Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (1): 122-130.doi: 10.12122/j.issn.1673-4254.2026.01.13
Kai LI(
), Wenqian ZENG, Yanzi ZHANG, Xiuling ZHU, Bing GUO(
)
Received:2025-07-07
Online:2026-01-20
Published:2026-01-16
Contact:
Bing GUO
E-mail:likai05132024@163.com;guobing0111@scu.edu.cn
Supported by:Kai LI, Wenqian ZENG, Yanzi ZHANG, Xiuling ZHU, Bing GUO. Exposures to volatile organic compounds are positively correlated with risks of metabolic dysfunction-associated steatotic liver disease[J]. Journal of Southern Medical University, 2026, 46(1): 122-130.
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URL: https://www.j-smu.com/EN/10.12122/j.issn.1673-4254.2026.01.13
| 92.6% | |||
| 99.5% | |||
| 99.7% | |||
| 94.0% | |||
| 99.4% | |||
| 99.1% | |||
| 87.2% | |||
| 99.9% | |||
| 95.2% | |||
| 99.8% | |||
| 98.8% | |||
| 99.4% | |||
| 99.9% | |||
| 97.2% | |||
| 99.9% |
Tab.1 Fifteen types of volatile organic compound metabolites (mVOCs) with detection rates exceeding 80% in the urine samples
| 92.6% | |||
| 99.5% | |||
| 99.7% | |||
| 94.0% | |||
| 99.4% | |||
| 99.1% | |||
| 87.2% | |||
| 99.9% | |||
| 95.2% | |||
| 99.8% | |||
| 98.8% | |||
| 99.4% | |||
| 99.9% | |||
| 97.2% | |||
| 99.9% |
| Variable | Overall (n=2122) | Non-MASLD (n=1149) | MASLD (n=973) | P |
|---|---|---|---|---|
| Age (year, Mean±SD) | 45.30 (14.17) | 43.43 (14.63) | 47.52 (13.27) | <0.001 |
| Gender [n (%)] | <0.001 | |||
| Male | 1168 (55%) | 602 (52%) | 566 (58%) | |
| Female | 954 (45%) | 547 (48%) | 407 (42%) | |
| Education level [n (%)] | 0.13 | |||
| Less than high school | 354 (17%) | 181 (16%) | 173 (18%) | |
| High school or equivalent | 413 (19%) | 212 (18%) | 201 (21%) | |
| College or above | 1355 (64%) | 756 (66%) | 599 (62%) | |
| Income level [n (%)] | 0.03 | |||
| Low income | 519 (24%) | 283 (25%) | 236 (24%) | |
| Middle income | 804 (38%) | 408 (36%) | 396 (41%) | |
| High income | 799 (38%) | 458 (40%) | 341 (35%) | |
| Marital status [n (%)] | <0.001 | |||
| Married | 1400 (66%) | 734 (64%) | 666 (68%) | |
| Divorced | 329 (16%) | 166 (14%) | 163 (17%) | |
| Single | 393 (19%) | 249 (22%) | 144 (15%) | |
| BMI (kg/m2, Mean±SD) | 29.19 (6.83) | 24.88 (3.48) | 34.27 (6.31) | <0.001 |
| Diabetes [n (%)] | 216 (10%) | 63 (5.5%) | 153 (16%) | <0.001 |
| Hypertension [n (%)] | 620 (29%) | 219 (19%) | 401 (41%) | <0.001 |
| Total cholesterol level (mmol/L, Mean±SD) | 4.94 (1.06) | 4.80 (1.00) | 5.11 (1.10) | <0.001 |
| Urinary creatinine level (mg/100 mL, Mean±SD) | 122.22 (79.13) | 113.38 (76.69) | 132.66 (80.72) | <0.001 |
Tab.2 General characteristics of the study population
| Variable | Overall (n=2122) | Non-MASLD (n=1149) | MASLD (n=973) | P |
|---|---|---|---|---|
| Age (year, Mean±SD) | 45.30 (14.17) | 43.43 (14.63) | 47.52 (13.27) | <0.001 |
| Gender [n (%)] | <0.001 | |||
| Male | 1168 (55%) | 602 (52%) | 566 (58%) | |
| Female | 954 (45%) | 547 (48%) | 407 (42%) | |
| Education level [n (%)] | 0.13 | |||
| Less than high school | 354 (17%) | 181 (16%) | 173 (18%) | |
| High school or equivalent | 413 (19%) | 212 (18%) | 201 (21%) | |
| College or above | 1355 (64%) | 756 (66%) | 599 (62%) | |
| Income level [n (%)] | 0.03 | |||
| Low income | 519 (24%) | 283 (25%) | 236 (24%) | |
| Middle income | 804 (38%) | 408 (36%) | 396 (41%) | |
| High income | 799 (38%) | 458 (40%) | 341 (35%) | |
| Marital status [n (%)] | <0.001 | |||
| Married | 1400 (66%) | 734 (64%) | 666 (68%) | |
| Divorced | 329 (16%) | 166 (14%) | 163 (17%) | |
| Single | 393 (19%) | 249 (22%) | 144 (15%) | |
| BMI (kg/m2, Mean±SD) | 29.19 (6.83) | 24.88 (3.48) | 34.27 (6.31) | <0.001 |
| Diabetes [n (%)] | 216 (10%) | 63 (5.5%) | 153 (16%) | <0.001 |
| Hypertension [n (%)] | 620 (29%) | 219 (19%) | 401 (41%) | <0.001 |
| Total cholesterol level (mmol/L, Mean±SD) | 4.94 (1.06) | 4.80 (1.00) | 5.11 (1.10) | <0.001 |
| Urinary creatinine level (mg/100 mL, Mean±SD) | 122.22 (79.13) | 113.38 (76.69) | 132.66 (80.72) | <0.001 |
| VOC | Model 1 | Model 2 | ||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| 2-MHA | 1.02 (0.77-1.36) | 0.871 | 1.05 (0.93-1.18) | 0.427 |
| 3,4-MHA | 0.95 (0.78-1.15) | 0.582 | 1.03 (0.94-1.13) | 0.532 |
| AMCC | 0.85 (0.48-1.48) | 0.558 | 0.98 (0.90-1.08) | 0.743 |
| ATCA | 1.06 (0.95-1.18) | 0.279 | 1.13 (1.03-1.25) | 0.008 |
| BMA | 1.02 (0.94-1.12) | 0.585 | 1.07 (0.94-1.22) | 0.297 |
| CEMA | 1.98 (1.53-2.56) | <0.001 | 1.28 (1.11-1.46) | <0.001 |
| CYMA | 0.76 (0.55-1.04) | 0.086 | 0.98 (0.89-1.08) | 0.682 |
| DHBMA | 1.33 (1.14-1.56) | <0.001 | 1.29 (1.17-1.43) | <0.001 |
| 2HPMA | 1.00 (0.91-1.09) | 0.975 | 1.03 (0.94-1.12) | 0.539 |
| 3HPMA | 0.59 (0.46-0.76) | <0.001 | 1.02 (0.93-1.12) | 0.697 |
| MA | 1.12 (0.60-2.09) | 0.713 | 1.17 (0.94-1.46) | 0.164 |
| PGA | 0.95 (0.54-1.66) | 0.849 | 1.15 (0.94-1.42) | 0.181 |
| AAMA | 0.81 (0.67-0.99) | 0.036 | 1.00 (0.91-1.10) | 0.963 |
| MHBMA3 | 0.87 (0.64-1.18) | 0.357 | 1.05 (0.95-1.15) | 0.359 |
| HMPMA | 1.57 (1.11-2.21) | 0.011 | 1.09 (0.99-1.21) | 0.083 |
Tab.3 Correlation between single mVOC and metabolic dysfunction-associated steatotic liver disease (MASLD)
| VOC | Model 1 | Model 2 | ||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| 2-MHA | 1.02 (0.77-1.36) | 0.871 | 1.05 (0.93-1.18) | 0.427 |
| 3,4-MHA | 0.95 (0.78-1.15) | 0.582 | 1.03 (0.94-1.13) | 0.532 |
| AMCC | 0.85 (0.48-1.48) | 0.558 | 0.98 (0.90-1.08) | 0.743 |
| ATCA | 1.06 (0.95-1.18) | 0.279 | 1.13 (1.03-1.25) | 0.008 |
| BMA | 1.02 (0.94-1.12) | 0.585 | 1.07 (0.94-1.22) | 0.297 |
| CEMA | 1.98 (1.53-2.56) | <0.001 | 1.28 (1.11-1.46) | <0.001 |
| CYMA | 0.76 (0.55-1.04) | 0.086 | 0.98 (0.89-1.08) | 0.682 |
| DHBMA | 1.33 (1.14-1.56) | <0.001 | 1.29 (1.17-1.43) | <0.001 |
| 2HPMA | 1.00 (0.91-1.09) | 0.975 | 1.03 (0.94-1.12) | 0.539 |
| 3HPMA | 0.59 (0.46-0.76) | <0.001 | 1.02 (0.93-1.12) | 0.697 |
| MA | 1.12 (0.60-2.09) | 0.713 | 1.17 (0.94-1.46) | 0.164 |
| PGA | 0.95 (0.54-1.66) | 0.849 | 1.15 (0.94-1.42) | 0.181 |
| AAMA | 0.81 (0.67-0.99) | 0.036 | 1.00 (0.91-1.10) | 0.963 |
| MHBMA3 | 0.87 (0.64-1.18) | 0.357 | 1.05 (0.95-1.15) | 0.359 |
| HMPMA | 1.57 (1.11-2.21) | 0.011 | 1.09 (0.99-1.21) | 0.083 |
Fig.1 Correlation between the 15 mVOCs included in the study. The numbers in the figure represent the correlation coefficients between two VOCs. Darkest shades indicate a strong positive correlation (close to 1), while the light shades indicate a strong negative correlation (close to -1). The asterisks (*) within the squares denote the statistical significance of the correlation coefficients. *P<0.05, ** P<0.01, ***P<0.001).
Fig.2 LASSO regression analysis of the 15 types of mVOCs included in the study. A: Screening pathway for mVOCs associated with MASLD risk. B: Correlation between log-transformed λ and MSE. The red dashed line and its error bars in B represent the mean MSE values and their corresponding 95% confidence intervals (95% CI). The left black dashed line indicates the optimal λ value yielding the minimum MSE, while the right black dashed line shows the λ value from the simplest model obtained at one standard error below the minimum MSE.
Fig. 3 WQS model forward regression of mVOCs mixtures with MASLD weight plots. The chart above were adjusted for age, gender, ethnicity, dietary patterns, income level, marital status, occupation, diabetes, hypertension, smoking status, and total cholesterol levels.
| Subgroup | OR (95% CI) | P | |
|---|---|---|---|
| Gender | |||
| Male | 1.21 (0.99, 1.47) | 0.056 | |
| Female | 1.06 (0.86, 1.30) | 0.579 | |
| Smoking status | |||
| Non-smoker | 1.10 (0.83, 1.46) | 0.501 | |
| Secondhand smoker | 1.27 (1.02, 1.57) | 0.031 | |
| Current smoker | 1.49 (1.19, 1.87) | <0.001 | |
Tab.4 Associations between different groupings of mVOCs and MASLD
| Subgroup | OR (95% CI) | P | |
|---|---|---|---|
| Gender | |||
| Male | 1.21 (0.99, 1.47) | 0.056 | |
| Female | 1.06 (0.86, 1.30) | 0.579 | |
| Smoking status | |||
| Non-smoker | 1.10 (0.83, 1.46) | 0.501 | |
| Secondhand smoker | 1.27 (1.02, 1.57) | 0.031 | |
| Current smoker | 1.49 (1.19, 1.87) | <0.001 | |
Fig. 4 WQS model forward regression of mVOCs mixtures with MASLD weight plots in groups with different smoking statuses. A: Weight map of mVOCs in the smoking group after adjusting for confounding factors. B: Weight map of mVOCs in the secondhand smoking exposure group after adjusting for confounding factors.
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