Abstract
Introduction
The impact of high altitude on patients with interstitial lung disease (ILD) remains unclear. This study aimed to describe the clinical characteristics of ILD patients in high-altitude regions.
Methods
This retrospective observational study included patients diagnosed with Idiopathic Pulmonary Fibrosis (IPF) and Connective Tissue Disease-associated ILD (CTD-ILD) hospitalized at two hospitals in Qinghai and Tibet between April 2018 and September 2021. Patients were categorized into high-altitude (≥ 2500 m) and low-altitude (< 2500 m) groups. Demographic, clinical, hematological, and pulmonary function data were collected and analysed.
Results
A total of 119 patients were enrolled, with 56 in the high-altitude group and 63 in the low-altitude group. Compared with the low-altitude group, the high-altitude group had a significantly greater proportion of CTD-ILD patients (42.2% vs. 82.1%, p < 0.001). Patients in the high-altitude group presented higher red blood cell counts and hemoglobin and hematocrit levels but lower PaO2/FiO2 ratio (P/F ratio), FVC, and DLCO.
Conclusions
This study revealed a higher prevalence of CTD-ILD, an increased proportion of Tibetan ethnicity, differences in the P/F ratio, MCV, FVC, MCHC, and HGB levels in the high-altitude group.
Keywords: Interstitial lung disease, Idiopathic pulmonary fibrosis, Connective tissue disease-associated interstitial lung disease, High altitude
Introduction
Interstitial lung diseases (ILDs) are a group of disorders characterized by inflammation and fibrosis of the lung parenchyma [1–3]. Idiopathic pulmonary fibrosis (IPF) and connective tissue disease-associated interstitial lung disease (CTD-ILD) are the two most common classes in clinical practice [4]. Both conditions significantly impact patients’ quality of life and pose challenges in management [5–7]. IPF is a chronic, progressive, and irreversible lung disease with a poor prognosis [5, 6, 8–11], whereas CTD-ILD encompasses a heterogeneous group of ILDs associated with underlying connective tissue diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and progressive systemic sclerosis (PSS) [5, 12–14].
The unique environmental conditions at high altitudes (HA), including low atmospheric pressure, hypoxia, low temperatures, low relative humidity, and elevated UV-B radiation, have been shown to impact various systems of the body [15–17]. Several studies have explored the potential influence of HA on CTD [14, 18–20]. A study by Qian reported that low-temperature and high-UV-B radiation exposure in HA environments can exacerbate the systemic symptoms of CTD diseases [20], and another study by Kondo reported differences in the proportions of T-cell and B-cell subgroups in RA patients living in HA compared with those in low-altitude areas [14]. These findings suggest that HA may influence the immune characteristics of CTD-related conditions.
Regarding the impact of HA on ILD, a study demonstrated that a significant proportion of patients with interstitial lung diseases living at high altitudes had high haematocrit levels [18]. Another study by Seccombe simulated conditions equivalent to an altitude of 2438 m and reported a notable decrease in the PaO2 levels of ILD patients, from an average of 78 ± 12 mmHg to 49 ± 8 mmHg [19]. Despite these findings, data on the potential influence of HA on the clinical characteristics of ILD patients, particularly those with IPF and CTD-ILD, are scarce.
Given the limited understanding of the impact of HA on IPF and CTD-ILD, this retrospective study aimed to investigate the distinctive clinical features of these conditions in individuals who have resided in HA areas (≥ 2500 m) compared with those in low-altitude (LA) areas (< 2500 m). By comparing the clinical, hematological, and pulmonary function test characteristics of IPF and CTD-ILD patients residing in the HA and LA regions, we aimed to identify potential associations between altitude and the clinical presentation, blood parameters, and pulmonary function in these diseases.
Methods
Study design and population
We conducted a retrospective cross-sectional study of ILD patients admitted to Qinghai People’s Hospital and Tibet Autonomous Region People’s Hospital between April 2018 and September 2021. Qinghai Provincial People’s Hospital is located in Xining (altitude ~ 2,260 m), and Tibet Autonomous Region People’s Hospital is located in Lhasa (altitude ~ 3,650 m). As the largest tertiary regional referral centers on the Qinghai-Tibet Plateau, these hospitals are equipped with comprehensive diagnostic facilities and serve patients from diverse altitude areas (ranging from ~ 1,500 to over 5,000 m). This study was approved by the Ethics Committee of Qinghai People’s Hospital, Tibet Autonomous Region People’s Hospital and West China Hospital of Sichuan University (Ethics number: Review No. 716 of 2021).
ILD patients were included in the study if they met the following criteria: (1) were over 18 years of age; (2) confirmed diagnosis of IPF or CTD-ILD. IPF diagnosis was established through multidisciplinary discussion (MDD) at each center involving pulmonologists, radiologists, and rheumatologists. A definitive diagnosis required unanimous consensus based on the 2018 ATS/ERS/JRS/ALAT guidelines; cases with diagnostic uncertainty were excluded [5]. CTD-ILD was diagnosed in patients with confirmed CTD and concomitant ILD features on HRCT. CTD diagnoses were adjudicated by rheumatologists according to validated classification criteria, including rheumatoid arthritis [21], systemic sclerosis [22], systemic lupus erythematosus [23], Sjögren’s syndrome [24], idiopathic inflammatory myopathies [25], and other CTDs.
Patients were excluded from the study if they had any of the following conditions: (1) comorbidities that could significantly affect the prognosis of ILD, such as malignant tumors; (2) pregnancy or lactation; (3) severe pulmonary infections upon admission, to minimize interference with ILD classification caused by acute lung lesions; or (4) incomplete hospitalization data. For patients who had multiple hospitalizations due to ILD exacerbation during the study period, only data from the initial presentation and hospital admission were included to avoid duplication and potential bias in the analysis.
Data collection
Demographic, clinical, blood routine, arterial blood gas, and pulmonary function data were extracted from the electronic medical records of the included patients via a standardized case report form (CRF).
The baseline information included age, sex, height, weight, body mass index (BMI), ethnicity, occupation, smoking history, family history, and altitude of residence. The altitude of each patient’s residence was determined on the basis of the county-level administrative unit, which is the smallest administrative division in China. The altitude data, measured in meters above sea level, were obtained from the Human–Earth System under the Institute of Geographical Sciences and Resources at the Chinese Academy of Sciences.
The clinical feature data included ILD subtype, symptoms, physical signs, comorbidities, admission method, and sex-age-physiology (ILD-GAP) score [26], a clinical prediction model based on sex, age, and lung physiology.
The laboratory examinations included in the analysis were limited to the initial tests performed upon patient admission or diagnosis. These tests included routine blood tests (hemoglobin, white blood cell count, and platelet count) and arterial blood gas parameters, including partial pressure of oxygen (PaO2), partial pressure of carbon dioxide (PaCO2), pH, and bicarbonate (HCO3).
Pulmonary function data, such as forced vital capacity (FVC), forced expiratory volume in one second (FEV1), total lung capacity (TLC), and diffusing capacity of the lung for carbon monoxide (DLCO), were also extracted.
Definitions and classifications
Patients were categorized into two main groups on the basis of the altitude of their residence: low altitude (LA, < 2,500 m) and high altitude (HA, ≥ 2,500 m), which were determined via county-level administrative unit data from the Chinese Academy of Sciences. Residence was defined as living in the respective altitude region for at least ten years. Within each altitude group, patients were further classified into two subgroups on the basis of their ILD diagnosis: CTD-ILD and IPF. IPF was diagnosed on the basis of the 2018 ATS/ERS/JRS/ALAT guidelines, which require the presence of a usual interstitial pneumonia (UIP) pattern or probable UIP on high-resolution computed tomography (HRCT) or a combination of high-resolution computed tomography (HRCT) and surgical lung biopsy results. CTD-ILD was identified in patients with a confirmed connective tissue disease (CTD) diagnosis, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), or progressive systemic sclerosis (PSS), who simultaneously presented with ILD features.
Statistical analysis
The sample size was determined by the number of eligible patients admitted to the participating hospitals who met the inclusion and exclusion criteria during the study period. Owing to the retrospective nature of the study and the limited availability of data on the prevalence of interstitial lung disease (ILD) in high-altitude regions, no formal sample size calculation was performed.
Continuous variables are expressed as the means ± standard deviations (SDs) for normally distributed data or medians (interquartile ranges, IQRs) for nonnormally distributed data. Categorical variables are presented as frequencies and percentages. Comparisons between the LA and HA groups, as well as between the CTD-ILD and IPF subgroups within each altitude group, were performed via Student’s t test for normally distributed continuous variables, the Mann‒Whitney U test for nonnormally distributed continuous variables, and the chi‒square test or Fisher’s exact test for categorical variables. A two-tailed P value < 0.05 was considered statistically significant.
Correlations between altitude and clinical, blood routine, arterial blood gas, and pulmonary function parameters were assessed separately for the IPF and CTD-ILD subgroups via Spearman’s rank correlation coefficient (ρ). The strength of the correlation was interpreted as weak (| ρ | < 0.3), moderate (0.3 ≤ (| ρ | < 0.7), or strong ((| ρ | ≥ 0.7). Linear regression analysis was performed to investigate the relationships between clinically relevant continuous variables and specific altitude levels within each subgroup. All the statistical analyses were performed via the statistical package IBM SPSS Statistics software (SPSS), version 29.
Results
Patient clinical characteristics
From April 2018 to September 2021, we enrolled 119 patients, including 56 patients (10 IPF and 46 CTD-ILD) in the high-altitude (HA) group and 63 patients (36 IPF and 27 CTD-ILD) in the low-altitude (LA) group (Table 1). The HA group had a significantly higher proportion of CTD-ILD patients compared to the LA group (82.1% vs. 42.2%, p < 0.001).
Table 1.
Demographic and clinical characteristics of IPF and CTD-ILD patients at low and high altitudes
| Characteristic | HA | LA | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Overall N = 56 |
IPF N = 10 |
CTD-ILD N = 46 |
Overall N = 63 |
IPF N = 36 |
CTD-ILD N = 27 |
|||||
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Age ± SD — years | 56 ± 12 | 64 ± 7 | 54 ± 12 | 59 ± 12 | 63 ± 13 | 54 ± 9 | ||||
| Sex, female — no. (%) | 43 (76.8%) | 5 (50.0%) | 38 (82.6%) | 30 (47.6%) | 11 (30.6%) | 19 (70.4%) | ||||
| BMI (IQR) — kg/m2 | 23.0 (21.8, 23.9) | 23.3 (22.6, 23.9) | 23.0 (21.6, 23.8) | 22.00 (18.92, 25.25) | 22.45 (19.40, 25.10) | 21.30 (18.60, 25.50) | ||||
| Admission type — no. (%) | ||||||||||
| Elective admission | 50 (89.3%) | 8 (80.0%) | 42 (91.3%) | 62 (98.4%) | 36 (100.0%) | 26 (96.3%) | ||||
| Emergency admission | 6 (10.7%) | 2 (20.0%) | 4 (8.7%) | 1 (1.6%) | 0 (0.0%) | 1 (3.7%) | ||||
| ILD-GAP Score (IQR) | 1.00 (0.00, 2.00) | 5.00 (4.25, 5.00) | 0.00 (0.00, 1.00) | 1.00 (−1.00, 3.00) | 3.00 (2.00, 4.00) | −1.00 (−1.00, −0.50) | ||||
| Ethnic group— no. (%) | ||||||||||
| Han | 7 (12.5%) | 4 (40.0%) | 3 (6.5%) | 41 (65.1%) | 24 (66.7%) | 17 (63.0%) | ||||
| Tibetan | 49 (87.5%) | 6 (60.0%) | 43 (93.5%) | 22 (34.9%) | 12 (33.3%) | 10 (37.0%) | ||||
| Smoking — no. (%) | 10 (17.9%) | 5 (50.0%) | 5 (10.9%) | 20 (31.7%) | 14 (38.9%) | 6 (22.2%) | ||||
| Comorbidity — no. (%) | 47 (83.9%) | 6 (60.0%) | 41 (89.1%) | 56 (88.9%) | 31 (86.1%) | 25 (92.6%) | ||||
| Cough — no. (%) | 43 (76.8%) | 9 (90.0%) | 34 (73.9%) | 54 (85.7%) | 34 (94.4%) | 20 (74.1%) | ||||
| Dyspnea — no. (%) | 17 (30.4%) | 7 (70.0%) | 10 (21.7%) | 37 (58.7%) | 27 (75.0%) | 18 (66.7%) | ||||
| Chest pain — no. (%) | 17 (30.4%) | 5 (50.0%) | 12 (26.1%) | 9 (14.3%) | 0 (0.0%) | 5 (18.5%) | ||||
| Cyanosis — no. (%) | 15 (26.8%) | 3 (30.0%) | 12 (26.1%) | 19 (30.2%) | 14 (38.9%) | 5 (18.5%) | ||||
| Respiratory failure— no. (%) | 8 (14.3%) | 0 (0.0%) | 8 (17.4%) | 12 (19.0%) | 9 (25.0%) | 3 (11.1%) | ||||
| Characteristic | P-value | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 vs. 4 | 2 vs. 3 | 2 vs. 5 | 3 vs. 6 | 5 vs.6 | ||||||
| Age ± SD — years | 0.098 | 0.002 | 0.767 | 0.821 | 0.002 | |||||
| Sex, female — no. (%) | 0.001 | 0.041 | 0.283 | 0.222 | 0.002 | |||||
| BMI (IQR) — kg/m2 | 0.064 | 0.401 | 0.09 | 0.239 | 0.578 | |||||
| Admission type — no. (%) | 0.051 | 0.289 | 0.043 | 0.645 | 0.429 | |||||
| ILD-GAP Score (IQR) | 0.656 | < 0.001 | < 0.001 | < 0.001 | < 0.001 | |||||
| Ethnic group— no. (%) | < 0.001 | 0.015 | 0.157 | < 0.001 | 0.76 | |||||
| Han | ||||||||||
| Tibetan | ||||||||||
| Smoking — no. (%) | 0.082 | 0.011 | 0.719 | 0.309 | 0.16 | |||||
| Comorbidity — no. (%) | 0.429 | 0.041 | 0.087 | > 0.999 | 0.689 | |||||
| Cough — no. (%) | 0.21 | 0.424 | 0.53 | 0.988 | 0.033 | |||||
| Dyspnea — no. (%) | 0.002 | 0.006 | 0.706 | 0.157 | 0.002 | |||||
| Chest pain — no. (%) | 0.034 | 0.152 | 0.015 | 0.46 | 0.48 | |||||
| Cyanosis — no. (%) | 0.684 | > 0.999 | 0.723 | 0.46 | 0.081 | |||||
| Respiratory failure— no. (%) | 0.488 | 0.326 | 0.172 | 0.736 | 0.165 | |||||
Percentages may not total 100 because of rounding. IQR denotes interquartile range. Summary statistics were reported for nonmissing values. the patient. One patient could present with several symptoms. Abbreviations: LA Low altitude, HA High altitude, IPF Idiopathic pulmonary fibrosis, CTD-ILD Connective tissue disease-associated interstitial lung disease, N Number of subjects, SD Standard deviation, IQR Interquartile range, BMI Body mass index, BMI Body mass index, ILD-GAP Gender-Age-Physiology scoring system for interstitial lung disease
When comparing IPF patients between the two groups, those in the HA group had significantly higher ILD-GAP scores than those in the LA group (5.00 [IQR: 4.25, 5.00] vs. 3.00 [IQR: 2.00, 4.00], p < 0.001).
Among CTD-ILD patients, those in the HA group had a significantly higher proportion of Tibetan ethnicity (93.5% vs. 37.0%, p < 0.001), and higher ILD-GAP scores (0.00 [IQR: 0.00, 1.00] vs. −1.00 [IQR: −1.00, −0.50], p < 0.001) compared to those in the LA group.
In both the HA and LA groups, IPF patients were significantly older, had a lower proportion of females, and had higher ILD-GAP scores than CTD-ILD patients (all p < 0.05). Furthermore, in the HA group, IPF patients had a significantly lower proportion of Tibetan ethnicity and a higher prevalence of dyspnea, while in the LA group, IPF patients had a significantly higher prevalence of cough and dyspnea than their CTD-ILD counterparts (p < 0.05).
Laboratory examination and pulmonary function test
Hematological parameters, including RBC, HGB, HCT, and MCHC were significantly higher in the HA group, while HCT, MCV, and MCH were significantly lower, compared to the LA group (all p < 0.05, Table 2). For IPF patients, WBC was significantly lower in the HA group than in the LA group (p = 0.02). CTD-ILD patients in the HA group had significantly higher RBC, HGB, HCT and MCHC compared to those in the LA group (all p < 0.01).
Table 2.
Hematological, blood gas analysis, and pulmonary function parameters of IPF and CTD-ILD patients at low and high altitudes
| Characteristic | HA | LA | ||||
|---|---|---|---|---|---|---|
| Overall N = 56 | IPF N = 10 | CTD-ILD N = 46 | Overall N = 63 | IPF N = 36 | CTD-ILD N = 27 | |
| 1 | 2 | 3 | 4 | 5 | 6 | |
| MCV±SD — fL | 89.0 ± 5.7 | 90.8 ± 3.8 | 88.7 ± 6.0 | 94.7 ± 6.2 | 93.4 ± 6.0 | 96.4 ± 6.2 |
| MCH±SD — pg | 29.03 ± 2.80 | 29.59 ± 3.28 | 28.91 ± 2.71 | 30.97 ± 2.30 | 31.16 ± 2.16 | 30.71 ± 2.48 |
| RBC (IQR) — *1012/L | 5.01 (4.56, 5.54) | 5.21 (4.43, 6.36) | 5.00 (4.58, 5.52) | 4.62 (4.06, 4.96) | 4.66 (4.16, 5.11) | 4.29 (4.01, 4.90) |
| WBC (IQR) — *1012/L | 5.10 (4.79, 5.73) | 5.06 (4.93, 5.55) | 5.10 (4.73, 5.80) | 5.46 (4.40, 6.43) | 5.49 (4.98, 6.63) | 5.45 (3.91, 6.39) |
| MCHC (IQR) — g/L | 332 (324, 337) | 329 (325, 332) | 334 (323, 338) | 321 (318, 328) | 321 (318, 325) | 322 (320, 328) |
| HGB (IQR) — g/L | 146 (137, 159) | 145 (142, 173) | 146 (136, 159) | 139 (127, 148) | 141 (129, 148) | 130 (121, 148) |
| HCT (IQR) — % | 45 (41, 49) | 43 (40, 54) | 46 (42, 48) | 42 (39, 46) | 43 (39, 46) | 41 (39, 45) |
| PH (IQR) | 7.39 (7.35, 7.45) | 7.42 (7.35, 7.45) | 7.38 (7.35, 7.45) | 7.44 (7.43, 7.46) | 7.44 (7.43, 7.46) | 7.45 (7.43, 7.46) |
| PO2 (IQR) — mmHg | 66 (63, 71) | 64 (61, 66) | 68 (63, 71) | 72 (66, 83) | 72 (66, 76) | 73 (67, 89) |
| P/F ratio (IQR) — mmHg | 201 (187, 215) | 188 (185, 200) | 205 (192, 215) | 229 (218, 258) | 229 (218, 257) | 229 (215, 258) |
| PCO2 (IQR) — mmHg | 36.8 (35.5, 36.8) | 36.8 (35.5, 36.8) | 36.8 (35.6, 36.8) | 35.8 (34.0, 39.0) | 36.0 (34.1, 37.8) | 34.0 (31.0, 41.0) |
| FEV1 (IQR) — % predicted | 74 (70, 77) | 77 (75, 77) | 73 (69, 77) | 76 (71, 82) | 75 (68, 84) | 77 (74, 79) |
| FVC (IQR) — % predicted | 65 (59, 66) | 50 (47, 54) | 65 (60, 66) | 76.1 (74.1, 78.5) | 75.6 (72.8, 77.9) | 77.1 (75.2, 79.3) |
| DLCO (IQR) — % predicted | 50 (47, 52) | 35 (35, 47) | 50 (48, 53) | 55.7 (52.3, 58.0) | 54.3 (51.9, 57.0) | 56.9 (53.7, 58.5) |
| TLC (IQR) — % | 70.5 (68.5, 72.7) | 70.8 (66.2, 76.8) | 70.5 (68.8, 72.6) | 75 (65, 78) | 76 (64, 80) | 74 (67, 77) |
| Characteristic | P-value | |||||
|---|---|---|---|---|---|---|
| 1 vs. 4 | 2 vs. 3 | 2 vs. 5 | 3 vs. 6 | 5 vs.6 | ||
| MCV±SD — fL | <0.001 | 0.163 | 0.112 | <0.001 | 0.065 | |
| MCH±SD — pg | <0.001 | 0.551 | 0.18 | 0.005 | 0.458 | |
| RBC (IQR) — *1012/L | <0.001 | 0.615 | 0.139 | <0.001 | 0.176 | |
| WBC (IQR) — *1012/L | 0.332 | 0.906 | 0.02 | 0.464 | 0.128 | |
| MCHC (IQR) — g/L | <0.001 | 0.266 | 0.341 | 0.002 | 0.527 | |
| HGB (IQR) — g/L | <0.001 | 0.822 | 0.111 | <0.001 | 0.15 | |
| HCT (IQR) — % | 0.004 | 0.806 | 0.202 | 0.002 | 0.24 | |
| PH (IQR) | <0.001 | 0.923 | 0.047 | <0.001 | 0.822 | |
| PO2 (IQR) — mmHg | <0.001 | 0.079 | 0.035 | 0.004 | 0.522 | |
| P/F ratio (IQR) — mmHg | <0.001 | 0.195 | 0.001 | <0.001 | 0.912 | |
| PCO2 (IQR) — mmHg | 0.586 | 0.683 | 0.697 | 0.763 | 0.395 | |
| FEV1 (IQR) — % predicted | 0.042 | 0.212 | 0.766 | 0.041 | 0.401 | |
| FVC (IQR) — % predicted | <0.001 | <0.001 | <0.001 | <0.001 | 0.133 | |
| DLCO (IQR) — % predicted | <0.001 | <0.001 | <0.001 | <0.001 | 0.034 | |
| TLC (IQR) — % | 0.058 | 0.831 | 0.957 | 0.577 | 0.437 | |
Data are shown as mean ± SD or median (quartile). Only data from the initial presentation and hospital admission were considered. Abbreviations: MCV Mean Corpuscular Volume, RBC Red Blood Cell, MCHC Mean Corpuscular Hemoglobin Concentration, HGB Hemoglobin, HCT Hematocrit, FVC Forced Vital Capacity, LA Low altitude, HA High altitude, IPF Idiopathic pulmonary fibrosis, CTD-ILD Connective tissue disease-associated interstitial lung disease, N Number of subjects, SD Standard deviation, IQR Interquartile range, MCH Mean corpuscular hemoglobin, WBC White blood cell count, PH Arterial pH, PO2 Arterial partial pressure of oxygen, P/F ratio PO2/FIO2 ratio, PCO2 Arterial partial pressure of carbon dioxide, FEV1 Forced expiratory volume in 1 s, DLCO Diffusing capacity of the lungs for carbon monoxide, TLC Total lung capacity
Blood gas analysis showed significantly lower PH, PO2, and P/F ratio in the HA group than in the LA group (all p < 0.05, Table 2). IPF patients in the HA group had lower PO2 and P/F ratio than those in the LA group (p < 0.05). CTD-ILD patients in the HA group had significantly lower PO2 and P/F ratio compared to those in the LA group (all p < 0.05).
Pulmonary function tests revealed significantly lower FEV1, FVC, and DLCO in the HA group (all p < 0.05, Table 2). Both IPF and CTD-ILD patients in the HA group had significantly lower FVC and DLCO compared to their counterparts in the LA group (all p < 0.05). Additionally, CTD-ILD patients in the HA group had significantly lower FEV1 compared to those in the LA group (p = 0.041). In both HA and LA groups, IPF patients had significantly lower DLCO than CTD-ILD patients (all p < 0.001).
Correlations between altitude and clinical, laboratory, and PFT parameters in IPF and CTD-ILD patients
Spearman’s correlation analysis was performed to assess the relationships between altitude groups (LA/HA) and various variables in IPF and CTD-ILD patients (Fig. 1; Table 3). Among IPF patients, altitude was strongly positively correlated with the ILD-GAP score (ρ = 0.56, p < 0.001) and was negatively correlated with the P/F ratio (ρ= −0.48, p < 0.001). In contrast, altitude was positively correlated with Tibetan ethnicity, RBC, MCHC, HGB, HCT, and ILD-GAP scores (all ρ > 0.3, all p < 0.05; Fig. 1B; Table 3) in CTD-ILD patients. Furthermore, altitude was negatively correlated with the MCV and P/F ratio (all ρ< −0.3, all p < 0.001) in this subgroup.
Fig. 1.
The absolute value of spearman correlation heatmap of variables in IPF patients (A) and CTD-ILD patients (B) Figure legend: The spearman correlation coefficient ranges from 0 (skyblue) to 1 (red). The absolute value greater than 0.3 are annotated with black squares. Abbreviations: IPF: Idiopathic Pulmonary Fibrosis; CTD-ILD: Connective Tissue Disease-Associated Interstitial Lung Disease
Table 3.
Spearman’s rank correlation analysis between altitude and clinical variables in IPF and CTD-ILD patients
| Values | IPF | CTD-ILD | ||
|---|---|---|---|---|
| rho(ρ) | p-value | rho(ρ) | p-value | |
| ILD-GAP score | 0.56 | < 0.001 | 0.68 | < 0.001 |
| P/F ratio, mmHg | −0.48 | < 0.001 | −0.51 | < 0.001 |
| Ethic Group | - | - | 0.61 | < 0.001 |
| MCV, fL | - | - | −0.54 | < 0.001 |
| RBC, *1012/L | - | - | 0.45 | < 0.001 |
| MCHC, g/L | - | - | 0.35 | 0.003 |
| HGB, g/L | - | - | 0.39 | < 0.001 |
| HCT, % | - | - | 0.33 | 0.004 |
Spearman’s rank correlation analysis was used; correlation coefficient rho (ρ) ranges [−1,1]; p < 0.05 was considered statistically significant. Abbreviations: ILD-GAP Gender-Age-Physiology scoring system for interstitial lung disease, MCV Mean Corpuscular Volume, RBC Red Blood Cell, MCHC Mean Corpuscular Hemoglobin Concentration, HGB Hemoglobin, HCT Hematocrit, P/F ratio PO2/FIO2 ratio (arterial partial pressure of oxygen/fraction of inspired oxygen ratio), FVC Forced Vital Capacity
These findings were further supported by Pearson’s correlation analysis, which yielded consistent results for both IPF (Fig. 2A) and CTD-ILD patients (Fig. 2B-C).
Fig. 2.
Linear Correlations between Altitude and Clinical Parameters in IPF (A) and CTD-ILD (B, C) Figure legend: Linear correlations between altitude and various clinical parameters: A Correlations with P/F ratio, and ILD-GAP score in IPF patients. B Correlations with MCV, MCHC, HGB, and HCT in CTD-ILD patients. C Correlations with P/F ratio, and ILD-GAP score in CTD-ILD patients
Discussion
This retrospective cross-sectional study aimed to investigate the impact of high altitude on the epidemiological, clinical, and hematological characteristics of patients with CTD-ILD and IPF on the Qinghai‒Tibet Plateau of China. Our findings suggest that high altitude may significantly influence the presentation and clinical course of these interstitial lung diseases (ILDs), providing valuable insights for the management of ILD patients living at high altitudes.
The demographic and clinical characteristics differed between IPF patients and CTD-ILD patients in both the LA and HA groups, with IPF patients being older, predominantly male, and having a higher prevalence of smoking history, which is consistent with the known epidemiology of IPF [8, 12, 27]. One of the key findings was the greater prevalence of CTD-ILD among ILD patients in the HA group, particularly in the Tibetan population. This observation is in line with previous studies reporting that high-altitude environments exacerbate the occurrence and progression of CTDs, such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) [20, 28, 29]. Moreover, the unique genetic background of Tibetan populations, characterized by specific adaptations to high-altitude environments, such as those related to hypoxia-inducible factors (HIFs), may influence their susceptibility to CTDs and, consequently, CTD-ILD [29, 30].
Our study also revealed that high altitude was positively correlated with mean corpuscular haemoglobin concentration (MCHC), haematocrit (HCT), and haemoglobin (HGB) levels in CTD-ILD patients, which is consistent with previous studies reporting adaptive changes to chronic hypoxia at high altitudes [16, 17]. These findings suggest that a high-altitude hypoxic environment may stimulate erythropoiesis and increase the oxygen-carrying capacity as a compensatory mechanism. However, despite these adaptive changes, high-altitude conditions are negatively correlated with the PaO2/FiO2 (P/F) ratio, diffusing capacity for carbon monoxide (DLCO), and forced vital capacity (FVC) in CTD-ILD patients. These findings suggest that a high-altitude hypoxic environment may exacerbate hypoxemia and ventilatory dysfunction in CTD-ILD patients. The chronic hypoxic environment may accelerate the progression of lung fibrosis and vascular remodelling by inducing oxidative stress, inflammation, and fibrotic processes, further compromising lung function and gas exchange in ILD patients living at high altitudes [31, 32].
This study has several limitations. First, the sample size was relatively small, which may limit the generalizability of the findings to the broader ILD population. However, we collected data from two centers to minimize this limitation. Second, the cross-sectional design of the study precludes the determination of causal relationships between altitude and the observed differences in ILD patients. Nevertheless, our results have important implications for the clinical management of ILD patients living at high altitudes. Third, the lack of a control group of healthy individuals is another limitation of this study. The inclusion of a control group would provide valuable reference points for comparing the impact of altitude on pulmonary function and hematological parameters in ILD patients with that in healthy subjects. Despite these limitations, our study provides valuable insights into the impact of altitude on ILD and underscores the need for further research. Future studies should address these limitations by conducting larger, multicenter, longitudinal investigations with healthy control groups to better understand the complex interplay between altitude, genetic factors, and ILD pathogenesis.
Conclusions
Our study demonstrated that, compared with patients living at low altitudes, ILD patients living at high altitudes present distinct clinical characteristics, laboratory findings, and pulmonary function parameters. The greater prevalence of CTD-ILD and Tibetan ethnicity, along with the differences in the P/F ratio and MCV, FVC, MCHC, and HGB levels, suggest that altitude plays a significant role in the pathophysiology and clinical presentation of ILD.
Acknowledgements
We thank the patients who participated in this study, their families, and the medical, nursing, and research staff at the study centres.
Authors’ contributions
Conceptualization, F.L.; Data curation, L.Z. and M.Z.; Formal Analysis, L.Z.; Funding acquisition, M.Z.; Investigation, F.L.; Methodology, F.L., J.L. and L.X.; Project Administration, L.Z. and M.Z.; Resources, Y.T., Q.M. and L.C. (Ling Chen); Supervision, F.L.; Validation, L.C. (Ling Chen) and L.Z.; Writing – Original Draft, L.Z. and M.Z.; Writing–Review & Editing, L.C. (Ling Chen), L.C. (Lei Chen) and F.L.L.Z., M.Z. and L.C. (Ling Chen) contributed equally as co-first authors. L.C. (Ling Chen) played a key role in data interpretation and addressing reviewers’ comments during the revision process. F.L. contributed as corresponding author. All authors had access to data reported in this study. All authors discussed the results, reviewed and edited the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Funding
This work was supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2025ZD05518013).
Data availability
The steering committee of the CENTERS study will consider reasonable requests for the sharing of deidentified individual participant data. Requests should be made to the corresponding author.
Declarations
Ethics approval and consent to participate
This study was performed in accordance with the Declaration of Helsinki. The study protocol was approved by the Ethics Committees of Qinghai People’s Hospital, Tibet Autonomous Region People’s Hospital, and West China Hospital of Sichuan University (Approval No. 716 of 2021). The requirement for informed consent was waived by the Ethics Committees due to the retrospective nature of the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Liang Zhou, Min Zhu and Ling Chen contributed equally to this work.
References
- 1.Travis WD, Costabel U, Hansell DM, King TE, Lynch DA, Nicholson AG, Ryerson CJ, Ryu JH, Selman M, Wells AU, et al. An official American thoracic society/European respiratory society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med. 2013;188(6):733–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Wijsenbeek M, Cottin V. Spectrum of fibrotic lung diseases. N Engl J Med. 2020;383(10):958–68. [DOI] [PubMed] [Google Scholar]
- 3.Lederer DJ, Martinez FJ. Idiopathic pulmonary fibrosis. N Engl J Med. 2018;378(19):1811–23. [DOI] [PubMed] [Google Scholar]
- 4.Ban C, Yan W, Xie B, Zhu M, Liu Y, Zhang S, Ye Q, Ren Y, Jiang D, Geng J et al. Spectrum of interstitial lung disease in China from 2000 to 2012. Eur Respir J. 2018;52(3):1701554. [DOI] [PubMed]
- 5.Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ, Behr J, Cottin V, Danoff SK, Morell F, et al. Diagnosis of idiopathic pulmonary Fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2018;198(5):e44–68. [DOI] [PubMed] [Google Scholar]
- 6.Raghu G, Remy-Jardin M, Richeldi L, Thomson CC, Inoue Y, Johkoh T, Kreuter M, Lynch DA, Maher TM, Martinez FJ, et al. Idiopathic pulmonary fibrosis (an Update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2022;205(9):e18–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Winstone TA, Assayag D, Wilcox PG, Dunne JV, Hague CJ, Leipsic J, Collard HR, Ryerson CJ. Predictors of mortality and progression in scleroderma-associated interstitial lung disease: a systematic review. Chest. 2014;146(2):422–36. [DOI] [PubMed] [Google Scholar]
- 8.Ley B, Collard HR, King TE Jr. Clinical course and prediction of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;183(4):431–40. [DOI] [PubMed] [Google Scholar]
- 9.Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Colby TV, Cordier JF, Flaherty KR, Lasky JA, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788–824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Richeldi L, Costabel U, Selman M, Kim DS, Hansell DM, Nicholson AG, Brown KK, Flaherty KR, Noble PW, Raghu G, et al. Efficacy of a tyrosine kinase inhibitor in idiopathic pulmonary fibrosis. N Engl J Med. 2011;365(12):1079–87. [DOI] [PubMed] [Google Scholar]
- 11.Wuyts WA, Antoniou KM, Borensztajn K, Costabel U, Cottin V, Crestani B, Grutters JC, Maher TM, Poletti V, Richeldi L, et al. Combination therapy: the future of management for idiopathic pulmonary fibrosis? Lancet Respir Med. 2014;2(11):933–42. [DOI] [PubMed] [Google Scholar]
- 12.Fischer A, du Bois R. Interstitial lung disease in connective tissue disorders. Lancet. 2012;380(9842):689–98. [DOI] [PubMed] [Google Scholar]
- 13.Mathai SC, Danoff SK. Management of interstitial lung disease associated with connective tissue disease. BMJ. 2016;352:h6819. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kondo Y, Abe S, Toko H, Hirota T, Takahashi H, Shimizu M, Noma H, Tsuboi H, Matsumoto I, Inaba T, et al. Effect of Climatic environment on immunological features of rheumatoid arthritis. Sci Rep. 2023;13(1):1304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Luks AM, Hackett PH. High Altitude and Common Medical Conditions. In: High Altitude. edn. 2014. p. 449–77.
- 16.Palmer BF. Physiology and pathophysiology with ascent to altitude. Am J Med Sci. 2010;340(1):69–77. [DOI] [PubMed] [Google Scholar]
- 17.Bartsch P, Saltin B. General introduction to altitude adaptation and mountain sickness. Scand J Med Sci Sports. 2008;18(Suppl 1):1–10. [DOI] [PubMed] [Google Scholar]
- 18.Perez-Padilla R, Salas J, Carrillo G, Selman M, Chapela R. Prevalence of high hematocrits in patients with interstitial lung disease in Mexico City. Chest. 1992;101(6):1691–3. [DOI] [PubMed] [Google Scholar]
- 19.Seccombe LM, Kelly PT, Wong CK, Rogers PG, Lim S, Peters MJ. Effect of simulated commercial flight on oxygenation in patients with interstitial lung disease and chronic obstructive pulmonary disease. Thorax. 2004;59(11):966–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Qian G, Ran X, Zhou CX, Deng DQ, Zhang PL, Guo Y, et al. Systemic lupus erythematosus patients in the low-latitude plateau of China: altitudinal influences. Lupus. 2014;23(14):1537–45. [DOI] [PubMed] [Google Scholar]
- 21.Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO 3rd, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, et al. 2010 rheumatoid arthritis classification criteria: an American college of Rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis. 2010;69(9):1580–8. [DOI] [PubMed] [Google Scholar]
- 22.van den Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A, Matucci-Cerinic M, Naden RP, Medsger TA Jr., Carreira PE, et al. 2013 classification criteria for systemic sclerosis: an American college of rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis. 2013;72(11):1747–55. [DOI] [PubMed] [Google Scholar]
- 23.Petri M, Orbai AM, Alarcon GS, Gordon C, Merrill JT, Fortin PR, Bruce IN, Isenberg D, Wallace DJ, Nived O, et al. Derivation and validation of the systemic lupus international collaborating clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012;64(8):2677–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Shiboski CH, Shiboski SC, Seror R, Criswell LA, Labetoulle M, Lietman TM, Rasmussen A, Scofield H, Vitali C, Bowman SJ, et al. 2016 American college of Rheumatology/European league against rheumatism classification criteria for primary Sjögren’s syndrome: A consensus and Data-Driven methodology involving three international patient cohorts. Arthritis Rheumatol. 2016;69(1):35–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lundberg IE, Tjärnlund A, Bottai M, Werth VP, Pilkington C, de Visser M, Alfredsson L, Amato AA, Barohn RJ, Liang MH, et al. 2017 European league against Rheumatism/American college of rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Arthritis Rheumatol. 2017;69(12):2271–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ley B, Ryerson CJ, Vittinghoff E, Ryu JH, Tomassetti S, Lee JS, Poletti V, Buccioli M, Elicker BM, Jones KD, et al. A multidimensional index and staging system for idiopathic pulmonary fibrosis. Ann Intern Med. 2012;156(10):684–91. [DOI] [PubMed] [Google Scholar]
- 27.Jee AS, Parker MJS, Bleasel JF, Troy LK, Lau EM, Jo HE, Teoh AKY, Adelstein S, Webster S, Corte TJ. Baseline characteristics and survival of an Australian interstitial pneumonia with autoimmune features cohort. Respiration. 2021;100(9):853–64. [DOI] [PubMed] [Google Scholar]
- 28.Pan Q, Chen J, Guo L, Lu X, Liao S, Zhao C, Wang S, Liu H. -f: mechanistic insights into environmental and genetic risk factors for systemic lupus erythematosus. Am J Translational Res. 2019;11 3:1241–54. [PMC free article] [PubMed] [Google Scholar]
- 29.Arima H, Koirala S, Nema K, Nakano M, Ito H, Poudel KM, et al. High prevalence of rheumatoid arthritis and its risk factors among Tibetan highlanders living in Tsarang, Mustang district of Nepal. J Physiol Anthropol. 2022. 10.1186/s40101-022-00283-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Majmundar AJ, Wong WJ, Simon MC. Hypoxia-inducible factors and the response to hypoxic stress. Mol Cell. 2010;40(2):294–309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Rimoldi SF, Rexhaj E, Pratali L, Bailey DM, Hutter D, Faita F, Salinas Salmon C, Villena M, Nicod P, Allemann Y, et al. Systemic vascular dysfunction in patients with chronic mountain sickness. Chest. 2012;141(1):139–46. [DOI] [PubMed] [Google Scholar]
- 32.Penaloza D, Arias-Stella J. The heart and pulmonary circulation at high altitudes: healthy highlanders and chronic mountain sickness. Circulation. 2007;115(9):1132–46. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The steering committee of the CENTERS study will consider reasonable requests for the sharing of deidentified individual participant data. Requests should be made to the corresponding author.