altitude chambers. Middle panel: sensor data showing the effective concentration of oxygen (%) inside 0m (20.4 ± 0.3%, n = 14 chambers), 1400m (17.4 ± 0.3%, n = 5 chambers), and 4500m (10.5 ± 0.2, n = 18 chambers) altitude chambers. After SD experiments (data shown in Figure 6 ), blood was collected, and Hct was measured. Group Hct data (right panel) shows that Hct is robustly increased in mice exposed to 4500m (n = 59 mice) compared to 1400m (n = 61) and 0m (n = 54) altitudes. One way ANOVA results ****P < 0.0001, *P = 0.0133. The duration of altitude exposures was selected based on prior research conducted at the University of Utah (elevation 1400m) which demonstrated that mice exposed to 10 days of 12% hypoxia (simulating 4,500m altitude) exhibited a Hct increase from ∼50% (local altitude) to ∼65-70%. 44 Red blood cell (RBC) expansion is a well-documented physiological response to hypoxia in humans living at moderate to very high altitudes, 45 – 49 making Hct measurements a useful secondary measure to validate our altitude manipulations. Comparable to prior work, 44 Hct values at the end of 10-day altitude periods were robustly increased in high-altitude mice (∼70% Hct) compared to both local (∼51% Hct) and sea-level (∼49% Hct), and there were significant differences in Hct for all elevations ( Figure 5B ). We first evaluated the influence of altitude on SD threshold in male C57Bl/6J mice. After altitude manipulations, SD threshold was examined using focal microinjection of 1M KCl, 33 , 37 the volume of which was incrementally increased (via stepped pressure pulses every 5 min) until SD was detected at the distant recording electrode ( Figure 6A ). The distant locations for SD initiation and detection (∼6mm) ensured unambiguous confirmation of SD, in that only KCl volumes capable of inducing SD propagation across the entire ipsilateral hemisphere were detected (i.e., no partial SDs). Results in Figure 6B show a significant decrease in SD threshold in mice exposed to higher simulated elevations. Moreover, SD was evoked using the lowest volume of KCl tested in the majority of high (14/22; 64%) compared to sea level (1/22; 4.5%) and local (7/22; 32%) altitude mice. Numerically, there was a decrement in the mean threshold as elevation progressed from 0 to 1400 to 4500m, with significant differences between 0 and 4500m and 1400 and 4500m, but the difference in threshold between 0 and 1400m was not significant. Download figure Open in new tab Figure 6. SD threshold is reduced in mice exposed to high altitude. A. Schematic showing the approximate location of burr holes utilized for SD induction (rostral; for KCl administration) and detection (caudal location; for recording of local field potentials (LFP). Stepped increases in pressure (psi) were applied to the micropipette at 5 minute intervals to deliver increasing volumes of KCl into the rostral location until SD was detected at the distant LFP electrode (see representative DC shift trace). Arrowheads indicate time points of KCl application. B. Group data shows significantly lower SD threshold in C57Bl/6J mice exposed to 4500m compared to those in 1400m and 0m altitude groups ( n = 22 mice/group). **** P < 0.0001, * P = 0.015, n.s. P = 0.053; ANOVA, Dunn’s multiple comparison test. C. Schematic showing experimental design for SD counting using intrinsic optical imaging (IOS) of the largely intact dorsal surface of the skull (see representative image and reflectance trace on right). After saline perfusion, 1M KCl was slowly perfused onto the exposed cortex of the right hemisphere for 2 hours. D. Group data showing a significant increase in the number of SDs detected over the testing period in mice from 4500m ( n = 14 mice) compared to 0m ( n = 12) and 1400m ( n = 14) altitude groups. * P = 0.025, ** P = 0.001, n.s. P = 0.67. E. Composite SD susceptibility score generated via z-score normalization of SD data from C57Bl/6J mice shown in B and D ( n = 34, 36, 36 for 0, 1400, and 4500m groups, respectively). Linear regression analysis revealed a significant positive association between altitude and SD susceptibility (Y = (3.12 × 10□□) X - 0.625, R² = 0.350, P < 0.0001) (Slope: 3.12 × 10□□, 95% CI: 2.29 × 10□□ to 3.95 × 10□□) suggesting a gradual but consistent increase in SD susceptibility with rising altitude. To further validate the SD susceptibility altitude phenotype, we employed SD counting, a second widely used SD induction technique. 37 , 50 In these experiments, cortical intrinsic signal imaging was used to monitor SD frequency over a 2-hour recording period, with continuous KCl perfusion serving as the evoking stimulus ( Figure 6C ). As shown in Figure 6D , the number of SDs was significantly increased at 4,500m (15.4 ± 1.0 SDs) compared to both 1,400m (12.3 ± 0.7 SDs) and 0m (10.8 ± 0.6 SDs), indicating an altitude-dependent enhancement in SD occurrence. Here again the altitude phenotype was driven by differences between 0 and 4500m and 1400 and 4500m, with differences between 0 and 1400m showing a progression but not significantly different. Given an apparent ‘dose response’ to altitude at all elevations with two different techniques, we suspected that the lack of a significant difference between 0 and 1400m might be a false negative, driven at least in part by the low signal-to-noise of SD susceptibility methods. 51 – 53 To quantify altitude related SD susceptibility across both models, we generated a composite SD susceptibility score ( Figure 6E ). First, a reciprocal transformation (1/x) was applied to SD threshold values ( Figure 6B ) to ensure that lower values corresponded to lower SD susceptibility. Next, a global z-score approach was applied to both SD thresholding and SD counting data (from Figure 6D ), to enable a standardized comparison of SD susceptibility across altitudes from both assessment techniques. Linear regression ( Figure 6E ) demonstrated a significant positive relationship between altitude and SD susceptibility score (Y = (3.12 × 10□□)X - 0.625, R² = 0.350, P < 0.0001), indicating that higher elevations – including moderate elevations - are associated with increased SD susceptibility. The slope (3.12 × 10□□, 95% CI: 2.29 × 10□□ to 3.95 × 10□□) suggests a gradual but consistent increase in SD susceptibility with rising altitude. Discussion We found that the occurrence of migraine aura increases with elevation, and that this increase is present at moderate elevations. This finding was consistent across different methods of ascertainment in various clinical samples, and was supported by mouse studies showing increased susceptibility to SD, the physiological mechanism of migraine aura, in mice exposed to altitude chambers. Migraine (and migraine aura) prevalence at altitude The first studies to examine migraine at altitude observed an increased prevalence of both migraine 54 , 55 (but see 56 ) and aura 54 at high to very high elevations (3200 to 4300m), compared with sea level in Peru. These studies took place before ICHD criteria were in common use. More recently larger studies confirmed an increased prevalence of (ICHD-defined) migraine with elevation in both Nepal and Peru. The study in Nepal found that the prevalence of migraine is up to 45% in moderate altitude dwellers (1,000m-2499,m) compared to those living <500m above sea level (∼28%) where living at higher altitudes (greater than or equal to 1000m was associated with greater odds of having a migraine (OR 1.5-2.2 vs. <500m). 57 Interestingly, migraine prevalence declined above 2500m in this study, but still remained higher (∼38%) than at 3,500m) compared to those residing below 350m, suggesting a continuous increase in prevalence in both moderate and high elevations. 58 However, neither of these larger, more modern studies examined aura. We show that, like migraine prevalence demonstrated in prior work, the rate of aura also increases with elevation and that this effect includes moderate elevations. Like the work from Peru, 58 our work supports a relatively monotonic rise in aura prevalence (across various cohort samples) as elevation increases, and contrasts with data from Nepal, where overall migraine prevalence increased at moderate elevations (1,000–2,499m) but declined above 2,500m. 57 Putting aside the obvious fact that different phenotypes were investigated (aura, and migraine-aura unspecified) the origins of this discrepancy are unclear and bear further investigation. The decline in barometric pressure and consequently oxygen availability with altitude are monotonic, 59 but hemoglobin binding of oxygen and thus oxygen saturation levels experience a marked decline above 2400m, even in acclimated individuals. 60 , 61 This physiological gradient may have differential effects on migraine susceptibility (but presumably not aura susceptibility, at least based on our data), potentially contributing to variations in prevalence across altitudes. Additionally, genetic adaptations to high-altitude living differentially affect the two locations. The Nepalese population, where altitude-specific genetic adaptations are likely more prevalent, 62 may respond differently to hypoxic stress than North American (this study) or South American population studies, 54 , 55 , 58 where a drop-off in aura or migraine prevalence above 2500 meters was not noted. It is also important to emphasize that increased aura prevalence with altitude conveys potentially different information, with different mechanistic and treatment implications, than a wholesale increase in migraine prevalence. For aura, the known physiological mechanism of SD may serve to narrow the possible mechanisms. As a first step, we can focus on what aspects of the altitude environment can contribute to SD (see below). Implications of the different clinical studies The primary message from the four different forms of clinical data ascertainment is that they all converge on an increased rate of migraine aura with increasing elevation. Each method was very different from the other and each dataset was completely separate, which increases confidence that the convergent signal is real. However, the differences in the studies also mean that each dataset might also provide different angles from which to view the clinical phenomena, along with particular advantages and disadvantages. The chart review provided the least rigorous dataset, but it served to validate our initial clinical observations and justified the more rigorous studies that followed. The prospective two-site comparison between SLC and Cleveland added significantly to the rigor of the inquiry by systematically using the validated VARS questionnaire to better detect aura among participants. We observed a significant increase in the proportion of patients with aura in SLC that was robust to controlling for other variables that differed between the two sites. In the cross-sectional ARMR study, we went beyond using VARS as a binary classification of aura, using specific aura features within VARS to test their relationships with elevation, and found scotoma alone to be significantly associated with elevation. The cross-sectional study of the ARMR tertiary headache clinic database helped generalize the finding of increased rates of aura with altitude beyond the single location of SLC by adding additional subjects who lived at moderate to high elevations. This is important because the phenotype in SLC could possibly have been due to unique features of the location, e.g. a genetic founder effect in the LDS population that accounts for ∼50% of the state population. The ARMR study also significantly increased the number of subjects examined, and the geographical and altitudinal diversity of that sampling. The cross-sectional examination of the Epic Cosmos dataset expanded our inquiry to the whole United States. It confirmed the positive association between migraine aura and altitude, albeit with a different definition of migraine aura. Because of the clinical nature of the dataset, VARS score was not available and we used the proportion of ICD-10 migraine with aura (G43.1) to migraine (G43) diagnosis as our proxy for aura prevalence. Though the definition of aura was less rigorous (dependent on coding and clinician-based factors, as in the chart review), the presence of an aura/elevation association in a nationwide sample was arguably the most rigorous confirmation of the phenotype across the studies. Notably, the estimated effect size of elevation on aura prevalence varied widely across our sub-studies, with a 3.7% and 2.9% increase in aura with 100m increased elevation in the prospective SLC/Cleveland comparison and ARMR dataset (respectively), contrasting with the much smaller 0.129-0.299% increase per 100m in the Epic Cosmos dataset. As discussed above, the studies differ considerably in both sampling and participant characteristics (tertiary/academic referral centers vs nationwide sample including both academic and community samples), as well in their use of different definitions of aura (clinician assigned vs. VARS vs. ICD-10). VARS is clearly advantaged by its migraine-specificity and quantitative nature, allowing for a more rigorous definition of aura compared to ICD-10 code based assessment, which introduces coding variability across time and across location. 63 On the other hand, the tertiary headache population represented in the SLC/Cleveland and ARMR datasets is less generalizable to the population as a whole, where the overall severity and complexity of patient presentation is higher than in the predominantly community based EMR-based sample. Thus, while we may have greater confidence in the sensitivity and specificity of the aura diagnosis in our smaller samples, the larger nationwide confirmation of the significant association between aura and altitude – albeit smaller in effect size – remains persuasive. Ultimately an accurate assessment of the true effect size of elevation on aura prevalence will require larger population-based samples with careful examination of migraine burden/severity, while accounting for clinical confounders, and confirmation of aura (ideally using VARS or other gold-standard equivalent). Possible mechanisms of SD susceptibility at altitude One of the key potential advantages of an aura phenotype with altitude is that it may narrow the parameter space for mechanistic investigation. It is known that there is an increase in migraine (aura unspecified) prevalence with altitude, but for the overall migraine population, there are multiple factors (each with multiple potential mechanisms) that could be involved: they include hypoxia, 57 , 58 , 64 – 69 barometric pressure differences, 70 – 74 increased solar radiation/sunlight, 75 – 77 metabolic challenge, 78 – 81 dehydration, 82 – 84 and inflammatory environment. 85 – 88 In contrast, the migraine aura has a known physiological mechanism in SD, 12 – 14 , 42 , 43 allowing us to focus on the mechanisms of SD initiation as we speculate about candidate mechanisms for why aura might be more prevalent at altitude. The two most likely SD triggers that occur at altitude are hypoxia and thrombosis/ischemia. Hypoxia Most evidence supporting hypoxia as a key trigger of migraine (and aura) at altitude comes from acute oxygen manipulations in humans, particularly normobaric hypoxia studies. In these studies reducing the fraction of inspired oxygen (FiO□) without altering barometric pressure can trigger of both migraine aura and migraine headache. 66 – 69 , 89 , 90 However, human physiology studies employing acute hypobaric hypoxia to examine both barometric and hypoxic changes at altitude on migraine and migraine aura are, to our knowledge, nonexistent. Despite the lack of controlled studies, there is evidence (from hikers and mountaineers) suggesting that high altitude headache, as well as headache accompanying acute mountain sickness (AMS) symptoms (e.g., nausea, vomiting), bear a striking resemblance to migraine 69 , 91 , 92 (though unconfirmed with ICHD criteria; distinguishing high-altitude headache, and AMS from migraine is challenging even when ICHD criteria and Lake Louise scores [for AMS] are present 92 ). In potential symmetry, migraine appears to be a risk factor for high altitude headache. 93 , 94 Additionally, one case study of a mountaineer at very high altitude (5000m) gives a convincing account of migraine aura followed by hemicrania, but again ICHD criteria were not used. 95 With this data in mind (acute) hypoxia has been proposed as a common unifying trigger of migraine aura, migraine, and altitude-induced headaches 66 , 67 , 69 – and thus is a factor worth examining in relation to SD. Acute hypoxia and anoxia are well-established triggers of SD in preclinical studies across multiple species and experimental models. 13 , 43 , 96 Severe hypoxia has been shown to directly stimulate glutamate release from neurons, 97 , 98 while simultaneously impairing sodium-potassium (Na□/K□-ATPase) pumps in both neurons and astrocytes, disrupting their ability to maintain electrochemical gradients. 99 , 100 Since Na□/K□-ATPase dysfunction, excessive glutamate release, and extracellular K□ accumulation are key drivers of SD initiation 13 , 43 , 101 , 102 , these findings support a direct mechanistic link between acute hypoxia and SD susceptibility. However, the oxygen manipulations typically used to induce acute migraine, aura, or SD in experimental settings are far greater than the oxygen fluctuations experienced at moderate elevations. 57 , 67 , 69 In the human studies, reducing FiO□ to ∼12% (from 21% at sea level) resulted in capillary hemoglobin saturations of 70–83% 67 – 69 , 89 which is comparable to physiological conditions at altitudes exceeding 4,000m. (For comparison, effective FiO 2 at a moderate elevation like SLC [1400m] is 17-18% and capillary hemoglobin saturation is 94-97%.) 60 , 103 While these manipulations are certainly relevant to very high-altitude exposure, their implications for more moderate elevations are unclear. Moreover, to our knowledge there is no physiological data on the chronic effects of either normobaric or hypobaric hypoxia on aura or SD susceptibility. This is relevant because individuals residing at altitude undergo physiological adaptations that help maintain adequate tissue oxygenation despite lower oxygen availability. 57 , 104 That said, while mild to moderate hypoxia alone may not be sufficient to directly trigger SD or aura, it could lower the threshold for SD initiation and/or have indirect metabolic effects (such as shift toward glycolysis/lactate production, oxidative pathway activation 105 – 107 ) that could also make the brain more susceptible to additional triggers. Our mouse findings support this concept, aligning with previous work showing that hypoxia exposure—without directly inducing SD—can reduce the threshold for subsequent SD induction. 108 , 109 Thrombosis In preclinical models, ischemia due to vascular occlusion was one of the first known triggers of SD. 13 , 43 , 110 And it is of note that large vessel occlusions/thromboses are not required to generate SD - models of transient thrombosis/embolism are sufficient to generate SD without leaving ischemic damage. 111 – 113 As such, possible mechanisms of SD at altitude could relate to changes in vascular rheology or thrombotic propensity. RBC expansion is well-documented at extreme, high, and moderate altitudes. 45 – 49 On an exploratory basis (not shown), we also noted increases in Hct in SLC migraine patients vs. Seattle and Cleveland cohorts, and more conclusively in mice housed at local SLC altitude vs. 0m controls ( Figure 5 ). Although RBC increases improve oxygen delivery to tissues, they also elevate blood viscosity and disrupt cerebrovascular perfusion, increasing thrombotic risk. 114 – 123 In a very high altitude population (4300m), migraine (mostly with aura) was significantly more likely in subjects with elevated hematocrit. 54 There is also evidence that increased RBC mass is correlated with migraine phenotypes, 124 in subjects with polycythemia (migraine with/without aura, 125 probable migraine, aura unspecified 126 ) and in subjects with elevated hematocrit in the setting of congenital cardiac disease (migraine with aura 127 ). There is also a correlation of RBC mass and migraine without aura in populations with normal or even low hematocrit. 128 , 129 RBC production at altitude is downstream of increases in erythropoietin (EPO), one of many genes upregulated by the cellular hypoxia sensing hypoxia inducible factor (HIF) pathway. 130 – 132 In addition to stimulating RBC production, HIF-controlled genes enhance the prothrombotic milieu of blood which increases blood coagulation propensity. 133 – 135 Interestingly, a small retrospective study found that thrombotic risk in polycythemia vera patients (characterized by elevated RBC mass unrelated to EPO levels) was increased in patients living in SLC (∼58% of patients had a history of documented thrombotic events) compared to those living near sea level (Baltimore area, ∼27%). 136 Such evidence suggests that hypoxia may be considered an independent prothrombotic risk factor even at moderate altitude where RBC increases are present, but less pronounced (vs those > 2500m). Lasting increases in HIF-targeted genes also contribute to chronic mountain sickness (CMS)– an illness characterized by high hematocrit, prominent increases in blood viscosity (recently described to correlate with the severity of CMS at 5100m 122 ), increased migraine attack prevalence, and elevated stroke risk in high-altitude dwellers. 54 , 137 Genome analyses of altitude adapted Tibetans reveal positive selection for genomic variants that attenuate HIF activity. 138 , 139 Taken together, these studies generate the hypothesis that chronic increases in RBC mass (and/or other HIF-controlled gene products) at altitude can disrupt vascular rheology and increase thrombotic risk in a way that might facilitate SD and thus aura induction. Unlike hypoxia, which begins to significantly affect physiologic function only above 2400m, 60 , 61 the erythropoietic response is significant (e.g. requiring different reference standards for clinical laboratories) at more moderate elevations. 140 , 141 Of course, hypoxia and RBC expansion are not the only candidate mechanisms that could influence SD/aura susceptibility. Increased solar radiation, dehydration, and metabolic challenge have all been advanced as independent potential triggers of aura (though definitive evidence is sparse). 142 – 144 Moreover, metabolic challenge 78 , 79 , 105 can amplify the effects of hypoxia, and dehydration 145 can increase thrombosis risk. Finally, (neurogenic) inflammation is a known consequence of SD 13 , 14 , 18 ; it could also be involved in SD initiation, e.g. via the prothrombotic effects of inflammation 146 , 147 . Limitations and considerations for future work Several key distinctions must be made regarding the data presented in this study. First, our findings pertain to the occurrence of migraine and related aura features in populations that are presumably adapted to their respective elevations. Therefore, while acute hypoxia studies have provided valuable insights towards candidate mechanisms, our results should not be directly extrapolated to all headache syndromes associated with ascent to high altitudes. Further mechanistic work is needed and current classification/diagnostic tools make distinguishing migraine from other altitude-induced headaches (such as high-altitude headache and headache accompanying AMS) challenging. Second, while our human sample does include individuals residing at high elevations (>2500m), it does not extend to very high elevations (>3500–5500m); thus we must exercise caution when comparing our data to studies conducted at higher altitudes. However, our mouse model did simulate 4500m conditions, allowing us to explore physiological responses beyond those observed in our human cohort. Third, within our human data, the sample is most representative of individuals living at low and moderate elevations (<2500m), which are not typically considered physiologically extreme, as evidenced by the large populations residing at these altitudes. The mechanisms driving migraine aura susceptibility at moderate altitudes may differ from those at higher elevations. That said, our mouse model suggests a graded relationship between altitude on SD susceptibility – an effect likely influenced by multiple (and/or overlapping) mechanisms. Our current working model (a hypothesis) proposes that both hypoxia and rheologic changes associated with hematopoietic adaptation to altitude contribute to SD susceptibility, but their relative importance shifts with elevation. At moderate altitudes, rheological factors (e.g., increased hematocrit and blood viscosity) may play a more dominant role in promoting SD initiation, whereas at higher altitudes, hypoxia and hematologic changes may act synergistically to maximize susceptibility. Future studies should aim to dissect these mechanisms further, clarifying how altitude-related physiological adaptations influence migraine risk and SD generation across elevations. Furthermore, now that we have a target mechanism to investigate, and methods to separate the elements of the physiological altitude response, the underlying questions are now amenable to investigation. Clinical implications and conclusions Globally, between 700 million and 1 billion people dwell above 1000m (3280ft). 20 , 21 , 148 Assuming (conservatively) global migraine prevalence of 12% and migraine with aura prevalence of 4%, 1 – 5 between 28 and 40 million individuals with migraine may be affected by the increased rate of aura with altitude ( Supplementary Figure 1 ). This estimate does not consider (because there is no data upon which to ground it) the possibility that altitude increases the likelihood that individuals with migraine who would otherwise be without aura, have aura at altitude. Aura increases the burden of migraine, 7 , 11 and it is also associated with an increased likelihood of stroke and other adverse cardiovascular outcomes. 149 , 150 The opportunity to address a mechanistic target (SD) that is also the earliest measurable physiological phenotype of migraine (aura) brings both immediate and potential future benefits. Clinically available drugs could be considered in the current care of higher altitude populations with migraine aura, with the rationale that suppressing an element of the migraine attack generation might have enhanced disease modifying effects compared to other methods. On an investigational level the mechanistic target represented by SD, along with the known and testable mechanisms involved in the altitude response, could generate testable drug targets, potentially relevant to all migraine with aura. Data Availability All data produced in the present study are available upon reasonable request to the authors Funding Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health T32NS115723 (KMR), R01 NS 102978, 104742 (KCB), and the Department of Defense Congressionally Designated Medical Research Program (CDMRP) PR20091 (KCB). Competing interests The authors report no competing interests. Abbreviations AMS acute mountain sickness ARMR American Registry of Migraine Research BMI body mass index CMS chronic mountain sickness EPO erythropoietin FiO2 fraction of inspired air HIF hypoxia-inducible factor Hct hematocrit ICD International Classification of Diseases ICHD International Classification of Headache Disorders KCl potassium chloride LDS Latter-Day Saints LFP local field potential MIDAS Migraine Disability Assessment PHQ Patient Health Questionnaire RBC red blood cell SD spreading depolarization SLC Salt Lake City VARS Visual Aura Rating Scale VIF variance inflation factors Acknowledgements The authors would like to acknowledge Ka-Ho Wong for his assistance in the retrospective chart review, Dr. Lindsay Hunter for her assistance with the ARMR registry study, and Lauren Pearson DO, MPH for her expertise in hematocrit testing and interpretation. References 1. ↵ Steiner TJ , Stovner LJ . 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