Urinary enterolignans and enterolignan-predicting microbial species are favourably associated with liver fat and other obesity markers

Abstract

Aims: Plant-derived lignans may protect against obesity, while their bioactivity needs gut microbial conversion to enterolignans. We used repeated measures to identify enterolignan-predicting microbial species and investigate whether enterolignans and enterolignan-predicting microbial species are associated with obesity. Methods: Urinary enterolignans, fecal microbiota, body weight, height, and circumferences of the waist (WC) and hips (HC) were repeatedly measured at the baseline and after 1 year in 305 community-dwelling adults in Huoshan, China. Body composition and liver fat [indicated by the controlled attenuation parameter (CAP)] were measured after 1 year. Multivariate-adjusted linear models and linear mixed-effects models were used to analyze single and repeated measurements, respectively. Results: Enterolactone and enterodiol levels were both inversely associated with the waist-to-hip ratio, body fat mass (BFM), visceral fat level (VFL), and liver fat accumulation (all P < 0.05). Enterolactone levels were also associated with lower WC (β = −0.0035 and P = 0.013) and HC (β = −0.0028 and P = 0.044). We identified multiple bacterial genera whose relative abundance was positively associated with the levels of enterolactone (26 genera) and enterodiol (22 genera, all P false discovery rate < 0.05), and constructed the enterolactone-predicting microbial score and enterodiol-predicting microbial score to reflect the overall enterolignan-producing potential of the host gut microbiota. Both these scores were associated with lower body weight and CAP (all P < 0.05). The enterolactone-predicting microbial score was also inversely associated with the BFM (β = −0.1128 and P = 0.027) and VFL (β = −0.1265 and P = 0.044). Conclusion: Our findings support that modulating the host gut microbiome could be a potential strategy to prevent obesity by enhancing the production of enterolignans.

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Introduction

Obesity and being overweight are associated with multiple adverse health outcomes throughout the life course.¹ In China, the prevalence of obesity in adults rose from 3.1% in 2004 to 8.1% in 2018.² Diet plays an important role in the prevention of obesity and obesity-related disorders.^1,3 Lignans are non-flavonoid polyphenols present in many plant foods such as seeds, vegetables, fruits, nuts, whole grains, coffee, tea, and wine.⁴ Lariciresinol, secoisolariciresinol, pinoresinol, and matairesinol are the major lignan compounds in the human diet and can be converted into more bioactive enterolignans including enterolactone (ENL) and enterodiol (END) by the host gut microbiota in the colon.^5–7 Experimental studies showed that enterolignans may influence signaling pathways related to fat synthesis and metabolism, including inhibiting fat accumulation, accelerating energy metabolism, affecting appetite, and exhibiting anti-inflammatory, anti-oxidant, and estrogenic effects.^8–10

However, the role of enterolignan or lignan intake in the management of obesity has been less studied in epidemiological research.^11–21 Most previous studies used the food frequency questionnaire (FFQ) to estimate habitual intake of plant foods,^11–13,21 which may have been subject to measurement errors. In addition, food composition tables/databases on lignans were not complete,²² which could have undermined the accuracy of the estimation of dietary lignan intake. Moreover, the bioavailability of lignans can be determined by not only the dietary intake levels but also many other factors such as genetic variations and the host gut microbiota.⁵ Therefore, further investigations using the biomarkers of dietary lignans (i.e., internal exposure) are clearly needed. Although few studies used serum or urinary ENL and END as the biomarkers of lignan intake,^14–17,20 these studies were all based on a single measure without considering the impact of day-to-day variations in enterolignans in urine or blood samples.

Persons with different gut microbiota compositions may have different capacities for producing enterolignans.^22–25 Although conventional culture-based studies identified multiple bacterial genera capable of metabolizing lignans into enterolignans,^23,26–30 these studies may not have considered all microbes that contribute to the production of enterolignans, given the known difficulties in culturing many of the microbes comprising the human gastrointestinal microbiome. Also, the evidence may not be applied to general populations, because the human diet is complex, and the gut microbial ability to produce enterolignans depends not only on microbial compositions but also on the habitual diet consumed.³¹ Of note, few studies have dissected microbial taxa associated with the production of enterolignans at the population level in the United States, Europe, and Japan,^21,32–36 while little is known in Chinese populations. This issue is important because dietary habits may differ across countries,³⁷ which could influence the gut microbial ability of producing metabolites.³⁸

Therefore, we repeatedly measured the fecal microbiome, urinary enterolignans, and body fat parameters twice, 1 year apart, in a representative sample of 305 community-dwelling adults in Huoshan, China. We longitudinally investigated the association between urinary enterolignans and body fat measures, and identified the gut microbial species associated with urinary enterolignan concentrations at the population level and evaluated their association with body fat measures using repeated measurements. We also separately utilized data from the baseline and data after 1 year to assess the cross-sectional associations among urinary enterolignans, enterolignan-predicting microbial species, and obesity markers to check the consistency in this multiomics study.

Methods

Study population

The Anhui Lifestyle Validation Study (ALVS) was a 1-year study that selected 754 Chinese Han community-dwelling adults aged 18 years or older who resided in Huoshan County during 2021 to 2022. To ensure the representativeness of the sample, a multistage probability sampling approach was applied. First, four towns within Huoshan County were randomly selected. Second, we randomly selected four villages in each town. Third, one residential group was randomly chosen within each village, and 50 households were selected from each residential group. Finally, using the Kish selection grid technique, one adult aged 18 years or older was selected in each household. The main aim of the ALVS was to validate a self-made structured questionnaire administered in the Anhui Liver Diseases Study and other cohorts in Anhui, China (ESI Fig. 1†). We collected demographic and lifestyle information and used the questionnaire through a face-to-face interview at the baseline, and again at the end of the study. Anthropometric data (height, weight, and circumferences of the waist and hips) and biospecimens (fasting blood, first-morning void urine, and stool) were also collected twice, 1 year apart, at the same time with questionnaire surveys. Additionally, liver fat content was non-invasively estimated by the controlled attenuation parameter (CAP) during transient elastography at the end of the study.

Participants in the present study were selected from the 754 ALVS participants in Huoshan County. We included 482 participants who completed the baseline survey and a follow-up interview after 1 year. Among the 482 participants, we excluded those who did not provide urine samples (n = 176) or who had a history of hormone replacement therapy (n = 1). After exclusion, 305 participants were included in the final analysis. In each analysis, the number of participants might differ and this depended on the availability of data on urinary enterolignans, gut microbiota, and obesity markers. Details on the study population selection are shown in ESI Fig. 2.†

All participants in this study provided written informed consent, and the study protocol was approved by the ethics committee of Anhui Medical University (Protocol Number: 20210730).

Urinary biomarker measurement

The participants were requested to provide first-morning urine samples twice, 1 year apart. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for the analysis of urinary enterolignans.

END and ENL standards were purchased from Sigma and used for calibration purposes. To initiate the enzymatic digestion process, frozen urine samples were thawed at room temperature and then centrifuged (10 000 rpm × 5 min, 4 °C), and 0.5 mL of the supernatant was taken and mixed with 0.5 mL of sodium acetate buffer (0.2 mol L⁻¹, pH = 5.2). To this mixture, 20 μL of the 4-hydroxybenzophenone internal standard solution (1 μg mL⁻¹) and 10 μL of β-glucuronidase/aryl sulfatase hydrochloride (≥85 000 units, 2 mL) were added. The mixture was then incubated in a 37 °C water bath for 2 hours. The sample preparation procedure was as follows: the enzymolyzed urine samples were centrifuged (10 000 rpm × 5 min, 4 °C), all the supernatant was taken into test tubes, and 3 mL of acetonitrile was added to them. After vortexing for 3 minutes, the samples were blow-dried under nitrogen at 40 °C in a water bath and then fixed with 1 mL of 80% methanol–water solution, passed through 0.22 μm organic microporous membranes, and finally analyzed using a liquid chromatography-tandem mass spectrometer. The separation was performed using an Agilent Eclipse Plus C18 chromatography column (50 mm × 2.1 mm, 1.8 μm), with acetonitrile and 5 mmol L⁻¹ ammonium formate solution as the mobile phase under gradient elution. The flow rate was 0.3 mL min⁻¹, the column temperature was 40 °C and the injection volume was 10 μL. Mass spectrometry detection, under multiple-reaction monitoring, was performed using an electrospray ionization source in negative mode. The quantification process was performed by employing the chromatographic peak area internal standard method.

Values under the detection limits were considered to be 0. Given that the concentrations of markers in urine can be influenced by the urine volume, the concentrations of urinary enterolignans were corrected using creatinine and expressed as μg g⁻¹ creatinine.³⁹

Fecal microbiome profiling

Fecal samples were repeatedly collected 1–3 days after the urine sample collection. Fecal samples were collected at home using a commode specimen collection system and a stool collection container (Fisher Scientific) by the participants and were delivered to the nearest Community Health Center within 4 hours. Upon arrival, each sample was immediately stored in −80 °C freezers until nucleic acid extraction. To reduce the impact of probiotic or antibiotic exposure, we only included participants without probiotic or antibiotic use at least 1.5 months before fecal sample collection in the study.⁴⁰

The fecal microbiome was profiled using 16S ribosomal RNA (rRNA) gene sequencing. To extract the DNA of the microbial community, the MagPure Stool DNA KF kit B (Magen, China) was used. The extracted DNA was quantified using a Qubit fluorometer with the Qubit dsDNA BR assay kit (Invitrogen, USA), and its quality was assessed by running an aliquot on 1% agarose gel. The V4 region of the 16S ribosomal RNA (rRNA) gene was amplified using degenerate PCR primers. Both forward and reverse primers were tagged with an Illumina adapter, pad, and linker sequences. PCR enrichment was performed in a 50-μL reaction solution containing 30 ng of template, fusion PCR primer, and PCR master mix. PCR cycling conditions were as follows: 95 °C for 3 minutes, 30 cycles at 95 °C for 45 seconds, 56 °C for 45 seconds, 72 °C for 45 seconds, and final extension at 72 °C for 10 minutes. The PCR products were purified using Agencourt AMPure XP beads and eluted in elution buffer. The quality of the libraries was validated using the Agilent Technologies Bioanalyzer-2100. Sequencing was performed on the Illumina HiSeq 2500 platform (BGI, Shenzhen, China) using validated libraries and generating 2 × 250 bp paired-end reads. The species with an average relative abundance of over 0.001% were selected for the analysis. A total of 87 genera were identified.

Covariate measurement

Demographic and lifestyle information, including age, race, gender, marital status, education level, annual household income, smoking, alcohol consumption, diet, and physical activities was obtained twice, 1 year apart, through a structured questionnaire. Physical activity was quantified as metabolic equivalent tasks (METS) in hours per week. Total energy intake was assessed from the FFQ as kilocalories per day. Considering the potential influence of dietary pattern or overall quality of the diet, a diet quality score, the dietary approaches to stop hypertension (DASH), was derived from a 141-item semi-quantitative FFQ according to the method by Fung et al.⁴¹ and was further adjusted in the analysis.

Ascertaining body fat markers

Body weight, height, and circumferences of the waist (WC) and hips (HC) were measured at the baseline and after 1 year by a professional investigator. The body mass index was calculated as weight in kilograms divided by height in meters squared. The waist-to-hips ratio (WHR) was calculated as WC divided by HC in centimeters. Vibration-controlled transient elastography (VCTE) using the FibroScan HANDY® device (Echosens, Paris, France) equipped with a medium or extra-large probe was performed by trained technicians after 1 year of follow-up. We used a VCTE-derived CAP to measure hepatic fat accumulation. Details of the quality control mechanisms for the VCTE examinations have been reported elsewhere.⁴² We used the InBody570 device to assess body fat mass (BFM), percentage body fat (PBF), and visceral fat level (VFL) after 1 year of follow-up.

Statistical analysis

Continuous variables were presented as means with standard deviation (SD) or medians with interquartile ranges (IQR), while categorical variables were reported as percentages. The differences of continuous variables were compared by one-way analysis of variance, or by the Kruskal–Wallis test, and the differences of classified variables by the chi-square test or Fisher's exact test. Linear mixed-effects models (LMMs) were used to estimate the longitudinal associations of urinary excretion of ENL and END with body weight, WC, HC, WHR, and body mass index (BMI), while linear models were used to analyze the associations with CAP, BFM, PBF, and VFL. The adjusted covariates in the multivariable models included age, sex, education level, household per capita income, physical activity, alcohol drinking, smoking, total energy intake, menopausal status, batch effect (first, second), and DASH score.

We used LMMs to identify bacterial genera that can predict urinary enterolignan concentrations, adjusting for age, sex, education level, household per capita income, physical activity, alcohol drinking, smoking, batch effect, and total energy intake. Time-varying covariates based on repeated measurements at the baseline and after 1 year were entered into models if possible. The relative abundances of taxonomic features were transformed by the Arc-sin square root prior to analysis. The Benjamini–Hochberg false discovery rate (FDR) method was used for multiple testing corrections. An FDR value of <0.05 was considered statistically significant.

To reflect the overall enterolignan-producing capacity of the host gut microbiota, we calculated an enterolactone-predicting microbial score (EnlMS) and an enterodiol-predicting microbial score (EndMS). These enterolignan-predicting microbial scores summarize the relative abundance of microbial genera that were positively associated with ENL or END in the LMM analyses at an FDR < 0.05, respectively. Microbial genera detected in 50% or more of the samples were defined as “high” (median levels or higher) or “low” (less than the median level) according to the median relative abundance, whereas microbial genera detected in less than 50% of the samples were dichotomously categorized according to the presence or absence of the genus. One point was assigned for higher abundance or presence of genera or 0 otherwise. The total scores were then calculated by summing the scores of all genera. We also investigated the associations between the two enterolignan-predicting microbial scores and body fat measures. In sensitivity analysis, to observe the reproducibility in multiomics studies, we separated data from the baseline and after the 1-year baseline to investigate the cross-sectional associations between urinary enterolignans, enterolignan-predicting microbial species, and body fat measures. All analyses were performed using R version 4.2.0.

Results

Baseline characteristics

This study included a total of 305 participants (mean [SD] age, 48.5 [14.9] years), with a median BMI of 24 (range: 14–37) kg/m². Compared to participants in the lowest tertile of urinary enterolignans, those in the highest tertile were more likely to be female, less likely to be smokers, and had a lower prevalence of hypertension at the baseline. Similar trends were observed after 1 year of follow-up albeit without statistical significance (Table 1).

Table 1 The characteristics of participants according to urinary enterolignan (enterolactone and enterodiol) concentrations at the baseline and after 1 year of follow-up in Huoshan, China^a

Characteristics	Urinary enterolignans at the baseline			Urinary enterolignans after one year of follow-up
	Tertile 1	Tertile 2	Tertile 3	Tertile 1	Tertile 2	Tertile 3
Abbreviations: BMI, body mass index; DASH, dietary approaches to stop hypertension; METS, metabolic equivalent tasks.a Continuous variables are expressed as the median (interquartile range) or mean (SD) according to the distribution of the variables, while categorical variables are presented as the percentage. P values were calculated from the one-way ANOVA or Kruskal–Wallis test for continuous variables and the chi-square test or Fisher's exact test for categorical variables.b Numbers of participants vary due to missing values for outcome variables, covariates, or outliers. Urinary enterolignans at the baseline: *P value <0.05 and **P value <0.01; urinary enterolignans after one year of follow-up: ^‡P value <0.05 and ^‡‡P value <0.01. P < 0.05 indicates a significant difference.
No. of participants^b	101	102	102	101	102	102
Age, years	50.0 (38.0–59.0)	50.5 (37.0–58.0)	49.0 (35.0–59.0)	50.0 (37.0–59.0)	51.5 (37.0–59.0)	53.0 (40.0–64.0)
Female**, %	42.6	55.9	65.7	48.5	52.9	61.8
Married, %	83.2	77.2	87.3	81.8	82.4	81.4
Household per capita income, %
<5000 Yuan	13.1	17.5	13.3	6.1	11.5	10.9
5000–10 000 Yuan	26.3	16.5	22.5	13.1	18.8	12.9
10 000–20 000 Yuan	25.3	22.7	29.6	25.3	22.9	29.7
>20 000 Yuan	35.4	43.3	34.7	55.6	46.9	46.5
Education level, %
Informal education	10.9	16.7	14.7	6.9	13.7	17.7
Primary school or below	32.7	21.6	27.5	24.8	29.4	23.5
Junior high school	25.7	25.5	29.4	33.7	27.5	27.5
Senior high school or above	30.7	36.3	28.4	34.7	29.4	31.4
Never smokers**, %	61.4	74.5	80.2	63.4	66.7	75.5
Never drinkers, %	74.0	83.7	86.9	77.0	84.2	86.1
BMI, %
<18.5 kg m^−2	2.0	3.9	2.9	6.9	5.9	4.9
18.5–24.0 kg m^−2	37.6	45.1	52.9	40.6	46.1	50.0
24.0–28.0 kg m^−2	37.6	37.3	28.4	37.6	32.4	36.3
≥28.0 kg m^−2	22.8	13.7	15.7	14.9	15.7	8.8
Total energy intake, kcal d^−1	2180 (1696–2962)	2197 (1696–2729)	2029 (1632–2660)	1905 (1557–2595)	2027 (1463–2715)	1884 (1540–2329)
Physical activities, METS-h per week	155.2 (112.1–223.5)	160.4 (109.8–206.1)	142.9 (100.0–218.3)	152.7 (112.4–214.7)	158.9 (111.0–242.3)	167.3 (105.9–257.5)
Hypertension*, %	60.4	44.1	41.2	49.5	52.9	42.2
Type 2 diabetes^‡, %	13.9	10.8	8.8	11.9	22.6	8.8
DASH score	24 (22–27)	24 (22–26)	24 (22–27)	23 (21–26)	24 (22–26)	24 (22–27)

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Urinary enterolignans and body fat measures

The median urinary concentrations of ENL and END were 209.4 μg g⁻¹ creatinine (IQR: 22.9 to 889.0 μg g⁻¹ creatinine) and 23.5 μg g⁻¹ creatinine (IQR: 3.7 to 55.3 μg g⁻¹ creatinine) at the baseline and 183.8 μg g⁻¹ creatinine (IQR: 34.2 to 720.5 μg g⁻¹ creatinine) and 30.9 μg g⁻¹ creatinine (IQR: 7.4 to 80.5 μg g⁻¹ creatinine) after 1 year of follow-up, respectively. Higher urinary concentrations of ENL were associated with lower WC (β = −0.0035 and P = 0.013), HC (β = −0.0028 and P = 0.044), and WHR (β = −0.0028 and P = 0.048). Similarly, END levels were inversely associated with WHR (β = −0.0034 and P = 0.049).

The urinary excretion of ENL was inversely associated with BFM (β = −0.0252 and P = 0.018), VFL (β = −0.0279 and P = 0.034), and CAP (β = −0.0176 and P = 0.001), while END showed a protective association with BFM (β = −0.0297 and P = 0.025), VFL (β = −0.0348 and P = 0.032), and CAP (β = −0.0174 and P = 0.010, Table 2).

Table 2 Longitudinal associations between urinary enterolignans and body fat measures among community-dwelling adults in Huoshan, China

Body fat measures	Enterolactone		Enterodiol
	β (SE)	p	β (SE)	p
Abbreviations: BFM, body fat mass; BMI, body mass index; CAP, controlled attenuation parameter; DASH, dietary approaches to stop hypertension; PBF, percentage body fat; VFL, visceral fat level; WHR, waist-to-hips ratio.a Linear mixed-effects models with repeated measures were adjusted for age (18–29, 30–39, 40–49, 50–59, and ≥60 years), sex (women and men), education level (informal education, primary school or below, junior high school, and senior high school or above), household per capita income (<5000, 5000–10 000, 10 000–20 000, and >20 000 Yuan), total energy intake (kcal day^−1, continuous), physical activity (metabolic equivalent tasks in h per week, continuous), menopausal status (yes, no), current or past smoking (yes, no), current or past alcohol drinking (yes, no), batch effect, and DASH diet index (continuous).b Linear regression models were adjusted for age (18–29, 30–39, 40–49, 50–59, and ≥60 years), sex (women, men), education level (informal education, primary school or below, junior high school, and senior high school or above), household per capita income (<5000, 5000–10 000, 10 000–20 000, and >20 000 Yuan), total energy intake (kcal day^−1, continuous), physical activity (metabolic equivalent tasks in h per week, continuous), menopausal status (yes, no), current or past smoking (yes, no), current or past alcohol drinking (yes, no), and DASH diet index (continuous). The values were log-transformed to obtain a normal distribution of the residuals.P < 0.05 indicates a significant difference.
Waist circumference (N = 610)^a	−0.0035 (0.0014)	0.013	−0.0025 (0.0016)	0.125
Hips circumference (N = 609)^a	−0.0028 (0.0014)	0.044	−0.0022 (0.0016)	0.173
WHR (N = 609)^a	−0.0028 (0.0014)	0.048	−0.0034 (0.0017)	0.049
Weight (N = 610)^a	−0.0020 (0.0011)	0.061	−0.0015 (0.0012)	0.218
BMI (N = 610)^a	−0.0007 (0.0013)	0.591	−0.0019 (0.0014)	0.171
BFM (N = 298)^b	−0.0252 (0.0106)	0.018	−0.0297 (0.0132)	0.025
PBF (N = 298)^b	−0.0137 (0.0072)	0.057	−0.0169 (0.0089)	0.057
VFL (N = 298)^b	−0.0279 (0.0131)	0.034	−0.0348 (0.0162)	0.032
CAP (N = 303)^b	−0.0176 (0.0053)	0.001	−0.0174 (0.0067)	0.010

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Fecal microbiome and body fat measures

Among the 87 bacterial genera, the relative abundance of 26 genera was positively associated with urinary ENL (ESI Table 2†), while 22 genera were positively associated with urinary END at P_FDR < 0.05 (ESI Table 3†).

These 26 genera associated with ENL levels belong to the following 4 phyla families: Firmicutes (Intestinimonas, Mogibacterium, Eisenbergiella, Coprococcus, Sporobacter, Subdoligranulum, Dorea, Oscillibacter, Clostridium_IV, Ruminococcus, Eubacterium, Fecalibacterium, Phascolarctobacterium, and Gemmiger), Proteobacteria (Oxalobacter, Bilophila, and Desulfovibrio), Actinobacteria (Gordonibacter and Collinsella), and Bacteroidetes (Odoribacter, Butyricimonas, Coprobacter, Barnesiella, Alistipes, and Paraprevotella), while the 22 genera associated with END levels belong to the following 4 phyla families: Firmicutes (Intestinimonas, Mogibacterium, Dorea, Sporobacter, Coprococcus, Oscillibacter, Subdoligranulum, Ruminococcus2, Ruminococcus, Eubacterium, Roseburia, and Fecalibacterium), Proteobacteria (Desulfovibrio), Actinobacteria (Adlercreutzia and Collinsella), and Bacteroidetes (Odoribacter, Butyricimonas, Barnesiella, Bilophila, Alistipes, Paraprevotella, and Prevotella).

We constructed 2 enterolignan-predicting microbial species scores and observed an inverse association between EnlMS and multiple obesity markers, including body weight (β = −0.0119 and P = 0.031), BFM (β =−0.1128 and P = 0.027), VFL (β = −0.1265 and P = 0.044), and CAP (β = −0.0883 and P < 0.001). Similarly, higher EndMS was associated with lower body weight (β = −0.0156 and P = 0.005) and CAP (β = −0.0599 and P = 0.014, Table 3).

Table 3 Longitudinal associations between enterolignan-predicting microbial scores and body fat measures among community-dwelling adults in Huoshan, China

Body fat measures	Enterolactone-predicting microbial score		Enterodiol-predicting microbial score
	β (SE)	p	β (SE)	p
Abbreviations: BFM, body fat mass; BMI, body mass index; CAP, controlled attenuation parameter; PBF, percentage body fat; VFL, visceral fat level; WHR, waist-to-hips ratio.a Linear mixed-effects models with repeated measures were adjusted for age (18–29, 30–39, 40–49, 50–59, and ≥60 years), sex (women and men), education level (informal education, primary school or below, junior high school, and senior high school or above), household per capita income (<5000, 5000–10 000, 10 000–20 000, and >20 000 Yuan), total energy intake (kcal day^−1, continuous), physical activity (metabolic equivalent tasks in h per week, continuous), current or past smoking (yes, no), current or past alcohol drinking (yes, no), and batch effect.b Linear regression models were adjusted for age (18–29, 30–39, 40–49, 50–59, and ≥60 years), sex (women and men), education level (informal education, primary school or below, junior high school, and senior high school or above), household per capita income (<5000, 5000–10 000, 10 000–20 000, and >20 000 Yuan), total energy intake (kcal day^−1, continuous), physical activity (metabolic equivalent tasks in h per week, continuous), current or past smoking (yes, no), and current or past alcohol drinking (yes, no). The values were log-transformed to obtain a normal distribution of the residuals. P < 0.05 indicates a significant difference.
Waist circumference (N = 485)^a	−0.0022 (0.0076)	0.773	−0.0047 (0.0076)	0.538
Hips circumference (N = 485)^a	0.0062 (0.0066)	0.350	0.0109 (0.0066)	0.100
WHR (N = 485)^a	0.0028 (0.0069)	0.686	0.0002 (0.0070)	0.979
Weight (N = 485)^a	−0.0119 (0.0055)	0.031	−0.0156 (0.0055)	0.005
BMI (N = 485)^a	−0.0101 (0.0064)	0.114	−0.0118 (0.0064)	0.066
BFM (N = 243)^b	−0.1128 (0.0505)	0.027	−0.0658 (0.0503)	0.192
PBF (N = 243)^b	−0.0566 (0.0352)	0.109	−0.0298 (0.0349)	0.394
VFL (N = 243)^b	−0.1265 (0.0624)	0.044	−0.0744 (0.0620)	0.231
CAP (N = 246)^b	−0.0883 (0.0243)	<0.001	−0.0599 (0.0243)	0.014

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Sensitivity analysis

We separately investigated the cross-sectional association using data at the baseline and data after 1 year, and the results were generally similar. When using data at the baseline, the urinary excretion of enterolignans was inversely associated with WC, WHR, body weight, BMI, BFM, PBF, VFL, and CAP (ESI Table 4†), while higher enterolignan-predicting microbial species scores were associated with decrease in liver fat content (all P < 0.05, ESI Table 5†). These beneficial associations remained statistically significant when using data after 1 year.

Discussion

In this one-year longitudinal study, we used repeated measures to examine the relationship between urinary enterolignan concentrations and enterolignan-predicting microbial scores with body fat measures, and dissected the gut microbial species associated with levels of urinary enterolignans. We found that urinary ENL and END are favorably associated with many body fat measures. We identified multiple bacterial genera whose relative abundance can predict higher urinary excretion of ENL and END, and constructed 2 enterolignan-predicting microbial scores to reflect the overall potential of producing ENL and END. The enterolignan-predicting microbial scores showed a beneficial association with several body fat measures. When separately using a single measure at the baseline and after 1 year, the results were consistent with those using repeated measures, indicating good reproducibility in multiomics studies.

Several studies on the association between lignans and obesity have been conducted. For example, four cross-sectional studies using the NHANES database reported an inverse association between urinary enterolignan concentrations and both obesity^14,16,18 and waist circumference.^14–16 Similarly, the Framingham Offspring Study found that postmenopausal women with a high dietary intake of lignans had a significantly lower WHR compared to those with lower lignan consumption.¹¹ Moreover, an observational study involving 115 postmenopausal women in Canada revealed that those with a high dietary lignan intake had significantly lower BMI and total body fat mass compared to those with a low lignan intake.¹² Another cohort study indicated that higher urinary excretion of lignan metabolites was associated with slower weight gain, and individuals with higher levels of END had lower baseline body mass indices and gained less weight over a ten-year follow-up period.¹⁷ These conclusions align with our results, suggesting that higher enterolignan concentrations may potentially contribute to lower obesity rates, reduced waist circumference, and improved WHR.

In our study, we observed an association between lignans and visceral fat, specifically liver fat. A recent cross-sectional study found an inverse association between elevated urinary ENL concentrations and the risk of non-alcoholic fatty liver disease (NAFLD) in middle-aged American males. Similarly, older American males exhibited an inverse association between urinary END concentration and NAFLD.¹⁹ Additionally, some animal studies suggested that lignans play a role in reducing visceral fat accumulation.^9,10,43,44 These findings further support our discovery of an inverse association between lignans and visceral fat.

We showed that multiple bacterial genera were associated with enterolignans, which is in line with previous studies. For example, a study that included postmenopausal women in the United States found that genera of Christensenellaceae, Prevotellaceae, Ruminococcaceae, and some Lachnospiraceae were positively associated with ENL.³² Similarly, two studies of adult men from the United States and Germany found that species belonging to Ruminococcus, Coprococcus, Fecalibacterium, Alistipes, Butyrivibrio, and Methanobrevibacter were associated with ENL.^21,35 In addition, two studies in the general population from the United States and Japan found a positive correlation with END and ENL excretion in several genera (e.g., Alistipes, Barnesiella, Ruminococcaceae, and Roseburia),^34,36 which were also observed in our study. Moreover, several bacterial genera, including Oxalobacter, Adlercreutzia, Mogibacterium, Sporobacter, and Gemmiger, can predict higher urinary enterolignan concentrations in our population, which have not yet been reported in other populations. This could be due to different habitual diets across different populations, which may affect the gut microbial composition and the capability of producing enterolignans.^37,38 Taken together, our study confirms previous reports at the genus level and explores bacterial genera that could potentially be involved in ENL and END in the Chinese population. We found that bacterial genera were positively associated with enterolignans and inversely associated with body weight and visceral fat. Previous studies suggested that obesity in adults is associated with the gut microbiome, and the altered functional potential of the obesogenic microbiome influences various negative health outcomes.⁴⁵

Although the exact mechanisms are not fully understood, several studies have suggested that lignans may inhibit adipogenesis and reduce obesity by activating the AMPK signaling pathway and suppressing the expression of adipogenesis-related genes.^8,9 Lignans may also reduce fatty acid synthesis by suppressing sterol regulatory element binding protein-1c expression in the liver and promote β-oxidation in muscle by inducing adiponectin expression. Furthermore, a study indicates that enterolignans potentially regulate adiponectin expression and peroxisome proliferator-activated receptor gamma activity.¹⁰ In addition, it has been shown that the gut microbiome is responsible for metabolizing lignans into enterolignans,²¹ suggesting that this may be one of the pathways through which they affect body weight and body fat mass.^46,47 Our findings and this mechanistic research underscore a better appreciation of the interactions between the gut microbiome and its metabolites to provide possible therapeutic targets for preventing obesity.

The strengths of our study include the prospective design, the use of urinary biomarkers to reflect dietary lignan intake, and the repeated measurement of urinary enterolignans, gut microbiota, and body fat indicators. However, several limitations should be noted. First, despite the prospective design, we were not able to investigate the urinary enterolignans in relation to the long-term risk of obesity due to the short follow-up period. Second, despite the adjustments for various covariates, residual confounding cannot be completely ruled out. Last, our study could be strengthened by deeper sequencing such as metagenomics or metatranscriptomics to evaluate fecal microbial gene expression.

Conclusions

In summary, we replicated previous studies at the genus level on enterolignan-predicting species, and found several novel bacterial genera that could be involved in producing enterolignans in a Chinese population. High urinary concentrations of enterolignans and enterolignan-predicting microbial species are favorably associated with body fat measures. Our findings support that modulating the host gut microbiome may be a potential strategy to reduce obesity risk by enhancing the production of enterolignans.

Data availability

The datasets supporting this article have been uploaded as part of the ESI.†

Author contributions

Yufeng Mo and Yamin Li had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of data analysis. Study concept and design: Wanshui Yang. Acquisition, analysis, or interpretation of data: all authors. Drafting of the manuscript: Yufeng Mo. Critical revision of the manuscript for important intellectual content: Wanshui Yang. Statistical analysis: Yufeng Mo. Obtained funding: Wanshui Yang. Administrative, technical, or material support: Wanshui Yang. Study supervision: Wanshui Yang.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (grant number 2023YFA1800800), the National Natural Science Foundation of China (grant number 82373673), the Research Funds of the Center for Big Data and Population Health of IHM (grant number JKS2022018), the grants from Anhui Medical University (grant number 2021xkjT007), and the Postgraduate Innovation Research and Practice Program of Anhui Medical University (grant numbers YJS20230048 and YJS20230150).

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3fo05632e

‡ These authors contributed equally as co-first authors for this article.

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