Retinol-Binding Protein-4 Predicts Visceral Adiposity and Related Inflammatory–Cardiometabolic Profiles in Women

Retinol-Binding Protein-4 Predicts Visceral Adiposity and Related Inflammatory–Cardiometabolic Profiles in Women | 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 Retinol-Binding Protein-4 Predicts Visceral Adiposity and Related Inflammatory–Cardiometabolic Profiles in Women Muna Al-Maqbali, Khamis Al Hashmi, Khalid Al-Rasadi, Maha Alriyami, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8310783/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Visceral adiposity is a key contributor to cardiometabolic risk through links to insulin resistance, inflammation, and atherogenic dyslipidemia. In women, this is especially relevant during menopausal transition, when hormonal shifts increase visceral fat and cardiometabolic vulnerability. Yet factors predisposing to visceral adiposity remain unclear, emphasizing the need to identify its determinants as targets to modulate visceral fat and associated metabolic risk. We studied 410 healthy women (290 premenopausal, 120 postmenopausal) under controlled hormonal conditions. Serum measures included fat-storage–related hormones/proteins, cardiovascular risk (atherogenic lipids, inflammatory and oxidation) parameters. Regional fat predictors were identified by regression analyses. Mediation models examined whether the predictor’s association with visceral fat corresponded to downstream cardiometabolic outcomes. Higher visceral fat (+ 50.7%), elevated retinol-binding-protein-4 (RBP4) (+ 23.6%), and lower growth hormone (GH) (− 63.8%) levels were found in postmenopausal compared to premenopausal women (p < 0.001). RBP4 was the main positive predictor of visceral adiposity (6.5% variance%, p < 0.001), surpassing insulin, while GH was the strongest negative predictor (− 13%). Mediation models showed that RBP4 associated with metabolic risk profiles through both visceral fat–related and fat-independent pathways. The strongest associations were observed for the inflammatory profile. RBP4 demonstrated visceral-fat–related associations with (hsCRP β = 0.0617; IL-6 β = 0.036, p < 0.001). Conversely, a fat-independent pathway showed inverse associations with these markers (hsCRP β= −0.0678; IL-6 β= −0.0650, p < 0.05), suggesting a weaker anti-inflammatory profile, and predominance of the visceral fat-associated pro-inflammatory pathway. Furthermore, RBP4 demonstrated visceral-fat–mediated associations with LDL-C, triglycerides, and ApoB (β = 0.0136, 0.0063, 0.0037; p < 0.001), alongside fat-independent associations (β 0.0279, 0.0129, 0.0082; p < 0.001) suggesting involvement of both visceral-fat–related and direct pathways. The association with insulin resistance was primarily through visceral fat (HOMA-IR β = 0.0172; p < 0.001), while the association with the oxidative parameter, homocysteine, was exclusively independent (β = 0.033; p = 0.029). Overall, RBP4 emerges as a key predictor of visceral adiposity, with both visceral-fat–related and fat-independent associations linked to cardiometabolic risk. These findings highlight RBP4 as a potential contributor to visceral fat–related vulnerability in women, warranting further investigations in longitudinal and male cohorts. Visceral Adiposity Retinol-Binding Protein 4 Growth Hormone Mediation analysis Cardiometabolic risk Atherogenesis high sensitivity C-reactive Protein Interleukin-6 Homocysteine Figures Figure 1 Introduction Visceral adiposity is increasingly recognized as the most metabolically detrimental fat depot due to its strong associations with insulin resistance, systemic inflammation, dyslipidemia, and elevated cardiometabolic disease risk ( 1 – 3 ). Several mechanisms underlie this adverse phenotype. Visceral adipocytes display heightened lipolytic activity, increasing free fatty acid (FFA) delivery to the portal circulation and disrupting hepatic lipid metabolism ( 2 , 4 , 5 ). In addition, visceral fat secretes pro-inflammatory adipokines, such as interleukin-6 (IL-6), contributing to chronic low-grade inflammation ( 6 , 7 ), Visceral fat accumulation is also linked to impaired expandability of subcutaneous adipose tissue, promoting ectopic fat deposition in metabolic organs including the liver and skeletal muscle ( 2 , 8 , 9 ) This association becomes particularly relevant in women during menopausal transition, a period characterized by a well-documented shift in fat distribution toward greater and persistent visceral accumulation (Farahmand et al., 2022; Greendale et al., 2021; Marlatt et al., 2022). Menopause also coincides with a sharp rise in cardiometabolic vulnerability, highlighting the need to better understand the biological processes that drive these changes ( 13 – 16 ). Although alterations in adipose tissue distribution during menopause are largely attributed to declining estrogen and female hormonal changes ( 17 – 19 ), the specific mechanisms driving fat distribution patterns remain incompletely defined ( 20 ). Available evidence suggests that sex steroids influence fat distribution primarily through indirect modulation of other hormonal and metabolic pathways. For example, estrogen protects against visceral fat accumulation by modifying lipoprotein lipase (LPL) activity, enhancing subcutaneous storage, and downregulating glucocorticoid activity in visceral depots. Estrogen deficiency after menopause promotes central fat accumulation, while hormone therapy reverses these changes ( 17 – 19 ). Progesterone enhances lipogenesis, but also antagonizes glucocorticoid receptor signaling in adipose tissue ( 21 ). Androgens, particularly in estrogen-deficient states, are linked with visceral adiposity through stimulation of lipogenic pathways and impairment of AMPK signaling ( 22 ). Collectively, sex steroids modulate several interacting pathways that shape adipose distribution, but their specific roles in depot-specific regulation, particularly regarding visceral fat accumulation and persistence, is not fully understood. As for systemic metabolic regulators, insulin acts as the principal lipogenic hormone, promoting triglyceride synthesis and storage by stimulating glucose uptake and lipoprotein lipase (LPL) activity, while concurrently suppressing hormone-sensitive lipase (HSL)–mediated lipolysis ( 23 ). Recent findings showed that these effects are depot-specific, maintaining subcutaneous fat insulin sensitivity ( 24 ). However, gender-specific effects is inconsistent and poorly defined. The Acylation-stimulating protein (ASP) is another lipogenic factor, identified as an adipokine, that has shown preliminary indications of regional effects. Acting independently and additively with insulin, ASP stimulates diacylglycerol acyltransferase (DGAT) and inhibits hormone-sensitive lipase (HSL). Early studies suggested greater responsiveness in subcutaneous compared to visceral fat, higher circulating levels in women than men, and fluctuations across the menstrual cycle with progesterone, pointing to a possible female-specific lipogenic role favoring subcutaneous storage ( 25 , 26 ). Although these studies represent important advances in understanding ASP biology, further in vivo investigations in humans are essential to expand upon these findings in the context of gender-and depot-specific fat distribution. Cortisol provides the most consistent evidence of depot-specific action. It enhances adipocyte differentiation and lipid accumulation preferentially in visceral depots and is strongly implicated in stress-related abdominal obesity and endocrine disorders such as Cushing’s syndrome. Despite its well-established role in promoting visceral adiposity, the role of cortisol as a gender-specific mediator of fat distribution has not been demonstrated( 27 ). Other regulators, including thyroid hormones, and the human growth hormone (HGH), exert central roles in adipose metabolism. Thyroid hormones regulate basal metabolic rate and adipose metabolism, whereas GH promotes lipolysis ( 28 , 29 ). Both decline with age and menopause, but without definitive evidence of gender or depot-specific actions ( 30 , 31 ). An array of specific molecular targets has been implicated in the regulation of adipogenesis. Key transcription factors, including CCAAT/enhancer-binding protein alpha (C/EBPα) and peroxisome proliferator–activated receptor gamma (PPARγ), together with downstream effectors such as fatty acid–binding protein 4 (FABP4), adiponectin, and fatty acid synthase (FAS), play essential roles in the differentiation and maturation of adipocytes ( 32 ). Retinol binding protein-4 (RBP4), a ~ 21-kDa protein, was first identified as an adipokine in 2005, when Yang et al. (2005) demonstrated that adipocyte-derived RBP4 is associated with systemic insulin resistance in both people with obesity and type 2 diabetes, suggesting a mechanistic link between RBP4 and adipose tissue dysfunction and impaired glucose metabolism ( 33 ). Adipose tissue, second only to the liver, expresses RBP4, particularly in visceral fat depots ( 34 , 35 ). Functionally, RBP4 transports retinol and forms a complex with transthyretin (TTR) to prevent renal clearance. Since then, more clinical and epidemiological studies have associated elevated circulating RBP4 with obesity, type 2 diabetes, metabolic syndrome, and cardiovascular abnormalities and was implicated in vascular inflammation, endothelial dysfunction, and cardiac remodeling ( 36 ). Beyond its metabolic effects, it is now recognized as a key driver of adipose tissue inflammation, emphasizing an integrative role in the convergence of endocrine and immune dysfunction ( 37 , 38 ). Notably, visceral fat RBP4 expression is consistently higher than in subcutaneous adipose tissue in people with obesity or diabetes ( 34 , 35 , 39 , 40 ). Moreover, genetic variations in or near the RBP4 locus have been linked to insulin resistance and related traits such as BMI, waist-to-hip ratio, insulin, and free fatty acid levels ( 38 ). Thus, RBP4 may represent a mechanistic link between visceral adiposity and metabolic dysfunction. Despite strong evidence, findings remain inconsistent. Some studies report weak or no correlation between circulating RBP4 and insulin resistance or obesity ( 41 , 42 ). Despite extensive associations of RBP4 with adiposity, insulin resistance, and inflammation, to our knowledge no direct evidence demonstrates that RBP4 promotes lipid storage or, and its role in regional adiposity is not yet established. A major limitation in studies involving women is the inherent biological variability introduced by hormonal fluctuations across the menstrual cycle and the menopausal transition, which can profoundly influence metabolic and cardiovascular parameters. This variability has contributed to inconsistent findings and limited reproducibility across studies. To overcome this methodological constraint, the present study implemented a rigorously standardized sampling design, with blood collection restricted to the early follicular phase (days 1–3), when circulating sex hormone levels are at their nadir in premenopausal women. This approach uniquely addresses one of the most persistent challenges in women-focused metabolic research, substantially minimizing hormonal variability, enhancing comparability across premenopausal and postmenopausal groups, and strengthening the validity of sex-specific metabolic assessments. The main aim of this study was to investigate determinants of regional adiposity and related cardiometabolic risk while minimizing the confounding effects of hormonal variability. Specifically, our objective was to identify predictors of adiposity distribution in women, including established fat-storage regulators, with particular focus on determinants of visceral adiposity, and to assess whether the positive predictors were associated with cardiometabolic risk across inflammatory, lipid, glycemic, insulin-resistance, and oxidative-stress pathways. Materials and Methods Study Design and Population This prospective cross-sectional study was approved by the Sultan Qaboos University Ethics Committee (SQU-EC/164/14, MREC #1019). A total of 410 apparently healthy women Omani women aged 18–65 years. Participants were apparently healthy women with no known comorbidities that could affect metabolic profiles. Recruitment was conducted through community engagement at women's society events, schools (including teaching and administrative staff), university student housing, university events, hospital visitation areas, and among university personnel and support staff and others. Exclusion criteria were as follows: age above 65 years, pregnancy, active smoking, and consumption of alcoholic beverages, usage of lipid- or cholesterol lowering drugs, corticosteroids, oral contraceptives, hormone replacement therapy, insulin or any vitamin or hormonal supplements, the presence of hemoglobinopathies, infectious or inflammatory disorders. All study participants provided written informed consent. Participants completed a structured questionnaire covering menstrual history, reproductive status and medical background. Classification Criteria: Women were stratified based on age, reproductive status and follicle-stimulating hormone (FSH) levels. Reproductive groups were defined by the presence of menstruation and FSH 12 months and FSH ≥ 25 IU/L. This classification ensured accurate hormonal profiling and minimized variability related to transitional menopausal phases. The samples were stratified as follows: Reproductive Group: Aged 18–51 years Regular menstrual cycles (21–35 days) FSH range: 4.7 to 21.5 mIU/mL Menopausal Group: Age > 51 years Cessation of menstruation for ≥ 12 consecutive months FSH range: 25.8 to 134.8 mIU/mL Venous blood samples were collected during the first three days of the follicular phase for reproductive-age women to ensure the most possible hormonal comparability with menopausal participants. Samples were collected in a standardized morning window to reduce variability due to diurnal hormonal changes. Anthropometric measures included weight, height, waist circumference, body fat percentage, visceral fat, and skinfold thickness. Body composition was estimated using the Omron Full Body Sensor Body Composition Monitor and Scale. This bioelectrical impedance analysis (BIA) device measures fat mass, skeletal muscle mass, visceral fat, and BMI. It operates via low-frequency current and compensates for diurnal fluid shifts by using both hand and foot electrodes, providing clinically valid readings. The use of bioelectrical impedance analysis (BIA) for assessing body composition has been validated in numerous studies using dual-energy X-ray absorptiometry (DXA) as the reference standard. Such validation work supported its adoption in large-scale and longitudinal studies ( 43 – 47 ). Measurement of subcutaneous fat depots Subcutaneous fat was measured using a standard plastic skinfold caliper (graduated in millimeters; range 0–60 mm). The caliper was applied to a pinched skinfold after grasping the skin and underlying subcutaneous fat between the thumb and forefinger. Abdominal subcutaneous fat was assessed 2 cm lateral to the umbilicus, and arm subcutaneous fat was assessed at the midline of the triceps. Measurements were performed by trained medical personnel. Biochemical Analyses All biochemical parameters were quantified using commercial kits and automated analyzers. Retinol Binding Protein-4 (RBP4) levels were assessed using the Quantikine Human RBP4 ELISA kit (DRB400, R&D Systems, USA). Acylation stimulating Protein (ASP) concentrations were measured using a competitive ELISA kit (HA0889, Neo Scientific, USA). Routine biochemical parameters were measured in the Clinical Biochemistry lab at Sultan Qaboos University Hospital. Serum hormone concentrations of progesterone, estradiol, FSH, cortisol, and insulin and HGH in addition to IL-6 were quantified using the Cobas 6000 analyzer (e-601 module; Roche Diagnostics). Total plasma homocysteine was quantified by an enzymatic cycling assay with NADH detection at 340 nm, and high-sensitivity C-reactive protein (hs-CRP) was measured by immunoturbidimetry on a Roche Cobas 6000 analyzer (c-501 module; Roche Diagnostics). The Homeostasis Model Assessment (HOMA) was measured by the standard formula: Fasting Insulin (µU/mL)×Fasting Glucose (mmol/L) ÷ 22.5. Lipids including total cholesterol, triglycerides, LDL-C, HDL-C, and fasting glucose were measured with the Cobas 6000 c-501 module using spectrophotometric enzymatic assays. LDL-C was calculated using the Friedewald formula: LDL-C = TC – [(TG/2.2) + HDL-C]. HbA1c was quantified using the Cobas Integra 400 plus (Roche Diagnostics), which employs turbidimetric inhibition immunoassay. Glutathione (GSH) was measured by BioVision’s ApoGSH TM Glutathione colorimetric assay kit (Catalog #K261-100, Biovision). RBP4 data were complete for all participants ( n = 410), and anthropometric measures ( n = 407–410). Biochemical parameters had variable sample sizes across analyses due to missing values as a result of limited sample availability or assay exclusion, resulting in sample sizes ( n = 350–410), (GSH, n = 312). Analyses were conducted using all available data for each variable (pairwise deletion). Statistical Analysis Data were analyzed using SPSS version 30. Normality of distributions was assessed using the Kolmogorov–Smirnov test. Group differences were evaluated using independent-samples t-tests for normally distributed variables and Mann–Whitney U tests for non-normally distributed variables. Results are presented as mean ± standard deviation. Bivariate correlations between RBP4 and metabolic markers were examined using Pearson’s or Spearman’s correlation coefficients, as appropriate. To identify hormone predictors of fat distribution patterns, stepwise linear regression analyses were performed with visceral and subcutaneous fat (abdominal and arm), as independent variables in separate models. Major fat-regulating hormones were included as dependent variables. To account for potential deviations from parametric assumptions, bootstrapped regression analyses were also conducted. Statistical significance was defined as p < 0.05. Mediation analysis was conducted using the PROCESS macro for SPSS (version 5.0) developed by Hayes (2022), with 5,000 bias-corrected bootstrap resamples. An indirect effect was considered statistically significant if the 95% confidence interval (CI) did not include zero ( 48 ). Results Participant Characteristics A total of 410 apparently healthy women were included in this study (290 premenopausal and 120 postmenopausal). Table 1 presents the anthropometric parameters of the cohort. Postmenopausal women had significantly higher BMI, waist circumference, fat percentage, and visceral fat, but lower muscle percentage compared with premenopausal women (p < 0.05 for all). In relative terms, BMI was 8.9% higher, waist circumference 14.0% higher, and total fat percentage 9.7% higher in the postmenopausal group. Visceral fat showed the most pronounced difference, being 50.7% higher, while muscle percentage was 5.4% lower, indicating a major shift toward central adiposity and reduced lean mass. Subcutaneous fat distribution also differed, with abdominal skinfold thickness 11.2% higher, whereas peripheral skinfold arm thickness remained essentially unchanged (0.4%). As visceral adiposity represented the most pronounced difference between postmenopausal and premenopausal women (~ 50.7%), subsequent analyses were conducted to examine predictors of visceral fat accumulation and their link to downstream cardiometabolic effects, including inflammation, insulin resistance, glycemic load, lipids, and oxidative markers. Table 1 Anthropometric and Fat Distribution Patterns in Pre- and Postmenopausal Women Variable Premenopausal (Mean ± SD) Postmenopausal (Mean ± SD) p -value Age (years) 33.7 ± 10.5 56.3 ± 6.5 < 0.001 BMI (kg/m²) 27.0 ± 6.1 29.4 ± 5.6 < 0.01 Waist circumference (cm) 86.0 ± 18.1 98.0 ± 11.0 < 0.001 Fat percentage (%) 40.3 ± 9.3 44.2 ± 7.5 < 0.01 Muscle percentage (%) 24.1 ± 2.8 22.8 ± 3.5 < 0.01 Visceral fat (%) 6.7 ± 3.3 10.1 ± 3.1 < 0.001 Skinfold abdomen (mm) 32.2 ± 14.2 35.8 ± 12.1 0.010 Peripheral skinfold arm (mm) 23.3 ± 9.4 23.4 ± 8.2 0.90 Fat Storage Hormone Profiles As shown in Table 2, and consistent with physiological changes after menopause, gonadotropins increased markedly in postmenopausal women, with FSH higher by approximately fivefold and LH nearly threefold ( p < 0.001 for both). Sex steroids were significantly lower, with estradiol and progesterone each lower by ~70% ( p < 0.001), while testosterone was lower by 17.8% ( p < 0.001). In contrast, fat regulating hormones, including insulin, cortisol, ASP, and thyroid hormones, showed no significant differences between groups, apart from a modest increase in FT3 ( p = 0.013). Notably, circulating RBP4 was higher by 23.6% ( p < 0.001) in postmenopausal women, while HGH levels showed an opposing pattern, by a marked 63.8% ( p < 0.001). Table 2: Fat-regulating Hormones/Factors in Pre and Postmenopausal women Variable Premenopausal (Mean ± SD) Postmenopausal (Mean ± SD) p -value Reproductive hormones FSH ( IU/L ) 11.4 ± 17.4 65.6 ± 25.4 <0.001 LH ( IU/L ) 9.1 ± 10.6 35.0 ± 14.2 <0.001 Estradiol ( pg/ml ) 51.9 ± 95.1 15.8 ± 36.5 <0.001 Progesterone ( ng/ml ) 0.71 ± 1.09 0.22 ± 0.21 <0.001 Testosterone ( ng/ml ) 0.26 ± 0.16 0.21 ± 0.17 <0.001 Fat Storage Hormones Insulin ( µU/ml ) 10.8 ± 5.9 9.6 ± 4.7 0.125 Cortisol ( nmol/L ) 347.8 ± 152.2 320.4 ± 144.6 0.070 ASP ( nM ) 52.0 ± 13.5 54.6 ± 21.3 0.886 TSH ( µIU/ml ) 2.90 ± 5.55 3.21 ± 6.92 0.465 FT3 ( pmol/L ) 4.76 ± 0.50 4.88 ± 0.56 0.013 FT4 ( pmol/L ) 15.1 ± 2.1 15.0 ± 2.5 0.942 HGH ( ng/ml ) 1847.0 ± 2619.8 669.2 ± 948.4 <0.001 RBP4( mg/ml ) 31.7 ± 8.9 39.2 ± 8.9 <0.001 FSH: Follicle stimulation hormone, LH: Luteinizing hormone, ASP: Acylation Stimulating Protein, TSH: Thyroid stimulating Hormone, FT3, FT4, Thyroid hormones, 3 and 4, HGH: Human Growth Hormone. Correlations of Fat-regulating Factors with Fat Depots To assess the associations between fat depots and known fat-regulating factors, we examined bivariate correlations of visceral, abdominal subcutaneous, and arm subcutaneous fat with all hormonal and fat-regulating markers in the study, including RBP4, insulin, HGH, cortisol, ASP, and thyroid hormones (TSH, FT3, FT4). Interestingly, among the assessed markers, only RBP4, insulin, and HGH showed consistent and significant correlations with fat depots ( Table 3 ). Specifically, RBP4 and insulin correlated positively with visceral and subcutaneous fat depots, whereas HGH showed inverse associations across all fat depots. Other markers, including cortisol, ASP, and thyroid hormones, were not significant or did not show meaningful correlations. Table 3: Bivariate Correlations of Fat-regulating Markers with Fat depots: Variable Visceral fat (r, p ) Subcutaneous abdomen (r, p ) Peripheral Subcutaneous (r, p ) RBP4 ( mg/ml ) 0.311, < 0.001 0.170, < 0.001 0.131, 0.008 Insulin( µU/ml ) 0.285, < 0.001 0.358, < 0.001 0.409, < 0.001 HGH( ng/ml ) –0.429, < 0.001 – 0.361, < 0.001 0.251, < 0.001 r= regression coefficient Regression Analysis for Predictors of Regional Adiposity: Table 4 demonstrates regression analysis that was performed to identify predictors of different fat depots. Visceral adiposity was mainly predicted by RBP4, surpassing insulin (Variance explained 6.5%, p< 0.001)). HGH emerged as the dominant negative predictor (13%, P< 0.001)), with a smaller opposing contribution from progesterone (1.7%). Overall, the model highlights RBP4 as the principal positive determinant opposed by the protective effect of HGH. For abdominal subcutaneous fat, insulin was the leading positive predictor (14.5%), with HGH again exerting a negative effect (8.5%). RBP4 had only a minor contribution (1.2%), suggesting insulin is the dominant determinant of abdominal subcutaneous fat. For peripheral subcutaneous fat, insulin remained the predominant predictor (14.4%), opposed by HGH (3.3%) and a modest effect of progesterone (1.2%). RBP4 and other hormones were excluded. In summary, insulin consistently predicted subcutaneous fat across depots, while RBP4 emerged as the dominant predictor of visceral adiposity, suggesting a depot-specific role with potential mechanistic relevance for cardiometabolic risk. Table 4. Independent Predictors of Fat Depots by Regression analysis Dependent variable Independent Variables Fat depot Positive predictors (Variance ΔR², p ) Negative predictors ( Variance ΔR², p ) Visceral fat RBP4 (6.5%, <0.001); Insulin (5.6%, <0.001) HGH (13.0%, <0.001); Progesterone (1.7%, 0.006) Abdominal subcutaneous Insulin (14.5%, <0.001); RBP4 (1.2%, 0.019) HGH (8.5%, <0.001) Peripheral subcutaneous Insulin (14.4%, <0.001) HGH (3.3%, <0.001); Progesterone (1.2%, 0.028) Retinol binding Protein mediation pathways Table 5 presents the mediation models evaluating the association between RBP4, as the key positive predictor, and cardiometabolic outcomes. Mediation analyses showed that RBP4 associated with cardiometabolic risk profiles through two components: Visceral-fat–mediated (indirect) pathway and a fat-independent (direct) pathway (Mediation interpreted when 95% bootstrap CI excluded zero; PROCESS v5, 5,000 resamples (PROCESS; 5,000 bootstraps). Table 5: RBP4 Mediated Links to Cardiometabolic Risk Factors Category Outcome Effect via Visceral Fat (β, 95% CI boot, p) Direct Effect of RBP4 (β, 95% CI param, p) Inflammation hsCRP 0.0617 (0.0355–0.0975), p<0.001 −0.0678 (−0.1280 to −0.0075), p =0.028 IL-6 0.0361 (0.0208–0.0565), p<0.001 −0.0650 (−0.1154 to −0.0145), p =0.012 Lipids LDL-C 0.0136 (0.0086–0.0198), p<0.001 0.0279 (0.0170–0.0388), p<0.001 TG 0.0063 (0.0037–0.0096), p<0.001 0.0129 (0.0070–0.0189), p<0.001 ApoB 0.0037 (0.0024–0.0053), p<0.001 0.0082 (0.0053–0.0112), p<0.001 HDL-C −0.0009 (−0.0024–0.0002), p =0.11 −0.0027 (−0.0069–0.0015), p =0.208 Glycemic / Insulin HOMA-IR 0.0172 (0.0092–0.0290), p<0.001 −0.0020 (−0.0227–0.0186), p =0.846 HbA1c 0.0067 (0.0041–0.0099), p<0.001 0.0080 (0.0003–0.0158), p =0.042 Oxidation GSH 0.0004 (0.0001–0.0007), p =0.015 −0.0001 (−0.0010–0.0008), p =0.791 Homocysteine 0.0054 (−0.0036–0.0155), p =0.32 0.0331 (0.0034–0.0628), p =0.029 Notes. β values are unstandardized (change in outcome per 1-unit increase in RBP4). “Effect via visceral fat” = indirect path (bootstrap 95% CI; 5,000 resamples). “Direct effect” = RBP4 → outcome adjusted for visceral fat (parametric 95% CI). hsCRP and IL-6 show competitive mediation (indirect positive, direct inverse). Table 6 provides a descriptive summary of the mediation findings, highlighting the predominant pathways linking RBP4 to cardiometabolic markers. The strongest associations were observed for the inflammatory profile. RBP4 demonstrated visceral-fat–mediated associations with (hsCRP, β = 0.0617, 95% CI 0.0355–0.0975; IL-6 β = 0.0361, 95% CI 0.0208–0.0565; both p < 0.001) indicating that each 1-mg/mL increase in RBP4 corresponded to approximately 6% higher hsCRP and 4% higher IL-6. Conversely, the fat-independent (direct) pathway showed inverse associations with these markers (hsCRP β= −0.0678, p = 0.028; IL-6 β= −0.0650, p< 0.012), suggesting a weaker anti-inflammatory profile that was outweighed by the visceral-fat–mediated pro-inflammatory pathway. Beyond inflammation, atherogenic Lipids demonstrated dual pathways with positive associations with RBP4. Triglycerides, LDL-C and ApoB, showed significant associations via visceral fat (all p<0.001), combined with strong fat-independent direct effects (all p<0.001), highlighting the association of RBP4 with atherogenic lipid risk even after accounting for adiposity. The results of the direct pathway appeared to be more prominent, and no significant effect was observed for HDL-C in both direct and indirect pathways. As for the glycemic load, RBP4 was associated with higher insulin resistance exclusively via visceral fat (2% higher HOMA-IR, p<0.001), with no direct effects (p=0.846), whereas the Glycemic burden (HbA1c) showed a smaller mediated signal (0.7%, p<0.001) together with a modest direct association. For oxidative stress markers, the mediated effect of RBP4 via visceral fat on GSH was minimal (~0.04%, p= 0.015), while the association with the oxidative parameter, homocysteine, was exclusively fat- independent (β= 0.033; p= 0.029). Table 6. Summary of Mediation Patterns Linking RBP4, Visceral Fat, and Cardiometabolic Risk Factors Outcome Via Visceral Fat Direct Effect of RBP4 Brief note Inflammation hsCRP ↑ ↓ Competitive mediation; Visceral path predominates IL-6 ↑ ↓ Competitive mediation; Visceral path predominates Lipids TG ↑ ↑ Dual pathway (mediated + strong direct) LDL-C ↑ ↑ Dual pathway (mediated + strong direct) ApoB ↑ ↑ Dual pathway (mediated + strong direct) Glycemia / Insulin Resistance HOMA-IR ↑ — Entirely/near-fully mediated HbA1c ↑ ↑ Mostly mediated; small direct Oxidative / Other Glutathione (GSH) ↑ — Small positive mediated effect only Homocysteine — ↑ Direct-only association HDL-C — — No consistent association Discussion Visceral adiposity in women is a critical determinant of metabolic health, with its accumulation strongly associated with increased cardiometabolic risk. Unlike peripheral fat depots, visceral fat contributes to insulin resistance, systemic inflammation, and adverse lipid profiles. The shift from a gynoid to an android fat distribution pattern is particularly concerning, as it marks a transition toward central obesity, which is more metabolically active and detrimental ( 15 , 49 ). Waist circumference closely tracks visceral fat levels and thus remains a reliable and practical surrogate marker in clinical assessments. Anthropometric and hormonal differences between pre and post-menopausal women: In the present study, visceral adiposity emerged as the most pronounced difference between pre- and postmenopausal women, increasing by 50.7%. This marked change highlights menopause as a key driver of fat redistribution. Abdominal subcutaneous fat also showed smaller but significant increases, whereas peripheral subcutaneous fat remained largely unchanged. These observations are consistent with prior reports indicating that menopause is linked to a preferential accumulation of visceral fat and a relative preservation of peripheral fat stores, reinforcing the risk shift toward a more atherogenic fat pattern ( 15 , 49 ). Hormonal shifts following menopause appear to underlie the observed redistribution of body fat. As expected, menopause was marked by significant increases in gonadotropins (FSH and LH) and sharp declines in sex steroids, particularly estradiol and progesterone. These changes likely contribute to the transition from a gynoid to an android fat pattern, as estrogen is known to regulate lipid metabolism and subcutaneous fat storage. As for fat-storage–related hormones such as insulin, cortisol, and thyroid hormones, no significant differences were observed between pre- and postmenopausal women. This suggests that, despite their known roles in energy balance and adiposity, these hormones may not be primary drivers of the metabolic changes associated with menopause. Notably, two significant hormonal changes emerged in postmenopausal women: a substantial rise in RBP4 and a marked decline in HGH. Importantly, these hormonal shifts were not only evident as between-group differences but were also reflected in association analyses, which revealed that both hormones were significantly linked to patterns of body fat distribution. This suggests that these metabolic hormones may contribute to key alterations in fat storage and metabolic risk associated with menopause. Association of fat storage regulators with regional fat depots: Among all fat-regulating hormones assessed, only RBP4, insulin, and HGH demonstrated consistent associations with specific fat depots. Insulin correlated positively with both subcutaneous (abdominal and peripheral) and visceral fat, aligning with its well-known potent anabolic and lipogenic roles ( 23 ). In contrast, HGH exhibited a strong inverse association with visceral adiposity, supporting its established function in promoting lipolysis and inhibiting fat accumulation ( 29 , 30 ) . The sharp decline in HGH levels observed postmenopause, along with its negative associations with multiple fat depots, likely reflects a reduced physiological capacity to oppose the effects of lipogenic hormones such as insulin and RBP4. This imbalance may contribute directly to visceral fat accumulation, consistent with prior evidence linking age-related reductions in HGH to central fat redistribution and increased cardiometabolic risk ( 50 ). Predictors of regional fat depots RBP4 emerged as the strongest correlate and independent predictor of visceral adiposity, with circulating levels elevated by 23.7% in postmenopausal women, a greater increase than observed for other adipokines. This aligns with previous studies linking RBP4 to insulin resistance, hepatic steatosis, and central obesity ( 37 , 40 , 51 ). Regression analysis further identified RBP4 as the primary determinant of visceral fat accumulation, explaining a larger proportion of variance than insulin, suggesting a mechanistic role beyond mere association. Functionally, RBP4 has been shown to enhance adipocyte differentiation, promote macrophage infiltration, and upregulate pro-inflammatory cytokines such as IL-6, thereby potentiating both lipogenic and inflammatory pathways in visceral depots ( 52 , 53 ) Conversely, HGH emerged as the dominant negative predictor of visceral fat, explaining 13% of the variance. Its inverse relationship across all fat depots is consistent with its established lipolytic and anti-adipogenic effects, primarily through hormone-sensitive lipase activation and inhibition of lipid uptake ( 54 ). The marked reduction in circulating GH observed after menopause likely diminishes this protective effect, thereby permitting greater RBP4-driven visceral expansion. This hormonal interplay, characterized by diminished GH activity and elevated RBP4 may represent a pivotal shift in adipose regulation with aging and estrogen decline. For subcutaneous fat depots, insulin emerged as the leading positive predictor, consistent with its established role in stimulating lipogenesis and glucose uptake in adipocytes. RBP4 contributed minimally to abdominal subcutaneous fat, and was excluded from the peripheral model, emphasizing its depot-specific impact. Progesterone exerted a modest opposing influence on both visceral and peripheral subcutaneous fat. Evidence suggests its influence varies by fat depot, sex, and hormonal context, likely due to differential receptor expression ( 55 – 57 ) Collectively, these findings describe distinct hormonal interplays in regional adiposity: RBP4 as the main independent positive determinant of visceral fat, insulin as the dominant predictor of subcutaneous fat, and HGH as the principal negative regulator across depots. Thereby, elevated RBP4, coupled with reduced HGH activity, may promote visceral fat distribution characteristic of menopause and aging. These findings highlight a potential depot-specific effect of RBP4 that warrants further mechanistic exploration. RBP4 is a transporter of retinol, the metabolic precursor of retinoic acid (RA), which functions as a potent regulator of cellular differentiation through nuclear retinoic acid receptors ( 58 ). Adipose tissue is an active site of RA biosynthesis. The conversion of retinol to retinaldehyde and subsequently to RA is catalyzed by retinol dehydrogenases and aldehyde dehydrogenases, which are expressed in adipose depots. Evidence indicates that this pathway operates in a depot-specific manner, with visceral adipose tissue demonstrating particularly high RA-generating capacity. In human visceral fat, aldehyde dehydrogenase (ALDH1A2) expression is enriched, and the rate of RA formation is approximately threefold higher than in subcutaneous depots ( 59 ). In addition, Experimental work in mice has shown that ALDH1A1 is the dominant enzyme for RA production during adipogenesis, driving the expression of key transcriptional regulators such as ZFP423 and PPARγ, thereby promoting fat formation, particularly in visceral depots ( 60 ). Moreover, ALDH1A1 expression in visceral fat has been linked to diet- and sex-specific differences in fat accumulation, with inhibition of this pathway protecting female mice from visceral obesity ( 61 ). Collectively, these findings establish that visceral adipose tissue possesses a uniquely active RA biosynthetic program, positioning retinol transport by RBP4 as a potentially important upstream regulator of depot-specific adipogenesis and metabolic risk. Elevated RBP4 released from visceral fat could therefore perpetuate a feed-forward loop: increased secretion from expanding adipose tissue may promote further adipogenesis, particularly in visceral depots, sustaining their growth and metabolic persistence. Retinol binding protein: A mediator of systemic inflammation and dyslipidemia Experimental evidence supports RBP4 as a mediator of adipose inflammation and systemic insulin resistance through activation of antigen-presenting cells and macrophages ( 36 , 39 , 62 ). Recent evidence, including a comprehensive review by Sontoro et al. (NEJM, 2023), reinforces the mechanistic relevance of RBP4 in promoting adipose-tissue inflammation and early metabolic dysfunction, preceding the onset of insulin resistance. Beyond its role as a retinol transport protein, RBP4 acts as an active mediator linking adipose inflammation to metabolic impairment, linked to visceral fat accumulation ( 63 ). Generally, our mediation analyses position RBP4 as a dual-mechanism factor: (i) acting through visceral fat to drive inflammation and insulin resistance, and (ii) exerting independent effects on atherogenic lipids. Our models suggest that much of the link between RBP4 and inflammation (CRP, IL-6) appeared to operate through visceral fat, positioning RBP4 as a key amplifier of the visceral–inflammation axis. Therefore, reducing visceral adiposity would be expected to substantially attenuate this inflammatory signal. Importantly, also, RBP4 was also associated with lipid abnormalities independent of visceral fat, suggesting that RBP4 contributes to dyslipidemia through fat-independent pathways, in line with studies linking it to vascular injury and atherosclerosis ( 36 , 39 , 62 ). This may require direct lipid-lowering strategies alongside adiposity management. These findings are summarized in a hypothetical schematic presentation shown in Fig. 1 . However, these are associational results from regression-based mediation in observational data; they support pathways but do not establish causality. A recent landmark study, by Ridker et. al (NEJM, 2024) strongly supports our findings by demonstrating that a combined measure of high-sensitivity CRP, LDL cholesterol, and lipoprotein(a) predicts incident cardiovascular events over 30 years in initially healthy U.S. women, highlighting the connection between inflammation and lipids as key drivers of cardiometabolic risk ( 64 ) Our findings expand this framework by implicating RBP4, a hepatoadipokine linked to both systemic inflammation and atherogenic dyslipidemia, as a potential upstream mediator of these pathways. Prior mechanistic studies have shown that RBP4 promotes adipose tissue inflammation through macrophage and T-cell activation and increases the production of interleukin-6, thereby boosting hepatic CRP synthesis ( 37 , 40 ). Alongside, clinical studies associate elevated RBP4 levels with adverse lipid profiles, including higher triglycerides and lower HDL cholesterol ( 65 – 67 ) . Boxes denote variables; arrows indicate positive associations tested in mediation models (PROCESS, Model 4; 5,000 bootstrap resamples). Results summarize significant indirect (via visceral fat) and/or direct paths. Here, “via visceral fat” denotes the mediated path; “direct” denotes effects after adjusting for visceral fat. Significance is based on 95% bootstrap CIs. RBP4 is associated with higher visceral fat, which further relates to higher inflammation ( hsCRP, IL-6 ), insulin resistance ( HOMA-IR ), glycemic burden ( HbA1c ) and atherogenic lipids ( triglycerides, LDL-C, ApoB ). Further, arrows from RBP4 directly to atherogenic lipids ( triglycerides, LDL-C, ApoB ) and homocysteine depicts a direct, fat-independent path. An inverse association of RBP4 with the anti-inflammatory profile (lower hsCRP and IL-6) is demonstrated as a direct fat independent path. Conclusion and future perspectives Overall, RBP4 emerges as a key predictor of visceral adiposity in women, displaying dual visceral-fat–related and fat-independent associations with cardiometabolic risk pathways. The pattern of findings suggests that higher RBP4 levels are linked to greater visceral adiposity, accompanied by a significant visceral-fat–related inflammatory and atherogenic lipid profile. In parallel, RBP4 also shows adiposity-independent associations with atherogenic lipids and oxidative markers, suggesting an additional pathway that operates irrespective of visceral fat. Although an anti-inflammatory direct association was observed, it was overridden by the stronger visceral-fat–related inflammatory signal. Together, these findings suggest that RBP4 may contribute to cardiometabolic vulnerability through two complementary mechanisms, one amplified by visceral fat accumulation and another independent of adiposity. This dual pattern positions RBP4 as a promising biomarker of visceral-adiposity–related risk and highlights its potential relevance as a prospective risk indicator and as a target for future strategies aimed at modulating fat storage and cardiometabolic risk in women, warranting further evaluation in longitudinal and male cohorts Yet, several cautions remain. Our findings are based on observational cross-sectional associations and mediation models, which support pathways but do not establish causality. The strength of this study lies in addressing a limitation present in women research, the lack of hormonal-phase control, which may have masked sex-specific regulation. We addressed this by selecting premenopausal women specifically in the early follicular phase. An additional advantage was conducting all sampling in the morning, which helped minimize daily hormonal fluctuations, such as cortisol and growth hormone that could confound the data. Our findings identify RBP4 as a novel and clinically relevant predictor of visceral adiposity in women, with downstream implications for inflammatory and lipid pathways that may be particularly accentuated after menopause. Given RBP4’s role as the principal carrier of retinol, and an established adipokine, these observations raise the possibility that retinoid signaling may influence adipocyte differentiation and depot-specific autocrine fat expansion. These insights open new avenues for mechanistic exploration. Specifically, future studies are needed to determine whether RBP4 actively contributes to visceral adiposity development, potentially through retinoid-regulated adipogenesis, or whether it reflects upstream metabolic disturbances that track with central fat accumulation. Clarifying these processes will be essential for understanding how RBP4 may shape inflammation, lipid metabolism, and cardiometabolic vulnerability in women. In addition, the sex-specific nature of these findings highlights the need for comparative studies in men to define whether RBP4 carries similar or distinct metabolic implications across sexes. Longitudinal research will also be crucial to establish temporal relationships and evaluate whether RBP4 predicts incident visceral adiposity or cardiometabolic outcomes. Collectively, this study provides a foundation for advancing both mechanistic and translational work, including the potential development of RBP4-directed strategies aimed at modulating visceral fat biology and its associated metabolic sequelae. Declarations AI Use Statement: AI tools were used to assist with language editing, structural clarity, and improving the general presentation and understanding of the manuscript. All scientific ideas, data sources, data analysis (SPSS), and conclusions including Figure 1 are original. Competing Interests: The authors declare no competing financial or non-financial interests related to the work described in this manuscript. Author Contribution JS conceived and designed the study, prepared funding proposal, synthesized the research narrative, analyzed and interpreted findings, wrote the original draft, and supervised all stages of the project. MAM contributed to study design, coordinated data collection and project management, ensured quality control, curated data, and participated in original draft writing and data analysis. KAH contributed to the original study design and funding acquisition, contributed to sample collection, contributed to critical manuscript revision, and clinical perspective and helped define research directions. KAR supported the conceptual framework by aligning study design with clinical relevance and advised on the overall study direction. MAR provided an informed perspective through critical review and integration of updated sources, and manuscript writing and review. NC ensured clinical applicability in study design, managed patient recruitment and communication, and handled questionnaire administration and sample/data collection. ME contributed conceptually by providing clinical and methodological insight on study limitations, critically revised the manuscript with interpretive feedback, and contributed to research directions. Acknowledgement This study was funded by the Research Council of Oman (TRC), Grant No. RC/MED/BIOC/15/01. The authors gratefully acknowledge the support of the Clinical Biochemistry Laboratory at Sultan Qaboos University Hospital (SQUH), particularly Dr. Mohsen Al-Lawati for assistance with sample processing and analysis. Special thanks are due to Mr. Marvin Enriquez from the Microbiology Laboratory at SQUH. We extend our sincere appreciation to all the women who volunteered to participate in this study, including Omani women societies, and nursing staff at Sultan Qaboos University hospital particularly physiology for their technical support and assistance with sample collection. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to participant confidentiality agreements approved by the Institutional Ethics Committee. Data access is restricted to the research team in accordance with the informed consent provided by participants and the ethics approval granted by the Institutional Ethics Committee (SQU-EC/164/14, MREC #1019).Additionally, these data are part of a larger research project with planned future publications that have not yet been released. However, de-identified data may be made available from the corresponding author upon reasonable request and with prior approval from the Institutional Ethics Committee. 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Rocha M, Bañuls C, Bellod L, Rovira-Llopis S, Morillas C, Solá E, et al. Association of serum retinol binding protein 4 with atherogenic dyslipidemia in morbid obese patients. PLoS ONE. 2013;8(11):e78670. Additional Declarations No competing interests reported. Supplementary Files SignificanceStatementCD.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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University","correspondingAuthor":true,"prefix":"","firstName":"Jumana","middleName":"","lastName":"Saleh","suffix":""}],"badges":[],"createdAt":"2025-12-08 19:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8310783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8310783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97864230,"identity":"86881d66-d8f0-44dd-b6f1-a5a6cc142dc1","added_by":"auto","created_at":"2025-12-10 09:22:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":476010,"visible":true,"origin":"","legend":"","description":"","filename":"RetinolbindingProteinSubmittedCD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/ec7b87d8ca2ea1ad95b786c6.docx"},{"id":97864223,"identity":"19a8c4d5-5204-4224-8b6d-b26372c424f4","added_by":"auto","created_at":"2025-12-10 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09:22:21","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176783,"visible":true,"origin":"","legend":"","description":"","filename":"17c0e113e7a649ef9ce72f543fe93a4c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/57e2153afdd03c3f825289c2.xml"},{"id":97864232,"identity":"125be0ad-1bd5-4a89-b117-e43163ded2db","added_by":"auto","created_at":"2025-12-10 09:22:28","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190206,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/0eddff9e33c7834a9be4a1a3.html"},{"id":97898139,"identity":"d123265b-5efd-4408-8a92-66a76236934f","added_by":"auto","created_at":"2025-12-10 15:38:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation scheme hypothetically linking RBP4 to cardiometabolic outcomes via visceral fat and independently\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxes denote variables; arrows indicate positive associations tested in mediation models (PROCESS, Model 4; 5,000 bootstrap resamples). Results summarize significant indirect (via visceral fat) and/or direct paths\u003cstrong\u003e. \u003c/strong\u003eHere, “via visceral fat” denotes the mediated path; “direct” denotes effects after adjusting for visceral fat. Significance is based on 95% bootstrap CIs. RBP4 is associated with higher visceral fat, which further relates to higher inflammation (\u003cem\u003ehsCRP, IL-6\u003c/em\u003e), insulin resistance (\u003cem\u003eHOMA-IR\u003c/em\u003e), glycemic burden (\u003cem\u003eHbA1c\u003c/em\u003e) and atherogenic lipids (\u003cem\u003etriglycerides, LDL-C, ApoB\u003c/em\u003e). Further, arrows from RBP4 directly to atherogenic lipids (\u003cem\u003etriglycerides, LDL-C, ApoB\u003c/em\u003e) and homocysteine depicts a direct, fat-independent path. An inverse association of RBP4 with the anti-inflammatory profile (lower hsCRP and IL-6) is demonstrated as a direct fat independent path.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/f4839b99e122837e198edd23.png"},{"id":98430440,"identity":"7de79ea2-43bf-4020-a9bf-3f3dd0fb1e1b","added_by":"auto","created_at":"2025-12-17 16:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1897300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/b7d23bb6-8169-4ac0-bb0f-30747eecd63b.pdf"},{"id":97864222,"identity":"838e9b7b-bdb7-4f34-a877-eebb347390f8","added_by":"auto","created_at":"2025-12-10 09:22:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15319,"visible":true,"origin":"","legend":"","description":"","filename":"SignificanceStatementCD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8310783/v1/424f05928e7fd3fe35fc5cc9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Retinol-Binding Protein-4 Predicts Visceral Adiposity and Related Inflammatory–Cardiometabolic Profiles in Women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVisceral adiposity is increasingly recognized as the most metabolically detrimental fat depot due to its strong associations with insulin resistance, systemic inflammation, dyslipidemia, and elevated cardiometabolic disease risk (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Several mechanisms underlie this adverse phenotype. Visceral adipocytes display heightened lipolytic activity, increasing free fatty acid (FFA) delivery to the portal circulation and disrupting hepatic lipid metabolism (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition, visceral fat secretes pro-inflammatory adipokines, such as interleukin-6 (IL-6), contributing to chronic low-grade inflammation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), Visceral fat accumulation is also linked to impaired expandability of subcutaneous adipose tissue, promoting ectopic fat deposition in metabolic organs including the liver and skeletal muscle (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis association becomes particularly relevant in women during menopausal transition, a period characterized by a well-documented shift in fat distribution toward greater and persistent visceral accumulation (Farahmand et al., 2022; Greendale et al., 2021; Marlatt et al., 2022). Menopause also coincides with a sharp rise in cardiometabolic vulnerability, highlighting the need to better understand the biological processes that drive these changes (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although alterations in adipose tissue distribution during menopause are largely attributed to declining estrogen and female hormonal changes (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), the specific mechanisms driving fat distribution patterns remain incompletely defined (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAvailable evidence suggests that sex steroids influence fat distribution primarily through indirect modulation of other hormonal and metabolic pathways. For example, estrogen protects against visceral fat accumulation by modifying lipoprotein lipase (LPL) activity, enhancing subcutaneous storage, and downregulating glucocorticoid activity in visceral depots. Estrogen deficiency after menopause promotes central fat accumulation, while hormone therapy reverses these changes (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Progesterone enhances lipogenesis, but also antagonizes glucocorticoid receptor signaling in adipose tissue (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Androgens, particularly in estrogen-deficient states, are linked with visceral adiposity through stimulation of lipogenic pathways and impairment of AMPK signaling (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Collectively, sex steroids modulate several interacting pathways that shape adipose distribution, but their specific roles in depot-specific regulation, particularly regarding visceral fat accumulation and persistence, is not fully understood.\u003c/p\u003e\u003cp\u003eAs for systemic metabolic regulators, insulin acts as the principal lipogenic hormone, promoting triglyceride synthesis and storage by stimulating glucose uptake and lipoprotein lipase (LPL) activity, while concurrently suppressing hormone-sensitive lipase (HSL)\u0026ndash;mediated lipolysis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Recent findings showed that these effects are depot-specific, maintaining subcutaneous fat insulin sensitivity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, gender-specific effects is inconsistent and poorly defined. The Acylation-stimulating protein (ASP) is another lipogenic factor, identified as an adipokine, that has shown preliminary indications of regional effects. Acting independently and additively with insulin, ASP stimulates diacylglycerol acyltransferase (DGAT) and inhibits hormone-sensitive lipase (HSL). Early studies suggested greater responsiveness in subcutaneous compared to visceral fat, higher circulating levels in women than men, and fluctuations across the menstrual cycle with progesterone, pointing to a possible female-specific lipogenic role favoring subcutaneous storage (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Although these studies represent important advances in understanding ASP biology, further \u003cem\u003ein vivo\u003c/em\u003e investigations in humans are essential to expand upon these findings in the context of gender-and depot-specific fat distribution. Cortisol provides the most consistent evidence of depot-specific action. It enhances adipocyte differentiation and lipid accumulation preferentially in visceral depots and is strongly implicated in stress-related abdominal obesity and endocrine disorders such as Cushing\u0026rsquo;s syndrome. Despite its well-established role in promoting visceral adiposity, the role of cortisol as a gender-specific mediator of fat distribution has not been demonstrated(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Other regulators, including thyroid hormones, and the human growth hormone (HGH), exert central roles in adipose metabolism. Thyroid hormones regulate basal metabolic rate and adipose metabolism, whereas GH promotes lipolysis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Both decline with age and menopause, but without definitive evidence of gender or depot-specific actions (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn array of specific molecular targets has been implicated in the regulation of adipogenesis. Key transcription factors, including CCAAT/enhancer-binding protein alpha (C/EBPα) and peroxisome proliferator\u0026ndash;activated receptor gamma (PPARγ), together with downstream effectors such as fatty acid\u0026ndash;binding protein 4 (FABP4), adiponectin, and fatty acid synthase (FAS), play essential roles in the differentiation and maturation of adipocytes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRetinol binding protein-4 (RBP4), a\u0026thinsp;~\u0026thinsp;21-kDa protein, was first identified as an adipokine in 2005, when Yang et al. (2005) demonstrated that adipocyte-derived RBP4 is associated with systemic insulin resistance in both people with obesity and type 2 diabetes, suggesting a mechanistic link between RBP4 and adipose tissue dysfunction and impaired glucose metabolism (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Adipose tissue, second only to the liver, expresses RBP4, particularly in visceral fat depots (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Functionally, RBP4 transports retinol and forms a complex with transthyretin (TTR) to prevent renal clearance. Since then, more clinical and epidemiological studies have associated elevated circulating RBP4 with obesity, type 2 diabetes, metabolic syndrome, and cardiovascular abnormalities and was implicated in vascular inflammation, endothelial dysfunction, and cardiac remodeling (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Beyond its metabolic effects, it is now recognized as a key driver of adipose tissue inflammation, emphasizing an integrative role in the convergence of endocrine and immune dysfunction (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Notably, visceral fat RBP4 expression is consistently higher than in subcutaneous adipose tissue in people with obesity or diabetes (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Moreover, genetic variations in or near the RBP4 locus have been linked to insulin resistance and related traits such as BMI, waist-to-hip ratio, insulin, and free fatty acid levels (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Thus, RBP4 may represent a mechanistic link between visceral adiposity and metabolic dysfunction. Despite strong evidence, findings remain inconsistent. Some studies report weak or no correlation between circulating RBP4 and insulin resistance or obesity (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Despite extensive associations of RBP4 with adiposity, insulin resistance, and inflammation, to our knowledge no direct evidence demonstrates that RBP4 promotes lipid storage or, and its role in regional adiposity is not yet established.\u003c/p\u003e\u003cp\u003eA major limitation in studies involving women is the inherent biological variability introduced by hormonal fluctuations across the menstrual cycle and the menopausal transition, which can profoundly influence metabolic and cardiovascular parameters. This variability has contributed to inconsistent findings and limited reproducibility across studies. To overcome this methodological constraint, the present study implemented a rigorously standardized sampling design, with blood collection restricted to the early follicular phase (days 1\u0026ndash;3), when circulating sex hormone levels are at their nadir in premenopausal women. This approach uniquely addresses one of the most persistent challenges in women-focused metabolic research, substantially minimizing hormonal variability, enhancing comparability across premenopausal and postmenopausal groups, and strengthening the validity of sex-specific metabolic assessments.\u003c/p\u003e\u003cp\u003eThe main aim of this study was to investigate determinants of regional adiposity and related cardiometabolic risk while minimizing the confounding effects of hormonal variability.\u003c/p\u003e\u003cp\u003eSpecifically, our objective was to identify predictors of adiposity distribution in women, including established fat-storage regulators, with particular focus on determinants of visceral adiposity, and to assess whether the \u003cem\u003epositive\u003c/em\u003e predictors were associated with cardiometabolic risk across inflammatory, lipid, glycemic, insulin-resistance, and oxidative-stress pathways.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003e This prospective cross-sectional study was approved by the Sultan Qaboos University Ethics Committee (SQU-EC/164/14, MREC #1019). A total of 410 apparently healthy women Omani women aged 18\u0026ndash;65 years. Participants were apparently healthy women with no known comorbidities that could affect metabolic profiles. Recruitment was conducted through community engagement at women's society events, schools (including teaching and administrative staff), university student housing, university events, hospital visitation areas, and among university personnel and support staff and others. Exclusion criteria were as follows: age above 65 years, pregnancy, active smoking, and consumption of alcoholic beverages, usage of lipid- or cholesterol lowering drugs, corticosteroids, oral contraceptives, hormone replacement therapy, insulin or any vitamin or hormonal supplements, the presence of hemoglobinopathies, infectious or inflammatory disorders. All study participants provided written informed consent. Participants completed a structured questionnaire covering menstrual history, reproductive status and medical background.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClassification Criteria:\u003c/h3\u003e\n\u003cp\u003eWomen were stratified based on age, reproductive status and follicle-stimulating hormone (FSH) levels. Reproductive groups were defined by the presence of menstruation and FSH\u0026thinsp;\u0026lt;\u0026thinsp;25 IU/L, while postmenopausal status was confirmed by amenorrhea\u0026thinsp;\u0026gt;\u0026thinsp;12 months and FSH\u0026thinsp;\u0026ge;\u0026thinsp;25 IU/L. This classification ensured accurate hormonal profiling and minimized variability related to transitional menopausal phases.\u003c/p\u003e\u003cp\u003eThe samples were stratified as follows:\u003c/p\u003e\u003cp\u003eReproductive Group:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAged 18\u0026ndash;51 years\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRegular menstrual cycles (21\u0026ndash;35 days)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFSH range: 4.7 to 21.5 mIU/mL\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eMenopausal Group:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;51 years\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCessation of menstruation for \u0026ge;\u0026thinsp;12 consecutive months\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFSH range: 25.8 to 134.8 mIU/mL\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eVenous blood samples were collected during the first three days of the follicular phase for reproductive-age women to ensure the most possible hormonal comparability with menopausal participants. Samples were collected in a standardized morning window to reduce variability due to diurnal hormonal changes. Anthropometric measures included weight, height, waist circumference, body fat percentage, visceral fat, and skinfold thickness.\u003c/p\u003e\u003cp\u003eBody composition was estimated using the Omron Full Body Sensor Body Composition Monitor and Scale. This bioelectrical impedance analysis (BIA) device measures fat mass, skeletal muscle mass, visceral fat, and BMI. It operates via low-frequency current and compensates for diurnal fluid shifts by using both hand and foot electrodes, providing clinically valid readings. The use of bioelectrical impedance analysis (BIA) for assessing body composition has been validated in numerous studies using dual-energy X-ray absorptiometry (DXA) as the reference standard. Such validation work supported its adoption in large-scale and longitudinal studies (\u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMeasurement of subcutaneous fat depots\u003c/h3\u003e\n\u003cp\u003eSubcutaneous fat was measured using a standard plastic skinfold caliper (graduated in millimeters; range 0\u0026ndash;60 mm). The caliper was applied to a pinched skinfold after grasping the skin and underlying subcutaneous fat between the thumb and forefinger. Abdominal subcutaneous fat was assessed 2 cm lateral to the umbilicus, and arm subcutaneous fat was assessed at the midline of the triceps. Measurements were performed by trained medical personnel.\u003c/p\u003e\n\u003ch3\u003eBiochemical Analyses\u003c/h3\u003e\n\u003cp\u003eAll biochemical parameters were quantified using commercial kits and automated analyzers. Retinol Binding Protein-4 (RBP4) levels were assessed using the Quantikine Human RBP4 ELISA kit (DRB400, R\u0026amp;D Systems, USA). Acylation stimulating Protein (ASP) concentrations were measured using a competitive ELISA kit (HA0889, Neo Scientific, USA). Routine biochemical parameters were measured in the Clinical Biochemistry lab at Sultan Qaboos University Hospital. Serum hormone concentrations of progesterone, estradiol, FSH, cortisol, and insulin and HGH in addition to IL-6 were quantified using the Cobas 6000 analyzer (e-601 module; Roche Diagnostics). Total plasma homocysteine was quantified by an enzymatic cycling assay with NADH detection at 340 nm, and high-sensitivity C-reactive protein (hs-CRP) was measured by immunoturbidimetry on a Roche Cobas 6000 analyzer (c-501 module; Roche Diagnostics). The Homeostasis Model Assessment (HOMA) was measured by the standard formula: Fasting Insulin (\u0026micro;U/mL)\u0026times;Fasting Glucose (mmol/L)\u0026thinsp;\u0026divide;\u0026thinsp;22.5.\u003c/p\u003e\u003cp\u003eLipids including total cholesterol, triglycerides, LDL-C, HDL-C, and fasting glucose were measured with the Cobas 6000 c-501 module using spectrophotometric enzymatic assays. LDL-C was calculated using the Friedewald formula: LDL-C\u0026thinsp;=\u0026thinsp;TC \u0026ndash; [(TG/2.2)\u0026thinsp;+\u0026thinsp;HDL-C]. HbA1c was quantified using the Cobas Integra 400 plus (Roche Diagnostics), which employs turbidimetric inhibition immunoassay. Glutathione (GSH) was measured by BioVision\u0026rsquo;s ApoGSH TM Glutathione colorimetric assay kit (Catalog #K261-100, Biovision).\u003c/p\u003e\u003cp\u003eRBP4 data were complete for all participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;410), and anthropometric measures (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;407\u0026ndash;410). Biochemical parameters had variable sample sizes across analyses due to missing values as a result of limited sample availability or assay exclusion, resulting in sample sizes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;350\u0026ndash;410), (GSH, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;312). Analyses were conducted using all available data for each variable (pairwise deletion).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS version 30. Normality of distributions was assessed using the Kolmogorov\u0026ndash;Smirnov test. Group differences were evaluated using independent-samples t-tests for normally distributed variables and Mann\u0026ndash;Whitney U tests for non-normally distributed variables. Results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Bivariate correlations between RBP4 and metabolic markers were examined using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients, as appropriate. To identify hormone predictors of fat distribution patterns, stepwise linear regression analyses were performed with visceral and subcutaneous fat (abdominal and arm), as independent variables in separate models. Major fat-regulating hormones were included as dependent variables. To account for potential deviations from parametric assumptions, bootstrapped regression analyses were also conducted. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Mediation analysis was conducted using the PROCESS macro for SPSS (version 5.0) developed by Hayes (2022), with 5,000 bias-corrected bootstrap resamples. An indirect effect was considered statistically significant if the 95% confidence interval (CI) did not include zero (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 410 apparently healthy women were included in this study (290 premenopausal and 120 postmenopausal). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the anthropometric parameters of the cohort. Postmenopausal women had significantly higher BMI, waist circumference, fat percentage, and visceral fat, but lower muscle percentage compared with premenopausal women (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). In relative terms, BMI was 8.9% higher, waist circumference 14.0% higher, and total fat percentage 9.7% higher in the postmenopausal group. Visceral fat showed the most pronounced difference, being 50.7% higher, while muscle percentage was 5.4% lower, indicating a major shift toward central adiposity and reduced lean mass. Subcutaneous fat distribution also differed, with abdominal skinfold thickness 11.2% higher, whereas peripheral skinfold arm thickness remained essentially unchanged (0.4%).\u003c/p\u003e\u003cp\u003eAs visceral adiposity represented the most pronounced difference between postmenopausal and premenopausal women (~\u0026thinsp;50.7%), subsequent analyses were conducted to examine predictors of visceral fat accumulation and their link to downstream cardiometabolic effects, including inflammation, insulin resistance, glycemic load, lipids, and oxidative markers.\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\u003eAnthropometric and Fat Distribution Patterns in Pre- and Postmenopausal Women\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePremenopausal\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePostmenopausal\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFat percentage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMuscle percentage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVisceral fat (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSkinfold abdomen (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePeripheral skinfold arm (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.90\u003c/b\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\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eFat Storage Hormone Profiles\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 2,\u003c/strong\u003e and consistent with physiological changes after menopause, gonadotropins increased markedly in postmenopausal women, with FSH higher by approximately fivefold and LH nearly threefold (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 for both). Sex steroids were significantly lower, with estradiol and progesterone each lower by ~70% (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while testosterone was lower by 17.8% (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, fat regulating hormones, including insulin, cortisol, ASP, and thyroid hormones, showed no significant differences between groups, apart from a modest increase in FT3 (\u003cem\u003ep\u003c/em\u003e = 0.013). Notably, circulating RBP4 was higher by 23.6% (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) in postmenopausal women, while HGH levels showed an opposing pattern, by a marked 63.8% (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Fat-regulating Hormones/Factors in Pre and Postmenopausal women\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"552\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePremenopausal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePostmenopausal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReproductive hormones\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFSH (\u003cem\u003eIU/L\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.4 \u0026plusmn; 17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.6 \u0026plusmn; 25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLH (\u003cem\u003eIU/L\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.1 \u0026plusmn; 10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.0 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstradiol (\u003cem\u003epg/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.9 \u0026plusmn; 95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.8 \u0026plusmn; 36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProgesterone (\u003cem\u003eng/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71 \u0026plusmn; 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTestosterone (\u003cem\u003eng/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.26 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFat Storage Hormones\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin (\u003cem\u003e\u0026micro;U/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.8 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.6 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.125\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCortisol (\u003cem\u003enmol/L\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347.8 \u0026plusmn; 152.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e320.4 \u0026plusmn; 144.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.070\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eASP (\u003cem\u003enM\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0 \u0026plusmn; 13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.6 \u0026plusmn; 21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.886\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSH (\u003cem\u003e\u0026micro;IU/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.90 \u0026plusmn; 5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.21 \u0026plusmn; 6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.465\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3 (\u003cem\u003epmol/L\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.76 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.88 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT4 (\u003cem\u003epmol/L\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.1 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.0 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.942\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHGH (\u003cem\u003eng/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1847.0 \u0026plusmn; 2619.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e669.2 \u0026plusmn; 948.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRBP4(\u003cem\u003emg/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.7 \u0026plusmn; 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.2 \u0026plusmn; 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFSH: Follicle stimulation hormone, LH: Luteinizing hormone, \u0026nbsp; ASP: Acylation Stimulating Protein, TSH: Thyroid stimulating Hormone, FT3, FT4, Thyroid hormones, 3 and 4, HGH: Human Growth Hormone.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations of Fat-regulating Factors with Fat Depots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the associations between fat depots and known fat-regulating factors, we examined bivariate correlations of visceral, abdominal subcutaneous, and arm subcutaneous fat with all hormonal and fat-regulating markers in the study, including RBP4, insulin, HGH, cortisol, ASP, and thyroid hormones (TSH, FT3, FT4). Interestingly, among the assessed markers, only RBP4, insulin, and HGH showed consistent and significant correlations with fat depots (\u003cstrong\u003eTable 3\u003c/strong\u003e). Specifically, RBP4 and insulin correlated positively with visceral and subcutaneous fat depots, whereas HGH showed inverse associations across all fat depots. Other markers, including cortisol, ASP, and thyroid hormones, were not significant or did not show meaningful correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Bivariate Correlations of Fat-regulating Markers with Fat depots:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"612\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVisceral fat (r, \u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous abdomen (r, \u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePeripheral Subcutaneous (r, \u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRBP4 (\u003cem\u003emg/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.311, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.131, 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInsulin(\u003cem\u003e\u0026micro;U/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.285, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.358, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.409, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHGH(\u003cem\u003eng/ml\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash;0.429, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ndash; 0.361, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.251, \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003er= regression coefficient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis for Predictors of Regional Adiposity:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e demonstrates regression analysis that was performed to identify predictors of different fat depots. Visceral adiposity was mainly predicted by RBP4, surpassing insulin (Variance explained 6.5%, p\u0026lt; 0.001)). HGH emerged as the dominant negative predictor (13%, P\u0026lt; 0.001)), with a smaller opposing contribution from progesterone (1.7%). Overall, the model highlights RBP4 as the principal positive determinant opposed by the protective effect of HGH.\u003c/p\u003e\n\u003cp\u003eFor abdominal subcutaneous fat, insulin was the leading positive predictor (14.5%), with HGH again exerting a negative effect (8.5%). RBP4 had only a minor contribution (1.2%), suggesting insulin is the dominant determinant of abdominal subcutaneous fat.\u003c/p\u003e\n\u003cp\u003eFor peripheral subcutaneous fat, insulin remained the predominant predictor (14.4%), opposed by HGH (3.3%) and a modest effect of progesterone (1.2%). RBP4 and other hormones were excluded. \u0026nbsp;In summary, insulin consistently predicted subcutaneous fat across depots, while RBP4 emerged as the dominant predictor of visceral adiposity, suggesting a depot-specific role with potential mechanistic relevance for cardiometabolic risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eIndependent Predictors of Fat Depots by Regression analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"476\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFat depot\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePositive predictors (Variance \u0026Delta;R\u0026sup2;, \u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNegative predictors ( Variance \u0026Delta;R\u0026sup2;, \u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVisceral fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRBP4 (6.5%, \u0026lt;0.001);\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInsulin (5.6%, \u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHGH (13.0%, \u0026lt;0.001);\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProgesterone (1.7%, 0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbdominal subcutaneous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInsulin (14.5%, \u0026lt;0.001);\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRBP4 (1.2%, 0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHGH (8.5%, \u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePeripheral subcutaneous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInsulin (14.4%, \u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHGH (3.3%, \u0026lt;0.001);\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProgesterone (1.2%, 0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRetinol binding Protein mediation pathways\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e presents the mediation models evaluating the association between RBP4, as the key positive predictor, and cardiometabolic outcomes. Mediation analyses showed that RBP4 associated with cardiometabolic risk profiles through two components: Visceral-fat\u0026ndash;mediated (indirect) pathway and a fat-independent (direct) pathway (Mediation interpreted when 95% bootstrap CI excluded zero; PROCESS v5, 5,000 resamples (PROCESS; 5,000 bootstraps).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: RBP4 Mediated Links to Cardiometabolic Risk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEffect via Visceral Fat\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026beta;, 95% CI boot, p)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Effect of RBP4\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026beta;, 95% CI param, p)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInflammation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ehsCRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0617\u003c/strong\u003e (0.0355\u0026ndash;0.0975), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;0.0678\u003c/strong\u003e (\u0026minus;0.1280 to \u0026minus;0.0075), \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e=0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0361\u003c/strong\u003e (0.0208\u0026ndash;0.0565), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;0.0650\u003c/strong\u003e (\u0026minus;0.1154 to \u0026minus;0.0145), \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e=0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0136\u003c/strong\u003e (0.0086\u0026ndash;0.0198), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0279\u003c/strong\u003e (0.0170\u0026ndash;0.0388), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0063\u003c/strong\u003e (0.0037\u0026ndash;0.0096), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0129\u003c/strong\u003e (0.0070\u0026ndash;0.0189), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eApoB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0037\u003c/strong\u003e (0.0024\u0026ndash;0.0053), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0082\u003c/strong\u003e (0.0053\u0026ndash;0.0112), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0009 (\u0026minus;0.0024\u0026ndash;0.0002), \u003cem\u003ep\u003c/em\u003e=0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0027 (\u0026minus;0.0069\u0026ndash;0.0015), \u003cem\u003ep\u003c/em\u003e=0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGlycemic / Insulin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHOMA-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0172\u003c/strong\u003e (0.0092\u0026ndash;0.0290), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0020 (\u0026minus;0.0227\u0026ndash;0.0186), \u003cem\u003ep\u003c/em\u003e=0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0067\u003c/strong\u003e (0.0041\u0026ndash;0.0099), \u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0080 (0.0003\u0026ndash;0.0158), \u003cem\u003ep\u003c/em\u003e=0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOxidation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0004\u003c/strong\u003e (0.0001\u0026ndash;0.0007), \u003cem\u003ep\u003c/em\u003e=0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0001 (\u0026minus;0.0010\u0026ndash;0.0008), \u003cem\u003ep\u003c/em\u003e=0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHomocysteine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0054 (\u0026minus;0.0036\u0026ndash;0.0155), \u003cem\u003ep\u003c/em\u003e=0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.0331\u003c/strong\u003e (0.0034\u0026ndash;0.0628), \u003cem\u003ep\u003c/em\u003e=0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNotes.\u003c/strong\u003e \u0026beta; values are unstandardized (change in outcome per 1-unit increase in RBP4). \u0026ldquo;Effect via visceral fat\u0026rdquo; = indirect path (bootstrap 95% CI; 5,000 resamples). \u0026ldquo;Direct effect\u0026rdquo; = RBP4 \u0026rarr; outcome adjusted for visceral fat (parametric 95% CI). hsCRP and IL-6 show competitive mediation (indirect positive, direct inverse).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e provides a descriptive summary of the mediation findings, highlighting the predominant pathways linking RBP4 to cardiometabolic markers. The strongest associations were observed for the inflammatory profile. RBP4 demonstrated visceral-fat\u0026ndash;mediated associations with (hsCRP, \u0026beta; = 0.0617, 95% CI 0.0355\u0026ndash;0.0975; IL-6 \u0026beta; = 0.0361, 95% CI 0.0208\u0026ndash;0.0565; both p \u0026lt; 0.001) indicating that each 1-mg/mL increase in RBP4 corresponded to approximately 6% higher hsCRP and 4% higher IL-6. Conversely, the fat-independent (direct) pathway showed inverse associations with these markers (hsCRP \u0026beta;= \u0026minus;0.0678, p = 0.028; IL-6 \u0026beta;= \u0026minus;0.0650, p\u0026lt; 0.012), suggesting a weaker anti-inflammatory profile that was outweighed by the visceral-fat\u0026ndash;mediated pro-inflammatory pathway.\u003c/p\u003e\n\u003cp\u003eBeyond inflammation, atherogenic Lipids demonstrated dual pathways with positive associations with RBP4. Triglycerides, LDL-C and ApoB, showed significant associations via visceral fat (all p\u0026lt;0.001), combined with strong fat-independent direct effects (all p\u0026lt;0.001), highlighting the association of RBP4 with atherogenic lipid risk even after accounting for adiposity. The results of the direct pathway appeared to be more prominent, and no significant effect was observed for HDL-C in both direct and indirect pathways.\u003c/p\u003e\n\u003cp\u003eAs for the glycemic load, RBP4 was associated with higher insulin resistance exclusively via visceral fat (2% higher HOMA-IR, p\u0026lt;0.001), with no direct effects (p=0.846), whereas the Glycemic burden (HbA1c) showed a smaller mediated signal (0.7%, p\u0026lt;0.001) together with a modest direct association. \u0026nbsp;For oxidative stress markers, the mediated effect of RBP4 via visceral fat on GSH was minimal (~0.04%, p= 0.015), while the association with the oxidative parameter, homocysteine, was exclusively fat- independent (\u0026beta;= 0.033; p= 0.029).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Summary of Mediation Patterns Linking RBP4, Visceral Fat, and Cardiometabolic Risk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVia Visceral Fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Effect of RBP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBrief note\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInflammation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCompetitive mediation; Visceral path predominates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026darr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCompetitive mediation; Visceral path predominates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLipids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDual pathway (mediated + strong direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDual pathway (mediated + strong direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eApoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDual pathway (mediated + strong direct)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGlycemia / Insulin Resistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEntirely/near-fully mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMostly mediated; small direct\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOxidative / Other\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlutathione (GSH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSmall positive mediated effect only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHomocysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026uarr;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDirect-only association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo consistent association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eVisceral adiposity in women is a critical determinant of metabolic health, with its accumulation strongly associated with increased cardiometabolic risk. Unlike peripheral fat depots, visceral fat contributes to insulin resistance, systemic inflammation, and adverse lipid profiles. The shift from a gynoid to an android fat distribution pattern is particularly concerning, as it marks a transition toward central obesity, which is more metabolically active and detrimental (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Waist circumference closely tracks visceral fat levels and thus remains a reliable and practical surrogate marker in clinical assessments.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAnthropometric and hormonal differences between pre and post-menopausal women:\u003c/h2\u003e\u003cp\u003eIn the present study, visceral adiposity emerged as the most pronounced difference between pre- and postmenopausal women, increasing by 50.7%. This marked change highlights menopause as a key driver of fat redistribution. Abdominal subcutaneous fat also showed smaller but significant increases, whereas peripheral subcutaneous fat remained largely unchanged. These observations are consistent with prior reports indicating that menopause is linked to a preferential accumulation of visceral fat and a relative preservation of peripheral fat stores, reinforcing the risk shift toward a more atherogenic fat pattern (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHormonal shifts following menopause appear to underlie the observed redistribution of body fat. As expected, menopause was marked by significant increases in gonadotropins (FSH and LH) and sharp declines in sex steroids, particularly estradiol and progesterone. These changes likely contribute to the transition from a gynoid to an android fat pattern, as estrogen is known to regulate lipid metabolism and subcutaneous fat storage.\u003c/p\u003e\u003cp\u003eAs for fat-storage\u0026ndash;related hormones such as insulin, cortisol, and thyroid hormones, no significant differences were observed between pre- and postmenopausal women. This suggests that, despite their known roles in energy balance and adiposity, these hormones may not be primary drivers of the metabolic changes associated with menopause.\u003c/p\u003e\u003cp\u003eNotably, two significant hormonal changes emerged in postmenopausal women: a substantial rise in RBP4 and a marked decline in HGH. Importantly, these hormonal shifts were not only evident as between-group differences but were also reflected in association analyses, which revealed that both hormones were significantly linked to patterns of body fat distribution. This suggests that these metabolic hormones may contribute to key alterations in fat storage and metabolic risk associated with menopause.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAssociation of fat storage regulators with regional fat depots:\u003c/h2\u003e\u003cp\u003eAmong all fat-regulating hormones assessed, only RBP4, insulin, and HGH demonstrated consistent associations with specific fat depots. Insulin correlated positively with both subcutaneous (abdominal and peripheral) and visceral fat, aligning with its well-known potent anabolic and lipogenic roles (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In contrast, HGH exhibited a strong inverse association with visceral adiposity, supporting its established function in promoting lipolysis and inhibiting fat accumulation (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eThe sharp decline in HGH levels observed postmenopause, along with its negative associations with multiple fat depots, likely reflects a reduced physiological capacity to oppose the effects of lipogenic hormones such as insulin and RBP4. This imbalance may contribute directly to visceral fat accumulation, consistent with prior evidence linking age-related reductions in HGH to central fat redistribution and increased cardiometabolic risk (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of regional fat depots\u003c/h2\u003e\u003cp\u003eRBP4 emerged as the strongest correlate and independent predictor of visceral adiposity, with circulating levels elevated by 23.7% in postmenopausal women, a greater increase than observed for other adipokines. This aligns with previous studies linking RBP4 to insulin resistance, hepatic steatosis, and central obesity (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Regression analysis further identified RBP4 as the primary determinant of visceral fat accumulation, explaining a larger proportion of variance than insulin, suggesting a mechanistic role beyond mere association. Functionally, RBP4 has been shown to enhance adipocyte differentiation, promote macrophage infiltration, and upregulate pro-inflammatory cytokines such as IL-6, thereby potentiating both lipogenic and inflammatory pathways in visceral depots (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eConversely, HGH emerged as the dominant negative predictor of visceral fat, explaining 13% of the variance. Its inverse relationship across all fat depots is consistent with its established lipolytic and anti-adipogenic effects, primarily through hormone-sensitive lipase activation and inhibition of lipid uptake (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The marked reduction in circulating GH observed after menopause likely diminishes this protective effect, thereby permitting greater RBP4-driven visceral expansion. This hormonal interplay, characterized by diminished GH activity and elevated RBP4 may represent a pivotal shift in adipose regulation with aging and estrogen decline.\u003c/p\u003e\u003cp\u003eFor subcutaneous fat depots, insulin emerged as the leading positive predictor, consistent with its established role in stimulating lipogenesis and glucose uptake in adipocytes. RBP4 contributed minimally to abdominal subcutaneous fat, and was excluded from the peripheral model, emphasizing its depot-specific impact. Progesterone exerted a modest opposing influence on both visceral and peripheral subcutaneous fat. Evidence suggests its influence varies by fat depot, sex, and hormonal context, likely due to differential receptor expression (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eCollectively, these findings describe distinct hormonal interplays in regional adiposity: RBP4 as the main independent positive determinant of visceral fat, insulin as the dominant predictor of subcutaneous fat, and HGH as the principal negative regulator across depots. Thereby, elevated RBP4, coupled with reduced HGH activity, may promote visceral fat distribution characteristic of menopause and aging.\u003c/p\u003e\u003cp\u003eThese findings highlight a potential depot-specific effect of RBP4 that warrants further mechanistic exploration. RBP4 is a transporter of retinol, the metabolic precursor of retinoic acid (RA), which functions as a potent regulator of cellular differentiation through nuclear retinoic acid receptors (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Adipose tissue is an active site of RA biosynthesis. The conversion of retinol to retinaldehyde and subsequently to RA is catalyzed by retinol dehydrogenases and aldehyde dehydrogenases, which are expressed in adipose depots. Evidence indicates that this pathway operates in a depot-specific manner, with visceral adipose tissue demonstrating particularly high RA-generating capacity. In human visceral fat, aldehyde dehydrogenase (ALDH1A2) expression is enriched, and the rate of RA formation is approximately threefold higher than in subcutaneous depots (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). In addition, Experimental work in mice has shown that ALDH1A1 is the dominant enzyme for RA production during adipogenesis, driving the expression of key transcriptional regulators such as ZFP423 and PPARγ, thereby promoting fat formation, particularly in visceral depots (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Moreover, ALDH1A1 expression in visceral fat has been linked to diet- and sex-specific differences in fat accumulation, with inhibition of this pathway protecting female mice from visceral obesity (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Collectively, these findings establish that visceral adipose tissue possesses a uniquely active RA biosynthetic program, positioning retinol transport by RBP4 as a potentially important upstream regulator of depot-specific adipogenesis and metabolic risk. Elevated RBP4 released from visceral fat could therefore perpetuate a feed-forward loop: increased secretion from expanding adipose tissue may promote further adipogenesis, particularly in visceral depots, sustaining their growth and metabolic persistence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eRetinol binding protein: A mediator of systemic inflammation and dyslipidemia\u003c/h2\u003e\u003cp\u003eExperimental evidence supports RBP4 as a mediator of adipose inflammation and systemic insulin resistance through activation of antigen-presenting cells and macrophages (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Recent evidence, including a comprehensive review by Sontoro et al. (NEJM, 2023), reinforces the mechanistic relevance of RBP4 in promoting adipose-tissue inflammation and early metabolic dysfunction, preceding the onset of insulin resistance. Beyond its role as a retinol transport protein, RBP4 acts as an active mediator linking adipose inflammation to metabolic impairment, linked to visceral fat accumulation (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenerally, our mediation analyses position RBP4 as a dual-mechanism factor: (i) acting through visceral fat to drive inflammation and insulin resistance, and (ii) exerting independent effects on atherogenic lipids. Our models suggest that much of the link between RBP4 and inflammation (CRP, IL-6) appeared to operate through visceral fat, positioning RBP4 as a key amplifier of the visceral\u0026ndash;inflammation axis. Therefore, reducing visceral adiposity would be expected to substantially attenuate this inflammatory signal. Importantly, also, RBP4 was also associated with lipid abnormalities independent of visceral fat, suggesting that RBP4 contributes to dyslipidemia through fat-independent pathways, in line with studies linking it to vascular injury and atherosclerosis (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). This may require direct lipid-lowering strategies alongside adiposity management. These findings are summarized in a hypothetical schematic presentation shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. However, these are associational results from regression-based mediation in observational data; they support pathways but do not establish causality.\u003c/p\u003e\u003cp\u003eA recent landmark study, by Ridker et. al (NEJM, 2024) strongly supports our findings by demonstrating that a combined measure of high-sensitivity CRP, LDL cholesterol, and lipoprotein(a) predicts incident cardiovascular events over 30 years in initially healthy U.S. women, highlighting the connection between inflammation and lipids as key drivers of cardiometabolic risk (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eOur findings expand this framework by implicating RBP4, a hepatoadipokine linked to both systemic inflammation and atherogenic dyslipidemia, as a potential upstream mediator of these pathways. Prior mechanistic studies have shown that RBP4 promotes adipose tissue inflammation through macrophage and T-cell activation and increases the production of interleukin-6, thereby boosting hepatic CRP synthesis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Alongside, clinical studies associate elevated RBP4 levels with adverse lipid profiles, including higher triglycerides and lower HDL cholesterol (\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBoxes denote variables; arrows indicate positive associations tested in mediation models (PROCESS, Model 4; 5,000 bootstrap resamples). Results summarize significant indirect (via visceral fat) and/or direct paths. Here, \u0026ldquo;via visceral fat\u0026rdquo; denotes the mediated path; \u0026ldquo;direct\u0026rdquo; denotes effects after adjusting for visceral fat. Significance is based on 95% bootstrap CIs. RBP4 is associated with higher visceral fat, which further relates to higher inflammation (\u003cem\u003ehsCRP, IL-6\u003c/em\u003e), insulin resistance (\u003cem\u003eHOMA-IR\u003c/em\u003e), glycemic burden (\u003cem\u003eHbA1c\u003c/em\u003e) and atherogenic lipids (\u003cem\u003etriglycerides, LDL-C, ApoB\u003c/em\u003e). Further, arrows from RBP4 directly to atherogenic lipids (\u003cem\u003etriglycerides, LDL-C, ApoB\u003c/em\u003e) and homocysteine depicts a direct, fat-independent path. An inverse association of RBP4 with the anti-inflammatory profile (lower hsCRP and IL-6) is demonstrated as a direct fat independent path.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eConclusion and future perspectives\u003c/h2\u003e\u003cp\u003eOverall, RBP4 emerges as a key predictor of visceral adiposity in women, displaying dual visceral-fat\u0026ndash;related and fat-independent associations with cardiometabolic risk pathways. The pattern of findings suggests that higher RBP4 levels are linked to greater visceral adiposity, accompanied by a significant visceral-fat\u0026ndash;related inflammatory and atherogenic lipid profile. In parallel, RBP4 also shows adiposity-independent associations with atherogenic lipids and oxidative markers, suggesting an additional pathway that operates irrespective of visceral fat. Although an anti-inflammatory direct association was observed, it was overridden by the stronger visceral-fat\u0026ndash;related inflammatory signal.\u003c/p\u003e\u003cp\u003eTogether, these findings suggest that RBP4 may contribute to cardiometabolic vulnerability through two complementary mechanisms, one amplified by visceral fat accumulation and another independent of adiposity. This dual pattern positions RBP4 as a promising biomarker of visceral-adiposity\u0026ndash;related risk and highlights its potential relevance as a prospective risk indicator and as a target for future strategies aimed at modulating fat storage and cardiometabolic risk in women, warranting further evaluation in longitudinal and male cohorts\u003c/p\u003e\u003cp\u003eYet, several cautions remain. Our findings are based on observational cross-sectional associations and mediation models, which support pathways but do not establish causality. The strength of this study lies in addressing a limitation present in women research, the lack of hormonal-phase control, which may have masked sex-specific regulation. We addressed this by selecting premenopausal women specifically in the early follicular phase. An additional advantage was conducting all sampling in the morning, which helped minimize daily hormonal fluctuations, such as cortisol and growth hormone that could confound the data.\u003c/p\u003e\u003cp\u003eOur findings identify RBP4 as a novel and clinically relevant predictor of visceral adiposity in women, with downstream implications for inflammatory and lipid pathways that may be particularly accentuated after menopause. Given RBP4\u0026rsquo;s role as the principal carrier of retinol, and an established adipokine, these observations raise the possibility that retinoid signaling may influence adipocyte differentiation and depot-specific autocrine fat expansion. These insights open new avenues for mechanistic exploration. Specifically, future studies are needed to determine whether RBP4 actively contributes to visceral adiposity development, potentially through retinoid-regulated adipogenesis, or whether it reflects upstream metabolic disturbances that track with central fat accumulation. Clarifying these processes will be essential for understanding how RBP4 may shape inflammation, lipid metabolism, and cardiometabolic vulnerability in women.\u003c/p\u003e\u003cp\u003eIn addition, the sex-specific nature of these findings highlights the need for comparative studies in men to define whether RBP4 carries similar or distinct metabolic implications across sexes. Longitudinal research will also be crucial to establish temporal relationships and evaluate whether RBP4 predicts incident visceral adiposity or cardiometabolic outcomes.\u003c/p\u003e\u003cp\u003eCollectively, this study provides a foundation for advancing both mechanistic and translational work, including the potential development of RBP4-directed strategies aimed at modulating visceral fat biology and its associated metabolic sequelae.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAI Use Statement:\u0026nbsp;\u003c/strong\u003eAI tools were used to assist with language editing, structural clarity, and improving the general presentation and understanding of the manuscript. All scientific ideas, data sources, data analysis (SPSS), and conclusions including \u003cstrong\u003eFigure 1\u003c/strong\u003e are original.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing financial or non-financial interests related to the work described in this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJS conceived and designed the study, prepared funding proposal, synthesized the research narrative, analyzed and interpreted findings, wrote the original draft, and supervised all stages of the project. MAM contributed to study design, coordinated data collection and project management, ensured quality control, curated data, and participated in original draft writing and data analysis. KAH contributed to the original study design and funding acquisition, contributed to sample collection, contributed to critical manuscript revision, and clinical perspective and helped define research directions. KAR supported the conceptual framework by aligning study design with clinical relevance and advised on the overall study direction. MAR provided an informed perspective through critical review and integration of updated sources, and manuscript writing and review. NC ensured clinical applicability in study design, managed patient recruitment and communication, and handled questionnaire administration and sample/data collection. ME contributed conceptually by providing clinical and methodological insight on study limitations, critically revised the manuscript with interpretive feedback, and contributed to research directions.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the Research Council of Oman (TRC), Grant No. RC/MED/BIOC/15/01. The authors gratefully acknowledge the support of the Clinical Biochemistry Laboratory at Sultan Qaboos University Hospital (SQUH), particularly Dr. Mohsen Al-Lawati for assistance with sample processing and analysis. Special thanks are due to Mr. Marvin Enriquez from the Microbiology Laboratory at SQUH. We extend our sincere appreciation to all the women who volunteered to participate in this study, including Omani women societies, and nursing staff at Sultan Qaboos University hospital particularly physiology for their technical support and assistance with sample collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to participant confidentiality agreements approved by the Institutional Ethics Committee. Data access is restricted to the research team in accordance with the informed consent provided by participants and the ethics approval granted by the Institutional Ethics Committee (SQU-EC/164/14, MREC #1019).Additionally, these data are part of a larger research project with planned future publications that have not yet been released. However, de-identified data may be made available from the corresponding author upon reasonable request and with prior approval from the Institutional Ethics Committee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNeeland IJ, Ross R, Despr\u0026eacute;s JP, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7(9):715\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee MJ, Kim J. The pathophysiology of visceral adipose tissues in cardiometabolic diseases. Biochem Pharmacol. 2024;222:116116.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZiegler AK, Scheele C. 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J Clin Med. 2019;8(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocha M, Ba\u0026ntilde;uls C, Bellod L, Rovira-Llopis S, Morillas C, Sol\u0026aacute; E, et al. Association of serum retinol binding protein 4 with atherogenic dyslipidemia in morbid obese patients. PLoS ONE. 2013;8(11):e78670.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Visceral Adiposity, Retinol-Binding Protein 4, Growth Hormone, Mediation analysis, Cardiometabolic risk, Atherogenesis, high sensitivity C-reactive Protein, Interleukin-6, Homocysteine","lastPublishedDoi":"10.21203/rs.3.rs-8310783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8310783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVisceral adiposity is a key contributor to cardiometabolic risk through links to insulin resistance, inflammation, and atherogenic dyslipidemia. In women, this is especially relevant during menopausal transition, when hormonal shifts increase visceral fat and cardiometabolic vulnerability. Yet factors predisposing to visceral adiposity remain unclear, emphasizing the need to identify its determinants as targets to modulate visceral fat and associated metabolic risk.\u003c/p\u003e\u003cp\u003eWe studied 410 healthy women (290 premenopausal, 120 postmenopausal) under controlled hormonal conditions. Serum measures included fat-storage\u0026ndash;related hormones/proteins, cardiovascular risk (atherogenic lipids, inflammatory and oxidation) parameters. Regional fat predictors were identified by regression analyses. Mediation models examined whether the predictor\u0026rsquo;s association with visceral fat corresponded to downstream cardiometabolic outcomes.\u003c/p\u003e\u003cp\u003eHigher visceral fat (+\u0026thinsp;50.7%), elevated retinol-binding-protein-4 (RBP4) (+\u0026thinsp;23.6%), and lower growth hormone (GH) (\u0026minus;\u0026thinsp;63.8%) levels were found in postmenopausal compared to premenopausal women (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RBP4 was the main positive predictor of visceral adiposity (6.5% variance%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), surpassing insulin, while GH was the strongest negative predictor (\u0026minus;\u0026thinsp;13%). Mediation models showed that RBP4 associated with metabolic risk profiles through both visceral fat\u0026ndash;related and fat-independent pathways. The strongest associations were observed for the inflammatory profile. RBP4 demonstrated visceral-fat\u0026ndash;related associations with (hsCRP β\u0026thinsp;=\u0026thinsp;0.0617; IL-6 β\u0026thinsp;=\u0026thinsp;0.036, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, a fat-independent pathway showed inverse associations with these markers (hsCRP β= \u0026minus;0.0678; IL-6 β= \u0026minus;0.0650, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a weaker anti-inflammatory profile, and predominance of the visceral fat-associated pro-inflammatory pathway.\u003c/p\u003e\u003cp\u003eFurthermore, RBP4 demonstrated visceral-fat\u0026ndash;mediated associations with LDL-C, triglycerides, and ApoB (β\u0026thinsp;=\u0026thinsp;0.0136, 0.0063, 0.0037; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside fat-independent associations (β 0.0279, 0.0129, 0.0082; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggesting involvement of both visceral-fat\u0026ndash;related and direct pathways. The association with insulin resistance was primarily through visceral fat (HOMA-IR β\u0026thinsp;=\u0026thinsp;0.0172; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the association with the oxidative parameter, homocysteine, was exclusively independent (β\u0026thinsp;=\u0026thinsp;0.033; p\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e\u003cp\u003eOverall, RBP4 emerges as a key predictor of visceral adiposity, with both visceral-fat\u0026ndash;related and fat-independent associations linked to cardiometabolic risk. These findings highlight RBP4 as a potential contributor to visceral fat\u0026ndash;related vulnerability in women, warranting further investigations in longitudinal and male cohorts.\u003c/p\u003e","manuscriptTitle":"Retinol-Binding Protein-4 Predicts Visceral Adiposity and Related Inflammatory–Cardiometabolic Profiles in Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 09:22:16","doi":"10.21203/rs.3.rs-8310783/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c0ec572-82b2-4c18-ad7e-6ec630c5dc61","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T22:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 09:22:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8310783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8310783","identity":"rs-8310783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}