{"paper_id":"0348f3c3-51f8-4406-adfe-ddd47ca3afc4","body_text":"Subcutaneous adipocyte glucocorticoid-responsive transcripts after modest weight gain in South Asian and White European men: an exploratory longitudinal study | 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 Subcutaneous adipocyte glucocorticoid-responsive transcripts after modest weight gain in South Asian and White European men: an exploratory longitudinal study Xuan Gao, Robin Lengton, Jenny A. Visser, James McLaren, Naveed Sattar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9199966/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Adipocyte glucocorticoid (GC) signalling influences lipid storage and insulin sensitivity, and South Asians develop insulin resistance at lower BMI than White Europeans. We tested whether early modest weight gain alters adipocyte GC-related transcripts differently by ancestry and whether within-person transcript changes track dynamic insulin responses. Methods White European (n = 21) and South Asian (n = 14) men underwent ~ 6% diet-induced weight gain. Abdominal subcutaneous adipocytes were sampled at baseline and post–weight gain for RT-qPCR assessment of GC-responsive transcripts ( FKBP5, TSC22D3/GILZ ) and related targets ( HSD11B1, HSD11B2, IL2, IL6 ). Metabolic responses were characterised using a standardised mixed-meal test with 5-hour profiles of glucose, insulin, C-peptide and triglycerides; hepatic triglyceride content was quantified by MRI. Results Weight gain reduced FKBP5 (− 23.65 ± 8.82% in White Europeans; −17.68 ± 11.62% in South Asians; P = 0.007) and GILZ (− 11.40 ± 2.83%; −5.95 ± 4.01%; P = 0.001 for change with weight gain), with no ethnicity×intervention interaction (P ≥ 0.26). HSD11B1/HSD11B2 and IL2/IL6 did not change. At baseline, FKBP5 and GILZ were associated with adiposity, liver fat and adipocyte size. Within-person ΔFKBP5 (post–weight gain minus baseline) correlated with Δpostprandial insulin (r = 0.46, P = 0.006) and ΔC-peptide (r = 0.34, P = 0.049). ΔGILZ correlated with Δfasting glucose (r = − 0.40, P = 0.017) and Δpostprandial insulin (r = 0.34, P = 0.049). In mixed-effects models, change in postprandial insulin remained an independent correlate of both transcripts. Conclusion Early modest weight gain downregulates adipocyte GC-responsive transcripts similarly across ancestries, and dynamic transcript changes track insulin exposure. These findings implicate insulin dynamics as a potential driver of adipocyte GC signalling adaptations during early weight gain, linking adipocyte transcriptional responses to clinically relevant postprandial insulin physiology. Adipocyte Ethnicity FKBP5 GILZ Glucocorticoid signalling Insulin South Asian Subcutaneous adipose tissue Type 2 diabetes Weight gain Highlights Modest short-term weight gain was associated with lower adipocyte FKBP5 and GILZ expression. These changes did not differ according to ethnicity. Baseline FKBP5 and GILZ expression was associated with adiposity, liver fat and adipocyte morphology. Within-person changes in glucocorticoid-responsive transcripts were associated with changes in postprandial insulin responses. Introduction Adipocyte glucocorticoid signalling, mediated by the nuclear glucocorticoid receptor, influences lipid storage and adipocyte differentiation, insulin action and systemic metabolic homeostasis. South Asian populations experience greater central adipose tissue accumulation, insulin resistance and type 2 diabetes at lower body mass index than White Europeans. These differences reflect ethnic variation in body fat distribution, hepatic fat, and fitness that manifest across the life course (1–10). We previously demonstrated in the GlasVEGAS cohort that weight gain leads to greater adverse metabolic responses in South Asians compared with White Europeans (6). In this context, glucocorticoid metabolism in adipose tissue, including local regeneration via 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), glucocorticoid receptor isoform balance, and downstream glucocorticoid-responsive transcripts - represents a plausible pathway through which modest weight gain could exacerbate metabolic risk (11, 12). Among these downstream targets, FK506 binding protein 5 ( FKBP5) and glucocorticoid-induced leucine zipper ( GILZ ) are well-established glucocorticoid-responsive genes in human adipose tissue that have been linked to insulin sensitivity, adipose inflammation, and adipogenesis (4, 13–18). More specifically, adipose tissue GILZ mRNA levels have been found to be reduced in obesity, and knockdown of GILZ in human adipocytes ex vivo coincided with enhanced expression of the proinflammatory factors interleukin-6 (IL6) and leptin (16). The latter may contribute to the generally more proinflammatory state observed in South Asians (19, 20). Important questions that remain include how weight gain affects adipocyte glucocorticoid signalling, as well as how this behaves in the metabolically vulnerable South Asian population. Against this background, we examined glucocorticoid-related transcript responses in mature abdominal subcutaneous adipocytes obtained from South Asian and White European men before and after modest weight gain. Specifically, we assessed transcripts related to local glucocorticoid metabolism ( HSD11B1 and HSD11B2 ), candidate glucocorticoid-responsive downstream targets ( FKBP5 and TSC22D3/GIL Z), and selected inflammation-related targets ( IL2 and IL6 ), while also attempting to quantify NR3C1 isoforms. We further examined whether baseline transcript abundance and within-person transcript changes were associated with indices of adiposity, adipocyte morphology and insulin-related metabolic responses. Given the exploratory nature of the study and the modest sample size, we aimed primarily to determine whether short-term weight gain was accompanied by measurable adipocyte transcript changes and whether these changes tracked contemporaneous metabolic alterations. Methods Study design and participants The Glasgow visceral and ectopic fat with weight gain in South Asians (GlasVEGAS) study was registered on ClinicalTrails.gov (NCT02399423). Ethical approval was obtained from the University of Glasgow College of Medical, Veterinary and Life Sciences Ethics Committee and all participants provided written informed consent. A within person weight gain study was conducted in two ancestry groups: White European men (n = 21) and South Asian men (n = 14). Participants attended two study visits, at baseline and after achieving a minimum of 5% (target ~ 7%) weight gain. Weight gain was induced through an overfeeding protocol over 4–6 weeks, whereby participants were asked to eat until they felt more full than usual and were provided with high-energy snacks (ice-cream, chocolate bars, potato crisps, cheese, dried fruit and nuts, sugary drinks) providing an additional ~ 6.2–8.4 MJ/day (1500–2000 kcal/day). Once participants gained the required weight, participants followed a weight-neutral diet for three days before post-weight gain assessments. All participants provided written informed consent. Inclusion and exclusion criteria, safety procedures, and weight-gain protocols have been described previously (6). Parent-study context The present analysis should be interpreted within the context of the broader GlasVEGAS human overfeeding study, from which additional physiological and metabolic phenotyping has been reported previously. The current manuscript focuses specifically on adipocyte glucocorticoid-related transcript responses in mature abdominal subcutaneous adipocytes, whereas broader phenotypic measurements obtained in the parent study included body composition, hepatic triglyceride content, and postprandial glucose, insulin, C-peptide and triglyceride responses to a standardised mixed-meal test, together with the broader adverse metabolic responses to short-term overfeeding reported previously in the GlasVEGAS study (6). This exploratory secondary analysis was undertaken to examine whether adipocyte glucocorticoid-responsive transcription changed during early weight gain and whether such changes were associated with contemporaneous metabolic responses. Adipose sampling and adipocyte isolation Subcutaneous abdominal white adipose tissue was obtained by needle biopsy at both visits. Mature adipocytes were isolated using established procedures as described previously (6) and processed for RNA extraction. All samples were frozen at − 80°C until use, and samples from a given participant were processed in parallel when feasible to minimise batch effects (6). Gene expression quantification Total RNA was extracted from purified mature adipocytes using the RNeasy Lipid Tissue Mini Kit (QIAGEN, 74804) according to the manufacturer’s instructions. RNA was eluted in nuclease-free water, and RNA concentration and purity were assessed using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific); optical density 260/280 nm ratios within the acceptable range for downstream analysis were considered suitable. To minimise genomic DNA contamination, RNA samples underwent DNase treatment using the DNA-free kit (Thermo Fisher Scientific, AM1906) according to the manufacturer’s instructions. Single-stranded cDNA was synthesised from purified RNA using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, 4368813) (6). Because RNA yield from isolated mature adipocytes was limited, target cDNA was pre-amplified using TaqMan PreAmp Master Mix (2×) (Thermo Fisher Scientific, 4384266). A pooled assay mix was prepared using equal amounts of each target assay, and pre-amplification was performed for 10 cycles on a StepOnePlus Real-Time PCR system, after which samples were diluted 1:5 in TE buffer before quantitative PCR. Pre-amplification uniformity was assessed by comparing Ct values obtained with and without pre-amplification, using CDKN1B as the endogenous control for the uniformity assessment. A ΔΔCt difference of less than ± 1.5 between the diluted pre-amplified sample and the corresponding non-amplified sample was considered acceptable (6). Quantitative PCR was performed in duplicate on a StepOnePlus Real-Time PCR system using TaqMan Universal PCR Master Mix (Thermo Fisher Scientific, 4304437). TaqMan Gene Expression Assays (Applied Biosystems) were used for HSD11B1 (Hs01547870_m1), HSD11B2 (Hs00388669_m1), FKBP5 (Hs01561006_m1), TSC22D3/GILZ (Hs00608272_m1), IL2 (Hs00174114_m1), and IL6 (Hs00174131_m1). NR3C1 isoforms (GRα and GRβ) were assessed using previously published primer pairs. The fluorescence threshold for the FAM-MGB reporter was set at 0.2 ΔRn in accordance with the manufacturer’s recommendations, and signals earlier than Ct 15 were regarded as background fluorescence and were not interpreted quantitatively. PPIA was used as the endogenous reference gene for ΔCt and ΔΔCt analyses and did not differ materially by ancestry or intervention (21). Each PCR plate included no-reverse-transcription controls and no-template controls to monitor genomic DNA contamination and non-specific amplification. Assay performance was reviewed before inferential analysis, and targets showing limited or unreliable amplification were excluded from formal statistical analysis and interpreted descriptively only. Body composition and adipocyte morphology Body mass index was recorded at both visits. Body composition indices, including abdominal subcutaneous adipose tissue volume and whole body lean mass, were measured by magnetic resonance imaging (MRI) using a 3.0-Tesla scanner (Magnetom, Siemens). Adipocyte morphology was assessed in isolated adipocytes by measuring intracellular diameter of 150 adipocytes per sample using ImageJ (version 1.52a, NIH) from digital images captured under a B×50 microscope (10× lens). Adipocytes were categorised as very small (≤ 30 µm), small (31–70 µm), medium (71–90 µm) or large (> 90 µm), and mean adipocyte diameter and the proportion within each size category were calculated as described previously (6). Metabolic assessments Participants underwent a standardised mixed meal test (800 kcal: 37% fat, 47% carbohydrate, 17% protein) following an overnight fast. Blood samples were collected at fasting and at 30, 45, 60, 90, 120, 180, 240 and 300 minutes postprandially for measurement of glucose, insulin, C-peptide and triglycerides. Time-averaged areas under the postprandial concentration versus time curves (AUC) were used as summary measures of postprandial metabolic responses. Full details of the metabolic test protocol have been described previously (6). Statistical analysis Baseline differences between ancestry groups were assessed using unpaired t-tests. Pearson correlations were used to examine associations between gene expression and indices of body composition, adipocyte morphology and metabolic function at baseline (univariable analyses). Within-person change (Δ) associations examined Pearson correlations between within-person change in gene expression (weight gain minus baseline) and changes in metabolic variables. Because FKBP5 and GILZ expression showed significant changes with weight gain and are established downstream glucocorticoid-responsive transcripts, we used mixed-effects ANOVA with subject-level random intercepts to model change in these genes as outcomes, with ethnicity (South Asian versus White European), baseline body mass index, mean adipocyte diameter and batch as covariates, and candidate metabolic predictors including change in insulin area under the curve. Results are presented as crude (unadjusted) and then sequentially adjusted models. Model 1 included ethnicity in addition to the crude model, and Model 2 further added baseline gene expression; subsequent models additionally incorporated candidate metabolic covariates, with sensitivity models adding each metabolic predictor separately to assess the contribution of individual covariates and potential correlated predictors. To assess regression to the mean, we examined pairwise Pearson correlations between baseline levels and changes (Electronic supplementary material [ESM] Table 1 ) and applied Oldham's method by modelling change versus the average of baseline and post-weight gain values (ESM Table 2 ). Table 1 Expression of glucocorticoid-related genes in adipocytes in white European (WE) and South Asian (SA) men at baseline and after weight gain Gene Baseline WE (n = 21) Baseline SA (n = 14) P a Δ WE (n = 21) Δ SA (n = 14) P b P c Relative expression to PPIA (%) FKBP5 104.23 ± 9.45 86.77 ± 9.88 0.23 -23.65 ± 8.82 -17.68 ± 11.62 0.007 0.68 GILZ 40.09 ± 2.61 31.85 ± 3.69 0.07 -11.4 ± 2.83 -5.95 ± 4.01 0.001 0.26 HSD11B1 11.65 ± 2.2 13.11 ± 2.49 0.97 -2.01 ± 1.34 -2.48 ± 1.62 0.14 0.62 HSD11B2 0.14 ± 0.02 0.12 ± 0.02 0.63 -0.05 ± 0.02 0.00 ± 0.02 0.39 0.23 IL2 d 0.00 ± 0.00 0.00 ± 0.00 — 2.80×10⁻⁵±1.70×10⁻⁵ 2.89×10⁻⁵±2.18×10⁻⁵ — — IL6 0.11 ± 0.04 0.38 ± 0.15 0.15 0.04 ± 0.02 -0.19 ± 0.16 0.91 0.19 Values are mean ± SEM Δ: within person change with weight gain P a : baseline ethnicity difference between white Europeans and South Asians at baseline (unpaired t-test) P b : weight gain main effect of weight gain intervention (2-way ANOVA) P c : ethnicity x weight gain intervention interaction (2-way ANOVA) d : Detectable in 3/21 ( Δ WE) and 2/14 ( Δ SA) —: not applicable / not calculated (baseline expression was 0 in both groups; therefore Pᵃ, Pᵇ and Pᶜ were not calculated for IL2). Table 2 Univariable correlations between FKBP5 and GILZ expression in subcutaneous adipose tissue and indices of body composition/ adipocyte size and metabolic function Gene expression Correlate N r 95% CI for r P Body composition/ adipocyte size FKBP5 ASAT (l) 33 -0.35 (-0.62, -0.006) 0.047 Liver fat fraction (%) 32 -0.35 (-0.62, -0.003) 0.049 Mean adipocyte diameter (µm) 30 -0.42 (-0.68, -0.07) 0.020 Proportion of small adipocytes (% cells) 30 0.42 (0.07, 0.68) 0.021 GILZ BMI 35 0.36 (0.03, 0.62) 0.033 Whole-body lean tissue (l) 33 0.41 (0.07, 0.66) 0.019 Liver fat fraction (%) 32 -0.38 (-0.65, -0.04) 0.030 Proportion of small adipocytes (% cells) 30 0.38 (0.02, 0.65) 0.041 Metabolic GILZ Postprandial C-peptide (nmol·l − 1 ) 35 -0.34 (-0.60, -0.004) 0.048 Fasting triglycerides (mmol·l − 1 ) 35 -0.38 (-0.64, -0.06) 0.023 Postprandial triglycerides (mmol·l − 1 ) 35 -0.39 (-0.64, -0.07) 0.020 CI, confidence interval; ASAT, abdominal subcutaneous adipose tissue; BMI, body mass index N indicates the number of participants with non-missing paired measurements for each correlation r and P values were obtained from two-sided, univariable Pearson’s correlations without adjustment for multiple comparisons. All tests were two-sided with an alpha of 0.05. Fitness and physical activity variables were excluded from primary analyses due to limited sample size. Statistical analyses were performed using Minitab (version 22.1, Minitab LLC) and figures were generated using GraphPad Prism (version 9.5.1). Results Baseline characteristics including age, BMI, and metabolic parameters have been described previously (6). Weight gain downregulates glucocorticoid-responsive transcripts At baseline, there were no statistically significant ethnic differences in adipocyte FKBP5 or GILZ expression (Table 1 ). Expression of HSD11B1 and HSD11B2 also did not differ significantly between South Asian and White European participants. IL6 was detectable at low levels and showed no significant ethnic difference, whereas IL2 showed limited and inconsistent amplification and was therefore considered only descriptively. NR3C1 isoforms (GRα and GRβ) could not be reliably quantified in this adipocyte material because transcript abundance was below the robust limit of detection and assay performance was insufficient for confident quantitative interpretation (22, 23). These targets were therefore not taken forward as informative quantitative outcome. Adipocyte FKBP5 and GILZ mRNA expression levels decreased similarly in response to weight gain in both ethnic groups ( FKBP5 : −23.65 ± 8.82% in White Europeans and − 17.68 ± 11.62% in South Asians, P weight gain = 0.007; GILZ : −11.4 ± 2.83% in White Europeans and − 5.95 ± 4.01% in South Asians, P weight gain = 0.001; Table 1 ). The ethnicity × weight gain interaction was not statistically significant for either transcript, indicating no evidence of an ethnic difference in the response to weight gain. HSD11B1 , HSD11B2 and IL6 did not change significantly with weight gain, and IL2 remained uninformative because of limited detectability (Table 1 ). Baseline glucocorticoid-responsive gene expression relates to adiposity and metabolic phenotype At baseline, FKBP5 expression was negatively correlated with ASAT volume (r = − 0.35, P = 0.047), liver fat fraction (r = − 0.35, P = 0.049) and adipocyte diameter (r = − 0.42, P = 0.020), while a positive correlation was observed between FKBP5 expression and the proportion of small adipocytes (r = 0.42, P = 0.021, Table 2 ; full correlation matrix in ESM Table 3 ). Representative scatterplots are shown in ESM Fig. 1. GILZ expression showed a complementary pattern: GILZ expression was correlated with BMI (r = 0.36, P = 0.033), lean mass (r = 0.41, P = 0.019) and the proportion of small adipocytes (r = 0.38, P = 0.041), whereas negative correlations were observed with liver fat fraction (r = − 0.38, P = 0.030), postprandial triglyceride (r = − 0.39, P = 0.020), fasting triglyceride (r = − 0.38, P = 0.023) and fasting C-peptide (r = − 0.34, P = 0.048,Table 2 ; ESM Table 3 ). Scatterplots of baseline GILZ expression versus metabolic parameters are shown in ESM Fig. 2. Table 3 Univariable correlations between weight gain–induced changes (Δ) in FKBP5 and GILZ expression and concurrent changes in metabolic variables. Δ Gene expression Correlate N r 95% CI for r P Metabolic Δ FKBP5 Δ postprandial C-peptide (nmol·l − 1 ) 35 0.34 (0.002, 0.60) 0.049 Δ postprandial insulin (µU·l − 1 ) 35 0.46 (0.14, 0.69) 0.006 Δ GILZ Δ fasting glucose (mmol·l − 1 ) 35 -0.40 (-0.65, -0.08) 0.017 Δ postprandial insulin (µU·l − 1 ) 35 0.34 (0.001, 0.60) 0.049 Δ indicates within-person change with weight gain. r and P values were obtained from two-sided, univariable Pearson’s correlations without adjustment for multiple comparisons. CI, confidence interval. N indicates the number of participants with non-missing paired measurements for each correlation. Changes in glucocorticoid-responsive transcripts correlate with changes in insulin dynamics Analyses of within-person change (Δ) indicated that dynamic alterations in expression of adipocyte glucocorticoid responsive transcripts tracked shifts in metabolic status. Δ FKBP5 expression correlated positively with change in postprandial insulin and C peptide (r = 0.46, P = 0.006; r = 0.34, P = 0.049), while Δ GILZ correlated negatively with change in fasting glucose and positively with Δpostprandial insulin (r = -0.40, P = 0.017; r = 0.34, P = 0.049). Full correlation coefficients and confidence intervals are provided in Table 3 (extended version in ESM Table 4 ). Scatterplots illustrating these within-person associations are shown in ESM Fig. 3. Table 4 Hierarchical mixed-effects models for within-person change (Δ) in (a) FKBP5 expression and (b) GILZ expression. (a) Δ FKBP5 Model 0 Model 1 Model 2 Model 3 Model 3s-A Model 3s-B Intercept -21.26 (6.95), 0.004 -20.67 (7.2), 0.007 43.66 (13.98), 0.004 25.10 (14.37), 0.091 34.57 (15.14), 0.029 25.79 (14.69), 0.89 Ethnicity (SA vs WE) — -2.98 (7.18), 0.68 2.89 (5.59), 0.61 6.90 (5.51), 0.22 6.31 (6.00), 0.30 8.20 (5.57), 0.15 Baseline FKBP5 — — -0.67 (0.13), < 0.001 -0.60 (0.13), < 0.001 -0.63 (0.14), < 0.001 -0.59 (0.13), < 0.001 Δ postprandial C-peptide — — — -14.5 (9.38), 0.13 6.95 (4.85), 0.16 — Δ postprandial insulin — — — 1.90 (0.73), 0.014 — 0.90 (0.35), 0.016 Model fit: AICc 354.89 348.96 332.09 317.57 325.07 326.25 Model fit: BIC 356.30 350.32 333.43 318.83 326.36 327.55 (b) Δ GILZ Model 0 Model 1 Model 2 Model 3 Model 3s-A Model 3s-B Intercept -9.22 (2.35), < 0.001 -8.68 (2.38), 0.001 14.70 (5.86), 0.017 8.49 (5.86), 0.16 11.56 (6.02), 0.064 11.72 (5.77), 0.051 Ethnicity (SA vs WE) — -2.73 (2.38), 0.26 -0.05 (2.04), 0.98 -1.31 (2.05), 0.53 -0.41 (2.00), 0.84 -1.67 (2.11), 0.44 Baseline GILZ — — -0.65 (0.15), < 0.001 -0.56 (0.15), 0.001 -0.57 (0.16), 0.001 -0.65 (0.15), < 0.001 Δ Fasting glucose — — — -4.48 (2.51), 0.085 -4.39 (2.66), 0.11 — Δ postprandial insulin — — — 0.26 (0.12), 0.038 — 0.26 (0.13), 0.047 Model fit: AICc 281.02 276.14 263.44 254.86 256.96 261.65 Model fit: BIC 282.42 277.51 264.77 256.12 258.26 262.94 Δ denotes within-person change with weight gain. Models included a subject-specific random intercept (Dummy ID) and were fitted using restricted maximum likelihood (REML); denominator degrees of freedom for fixed effects were estimated using the Kenward–Roger method. Model 0: crude (intercept-only). Model 1: Model 0 + ethnicity. Model 2: Model 1 + baseline gene expression. Model 3 (full): Model 2 + metabolic covariates (for FKBP5 : Δ postprandial C-peptide and Δ postprandial insulin; for GILZ : Δ fasting glucose and Δ postprandial insulin). Model 3s-A and Model 3s-B: sensitivity models based on Model 2 adding each metabolic covariate separately to assess the impact of each covariate and potential collinearity among metabolic predictors. Fixed-effect estimates are reported as β (SE), P (two-sided). Model fit is summarised using AICc and BIC. Abbreviations: WE, White European; SA, South Asian. Insulin exposure independently predicts changes in FKBP5 and GILZ after accounting for regression to the mean To identify independent predictors of Δ FKBP5 and Δ GILZ while accounting for regression to the mean, we fitted hierarchical linear mixed-effects models with subject-specific random intercepts (Table 4 ). For Δ FKBP5 , the crude model confirmed an overall decrease with weight gain. Ethnicity was not associated with Δ FKBP5 (Model 1). After adjustment for baseline FKBP5 expression (Model 2), baseline FKBP5 was strongly and inversely associated with its own change (β = −0.67 [SE 0.13], P < 0.001), with marked improvement in model fit. In the full model (Model 3), baseline FKBP5 remained inversely associated (β = −0.60 [0.13], P < 0.001) and Δ postprandial insulin showed a positive association (β = 1.90 [0.73], P = 0.014), whereas Δ postprandial C-peptide was not significant (P = 0.13). Sensitivity models adding each metabolic predictor separately (Models 3s-A, 3s-B) yielded consistent inferences regarding baseline FKBP5 and ethnicity; the attenuated effect of Δ postprandial insulin when entered with Δ C-peptide is consistent with partial collinearity among postprandial metabolic measures. For Δ GILZ , the crude model also indicated an overall decrease with weight gain. Ethnicity was not associated with Δ GILZ (Model 1). After adjustment for baseline GILZ (Model 2), baseline expression was strongly and inversely associated with Δ GILZ (β = −0.65 [0.15], P < 0.001). In the full model (Model 3), baseline GILZ remained inversely associated (β = −0.56 [0.15], P = 0.001) and Δ postprandial insulin was positively associated (β = 0.26 [0.12], P = 0.038), while Δ fasting glucose was not significant (P = 0.085). Sensitivity models confirmed that the Δ postprandial insulin association persisted when entered alone (Model 3s-B: β = 0.26 [0.13], P = 0.047), whereas the Δ fasting glucose effect attenuated. Conclusions regarding ethnicity and baseline gene expression were robust across all model specifications. Discussion This exploratory secondary analysis of the GlasVEGAS study examined whether modest short-term weight gain was associated with altered glucocorticoid-related transcript abundance in mature abdominal subcutaneous adipocytes and whether any such changes differed by ethnicity. FKBP5 and GILZ expression decreased after weight gain, and within-person changes in these transcripts were associated with changes in postprandial insulin-related measures. However, we did not detect statistically supported ethnicity-specific differences in transcript responses. Interpretation of the null ancestry interaction should nevertheless remain cautious. Although the direction and magnitude of change appeared broadly similar in South Asian and White European men, the present study was not powered to establish equivalence or to exclude modest between-group differences. The current findings therefore indicate that a clear ethnicity-by-weight gain interaction was not detected in this sample, rather than demonstrating that ethnicity has no influence on adipocyte glucocorticoid-responsive transcription during early weight gain. The cross-sectional and longitudinal analyses suggest that adipocyte FKBP5 and GILZ are linked to metabolic phenotype and to contemporaneous variation in insulin exposure during early weight gain. At baseline, both transcripts related to indices of adiposity, liver fat and adipocyte morphology, and within-person changes in transcript abundance were associated with changes in postprandial insulin-related measures (14, 15, 17, 18, 24). These findings are biologically plausible in light of prior literature identifying FKBP5 and GILZ as glucocorticoid-responsive and metabolically relevant adipocyte transcripts. Nevertheless, the present data remain observational and restricted to transcript-level measurements. Accordingly, they do not establish the direction of effect and cannot distinguish whether altered insulin exposure influences adipocyte transcription, whether adipocyte transcription contributes to metabolic change, or whether both are parallel responses to the overfeeding intervention. The present findings should therefore be interpreted as hypothesis-generating evidence of association rather than as proof that insulin dynamics directly drive adipocyte glucocorticoid signalling. Two additional features warrant discussion. First, the inverse relationship between baseline levels and subsequent changes observed across most glucocorticoid related genes raises the possibility of regression to the mean. We formally evaluated this using Oldham’s method and found evidence that regression to the mean contributes but does not fully account for the pattern, strengthening the inference that the observed decreases in FKBP5 and GILZ reflect true biological change. Second, as both transcripts are downstream effectors of glucocorticoid receptor signalling, their coordinated behaviour may reflect upstream fluctuations in receptor isoform abundance or activity, or alterations in co-activators and co-repressors that modulate GR signalling. However, measurement of GRα and GRβ remains technically challenging in human adipose tissue because GRβ is expressed at low levels, alternative splicing yields multiple isoforms, and commonly used qPCR assays, including some TaqMan designs, have limited specificity for individual isoforms. These constraints, described in foundational reviews of GR isoforms and GRβ biology, caution against overinterpreting null or noisy receptor isoform readouts and motivate the use of orthogonal quantification approaches in future work (22, 23). The absence of consistent changes in local glucocorticoid activation and in inflammatory markers in this young, relatively lean cohort is also informative. HSD11B1 and HSD11B2 did not change significantly with six percent weight gain, consistent with reports that tissue-specific regulation of glucocorticoid metabolism in human obesity is heterogeneous across depots and may require larger changes in adiposity, more prolonged exposure, or more severe metabolic stress to shift detectably at the mRNA level (11, 12, 25), with pharmacological studies further underscoring context-dependent effects of 11β-HSD1 modulation (26–29). Direct measurement of local glucocorticoid concentrations in adipose tissue would have provided additional mechanistic insight but remains technically challenging and was not feasible in this study. Likewise, IL2 remained effectively undetectable at baseline and showed no meaningful change with weight gain, which is concordant with limited inflammatory activation expected in lean young adults. These null findings help focus the mechanistic narrative on downstream transcriptional responsiveness in mature adipocytes rather than on proximal steroid regeneration or overt inflammatory signalling. This study has several important limitations. First, the sample size was modest, which limited power for interaction analyses and restricts confidence in null findings, particularly with respect to ethnic comparisons. Second, only men were studied, and the findings therefore cannot be generalised to women. Third, the intervention modelled modest short-term overfeeding rather than chronic obesity, so extrapolation to long-standing obesity should be made cautiously. Fourth, only mature abdominal subcutaneous adipocytes were studied; the findings cannot be assumed to apply to visceral adipose tissue or to other adipose depots. Fifth, the mechanistic assessment was restricted to transcript-level measurements and did not include protein abundance, receptor binding, tissue glucocorticoid concentrations or functional adipocyte assays. Finally, some candidate targets, particularly IL2 and NR3C1 isoforms, showed limited or unreliable amplification, which further constrains mechanistic interpretation. Taken together, these considerations support interpreting the study as a focused exploratory human translational analysis that requires replication in larger and more diverse cohorts. Conclusions Modest short-term weight gain was associated with lower FKBP5 and GILZ transcript abundance in mature abdominal subcutaneous adipocytes in both South Asian and White European men. We did not detect statistically supported ethnicity-specific differences, although the present study was not powered to exclude modest between-group effects. Associations between within-person transcript changes and postprandial insulin-related measures support further investigation of links between insulin exposure and adipocyte glucocorticoid-responsive transcription during early weight gain, but causal, sex-specific and depot-specific inferences remain premature. Abbreviations ASAT, abdominal subcutaneous adipose tissue; AUC, area under the curve; BMI, body mass index; FKBP5, FK506 binding protein 5; GILZ, glucocorticoid-induced leucine zipper; GR, glucocorticoid receptor; HSD11B1, 11β-hydroxysteroid dehydrogenase type 1; HSD11B2, 11β-hydroxysteroid dehydrogenase type 2; IL2, interleukin-2; IL6, interleukin-6; MRI, magnetic resonance imaging; NR3C1, nuclear receptor subfamily 3 group C member 1; qPCR, quantitative polymerase chain reaction; SA, South Asian; WE, White European. Declarations Competing Interests JAV has received royalties from AMH assays, paid to the institute/lab with no personal financial gain. NS reports consulting and/or speaker fees from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his institution from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. Funding: The study was supported by funding from the European Federation of Pharmaceutical Industries Associations (EFPIA)-Innovative Medicines Initiative (IMI) Joint Undertaking-European Medical Information Framework (EMIF; grant no. 115372 to JMR Gill and N Sattar) and a Dutch Diabetes Foundation Senior Fellowship (2023.82.010 to MR Boon). Author Contribution XG conceived and conducted the analyses, curated the data, performed the statistical analyses, interpreted the results, and drafted the manuscript. RL designed the glucocorticoid receptor isoform TaqMan assays and contributed to gene expression methodology. JAV contributed to study design and target gene selection. JM led participant recruitment, sample collection, and overall study execution. NS and DJF contributed to study conception and oversight. XG, MRB, NS and JMRG generated the original study idea. MRB supervised the project and contributed to manuscript writing. JMRG supervised the study, contributed to interpretation, and critically revised the manuscript. All authors contributed to manuscript revision and approved the final version. References Saxena A SS, Choudhary P, Sharma B, Gupta S, Mandia R, Banshiwal RC, Lamoria RK, Dhakad A, Raj U, Tiwari P, Mathur SK. Adipose tissue quantity, distribution and pathology and its relationship with type-2 diabetes, insulin resistance and other clustering disease risk in South Asians: a cross-sectional study. Frontiers in Endocrinology. 2025. Consultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004. Ghouri N PD, McConnachie A, Wilson J, Gill JM, Sattar N. Lower cardiorespiratory fitness contributes to increased insulin resistance and fasting glycaemia in middle-aged South Asian compared with European men living in the UK. Diabetologia. 2013. Iliodromiti S MJ, Ghouri N, Miller MR, Dahlqvist Leinhard O, Linge J, Ballantyne S, Platt J, Foster J, Hanvey S, Gujral UP, Kanaya A, Sattar N, Lumsden MA, Gill JMR. Liver, visceral and subcutaneous fat in men and women of South Asian and white European descent: a systematic review and meta-analysis of new and published data. Diabetologia. 2022. Karamali NS AG, Kanhai HH, de Groot CJ, Tamsma JT, Middelkoop BJ. Thin-fat insulin-resistant phenotype also present in South Asian neonates born in the Netherlands. Journal of Developmental Origins of Health and Disease. 2015. McLaren J GX, Ghouri N, Freeman DJ, Richardson J, Sattar N, Gill JMR. Weight gain leads to greater adverse metabolic responses in South Asian compared with white European men: the GlasVEGAS study. Nature Metabolism. 2024. V. M. Lessons Learned From Epidemiology of Type 2 Diabetes in South Asians: Kelly West Award Lecture 2024. Diabetes Care. 2025. Hudda MT DA, Owen CG, Rudnicka AR, Sattar N, Cook DG, Whincup PH, Nightingale CM. Exploring the use of adjusted body mass index thresholds based on equivalent insulin resistance for defining overweight and obesity in UK South Asian children. International Journal of Obesity. 2019. Tillin T SN, Godsland IF, Hughes AD, Chaturvedi N, Forouhi NG. Ethnicity-specific obesity cut-points in the development of Type 2 diabetes - a prospective study including three ethnic groups in the United Kingdom. Diabetic Medicine. 2015. Bakker LE GB, van Schinkel LD, van der Zon GC, Streefland TC, van Klinken JB, Jonker JT, Lamb HJ, Smit JW, Pijl H, Meinders AE, Jazet IM.. Middle-aged overweight South Asian men exhibit a different metabolic adaptation to short-term energy restriction compared with Europeans. Diabetologia. 2015. Crowley RK WC, Hughes BA, Gray J, McCarthy T, Taylor AE, Gathercole LL, Shackleton CHL, Crabtree N, Arlt W, Stewart PM, Tomlinson JW. Increased central adiposity and decreased subcutaneous adipose tissue 11β-hydroxysteroid dehydrogenase type 1 are associated with deterioration in glucose tolerance-A longitudinal cohort study. Clinical Endocrinology (Oxford). 2019. Woods CP CM, Gathercole L, Taylor A, Hughes B, Gaoatswe G, Manolopoulos K, Hogan AE, O'Connell J, Stewart PM, Tomlinson JW, O'Shea D, Sherlock M. Tissue specific regulation of glucocorticoids in severe obesity and the response to significant weight loss following bariatric surgery (BARICORT). The Journal of Clinical Endocrinology and Metabolism. 2015. Scaron;iklová M ŠV, Koc M, Krauzová E, Čížková T, Ondrůjová B, Wilhelm M, Varaliová Z, Kuda O, Neubert J, Lambert L, Elkalaf M, Gojda J, Rossmeislová L. The role of adipogenic capacity and dysfunctional subcutaneous adipose tissue in the inheritance of type 2 diabetes mellitus: cross-sectional study. Obesity (Silver Spring). 2024. Willmer T GJ, Dias S, Louw J, Pheiffer C. DNA methylation of FKBP5 in South African women: associations with obesity and insulin resistance. Clinical Epigenetics. 2020. Willmer T OA, Dias S, Mendham AE, Goedecke JH, Pheiffer C. A pilot investigation of genetic and epigenetic variation of FKBP5 and response to exercise intervention in African women with obesity. Scientific Reports. 2022. Lee MJ YR, Karastergiou K, Smith SR, Chang JR, Gong DW, Fried SK. Low expression of the GILZ may contribute to adipose inflammation and altered adipokine production in human obesity. Journal of Lipid Research. 2016. Pereira MJ PJ, Svensson MK, Rizell M, Dalenbäck J, Hammar M, Fall T, Sidibeh CO, Svensson PA, Eriksson JW. FKBP5 expression in human adipose tissue increases following dexamethasone exposure and is associated with insulin resistance. Metabolism. 2014. Sidibeh CO PM, Abalo XM, J Boersma G, Skrtic S, Lundkvist P, Katsogiannos P, Hausch F, Castillejo-López C, Eriksson JW. FKBP5 expression in human adipose tissue: potential role in glucose and lipid metabolism, adipogenesis and type 2 diabetes. Endocrine. 2018. Straat ME, Martinez-Tellez B, van Eyk HJ, Bizino MB, van Veen S, Vianello E, et al. Differences in Inflammatory Pathways Between Dutch South Asians vs Dutch Europids With Type 2 Diabetes. J Clin Endocrinol Metab. 2023;108(4):931–40. Boon MR, Karamali NS, de Groot CJ, van Steijn L, Kanhai HH, van der Bent C, et al. E-selectin is elevated in cord blood of South Asian neonates compared with Caucasian neonates. J Pediatr. 2012;160(5):844–8.e1. Neville MJ, Collins JM, Gloyn AL, McCarthy MI, Karpe F. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity (Silver Spring). 2011;19(4):888–92. Lewis-Tuffin LJ CJ. The physiology of human glucocorticoid receptor beta (hGRbeta) and glucocorticoid resistance. Annals of the New York Academy of Sciences. 2006. Lu NZ CJ. The origin and functions of multiple human glucocorticoid receptor isoforms. Annals of the New York Academy of Sciences. 2004. Strączkowski M SM, Matulewicz N, Nikołajuk A, Karczewska-Kupczewska M. Relation of adipose tissue and skeletal muscle FKBP5 expression with insulin sensitivity and the regulation of FKBP5 by insulin and free fatty acids. Endocrine. 2022. Bianzano S SC, Wolff M, Heise T, Plum-Moerschel L. Selective Inhibition of 11beta-Hydroxysteroiddehydrogenase-1 with BI 187004 in Patients with Type 2 Diabetes and Overweight or Obesity: Safety, Pharmacokinetics, and Pharmacodynamics After Multiple Dosing Over 14 Days. Experimental and Clinical Endocrinology & Diabetes. 2022. Stefan N RM, Jordan P, Nowotny B, Kantartzis K, Machann J, Hwang JH, Nowotny P, Kahl S, Harreiter J, Hornemann S, Sanyal AJ, Stewart PM, Pfeiffer AF, Kautzky-Willer A, Roden M, Häring HU, Fürst-Recktenwald S. Inhibition of 11β-HSD1 with RO5093151 for non-alcoholic fatty liver disease: a multicentre, randomised, double-blind, placebo-controlled trial. The Lancet Diabetes & Endocrinology. 2014. Othonos N PR, Arvaniti A, White S, Bonaventura I, Nikolaou N, Moolla A, Marjot T, Stimson RH, van Beek AP, van Faassen M, Isidori AM, Bateman E, Sadler R, Karpe F, Stewart PM, Webster C, Duffy J, Eastell R, Gossiel F, Cornfield T, Hodson L, Jane Escott K, Whittaker A, Kirik U, Coleman RL, Scott CAB, Milton JE, Agbaje O, Holman RR, Tomlinson JW.. 11β-HSD1 inhibition in men mitigates prednisolone-induced adverse effects in a proof-of-concept randomised double-blind placebo-controlled trial. Nature Communications. 2023. Yadav Y DK, Khot R, Venkatesh SK, Port J, Galderisi A, Cobelli C, Wegner C, Basu A, Carter R, Basu R.. Inhibition of 11β-Hydroxysteroid dehydrogenase-1 with AZD4017 in patients with nonalcoholic steatohepatitis or nonalcoholic fatty liver disease: A randomized, double-blind, placebo-controlled, phase II study. Diabetes, Obesity & Metabolism. 2022. Heise T ML, Hompesch M, Häring HU, Kapitza C, Abt M, Ramsauer M, Magnone MC, Fuerst-Recktenwald S. Safety, efficacy and weight effect of two 11β-HSD1 inhibitors in metformin-treated patients with type 2 diabetes. Diabetes, Obesity & Metabolism. 2014. Additional Declarations Competing interest reported. JAV has received royalties from AMH assays, paid to the institute/lab with no personal financial gain. NS reports consulting and/or speaker fees from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his institution from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. Supplementary Files MetabologiaGlasVEGASGCESM.pptx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 01 Apr, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9199966\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":625398673,\"identity\":\"11e1e959-ff92-4a0d-a372-8db679230617\",\"order_by\":0,\"name\":\"Xuan Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Glasgow\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuan\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":625398678,\"identity\":\"90af246c-2162-4a3f-bd0c-09e65ff93066\",\"order_by\":1,\"name\":\"Robin Lengton\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Erasmus Medical Centre Rotterdam\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Robin\",\"middleName\":\"\",\"lastName\":\"Lengton\",\"suffix\":\"\"},{\"id\":625398688,\"identity\":\"8959fc43-8ff3-4210-a876-ff80461b433a\",\"order_by\":2,\"name\":\"Jenny A. 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JAV has received royalties from AMH assays, paid to the institute/lab with no personal financial gain. NS reports consulting and/or speaker fees from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp \\u0026 Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his institution from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work.\",\"formattedTitle\":\"Subcutaneous adipocyte glucocorticoid-responsive transcripts after modest weight gain in South Asian and White European men: an exploratory longitudinal study\",\"fulltext\":[{\"header\":\"Highlights\",\"content\":\"\\u003cp\\u003eModest short-term weight gain was associated with lower adipocyte \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression. These changes did not differ according to ethnicity.\\u003c/p\\u003e\\n\\u003cp\\u003eBaseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression was associated with adiposity, liver fat and adipocyte morphology.\\u003c/p\\u003e\\n\\u003cp\\u003eWithin-person changes in glucocorticoid-responsive transcripts were associated with changes in postprandial insulin responses.\\u003c/p\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAdipocyte glucocorticoid signalling, mediated by the nuclear glucocorticoid receptor, influences lipid storage and adipocyte differentiation, insulin action and systemic metabolic homeostasis. South Asian populations experience greater central adipose tissue accumulation, insulin resistance and type 2 diabetes at lower body mass index than White Europeans. These differences reflect ethnic variation in body fat distribution, hepatic fat, and fitness that manifest across the life course (1\\u0026ndash;10). We previously demonstrated in the GlasVEGAS cohort that weight gain leads to greater adverse metabolic responses in South Asians compared with White Europeans (6). In this context, glucocorticoid metabolism in adipose tissue, including local regeneration via 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), glucocorticoid receptor isoform balance, and downstream glucocorticoid-responsive transcripts - represents a plausible pathway through which modest weight gain could exacerbate metabolic risk (11, 12). Among these downstream targets, FK506 binding protein 5 (\\u003cem\\u003eFKBP5)\\u003c/em\\u003e and glucocorticoid-induced leucine zipper (\\u003cem\\u003eGILZ\\u003c/em\\u003e) are well-established glucocorticoid-responsive genes in human adipose tissue that have been linked to insulin sensitivity, adipose inflammation, and adipogenesis (4, 13\\u0026ndash;18). More specifically, adipose tissue \\u003cem\\u003eGILZ\\u003c/em\\u003e mRNA levels have been found to be reduced in obesity, and knockdown of \\u003cem\\u003eGILZ\\u003c/em\\u003e in human adipocytes \\u003cem\\u003eex vivo\\u003c/em\\u003e coincided with enhanced expression of the proinflammatory factors interleukin-6 (IL6) and leptin (16). The latter may contribute to the generally more proinflammatory state observed in South Asians (19, 20). Important questions that remain include how weight gain affects adipocyte glucocorticoid signalling, as well as how this behaves in the metabolically vulnerable South Asian population.\\u003c/p\\u003e \\u003cp\\u003eAgainst this background, we examined glucocorticoid-related transcript responses in mature abdominal subcutaneous adipocytes obtained from South Asian and White European men before and after modest weight gain. Specifically, we assessed transcripts related to local glucocorticoid metabolism (\\u003cem\\u003eHSD11B1\\u003c/em\\u003e and \\u003cem\\u003eHSD11B2\\u003c/em\\u003e), candidate glucocorticoid-responsive downstream targets (\\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eTSC22D3/GIL\\u003c/em\\u003eZ), and selected inflammation-related targets (\\u003cem\\u003eIL2\\u003c/em\\u003e and \\u003cem\\u003eIL6\\u003c/em\\u003e), while also attempting to quantify NR3C1 isoforms. We further examined whether baseline transcript abundance and within-person transcript changes were associated with indices of adiposity, adipocyte morphology and insulin-related metabolic responses. Given the exploratory nature of the study and the modest sample size, we aimed primarily to determine whether short-term weight gain was accompanied by measurable adipocyte transcript changes and whether these changes tracked contemporaneous metabolic alterations.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and participants\\u003c/h2\\u003e \\u003cp\\u003eThe Glasgow visceral and ectopic fat with weight gain in South Asians (GlasVEGAS) study was registered on ClinicalTrails.gov (NCT02399423). Ethical approval was obtained from the University of Glasgow College of Medical, Veterinary and Life Sciences Ethics Committee and all participants provided written informed consent. A within person weight gain study was conducted in two ancestry groups: White European men (n\\u0026thinsp;=\\u0026thinsp;21) and South Asian men (n\\u0026thinsp;=\\u0026thinsp;14). Participants attended two study visits, at baseline and after achieving a minimum of 5% (target\\u0026thinsp;~\\u0026thinsp;7%) weight gain. Weight gain was induced through an overfeeding protocol over 4\\u0026ndash;6 weeks, whereby participants were asked to eat until they felt more full than usual and were provided with high-energy snacks (ice-cream, chocolate bars, potato crisps, cheese, dried fruit and nuts, sugary drinks) providing an additional\\u0026thinsp;~\\u0026thinsp;6.2\\u0026ndash;8.4 MJ/day (1500\\u0026ndash;2000 kcal/day). Once participants gained the required weight, participants followed a weight-neutral diet for three days before post-weight gain assessments. All participants provided written informed consent. Inclusion and exclusion criteria, safety procedures, and weight-gain protocols have been described previously (6).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eParent-study context\\u003c/h3\\u003e\\n\\u003cp\\u003eThe present analysis should be interpreted within the context of the broader GlasVEGAS human overfeeding study, from which additional physiological and metabolic phenotyping has been reported previously. The current manuscript focuses specifically on adipocyte glucocorticoid-related transcript responses in mature abdominal subcutaneous adipocytes, whereas broader phenotypic measurements obtained in the parent study included body composition, hepatic triglyceride content, and postprandial glucose, insulin, C-peptide and triglyceride responses to a standardised mixed-meal test, together with the broader adverse metabolic responses to short-term overfeeding reported previously in the GlasVEGAS study (6). This exploratory secondary analysis was undertaken to examine whether adipocyte glucocorticoid-responsive transcription changed during early weight gain and whether such changes were associated with contemporaneous metabolic responses.\\u003c/p\\u003e\\n\\u003ch3\\u003eAdipose sampling and adipocyte isolation\\u003c/h3\\u003e\\n\\u003cp\\u003eSubcutaneous abdominal white adipose tissue was obtained by needle biopsy at both visits. Mature adipocytes were isolated using established procedures as described previously (6) and processed for RNA extraction. All samples were frozen at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until use, and samples from a given participant were processed in parallel when feasible to minimise batch effects (6).\\u003c/p\\u003e\\n\\u003ch3\\u003eGene expression quantification\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA was extracted from purified mature adipocytes using the RNeasy Lipid Tissue Mini Kit (QIAGEN, 74804) according to the manufacturer\\u0026rsquo;s instructions. RNA was eluted in nuclease-free water, and RNA concentration and purity were assessed using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific); optical density 260/280 nm ratios within the acceptable range for downstream analysis were considered suitable. To minimise genomic DNA contamination, RNA samples underwent DNase treatment using the DNA-free kit (Thermo Fisher Scientific, AM1906) according to the manufacturer\\u0026rsquo;s instructions. Single-stranded cDNA was synthesised from purified RNA using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, 4368813) (6).\\u003c/p\\u003e \\u003cp\\u003eBecause RNA yield from isolated mature adipocytes was limited, target cDNA was pre-amplified using TaqMan PreAmp Master Mix (2\\u0026times;) (Thermo Fisher Scientific, 4384266). A pooled assay mix was prepared using equal amounts of each target assay, and pre-amplification was performed for 10 cycles on a StepOnePlus Real-Time PCR system, after which samples were diluted 1:5 in TE buffer before quantitative PCR. Pre-amplification uniformity was assessed by comparing Ct values obtained with and without pre-amplification, using CDKN1B as the endogenous control for the uniformity assessment. A ΔΔCt difference of less than \\u0026plusmn;\\u0026thinsp;1.5 between the diluted pre-amplified sample and the corresponding non-amplified sample was considered acceptable (6).\\u003c/p\\u003e \\u003cp\\u003eQuantitative PCR was performed in duplicate on a StepOnePlus Real-Time PCR system using TaqMan Universal PCR Master Mix (Thermo Fisher Scientific, 4304437). TaqMan Gene Expression Assays (Applied Biosystems) were used for \\u003cem\\u003eHSD11B1\\u003c/em\\u003e (Hs01547870_m1), \\u003cem\\u003eHSD11B2\\u003c/em\\u003e (Hs00388669_m1), \\u003cem\\u003eFKBP5\\u003c/em\\u003e (Hs01561006_m1), \\u003cem\\u003eTSC22D3/GILZ\\u003c/em\\u003e (Hs00608272_m1), \\u003cem\\u003eIL2\\u003c/em\\u003e (Hs00174114_m1), and \\u003cem\\u003eIL6\\u003c/em\\u003e (Hs00174131_m1). NR3C1 isoforms (GRα and GRβ) were assessed using previously published primer pairs. The fluorescence threshold for the FAM-MGB reporter was set at 0.2 ΔRn in accordance with the manufacturer\\u0026rsquo;s recommendations, and signals earlier than Ct 15 were regarded as background fluorescence and were not interpreted quantitatively. \\u003cem\\u003ePPIA\\u003c/em\\u003e was used as the endogenous reference gene for ΔCt and ΔΔCt analyses and did not differ materially by ancestry or intervention (21). Each PCR plate included no-reverse-transcription controls and no-template controls to monitor genomic DNA contamination and non-specific amplification. Assay performance was reviewed before inferential analysis, and targets showing limited or unreliable amplification were excluded from formal statistical analysis and interpreted descriptively only.\\u003c/p\\u003e\\n\\u003ch3\\u003eBody composition and adipocyte morphology\\u003c/h3\\u003e\\n\\u003cp\\u003eBody mass index was recorded at both visits. Body composition indices, including abdominal subcutaneous adipose tissue volume and whole body lean mass, were measured by magnetic resonance imaging (MRI) using a 3.0-Tesla scanner (Magnetom, Siemens). Adipocyte morphology was assessed in isolated adipocytes by measuring intracellular diameter of 150 adipocytes per sample using ImageJ (version 1.52a, NIH) from digital images captured under a B\\u0026times;50 microscope (10\\u0026times; lens). Adipocytes were categorised as very small (\\u0026le;\\u0026thinsp;30 \\u0026micro;m), small (31\\u0026ndash;70 \\u0026micro;m), medium (71\\u0026ndash;90 \\u0026micro;m) or large (\\u0026gt;\\u0026thinsp;90 \\u0026micro;m), and mean adipocyte diameter and the proportion within each size category were calculated as described previously (6).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMetabolic assessments\\u003c/h2\\u003e \\u003cp\\u003eParticipants underwent a standardised mixed meal test (800 kcal: 37% fat, 47% carbohydrate, 17% protein) following an overnight fast. Blood samples were collected at fasting and at 30, 45, 60, 90, 120, 180, 240 and 300 minutes postprandially for measurement of glucose, insulin, C-peptide and triglycerides. Time-averaged areas under the postprandial concentration versus time curves (AUC) were used as summary measures of postprandial metabolic responses. Full details of the metabolic test protocol have been described previously (6).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eBaseline differences between ancestry groups were assessed using unpaired t-tests. Pearson correlations were used to examine associations between gene expression and indices of body composition, adipocyte morphology and metabolic function at baseline (univariable analyses). Within-person change (Δ) associations examined Pearson correlations between within-person change in gene expression (weight gain minus baseline) and changes in metabolic variables.\\u003c/p\\u003e \\u003cp\\u003eBecause \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression showed significant changes with weight gain and are established downstream glucocorticoid-responsive transcripts, we used mixed-effects ANOVA with subject-level random intercepts to model change in these genes as outcomes, with ethnicity (South Asian versus White European), baseline body mass index, mean adipocyte diameter and batch as covariates, and candidate metabolic predictors including change in insulin area under the curve. Results are presented as crude (unadjusted) and then sequentially adjusted models. Model 1 included ethnicity in addition to the crude model, and Model 2 further added baseline gene expression; subsequent models additionally incorporated candidate metabolic covariates, with sensitivity models adding each metabolic predictor separately to assess the contribution of individual covariates and potential correlated predictors.\\u003c/p\\u003e \\u003cp\\u003eTo assess regression to the mean, we examined pairwise Pearson correlations between baseline levels and changes (Electronic supplementary material [ESM] Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) and applied Oldham's method by modelling change versus the average of baseline and post-weight gain values (ESM Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eExpression of glucocorticoid-related genes in adipocytes in white European (WE) and South Asian (SA) men at baseline and after weight gain\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGene\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBaseline WE (n\\u0026thinsp;=\\u0026thinsp;21)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBaseline SA (n\\u0026thinsp;=\\u0026thinsp;14)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eΔ WE (n\\u0026thinsp;=\\u0026thinsp;21)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eΔ SA (n\\u0026thinsp;=\\u0026thinsp;14)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eP\\u003csup\\u003ec\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRelative expression to \\u003cem\\u003ePPIA\\u003c/em\\u003e (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eFKBP5\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e104.23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-23.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-17.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGILZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40.09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-11.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-5.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eHSD11B1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-2.48\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eHSD11B2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eIL2\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003ed\\u003c/em\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.80\\u0026times;10⁻⁵\\u0026plusmn;1.70\\u0026times;10⁻⁵\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.89\\u0026times;10⁻⁵\\u0026plusmn;2.18\\u0026times;10⁻⁵\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eIL6\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eValues are mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SEM\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eΔ: within person change with weight gain\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eP\\u003csup\\u003ea\\u003c/sup\\u003e: baseline ethnicity difference between white Europeans and South Asians at baseline (unpaired t-test)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eP\\u003csup\\u003eb\\u003c/sup\\u003e: weight gain main effect of weight gain intervention (2-way ANOVA)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eP\\u003csup\\u003ec\\u003c/sup\\u003e: ethnicity x weight gain intervention interaction (2-way ANOVA)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003e\\u003csup\\u003ed\\u003c/sup\\u003e: Detectable in 3/21 (\\u003cb\\u003eΔ\\u003c/b\\u003e WE) and 2/14 (\\u003cb\\u003eΔ\\u003c/b\\u003e SA)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003e\\u0026mdash;: not applicable / not calculated (baseline expression was 0 in both groups; therefore Pᵃ, Pᵇ and Pᶜ were not calculated for IL2).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnivariable correlations between \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression in subcutaneous adipose tissue and indices of body composition/ adipocyte size and metabolic function\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGene expression\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCorrelate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% CI for r\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody composition/ adipocyte size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eFKBP5\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eASAT (l)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.62, -0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLiver fat fraction (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.62, -0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean adipocyte diameter (\\u0026micro;m)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.68, -0.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.020\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProportion of small adipocytes (% cells)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.07, 0.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.021\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGILZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBMI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.03, 0.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.033\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWhole-body lean tissue (l)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.07, 0.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLiver fat fraction (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.65, -0.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.030\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eProportion of small adipocytes (% cells)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.02, 0.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.041\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMetabolic\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGILZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePostprandial C-peptide (nmol\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.60, -0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFasting triglycerides (mmol\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.64, -0.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.023\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePostprandial triglycerides (mmol\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.64, -0.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.020\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eCI, confidence interval; ASAT, abdominal subcutaneous adipose tissue; BMI, body mass index\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eN indicates the number of participants with non-missing paired measurements for each correlation\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003er and P values were obtained from two-sided, univariable Pearson\\u0026rsquo;s correlations without adjustment for multiple comparisons.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll tests were two-sided with an alpha of 0.05. Fitness and physical activity variables were excluded from primary analyses due to limited sample size. Statistical analyses were performed using Minitab (version 22.1, Minitab LLC) and figures were generated using GraphPad Prism (version 9.5.1).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eBaseline characteristics including age, BMI, and metabolic parameters have been described previously (6).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eWeight gain downregulates glucocorticoid-responsive transcripts\\u003c/h2\\u003e \\u003cp\\u003eAt baseline, there were no statistically significant ethnic differences in adipocyte \\u003cem\\u003eFKBP5\\u003c/em\\u003e or \\u003cem\\u003eGILZ\\u003c/em\\u003e expression (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Expression of \\u003cem\\u003eHSD11B1\\u003c/em\\u003e and \\u003cem\\u003eHSD11B2\\u003c/em\\u003e also did not differ significantly between South Asian and White European participants. \\u003cem\\u003eIL6\\u003c/em\\u003e was detectable at low levels and showed no significant ethnic difference, whereas \\u003cem\\u003eIL2\\u003c/em\\u003e showed limited and inconsistent amplification and was therefore considered only descriptively. NR3C1 isoforms (GRα and GRβ) could not be reliably quantified in this adipocyte material because transcript abundance was below the robust limit of detection and assay performance was insufficient for confident quantitative interpretation (22, 23). These targets were therefore not taken forward as informative quantitative outcome.\\u003c/p\\u003e \\u003cp\\u003eAdipocyte \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e mRNA expression levels decreased similarly in response to weight gain in both ethnic groups (\\u003cem\\u003eFKBP5\\u003c/em\\u003e: \\u0026minus;23.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.82% in White Europeans and \\u0026minus;\\u0026thinsp;17.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.62% in South Asians, P\\u003csub\\u003eweight gain\\u003c/sub\\u003e = 0.007; \\u003cem\\u003eGILZ\\u003c/em\\u003e: \\u0026minus;11.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.83% in White Europeans and \\u0026minus;\\u0026thinsp;5.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.01% in South Asians, P\\u003csub\\u003eweight gain\\u003c/sub\\u003e = 0.001; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The ethnicity \\u0026times; weight gain interaction was not statistically significant for either transcript, indicating no evidence of an ethnic difference in the response to weight gain. \\u003cem\\u003eHSD11B1\\u003c/em\\u003e, \\u003cem\\u003eHSD11B2\\u003c/em\\u003e and \\u003cem\\u003eIL6\\u003c/em\\u003e did not change significantly with weight gain, and \\u003cem\\u003eIL2\\u003c/em\\u003e remained uninformative because of limited detectability (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline glucocorticoid-responsive gene expression relates to adiposity and metabolic phenotype\\u003c/h2\\u003e \\u003cp\\u003eAt baseline, \\u003cem\\u003eFKBP5\\u003c/em\\u003e expression was negatively correlated with ASAT volume (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.35, P\\u0026thinsp;=\\u0026thinsp;0.047), liver fat fraction (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.35, P\\u0026thinsp;=\\u0026thinsp;0.049) and adipocyte diameter (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.42, P\\u0026thinsp;=\\u0026thinsp;0.020), while a positive correlation was observed between FKBP5 expression and the proportion of small adipocytes (r\\u0026thinsp;=\\u0026thinsp;0.42, P\\u0026thinsp;=\\u0026thinsp;0.021, Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; full correlation matrix in ESM Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Representative scatterplots are shown in ESM Fig.\\u0026nbsp;1. \\u003cem\\u003eGILZ\\u003c/em\\u003e expression showed a complementary pattern: \\u003cem\\u003eGILZ\\u003c/em\\u003e expression was correlated with BMI (r\\u0026thinsp;=\\u0026thinsp;0.36, P\\u0026thinsp;=\\u0026thinsp;0.033), lean mass (r\\u0026thinsp;=\\u0026thinsp;0.41, P\\u0026thinsp;=\\u0026thinsp;0.019) and the proportion of small adipocytes (r\\u0026thinsp;=\\u0026thinsp;0.38, P\\u0026thinsp;=\\u0026thinsp;0.041), whereas negative correlations were observed with liver fat fraction (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.38, P\\u0026thinsp;=\\u0026thinsp;0.030), postprandial triglyceride (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.39, P\\u0026thinsp;=\\u0026thinsp;0.020), fasting triglyceride (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.38, P\\u0026thinsp;=\\u0026thinsp;0.023) and fasting C-peptide (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.34, P\\u0026thinsp;=\\u0026thinsp;0.048,Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; ESM Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Scatterplots of baseline \\u003cem\\u003eGILZ\\u003c/em\\u003e expression versus metabolic parameters are shown in ESM Fig.\\u0026nbsp;2.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnivariable correlations between weight gain\\u0026ndash;induced changes (Δ) in \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression and concurrent changes in metabolic variables.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ Gene expression\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCorrelate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% CI for r\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetabolic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ \\u003cem\\u003eFKBP5\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eΔ postprandial C-peptide (nmol\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.002, 0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eΔ postprandial insulin (\\u0026micro;U\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.14, 0.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ GILZ\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eΔ fasting glucose (mmol\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(-0.65, -0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.017\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eΔ postprandial insulin (\\u0026micro;U\\u0026middot;l\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(0.001, 0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eΔ indicates within-person change with weight gain.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003er and P values were obtained from two-sided, univariable Pearson\\u0026rsquo;s correlations without adjustment for multiple comparisons.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eCI, confidence interval. N indicates the number of participants with non-missing paired measurements for each correlation.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChanges in glucocorticoid-responsive transcripts correlate with changes in insulin dynamics\\u003c/h2\\u003e \\u003cp\\u003eAnalyses of within-person change (Δ) indicated that dynamic alterations in expression of adipocyte glucocorticoid responsive transcripts tracked shifts in metabolic status. Δ\\u003cem\\u003eFKBP5\\u003c/em\\u003e expression correlated positively with change in postprandial insulin and C peptide (r\\u0026thinsp;=\\u0026thinsp;0.46, P\\u0026thinsp;=\\u0026thinsp;0.006; r\\u0026thinsp;=\\u0026thinsp;0.34, P\\u0026thinsp;=\\u0026thinsp;0.049), while Δ\\u003cem\\u003eGILZ\\u003c/em\\u003e correlated negatively with change in fasting glucose and positively with Δpostprandial insulin (r = -0.40, P\\u0026thinsp;=\\u0026thinsp;0.017; r\\u0026thinsp;=\\u0026thinsp;0.34, P\\u0026thinsp;=\\u0026thinsp;0.049). Full correlation coefficients and confidence intervals are provided in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e (extended version in ESM Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Scatterplots illustrating these within-person associations are shown in ESM Fig.\\u0026nbsp;3.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eHierarchical mixed-effects models for within-person change (Δ) in (a) \\u003cem\\u003eFKBP5\\u003c/em\\u003e expression and (b) \\u003cem\\u003eGILZ\\u003c/em\\u003e expression.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(a) Δ \\u003cem\\u003eFKBP5\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 0\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eModel 3s-A\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eModel 3s-B\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIntercept\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-21.26 (6.95), 0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-20.67 (7.2), 0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43.66 (13.98), 0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.10 (14.37), 0.091\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e34.57 (15.14), 0.029\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e25.79 (14.69), 0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEthnicity (SA vs WE)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-2.98 (7.18), 0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.89 (5.59), 0.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.90 (5.51), 0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.31 (6.00), 0.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8.20 (5.57), 0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.67 (0.13), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.60 (0.13), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.63 (0.14), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.59 (0.13), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ postprandial C-peptide\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-14.5 (9.38), 0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.95 (4.85), 0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ postprandial insulin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.90 (0.73), 0.014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.90 (0.35), 0.016\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel fit: AICc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e354.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e348.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e332.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e317.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e325.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e326.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel fit: BIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e356.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e350.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e333.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e318.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e326.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e327.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(b) Δ \\u003cem\\u003eGILZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 0\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eModel 3s-A\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eModel 3s-B\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIntercept\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-9.22 (2.35), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-8.68 (2.38), 0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.70 (5.86), 0.017\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.49 (5.86), 0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.56 (6.02), 0.064\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e11.72 (5.77), 0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEthnicity (SA vs WE)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-2.73 (2.38), 0.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.05 (2.04), 0.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.31 (2.05), 0.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.41 (2.00), 0.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-1.67 (2.11), 0.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaseline \\u003cem\\u003eGILZ\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.65 (0.15), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.56 (0.15), 0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.57 (0.16), 0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.65 (0.15), \\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ Fasting glucose\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-4.48 (2.51), 0.085\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-4.39 (2.66), 0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eΔ postprandial insulin\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.26 (0.12), 0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.26 (0.13), 0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel fit: AICc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e281.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e276.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e263.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e254.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e256.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e261.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel fit: BIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e282.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e277.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e264.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e256.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e258.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e262.94\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eΔ denotes within-person change with weight gain. Models included a subject-specific random intercept (Dummy ID) and were fitted using restricted maximum likelihood (REML); denominator degrees of freedom for fixed effects were estimated using the Kenward\\u0026ndash;Roger method. Model 0: crude (intercept-only). Model 1: Model 0\\u0026thinsp;+\\u0026thinsp;ethnicity. Model 2: Model 1\\u0026thinsp;+\\u0026thinsp;baseline gene expression. Model 3 (full): Model 2\\u0026thinsp;+\\u0026thinsp;metabolic covariates (for \\u003cem\\u003eFKBP5\\u003c/em\\u003e: Δ postprandial C-peptide and Δ postprandial insulin; for \\u003cem\\u003eGILZ\\u003c/em\\u003e: Δ fasting glucose and Δ postprandial insulin). Model 3s-A and Model 3s-B: sensitivity models based on Model 2 adding each metabolic covariate separately to assess the impact of each covariate and potential collinearity among metabolic predictors. Fixed-effect estimates are reported as β (SE), P (two-sided). Model fit is summarised using AICc and BIC. Abbreviations: WE, White European; SA, South Asian.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eInsulin exposure independently predicts changes in FKBP5 and GILZ after accounting for regression to the mean\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo identify independent predictors of Δ\\u003cem\\u003eFKBP5\\u003c/em\\u003e and Δ\\u003cem\\u003eGILZ\\u003c/em\\u003e while accounting for regression to the mean, we fitted hierarchical linear mixed-effects models with subject-specific random intercepts (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFor Δ\\u003cem\\u003eFKBP5\\u003c/em\\u003e, the crude model confirmed an overall decrease with weight gain. Ethnicity was not associated with Δ\\u003cem\\u003eFKBP5\\u003c/em\\u003e (Model 1). After adjustment for baseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e expression (Model 2), baseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e was strongly and inversely associated with its own change (β = \\u0026minus;0.67 [SE 0.13], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with marked improvement in model fit. In the full model (Model 3), baseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e remained inversely associated (β = \\u0026minus;0.60 [0.13], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and Δ postprandial insulin showed a positive association (β\\u0026thinsp;=\\u0026thinsp;1.90 [0.73], P\\u0026thinsp;=\\u0026thinsp;0.014), whereas Δ postprandial C-peptide was not significant (P\\u0026thinsp;=\\u0026thinsp;0.13). Sensitivity models adding each metabolic predictor separately (Models 3s-A, 3s-B) yielded consistent inferences regarding baseline \\u003cem\\u003eFKBP5\\u003c/em\\u003e and ethnicity; the attenuated effect of Δ postprandial insulin when entered with Δ C-peptide is consistent with partial collinearity among postprandial metabolic measures.\\u003c/p\\u003e \\u003cp\\u003eFor Δ\\u003cem\\u003eGILZ\\u003c/em\\u003e, the crude model also indicated an overall decrease with weight gain. Ethnicity was not associated with Δ\\u003cem\\u003eGILZ\\u003c/em\\u003e (Model 1). After adjustment for baseline \\u003cem\\u003eGILZ\\u003c/em\\u003e (Model 2), baseline expression was strongly and inversely associated with Δ\\u003cem\\u003eGILZ\\u003c/em\\u003e (β = \\u0026minus;0.65 [0.15], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In the full model (Model 3), baseline \\u003cem\\u003eGILZ\\u003c/em\\u003e remained inversely associated (β = \\u0026minus;0.56 [0.15], P\\u0026thinsp;=\\u0026thinsp;0.001) and Δ postprandial insulin was positively associated (β\\u0026thinsp;=\\u0026thinsp;0.26 [0.12], P\\u0026thinsp;=\\u0026thinsp;0.038), while Δ fasting glucose was not significant (P\\u0026thinsp;=\\u0026thinsp;0.085). Sensitivity models confirmed that the Δ postprandial insulin association persisted when entered alone (Model 3s-B: β\\u0026thinsp;=\\u0026thinsp;0.26 [0.13], P\\u0026thinsp;=\\u0026thinsp;0.047), whereas the Δ fasting glucose effect attenuated. Conclusions regarding ethnicity and baseline gene expression were robust across all model specifications.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis exploratory secondary analysis of the GlasVEGAS study examined whether modest short-term weight gain was associated with altered glucocorticoid-related transcript abundance in mature abdominal subcutaneous adipocytes and whether any such changes differed by ethnicity. \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e expression decreased after weight gain, and within-person changes in these transcripts were associated with changes in postprandial insulin-related measures. However, we did not detect statistically supported ethnicity-specific differences in transcript responses.\\u003c/p\\u003e \\u003cp\\u003eInterpretation of the null ancestry interaction should nevertheless remain cautious. Although the direction and magnitude of change appeared broadly similar in South Asian and White European men, the present study was not powered to establish equivalence or to exclude modest between-group differences. The current findings therefore indicate that a clear ethnicity-by-weight gain interaction was not detected in this sample, rather than demonstrating that ethnicity has no influence on adipocyte glucocorticoid-responsive transcription during early weight gain.\\u003c/p\\u003e \\u003cp\\u003eThe cross-sectional and longitudinal analyses suggest that adipocyte FKBP5 and \\u003cem\\u003eGILZ\\u003c/em\\u003e are linked to metabolic phenotype and to contemporaneous variation in insulin exposure during early weight gain. At baseline, both transcripts related to indices of adiposity, liver fat and adipocyte morphology, and within-person changes in transcript abundance were associated with changes in postprandial insulin-related measures (14, 15, 17, 18, 24). These findings are biologically plausible in light of prior literature identifying FKBP5 and \\u003cem\\u003eGILZ\\u003c/em\\u003e as glucocorticoid-responsive and metabolically relevant adipocyte transcripts. Nevertheless, the present data remain observational and restricted to transcript-level measurements. Accordingly, they do not establish the direction of effect and cannot distinguish whether altered insulin exposure influences adipocyte transcription, whether adipocyte transcription contributes to metabolic change, or whether both are parallel responses to the overfeeding intervention. The present findings should therefore be interpreted as hypothesis-generating evidence of association rather than as proof that insulin dynamics directly drive adipocyte glucocorticoid signalling.\\u003c/p\\u003e \\u003cp\\u003eTwo additional features warrant discussion. First, the inverse relationship between baseline levels and subsequent changes observed across most glucocorticoid related genes raises the possibility of regression to the mean. We formally evaluated this using Oldham\\u0026rsquo;s method and found evidence that regression to the mean contributes but does not fully account for the pattern, strengthening the inference that the observed decreases in \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e reflect true biological change. Second, as both transcripts are downstream effectors of glucocorticoid receptor signalling, their coordinated behaviour may reflect upstream fluctuations in receptor isoform abundance or activity, or alterations in co-activators and co-repressors that modulate GR signalling. However, measurement of GRα and GRβ remains technically challenging in human adipose tissue because GRβ is expressed at low levels, alternative splicing yields multiple isoforms, and commonly used qPCR assays, including some TaqMan designs, have limited specificity for individual isoforms. These constraints, described in foundational reviews of GR isoforms and GRβ biology, caution against overinterpreting null or noisy receptor isoform readouts and motivate the use of orthogonal quantification approaches in future work (22, 23).\\u003c/p\\u003e \\u003cp\\u003eThe absence of consistent changes in local glucocorticoid activation and in inflammatory markers in this young, relatively lean cohort is also informative. \\u003cem\\u003eHSD11B1\\u003c/em\\u003e and \\u003cem\\u003eHSD11B2\\u003c/em\\u003e did not change significantly with six percent weight gain, consistent with reports that tissue-specific regulation of glucocorticoid metabolism in human obesity is heterogeneous across depots and may require larger changes in adiposity, more prolonged exposure, or more severe metabolic stress to shift detectably at the mRNA level (11, 12, 25), with pharmacological studies further underscoring context-dependent effects of 11β-HSD1 modulation (26\\u0026ndash;29). Direct measurement of local glucocorticoid concentrations in adipose tissue would have provided additional mechanistic insight but remains technically challenging and was not feasible in this study. Likewise, IL2 remained effectively undetectable at baseline and showed no meaningful change with weight gain, which is concordant with limited inflammatory activation expected in lean young adults. These null findings help focus the mechanistic narrative on downstream transcriptional responsiveness in mature adipocytes rather than on proximal steroid regeneration or overt inflammatory signalling.\\u003c/p\\u003e \\u003cp\\u003eThis study has several important limitations. First, the sample size was modest, which limited power for interaction analyses and restricts confidence in null findings, particularly with respect to ethnic comparisons. Second, only men were studied, and the findings therefore cannot be generalised to women. Third, the intervention modelled modest short-term overfeeding rather than chronic obesity, so extrapolation to long-standing obesity should be made cautiously. Fourth, only mature abdominal subcutaneous adipocytes were studied; the findings cannot be assumed to apply to visceral adipose tissue or to other adipose depots. Fifth, the mechanistic assessment was restricted to transcript-level measurements and did not include protein abundance, receptor binding, tissue glucocorticoid concentrations or functional adipocyte assays. Finally, some candidate targets, particularly \\u003cem\\u003eIL2\\u003c/em\\u003e and \\u003cem\\u003eNR3C1\\u003c/em\\u003e isoforms, showed limited or unreliable amplification, which further constrains mechanistic interpretation. Taken together, these considerations support interpreting the study as a focused exploratory human translational analysis that requires replication in larger and more diverse cohorts.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eModest short-term weight gain was associated with lower \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e transcript abundance in mature abdominal subcutaneous adipocytes in both South Asian and White European men. We did not detect statistically supported ethnicity-specific differences, although the present study was not powered to exclude modest between-group effects. Associations between within-person transcript changes and postprandial insulin-related measures support further investigation of links between insulin exposure and adipocyte glucocorticoid-responsive transcription during early weight gain, but causal, sex-specific and depot-specific inferences remain premature.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eASAT, abdominal subcutaneous adipose tissue; AUC, area under the curve; BMI, body mass index; FKBP5, FK506 binding protein 5; GILZ, glucocorticoid-induced leucine zipper; GR, glucocorticoid receptor; HSD11B1, 11\\u0026beta;-hydroxysteroid dehydrogenase type 1; HSD11B2, 11\\u0026beta;-hydroxysteroid dehydrogenase type 2; IL2, interleukin-2; IL6, interleukin-6; MRI, magnetic resonance imaging; NR3C1, nuclear receptor subfamily 3 group C member 1; qPCR, quantitative polymerase chain reaction; SA, South Asian; WE, White European.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eCompeting Interests\\u003c/h2\\u003e\\u003cp\\u003eJAV has received royalties from AMH assays, paid to the institute/lab with no personal financial gain. NS reports consulting and/or speaker fees from Abbott Laboratories, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Merck Sharp \\u0026amp; Dohme, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and grant support paid to his institution from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding:\\u003c/h2\\u003e \\u003cp\\u003eThe study was supported by funding from the European Federation of Pharmaceutical Industries Associations (EFPIA)-Innovative Medicines Initiative (IMI) Joint Undertaking-European Medical Information Framework (EMIF; grant no. 115372 to JMR Gill and N Sattar) and a Dutch Diabetes Foundation Senior Fellowship (2023.82.010 to MR Boon).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eXG conceived and conducted the analyses, curated the data, performed the statistical analyses, interpreted the results, and drafted the manuscript. RL designed the glucocorticoid receptor isoform TaqMan assays and contributed to gene expression methodology. JAV contributed to study design and target gene selection. JM led participant recruitment, sample collection, and overall study execution. NS and DJF contributed to study conception and oversight. XG, MRB, NS and JMRG generated the original study idea. MRB supervised the project and contributed to manuscript writing. JMRG supervised the study, contributed to interpretation, and critically revised the manuscript. All authors contributed to manuscript revision and approved the final version.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eSaxena A SS, Choudhary P, Sharma B, Gupta S, Mandia R, Banshiwal RC, Lamoria RK, Dhakad A, Raj U, Tiwari P, Mathur SK. 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The Journal of Clinical Endocrinology and Metabolism. 2015.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eScaron;iklov\\u0026aacute; M \\u0026Scaron;V, Koc M, Krauzov\\u0026aacute; E, Č\\u0026iacute;žkov\\u0026aacute; T, Ondrůjov\\u0026aacute; B, Wilhelm M, Varaliov\\u0026aacute; Z, Kuda O, Neubert J, Lambert L, Elkalaf M, Gojda J, Rossmeislov\\u0026aacute; L. The role of adipogenic capacity and dysfunctional subcutaneous adipose tissue in the inheritance of type 2 diabetes mellitus: cross-sectional study. Obesity (Silver Spring). 2024.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eWillmer T GJ, Dias S, Louw J, Pheiffer C. DNA methylation of FKBP5 in South African women: associations with obesity and insulin resistance. Clinical Epigenetics. 2020.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eWillmer T OA, Dias S, Mendham AE, Goedecke JH, Pheiffer C. 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FKBP5 expression in human adipose tissue: potential role in glucose and lipid metabolism, adipogenesis and type 2 diabetes. Endocrine. 2018.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eStraat ME, Martinez-Tellez B, van Eyk HJ, Bizino MB, van Veen S, Vianello E, et al. Differences in Inflammatory Pathways Between Dutch South Asians vs Dutch Europids With Type 2 Diabetes. J Clin Endocrinol Metab. 2023;108(4):931\\u0026ndash;40.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eBoon MR, Karamali NS, de Groot CJ, van Steijn L, Kanhai HH, van der Bent C, et al. E-selectin is elevated in cord blood of South Asian neonates compared with Caucasian neonates. J Pediatr. 2012;160(5):844\\u0026ndash;8.e1.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eNeville MJ, Collins JM, Gloyn AL, McCarthy MI, Karpe F. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity (Silver Spring). 2011;19(4):888\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eLewis-Tuffin LJ CJ. The physiology of human glucocorticoid receptor beta (hGRbeta) and glucocorticoid resistance. Annals of the New York Academy of Sciences. 2006.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eLu NZ CJ. The origin and functions of multiple human glucocorticoid receptor isoforms. Annals of the New York Academy of Sciences. 2004.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eStrączkowski M SM, Matulewicz N, Nikołajuk A, Karczewska-Kupczewska M. Relation of adipose tissue and skeletal muscle FKBP5 expression with insulin sensitivity and the regulation of FKBP5 by insulin and free fatty acids. Endocrine. 2022.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eBianzano S SC, Wolff M, Heise T, Plum-Moerschel L. Selective Inhibition of 11beta-Hydroxysteroiddehydrogenase-1 with BI 187004 in Patients with Type 2 Diabetes and Overweight or Obesity: Safety, Pharmacokinetics, and Pharmacodynamics After Multiple Dosing Over 14 Days. Experimental and Clinical Endocrinology \\u0026amp; Diabetes. 2022.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eStefan N RM, Jordan P, Nowotny B, Kantartzis K, Machann J, Hwang JH, Nowotny P, Kahl S, Harreiter J, Hornemann S, Sanyal AJ, Stewart PM, Pfeiffer AF, Kautzky-Willer A, Roden M, H\\u0026auml;ring HU, F\\u0026uuml;rst-Recktenwald S. Inhibition of 11\\u0026beta;-HSD1 with RO5093151 for non-alcoholic fatty liver disease: a multicentre, randomised, double-blind, placebo-controlled trial. The Lancet Diabetes \\u0026amp; Endocrinology. 2014.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eOthonos N PR, Arvaniti A, White S, Bonaventura I, Nikolaou N, Moolla A, Marjot T, Stimson RH, van Beek AP, van Faassen M, Isidori AM, Bateman E, Sadler R, Karpe F, Stewart PM, Webster C, Duffy J, Eastell R, Gossiel F, Cornfield T, Hodson L, Jane Escott K, Whittaker A, Kirik U, Coleman RL, Scott CAB, Milton JE, Agbaje O, Holman RR, Tomlinson JW.. 11\\u0026beta;-HSD1 inhibition in men mitigates prednisolone-induced adverse effects in a proof-of-concept randomised double-blind placebo-controlled trial. Nature Communications. 2023.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eYadav Y DK, Khot R, Venkatesh SK, Port J, Galderisi A, Cobelli C, Wegner C, Basu A, Carter R, Basu R.. Inhibition of 11\\u0026beta;-Hydroxysteroid dehydrogenase-1 with AZD4017 in patients with nonalcoholic steatohepatitis or nonalcoholic fatty liver disease: A randomized, double-blind, placebo-controlled, phase II study. Diabetes, Obesity \\u0026amp; Metabolism. 2022.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cspan\\u003eHeise T ML, Hompesch M, H\\u0026auml;ring HU, Kapitza C, Abt M, Ramsauer M, Magnone MC, Fuerst-Recktenwald S. Safety, efficacy and weight effect of two 11\\u0026beta;-HSD1 inhibitors in metformin-treated patients with type 2 diabetes. Diabetes, Obesity \\u0026amp; Metabolism. 2014.\\u003c/span\\u003e\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"metabologia\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Metabologia](https://link.springer.com/journal/44357)\",\"snPcode\":\"44357\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44357/3?\",\"title\":\"Metabologia\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Adipocyte, Ethnicity, FKBP5, GILZ, Glucocorticoid signalling, Insulin, South Asian, Subcutaneous adipose tissue, Type 2 diabetes, Weight gain\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9199966/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9199966/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003ePurpose\\u003c/h2\\u003e \\u003cp\\u003eAdipocyte glucocorticoid (GC) signalling influences lipid storage and insulin sensitivity, and South Asians develop insulin resistance at lower BMI than White Europeans. We tested whether early modest weight gain alters adipocyte GC-related transcripts differently by ancestry and whether within-person transcript changes track dynamic insulin responses.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWhite European (n\\u0026thinsp;=\\u0026thinsp;21) and South Asian (n\\u0026thinsp;=\\u0026thinsp;14) men underwent\\u0026thinsp;~\\u0026thinsp;6% diet-induced weight gain. Abdominal subcutaneous adipocytes were sampled at baseline and post\\u0026ndash;weight gain for RT-qPCR assessment of GC-responsive transcripts (\\u003cem\\u003eFKBP5, TSC22D3/GILZ\\u003c/em\\u003e) and related targets (\\u003cem\\u003eHSD11B1, HSD11B2, IL2, IL6\\u003c/em\\u003e). Metabolic responses were characterised using a standardised mixed-meal test with 5-hour profiles of glucose, insulin, C-peptide and triglycerides; hepatic triglyceride content was quantified by MRI.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWeight gain reduced \\u003cem\\u003eFKBP5\\u003c/em\\u003e (\\u0026minus;\\u0026thinsp;23.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.82% in White Europeans; \\u0026minus;17.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.62% in South Asians; P\\u0026thinsp;=\\u0026thinsp;0.007) and \\u003cem\\u003eGILZ\\u003c/em\\u003e (\\u0026minus;\\u0026thinsp;11.40\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.83%; \\u0026minus;5.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.01%; P\\u0026thinsp;=\\u0026thinsp;0.001 for change with weight gain), with no ethnicity\\u0026times;intervention interaction (P\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.26). HSD11B1/HSD11B2 and IL2/IL6 did not change. At baseline, \\u003cem\\u003eFKBP5\\u003c/em\\u003e and \\u003cem\\u003eGILZ\\u003c/em\\u003e were associated with adiposity, liver fat and adipocyte size. Within-person ΔFKBP5 (post\\u0026ndash;weight gain minus baseline) correlated with Δpostprandial insulin (r\\u0026thinsp;=\\u0026thinsp;0.46, P\\u0026thinsp;=\\u0026thinsp;0.006) and ΔC-peptide (r\\u0026thinsp;=\\u0026thinsp;0.34, P\\u0026thinsp;=\\u0026thinsp;0.049). ΔGILZ correlated with Δfasting glucose (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.40, P\\u0026thinsp;=\\u0026thinsp;0.017) and Δpostprandial insulin (r\\u0026thinsp;=\\u0026thinsp;0.34, P\\u0026thinsp;=\\u0026thinsp;0.049). In mixed-effects models, change in postprandial insulin remained an independent correlate of both transcripts.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eEarly modest weight gain downregulates adipocyte GC-responsive transcripts similarly across ancestries, and dynamic transcript changes track insulin exposure. These findings implicate insulin dynamics as a potential driver of adipocyte GC signalling adaptations during early weight gain, linking adipocyte transcriptional responses to clinically relevant postprandial insulin physiology.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Subcutaneous adipocyte glucocorticoid-responsive transcripts after modest weight gain in South Asian and White European men: an exploratory longitudinal study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-20 17:52:50\",\"doi\":\"10.21203/rs.3.rs-9199966/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-24T16:46:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"219323376546476352043818240304390871520\",\"date\":\"2026-04-18T10:55:02+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"332256936747804412015951635496001070168\",\"date\":\"2026-04-13T11:47:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-13T10:45:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-09T07:43:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-06T01:40:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Metabologia\",\"date\":\"2026-04-01T15:01:59+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"metabologia\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Metabologia](https://link.springer.com/journal/44357)\",\"snPcode\":\"44357\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44357/3?\",\"title\":\"Metabologia\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1ab40148-4b1f-43ea-a312-31b0b46a88ef\",\"owner\":[],\"postedDate\":\"April 20th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-20T17:52:51+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-20 17:52:50\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9199966\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9199966\",\"identity\":\"rs-9199966\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}