Meteorological and Environmental Factors Associated with Sudden Cardiac Arrest during Marathons in Japan

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While underlying cardiovascular conditions have been linked to SCA, the role of environmental factors, such as ambient air quality and meteorological conditions, remains unclear. We conducted a nationwide study in Japan to examine the association between meteorological and air pollution variables and the occurrence of SCA during marathons. Data from approximately 4.53 million runners participating in full marathons certified by the Japan Association of Athletics Federations from April 2011 to March 2020 were analyzed. SCA cases were linked to meteorological variables (temperature, humidity, solar radiation, wind speed, precipitation, and air pressure) and air pollutants (particulate matter and gaseous pollutants). Results Among 4.53 million runners, 74 SCA cases were identified. Poisson regression analysis showed that lower ambient temperatures at race start were significantly associated with increased SCA risk (adjusted incidence risk ratio per 1°C decrease: 1.06; 95% confidence interval: 1.02–1.12). No significant associations were found between air pollutants and SCA risk. Conclusions Lower temperatures at the start of marathons were associated with a higher risk of SCA, while no significant correlation was observed with air pollutants. These findings suggest that temperature may be an important environmental factor influencing the risk of SCA during marathons. sudden cardiac arrest marathon running ambient temperature air pollution Figures Figure 1 Figure 2 1 INTRODUCTION Sudden cardiac arrest (SCA) during sporting activities is considered as tragic in that a hitherto fit athlete suddenly runs the risk of death. SCA events in road races, including marathons, occur in about 0.5 to 2 cases per 100,000 runners [ 1 – 4 ], garnering much public attention. The Race Associated Cardiac Arrest Event Registry (RACER 2) study [ 2 ], involving 29 million participants in the US, recently reported that nearly half of SCA cases were attributed to coronary artery disease, aligning with findings from other national registries [ 1 , 3 ]. To date, most studies on etiology have focused on the characteristics of individual runners, and not on external or environmental factors. In general, meteorological variables and air pollutants are associated with cardiovascular diseases and deaths [ 5 – 7 ]. However, to the best of our knowledge, the association between these variables and SCA during sports activities including running has not been fully studied. Road races are characterized by large numbers of runners experiencing the same air conditions. This provides a valuable opportunity for a comprehensive assessment of the relationship between environmental exposure factors and SCA during exercise. By linking a nationwide registry of SCA during marathon in the Japan Association of Athletics Federations (JAAF) with the Japan Meteorological Agency and the National Institute for Environmental Studies databases, we examined whether any meteorological and air pollutant variable(s) were associated with the risk of SCA. 2 METHODS 2.1 Study Overview This is an observational study with a nationwide prospective using the SCA registry during marathon races. The reporting of the studies complied with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 8 ]. This study was approved by the Institutional Ethics Committee of the Sports Medicine Research Centre, Keio University (No. 2013-03); individual consent was waived as the study does not use personal patient identifiers. 2.2 Data sources 2.2.1 Study population JAAF certifies more than 70 full marathon races annually in 42 of the 47 prefectures (Fig. 1 ). As reported, the JAAF Medical Committee has been involved in an SCA registry study since April 2011 [ 9 ]. Members of the medical committee mailed questionnaires to race offices after races to investigate medical activities. In addition, local newspapers and internet news articles were reviewed; any discrepancies were resolved by contacting race offices directly. SCA was defined as a runner collapsing suddenly between the start and one hour after the end of races and receiving life-saving treatment such as chest compressions or an automated external defibrillator (AED). To maximize questionnaire responses, the causes of cardiac arrest were not pursued. The number of marathon participants was defined as the number of runners who crossed the start line. Age group data were collected for about half the races (290 races). For races with missing demographic data but available registrant or finisher lists, those distributions were used to estimate participants’ age groups [ 9 ]. Participants were categorized as under 40 (< 40s), aged 40–59 (40–50s) and over 60 (≥ 60s). Gender data were based on participants’ self-reported registration information. The route of the JAAF-certified races follows World Athletics rules [ 10 ], requiring the straight-line distance between the start and finish points to be less than 50% of the total route. Thus, air quality at the start was assumed to represent the entire air route. 2.2.2 Meteorological and air pollutant variables Weather data were obtained from the Japan Meteorological Agency, which operates 56 meteorological observatories and approximately 1,300 automated meteorological data acquisition system stations across Japan [ 11 ]. Hourly observations of ambient temperature, relative humidity, solar radiation, wind speed, precipitation, and air pressure were extracted. Air pollutant concentrations, hourly observations of particulate pollutants (fine particulate matter [PM 2.5 ] and suspended particulate matter [SPM]) and gaseous pollutants (photochemical oxidants [Ox], sulphur dioxide [SO 2 ], nitrogen dioxide [NO 2 ], and carbon monoxide [CO]) were retrieved from the National Institute for Environmental Studies database [ 12 ]. There were approximately 1,900 air pollution monitoring stations distributed throughout Japan, the respective observations were based on measurements at ambient air quality monitoring stations (AAQMS), which are representative of the air quality in the area. For CO, measurements from roadside stations were included due to limited AAQMS availability nearby. If data were missing for more than three consecutive hours at the nearest station, the next nearest station’s data were used. If an observation was missing for up to two consecutive hours, the average value of the previous and following observations was used. These processes allowed missing observations to be completely eliminated, while the imputation during the race time was almost negligible as it accounted for less than 0.1% of the total observations. 2.3 Statistical analysis 2.3.1 Main analysis Observed meteorological and air pollutant data were both indexed to the start time of the race day and averaged over a three-hour period, including one hour before and after the start of the race. According to the distribution of the data, they were summarized as mean and standard deviation (SD), median and interquartile range (IQR). Data for races with and without cases of SCA were compared using the t-test or Wilcoxon signed rank tests for continuous variables, while categorical variables were compared using the chi-squared or Fisher’s exact tests, as appropriate. The main analysis was based on a generalized linear model with a Poisson distribution, for the number of SCA cases in the race, offset by the logarithm of the number of race participants. Given the low incidence of SCA events, the Poisson regression model was chosen for its simplicity and interpretability in estimating event rates across the study cohort. Results are presented as point estimates of incidence risk ratios and their 95% confidence intervals (95% CI), calculated per 1-unit increase for all covariates except CO, which is calculated per 0.1 ppm increase. In addition to the bivariate analysis, a multivariate analysis was performed incorporating the following covariates. Model 1 adjusted for the four Japanese regional divisions (north, east, west, and Okinawa [Figure 1 ]), elevation from sea level, and marathon start time. In addition to the covariates in Model 1, Model 2 included temperature and all air pollutants collectively. To account for potential non-linear relationships, natural cubic splines with 3 degrees of freedom were applied to all variables. The reference values were set at the median (50th percentile) of each variable distribution, with knots placed at the 25th and 75th percentiles. For simple comparisons, values of p < 0.05 were considered significant obtained using a two-tailed test, and p-values were not indicated for estimating relationships due to their exploratory nature. These statistical evaluations were computed using JMP® version 11 software (SAS Institute Inc., Cary, NC, USA). Non-linear regression was calculated using R, version 4.5.0 (R Foundation, Vienna, Austria). 2.3.2 Sensitivity analysis We performed several further analyses with the aim of validating the accuracy of the main analysis. First, the air temperature parameters were converted to wet bulb globe temperatures (WBGT), which is used for stratification of thermal health risks in road racing [ 13 ]. Although WBGT was not directly measured using instruments, the estimation formula by Ono et al [ 14 ]. was adopted as a surrogate (Supplementary methods). Second, to consider only those cases of SCA with cardiogenic origin, we repeated the analysis with the outcome restricted to cases with AED use. Third, to assess potential lags in time phases, instead of using the 3-hour average including the start as the reference frame (Lag 0) for the main analysis, we used average values from 7 to 5 hours before (Lag − 6), 4 to 2 hours before (Lag − 3), 2 to 4 hours after (Lag + 3) and 5 to 7 hours after (Lag + 6) the start time of the race as an exposure in the regression model. Fourth, with the aim of eliminating runner background bias, elite races with a 40 km barrier of less than 4 hours, as well as women-only races, were excluded. Fifth, to account for possible geographical noise, races, where the monitoring station was more than 21.0975 km from the starting point, were excluded. Sixth, we excluded host cities for races with population densities exceeding 5000 inhabitants per square km in order to exclude potential confounding by unmeasured pollutants. Seventh, gender (the number of log-transformed male participants) and age groups (the number of log-transformed participants under 40 and over 60 years) were included in the main analysis to adjust for runner demographics for each race. 3 RESULTS Between April 2011 and March 2020, 571 JAAF-certified full marathon races were held (Fig. 1 ), with a total of 4,528,134 runners involved. Data on the distribution of gender and age groups for each race were available for 95 and 74%, respectively, suggesting that there were no appreciable variations in the demographic composition of runners among events (Table 1 ). Table 1 Baseline race characteristics JAAF-certified marathons (N = 571) Number of Participants, n median (IQR) 6258 (2028, 11520) Age group a , % median (IQR) < 40s 34.0 (29.2, 38.5) 40–50s 56.5 (52.8, 60.8) ≥ 60s 9.0 (6.8, 11) Male, % median (IQR) 84.7 (80.6, 88.0) Completion rate b , % median (IQR) 89.9 (84.0, 93.3) Season c , n (%) Autumn 218 (38.2) Winter 208 (36.4) Spring 131 (22.9) Summer 14 (2.5) Regional division d , n (%) North 63 (11.0) East 205 (35.9) West 285 (49.9) Okinawa 18 (3.2) Start time, hour:minute median (IQR) 9:00 (9:00, 10:00) Latitude at starting site e , degree median (IQR) 35.0 (33.7, 36.4) Longitude at starting site e , degree median (IQR) 135.9 (132.8, 139.6) Elevation from sea level, m median (IQR) 12 (3, 52) a 544 races (95%) were included. †422 races (74%) were included. b The ratio of the number of finishers to the number of participants. c Autumn was defined as September to November, winter as December to February, spring as March to May and summer as June to August. d Refer to Fig. 1 . e Numerical values are expressed in the 100 decimal system. JAAF, Japan Association of Athletics Federations; IQR, inter-quartile range. Of the 4.53 million participants, 74 SCA cases occurred in 61 races (rate per 100 000; 1.63 [95% CI: 1.26, 2.01]); the median age of SCA patients was 52 years (IQR: 44, 61), and 69 (93%) were male. Shockable rhythms were detected by the AED and defibrillated in 55 (74%). Only one death occurred, resulting in a survival rate of approximately 99% for all SCA cases (Table S1 ). A summary of the meteorological and air pollution data in each race is presented in Table 2 . The marathons were often held in the early morning hours of autumn and winter, with an average temperature of 11.5°C (SD: 5.8). Only 25 races (4%) exceeded 20.5°C by WBGT, which is regarded as the "do not start" criterion for marathons [ 15 ]. Air pollutant concentrations during the start time tended to be low, reflecting the fact that the races were held on Sundays and public holidays. However, 158 races (28%) exceeded the daily average of 15 µg/m 3 for PM 2.5 , an environmental quality standard according to the World Health Organization [ 16 ]. Races with SCA events were more frequent in winter, and air temperature were lower (10.4°C [SD: 5.3] vs. 11.7°C [5.8], p = 0.11). Other meteorological variables were similar between those with and without SCA. For air pollutants, NO 2 was significantly higher in races with SCA events, but no other air pollutants showed significant differences. The median distance between the weather station measuring the temperature and the starting point was 3 km (IQR: 1.7, 6.5) and the median distance between the PM 2.5 measuring station and the starting point was 5 km (IQR: 2.2, 11.9). Table 2 Meteorological variables and air pollution concentrations around the starting point for the overall race and with/without sudden cardiac arrest. Overall (N = 571) non-SCA race (N = 510) SCA race (N = 61) P Meteorological variables Temperature, °C 11.5 (5.8) 11.7 (5.8) 10.4 (5.3) 0.11 Humidity, % 66.4 (16.8) 66.5 (17.0) 65.4 (15.5) 0.63 Solar radiation, MJ/㎡ 1.3 [0.7, 1.7] 1.3 [0.7, 1.8] 1.3 [0.7, 1.6] 0.82 Wind speed, m/s 2.5 (1.7) 2.5 (1.7) 2.5 (1.6) 0.86 Precipitation, mm/h 0 [0, 0] 0 [0, 0] 0 [0, 0] 0.29 Air pressure, hPa 1011 (17) 1011 (18) 1015 (7.1) 0.05 Air pollution variables Fine particulate matter, µg/m 3 10.3 [6.0, 16] 10.3 [6.0, 16] 9.3 [6.5, 14] 0.77 Suspended particulate matter, µg/m 3 13.7 [7.7, 22] 13.7 [7.7, 22] 13.3 [ 8 , 21 ] 0.88 Photochemical oxidants, ppm 27.7 [18, 36] 27.7 [18, 36] 26.7 [17, 34] 0.47 Sulfur dioxide, ppm 1.0 [0.7, 2.7] 1.0 [0.3, 2.7] 1.0 [0.8, 2.8] 0.37 Nitrogen dioxide, ppm 6.0 [3.3, 10.3] 5.7 [3.3, 10] 8.0 [5.0, 15] 0.001 Carbon monoxide, 0.1 ppm 3.7 [2.7, 5.0] 3.7 [2.7, 5.0] 4.0 [3.0, 5.3] 0.09 Values are presented as mean (standard deviation) or median [interquartile range]. SCA, sudden cardiac arrest; MJ, megajoule; hPA, hectopascal; ppm, part per million. Table 3 shows the results of Poisson regression analyses between air quality variables and SCA. A positive relationship was found for air pressure with an incidence risk ratio of 1.02 (95% CI: 1.00, 1.05), but this relationship disappeared after multivariate adjustment. Meanwhile, the multivariate analyses showed a negative linear association of ambient temperature with SCA, with an incidence risk ratio /°C increase of 0.94 (95% CI: 0.89, 0.98) for model 1 and 0.94 (95% CI: 0.89, 0.99) for model 2. Figure 2 shows the results of the non-linear regression analysis between temperature and SCA. Figure 2 shows the results of the non-linear regression analysis between temperature and SCA. A bimodal risk pattern was observed, with a peak at 6.3°C where the IRR reached 2.90 (95% CI: 0.80, 10.5). However, the wide CI across all temperature ranges indicate substantial uncertainty in the estimated risk pattern. Analyses of other variables are provided in Figure S2, with no specific risk trends observed. Table 3 Associations of each meteorological and pollutant measurements with sudden cardiac arrest during marathon Bivariate Multivariate Model 1 Model 2 Incidence risk ratio 95% CI Incidence risk ratio 95% CI Incidence risk ratio 95% CI Temperature, per 1°C increase 0.96 0.92, 1.00 0.94 0.89, 0.98 0.94 0.89, 0.99 Humidity, per 1 hPa increase 1.00 0.98, 1.01 0.99 0.98, 1.01 Solar radiation, per 1 MJ/㎡ increase 0.94 0.67, 1.33 1.08 0.75, 1.56 Wind speed, per 1 m/s increase 0.97 0.83, 1.10 0.95 0.81, 1.09 Precipitation, per 1 mm/h increase 0.96 0.59, 1.29 0.94 0.58, 1.27 Air pressure, per 1 hPa increase 1.02 1.00, 1.05 1.01 0.98, 1.04 Fine particulate matter, per 1 µg/m 3 increase 0.99 0.96, 1.02 0.98 0.95, 1.01 0.98 0.93, 1.03 Suspended particulate matter, per 1 µg/m 3 increase 0.99 0.97, 1.01 0.98 0.96, 1.00 0.99 0.95, 1.03 Photochemical oxidants, per 1 ppm increase 0.99 0.97, 1.01 0.99 0.97, 1.01 0.99 0.97, 1.02 Sulfur dioxide, per 1 ppm increase 1.02 0.90, 1.13 0.98 0.85, 1.11 1.00 0.86, 1.15 Nitrogen dioxide, per 1 ppm increase 1.03 0.99, 1.06 1.01 0.98, 1.04 1.01 0.97, 1.06 Carbon monoxide, per 0.1 ppm increase 1.03 0.93, 1.14 1.02 0.91, 1.12 1.02 0.89, 1.15 Model 1; adjusted for 4 regional divisions in Japan and elevation from sea level, and marathon start time. Model 2; fitted with the temperature and each air pollutant on Model 1. CI, confidence interval; see Table 2 for other abbreviation. Several sensitivity analyses generally showed similar results to the main analysis. First, using WBGT instead of temperature, in multivariate analyses showed similar results (Table S2, Model 1: 0.93, 95% CI: 0.89, 0.98; Model 2: 0.94, 95% CI: 0.89, 0.99). Second, focusing on SCA patients with AED use, the results were also similar (Table S3); none of the variables showed a relationship with the meteorological or air pollutant variables. Third, the lagged exposure models (Figure S3) indicated that temperature showed a consistent negative association with SCA at both Lag 0 and Lag + 3, while PM 2.5 showed a positive association with SCA only at Lag + 3. Other sensitivity analysis, excluding elite-only or women-only races (Table S4), excluding races where the measuring station is more 21.0975 km from the starting point (Table S5), excluding overpopulated cities that hosted a race (Table S6), and adjusted the main analysis for gender and age groups (Table S7) showed similar results to the main analysis, with temperature consistently showing a negative linear relationship with SCA in the multivariate analysis. Overall, these analyses suggest that the main analysis is robust to variations in exposure definitions and data subsets. 4 DISCUSSION 4.1 Main findings We found that lower temperature was significantly associated with increased risk of SCA during races, with a 1°C decrease associated with a 6% increase in risk; however, other meteorological factors and air pollutants were not. Lagged exposure models further indicated that temperatures around the race start time were strongly associated with SCA risk. Although the sensitivity analyses supported the negative association with lower temperature, non-linear regression suggested a potential risk increase at higher temperatures, though the trend was less pronounced. 4.2 Association between ambient temperature and sudden cardiac arrest To the best of our knowledge, this is the first study to suggest the association between cold temperature and SCA during marathons, whereas epidemiological studies in the general population have shown that colder temperatures are associated with a higher incidence of events of myocardial infarction [ 17 ], out-of-hospital cardiac arrest [ 18 ], and ventricular arrhythmias [ 19 ]. In cold environments, physiological responses include vasoconstriction of peripheral tissues via sympathetic activation to maintain core body temperature and the production of body heat by shivering, with a concomitant increase in blood pressure and heart rate, leading to an increased cardiac burden [ 20 ]. Furthermore, hypothermia prolongs the QT interval on the electrocardiogram, contributing to increased electrical susceptibility [ 21 ]. A 1°C drop in body temperature leads to a 2% increase in hematocrit [ 22 ], which may also contribute to hemorheology. The physiological response to exercise in a cold environment is more complex. Although peripheral tissue blood flow is essentially reduced to maintain core body temperature, the increased oxygen demand of active muscles results in passive blood deprivation that antagonizes the redistribution of blood flow. If there is organic stenosis in the coronary arteries, which is known to develop with repeated endurance exercise training [ 23 ], it may be predisposed to induce relative myocardial ischemia. In fact, a Japanese study, in which some SCA cases overlap with the present study, have reported that approximately half of cases with SCA during long-distance road races derived from culprit coronary lesion [ 3 ]. Moreover, William et al. performed in vivo in-human experiments in which a wire with a pressure sensor was placed in the coronary artery and subjected to a cycling exercise after inhaling cold air of -15°C [ 24 ]. Normally, exercise decreases myocardial microcirculatory vascular resistance and increases coronary artery flow, but in patients with coronary artery stenosis, cold air inhalation caused increased myocardial afterload and myocardial diastolic dysfunction, resulting in a blunted reduction in myocardial microcirculatory vascular resistance and an inadequate coronary blood flow supply on demand, leading to subendocardial ischemia. Thus, endurance exercise in a cold environment may exacerbate myocardial ischemia, even in the absence of typical acute coronary obstruction or thrombosis. Although the main analysis identified colder temperatures as a significant risk factor, the non-linear regression analysis suggested a potential, albeit less pronounced, increase in SCA risk at temperatures above 15°C. This observation indicates that the physiological strain induced by higher temperatures could still contribute to cardiovascular stress in marathon runners. 4.3 Association of air pollutants with sudden cardiac arrest This study did not identify a statistically significant connection between exposure to air pollutants and the SCA risk. Nevertheless, previous study has demonstrated that higher concentration of particulate and gaseous pollutants are associated with an increased risk of acute myocardial infarction [ 25 , 26 ] and out-of-hospital cardiac arrest [ 27 , 28 ] in the general population. Air quality remains a critical consideration during endurance events, because pollutants such as particulate matter have effects on marathon performance in athletes [ 29 ]. A study of Gerardin et al. observed a higher incidence of cardiac events in road races with elevated pollution indices comprising NO 2 , SO 2 , O 3 and PM 10 [ 30 ]. Although our study provides a more focused analysis of SCA, the low levels of air pollution during the events we examined may partly explain the lack of significant findings. Further research is warranted to clarify the potential role of air pollutants under different conditions. 4.4 Implications of this study for current challenges in sports Cold-weather marathons present both advantages and disadvantages, always accompanied by a dilemma, in that cooler temperatures can enhance marathon running performance [ 31 ], but they are associated with an increased risk of SCA. Meanwhile, the rising prevalence of marathons held in hot conditions due to global warming has raised concerns about the appropriateness of such events, given the heightened risk of heat-related illnesses. Considering this context, it is not a feasible strategy to adjust marathon dates or start times solely to avoid the risk of SCA. Instead, the findings of this study should guide organizers, medical personnel, volunteers, and other stakeholders to improve their preparations. A shared understanding that colder temperatures are associated with SCA risk allows more effective first aid planning and resource allocation, ultimately ensuring safety for all runners. 4.5 Limitations There are several limitations to this study. First, the present study only included Japanese races and the participants were almost exclusively Japanese. Therefore, the generalizability of our findings to other countries may be limited. There may be racial/ethnic differences in the risk of SCA, although previous literature shows that the incidence of SCA during road racing has been similar both in the East and the West, over a period of about 30 years [ 1 – 4 ]. In addition, concentrations of air pollutants were low in most marathons in Japan, which may partly explain the non-significant association between air pollution and the SCA risk. However, the air pollution levels reported in this study, particularly for PM 2.5 , are comparable to those observed at major marathons in the United States [ 32 ]. Moreover, the study included a small proportion of marathons conducted during high temperatures. Therefore, the relationship between high temperature and SCA could not be fully addressed. Finally, we were unable to collect information on background diseases (such as ischemic heart disease or cardiomyopathy) or other individual-level factors (such as smoking status, body weight, overall physical fitness, socioeconomic position, ethnicity, and additional environmental exposure, as well as body temperature during the event) in runners participating in races. We have assumed that the background of runners is less heterogeneous across races based on the distribution of age groups and gender. A sensitivity analysis restricted to races adjusted for these covariates also produced similar results to the main analysis. 5 CONCLUSION During the study period, 1.63 SCA cases per 100,000 runners occurred during marathons. Lower temperature around the start of the race was associated with an increased risk of SCA. Additionally, no statistically significant correlation between air pollutant concentrations and SCA risk was observed. Declarations Ethics approval and consent to participate: This study was approved by the Ethics Review Committee of the Sports Medicine Research Centre, Keio University (No. 2013-03). Consent for publication: Not applicable Competing interests: The authors declare that they have no competing interests. Funding: There are no research funds in this study. Authors' contributions: JK contributed to the conception of the study, data analysis and interpretation, and drafting of the manuscript. TM and FY contributed to data collection and analysis. MI contributed to data modelling and revision of the manuscript. YH contributed to data analysis and final review of the manuscript. All authors read and approved the final manuscript. Acknowledgements: We would like to thank Dr. Tomohiro Shinozaki in the Tokyo University of Science and Dr. Yoshihisa Miyamoto in the National Cancer Centre for their statistical advice. Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Gerardin B, Collet JP, Mustafic H, Bellemain-Appaix A, Benamer H, Monsegu J, Teiger E, Livarek B, Jaffry M, Lamhaut L, Fleischel C, Aubry P. Registry on acute cardiovascular events during endurance running races: the prospective RACE Paris registry. 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Williams RP, Asrress KN, Lumley M, Arri S, Patterson T, Ellis H, Manou-Stathopoulou V, Macfarlane C, Chandran S, Moschonas K, Oakeshott P, Lockie T, Chiribiri A, Clapp B, Perera D, Plein S, Marber MS, Redwood SR. Deleterious Effects of Cold Air Inhalation on Coronary Physiological Indices in Patients With Obstructive Coronary Artery Disease. J Am Heart Assoc. 2018;7:e008837. Mustafic H, Jabre P, Caussin C, Murad MH, Escolano S, Tafflet M, Périer MC, Marijon E, Vernerey D, Empana JP, Jouven X. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. JAMA. 2012;307:713–21. Chen R, Jiang Y, Hu J, Chen H, Li H, Meng X, Ji JS, Gao Y, Wang W, Liu C, Fang W, Yan H, Chen J, Wang W, Xiang D, Su X, Yu B, Wang Y, Xu Y, Wang L, Li C, Chen Y, Bell ML, Cohen AJ, Ge J, Huo Y, Kan H. Hourly Air Pollutants and Acute Coronary Syndrome Onset in 1.29 Million Patients. Circulation. 2022;145:1749–60. Zhao B, Johnston FH, Salimi F, Kurabayashi M, Negishi K. Short-term exposure to ambient fine particulate matter and out-of-hospital cardiac arrest: a nationwide case-crossover study in Japan. Lancet Planet Health. 2020;4:e15–23. Kojima S, Michikawa T, Matsui K, Ogawa H, Yamazaki S, Nitta H, Takami A, Ueda K, Tahara Y, Yonemoto N, Nonogi H, Nagao K, Ikeda T, Sato N, Tsutsui H. Association of Fine Particulate Matter Exposure With Bystander-Witnessed Out-of-Hospital Cardiac Arrest of Cardiac Origin in Japan. JAMA Netw Open. 2020;3:e203043. Marr LC, Ely MR. Effect of air pollution on marathon running performance. Med Sci Sports Exerc. 2010. 10.1249/MSS.0b013e3181b84a85 . https://www.doi.org/ . Gerardin B, Guedeney P, Bellemain-Appaix A, Levasseur T, Mustafic H, Benamer H, Monsegu J, Lamhaut L, Montalescot G, Aubry P, Collet JP. Life-threatening and major cardiac events during long-distance races: updates from the prospective RACE PARIS registry with a systematic review and meta-analysis. Eur J Prev Cardiol. 2021;28:679–86. El Helou N, Tafflet M, Berthelot G, Tolaini J, Marc A, Guillaume M, Hausswirth C, Toussaint JF. Impact of environmental parameters on marathon running performance. PLoS ONE. 2012;7:e37407. Fleury ES, Bittker GS, Just AC, Braun JM. Running on fumes: An analysis of fine particulate matter's impact on finish times in nine major US marathons, 2003–2019. Sports Med. 2025;55:1505–14. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7045064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530907153,"identity":"67a22fcf-9f24-474d-8238-df78fca301f3","order_by":0,"name":"Jo 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12:08:13","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125366,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7045064/v1/eba28812137f4bf0bbdc34b2.html"},{"id":94824019,"identity":"bbbdd883-72a5-4681-bc81-8f5be6e3e1f7","added_by":"auto","created_at":"2025-10-31 06:48:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":398946,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Japan Association of Athletics Federations-certified marathons and four regional divisions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRed dots indicate the location of each marathon. Marathons held in close proximity omit one or the other. The four regional divisions followed the Japanese division method based on general seasonal forecasts.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-7045064/v1/86c7e94664f30deaca0367ab.png"},{"id":94761573,"identity":"2ee15a97-a6dc-453a-b951-d668fd474fce","added_by":"auto","created_at":"2025-10-30 12:08:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-linear relationship between temperature and the risk of sudden cardiac arrests during marathons.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe risk curve for sudden cardiac arrest (SCA) is shown in blue, and the 95% confidence interval (CI) is shown in light blue. The vertical axis depicts the incidence rate ratio (IRR) of SCA for each 1°C change from the baseline temperature (median), adjusted for Japanese regional division, elevation, and start time. Shown below are the distributions of temperatures for all races, with black vertical lines indicating individual races. Races with SCA are marked by colored vertical lines, with orange representing one case, red for two cases, and dark red for three cases.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-7045064/v1/b80d0aa2a68b7192d2284aab.png"},{"id":98426021,"identity":"c6d017d4-d4ad-458a-9649-6c01adc3b700","added_by":"auto","created_at":"2025-12-17 16:35:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414110,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7045064/v1/35a27054-2765-46d2-91e8-37052c411590.pdf"},{"id":94761584,"identity":"4e506189-ff8e-4761-82b1-07592cdcf1bf","added_by":"auto","created_at":"2025-10-30 12:08:12","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2434539,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7045064/v1/cdd2a012afca29e374dea129.docx"}],"financialInterests":"","formattedTitle":"Meteorological and Environmental Factors Associated with Sudden Cardiac Arrest during Marathons in Japan","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eSudden cardiac arrest (SCA) during sporting activities is considered as tragic in that a hitherto fit athlete suddenly runs the risk of death. SCA events in road races, including marathons, occur in about 0.5 to 2 cases per 100,000 runners [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], garnering much public attention. The Race Associated Cardiac Arrest Event Registry (RACER 2) study [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], involving 29\u0026nbsp;million participants in the US, recently reported that nearly half of SCA cases were attributed to coronary artery disease, aligning with findings from other national registries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To date, most studies on etiology have focused on the characteristics of individual runners, and not on external or environmental factors. In general, meteorological variables and air pollutants are associated with cardiovascular diseases and deaths [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, to the best of our knowledge, the association between these variables and SCA during sports activities including running has not been fully studied.\u003c/p\u003e\u003cp\u003eRoad races are characterized by large numbers of runners experiencing the same air conditions. This provides a valuable opportunity for a comprehensive assessment of the relationship between environmental exposure factors and SCA during exercise. By linking a nationwide registry of SCA during marathon in the Japan Association of Athletics Federations (JAAF) with the Japan Meteorological Agency and the National Institute for Environmental Studies databases, we examined whether any meteorological and air pollutant variable(s) were associated with the risk of SCA.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Overview\u003c/h2\u003e\u003cp\u003eThis is an observational study with a nationwide prospective using the SCA registry during marathon races. The reporting of the studies complied with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study was approved by the Institutional Ethics Committee of the Sports Medicine Research Centre, Keio University (No. 2013-03); individual consent was waived as the study does not use personal patient identifiers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data sources\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Study population\u003c/h2\u003e\u003cp\u003eJAAF certifies more than 70 full marathon races annually in 42 of the 47 prefectures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As reported, the JAAF Medical Committee has been involved in an SCA registry study since April 2011 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Members of the medical committee mailed questionnaires to race offices after races to investigate medical activities. In addition, local newspapers and internet news articles were reviewed; any discrepancies were resolved by contacting race offices directly. SCA was defined as a runner collapsing suddenly between the start and one hour after the end of races and receiving life-saving treatment such as chest compressions or an automated external defibrillator (AED). To maximize questionnaire responses, the causes of cardiac arrest were not pursued.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe number of marathon participants was defined as the number of runners who crossed the start line. Age group data were collected for about half the races (290 races). For races with missing demographic data but available registrant or finisher lists, those distributions were used to estimate participants\u0026rsquo; age groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Participants were categorized as under 40 (\u0026lt;\u0026thinsp;40s), aged 40\u0026ndash;59 (40\u0026ndash;50s) and over 60 (\u0026ge;\u0026thinsp;60s). Gender data were based on participants\u0026rsquo; self-reported registration information.\u003c/p\u003e\u003cp\u003eThe route of the JAAF-certified races follows World Athletics rules [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], requiring the straight-line distance between the start and finish points to be less than 50% of the total route. Thus, air quality at the start was assumed to represent the entire air route.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Meteorological and air pollutant variables\u003c/h2\u003e\u003cp\u003eWeather data were obtained from the Japan Meteorological Agency, which operates 56 meteorological observatories and approximately 1,300 automated meteorological data acquisition system stations across Japan [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hourly observations of ambient temperature, relative humidity, solar radiation, wind speed, precipitation, and air pressure were extracted. Air pollutant concentrations, hourly observations of particulate pollutants (fine particulate matter [PM\u003csub\u003e2.5\u003c/sub\u003e] and suspended particulate matter [SPM]) and gaseous pollutants (photochemical oxidants [Ox], sulphur dioxide [SO\u003csub\u003e2\u003c/sub\u003e], nitrogen dioxide [NO\u003csub\u003e2\u003c/sub\u003e], and carbon monoxide [CO]) were retrieved from the National Institute for Environmental Studies database [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. There were approximately 1,900 air pollution monitoring stations distributed throughout Japan, the respective observations were based on measurements at ambient air quality monitoring stations (AAQMS), which are representative of the air quality in the area. For CO, measurements from roadside stations were included due to limited AAQMS availability nearby. If data were missing for more than three consecutive hours at the nearest station, the next nearest station\u0026rsquo;s data were used. If an observation was missing for up to two consecutive hours, the average value of the previous and following observations was used. These processes allowed missing observations to be completely eliminated, while the imputation during the race time was almost negligible as it accounted for less than 0.1% of the total observations.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Main analysis\u003c/h2\u003e\u003cp\u003eObserved meteorological and air pollutant data were both indexed to the start time of the race day and averaged over a three-hour period, including one hour before and after the start of the race. According to the distribution of the data, they were summarized as mean and standard deviation (SD), median and interquartile range (IQR). Data for races with and without cases of SCA were compared using the t-test or Wilcoxon signed rank tests for continuous variables, while categorical variables were compared using the chi-squared or Fisher\u0026rsquo;s exact tests, as appropriate. The main analysis was based on a generalized linear model with a Poisson distribution, for the number of SCA cases in the race, offset by the logarithm of the number of race participants. Given the low incidence of SCA events, the Poisson regression model was chosen for its simplicity and interpretability in estimating event rates across the study cohort. Results are presented as point estimates of incidence risk ratios and their 95% confidence intervals (95% CI), calculated per 1-unit increase for all covariates except CO, which is calculated per 0.1 ppm increase. In addition to the bivariate analysis, a multivariate analysis was performed incorporating the following covariates. Model 1 adjusted for the four Japanese regional divisions (north, east, west, and Okinawa [Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]), elevation from sea level, and marathon start time. In addition to the covariates in Model 1, Model 2 included temperature and all air pollutants collectively. To account for potential non-linear relationships, natural cubic splines with 3 degrees of freedom were applied to all variables. The reference values were set at the median (50th percentile) of each variable distribution, with knots placed at the 25th and 75th percentiles.\u003c/p\u003e\u003cp\u003eFor simple comparisons, values of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant obtained using a two-tailed test, and p-values were not indicated for estimating relationships due to their exploratory nature. These statistical evaluations were computed using JMP\u0026reg; version 11 software (SAS Institute Inc., Cary, NC, USA). Non-linear regression was calculated using R, version 4.5.0 (R Foundation, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eWe performed several further analyses with the aim of validating the accuracy of the main analysis. First, the air temperature parameters were converted to wet bulb globe temperatures (WBGT), which is used for stratification of thermal health risks in road racing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although WBGT was not directly measured using instruments, the estimation formula by Ono et al [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. was adopted as a surrogate (Supplementary methods). Second, to consider only those cases of SCA with cardiogenic origin, we repeated the analysis with the outcome restricted to cases with AED use. Third, to assess potential lags in time phases, instead of using the 3-hour average including the start as the reference frame (Lag 0) for the main analysis, we used average values from 7 to 5 hours before (Lag \u0026minus;\u0026thinsp;6), 4 to 2 hours before (Lag \u0026minus;\u0026thinsp;3), 2 to 4 hours after (Lag\u0026thinsp;+\u0026thinsp;3) and 5 to 7 hours after (Lag\u0026thinsp;+\u0026thinsp;6) the start time of the race as an exposure in the regression model. Fourth, with the aim of eliminating runner background bias, elite races with a 40 km barrier of less than 4 hours, as well as women-only races, were excluded. Fifth, to account for possible geographical noise, races, where the monitoring station was more than 21.0975 km from the starting point, were excluded. Sixth, we excluded host cities for races with population densities exceeding 5000 inhabitants per square km in order to exclude potential confounding by unmeasured pollutants. Seventh, gender (the number of log-transformed male participants) and age groups (the number of log-transformed participants under 40 and over 60 years) were included in the main analysis to adjust for runner demographics for each race.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003eBetween April 2011 and March 2020, 571 JAAF-certified full marathon races were held (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a total of 4,528,134 runners involved. Data on the distribution of gender and age groups for each race were available for 95 and 74%, respectively, suggesting that there were no appreciable variations in the demographic composition of runners among events (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline race characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJAAF-certified marathons\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;571)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Participants, n\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6258 (2028, 11520)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group\u003csup\u003ea\u003c/sup\u003e, %\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;40s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.0 (29.2, 38.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;50s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56.5 (52.8, 60.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.0 (6.8, 11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, %\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.7 (80.6, 88.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompletion rate\u003csup\u003eb\u003c/sup\u003e, %\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.9 (84.0, 93.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003csup\u003ec\u003c/sup\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAutumn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e218 (38.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWinter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e208 (36.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e131 (22.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSummer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional division\u003csup\u003ed\u003c/sup\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63 (11.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205 (35.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e285 (49.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOkinawa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStart time, hour:minute\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00 (9:00, 10:00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatitude at starting site\u003csup\u003ee\u003c/sup\u003e, degree\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.0 (33.7, 36.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitude at starting site\u003csup\u003ee\u003c/sup\u003e, degree\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135.9 (132.8, 139.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation from sea level, m\u003c/p\u003e\u003cp\u003emedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (3, 52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003e544 races (95%) were included. \u0026dagger;422 races (74%) were included. \u003csup\u003eb\u003c/sup\u003eThe ratio of the number of finishers to the number of participants. \u003csup\u003ec\u003c/sup\u003eAutumn was defined as September to November, winter as December to February, spring as March to May and summer as June to August. \u003csup\u003ed\u003c/sup\u003eRefer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003csup\u003ee\u003c/sup\u003eNumerical values are expressed in the 100 decimal system.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eJAAF, Japan Association of Athletics Federations; IQR, inter-quartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOf the 4.53\u0026nbsp;million participants, 74 SCA cases occurred in 61 races (rate per 100 000; 1.63 [95% CI: 1.26, 2.01]); the median age of SCA patients was 52 years (IQR: 44, 61), and 69 (93%) were male. Shockable rhythms were detected by the AED and defibrillated in 55 (74%). Only one death occurred, resulting in a survival rate of approximately 99% for all SCA cases (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA summary of the meteorological and air pollution data in each race is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The marathons were often held in the early morning hours of autumn and winter, with an average temperature of 11.5\u0026deg;C (SD: 5.8). Only 25 races (4%) exceeded 20.5\u0026deg;C by WBGT, which is regarded as the \"do not start\" criterion for marathons [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Air pollutant concentrations during the start time tended to be low, reflecting the fact that the races were held on Sundays and public holidays. However, 158 races (28%) exceeded the daily average of 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e2.5\u003c/sub\u003e, an environmental quality standard according to the World Health Organization [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Races with SCA events were more frequent in winter, and air temperature were lower (10.4\u0026deg;C [SD: 5.3] vs. 11.7\u0026deg;C [5.8], p\u0026thinsp;=\u0026thinsp;0.11). Other meteorological variables were similar between those with and without SCA. For air pollutants, NO\u003csub\u003e2\u003c/sub\u003e was significantly higher in races with SCA events, but no other air pollutants showed significant differences. The median distance between the weather station measuring the temperature and the starting point was 3 km (IQR: 1.7, 6.5) and the median distance between the PM\u003csub\u003e2.5\u003c/sub\u003e measuring station and the starting point was 5 km (IQR: 2.2, 11.9).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeteorological variables and air pollution concentrations around the starting point for the overall race and with/without sudden cardiac arrest.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;571)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-SCA race\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;510)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSCA race\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeteorological variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, \u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.5 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.4 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHumidity, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.4 (16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.5 (17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.4 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolar radiation, MJ/㎡\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 [0.7, 1.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 [0.7, 1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3 [0.7, 1.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed, m/s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation, mm/h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 [0, 0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 [0, 0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 [0, 0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pressure, hPa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1011 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1011 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1015 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAir pollution variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFine particulate matter, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.3 [6.0, 16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.3 [6.0, 16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3 [6.5, 14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuspended particulate matter, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.7 [7.7, 22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7 [7.7, 22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.3 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhotochemical oxidants, ppm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.7 [18, 36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.7 [18, 36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.7 [17, 34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSulfur dioxide, ppm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 [0.7, 2.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 [0.3, 2.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 [0.8, 2.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrogen dioxide, ppm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 [3.3, 10.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.7 [3.3, 10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0 [5.0, 15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon monoxide, 0.1 ppm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.7 [2.7, 5.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.7 [2.7, 5.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0 [3.0, 5.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are presented as mean (standard deviation) or median [interquartile range]. SCA, sudden cardiac arrest; MJ, megajoule; hPA, hectopascal; ppm, part per million.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of Poisson regression analyses between air quality variables and SCA. A positive relationship was found for air pressure with an incidence risk ratio of 1.02 (95% CI: 1.00, 1.05), but this relationship disappeared after multivariate adjustment. Meanwhile, the multivariate analyses showed a negative linear association of ambient temperature with SCA, with an incidence risk ratio /\u0026deg;C increase of 0.94 (95% CI: 0.89, 0.98) for model 1 and 0.94 (95% CI: 0.89, 0.99) for model 2. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the non-linear regression analysis between temperature and SCA. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the non-linear regression analysis between temperature and SCA. A bimodal risk pattern was observed, with a peak at 6.3\u0026deg;C where the IRR reached 2.90 (95% CI: 0.80, 10.5). However, the wide CI across all temperature ranges indicate substantial uncertainty in the estimated risk pattern. Analyses of other variables are provided in Figure S2, with no specific risk trends observed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of each meteorological and pollutant measurements with sudden cardiac arrest during marathon\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncidence risk ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncidence risk ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncidence risk ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature,\u003c/p\u003e\u003cp\u003eper 1\u0026deg;C increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92, 1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89, 0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89, 0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHumidity,\u003c/p\u003e\u003cp\u003eper 1 hPa increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolar radiation,\u003c/p\u003e\u003cp\u003eper 1 MJ/㎡ increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67, 1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75, 1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWind speed,\u003c/p\u003e\u003cp\u003eper 1 m/s increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83, 1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81, 1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation,\u003c/p\u003e\u003cp\u003eper 1 mm/h increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59, 1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58, 1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pressure,\u003c/p\u003e\u003cp\u003eper 1 hPa increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00, 1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98, 1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFine particulate matter,\u003c/p\u003e\u003cp\u003eper 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96, 1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.95, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.93, 1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuspended particulate matter,\u003c/p\u003e\u003cp\u003eper 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96, 1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95, 1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhotochemical oxidants,\u003c/p\u003e\u003cp\u003eper 1 ppm increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97, 1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97, 1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSulfur dioxide,\u003c/p\u003e\u003cp\u003eper 1 ppm increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90, 1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.85, 1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86, 1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrogen dioxide,\u003c/p\u003e\u003cp\u003e per 1 ppm increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99, 1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.98, 1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97, 1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon monoxide,\u003c/p\u003e\u003cp\u003e per 0.1 ppm increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93, 1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91, 1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89, 1.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1; adjusted for 4 regional divisions in Japan and elevation from sea level, and marathon start time. Model 2; fitted with the temperature and each air pollutant on Model 1. CI, confidence interval; see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for other abbreviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSeveral sensitivity analyses generally showed similar results to the main analysis. First, using WBGT instead of temperature, in multivariate analyses showed similar results (Table S2, Model 1: 0.93, 95% CI: 0.89, 0.98; Model 2: 0.94, 95% CI: 0.89, 0.99). Second, focusing on SCA patients with AED use, the results were also similar (Table S3); none of the variables showed a relationship with the meteorological or air pollutant variables. Third, the lagged exposure models (Figure S3) indicated that temperature showed a consistent negative association with SCA at both Lag 0 and Lag\u0026thinsp;+\u0026thinsp;3, while PM\u003csub\u003e2.5\u003c/sub\u003e showed a positive association with SCA only at Lag\u0026thinsp;+\u0026thinsp;3. Other sensitivity analysis, excluding elite-only or women-only races (Table S4), excluding races where the measuring station is more 21.0975 km from the starting point (Table S5), excluding overpopulated cities that hosted a race (Table S6), and adjusted the main analysis for gender and age groups (Table S7) showed similar results to the main analysis, with temperature consistently showing a negative linear relationship with SCA in the multivariate analysis. Overall, these analyses suggest that the main analysis is robust to variations in exposure definitions and data subsets.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4 DISCUSSION","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Main findings\u003c/h2\u003e\u003cp\u003eWe found that lower temperature was significantly associated with increased risk of SCA during races, with a 1\u0026deg;C decrease associated with a 6% increase in risk; however, other meteorological factors and air pollutants were not. Lagged exposure models further indicated that temperatures around the race start time were strongly associated with SCA risk. Although the sensitivity analyses supported the negative association with lower temperature, non-linear regression suggested a potential risk increase at higher temperatures, though the trend was less pronounced.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Association between ambient temperature and sudden cardiac arrest\u003c/h2\u003e\u003cp\u003eTo the best of our knowledge, this is the first study to suggest the association between cold temperature and SCA during marathons, whereas epidemiological studies in the general population have shown that colder temperatures are associated with a higher incidence of events of myocardial infarction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], out-of-hospital cardiac arrest [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and ventricular arrhythmias [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In cold environments, physiological responses include vasoconstriction of peripheral tissues via sympathetic activation to maintain core body temperature and the production of body heat by shivering, with a concomitant increase in blood pressure and heart rate, leading to an increased cardiac burden [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, hypothermia prolongs the QT interval on the electrocardiogram, contributing to increased electrical susceptibility [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A 1\u0026deg;C drop in body temperature leads to a 2% increase in hematocrit [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which may also contribute to hemorheology. The physiological response to exercise in a cold environment is more complex. Although peripheral tissue blood flow is essentially reduced to maintain core body temperature, the increased oxygen demand of active muscles results in passive blood deprivation that antagonizes the redistribution of blood flow. If there is organic stenosis in the coronary arteries, which is known to develop with repeated endurance exercise training [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], it may be predisposed to induce relative myocardial ischemia. In fact, a Japanese study, in which some SCA cases overlap with the present study, have reported that approximately half of cases with SCA during long-distance road races derived from culprit coronary lesion [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, William et al. performed \u003cem\u003ein vivo\u003c/em\u003e in-human experiments in which a wire with a pressure sensor was placed in the coronary artery and subjected to a cycling exercise after inhaling cold air of -15\u0026deg;C [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Normally, exercise decreases myocardial microcirculatory vascular resistance and increases coronary artery flow, but in patients with coronary artery stenosis, cold air inhalation caused increased myocardial afterload and myocardial diastolic dysfunction, resulting in a blunted reduction in myocardial microcirculatory vascular resistance and an inadequate coronary blood flow supply on demand, leading to subendocardial ischemia. Thus, endurance exercise in a cold environment may exacerbate myocardial ischemia, even in the absence of typical acute coronary obstruction or thrombosis.\u003c/p\u003e\u003cp\u003eAlthough the main analysis identified colder temperatures as a significant risk factor, the non-linear regression analysis suggested a potential, albeit less pronounced, increase in SCA risk at temperatures above 15\u0026deg;C. This observation indicates that the physiological strain induced by higher temperatures could still contribute to cardiovascular stress in marathon runners.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Association of air pollutants with sudden cardiac arrest\u003c/h2\u003e\u003cp\u003eThis study did not identify a statistically significant connection between exposure to air pollutants and the SCA risk. Nevertheless, previous study has demonstrated that higher concentration of particulate and gaseous pollutants are associated with an increased risk of acute myocardial infarction [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and out-of-hospital cardiac arrest [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] in the general population. Air quality remains a critical consideration during endurance events, because pollutants such as particulate matter have effects on marathon performance in athletes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A study of Gerardin et al. observed a higher incidence of cardiac events in road races with elevated pollution indices comprising NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although our study provides a more focused analysis of SCA, the low levels of air pollution during the events we examined may partly explain the lack of significant findings. Further research is warranted to clarify the potential role of air pollutants under different conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Implications of this study for current challenges in sports\u003c/h2\u003e\u003cp\u003eCold-weather marathons present both advantages and disadvantages, always accompanied by a dilemma, in that cooler temperatures can enhance marathon running performance [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but they are associated with an increased risk of SCA. Meanwhile, the rising prevalence of marathons held in hot conditions due to global warming has raised concerns about the appropriateness of such events, given the heightened risk of heat-related illnesses. Considering this context, it is not a feasible strategy to adjust marathon dates or start times solely to avoid the risk of SCA. Instead, the findings of this study should guide organizers, medical personnel, volunteers, and other stakeholders to improve their preparations. A shared understanding that colder temperatures are associated with SCA risk allows more effective first aid planning and resource allocation, ultimately ensuring safety for all runners.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Limitations\u003c/h2\u003e\u003cp\u003eThere are several limitations to this study. First, the present study only included Japanese races and the participants were almost exclusively Japanese. Therefore, the generalizability of our findings to other countries may be limited. There may be racial/ethnic differences in the risk of SCA, although previous literature shows that the incidence of SCA during road racing has been similar both in the East and the West, over a period of about 30 years [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, concentrations of air pollutants were low in most marathons in Japan, which may partly explain the non-significant association between air pollution and the SCA risk. However, the air pollution levels reported in this study, particularly for PM\u003csub\u003e2.5\u003c/sub\u003e, are comparable to those observed at major marathons in the United States [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, the study included a small proportion of marathons conducted during high temperatures. Therefore, the relationship between high temperature and SCA could not be fully addressed. Finally, we were unable to collect information on background diseases (such as ischemic heart disease or cardiomyopathy) or other individual-level factors (such as smoking status, body weight, overall physical fitness, socioeconomic position, ethnicity, and additional environmental exposure, as well as body temperature during the event) in runners participating in races. We have assumed that the background of runners is less heterogeneous across races based on the distribution of age groups and gender. A sensitivity analysis restricted to races adjusted for these covariates also produced similar results to the main analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eDuring the study period, 1.63 SCA cases per 100,000 runners occurred during marathons. Lower temperature around the start of the race was associated with an increased risk of SCA. Additionally, no statistically significant correlation between air pollutant concentrations and SCA risk was observed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003e This study was approved by the Ethics Review Committee of the Sports Medicine Research Centre, Keio University (No. 2013-03).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThere are no research funds in this study.\u003c/p\u003e\u003ch2\u003eAuthors' contributions:\u003c/h2\u003e\u003cp\u003eJK contributed to the conception of the study, data analysis and interpretation, and drafting of the manuscript. TM and FY contributed to data collection and analysis. MI contributed to data modelling and revision of the manuscript. YH contributed to data analysis and final review of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eWe would like to thank Dr. Tomohiro Shinozaki in the Tokyo University of Science and Dr. Yoshihisa Miyamoto in the National Cancer Centre for their statistical advice.\u003c/p\u003e\u003ch2\u003eAvailability of data and material:\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGerardin B, Collet JP, Mustafic H, Bellemain-Appaix A, Benamer H, Monsegu J, Teiger E, Livarek B, Jaffry M, Lamhaut L, Fleischel C, Aubry P. 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Sports Med. 2025;55:1505\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sudden cardiac arrest, marathon running, ambient temperature, air pollution","lastPublishedDoi":"10.21203/rs.3.rs-7045064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7045064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSudden cardiac arrest (SCA) during marathons is a rare but critical event. While underlying cardiovascular conditions have been linked to SCA, the role of environmental factors, such as ambient air quality and meteorological conditions, remains unclear. We conducted a nationwide study in Japan to examine the association between meteorological and air pollution variables and the occurrence of SCA during marathons. Data from approximately 4.53\u0026nbsp;million runners participating in full marathons certified by the Japan Association of Athletics Federations from April 2011 to March 2020 were analyzed. SCA cases were linked to meteorological variables (temperature, humidity, solar radiation, wind speed, precipitation, and air pressure) and air pollutants (particulate matter and gaseous pollutants).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 4.53\u0026nbsp;million runners, 74 SCA cases were identified. Poisson regression analysis showed that lower ambient temperatures at race start were significantly associated with increased SCA risk (adjusted incidence risk ratio per 1\u0026deg;C decrease: 1.06; 95% confidence interval: 1.02\u0026ndash;1.12). No significant associations were found between air pollutants and SCA risk.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLower temperatures at the start of marathons were associated with a higher risk of SCA, while no significant correlation was observed with air pollutants. These findings suggest that temperature may be an important environmental factor influencing the risk of SCA during marathons.\u003c/p\u003e","manuscriptTitle":"Meteorological and Environmental Factors Associated with Sudden Cardiac Arrest during Marathons in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 12:08:07","doi":"10.21203/rs.3.rs-7045064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"463da6d0-3fee-46c3-9815-74b51d46d5c5","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T22:29:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 12:08:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7045064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7045064","identity":"rs-7045064","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}