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Association between humidity and respiratory health: the 2016–2018 Korea National Health and Nutrition Examination Survey

Respiratory Research volume 25, Article number: 424 (2024) Cite this article

Abstract

Background

Ambient humidity has a significant impact on respiratory health and influences disease and symptoms. However, large-scale studies are required to clarify its specific effects on lung function and respiratory symptoms. We examined the relationship between relative humidity (RH), lung function, and respiratory symptoms using data from the Korea National Health and Nutrition Examination Survey (KNHANES).

Methods

In this cross-sectional study, we analyzed data from KNHANES participants aged ≥ 40 years, collected between 2016 and 2018. Pulmonary function tests (PFTs) and health questionnaires were used to assess lung function and respiratory symptoms. Individual environmental data, including RH, were obtained from the Community Multiscale Air Quality model and linked to the participants’ addresses. Short-term (0–14 days), mid-term (30–180 days), and long-term (1–5 years) RH exposures were examined. Linear regression models were used to evaluate the associations between RH and PFTs. Univariate and multivariable logistic regression models were applied to assess the risk of lung function abnormalities and respiratory symptoms.

Results

In total, 10,396 participants were included (mean age: 58.3 years, male: 43.6%). In multiple regression analysis, higher RH was negatively associated with the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) ratio across various time lags, while FVC was positively correlated with long-term RH exposure. In multiple logistic analysis adjusted for clinical and environmental covariates, long-term higher RH exposure was associated with a lower risk of restrictive lung disease (odds ratio [OR] at 4-year moving average [MA]: 0.978, 95% confidence interval [CI]: 0.959–0.997), while mid-term RH exposure decreased the risk of chronic cough (OR at 90-day MA: 0.968, 95% CI: 0.948–0.987) and sputum production (OR at 90-day MA: 0.985, 95% CI: 0.969–1.001).

Conclusions

Higher RH was negatively associated with lung function and increased the risk of obstructive lung disease, whereas mid-term RH exposure reduced the risk of chronic cough and sputum production.

Background

The relationship between environmental factors and respiratory health is a growing area of research, particularly in the context of climate change and urbanization [1]. Numerous studies have highlighted the adverse effects of air pollution on respiratory health and lung function, emphasizing the need for environmental control [2,3,4,5]. Among the various environmental factors, ambient humidity plays a significant role in human health. Research has shown that humidity affects human health, particularly regarding infectious diseases, and indoor relative humidity (RH) is often used to model respiratory virus transmission [6]. A Korean study found that the risk of influenza incidence significantly increased at low (30–40%) or high (70%) RH combined with low daily temperatures of 0–5 °C [7]. Other studies have linked RH to the transmission and outcomes of coronavirus disease 2019 [8, 9].

Several studies have investigated the effects of humidity on respiratory health [10,11,12,13]. Extreme humidity levels, particularly in hot and humid or cold and dry conditions, are associated with worsened health, decreased physical activity, and increased exacerbations in patients with chronic obstructive pulmonary disease (COPD) [10]. Furthermore, humidity has been linked to increased respiratory mortality and outpatient visits [11, 12]. However, one study found that, while indoor and outdoor temperatures were negatively correlated with self-reported COPD symptoms, indoor and outdoor humidity were not significantly related to these symptoms [13]. In addition, several studies have reported an association between humidity and lung function [14,15,16,17], but the results are conflicting, and they did not examine humidity alone over various periods. Since humidity is closely linked to other meteorological factors [18], it is necessary to consider these factors together. Therefore, large-scale studies are needed to examine the effects of humidity on lung function and respiratory symptoms across different time lags, adjusting for clinical and environmental confounders. The Korea National Health and Nutrition Examination Survey (KNHANES) now includes meteorological and air pollution data. Recent studies using this data have examined the impact of air pollution on lung function [19]. Thus, we aimed to investigate the relationship between RH and both lung function and respiratory symptoms in a large Korean population using data from the KNHANES.

Methods

Study design and population

This cross-sectional study used data from the KNHANES, conducted by the Korea Centers for Disease Control and Prevention since 1988 [20, 21]. The KNHANES is a nationally representative survey that assesses the health and nutritional status of the non-institutionalized Korean population through a stratified, multistage probability sampling method involving approximately 10,000 individuals annually. It includes health interviews, examinations, and nutrition surveys, and collects data on socioeconomic status, health behaviors, quality of life, healthcare utilization, anthropometric measurements, biochemical profiles, and dietary intake.

Pulmonary function tests (PFTs) were conducted as part of the survey using portable spirometry units. Since the fourth phase of the survey in 2007, PFTs have been conducted on adults aged 18 years and older, with the age criterion adjusted to 40 years and older since 2010. Due to equipment updates, the spirometry devices were changed from dry-seal spirometer (Vmax series 2130; SensorMedics Corp., Yorba Linda, CA, USA) to Vyntus Spiro (Vyaire Medical Inc., Hoechberg, Germany) on June 28, 2016. The data used in this study were collected between 2016 and 2018.

Participants

A total of 24,269 participants aged ≥ 40 years who were involved in the KNHANES between 2016 and 2018 were screened. We excluded participants who did not undergo PFTs (n = 13,450) and those with missing data on major covariates (n = 423). Consequently, 10,396 participants with complete data on lung function and relevant covariates were included in the final analysis (Fig. 1). This study was approved by the Institutional Review Board of the Soonchunhyang University Seoul Hospital (SCHUH 2023-08-002).

Fig. 1
figure 1

Enrollment of participants. KNHANES, Korea National Health and Nutrition Examination Survey; PFT, pulmonary function test

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Clinical data collection

To measure lung function, a portable spirometer (Vyntus Spiro, Vyaire Medical Inc., Hoechberg, Germany) was used on individuals aged ≥ 40 years, following the American Thoracic Society/European Respiratory Society guidelines [22]. Four trained technicians ensured quality control. Each participant performed three–eight acceptable maneuvers. The correlation between conventional and portable spirometry was high, with Pearson’s coefficients of 0.986 for forced vital capacity (FVC) and 0.994 for forced expiratory volume in one second (FEV1) [23]. The predicted values for FVC and FEV1 were derived using Korean reference standards [24]. Participants were categorized into obstructive (FEV1/FVC < 0.7) and restrictive (FVC < 80% predicted, FEV1/FVC ≥ 0.7) groups based on spirometry results [25]. Data on other PFT metrices, including forced expiratory flow at 25–75% of FVC (FEF25 − 75%) and peak expiratory flow (PEF), were also collected. Medical history and chronic respiratory symptoms, such as sputum production and cough lasting > three months, were assessed using the KNHANES health questionnaire.

Environmental data collection

To assess the impact of humidity on lung function, detailed environmental data were linked to KNHANES clinical data [26]. Meteorological data, including daily averages of temperature, wind speed, humidity, precipitation, wind direction, solar radiation, and surface pressure, were obtained from the Korea Meteorological Administration. These data were created using emission quantity and chemical transport models with a spatial resolution of 9 km grids, specific to city-county-district units. The Community Multiscale Air Quality (CMAQ) model estimates high-resolution relative humidity, air pollution, and other atmospheric conditions by integrating data from the Weather Research and Forecasting model version 3.6.1 [27] based on inputs from the National Centers for Environmental Prediction and the Global Forecast System final analysis data [28]. The participants’ geocoded addresses enabled the precise matching of daily humidity levels through spatial interpolation techniques such as Inverse Distance Weighting or Kriging.

Short-term exposure was assessed using daily averages for the survey date (day 0) and 1, 3, 7, and 14 days prior to the survey (lags). Mid-term exposure was calculated by the moving average (MA) of daily relative humidity over cumulative periods of 30, 90, and 180 days preceding the survey. Long-term exposure was determined by the MA of daily relative humidity over periods of 1 year (365 days), 2 years (730 days), 3 years (1,095 days), 4 years (1,460 days), and 5 years (1,825 days) preceding the survey (Additional file 1: Table S1).

Statistical analysis

Descriptive statistics summarized participants’ baseline characteristics, with continuous variables presented as mean ± standard deviation and categorical variables as numbers (percentages). Associations between RH and lung function were evaluated using simple and multivariable linear regression models, with results expressed as beta coefficients (β) and standard errors. Logistic regression models categorized participants according to lung function (obstructive or restrictive) and respiratory symptoms (chronic cough or sputum production), and odds ratios (OR) and 95% confidence intervals (CI) were calculated.

Model 1 was an unadjusted model; Model 2 adjusted for age, sex, income, education, region of residence (9 provinces, 6 metropolitan cities, 1 special city, and 1 special self-governing city), smoking status, body mass index (BMI), and history of asthma. The multivariable Model 3 (main model) further adjusted for environmental covariates including mean temperature, precipitation, wind speed, and air pollution levels (particulate matter with a diameter ≤ 10 μm [PM10], particulate matter with a diameter ≤ 2.5 μm [PM2.5], sulfur dioxide [SO2], nitrogen dioxide [NO2], carbon monoxide [CO], and ozone [O3]). Different lag periods for humidity exposure were analyzed for robustness. For effect modification, we performed analyses stratified by age (under 65 years vs. 65 years or older), sex (male vs. female), and BMI categories based on Asian-Pacific classification (underweight [BMI < 18.5 kg/m2], normal [18.5–24.9 kg/m2], and obese [≥ 25.0 kg/m2]) [29]. Interaction analyses were performed using multiple linear regression models with interaction terms (RHxair pollutant) to assess the effects of RH and air pollutants on lung function outcomes at lag 0 (index date). All analyses were performed using R software (version 4.0.3), with p-value of < 0.05 considered statistically significant.

Results

Baseline characteristics

This study included 10,396 participants with a mean age of 58.3 years, 43.6% of whom were male. Household income was evenly distributed across the quartiles. The educational levels were as follows: primary school or lower (24.6%), middle school (13.5%), high school (31.8%), and college degree or higher (30.1%). The regional distribution covered major areas of South Korea, including Seoul (19.0%), Busan (7.1%), and Gyeonggi (22.1%). Ever-smokers accounted for 40.6% of the participants. The mean BMI was 24.2 kg/m².

Environmental data on the index date (lag 0) showed a mean RH of 64.5%, mean temperature of 13.1 °C, wind speed of 2.7 m/s, and precipitation rate of 0.6 mm/hr. Mean levels of air pollutants were: PM10 at 48.6 µg/m³, PM2.5 at 22.9 µg/m³, SO2 at 4.1 ppb, NO2 at 27.2 ppb, CO at 457.8 ppb, and O3 at 29.3 ppb (Table 1). Table S2 in Additional file 1 shows consistent RH values over different lag days, which increased slightly to 67.8% by the fifth year, with the quartile values indicating consistent trends over time.

Table 1 Baseline characteristics of total participants
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Lung function and respiratory symptoms

The mean predicted FEV1 was 88.5%, with a mean measured FEV1 of 2.6 L, and the mean predicted FVC was 88.5%, with a mean measured FVC of 3.3 L. The mean FEV1/FVC ratio was 70.1%. Obstructive lung disease (FEV1/FVC < 0.7) was identified in 1,415 participants (13.6%), with 605 (5.8%) classified as mild and 810 (7.8%) as moderate. Restrictive lung disease (FVC < 80% predicted, FEV1/FVC ≥ 0.7) was found in 1,918 participants (18.5%). Chronic cough was reported by 272 participants (2.6%) and sputum production by 438 participants (4.2%), both with an average duration of 7.6 years (Table 2).

Table 2 Lung function and respiratory symptoms in total participants
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Association between RH and lung function

In simple regression, there was a consistent negative association between FEV1/FVC and RH across all time lags, with β values ranging from -0.015 to -0.151, all of which were statistically significant. For FEV1, the negative association with RH was significant for many time lags, particularly in the short term (lags 0,1,3,7-day) with β values ranging from − 0.018 to -0.023, and in the mid-term (30, 90-day MAs) with β values ranging from − 0.041 to -0.042. For FVC, most time lags did not show a significant association with RH. However, similar to FEV1/FVC, both FEF25 − 75% and PEF showed negative associations with RH across all time lags, with a tendency for the negative effect to become stronger over longer time periods (FEF25 − 75%: β = -0.003% to -0.019%; PEF: β = -0.001% to -0.040%) (Additional file 1: Table S3).

In multiple regression analysis (Table 3), FEV1/FVC showed a consistent negative association with RH across various time lags, with statistically significant β values in the short term (lags 0–1 day), mid-term (30 to 180-day MAs), and long term (1 to 5-year MAs), with β values decreasing as the lag length increased (Fig. 2A). For FEV1, there was a tendency towards negative associations at lag 0 day (β = -0.02, p = 0.088); however, most associations were not statistically significant. FVC generally showed a positive correlation with RH, with statistically significant β values observed at 3-year MA (β = 0.077, p = 0.014), and 4-year MA (β = 0.087, p = 0.006) (Fig. 2B). FEF25 − 75% showed a consistent negative association with RH across many time lags with β values ranging from -0.001 to -0.004, except for lag 14 days, and 180-day, 1-year, and 4-year MAs. PEF also showed consistent negative associations with RH in both the mid- and long-term, with β values ranging from -0.007 to -0.010 for mid-term and -0.012 to -0.018 for long-term, although negative correlations in short-term failed to reach statistical significance.

Table 3 Multivariable regression analysis showing effect of relative humidity on lung function over various time lags
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Fig. 2
figure 2

Representative figures of the effect of various lagged relative humidity levels on lung function. (A) FEV1/FVC and 0 day-lagged relative humidity. (B) FVC and 4 year-lagged relative humidity. This scatter plot depicts the relationship between relative humidity lagged by various time periods and lung function. The X-axis represents the relative humidity at different lag periods, while the Y-axis represents the lung function. The blue line represents the regression line, indicating the trend observed through multivariable regression analysis. The beta coefficient and p-value are displayed in the plot, highlighting the statistical significance of the observed effect. RH, relative humidity; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity

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Subgroup analysis of the association between RH and lung function

In the younger age group, FEV1/FVC showed significant negative associations at short-term RH lags, including the 0-day (β = -0.021, p = 0.002) and 1-day (β = -0.017, p = 0.005) lags. FEF25 − 75% was significantly reduced in the short-term RH exposure including the 0-day (β = -0.003, p < 0.001), 1-day (β = -0.002, p = 0.004), and 3-day (β = -0.002, p = 0.002) lags, and the mid-term including the 30-day (β = -0.004, p = 0.008) and 90-day (β = -0.004, p = 0.032) lags. However, FEV1 (β = 0.120 to 0.187), FVC (β = 0.132 to 0.196), and PEF (β = 0.013 to 0.016) demonstrated significant positive associations with long-term RH exposures, including the 1-year to 5-year MAs. In older individuals, long-term exposure to RH was negatively associated with FEV1/FVC (β = -0.118 to -0.124), FEF25 − 75% (β = -0.010 to -0.016), PEF (β = -0.023 to -0.031). PEF was also negatively associated with mid-term exposure to RH (Additional file 1: Table S4).

In subgroup analysis stratified by the sex, significant negative associations were observed in males for FEV1/FVC at both the 0-day (β = -0.023, p = 0.021) and 1-day (β = -0.023, p = 0.014) lags, while a positive association was found at the 14-day lag (β = 0.016, p = 0.045). FVC showed significant positive associations over the 1- to 5-year MAs (β = 0.128 to 0.201). For FEF25 − 75%, significant negative associations were identified at the 0-day (β = -0.003, p = 0.007) and 1-day (β = -0.003, p = 0.008) lags. In females, significant negative associations were found for FEV1/FVC (β = -0.034 to -0.039), FEF25 − 75% (β = -0.004 to -0.006), and PEF (β = -0.006 to -0.007) at mid-term RH lags, including the 30-, 90-, and 180-day MAs. Additionally, FEV1 and FEF25 − 75% showed negative associations at the 7-day lag (FEV1: β = -0.030, p = 0.021; FEF25 − 75%: β = -0.002, p = 0.018) (Additional file 1: Table S5).

When stratified by BMI, the underweight group showed significant negative associations for FEF25 − 75% at the 0-day lag (β = -0.015, p = 0.027). In the normal weight group, both FEV1/FVC and FEF25 − 75% showed significant negative associations at mid-term lags, including the 30-day (FEV1/FVC: β = -0.035, p = 0.006; FEF25 − 75%: β = -0.005, p = 0.002) and 90-day averages (FEV1/FVC: β = -0.042, p = 0.004; FEF25 − 75%: β = -0.005, p = 0.005). FEF25 − 75% also showed a significant negative association at the 3-day lag (β = -0.002%, p = 0.028). Additionally, FVC was positively correlated with long-term RH exposures (β = 0.115–0.169%), while PEF was positively associated with RH at the 1-year MA (β = 0.012, p = 0.039). In the obesity group, FEV1/FVC showed significant, but mixed associations at short-term RH exposures, including 1-day (β = -0.017, p = 0.038) and 14-day (β = 0.014, p = 0.039) lags. FEF25 − 75% was negatively associated with short-term RH exposures, including the 0-day (β = -0.003, p = 0.008), 1-day (β = -0.002, p = 0.047), and 7-day (β = -0.002, p = 0.019) lags (Additional file 1: Table S6).

Interaction analysis between RH and air pollutants

We observed significant negative interaction effects between RH and SO2 on FEV1/FVC (β = -0.040, p = 0.050), as well as between RH and NO2 on FVC (β = -1.716, p = 0.040) Positive interaction effects were found between RH and PM2.5 on FEF25–75% (β = 0.000, p = 0.047) and PEF (β = 0.001, p = 0.001), though the magnitudes were small. In contrast, RH showed negative interactions with O3 on both FEF25–75% (β = -0.161, p = 0.004) and PEF (β = -0.419, p < 0.001). (Additional file 1: Table S7).

Association between RH and lung function abnormalities

In the unadjusted model (Model 1), significant positive associations between RH and obstructive lung disease were found in the short-term (lag 1 day) and long-term (1 to 5-year MAs). In the clinical covariate-adjusted model (Model 2), marginal associations were observed at 1 to 5-year MAs (Additional file 1: Fig. S1). In the main model (Model 3), which was adjusted for environmental factors, there was a tendency for the OR to increase above 1 as the exposure lag period increased, compared to short-term exposure. However, at 2 and 3-year MAs, there was a marginal association, but this was not statistically significant (OR: 1.023, 95% CI: 1.000–1.048, p = 0.054 for 2-year MA, OR: 1.024, 95% CI: 1–1.049, p = 0.054 for 3-year MA) (Fig. 3A).

Fig. 3
figure 3

Effect of various lagged relative humidity levels on lung function abnormalities. (A) Obstructive pattern. (B) Restrictive pattern. This forest plot represents the ORs and 95% CIs for the effect of relative humidity at various lag periods on lung function abnormalities, as determined by multivariable logistic regression analysis (Model 3). The X-axis displays the OR, and the variables on the Y-axis represent relative humidity lagged by different time periods, ranging from the same day (lag 0 day) to 5 years (5- year moving average). OR, odds ratio; CI, confidence interval

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Restrictive lung disease showed little significance until mid-term, but there was a tendency for the risk to increase with higher RH. However, with lags of more than one year, an increase in RH was associated with a decreased risk of restrictive lung disease in both Model 1 (significant at 1-, 3-, and 4-year MAs) and Model 2 (significant at 1 to 5-year MA) (Additional file 1: Fig. S2). The main model showed a tendency for the risk to increase with higher RH in short and mid-term, though this was not statistically significant. However, in the long term, higher RH showed a negative trend in relation to the risk of restrictive lung disease, with significant negative association observed at 4-year MA (OR: 0.978, 95% CI: 0.959–0.997, p = 0.022) (Fig. 3B).

Association between RH and respiratory symptoms

Both Models 1 and 2 indicated that mid-term exposure (90 to 180-day MAs) to higher RH was associated with a decreased risk of chronic cough, whereas short-term and long-term exposures did not show significant associations. However, the OR tended to increase above one with long-term exposure (Additional file 1: Fig. S3). In the main model, statistically significant negative associations between RH and chronic cough were observed for mid-term exposures, including the 90-day (OR: 0.968, 95% CI: 0.948–0.987, p = 0.002) and 180-day (OR: 0.957, 95% CI: 0.930–0.983, p = 0.002) MA. However, for MA longer than one year, the statistical significance disappeared (Fig. 4A).

Fig. 4
figure 4

Effect of various lagged relative humidity levels on respiratory symptoms. (A) Chronic cough. (B) Sputum production. This forest plot represents the ORs and 95% CIs for the effect of relative humidity at various lag periods on respiratory symptoms, as determined by multivariable logistic regression analysis (Model 3). The X-axis displays the OR, and the variables on the Y-axis represent relative humidity lagged by different time periods, ranging from the same day (lag 0 day) to 5 years (5-year moving average). OR, odds ratio; CI, confidence interval

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Similar to the findings for chronic cough, mid-term exposure (180-day MA) to higher RH was significantly linked to a decrease in sputum production, whereas no significant associations were found for short-term and long-term exposures in both Models 1 and 2 (Additional file 1: Fig. S4). The main model also showed consistent results, with a trend toward negative associations between RH and sputum production observed for mid-term exposures at: MA 90-day (OR: 0.985, 95% CI: 0.969–1.001, p = 0.062), and 180-day (OR: 0.980, 95% CI: 0.959–1.001, p = 0.065) (Fig. 4B).

Discussion

To the best of our knowledge, this is the first study to comprehensively analyze the association between RH and respiratory health, lung function, and symptoms. FEV1/FVC showed a negative association with RH across short-term, mid-term, and long-term exposures, whereas FVC exhibited a positive association, particularly in the long-term. The obstructive pattern had few significant associations but showed an increasing risk with longer-term higher RH exposure. In contrast, short-term exposure to higher RH increased the risk of restrictive pattern, whereas long-term exposure reduced the risk of restrictive lung disease. Mid-term exposure to higher RH was significantly associated with a decreased risk of chronic cough. Additionally, there was a suggestive association between mid-term higher RH exposure and decreased sputum production, while short-term and long-term exposure demonstrated no significant association with either outcome.

In our study, after adjusting for covariates, RH was negatively correlated with FEV1/FVC or FEF25 − 75% but positively correlated with FVC. Previous studies on RH and lung function have reported inconsistent results [14,15,16,17]. Lepeule et al. found that a 5% increase in the 7-day average RH was associated with a 0.2% decrease in both FVC and FEV1 among elderly men in the USA (n = 1,103) [14]. Chen et al. observed a decline in PEF rates with a high 14-day lag RH in elderly Chinese individuals; however, this association disappeared after adjusting for pollutant exposure [15]. A Swedish study of young adults (n = 1,853) found no significant association between building humidity indicators and FEV1 or FVC [16]. However, the European Respiratory Health Survey found that women in homes with dampness (water damage or damp spots) (n = 6,443) experienced an additional FEV1 decline of -2.25 ml/year, with increased lung function decline as dampness scores rose [17]. In addition, a Canadian cross-sectional survey (n = 36) showed that extremes of humidity, particularly in hot and humid (≥ 25 °C, > 50% RH) conditions, were associated with worsened health status, decreased physical activity, and increased exacerbations in patients with COPD, compared with moderate conditions (14–21 °C, 30–50% RH) [10]. Although our study did not find a direct relationship between RH and FEV1 in multivariable analysis, FEV1/FVC, an obstruction indicator, showed a negative correlation with RH, consistent with previous findings [10, 14, 15, 17]. This may be due to the complex interplay in which higher RH is associated with elevated air pollution levels, leading to impaired lung function. Although we adjusted for air pollutants, previous studies suggest that higher humidity can increase PM concentrations, which may contribute to lung function decline [30, 31]. A meta-analysis of 25 studies on children’s short-term exposure to PM2.5 found that higher RH (≥ average) led to a greater decrease in PEF (-4.02 L/min [high] vs. -1.20 L/min [low]) compared to lower RH (< average) [32]. Additionally, higher humidity can increase the release of volatile organic compounds and black carbon [33, 34], which were not controlled for in our study, potentially worsening respiratory irritation and lung function [35, 36]. Higher humidity can also increase allergens, triggering bronchospasm, and exacerbating obstruction [37]. These findings collectively suggest that humidity may contribute to lung function obstruction, but further research is needed to clarify the underlying mechanisms.

Our study found that long-term exposure to high RH was associated with a lower risk of restrictive patterns and was positively correlated with FVC. This may be due to higher humidity maintaining mucosal hydration, which prevents mucosal dryness and subsequent inflammation [38]. Dry and damaged mucosal surfaces can trigger the release of inflammatory mediators that promote a type 2 inflammatory response characterized by the release of cytokines such as interleukin (IL)-4 and IL-13 [39, 40]. There is evidence that IL-13 contributes to pulmonary fibrosis, including idiopathic pulmonary fibrosis, and that anti-IL-13 therapy can reduce fibrosis and enhance airway epithelium repair through both transforming growth factor-β-dependent and independent mechanisms [41]. Collectively, these findings suggest that high humidity is associated with a reduced risk of epithelial injury and restrictive patterns.

In our study, FVC, a measure of lung volume indicative of restrictive lung defects, did not show significant short-term associations. FEV1 and FEV1/FVC, widely recognized as a marker of airflow limitation in airway diseases [42], is often studied in relation to short-term environmental exposures due to its sensitivity to these changes [43]. In contrast, FVC reflects restrictive patterns typically associated with underlying lung parenchymal inflammation, which requires structural changes in lung tissue and may be therefore less responsive to short-term exposures. These findings are supported by studies linking restrictive ventilatory defects to long-term exposures [44,45,46]. This makes FVC more likely to be influenced by long-term environmental exposure. Indeed, restrictive pattern diseases, such as interstitial lung disease, are often studied in the context of long-term exposures, sometimes spanning decades [47]. These findings collectively suggest that FVC may be more likely to show associations with long-term, rather than short-term, environmental exposures.

Our study found that the effects of RH on lung function varied by age, sex, and BMI. In younger individuals, long-term exposure to higher RH was associated with improved lung function, likely due to better airway hydration and preserved lung elasticity [48]. In contrast, older adults showed declines in FEV1/FVC and PEF with prolonged RH exposure, suggesting increased susceptibility to airway obstruction. These findings indicate that while younger individuals may adapt positively to sustained humidity, older adults are more vulnerable to its cumulative effects, which can exacerbate respiratory issues [49]. In males, short-term exposure to RH was negatively correlated with FEV1 and FEF25-75%, possibly due to the higher prevalence of smoking, which can lead to pre-existing airway diseases [50]. These factors may make males more sensitive to short-term humidity fluctuations, exacerbating airway obstruction. In contrast, long-term exposure to RH may improve airway hydration, potentially reducing sputum retention in individuals with chronic airway conditions [38, 51,52,53], thereby improving lung volumes, as reflected in the positive correlation with FVC.

Unlike males, in females, mid-term exposure, and to a lesser extent short-term exposure, to RH has shown a negative correlation with lung function parameters related to obstructive airflow limitations such as FEV1/FVC, FEV1, FEF25-75%, and PEF. This is likely due to females’ relatively smaller lung and airway volumes compared to males, making them more vulnerable to environmental stressors like changes in RH [54, 55]. In the normal weight group, the RH exposure followed the main findings’ trend, with short-term negative and long-term protective impacts on lung function. However, in underweight and obese individuals, significant associations were only observed with short-term RH exposure. This may be due to reduced muscle mass and lung reserves in underweight individuals, making them more susceptible to immediate environmental stressor [56]. Excess weight in obese individuals may compromise lung mechanics and increase airway resistance [57], may limit their ability to benefit from prolonged RH exposure. Our study highlights that the effects of RH on lung function are strongly influenced by individual factors, emphasizing the importance of personalized considerations when evaluating environmental impacts on respiratory health.

Higher humidity may preserve lung function by maintaining mucociliary clearance and reducing chronic inflammation [52, 58]. Rea et al. revealed that long-term (12-month) humidification therapy with fully humidified air at 37 °C, delivered daily through nasal cannulae to COPD and bronchiectasis patients (n = 108), significantly reduced exacerbation days (18.2 days vs. 33.5 days; p = 0.045), increased time to first exacerbation (median 52 days vs. 27 days; p = 0.0495), and improved lung function and quality of life compared to usual care [53]. We also observed that chronic cough and sputum production decreased with higher mid-term RH. However, this trend did not persist for over a year in our study, likely because of seasonal variations. High summer humidity is counterbalanced by dry conditions in other seasons, making the benefits of higher RH on chronic respiratory symptoms transient.

This study had several limitations. First, its cross-sectional design limits the ability to establish causality between RH and respiratory health outcomes, and longitudinal studies are needed to confirm these associations over time. Second, despite adjusting for various clinical and environmental covariates, residual confounding factors may have been present. However, we adjusted comprehensively for a range of individual environmental factors and air pollutants. Third, while the accuracy of individual RH values predicted by the CMAQ might have introduced some discrepancies compared to actual measurements, a previous study showed a high correlation (r = 0.78) between the measured and predicted values [59]. Additionally, previous studies have reported a high correlation between indoor and outdoor humidity levels (r = 0.6 to 0.8), supporting the use of outdoor RH as a reasonable proxy for overall humidity exposure [60, 61]. Finally, the study population was limited to Korea; therefore, the findings may not be generalizable to other regions with different climatic conditions and population characteristics. Despite these limitations, our comprehensive adjustments for environmental and clinical factors enhanced the robustness of our findings using large-scale data with individual levels of RH exposure.

Conclusions

This study found that RH was associated with lung function and respiratory symptoms in a complex manner, varying with exposure duration. Different RH levels have both positive and negative effects on respiratory health. These findings highlight the importance of considering RH in public health strategies. Further longitudinal studies are needed to confirm these associations and explore the long-term effects of RH on respiratory health.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

RH:

Relative humidity

COPD:

Chronic obstructive pulmonary disease

KNHANES:

Korea National Health and Nutrition Examination Survey

PFT:

Pulmonary function test

FVC:

Forced vital capacity

FEV1 :

Forced expiratory volume in one second

FEF25 − 75% :

Forced expiratory flow at 25–75% of forced vital capacity

PEF:

Peak expiratory flow

CMAQ:

Community Multiscale Air Quality

OR:

Odds ratio

CI:

Confidence interval

BMI:

Body mass index

PM10 :

Particulate matter with a diameter ≤ 10 μm

PM2.5 :

Particulate matter with a diameter ≤ 2.5 μm

SO2 :

Sulfur dioxide

NO2 :

Nitrogen dioxide

CO:

Carbon monoxide

O3 :

Ozone

IL:

Interleukin

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Acknowledgements

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Funding

This study was supported by the Soonchunhyang University Research Fund.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1G1A1010730).

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  1. Jinwoo Seok and Bo Young Lee contributed equally to this work.

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  1. Division of Allergy and Respiratory Diseases, Soonchunhyang University Seoul Hospital, 59 Daesagwan-ro, Seoul, 04401, Republic of Korea

    Jinwoo Seok, Bo Young Lee & Hee-Young Yoon

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Contributions

H-Y.Y. takes full responsibility for the study conception and design and the content of this manuscript, including data and analysis. J.S. and BY.L. performed statistical analyses and data interpretation. J.S. drafted the initial manuscript, and BY.L. edited it. All authors reviewed and approved the final version of the manuscript.

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Correspondence to Hee-Young Yoon.

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Seok, J., Lee, B. & Yoon, HY. Association between humidity and respiratory health: the 2016–2018 Korea National Health and Nutrition Examination Survey. Respir Res 25, 424 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12931-024-03054-z

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