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Verticchio Vercellin, Alice; Harris, Alon; Belamkar, Aditya; Zukerman, Ryan; Carichino, Lucia; Szopos, Marcela; Siesky, Brent; Quaranta, Luciano; Bruttini, Carlo; Oddone, Francesco; Riva, Ivano; Guidoboni, Giovanna

Photonics
, Volume 9 (3) – Mar 5, 2022

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hv photonics Article Physiology-Enhanced Data Analytics to Evaluate the Effect of Altitude on Intraocular Pressure and Ocular Hemodynamics 1 1 , 2 3 4 Alice Verticchio Vercellin , Alon Harris *, Aditya Belamkar , Ryan Zukerman , Lucia Carichino , 5 1 6 7 8 9 Marcela Szopos , Brent Siesky , Luciano Quaranta , Carlo Bruttini , Francesco Oddone , Ivano Riva 10 , 11 and Giovanna Guidoboni Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA; alice.verticchio@mssm.edu (A.V.V.); brent.siesky@mssm.edu (B.S.) Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, IN 46202, USA; abelamka@iu.edu Department of Ophthalmology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; rxz239@miami.edu School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA; lcsma1@rit.edu MAP5 UMR CNRS 8145, Université de Paris, 75006 Paris, France; marcela.szopos@u-paris.fr Department of Ophthalmology, Centro Oculistico Italiano, 25122 Brescia, Italy; luciano_quaranta@mac.com Department of Surgical & Clinical, Diagnostic and Pediatric Sciences, Section of Ophthalmology, University of Pavia—IRCCS Fondazione Policlinico San Matteo, 27100 Pavia, Italy; carlo.bruttini01@universitadipavia.it Glaucoma Unit, IRCCS—Fondazione Bietti, 00198 Rome, Italy; francesco.oddone@fondazionebietti.it Department of Ophthalmology, Istituto Clinico Sant’Anna, 25127 Brescia, Italy; ivano.riva@virgilio.it Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; guidobonig@missouri.edu Department of Mathematics, University of Missouri, Columbia, MO 65211, USA * Correspondence: alon.harris@mssm.edu; Tel.: +212-241-0250 Citation: Verticchio Vercellin, A.; Harris, A.; Belamkar, A.; Zukerman, Abstract: Altitude affects intraocular pressure (IOP); however, the underlying mechanisms involved R.; Carichino, L.; Szopos, M.; Siesky, and its relationship with ocular hemodynamics remain unknown. Herein, a validated mathematical B.; Quaranta, L.; Bruttini, C.; Oddone, modeling approach was used for a physiology-enhanced (pe-) analysis of the Mont Blanc study F.; et al. Physiology-Enhanced Data (MBS), estimating the effects of altitude on IOP, blood pressure (BP), and retinal hemodynamics. In Analytics to Evaluate the Effect of the MBS, IOP and BP were measured in 33 healthy volunteers at 77 and 3466 m above sea level. Altitude on Intraocular Pressure and Pe-retinal hemodynamics analysis predicted a statistically signiﬁcant increase (p < 0.001) in the model Ocular Hemodynamics. Photonics predicted blood ﬂow and pressure within the retinal vasculature following increases in systemic BP 2022, 9, 158. https://doi.org/ with altitude measured in the MBS. Decreased IOP with altitude led to a non-monotonic behavior of 10.3390/photonics9030158 the model predicted retinal vascular resistances, with signiﬁcant decreases in the resistance of the Received: 14 January 2022 central retinal artery (p < 0.001) and retinal venules (p = 0.003) and a non-signiﬁcant increase in the Accepted: 20 February 2022 resistance in the central retinal vein (p = 0.253). Pe-aqueous humor analysis showed that a decrease in Published: 5 March 2022 osmotic pressure difference (OPD) may underlie the difference in IOP measured at different altitudes Publisher’s Note: MDPI stays neutral in the MBS. Our analysis suggests that venules bear the signiﬁcant portion of the IOP pressure load with regard to jurisdictional claims in within the ocular vasculature, and that OPD plays an important role in regulating IOP with changes published maps and institutional afﬁl- in altitude. iations. Keywords: glaucoma; altitude; intraocular pressure; mathematical modeling; physiology-enhanced data analytics Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons Glaucoma is characterized by the loss of retinal ganglion cells and changes to the Attribution (CC BY) license (https:// optic nerve head that are associated with progressive irreversible vision loss. Glaucoma creativecommons.org/licenses/by/ is the second leading cause of blindness worldwide, and by 2040, it is estimated to afﬂict 4.0/). Photonics 2022, 9, 158. https://doi.org/10.3390/photonics9030158 https://www.mdpi.com/journal/photonics Photonics 2022, 9, 158 2 of 11 111.8 million persons [1]. Currently, intraocular pressure (IOP) is the only modiﬁable risk factor for glaucoma, with research having established IOP reduction as effective in delaying or preventing the onset and progression of the disease [2]. Other, currently less-modiﬁable risk factors have been identiﬁed in the disease pathogenesis including deﬁciencies in both systemic and ocular hemodynamics [3–5]. Importantly, these risk factors are known to be inﬂuenced by altitude; however, the underlying mechanisms and the clinical signiﬁcance of altitude’s effects on them have not been established. During increasing elevation, oxygen saturation within the blood decreases due to the depressed atmospheric pressure [6,7]. This results in a cascade of systemic responses, including an increase in resting blood pressure and heart rate and autoregulatory alterations to cerebral blood ﬂow secondary to hypoxia [8–11]. In comparison, the literature regarding altitude-related alterations in IOP and ocular hemodynamics is much less clear. While several studies have indicated IOP may increase with altitude [6,12–22], many have also found an opposing decrease in IOP with altitude [7,8,23–32]. Additionally, studies have shown that short-term exposure to high altitudes can alter the retinal microcirculation in young, healthy subjects secondary to hypoxic endothelial dysfunction [33]. Several studies have also noted ocular vascular changes with ascension including retinal vessel dilation and alterations in retinal and choroidal blood velocity and pressure [8,10,21,33,34]. However, these effects are generally self-limiting and nonindicative of lasting ocular or systemic hemodynamic changes [35–37]. The mechanisms involved and the consequences of altitude-related changes to IOP and ocular hemodynamics are important to understand for mitigation of disease risk, including glaucoma. Mathematical modeling techniques allow for the creation of a virtual laboratory for the testing of hypotheses in a controlled environment, allowing researchers to better understand the inputs, proposed mechanisms, and outcomes of modeled experiments [38]. In this perspective, herein, we combined experimental measurements with mathematical modeling to better understand the relationships between altitude, IOP, and hemodynamic changes and the clinical signiﬁcance of these alterations on ocular disease. Speciﬁcally, the data previously collected from the Mont Blanc Study (MBS) [32] were used as inputs for two mathematical models describing the physiology of ocular hemodynamics [39] and aqueous humor ﬂow [40]. The outputs of these models provide estimates for variables that bear physiological interest but that cannot be measured directly. Thus, the methodological approach proposed in this study, henceforth referred to as physiology-enhanced data analytics, will model and quantify the physiologic relationships between changes in IOP, blood pres- sure (BP), and ocular hemodynamics. Outcomes from the model will provide a framework for future altitude studies and provide needed clarity on the inﬂuence of altitude on IOP, BP, ocular hemodynamics, and associated risks for ocular pathologies. 2. Materials and Methods 2.1. The Mont Blanc Study The MBS measured IOP, central corneal thickness (CCT), systolic BP (SBP), diastolic BP (DBP), heart rate (HR), and oxygen saturation in 33 healthy volunteers (17 females, 16 males; mean age: 24.8 3.3) at 77 m (Pavia, Italy (PV)), at 1300 m (Courmayeur, Italy (CM)), and at 3466m (Pointe Helbronner, Mont Blanc Mountain, Italy (PH)) above sea level [32]. The ascent from CM was performed via a high-speed cableway, which took 15 min to reach PH (Skyway Mont Blanc cable car, Skyway Monte Bianco, Funivie Monte Bianco S.p.a, Aosta Valley, Italy). IOP was measured by applanation tonometry (Perkins MK2, Clement and Clarke) and rebound tonometry (I care TAO1i tonometer, Icare Finland Oy), under topical anesthesia with Oxybuprocaine drops. CCT was assessed by ultrasound pachymetry (PachMATE2). Subjects were examined at 9 a.m., 11 a.m., 1 p.m., and 3 p.m. in an indoor setting at PV and PH. IOP and CCT were also measured outdoors at PH at 9 a.m. [32]. Subjects were not acclimatized, nor were they required to exert any physical effort. One eye was selected for study throughout its course. Photonics 2022, 9, x FOR PEER REVIEW 3 of 12 a.m. [32]. Subjects were not acclimatized, nor were they required to exert any physical Photonics 2022, 9, 158 3 of 11 effort. One eye was selected for study throughout its course. The study found mean values of IOP to significantly decrease from PV to PH with multiple tonometers [32]. Additionally, CCT was found to increase with ascension, largely The study found mean values of IOP to signiﬁcantly decrease from PV to PH with matching the current literature [12,13,19,25,32]. The study initially hypothesized that hy- multiple tonometers [32]. Additionally, CCT was found to increase with ascension, largely poxic conditions found at higher altitudes may deplete the oxygen supply to nonpig- matching the current literature [12,13,19,25,32]. The study initially hypothesized that hy- mented ciliary epithelial cells, reducing the production of aqueous humor and thus IOP poxic conditions found at higher altitudes may deplete the oxygen supply to nonpigmented [32]. The MBS provides an essential experimental framework for this study to better un- ciliary epithelial cells, reducing the production of aqueous humor and thus IOP [32]. The derstand the physiologic association between IOP, BP, ocular hemodynamics, and alti- MBS provides an essential experimental framework for this study to better understand the tude. physiologic association between IOP, BP, ocular hemodynamics, and altitude. 2.2. Mathematical Model of Retinal Hemodynamics 2.2. Mathematical Model of Retinal Hemodynamics The mathematical model proposed and validated in [39] to study the hemodynamics The mathematical model proposed and validated in [39] to study the hemodynamics in the retina was used in combination with the data from the MBS to assess how changes in the retina was used in combination with the data from the MBS to assess how changes in in BP and IOP due to different altitudes affect retinal hemodynamics. In the model, arterial BP and IOP due to different altitudes affect retinal hemodynamics. In the model, arterial BP is the driving force of the blood flow through the vasculature, which includes the cen- BP is the driving force of the blood ﬂow through the vasculature, which includes the tral retinal artery and vein (CRA and CRV) and the retinal microvasculature (retinal arte- central retinal artery and vein (CRA and CRV) and the retinal microvasculature (retinal rioles, capillaries, and venules), whereas IOP acts as an external pressure on the vessels. arterioles, capillaries, and venules), whereas IOP acts as an external pressure on the vessels. In this study, the model was used in a novel way to obtain individualized estimates of In this study, the model was used in a novel way to obtain individualized estimates hemodynamic variables based on individualized pressure inputs, thereby enabling phys- of hemodynamic variables based on individualized pressure inputs, thereby enabling iology-enhanced d physiology-enhanced ata analytics. data analytics. This analysis, hencefor This analysis, th referre henceforth d to as physio referred logy to-e as nhanced ret physiology-enhanced inal hemodyn retinal amics hemodynamics analysis (pe-RH analysis analysis( ),pe-RH utilized analysis), the values utilized of IOP, the SBP, D values BP, of and IOP HR measure , SBP, DBPd , on e and a HR ch measured on subject at each altitude each subject as indi atvi each dualialtit zed i ude nputs as for the individualized retinal hem inputs ody for namic model. the retinal hemodynamic Specif- model. Speciﬁcally, SBP, DBP, and HR were combined using formula (3.9) in [41] to deﬁne the ically, SBP, DBP, and HR were combined using formula (3.9) in [41] to define the individ- individualized pressure P at the inlet of the retinal vasculature. In the model, the combined ualized pressure Pin at the inlet of the retinal vasculature. In the model, the combined ac- in action of BP and IOP induces variations in the vascular resistances characterizing the tion of BP and IOP induces variations in the vascular resistances characterizing the intra- intraocular segments of the CRA (R ), CRV (R ), and the retinal venules (R = R + R ). ocular segments of the CRA (R1c), CRV (R5a), and the retinal venules (R4 = R4a + R4b). These 1c 5a 4 4a 4b These variable resistances are indicated by arrows in the mathematical model in the center variable resistances are indicated by arrows in the mathematical model in the center of of Figure 1. We remark that, in this study, we assumed the diameter of arterioles to be Figure 1. We remark that, in this study, we assumed the diameter of arterioles to be con- constant. The model predicted values of BP, blood ﬂow, and vascular resistances across the stant. The model predicted values of BP, blood flow, and vascular resistances across the retinal vasculature based on individualized inputs yield individualized outputs on which retinal vasculature based on individualized inputs yield individualized outputs on which statistical analysis can be performed. Results between the two locations were analyzed statistical analysis can be performed. Results between the two locations were analyzed using a t-test with p < 0.05 being statistically signiﬁcant. A schematic illustration of the using a t-test with p < 0.05 being statistically significant. A schematic illustration of the pe- pe-RH analysis is reported in Figure 1. RH analysis is reported in Figure 1. Figure 1. Schematic of the physiology-enhanced retinal hemodynamics (pe-RH) analysis. Starting Figure 1. Schematic of the physiology-enhanced retinal hemodynamics (pe-RH) analysis. Starting from clinical data (left) of intraocular pressure, blood pressure, and heart rate from the Mont Blanc from clinical data (left) of intraocular pressure, blood pressure, and heart rate from the Mont Blanc Study, the mathematical model for retinal hemodynamics (center) was used to predict pressures, Study, the mathematical model for retinal hemodynamics (center) was used to predict pressures, blood flow, and vascular resistances in the retina as individualized outputs, resulting in the pe-RH blood ﬂow, and vascular resistances in the retina as individualized outputs, resulting in the pe-RH dataset (right). dataset (right). 2.3. Mathematical Model of Aqueous Humor Dynamics 2.3. Mathematical Model of Aqueous Humor Dynamics The mathematical model proposed and validated in [40] to study aqueous humor (AH) dynamics was also used in combination with the data from the MBS to gain insights into Photonics 2022, 9, x FOR PEER REVIEW 4 of 12 Photonics 2022, 9, 158 4 of 11 The mathematical model proposed and validated in [40] to study aqueous humor (AH) dynamics was also used in combination with the data from the MBS to gain insights into the relationship between altitude and IOP physiology. In the model, IOP is calculated as the result of the balance between AH inflow (JIN) and outflow (JOUT). AH inflow is as- the relationship between altitude and IOP physiology. In the model, IOP is calculated as the sumed to be due to (i) active secretion (Js), which is determined by the osmotic pressure result of the balance between AH inﬂow (J ) and outﬂow (J ). AH inﬂow is assumed to IN OUT difference (OPD), and (ii) ultrafiltration, which is determined by the oncotic pressure dif- be due to (i) active secretion (J ), which is determined by the osmotic pressure difference ference (Jp) an(OPD), d the cap and illa (ii) ry blood pr ultraﬁltration, essure which (cBP).is AH o determined utflow is by athe ssumed oncotic to occ pressur ur e difference through the tr(ab J ecu ) and lar m theecapillary shwork (J blood tm) and uveosc pressurele (cBP). ral pathw AH outﬂow ays (Juv).is In assumed this stud to y, t occur he through the model was ustrabecular ed in a nov meshwork el way to (Jinvest ) and igate uveoscleral the physiol pathways ogical mechan (J ). In ism this s rel study ating , the model was tm uv changes in IOP wi used th ch in aang novel es in way altito tud investigate e and BP. Two the physiological different analyses were pe mechanisms r relating formedchanges , in IOP with changes in altitude and BP. Two different analyses were performed, as detailed below. as detailed below. The ﬁrst analysis, henceforth referred to as physiology-enhanced intraocular pressure The first analysis, henceforth referred to as physiology-enhanced intraocular pres- analysis (pe-IOP analysis), was conducted to test whether the changes in BP associated with sure analysis (pe-IOP analysis), was conducted to test whether the changes in BP associ- changes in altitude can explain the changes in IOP measured via tonometry (Figure 2). For ated with changes in altitude can explain the changes in IOP measured via tonometry each subject in the study, we computed the mean arterial pressure (MAP) as MAP = 1/3 (Figure 2). For each subject in the study, we computed the mean arterial pressure (MAP) SBP + 2/3 DBP, and we used it to set the value of cBP as an individualized input for the as MAP = 1/3 SBP + 2/3 DBP, and we used it to set the value of cBP as an individualized model. Following [39], we set cBP = a MAP, with a = 0.29, and we kept other model input for the model. Following [39], we set cBP = 𝛼 MAP, with 𝛼 0.29, and we kept parameters at their baseline values as listed in [40]. Upon utilizing cBP as an individualized other model parameters at their baseline values as listed in [40]. Upon utilizing cBP as an input, the AH model was used to predict the corresponding IOP value as an individualized individualized input, the AH model was used to predict the corresponding IOP value as output, which was then compared with the actual IOP value measured on that speciﬁc an individualized output, which was then compared with the actual IOP value measured on subject at different altitudes (PV, CM, PH). that specific subject at different altitudes (PV, CM, PH). Figure 2. Schematic of the physiology-enhanced intraocular pressure (pe-IOP) analysis. Starting Figure 2. Schematic of the physiology-enhanced intraocular pressure (pe-IOP) analysis. Starting from from clinical data (left) of blood pressure (BP) from the Mont Blanc Study, the mathematical model clinical data (left) of blood pressure (BP) from the Mont Blanc Study, the mathematical model of of aqueous humor dynamics (right) was used to predict the intraocular pressure (IOP) value that aqueous humor dynamics (right) was used to predict the intraocular pressure (IOP) value that would would correspond to the altitude-related change in BP and then compared to the IOP value meas- correspond to the altitude-related change in BP and then compared to the IOP value measured via ured via tonometry (center). tonometry (center). The second analysis, henceforth referred to as physiology-enhanced aqueous humor The second analysis, henceforth referred to as physiology-enhanced aqueous humor analysis (pe-AH analysis), was conducted to test whether and to what extent changes in analysis (pe-AH analysis), was conducted to test whether and to what extent changes in OPD may be contributing to IOP changes observed with altitude (Figure 3). For each sub- OPD may be contributing to IOP changes observed with altitude (Figure 3). For each ject in the study, we used the measured values of MAP and IOP as individualized inputs, subject in the study, we used the measured values of MAP and IOP as individualized inputs, with cBP scaled from MAP as described above, and we utilized the model to estimate the with cBP scaled from MAP as described above, and we utilized the model to estimate the OPD value that would be necessary to attain a balance between AH inflow and outflow OPD value that would be necessary to attain a balance between AH inﬂow and outﬂow for for that specific subject at different altitudes (PV, CM, PH). that speciﬁc subject at different altitudes (PV, CM, PH). Photonics 2022, 9, 158 5 of 11 Photonics 2022, 9, x FOR PEER REVIEW 5 of 12 Photonics 2022, 9, x FOR PEER REVIEW 5 of 12 Figure 3. Schematic of the physiology-enhanced aqueous humor (pe-AH) analysis. Starting from Figure 3. Schematic of the physiology-enhanced aqueous humor (pe-AH) analysis. Starting from Figure 3. Schematic of the physiology-enhanced aqueous humor (pe-AH) analysis. Starting from clinical data (left) of intraocular pressure (IOP) and blood pressure (BP) from the Mont Blanc Study, clinical data (left) of intraocular pressure (IOP) and blood pressure (BP) from the Mont Blanc Study, clinical data (left) of intraocular pressure (IOP) and blood pressure (BP) from the Mont Blanc Study, the mathematical model of aqueous humor dynamics (center) was used to estimate the value of the the mathematical model of aqueous humor dynamics (center) was used to estimate the value of the the mathematical model of aqueous humor dynamics (center) was used to estimate the value of the osmotic pressure difference (OPD) and enhance the dataset (right). osmotic pressure difference (OPD) and enhance the dataset (right). osmotic pressure difference (OPD) and enhance the dataset (right). 3. Results 3. Results 3. Results 3.1. Physiology-Enhanced Data Analytics: Retinal Hemodynamics 3.1. Physiology-Enhanced Data Analytics: Retinal Hemodynamics 3.1. Physiology-Enhanced Data Analytics: Retinal Hemodynamics The results of the pe-RH analysis are reported in Figures 4 and 5. The measured BP The results of the pe-RH analysis are reported in Figures 4 and 5. The measured BP The results of the pe-RH analysis are reported in Figures 4 and 5. The measured BP values exhibit an increasing trend with altitude, with values of MAP of 83.69 ( 6.62) values exhibit an increasing trend with altitude, with values of MAP of 83.69 ( 6.62) values exhibit an increasing trend with altitude, with values of MAP of 83.69 (6.62) mmHg mmHg at PV and 92.82 ( 8.43) at PH [32]. Following this increase in systemic BP with mmHg at PV and 92.82 ( 8.43) at PH [32]. Following this increase in systemic BP with at PV and 92.82 (8.43) at PH [32]. Following this increase in systemic BP with altitude, the altitude, the model predicted blood flow supplying the retina, the model predicted BPs in altitude, the model predicted blood flow supplying the retina, the model predicted BPs in model predicted blood ﬂow supplying the retina, the model predicted BPs in the CRA, CRV, the CRA, CRV, and retinal arterioles and venules, and the inflow retinal blood flow show the CRA, CRV, and and r retinal etinalarterioles arteriolesand and venu venules, les, an and d the the inﬂow inflow retin retinal al blood blood flow show ﬂow show a statistically a statistically significant increase (p < 0.001) with altitude (Figure 4). a statistically sign signiﬁcant ificant in incr creas ease e (( pp << 00.001) .001) wi with th al altitude titude (F (Figur igure 4) e 4). . Figure 4. The physiology−enhanced retinal hemodynamics analysis predicted average values and Figure 4. The physiologyenhanced retinal hemodynamics analysis predicted average values and standard deviation of the vascular pressures and blood flow rates in different retinal vascular beds Figure 4. The physiology standard deviation −enhanced retinal of the vascular hemodynamics analys pressures and blood is predi ﬂow cted rates average v in differ alues and ent retinal vascular beds for the two locations/altitudes considered. CRA: central retinal artery; CRV: central retinal vein. At- standard deviation of the vascular pressures and blood flow rates in different retinal vascular beds for the two locations/altitudes considered. CRA: central retinal artery; CRV: central retinal vein. tribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini C.; Riva, I.; for the two locations/altitudes considered. CRA: central retinal artery; CRV: central retinal vein. At- Attribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini C.; Riva, Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A physiology-en- tribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini C.; Riva, I.; I.; Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A physiology- hanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 560. Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A physiology-en- enhanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 560. (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). hanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 560. (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). The measured IOP values exhibit a decreasing trend with altitude, with values of 11.97 ( 2.39) mmHg reported at PV and 11.34 ( 2.74) at PH [32]. The pe-RH analysis pre- The measured IOP values exhibit a decreasing trend with altitude, with values of dicted that this decrease in IOP with altitude is linked to a non-monotonic behavior of the 11.97 ( 2.39) mmHg reported at PV and 11.34 ( 2.74) at PH [32]. The pe-RH analysis pre- retinal vascular resistances shown in Figure 5. In particular, the model shows a statistically dicted that this decrease in IOP with altitude is linked to a non-monotonic behavior of the significant decrease in the CRA resistance (R1c) (p < 0.001) and the retinal venules total retinal vascular resistances shown in Figure 5. In particular, the model shows a statistically resistance (R4 = R4a + R4b) (p = 0.003) and a non-significant increase in the CRV resistance significant decrease in the CRA resistance (R1c) (p < 0.001) and the retinal venules total (R5a) (p = 0.253) with altitude. Note that the total resistance in the retinal venules is mark- resistance (R4 = R4a + R4b) (p = 0.003) and a non-significant increase in the CRV resistance edly higher than in the CRA and CRV, suggesting that the venules withstand most of the (R5a) (p = 0.253) with altitude. Note that the total resistance in the retinal venules is mark- IOP load on the vasculature. edly higher than in the CRA and CRV, suggesting that the venules withstand most of the IOP load on the vasculature. Photonics 2022, 9, x FOR PEER REVIEW 6 of 12 Photonics 2022, 9, 158 6 of 11 p=0.003 Pavia Pointe Helbronner p<0.001 p=0.253 CRA ( R ) Venules ( R + R ) CRV ( R ) 1c 4a 4b 5a Figure 5. The physiology−enhanced retinal hemodynamics analysis predicted average values and Figure 5. The physiologyenhanced retinal hemodynamics analysis predicted average values and standard deviation of resistances of the intraocular segments of the central retinal artery (CRA) (R1c), standard deviation of resistances of the intraocular segments of the central retinal artery (CRA) (R ), 1c central retinal vein (CRV) (R5a), and retinal venules (R4 = R4a + R4b) for the two locations/altitudes central retinal vein (CRV) (R ), and retinal venules (R = R + R ) for the two locations/altitudes 5a 4 4a 4b considered. Attribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini considered. Attribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini C.; C.; Riva, I.; Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A phys- Riva, I.; Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A physiology- iology-enhanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, enhanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 560. Vol.62, 560. (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Pub- (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). lic License). The measured IOP values exhibit a decreasing trend with altitude, with values of 3.2. Physiology-Enhanced Data Analytics: Aqueous Humor Dynamics 11.97 (2.39) mmHg reported at PV and 11.34 (2.74) at PH [32]. The pe-RH analysis The results of the pe-IOP analysis are reported in Figure 6. As previously indicated, predicted that this decrease in IOP with altitude is linked to a non-monotonic behavior the measured IOP values exhibit a decreasing trend with altitude [32]. The model pre- of the retinal vascular resistances shown in Figure 5. In particular, the model shows a dicted IOP values exhibit the opposite trend, with mean values of 13.7 mmHg, 15.2 statistically signiﬁcant decrease in the CRA resistance (R ) (p < 0.001) and the retinal 1c mmHg, and 14.9 mmHg obtained for PV, CM, and PH. This finding shows that changes venules total resistance (R = R + R ) (p = 0.003) and a non-signiﬁcant increase in the 4 4a 4b in MAP are not sufficient to explain the measured IOP differences at different altitudes. CRV resistance (R ) (p = 0.253) with altitude. Note that the total resistance in the retinal 5a As a matter of fact, in this analysis, the MAP value was the only model input to be modi- venules is markedly higher than in the CRA and CRV, suggesting that the venules withstand fied with altitude, thereby assuming that all other factors, including OPD, were given con- most of the IOP load on the vasculature. stants that are independent of the subject and the altitude. 3.2. Physiology-Enhanced Data Analytics: Aqueous Humor Dynamics The results of the pe-IOP analysis are reported in Figure 6. As previously indicated, the measured IOP values exhibit a decreasing trend with altitude [32]. The model predicted IOP values exhibit the opposite trend, with mean values of 13.7 mmHg, 15.2 mmHg, and 14.9 mmHg obtained for PV, CM, and PH. This ﬁnding shows that changes in MAP are not sufﬁcient to explain the measured IOP differences at different altitudes. As a matter of fact, in this analysis, the MAP value was the only model input to be modiﬁed with altitude, thereby assuming that all other factors, including OPD, were given constants that are independent of the subject and the altitude. Figure 6. The physiology−enhanced intraocular pressure analysis shows that changes in mean arte- rial pressure are not sufficient to explain the measured intraocular pressure (IOP) differences at different altitudes, since the measured IOP decreases with altitude while the model predicted IOP increases. Attribution: Guidoboni, G.; Szopos, M.; Verticchio Vercellin, A.C.; Bruttini C.; Riva, I.; Siesky, B.A.; Harris A.; Quaranta L. Physiology-enhanced data analytics to evaluate the effect of Resistance [mmHg s/mL] Photonics 2022, 9, x FOR PEER REVIEW 6 of 12 p=0.003 Pavia Pointe Helbronner p<0.001 p=0.253 CRA ( R ) Venules ( R + R ) CRV ( R ) 1c 4a 4b 5a Figure 5. The physiology−enhanced retinal hemodynamics analysis predicted average values and standard deviation of resistances of the intraocular segments of the central retinal artery (CRA) (R1c), central retinal vein (CRV) (R5a), and retinal venules (R4 = R4a + R4b) for the two locations/altitudes considered. Attribution: Carichino L.; Guidoboni G.; Szopos M.; Verticchio Vercellin A.C.; Bruttini C.; Riva, I.; Siesky B.A.; Quaranta L.; Harris A. Effect of altitude on retinal hemodynamics: A phys- iology-enhanced theoretical analysis. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 560. (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Pub- lic License). 3.2. Physiology-Enhanced Data Analytics: Aqueous Humor Dynamics The results of the pe-IOP analysis are reported in Figure 6. As previously indicated, the measured IOP values exhibit a decreasing trend with altitude [32]. The model pre- dicted IOP values exhibit the opposite trend, with mean values of 13.7 mmHg, 15.2 mmHg, and 14.9 mmHg obtained for PV, CM, and PH. This finding shows that changes in MAP are not sufficient to explain the measured IOP differences at different altitudes. As a matter of fact, in this analysis, the MAP value was the only model input to be modi- Photonics 2022, 9, 158 7 of 11 fied with altitude, thereby assuming that all other factors, including OPD, were given con- stants that are independent of the subject and the altitude. Figure 6. The physiologyenhanced intraocular pressure analysis shows that changes in mean Photonics 2022, 9, x FOR PEER REVIEW Figure 6. The physiology−enhanced intraocular pressure analysis shows that changes in mean arte 7 of 12 - arterial pressure are not sufﬁcient to explain the measured intraocular pressure (IOP) differences rial pressure are not sufficient to explain the measured intraocular pressure (IOP) differences at at different altitudes, since the measured IOP decreases with altitude while the model predicted different altitudes, since the measured IOP decreases with altitude while the model predicted IOP IOP increases. Attribution: Guidoboni, G.; Szopos, M.; Verticchio Vercellin, A.C.; Bruttini C.; Riva, increases. Attribution: Guidoboni, G.; Szopos, M.; Verticchio Vercellin, A.C.; Bruttini C.; Riva, I.; altitude on intraocular pressure. Investigative Ophthalmology & Visual Science June 2021, Vol.62, Siesky, B.A.; Harris A.; Quaranta L. Physiology-enhanced data analytics to evaluate the effect of I.; Siesky, B.A.; Harris A.; Quaranta L. Physiology-enhanced data analytics to evaluate the effect of 559 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public Li- altitude on intraocular pressure. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 559 cense). (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). The results of the pe-AH analysis are reported in Figure 7. The results show that The results of the pe-AH analysis are reported in Figure 7. The results show that changes in OPD, in conjunction with MAP, capture the decreasing trend of IOP with alti- changes in OPD, in conjunction with MAP, capture the decreasing trend of IOP with tude as observed in the MBS. Specifically, MAP values were measured at PV, CM, and PH altitude as observed in the MBS. Speciﬁcally, MAP values were measured at PV, CM, [32], and the corresponding model predicted values of OPD are 367.0 mmHg, 266.5 and PH [32], and the corresponding model predicted values of OPD are 367.0 mmHg, mmHg, and 287.0 mmHg. 266.5 mmHg, and 287.0 mmHg. Pavia (PV) Courmayeur (CM) Pointe Helbronner (PH) Figure 7. The physiologyenhanced aqueous humor analysis shows that a decrease in osmotic Figure 7. The physiology−enhanced aqueous humor analysis shows that a decrease in osmotic pres- pressure difference (OPD) may underlie the difference in intraocular pressure measured at different sure difference (OPD) may underlie the difference in intraocular pressure measured at different alaltitudes. titudes. Attributi Attribution: on: GuGuidoboni, idoboni, G.; S G.; zopos Szopos, , M.; Verti M.; Verticchio cchio Verc Ver ell cellin, in, A.C. A.C.; ; Brutti Bruttini ni C.; C.; RiRiva, va, I.; I.; Siesky, B.A.; Harris A.; Quaranta L. Physiology-enhanced data analytics to evaluate the effect of Siesky, B.A.; Harris A.; Quaranta L. Physiology-enhanced data analytics to evaluate the effect of altitude on intraocular pressure. Investigative Ophthalmology & Visual Science June 2021, Vol.62, altitude on intraocular pressure. Investigative Ophthalmology & Visual Science June 2021, Vol.62, 559 559 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public Li- (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License). cense). 4. Discussion 4. Discussion The relationships between IOP, BP, and ocular hemodynamics during varied elevation are The relationships be important considerations tween IOP, BP in the risk , and management ocular hemoof dynam ophthalmic ics during v diseases aried e such leva- as tion glaucoma, are impo yet rtan they t cons are ide particularly rations in the difﬁcult risk m to anag investigate ement oin f oph clinical thalm practice. ic diseasThe es such curr a ent s glaucoma, yet they are particularly difficult to investigate in clinical practice. The current literature on altitude and physiologic variables such as IOP, BP, and hemodynamics is particularly difficult to interpret due to the conflicting presence of environmental varia- bles and methodological variances [12–14,23–25]. Specifically, some studies utilized hy- pobaric chambers to simulate altitude changes, while others followed participants through climbs or lifts to higher elevation. Additionally, each study had wide variation in its measurement timelines and temperature at which the measurements were assessed, which may further influence comparisons. Finally, no specific altitude was consistently utilized across studies, therefore complicating the understanding of what elevation level is required to significantly impact IOP and/or ocular hemodynamics. In order to overcome these limitations, in this study, we used an innovative methodological approach indicated as physiology-enhanced data analytics able to integrate experimental data—derived from the MBS [32]—with validated mathematical models [39,40] in order to shed further light on the physiologic relationships between IOP, BP, and ocular hemodynamics with altitude. First, the mathematical model validated in [39] was here used in combination with the MBS to evaluate how BP and IOP changes at different altitudes may influence retinal hemodynamics. In detail, the pe-RH analysis utilized the values of IOP, SBP, DBP, and HR measured on each subject at each altitude in the MBS as individualized inputs and provided Resistance [mmHg s/mL] Photonics 2022, 9, 158 8 of 11 literature on altitude and physiologic variables such as IOP, BP, and hemodynamics is particularly difﬁcult to interpret due to the conﬂicting presence of environmental variables and methodological variances [12–14,23–25]. Speciﬁcally, some studies utilized hypobaric chambers to simulate altitude changes, while others followed participants through climbs or lifts to higher elevation. Additionally, each study had wide variation in its measurement timelines and temperature at which the measurements were assessed, which may further inﬂuence comparisons. Finally, no speciﬁc altitude was consistently utilized across studies, therefore complicating the understanding of what elevation level is required to signiﬁcantly impact IOP and/or ocular hemodynamics. In order to overcome these limitations, in this study, we used an innovative methodological approach indicated as physiology-enhanced data analytics able to integrate experimental data—derived from the MBS [32]—with validated mathematical models [39,40] in order to shed further light on the physiologic relationships between IOP, BP, and ocular hemodynamics with altitude. First, the mathematical model validated in [39] was here used in combination with the MBS to evaluate how BP and IOP changes at different altitudes may inﬂuence retinal hemodynamics. In detail, the pe-RH analysis utilized the values of IOP, SBP, DBP, and HR measured on each subject at each altitude in the MBS as individualized inputs and provided the values of BP, blood ﬂow, and vascular resistances across the retinal vasculature as individualized outputs (Figure 1). With altitude, the experimental data from the MBS showed an increase in BP, which corresponded to model predicted statistically signiﬁcant increases in the blood ﬂow supplying the retina and BPs in the CRA, CRV, and retinal arterioles and venules (Figure 4). Additionally, the model predicted that the decrease in IOP with altitude found in the MBS was linked, in particular, to a statistically signiﬁcant decrease with elevation in the total resistance at the level of the retinal venules (Figure 5). Generally, the literature agrees with the ﬁndings of this study as several pilot investigations demonstrated short-term increases in retinal blood ﬂow, velocity, and pressures with increasing altitude [8,10,21,33,34]. Our results from the pe-RH analysis conﬁrm the data from the literature and also suggest that altitude may signiﬁcantly increase retinal vessel pressures, alter localized tissue perfusion, and indicate that venules bear the signiﬁcant portion of the IOP load within the ocular vasculature. This is a novel and important ﬁnding that may explain how elevation results in acute increases in retinal perfusion and pressures. In fact, the venous system’s blood vessel walls are less rigid and internally pressurized than on the arterial side, making venous vessels possibly more susceptible to pressure increases from elevation. These results also suggest that the retinal venules may play an important role in the regulation of the blood ﬂow in response to IOP and BP changes with altitude. We recall that, in this study, the mathematical model did not include active regulatory mechanisms that would lead to dilation or constriction of arterioles. Our analysis suggests that such mechanisms may be called into action as a response to changes that are occurring at the level of the venules downstream. Therefore, further studies are needed to investigate the retinal venous compartment as a potential target for future estimation of risk and treatment development in the management of glaucoma. In addition, we also used the mathematical model validated in [40] on AH dynamics in combination with the experimental data from the MBS to shed further light on the physiological mechanisms between changes in IOP and BP with altitude through two sub-analyses. The pe-IOP analysis used the values of MAP derived from the MBS as individualized inputs and provided the corresponding IOP values at different altitudes as individualized outputs, which were then compared with the actual IOP values collected in the real word (Figure 2). The IOP values predicted by the model exhibited an increasing trend with altitude, while the experimental IOP decreased with elevation, thus showing that changes in MAP are not sufﬁcient to explain the measured IOP differences at different altitudes (Figure 6). Important ﬁndings in this context were then derived from the pe-AH analysis which used the measured values of MAP and IOP as individualized inputs and estimated the OPD values needed to achieve a balance between inﬂow and outﬂow of aqueous humor for each subject at different altitudes from the model as individualized outputs Photonics 2022, 9, 158 9 of 11 (Figure 3). Interestingly, the pe-AH analysis showed that a decrease in OPD may be the physiologic mechanism underlying the differences in IOP measured at different altitudes, thus suggesting that OPD may play an important role in regulating IOP during changes in altitude. This result is particularly signiﬁcant because it highlights how mathematical modeling, by estimating parameters that cannot be easily measured experimentally, may help elucidate pathophysiological mechanisms that occur with altitude changes. Further studies will be needed to implement the model to include variables such as temperature and oxygen saturation that may further inﬂuence the relationship between altitude, BP, and IOP. The relationships between altitude, IOP, and hemodynamics are complex, and the current literature does not provide consensus on their associations or possible inﬂuence on the risk of glaucoma. Importantly, further research should be speciﬁcally designed to ac- count for environmental variables and methodological variances to address the conclusion regarding the association between IOP and altitude. As these relationships are interwoven, ocular hemodynamic variables should also be measured in patients during ascension or ascension-simulated conditions affecting IOP. Mathematical modeling provides an effective tool for enhancing the analysis options of clinical datasets and allows for the testing of hypotheses in a virtual laboratory. These tools are especially important for understanding complex interactions that cannot be viewed in vivo due to anatomical target inaccessibility or imaging and measurement limitations. Identifying and modeling biomarkers such as venous resistance and OPD may provide a framework for understanding altitude’s effects on IOP, ocular hemodynamics, and the risk of ocular pathologies. Additionally, future model analytics may include the effect of temperature and CCT for enhanced speciﬁcity and eventually include the effect of altitude on oxygen saturation via autoregulation to better evaluate and understand ocular physiology and potential mechanisms of pathophysiology. Our study is not without limitations. It is important to acknowledge that while mathematical modeling represents an effective tool for enhancing the analysis options of clinical datasets and allows for the testing of hypotheses in a virtual laboratory, only real-world measurement can provide the ﬁnal conﬁrmation of the tested hypotheses, and these should be the next steps in conﬁrming our results. Furthermore, the analysis presented in this study does not explicitly include vascular regulatory mechanisms, whose recruitment in response to alterations in the venous side of the circulation remains largely understudied. In addition, the mathematical models presented in our study used data previously collected from the MBS as inputs [32], which presented several limitations as follows: (i) the IOP measurements were performed with tonometers that have not been previously validated to be used at high altitudes; (ii) the inﬂuence of acclimatization on the physiologic parameters was not evaluated; and (iii) the study subjects were all healthy subjects with a mean age of 24.8 years. Therefore, further experimental studies are needed to investigate the effect of acclimatization on the physiologic parameters analyzed and should be conducted speciﬁcally in older patients with glaucoma as this population may have differing pathophysiological mechanisms that underlie their condition and differentially inﬂuence IOP and other physiologic variables at high altitude. 5. Conclusions Our physiology-enhanced data analytics methodological approach applied to experimen- tal data derived from the MBS on healthy subjects predicted elevation-induced increases in the blood ﬂow and pressures supplying the retina and a decrease in the total resistance at the level of the retinal venules. In addition, the model predicted that OPD plays a signiﬁcant role in IOP regulation at different altitudes, and that high altitude should be considered when assessing IOP and risk in patients with glaucoma. These pilot results need to be conﬁrmed in glaucomatous patients to ensure their applicability in clinical practice. High altitude may potentially represent an environmental factor that could assist in assessing individual risk and/or potentially the manipulation of IOP. Further, these novel experimental data may be used Photonics 2022, 9, 158 10 of 11 as inputs to mathematical models that can have a crucial role as tools to further the understanding of the mechanisms involved in IOP and ocular hemodynamics. These results are applicable not only in physiologic conditions, as discussed in this study, but also in pathological conditions including glaucoma and other diseases of the retina and optic nerve. Author Contributions: Conceptualization, A.V.V., A.H., G.G.; formal analysis, L.C., M.S., G.G.; writing—original draft preparation, A.V.V., A.B., R.Z., G.G.; writing—review and editing., B.S., L.Q., C.B., I.R., F.O.; supervision, A.H., G.G. All authors have read and agreed to the published version of the manuscript. Funding: This work has been partially supported by NIH grant R01EY030851, NSF DMS 1853222/2021192, NSF DMS 2108711/2108665, and a Challenge Grant award from Research to Prevent Blindness, NY. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conﬂicts of Interest: A. Harris would like to disclose that he received remuneration from AdOM, Qlaris, Luseed, and Cipla for serving as a consultant, and he serves on the board of AdOM, Qlaris, and Phileas Pharma. A. Harris holds an ownership interest in AdOM, Luseed, Oxymap, Qlaris, Phileas Pharma, SlitLed, and QuLent. All relationships listed above are pursuant to the Icahn School of Medicine’s policy on outside activities. I. Riva would like to disclose that he received remuneration as honoraria and congress expenses from Allergan, Santen, Visufarma, and SIFI. G. Guidoboni would like to disclose that she received remuneration from Foreside Healthcare LLC for serving as a consultant. These relationships are pursuant to the University of Missouri’s policy on outside activities. The contribution of the author F. 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Photonics – Multidisciplinary Digital Publishing Institute

**Published: ** Mar 5, 2022

**Keywords: **glaucoma; altitude; intraocular pressure; mathematical modeling; physiology-enhanced data analytics

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