Prevalence and Impact of Chronic Mountain Sickness on Quality of Life Among Urban Residents in Northwestern Sichuan, China: A Cross-Sectional Study

Prevalence and Impact of Chronic Mountain Sickness on Quality of Life Among Urban Residents in Northwestern Sichuan, China: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prevalence and Impact of Chronic Mountain Sickness on Quality of Life Among Urban Residents in Northwestern Sichuan, China: A Cross-Sectional Study Hong Chang, Jiawei Zhou, Bin Peng, Yuxing Liu, Miaomiao Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7033332/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract With rapid urbanization in high-altitude regions, the health impacts of chronic hypoxia on plateau urban residents remain understudied. This study aims to determine the prevalence of chronic mountain sickness (CMS) and its associated factors among urban residents of the Northwestern Sichuan Plateau and further assess the health-related quality of life (HRQoL) in this population. A cross-sectional survey study was conducted in urban residents of the Northwestern Sichuan Plateau aged ≥20 years residing at an altitude of 3500 to 4000 meters. Demographics, physiological parameters, hematological parameters, specified symptoms of CMS were recorded. CMS was diagnosed using the Qinghai CMS score criteria. Propensity score matching (1:3) was performed to adjust for age differences between the CMS group and the group without CMS. A segmented linear regression model was employed to identify the hemoglobin threshold associated with CMS. Multivariable conditional logistic regression was used to evaluate independent associated factors for CMS after age matching. 12-Item Short Form Health Survey (SF-12) was employed to assess the HRQoL in this population. Prevalence of CMS was 20.5% (95% CI: 16.2%-24.8%). Female and hemoglobin (Hb) levels ≥ 186g/L were independent associated factors for CMS after controlling for age with odds ratio (OR) as 3.02 (95% CI: 1.19–7.64) and OR 4.76 (95% CI: 1.41–16.06), respectively. The SF-12 assessment demonstrated significantly impaired physical and mental health status in CMS patients, with a more pronounced deterioration in physical health components. This study enhances understanding of CMS burden and associated factors in plateau urban populations, supporting sustainable development in these areas. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Chronic mountain sickness Urban residents The Northwestern Sichuan Plateau Health-related quality of life Figures Figure 1 Introduction Chronic mountain sickness (CMS) is a frequent and potentially fatal chronic condition caused by hypoxia in high-altitude population. It is characterized by excessive erythrocytosis, severe hypoxemia, and, in some cases, moderate to severe pulmonary hypertension. Patients of CMS are disabled with impaired memory, headache, breathlessness, fatigue, disturbed sleep, anorexia, and tinnitus that affect the quality of life. Currently, the most effective treatment for CMS is relocation of the patient to a lower elevation, but this is seldom feasible due to the socioeconomic. The prevalence of CMS varies considerably across the world, influenced by multiple factors such as altitude of residence, ethnicity, duration of high-altitude residence, among others [ 1 ]. The prevalence of CMS ranges from 5–33% [ 2 ]. Based on the Qinghai Scoring System published in 2004, a study reported that prevalence of CMS among natives of Spiti Valley in the Greater Himalayas residing at an altitude of 3000−4200 meters was 28.7% [ 3 ]. With the progression of urbanization in high-altitude regions, a distinct gradient in socioeconomic status and lifestyle patterns exists between urban populations and pastoral populations. Previous research has indicated that urbanization and associated changes in lifestyle also could have role in development of CMS [ 4 ]. Moreover, studies have indicated that the mean Hb concentration of urban populations was higher than that of rural populations in the Andes and the Himalayas, with concomitant differences in oxygen saturation levels [ 5 ]. Thus, measuring the burden of CMS among urban population is important for high altitude researchers, clinicians and policy‑makers. However, the role of urbanization in modulating the prevalence of CMS remains poorly understood and reliable epidemiological data from urban populations are notably lacking globally. The burden, associated factors, and health-related quality of life (HRQoL) of CMS have never been studied among urban populations of the Northwestern Sichuan Plateau in China. Hongyuan County, with an average altitudes of 3500 to 4000 meters, has emerged as a representative high-altitude city on the Northwestern Sichuan Plateau. The present epidemiological study was conducted in urban areas of Hongyuan County. CMS significantly impacts the physical and mental health of high-altitude populations. To effectively assess the health status of urban population at high altitude, particularly CMS patients, we evaluated their physiological function, psychological well-being, daily living activities, and social participation. We utilized the 12-Item Short Form Health Survey (SF-12). This instrument has been widely adopted in epidemiological studies due to its ability to capture both physical and mental health dimensions through 12 concise items, with validated Chinese versions available for local populations. This study aimed to determine the prevalence of CMS and its associated factors among urban residents in the Northwestern Sichuan Plateau, while assessing their HRQoL. Our findings contribute to supplement the existing epidemiological data on CMS and provide valuable insights for early disease detection and sustainable development of urban populations in plateau regions. Subjects and Methods The study population was selected by a random cluster sampling method. The communities of Hongyuan County were listed and were selected using a random number table. From the selected communities, all individuals aged 20 years and above consenting to participate were screened, informed consent from each participant was obtained. After screening, 341 subjects were included in the analysis who met the following criteria: (1) no underlying cardiorespiratory diseases, (2) completed questionnaires, and (3) availability of all laboratory data necessary for CMS diagnosis. The diagnosis of CMS was made using Qinghai CMS scoring criteria. This study was conducted according to the Declaration of Helsinki and approved by the Ethics Review Committee of Mianyang Third People’s Hospital. We designed a standardized questionnaire including questions on the subjects’ demographic characteristics, medical history, specified symptoms of CMS and SF-12, a professional research assistant was responsible for conducting face-to-face interviews with subjects and taking detailed records of their information. As per Qinghai CMS scoring system standard operating protocol to document the presence and severity of symptoms and signs related to CMS to maintain the uniformity in scoring CMS symptoms and signs. SF-12 standard scores (NBS) were calculated according to the original scoring manual. All anthropometric measurements followed WHO STEPS protocols. Height and weight were measured in triplicate, respectively, the average of three consecutive measurements was used for analysis. Blood pressure (BP) was measured twice at 5-minute intervals using a daily-calibrated electronic blood pressure monitor, and the average value was taken as the BP value. Heart rate (HR) was measured in triplicate via 12-lead ECG after 15-minute rest, the median value was used to exclude arrhythmic outliers. Peripheral oxygen saturation (SpO₂) was measured after 5 minutes of seated rest using a handheld pulse oximeter (Nyco Pulse O2, Nyco Devices, Switzerland) with finger probe. Each subject were subjected to blood tests in a fasting state to estimate Hb level using an automated hematology analyzer (Sysmex XN-9000, Sysmex Corporation, Japan) at Hongyuan County People's Hospital. Assuming a CMS prevalence of 10% based on the reported range of 5%-33% in previous studies [ 2 ], the minimum sample size required to estimate the prevalence with 95% confidence and a 5% margin of error was 138. Using a cluster correction factor of 2 [ 6 ], the approximate sample required was about 270. Statistical analyses were performed in R software (version 4.4.3). Continuous variables were described as mean ± standard deviation (SD) if normally distributed and compared using the t -test. Non-normally distributed continuous variables were expressed as median (interquartile range [IQR]) and analyzed using the Mann–Whitney U test. Categorical variables were presented as count and proportions ( n , %) and compared using the Chi-square test. To minimize age-related confounding, we performed 1:3 nearest-neighbor propensity score matching (PSM), resulting in a final cohort of 223 subjects for univariable analysis. Multivariable conditional logistic regression analysis was used to calculate the odds ratio (OR) and its 95% confidence interval (CI) to identify the associated independent risk factors. Variance inflation factors (VIF) were used to test for the multicollinearity of variables in the model. Restricted cubic spline (RCS) analysis was used to assess potential nonlinear relationships of continuous variables. To model potential nonlinear associations, we applied segmented linear regression analysis, the optimal inflection points were determined through iterative optimization by minimizing the model's deviance. Based on these thresholds, Hb levels were then categorized into three groups (<166 g/L, 166–185 g/L, and ≥186 g/L) and incorporated into the multivariable logistic regression model. A two-sided P value of < 0.05 indicated statistical significance. HRQoL was evaluated using the SF-12. The SF-12 generates two summary scores: the Physical Component Summary (PCS) and the Mental Component Summary (MCS). Raw scores were first calculated according to the standard SF-12 scoring algorithm and then transformed into norm-based T-scores (mean = 50, SD = 10), with higher scores indicating better health-related quality of life. Comparisons between the CMS group and the non-CMS group were performed using t-tests. Effect sizes were reported using Cohen’s d, with thresholds defined as: small effect (Cohen’s d ≥ 0.2), medium effect (Cohen’s d ≥ 0.5), large effect (Cohen’s d ≥ 0.8). These criteria are consistent with Cohen’s conventions for behavioral and health sciences [ 7 ]. Results A total of 377 urban residents aged 20 years and above were screened. 31 subjects were excluded due to presence of cardiorespiratory diseases, and an additional 5 were excluded because of incomplete questionnaires. The final 341 subjects were analyzed. Prevalence of CMS, erythrocytosis, hypoxemia The overall prevalence of CMS was 20.5% (95% CI: 16.2–24.8%). Severity stratification revealed mild cases accounted for 17.9% (95% CI: 14.0-22.4%), moderate cases for 2.3% (95% CI: 1.0-4.6%), and severe cases for 0.3% (95% CI: 0-1.6%). The prevalence of CMS was slightly higher in females (21.5%) compared to males (19.4%), although this difference did not reach statistical significance ( p = 0.718). The prevalence of erythrocytosis (defined as Hb ≥ 210 g/L in males and ≥190 g/L in females) was 4.1% (95% CI: 2.3–6.8%) in the study population, males demonstrated a significantly higher prevalence than females (7.5%, 95% CI: 3.9–12.7% vs. 1.1%, 95% CI: 0.1–3.9%; p = 0.007). Hypoxemia (SpO 2 < 85%) was observed in 8.2% (95% CI: 5.6–11.8) of the study population, with no significant difference between males and females (8.1%, 95% CI: 4.4–13.5% vs. 8.3%, 95% CI: 4.7–13.3%; p = 1.000). Associated factors of CMS Comparison of demographic, clinical characteristics between group with CMS and without CMS were described in Table 1 . The mean age of population with CMS was significantly higher than population without CMS (47.03 ± 8.12 vs. 39.23 ± 9.30, p < 0.001) and as expected, the duration of high-altitude residence of population with CMS was significantly higher than the population without CMS (37.69 ± 16.82 vs. 29.70 ± 16.10, p < 0.001). The population mean of BMI (26.22 ± 4.62 vs. 24.61 ± 3.51, p = 0.008), SBP ( 132.03 ± 17.64 vs. 125.98 ± 17.41, p = 0.01), and HB ( 173.87 ± 27.61 vs. 163.18 ± 19.84, p = 0.003) were significantly higher among group with CMS than group without CMS, however, no intergroup disparity was observed in obesity rates (30% vs. 19.6%, p = 0.084). Compared to the non-CMS group, the CMS group demonstrated significantly lower SpO₂ (%) (88.79 ± 3.94 vs. 90.10 ± 3.49; p = 0.007). Age, as a well-established risk factor for CMS, may confound the results through its associations with variables such as menstrual status and duration of high-altitude residence. Given significant baseline age disparities between group with CMS and group without CMS (standardized mean differences [SMD] = 0.961), we performed 1:3 nearest-neighbor PSM to minimize age-related confounding. The final matched cohort comprised 223 subjects (67 CMS cases and 156 non-CMS cases), demonstrating excellent age balance between groups (SMD = 0.032). Comparison of demographic, clinical characteristics between groups after age matching were described in Table 1 . In the univariate analysis (Table 2 ), Hb level (OR = 1.01, p = 0.034) were significantly associated with the outcome. CMS was more prevalent among postmenopausal women (44.8% vs. 29.3%) and people with obesity (39.6% vs. 27.4%), though the differences were not statistically significant. Variables with clinical significance demonstrated no evidence of multicollinearity (all VIFs < 3) were included in multivariable conditional logistic regression analysis. Nonlinearity analysis via RCS indicated a threshold effect of Hb levels on CMS risk. Segmented linear regression identified two inflection points at 166 g/L and 186 g/L in the Hb-CMS relationship (Fig. 1 ). Below 166 g/L, Hb showed no significant effect (β = 0.002, Z value = 0.137). Between 166–185 g/L, increase in Hb non-significantly reduced the log-odds of CMS (β = -0.039, Z value = -0.912). Above 186 g/L, each 1 g/L increase in Hb significantly raised the odds of CMS by 12% (β = 0.117, Z value = 2.451). Based on these thresholds, Hb levels were then categorized into three groups (<166 g/L, 166–185 g/L, and ≥186 g/L) and incorporated into the multivariable conditional logistic regression model again. Key results of the multivariable conditional logistic regression were summarized in Table 3 . Females had 3.02 times higher odds of CMS compared to males (OR = 3.02, 95% CI: 1.19–7.64, p = 0.020), Hb levels ≥ 186 g/L were associated with a 4.76-fold increased odds of CMS (OR = 4.76, 95% CI: 1.41–16.06, p = 0.012). Prevalence and intensity of symptoms and signs of CMS Among the total study population, the most prevalent symptoms and signs were cyanosis (52.8%), headache (49.0%), breathlessness (46.9%) and sleep disturbance (43.7%). The majority of cases reported mild intensity across symptoms, except for sleep disturbance, which was predominantly moderate (75.8% of affected cases). Among the CMS patients, the most common symptoms and signs remained breathlessness (88.6%), sleep disturbance (85.7%), headache (80%), and cyanosis (78.6%), however, moderate/severe breathlessness increased to 29.1% (vs. 14.4% overall), moderate/severe cyanosis increased to 50.9% (vs. 27.7% overall), and severe headache to 16% (vs. 6.6% overall). Among these most prevalent symptoms, females demonstrated significantly higher rates of both breathlessness (54.7% vs 38.1%, p = 0.003) and headache (60.8% vs 35.6%, p < 0.001) compared to males (Table 4 ). HRQoL assessment The SF-12 assessment revealed impairments in both physical and mental health among CMS patients with physical health being more severely affected (Table 5 ). PCS scores were markedly lower in the CMS group (33.50 ± 14.35 vs. 60.44 ± 13.69; p < 0.001, Cohen’s d = -1.95, large effect). Similarly, MCS scores were significantly reduced in the CMS group (46.96 ± 12.96 vs. 53.71 ± 12.59; p < 0.001, Cohen’s d = -0.53, medium effect). These effects exceeded the minimal important difference (MID) thresholds for both PCS (≥ 3 points) and MCS (≥ 5 points), indicating clinically meaningful deteriorations. The CMS group exhibited statistically significant impairments in all physical health domains compared to the group without CMS (all p 0.8) were observed for General Health (GH) (Cohen’s d = -0.83), Physical Functioning (PF) (Cohen’s d = -1.40), and Role Physical (RP) (Cohen’s d = -2.15), while Bodily Pain (BP) demonstrated a medium effect size (Cohen’s d = -0.58). For mental health domains, significant between-group differences were only found in Mental Health (MH) ( p < 0.05)and Role Emotional (RE). The MH showed a medium effect size(Cohen’s d = -0.78), whereas the RE exhibited a small effect size (Cohen’s d = -0.32). No significant differences were observed in other domains. Table 1 Comparison of descriptive statistics between group with CMS and without CMS before and after age matching. Unmatched Matched Group with CMS ( n = 70) Group without CMS ( n = 271) P - value Group with CMS ( n = 67) Group without CMS ( n = 156) P - value Gender * Male Female 31 (19.4) 39 (21.5) 129 (80.6) 142 (78.5) 0.718 30 (26.8) 37 (33.3) 82 (73.2) 74 (66.7) 0.357 Age (years)** 47.03 ± 8.12 39.23 ± 9.30 < 0.001 46.49 ± 7.88 44.59 ± 7.68 0.094 Residents * Migrant Native 27 (20.5) 43 (20.6) 105 (79.5) 166 (79.4) 1 27 (31.8) 40 (29.0) 58 (68.2) 98 (71.0) 0.772 Menstrual status * Postmenopausal Premenopausal 15 (48.4) 24 (16) 16 (51.6) 126 (84) <0.001 13 (44.8) 24 (29.3) 16 (55.2) 58 (70.7) 0.194 Years of high-altitude residence ** 37.69 ± 16.82 29.70 ± 16.10 < 0.001 36.73 ± 16.56 35.39 ± 15.37 0.559 Obesity * 21 (28.4) 53 (71.6) 0.084 19 (39.6) 29 (60.4) 0.147 Height** 161.49 ± 6.77 163.10 ± 7.89 0.117 161.66 ± 6.81 163.03 ± 8.06 0.226 Weight ** 68.41 ± 12.56 65.76 ± 12.27 0.110 68.20 ± 12.55 66.62 ± 11.46 0.357 BMI** 26.22 ± 4.62 24.61 ± 3.51 0.008 26.08 ± 4.59 24.96 ± 3.14 0.036 SBP** 132.03 ± 17.64 125.98 ± 17.41 0.010 131.81 ± 17.54 129.94 ± 17.84 0.472 DBP ** 81.71 ± 14.43 78.96 ± 14.37 0.155 81.57 ± 14.36 81.62 ± 15.08 0.98 HR ** 81.94 ± 12.52 81.48 ± 12.75 0.788 82.58 ± 12.23 81.34 ± 12.30 0.489 SpO 2 (%) ** 88.79 ± 3.94 90.10 ± 3.49 0.007 89 ± 3.81 89.65 ± 3.47 0.231 Hb (g/L) ** 173.87 ± 27.61 163.18 ± 19.84 0.003 171.66 ± 30.31 164.10 ± 19.92 0.029 * n (%); ** mean ± sd. Table 2 Univariate analysis of baseline characteristics after PSM between group with CMS and without CMS Group with CMS ( n = 67) Group without CMS ( n = 156) OR(95%CI) P -value Gender* Male Female 30(26.8) 37(33.3) 82(73.2) 74(66.7) 1.49 (0.81–2.73) 0.195 Residents * Migrant Native 27(31.8) 40(29.0) 58(68.2) 98(71.0) 0.82 (0.46–1.48) 0.514 Menstrual status* Postmenopausal Premenopausal 13(44.8) 24(29.3) 16(55.2) 58(70.7) 1.96(0.81–4.72) 0.130 Years of high-altitude residence** 36.73 ±16.56 35.39 ±15.37 1.00 (0.97–1.02) 0.732 Obesity* 19 (39.6) 29 (60.4) 1.51 (0.77–2.96) 0.235 Height** 161.66 ± 6.81 163.03 ± 8.06 0.97 (0.94–1.01) 0.175 Weight** 68.20 ± 12.55 66.62 ± 11.46 1.00 (0.98–1.03) 0.741 BMI** 26.08 ± 4.59 24.96 ± 3.14 1.06 (0.98–1.15) 0.148 SBP** 131.81± 17.54 129.94 ± 17.84 1.00 (0.98–1.02) 0.908 DBP** 81.57 ± 14.36 81.62 ± 15.08 1.00 (0.98–1.02) 0.682 HR** 82.58 ± 12.23 81.34 ± 12.30 1.01 (0.99–1.04) 0.317 SpO 2 (%)** 89.00 ± 3.81 89.65 ± 3.47 0.95 (0.88–1.03) 0.236 Hb** 171.66± 30.31 164.10 ± 19.92 1.01 (1.00-1.03) 0.034 * n (%); ** mean ± sd. Table 3 Multivariable conditional logistic regression analysis for independent predisposing factors of CMS β S.E OR 95% CI Z-value P -value Gender(Female) 1.105 0.474 3.02 1.19–7.64 2.332 0.020 Residents(Native) -0.100 0.321 0.90 0.48–1.70 -0.311 0.756 Hb≥186 1.561 0.620 4.76 1.41–16.06 2.517 0.012 BMI 0.037 0.082 1.04 0.88–1.22 0.456 0.648 SBP 0.003 0.010 1.00 0.98–1.02 0.280 0.780 SPO2 -0.026 0.045 0.97 0.89–1.06 -0.589 0.556 Obesity -0.014 0.632 0.90 0.29–3.40 -0.023 0.982 Menstrual status (Postmenopausal)* 0.497 0.482 1.64 0.63–4.22 1.032 0.302 * Analyzed among females only. Table 4 Gender comparison of symptoms and signs of CMS among the total study population Male ( n = 160) Female ( n = 181) χ2 P -value Breathlessness n (%) 61(38.1) 99(54.7) 8.71 0.003 Sleep disturbance n (%) 63(39.4) 86(47.5) 1.97 0.161 Cyanosis n (%) 103(64.4) 77(42.5) 15.38 <0.001 Dilatation of veins n (%) 22(13.8) 9(5.0) 6.89 0.009 Paresthesia n (%) 42(26.3) 58(32.0) 1.11 0.292 Headache n (%) 57(35.6) 110(60.8) 20.50 <0.001 Tinnitus n (%) 52(32.5) 60(33.1) 0 0.991 Table 5 comparison of HRQoL between group with CMS and without CMS. Group with CMS ( n = 70) Group without CMS ( n = 271) P - value Cohen’s d PCS* 33.50 ± 14.35 60.44 ± 13.69 < 0.001 -1.95 GH* 27.14 ± 17.42 45.30 ± 22.74 < 0.001 -0.83 PF* 50.00 ± 20.41 76.59 ± 18.63 < 0.001 -1.40 RP* 21.61 ± 19.26 61.30 ± 18.28 < 0.001 -2.15 BP* 47.14 ± 18.33 57.75 ± 18.26 < 0.001 -0.58 MCS* 46.96 ± 12.96 53.71 ± 12.59 < 0.001 -0.53 RE* 47.86 ± 22.32 53.74 ± 17.09 0.04 -0.32 VT* 44.64 ± 17.49 48.25 ± 18.49 0.13 0.20 MH* 39.64 ± 13.95 50.65 ± 14.42 < 0.001 -0.78 SF* 62.14 ± 17.42 65.22 ± 17.52 0.19 -0.18 * mean ± sd. Discussion In past decades, thousands of residents on the plateau have been investigated to determine prevalence of CMS, erythrocytosis, hypoxemia. The prevalence of CMS varies significantly across different regions worldwide from 5–33% [ 2 ]. Several studies examining physiologic adaptation to high-altitude in the East African Mountains have noted absence of CMS in this region of the world [ 8 ]. In Bolivia as a whole approximately two‑thirds of the population live at altitudes greater than 3000 m and CMS is deemed a considerable public health problem, studies there have found CMS rates between 8% and 10% in the male active population [ 9 ]. More extensive research into the prevalence of CMS has been conducted in the Andes, several studies in Cerro de Pasco have found CMS prevalence rates of 14.8% and 18.2% [ 10 ], studies in the Peruvian central Andes located at 4100 m have found CMS prevalence rates as high as 32.6% [ 11 ]. Epidemiological investigations by Chinese researchers documented that prevalence of CMS among native Tibetans, Han Chinese immigrated to Qinghai plateau in Tibet were 1.21% and 5.57%, respectively [ 2 ]. The reasons for this large variability are not well‑understood, though they may be associated with ethnic differences, residential altitude, lifestyle, and the diagnostic criteria for CMS used in different studies. Previous studies have also reported that urbanization and associated changes in lifestyle could have role in development of CMS [ 4 ]. Nevertheless, epidemiological studies on CMS remain relatively scarce for certain subpopulations, particularly among urban residents. This epidemiological study of CMS among urban residents of the Northwestern Sichuan Plateau in China revealed 20.5% (95% CI: 16.2–24.8%) of the population were affected with CMS, with the clinical spectrum dominated by mild presentations. Females have a higher prevalence of CMS compared to males. The prevalence of CMS observed in the present study was higher than that reported in previous studies from the Indian Himalayas at comparable elevations [ 12 ]. The relatively high prevalence of CMS observed in our study may be explained by several factors. Our study primarily focused on urban populations at high altitude. Previous research has reported that urbanization and associated changes in lifestyle could have role in development of CMS, and our findings support this hypothesis. Existing evidence suggests that physical inactivity may contribute to the development and progression of CMS [ 3 ]. We speculate that the predominantly sedentary lifestyle characteristic of urban high-altitude residents may represent a potential contributing factor to their increased susceptibility to CMS. Recent studies have demonstrated that regular physical exercise provides numerous physiological benefits, not only in healthy individuals at high altitude, but also in patients with CMS. Exercise training, especially moderate-intensity aerobic exercise, might be used as a non-pharmacological therapy for CMS [ 13 ]. The participants in our study live at elevations ranging from 3,500 to 4,000 meters, which is higher than the altitudes reported in many prior studies, altitude of residence is a significant risk factor for the development of chronic mountain sickness. Much of the historical research on CMS has focused on male populations, while our study included both male and female participants, notably, our findings revealed a higher prevalence of CMS among females compared to males in the studied population, this could be one of the contributing factors. In this study, we applied the latest diagnostic criteria for CMSࣧthe Qinghai CMS score criteria. Differences in diagnostic criteria may partially account for this discrepancy. It should be noted that some researchers have raised concerns that current CMS diagnostic criteria may be overly sensitive but lack specificity, furthermore, some researchers argue that most CMS symptoms are also commonly shared with cardiac, pulmonary, and other diseases, which lead the scoring system less reflect maladaptation to high altitudes after long-term hypoxic exposure, but instead may reflect lung or cardiac malfunction [ 14 ]. The diagnostic setting of Hb threshold for erythrocytosis remains a contentious issue in clinical practice. Erythrocytosis is essentially an adaptive response to long-term hypoxic exposure, making it difficult to be distinguished from a maladaptive response. Currently, the Qinghai CMS score criteria is used to diagnose CMS, with Hb levels of more than 210 g/L in male and more than 190 g/L in female as the cut-off values for diagnosing excessive erythrocytosis (EE) [ 2 ]. The cut-off points were originally established based on the Gaussian distribution. However, several studies suggest that EE defined by these cutoffs shows no significant association with the symptoms and signs of CMS, despite it has been presumed this elevated Hb levels as responsible of the signs and symptoms of CMS. Given this discrepancy between Hb concentration and symptoms, different Hb thresholds have been proposed by medical researchers from different groups and countries [ 15 , 16 ]. For instance, Jiang et al. have applied k-means clustering methods, recommending Hb ≥ 200 g/L as a more clinically relevant threshold for EE in CMS diagnosis [ 17 ]. In the first population (Hb < 200 g/L), which represents the physiologic response of erythrocytosis, the SaO2 remains stable as Hb increases; in the second population (Hb ≥ 200 g/L), which represents the pathologic response part of erythrocytosis, the SaO 2 decreases as the Hb increases. Reeves and Leon-Velarde in their study showed that optimal Hb level at high altitude, which allows greater oxygen extraction, was 175 g/L [ 18 ] . In our study, we employed segmented linear regression to identify two inflection points in the HB-CMS relationship, which were 166 g/L and 186 g/L, respectively. When hemoglobin levels were < 166 g/L, the prevalence of CMS showed no significant change as Hb increases. Between 166–185 g/L, the prevalence of CMS slightly decreased as the Hb increases. However, when hemoglobin levels exceeded 186 g/L, the prevalence of CMS significantly increased with further rises in hemoglobin. So, we recommended the hemoglobin threshold for this population to be 186 g/L. The association we observed between Hb levels and CMS is consistent with the known relationship between Hb levels and blood oxygen-transport capacity. When the Hb concentration is below the “optimal” value, physiologic erythrocytosis response helps to elevate ability of blood to transport oxygen. When the Hb exceeds this “optimal” value, the erythrocytosis response aggravates the oxygen supplication-demanding imbalance and triggers CMS pathologic processes[ 19 ]. Of course, further studies are needed to explore the relationship between this threshold and the symptoms and signs of CMS in subsequent research. Increasing age are well established risk factors for CMS [ 20 – 22 ].Given significant baseline age disparities between group with CMS and group without CMS, which may confound outcomes through associations with variable, such as menstrual status and duration of high-altitude residence. 1:3 nearest-neighbor PSM was performed to minimize age-related confounding. After age matching, univariate analysis revealed that Hb levels were significantly associated with CMS. Although CMS was more prevalent among postmenopausal women, the differences were not statistically significant. Multivariable conditional logistic regression analysis revealed that females and Hb level emerged as independent associated factors for CMS. Menopausal status was analyzed only in the female subgroup due to its inapplicability to males. No significant association was observed between menopausal status and the prevalence of CMS. In contrast to most previous studies identifying males as a primary risk factor for CMS [ 2 , 23 ], our study demonstrate females as an independent associated factor. This finding is consistent with a epidemiological study conducted in natives of Spiti in greater Himalayas, which similarly reported higher CMS prevalence among females [ 3 ]. Notably, this study attributed this sex disparity to women's prolonged exposure to indoor air pollution and consequent pulmonary dysfunction - a potential contributory mechanism that may partially explain elevated CMS susceptibility in females. In our study, the higher CMS symptom score was recorded in women than in men, but the lower prevalence of excessive erythrocytosis and comparable hypoxemia rates were observed. This may point out the lack of specificity of contemporary symptoms based diagnostic criteria in females for diagnosis of CMS or may be that the threshold for symptoms related to hypoxemia and erythrocytosis in females is lower than in males. Moreover, in contrast to prior researches, our study employed age-based matching to mitigate age-related confounding effects between genders. The observed inconsistency in the association between female sex and CMS risk—initially non-significant in univariate analysis but significant in multivariable analysis—may result from confounding protective factors in the female population, including younger age (43 vs. 46 years), shorter high-altitude exposure (34 vs. 37 years), and lower Hb levels (152 vs. 181 g/L). These factors likely attenuated the true physiological susceptibility of females in unadjusted models, which became apparent only after multivariate adjustment. Building upon our segmented regression results identifying 186 g/L as the critical hemoglobin threshold, subsequent multivariable analysis demonstrated that Hb levels ≥ 186 g/L was an independent associated factor. Many studies of adaptation to high-altitude hypoxia have focused on the blood because of its role in oxygen transport. Below the optimal hemoglobin threshold, numerous of individuals effectively increase oxygen delivery capacity through elevated hemoglobin concentrations, thereby reducing CMS risk. However, beyond this optimal level, excessive erythrocytosis impairs oxygen transport through increased blood viscosity, triggering a vicious cycle of tissue hypoxia and further erythrocytosis. Previous studies have suggested that the Tibetans lived at altitudes of 3,800–4,065 meters without significant erythrocytosis [ 24 ]. Relatively low Hb concentrations among Tibetan high-altitude natives have also been widely reported in numerous studies [ 5 ]. In our study population, the prevalence of EE was 4.1%, which is higher than previously reported rates among Tibetan pastoralists. Our findings suggest potential differences in hemoglobin concentrations between urban residents and pastoral populations in high-altitude regions. These findings also suggest that CMS screening and intervention should be prioritized for urban residents in the northwestern Sichuan plateau when hemoglobin levels exceed 186 g/L. Further studies with more diverse populations are required to establish population-specific hemoglobin thresholds. Previous studies identified menopause as a risk factor for CMS [ 25 ]. This phenomenon may be attributed to postmenopausal elevation of the testosterone-to-estradiol ratio and cessation of periodic blood loss, both contributing to increased hemoglobin levels. Contrary to previous reports, our analysis revealed no significant association between menopausal status and CMS among female participants. A previous study also failed to observe an increase in Hb levels after menopause [ 26 ]. The exact cause underlying this phenomenon require further investigation. One possible explanation for this difference is that they did not account for the confounding effect of age, as postmenopausal women are generally older than premenopausal women and advancing age is a well-established risk factor for CMS. The growing recognition of the value of real-world data on HRQoL in informing disease management and policy development is increasingly evident. Patients of CMS are disabled with impaired memory, headache, breathlessness, fatigue, disturbed sleep, anorexia, and tinnitus that affect the quality of life. These limitations may force patients to discontinue prolonged residence at high altitudes or full-time work. Even clinicians may underestimate this impact. Study has demonstrated a significant correlation between the HRQoL of high-altitude urban residents and the occurrence of CMS [ 11 ]. Therefore, research on the effects of CMS on patients' quality of life is crucial for understanding the characteristics of this disease and the associated disease burden it imposes. The SF-12 is a widely used and validated HRQoL assessment instrument in populations at sea level and at high altitude, serving as an abbreviated version of the SF-36 (Short Form-36). This 12-item questionnaire evaluates an individual's overall physical and mental health status. The results of our study indicate that populations without CMS exhibit significantly better health scores compared to those with CMS. CMS adversely affects both the physical and mental health of patients, with a more pronounced impact on physical health. In terms of physical health, compared to non-CMS high-altitude populations, CMS patients showed poorer performance across multiple dimensions of physical health, with the most pronounced impairment observed in the RP dimension. This suggests that physical health limitations predominantly manifest as restrictions in occupational functioning, which is particularly critical for high-altitude urban populations who constitute the primary workforce in plateau development. Regarding mental health, CMS patients demonstrated the most significant deterioration in the MH domain compared to non-CMS populations, suggesting that emotional disturbances (e.g., depression and anxiety) likely constitute the primary psychological burden. This phenomenon may be attributed to chronic hypoxic-induced hippocampal atrophy and prefrontal cortex dysfunction, which elevate susceptibility to mood disorders, compounded by persistent sleep disturbances exacerbating emotional dysregulation [ 27 – 29 ]. Furthermore, limited healthcare resources and inadequate psychological support in high-altitude regions often result in prolonged untreated mental health conditions. Given the substantial MH domain impairment, routine depression/anxiety screening and targeted psychological interventions should be integrated into CMS patient management. To our knowledge, this represents the first study to investigate both the burden of CMS and its HRQoL impacts among urban residents in high-altitude regions. this study also represents the first application combining matching with multivariable logistic regression in cross-sectional research on CMS. The integrated use of matching and multivariable logistic regression addresses the limitations of conventional approaches: it compensates for traditional regression's neglect of intergroup comparability while overcoming matching's inability to perform model-based adjustments. This dual-control strategy for confounding factors creates synergistic effects where 'the whole is greater than the sum of its parts' (1 + 1 > 2). The combined methodology can be extended to other high-altitude-related disorders (e.g., high-altitude pulmonary hypertension) or similar cross-sectional studies, providing a replicable methodological framework for future research in high-altitude medicine. conclusion Prevalence of CMS amongst urban residents of the Northwestern Sichuan Plateau in China residing at an altitude of 3500 to 4000 meters was 20.5%. After age matching, female, and hemoglobin levels ≥ 186 g/L were identified as significant associated factors for CMS. Populations with CMS exhibit poorer health status compared to those without CMS, with more pronounced impairments in physical health. Observations made in the present study have important implications for health policy makers and public health providers for creating awareness among urban residents of the Northwestern Sichuan Plateau about CMS burden, associated factors and appropriate preventive measures in order to reduce morbidity and improve quality of life. Declarations Additional information Competing interests The authors declare no competing interests. Funding No funding. Author Contribution C.H. conceived the study, analyzed data, drafted the manuscript and gave final approval. P.B. and Z.J. contributed to conception and design of study, analysis of data, and gave final approval. L.Y., H.M., D.P., and F.S. contributed to acquisition of data and gave final approval. L.W. contributed to conception and design of study, analysis and interpretation of results, critically revised the manuscript and gave final approval. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy. Data Availability The data presented in this study are available upon request by contacting Dr. Wenjun Li [email protected] References Monge, C. C., Arregui, A. & León-Velarde, F. Pathophysiology and epidemiology of chronic mountain sickness. Int. J. Sports Med. 13 (Suppl 1), S79–81 (1992). León-Velarde, F. et al. Consensus statement on chronic and subacute high altitude diseases. High. Alt Med. Biol. 6 (2), 147–157 (2005). Negi, P. C. et al. Epidemiological study of chronic mountain sickness in natives of Spiti Valley in the Greater Himalayas. High. Alt Med. Biol. 14 (3), 220–229 (2013). John, B. & West La, M. D. Hypoxia, polycythemia, and chronic mountain sickness. Chest 94 (1), A22–A23 (1988). Beall, C. M., Brittenham, G. M., Macuaga, F. & Barragan, M. Variation in hemoglobin concentration among samples of high-altitude natives in the Andes and the Himalayas. Am. J. Hum. Biol. 2 (6), 639–651 (1990). Chadha, V. Sample size determination in health studies. NTI Bull. 42 (3&4), 55–62 (2006). R., K. T., Book Reviews -- Statistical Power Analysis for the Behavioral Sciences (2nd ed.) by Jacob Cohen. Educational and Psychological Measurement . Vol.50(No.1), 225 (1990). Beall, C. M. High-altitude adaptations. Lancet (2003). 362 Suppl, s14-15 Vargas, E. & Spielvogel, H. Chronic mountain sickness, optimal hemoglobin, and heart disease. High. Alt Med. Biol. 7 (2), 138–149 (2006). Arregui, A. et al. Migraine, polycythemia and chronic mountain sickness. Cephalalgia 14 (5), 339–341 (1994). Gonzales, G. F., Rubio, J. & Gasco, M. Chronic mountain sickness score was related with health status score but not with hemoglobin levels at high altitudes. Respir Physiol. Neurobiol. 188 (2), 152–160 (2013). Sahota, I. S. & Panwar, N. S. Prevalence of Chronic Mountain Sickness in high altitude districts of Himachal Pradesh. Indian J. Occup. Environ. Med. 17 (3), 94–100 (2013). Teixeira, A. L. & Lang, J. A. Exercise is medicine for chronic mountain sickness. Exp. Physiol. 106 (11), 2153–2154 (2021). Gonzales, G. F., Tapia, V., Gasco, M. & Gonzales-Castañeda, C. Serum testosterone levels and score of chronic mountain sickness in Peruvian men natives at 4340 m. Andrologia 43 (3), 189–195 (2011). Pasha, M. A. & Newman, J. H. High-altitude disorders: pulmonary hypertension: pulmonary vascular disease: the global perspective. Chest 137 (6 Suppl), 13s–19s (2010). Groepenhoff, H. et al. Exercise pathophysiology in patients with chronic mountain sickness exercise in chronic mountain sickness. Chest 142 (4), 877–884 (2012). Jiang, C. et al. Chronic mountain sickness in Chinese Han males who migrated to the Qinghai-Tibetan plateau: application and evaluation of diagnostic criteria for chronic mountain sickness. BMC Public. Health . 14 , 701 (2014). Reeves, J. T. & Leon-Velarde, F. Chronic mountain sickness: recent studies of the relationship between hemoglobin concentration and oxygen transport. High. Alt. Med. Biol. 5 (2), 147–155 (2004). Lenfant, C. & Sullivan, K. Adaptation to high altitude. N Engl. J. Med. 284 (23), 1298–1309 (1971). Sime, F., Monge, C. & Whittembury, J. Age as a cause of chronic mountain sickness (Monge's disease). Int. J. Biometeorol. 19 (2), 93–98 (1975). León-Velarde, F., Arregui, A., Vargas, M., Huicho, L. & Acosta, R. Chronic mountain sickness and chronic lower respiratory tract disorders. Chest 106 (1), 151–155 (1994). Spicuzza, L. et al. Sleep-related hypoxaemia and excessive erythrocytosis in Andean high-altitude natives. Eur. Respir J. 23 (1), 41–46 (2004). Julian, C. G. & Moore, L. G. Human Genetic Adaptation to High Altitude: Evidence from the Andes. Genes (Basel) 10 (2) (2019). Beall, C. M. et al. Hemoglobin concentration of high-altitude Tibetans and Bolivian Aymara. Am. J. Phys. Anthropol. 106 (3), 385–400 (1998). León-Velarde, F. et al. The role of menopause in the development of chronic mountain sickness. Am. J. Physiol. 272 (1 Pt 2), R90–94 (1997). Winslow, R. M. et al. Different hematologic responses to hypoxia in Sherpas and Quechua Indians. J. Appl. Physiol. (1985) . 66 (4), 1561–1569 (1989). Hota, S. K., Barhwal, K., Singh, S. B. & Ilavazhagan, G. Differential temporal response of hippocampus, cortex and cerebellum to hypobaric hypoxia: a biochemical approach. Neurochem Int. 51 (6–7), 384–390 (2007). Hou, Y. et al. Establishment and evaluation of a simulated high–altitude hypoxic brain injury model in SD rats. Mol. Med. Rep. 19 (4), 2758–2766 (2019). Prescot, A. et al. Effect of moderate altitude on human cerebral metabolite levels: A preliminary, multi-site, proton magnetic resonance spectroscopy investigation. Psychiatry Res. Neuroimaging . 314 , 111314 (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers invited by journal 24 Oct, 2025 Editor invited by journal 09 Jul, 2025 Editor assigned by journal 04 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 02 Jul, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7033332","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539096255,"identity":"be5542d2-ccc7-42b2-abfb-23f648d2b24a","order_by":0,"name":"Hong Chang","email":"","orcid":"","institution":"The Third Hospital of Mianyang, Sichuan Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chang","suffix":""},{"id":539096256,"identity":"1bc23413-5540-4ead-b26f-d7e6fa352597","order_by":1,"name":"Jiawei Zhou","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiawei","middleName":"","lastName":"Zhou","suffix":""},{"id":539096257,"identity":"460820a9-fef5-47f7-b0b7-f7fdd3996c1e","order_by":2,"name":"Bin Peng","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Peng","suffix":""},{"id":539096258,"identity":"4db70737-4947-4906-9f4c-9312e982f2b6","order_by":3,"name":"Yuxing Liu","email":"","orcid":"","institution":"Hongyuan County People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxing","middleName":"","lastName":"Liu","suffix":""},{"id":539096259,"identity":"adb95b51-a2d0-489d-8d79-9d513430c962","order_by":4,"name":"Miaomiao Huang","email":"","orcid":"","institution":"The Third Hospital of Mianyang, Sichuan Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Miaomiao","middleName":"","lastName":"Huang","suffix":""},{"id":539096260,"identity":"d08179d7-13c1-4ffe-8448-69dd36293d1d","order_by":5,"name":"Piaopiao Dai","email":"","orcid":"","institution":"The Third Hospital of Mianyang, Sichuan Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Piaopiao","middleName":"","lastName":"Dai","suffix":""},{"id":539096261,"identity":"5b81f022-a273-4ea5-965b-89cde702bb3e","order_by":6,"name":"Shuai Feng","email":"","orcid":"","institution":"The Third Hospital of Mianyang, Sichuan Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Feng","suffix":""},{"id":539096262,"identity":"fed049e2-2b2d-40d6-8abe-93ba170a8112","order_by":7,"name":"Wenjun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3SsQrCMBCA4QsdXAp2vFIM+ACFQKDoUl/F0MHVsZO0FNLFB6gv4lzp0CUP0NG8QUdH4+TYGwXzz/cNyR2Az/eTBRWgQH4Y69rONMIcKXcSzNBIJBMwpaqmk45CCkg7pXGvUdU3qwEh52m1QLLJkVijbBKln2coZNYTyOTIRieqFQi9ulMJu8YPjSGZoMFth4xKjG1fWKIUoXKfLChvGYvBTV64WI3WzmXOFwlERwi+GxRL45/WPTDinfh8Pt+/9gYG9EXVKgKj2gAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Hospital of Mianyang, Sichuan Mental Health Center","correspondingAuthor":true,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-03 02:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7033332/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7033332/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95186136,"identity":"c80deec7-a252-4502-b8fd-3187a61d60ed","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/8d6f533ab75353b4b6c79807.docx"},{"id":95186135,"identity":"e6a04ac2-caab-4d9c-8afe-0a0a0433e201","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9251,"visible":true,"origin":"","legend":"","description":"","filename":"54ee50d17b1c4dfd9991c57d1cb1d19c.json","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/2056bc289f4e5c1282437c35.json"},{"id":95186140,"identity":"960b2357-81a2-43cf-91d3-8536cb1f2296","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115011,"visible":true,"origin":"","legend":"","description":"","filename":"54ee50d17b1c4dfd9991c57d1cb1d19c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/24e823d0aa2093e0519ac8a7.xml"},{"id":95186137,"identity":"d1db61fd-8dca-48df-bc32-eac03b2c2ac6","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":54223,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/1413021e943348edc61ea0f1.png"},{"id":95186138,"identity":"6e3b756e-23e8-4632-8f43-f8945a125fc8","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113629,"visible":true,"origin":"","legend":"","description":"","filename":"54ee50d17b1c4dfd9991c57d1cb1d19c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/3bd5640be8d63dc8be7c5f5c.xml"},{"id":95186139,"identity":"155dfcfa-ba70-48b6-9916-c2abc26b1724","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124279,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/12e539399dd8850c51122f08.html"},{"id":95186133,"identity":"e6a7f2c2-256a-4463-8bea-75da3875cb62","added_by":"auto","created_at":"2025-11-05 09:18:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61505,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented linear regression of hemoglobin (Hb) levels on CMS.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/1cce7c522a88880e4e7a90f6.png"},{"id":95227108,"identity":"f40e4ff6-58d8-43c2-b210-973b75bc3728","added_by":"auto","created_at":"2025-11-05 16:32:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840216,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7033332/v1/fa44a6c6-fc25-410e-9f57-bcc85fe06a1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Impact of Chronic Mountain Sickness on Quality of Life Among Urban Residents in Northwestern Sichuan, China: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic mountain sickness (CMS) is a frequent and potentially fatal chronic condition caused by hypoxia in high-altitude population. It is characterized by excessive erythrocytosis, severe hypoxemia, and, in some cases, moderate to severe pulmonary hypertension. Patients of CMS are disabled with impaired memory, headache, breathlessness, fatigue, disturbed sleep, anorexia, and tinnitus that affect the quality of life. Currently, the most effective treatment for CMS is relocation of the patient to a lower elevation, but this is seldom feasible due to the socioeconomic.\u003c/p\u003e\u003cp\u003eThe prevalence of CMS varies considerably across the world, influenced by multiple factors such as altitude of residence, ethnicity, duration of high-altitude residence, among others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of CMS ranges from 5–33% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Based on the Qinghai Scoring System published in 2004, a study reported that prevalence of CMS among natives of Spiti Valley in the Greater Himalayas residing at an altitude of 3000−4200 meters was 28.7% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the progression of urbanization in high-altitude regions, a distinct gradient in socioeconomic status and lifestyle patterns exists between urban populations and pastoral populations. Previous research has indicated that urbanization and associated changes in lifestyle also could have role in development of CMS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, studies have indicated that the mean Hb concentration of urban populations was higher than that of rural populations in the Andes and the Himalayas, with concomitant differences in oxygen saturation levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, measuring the burden of CMS among urban population is important for high altitude researchers, clinicians and policy‑makers. However, the role of urbanization in modulating the prevalence of CMS remains poorly understood and reliable epidemiological data from urban populations are notably lacking globally.\u003c/p\u003e\u003cp\u003eThe burden, associated factors, and health-related quality of life (HRQoL) of CMS have never been studied among urban populations of the Northwestern Sichuan Plateau in China. Hongyuan County, with an average altitudes of 3500 to 4000 meters, has emerged as a representative high-altitude city on the Northwestern Sichuan Plateau. The present epidemiological study was conducted in urban areas of Hongyuan County. CMS significantly impacts the physical and mental health of high-altitude populations. To effectively assess the health status of urban population at high altitude, particularly CMS patients, we evaluated their physiological function, psychological well-being, daily living activities, and social participation. We utilized the 12-Item Short Form Health Survey (SF-12). This instrument has been widely adopted in epidemiological studies due to its ability to capture both physical and mental health dimensions through 12 concise items, with validated Chinese versions available for local populations.\u003c/p\u003e\u003cp\u003eThis study aimed to determine the prevalence of CMS and its associated factors among urban residents in the Northwestern Sichuan Plateau, while assessing their HRQoL. Our findings contribute to supplement the existing epidemiological data on CMS and provide valuable insights for early disease detection and sustainable development of urban populations in plateau regions.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cp\u003eThe study population was selected by a random cluster sampling method. The communities of Hongyuan County were listed and were selected using a random number table. From the selected communities, all individuals aged 20 years and above consenting to participate were screened, informed consent from each participant was obtained. After screening, 341 subjects were included in the analysis who met the following criteria: (1) no underlying cardiorespiratory diseases, (2) completed questionnaires, and (3) availability of all laboratory data necessary for CMS diagnosis. The diagnosis of CMS was made using Qinghai CMS scoring criteria. This study was conducted according to the Declaration of Helsinki and approved by the Ethics Review Committee of Mianyang Third People’s Hospital.\u003c/p\u003e\u003cp\u003eWe designed a standardized questionnaire including questions on the subjects’ demographic characteristics, medical history, specified symptoms of CMS and SF-12, a professional research assistant was responsible for conducting face-to-face interviews with subjects and taking detailed records of their information. As per Qinghai CMS scoring system standard operating protocol to document the presence and severity of symptoms and signs related to CMS to maintain the uniformity in scoring CMS symptoms and signs. SF-12 standard scores (NBS) were calculated according to the original scoring manual. All anthropometric measurements followed WHO STEPS protocols. Height and weight were measured in triplicate, respectively, the average of three consecutive measurements was used for analysis. Blood pressure (BP) was measured twice at 5-minute intervals using a daily-calibrated electronic blood pressure monitor, and the average value was taken as the BP value. Heart rate (HR) was measured in triplicate via 12-lead ECG after 15-minute rest, the median value was used to exclude arrhythmic outliers. Peripheral oxygen saturation (SpO₂) was measured after 5 minutes of seated rest using a handheld pulse oximeter (Nyco Pulse O2, Nyco Devices, Switzerland) with finger probe. Each subject were subjected to blood tests in a fasting state to estimate Hb level using an automated hematology analyzer (Sysmex XN-9000, Sysmex Corporation, Japan) at Hongyuan County People's Hospital.\u003c/p\u003e\u003cp\u003eAssuming a CMS prevalence of 10% based on the reported range of 5%-33% in previous studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the minimum sample size required to estimate the prevalence with 95% confidence and a 5% margin of error was 138. Using a cluster correction factor of 2 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the approximate sample required was about 270. Statistical analyses were performed in R software (version 4.4.3). Continuous variables were described as mean ± standard deviation (SD) if normally distributed and compared using the \u003cem\u003et\u003c/em\u003e-test. Non-normally distributed continuous variables were expressed as median (interquartile range [IQR]) and analyzed using the Mann–Whitney \u003cem\u003eU\u003c/em\u003e test. Categorical variables were presented as count and proportions (\u003cem\u003en\u003c/em\u003e, %) and compared using the Chi-square test. To minimize age-related confounding, we performed 1:3 nearest-neighbor propensity score matching (PSM), resulting in a final cohort of 223 subjects for univariable analysis. Multivariable conditional logistic regression analysis was used to calculate the odds ratio (OR) and its 95% confidence interval (CI) to identify the associated independent risk factors. Variance inflation factors (VIF) were used to test for the multicollinearity of variables in the model. Restricted cubic spline (RCS) analysis was used to assess potential nonlinear relationships of continuous variables. To model potential nonlinear associations, we applied segmented linear regression analysis, the optimal inflection points were determined through iterative optimization by minimizing the model's deviance. Based on these thresholds, Hb levels were then categorized into three groups (\u0026lt;166 g/L, 166–185 g/L, and ≥186 g/L) and incorporated into the multivariable logistic regression model. A two-sided P value of \u0026lt; 0.05 indicated statistical significance.\u003c/p\u003e\u003cp\u003eHRQoL was evaluated using the SF-12. The SF-12 generates two summary scores: the Physical Component Summary (PCS) and the Mental Component Summary (MCS). Raw scores were first calculated according to the standard SF-12 scoring algorithm and then transformed into norm-based T-scores (mean = 50, SD = 10), with higher scores indicating better health-related quality of life. Comparisons between the CMS group and the non-CMS group were performed using t-tests. Effect sizes were reported using Cohen’s d, with thresholds defined as: small effect (Cohen’s d ≥ 0.2), medium effect (Cohen’s d ≥ 0.5), large effect (Cohen’s d ≥ 0.8). These criteria are consistent with Cohen’s conventions for behavioral and health sciences [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 377 urban residents aged 20 years and above were screened. 31 subjects were excluded due to presence of cardiorespiratory diseases, and an additional 5 were excluded because of incomplete questionnaires. The final 341 subjects were analyzed.\u003c/p\u003e\u003cp\u003ePrevalence of CMS, erythrocytosis, hypoxemia\u003c/p\u003e\u003cp\u003eThe overall prevalence of CMS was 20.5% (95% CI: 16.2\u0026ndash;24.8%). Severity stratification revealed mild cases accounted for 17.9% (95% CI: 14.0-22.4%), moderate cases for 2.3% (95% CI: 1.0-4.6%), and severe cases for 0.3% (95% CI: 0-1.6%). The prevalence of CMS was slightly higher in females (21.5%) compared to males (19.4%), although this difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.718). The prevalence of erythrocytosis (defined as Hb \u0026ge; 210 g/L in males and \u0026ge;190 g/L in females) was 4.1% (95% CI: 2.3\u0026ndash;6.8%) in the study population, males demonstrated a significantly higher prevalence than females (7.5%, 95% CI: 3.9\u0026ndash;12.7% vs. 1.1%, 95% CI: 0.1\u0026ndash;3.9%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Hypoxemia (SpO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;85%) was observed in 8.2% (95% CI: 5.6\u0026ndash;11.8) of the study population, with no significant difference between males and females (8.1%, 95% CI: 4.4\u0026ndash;13.5% vs. 8.3%, 95% CI: 4.7\u0026ndash;13.3%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000).\u003c/p\u003e\u003cp\u003eAssociated factors of CMS\u003c/p\u003e\u003cp\u003eComparison of demographic, clinical characteristics between group with CMS and without CMS were described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of population with CMS was significantly higher than population without CMS (47.03 \u0026plusmn; 8.12 vs. 39.23 \u0026plusmn; 9.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and as expected, the duration of high-altitude residence of population with CMS was significantly higher than the population without CMS (37.69 \u0026plusmn; 16.82 vs. 29.70 \u0026plusmn; 16.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The population mean of BMI (26.22 \u0026plusmn; 4.62 vs. 24.61 \u0026plusmn; 3.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), SBP ( 132.03 \u0026plusmn; 17.64 vs. 125.98 \u0026plusmn; 17.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and HB ( 173.87 \u0026plusmn; 27.61 vs. 163.18 \u0026plusmn; 19.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were significantly higher among group with CMS than group without CMS, however, no intergroup disparity was observed in obesity rates (30% vs. 19.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.084). Compared to the non-CMS group, the CMS group demonstrated significantly lower SpO₂ (%) (88.79 \u0026plusmn; 3.94 vs. 90.10 \u0026plusmn; 3.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003cp\u003eAge, as a well-established risk factor for CMS, may confound the results through its associations with variables such as menstrual status and duration of high-altitude residence. Given significant baseline age disparities between group with CMS and group without CMS (standardized mean differences [SMD]\u0026thinsp;=\u0026thinsp;0.961), we performed 1:3 nearest-neighbor PSM to minimize age-related confounding. The final matched cohort comprised 223 subjects (67 CMS cases and 156 non-CMS cases), demonstrating excellent age balance between groups (SMD\u0026thinsp;=\u0026thinsp;0.032). Comparison of demographic, clinical characteristics between groups after age matching were described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the univariate analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), Hb level (OR\u0026thinsp;=\u0026thinsp;1.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) were significantly associated with the outcome. CMS was more prevalent among postmenopausal women (44.8% vs. 29.3%) and people with obesity (39.6% vs. 27.4%), though the differences were not statistically significant.\u003c/p\u003e\u003cp\u003eVariables with clinical significance demonstrated no evidence of multicollinearity (all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;3) were included in multivariable conditional logistic regression analysis. Nonlinearity analysis via RCS indicated a threshold effect of Hb levels on CMS risk. Segmented linear regression identified two inflection points at 166 g/L and 186 g/L in the Hb-CMS relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Below 166 g/L, Hb showed no significant effect (β\u0026thinsp;=\u0026thinsp;0.002, Z value\u0026thinsp;=\u0026thinsp;0.137). Between 166\u0026ndash;185 g/L, increase in Hb non-significantly reduced the log-odds of CMS (β = -0.039, Z value = -0.912). Above 186 g/L, each 1 g/L increase in Hb significantly raised the odds of CMS by 12% (β\u0026thinsp;=\u0026thinsp;0.117, Z value\u0026thinsp;=\u0026thinsp;2.451). Based on these thresholds, Hb levels were then categorized into three groups (\u0026lt;166 g/L, 166\u0026ndash;185 g/L, and \u0026ge;186 g/L) and incorporated into the multivariable conditional logistic regression model again. Key results of the multivariable conditional logistic regression were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Females had 3.02 times higher odds of CMS compared to males (OR\u0026thinsp;=\u0026thinsp;3.02, 95% CI: 1.19\u0026ndash;7.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), Hb levels\u0026thinsp;\u0026ge;\u0026thinsp;186 g/L were associated with a 4.76-fold increased odds of CMS (OR\u0026thinsp;=\u0026thinsp;4.76, 95% CI: 1.41\u0026ndash;16.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e\u003cp\u003ePrevalence and intensity of symptoms and signs of CMS\u003c/p\u003e\u003cp\u003eAmong the total study population, the most prevalent symptoms and signs were cyanosis (52.8%), headache (49.0%), breathlessness (46.9%) and sleep disturbance (43.7%). The majority of cases reported mild intensity across symptoms, except for sleep disturbance, which was predominantly moderate (75.8% of affected cases). Among the CMS patients, the most common symptoms and signs remained breathlessness (88.6%), sleep disturbance (85.7%), headache (80%), and cyanosis (78.6%), however, moderate/severe breathlessness increased to 29.1% (vs. 14.4% overall), moderate/severe cyanosis increased to 50.9% (vs. 27.7% overall), and severe headache to 16% (vs. 6.6% overall). Among these most prevalent symptoms, females demonstrated significantly higher rates of both breathlessness (54.7% vs 38.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and headache (60.8% vs 35.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to males (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHRQoL assessment\u003c/p\u003e\u003cp\u003eThe SF-12 assessment revealed impairments in both physical and mental health among CMS patients with physical health being more severely affected (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). PCS scores were markedly lower in the CMS group (33.50 \u0026plusmn; 14.35 vs. 60.44 \u0026plusmn; 13.69; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d = -1.95, large effect). Similarly, MCS scores were significantly reduced in the CMS group (46.96 \u0026plusmn; 12.96 vs. 53.71 \u0026plusmn; 12.59; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d = -0.53, medium effect). These effects exceeded the minimal important difference (MID) thresholds for both PCS (\u0026ge;\u0026thinsp;3 points) and MCS (\u0026ge;\u0026thinsp;5 points), indicating clinically meaningful deteriorations. The CMS group exhibited statistically significant impairments in all physical health domains compared to the group without CMS (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Large effect sizes (Cohen's d\u0026thinsp;\u0026gt;\u0026thinsp;0.8) were observed for General Health (GH) (Cohen\u0026rsquo;s d = -0.83), Physical Functioning (PF) (Cohen\u0026rsquo;s d = -1.40), and Role Physical (RP) (Cohen\u0026rsquo;s d = -2.15), while Bodily Pain (BP) demonstrated a medium effect size (Cohen\u0026rsquo;s d = -0.58). For mental health domains, significant between-group differences were only found in Mental Health (MH) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)and Role Emotional (RE). The MH showed a medium effect size(Cohen\u0026rsquo;s d = -0.78), whereas the RE exhibited a small effect size (Cohen\u0026rsquo;s d = -0.32). No significant differences were observed in other domains.\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\u003eComparison of descriptive statistics between group with CMS and without CMS before and after age matching.\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eUnmatched\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMatched\u003c/p\u003e\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\u003cp\u003eGroup with CMS (\u003cem\u003en\u003c/em\u003e = 70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup without CMS (\u003cem\u003en\u003c/em\u003e = 271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e- value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGroup with CMS (\u003cem\u003en\u003c/em\u003e = 67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGroup without CMS (\u003cem\u003en\u003c/em\u003e = 156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e- value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender *\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (19.4)\u003c/p\u003e\u003cp\u003e39 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129 (80.6)\u003c/p\u003e\u003cp\u003e142 (78.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (26.8)\u003c/p\u003e\u003cp\u003e37 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 (73.2)\u003c/p\u003e\u003cp\u003e74 (66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.03 \u0026plusmn; 8.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.23 \u0026plusmn; 9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.49 \u0026plusmn; 7.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.59 \u0026plusmn; 7.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidents *\u003c/p\u003e\u003cp\u003eMigrant\u003c/p\u003e\u003cp\u003eNative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (20.5)\u003c/p\u003e\u003cp\u003e43 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (79.5)\u003c/p\u003e\u003cp\u003e166 (79.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (31.8)\u003c/p\u003e\u003cp\u003e40 (29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58 (68.2)\u003c/p\u003e\u003cp\u003e98 (71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual status *\u003c/p\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (48.4)\u003c/p\u003e\u003cp\u003e24 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (51.6)\u003c/p\u003e\u003cp\u003e126 (84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (44.8)\u003c/p\u003e\u003cp\u003e24 (29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (55.2)\u003c/p\u003e\u003cp\u003e58 (70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of high-altitude residence **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.69 \u0026plusmn; 16.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.70 \u0026plusmn; 16.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.73 \u0026plusmn; 16.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.39 \u0026plusmn; 15.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161.49 \u0026plusmn; 6.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163.10 \u0026plusmn; 7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161.66 \u0026plusmn; 6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e163.03 \u0026plusmn; 8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.41 \u0026plusmn; 12.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.76 \u0026plusmn; 12.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.20 \u0026plusmn; 12.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.62 \u0026plusmn; 11.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.22 \u0026plusmn; 4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.61 \u0026plusmn; 3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.08 \u0026plusmn; 4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.96 \u0026plusmn; 3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132.03 \u0026plusmn; 17.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.98 \u0026plusmn; 17.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131.81 \u0026plusmn; 17.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129.94 \u0026plusmn; 17.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.71 \u0026plusmn; 14.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.96 \u0026plusmn; 14.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.57 \u0026plusmn; 14.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.62 \u0026plusmn; 15.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.94 \u0026plusmn; 12.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.48 \u0026plusmn; 12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.58 \u0026plusmn; 12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.34 \u0026plusmn; 12.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.79 \u0026plusmn; 3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.10 \u0026plusmn; 3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 \u0026plusmn; 3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89.65 \u0026plusmn; 3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb (g/L) **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173.87 \u0026plusmn; 27.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163.18 \u0026plusmn; 19.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171.66 \u0026plusmn; 30.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e164.10 \u0026plusmn; 19.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* n (%); ** mean \u0026plusmn; sd.\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\u003eUnivariate analysis of baseline characteristics after PSM between group with CMS and without CMS\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=\"char\" char=\".\" 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\u003eGroup with CMS (\u003cem\u003en\u003c/em\u003e = 67)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup without CMS (\u003cem\u003en\u003c/em\u003e = 156)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender*\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30(26.8)\u003c/p\u003e\u003cp\u003e37(33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82(73.2)\u003c/p\u003e\u003cp\u003e74(66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49 (0.81\u0026ndash;2.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidents *\u003c/p\u003e\u003cp\u003eMigrant\u003c/p\u003e\u003cp\u003eNative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27(31.8)\u003c/p\u003e\u003cp\u003e40(29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(68.2)\u003c/p\u003e\u003cp\u003e98(71.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82 (0.46\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual status*\u003c/p\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(44.8)\u003c/p\u003e\u003cp\u003e24(29.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(55.2)\u003c/p\u003e\u003cp\u003e58(70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.96(0.81\u0026ndash;4.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of high-altitude residence**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.73 \u0026plusmn;16.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.39 \u0026plusmn;15.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.97\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (39.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (60.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51 (0.77\u0026ndash;2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e161.66 \u0026plusmn; 6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163.03 \u0026plusmn; 8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97 (0.94\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.20 \u0026plusmn; 12.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.62 \u0026plusmn; 11.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.98\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.08 \u0026plusmn; 4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.96 \u0026plusmn; 3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.98\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.81\u0026plusmn; 17.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.94 \u0026plusmn; 17.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.57 \u0026plusmn; 14.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.62 \u0026plusmn; 15.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.58 \u0026plusmn; 12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.34 \u0026plusmn; 12.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.99\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.00 \u0026plusmn; 3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.65 \u0026plusmn; 3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.88\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171.66\u0026plusmn; 30.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164.10 \u0026plusmn; 19.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* n (%); ** mean \u0026plusmn; sd.\u003c/p\u003e\u003cp\u003e\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\u003eMultivariable conditional logistic regression analysis for independent predisposing factors of CMS\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eZ-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender(Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.19\u0026ndash;7.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidents(Native)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u0026ndash;1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHb\u0026ge;186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.41\u0026ndash;16.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u0026ndash;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.98\u0026ndash;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u0026ndash;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.29\u0026ndash;3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual status (Postmenopausal)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u0026ndash;4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* Analyzed among females only.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGender comparison of symptoms and signs of CMS among the total study population\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003eMale\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreathlessness \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61(38.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99(54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep disturbance \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63(39.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86(47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyanosis \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e103(64.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77(42.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDilatation of veins \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9(5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParesthesia \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42(26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58(32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeadache \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57(35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110(60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTinnitus \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52(32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60(33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ecomparison of HRQoL between group with CMS and without CMS.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003eGroup with CMS (\u003cem\u003en\u003c/em\u003e = 70)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup without CMS (\u003cem\u003en\u003c/em\u003e = 271)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e- value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCS*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e33.50\u0026thinsp;\u0026plusmn;\u0026thinsp;14.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e60.44\u0026thinsp;\u0026plusmn;\u0026thinsp;13.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGH*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e27.14\u0026thinsp;\u0026plusmn;\u0026thinsp;17.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e45.30\u0026thinsp;\u0026plusmn;\u0026thinsp;22.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePF*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e50.00\u0026thinsp;\u0026plusmn;\u0026thinsp;20.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e76.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRP*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e21.61\u0026thinsp;\u0026plusmn;\u0026thinsp;19.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e61.30\u0026thinsp;\u0026plusmn;\u0026thinsp;18.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e47.14\u0026thinsp;\u0026plusmn;\u0026thinsp;18.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e57.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCS*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e46.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e53.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRE*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e47.86\u0026thinsp;\u0026plusmn;\u0026thinsp;22.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e53.74\u0026thinsp;\u0026plusmn;\u0026thinsp;17.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVT*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e44.64\u0026thinsp;\u0026plusmn;\u0026thinsp;17.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e48.25\u0026thinsp;\u0026plusmn;\u0026thinsp;18.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMH*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e39.64\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e50.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSF*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e62.14\u0026thinsp;\u0026plusmn;\u0026thinsp;17.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e65.22\u0026thinsp;\u0026plusmn;\u0026thinsp;17.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* mean \u0026plusmn; sd.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn past decades, thousands of residents on the plateau have been investigated to determine prevalence of CMS, erythrocytosis, hypoxemia. The prevalence of CMS varies significantly across different regions worldwide from 5\u0026ndash;33% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several studies examining physiologic adaptation to high-altitude in the East African Mountains have noted absence of CMS in this region of the world [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In Bolivia as a whole approximately two‑thirds of the population live at altitudes greater than 3000 m and CMS is deemed a considerable public health problem, studies there have found CMS rates between 8% and 10% in the male active population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. More extensive research into the prevalence of CMS has been conducted in the Andes, several studies in Cerro de Pasco have found CMS prevalence rates of 14.8% and 18.2% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], studies in the Peruvian central Andes located at 4100 m have found CMS prevalence rates as high as 32.6% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Epidemiological investigations by Chinese researchers documented that prevalence of CMS among native Tibetans, Han Chinese immigrated to Qinghai plateau in Tibet were 1.21% and 5.57%, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The reasons for this large variability are not well‑understood, though they may be associated with ethnic differences, residential altitude, lifestyle, and the diagnostic criteria for CMS used in different studies. Previous studies have also reported that urbanization and associated changes in lifestyle could have role in development of CMS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, epidemiological studies on CMS remain relatively scarce for certain subpopulations, particularly among urban residents.\u003c/p\u003e\u003cp\u003eThis epidemiological study of CMS among urban residents of the Northwestern Sichuan Plateau in China revealed 20.5% (95% CI: 16.2\u0026ndash;24.8%) of the population were affected with CMS, with the clinical spectrum dominated by mild presentations. Females have a higher prevalence of CMS compared to males. The prevalence of CMS observed in the present study was higher than that reported in previous studies from the Indian Himalayas at comparable elevations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The relatively high prevalence of CMS observed in our study may be explained by several factors. Our study primarily focused on urban populations at high altitude. Previous research has reported that urbanization and associated changes in lifestyle could have role in development of CMS, and our findings support this hypothesis. Existing evidence suggests that physical inactivity may contribute to the development and progression of CMS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. We speculate that the predominantly sedentary lifestyle characteristic of urban high-altitude residents may represent a potential contributing factor to their increased susceptibility to CMS. Recent studies have demonstrated that regular physical exercise provides numerous physiological benefits, not only in healthy individuals at high altitude, but also in patients with CMS. Exercise training, especially moderate-intensity aerobic exercise, might be used as a non-pharmacological therapy for CMS [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The participants in our study live at elevations ranging from 3,500 to 4,000 meters, which is higher than the altitudes reported in many prior studies, altitude of residence is a significant risk factor for the development of chronic mountain sickness. Much of the historical research on CMS has focused on male populations, while our study included both male and female participants, notably, our findings revealed a higher prevalence of CMS among females compared to males in the studied population, this could be one of the contributing factors. In this study, we applied the latest diagnostic criteria for CMSࣧthe Qinghai CMS score criteria. Differences in diagnostic criteria may partially account for this discrepancy. It should be noted that some researchers have raised concerns that current CMS diagnostic criteria may be overly sensitive but lack specificity, furthermore, some researchers argue that most CMS symptoms are also commonly shared with cardiac, pulmonary, and other diseases, which lead the scoring system less reflect maladaptation to high altitudes after long-term hypoxic exposure, but instead may reflect lung or cardiac malfunction [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe diagnostic setting of Hb threshold for erythrocytosis remains a contentious issue in clinical practice. Erythrocytosis is essentially an adaptive response to long-term hypoxic exposure, making it difficult to be distinguished from a maladaptive response. Currently, the Qinghai CMS score criteria is used to diagnose CMS, with Hb levels of more than 210 g/L in male and more than 190 g/L in female as the cut-off values for diagnosing excessive erythrocytosis (EE) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The cut-off points were originally established based on the Gaussian distribution. However, several studies suggest that EE defined by these cutoffs shows no significant association with the symptoms and signs of CMS, despite it has been presumed this elevated Hb levels as responsible of the signs and symptoms of CMS. Given this discrepancy between Hb concentration and symptoms, different Hb thresholds have been proposed by medical researchers from different groups and countries [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, Jiang et al. have applied k-means clustering methods, recommending Hb \u0026ge; 200 g/L as a more clinically relevant threshold for EE in CMS diagnosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the first population (Hb\u0026thinsp;\u0026lt;\u0026thinsp;200 g/L), which represents the physiologic response of erythrocytosis, the SaO2 remains stable as Hb increases; in the second population (Hb \u0026ge; 200 g/L), which represents the pathologic response part of erythrocytosis, the SaO\u003csub\u003e2\u003c/sub\u003e decreases as the Hb increases. Reeves and Leon-Velarde in their study showed that optimal Hb level at high altitude, which allows greater oxygen extraction, was 175 g/L [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eIn our study, we employed segmented linear regression to identify two inflection points in the HB-CMS relationship, which were 166 g/L and 186 g/L, respectively. When hemoglobin levels were \u0026lt; 166 g/L, the prevalence of CMS showed no significant change as Hb increases. Between 166\u0026ndash;185 g/L, the prevalence of CMS slightly decreased as the Hb increases. However, when hemoglobin levels exceeded 186 g/L, the prevalence of CMS significantly increased with further rises in hemoglobin. So, we recommended the hemoglobin threshold for this population to be 186 g/L. The association we observed between Hb levels and CMS is consistent with the known relationship between Hb levels and blood oxygen-transport capacity. When the Hb concentration is below the \u0026ldquo;optimal\u0026rdquo; value, physiologic erythrocytosis response helps\u003c/p\u003e\u003cp\u003eto elevate ability of blood to transport oxygen. When the Hb exceeds this \u0026ldquo;optimal\u0026rdquo; value, the erythrocytosis response aggravates the oxygen supplication-demanding imbalance and triggers CMS pathologic processes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Of course, further studies are needed to explore the relationship between this threshold and the symptoms and signs of CMS in subsequent research.\u003c/p\u003e\u003cp\u003eIncreasing age are well established risk factors for CMS [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Given significant baseline age disparities between group with CMS and group without CMS, which may confound outcomes through associations with variable, such as menstrual status and duration of high-altitude residence. 1:3 nearest-neighbor PSM was performed to minimize age-related confounding. After age matching, univariate analysis revealed that Hb levels were significantly associated with CMS. Although CMS was more prevalent among postmenopausal women, the differences were not statistically significant. Multivariable conditional logistic regression analysis revealed that females and Hb level emerged as independent associated factors for CMS. Menopausal status was analyzed only in the female subgroup due to its inapplicability to males. No significant association was observed between menopausal status and the prevalence of CMS.\u003c/p\u003e\u003cp\u003eIn contrast to most previous studies identifying males as a primary risk factor for CMS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our study demonstrate females as an independent associated factor. This finding is consistent with a epidemiological study conducted in natives of Spiti in greater Himalayas, which similarly reported higher CMS prevalence among females [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, this study attributed this sex disparity to women's prolonged exposure to indoor air pollution and consequent pulmonary dysfunction - a potential contributory mechanism that may partially explain elevated CMS susceptibility in females. In our study, the higher CMS symptom score was recorded in women than in men, but the lower prevalence of excessive erythrocytosis and comparable hypoxemia rates were observed. This may point out the lack of specificity of contemporary symptoms based diagnostic criteria in females for diagnosis of CMS or may be that the threshold for symptoms related to hypoxemia and erythrocytosis in females is lower than in males. Moreover, in contrast to prior researches, our study employed age-based matching to mitigate age-related confounding effects between genders. The observed inconsistency in the association between female sex and CMS risk\u0026mdash;initially non-significant in univariate analysis but significant in multivariable analysis\u0026mdash;may result from confounding protective factors in the female population, including younger age (43 vs. 46 years), shorter high-altitude exposure (34 vs. 37 years), and lower Hb levels (152 vs. 181 g/L). These factors likely attenuated the true physiological susceptibility of females in unadjusted models, which became apparent only after multivariate adjustment.\u003c/p\u003e\u003cp\u003eBuilding upon our segmented regression results identifying 186 g/L as the critical hemoglobin threshold, subsequent multivariable analysis demonstrated that Hb levels\u0026thinsp;\u0026ge;\u0026thinsp;186 g/L was an independent associated factor. Many studies of adaptation to high-altitude hypoxia have focused on the blood because of its role in oxygen transport. Below the optimal hemoglobin threshold, numerous of individuals effectively increase oxygen delivery capacity through elevated hemoglobin concentrations, thereby reducing CMS risk. However, beyond this optimal level, excessive erythrocytosis impairs oxygen transport through increased blood viscosity, triggering a vicious cycle of tissue hypoxia and further erythrocytosis. Previous studies have suggested that the Tibetans lived at altitudes of 3,800\u0026ndash;4,065 meters without significant erythrocytosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Relatively low Hb concentrations among Tibetan high-altitude natives have also been widely reported in numerous studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In our study population, the prevalence of EE was 4.1%, which is higher than previously reported rates among Tibetan pastoralists. Our findings suggest potential differences in hemoglobin concentrations between urban residents and pastoral populations in high-altitude regions. These findings also suggest that CMS screening and intervention should be prioritized for urban residents in the northwestern Sichuan plateau when hemoglobin levels exceed 186 g/L. Further studies with more diverse populations are required to establish population-specific hemoglobin thresholds.\u003c/p\u003e\u003cp\u003ePrevious studies identified menopause as a risk factor for CMS [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This phenomenon may be attributed to postmenopausal elevation of the testosterone-to-estradiol ratio and cessation of periodic blood loss, both contributing to increased hemoglobin levels. Contrary to previous reports, our analysis revealed no significant association between menopausal status and CMS among female participants. A previous study also failed to observe an increase in Hb levels after menopause [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The exact cause underlying this phenomenon require further investigation. One possible explanation for this difference is that they did not account for the confounding effect of age, as postmenopausal women are generally older than premenopausal women and advancing age is a well-established risk factor for CMS.\u003c/p\u003e\u003cp\u003eThe growing recognition of the value of real-world data on HRQoL in informing disease management and policy development is increasingly evident. Patients of CMS are disabled with impaired memory, headache, breathlessness, fatigue, disturbed sleep, anorexia, and tinnitus that affect the quality of life. These limitations may force patients to discontinue prolonged residence at high altitudes or full-time work. Even clinicians may underestimate this impact. Study has demonstrated a significant correlation between the HRQoL of high-altitude urban residents and the occurrence of CMS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, research on the effects of CMS on patients' quality of life is crucial for understanding the characteristics of this disease and the associated disease burden it imposes.\u003c/p\u003e\u003cp\u003eThe SF-12 is a widely used and validated HRQoL assessment instrument in populations at sea level and at high altitude, serving as an abbreviated version of the SF-36 (Short Form-36). This 12-item questionnaire evaluates an individual's overall physical and mental health status. The results of our study indicate that populations without CMS exhibit significantly better health scores compared to those with CMS. CMS adversely affects both the physical and mental health of patients, with a more pronounced impact on physical health. In terms of physical health, compared to non-CMS high-altitude populations, CMS patients showed poorer performance across multiple dimensions of physical health, with the most pronounced impairment observed in the RP dimension. This suggests that physical health limitations predominantly manifest as restrictions in occupational functioning, which is particularly critical for high-altitude urban populations who constitute the primary workforce in plateau development. Regarding mental health, CMS patients demonstrated the most significant deterioration in the MH domain compared to non-CMS populations, suggesting that emotional disturbances (e.g., depression and anxiety) likely constitute the primary psychological burden. This phenomenon may be attributed to chronic hypoxic-induced hippocampal atrophy and prefrontal cortex dysfunction, which elevate susceptibility to mood disorders, compounded by persistent sleep disturbances exacerbating emotional dysregulation [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, limited healthcare resources and inadequate psychological support in high-altitude regions often result in prolonged untreated mental health conditions. Given the substantial MH domain impairment, routine depression/anxiety screening and targeted psychological interventions should be integrated into CMS patient management.\u003c/p\u003e\u003cp\u003eTo our knowledge, this represents the first study to investigate both the burden of CMS and its HRQoL impacts among urban residents in high-altitude regions. this study also represents the first application combining matching with multivariable logistic regression in cross-sectional research on CMS. The integrated use of matching and multivariable logistic regression addresses the limitations of conventional approaches: it compensates for traditional regression's neglect of intergroup comparability while overcoming matching's inability to perform model-based adjustments. This dual-control strategy for confounding factors creates synergistic effects where 'the whole is greater than the sum of its parts' (1\u0026thinsp;+\u0026thinsp;1\u0026thinsp;\u0026gt;\u0026thinsp;2). The combined methodology can be extended to other high-altitude-related disorders (e.g., high-altitude pulmonary hypertension) or similar cross-sectional studies, providing a replicable methodological framework for future research in high-altitude medicine.\u003c/p\u003e"},{"header":"conclusion","content":"\u003cp\u003ePrevalence of CMS amongst urban residents of the Northwestern Sichuan Plateau in China residing at an altitude of 3500 to 4000 meters was 20.5%. After age matching, female, and hemoglobin levels \u0026ge; 186 g/L were identified as significant associated factors for CMS. Populations with CMS exhibit poorer health status compared to those without CMS, with more pronounced impairments in physical health. Observations made in the present study have important implications for health policy makers and public health providers for creating awareness among urban residents of the Northwestern Sichuan Plateau about CMS burden, associated factors and appropriate preventive measures in order to reduce morbidity and improve quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAdditional information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.H. conceived the study, analyzed data, drafted the manuscript and gave final approval. P.B. and Z.J. contributed to conception and design of study, analysis of data, and gave final approval. L.Y., H.M., D.P., and F.S. contributed to acquisition of data and gave final approval. L.W. contributed to conception and design of study, analysis and interpretation of results, critically revised the manuscript and gave final approval. All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are available upon request by contacting Dr. Wenjun Li [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMonge, C. C., Arregui, A. \u0026amp; Le\u0026oacute;n-Velarde, F. Pathophysiology and epidemiology of chronic mountain sickness. \u003cem\u003eInt. J. Sports Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (Suppl 1), S79\u0026ndash;81 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Velarde, F. et al. Consensus statement on chronic and subacute high altitude diseases. \u003cem\u003eHigh. Alt Med. Biol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (2), 147\u0026ndash;157 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNegi, P. C. et al. Epidemiological study of chronic mountain sickness in natives of Spiti Valley in the Greater Himalayas. \u003cem\u003eHigh. Alt Med. Biol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (3), 220\u0026ndash;229 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohn, B. \u0026amp; West La, M. D. Hypoxia, polycythemia, and chronic mountain sickness. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e94\u003c/b\u003e (1), A22\u0026ndash;A23 (1988).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeall, C. M., Brittenham, G. M., Macuaga, F. \u0026amp; Barragan, M. Variation in hemoglobin concentration among samples of high-altitude natives in the Andes and the Himalayas. \u003cem\u003eAm. J. Hum. Biol.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e (6), 639\u0026ndash;651 (1990).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChadha, V. Sample size determination in health studies. \u003cem\u003eNTI Bull.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (3\u0026amp;4), 55\u0026ndash;62 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR., K. T., Book Reviews -- Statistical Power Analysis for the Behavioral Sciences (2nd ed.) by Jacob Cohen. \u003cem\u003eEducational and Psychological Measurement\u003c/em\u003e. Vol.50(No.1), 225 (1990).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeall, C. M. High-altitude adaptations. \u003cem\u003eLancet\u003c/em\u003e (2003). 362 Suppl, s14-15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVargas, E. \u0026amp; Spielvogel, H. Chronic mountain sickness, optimal hemoglobin, and heart disease. \u003cem\u003eHigh. Alt Med. Biol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (2), 138\u0026ndash;149 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArregui, A. et al. Migraine, polycythemia and chronic mountain sickness. \u003cem\u003eCephalalgia\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 339\u0026ndash;341 (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzales, G. F., Rubio, J. \u0026amp; Gasco, M. Chronic mountain sickness score was related with health status score but not with hemoglobin levels at high altitudes. \u003cem\u003eRespir Physiol. Neurobiol.\u003c/em\u003e \u003cb\u003e188\u003c/b\u003e (2), 152\u0026ndash;160 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahota, I. S. \u0026amp; Panwar, N. S. Prevalence of Chronic Mountain Sickness in high altitude districts of Himachal Pradesh. \u003cem\u003eIndian J. Occup. Environ. Med.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (3), 94\u0026ndash;100 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeixeira, A. L. \u0026amp; Lang, J. A. Exercise is medicine for chronic mountain sickness. \u003cem\u003eExp. Physiol.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (11), 2153\u0026ndash;2154 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzales, G. F., Tapia, V., Gasco, M. \u0026amp; Gonzales-Casta\u0026ntilde;eda, C. Serum testosterone levels and score of chronic mountain sickness in Peruvian men natives at 4340 m. \u003cem\u003eAndrologia\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (3), 189\u0026ndash;195 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePasha, M. A. \u0026amp; Newman, J. H. High-altitude disorders: pulmonary hypertension: pulmonary vascular disease: the global perspective. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e (6 Suppl), 13s\u0026ndash;19s (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGroepenhoff, H. et al. Exercise pathophysiology in patients with chronic mountain sickness exercise in chronic mountain sickness. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e142\u003c/b\u003e (4), 877\u0026ndash;884 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang, C. et al. Chronic mountain sickness in Chinese Han males who migrated to the Qinghai-Tibetan plateau: application and evaluation of diagnostic criteria for chronic mountain sickness. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 701 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReeves, J. T. \u0026amp; Leon-Velarde, F. Chronic mountain sickness: recent studies of the relationship between hemoglobin concentration and oxygen transport. \u003cem\u003eHigh. Alt. Med. Biol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (2), 147\u0026ndash;155 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLenfant, C. \u0026amp; Sullivan, K. Adaptation to high altitude. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e284\u003c/b\u003e (23), 1298\u0026ndash;1309 (1971).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSime, F., Monge, C. \u0026amp; Whittembury, J. Age as a cause of chronic mountain sickness (Monge's disease). \u003cem\u003eInt. J. Biometeorol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (2), 93\u0026ndash;98 (1975).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Velarde, F., Arregui, A., Vargas, M., Huicho, L. \u0026amp; Acosta, R. Chronic mountain sickness and chronic lower respiratory tract disorders. \u003cem\u003eChest\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (1), 151\u0026ndash;155 (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpicuzza, L. et al. Sleep-related hypoxaemia and excessive erythrocytosis in Andean high-altitude natives. \u003cem\u003eEur. Respir J.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 41\u0026ndash;46 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJulian, C. G. \u0026amp; Moore, L. G. Human Genetic Adaptation to High Altitude: Evidence from the Andes. \u003cem\u003eGenes (Basel)\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e(2) (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeall, C. M. et al. Hemoglobin concentration of high-altitude Tibetans and Bolivian Aymara. \u003cem\u003eAm. J. Phys. Anthropol.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (3), 385\u0026ndash;400 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe\u0026oacute;n-Velarde, F. et al. The role of menopause in the development of chronic mountain sickness. \u003cem\u003eAm. J. Physiol.\u003c/em\u003e \u003cb\u003e272\u003c/b\u003e (1 Pt 2), R90\u0026ndash;94 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinslow, R. M. et al. Different hematologic responses to hypoxia in Sherpas and Quechua Indians. \u003cem\u003eJ. Appl. Physiol. (1985)\u003c/em\u003e. \u003cb\u003e66\u003c/b\u003e (4), 1561\u0026ndash;1569 (1989).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHota, S. K., Barhwal, K., Singh, S. B. \u0026amp; Ilavazhagan, G. Differential temporal response of hippocampus, cortex and cerebellum to hypobaric hypoxia: a biochemical approach. \u003cem\u003eNeurochem Int.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (6\u0026ndash;7), 384\u0026ndash;390 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou, Y. et al. Establishment and evaluation of a simulated high\u0026ndash;altitude hypoxic brain injury model in SD rats. \u003cem\u003eMol. Med. Rep.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (4), 2758\u0026ndash;2766 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrescot, A. et al. Effect of moderate altitude on human cerebral metabolite levels: A preliminary, multi-site, proton magnetic resonance spectroscopy investigation. \u003cem\u003ePsychiatry Res. Neuroimaging\u003c/em\u003e. \u003cb\u003e314\u003c/b\u003e, 111314 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic mountain sickness, Urban residents, The Northwestern Sichuan Plateau, Health-related quality of life","lastPublishedDoi":"10.21203/rs.3.rs-7033332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7033332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith rapid urbanization in high-altitude regions, the health impacts of chronic hypoxia on plateau urban residents remain understudied. This study aims to determine the prevalence of chronic mountain sickness (CMS) and its associated factors among urban residents of the Northwestern Sichuan Plateau and further assess the health-related quality of life (HRQoL) in this population. A cross-sectional survey study was conducted in urban residents of the Northwestern Sichuan Plateau aged \u0026ge;20 years residing at an altitude of 3500 to 4000 meters. Demographics, physiological parameters, hematological parameters, specified symptoms of CMS were recorded. CMS was diagnosed using the Qinghai CMS score criteria. Propensity score matching (1:3) was performed to adjust for age differences between the CMS group and the group without CMS. A segmented linear regression model was employed to identify the hemoglobin threshold associated with CMS. Multivariable conditional logistic regression was used to evaluate independent associated factors for CMS after age matching. 12-Item Short Form Health Survey (SF-12) was employed to assess the HRQoL in this population. Prevalence of CMS was 20.5% (95% CI: 16.2%-24.8%). Female and hemoglobin (Hb) levels \u0026ge; 186g/L were independent associated factors for CMS after controlling for age with odds ratio (OR) as 3.02 (95% CI: 1.19\u0026ndash;7.64) and OR 4.76 (95% CI: 1.41\u0026ndash;16.06), respectively. The SF-12 assessment demonstrated significantly impaired physical and mental health status in CMS patients, with a more pronounced deterioration in physical health components. This study enhances understanding of CMS burden and associated factors in plateau urban populations, supporting sustainable development in these areas.\u003c/p\u003e","manuscriptTitle":"Prevalence and Impact of Chronic Mountain Sickness on Quality of Life Among Urban Residents in Northwestern Sichuan, China: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 09:18:11","doi":"10.21203/rs.3.rs-7033332/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-20T07:34:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14257572945543023238642287055128974240","date":"2025-11-10T03:43:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T22:05:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244764856349627252713322594048071335167","date":"2025-10-27T01:16:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-24T07:25:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-09T20:48:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-04T18:22:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T15:04:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-03T01:59:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33880f18-ad95-4fe3-8b72-0387f47ad9e9","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57329833,"name":"Health sciences/Diseases"},{"id":57329834,"name":"Health sciences/Health care"},{"id":57329835,"name":"Health sciences/Medical research"},{"id":57329836,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-05T09:18:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 09:18:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7033332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7033332","identity":"rs-7033332","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}