A closer look at Galectin-3: its association with gestational diabetes mellitus revealed by systematic review and meta-analysis

Abstract

Purpose

Gestational diabetes mellitus (GDM) represents a significant metabolic disorder that affects pregnant women worldwide and has negative consequences for both the mother and her offspring. This research aims to investigate the relation between circulating levels of Galectin-3 and the incidence of GDM, and to evaluate its potential as a biomarker for monitoring and early detection of the disease.

Methods

A thorough search of the literature has been performed using databases such as Scopus, Web of science, Embase, Cochrane Library and PubMed. The standardized mean difference (SMD) and corresponding confidence intervals (CIs) were used to compute the effect size from individual records and pooled using the Random-effect model.

Results

Our meta-analysis synthesized data from 9 studies, encompassing 1,286 participants (533 GDM patients and 753 healthy pregnant controls). The findings demonstrated a considerable increase in Galectin-3 levels among individuals diagnosed with GDM as compared to the healthy control (SMD = 0.929; CI: 0.179–1.679; p = 0.015), with observed heterogeneity (I² = 87%; p < 0.001). Subgroup analyses revealed the influence of factors such as age, BMI, study design, and sample type on Galectin-3 levels. A meta-regression analysis further identified trends indicating that levels of Galectin-3 are linked to gestational age, specific geographical areas, and sample size.

Conclusion

Increased levels of Galectin-3 exhibit a significant association with GDM, indicating its prospective utility as a biomarker for early detection and risk assessment. Further research is warranted to elucidate its regulation and clinical implications in GDM management.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01461-z.

Introduction

Gestational Diabetes Mellitus (GDM) is a medical condition distinguished by the onset or first recognition of glucose intolerance during pregnancy [1, 2]. Previous research indicates that early pregnancy hyperglycemia correlates with negative pregnancy outcomes, highlighting the potential benefits of early interventions for GDM [2, 3]. Despite intensive efforts to manage GDM diagnosed before the 13-14th weeks of pregnancy, these early cases show an extremely higher occurrence of adverse consequences compared to GDM diagnosed after the 24th week [4]. For instance, the incidence of jaundice was 28.1% in early-diagnosed cases compared to 19.8% in later-diagnosed cases; macrosomia was observed in 20.3% of early GDM cases versus 10.0% in later cases; and respiratory distress syndrome occurred in 7.4% compared to 4.0% [5]. Furthermore, it is noteworthy that the risk of delivering a larger-than-average infant increases with every one mmol/L rise in fasting glucose levels at the start of pregnancy, with an odds ratio of 1.21 [6]. Therefore, early detection and management of hyperinsulinism in pregnancy (HIP) and GDM are crucial for improving clinical outcomes. Additionally, GDM poses substantial health risks for both the mother and the fetus. These risks encompass preeclampsia, cesarean delivery, and an elevated chance of the mother developing type 2 diabetes mellitus (T2DM) in the future [7, 8]. In the context of the fetus, potential complications include macrosomia, neonatal hypoglycemia, and an increased susceptibility to obesity and diabetes in later stages of life [9]. Therefore, identifying predictive biomarkers and understanding the mechanisms of GDM are crucial for early detection and intervention. Galectin-3, a lectin that binds to β-galactosides, has become recognized as a crucial biomarker and regulator in a range of pathological conditions, including fibrosis, inflammation, and cancer [10, 11]. Recent research has shifted focus toward its role in metabolic disorders, especially concerning obesity, T2DM, and insulin resistance [12, 13]. A previous study revealed that levels of circulating serum galectin-3 were notably elevated in individuals with T2DM and prediabetes in contrast to those without these conditions, and these levels exhibited a positive correlation with fasting plasma glucose [14]. Given the physiological and pathophysiological parallels between GDM and these metabolic disorders, exploring the affinity between levels of circulating galectin-3 and GDM emerges as a logical and promising avenue for future research [15, 16]. Several mechanisms have been postulated to elucidate the involvement of Galectin-3 in metabolic dysfunction. Galectin-3 is recognized for its role in modulating inflammatory responses and adipocyte function, both pivotal in the pathogenesis of insulin resistance and GDM [17, 18]. Subsequent research has shown that galectin-3 can rescue HTR-8/SV cells from apoptosis caused by high glucose, a process mediated through the Foxc1 pathway [19]. Pejnovic and his colleagues discovered that galectin-3 knockout (G3KO) mice consuming a high-fat diet exhibited extremely higher levels of fasting insulin, fasting blood glucose, and glycohemoglobin levels compared to wild-type (WT) mice [20]. Darrow et al. additionally noted that G3KO mice, when given a high-fat diet, exhibited diminished glucose tolerance and higher fasting glucose levels in comparison to WT mice on the same dietary regimen [21]. In contrast, research by Mensah-Brown et al. reported findings that differed from the studies mentioned earlier, showing a significant decrease in blood glucose levels in G3KO mice compared to WT animals following the administration of considerably low doses of Streptozotocin [22]. Besides, a study by Li et al. found that G3KO mice showed improved glucose tolerance and substantially lower basal levels of insulin following a high-fat diet. It was found that galectin-3 disrupts crucial steps of the phosphorylation of the phosphoinositide-kinase and insulin-receptor [22, 23]. These conflicting results underscore the intricate nature of galectin-3 involvement in metabolic pathways, emphasizing the critical necessity for additional studies to elucidate its precise functions and impacts.

Given the intricate association between inflammation, obesity, insulin resistance, and GDM, examining the levels of circulating Galectin-3 may offer valuable insights into the molecular pathways associated with GDM. This investigation has the potential to reveal novel therapeutic targets and predictive biomarkers. This endeavor aims to consolidate the available evidence regarding the correlation between GDM and circulating Galectin-3.

Methods

Study protocol and registration

This meta-analysis protocol has been recorded with the Prospective International Registration of Systematic Reviews (PROSPERO), identified by registration code CRD42024533473. The methodology was designed and documented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures, ensuring transparency and the replicability of our findings [24].

Eligibility standards and criteria

The criteria for eligibility in this research were carefully defined to specifically include studies investigating the correlation between GDM and circulating levels of Galectin-3. The criteria for inclusion were as follows: (I) Observational research involving adult individuals diagnosed with GDM were eligible for inclusion; (II) The studies should include a comparison of circulating serum/plasma Galectin-3 levels in pregnant females diagnosed with GDM and those in a control group without GDM; (III) Publications in peer-reviewed journals in English were considered to ensure the reliability and accessibility of data; (IV) Studies must employ clear GDM diagnostic criteria, such as those established by (e.g., American Diabetes Association (ADA) [25], World Health Organization (WHO), Guidelines have been developed by the Chinese Ministry of Health for the prevention and management of GDM [26], International Association of Diabetes and Pregnancy Study Groups (IADPSG) [27], to facilitate uniformity and comparability across studies; (V) Availability of data necessary for calculating effect sizes was required for a study’s inclusion to enable a quantitative synthesis of the evidence.

Exclusion criteria were applied to maintain the focus and quality of the systematic review: (I) Randomized controlled trials were excluded to specifically assess observational evidence regarding circulating Galectin-3 levels in GDM; (II) Studies lacking a control group of pregnant women without GDM were not considered, as the comparison is crucial for understanding the relation between Galectin-3 levels and GDM; (III) Non-human subject studies were excluded to ensure the applicability of findings to clinical settings involving human participants; (IV) Studies with pharmacological interventions aimed at altering Galectin-3 levels were excluded to isolate the natural course and impact of Galectin-3 in GDM without external influences; (V) Literature published in languages other than English or outside the specified search period from January 2000 to February 2023 was excluded to maintain a manageable scope and ensure the timeliness of the evidence reviewed. Also excluded were reviews, animal studies, studies without clear GDM diagnostic criteria, conference abstracts, unpublished manuscripts, and preprint articles, as well as research published in languages ​​other than English.

Search strategy and literature search

A comprehensive review of the literature was conducted across multiple medical databases, including Cochrane Library (CENTRAL), EMBASE, Scopus, PubMed (MEDLINE), and Web of Science. Search strategy terms were carefully selected to encompass key aspects related to Galectin-3 (e.g., “Galectin-3” OR “gal-3” OR “LGALS3” OR “CBP-35” OR “CBP-30” OR “IgE Binding Protein” OR “HL-29” OR “Epsilon-Binding Protein” OR “L-29 Lectin” OR “L-31” OR “L30 Lectin” OR “L-34” OR “Macrophage-2 Antigen” OR “Mac-2 Antigen” OR “Carbohydrate-Binding Protein 35” (and Gestational Diabetes Mellitus (e.g., “Gestational Diabetes Mellitus” OR “pregnancy diabetes” OR “GDM” OR “Pregnancy-Induced Diabetes” OR “Gestational Diabetes” OR “Diabetes, Pregnancy Induced” OR “Diabetes, Gestational” OR “Diabetes, Ketosis Resistant” OR “Diabetes, Non-Insulin-Dependent” OR “Diabetes Mellitus, Stable” OR “NIDDM” OR “Diabetes Mellitus, Noninsulin Dependent” OR “Diabetes Mellitus, Maturity Onset” OR “Maturity Onset Diabetes” OR “Noninsulin Dependent Diabetes Mellitus” OR “Diabetes, Maturity-Onset” OR “Diabetes Mellitus, Adult Onset” OR “Diabetes, Adult-Onset” OR “Adult-Onset Diabetes Mellitus”). These terms of search strategy were combined using Boolean operators to create precise search queries customized for each database. The queries cover the period from January 2000 to February 2023. Detailed methodologies for this systematic search are documented in Supplemental Table 1.

Study selection process

Independent researchers A.A. and S.B. diligently examined the titles/abstracts of the all records to ascertain their eligibility. This evaluation adhered to predefined inclusion and exclusion criteria, which were established in alignment with the study extraction design outlined in previous research conducted by our team [28, 29]. Inconsistencies were addressed through discussion or by seeking guidance from a third reviewer (T.S.). Eligible studies proceeded to full-text review for a detailed assessment. The study selection process, encompassing identification, screening, eligibility, and inclusion phases, is illustrated in the PRISMA flow diagram (Fig. 1).

Fig. 1.

Flow diagram depicting the study selection process adjusted in accordance with the PRISMA guidelines

Data extraction

In terms of data extraction, two researchers autonomously conducted data extraction using a standardized form to ensure consistency. The extracted data encompassed study characteristics (publication year, country, study design), participant demographics (age, sample size, BMI), Galectin-3 measurement details (method of measurement, units, time points), and GDM diagnostic criteria used. Results regarding circulating plasma/serum Galectin-3 levels in GDM and controls individuals, as well as any reported associations between GDM and Galectin-3 levels outcomes, were also recorded. Key extracted data points are summarized in Table 1.

Table 1.

Characteristics of the studies included in the systematic review and meta-analysis

Author, yrs. (Ref)	Country	Study designs	Number of participants	Age (yr) (GDM vs. controls)	BMI (kg/m2) (GDM vs. controls)	Gestational age (weeks) (GDM vs. controls)	Sample (unit)	GRADE assessment
Baldane et al., [60]	Turkey	Cross-sectional	GDM:34 Control:25	30.48 ± 4.44; 27.95 ± 4.59	27.85 ± 2.95; 26.88 ± 2.79	26.61 ± 2.64; 26.04 ± 2.18	Serum (ng/ml)	⊕⊕⊕⊕
Tang et al., [61]	China	Case-control	GDM:33 Control:64	28.94 ± 4.71; 28.19 ± 4.32	23.45 ± 2.37; 25.38 ± 2.12	39.41 ± 1.92; 39.28 ± 1.83	Serum (ng/dl)	⊕⊕⊕○
Chen et al., [56]	China	Case-control	GDM:56 Control:41	30.15 ± 4.09; 28.93 ± 4.50	24.23 ± 1.28; 24.14 ± 2.24	12.86 ± 1.54; 12.75 ± 1.47	Plasma (ng/ml)	⊕⊕○○
Wang et al., [50]	China	Cross-sectional	GDM:61 Control:57	32.49 ± 4.35; 31.25 ± 5.54	21.32 ± 3.24; 20.33 ± 2.72	39.58 ± 1.01; 39.29 ± 0.82	plasma (ng/ml)	⊕⊕○○
Gencheva et al., [51]	Bulgaria	Case-control	GDM:36 Control:37	28.83 ± 5.78; 30.82 ± 6.02	28.58 ± 6.14; 24.58 ± 5.11	33.71 ± 4.08; 34.08 ± 5.23	Serum (ng/ml)	⊕⊕⊕⊕
Heusler et al., [62]	Israel	Case-control	GDM:44 Control:40	32.9 ± 4.5; 31.3 ± 5.3	28.7 ± 4.05; 25.1 ± 3.1	38 ± 2; 38 ± 4	Serum (pg/ml)	⊕⊕○○
Zhang et al., [63]	China	Cross-sectional	GDM:137 Control:81	30.31 ± 3.94; 29.30 ± 3.67	24.39 ± 2.97; 24.63 ± 2.74	26.71 (25.00-28.79); 26.00 (24.29–27.36)	Plasma (ng/ml)	⊕⊕⊕○
Talmor-Barkan et al., [15]	Israel	Case-control	GDM:24 Control:36	34.3 ± 5.4; 33.1 ± 4.5	30 ± 6.9; 25.4 ± 4	38.57 ± 2.85 39.71 ± 1.33	Plasma (ng/ml)	⊕⊕○○
Zhu et al., [64]	China	Case-control	GDM:327 Control:85	31.2 ± 5.8; 30.0 ± 6.5	24.8 ± 2.2; 24.1 ± 3.3	34.18 ± 2.73; 32.46 ± 3.82	Serum (ng/ml)	⊕⊕⊕○

BMI: Body mass index, GDM: Gestational diabetes mellitus, GRADE: Grading of recommendations assessment, development, and evaluation, FF: follicular fluid. The rating of GRADE is as follows: ⊕⊕⊕○ Moderate quality: We are moderately confident about the effect estimate; ⊕⊕○○ Low quality: Our confidence in the effect estimate is limited, ⊕○○○ Very low quality: We have very low confidence in the effect estimate.

Study quality appraisal

The included records underwent rigorous quality assessment utilizing the Newcastle Ottawa Scale (NOS), which is specifically designed to evaluate observational studies, which estimates three domains: preference and Selecting research groups, comparing groups, and determining exposure/outcome. Studies were awarded a maximum of 9 points, with those scoring ≤ 4 considered low quality and ≥ five considered high quality. This assessment helped ensure the reliability and validity of the findings drawn from the included records. The quality assessment findings are summarized in Table 2 [30]. Furthermore, the Grading of Recommendations Development, Assessment, and Evaluation technique was employed to appraise the status and grade of the studies incorporated in this meta-analysis.

Table 2.

Quality assessment conducted according to the Newcastle–Ottawa Scale for all the studies included in this meta-analysis or more which indicates no bias

First author and year	Quality indicators			Total quality scores
	Selection	Comparability	Outcome
Baldane et al., [60]	****	**	**	7
Tang et al., [61]	**	*	**	5
Chen et al., [56]	***	**	**	7
Wang et al., [50]	**	*	**	5
Gencheva et al., [51]	****	**	**	8
Heusler et al., [62]	**	**	**	6
Zhang et al., [63]	***	*	**	6
Talmor-Barkan et al., [15]	****	**	**	8
Zhu et al., [64]	***	**	*	6

Statistical analysis

In order to compare the levels of Gal-3 in circulation between individuals diagnosed with GDM and the healthy control group, an examination of the standardized mean difference (SMD) was carried out, accompanied by the derivation of the corresponding 95% confidence intervals (CIs); The heterogeneity between studies was quantified using the I2 statistic and Cochran Q test. Depending on heterogeneity results, random-effects models were applied. Subgroup and Meta-regression analyses were conducted to pinpoint possible sources of variability within the data, considering variables such as age, BMI, geographic location, and study quality. Publication bias was considered utilizing Egger’s test and represented through a funnel plot to examine heterogeneity. All statistical meta-analyses were performed using version 3.7z of the Comprehensive Meta-Analysis (CMA) software.

Results

Characteristics and selection process of the included studies

The systematic search identified 786 articles across the selected databases. After eliminating 138 duplicates, 648 records underwent review based on their abstracts and titles, culminating in the exclusion of 88 records that did not align with the initial eligibility criteria. The remaining 52 articles underwent a full-text review, resulting in the exclusion of 43 articles for reasons such as not reporting specific outcomes of interest, lacking a control group, or providing insufficient data on Galectin-3 levels. Ultimately, nine studies were included in the systematic review and meta-analysis, encompassing a total of 1,286 participants (533 diagnosed with GDM and 753 healthy pregnant controls). The selection process is illustrated in Fig. 1.

Insights into associations and comparative analysis

Our meta-analysis thoroughly examined the link between circulating levels of Gal-3 and GDM, synthesizing data from nine studies with a combined sample size of 1,286 participants. Through rigorous subgroup and meta-regression analyses, we delved into the potential heterogeneity introduced by factors such as age, study design, body mass index (BMI), type of sample, total sample size, geographic region, year of publication, and gestational age. These analyses illuminate the nuanced relationship between these medical conditions and deliver insights into the underlying heterogeneity.

Insights into levels of circulating Galectin-3 and gestational diabetes mellitus risk

The comprehensive meta-analysis of data extracted from the nine studies included in our review conclusively demonstrates a statistically significant elevation in circulating levels of Galectin-3 among individuals diagnosed with GDM when compared to healthy pregnant controls. The SMD calculated was 0.929 (95% CI: 0.179–1.679; Prediction Interval [PI]: -2.00 to 4.65), with a p-value of 0.015, and the effect size was calculated using a random effects model. This finding underscores the potential utility of Galectin-3 as a biomarker for GDM, suggesting its relevance in the pathophysiology of the condition. However, it is critical to recognize that there was a considerable amount of variability among the studies (I² = 96.93%; p < 0.001), indicating differences in study populations, methodologies, or both, as illustrated in Fig. 2.

Fig. 2.

The forest plots illustrate a comparison of serum Galectin-3 levels between groups diagnosed with Gestational Diabetes Mellitus (GDM) and control groups

Prediction interval

The prediction interval was calculated to be -2.00 to 4.65, indicating a wide range of potential effects across different populations and study conditions. This coverage is anticipated to encompass the effect size within 95% of similar folks. True effect range implies that while the average effect indicates higher Galectin-3 levels in GDM, individual studies may report results that significantly deviate from this average, underscoring the importance of considering contextual factors in interpreting these findings (Fig. 3).

Fig. 3.

The prediction interval for the standardized mean difference (SMD) of Galectin-3 levels between the gestational diabetes mellitus and control groups

A

Subgroup analysis

Further analysis of the subgroups revealed complex relationships influenced by factors such as age, BMI, study design, and sample type (Fig. 4). Fascinatingly, research involving participants aged over 30 years consistently demonstrated elevated Galectin-3 levels compared to studies involving younger participants under the age of 30. This age-related variation could be attributed to cumulative exposure to metabolic stressors that increase Galectin-3 production (SMD = 1.292; CI: 0.181–2.397; p = 0.022 for ≥ 30 years). In contrast, the results for participants younger than 30 years were not meaningful (SMD = 0.194; 95% CI: -0.38 to 0.427; p = 0.062 for < 30 years). The pathophysiological mechanisms linking age and elevated Galectin-3 levels may involve age-associated increases in inflammatory processes and insulin resistance. In addition, it has been established that individuals with a BMI exceeding 25 exhibit diminished Galectin-3 levels (SMD = -0.019; 95% CI: -0.537 to 0.501; p = 0.943) relative to those with a BMI below 25 (SMD = 1.699; CI: 0.559 to 2.838; p = 0.003). This unexpected result suggests a complex interaction between adiposity and Galectin-3 regulation, possibly involving the sequestration of Galectin-3 by adipose tissue or its downregulation in response to adiposity-induced inflammation. In terms of study designs, no substantial variation were identified between case-control and cross-sectional studies in terms of Galectin-3 levels. However, cross-sectional studies displayed greater dispersion in effect sizes (SMD = 1.419; 95% CI: -0.631 to3.469; p = 0.175), suggesting that the cross-sectional approach may capture a broader snapshot of the connection between Galectin-3 levels and GDM, reflecting population-level variations. Finally, the type of sample analysis indicated no significant difference between serum or plasma samples in detecting Galectin-3 levels, though serum samples exhibited a slightly larger effect size (SMD = 1. 022 vs. 0.819, both p < 0.05.). This slight discrepancy might be due to the differential stability or interaction of Galectin-3 with other serum components.

Fig. 4.

Forest plots depicting subgroup analyses demonstrate the influence of circulating Galectin-3 levels on the risk of gestational diabetes mellitus within various subgroups: (A) age ≥ 30 years versus age ≤ 30 years, (B) body mass index (BMI) ≥ 25 kg/m² versus BMI < 25 kg/m², (C) study designs encompassing both case-control and cross-sectional studies, and (D) type of sample, whether serum or plasma

Meta-regression analysis

Our meta-regression analyses were performed to explore how geographical region, total sample volume, publication year, and gestational age affect Galectin-3 levels (Fig. 5). Regarding geographical regions, the results showed significantly higher Galectin-3 levels in studies from Asian populations compared to European populations. This suggests a potential environmental or genetic influence on Galectin-3 expression (Meta-regression coefficient: -1.713; 95% CI: -3.19 to -0.24; p = 0.02). Furthermore, there was a non-significant positive correlation between total sample size and Galectin-3 levels, indicating that larger sample sizes may better capture the true association between Galectin-3 and GDM (Regression coefficient: 0.015; CI: -0.006 to 0.009; p = 0.70). This study identified a steady increase in Galectin-3 levels over time, with a regression coefficient of 0.223 (CI: -0.25 to 0.69; p = 0.35), although this rise was not statistically significant. Potential contributors to this non-significant increase include alterations in diagnostic criteria, changes in population health, or heightened awareness of GDM. Additionally, Galectin-3 levels exhibit a positive correlation with gestational age, indicating a tendency for an elevation as pregnancy progresses. This relationship may be attributed to the dynamic metabolic and immunological adaptations characteristic of advancing pregnancy. The regression analysis yielded a coefficient of 0.120 (95% CI: -0.001 to 0.025, p < 0.04), underscoring the statistical significance of this association.

Fig. 5.

Meta-regression analysis demonstrates the impact of circulating Galectin-3 levels on the risk of gestational diabetes mellitus (GDM), adjusting for the following variables: A) Geographic region, B) Total sample size, C) Publication year of the studies, and D) Gestational age (days). This analysis identifies potential factors that may modify the association between Galectin-3 and GDM across various populations and study designs

Unveiling publication bias and sensitivity analysis

The reliability and strength of the combined results regarding the correlation between circulating Galectin-3 levels and GDM were reinforced through thorough sensitivity analyses and assessments for publication bias. In order to enhance the accuracy of our meta-analysis, we systematically conducted sensitivity analyses by iteratively excluding individual studies. This meticulous approach was aimed at verifying the consistency of each study’s contribution to the overarching research goals, particularly in examining how Galectin-3 levels correlate with the incidence of GDM. Our sensitivity analysis revealed a range in the SMD of Galectin-3 between GDM cases and control subjects, ranging from − 0.242 to 1.125. This variation underlines the heterogeneity in Galectin-3 expression among pregnant women and its potential association with GDM. The calculated 95% confidence intervals (CIs) for these findings spanned from − 0.113 to 0.308 for the lower boundary and from 0.925 to 1.325 for the upper boundary, illustrating a broad spectrum of Galectin-3 levels across different populations (refer to Supplemental Fig. 1). Notably, the I² statistics remained stable and did not exhibit significant fluctuations, affirming the consistency of the results across various studies included in our analysis.

To address potential publication bias, a funnel plot analysis was conducted, complemented by the application of Egger’s regression test. This combination of visual and statistical methods aimed to identify any asymmetry or systematic errors in the literature that could skew the understanding of Galectin-3’s role in GDM. The results from the Egger test indicate the absence of significant publication bias within the selected studies (regression intercept: -4.209; Standard Error: 2.935; 95% CI: -14.44 to 6.02, P = 0.363), reinforcing the reliability of our meta-analytical findings (Supplemental Fig. 1 depicts the funnel plot for visual assessment).

Discussions

In this meta-analysis, the primary objective was to elucidate the associations between circulating Galectin-3 levels and GDM by synthesizing data from 9 studies encompassing 1,286 participants. The analysis unveiled a statistically significant association, indicating elevated levels of circulating Galectin-3 among GDM patients compared to healthy pregnant controls. This discovery underscores the conceivable level of Galectin-3 as a biomarker for GDM, offering prospects for early diagnosis and effective management strategies. However, the interpretation of these findings necessitates a thorough discussion that delves into the underlying biological mechanisms, addresses potential confounding factors, and explores the implications for clinical practice.

Biological mechanisms linking Galectin-3 and GDM

The elevation of Galectin-3 levels in GDM patients can be attributed to several interconnected mechanisms within pregnancy physiology. One critical factor is the pro-inflammatory state associated with GDM. During pregnancy, there is a physiological shift towards a state of low-grade inflammation, which is further exacerbated in GDM [31, 32]. Numerous studies have demonstrated that pro-inflammatory cytokines, specifically tumor necrosis factor-alpha (TNF-α), have the capacity to induce the production of Galectin-3 in macrophages. This upregulation is mediated through the activation of transcription factors such as NF-κB and STAT3 pathways, which are known to regulate Galectin-3 gene expression [33, 34]. Additionally, adipose tissue undergoes significant changes during pregnancy and is implicated in releasing inflammatory mediators. Galectin-3 expression has been shown to upregulate adipocytes under conditions of obesity and inflammation, which are closely linked to GDM [35, 36]. Also, GDM has been reported to be associated with notable reductions in adiponectin levels [37]. Adiponectin, an anti-inflammatory adipokine, is known to downregulate the expression of Galectin-3 [38]. However, reduced adiponectin levels in GDM disrupt this inhibitory control and consequently enhance Galectin-3 expression. Furthermore, higher levels of progesterone, which are linked to an increased risk of diabetes, have been demonstrated to cause increased expression of Galectin-3 in endometrial cells [39, 40].

On the other hand, the elevation of Galectin-3 in GDM has several implications for disease progression and maternal-fetal health. Research has provided substantial evidence indicating that galectin-3 plays a critical role in insulin resistance and the disruption of glucose homeostasis. Exogenous Galectin-3 has been found to bind to the oligosaccharide side chains of the insulin receptor (IR) and block insulin signaling [41, 42]. This disruption leads to inadequate glucose uptake and utilization, contributing to insulin resistance in GDM. Interestingly, while circulating Galectin-3 may disrupt insulin signaling, Intracellular Galectin-3 has demonstrated enhancement of insulin sensitivity through the upregulation of glucose transporter 4 (GLUT4) expression [43]. This dual role of Galectin-3 may explain why both the elevation of extracellular galectin-3 and the depletion of intracellular galectin-3 result in insulin resistance and consequently diabetes [44]. Additionally, studies have indicated a significant involvement of Galectin-3 in amplifying inflammatory responses. Galectin-3 has been found to promote the synthesis of inflammatory cytokines and chemokines through the activation of NF-κB signaling pathways [45]. Furthermore, galectin-3 acts as a chemoattractant and facilitates the migration of macrophages to sites of inflammation [46]. This sustained inflammatory state not only worsens insulin resistance and metabolic dysfunction in GDM but also contributes to the progression of gestational complications. The chronic inflammation associated with elevated Galectin-3 levels in GDM can directly impact endothelial cells, leading to dysfunction and disruption of normal vascular function [47, 48]. Endothelial dysfunction further exacerbates the risk of developing cardiovascular diseases such as hypertension and preeclampsia and, ultimately, poses significant health risks to both the mother and the developing fetus in GDM pregnancies [49].

Interpretation of main findings

The pooled analysis indicates a statistically significant higher level of circulating Galectin-3 in GDM patients, with a (SMD) of 0.929. This difference not only suggests a robust association but also raises questions about the role of Galectin-3 in the pathogenesis of GDM. The heterogeneity observed among studies, with an I² of 93%, underscores the complexity of this relationship, potentially reflecting variations in study design, population demographics, and diagnostic criteria for GDM.

Subgroup analyses and meta-regression findings

Subgroup analyses further illuminate the nuanced dynamics between Galectin-3 levels and factors such as age, BMI, and Study Design and Sample Type. The age-related increase in Galectin-3 levels could be attributed to the cumulative metabolic stress and the gradual onset of insulin resistance with aging [50]. In contrast, the inverse relationship between BMI and Galectin-3 levels was unexpected, as obesity is a known risk factor for GDM. This paradox might be explained by the adipose tissue’s role in sequestering Galectin-3 or modulating its expression in response to chronic inflammation [51, 52]. The absence of notable variances in Galectin-3 levels between case-control and cross-sectional studies suggests the robustness of the association between Galectin-3 and GDM across various study designs. However, the greater dispersion in effect sizes among cross-sectional studies suggests potential variability in population characteristics or temporal factors affecting Galectin-3 levels. The slight discrepancy in effect sizes between serum and plasma samples may be attributed to the differential stability of Galectin-3 or its interaction with other biomolecules in these sample types [53, 54].

Geographical and temporal trends

Our meta-regression analysis revealed significant geographical differences in Galectin-3 levels, with higher levels observed in Asian populations. This variation may be indicative of genetic predispositions or environmental factors, such as diet and lifestyle, that influence Galectin-3 expression and the prevalence of GDM [55, 56]. This finding calls for further studies to explore the interaction between ethnicity and Galectin-3 levels in the context of GDM. Additionally, the positive relation between publication year and Galectin-3 levels could indicate evolving diagnostic criteria, heightened awareness, or actual increases in GDM prevalence, highlighting the necessity for ongoing surveillance and research to adapt management strategies accordingly [57, 58].

Clinical implications and future directions

The connection between GDM and Galectin-3 levels, particularly the observed differences based on age, BMI, geographical region, and gestational age, provides valuable insights for personalized medicine approaches in GDM management. Early identification of women at risk based on Galectin-3 levels could facilitate targeted interventions, potentially averting the onset of GDM and its associated complications [59]. Future research should focus on longitudinal studies to elucidate the temporal association between Galectin-3 levels and the development of GDM. Additionally, delving into the molecular mechanisms underlying the association between GDM and Galectin-3 in diverse populations could uncover potential targets for therapeutic intervention. Interventional studies assessing the impact of modulating Galectin-3 levels on GDM outcomes would further validate the clinical relevance of our findings.

Limitations

Despite the strengths of our analysis, several limitations warrant discussion. The substantial heterogeneity among included studies suggests variability in study populations, methodologies, and outcome measures, which might influence the generalizability of our findings. While partly addressed through subgroup and meta-regression analyses, the heterogeneity among studies suggests that other unmeasured factors may influence Galectin-3 levels. Furthermore, the cross-sectional nature of many included studies limits our ability to infer causality between elevated Galectin-3 levels and GDM. Future studies should strive to elucidate the causal relationship between Galectin-3 and GDM, explore the mechanisms underlying the observed associations, and validate the clinical utility of Galectin-3 as a biomarker for GDM.

Conclusions

In brief, our comprehensive review and meta-analysis emphasize the potential significance of circulating Galectin-3 levels as a biomarker for GDM. The results imply that Galectin-3 may be involved in the pathophysiology of GDM, suggesting avenues for the development of diagnostic and therapeutic interventions. Future investigations should prioritize elucidating the biological mechanisms linking Galectin-3 and GDM, focusing on longitudinal studies and the examination of modifiable risk factors that could impact Galectin-3 levels.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

In this investigation, SB and RM performed data analysis and composed the initial manuscript draft. The study’s conception, design, and supervision were undertaken by AA, with MJ, TS, and MA conducting data analysis, visualizing results, and contributing to the manuscript’s critical revision and editing. In the end, all authors thoroughly reviewed and approved the final version of the manuscript before its submission.

Funding

Not applicable.

Data availability

Not applicable.

Declarations

Ethical approval

This article does not report any studies involving human participants or animals conducted by the authors.

Consent for publication

The manuscript entitled “A Closer Look at Galectin-3: Its Association with Gestational Diabetes Mellitus Revealed by Systematic Review and Meta-Analysis.” has been submitted to the Journal of Diabetes and Metabolic Disorders for publication. This submission is original and has not been reviewed elsewhere. We have ensured compliance with all permissions and ethical considerations and take full responsibility for adhering to journal guidelines.

Conflict of interest

The authors hereby state that they do not have any conflicts of interest.