Is low birth weight associated with lower adiponectin levels? - A systematic review and meta-analysis

Afnan Sabah Alhakeem; Aleyna Hasturk; Trine Witzner Hessel Lawaetz; Tue Helms Andersen; Charlotte Brøns; Allan Arthur Vaag

doi:10.1371/journal.pone.0335598

Abstract

Individuals born with low birth weight are at increased risk of type 2 diabetes, which potentially may be attributed to immature adipose tissue development and reduced levels of the insulin-sensitizing adipokine, adiponectin. This systematic review and meta-analysis synthesize data from 67 studies, comprising over 8000 individuals across various age groups, to examine the relationship between circulating adiponectin levels and birth weight. The results revealed that individuals with low birth weight have significantly lower adiponectin levels compared to those born with normal birth weight (SMD = −0.46 μg/ml [95% CI: −0.57; −0.35], P < 0.0001). Moderate heterogeneity was observed (I²= 67%, P < 0.01), but sensitivity analysis and meta-regression did not identify specific factors driving this variation. Pooled Pearson correlation analysis indicated a moderate but statistically significant positive correlation between birth weight and adiponectin levels (correlation estimate = 0.31 [95% CI: 0.16; 0.46], P < 0.0001). These findings suggest that reduced adiponectin levels in low birth weight individuals may contribute to their elevated risk of type 2 diabetes, potentially offering new insights into the developmental origin of this disease.

Citation: Alhakeem AS, Hasturk A, Lawaetz TWH, Andersen TH, Brøns C, Vaag AA (2025) Is low birth weight associated with lower adiponectin levels? - A systematic review and meta-analysis. PLoS One 20(12): e0335598. https://doi.org/10.1371/journal.pone.0335598

Editor: Kwang-Sig Lee, Korea University - Seoul Campus: Korea University, KOREA, REPUBLIC OF

Received: November 8, 2024; Accepted: September 29, 2025; Published: December 2, 2025

Copyright: © 2025 Alhakeem et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All studies identified in the literature search, along with the data script, are available in the linked repository, which is also referenced to in the manuscript. (https://github.com/TT2DR-StenoDiabetesCenterCopenhagen/Systematic_review_and_meta-analysis_adiponectin.git).

Funding: This work was supported by Novo Nordisk Foundation [NNF23OC0086443, NNF21OC0071777], European Foundation for the Study of Diabetes, Swedish Region Skåne Alf 2022 (2025-BudgetProjekt0059), The Crafoord Foundation, and The Swedish Diabetes Association. All authors are employed by the Steno Diabetes Center Copenhagen, a public hospital and research institution under Copenhagen University Hospital, which is partly funded by a grant from the Novo Nordisk Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The association between being born with a low birth weight (LBW) and later increased risk of developing type 2 diabetes (T2D) and associated cardiometabolic diseases has been firmly established [1–4]. The concept of developmental programming influenced by adverse environmental factors during early growth provides a conceptual framework to explain how immature development and age-dependent dysfunctions of multiple organs are involved in T2D development [5]. T2D is a multifactorial heterogeneous disease that involves a complex interplay between pancreatic beta-cells, systemic insulin resistance, and impaired adipose tissue expandability [6–8]. Notably, emerging evidence indicates that adverse fetal conditions associated with LBW can lead to epigenetic changes that affect genes involved in adipogenesis, resulting in immature development of the subcutaneous adipose tissue (SAT) [9]. This may disrupt the normal endocrine functions of the tissue, including the secretion of adiponectin. Adiponectin is considered to play a central role in enhancing insulin sensitivity [10]. Notably, studies have reported reduced adiponectin levels in LBW subjects [8,11–13], which therefore may contribute to insulin resistance, ectopic fat deposition, metabolic dysfunction-associated steatotic liver disease (MASLD), and eventually overt T2D in these individuals [10,8,14]. Accordingly, reduced adiponectin levels may play a pivotal role in mediating the risk of developing T2D and associated cardiometabolic comorbidities in LBW subjects [15–17]. Therefore, we performed a systematic literature review and meta-analysis to examine whether LBW is associated with low circulating adiponectin levels when compared with normal birth weight (NBW).

Methods

This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) criteria [15]. The protocol was registered in the PROSPERO Database (registration ID: CRD42023461784).

Search strategy

Potentially relevant studies were identified through a comprehensive search in MEDLINE, Embase, and Web of Science core collection on July 4, 2024. An information specialist (THA) conducted a systematic search with the concepts “adiponectin/adipokines” and “low birth weight”. Both concepts were searched using Medical Subject Headings (MeSH) and free text words. The search string was peer reviewed by another information specialist. Further, we evaluated it by testing whether 21 key articles within the field were among the search results. The search string was developed in MEDLINE and subsequently translated to the other databases. No limit on date, country, or language was applied. Details on the search strategy for each database can be found in S1 Table.

Citation tracking was performed on all articles included in the review to retrieve references and articles citing the included articles with the software tool Citationchaser [16].

Eligibility criteria

Observational studies that provided data on birth weight and circulating adiponectin levels in healthy human participants across various age groups were included. Studies were included irrespective of whether they reported categorical comparisons between groups (i.e., LBW and/or high birth weight (HBW) versus NBW subjects) or adiponectin measurements across the entire spectrum of birth weights. We intended to adhere to the original studies’ criteria for birth weight categories, where small and large for gestational age (GA) were considered equivalent to low and high birth weight, respectively [17]. The operational definition of LBW was a birth weight below 2500 grams, irrespective of GA, and includes both preterm infants with appropriate growth (>37 completed weeks of gestation) as well as term and preterm growth-restricted infants (<10th centile of weight for gestational age and sex), commonly referred to as small for gestational age (SGA) [18,19]. We incorporated both absolute weight (grams) and percentiles in defining LBW to reflect the equivalence between LBW and SGA, as absolute weight typically defines LBW while percentiles are used for SGA. Importantly, some studies use a threshold of less than 2 standard deviations (SD) or the 10th percentile to define this category. HBW was categorized as either a birth weight above the 90th percentile or over 4000 grams. For control groups, birth weight was defined as between the 10th and 90th percentiles, within ± 1 SD, or within the range of 2500–4000 grams. Studies reporting birth weight in grams according to clinical definitions all accounted for GA by including full-term infants (GA = 37–41 weeks) [20–24]. Subjects from the studies were divided into different age groups, including neonates (birth to 28 days) [25], infants (28 days to 12 months) [25], children (1 year to 17 years) [26], and adults (18 years and older) [26], reflecting the aim to explore adiponectin levels across various age groups. Adiponectin concentrations had to be reported in either plasma, serum, or cord blood. Studies meeting the following criteria were excluded: a) animal studies, b) clinical trials, c) populations with acute and/or chronic diseases, including diabetes, d) studies with fewer than 12 subjects, and e) missing information on the applied adiponectin assay. All studies meeting the inclusion criteria and providing extractable data were considered for inclusion in this analysis. The specific inclusion and exclusion criteria applied in the study selection process are summarized in Table 1.

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Table 1. Inclusion and exclusion criteria.

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Study selection and data extraction

Records found in the database search were uploaded to EPPI reviewer 6 [27], and duplicates were removed. Two independent reviewers (ASA, AH) screened the titles and abstracts of all retrieved studies. The full text of potentially eligible studies was then screened by the same reviewers according to the inclusion criteria. Data were independently extracted by each reviewer and subsequently compared to ensure accuracy and consistency in data extraction. Information on authorship, publication year, country, study design, survey period, sample size, age, mean or median birth weight, mean or median adiponectin levels, assay type, and blood sample type was extracted. Any discrepancy between reviewers was resolved through discussion.

Articles in languages not understood by the review team (excluding English, Arabic, Turkish, Danish, Swedish, and Norwegian) that were deemed eligible by title and abstract were not included in the synthesis but have been listed for others to analyze (S2 Table).

Quality assessment

The JBI critical appraisal tool [28–30] was applied to evaluate study quality. Each study underwent assessment regarding the selection of study population, description of study settings, exposure assessment, outcome assessment, identification of confounders, strategies for confounder adjustment, and appropriate statistical analysis. The two reviewers independently evaluated each study, and through discussion, a final score indicating either low, unclear, or high risk of bias was assigned. Of the included studies, a total of 33 were rated as low risk of bias, 24 had an unclear risk of bias, and 10 were rated as high risk of bias. A more detailed quality assessment is described in S1-S3 Figs. The most important limitations pertaining to the quality of the original reports were inadequate adjustment for confounders (29 studies), lack of clear description of the study population (4 studies), and reliance on self-reported birth weight information rather than register-based data (2 studies). Conversely, high-quality studies tended to use well-defined inclusion criteria and robust exposure and outcome assessment methods.

Statistical analysis

The standardized mean difference (SMD) and corresponding 95% confidence interval (CI) were used to measure the difference in adiponectin levels between LBW and NBW subjects. Studies providing birth weight and adiponectin data as mean and SD were incorporated in the meta-analysis. In cases where a standard error of a mean (SEM) was given, it was converted to SD by multiplying SEM by the square root of the sample size [31]. The overall effect estimate was determined through the random effects model, where heterogeneity was assessed through Cochran’s Q and I² statistics. Values below 25% indicate low heterogeneity, 25–75% moderate heterogeneity, and above 75% high heterogeneity. To explore the sources of heterogeneity, sensitivity analysis and meta-regression were conducted considering the following factors: age category, risk of bias, and sample type.

An influence analysis was performed to further explore the between-study heterogeneity by excluding studies one by one to identify potential studies contributing to distortions of the effect estimate. Robustness of the meta-analysis was explored using a GOSH Plot analysis to identify studies with high impact on heterogeneity. Publication bias was assessed through visual inspection of the funnel plot and Egger’s test, with a P < 0.05 indicating significant publication bias.

All statistical analyses were performed using R Software (version 4.3.0) with the metafor, dmetar, and tidyverse packages [32–34]. P-value < 0.05 was considered statistically significant. The analysis script is available at the following repository: https://github.com/TT2DR-StenoDiabetesCenterCopenhagen/Systematic_review_and_meta-analysis_adiponectin.git.

Results

Search results

A total of 2181 studies were identified through a systematic search in MEDLINE, Embase, and Web of Science from inception to July 4, 2024. After removing duplicates, 1523 studies were screened by title and abstract yielding a total of 99 eligible studies. Evaluating the full text of these studies resulted in 66 studies meeting the inclusion criteria. Citation tracking retrieved 2455 additional studies of which 660 were duplicates. One additional study from the citation tracking was deemed relevant, and a total of 67 studies were included in the systematic review (Fig 1).

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Fig 1. Flow diagram of the selection process.

*Full-text articles only available in languages other than English, Danish, Swedish, Norwegian, Turkish, and Arabic.

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Study characteristics and quality assessment

Table 2 displays the comprehensive characteristics of the studies, including 8204 individuals in the systematic review. Most studies categorized subjects based on LBW (n = 27) or HBW (n = 17), while others were stratified by maternal gestational diabetes (n = 6) or maternal smoking status (n = 2). Additionally, a few studies reported adiponectin levels in preterm (n = 5) or twin births (n = 3), where these subjects were grouped into their respective birth weight categories. Only observational studies were included, hereof 43 cross-sectional, 14 case-control, and 10 cohort studies. Information on both periods of data collection and population origins was documented. Adiponectin levels were measured in blood samples (n = 30), cord blood (n = 36), and one in a dried blood spot sample [35] and analyzed by enzyme-linked immunosorbent assay (ELISA, n = 47), radioimmunoassay (RIA, n = 18), multiplex assay (n = 1), or by immunoassay (n = 1). As for the specific molecular form of adiponectin, most studies reported total adiponectin levels. The studies were categorized according to the age groups of the subjects, among them, 28 studies focused on neonates, 19 on infants, 18 on children, and 2 studies on adults.

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Table 2. Study characteristics.

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Meta-analysis

Out of the 67 studies included in the systematic review, only 34 studies were eligible for inclusion in the meta-analysis. Eleven studies were not incorporated as they reported data solely as medians or SD scores, 12 studies lacked a control group, and 10 studies did not compare different birth weight categories, instead focusing on variables such as smoking or gestational diabetes. The objective of this study was to investigate the difference in adiponectin levels between LBW and NBW subjects, as analyzed in a total of 24 studies included in our review. Given the broad inclusion criteria for birth weight, a subset of the included studies also examined adiponectin levels in HBW subjects. These data were incorporated into a pre-defined exploratory analysis to gain further insight into the broader relationship between adiponectin and birth weight. It is important to note that the primary focus of the search was on LBW, and as such, we cannot be certain that all studies concerning HBW were included. Although this exploratory analysis offers valuable preliminary insights, we do not believe that this due to the number and quality of studies, establishes any definitive relationship between HBW and adiponectin levels. Well-designed studies with robust methodologies are needed to confirm this association.

Main analysis

Data from the 24 studies examining adiponectin levels in LBW and NBW subjects were included in the meta-analysis, which showed that LBW subjects had significantly lower adiponectin levels compared to NBW subjects (SMD = −0.56 μg/ml [95% CI: −0.99; −0.14], P = 0.01). The studies were characterized by substantial heterogeneity (I²= 92%, P < 0.01), which was investigated through sensitivity analysis and meta-regression on variables such as age, blood sample type, and risk of bias, supplemented by visual assessment (S4-S6 Figs). However, none of these variables were able to elucidate the underlying reasons for the observed high heterogeneity, as the outcome did not achieve statistical significance (Table 3).

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Table 3. SMD and associated heterogeneity in a sensitivity analysis of 24 studies comparing adiponectin levels in LBW individuals with NBW controls (including outliers¹).

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An influence analysis was conducted to determine whether the presence of possible outliers influenced the results, and with an additional GOSH Plot analysis, nine studies were identified as primary contributors to the overall heterogeneity. Following the exclusion of outliers [24,36–43], the meta-analysis of the remaining 16 studies still showed significantly lower adiponectin levels in LBW individuals as compared to NBW (SMD = −0.46 μg/ml [95% CI: −0.57; −0.35], P < 0.0001) with moderate heterogeneity (I²= 67%, P < 0.01) (Fig 2). Notably, the dataset comprising 24 studies yielded 26 data points, as two studies each contributed with two separate data points [38,44]. One of these studies had one data points identified as an outlier and thus excluded, while the other data point remained in the analysis [38]. Therefore, although nine outlier studies were excluded, the final analysis included 16 studies.

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Fig 2. The association between circulating adiponectin levels and low birth weight as compared to normal birth weight controls after the exclusion of outlier studies.

Forest plot comparing mean circulating adiponectin levels in LBW subjects and NBW controls after outlier removal. The overall effect size is SMD = −0.46 μg/ml [95% CI: −0.57; −0.35], P < 0.0001. The blue diamond represents the overall effect size and confidence interval. Heterogeneity is estimated at I² = 67% (P < 0.01), assessed using Cochran’s Q and I² statistics.

LBW, low birth weight; NBW, normal birth weight; SMD, standardized mean difference; SD, standard deviation; CI, confidence interval; I², heterogeneity.

Seventeen data points are presented for 16 studies, as Ibäñez et al. [38] included two LBW groups compared to one NBW group.

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The extended meta-analysis consisting of 14 studies comparing adiponectin levels between HBW and NBW subjects failed to establish significant evidence of a difference (SMD = −0.36 μg/ml [95% CI: −1.06; 0.33], P = 0.27; I²= 95%) and therefore no further assessments were made regarding the role of HBW.

Subgroup analysis assessing the impact of postnatal growth

To investigate the postnatal growth patterns, a subgroup analysis was performed to ascertain whether the occurrence of catch-up growth (CU-growth) among LBW individuals influenced adiponectin levels later in life. Out of all the studies included, four specifically explored this phenomenon, revealing compelling evidence of decreased adiponectin levels in LBW individuals who underwent CU-growth compared to those who did not (SMD = −0.73 [95% CI: −1.41; −0.05], P = 0.04) (Fig 3).

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Fig 3. The association between circulating adiponectin levels and catch-up growth vs. non-catch-up growth.

Forest plot comparing mean circulating adiponectin levels in CU-growth versus non-CU-growth groups across four studies. The overall effect size is SMD = −0.73 [95% CI: −1.41; −0.05], P = 0.04. The blue diamond represents the overall effect size and confidence interval. Heterogeneity is estimated at I² = 41.3% (P = 0.16) assessed using Cochran’s Q and I² statistics.

SMD, standardized mean difference; SD, standard deviation; CI, confidence interval; I², heterogeneity; CU, catch-up growth.

Catch-up growth is defined as either corrected height SDS ≥ 0 [36,37,44] or matched height, weight, and BMI to NBW controls [38].

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Sensitivity analysis and meta regression

Sensitivity analysis of age categories, sample type, and risk of bias was conducted to determine whether these factors had any influence on the high heterogeneity. The finding of lower circulating adiponectin levels in LBW subjects tended to be more robust in neonates compared to older individuals with also the heterogeneity being lowest within this group (SMD = −0.52, I²= 67%, P = 0.55). Studies with low risk of bias showed a stronger correlation between low adiponectin and LBW but were not statistically significant (SMD = −0.48, I²= 63%, P = 0.70) (Table 4). In addition, a meta-regression was performed with the same moderators investigated in the sensitivity analysis with the addition of publication year to examine possible influence. Neither of the moderators, including age category, sample type, risk of bias, or publication year had any significant impact on the results.

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Table 4. SMD and associated heterogeneity in a sensitivity analysis of 16 studies comparing adiponectin levels in LBW individuals with NBW controls (excluding outliers¹).

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Publication bias and funnel plot

Publication bias was assessed through visual inspection of the funnel plot illustrating slight asymmetry (Fig 4). However, Egger’s test did not indicate the presence of publication bias (P = 0.07).

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Fig 4. Funnel plot with Egger’s test.

Funnel plot with Egger’s Test was performed to evaluate the publication bias.

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Strength and direction of the association between adiponectin and birth weight

The relationship between adiponectin levels and birth weight was further explored by inferential statistics. The provided Pearson correlations from 19 studies were included for analysis using a random-effects model (Fig 5). The pooled overall estimate of 0.31 ([95% CI: 0.16; 0.46], P < 0.0001) indicates a moderate but positive correlation between adiponectin and birth weight, suggesting a linear relationship between the two variables. Substantial heterogeneity was observed (I²= 89%, P < 0.0001), though no further analyses were made to assess variability.

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Fig 5. Analysis of pooled Pearson correlations from 19 studies examining the relationship between adiponectin levels and birth weight.

Forest plot showing the correlation between circulating adiponectin levels and birth weight across 19 studies. The pooled estimate is r = 0.31. Heterogeneity is estimated at I² = 89% (P < 0.0001), assessed using Cochran’s Q and I² statistics. Pearson correlations (r) were extracted from eligible studies, and 95% CIs were calculated using the random effects model.

SMD, standardized mean difference; CI, confidence interval; I², heterogeneity.

Twenty data points are presented for 19 studies, as Meral et al. [38] provide a correlation coefficient for both small and large for gestational age.

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Discussion

This systematic review provides firm evidence of lower circulating adiponectin levels in a categorical analysis of individuals born with LBW in comparison to NBW, which was further supported by a statistically significant positive correlation between circulating adiponectin levels and birth weight. The results all together support the notion of reduced circulating adiponectin levels as a potential key mediator of increased cardiometabolic disease risk in people born with LBW.

The result from the meta-analysis comes with moderate heterogeneity after the exclusion of the outliers, which were based on their relative effect sizes, defined as either extremely small or large, thereby disproportionally influencing the overall effect size and heterogeneity, potentially compromising the study’s reliability. While some heterogeneity is expected in meta-analyses, excluding outliers that disproportionately influence the heterogeneity helps preserve the robustness of the findings without altering the overall results of the meta-analysis. Sensitivity and meta-regression analyses did not identify any moderators that could fully account for the remaining heterogeneity. However, differences in ethnicity, geographic location, assay type, and study settings are the most likely contributors to the seen variability. The heterogeneity can to some extent be attributed to variation in study design as the meta-analysis encompasses cross-sectional, cohort, and case-control studies. Therefore, the diversity in study design, along with the unexplored variables, inherently contributes to the remaining heterogeneity. Additionally, differences in study populations and/or designs among outliers may further contribute to the heterogeneity. For instance, some studies included LBW children with catch-up growth [39], short LBW children [38], or preterm neonates [43], and when visually examining the funnel plot, these outliers appeared to be linked with publication bias. However, Egger’s test did not detect significant bias, suggesting the asymmetry is more likely to be due to heterogeneity rather than bias. Notably, the asymmetry was subsequently eliminated after excluding the outliers, which did not alter the overall result of the meta-analysis. Furthermore, it is important to point out that the excluded outliers also found lower levels of adiponectin in LBW individuals [36,39,40,42,43], indicating that their exclusion was not based on their findings, but rather because of their attribution to the high heterogeneity (S7 Fig). An exception to this is the study by Evagelidou et al. [37], which did not find lower serum adiponectin levels in a cohort of pre-pubertal LBW children. Interestingly, the authors interpreted their negative findings as indicative of a transient adaptation mechanism involving a compensatory elevated adiponectin release to counteract peripubertal insulin resistance, further speculating that a subsequent decrease in adiponectin levels may follow later in life in these individuals. In support of this, Jaquet et al. [40] reported a U-shaped relationship between serum adiponectin levels and insulin resistance in young adults with LBW, which consistent with Evagelidou et al., was interpreted to indicate a compensatory and somewhat disproportional increase in circulating adiponectin levels in LBW during the peripubertal period. This, in turn, could also be explained by some kind of transient adiponectin resistance in LBW adolescents [40]. While further studies are needed to understand these observations in LBW adolescents, they do not change the overall interpretation of the current study, providing firm evidence of reduced circulating adiponectin levels in LBW subjects.

Besides the puberty-associated changes, our analysis furthermore indicates a relatively stronger correlation between LBW and adiponectin levels in early life, subsequently diminishing with aging (Tables 3 and 4). This trend aligns with prior studies showing a positive correlation between body weight and circulating adiponectin levels during infancy, which is replaced by an inverse relationship in adulthood [45–47]. Indeed, the observed changes in the associations with birth weight over time might partly be explained by this shift. These associations are further influenced and complicated by the notion that LBW subjects retain lower body weight, adult height, and even to some extent, lower BMI throughout life [48–51]. To this end, LBW subjects tend to accumulate excessive visceral and hepatic fat [8,39], which in turn may represent ectopic fat deposition associated with immature and epigenetically mediated changes in subcutaneous adipose tissue cells [9,52]. While further studies are needed to delineate the causal inferences underlying these observations, the current results support the notion of a potentially important role of adiponectin in mediating the increased risk of cardiometabolic disease among people born with LBW.

LBW infants and children often experience postnatal CU-growth [53], which in turn has been associated with a further increased risk of adult cardiometabolic diseases [54,55]. Notably, our subgroup analysis of the four studies that addressed this topic indicated that CU-growth in LBW is associated with further reduced circulating adiponectin levels compared with LBW individuals who did not experience CU-growth [36–38,56] (Fig 3). This suggests that reduced adiponectin levels may not only be causally linked to increased cardiometabolic disease risk in LBW individuals per se but that a further distinct reduction in adiponectin is observed in those experiencing CU-growth, placing them at an even higher risk. Nevertheless, the causal inference related to this finding needs to be substantiated by mechanistic as well as prospective studies, and particularly it must be determined whether the further reduction in adiponectin levels is a direct contributor to cardiometabolic risk or a secondary consequence of the excess adiposity characterizing LBW CU-growth individuals.

In addition to LBW, HBW has also been associated with an increased risk of adult cardiometabolic diseases, which in turn may be related to and/or explained by maternal hyperglycemia in pregnancy. Indeed, some – but not all – studies have reported a U- or J-shaped relationship between birth weight and risk of T2D [4, 57,58]. In the current meta-analysis, we found an overall positive correlation between adiponectin levels and birth weight across the entire spectrum (Fig 5), with the association being further strengthened by a z-value of 3.95. This finding of a high z-value indicates that the observed correlation coefficient is 3.95 standard errors away from zero, suggesting a highly significant result caused by a consistent statistical pattern within the data. However, with some non-significant trend towards relatively lower adiponectin levels among HBW individuals (Fig 6), further studies are needed to understand the extent to which reduced circulating adiponectin levels may be implicated in mediating the association between HBW and the risk of cardiometabolic diseases.

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Fig 6. The association between circulating adiponectin levels and high birth weight as compared to normal birth weight controls.

Forest plot comparing mean circulating adiponectin levels in HBW versus NBW groups. The overall effect size is SMD = −0.36 μg/ml [95% CI: −1.06; 0.33], P = 0.27. The blue diamond represents the overall effect size and confidence interval. Heterogeneity is estimated at I² = 95% (P < 0.01), assessed by Cochran’s Q and I² statistics.

NBW, normal birth weight; HBW, high birth weight; SMD, standardized mean difference; SD, standard deviation; CI, confidence interval; I², heterogeneity.

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Strengths and limitations

To the best of our knowledge, this is the first meta-analysis investigating the association between adiponectin levels and birth weight across various age groups. Our findings of reduced adiponectin levels in LBW individuals remain robust across all sensitivity and subgroup analyses, including in- or exclusion of outliers. The absence of comparable meta-analyses presents both a strength and a limitation. As the first study to systematically synthesize evidence across different age groups, we offer a thorough understanding of how fetal programming may influence the development of T2D and cardiovascular disease. However, the lack of similar meta-analyses limits direct validation. Nevertheless, our results align with smaller yet high-quality deep phenotyping studies comparing LBW and NBW individuals [8,13], which, despite their relevance, were not included in our meta-analysis due to misalignment with our inclusion criteria.

As for potential limitations in our study, we applied varying definitions of LBW, equating it with SGA, potentially contributing to observed heterogeneity. This decision was made to adhere to the definitions applied by the authors of the original studies. In support of this approach, it was recently argued that LBW and SGA should be encompassed under one category, as these infants are all adversely affected by prenatal exposures leading to some level of alteration in their fetal programming [17]. Additionally, the sensitivity analyses were limited by the smaller sample size available for older age groups. Furthermore, adiponectin exists in low as well as in a high molecular form, with the high molecular form being proposed to have the biggest impact in vivo [45,59]. However, due to limited information available from the applied assays [45], we were unable to determine the impact of the molecular forms of adiponectin in the different studies. Despite this limitation, our findings remain robust, as adiponectin – regardless of its molecular form – is considered to improve insulin sensitivity. Nevertheless, there is a need for large clinical and prospective studies interrogating the extent to which the different molecular forms of adiponectin may be involved in differential physiological or pathophysiological mechanisms, and/or potentially associated with differential cardiometabolic outcomes or disease manifestations.

Our finding of a robust association between LBW and lower circulating adiponectin levels is clinically relevant as it raises the possibility of using adiponectin as a biomarker to determine cardiometabolic disease risk later in life in LBW subjects, as well as subjects with unknown, normal, or high birth weight. However, larger prospective studies are needed to determine the sensitivity, including false positive and negative predictions, and to benchmark it against other clinical or biomarker predictors.

Moreover, our findings could have implications for strategies aimed at improving prenatal and nutritional care. As LBW is a recognized risk factor for the later development of T2D [1–4], potentially mediated by lower adiponectin levels, interventions focused on enhancing maternal nutrition and optimizing prenatal care may play a vital role in mitigating this risk. With the addition of adiponectin’s potential as a biomarker for risk monitoring after birth, targeted prevention of T2D in LBW subjects may include both antenatal and postnatal prevention and risk mitigation efforts. Additionally, longitudinal studies exploring adiponectin across different life stages in LBW individuals could help identify critical periods when adiponectin levels are most influential, uncovering opportunities for targeted interventions at key developmental stages.

Finally, although the exclusion of studies due to language restriction is a potential limitation, only three studies were excluded for this reason. The large number of included studies with high-quality data minimizes the likelihood of a significant impact on the overall findings. Furthermore, since most relevant studies in this field are published in English, the risk of systematic bias from language restriction is likely to be minimal.

Conclusion

In conclusion, this current meta-analysis provides firm evidence that individuals born with LBW exhibit decreased levels of adiponectin, potentially contributing to their elevated risk of T2D. These findings highlight the potential of adiponectin as an early biomarker of metabolic dysfunction and cardiometabolic risk in LBW individuals, offering a valuable tool in clinical practice for the prevention of T2D.

Supporting information

S1 Table. Search strategy.

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S2 Table. Studies written in languages not understood by the review team for consideration in future analysis by fluent readers.

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S1 Fig. Risk of bias assessment for case-control studies.

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S2 Fig. Risk of bias assessment for cohort studies.

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S3 Fig. Risk of bias assessment for cross-sectional studies.

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S4 Fig. Relationship between age groups and adiponectin levels.

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S5 Fig. Influence of blood sample type on adiponectin levels.

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S6 Fig. Impact of risk of bias on adiponectin levels.

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S7 Fig. Analysis of the excluded outliers comparing adiponectin levels in LBW and NBW subjects.

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S1 Checklist. PRISMA Checklist.

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Figures

Abstract

Introduction

Methods

Search strategy

Eligibility criteria

Study selection and data extraction

Quality assessment

Statistical analysis

Results

Search results

Study characteristics and quality assessment

Meta-analysis

Main analysis

Subgroup analysis assessing the impact of postnatal growth

Sensitivity analysis and meta regression

Publication bias and funnel plot

Strength and direction of the association between adiponectin and birth weight

Discussion

Strengths and limitations

Conclusion

Supporting information

S1 Table. Search strategy.

S2 Table. Studies written in languages not understood by the review team for consideration in future analysis by fluent readers.

S1 Fig. Risk of bias assessment for case-control studies.

S2 Fig. Risk of bias assessment for cohort studies.

S3 Fig. Risk of bias assessment for cross-sectional studies.

S4 Fig. Relationship between age groups and adiponectin levels.

S5 Fig. Influence of blood sample type on adiponectin levels.

S6 Fig. Impact of risk of bias on adiponectin levels.

S7 Fig. Analysis of the excluded outliers comparing adiponectin levels in LBW and NBW subjects.

S1 Checklist. PRISMA Checklist.

References