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. 2026 Jan 21;4(6):1096–1108. doi: 10.1021/envhealth.5c00186

Effect of Co-Exposure to Temperature and Fine Particulate Matter on Metabolomic and Immune Processes among Chinese Women of Childbearing Age

Nan Li †,‡, Ming Jin †,‡, Changxin Lan †,‡, Jia Xu ⊥, Bin Han ⊥, Xiaojing Liu †,‡, Bo Pan ⊥,*, Bin Wang †,‡,§,∥
PMCID: PMC13288245  PMID: 42344589

Abstract

Short-term exposures to temperature and fine particulate matter (PM2.5) have been associated with metabolomic perturbations. However, their combined effect on the metabolome has not been evaluated. We investigated the effect of short-term coexposure to temperature and PM2.5 on metabolomic signatures and the potential roles of serum lipids and biomarkers using a repeated-measures study among Chinese women of childbearing age. We performed untargeted metabolomic profiling to quantify plasma metabolites. Data on temperature and PM2.5 exposures were estimated using a fused estimator. Serum lipids and biomarkers of oxidative stress and inflammation were detected. The independent and combined effects of temperature and PM2.5 were estimated using a linear mixed-effect model and quantile-based g-computation, respectively. Pathway analysis was conducted to identify perturbed metabolic pathways. Ten women provided 46 blood samples, from which 139 metabolites were quantified. Temperature and PM2.5 had independent effects on several metabolites and pathways. The largest positive and negative combined effects were observed for benzyl sulfate [β = 1.460, 95% confidence interval (CI): 0.438, 2.481] and chenodeoxycholic acid glycine conjugate (β = −1.933, 95% CI: −3.473, −0.392), respectively. Co-exposure to temperature and PM2.5 perturbed four pathways, including biosynthesis of unsaturated fatty acids; phenylalanine, tyrosine and tryptophan biosynthesis; linoleic acid metabolism; and phenylalanine metabolism. The metabolomic perturbations were mainly related to oxidative stress and the inflammatory response. We observed a positive combined effect of temperature and PM2.5 on interleukin-8. Our findings demonstrate that the metabolic mechanisms induced by temperature and PM2.5 involve oxidative stress and the inflammatory response and suggest that these metabolomic perturbations might promote an inflammatory response via release of interleukin-8.

Keywords: metabolomic signature, temperature, fine particulate matter, co-exposure, interleukin-8, women of childbearing age

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1. Introduction

Both temperature and fine particulate matter (PM2.5) are major environmental risk factors for various adverse health outcomes, including mortality, cardiovascular diseases, , respiratory diseases and adverse pregnancy outcomes. , Metabolomic profiling is a powerful tool that can provide mechanistic information to elucidate biological responses to environmental exposures at the molecular level while closely reflecting phenotype and physiological status. , Several recent studies have evaluated the associations of PM2.5 with metabolomic signatures, and their results varied by age and sex. − Limited research has investigated the effect of temperature on untargeted metabolomic signatures among white men. , Short-term exposure to high temperature was associated with four metabolic pathways related to oxidative stress or nucleic acid damage and repair. In real-world scenarios, temperature and PM2.5 exposure do not occur in isolation, leading to interactive health effects. , To our knowledge, no study has yet evaluated the effect of coexposure to temperature and PM2.5 on metabolomic processes.

Women are susceptible to environmental exposures; however, few studies have evaluated the metabolomic signatures of PM2.5 exposure in women. Both short-term and long-term PM2.5 exposures , are associated with altered metabolic pathways in women undergoing infertility treatment. The main metabolites and pathways detected were related to inflammation and oxidative stress. An untargeted metabolomic study reported that maternal exposure to traffic-related air pollution was associated with metabolic perturbations of serum linoleic acid and arachidonic acid. The downstream products of those metabolites are potential biomarkers of oxidative stress and inflammation. A targeted study detected associations of in utero PM2.5 exposure with alteration of cord blood oxylipins, which might play important roles in inflammation. However, pregnant and infertile women might differ from the general population of women in terms of pre-existing health conditions, susceptibility to environmental factors and metabolic characteristics. , Studies on the associations between environmental exposure and metabolomic signatures in women of childbearing age remain lacking.

Oxidative stress and inflammation have been detected as critical underlying mechanisms through which temperature and PM2.5 affect women’s health. Although previous studies ,, have shown that temperature and PM2.5 exposure can perturb metabolic pathways involved in oxidative stress and inflammation, those studies have not investigated specific biomarkers and therefore failed to elucidate fully the metabolomic mechanism underlying oxidative stress and the inflammatory response induced by temperature and PM2.5. Epidemiological studies have used mediation models to reveal the impact of PM2.5 on related metabolites and biomarkers. In addition, animal models , and in vitro studies , have revealed that environmental factors can induce metabolomic perturbations and alter biomarkers of oxidative stress and inflammatory response. Therefore, we aimed to investigate the combined effect of short-term exposure to temperature and PM2.5 on the metabolomic signatures of Chinese women of childbearing age and to explore further the metabolic mechanism underlying oxidative and inflammatory responses by investigating specific biomarkers in the present study.

2. Materials and Methods

2.1. Study Design and Population

This repeated-measures study was conducted in the Mancheng District of Baoding City in Hebei Province, China. Participant recruitment and the first survey occurred in January 2015. Four follow-up visits were subsequently conducted in March 2015, June 2015, January 2016 and April 2016. The inclusion criteria have been previously described in detail. In short, women who were local residents, aged 18 to 50, and with no medical history were invited to participate. In total, 10 women who were representative of the typical characteristics of the local population were included in this study. All participants were surveyed at least four times. Fasting venous blood samples were collected during each survey and a structured questionnaire was employed to obtain information on participants’ demographic characteristics, lifestyles and other health-related factors on the day of sampling. Our repeated investigation of time-varying confounders, such as diet, physical activity and air ventilation when heating, can eliminate seasonal influences to some extent. All participants provided written informed consent agreeing to participate and to the use of their biological samples for this study. The study protocol was approved by the institutional review board of Peking University (IRB00001052–14084) and proof of approval is available upon request.

2.2. Quantification of Temperature and PM2.5

Using the questionnaire and the Baidu Map application, we obtained longitude and latitude information corresponding to each participant’s residential address at the village or street level. Daily temperature and PM2.5 exposure data were estimated for each participant using a well-developed fused estimator that combines data from monitoring, satellite remote sensing and simulations from air quality models. These estimates reflect the actual temperature and PM2.5 concentration, as described previously. Celsius is the primary unit used to communicate temperature in China, with fixed points of 0 and 100 degrees representing the freezing point and boiling point of water at standard atmospheric pressure, respectively. We used the following formula to convert temperatures from the Fahrenheit scale to Celsius: Temperature in degrees Celsius (°C) = (Temperature in degrees Fahrenheit (°F) – 32) × 5/9. Temperatures in our study range from −5.87 to 26.49 °C. We focused on the effects of short-term exposure to temperature and PM2.5. Average values of temperature and PM2.5 concentration for 1 to 7 days prior to each blood collection date were calculated and denoted as Lag-1 to Lag-7, respectively.

2.3. Measurement of Metabolites, Lipids, Biomarkers of Oxidative Stress and Inflammation

Plasma metabolites were detected using ultraperformance liquid chromatography (1290 II, Agilent) coupled with mass spectrometry (TripleTOF 5600+, AB Sciex). Information-dependent acquisition mode was used for MS/MS analyses of the metabolites. Data acquisition and processing were performed using Analyst TF 1.7.1 Software (AB Sciex, Concord, ON, Canada). The mass-to-charge ratios, retention times and peak areas of metabolites were extracted using MarkerView 1.3 software. PeakView 2.2 (AB Sciex, Concord, ON, Canada) was applied to extract MS/MS data and perform comparisons with the Metabolites database (AB Sciex, Concord, ON, Canada), HMDB, METLIN, PubChem, Lipid Maps, Kegg, and standard references to annotate ion identities. Four levels of metabolites were annotated. , Level 1 means metabolites annotated through matching of MS1, RT and MS/MS spectra of standards (87 metabolites). Level 2 means metabolites annotated through matching MS1 and MS/MS2 with public metabolite spectral library (44 metabolites). Level 3 means metabolites annotated based on MS1 and MS2 spectral similarity to known compounds of a chemical class (2 metabolites). Level 4 means metabolites annotated based on MS1 and database searches (6 metabolites). Detailed information is presented in Supplementary Data 1. Serum lipids, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and lipoprotein­(a) (LP­(a)), were determined using the oxidative method, direct testing and latex particle enhanced immune-turbidimetric assay, as detailed previously. Biomarkers of oxidative (heme oxygenase 1, HO-1) and inflammatory (interleukin-8, IL-8; monocyte chemotactic protein, MCP-1) responses were measured using enzyme-linked immunosorbent assays.

2.4. Statistical Analysis

We summarized the participants’ characteristics and illustrated the distribution trends of temperature and PM2.5 over the study period. In consideration of the repeated measurements, linear mixed-effect models (LMEMs) that included random participant-specific intercepts were employed to investigate the independent effects of temperature and PM2.5 exposure on metabolites. Prior to regression analysis, we conducted log-10 transformation to normalize the metabolite data. Percentage changes (PC%) and 95% confidence intervals (CI) of log-transformed metabolites associated with an interquartile range (IQR) increase in exposure were calculated as [exp(IQR×β) – 1] × 100% and (exp[IQR×(β ± 1.96 × SE)] – 1) × 100%, where β is the coefficient estimated from the LMEM and SE is the standard error. To analyze the combined effect of temperature and PM2.5, we employed the quantile-based g-computation (qgcomp) approach, which enabled estimation of mixture effects and identification of independent contributions from individual exposures using exposure quartiles without assuming directional homogeneity. The q-parameter of the qgcomp model was set to 4, and the results included the effect of the mixture with each quartile increase as well as the contribution weight and effect direction of each component. We evaluated the associations of temperature and PM2.5 with serum lipids and biomarkers using LMEMs and the qgcomp model. We further used the Benjamini–Hochberg false discovery rate (FDR) procedure to correct for multiple testing. To strengthen our findings, we used postvisit temperature and PM2.5 as indicators to detect possible residual confounding. Briefly, residual confounding is possible if significant associations of future temperature and PM2.5 with metabolites are detected when adjusting for current exposures and confounders.

Pathway analysis was conducted for significant metabolites from the LMEMs using MetaboAnalyst 5.0 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) library. We selected the 10 most impacted pathways for each lag day according to the ranking of P values and assigned them scores of 1–10, with lower P values indicating higher scores. We then aggregated all selected pathways for Lag-1–7 and calculated their total scores. The top 10 pathways were identified as those most impacted by short-term exposure. We assigned ranks of 1–10 to these pathways, with higher rank indicating a stronger impact. Their average P values and the impacts of 1–7 day lag periods were calculated. We adjusted for potential confounders including age, body mass index, location, education, occupation, active and passive smoking status, alcohol consumption, physical activity, and air ventilation when heating. All metabolites and biomarkers were log10-transformed prior to analysis to improve normality. Statistical analyses were performed using R version 4.3.1 and two-tailed P < 0.05 was considered statistically significant.

3. Results

3.1. Population Characteristics

Table S1 presents the characteristics of the recruited subjects. Across all five visits, the mean age and body mass index were 36.22 ± 4.66 years and 25.48 ± 3.12 kg/m2, respectively. Half of the participants had education levels of high school or below. Most of the participants were technicians (50%), followed by workers (30%) and farmers (20%). Half of participants lived in rural areas and counties. Only one subject reported active smoking at the fifth visit. However, the percentage of passive smoking reached 48%. In addition, 24% of participants consumed alcohol, 61% exercised less than once a week, and 63% employed ventilation when heating indoors. All subjects were of Han ethnicity.

3.2. Distribution of Temperature and PM2.5

The distribution trends of environmental temperature and PM2.5 are presented in Figure S1. The concentration of PM2.5 tended to decrease with increasing temperature, exhibiting a trend of moderate, moderate, low, moderate, and low over the five visits. Detailed descriptions of temperature and PM2.5 concentrations at Lag-1–7 days for the five visits are presented in Table S2.

3.3. Independent Effects of Temperature and PM2.5 on Plasma Metabolites

The β coefficients and log10-transformed P values estimated with the LMEMs for all 139 metabolites are presented in Figures (A) and (A), with colored points representing significant metabolites. The transverse line represents the statistical significance level of P = 0.05. PC% and 95% CIs of metabolites associated with IQR increments of temperature and PM2.5 were also calculated, and the significant results are shown in Figure (B), (B), and S2.

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Effect of short-term exposure to temperature (°C) on metabolites. (A) Volcano plots showing associations between average temperature at Lag-1–7 days and metabolites. (B) Percent changes and 95% confidence intervals (CIs) for amino acids and acylcarnitines associated with an IQR increase in temperature. Estimates were obtained using a linear mixed-effect model with adjustments for age, body mass index, education, occupation, location, active and passive smoking status, alcohol drinking, physical activity level, and air ventilation when heating.

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Effect of short-term exposure to PM2.5 (μg/m3) on metabolites. (A) Volcano plots showing associations between average PM2.5 at Lag-1–7 days and metabolites. (B) Percent changes and 95% confidence intervals (CIs) for amino acids, acylcarnitines and fatty acids associated with an IQR increase in PM2.5. Estimates were obtained using a linear mixed-effect model with adjustments for age, body mass index, education, occupation, location, active and passive smoking status, alcohol drinking, physical activity level, and air ventilation when heating.

As illustrated in Figure , temperature had negative effects on six amino acids and 12 acylcarnitines. However, significant associations with γ-glutamylisoleucine and γ-glutamylmethionine were found only at Lag-1 and Lag-2. As shown in Figure S2, temperature had negative associations with glutaric acid (carboxylic acid), l-lactic acid (hydroxy acid), tauroursodeoxycholic acid (bile acid) and oleamide (carbohydrate). Fumaric acid (carboxylic acid), oxoglutaric acid (keto acid), pyruvic acid (fatty amide), and arabinonic acid (carbohydrate) showed consistent positive associations with temperature at Lag-1–7. Temperatures at Lag-2–3 and Lag-1–2 were associated with increased levels of p-cresol sulfate (organic sulfuric acid) and glycerophosphocholine, respectively.

PM2.5 concentration had negative associations with the levels of six amino acids, five acylcarnitines and five fatty acids, as shown in Figure . However, significant decreases observed in 4-methyleneglutamate, n-acetylglutamine, oleoylcarnitine, stearoylcarnitine, 2-tetradecenoyl carnitine and all five fatty acids were temporary at Lag-1, with the exceptions of pyroglutamic acid at Lag-6 and valerylcarnitine at Lag-5. As shown in Figure S2, PM2.5 had negative effects on uric acid (purine), fumaric acid, oxoglutaric acid and pyruvic acid at lag periods of more than 4 days. Relatively consistent positive associations with PM2.5 were found for two amino acids (Figure ) and l-lactic acid (Figure S2). Correction for multiple comparisons was conducted by calculating P FDR. Exact P values and P FDR values for the independent effects of temperature and PM2.5 on metabolites are provided in Supplementary Data 2 and 3, respectively. We assessed the associations of future temperature and PM2.5 with metabolites to detect potential residual confounding. Detailed results are presented in Supplementary Data 5 and 6.

3.4. Combined Effect of Temperature and PM2.5 on Metabolomic Signatures

We used a quantile-based multipollutant qgcomp model to analyze the effect of coexposure to temperature and PM2.5 at Lag-1–7 on each metabolite, and the significant results are presented in Figure . The concentrations of six fatty acids increased with 1 quantile increment in a set of all exposures of interest, namely 3-hydroxyhexadecanoic acid (β = 0.761; 95% CI: 0.360, 1.161), α-linolenic acid (β = 0.830; 95% CI: 0.238, 1.422), arachidonic acid (β = 1.033; 95% CI: 0.385, 1.680), linoleic acid (β = 0.773; 95% CI: 0.202, 1.345), oleic acid (β = 0.823; 95% CI: 0.200, 1.447) and palmitoleic acid (β = 0.801; 95% CI: 0.188, 1.415). Temperature at Lag-6 was assigned the negative weight with the highest absolute value. PM2.5 at Lag-4 was assigned the highest positive weight, as shown in Figure (A1–A6).

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The significant combined effects of average temperature (°C) and PM2.5 (μg/m3) at Lag 1–7 on fatty acids (A1–A6), amino acids (B1–B5), benzene, indolyl carboxylic acids, bile acids and acylcarnitines (C1–C5). The X-axis represents the estimated weight for each exposure. The Y-axis represents temperature (T) and PM2.5 exposure by number of lag days, and β represents the change in log-transformed metabolites corresponding to a 1-quantile increase in a set of all exposures. Estimates were obtained through quantile-based g-computation with adjustments for age, body mass index, education, occupation, location, active and passive smoking status, alcohol drinking, physical activity level, and air ventilation when heating.

Figure (B1–B4) illustrates negative combined effects on four amino acids, namely l-cystine (β = −0.355; 95% CI: −0.668, −0.042), l-histidine (β = −0.120; 95% CI: −0.227, −0.013), l-tyrosine (β = −0.208; 95% CI: −0.402, −0.015) and proline betaine (β = −1.553; 95% CI: −2.896, −0.210). PM2.5 at Lag-6 and Lag-3 was assigned the highest positive weight for l-cystine and l-histidine, respectively. Temperature at Lag-2 and Lag-4 made the strongest positive contributions for l-tyrosine and proline betaine, respectively. Temperature at Lag-3 was assigned the negative weight with the highest absolute value for both l-histidine and l-tyrosine. The contributions with negative weight for PM2.5 at Lag-7 and temperature at Lag-6 were the largest for l-cystine and proline betaine, respectively. As shown in Figure (B5), temperature and PM2.5 had a positive combined effect on symmetric dimethylarginine. Temperatures at Lag-2 and Lag-3 were assigned highest negative and positive weights, respectively.

Co-exposure to temperature and PM2.5 at Lag-1–7 increased the concentrations of benzyl sulfate (β = 1.460; 95% CI: 0.438, 2.481) and phenacemide (β = 0.429; 95% CI: 0.068, 0.789), as illustrated in Figure (C1–C2). PM2.5 at Lag-6 and temperature at Lag-3 were assigned the highest positive weights, while temperatures at Lag-6 and Lag-2 made the largest negative contributions. Negative combined effects on l-tryptophan (β = −0.196; 95% CI: −0.350, −0.042), chenodeoxycholic acid glycine conjugate (β = −1.933; 95% CI: −3.473, −0.392) and isobutyrylcarnitine (β = −0.577; 95% CI: −1.119, −0.034) were found, as shown in Figure (C3–C5). PM2.5 at Lag-5 and temperature at Lag-3 were assigned the negative weights with the highest absolute values for l-tryptophan and isobutyrylcarnitine, while temperature at Lag-2 made the largest positive contribution. Exact P values and P FDR values for the associations of coexposure to temperature and PM2.5 with metabolites are provided in Supplementary Data 2. The combined effects of postvisit temperature and PM2.5 on metabolites are presented in Supplementary Data 7.

3.5. Metabolic Pathway Analysis

We conducted pathway analysis for metabolites that showed significant associations with temperature based on LMEMs at Lag-1–7. As shown in Figure (A1–A3), the top two most significant metabolic pathways were arginine biosynthesis and the citrate cycle (tricarboxylic acid [TCA] cycle), consistently across Lag-1–7. As shown in Figure (A4), we aggregated the results for Lag-1–7 and selected the top ten impacted pathways. The top three pathways were arginine biosynthesis (P < 0.001), the citrate cycle (TCA cycle) (P < 0.001) and pyruvate metabolism (P = 0.001).

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Scatter plots of metabolic pathways associated with short-term exposure to temperature (A1–A4) and PM2.5 (B1–B8) at various lag windows (denoted at the top left of each panel). (A1) and (A3) show the results for temperature at Lag-1–2 and Lag-4–7, respectively, and (A2) represents the result at Lag-3. (B1–B7) show results for PM2.5 at Lag-1–7. In (A1–A3) and (B1–B7), the X-axis represents pathway impact values and the Y-axis represents P values from pathway analysis, with the most impacted pathways shown in red. Aggregated results from Lag-1–7 for temperature and PM2.5 are shown in (A4) and (B8), respectively, with higher rank indicating greater importance. The Y-axis and circle size represent the calculated average P and impact values, respectively.

The results of pathway analysis for metabolites associated with PM2.5 at Lag-1–7 are presented in Figure (B1–B7). The citrate cycle (TCA cycle) and pyruvate metabolism were the top two significant metabolic pathways at Lag-4–7. Biosynthesis of unsaturated fatty acids was the most impacted pathway at Lag-1, but was not significant at Lag-2–6. The aggregated results for Lag-1–7 are shown in Figure (B8). The top three most significant metabolic pathways were pyruvate metabolism (P < 0.001), the citrate cycle (TCA cycle) (P = 0.011) and alanine, aspartate and glutamate metabolism (P = 0.024).

We conducted pathway analysis for the 14 metabolites that showed significant associations (P < 0.05) with coexposure to temperature and PM2.5 at Lag-1–7 using qgcomp (Figure ). The X-axis represents pathway impacts and the Y-axis represents P values from pathway analysis, with the most impacted pathways shown in red. Four metabolic pathways had significant results, including biosynthesis of unsaturated fatty acids (P < 0.001); phenylalanine, tyrosine and tryptophan biosynthesis (P = 0.022); linoleic acid metabolism (P = 0.028) and phenylalanine metabolism (P = 0.044).

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Scatter plots of metabolic pathways associated with coexposure to temperature and PM2.5 at Lag-1–7. The 14 notable metabolites in quantile-based g-computation were included in the pathway analysis.

3.6. The Individual and Combined Effects of Temperature and PM2.5 on Lipids, Biomarkers of Oxidative Stress and Inflammation

LMEMs were employed to assess the individual effects of temperature and PM2.5 on five lipids as well as three biomarkers of oxidative stress and inflammation. Table presents PC% and 95% CIs for lipids and biomarkers associated with IQR increases of temperature and PM2.5 at Lag-1–7. Temperature had consistent positive effects on MCP-1 and IL-8 at 7 lag days in both the crude and adjusted models. PM2.5 had negative associations with MCP-1 at Lag-2–7 in both models. The concentration of IL-8 decreased with increasing PM2.5 at Lag-1–7 in the crude models, and these associations remained significant after adjusting for potential confounders. No significant associations of temperature or PM2.5 with lipids were observed.

1. Associations of Short-term Exposure to Temperature and PM2.5 with Lipids and Biomarkers of Oxidative Stress and Inflammation .

    percent change (95% CI)
models biomarkers Lag-1 Lag-2 Lag-3 Lag-4 Lag-5 Lag-6 Lag-7
Temperature
crude HO-1 3.23 (−7.12, 14.7) 3.47 (−7.80, 16.1) 3.01 (−8.52, 16.0) 3.68 (−8.31, 17.2) 3.72 (−7.88, 16.8) 3.75 (−8.00, 17.0) 3.95 (−7.87, 17.3)
  MCP-1 24.0 (10.2, 39.5) 26.2 (10.9, 43.6) 26.4 (10.6, 44.5) 27.9 (11.4, 46.8) 26.9 (11.0, 44.9) 27.4 (11.3, 45.8) 27.1 (10.9, 45.6)
  IL-8 119 (26.3, 280) 142 (32.8, 341) 160 (40.9, 380) 162 (38.0, 396) 156 (38.4, 374) 158 (38.0, 381) 163 (40.7, 392)
  Lp(a) –1.73 (−8.66, 5.72) –1.66 (−9.21, 6.52) –1.83 (−9.60, 6.61) –2.24 (−10.2, 6.45) –2.69 (−10.4, 5.64) –2.97 (−10.7, 5.45) –3.37 (−11.1, 5.04)
  TC 19.1 (−10.6, 58.6) 22.7 (−10.3, 67.8) 24.2 (−10.2, 71.7) 25.2 (−10.5, 74.9) 23.9 (−10.3, 71.3) 24.5 (−10.3, 72.8) 23.4 (−11.4, 71.8)
  TG 24.7 (−5.20, 63.9) 28.5 (−4.76, 73.3) 29.7 (−4.83, 76.8) 31.3 (−4.59, 80.8) 30.3 (−4.28, 77.4) 31.0 (−4.21, 79.1) 30.5 (−4.82, 78.8)
  HDL –0.914 (−6.78, 5.32) –0.804 (−7.22, 6.05) –0.742 (−7.37, 6.36) –0.715 (−7.55, 6.63) –1.02 (−7.61, 6.05) –1.16 (−7.84, 5.99) –1.42 (−8.12, 5.77)
  LDL 1.36 (−4.45, 7.53) 1.17 (−5.16, 7.92) 0.801 (−5.71, 7.77) 0.768 (−5.95, 7.97) 0.615 (−5.88, 7.56) 0.473 (−6.11, 7.51) 0.269 (−6.34, 7.34)
adjusted HO-1 5.94 (−6.47, 20.0) 5.40 (−8.01, 20.8) 3.78 (−9.65, 19.2) 4.17 (−9.71, 20.2) 4.56 (−8.81, 19.9) 4.51 (−9.01, 20.1) 4.79 (−8.74, 20.3)
  MCP-1 20.4 (4.12, 39.2) 22.4 (4.53, 43.3) 22.9 (4.73, 44.2) 24.7 (5.94, 46.8) 23.7 (5.79, 44.7) 24.4 (6.23, 45.6) 24.0 (5.89, 45.1)
  IL-8 202 (56.2, 485) 251 (73.5, 611) 281 (89.6, 667) 283 (84.9, 693) 263 (80.4, 629) 265 (79.9, 641) 269 (82.9, 645)
  Lp(a) –2.92 (−12.1, 7.19) –3.10 (−13.0, 7.99) –3.20 (−13.4, 8.19) –3.80 (−14.3, 7.93) –4.28 (−14.3, 6.88) –4.63 (−14.7, 6.66) –5.09 (−15.1, 6.13)
  TC –12.4 (−30.1, 9.85) –12.8 (−31.9, 11.7) –11.5 (−31.5, 14.3) –11.0 (−31.7, 16.1) –10.3 (−30.5, 15.8) –10.3 (−30.8, 16.2) –10.6 (−31.0, 15.9)
  TG –4.04 (−25.2, 23.0) –3.58 (−26.5, 26.6) –2.47 (−26.3, 29.0) –1.08 (−26.0, 32.2) –0.447 (−24.7, 31.5) –0.484 (−25.0, 32.1) –0.21 (−24.8, 32.5)
  HDL –4.44 (−11.5, 3.13) –4.87 (−12.5, 3.37) –4.86 (−12.6, 3.60) –4.94 (−12.9, 3.76) –5.05 (−12.7, 3.26) –5.24 (−12.9, 3.15) –5.51 (−13.2, 2.82)
  LDL –1.31 (−8.68, 6.66) –1.67 (−9.66, 7.03) –1.84 (−9.99, 7.06) –1.67 (−10.1, 7.53) –1.69 (−9.78, 7.12) –1.77 (−9.95, 7.16) –2.01 (−10.2, 6.89)
PM2.5
crude HO-1 –8.63 (−21.1, 5.83) –6.89 (−19.0, 7.04) –8.14 (−21.1, 6.92) –8.30 (−20.0, 5.08) –7.50 (−18.4, 4.85) –6.37 (−16.2, 4.61) –5.99 (−15.6, 4.69)
  MCP-1 –8.06 (−24.0, 11.3) -16.4 (−29.8, −0.613) -20.0 (−33.6, −3.62) -18.2 (−30.9, −3.22) -18.8 (−30.3, −5.51) -17.0 (−27.4, −5.18) -17.0 (−27.0, −5.67)
  IL-8 -56.3 (−80.2, −3.50) -63.3 (−82.4, −23.6) -62.1 (−83.3, −14.0) -53.6 (−78.0, −1.99) -53.7 (−76.6, −8.33) -52.7 (−73.9, −14.3) -51.8 (−72.9, −14.5)
  Lp(a) 10.1 (−0.0985, 21.4) 8.46 (−1.21, 19.1) 8.67 (−1.89, 20.4) 7.84 (−1.68, 18.3) 7.50 (−1.25, 17.0) 6.37 (−1.36, 14.7) 4.95 (−2.50, 13.0)
  TC 1.26 (−32.9, 52.8) –14.6 (−42.1, 26.1) –17.3 (−45.8, 26.3) –13.6 (−41.0, 26.7) –14.8 (−40.0, 20.9) –13.7 (−36.7, 17.7) –14.8 (−36.8, 14.8)
  TG –8.20 (−38.3, 36.7) –20.8 (−45.4, 14.9) –24.0 (−49.2, 13.8) –20.4 (−44.7, 14.7) –21.1 (−43.5, 10.2) –19.2 (−39.9, 8.62) –19.0 (−39.1, 7.61)
  HDL 2.49 (−5.86, 11.6) 2.70 (−5.27, 11.4) 2.41 (−6.25, 11.9) 1.80 (−6.01, 10.3) 2.29 (−4.96, 10.1) 2.16 (−4.27, 9.02) 1.11 (−5.05, 7.66)
  LDL 2.80 (−5.34, 11.6) 2.30 (−5.42, 10.6) 1.66 (−6.67, 10.7) 0.539 (−6.95, 8.63) 0.387 (−6.53, 7.81) 0.532 (−5.62, 7.09) –0.20 (−6.10, 6.07)
adjusted HO-1 –7.15 (−20.8, 8.79) –5.64 (−18.7, 9.49) –7.33 (−21.6, 9.53) –8.62 (−21.7, 6.61) –8.70 (−20.8, 5.19) –7.45 (−18.3, 4.88) –6.65 (−17.4, 5.51)
  MCP-1 –11.5 (−26.6, 6.59) -16.2 (−29.5, −0.28) -19.7 (−33.6, −2.95) -18.0 (−31.1, −2.45) -17.6 (−29.9, −3.28) -15.8 (−27.0, −2.82) -15.7 (−26.8, −3.03)
  IL-8 -61.0 (−83.5, −7.96) -66.5 (−84.9, −25.5) -67.7 (−86.9, −20.2) -59.6 (−82.7, −5.90) -59.2 (−81.4, −10.8) -59.4 (−79.4, −19.9) -59.3 (−79.1, −20.9)
  Lp(a) 12.6 (−1.54, 28.7) 10.6 (−2.28, 25.1) 11.6 (−3.10, 28.4) 10.1 (−3.57, 25.8) 9.41 (−2.91, 23.3) 7.96 (−2.75, 19.8) 5.83 (−4.42, 17.2)
  TC –1.55 (−28.8, 36.2) –0.174 (−25.9, 34.5) 3.61 (−26.0, 45.0) 4.97 (−23.3, 43.6) 7.81 (−18.8, 43.1) 8.84 (−15.0, 39.3) 7.30 (−15.4, 36.2)
  TG –14.1 (−39.1, 21.3) –12.2 (−36.1, 20.7) –11.2 (−38.0, 27.4) –9.03 (−35.0, 27.4) –6.22 (−30.9, 27.3) –3.81 (−26.4, 25.7) –3.37 (−25.4, 25.1)
  HDL 5.72 (−3.55, 15.9) 6.27 (−2.75, 16.1) 6.96 (−3.05, 18.0) 5.99 (−3.08, 15.9) 6.28 (−2.17, 15.5) 5.97 (−1.55, 14.1) 4.94 (−2.47, 12.9)
  LDL 4.57 (−5.42, 15.6) 3.36 (−5.95, 13.6) 3.47 (−6.90, 15.0) 2.80 (−6.75, 13.3) 2.58 (−6.21, 12.2) 2.78 (−5.03, 11.2) 2.10 (−5.47, 10.3)
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a

Liner mixed-effect model adjusted for age, body mass index, education, occupation, location, active and passive smoking status, alcohol drinking, physical activity level, and air ventilation when heating.

b

Abbreviations: HO-1, heme oxygenase-1; MCP-1, monocyte chemotactic protein-1; IL-8, interleukin-8; Lp­(a), lipoprotein­(a); TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; CI, confidence interval.

Qgcomp analysis was used to estimate the combined effects of temperature and PM2.5 on lipids and biomarkers. As shown in Figure , coexposure to temperature and PM2.5 at Lag-1–7 significantly increased the concentration of IL-8 (β = 2.112; 95% CI: 0.560, 3.664). Temperature at Lag-7 and PM2.5 at Lag-2 were assigned the positive (weight = 0.193) and negative (weight = −0.509) weights with the largest absolute values, respectively. PM2.5 at Lag-6 (weight = 0.173) and temperatures at Lag-1 (weight = 0.170) and Lag-3 (weight = 0.168) also made considerable positive contributions. The combined effects of temperature and PM2.5 on the other two biomarkers and five lipids were not significant.

6.

6

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Combined effect of average temperature (°C) and PM2.5 (μg/m3) on IL-8 at Lag-1–7. The X-axis represents the estimated weight of each exposure. The Y-axis represents temperature (T) and PM2.5 exposure by number of lag days, and β represents the change in log-transformed IL-8 concentration corresponding to 1 quantile increase in a set of all exposures. Estimates were obtained using quantile-based g-computation with adjustments for age, body mass index, education, occupation, location, active and passive smoking status, alcohol drinking, physical activity level, and air ventilation when heating.

4. Discussion

In this repeated-measures study, we evaluated associations of short-term exposure to temperature and PM2.5 with untargeted metabolomic signatures among women of childbearing age. We observed significant independent effects of temperature and PM2.5 on several metabolites and pathways, mainly those related to oxidative stress and inflammatory responses. This study is the first to investigate the combined effect of temperature and PM2.5 on the metabolomic signature. Four pathways related to oxidative stress and the inflammatory response were found to be associated with coexposure to temperature and PM2.5, including biosynthesis of unsaturated fatty acids; phenylalanine, tyrosine and tryptophan biosynthesis; linoleic acid metabolism; and phenylalanine metabolism. We further examined the role of serum biomarkers in the inflammatory responses induced by metabolomic perturbations.

We observed significant associations of short-term exposure to temperature with 28 metabolites, including 6 amino acids and 12 acylcarnitines. A study in the United States evaluated the short-term effects of temperature on targeted metabolites related to cardiovascular disease and found perturbation in acylcarnitine (C16-OH:C14:1-DC). Acylcarnitine is a cofactor that transports free fatty acids into mitochondria. Higher concentrations of acylcarnitine indicate insufficient mitochondrial fatty acid oxidation, which may induce oxidative stress. We identified temperature-perturbed pathways related to oxidative stress including glycolysis/gluconeogenesis, pyruvate metabolism, taurine and hypotaurine metabolism, and lipoic acid metabolism. Glycolysis/gluconeogenesis is essential to maintaining oxidation–reduction balance. Pyruvate neutralizes reactive oxygen species (ROS) and reduces mitochondrial ROS production. Taurine and hypotaurine are sulfur-containing amino acids that are readily oxidized. Glycolysis/gluconeogenesis, as well as pyruvate, taurine and hypotaurine metabolism, were detected as perturbed pathways associated with temperature among men in an American study. To our knowledge, it is the first study using an untargeted approach to investigate the effects of short-term exposure to environmental temperature on the metabolomic signature. Lipoic acid, which has antioxidant properties, has not been associated with temperature previously. We also detected novel perturbations in arginine biosynthesis, as well as tyrosine, alanine, aspartate and glutamate metabolism, and these processes are involved in the inflammatory response. We observed an effect of temperature on the TCA cycle, which is detrimental to energy metabolism and is associated with oxidative stress and inflammation. Butanoate is involved in the TCA cycle and related to PM2.5, but was associated only with temperature in our study. Previous research has supported associations of temperature with oxidative stress and the inflammatory response. Our results highlight the potential metabolic mechanisms underlying those observed associations, providing a unique insight into the health of women of childbearing age in China.

Eight of the top 10 PM2.5-perturbed pathways overlapped with temperature-associated pathways in this study. These overlapping pathways were mainly related to oxidative stress (glycolysis/gluconeogenesis, TCA cycle, lipoic acid, pyruvate, taurine and hypotaurine metabolism) and inflammation (TCA cycle, arginine biosynthesis, tyrosine, alanine, aspartate and glutamate metabolism). We observed perturbations in fatty acids and a related pathway (biosynthesis of unsaturated fatty acids) that were associated only with PM2.5. Several studies ,,,,− have investigated PM2.5-related metabolomic signatures, among which few − have focused on women. A study in California found that prenatal exposure to traffic-related air pollution altered maternal levels of serum linoleic acid and arachidonic acid during the second trimester. Air pollution-induced oxidative stress can promote the hydrolysis of cell membranes. Linoleic acid and arachidonic acid are polyunsaturated fatty acids that can be released from hydrolyzed cell membranes and subsequently oxidized. The downstream products of linoleic acid are potential biomarkers of oxidative stress. Arachidonic acid can be converted into pro-inflammatory cytokines such as leukotrienes and prostaglandins through the lipoxygenase and cyclooxygenase pathways, respectively. A study in Belgium showed that in utero exposure to PM2.5 perturbed oxylipins involved in the lipoxygenase pathway in cord blood. In addition, metabolites and pathways related to oxidative stress and inflammatory responses were associated with PM2.5 , exposure among women seeking infertility treatment in Boston. Associations of PM2.5 with oxidative stress , and inflammatory responses among women have been widely reported. Validating and complementing these previous findings, our study elucidated the metabolic mechanisms of PM2.5-induced oxidative stress and inflammation among Chinese women of childbearing age, in which fatty acid metabolism might play a critical role.

Under realistic conditions, temperature and PM2.5 coexposure occurs, and the combined effect might differ from the independent effects of these factors. Meteorological conditions, such as temperature, can alter PM2.5 in terms of particle size, aggregation characteristics, and shape, thereby affecting its chemical constituents and toxicity. Therefore, we employed a multiexposure qgcomp model to evaluate the combined effects of temperature and PM2.5 on metabolomic signatures. We found that coexposure to temperature and PM2.5 increased the concentrations of arachidonic acid, linoleic acid, α-linolenic acid, oleic acid and palmitoleic acid, while PM2.5 showed an independent negative effect. As reported in a previous study, cold exposure may affect the α-linolenic acid metabolism pathway. Our results suggest that complex interactions exist between temperature- and PM2.5-induced changes in fatty acids and related pathways. We observed negative combined effects of temperature and PM2.5 on l-cystine and l-histidine, while no independent effects of either temperature or PM2.5 were found. l-cystine can increase the glutathione level and may lead to an antioxidant response. l-histidine is involved in the synthesis of the antioxidant carnosine and has anti-inflammatory effects. In a study of a cold-acclimated population, acute cold stress decreased the concentrations of l-cystine and l-histidine. This discrepancy may be due to heterogeneity among study participants, U-shaped health effects of temperature, or potential interactive effects of temperature and PM2.5. We identified four pathways associated with coexposure to temperature and PM2.5, including biosynthesis of unsaturated fatty acids; phenylalanine, tyrosine and tryptophan biosynthesis; linoleic acid metabolism; and phenylalanine metabolism. Phenylalanine is an amino acid with anti-inflammatory effects that has been associated with air pollution. The increase in phenylalanine may indicate elevated oxidative stress and an anti-inflammatory response to environmental exposure. Tryptophan and linoleic acid metabolism can lead to cell membrane damage, oxidative stress, and inflammation, and have previously been linked to PM2.5 exposure. Findings in female mice showed that PM2.5 could affect the expression of genes associated with lipid metabolism and interfere with related metabolic pathways. Another animal experiment reported that exposure to environmental stress, including low temperature, could alter mitochondria-related gene expression and perturb fatty acid metabolism. Further studies are needed to construct a gene–protein–metabolite network associated with coexposure to temperature and PM2.5 using multiomics data.

Our findings suggest that pro-inflammatory cytokines, such as MCP-1 and IL-8, may play an important role in inflammatory responses driven by temperature- and PM2.5-induced metabolomic perturbations. We observed positive effects of temperature on MCP-1 and IL-8, while elevated PM2.5 concentrations decreased the levels of MCP-1 and IL-8. Previous studies on the associations of PM2.5 with MCP-1 and IL-8 have reported inconsistent results, , with limited evidence linking ambient temperature to MCP-1 and IL-8. , We observed a positive combined effect of temperature and PM2.5 on the concentration of IL-8, indicting a potential interactive effect of temperature and PM2.5 on serum pro-inflammatory cytokine levels. Several temperature- and PM2.5-perturbed metabolites and pathways identified in our study have been reported to impact the production of IL-8. α-linolenic acid may improve the inflammatory response by reducing the IL-8 concentration. Acylcarnitines can activate inflammatory signaling pathways by inducing IL-8 production. Phenylalanine and histidine showed positive and negative correlations with IL-8, respectively. In addition, an in vitro study demonstrated that PM2.5 treatment altered amino acid metabolism and increased pro-inflammatory cytokine levels in human lung bronchial epithelial cells. Benzo­[a]­pyrene, one of the most toxic components of PM2.5, perturbed lipid metabolism and increased the level of IL-8 in human alveolar type II cells. Thus, we hypothesize that temperature and PM2.5 exposure can induce dysfunction in metabolism of amino acids, acylcarnitines and fatty acids, which might cause inflammation through the release of inflammatory cytokines, such as IL-8.

The present study has several strengths. To the best of our knowledge, this study is the first to evaluate the combined effect of temperature and PM2.5 on metabolomic signatures. Our findings provide possible evidence of the metabolic mechanisms underlying temperature- and PM2.5-induced oxidative stress and inflammation, suggesting potential interactive effects of temperature and PM2.5 on metabolomic perturbations. Moreover, we focused on women of childbearing age and provided insights into this subpopulation, who are susceptible to environmental factors. Our results confirm and complement previous findings in pregnant women and women with infertility. Additionally, we integrated metabolomic signatures with serum biomarkers of inflammation and proposed that temperature- and PM2.5-induced metabolomic perturbations may promote inflammation through release of the cytokine IL-8. We accounted for additional confounders in the present study, particularly passive smoking status, which could affect the concentrations of metabolites and other biomarkers. The inclusion of such confounders ensured the accuracy and reliability of our conclusions. Finally, our repeated-measures study design effectively reduced the influence of potential confounders.

We acknowledge that our study has some limitations. First, we measured a limited number of plasma metabolites and failed to cover the entire metabolome. Thus, we were unable to assess fully some crucial pathways such as fatty acid metabolism, as undetected downstream metabolites in this process are important biomarkers of oxidative stress or mediators of the inflammatory response. We did not detect metabolites of exogenous origin, which might be of great importance. Second, our data on temperature and PM2.5 exposure were constructed using a fused estimator and, thus, may differ from actual personal exposure. We note that our exposure assessment may not be as accurate as studies using individual exposure monitoring. Further research is needed to validate our results based on real-time individual exposure monitoring or validation analysis. Third, we did not consider potential U-shaped effects of temperature. However, metabolism is a complex process involving feedback loops, and the present pathway analysis was exploratory, with no consideration of effect direction. Any effect on a pathway is considered a perturbation worthy of further study. Fourth, we cannot draw definitive conclusions about whether pro-inflammatory cytokines are downstream products or upstream biological molecules associated with metabolic disturbances. Alteration of relevant gene expression induced by environmental exposure may also play a role in metabolomic perturbation and inflammatory responses. Fifth, we believe that multiomics studies, particularly studies incorporating gene and protein data, are needed to elucidate further the mechanisms underlying the toxic effects of environmental factors from the perspective of the exposome. − Finally, this study used a relatively small sample size and further studies with larger sample sizes are needed to strengthen its findings.

5. Conclusions

We identified several metabolites and metabolic pathways related to short-term temperature and PM2.5 exposure and demonstrated potential interactive effects of temperature and PM2.5 on metabolomic signatures in a population of women of childbearing age. Our findings elucidate the metabolic mechanisms underlying oxidative stress and inflammatory responses induced by temperature and PM2.5, suggesting that temperature- and PM2.5-related metabolomic perturbations may lead to inflammatory response through the release of the cytokine IL-8. In the context of global air pollution and climate change, our findings clarify the mechanisms underlying the health impacts of air pollution and temperature and support the development of biomarkers for preventative and therapeutic purposes.

Supplementary Material

eh5c00186_si_001.pdf (432.2KB, pdf)
eh5c00186_si_002.xlsx (197.9KB, xlsx)

Acknowledgments

We would like to express our gratitude to the working group for environmental exposure and human health of the China Cohort Consortium (http://chinacohort.bjmu.edu.cn/).

The original data will be available upon reasonable request by contacting panbocai@gmail.com, and the codes to reproduce the main study results can be downloaded on the Github Web site (https://github.com/pekingnn/Code-PM2.5-Metabolome).

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/envhealth.5c00186.

  • Table S1, Characteristics of the 10 recruited women; Table S2, Descriptions of environmental temperature and PM2.5 concentration; Figure S1, Distributions of environmental temperature and fine particulate matter (PM2.5) during the study period; Figure S2, Significant effects of short-term exposure to temperature and PM2.5 on metabolites (PDF)

  • Supplementary Data 1, Identification methods for 139 metabolites; Supplementary Data 2, Effect of short-term exposure to temperature on metabolites; Supplementary Data 3, Effect of short-term exposure to PM2.5 on metabolites; Supplementary Data 4, Combined effects of temperature and PM2.5 at Lag 1–7 on metabolites; Supplementary Data 5, Effect of temperature exposure after visits on metabolites; Supplementary Data 6, Effect of PM2.5 exposure after visits on metabolites; Supplementary Data 7, Combined effects of temperature and PM2.5 exposure after visits on metabolites (XLSX)

#.

N.L. and M.J. have equal contributions to this work. N.L.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writingoriginal draft, Writingreview and editing. M.J.: Methodology, Investigation, Formal analysis, Data curation, Writingreview and editing. C.L.: Methodology, Investigation, Data curation. J.X.: Methodology, Investigation, Data curation. B.H.: Methodology, Investigation, Data curation. X.L.: Methodology, Investigation, Data curation. B.P.: Methodology, Supervision, Project administration. B.W.: Methodology, Supervision, Investigation, Project administration, Funding acquisition.

This work was supported by the National Key Research & Development Program of the Ministry of Science and Technology of China (Grant No. 2023YFC3708305); the National Natural Science Foundation of China (Grant No. 42477455, 42077390); and Yunnan Major Scientific and Technological Projects (Grant No. 202202AG050019).

The authors declare no competing financial interest.

The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/bnRRwQ.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

eh5c00186_si_001.pdf (432.2KB, pdf)
eh5c00186_si_002.xlsx (197.9KB, xlsx)

Data Availability Statement

The original data will be available upon reasonable request by contacting panbocai@gmail.com, and the codes to reproduce the main study results can be downloaded on the Github Web site (https://github.com/pekingnn/Code-PM2.5-Metabolome).

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