From adipokine to integrator: longitudinal galectin-1 changes mirror glycemic, hepatic, renal, and lipid remodeling after bariatric surgery

Abstract

Background

Circulating galectin-1 levels increase with adiposity, and experimental data implicate this adipokine in the development of metabolic dysfunction. However, its longitudinal trajectory and organ-specific associations during weight loss remain poorly defined.

Methods

In this prospective study, we enrolled 117 adults with severe obesity, with a mean body mass index (BMI) 40.3 ± 5.3 kg/m², undergoing sleeve gastrectomy. Circulating galectin-1 levels and comprehensive metabolic profiles were assessed preoperatively and one year after surgery.

Results

Participants achieved a mean total weight loss of 32.2 ± 7.8%, accompanied by significant improvements in blood pressure, glycemic control, liver enzymes, and lipid profiles. Baseline galectin-1 concentrations did not differ between participants with and without type 2 diabetes and were not associated with diabetes remission at one year. Galectin-1 levels significantly decreased from 13.2 ± 5.8 to 11.0 ± 6.5 pg/mL (p < 0.001). The percentage reduction in galectin-1 was significantly correlated with improvements in aspartate aminotransferase (AST), alanine transaminase (ALT), creatinine, total cholesterol, and low-density lipoprotein (LDL) cholesterol, but not with glycemic indices. In multivariable stepwise regression, baseline galectin-1 was independently associated with BMI, creatinine, and estimated glomerular filtration rate (eGFR). One year postoperatively, galectin-1 remained independently associated with glycated hemoglobin (A1c), quantitative insulin sensitivity check index (QUICKI), AST, ALT, creatinine, and high-density lipoprotein (HDL) cholesterol.

Conclusions

Galectin-1 levels declined after weight loss and exhibited distinct associations with multiple metabolic systems. These results position galectin-1 as a potential integrative biomarker that mirrors glycemic, hepatic, renal, and lipid remodeling in response to bariatric surgery.

Introduction

Obesity has emerged as a major global health challenge, driving a broad spectrum of metabolic derangements, including type 2 diabetes (T2D), dyslipidemia, hypertension, cardiovascular disease, metabolic dysfunction‑associated steatotic liver disease (MASLD), and chronic kidney disease [1]. Elucidating the mechanisms that link excess adiposity to these multisystem complications remains a major research priority and is pivotal for the development of targeted therapies.

Galectin‑1, a carbohydrate-binding protein, has emerged as a key mediator linking obesity to systemic metabolic derangement [2, 3], while also exerting pleiotropic functions in immunometabolism, tissue remodeling, fibrosis, and tumor biology [2, 4, 5]. Circulating galectin‑1 concentrations are higher in individuals with obesity than in lean controls [6]. Pre‑clinical studies demonstrate that genetic or pharmacological inhibition of galectin‑1 attenuates diet‑induced obesity and improves metabolic parameters in rodents [2, 7–9]. Nevertheless, the precise mechanisms through which galectin‑1 influences human metabolic homeostasis remain incompletely defined. Beyond its association with adiposity, emerging human data also implicate galectin-1 in glucose dysregulation and T2D: in a population-based cohort, circulating galectin-1 was inversely associated with prevalent T2D and fasting glucose independently of obesity [6], and a recent review has highlighted galectin-1 as a potential link at the interface between obesity and T2D pathophysiology [2].

Bariatric surgery—particularly sleeve gastrectomy (SG) and Roux‑en‑Y gastric bypass—produces durable weight loss and profound improvements of obesity‑related comorbidities [10]. The marked and rapid metabolic shift that follows SG provides a unique model to interrogate dynamic changes in candidate mediators such as galectin‑1. To date, however, only sparse data are available on how circulating galectin‑1 responds to surgically induced weight loss and whether its kinetics reflect improvements across multiple organ systems.

The present study aimed to (i) quantify changes in circulating galectin‑1 before and after SG and (ii) delineate its cross‑sectional and longitudinal associations with indices of adiposity, glucose metabolism, hepatic and renal function and lipid homeostasis. By mapping the temporal and organ‑specific patterns of galectin‑1, we sought to clarify its potential as an integrative biomarker of metabolic recovery in severe obesity.

Materials and methods

This study was designed as an observational analysis based on a prospectively maintained cohort from a single tertiary medical center. We enrolled adults with obesity who underwent laparoscopic SG between 2018 and 2021 and had available longitudinal data, including paired preoperative and one-year postoperative blood samples. All surgical procedures were performed according to previously published techniques [11, 12]. Written informed consent was obtained from all participants prior to study enrollment. The study protocol received approval from the Institutional Review Board of National Taiwan University Hospital (IRB Nos. 201606111RINC and 202006050RINA).

Fasting blood samples were collected following a minimum eight-hour fast before surgery and at the one-year postoperative follow-up. Laboratory evaluations included complete blood count, glycemic indices, lipid profiles, aspartate aminotransferase (AST), alanine transaminase (ALT), creatinine, and high-sensitivity C-reactive protein (hs-CRP), all measured using standardized automated clinical analyzers. Residual serum samples were aliquoted and preserved at -80 °C for subsequent analysis. Circulating galectin-1 concentrations were determined using a commercially available human enzyme-linked immunosorbent assay (ELISA) kit (R&D Systems, Minneapolis, MN, USA).

Anthropometric measurements, including body weight, body mass index (BMI), waist circumference (WC), and blood pressure (BP), were recorded preoperatively and at each postoperative outpatient visit. Weight loss outcomes were expressed as the percentage of total weight loss (TWL%), calculated as: (baseline body weight – postoperative body weight)/baseline body weight × 100%. The diagnosis and remission of T2D followed the American Diabetes Association criteria [13, 14]. Insulin resistance was assessed using the homeostasis model assessment of insulin resistance (HOMA-IR), defined as fasting insulin (µIU/mL) multiplied by fasting glucose (mg/dL), divided by 405. In addition, insulin sensitivity was further evaluated using the quantitative insulin sensitivity check index (QUICKI), calculated as 1 divided by the sum of the logarithmic values of fasting glucose and fasting insulin. Estimated glomerular filtration rate (eGFR) was calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on serum creatinine levels [15]. The percentage change for each clinical variable was computed as: (postoperative value – baseline value)/baseline value × 100%.

Continuous variables are reported as means ± standard deviations, while categorical variables are presented as counts and percentages. Paired t-tests and McNemar’s tests were used to compare continuous and categorical variables, respectively, between baseline and 1-year follow-up. Between-group comparisons were conducted using Student’s t test for continuous variables, and the chi-square test or Fisher’s exact test for categorical variables. Among patients with T2D, differences in galectin-1 levels across categories of the number of oral antidiabetic agents (1–4 classes) were evaluated using the Kruskal–Wallis test. Pearson correlation coefficients were calculated to explore associations between galectin-1 levels and clinical parameters. Multivariable linear regression analyses with stepwise selection were performed to identify independent predictors of galectin-1, adjusting for potential confounders. A multivariable logistic regression model was constructed to examine whether circulating galectin-1 was independently associated with T2D. Statistical significance was defined as a two-tailed p-value < 0.05. All statistical analyses were conducted using IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA).

Results

A total of 117 individuals with obesity (58 men; mean age 39.0 ± 10.3 years) were included in the analysis. The baseline and one-year postoperative demographic, clinical, and laboratory profiles are detailed in Table 1. At one year following SG, participants achieved a mean TWL% of 32.2 ± 7.8%. Significant improvements were observed in body weight, BMI, waist circumference, blood pressure, glycemic parameters, AST and ALT, triglyceride levels, high-density lipoprotein (HDL) cholesterol, leukocyte count, and hsCRP compared with preoperative values. Circulating galectin-1 levels also showed a significant reduction, decreasing from 13.2 ± 5.8 pg/mL at baseline to 11.0 ± 6.5 pg/mL at one year postoperatively (p < 0.001), corresponding to a mean absolute change of −2.3 ± 5.8 pg/mL and a relative reduction of 12.2 ± 32.9%. Although the absolute change in galectin-1 was modest, this reduction paralleled the overall metabolic improvements observed after surgery.

Table 1.

Baseline and one-year postoperative demographic, clinical, and biochemical profiles, including circulating galectin-1 concentrations, in patients undergoing sleeve gastrectomy (n = 117).

	Baseline	1 year post-SG	p value
Age (year)	39.0 ± 10.3	40.0 ± 10.3
Male (n, %)	58 (49.6%)
T2D (n, %)	57 (48.7%)	20 (17.1%)
Body weight (kg)	112.2 ± 18.7	75.7 ± 13.1	<0.001
BMI (kg/m2)	40.3 ± 5.3	27.2 ± 3.9	<0.001
TWL% (%)		32.2 ± 7.8
Waist circumference (cm)	118.3 ± 12.3	88.8 ± 10.1	<0.001
Systolic BP (mmHg)	134.5 ± 14.9	122.9 ± 14.6	<0.001
Diastolic BP (mmHg)	83.9 ± 9.6	75.7 ± 10.4	<0.001
Leukocyte count (103/uL)	9.01 ± 2.21	6.62 ± 1.85	<0.001
hsCRP (mg/dL)	0.63 ± 0.51	0.18 ± 0.41	<0.001
Insulin (uIU/mL)	21.2 ± 12.4	6.8 ± 3.9	<0.001
Glucose (mg/dL)	115.7 ± 43.8	88.4 ± 12.5	<0.001
A1c (%)	6.44 ± 1.26	5.49 ± 0.51	<0.001
HOMA-IR	6.27 ± 4.74	1.52 ± 1.10	<0.001
QUICKI	0.31 ± 0.03	0.37 ± 0.03	<0.001
AST (U/L)	26.7 ± 15.0	17.6 ± 8.8	<0.001
ALT (U/L)	39.8 ± 30.2	15.4 ± 11.0	<0.001
Creatinine (mg/dL)	0.86 ± 0.39	0.89 ± 0.87	0.634
eGFR (mL/min/1.73 m2)	105.0 ± 23.0	105.8 ± 21.3	0.461
Triglyceride (mg/dL)	176.9 ± 100.4	94.7 ± 63.0	<0.001
Total cholesterol (mg/dL)	183.8 ± 37.8	184.0 ± 34.5	0.967
LDL cholesterol (mg/dL)	115.0 ± 33.7	110.1 ± 32.1	0.108
HDL cholesterol (mg/dL)	45.8 ± 9.5	57.4 ± 14.3	<0.001
Galectin-1 (pg/mL)	13.2 ± 5.8	11.0 ± 6.5	<0.001

SG sleeve gastrectomy, T2D type 2 diabetes, BMI body mass index, TWL% percentage of total weight loss, BP blood pressure, hsCRP high-sensitivity C-reactive protein, A1c glycated hemoglobin, HOMA-IR homeostatic model assessment for insulin resistance, QUICKI quantitative insulin sensitivity check index, AST aspartate aminotransferase, ALT alanine transaminase, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein, HDL high-density lipoprotein.

Galectin-1 concentrations did not differ significantly between male and female participants (13.6 ± 5.7 vs. 12.8 ± 6.0 pg/mL, p = 0.476). Among the cohort, 57 patients were diagnosed with T2D prior to surgery, and 20 of them remained diabetic at one year post-SG. In supplementary analyses, clinical and biochemical characteristics were stratified according to diabetes status (Supplementary Tables S1–S3). At baseline, galectin-1 levels were comparable between patients with and without T2D (14.1 ± 6.7 vs. 12.4 ± 4.8 pg/mL, p = 0.120; Table S1), and preoperative galectin-1 was not independently associated with T2D after adjustment for age and gender [odds ratio (OR) 1.036; 95% confidence interval (CI) 0.967–1.110; p = 0.312]. One year postoperatively, galectin-1 concentrations showed no statistically significant difference between patients with and without T2D (13.5 ± 9.1 vs. 10.4 ± 5.7 pg/mL, p = 0.051; Table S2), and postoperative galectin-1 again showed no independent association with T2D in age- and gender-adjusted logistic regression (OR 1.037; 95% CI 0.970–1.109; p = 0.289). Among patients with preoperative T2D, one-year galectin-1 concentrations did not differ between patients who achieved T2D remission and those who did not (11.6 ± 8.6 vs. 13.5 ± 9.1 pg/mL, p = 0.444; Table S3), and postoperative galectin-1 was not independently associated with persistent diabetes after adjustment for age and gender (OR 1.004; 95% CI 0.940–1.073; p = 0.903).

Among the 57 patients with preoperative T2D, 26, 15, 12, and 4 were receiving 1, 2, 3, and 4 oral antidiabetic drug classes, with galectin-1 levels of 13.3 ± 4.7, 17.2 ± 9.5, 12.7 ± 6.6, and 11.9 ± 2.2 pg/mL, respectively. Circulating galectin-1 levels did not differ significantly according to the number of oral antidiabetic drug classes used (1–4 classes; Kruskal–Wallis H = 3.247, df = 3, p = 0.355). Ten patients were treated with insulin, and these patients had higher galectin-1 concentrations than those not receiving insulin (22.1 ± 10.5 vs. 12.4 ± 4.0 pg/mL; p = 0.017). Only two individuals received glucagon-like peptide-1 receptor agonists (GLP-1 RA) (galectin-1 10.0 and 28.4 pg/mL), precluding any meaningful statistical comparison with non-users. Among the 20 patients with persistent T2D one year after surgery, 17 and 3 were taking 1 and 2 oral antidiabetic agents, respectively, with galectin-1 levels of 15.5 ± 9.3 and 14.6 ± 5.5 pg/mL (p = 0.880). None of the patients used insulin or GLP-1 RA postoperatively.

At baseline, circulating galectin-1 was positively correlated with fasting glucose, glycated hemoglobin (A1c), HOMA-IR, serum creatinine, and triglycerides, while showing an inverse correlation with QUICKI and eGFR (Table 2). Multivariable stepwise linear regression identified BMI, creatinine, and eGFR as independent baseline correlates of galectin-1 (Table 3).

Table 2.

Pearson correlations between baseline galectin-1 levels and baseline clinical and biochemical measures (n = 117).

	Pearson correlation coefficient (r)	p value
Age (year)	0.171	0.065
Body weight (kg)	0.135	0.145
BMI (kg/m2)	0.175	0.059
Waist circumference (cm)	0.165	0.075
Systolic BP (mmHg)	−0.026	0.784
Diastolic BP (mmHg)	−0.135	0.147
Leukocyte count (103/uL)	0.004	0.967
hsCRP (mg/dL)	−0.016	0.862
Insulin (uIU/mL)	0.157	0.091
Glucose (mg/dL)	0.229	0.013
A1c (%)	0.226	0.014
HOMA-IR	0.283	0.002
QUICKI	−0.207	0.025
AST (U/L)	0.004	0.969
ALT (U/L)	−0.006	0.950
Creatinine (mg/dL)	0.627	<0.001
eGFR (mL/min/1.73 m2)	−0.593	<0.001
Triglyceride (mg/dL)	0.200	0.030
Total cholesterol (mg/dL)	0.144	0.120
LDL cholesterol (mg/dL)	0.040	0.670
HDL cholesterol (mg/dL)	−0.136	0.143

BMI body mass index, BP blood pressure, hsCRP high-sensitivity C-reactive protein, A1c glycated hemoglobin, HOMA-IR homeostatic model assessment for insulin resistance, QUICKI quantitative insulin sensitivity check index, AST aspartate aminotransferase, ALT alanine transaminase, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein, HDL high-density lipoprotein. Bold values indicate statistical significance (p < 0.05).

Table 3.

Multivariable stepwise regression identifying baseline clinical and biochemical predictors of circulating galectin-1 levels prior to surgery.

	Unstandardized coefficient (95% CI)	p value
BMI (kg/m2)	0.299 (0.148–0.450)	<0.001
Creatinine (mg/dL)	6.657 (2.801–10.512)	0.001
eGFR (mL/min/1.73m2)	−0.066 (−0.131 to −0.001)	0.045
Constant	2.383 (−8.814–13.581)	0.674

BMI body mass index, eGFR estimated glomerular filtration rate.

One year after SG, galectin-1 levels were positively associated with age, hsCRP, A1c, AST, creatinine, triglycerides, and total cholesterol, and inversely associated with QUICKI, eGFR, and HDL cholesterol (Table 4). In multivariable regression analysis, postoperative galectin-1 remained independently associated with A1c, QUICKI, AST, ALT, creatinine, and HDL cholesterol (Table 5).

Table 4.

Pearson correlations between galectin-1 concentrations and clinical and biochemical markers at one year following sleeve gastrectomy (n = 117).

	Pearson correlation coefficient (r)	p value
Age (year)	0.211	0.022
Body weight (kg)	0.013	0.893
BMI (kg/m2)	0.078	0.407
Waist circumference (cm)	0.144	0.127
Systolic BP (mmHg)	0.003	0.974
Diastolic BP (mmHg)	0.080	0.398
Leukocyte count (103/uL)	−0.081	0.385
hsCRP (mg/dL)	0.197	0.034
Insulin (uIU/mL)	0.165	0.075
Glucose (mg/dL)	0.136	0.145
A1c (%)	0.272	0.003
HOMA-IR	0.133	0.154
QUICKI	−0.202	0.029
AST (U/L)	0.506	<0.001
ALT (U/L)	0.093	0.318
Creatinine (mg/dL)	0.661	<0.001
eGFR (mL/min/1.73 m2)	−0.656	<0.001
Triglyceride (mg/dL)	0.259	0.005
Total cholesterol (mg/dL)	0.200	0.031
LDL cholesterol (mg/dL)	0.157	0.092
HDL cholesterol (mg/dL)	−0.195	0.034

BMI body mass index, BP blood pressure, hsCRP high-sensitivity C-reactive protein, A1c glycated hemoglobin, HOMA-IR homeostatic model assessment for insulin resistance, QUICKI quantitative insulin sensitivity check index, AST aspartate aminotransferase, ALT alanine transaminase, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein, HDL high-density lipoprotein. Bold values indicate statistical significance (p < 0.05).

Table 5.

Multivariable stepwise regression evaluating clinical and biochemical factors at one year post-surgery that are independently associated with circulating galectin-1 levels.

	Unstandardized coefficient (95% CI)	p value
A1c (%)	1.100 (0.043–2.156)	0.043
QUICKI	−21.451 (−39.293 to −3.610)	0.019
AST (U/L)	0.608 (0.527–0.689)	<0.001
ALT (U/L)	−0.272 (-0.336 to −0.207)	<0.001
Creatinine (mg/dL)	4.295 (3.701–4.890)	<0.001
HDL cholesterol (mg/dL)	−0.071 (−0.109 to −0.032)	<0.001
Constant	6.486 (−3.021–15.993)	0.179

A1c glycated hemoglobin, QUICKI quantitative insulin sensitivity check index, AST aspartate aminotransferase, ALT alanine transaminase, HDL high-density lipoprotein.

The percentage change in circulating galectin-1 over the 1-year period was positively correlated with percentage changes in AST, ALT, creatinine, total cholesterol, and low-density lipoprotein (LDL) cholesterol (Table 6). Overall, these correlation and regression analyses indicate that galectin-1 is linked to multiple facets of metabolic dysfunction, with association strengths that are modest for most metabolic variables but more pronounced for renal indices such as creatinine, consistent with the concept that a single circulating protein captures only part of a complex metabolic network and functions as an integrative rather than disease-specific marker.

Table 6.

Associations between percent changes in circulating galectin-1 and corresponding changes in clinical and biochemical parameters from baseline to one year after sleeve gastrectomy (n = 117).

	Pearson correlation coefficient (r)	p value
%ΔBody weight	−0.021	0.823
%ΔBMI	−0.021	0.823
%ΔWaist circumference	0.092	0.331
%ΔSystolic BP	−0.073	0.441
%ΔDiastolic BP	−0.060	0.526
%ΔLeukocyte count	−0.007	0.941
%ΔhsCRP	−0.001	0.989
%ΔInsulin	0.131	0.159
%ΔGlucose	−0.144	0.121
%ΔA1c	−0.113	0.227
%ΔHOMA-IR	0.068	0.465
%ΔQUICKI	−0.135	0.146
%ΔAST	0.374	<0.001
%ΔALT	0.343	<0.001
%ΔCreatinine	0.215	0.020
%ΔeGFR	−0.032	0.728
%ΔTriglyceride	0.122	0.192
%ΔTotal cholesterol	0.277	0.003
%ΔLDL cholesterol	0.285	0.002
%ΔHDL cholesterol	−0.077	0.407

BMI body mass index, BP blood pressure, hsCRP high-sensitivity C-reactive protein, A1c glycated hemoglobin, HOMA-IR homeostatic model assessment for insulin resistance, QUICKI quantitative insulin sensitivity check index, AST aspartate aminotransferase, ALT alanine transaminase, eGFR estimated glomerular filtration rate, LDL low-density lipoprotein, HDL high-density lipoprotein. Bold values indicate statistical significance (p < 0.05).

Discussion

Our study represents one of the first human investigations to evaluate circulating galectin-1 levels in relation to obesity and its associated metabolic disturbances, both before and after substantial weight loss achieved through bariatric surgery. We demonstrated that galectin-1 levels significantly declined one year after SG, paralleling improvements in obesity severity and multiple metabolic parameters. Notably, galectin-1 exhibited varying degrees of association with indices of adiposity, glucose homeostasis, hepatic and renal function, and lipid metabolism across the preoperative and postoperative periods. These findings suggest that galectin-1 may serve as a sensitive biomarker reflecting the systemic metabolic burden of obesity.

Our findings indicate that obesity is associated with elevated circulating galectin-1, and substantial weight loss via SG leads to a significant decline in galectin-1 levels. Preoperatively, there was a trend toward higher galectin-1 in patients with greater adiposity (BMI and waist circumference), and BMI emerged as an independent positive predictor of baseline galectin-1 in multivariable analysis. This is consistent with the characterization of galectin-1 as an adipokine elevated in obesity [6]. Notably, galectin-1 is predominantly expressed in adipose tissue, which serves as a key source of circulating galectin-1 [16, 17]. For example, in children with obesity, serum galectin-1 was significantly higher than in children with normal weight and positively correlated with body fat percentage and waist circumference [16]. Likewise, a population study reported galectin-1 levels rising with increasing BMI and adiposity, even after adjusting for other factors [18]. However, at one year after SG, galectin-1 levels were no longer significantly associated with indices of adiposity. Moreover, change in galectin-1 did not correlate strongly with the magnitude of weight loss, suggesting that inter-individual differences in galectin-1 after surgery are not solely explained by the extent of weight loss. Thus, even though the mean relative change in galectin-1 over one year was only about 12% in magnitude, its longitudinal trajectory and organ-specific associations suggest that it reflects qualitative shifts in adipose and organ metabolic status rather than serving as a direct surrogate for specific anthropometric changes.

The present study demonstrates that, at baseline, circulating galectin-1 levels in individuals with severe obesity were not independently associated with the presence of T2D or glycemic parameters after adjusting for potential confounders in multivariate analyses. Additionally, baseline galectin-1 concentrations did not predict diabetes remission following SG. However, postoperative galectin-1 levels exhibited a positive correlation with A1c and a negative correlation with QUICKI, suggesting a potential relationship between galectin-1 and residual glycemic disturbances even after substantial metabolic improvements. In our cohort, patients with persistent T2D at one year were older and experienced a smaller degree of weight loss than those who entered remission, which is compatible with a higher cumulative metabolic burden and more treatment-resistant diabetes and may blunt between-group differences in galectin-1 when patients are classified only by remission status. In addition, among patients with T2D, those treated with insulin had higher galectin-1 levels than non-insulin users, whereas the number of oral antidiabetic drug classes was not related to galectin-1 concentrations. This pattern most likely reflects greater metabolic disease burden and treatment intensity among insulin users rather than a direct effect of insulin itself, and our ability to dissect drug-specific effects is further limited by the lack of systematic data on other medications for obesity-related comorbidities.

These findings contrast with previous reports, which indicated an inverse association between serum galectin-1 and T2D, fasting glucose levels, and a positive correlation with insulin, independent of BMI, observed in a large middle-aged population with relatively lower BMI (median BMI of 26.0 kg/m² in women and 27.2 kg/m² in men) [6]. Similarly, previous research also found galectin-1 negatively associated with fasting glucose in children with obesity (mean BMI 32.8 ± 4.7 kg/m²) [16]. It is important to note that the BMI in our study cohort (40.3 ± 5.3 kg/m²) is considerably higher than that of previously studied populations, suggesting that the substantial adiposity in severe obesity might alter or mask the interaction between circulating galectin-1 and glucose metabolism. Experimental and clinical data support this notion. Galectin-1 expression is upregulated in hypertrophic and hypoxic adipose tissue, where it participates in immunometabolic crosstalk between adipocytes, endothelial cells, and immune cells, promotes adipose tissue remodeling, and is closely linked to insulin resistance and ectopic lipid deposition [2, 7, 8, 17, 19]. In high-fat diet models, genetic deletion or inhibition of galectin-1 protects against obesity, steatosis, and hepatic expression of gluconeogenic and lipogenic genes [7, 8, 17], and multi-omics profiling in humans has shown that circulating galectin-1 clusters with markers of adiposity, insulin secretion and sensitivity, triglyceride, lipoproteins, and inflammatory pathways [18]. Given that adipose tissue is recognized as a significant source of circulating galectin-1 [17], extreme adiposity could induce altered secretion patterns and downstream organ interactions, thereby modifying the metabolic relevance of galectin-1 in severe obesity. Taken together, these observations support the view that in severe obesity galectin-1 behaves less as a simple surrogate of glycemic status and more as an integrative marker of global metabolic and inflammatory stress, which may explain the weaker and more complex relationships with blood glucose and diabetes status observed in our cohort compared with populations with lower BMI.

In our cohort, the percentage decrease in galectin-1 over one year was positively correlated with reductions in ALT and AST. Moreover, in the multivariate model, postoperative galectin-1 was independently associated positively with AST and negatively with ALT. This intriguing divergence suggests that galectin-1 may be linked to distinct aspects of liver pathology. Emerging evidence indicates that galectin-1 is involved in hepatic fibrosis. It is highly expressed by activated hepatic stellate cells, the principal effectors of liver fibrogenesis, and promotes their proliferation and collagen production—both critical steps in the progression of hepatic fibrosis [4]. Beyond fibrosis, galectin-1 levels also appear to reflect the burden of hepatic steatosis. A recent metabolic profiling study reported that serum galectin-1 correlates with liver size and liver fat content [18]. Our findings align well with this evidence, demonstrating substantial improvements in AST and ALT and reductions in galectin-1 levels following SG. Collectively, these observations reinforce the concept that galectin-1 intricately connects metabolic dysfunction, steatosis, inflammation, and fibrosis within the spectrum of MASLD. Even though the correlations with AST and ALT were not particularly strong, this degree of association is compatible with a multifactorial liver disease process in which galectin-1 is one of several interacting mediators.

Our findings demonstrate a consistent association between galectin-1 and renal function, supporting its potential as a biomarker of renal impairment. At both preoperative and postoperative time points, circulating galectin-1 correlated positively with serum creatinine and inversely with eGFR, and these relationships remained significant in multivariable analyses. Moreover, the percentage change in galectin-1 after SG paralleled changes in creatinine, indicating that galectin-1 is sensitive to dynamic alterations in renal function accompanying substantial weight loss. These observations are in line with previous cross-sectional and longitudinal studies reporting higher circulating galectin-1 levels in individuals with reduced kidney function [20, 21]. Mechanistically, the interplay between galectin-1 and the kidney appears complex and context-dependent. Galectin-1 has been implicated in both protective and pathogenic processes within the kidney [2]. Experimental studies in mouse models have suggested renoprotective effects, potentially via anti-inflammatory actions and attenuation of acute kidney injury [22], whereas in vitro data have yielded divergent findings regarding its role in renal fibrosis, with associations to different fibrotic markers and pathways [23, 24]. Taken together, our results suggest that galectin-1 may reflect renal functional changes driven by metabolic alterations, inflammation, and fibrotic signaling, although further work is needed to clarify its precise role in renal health.

Dyslipidemia is a hallmark of obesity-related metabolic disorders, and our data highlight a significant relationship between galectin-1 and an atherogenic lipid profile. At baseline, serum galectin-1 levels positively correlated with triglycerides, whereas 1 year after SG, galectin-1 showed positive correlations with triglycerides and total cholesterol and a negative correlation with HDL cholesterol. Notably, HDL cholesterol remained independently associated with galectin-1 levels postoperatively. Additionally, the change in galectin-1 from baseline to one year post-SG was significantly associated with changes in LDL and total cholesterol, aligning closely with previous cross-sectional findings [6]. Supporting our findings, a recent metabolomics analysis also revealed significant associations between galectin-1 and various lipoproteins and triglycerides [18]. Biologically, galectin-1 has been demonstrated to promote lipid accumulation in adipocytes and the liver, thus driving increased lipoprotein secretion and elevated blood triglycerides [2]. Importantly, inhibition of galectin-1 in obese animal models reduces hepatic lipogenesis and circulating triglycerides [25], reinforcing its role in mediating hypertriglyceridemia.

The present study provides comprehensive clinical data on obesity-related metabolic disorders before and after SG, alongside their associations with circulating galectin-1 levels. However, several limitations should be acknowledged. First, this is a single-center study conducted in an Asian population with a relatively small sample size, which may limit the generalizability of our findings to broader or more diverse populations. Second, the study did not include a healthy, lean control group, thereby restricting our ability to contextualize galectin-1 levels across the full metabolic spectrum. Third, direct measures of body composition, such as body fat percentage or imaging-based quantification of adipose tissue, were not available; thus, our assessment of adiposity relied on BMI and waist circumference as surrogate markers. Fourth, physical activity levels were not systematically recorded before and after SG, so we could not evaluate the potential influence of changes in physical activity on galectin-1 or metabolic outcomes. Fifth, we did not measure other adipokines, such as leptin or adiponectin, and therefore could not determine whether the observed changes in galectin-1 were specific or part of a broader adipokine remodeling; future studies should incorporate multi-marker adipokine profiling and joint analyses to better delineate these interrelationships. Moreover, due to the observational study design, we are unable to establish causality between galectin-1 and the metabolic improvements observed after SG. While our findings strongly suggest associations between galectin-1 and multiple metabolic pathways—including adiposity, glucose metabolism, liver and renal function, and lipid profiles—future mechanistic and interventional studies are needed to clarify the causal role of galectin-1 in obesity-related disease progression and remission during weight loss.

In summary, circulating galectin-1 levels decreased following weight loss and metabolic improvement after SG, and showed varying degrees of association with measures of adiposity, glucose metabolism, hepatic enzymes, renal function, and lipid profiles. These findings suggest a functional and integrative role for galectin-1 in the pathophysiology of obesity, potentially linking adipose tissue dysfunction with systemic metabolic disturbances.

Author contributions

THC and YJL contributed to the conception of the study, data analysis, and drafting of the manuscript. WSY contributed to the study design, data analysis, and interpretation. PCL, CNC, and MTL were involved in data acquisition and analysis. PJY contributed to the study conception and design, data analysis and interpretation, and made substantial revisions to the manuscript. All authors reviewed and approved the final submitted version.

Funding

PJY discloses support for the research of this work from the Taiwan Health Foundation (THF 2024001). THC, YJL, WSY, PCL, CNC, and MTL declare no relevant funding.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

This research was conducted following the principles of the Declaration of Helsinki. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants prior to study enrollment. The study protocol received approval from the Institutional Review Board of National Taiwan University Hospital (IRB Nos. 201606111RINC and 202006050RINA).

Supplementary information

The online version contains supplementary material available at 10.1038/s41387-026-00437-7.