Hepatic steatosis in postmenopausal women is characterized by distinct serum extracellular vesicle proteomic signatures

Hepatic steatosis in postmenopausal women is characterized by distinct serum extracellular vesicle proteomic signatures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hepatic steatosis in postmenopausal women is characterized by distinct serum extracellular vesicle proteomic signatures Patrick Pirrotte, Brooke Lovell, Siobán D. Harlow, Carrie A. Karvonen-Gutierrez, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7293767/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Dec, 2025 Read the published version in BMC Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract Background. Metabolic dysfunction-associated steatotic liver disease (MASLD) is common among midlife women. Circulating extracellular vesicles (EVs) carry bioactive cargo that may mediate or reflect disease processes, but their role in hepatic steatosis in postmenopausal women remains unexplored. Methods We conducted Liquid Chromatography Data-Independent Acquisition–Mass Spectrometry on serum-derived EVs from 275 postmenopausal women enrolled in the Michigan site of the Study of Women’s Health Across the Nation (MI-SWAN). Participants were grouped by hepatic steatosis status (n = 75), assessed via standardized ultrasound at the 2010 follow-up visit. Fasting serum samples were processed using size exclusion chromatography to isolate EVs. Differential EV protein abundance was evaluated by ANCOVA, adjusting for ethnicity and diabetes status, and applying Benjamini-Hochberg correction. Gene Set Enrichment Analysis (GSEA) was performed to identify enriched biological pathways. Results. Among 469 detected EV proteins, 60 differed by hepatic steatosis status (p < 0.05), with two proteins remaining significant after multiple testing correction: complement C4A (C4A) and afamin (AFM). GSEA indicated enrichment in lipid metabolism and innate immune activation pathways. Subgroup analyses revealed racial and disease severity-specific differences in EV protein profiles. In Black women (n = 172), AFM, C4A, and APOA1 were significantly elevated, while in White participants (n = 103), no proteins reached significance, although AFM displayed a nonsignificant trend toward higher abundance. In participants with severe hepatic steatosis (n = 43), subgroup analysis showed increased COL18A1, AFM, PRG4, and INHBE and decreased C4A and APOA1. INHBE was the only protein consistently elevated across all three subgroups, whereas others showed subgroup-specific enrichment, such as immunoglobulins in Black women and complement or coagulation proteins in White participants and those with severe steatosis. Analysis of hepatic transcriptomic datasets demonstrated consistently higher INHBE expression across the MASLD spectrum, including metabolic dysfunction-associated steatohepatitis (MASH), while AFM expression was significantly higher in the MASH vs. steatosis comparison. Conclusions. Serum EVs carry protein signatures reflective of hepatic steatosis and its severity in postmenopausal women. EV profiling may offer insights into mechanisms of disease progression and serve as potential biomarkers for risk stratification in midlife women. Liver hepatic steatosis proteomics extracellular vesicles Figures Figure 1 Figure 2 Figure 3 Figure 4 Background With increasing longevity, women now spend nearly 40% of their lives post-menopause (1). By 2030, postmenopausal women will comprise nearly half of the female population in the United States (2), yet the metabolic consequences of this transition remain incompletely defined. The reduction in estrogen production triggers profound shifts in fat distribution, insulin sensitivity, and dyslipidemia, all of which are associated with metabolic dysfunction-associated steatotic liver disease (MASLD) (3–10). The biological mechanisms linking hormonal decline to metabolic dysfunction represent an underexplored area of research. As the number of postmenopausal women continues to rise, there is an urgent need to disentangle the hormonal drivers of metabolic dysfunction from other risk factors to inform targeted prevention and treatment strategies. MASLD is a chronic condition defined by excessive triglyceride accumulation in the liver, spanning a disease spectrum from isolated steatosis, where at least 5% of liver weight consists of intracellular fat, to metabolic dysfunction-associated steatohepatitis (MASH), which involves liver inflammation and hepatocellular injury (11). While MASLD has historically been more prevalent in men, its burden among women has risen sharply in recent decades, increasing from 18.5% in 1988–1994 to 24.9% in 2007–2014 (12). This increase has outpaced that seen in men (13) and has been accompanied by a higher mortality rate in affected women (14). Furthermore, the prevalence of MASLD in postmenopausal women is variable, ranging from 15–62% depending on geography, ethnicity, and diagnostic criteria (4, 15, 16). According to one study, postmenopausal women showed a nearly two-fold higher likelihood of advanced fibrosis (36.1%) compared to men (17.7%) (17). Furthermore, MASH is currently the leading indication for liver transplantation in women (18). Animal studies provide evidence that estrogen loss directly increases susceptibility to hepatic steatosis and steatohepatitis, particularly in the presence of mild metabolic dysfunction, such as moderate adiposity, dyslipidemia, or prediabetes (4). Despite these trends, our understanding of the mechanisms by which MASLD develops and progresses in postmenopausal women remains limited. Extracellular vesicles (EVs) are small membrane-bound vesicles released by cells that mediate intercellular communication through the transfer of bioactive molecules (19, 20). EVs reflect the physiological state of their cell of origin, carrying a cargo that mirrors the metabolic or pathological environment of the parent cell (21). Increasing evidence suggests that EVs play a pivotal role in promoting metabolic dysfunction through the intracellular transport of signaling molecules that influence a variety of disease processes, including MASLD (22–28). Elevated plasma EVs have been observed in individuals with MASLD, with levels positively correlated with disease severity (27, 29, 30). Circulating EVs influence diverse processes in liver cells, including intracellular signaling, tissue injury and repair, and matrix remodeling (31). In animal models, EVs from steatotic hepatocytes affect the metabolome, alter hepatic gene expression, and exacerbate liver fibrosis and inflammation. For instance, in mice fed a diet high in saturated fat, cholesterol, and fructose, increased levels of hepatocyte-specific EVs in the circulation correlated with disease markers, while inhibition of EV release attenuated hepatic injury (32). These findings collectively indicate that EVs may both serve as biomarkers of MASLD severity and actively participate in the pathological processes underlying MASLD and MASH. Although EV research in the context of MASLD is advancing, current studies of EVs in postmenopausal women are scarce (33–37). To address this gap, we utilized clinical data and serum samples from the Study of Women’s Health Across the Nation (SWAN), a multi-site, longitudinal cohort study designed to investigate the health of women throughout midlife and the menopausal transition (38). In 2010, the Michigan site (MI-SWAN) conducted a liver ultrasound study focusing on the presence of hepatic steatosis among a cohort of predominantly postmenopausal women (39), with associated serum samples stored in the MI-SWAN biorepository. As a result, MI-SWAN provides a unique opportunity to investigate EV-derived proteomic profiles within a diverse group of postmenopausal women with hepatic steatosis. Here, we sought to identify and characterize EV-derived proteomic profiles associated with abdominal ultrasound-determined hepatic steatosis in postmenopausal women, leveraging the comprehensive clinical characterization of MI-SWAN participants to deepen understanding of EVs as potential biomarkers and therapeutic targets for MASLD. Methods Study Participants. SWAN is an ongoing community-based cohort study involving multiple racial and ethnic groups, designed to characterize the menopausal transition, post-menopause, and related health changes. Women aged 42–52 years were recruited from defined sampling frames at seven clinical sites in 1996–1997 (study design details are available elsewhere (38)). To be eligible, participants were required to have an intact uterus and at least one ovary, and to have experienced a menstrual period within the preceding three months. Exclusion criteria included hormone therapy use in the prior three months, pregnancy, or breastfeeding. Participants subsequently completed up to seventeen follow-up visits. All participants provided informed consent, and study procedures were approved by the institutional review board at each clinical site. The MI-SWAN recruited 543 women, with participants self-identifying as Black (66%) or White (34%). Participants were classified as postmenopausal based on the absence of menses for at least twelve consecutive months. At the 2010 annual follow-up visit, hepatic steatosis was assessed using ultrasound imaging (39). Of the 403 women who participated in that visit, 345 (86%) underwent hepatic ultrasound. Participants with a history of cirrhosis or chronic liver disease attributable to viral hepatitis or hemochromatosis (n = 14) were excluded, resulting in 331 potentially eligible participants. Stored serum samples were available for 278 of these participants and three women who were not postmenopausal at the time were excluded. Thus, 275 women comprised the analytical sample set for this study. At each study visit, women completed questionnaires providing information on sociodemographic characteristics, menopausal status, health status, and medications. Anthropometric measurements were obtained including height, weight, and waist and hip circumferences. A fasting blood draw was also obtained. Diabetes was defined as the use of glucose-lowering medication during the study, fasting glucose ≥ 126 mg/dL at two consecutive visits, or self-reported diabetes at two visits with fasting glucose ≥ 126 mg/dL at one visit. Blood was refrigerated for 1–2 hours prior to centrifugation, and serum was aliquoted, frozen, and stored at − 80ºC. Abdominal Ultrasound for Hepatic Fat Evaluation . A single ultrasound technician, blinded to the medical history of participants, performed abdominal ultrasounds using a Sonoline Elegra Ultrasound Imaging System (Siemens Medical Systems Inc.) equipped with a 3.5 MHz transducer and a 411 LE 0.5 phantom (GAMMEX-RMI Ltd). All images were evaluated for markers of hepatic steatosis by a single radiologist applying a standardized protocol and blinded to participant profile, as previously reported (39). The liver was graded for markers of hepatic steatosis including bright hepatic echo pattern compared with echo response of the right kidney, attenuation of the echo beam, and presence of focal fatty sparing (40). Hepatic steatosis was categorized as “moderate/severe” or “none/mild” based on echogenicity and clarity of structures within the liver (41). Isolation of EVs from Serum . EVs were isolated by size exclusion chromatography using qEVoriginal 70 nm Gen 2 columns (Izon Science; Medford, MA) from fasting serum samples. Following column equilibration with 15 mL of phosphate buffered saline (PBS), 500 µL of serum was loaded onto the column and 6 × 500 µL fractions (F) were collected (F7-F12), following the collection of the 3 mL void volume. fractions F7-F12 were combined and concentrated with 50K Amicon filters (Millipore, UFC805096) to a final volume of 500 µL. The combined concentrated EVs were used for downstream analyses. Sample Preparation for Proteomics Analysis. Isolated EVs were solubilized in a 2% sodium deoxycholate-based lysis buffer and sonicated using a cup-horn shaped sonotrode (UTR2000, Hielscher Ultrasonics) for 30 seconds (15 second on 1 second off) at 50% amplitude for three rounds of sonication. Protein extracts were clarified by centrifugation and protein concentration was determined using the BCA assay (Pierce). Proteins (40 µg) from isolated EVs were processed as previously described (28). To generate a reference spectral library, equal amounts of protein (i.e., 5 µg) were combined from each sample to create a 1.39 mg pool. The pooled sample was processed as described (28). Peptides were subjected to offline fractionation via high pH reverse phase chromatography on an Ultimate 3000 HPLC system (Thermo Scientific; Waltham, MA). Peptides were loaded onto a 10 cm C18 column (Waters XBridge C18, 4.6 mm ID, 3.5 µm particle size) and eluted over a 96-minute method into a 96-well plate. The resulting 96 fractions were combined to 24 peptide fractions for LC-MS/MS acquisition. All samples and library fractions were spiked with iRT peptides (Biognosys; Switzerland). Liquid Chromatography–Data-Independent Acquisition Mass Spectrometry ( LC-DIA/MS ). All mass spectrometry data were acquired on a nanoElute liquid chromatography system coupled to a timsTOF HT Pro 2 (Bruker Daltonics; Billerica, MA) mass spectrometer with a captive spray source (Bruker) using a 62-minute LC gradient at a flowrate of 850 nL/min on a 25 cm C18 column (Bruker PepSep, 150 µm ID, 1.5 µm particle size). Individual library fractions were acquired in DDA-PASEF (Data Dependent Acquisition – Parallel Accumulation Serial Fragmentation) mode with MS1 scans covering a mass range of 100–1700 m/z, TIMS mobility window (1/K0) between 0.70 and 1.50 with 75 ms accumulation and ramp time. DDA scans involved 7 PASEF ramps for a total cycle time of 0.65 second and a collision energy ramp of 20 eV to 65 eV for ion mobility window (1/K0) of 0.6–1.6. Each EV sample was acquired in DIA (Data Independent Acquisition)-PASEF mode keeping the same accumulation and ramp time as DDA runs. The capillary voltage was kept at 1700 V and dry gas temperature was kept at 200°C. Proteomics Data Analysis . Spectral libraries from the DDA-PASEF runs were created with Spectronaut 19.8 software against a human SwissProt database (UP000005640, downloaded June 2025). Theoretical digestion was performed using trypsin allowing for a maximum of two missed cleavages. Cysteine carbamidomethylation was set as a fixed modification, while methionine oxidation and protein N-terminal acetylation were set as variable modifications. PSMs and peptides were filtered for False Discovery Rate (FDR) < 1%. DIA-MS data was searched against data-specific spectral libraries using default parameters (cross-run normalization, data imputation, and scaling were disabled). Protein abundances were normalized using variance stabilization normalization ( vsn package) (42). To determine the amount of variation in protein abundance explained by clinical variability, principal component analysis (PCA) from PCAtools was run on proteins present in 100% of samples and clinical variables reporting the first 25 principal components (PCs). The significance of the relationship between confounding factors and protein abundance was calculated via eigencorplot function on variables for the first five PCs. Differential abundance was calculated using an ANCOVA with a Benjamini-Hochberg correction for multiple testing, accounting for ethnicity and diabetes status. Significant proteins (p < 0.05) were filtered for presence in at least 50% of samples and Partial Least Squares-Discriminate Analysis (PLS-DA) was used to assess group separation at 95% confidence with the plsda function in the mixOmics package (43). A Hotelling’s t-squared statistic was calculated using the Hotelling package to determine significance of group separation (p < 0.05). A significance score, defined as − log 10 ​(p-value) × sign(log 2 ​FC), was calculated for all identified EV proteins. This score was used to order proteins based on statistical significance and fold-change direction for subsequent Gene Set Enrichment Analysis (GSEA). Analysis was performed in GSEA software v 4.4.0 (44) against the Gene Ontology Biological Processes (GOBP) database (v2025). Results Characteristics of the study cohort Table 1 presents demographic information and clinical characteristics for the 275 postmenopausal participants at the 2010 follow-up visit. Participants with hepatic steatosis (n = 75) had higher BMI, greater waist circumference, and a higher prevalence of T2D compared to those without steatosis (n = 200). They also exhibited elevated triglyceride (TG), fasting glucose, and HbA1c levels. Among those with hepatic steatosis, women with T2D had greater adiposity and more pronounced metabolic abnormalities, including higher TG, fasting glucose, and HbA1c levels ( Table S1 ). Table 1 Study cohort demographic information and clinical characteristics (N = 275) Parameter Hepatic steatosis No hepatic steatosis N 75 200 Age (y) 59.48 ± 2.89 58.89 ± 2.76 Race n (%) Black 38 (50.7) 134 (67.0) White 37 (49.3) 66 (33.0) BMI (kg/m 2 ) 37.2 ± 7.6 32.7 ± 8.1 T2D, N (%) 32 (42.7) 47 (23.5) WC (cm) 109.6 ± 13.1 98.0 ± 16.4 TG (mg/dL) 153.8 ± 90.7 109.5 ± 51.3 FG (mg/dL) 128.1 ± 76.1 96.9 ± 31.1 HbA1c (%) 6.8 ± 1.8 6.0 ± 0.8 Data are means ± standard deviation, unless otherwise indicated; WC: waist circumference; TG: fasting serum triglyceride levels; FG: fasting plasma glucose; HbA1c: glycosylated hemoglobin Table 2. Hepatic expression of genes encoding differentially abundant proteins in hepatic steatosis and MASH HS vs CTL MASH vs CTL MASH vs HS Gene PRJNA512027 Meta-analysis PRJNA512027 Meta-analysis PRJNA512027 Meta-analysis z-score FDR z-score FDR z-score FDR z-score FDR z-score FDR z-score FDR AFM 1.971 5.95E-01 2.074 1.74E-01 1.819 1.49E-01 2.076 1.21E-01 -0.111 9.45E-01 2.492 3.69E-02 APOA1 0.167 9.88E-01 1.093 5.31E-01 -0.515 1.69E-05 0.312 8.64E-01 -5.909 < 1.0E-08 -0.853 5.21E-01 C4A -1.043 8.41E-01 -1.471 3.71E-01 -0.471 7.52E-01 -1.968 1.45E-01 0.663 6.38E-01 -1.085 4.01E-01 COL18A1 0.268 9.81E-01 0.945 6.00E-01 -4.606 7.78E-05 -0.381 8.29E-01 -5.711 4.58E-07 -1.893 1.20E-01 INHBE 2.676 3.49E-01 4.115 2.01E-03 3.678 1.59E-03 4.587 1.05E-04 0.388 7.94E-01 3.867 8.82E-04 PRG4 1.367 7.73E-01 -0.195 9.27E-01 4.111 3.99E-04 0.135 9.45E-01 3.271 4.69E-03 1.552 2.12E-01 Gene expression data was extracted from PRJNA512027 (n = 192) (46) and our previously published meta-analysis of transcriptomic datasets (n = 1,058) (47). HS: hepatic steatosis; CTL: control (healthy liver); MASH: metabolic dysfunction-associated steatohepatitis. Serum EVs carry steatosis-associated protein signatures A total of 469 proteins were identified by label-free LC-DIA/MS proteomic analysis of serum-derived EVs from study participants. Covariate analysis identified TG levels and ethnicity as potential confounders ( Fig S1 ). Analyses were adjusted for ethnicity and T2D status, as these are known to influence MASLD risk independent of lipid metabolism. However, we did not adjust for TG levels, as they are a feature of MASLD pathology rather than a confounding variable (45). We identified 60 differentially abundant proteins (DAPs) between participants with hepatic steatosis and controls (p < 0.05; Fig. 1 A). This result was further supported by PLS-DA from the significant proteins (p < 0.05) identified in at least 50% of samples, which demonstrated clear separation between samples in the two groups (Fig. 1 B). Additionally, Hotelling’s T 2 test confirmed significant group separation (p < 0.05, reinforcing the distinction in profiles between the conditions. After adjusting for multiple comparisons, two proteins remained significantly differentially abundant between the two groups (q < 0.05). C4A (complement factor 4A) was significantly decreased in hepatic steatosis, whereas afamin (AFM) was significantly increased (Fig. 1 A). We performed GSEA (GO Biological Processes) to gain functional insights into protein cargoes carried by EVs from participants with hepatic steatosis (Fig. 1 C ) . Significant and positively enriched gene sets (p < 0.05) were generally involved in lipid biology (i.e., triglyceride metabolic process, neutral lipid metabolic process, regulation of lipid biosynthesis process) and inflammation and immunity (e.g., regulation of innate immune response, regulation of humoral immune response, positive regulation of defense response, and positive regulation of response to external stimulus). Steatotic protein signatures from serum EVs differ between Black and non-Hispanic White participants Given our previous observation of a higher prevalence of hepatic steatosis among non-Hispanic White participants compared to Black participants in this cohort (39), we further examined the effect of steatosis by race/ethnicity. In White participants (n = 103), differential abundance analysis identified 52 DAPs (p < 0.05) after adjusting for T2D status; however, none remained statistically significant following multiple testing correction (Fig. 2 A). In Black women (n = 172), 34 DAPs (p < 0.05) were detected; three of which, AFM, C4A, and APOA1, remained significantly different following multiple testing correction (Fig. 2 B). Although AFM levels were not significantly altered in White participants, a trend toward higher abundance was observed (p = 0.138). PLS-DA showed clear distinction between the control and the hepatic steatosis group in both comparisons (95% CI; Fig. 2 C and 2 D). Hotelling’s T 2 testing showed significant separation between groups (p < 0.05). GSEA revealed additional distinctions between serum EV proteins from White and Black participants with hepatic steatosis. In White participants, positively enriched processes (p < 0.05) were broadly involved in innate and humoral responses, such as acute phase response, positive regulation of immune system process, complement activation pathway, and acute inflammatory response, while negatively enriched processes included supramolecular fiber organization, cell projection organization, and similar cytoskeleton organization processes (Fig. 2 E). In Black participants, processes related to protein containing complex organization, small molecule metabolic process, lipid localization, and regulation of transport were negatively enriched ( Fig. 2 F ) . Patients with severe steatosis exhibit signatures associated with lipid transport dysregulation, extracellular matrix remodeling, and inflammation We explored protein signatures associated with severe steatosis by comparing participants with severe hepatic steatosis (n = 43) and without hepatic steatosis (n = 156). Differential abundance analysis identified seven proteins significantly altered in severe hepatic steatosis after multiple testing correction, alongside an additional 63 proteins with a nominal p-value < 0.05. Of the significantly altered proteins (q < 0.05), COL18A1, AFM, APOA1, INHBE, and PRG4 isoform C and F were increased, while C4A and APOA1 were decreased (Fig. 3 A). PLS-DA and Hotelling’s T 2 testing of significant proteins (p < 0.05) identified in more than 50% of samples shows significant (p < 0.05) separation between the two groups (95% CI) (Fig. 3 B). These changes suggest a combined signature of enhanced extracellular matrix remodeling (COL18A1), lipid transport dysregulation (AFM), and impaired complement-mediated immunity (C4A), highlighting distinct molecular pathways linked to severe hepatic steatosis. GSEA of biological processes showed significant positive enrichment for inflammation-related pathways, including regulation of humoral immune response, positive regulation of response to external stimulus, and complement activation via the alternative pathway. Lipid dysbiosis, specifically regulation of lipid biosynthetic process, was also positively enriched (Fig. 3 C). Investigation of EV signatures associated with hepatic steatosis across subgroups To identify EV protein signatures potentially unique to each subgroup, we compared proteins that were significantly more abundant (p < 0.05) across cohort-level analyses, including the overall cohort, White participants, Black participants, and those with severe hepatic steatosis (Fig. 4 ). INHBE emerged as the only protein consistently elevated in hepatic steatosis across all subgroup analyses (Fig. 4 : Whole Up, White Up, Black Up, Severe Up). In the overall cohort (n = 275), three proteins—APOC2, APOA4, and IGLC6—were uniquely enriched (Fig. 4 : Whole Up). Among White participants (n = 103), 11 proteins showed subgroup-specific enrichment: IGHA1, C1QB, C4BPA, C4BPB, LCAT, PROS1, MMRN1, SERPINA10, CFI, ANK1, and LBP (Fig. 4 : White Up). In Black participants (n = 172), seven proteins were uniquely enriched: IGHG1, IGHG2, IGHV2-5, CFHR2, LUM, IGLV3-21, and ANTXR1 (Fig. 4 : Black Up). In the severe hepatic steatosis subgroup (n = 43), proteins with subgroup-specific enrichment included IGKV1D-13; IGKV1-13, ICAM1, C2, C7, APOC4, and ADGRF5 (Fig. 4 : Severe Up). To explore these candidates further, we examined the expression of the encoding genes in two cohorts, one representing our previously published RNA-seq dataset (NCBI Bioproject Accession PRJNA512027; n = 192) (46) and the other a meta-analysis of hepatic gene expression from liver biopsy samples, integrating ten RNA-sequencing and microarray datasets, and also including PRJNA512027 (1,058 samples) (47). INHBE was consistently overexpressed across all three meta-analyses: hepatic steatosis vs. controls, MASH vs. controls, and MASH vs steatosis. AFM also showed increased expression in each comparison, but statistical significance was only observed in the MASH vs. steatosis analysis (Z = 2.492; FDR = 0.0369). In contrast, C4A was underexpressed in all the three comparisons, though none reached statistical significance (lowest FDR = 0.145 in MASH vs. controls; Table 2 ). Finally, in the PRJNA512027 dataset, APOA1 and COL18A1 were significantly underexpressed in MASH relative to both controls and steatotic samples, but this pattern was not supported by the meta-analysis. Discussion Current clinical guidelines often overlook sex- and age-specific differences in metabolic health, revealing a gap in targeted approaches to reduce the burden of MASLD, particularly in postmenopausal women. In this study, we profiled the circulating EV proteome in a diverse cohort of postmenopausal women and identified proteomic signatures associated with hepatic steatosis. Our findings suggest that EV cargo reflects molecular alterations relevant to liver fat accumulation in this population, supporting their potential as biomarkers for both hepatic steatosis and metabolic shifts that may precede overt liver dysfunction. Notably, we observed distinct proteomic patterns not only by steatosis status but also by race/ethnicity, indicating that EVs may capture both common and ancestry-specific disease mechanisms. This observation raises the possibility that circulating EVs integrate environmental, genetic, and metabolic influences relevant to MASLD development. Two EV proteins, C4A and AFM, were significantly altered between women with and without hepatic steatosis. C4A was reduced, while AFM was elevated, suggesting roles in dysregulated complement activation and lipid transport, respectively, both central to MASLD pathogenesis. Elevated AFM, a vitamin E–binding hepatokine, has been linked to metabolic syndrome and hepatic inflammation (48–50). Reduced C4A, part of the classical complement cascade, may reflect impaired immune surveillance or increased complement turnover, consistent with hepatic inflammation (28, 51, 52). Subgroup analyses further revealed distinct EV proteomic signatures by race. In White women, proteins involved in coagulation, complement regulation, and lipid transport, including complement C4 binding protein alpha (C4BPA), lecithin-cholesterol acyltransferase (LCAT), protein S (PROS1), complement C4 binding protein beta (C4BPB), and multimerin 1 (MMRN1), were elevated in hepatic steatosis. These proteins implicate dysregulation of innate immunity and lipoprotein remodeling and warrant further study to clarify their specific contributions. In contrast, Black women exhibited enrichment of immunoglobulin-related proteins (e.g., IGHG1, IGHG2, IGHV2-5, IGLV3-21), suggesting differential activation of humoral immune response. Additional subgroup-specific proteins such as complement factor H related 2 (CFHR2), thrombospondin 4 (THBS4), lumican (LUM), and anthrax toxin receptor 1 (ANTXR1) suggest potential ancestry-associated differences in inflammatory and matrix remodeling pathways (53–55). However, these results require cautious interpretation. Though a useful proxy, self-reported race/ethnicity cannot fully capture genetic ancestry or account for its continuous variation and substructure across population groups (56). In participants with severe hepatic steatosis, EV protein signatures were enriched for markers of inflammation, complement activation, and dysregulated lipid metabolism. Among these, INHBE, COL18A1, AFM, and PRG4 were significantly elevated, while C4A and APOA1 were reduced. Enrichment of pathways involved in humoral immune responses and lipid biosynthesis further supports the hypothesis that these EV changes reflect worsening metabolic and immune dysregulation in advanced steatosis. These findings suggest that EV profiling may not only detect early disease but also stratify severity and capture pathophysiologic processes driving progression toward MASH. To determine whether EV protein cargo changes reflect underlying hepatic gene expression, we analyzed transcript levels of the corresponding genes in two liver transcriptomic datasets comprising over 1,000 samples. INHBE was significantly overexpressed in both hepatic steatosis and MASH compared to controls, as well as in MASH compared to steatosis. Additionally, AFM was significantly upregulated in MASH compared to hepatic steatosis, with similar but nonsignificant trends in both MASH and hepatic steatosis relative to controls. C4A expression was reduced in MASH compared to both control and steatotic livers. APOA1 and COL18A1 were significantly underexpressed in the PRJNA512027 dataset in MASH compared to both controls and steatosis; however, these differences were not confirmed in the meta-analysis. These findings suggest that although EV protein abundance does not consistently parallel hepatic gene expression, some convergence emerges in advanced disease. This partial overlap may reflect posttranscriptional regulation, differences in protein turnover or secretion, or contributions from non-parenchymal cell types to the EV proteome. Thus, EVs may offer complementary insights into disease biology, particularly when transcriptomic alterations are subtle or heterogeneous. Although classical inhibins A and B decline with menopause due to loss of ovarian function (57), INHBE encodes a distinct β-subunit of activin E, a hepatokine increasingly recognized for its role in metabolic regulation. Recent studies have shown that INHBE expression is induced by hepatic steatosis and insulin resistance (58–60), consistent with our finding of elevated EV-derived INHBE across all subgroups. These data suggest that INHBE upregulation in hepatic steatosis may represent a liver-derived compensatory response to metabolic stress rather than a reflection of altered gonadal hormone signaling. In this context, INHBE may serve as a relevant biomarker of liver–endocrine crosstalk in postmenopausal women with MASLD. Despite the strengths of this study, including the use of a well-characterized, community-based cohort and a focus on a high-risk population, several limitations should be acknowledged. Hepatic steatosis was assessed via ultrasound, an imaging technique with limited sensitivity for mild fat accumulation and lacking quantitative resolution. Future studies incorporating MRI-proton density fat fraction (MRI-PDFF) or liver biopsy would allow more accurate phenotyping. Additionally, the relatively small number of woman with steatosis (n = 75) may have limited power to detect subtle differences; however, our sample size exceeds those of many prior proteomic studies in MASLD (61, 62). Generalizability may also be limited due to the single-site design and its focus exclusively on Black and non-Hispanic White women. Finally, the cross-sectional design precludes inferences about the temporal relationship between EV cargo and progression/reversal of steatosis. Even with these limitations, our findings contribute to a growing body of evidence highlighting potential biomarkers and mechanistic pathways underlying hepatic steatosis in midlife women. Conclusions This study demonstrates that circulating EV proteomes reflect hepatic steatosis status in postmenopausal women and reveal both shared and ancestry-specific molecular signatures. These findings suggest that EVs may capture early and systemic metabolic perturbations relevant to MASLD pathogenesis, even in the absence of overt hepatic transcriptomic changes. As minimally invasive biomarkers, EVs hold promise for identifying individuals at risk for MASLD progression and uncovering novel pathways for intervention. Future research should prioritize longitudinal studies to determine whether EV profiles can predict disease trajectory and integrate multi-omics data, including genomics and metabolomics, to elucidate the full spectrum of MASLD pathophysiology across diverse populations. Abbreviations Afamin (AFM); Anthrax toxin receptor 1 (ANTXR1); Analysis of covariance (ANCOVA); Ankyrin 1 (ANK1); Apolipoprotein A1 (APOA1); Apolipoprotein A4 (APOA4); Apolipoprotein C2 (APOC2); Apolipoprotein C4 (APOC4); Body mass index (BMI); Complement component 2 (C2); Complement component 4A (C4A); Complement component 4 binding protein alpha (C4BPA); Complement component 4 binding protein beta (C4BPB); Complement component 7 (C7); Complement factor H-related protein 2 (CFHR2); Complement factor I (CFI); Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Differentially abundant proteins (DAPs); Diabetes mellitus (T2D); Extracellular vesicles (EVs); False discovery rate (FDR); Gene Ontology Biological Processes (GOBP); Gene Set Enrichment Analysis (GSEA); Hemoglobin A1c (HbA1c); High-performance liquid chromatography (HPLC); Immunoglobulin A1 (IGHA1); Immunoglobulin heavy constant gamma 1 (IGHG1); Immunoglobulin heavy constant gamma 2 (IGHG2); Immunoglobulin heavy variable 2-5 (IGHV2-5); Immunoglobulin kappa variable 1-13 (IGKV1-13); Immunoglobulin kappa variable 1D-13 (IGKV1D-13); Immunoglobulin lambda constant 6 (IGLC6); Immunoglobulin lambda variable 3-21 (IGLV3-21); Inhibin beta E (INHBE); Institutional Review Board (IRB); Integrated retention time (iRT); Intercellular adhesion molecule 1 (ICAM1); Lecithin-cholesterol acyltransferase (LCAT); Lipopolysaccharide binding protein (LBP); Liquid chromatography (LC); Liquid chromatography–mass spectrometry (LC-MS); Lumican (LUM); Mass spectrometry (MS); Metabolic dysfunction-associated steatohepatitis (MASH); Metabolic dysfunction-associated steatotic liver disease (MASLD); Michigan site of the Study of Women’s Health Across the Nation (MI-SWAN); Multimerin 1 (MMRN1); Partial least squares discriminant analysis (PLS-DA); Parallel accumulation serial fragmentation (PASEF); Phosphate buffered saline (PBS); Principal component (PC); Principal component analysis (PCA); Protein S (PROS1); Proteome Spectral Matches (PSMs); Serpin family A member 10 (SERPINA10); Size exclusion chromatography (SEC); Study of Women’s Health Across the Nation (SWAN); Type 2 diabetes (T2D); Triglyceride (TG); Variance stabilization normalization (VSN) Declarations Ethics approval and consent to participate All participants provided informed written consent, and study procedures were approved by the Health and Behavioral Sciences Institutional Review Board, University of Michigan. Consent for publication Not applicable. Availability of data and materials The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (63) partner repository. SWAN data are archived at https://www.icpsr.umich.edu/web/ICPSR/series/00253 and the Aging Research Biobank https://agingresearchbiobank.nia.nih.gov/studies/swan/?search_term=SWAN. The Michigan site- specific liver datasets used and/or analyzed during the current study are available from author Carrie Karvonen-Gutierrez ( [email protected] ) on reasonable request. Competing interests JKD is a member of the BMC Medicine Editorial Board. Funding P30CA33572, U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720, R01127015 Authors' contributions PP: supervision, methodology, writing—original draft preparation. BL: formal analysis, investigation, writing—original draft preparation, visualization. SDH: conceptualization, data and specimen collection, project administration, methodology, writing—original draft preparation, funding acquisition. CKM: data and specimen collection, methodology, manuscript editing. MMH: data curation, manuscript editing. ISP: formal analysis, editing of manuscript. XW: investigation. MNM: data curation, investigation. RS: formal analysis, investigation. KGM: formal analysis. MW: formal analysis, investigation. JKD: conceptualization, writing—original draft preparation, visualization, supervision, project administration, funding acquisition. All authors contributed significantly to this manuscript and read and agreed to the submitted version of this manuscript. Acknowledgements This research was supported by the NIDDK (R01DK127015; JKD). Research reported in this publication included work performed in the Integrated Mass Spectrometry Shared Resource supported by the National Cancer Institute of the National Institutes of Health under award number P30CA33572. The Study of Women's Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH or the National Institutes of Health. SWAN Clinical Centers: University of Michigan, Ann Arbor – Carrie Karvonen-Gutierrez, PI 2021 – present, Siobán Harlow, PI 2011 – 2021, MaryFran Sowers, PI 1994-2011; Massachusetts General Hospital, Boston, MA – Sherri‐Ann Burnett‐Bowie, PI 2020 – Present; Joel Finkelstein, PI 1999 – 2020; Robert Neer, PI 1994 – 1999; Rush University, Rush University Medical Center, Chicago, IL – Imke Janssen, PI 2020 – Present; Howard Kravitz, PI 2009 – 2020; Lynda Powell, PI 1994 – 2009; University of California, Davis/Kaiser – Elaine Waetjen and Monique Hedderson, PIs 2020 – Present; Ellen Gold, PI 1994 - 2020; University of California, Los Angeles – Arun Karlamangla, PI 2020 – Present; Gail Greendale, PI 1994 - 2020; Albert Einstein College of Medicine, Bronx, NY – Carol Derby, PI 2011 – present, Rachel Wildman, PI 2010 – 2011; Nanette Santoro, PI 2004 – 2010; University of Medicine and Dentistry – New Jersey Medical School, Newark – Gerson Weiss, PI 1994 – 2004; and the University of Pittsburgh, Pittsburgh, PA – Rebecca Thurston, PI 2020 – Present; Karen Matthews, PI 1994 - 2020. NIH Program Office: National Institute on Aging, Bethesda, MD – Rosaly Correa-de-Araujo 2020 - present; Chhanda Dutta 2016- present; Winifred Rossi 2012–2016; Sherry Sherman 1994 – 2012; Marcia Ory 1994 – 2001; National Institute of Nursing Research, Bethesda, MD – Program Officers. Central Laboratory: University of Michigan, Ann Arbor – Daniel McConnell (Central Ligand Assay Satellite Services). Coordinating Center: University of Pittsburgh, Pittsburgh, PA – Maria Mori Brooks, PI 2012 - present; Kim Sutton-Tyrrell, PI 2001 – 2012; New England Research Institutes, Watertown, MA - Sonja McKinlay, PI 1995 – 2001. Steering Committee: Susan Johnson, Current Chair; Chris Gallagher, Former Chair We thank the study staff at each site and all the women who participated in SWAN. References Hill K. The demography of menopause. Maturitas. 1996;23(2):113-27. Vespa JM, L.; Armstrong, D.M. Demographic Turning Points for the United States: Population Projections for 2020 to 2060. 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Martinez","email":"","orcid":"","institution":"Translational Genomics Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"N.","lastName":"Martinez","suffix":""},{"id":498280149,"identity":"fd901e61-0cb4-424c-ae94-25f25f36cae2","order_by":8,"name":"Ritin Sharma","email":"","orcid":"","institution":"Translational Genomics Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Ritin","middleName":"","lastName":"Sharma","suffix":""},{"id":498280150,"identity":"af98d65d-bb3e-4dba-be8d-820ca0dbca82","order_by":9,"name":"Krystine Garcia-Mansfield","email":"","orcid":"","institution":"Translational Genomics Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Krystine","middleName":"","lastName":"Garcia-Mansfield","suffix":""},{"id":498280151,"identity":"e7407da9-f5ff-4841-b031-4f7da69c4f98","order_by":10,"name":"Maya Willey","email":"","orcid":"","institution":"Translational Genomics Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Maya","middleName":"","lastName":"Willey","suffix":""},{"id":498280153,"identity":"36698034-8db8-42c7-b884-19a9e7525159","order_by":11,"name":"Johanna K DiStefano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxobEBiAlB8TMB0jQcoDBGMhkA2smBiQwQLXwGBKnhbk9ufnzhwoDeXP2nu8Pv1TcYeCXPn4Bv8N6HrZJHDhjYLiz5+zGZpkzzxgk+3IK8GuZkdjGcLDtD+OGG7kbmyXbDjMYnOFJIKSl+cPBfwb2G27kPGyW/EeclgaJgw0GiUAtjI0fG0Ba2A8Q9suZYwbJG84cM5zNcOwwj2QPD14dDIbt6Y8/VNQY2G443vzg44+aw3L8POwP8GtpQOIwA80HIQO8WuRRXPkDTBGwZRSMglEwCkYcAAB7zFOj7AxFJAAAAABJRU5ErkJggg==","orcid":"","institution":"Translational Genomics Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Johanna","middleName":"K","lastName":"DiStefano","suffix":""}],"badges":[],"createdAt":"2025-08-04 18:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7293767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7293767/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-025-04571-4","type":"published","date":"2025-12-07T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89232919,"identity":"05f76ed1-60f1-404d-9364-7c50aa831316","added_by":"auto","created_at":"2025-08-17 14:30:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1543620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially abundant EV proteins in participants with hepatic steatosis (HS) vs. controls.\u003cbr\u003e\nA) \u003c/strong\u003eVolcano plot displaying differentially abundant proteins. The horizontal dotted line indicates -log\u003csub\u003e10\u003c/sub\u003e (0.05). Red and pink dots represent proteins significantly increased in hepatic steatosis (p \u0026lt; 0.05 and FC \u0026gt; 0; q \u0026lt; 0.05 and FC \u0026gt; 0). Blue and green dots represent proteins significantly decreased (p \u0026lt; 0.05 and FC \u0026lt; 0; q \u0026lt; 0.05 and FC \u0026lt; 0). Grey dots are not significant. \u003cstrong\u003e(B) \u003c/strong\u003ePartial least squares-discriminant analysis (PLS-DA) plot showing separation between participants with hepatic steatosis (green circles) and controls (grey circles). Proteins were filtered for p \u0026lt; 0.05 and presence in ≥50% of samples. Ovals represent 95% confidence intervals.\u003cbr\u003e\n\u003cstrong\u003e(C) \u003c/strong\u003eDot plot showing significantly enriched Gene Ontology (GO) Biological Processes. Dots represent gene sets and are colored by normalized enrichment score (NES); dot size reflects the number of genes in each set.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/12a3b4cd0e5c010f0c3881b8.png"},{"id":89232921,"identity":"d2b8b49c-2274-4e75-b251-dad18edb18aa","added_by":"auto","created_at":"2025-08-17 14:30:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2766275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEV proteomic differences by race. (A–B)\u003c/strong\u003e Volcano plots displaying differentially abundant proteins within the White \u003cstrong\u003e(A)\u003c/strong\u003e and Black \u003cstrong\u003e(B)\u003c/strong\u003e subgroups. The horizontal dotted line indicates --log\u003csub\u003e10\u003c/sub\u003e (0.05). Red and pink dots represent proteins significantly increased in hepatic steatosis (p \u0026lt; 0.05 and FC \u0026gt; 0; q \u0026lt; 0.05 and FC \u0026gt; 0). Blue dots indicate proteins significantly decreased (p \u0026lt; 0.05 and FC \u0026lt; 0). Grey dots are not significant. \u003cstrong\u003e(C–D)\u003c/strong\u003e PLS-DA plots showing separation of hepatic steatosis (green circles) and controls (grey circles) within the White \u003cstrong\u003e(C)\u003c/strong\u003e and Black \u003cstrong\u003e(D)\u003c/strong\u003e subgroups. Proteins were filtered for p \u0026lt; 0.05 and presence in ≥50% of samples. Ovals represent 95% confidence intervals. \u003cstrong\u003e(E–F)\u003c/strong\u003e Dot plots showing significantly enriched GO Biological Processes in the White \u003cstrong\u003e(E)\u003c/strong\u003e and Black \u003cstrong\u003e(F)\u003c/strong\u003e cohorts. Dots represent gene sets and are colored by NES; dot size reflects the number of genes in each set. HS: hepatic steatosis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/f4f9ecb155470a981b1cac2c.png"},{"id":89232934,"identity":"60f3c21f-8727-41e0-8a66-d86daba605a3","added_by":"auto","created_at":"2025-08-17 14:30:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1253519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEV protein signatures in participants with severe hepatic steatosis (HS). \u0026nbsp;(A) \u003c/strong\u003eVolcano plot displaying differentially abundant proteins in participants with severe hepatic steatosis. The horizontal dotted line indicates -log\u003csub\u003e10\u003c/sub\u003e (0.05). Red and pink dots represent proteins significantly increased in hepatic steatosis (p \u0026lt; 0.05 and FC \u0026gt; 0; q \u0026lt; 0.05 and FC \u0026gt; 0). Blue dots represent significantly decreased proteins (p \u0026lt; 0.05 and FC \u0026lt; 0). Grey dots are not significant.\u003cstrong\u003e (B) \u003c/strong\u003ePLS-DA plot showing separation of participants with severe hepatic steatosis (green circles) and those without steatosis (grey circles). Proteins were filtered for p \u0026lt; 0.05 and presence in ≥50% of samples. Ovals represent 95% confidence intervals.\u003cstrong\u003e (C) \u003c/strong\u003eDot plot showing significantly enriched GO Biological Processes. Dots represent gene sets and are colored by NES; dot size reflects the number of genes in each set.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/f58bc7da27f4393a04f66063.png"},{"id":89233602,"identity":"5b90ffb0-3a6b-49f0-aadc-a62965e62a28","added_by":"auto","created_at":"2025-08-17 14:38:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":454334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverlap of significantly increased EV proteins across subgroups. \u003c/strong\u003eUpSet plot displaying the intersection of proteins significantly more abundant (p \u0026lt; 0.05) in each subgroup analysis. The bottom matrix shows all possible set intersections, with filled dots indicating the sets included in each intersection and connecting lines denoting shared membership. The top bar chart shows the number of proteins in each intersection. Horizontal bars to the left indicate the total number of significant proteins in each individual subgroup. HS: hepatic steatosis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/7d56253e0997a2c7ab41ad98.png"},{"id":97724009,"identity":"b4c8c83a-88f2-4920-9f13-154a550a957a","added_by":"auto","created_at":"2025-12-08 16:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7669527,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/d60c00ad-52ba-4bf5-84dc-7bc52e144dc9.pdf"},{"id":89232918,"identity":"54bf9e88-8da6-4d9b-a66b-93af82faa238","added_by":"auto","created_at":"2025-08-17 14:30:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":277996,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7293767/v1/dadfed0dd63debe5943528f7.docx"}],"financialInterests":"Competing interest reported. JKD is a member of the BMC Medicine Editorial Board.","formattedTitle":"Hepatic steatosis in postmenopausal women is characterized by distinct serum extracellular vesicle proteomic signatures","fulltext":[{"header":"Background","content":"\u003cp\u003eWith increasing longevity, women now spend nearly 40% of their lives post-menopause (1). By 2030, postmenopausal women will comprise nearly half of the female population in the United States (2), yet the metabolic consequences of this transition remain incompletely defined. The reduction in estrogen production triggers profound shifts in fat distribution, insulin sensitivity, and dyslipidemia, all of which are associated with metabolic dysfunction-associated steatotic liver disease (MASLD) (3–10). The biological mechanisms linking hormonal decline to metabolic dysfunction represent an underexplored area of research. As the number of postmenopausal women continues to rise, there is an urgent need to disentangle the hormonal drivers of metabolic dysfunction from other risk factors to inform targeted prevention and treatment strategies.\u003c/p\u003e\u003cp\u003eMASLD is a chronic condition defined by excessive triglyceride accumulation in the liver, spanning a disease spectrum from isolated steatosis, where at least 5% of liver weight consists of intracellular fat, to metabolic dysfunction-associated steatohepatitis (MASH), which involves liver inflammation and hepatocellular injury (11). While MASLD has historically been more prevalent in men, its burden among women has risen sharply in recent decades, increasing from 18.5% in 1988–1994 to 24.9% in 2007–2014 (12). This increase has outpaced that seen in men (13) and has been accompanied by a higher mortality rate in affected women (14). Furthermore, the prevalence of MASLD in postmenopausal women is variable, ranging from 15–62% depending on geography, ethnicity, and diagnostic criteria (4, 15, 16). According to one study, postmenopausal women showed a nearly two-fold higher likelihood of advanced fibrosis (36.1%) compared to men (17.7%) (17). Furthermore, MASH is currently the leading indication for liver transplantation in women (18). Animal studies provide evidence that estrogen loss directly increases susceptibility to hepatic steatosis and steatohepatitis, particularly in the presence of mild metabolic dysfunction, such as moderate adiposity, dyslipidemia, or prediabetes (4). Despite these trends, our understanding of the mechanisms by which MASLD develops and progresses in postmenopausal women remains limited.\u003c/p\u003e\u003cp\u003eExtracellular vesicles (EVs) are small membrane-bound vesicles released by cells that mediate intercellular communication through the transfer of bioactive molecules (19, 20). EVs reflect the physiological state of their cell of origin, carrying a cargo that mirrors the metabolic or pathological environment of the parent cell (21). Increasing evidence suggests that EVs play a pivotal role in promoting metabolic dysfunction through the intracellular transport of signaling molecules that influence a variety of disease processes, including MASLD (22–28). Elevated plasma EVs have been observed in individuals with MASLD, with levels positively correlated with disease severity (27, 29, 30). Circulating EVs influence diverse processes in liver cells, including intracellular signaling, tissue injury and repair, and matrix remodeling (31). In animal models, EVs from steatotic hepatocytes affect the metabolome, alter hepatic gene expression, and exacerbate liver fibrosis and inflammation. For instance, in mice fed a diet high in saturated fat, cholesterol, and fructose, increased levels of hepatocyte-specific EVs in the circulation correlated with disease markers, while inhibition of EV release attenuated hepatic injury (32). These findings collectively indicate that EVs may both serve as biomarkers of MASLD severity and actively participate in the pathological processes underlying MASLD and MASH.\u003c/p\u003e\u003cp\u003eAlthough EV research in the context of MASLD is advancing, current studies of EVs in postmenopausal women are scarce (33–37). To address this gap, we utilized clinical data and serum samples from the Study of Women’s Health Across the Nation (SWAN), a multi-site, longitudinal cohort study designed to investigate the health of women throughout midlife and the menopausal transition (38). In 2010, the Michigan site (MI-SWAN) conducted a liver ultrasound study focusing on the presence of hepatic steatosis among a cohort of predominantly postmenopausal women (39), with associated serum samples stored in the MI-SWAN biorepository. As a result, MI-SWAN provides a unique opportunity to investigate EV-derived proteomic profiles within a diverse group of postmenopausal women with hepatic steatosis.\u003c/p\u003e\u003cp\u003eHere, we sought to identify and characterize EV-derived proteomic profiles associated with abdominal ultrasound-determined hepatic steatosis in postmenopausal women, leveraging the comprehensive clinical characterization of MI-SWAN participants to deepen understanding of EVs as potential biomarkers and therapeutic targets for MASLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Participants.\u003c/em\u003e SWAN is an ongoing community-based cohort study involving multiple racial and ethnic groups, designed to characterize the menopausal transition, post-menopause, and related health changes. Women aged 42–52 years were recruited from defined sampling frames at seven clinical sites in 1996–1997 (study design details are available elsewhere (38)). To be eligible, participants were required to have an intact uterus and at least one ovary, and to have experienced a menstrual period within the preceding three months. Exclusion criteria included hormone therapy use in the prior three months, pregnancy, or breastfeeding. Participants subsequently completed up to seventeen follow-up visits. All participants provided informed consent, and study procedures were approved by the institutional review board at each clinical site.\u003c/p\u003e\u003cp\u003eThe MI-SWAN recruited 543 women, with participants self-identifying as Black (66%) or White (34%). Participants were classified as postmenopausal based on the absence of menses for at least twelve consecutive months. At the 2010 annual follow-up visit, hepatic steatosis was assessed using ultrasound imaging (39). Of the 403 women who participated in that visit, 345 (86%) underwent hepatic ultrasound. Participants with a history of cirrhosis or chronic liver disease attributable to viral hepatitis or hemochromatosis (n = 14) were excluded, resulting in 331 potentially eligible participants. Stored serum samples were available for 278 of these participants and three women who were not postmenopausal at the time were excluded. Thus, 275 women comprised the analytical sample set for this study.\u003c/p\u003e\u003cp\u003eAt each study visit, women completed questionnaires providing information on sociodemographic characteristics, menopausal status, health status, and medications. Anthropometric measurements were obtained including height, weight, and waist and hip circumferences. A fasting blood draw was also obtained. Diabetes was defined as the use of glucose-lowering medication during the study, fasting glucose ≥ 126 mg/dL at two consecutive visits, or self-reported diabetes at two visits with fasting glucose ≥ 126 mg/dL at one visit. Blood was refrigerated for 1–2 hours prior to centrifugation, and serum was aliquoted, frozen, and stored at − 80ºC.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAbdominal Ultrasound for Hepatic Fat Evaluation\u003c/em\u003e. A single ultrasound technician, blinded to the medical history of participants, performed abdominal ultrasounds using a Sonoline Elegra Ultrasound Imaging System (Siemens Medical Systems Inc.) equipped with a 3.5 MHz transducer and a 411 LE 0.5 phantom (GAMMEX-RMI Ltd). All images were evaluated for markers of hepatic steatosis by a single radiologist applying a standardized protocol and blinded to participant profile, as previously reported (39). The liver was graded for markers of hepatic steatosis including bright hepatic echo pattern compared with echo response of the right kidney, attenuation of the echo beam, and presence of focal fatty sparing (40). Hepatic steatosis was categorized as “moderate/severe” or “none/mild” based on echogenicity and clarity of structures within the liver (41).\u003c/p\u003e\u003cp\u003e\u003cem\u003eIsolation of EVs from Serum\u003c/em\u003e. EVs were isolated by size exclusion chromatography using qEVoriginal 70 nm Gen 2 columns (Izon Science; Medford, MA) from fasting serum samples. Following column equilibration with 15 mL of phosphate buffered saline (PBS), 500 µL of serum was loaded onto the column and 6 × 500 µL fractions (F) were collected (F7-F12), following the collection of the 3 mL void volume. fractions F7-F12 were combined and concentrated with 50K Amicon filters (Millipore, UFC805096) to a final volume of 500 µL. The combined concentrated EVs were used for downstream analyses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSample Preparation for Proteomics Analysis.\u003c/em\u003e Isolated EVs were solubilized in a 2% sodium deoxycholate-based lysis buffer and sonicated using a cup-horn shaped sonotrode (UTR2000, Hielscher Ultrasonics) for 30 seconds (15 second on 1 second off) at 50% amplitude for three rounds of sonication. Protein extracts were clarified by centrifugation and protein concentration was determined using the BCA assay (Pierce). Proteins (40 µg) from isolated EVs were processed as previously described (28). To generate a reference spectral library, equal amounts of protein (i.e., 5 µg) were combined from each sample to create a 1.39 mg pool. The pooled sample was processed as described (28). Peptides were subjected to offline fractionation via high pH reverse phase chromatography on an Ultimate 3000 HPLC system (Thermo Scientific; Waltham, MA). Peptides were loaded onto a 10 cm C18 column (Waters XBridge C18, 4.6 mm ID, 3.5 µm particle size) and eluted over a 96-minute method into a 96-well plate. The resulting 96 fractions were combined to 24 peptide fractions for LC-MS/MS acquisition. All samples and library fractions were spiked with iRT peptides (Biognosys; Switzerland).\u003c/p\u003e\u003cp\u003e\u003cem\u003eLiquid Chromatography–Data-Independent Acquisition Mass Spectrometry (\u003c/em\u003eLC-DIA/MS\u003cem\u003e).\u003c/em\u003e All mass spectrometry data were acquired on a nanoElute liquid chromatography system coupled to a timsTOF HT Pro 2 (Bruker Daltonics; Billerica, MA) mass spectrometer with a captive spray source (Bruker) using a 62-minute LC gradient at a flowrate of 850 nL/min on a 25 cm C18 column (Bruker PepSep, 150 µm ID, 1.5 µm particle size). Individual library fractions were acquired in DDA-PASEF (Data Dependent Acquisition – Parallel Accumulation Serial Fragmentation) mode with MS1 scans covering a mass range of 100–1700 m/z, TIMS mobility window (1/K0) between 0.70 and 1.50 with 75 ms accumulation and ramp time. DDA scans involved 7 PASEF ramps for a total cycle time of 0.65 second and a collision energy ramp of 20 eV to 65 eV for ion mobility window (1/K0) of 0.6–1.6. Each EV sample was acquired in DIA (Data Independent Acquisition)-PASEF mode keeping the same accumulation and ramp time as DDA runs. The capillary voltage was kept at 1700 V and dry gas temperature was kept at 200°C.\u003c/p\u003e\u003cp\u003e\u003cem\u003eProteomics Data Analysis\u003c/em\u003e. Spectral libraries from the DDA-PASEF runs were created with Spectronaut 19.8 software against a human SwissProt database (UP000005640, downloaded June 2025). Theoretical digestion was performed using trypsin allowing for a maximum of two missed cleavages. Cysteine carbamidomethylation was set as a fixed modification, while methionine oxidation and protein N-terminal acetylation were set as variable modifications. PSMs and peptides were filtered for False Discovery Rate (FDR) \u0026lt; 1%. DIA-MS data was searched against data-specific spectral libraries using default parameters (cross-run normalization, data imputation, and scaling were disabled). Protein abundances were normalized using variance stabilization normalization (\u003cem\u003evsn\u003c/em\u003e package) (42). To determine the amount of variation in protein abundance explained by clinical variability, principal component analysis (PCA) from \u003cem\u003ePCAtools\u003c/em\u003e was run on proteins present in 100% of samples and clinical variables reporting the first 25 principal components (PCs). The significance of the relationship between confounding factors and protein abundance was calculated via \u003cem\u003eeigencorplot\u003c/em\u003e function on variables for the first five PCs. Differential abundance was calculated using an ANCOVA with a Benjamini-Hochberg correction for multiple testing, accounting for ethnicity and diabetes status. Significant proteins (p \u0026lt; 0.05) were filtered for presence in at least 50% of samples and Partial Least Squares-Discriminate Analysis (PLS-DA) was used to assess group separation at 95% confidence with the \u003cem\u003eplsda\u003c/em\u003e function in the \u003cem\u003emixOmics\u003c/em\u003e package (43). A Hotelling’s t-squared statistic was calculated using the \u003cem\u003eHotelling\u003c/em\u003e package to determine significance of group separation (p \u0026lt; 0.05). A significance score, defined as − log\u003csub\u003e10\u003c/sub\u003e​(p-value) × sign(log\u003csub\u003e2\u003c/sub\u003e​FC), was calculated for all identified EV proteins. This score was used to order proteins based on statistical significance and fold-change direction for subsequent Gene Set Enrichment Analysis (GSEA). Analysis was performed in GSEA software v 4.4.0 (44) against the Gene Ontology Biological Processes (GOBP) database (v2025).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eCharacteristics of the study cohort\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents demographic information and clinical characteristics for the 275 postmenopausal participants at the 2010 follow-up visit. Participants with hepatic steatosis (n\u0026thinsp;=\u0026thinsp;75) had higher BMI, greater waist circumference, and a higher prevalence of T2D compared to those without steatosis (n\u0026thinsp;=\u0026thinsp;200). They also exhibited elevated triglyceride (TG), fasting glucose, and HbA1c levels. Among those with hepatic steatosis, women with T2D had greater adiposity and more pronounced metabolic abnormalities, including higher TG, fasting glucose, and HbA1c levels (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy cohort demographic information and clinical characteristics (N\u0026thinsp;=\u0026thinsp;275)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepatic steatosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo hepatic steatosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (67.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (33.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2D, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (23.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153.8\u0026thinsp;\u0026plusmn;\u0026thinsp;90.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.5\u0026thinsp;\u0026plusmn;\u0026thinsp;51.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;76.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.9\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eData are means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, unless otherwise indicated; WC: waist circumference; TG: fasting serum triglyceride levels; FG: fasting plasma glucose; HbA1c: glycosylated hemoglobin\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e\u003cp\u003eTable\u0026nbsp;2. Hepatic expression of genes encoding differentially abundant proteins in hepatic steatosis and MASH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c17\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eHS vs CTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c12\" namest=\"c6\"\u003e\u003cp\u003eMASH vs CTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c17\" namest=\"c13\"\u003e\u003cp\u003eMASH vs HS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePRJNA512027\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMeta-analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003ePRJNA512027\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eMeta-analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u003cp\u003ePRJNA512027\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e\u003cp\u003eMeta-analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003ez-score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAFM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.95E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.74E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.49E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e2.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.21E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9.45E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e2.492\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e\u003cb\u003e3.69E-02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAPOA1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.88E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.31E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.515\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.69E-05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8.64E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e-5.909\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt; 1.0E-08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e5.21E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC4A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.41E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.71E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e7.52E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e-1.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.45E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e6.38E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e4.01E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCOL18A1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.81E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.00E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-4.606\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003e7.78E-05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e-0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8.29E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e-5.711\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e4.58E-07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-1.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e1.20E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eINHBE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.49E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e4.115\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.01E-03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e3.678\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1.59E-03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e4.587\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e1.05E-04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e7.94E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e3.867\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e\u003cb\u003e8.82E-04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePRG4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.73E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.27E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4.111\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e3.99E-04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9.45E-01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e3.271\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e4.69E-03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e\u003cp\u003e2.12E-01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGene expression data was extracted from PRJNA512027 (n\u0026thinsp;=\u0026thinsp;192) (46) and our previously published meta-analysis of transcriptomic datasets (n\u0026thinsp;=\u0026thinsp;1,058) (47). HS: hepatic steatosis; CTL: control (healthy liver); MASH: metabolic dysfunction-associated steatohepatitis.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSerum EVs carry steatosis-associated protein signatures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 469 proteins were identified by label-free LC-DIA/MS proteomic analysis of serum-derived EVs from study participants. Covariate analysis identified TG levels and ethnicity as potential confounders (\u003cb\u003eFig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Analyses were adjusted for ethnicity and T2D status, as these are known to influence MASLD risk independent of lipid metabolism. However, we did not adjust for TG levels, as they are a feature of MASLD pathology rather than a confounding variable (45).\u003c/p\u003e\u003cp\u003eWe identified 60 differentially abundant proteins (DAPs) between participants with hepatic steatosis and controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This result was further supported by PLS-DA from the significant proteins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified in at least 50% of samples, which demonstrated clear separation between samples in the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Additionally, Hotelling\u0026rsquo;s T\u003csup\u003e2\u003c/sup\u003e test confirmed significant group separation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, reinforcing the distinction in profiles between the conditions. After adjusting for multiple comparisons, two proteins remained significantly differentially abundant between the two groups (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). C4A (complement factor 4A) was significantly decreased in hepatic steatosis, whereas afamin (AFM) was significantly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe performed GSEA (GO Biological Processes) to gain functional insights into protein cargoes carried by EVs from participants with hepatic steatosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Significant and positively enriched gene sets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were generally involved in lipid biology (i.e., triglyceride metabolic process, neutral lipid metabolic process, regulation of lipid biosynthesis process) and inflammation and immunity (e.g., regulation of innate immune response, regulation of humoral immune response, positive regulation of defense response, and positive regulation of response to external stimulus).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSteatotic protein signatures from serum EVs differ between Black and non-Hispanic White participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven our previous observation of a higher prevalence of hepatic steatosis among non-Hispanic White participants compared to Black participants in this cohort (39), we further examined the effect of steatosis by race/ethnicity. In White participants (n\u0026thinsp;=\u0026thinsp;103), differential abundance analysis identified 52 DAPs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) after adjusting for T2D status; however, none remained statistically significant following multiple testing correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In Black women (n\u0026thinsp;=\u0026thinsp;172), 34 DAPs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were detected; three of which, AFM, C4A, and APOA1, remained significantly different following multiple testing correction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Although AFM levels were not significantly altered in White participants, a trend toward higher abundance was observed (p\u0026thinsp;=\u0026thinsp;0.138). PLS-DA showed clear distinction between the control and the hepatic steatosis group in both comparisons (95% CI; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Hotelling\u0026rsquo;s T\u003csup\u003e2\u003c/sup\u003e testing showed significant separation between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGSEA revealed additional distinctions between serum EV proteins from White and Black participants with hepatic steatosis. In White participants, positively enriched processes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were broadly involved in innate and humoral responses, such as acute phase response, positive regulation of immune system process, complement activation pathway, and acute inflammatory response, while negatively enriched processes included supramolecular fiber organization, cell projection organization, and similar cytoskeleton organization processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In Black participants, processes related to protein containing complex organization, small molecule metabolic process, lipid localization, and regulation of transport were negatively enriched \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePatients with severe steatosis exhibit signatures associated with lipid transport dysregulation, extracellular matrix remodeling, and inflammation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe explored protein signatures associated with severe steatosis by comparing participants with severe hepatic steatosis (n\u0026thinsp;=\u0026thinsp;43) and without hepatic steatosis (n\u0026thinsp;=\u0026thinsp;156). Differential abundance analysis identified seven proteins significantly altered in severe hepatic steatosis after multiple testing correction, alongside an additional 63 proteins with a nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Of the significantly altered proteins (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05), COL18A1, AFM, APOA1, INHBE, and PRG4 isoform C and F were increased, while C4A and APOA1 were decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). PLS-DA and Hotelling\u0026rsquo;s T\u003csup\u003e2\u003c/sup\u003e testing of significant proteins (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified in more than 50% of samples shows significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) separation between the two groups (95% CI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These changes suggest a combined signature of enhanced extracellular matrix remodeling (COL18A1), lipid transport dysregulation (AFM), and impaired complement-mediated immunity (C4A), highlighting distinct molecular pathways linked to severe hepatic steatosis. GSEA of biological processes showed significant positive enrichment for inflammation-related pathways, including regulation of humoral immune response, positive regulation of response to external stimulus, and complement activation via the alternative pathway. Lipid dysbiosis, specifically regulation of lipid biosynthetic process, was also positively enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInvestigation of EV signatures associated with hepatic steatosis across subgroups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify EV protein signatures potentially unique to each subgroup, we compared proteins that were significantly more abundant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across cohort-level analyses, including the overall cohort, White participants, Black participants, and those with severe hepatic steatosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). INHBE emerged as the only protein consistently elevated in hepatic steatosis across all subgroup analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Whole Up, White Up, Black Up, Severe Up). In the overall cohort (n\u0026thinsp;=\u0026thinsp;275), three proteins\u0026mdash;APOC2, APOA4, and IGLC6\u0026mdash;were uniquely enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Whole Up). Among White participants (n\u0026thinsp;=\u0026thinsp;103), 11 proteins showed subgroup-specific enrichment: IGHA1, C1QB, C4BPA, C4BPB, LCAT, PROS1, MMRN1, SERPINA10, CFI, ANK1, and LBP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: White Up). In Black participants (n\u0026thinsp;=\u0026thinsp;172), seven proteins were uniquely enriched: IGHG1, IGHG2, IGHV2-5, CFHR2, LUM, IGLV3-21, and ANTXR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Black Up). In the severe hepatic steatosis subgroup (n\u0026thinsp;=\u0026thinsp;43), proteins with subgroup-specific enrichment included IGKV1D-13; IGKV1-13, ICAM1, C2, C7, APOC4, and ADGRF5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Severe Up).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore these candidates further, we examined the expression of the encoding genes in two cohorts, one representing our previously published RNA-seq dataset (NCBI Bioproject Accession PRJNA512027; n\u0026thinsp;=\u0026thinsp;192) (46) and the other a meta-analysis of hepatic gene expression from liver biopsy samples, integrating ten RNA-sequencing and microarray datasets, and also including PRJNA512027 (1,058 samples) (47). \u003cem\u003eINHBE\u003c/em\u003e was consistently overexpressed across all three meta-analyses: hepatic steatosis vs. controls, MASH vs. controls, and MASH vs steatosis. \u003cem\u003eAFM\u003c/em\u003e also showed increased expression in each comparison, but statistical significance was only observed in the MASH vs. steatosis analysis (Z\u0026thinsp;=\u0026thinsp;2.492; FDR\u0026thinsp;=\u0026thinsp;0.0369). In contrast, \u003cem\u003eC4A\u003c/em\u003e was underexpressed in all the three comparisons, though none reached statistical significance (lowest FDR\u0026thinsp;=\u0026thinsp;0.145 in MASH vs. controls; \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). Finally, in the PRJNA512027 dataset, APOA1 and COL18A1 were significantly underexpressed in MASH relative to both controls and steatotic samples, but this pattern was not supported by the meta-analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e Current clinical guidelines often overlook sex- and age-specific differences in metabolic health, revealing a gap in targeted approaches to reduce the burden of MASLD, particularly in postmenopausal women. In this study, we profiled the circulating EV proteome in a diverse cohort of postmenopausal women and identified proteomic signatures associated with hepatic steatosis. Our findings suggest that EV cargo reflects molecular alterations relevant to liver fat accumulation in this population, supporting their potential as biomarkers for both hepatic steatosis and metabolic shifts that may precede overt liver dysfunction. Notably, we observed distinct proteomic patterns not only by steatosis status but also by race/ethnicity, indicating that EVs may capture both common and ancestry-specific disease mechanisms. This observation raises the possibility that circulating EVs integrate environmental, genetic, and metabolic influences relevant to MASLD development.\u003c/p\u003e\u003cp\u003eTwo EV proteins, C4A and AFM, were significantly altered between women with and without hepatic steatosis. C4A was reduced, while AFM was elevated, suggesting roles in dysregulated complement activation and lipid transport, respectively, both central to MASLD pathogenesis. Elevated AFM, a vitamin E\u0026ndash;binding hepatokine, has been linked to metabolic syndrome and hepatic inflammation (48\u0026ndash;50). Reduced C4A, part of the classical complement cascade, may reflect impaired immune surveillance or increased complement turnover, consistent with hepatic inflammation (28, 51, 52).\u003c/p\u003e\u003cp\u003eSubgroup analyses further revealed distinct EV proteomic signatures by race. In White women, proteins involved in coagulation, complement regulation, and lipid transport, including complement C4 binding protein alpha (C4BPA), lecithin-cholesterol acyltransferase (LCAT), protein S (PROS1), complement C4 binding protein beta (C4BPB), and multimerin 1 (MMRN1), were elevated in hepatic steatosis. These proteins implicate dysregulation of innate immunity and lipoprotein remodeling and warrant further study to clarify their specific contributions. In contrast, Black women exhibited enrichment of immunoglobulin-related proteins (e.g., IGHG1, IGHG2, IGHV2-5, IGLV3-21), suggesting differential activation of humoral immune response. Additional subgroup-specific proteins such as complement factor H related 2 (CFHR2), thrombospondin 4 (THBS4), lumican (LUM), and anthrax toxin receptor 1 (ANTXR1) suggest potential ancestry-associated differences in inflammatory and matrix remodeling pathways (53\u0026ndash;55). However, these results require cautious interpretation. Though a useful proxy, self-reported race/ethnicity cannot fully capture genetic ancestry or account for its continuous variation and substructure across population groups (56).\u003c/p\u003e\u003cp\u003eIn participants with severe hepatic steatosis, EV protein signatures were enriched for markers of inflammation, complement activation, and dysregulated lipid metabolism. Among these, INHBE, COL18A1, AFM, and PRG4 were significantly elevated, while C4A and APOA1 were reduced. Enrichment of pathways involved in humoral immune responses and lipid biosynthesis further supports the hypothesis that these EV changes reflect worsening metabolic and immune dysregulation in advanced steatosis. These findings suggest that EV profiling may not only detect early disease but also stratify severity and capture pathophysiologic processes driving progression toward MASH.\u003c/p\u003e\u003cp\u003eTo determine whether EV protein cargo changes reflect underlying hepatic gene expression, we analyzed transcript levels of the corresponding genes in two liver transcriptomic datasets comprising over 1,000 samples. INHBE was significantly overexpressed in both hepatic steatosis and MASH compared to controls, as well as in MASH compared to steatosis. Additionally, AFM was significantly upregulated in MASH compared to hepatic steatosis, with similar but nonsignificant trends in both MASH and hepatic steatosis relative to controls. C4A expression was reduced in MASH compared to both control and steatotic livers. APOA1 and COL18A1 were significantly underexpressed in the PRJNA512027 dataset in MASH compared to both controls and steatosis; however, these differences were not confirmed in the meta-analysis.\u003c/p\u003e\u003cp\u003eThese findings suggest that although EV protein abundance does not consistently parallel hepatic gene expression, some convergence emerges in advanced disease. This partial overlap may reflect posttranscriptional regulation, differences in protein turnover or secretion, or contributions from non-parenchymal cell types to the EV proteome. Thus, EVs may offer complementary insights into disease biology, particularly when transcriptomic alterations are subtle or heterogeneous.\u003c/p\u003e\u003cp\u003eAlthough classical inhibins A and B decline with menopause due to loss of ovarian function (57), INHBE encodes a distinct β-subunit of activin E, a hepatokine increasingly recognized for its role in metabolic regulation. Recent studies have shown that \u003cem\u003eINHBE\u003c/em\u003e expression is induced by hepatic steatosis and insulin resistance (58\u0026ndash;60), consistent with our finding of elevated EV-derived INHBE across all subgroups. These data suggest that INHBE upregulation in hepatic steatosis may represent a liver-derived compensatory response to metabolic stress rather than a reflection of altered gonadal hormone signaling. In this context, INHBE may serve as a relevant biomarker of liver\u0026ndash;endocrine crosstalk in postmenopausal women with MASLD.\u003c/p\u003e\u003cp\u003eDespite the strengths of this study, including the use of a well-characterized, community-based cohort and a focus on a high-risk population, several limitations should be acknowledged. Hepatic steatosis was assessed via ultrasound, an imaging technique with limited sensitivity for mild fat accumulation and lacking quantitative resolution. Future studies incorporating MRI-proton density fat fraction (MRI-PDFF) or liver biopsy would allow more accurate phenotyping. Additionally, the relatively small number of woman with steatosis (n\u0026thinsp;=\u0026thinsp;75) may have limited power to detect subtle differences; however, our sample size exceeds those of many prior proteomic studies in MASLD (61, 62). Generalizability may also be limited due to the single-site design and its focus exclusively on Black and non-Hispanic White women. Finally, the cross-sectional design precludes inferences about the temporal relationship between EV cargo and progression/reversal of steatosis. Even with these limitations, our findings contribute to a growing body of evidence highlighting potential biomarkers and mechanistic pathways underlying hepatic steatosis in midlife women.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that circulating EV proteomes reflect hepatic steatosis status in postmenopausal women and reveal both shared and ancestry-specific molecular signatures. These findings suggest that EVs may capture early and systemic metabolic perturbations relevant to MASLD pathogenesis, even in the absence of overt hepatic transcriptomic changes. As minimally invasive biomarkers, EVs hold promise for identifying individuals at risk for MASLD progression and uncovering novel pathways for intervention. Future research should prioritize longitudinal studies to determine whether EV profiles can predict disease trajectory and integrate multi-omics data, including genomics and metabolomics, to elucidate the full spectrum of MASLD pathophysiology across diverse populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAfamin (AFM); Anthrax toxin receptor 1 (ANTXR1); Analysis of covariance (ANCOVA); Ankyrin 1 (ANK1); Apolipoprotein A1 (APOA1); Apolipoprotein A4 (APOA4); Apolipoprotein C2 (APOC2); Apolipoprotein C4 (APOC4); Body mass index (BMI); Complement component 2 (C2); Complement component 4A (C4A); Complement component 4 binding protein alpha (C4BPA); Complement component 4 binding protein beta (C4BPB); Complement component 7 (C7); Complement factor H-related protein 2 (CFHR2); Complement factor I (CFI); Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Differentially abundant proteins (DAPs); Diabetes mellitus (T2D); Extracellular vesicles (EVs); False discovery rate (FDR); Gene Ontology Biological Processes (GOBP); Gene Set Enrichment Analysis (GSEA); Hemoglobin A1c (HbA1c); High-performance liquid chromatography (HPLC); Immunoglobulin A1 (IGHA1); Immunoglobulin heavy constant gamma 1 (IGHG1); Immunoglobulin heavy constant gamma 2 (IGHG2); Immunoglobulin heavy variable 2-5 (IGHV2-5); Immunoglobulin kappa variable 1-13 (IGKV1-13); Immunoglobulin kappa variable 1D-13 (IGKV1D-13); Immunoglobulin lambda constant 6 (IGLC6); Immunoglobulin lambda variable 3-21 (IGLV3-21); Inhibin beta E (INHBE); Institutional Review Board (IRB); Integrated retention time (iRT); Intercellular adhesion molecule 1 (ICAM1); Lecithin-cholesterol acyltransferase (LCAT); Lipopolysaccharide binding protein (LBP); Liquid chromatography (LC); Liquid chromatography\u0026ndash;mass spectrometry (LC-MS); Lumican (LUM); Mass spectrometry (MS); Metabolic dysfunction-associated steatohepatitis (MASH); Metabolic dysfunction-associated steatotic liver disease (MASLD); Michigan site of the Study of Women\u0026rsquo;s Health Across the Nation (MI-SWAN); Multimerin 1 (MMRN1); Partial least squares discriminant analysis (PLS-DA); Parallel accumulation serial fragmentation (PASEF); Phosphate buffered saline (PBS); Principal component (PC); Principal component analysis (PCA); Protein S (PROS1); Proteome Spectral Matches (PSMs); Serpin family A member 10 (SERPINA10); Size exclusion chromatography (SEC); Study of Women\u0026rsquo;s Health Across the Nation (SWAN); Type 2 diabetes (T2D); Triglyceride (TG); Variance stabilization normalization (VSN)\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll participants provided informed written consent, and study procedures were approved by the Health and Behavioral Sciences Institutional Review Board, University of Michigan.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (63) partner repository. SWAN data are archived at https://www.icpsr.umich.edu/web/ICPSR/series/00253 and the Aging Research Biobank https://agingresearchbiobank.nia.nih.gov/studies/swan/?search_term=SWAN. The Michigan site- specific liver datasets used and/or analyzed during the current study are available from author Carrie Karvonen-Gutierrez ([email protected]) on reasonable request.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eJKD is a member of the BMC Medicine Editorial Board.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eP30CA33572, U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720, R01127015\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePP: supervision, methodology, writing\u0026mdash;original draft preparation. BL: formal analysis, investigation, writing\u0026mdash;original draft preparation, visualization. SDH: conceptualization, data and specimen collection, project administration, methodology, writing\u0026mdash;original draft preparation, funding acquisition. CKM: data and specimen collection, methodology, manuscript editing. MMH: data curation, manuscript editing. ISP: formal analysis, editing of manuscript. XW: investigation. MNM: data curation, investigation. RS: formal analysis, investigation. KGM: formal analysis. MW: formal analysis, investigation. JKD: conceptualization, writing\u0026mdash;original draft preparation, visualization, supervision, project administration, funding acquisition. All authors contributed significantly to this manuscript and read and agreed to the submitted version of this manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis research was supported by the NIDDK (R01DK127015; JKD). Research reported in this publication included work performed in the Integrated Mass Spectrometry Shared Resource supported by the National Cancer Institute of the National Institutes of Health under award number P30CA33572. The Study of Women\u0026apos;s Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women\u0026rsquo;s Health (ORWH) (Grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495, and U19AG063720). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH or the National Institutes of Health. \u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSWAN Clinical Centers:\u003c/u\u003e University of Michigan, Ann Arbor \u0026ndash; Carrie Karvonen-Gutierrez, PI 2021 \u0026ndash; present, Siob\u0026aacute;n Harlow, PI 2011 \u0026ndash; 2021, MaryFran Sowers, PI 1994-2011; Massachusetts General Hospital, Boston, MA \u0026ndash; Sherri‐Ann Burnett‐Bowie, PI 2020 \u0026ndash; Present; Joel Finkelstein, PI 1999 \u0026ndash; 2020; Robert Neer, PI 1994 \u0026ndash; 1999; Rush University, Rush University Medical Center, Chicago, IL \u0026ndash; Imke Janssen, PI 2020 \u0026ndash; Present; Howard Kravitz, PI 2009 \u0026ndash; 2020; Lynda Powell, PI 1994 \u0026ndash; 2009; University of California, Davis/Kaiser \u0026ndash; Elaine Waetjen and Monique Hedderson, PIs 2020 \u0026ndash; Present; Ellen Gold, PI 1994 - 2020; University of California, Los Angeles \u0026ndash; Arun Karlamangla, PI 2020 \u0026ndash; Present; Gail Greendale, PI 1994 - 2020; Albert Einstein College of Medicine, Bronx, NY \u0026ndash; Carol Derby, PI 2011 \u0026ndash; present, Rachel Wildman, PI 2010 \u0026ndash; 2011; Nanette Santoro, PI 2004 \u0026ndash; 2010; University of Medicine and Dentistry \u0026ndash; New Jersey Medical School, Newark \u0026ndash; Gerson Weiss, PI 1994 \u0026ndash; 2004; and the University of Pittsburgh, Pittsburgh, PA \u0026ndash; Rebecca Thurston, PI 2020 \u0026ndash; Present; Karen Matthews, PI 1994 - 2020. \u003cu\u003eNIH Program Office:\u003c/u\u003e National Institute on Aging, Bethesda, MD \u0026ndash; Rosaly Correa-de-Araujo 2020 - present; Chhanda Dutta 2016- present; Winifred Rossi 2012\u0026ndash;2016; Sherry Sherman 1994 \u0026ndash; 2012; Marcia Ory 1994 \u0026ndash; 2001; National Institute of Nursing Research, Bethesda, MD \u0026ndash; Program Officers. \u003cu\u003eCentral Laboratory:\u003c/u\u003e University of Michigan, Ann Arbor \u0026ndash; Daniel McConnell (Central Ligand Assay Satellite Services). \u003cu\u003eCoordinating Center:\u003c/u\u003e University of Pittsburgh, Pittsburgh, PA \u0026ndash; Maria Mori Brooks, PI 2012 - present; Kim Sutton-Tyrrell, PI 2001 \u0026ndash; 2012; New England Research Institutes, Watertown, MA - Sonja McKinlay, PI 1995 \u0026ndash; 2001.\u003c/p\u003e\n\u003cp\u003eSteering Committee: Susan Johnson, Current Chair; Chris Gallagher, Former Chair \u003c/p\u003e\n\u003cp\u003eWe thank the study staff at each site and all the women who participated in SWAN.\u003c/p\u003e\n\n\n\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHill K. 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The genetic ancestry of African Americans, Latinos, and European Americans across the United States. Am J Hum Genet. 2015;96(1):37-53.\u003c/li\u003e\n\u003cli\u003eAla-Fossi SL, Maenpaa J, Blauer M, Aine R, Tuohimaa P, Punnonen R. Inhibin A and B in peri- and postmenopause. Maturitas. 1998;30(3):273-81.\u003c/li\u003e\n\u003cli\u003eSugiyama M, Kikuchi A, Misu H, Igawa H, Ashihara M, Kushima Y, et al. Inhibin betaE (INHBE) is a possible insulin resistance-associated hepatokine identified by comprehensive gene expression analysis in human liver biopsy samples. PLoS One. 2018;13(3):e0194798.\u003c/li\u003e\n\u003cli\u003eGriffin JD, Buxton JM, Culver JA, Barnes R, Jordan EA, White AR, et al. Hepatic Activin E mediates liver-adipose inter-organ communication, suppressing adipose lipolysis in response to elevated serum fatty acids. Mol Metab. 2023;78:101830.\u003c/li\u003e\n\u003cli\u003eJensen-Cody SO, Potthoff MJ. Hepatokines and metabolism: Deciphering communication from the liver. Mol Metab. 2021;44:101138.\u003c/li\u003e\n\u003cli\u003eNiu L, Geyer PE, Wewer Albrechtsen NJ, Gluud LL, Santos A, Doll S, et al. Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease. Mol Syst Biol. 2019;15(3):e8793.\u003c/li\u003e\n\u003cli\u003eSourianarayanane A, Salemi MR, Phinney BS, McCullough AJ. Liver Tissue Proteins Improve the Accuracy of Plasma Proteins as Biomarkers in Diagnosing Metabolic Dysfunction-Associated Steatohepatitis. Proteomics Clin Appl. 2024;18(6):e202300236.\u003c/li\u003e\n\u003cli\u003ePerez-Riverol Y, Bai J, Bandla C, Garcia-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543-D52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Liver, hepatic steatosis, proteomics, extracellular vesicles","lastPublishedDoi":"10.21203/rs.3.rs-7293767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7293767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is common among midlife women. Circulating extracellular vesicles (EVs) carry bioactive cargo that may mediate or reflect disease processes, but their role in hepatic steatosis in postmenopausal women remains unexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted Liquid Chromatography Data-Independent Acquisition–Mass Spectrometry on serum-derived EVs from 275 postmenopausal women enrolled in the Michigan site of the Study of Women’s Health Across the Nation (MI-SWAN). Participants were grouped by hepatic steatosis status (n = 75), assessed via standardized ultrasound at the 2010 follow-up visit. Fasting serum samples were processed using size exclusion chromatography to isolate EVs. Differential EV protein abundance was evaluated by ANCOVA, adjusting for ethnicity and diabetes status, and applying Benjamini-Hochberg correction. Gene Set Enrichment Analysis (GSEA) was performed to identify enriched biological pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 469 detected EV proteins, 60 differed by hepatic steatosis status (p \u0026lt; 0.05), with two proteins remaining significant after multiple testing correction: complement C4A (C4A) and afamin (AFM). GSEA indicated enrichment in lipid metabolism and innate immune activation pathways. Subgroup analyses revealed racial and disease severity-specific differences in EV protein profiles. In Black women (n = 172), AFM, C4A, and APOA1 were significantly elevated, while in White participants (n = 103), no proteins reached significance, although AFM displayed a nonsignificant trend toward higher abundance. In participants with severe hepatic steatosis (n = 43), subgroup analysis showed increased COL18A1, AFM, PRG4, and INHBE and decreased C4A and APOA1. INHBE was the only protein consistently elevated across all three subgroups, whereas others showed subgroup-specific enrichment, such as immunoglobulins in Black women and complement or coagulation proteins in White participants and those with severe steatosis. Analysis of hepatic transcriptomic datasets demonstrated consistently higher \u003cem\u003eINHBE\u003c/em\u003e expression across the MASLD spectrum, including metabolic dysfunction-associated steatohepatitis (MASH), while \u003cem\u003eAFM\u003c/em\u003e expression was significantly higher in the MASH vs. steatosis comparison.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum EVs carry protein signatures reflective of hepatic steatosis and its severity in postmenopausal women. EV profiling may offer insights into mechanisms of disease progression and serve as potential biomarkers for risk stratification in midlife women.\u003c/p\u003e","manuscriptTitle":"Hepatic steatosis in postmenopausal women is characterized by distinct serum extracellular vesicle proteomic signatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:30:29","doi":"10.21203/rs.3.rs-7293767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T08:23:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-30T15:54:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-24T11:21:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159648234608125468131732007798395482421","date":"2025-08-23T09:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43656921299826239974001556973949890135","date":"2025-08-15T12:42:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T07:06:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T05:54:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T05:41:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2025-08-04T17:58:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a575e9a-c4fe-468f-ba00-dbbf46db2301","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:04:50+00:00","versionOfRecord":{"articleIdentity":"rs-7293767","link":"https://doi.org/10.1186/s12916-025-04571-4","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2025-12-07 15:57:30","publishedOnDateReadable":"December 7th, 2025"},"versionCreatedAt":"2025-08-17 14:30:29","video":"","vorDoi":"10.1186/s12916-025-04571-4","vorDoiUrl":"https://doi.org/10.1186/s12916-025-04571-4","workflowStages":[]},"version":"v1","identity":"rs-7293767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7293767","identity":"rs-7293767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}