Next Article in Journal
Surviving Adulthood with Rare Combined Congenital Heart Defects: Complete AV Canal Defect, Ebstein’s Anomaly, and Right Ventricular Hypoplasia
Previous Article in Journal
Systematic Review and Meta-Analysis of RCTs on Efficacy of Conventional vs. Emerging Treatments for Amblyopia
Previous Article in Special Issue
Proteome-Based Maternal Plasma and Serum Biomarkers for Preeclampsia: A Systematic Review and Meta-Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Life
Volume 16
Issue 2
10.3390/life16020223
life-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Pre-Eclampsia-Induced Maternal Liver Dysfunction: Systematic Review, Meta-Analysis and Meta-Regression of Observation Studies

by
Kay-Lee E. Strauss
,
Wendy N. Phoswa
and
Kabelo Mokgalaboni
*
Department of Life and Consumer Sciences, College of Agriculture and Environmental Sciences, University of South Africa, Florida Campus, Calabash Building, Office 02-047, Roodepoort 1710, South Africa
*
Author to whom correspondence should be addressed.
Life 2026, 16(2), 223; https://doi.org/10.3390/life16020223
Submission received: 30 October 2025 / Revised: 19 November 2025 / Accepted: 20 November 2025 / Published: 29 January 2026
(This article belongs to the Special Issue Molecular Mechanisms of Preeclampsia)
Browse Figures
Versions Notes

Abstract

Introduction: Pre-eclampsia (PE) is a pregnancy-related hypertensive condition defined by the onset of hypertension after 20 weeks of gestation that is associated with proteinuria and maternal organ damage or uteroplacental dysfunction. It continues to be a leading cause of maternal and perinatal morbidity and mortality globally. PE is linked to systemic inflammation, endothelial dysfunction, and oxidative stress, which may compromise hepatic function. Aim: This meta-analysis assesses the impact of PE on maternal liver function by evaluating hepatic biomarkers, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total serum bilirubin. Methods: This meta-analysis of observational studies in Epidemiology (MOOSE) involved a search of PubMed and Scopus and manual screening of studies published between 2000 and 2025. Eligible studies included cross-sectional, case–control, and cohort designs. The quality of the studies was evaluated using the Newcastle–Ottawa Scale. Statistical analysis was conducted using the online meta-analysis, Jamovi version 2.6.44, and IBM SPSS Statistics version 30, and effect estimates were reported as standardized mean differences (SMDs) with 95% confidence intervals (CIs). Results: Forty-five studies, comprising 257,929 women 9420 with PE; 248,509 normotensive, were included. Women with PE had elevated AST, MD = 1.81 (95% CI: 1.51 to 2.10; p < 0.0001) and ALT, SMD = 1.73 (95% CI: 1.38 to 2.07; p < 0.0001); ALP, SMD = 1.43 (95% CI: 0.97 to 1.88; p < 0.0001); and total serum bilirubin (TSB), SMD = 0.62 (95% CI: 0.36 to 0.88; p < 0.0001) in comparison to normotensive controls. In the meta-regression, maternal age and quality were significant moderators, with older age and high-quality studies associated with smaller and larger effect sizes, respectively, for ALP (β = −0.720 and β = 1.444) and TSB (β = −0.304 and β = 0.761). For every 1-unit increase in body mass index, there was a significant 0.406-unit decrease in ALT effect size. Conclusions: PE is significantly associated with elevated maternal hepatic enzyme levels, indicating hepatocellular damage and impaired liver function. These findings emphasise the necessity for routine liver function monitoring in pregnant women with hypertensive disorders.

1. Introduction

Pre-eclampsia (PE) is a hypertensive disorder occurring during pregnancy, characterised by new-onset hypertension defined as systolic blood pressure (SBP) ≥ 140 mmHg and diastolic blood pressure (DBP) ≥ 90 mmHg after 20 weeks of gestation [1,2,3]. It is accompanied by either proteinuria or maternal organ dysfunction and or uteroplacental dysfunction. It continues to be a significant contributor to maternal and perinatal morbidity and mortality globally, impacting around 2–8% of pregnancies [4]. Pregnancy is associated with substantial metabolic, haemodynamic, and hormonal changes that promote maternal well-being and foetal growth [5]. In most cases, these changes do not result in substantial impairment. However, in the case of PE, these gestational changes may be significant, resulting in compromised liver function. This is attributed to the pathophysiology of PE, which is centred around the abnormal placental growth and function, resulting in systemic inflammation, oxidative stress, and endothelial dysfunction [6,7,8]. Moreover, inflammation is associated with liver dysfunction, as the liver serves as both a major immunological organ and a target of inflammatory mechanisms [9]. The liver is an essential organ in human physiology, playing a vital role in metabolism, detoxification, protein synthesis, and the regulation of biochemical homeostasis [10,11]. The ability of the liver to maintain stable levels of circulating biomarkers, such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and bilirubin, is essential, as these are sensitive indicators of hepatic integrity and function [12]. Due to its vital role, any impairment in liver function can lead to systemic repercussions, especially during pregnancy, when maternal physiology experiences heightened metabolic and haemodynamic demands [13].
Initiation of an inflammatory response promotes the activation of liver macrophages (Kupffer cells) and neutrophils [14]. These cells secrete pro-inflammatory cytokines such as tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β), as well as reactive oxygen species [15]. This sequence of mediators induces hepatocellular oxidative stress and damage, which presents as elevated liver enzyme levels [16]. Therefore, a timely identification and intervention for PE are crucial to prevent complications and to preserve the health of both the mother and the infant. The hepatic symptoms of PE differ from moderate biochemical anomalies, such as increased liver enzymes, to severe complications such as hepatic rupture, infarction, and in severe cases, HELLP (Haemolysis, Elevated Liver enzymes, and Low Platelets) syndrome [17]. These complications not only compromise the mother’s health but also complicate clinical management and affect pregnancy outcomes. Moreover, liver dysfunction exacerbates negative pregnancy outcomes, such as preterm delivery, intrauterine growth restriction, and stillbirth, highlighting the combined risk to mother and foetal health. Therefore, this study aims to examine the effects of PE on maternal liver function by analysing changes in hepatic biomarkers (AST, ALT, ALP, and bilirubin).

2. Methodology

2.1. Study Design

This study is a meta-analysis of observational studies and adheres to the Meta-analysis of Observational Studies in Epidemiological (MOOSE) guideline [18] (Supplementary File S1). This study also followed the PICO framework in designing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow chart [19]. It also adheres to PECOS criteria as outlined in Table 1.

2.2. Search Strategy, Literature Search, and Selection Criteria

Two independent researchers (KES and KM) conducted an extensive literature search using the PubMed and Scopus databases, employing Medical Subject Headings (MeSH) terms to identify relevant published studies, as well as a comprehensive bibliography search. The MeSH terms and Boolean operators used for the search included “Liver Function” OR “Aspartate Aminotransferase” OR “AST” OR “Alanine Aminotransferase” OR “ALT” OR “Alkaline Phosphatase” OR “ALP” OR “Bilirubin” AND “Pre-eclampsia”. The review focused on studies published between 2000 and 2025 to identify evidence that reflects current diagnostic criteria, clinical practices, and methodological standards. Studies published before 2000 were excluded because of changes in the definition and management of PE, which could introduce variability.

2.3. Data Extraction and Quality Assurance

Two independent researchers (KES and KM) used a preformatted Excel spreadsheet to extract data from each study. Two researchers evaluated the two sheets, and in cases of disagreement over key items, a third researcher, WNP, was consulted to review the study and the disputed variables before a decision was reached. The primary data obtained from each study comprised the lead author’s family name, country of publication, study design, population size, blood pressure (SBP, DBP) and body mass index (BMI) of the participants, and the findings, mean, standard deviation (SD), and sample size of AST, ALT, ALP, and total serum bilirubin. The Newcastle–Ottawa Scale was used to assess the quality of the included case–control, cohort, and cross-sectional studies [20]. This tool focuses on selection, comparability, exposure, and outcome based on the design. Each item was rated with a shaded star or no empty star, and overall quality was judged accordingly. The study was considered high quality (low risk of bias) if it scored 8–9 stars, moderate if it scored 5–7 stars, and low if it scored 0–4 stars.

2.4. Statistical Analysis

For the meta-analysis, we used online meta-analysis software [21]. Jamovi version 2.6.44 and IBM SPSS Statistics version 30 were used for meta-regression and subgroup analysis, respectively. We calculated the effect estimates for all indicators by computing the mean, SD, and sample size for each study group. The mean and SD were estimated from the median and range using the protocols reported by Hozo et al. (2005) [22]. When the study reported the standard error of the mean (SEM), SD was estimated as SD = SEM × √n [23]. If the study reported the median and interquartile range (IQR), the mean was used as the median for a larger sample, and the SD was estimated as SD = IQR/1.35. In studies with multiple PE groups (moderate, mild, or severe), we used the Cochrane method to combine them into a single PE group. We employed the I2 statistic test to assess statistical heterogeneity [24,25]. I2 values of ≤50% and ≥75% were categorised as low and substantial statistical heterogeneity, respectively [26]. Moreover, the Egger regression test, Beggs and Mazumdar’s rank correlation, trim and fill, including safe-fail N assessment, were used to assess and adjust for publication bias. Sensitivity analysis was performed using one-study-exclusion methods to assess the stability of the effect size [27]. Subgroup analyses were conducted by study design, maternal age, gestational age at diagnosis of PE, study quality, BMI, and continent of publication. For meta-regression, different moderators including (maternal age and BMI, duration of gestation at time of diagnosis of PE, study design, and content of publication) were explored. A p-value below 0.05 was considered statistically significant.

3. Results

3.1. Literature Search and Screening

The preliminary search on the PubMed and Scopus databases produced 332 records. Furthermore, we conducted a bibliographic search and identified 33 relevant studies, bringing the total to 365 for review. Initially, we used an Excel spreadsheet to group all records; 2 duplicates were thus identified and excluded. Of the records that underwent initial full screening, 13 were excluded because their titles and abstracts were deemed irrelevant to the topic. Additionally, 305 were excluded for various reasons, including studies in animals, children, irrelevant markers reported, results presented graphically, not containing a control group, article retracted, no liver function test or pre-eclampsia present, the study was conducted after delivery of the baby, study not published in English, articles published before 2000, and review articles. Hence, 45 studies [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72] were deemed relevant as they satisfied the PECOS criteria outlined in Table 1. To address any discrepancies and prevent bias, a third independent researcher (WNP) participated in the screening and selection process. Refer to Figure 1 for a comprehensive explanation of the screening and selection procedure.

3.2. Characteristics of the Studies Included

We analysed data from 45 studies [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72] published in peer-reviewed journals between 2000 and 2025, evaluating the effect of PE on liver function in pregnant women. The sample sizes showed significant heterogeneity across research, ranging from small samples [51] to larger population studies [40,42,43]. The overall sample comprised 257,929 pregnant women: 9420 with PE and 248,509 normotensives. The published studies employed various designs, including 26 cross-sectional [28,29,30,31,38,39,46,49,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,71], 11 case–control [32,33,34,35,36,37,45,48,69,70,72], and 8 cohort designs [40,41,42,43,44,47,54,71]. Studies from 15 countries were analysed (Figure 2), with the majority from India [31,33,55,56,59,60,61,63,66,69,72], China [32,40,43,47,54,70], Nigeria [28,37,57,67,68], Iraq [34,39,58,62], Pakistan [41,50,52], Ethiopia [29,49], Bangladesh [30,35], Zimbabwe [65], Turkey [36,45], Libya [46], Iran [48], Saudi Arabia [38,64], Egypt [51], Korea [42], Israel [44], Russia [53] and Türkiye [71] (Figure 2). The average blood pressure of the PE group was 150.20 ± 15.14 mmHg systolic and 96.93 ± 9.40 mmHg diastolic. In contrast, the normotensive group had an average SBD and DBP of 115.42 ± 8.95 mmHg and 72.93 ± 6.55 mmHg, respectively. The average maternal age in the PE group reported in 33 studies was 29.65 ± 5.14 years, compared with 32 studies in normotensive women, which reported an average age of 28.75 ± 5.06 years. Moreover, the average BMI of the 17 students in the PE group was 26.77 ± 4.17 kg/m2, whereas in the normotensive group, it was 24.22 ± 3.64 kg/m2. The overall features of the included studies are presented in Table 2.

3.3. AST Levels in Pregnant Women with Pre-Eclampsia Versus Normotensive Pregnant Women

A total of 42 studies examined AST levels, including 9256 participants in the PE group and 248,386 subjects in the normotensive group. The analysis employed a random-effects model, revealing a statistically significant difference between the two groups (Figure 3), with an overall SMD of 1.81 (95% CI: 1.51 to 2.10). The overall effect estimates were statistically significant (p < 0.0001). However, significant heterogeneity was noted (p = 0), with an I2 = 99.1%.

3.4. ALT Levels in Pre-Eclampsia Compared to Normotensive

A total of 45 studies examined ALT levels, comprising 9419 participants in the PE group and 248,503 participants in the normotensive group. The analysis employed a random-effects model due to high heterogeneity (I2 = 99.3%; p = 0.0). The results revealed a statistically significant difference between the two groups, with an SMD of 1.73 (95% CI: 1.38 to 2.07), p < 0.0001, as shown in Figure 4.

3.5. ALP Levels in Pre-Eclampsia Compared to Normotensive Pregnant Women

Twenty-four studies analysed ALP levels, comprising 3069 participants in the PE group and 43,318 subjects in the normotensive group. The random effects model meta-analysis revealed an increase in ALP, with an SMD of 1.43 (95% CI: 0.97 to 1.88), as shown in Figure 5. The overall effect results demonstrated a statistically significant difference between the groups (p < 0.0001). However, substantial heterogeneity was observed (p < 0.01), with an I2 value of 98.3%.

3.6. Total Bilirubin Levels in Pre-Eclampsia Versus Normotensive Pregnant Women

In total, 24 studies examined total serum bilirubin (TSB) levels, including 2392 pregnant women with PE and 13,612 normotensive women. A random-effects meta-analysis was employed, and the effect estimates revealed an increased TSB (SMD = 0.62, 95% CI: 0.36 to 0.88). The overall effect was statistically significant (p < 0.0001). Significant heterogeneity was observed (p < 0.0001), with an I2 value of 93.6% (Figure 6).

3.7. Publication Bias Assessments

This meta-analysis included more than 10 studies, allowing for the assessment of publication bias. Funnel plot inspection, Egger’s and Beggs’ tests indicated potential publication bias for AST levels (Figure 7A) (E value = 9.596, p < 0.001) and Begg and Mazumdar’s rank correlation (value = 0.422, p < 0.001). Moreover, the trim-and-fill value was 0.00, with a fail-safe N of 42,802 and p < 0.001. Similarly, for ALT (Figure 7C), the Egger regression test suggested evidence of publication bias (value = 10.430, p < 0.001), and the Beggs’ value was 0.335 (p = 0.001). The Fail-Safe N value was 51,532, p < 0.001, whereas the trim and fill value was 0.00. Likewise, for TSB levels, funnel plot inspection (Figure 7B), Egger regression (value = 6.172, p < 0.001), and Beggs’s test (value = 0.297, p = 0.044) all indicated publication bias. Moreover, the evidence showed a significantly high fail-safe N value (215,000; p = 0.001) and a trim-and-fill value of 0.00. In contrast, for ALP, visual inspection of the funnel plot suggested no evidence of publication bias (Figure 7D). This is confirmed statistically by the Egger regression test (p = 0.05, regression coefficient = −0.2009) and the Beggs test (p = 0.131, test statistic = 0.225). The fail-safe N value was 3965, with a trim-and-fill of 0.000. These results altogether suggest that the pooled effect size from the ALP meta-analysis is robust and unlikely to be substantially influenced by publication bias.

3.8. Subgroup Analysis

A subgroup analysis was conducted to identify sources of heterogeneity in AST, ALT, ALP, and bilirubin levels by study design, maternal age, period of gestation during diagnosis of PE, continent, study quality, and BMI, as presented in Supplementary File S2, Table S1. For the AST subgroup, factors including study design, quality, continent, BMI, gestational duration, and maternal age did not account for the observed heterogeneity, as they did not reduce heterogeneity (Supplementary File S2, Table S1). Similarly, for ALT and ALP, none of these factors altered heterogeneity. In contrast, in the TSB subgroup, we found that study design, particularly cohort (I2 = 0%) and gestational age at diagnosis (I2 = 51.1%), were potential sources of observed heterogeneity.

3.9. Meta-Regression

The meta-regression output is presented in Table 3. For AST, among these moderators, study design (β = −0.630, p < 0.001) was the significant moderator. Additionally, the significant positive regression coefficient (β = 1.084, p < 0.001) indicates that for every 1-unit increase in the quality score, the effect size increases by 0.761 on average (Table 3). Therefore, there was no evidence that maternal age, gestational age at diagnosis, continent, or BMI explained the heterogeneity observed for AST. Similarly, we found that study design (β = 1.084, p < 0.019) and quality (β = 0.729, p = 0.039) were significant moderators of the ALT effect size. Moreover, the significant negative regression coefficient on ALT BMI (β = −0.406, p = 0.006) suggests that for every 1-unit increase in BMI, the effect size decreases by 0.406 units. For ALP, maternal age (β = −0.720, p = 0.010), quality (β = 1.444, p = 0.003), and continent (β = 2.07, p < 0.0001) were significant moderators. For TSB, study design (β = −0.384, p = 0.004), quality (β = 0.761, p < 0.001), and maternal age (β = −0.304, p = 0.019) were significant moderators.

3.10. The Sensitivity Analysis for Robustness and Stability of Effect Size

For AST, excluding four studies individually resulted in a change in effect size. Briefly, the exclusion of Salman [58] led to SMD = 1.74, 95% CI (1.44 to 2.03, p < 0.0001), Ekun [68], SMD = 1.70, 95% CI (1.41 to 1.99, p < 0.0001), Roy and Lodhi [61], SMD = 1.73, 95% CI (1.43 to 2.02, p < 0.0001) and Hendawy [38], SMD = 1.70, 95% CI (1.41 to 1.99, p < 0.0001). For ALT, six studies changed the effect size. The exclusion of Ohotu [57] changed effext to SMD = 1.65, p < 0.0001; Edebiri [67], SMD = 1.82, p < 0.0001; Mishra [33], SMD = 1.62, p < 0.0001; Hendawy [38], SMD = 1.54, p < 0.0001; Ekun [68], SMD = 1.060, p < 0.0001; and Salman [58], SMD = 1.61, p < 0.0001. We also noted that excluding only 4 of 25 studies for ALP changes the effect size. For instance, the effect changed for the exclusion of Salman [58] SMD = 1.21 (0.76 to 1.65, p < 0.0001), Mishra [33], SMD = 1.28, 95% CI (0.82 to 1.73, p < 0.0001); Ohotu [57], SMD = 1.73, 95% CI (1.30 to 2.17, p < 0.0001); Nainani [56], SMD = 1.28, 95% CI (0.83 to 1.73, p < 0.0001). For TSB, only excluding the Roy and Lodhi (2019) study [61] changed the effect size to SMD = 0.49 (0.26 to 0.72, p < 0.0001), a 21% decrease from the initial effect in the same direction.

3.11. Quality Assessment of Included Observational Studies

The quality of observational studies is presented in Supplementary File S2, Tables S2–S4. For the seven evaluated cohorts, six studies received 10 stars across all domains, and one study received 8 stars due to poor reporting of the adequacy of follow-up; however, all were rated as high quality (Supplementary File S2, Table S2). In 26 cross-sectional studies, 20 scored 7 stars, and one scored 5 stars [68], all of which were considered of moderate quality. Four studies received 8 stars, and 1 study scored 9 stars; thus, all were rated as high quality (Supplementary File S2, Table S3). For case–control studies, 11 studies scored 8–10 stars and were regarded as high quality, except for a study by Asha and Varghese (2017) [69], which was rated 6/10, indicating moderate quality (Supplementary File S2, Table S4). Overall, 51% of the studies were rated as high quality and 49% rated as moderate quality.

4. Discussion

This systematic review and meta-analysis evaluated data from 45 observational studies investigating the impact of PE on maternal liver function by analysing four biochemical markers: AST, ALT, ALP, and TSB. The results demonstrate that pregnant women with PE have elevated levels of liver function enzymes when compared to normotensive pregnancies. These results suggest that PE may impair hepatic function and induce hepatocellular stress in pregnancy. Our findings are consistent with evidence from previous studies, which show a consistent trend in the association between PE and liver dysfunction [29,31,32,40,72]. This is also confirmed by HIV patients, irrespective of treatment [73,74]. The overall data demonstrated statistically significant increases in AST, ALT, ALP, and bilirubin levels in women with PE, demonstrating hepatocellular damage and supporting the notion that systemic inflammation and endothelial dysfunction impair hepatocyte function [75].
Although the mechanism by which PE initiates liver dysfunction is complex, emerging evidence distinguishes two primary pathogenic subtypes of PE, with distinct mechanisms promoting liver dysfunction that depend on the stage of PE: early- and late-onset PE [76]. Early-onset PE is associated with defects in trophoblast invasion, resulting in abnormal placentation, placental ischaemia, and endothelial injury [76,77]. It’s essential to note that the trophoblast plays a crucial role in the attachment of the developing embryo to the endometrium, providing protection and forming part of the placenta [78]. However, any impairment in its function would disrupt all these delicate functions. On the other hand, defects in the trophoblast cause an imbalance in the production of angiogenic and antiangiogenic factors. Whereby, the release of antiangiogenic factors, such as soluble fms-like tyrosine kinase-1 receptor (sFlt-1), is increased compared to angiogenic factors such as vascular endothelial growth factor (VEGF) and placental growth factor (PGF) [79,80,81]. The imbalance between angiogenic and anti-angiogenic factors ultimately leads to endothelial dysfunction, which affects maternal organs, including the heart, liver, and kidneys, and, in severe cases, the brain (Figure 8). On the other hand, late-onset PE is associated with maternal metabolic dysfunction and systemic inflammation. Therefore, these contrasting pathways suggest that liver dysfunction in early-onset PE may arise due to placental ischemia-induced hepatic endothelial damage [74,82,83,84], whereas in late-onset PE, maternal metabolic abnormalities and chronic inflammation may exert a greater influence.
Endothelial dysfunction suppresses the release of vasodilators, such as prostacyclin, and promotes the release of vasoconstrictors, like thromboxane [85]. The activity promotes vasoconstriction of hepatic blood vessels, thereby inducing hypoxia, necrosis, and hepatocyte degeneration. This subsequently increases the levels of AST and ALT in the blood, both of which reflect liver injury [17]. The significant increase in ALP found in PE women likely indicates both hepatic impairment and placental involvement, as placental isoforms of ALP are physiologically elevated during pregnancy [86,87]. Therefore, PE-induced liver dysfunction is mediated by endothelial injury and inflammation that impair hepatic function [88,89]. Furthermore, vascular damage in PE impairs normal hepatic blood flow, leading to hepatocyte injury and reduced liver function, including bilirubin processing and clearance. For example, other studies have shown reduced haemoglobin levels in PE compared to controls [86,87], suggesting that haemoglobin may have been broken down through haemolysis, as reflected in elevated blood bilirubin levels. Previous reports have shown a substantial increase in red blood cell breakdown in severe cases, such as HELLP syndrome, resulting in elevated circulating bilirubin levels [90]. Altogether, impaired hepatic clearance and increased haemoglobin breakdown resulted in the accumulation of serum bilirubin in PE [9,17,48,52]. Our results are consistent with previous reports that showed higher bilirubin levels in PE compared with normotensive individuals [50,52,91]. This activity is associated with an increased rate of haemolysis (Figure 9).
Indeed, elevated AST, ALT, ALP, and bilirubin in PE indicate compromised hepatic clearance or increased haemolysis, both of which suggest liver injury and dysfunction.
This study has several strengths, the main one being that it is the first meta-analysis to examine the impact of PE on liver function in pregnant women. Additionally, it has employed an extensive literature review, with a substantial sample size, and strict adherence to the MOOSE guideline. The studies included were of good quality, as none were rated as poor on the Newcastle–Ottawa Scale. By integrating data from multiple countries (Figure 2), it provides a comprehensive overview of PE’s involvement in hepatic health during pregnancy on a global scale. Interestingly 52% and 48% of studies were of higher and moderate quality, respectively. Nevertheless, limitations must also be acknowledged. The evidence presented substantial heterogeneity across all outcomes. However, subgroup analysis and meta-regression were thoroughly conducted to identify sources and their association with the effect size. To some extent, variation in outcomes was attributed to the study design, particularly the cohorts and the gestational age at diagnosis of PE (more than 30 weeks), which were noted as potential sources of heterogeneity for bilirubin. However, for other outcomes (AST, ALT, and ALP), the variation could not be explained through the subgroup. A thorough meta-regression showed an association between moderators and effect sizes across outcomes. We noted publication bias, as evidenced by funnel plot asymmetry, Egger’s regression, Beggs’s test, trim-and-fill, and fail-safe n assessment. Not all studies documented baseline data, such as blood pressure, BMI, maternal age, and gestational age at the time of PE diagnosis, which remain critical for PE diagnosis. The information about the assay method used to assess these liver enzyme tests is limited; therefore, it was not used for subgroup analysis or meta-regression. As evidence is gathered from observational studies, it is essential to interpret the results with caution, as they do not establish causality and are susceptible to confounding, selection bias, and measurement bias. Moreover, for cross-sectional studies, while they may suggest an association, they fail to assess incidence or risk.

5. Conclusions

This meta-analysis reveals that PE induces liver dysfunction during pregnancy, as evidenced by significant elevations in liver function tests, including AST, ALT, ALP, and total bilirubin. These findings highlight the importance of monitoring liver function in pregnancy, especially if this is associated with hypertensive disorders. However, substantial variability and publication bias warrant careful interpretation. Based on evidence from observational studies, we recommend that future research focus on high-quality studies, including clinical trials, to investigate the potential treatment of liver dysfunction in PE during pregnancy and to prevent severe maternal and foetal complications.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life16020223/s1, Supplementary File S1: MOOSE Checklist; Supplementary File S2: Table S1: Subgroup analysis showing the effect of different factors on liver function, Table S2: Quality assessment of Cohort Studies, Table S3: Quality assessment of Cross-sectional studies, Table S4: Quality assessment of case–control studies.

Author Contributions

Conceptualization, K.-L.E.S. and K.M.; methodology, K.-L.E.S., K.M. and W.N.P.; software, K.-L.E.S. and K.M.; validation, K.M. and W.N.P.; formal analysis, K.-L.E.S. and K.M.; investigation, K.-L.E.S., K.M. and W.N.P.; resources, K.M.; data curation, K.-L.E.S., K.M. and W.N.P.; original draft preparation, K.-L.E.S. and K.M.; review and editing, K.-L.E.S., K.M. and W.N.P.; visualization, K.-L.E.S., K.M. and W.N.P.; supervision, K.M. and W.N.P.; project administration, K.M.; funding acquisition, K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by Research Development Grants for nGAP Scholars (NGAP23022780506) and the Black Academics Advancement Programme PhD Track (NFSG230512105121). The NRF had no role in the design of this study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of the NRF.

Institutional Review Board Statement

This work forms part of a larger project that received ethical consideration from the University of South Africa College of Agriculture and Environmental Sciences Health Research Ethics Committee (2025/CAES_HREC/7327).

Data Availability Statement

All data supporting this manuscript are provided in the Supplementary Materials.

Acknowledgments

We would like to thank the University of South Africa (UNISA) Research Office for covering the publication fees. Mendeley Reference Manager, FLATICON and BIOICONS were used for reference management and diagram creation. https://www.flaticon.com/free-icons/kidney (accessed on 28 October 2025), https://www.flaticon.com/free-icons/liver (accessed on 28 October 2025), https://www.flaticon.com/free-icons/placenta (accessed on 28 October 2025), https://www.flaticon.com/free-icons/placenta (accessed on 28 October 2025), https://www.flaticon.com/free-icons/blood-vessel (accessed on 28 October 2025), https://www.flaticon.com/free-icons/oxygen (accessed on 28 October 2025), https://www.flaticon.com/free-icons/blood (accessed on 28 October 2025) bone_marrow icon by El-Jayawant https://www.eleanorjayawant.com/ (accessed on 28 October 2025) is licensed under CC-BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ (accessed on 28 October 2025).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Tranquilli, A.L.; Brown, M.A.; Zeeman, G.G.; Dekker, G.; Sibai, B.M. The Definition of Severe and Early-Onset Preeclampsia. Statements from the International Society for the Study of Hypertension in Pregnancy (ISSHP). Pregnancy Hypertens. 2013, 3, 44–47. [Google Scholar] [CrossRef] [PubMed]
  2. Magee, L.A.; Brown, M.A.; Hall, D.R.; Gupte, S.; Hennessy, A.; Karumanchi, S.A.; Kenny, L.C.; McCarthy, F.; Myers, J.; Poon, L.C.; et al. The 2021 International Society for the Study of Hypertension in Pregnancy Classification, Diagnosis & Management Recommendations for International Practice. Pregnancy Hypertens. 2022, 27, 148–169. [Google Scholar] [CrossRef] [PubMed]
  3. Brown, M.A.; Magee, L.A.; Kenny, L.C.; Karumanchi, S.A.; McCarthy, F.P.; Saito, S.; Hall, D.R.; Warren, C.E.; Adoyi, G.; Ishaku, S. The Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis & Management Recommendations for International Practice. Pregnancy Hypertens. 2018, 13, 291–310. [Google Scholar]
  4. Duley, L. The Global Impact of Pre-Eclampsia and Eclampsia. Semin. Perinatol. 2009, 33, 130–137. [Google Scholar] [CrossRef] [PubMed]
  5. Soma-Pillay, P.; Nelson-Piercy, C.; Tolppanen, H.; Mebazaa, A. Physiological Changes in Pregnancy. Cardiovasc. J. Afr. 2016, 27, 89–94. [Google Scholar] [CrossRef]
  6. Guerby, P.; Tasta, O.; Swiader, A.; Pont, F.; Bujold, E.; Parant, O.; Vayssiere, C.; Salvayre, R.; Negre-Salvayre, A. Role of Oxidative Stress in the Dysfunction of the Placental Endothelial Nitric Oxide Synthase in Preeclampsia. Redox Biol. 2021, 40, 101861. [Google Scholar] [CrossRef]
  7. McElwain, C.J.; Tuboly, E.; McCarthy, F.P.; McCarthy, C.M. Mechanisms of Endothelial Dysfunction in Pre-Eclampsia and Gestational Diabetes Mellitus: Windows Into Future Cardiometabolic Health? Front. Endocrinol. 2020, 11, 655. [Google Scholar] [CrossRef]
  8. Michalczyk, M.; Celewicz, A.; Celewicz, M.; Wozniakowska-Gondek, P.; Rzepka, R. The Role of Inflammation in the Pathogenesis of Preeclampsia. Mediat. Inflamm. 2020, 2020, 3864941. [Google Scholar] [CrossRef]
  9. Robinson, M.W.; Harmon, C.; O’Farrelly, C. Liver Immunology and Its Role in Inflammation and Homeostasis. Cell. Mol. Immunol. 2016, 13, 267–276. [Google Scholar] [CrossRef]
  10. Mohajan, H.K. A Study on Functions of Liver to Sustain a Healthy Liver. Innov. Sci. Technol. 2025, 4, 77–87. [Google Scholar] [CrossRef]
  11. Trefts, E.; Gannon, M.; Wasserman, D.H. The Liver. Curr. Biol. 2017, 27, R1147–R1151. [Google Scholar] [CrossRef]
  12. Ling, S.; Diao, H.; Lu, G.; Shi, L. Associations between Serum Levels of Liver Function Biomarkers and All-Cause and Cause-Specific Mortality: A Prospective Cohort Study. BMC Public Health 2024, 24, 3302. [Google Scholar] [CrossRef]
  13. Lim, E.; Mouyis, M.; MacKillop, L. Liver Diseases in Pregnancy. Clin. Med. J. R. Coll. Physicians Lond. 2021, 21, E441–E445. [Google Scholar] [CrossRef]
  14. Thibaut, R.; Gage, M.C.; Pineda-Torra, I.; Chabrier, G.; Venteclef, N.; Alzaid, F. Liver Macrophages and Inflammation in Physiology and Physiopathology of Non-Alcoholic Fatty Liver Disease. FEBS J. 2022, 289, 3024–3057. [Google Scholar] [CrossRef]
  15. Ma, Y.; Yang, M.; He, Z.; Wei, Q.; Li, J. The Biological Function of Kupffer Cells in Liver Disease. In Biology of Myelomonocytic Cells; InTech: London, UK, 2017. [Google Scholar]
  16. Torres-Torres, J.; Espino-y-Sosa, S.; Martinez-Portilla, R.; Borboa-Olivares, H.; Estrada-Gutierrez, G.; Acevedo-Gallegos, S.; Ruiz-Ramirez, E.; Velasco-Espin, M.; Cerda-Flores, P.; Ramirez-Gonzalez, A.; et al. A Narrative Review on the Pathophysiology of Preeclampsia. Int. J. Mol. Sci. 2024, 25, 7569. [Google Scholar] [CrossRef]
  17. Mei, J.Y.; Afshar, Y. Hypertensive Complications of Pregnancy: Hepatic Consequences of Preeclampsia through HELLP Syndrome. Clin. Liver Dis. 2023, 22, 195–199. [Google Scholar] [CrossRef]
  18. Stroup, D.F.; Berlin, J.A.; Morton, S.C.; Olkin, I.; Williamson, G.D.; Rennie, D.; Moher, D.; Becker, B.J.; Sipe, T.A.; Thacker, S.B. Meta-Analysis of Observational Studies in Epidemiology: A Proposal for Reporting. Meta-Analysis of Observational Studies in Epidemiology (MOOSE) Group. JAMA 2000, 283, 2008–2012. [Google Scholar] [CrossRef] [PubMed]
  19. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  20. Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomized Studies in Meta-Analyses. Available online: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 28 May 2023).
  21. Fekete, J.T.; Győrffy, B. MetaAnalysisOnline.Com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies. J. Med. Internet Res. 2025, 27, e64016. [Google Scholar] [CrossRef] [PubMed]
  22. Hozo, S.P.; Djulbegovic, B.; Hozo, I. Estimating the Mean and Variance from the Median, Range, and the Size of a Sample. BMC Med. Res. Methodol. 2005, 5, 13. [Google Scholar] [CrossRef]
  23. Lee, D.K.; In, J.; Lee, S. Standard Deviation and Standard Error of the Mean. Korean J. Anesthesiol. 2015, 68, 220–223. [Google Scholar] [CrossRef]
  24. Huedo-Medina, T.B.; Sánchez-Meca, J.; Marín-Martínez, F.; Botella, J. Assessing Heterogeneity in Meta-Analysis: Q Statistic or I 2 Index? Psychol. Methods 2006, 11, 193–206. [Google Scholar] [CrossRef]
  25. Spineli, L.M.; Pandis, N. Statistical Heterogeneity: Notion and Estimation in Meta-Analysis. Am. J. Orthod. Dentofac. Orthop. 2020, 157, 856–859.e2. [Google Scholar] [CrossRef]
  26. Higgins, J.P.T.; Thompson, S.G. Quantifying Heterogeneity in a Meta-Analysis. Stat. Med. 2002, 21, 1539–1558. [Google Scholar] [CrossRef]
  27. Mathur, M.B.; VanderWeele, T.J. Sensitivity Analysis for Publication Bias in Meta-Analyses. J. R. Stat. Soc. Ser. C Appl. Stat. 2020, 69, 1091–1119. [Google Scholar] [CrossRef] [PubMed]
  28. Atiba, A.S.; Abbiyesuku, F.M.; Oparinde, D.P.; ‘Niran-Atiba, T.A.; Akindele, R.A. Plasma Malondialdehyde (MDA): An Indication of Liver Damage in Women with Pre-Eclamsia. Ethiop. J. Health Sci. 2016, 26, 479–486. [Google Scholar] [CrossRef] [PubMed]
  29. Hassen, F.S.; Malik, T.; Dejenie, T.A. Evaluation of Serum Uric Acid and Liver Function Tests among Pregnant Women with and without Preeclampsia at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia. PLoS ONE 2022, 17, e0272165. [Google Scholar] [CrossRef] [PubMed]
  30. Mondal, B.R.; Ahmed, S.; Saha, S.; Parveen, S.I.; Sultana, T.; Rahman, M.Q.; Sarker, U.K.; Aminotransferase, A.A.N.A.; Bilirubin, T. Concentration in Preeclampsia and Eclampsia. Mymensingh Med. J. 2016, 25, 85–90. [Google Scholar]
  31. Khan, J.A.; Ashraf, A.; Fayaz, F.; Qureshi, W.; Sheikh, A.T. Liver and Renal Biochemical Parameters in Preeclampsia: A Cross Sectional Study. Int. J. Res. Med. Sci. 2023, 11, 929–935. [Google Scholar] [CrossRef]
  32. Chen, L.; Pi, Y.; Chang, K.; Luo, S.; Peng, Z.; Chen, M.; Yu, L. Screening Models Combining Maternal Characteristics and Multiple Markers for the Early Prediction of Preeclampsia in Pregnancy: A Nested Case–Control Study. J. Obstet. Gynaecol. 2022, 42, 1889–1896. [Google Scholar] [CrossRef]
  33. Mishra, J.; Srivastava, S.K.; Pandey, K.B. Compromised Renal and Hepatic Functions and Unsteady Cellular Redox State during Preeclampsia and Gestational Diabetes Mellitus. Arch. Med. Res. 2021, 52, 635–640. [Google Scholar] [CrossRef] [PubMed]
  34. Qassim, A.A.; Ameen, M.A. Evaluation of the Effect of Preeclampsia on Liver and Renal Function Biomarkers Level. Biochem. Cell. Arch. 2021, 21, 4887–4891. [Google Scholar]
  35. Sultana, R.; Ahmed, S.; Sultana, N.; Diba, F. ALT in Preeclampsia. Delta Med. Col. J. 2021, 9, 65–68. [Google Scholar] [CrossRef]
  36. Uckan, K.; Sahin, H.G. Serum Amyloid A, Procalcitonin, Highly Sensitive C Reactive Protein and Tumor Necrosis Factor Alpha Levels and Acute Inflammatory Response in Patients with Hemolysis, Elevated Liver Enzymes, Low Platelet Count (HELLP) and Eclampsia. J. Obstet. Gynaecol. Res. 2018, 44, 440–447. [Google Scholar] [CrossRef]
  37. Udenze, I.; Arikawe, A.; Azinge, E.; Egbuagha, E. Liver Function Tests in Nigerian Women with Severe Preeclampsia. J. Clin. Sci. 2014, 11, 7. [Google Scholar] [CrossRef]
  38. Hendawy, M.O.; Hussein, S.; Harahsheh, E.A. Relationship between Pre-Eclampsia, Renal Impairment and Hepatic Insufficiency among Pregnant Women in Al-Jouf Area. J. Pharm. Nutr. Sci. 2020, 10, 295–301. [Google Scholar] [CrossRef]
  39. Al Ghazali, B.; Al-Taie, A.A.-H.; Hameed, R.J. Study of the Clinical Significance of Serum Albumin Level in Preeclampsia and in the Detection of Its Severity. Am. J. Biomed. 2014, 2, 964–974. [Google Scholar] [CrossRef]
  40. Nie, L.; Zhang, Z.; Yao, Q.; Chen, H.; Xu, C.; Chen, L.; Liu, C.; Tu, L.; Yi, Y.; Huang, T.; et al. The New Era of Risk Assessment for Hypertension in Pregnancy: From Clinical to Biochemical Markers in a Comprehensive Predictive Model. Taiwan J. Obstet. Gynecol. 2025, 64, 253–264. [Google Scholar] [CrossRef]
  41. Shahid, S.; Khalid, E.; Fatima, S.S.; Khan, G.M. Evaluation of Soluble TNF-like Weak Inducer of Apoptosis (STWEAK) Levels to Predict Preeclampsia in Early Weeks of Pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 2019, 234, 165–170. [Google Scholar] [CrossRef]
  42. Cho, G.J.; Kim, H.Y.; Park, J.H.; Ahn, K.H.; Hong, S.C.; Oh, M.J.; Kim, H.J. Prepregnancy Liver Enzyme Levels and Risk of Preeclampsia in a Subsequent Pregnancy: A Population-Based Cohort Study. Liver Int. 2018, 38, 949–954. [Google Scholar] [CrossRef]
  43. Zhang, L.; Gao, S.; Luan, Y.; Su, S.; Zhang, E.; Liu, J.; Xie, S.; Zhang, Y.; Yue, W.; Liu, R.; et al. Predictivity of Hepatic Steatosis Index for Gestational Hypertension and Preeclampsia: A Prospective Cohort Study. Int. J. Med. Sci. 2025, 22, 834–844. [Google Scholar] [CrossRef] [PubMed]
  44. Haggai, C.M.; Inshirah, S.; Jacob, B.; Marwan, O.; Lior, L.; Maya, F.W. Liver Stiffness and Steatosis in Preeclampsia as Shown by Transient Elastography–a Prospective Cohort Study. Am. J. Obstet. Gynecol. 2022, 227, 515.e1–515.e9. [Google Scholar] [CrossRef] [PubMed]
  45. İpek, G.; Tanaçan, A.; Ağaoğlu, Z.; Gülçin Baştemur, A.; Gülen Yıldız, E.; Şahin, D. The Role of Aspartate Aminotransferase to Platelet Ratio Index (APRI) in the First Trimester for the Prediction of Superimposed Preeclampsia: A Case-Control Study from a Tertiary Center. Pregnancy Hypertens. 2024, 37, 101132. [Google Scholar] [CrossRef] [PubMed]
  46. Hamed, S.; Hamed, S.S.M.; Khalifa, T.; Ali, M.S. Preeclampsia Symptoms and Liver Function Tests in Women with Pre-Eclampsia: Comparison with a Normal Pregnant Woman. Sci. J. Fac. Sci.-Sirte Univ. 2023, 3, 141–148. [Google Scholar]
  47. Fang, Y.; Liu, H.; Li, Y.; Cheng, J.; Wang, X.; Shen, B.; Wang, Q.; Chen, H. A Prediction Model of Preeclampsia in Hyperglycemia Pregnancy. Diabetes Metab. Syndr. Obes. 2024, 17, 1321–1333. [Google Scholar] [CrossRef]
  48. Hassanpour, S.H.; Zeinab Karami, S. Evaluation of Hepatic Biomarkers in Pregnant Women with Preeclampsia. Gynecol. Obstet. 2018, 8, 1000487. [Google Scholar] [CrossRef]
  49. Walle, M.; Getu, F.; Gelaw, Y.; Getaneh, Z. The Diagnostic Value of Hepatic and Renal Biochemical Tests for the Detection of Preeclampsia Among Pregnant Women Attending the Antenatal Care Clinic at the University of Gondar Comprehensive Specialized Hospital, Gondar, Northwest Ethiopia. Int. J. Gen. Med. 2022, 15, 7761–7771. [Google Scholar] [CrossRef]
  50. Taimoor, A.; Nazir, A.; Raza, N.; Qureshi, S.A.; Ayub, M.; Shirwany, T.A.K. Liver function tests in second and third Trimester Primigravida in normal Pregnancy and Preeclampsia. Pak. J. Physiol. 2017, 13, 25–28. [Google Scholar]
  51. Sakr, I.H.; Khowailed, A.A.; Kamel, M.M.; Farghaly, E.M.; Farid, Z.E. Endothelial-Platelet Dysfunction as an Indicator of Pre-Eclampsia and Its Severity. Med. J. Cairo Univ. 2019, 87, 1775–1782. [Google Scholar] [CrossRef]
  52. Munazza, B.; Raza, N.; Naureen, A.; Khan, S.A.; Fatima, F.; Ayub, M.; Sulaman, M. Liver Function Tests in Preeclampsia. J. Ayub Med. Coll. Abbottabad 2013, 23, 3–5. [Google Scholar]
  53. Zhestkova, N.V.; Ailamazyan, E.K.; Kuzminykh, T.U.; Marchenko, N.V. Characteristics of Liver Function in Patients with Preeclampsia. J. Obstet. Women’s Dis. 2023, 72, 59–69. [Google Scholar] [CrossRef]
  54. Zhang, Y.; Sheng, C.; Wang, D.; Chen, X.; Chen, X.; Jiang, Y.; Dou, Y.; Wang, Y.; Li, M.; Chen, H.; et al. High-Normal Liver Enzyme Levels in Early Pregnancy Predispose the Risk of Gestational Hypertension and Preeclampsia: A Prospective Cohort Study. Front. Cardiovasc. Med. 2022, 9, 963957. [Google Scholar] [CrossRef] [PubMed]
  55. Singh, A.; Singh, N.P.; Sant, S.K.; Jaiswal, K. Comparative Evaluation of Liver Functions in Pre-Eclamptic and Normal Pregnancy. J. Evid. Based Med. Healthc. 2017, 4, 5192–5195. [Google Scholar] [CrossRef] [PubMed]
  56. Nainani, M.; Bhargava, A.K. A Comparison of Liver Enzymes, Bilirubin and Uric Acid in Preeclampsia, Eclampsia and Normotensive Subjects. Int. J. Clin. Obstet. Gynaecol. 2019, 3, 19–20. [Google Scholar] [CrossRef]
  57. Ohotu, E.O.; Queendalyn, M.N.; Onah, E.S.; Ogbuabor, A.O. Comparative Evaluation of Some Liver Enzymes in Preeclamptic and Non-Preeclamptic Patients in the Enugu Metropolis South East Nigeria. Int. J. Med. Sci. Dent. Res. 2023, 6, 1–7. [Google Scholar]
  58. Ibrahim Salman, M. Evaluation of Liver Function Tests in Normotensive and Hypertensive Pregnancy. J. Univ. Anbar Pure Sci. 2016, 10, 7–10. [Google Scholar] [CrossRef]
  59. Saha, A.; Gupta, A. Das Study of Changes in Biochemical Parameters of Preeclampsia Patients, a Prospective Five Year Study. Int. J. Reprod. Contracept. Obstet. Gynecol. 2022, 11, 517. [Google Scholar] [CrossRef]
  60. Das, S.; Char, D.; Sarkar, S.; Kanti Saha, T.; Biswas, S.; Rudra, B. Evaluation of Liver Function Test in Normal Pregnancy and Pre-Eclampsia: A Case Control. IOSR J. Dent. Med. Sci. 2013, 12, 30–32. [Google Scholar] [CrossRef]
  61. Roy, N.; Lodhi, R.A. Evaluation of Liver Function Test and Renal Function Test in Pre-Eclampsia: A Case Control Study. People’s J. Sci. Res. 2019, 12, 18–23. [Google Scholar]
  62. Al-Sultan, A.M.; Jankeer, M.H. Evaluation of Liver and Renal Functions Tests in Pregnant Women with Preeclampsia. Texila Int. J. Public Health 2025, 13. [Google Scholar] [CrossRef]
  63. Afroz, F.; Sultana, N.; Rahman, A.; Zerin, N.; Mohammad Samsuzzaman, S.; Chowdhury, P.P.; Andalib, M.H.; Morshed, M.; Rahman, M.M.; Kamal, M.M. A Comparative Study of Hepatic Enzymes Between Preeclampsia and Normal Pregnant Women. J. Dhaka Med. Coll. 2021, 29, 18–22. [Google Scholar] [CrossRef]
  64. Al-Jameil, N.; Tabassum, H.; Al-Mayouf, H.; Al-Otay, L.; Aziz Khan, F. Liver Function Tests as Probable Markers of Preeclampsia—A Prospective Study Conducted in Riyadh. J. Clin. Anal. Med. 2015, 6, 461–464. [Google Scholar] [CrossRef]
  65. Makuyana, D.; Mahomed, K.; Shukusho, F.D.; Majoko, F. Liver and Kidney Function Tests in Normal and Pre-Eclamptic Gestation-a Comparison with Non-Gestational Reference Values. Cent. Afr. J. Med. 2002, 48, 55–59. [Google Scholar] [PubMed]
  66. Hazari, N.R.; Hatolkar, V.S.; Munde, S.M. Study of Serum Hepatic Enzymes in Preeclampsia. Int. J. Curr. Med. Appl. Sci. 2014, 2, 1–8. [Google Scholar]
  67. Edebiri, O.E.; Adewole, A.S.; Akpe, C.I.; Ehigiamusoe, E.A.; Ikuenobe, V.E.; Ohiwerei, W.O.; Orunta, E.D. Evaluation Of Liver Enzymes (ALP, ALT, AST and GGT) in Preeclamptic Pregnant Women in the Third Trimester Of Pregnancy. Int. J. Med. Health 2025, 4, 101–113. [Google Scholar] [CrossRef]
  68. Ekun, O.A.; Olawumi, O.M.; Makwe, C.C.; Ogidi, N.O. Biochemical Assessment of Renal and Liver Function among Preeclamptics in Lagos Metropolis. Int. J. Reprod. Med. 2018, 2018, 1594182. [Google Scholar] [CrossRef]
  69. Asha, N.S.; Varghese, A. Study of Liver Enzymes in Preeclampsia. J. Med. Sci. Clin. Res. 2017, 05, 15169–15172. [Google Scholar] [CrossRef]
  70. Lu, Y.; Yang, L.; Li, X.; Kuai, D.; Tian, W.; Zhang, H. A Prediction Model of Superimposed Preeclampsia in Women with Chronic Hypertension. Front. Cardiovasc. Med. 2025, 12, 1641662. [Google Scholar] [CrossRef]
  71. Albayrak, M.; Arslan, H.F. Useful Biomarkers for Preeclampsia: Evaluating the Diagnostic Potential of FIB-4 and FIB-5 Indices. Diagnostics 2025, 15, 693. [Google Scholar] [CrossRef]
  72. Singh, P.A.; Rachna, K. Association between LFT Test and Preeclampsia. Int. Res. J. Mod. Eng. Technol. Sci. 2021, 3, 93–98. [Google Scholar]
  73. Strauss, K.L.E.; Phoswa, W.N.; Hanser, S.; Mokgalaboni, K. HIV Infection and Antiretroviral Therapy Impair Liver Function in People Living with HIV: Systematic Review and Meta-Analysis. Pharmaceuticals 2025, 18, 955. [Google Scholar] [CrossRef]
  74. Strauss, K.L.E.; Phoswa, W.N.; Mokgalaboni, K. The Impact of Antiretroviral Therapy on Liver Function Among Pregnant Women Living with HIV in Co-Existence with and Without Pre-Eclampsia. Viruses 2025, 17, 28. [Google Scholar] [CrossRef]
  75. Chaudhary, S.; Mubaral, H.A.; Sabir, A.; Sadai, H.; Khali, A.; Mahmood, Z.; Shoukat, M.; Tahir, F.N. Elevated Liver Function Tests as Predictors of Severe Maternal Outcomes in Women with Preeclampsia. Pak. J. Med. Dent. 2025, 14, 116. [Google Scholar] [CrossRef]
  76. Aplin, J.D.; Myers, J.E.; Timms, K.; Westwood, M. Tracking Placental Development in Health and Disease. Nat. Rev. Endocrinol. 2020, 16, 479–494. [Google Scholar] [CrossRef]
  77. El Sayed, S.; Noel, L.; Lorquet, S.; Chantraine, F. Placenta Accreta Spectrum Disorder Associated With Late Onset Pre-Eclampsia: A Case Report. Clin. Case Rep. 2025, 13, e70346. [Google Scholar] [CrossRef]
  78. Gauster, M.; Moser, G.; Wernitznig, S.; Kupper, N.; Huppertz, B. Early Human Trophoblast Development: From Morphology to Function. Cell. Mol. Life Sci. 2022, 79, 345. [Google Scholar] [CrossRef]
  79. Rana, S.; Burke, S.D.; Karumanchi, S.A. Imbalances in Circulating Angiogenic Factors in the Pathophysiology of Preeclampsia and Related Disorders. Am. J. Obstet. Gynecol. 2022, 226, S1019–S1034. [Google Scholar] [CrossRef] [PubMed]
  80. Ali, L.E.; Salih, M.M.; Elhassan, E.M.; Mohmmed, A.A.; Adam, I. Placental Growth Factor, Vascular Endothelial Growth Factor, and Hypoxia-Inducible Factor-1α in the Placentas of Women with Pre-Eclampsia. J. Matern.-Fetal Neonatal Med. 2019, 32, 2628–2632. [Google Scholar] [CrossRef] [PubMed]
  81. Molbay, M.; Kipmen-Korgun, D.; Korkmaz, G.; Ozekinci, M.; Korgun, E.T. Human Trophoblast Progenitor Cells Express and Release Angiogenic Factors. Int. J. Mol. Cell Med. 2018, 7, 203–211. [Google Scholar] [CrossRef]
  82. Bakrania, B.A.; Spradley, F.T.; Drummond, H.A.; LaMarca, B.; Ryan, M.J.; Granger, J.P. Preeclampsia: Linking Placental Ischemia with Maternal Endothelial and Vascular Dysfunction. Compr. Physiol. 2021, 11, 1315–1349. [Google Scholar] [CrossRef]
  83. Tamil Barathi, P.; Mohanapriya, A. Pre-Eclampsia: Re-Visiting Pathophysiology, Role of Immune Cells, Biomarker Identification and Recent Advances in Its Management. J. Reprod. Immunol. 2024, 163, 104236. [Google Scholar] [CrossRef] [PubMed]
  84. Boeldt, D.S.; Bird, I.M. Vascular Adaptation in Pregnancy and Endothelial Dysfunction in Preeclampsia. J. Endocrinol. 2017, 232, R27–R44. [Google Scholar] [CrossRef] [PubMed]
  85. Shabani, M.; Irandoost, M.; Kashnian, M.; Monfared, Y.K. Comparation Level of Nitric Oxide (NO), Thromboxane A2(TXA2), Prostaglandin E2(PGE2) and Prostacyclin (PGI2) in the Plasma among Normal and Preeclampsia Pregnantwomen. J. Biochem. Technol. 2018, 9, 102–106. [Google Scholar]
  86. Zhang, B.; Zhan, Z.; Xi, S.; Zhang, Y.; Yuan, X. Alkaline Phosphatase of Late Pregnancy Promotes the Prediction of Adverse Birth Outcomes. J. Glob. Health 2025, 15, 04028. [Google Scholar] [CrossRef]
  87. Li, Q.; Wang, H.; Wang, H.; Deng, J.; Cheng, Z.; Lin, W.; Zhu, R.; Chen, S.; Guo, J.; Tang, L.V.; et al. Association between Serum Alkaline Phosphatase Levels in Late Pregnancy and the Incidence of Venous Thromboembolism Postpartum: A Retrospective Cohort Study. EClinicalMedicine 2023, 62, 102088. [Google Scholar] [CrossRef]
  88. Hammoud, G.M.; Ibdah, J.A. Preeclampsia-Induced Liver Dysfunction, HELLP Syndrome, and Acute Fatty Liver of Pregnancy. Clin. Liver Dis. 2014, 4, 69–73. [Google Scholar] [CrossRef]
  89. Auger, N.; Jutras, G.; Paradis, G.; Ayoub, A.; Lewin, A.; Maniraho, A.; Potter, B.J. Long-Term Risk of Chronic Liver Disease after Pre-Eclampsia. Int. J. Epidemiol. 2025, 54, dyaf072. [Google Scholar] [CrossRef]
  90. Guerra Ruiz, A.R.; Crespo, J.; López Martínez, R.M.; Iruzubieta, P.; Casals Mercadal, G.; Lalana Garcés, M.; Lavin, B.; Morales Ruiz, M. Measurement and Clinical Usefulness of Bilirubin in Liver Disease. Adv. Lab. Med. 2021, 2, 352–361. [Google Scholar] [CrossRef]
  91. Lodhi, R.; Roy, N. Liver Function Tests in Patients of Pre-Eclampsia in Bhilai, Chhattisgarh, India: A Clinical Study. Int. J. Reprod. Contracept. Obstet. Gynecol. 2018, 7, 5102. [Google Scholar] [CrossRef]
Life 16 00223 g001
Figure 1. A flowchart illustrating the study selection process.
Figure 1. A flowchart illustrating the study selection process.
Life 16 00223 g001
Life 16 00223 g002
Figure 2. Geographical distribution of publications.
Figure 2. Geographical distribution of publications.
Life 16 00223 g002
Life 16 00223 g003
Figure 3. Random effects meta-analysis assessing the impact of PE on aspartate aminotransferase levels [28,29,31,32,33,34,36,37,38,39,40,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
Figure 3. Random effects meta-analysis assessing the impact of PE on aspartate aminotransferase levels [28,29,31,32,33,34,36,37,38,39,40,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
Life 16 00223 g003
Life 16 00223 g004
Figure 4. Random effects meta-analysis assessing the impact of PE on alanine aminotransferase levels [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
Figure 4. Random effects meta-analysis assessing the impact of PE on alanine aminotransferase levels [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].
Life 16 00223 g004
Life 16 00223 g005
Figure 5. Random effects meta-analysis assessing the impact of PE on alkaline phosphatase levels [33,37,41,43,47,48,50,52,54,55,56,57,58,59,60,62,64,65,66,67,69,70,71,72].
Figure 5. Random effects meta-analysis assessing the impact of PE on alkaline phosphatase levels [33,37,41,43,47,48,50,52,54,55,56,57,58,59,60,62,64,65,66,67,69,70,71,72].
Life 16 00223 g005
Life 16 00223 g006
Figure 6. Random effects meta-analysis assessing the impact of PE on total serum bilirubin levels in pregnant women [29,30,33,37,40,41,46,47,48,49,50,52,53,55,56,58,59,60,61,64,65,66,70,72].
Figure 6. Random effects meta-analysis assessing the impact of PE on total serum bilirubin levels in pregnant women [29,30,33,37,40,41,46,47,48,49,50,52,53,55,56,58,59,60,61,64,65,66,70,72].
Life 16 00223 g006
Life 16 00223 g007
Figure 7. Funnel plots illustrate the possible publication bias among the studies included in the meta-analysis. (A) Studies that analysed AST levels in pregnant women with PE and normotensive pregnant women. (B) Studies that analysed total bilirubin levels in pregnant women with PE and normotensive pregnant women. (C) Studies that analysed ALT levels in pregnant women with PE and normotensive pregnant women. (D) Studies that analysed ALP levels in pregnant women with PE and normotensive pregnant women. The black dot indicates an individual study. A gray colour region indicates a pseudo-confidence interval region.
Figure 7. Funnel plots illustrate the possible publication bias among the studies included in the meta-analysis. (A) Studies that analysed AST levels in pregnant women with PE and normotensive pregnant women. (B) Studies that analysed total bilirubin levels in pregnant women with PE and normotensive pregnant women. (C) Studies that analysed ALT levels in pregnant women with PE and normotensive pregnant women. (D) Studies that analysed ALP levels in pregnant women with PE and normotensive pregnant women. The black dot indicates an individual study. A gray colour region indicates a pseudo-confidence interval region.
Life 16 00223 g007
Life 16 00223 g008
Figure 8. Schematic depiction of the disparity between angiogenic and anti-angiogenic factors resulting in endothelial dysfunction. This malfunction leads to multi-organ involvement in pre-eclampsia, impacting essential maternal organs such as the liver, kidney, and brain. Figure created using FLATICON and Microsoft PowerPoint.
Figure 8. Schematic depiction of the disparity between angiogenic and anti-angiogenic factors resulting in endothelial dysfunction. This malfunction leads to multi-organ involvement in pre-eclampsia, impacting essential maternal organs such as the liver, kidney, and brain. Figure created using FLATICON and Microsoft PowerPoint.
Life 16 00223 g008
Life 16 00223 g009
Figure 9. A. Schematic depiction of how ineffective erythropoiesis and aging red blood cells contribute to the elevation of bilirubin. UDP: uridine diphosphate. Figure created using FLATICON and BIOICONS.
Figure 9. A. Schematic depiction of how ineffective erythropoiesis and aging red blood cells contribute to the elevation of bilirubin. UDP: uridine diphosphate. Figure created using FLATICON and BIOICONS.
Life 16 00223 g009
Table 1. PECOS criteria.
Table 1. PECOS criteria.
Population (P)Pre-eclamptic pregnant women
Exposure (E)Pre-eclampsia
Comparison (C)Healthy pregnant women (normotensive)
Outcome (O)Liver function (AST, ALT, ALP, and bilirubin)
Study design (S)Cross-sectional, case–control, and cohort
Table 2. Overview of features of included studies (n = 45).
Table 2. Overview of features of included studies (n = 45).
Author Name and Publication YearCountryStudy DesignPE GroupNormotensive GroupAge (Years)
PE Group		Age (Years)
Normotensive Group		SBP and DBP (mmHg)
PE Group		SBP and DBP (mmHg)
Normotensive Group		Markers
Atiba et al., 2016 [28]NigeriaCross-sectional9811528.87 ± 4.3328.87 ± 6.62166.15 ± 9.40
99.80 ± 2.66		117.83 ± 13.03
70.87 ± 9.65		AST
Ekun et al., 2018 [68]NigeriaCross-sectional495033.18 ± 4.5532.44 ± 4.99172.55 ± 24.16
112.47 ± 17.73		113.02 ± 9.95
70.20 ± 9.09		AST and ALT
Asha and Varghese, 2017 [69]IndiaCase–control5050NRNRNRNRAST, ALT, and ALP
Chen et al., 2022 [32]ChinaCase–Control737330.17 ± 4.3329.87 ± 4.14116.43 ± 10.3
71.96 ± 9.65		106.07 ± 11.91
65.59 ± 9.78		AST and ALT
Cho et al., 2018 [42]South KoreaCohort3973192,57130.92 ± 3.7530.25 ± 3.38117.60 ± 13.46
74.76 ± 10.00		109.6 ± 10.69
69.00 ± 7.95		AST and ALT
Hamed et al., 2023 [46]LibyaCross-sectional604033.62 ± 6.5028.77 ± 7.26NRNRALT and AST
Fang et al., 2024 [47]ChinaRetrospective cohort5389829.23 ± 1.6529.77 ± 1.77112.5 ± 2.92
72.5 ± 2.92		108 ± 3.45
67.5 ± 2.84		AST, ALT, and ALP
Albayrak and Arslan, 2025 [71]TürkiyeRetrospective case–control20720531.2 ± 6.8929.5 ± 5.13156.5 ± 9.2
96.8 ± 7.5		110.6 ± 7.3
63.5 ± 6.8		AST, ALP, and ALP
Lu et al., 2025 [70]ChinaRetrospective case–control11321731.32 ± 5.1031.53 ± 4.06140 ± 0.75
90 ± 0.2		140 ± 0.67
90 ± 0.33		AST, ALT, ALP, and bilirubin
Hassanpour and Karami, 2018 [48]IranCase–control5049NR NRNRALT, AST, ALP, and bilirubin
Hassen et al., 2022 [29]EthiopiaCross-sectional515132.9 ± 6.329.5 ± 3.3142.8 ± 6.34
92.8 ± 5.22		NRALT, AST, ALP, and bilirubin
Hendawy et al., 2020 [38]Saudi ArabiaCross-sectional100100NRNRNRNRALT and AST
İpek et al., 2024 [45]TurkeyCase–Control929131.0 ± 10.027.0 ± 8.0NRNRALT and AST
Khan et al., 2023 [31]IndiaCross-sectional15015029.24 ± 3.4329.09 ± 3.08154.28 ± 22.31
100.21 ± 12.34		119.08 ± 12.20
74.34 ± 6.22		ALT and AST
Mishra et al., 2021 [33]IndiaCase–control332530 ± 5NR151.3 ± 3.0
70.83 ± 3.41		100.0 ± 1.82
111.2 ± 4.94		AST and bilirubin
Mondal et al., 2016 [30]BangladeshCross-sectional505026.58 ± 3.9726.06 ± 5.02156.56 ± 14.05
104.69 ± 8.88		107.5 ± 12.95
69.69 ± 7.82		ALT and bilirubin
Munazza et al., 2013 [52]PakistanComparative cross-sectional505015–4515–45166.60 ± 24.04
106.50 ± 13.18		116.80 ± 9.022
73.44 ± 7.29		ALT, AST, and bilirubin
Qassim and Ameen, 2021 [34]IraqCase–control505016–4016–40NRNRALT and AST
Sakr et al., 2019 [51]EgyptCross-sectional252518–3518–35NRNRAST and ALT
Singh and Rachna, 2021 [72] IndiaCase–control303018 and above18 and above167.33 ± 25.45
103.33 ± 12.41		113.33 ± 7.58
75.00 ± 5.08		ALT, AST, and ALP
Sultana et al., 2021 [35]BangladeshCase–control505024.66 ± 3.2224.06 ± 3.71NRNRALT
Uckan and Sahin, 2018 [36]TurkeyCase–control303030.7 ± 8.0128.5 ± 8.03175.6 ± 16.82
95.8 ± 7.97		122.5 ± 7.2
72.6 ± 8.73		AST and ALT
Udenze et al., 2014 [37]NigeriaCase–control212131.73 ± 5.532.57 ± 5.5153.4 ± 52
99.1 ± 34.5		116.19 ± 12.00
70.7 ± 1.12		AST, ALT, and ALP
Al Ghazali et al., 2014 [39]IraqCross-sectional532125.23 ± 6.2327.28 ± 6.14168.58 ± 20.54
110.39 ± 9.025		117.4 ± 9.95
76.7 ± 9.13		AST and ALT
Nie et al., 2025 [40]ChinaRetrospective cohort120011,49930.09 ± 5.4029.19 ± 4.64134.76 ± 7.95
84.53 ± 7.36		115.28 ± 12.95
71.56 ± 6.57		ALT, AST, and bilirubin
Shahid et al., 2019 [41]PakistanProspective cohort631727.05 ± 5.8930.65 ± 9.40125.44 ± 16.23
83.95 ± 7.6		108.22 ± 5.89
79.55 ± 8.45		ALP and bilirubin
Taimoor et al., 2017 [50]PakistanComparative cross-sectional505025.92 ± 5.5625.92 ± 5.56NRNRAST, ALP, and bilirubin
Walle et al., 2022 [49]EthiopiaComparative cross-sectional 636328.1 ± 4.6127.5 ± 4.77145.4 ± 8.6
94.5 ± 6.1		103.7 ± 10.2
69.5 ± 7.5		AST, ALT, and bilirubin
Haggai et al., 2022 [44]IsraelProspective cohort363731.17 ± 7.6028.59 ± 4.77156.0 ± 14.2
94.25 ± 12.1		117.92 ± 10.8
70.0 ± 8.4		AST and ALT
Zhang et al., 2025 [43]ChinaProspective cohort158835,85832.4 ± 4.232.4 ± 4.2NRNRAST and ALT
Zhestkova et al., 2023 [53]RussiaCross-sectional735030.65 ± 2.3931.75 ± 2.01150.23 ± 6.73
93.43 ± 3.06		108 ± 3.45
65 ± 2.9		ALP and bilirubin
Singh et al., 2017 [55]IndiaCross-sectional707025.6 ± 3.725.1 ± 3.9156.5 ± 18.4
102.0 ± 16.3		117.2 ± 8.9
76.7 ± 4.5		AST, ALT, ALP, and bilirubin
Zhang et al., 2022 [54]ChinaCohort244528131.9 ± 4.530.9 ± 4.0NRNRAST, ALT, and ALP
Nainani and Bhargava, 2019 [56]IndiaComparative cross-sectional3910020–45NRNRNRAST, ALT, ALP and bilirubin
Ohotu et al., 2023 [57]NigeriaCross-sectional3535NRNR169.56 ± 20.02
107.45 ± 8.14		117.42 ± 6.01
75.36 ± 8.20		AST, ALT, and ALP
Salman, 2016 [58]IraqComparative cross-sectional 4040NRNR158.5 ± 8.92
111.75 ± 7.1		108.75 ± 7.9
73.0 ± 5.16		AST, ALT and bilirubin
Das et al., 2013 [60]IndiaCross-sectional5050NRNR164.58 ± 22.04
104.48 ± 12.16		114.72 ± 8.01
72.41 ± 6.28		ALT, AST, ALP, and bilirubin
Roy and Lodhi, 2019 [61]IndiaCross-sectional303027.53 ± 5.1529.23 ± 6.08NRNRAST, ALT, and bilirubin
Al-Sultan et al. [62]IraqComparative cross-sectional6035NRNRNRNRAST, ALT, and ALP
Afroz et al., 2021 [63]IndiaCross-sectional505028.0 ± 5.9326.20 ± 5.32153.4 ± 16.7
91.7 ± 5.2		113.4 ± 4.5
74.1 ± 6.6		AST and ALT
Saha et al., 2022 [59]IndiaCross-sectional606032.42 ± 6.4526.44 ± 8.14146.32 ± 8.21
94.46 ± 6.84		110.86 ± 12.54
79.44 ± 6.47		AST, ALT, ALP, and bilirubin
Al-Jameil et al., 2015 [64]Saudi ArabiaCross-sectional404031.55 ± 6.1431.20 ± 5.84167.0 ± 24.43
98.51 ± 11.16		113.56 ± 13.93
67.66 ± 9.38		AST, ALT, ALP, and bilirubin
Makuyana et al., 2002 [65]ZimbabweCross-sectional387227 ± 625 ± 6165 ± 20
109 ± 13		118 ± 11
74 ± 8		AST, ALT, ALP, and bilirubin
Hazari et al., 2014 [66]IndiaCross-sectional404032.42 ± 6.4525.13 ± 2.34146.32 ± 8.21
94.46 ± 6.84		110.0 ± 10.4
67.4 ± 4.8		AST, ALP. ALP and bilirubin
Edebiri et al., 2025 [67]NigeriaComparative cross-sectional 4020NRNRNRNRALT, AST, and ALP
SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; PE: Pre-eclampsia; NR: Not Reported; ALT: Alanine Aminotransferase; AST: Aspartate Aminotransferase; ALP: Alkaline Phosphatase. Age, SBP, and DBP are reported as means ± SD or range.
Table 3. Meta-Regression Examining the Influence of Moderator Variables on the Effect Estimates.
Table 3. Meta-Regression Examining the Influence of Moderator Variables on the Effect Estimates.
OutcomesModeratorsβsepLower CIUpper
CI
ASTIntercept2.8270.335<0.0012.1703.484
Study design−0.630 *0.183<0.001−0.989−0.270
Intercept1.75350.424<0.0010.9232.584
Maternal age0.01980.1720.909−0.3180.358
Intercept2.3060.322<0.0011.6752.936
BMI−0.2810.1570.075−0.5890.028
Intercept2.0780.2617<0.0011.5652.591
Gestation age−0.1200.09100.187−0.2980.058
Intercept0.1820.4700.698−0.7391.103
Quality1.084 *0.299<0.0010.4981.670
Intercept2.1310.566<0.0011.0213.241
Continent−0.1790.2930.542−0.7530.395
ALTIntercept3.010.573<0.0011.8844.131
Study design−1.08 *0.4590.019−1.979−0.180
Intercept1.4430.4510.0010.5582.327
Maternal age0.1370.2030.499−0.2600.534
Intercept2.4540.318<0.0011.8313.078
BMI−0.406 *0.1470.006−0.694−0.117
Intercept1.3400.363<0.0010.6282.052
Gestation age0.2280.1900.231−0.1450.601
Intercept0.6530.5470.232−0.4191.725
Quality0.729 *0.3530.0390.0371.421
Intercept1.80340.6500.0060.5293.078
Continent−0.04520.3380.894−0.7090.618
ALPIntercept1.8160.5830.0020.6732.958
Study design−0.2310.2970.435−0.8130.350
Intercept3.0270.673<0.0011.7094.345
Maternal age−0.720 *0.2780.010−1.266−0.175
Intercept1.6890.5390.0020.6332.745
BMI−0.1770.2860.535−0.7370.383
Intercept0.9930.4900.0430.0321.954
Gestation age0.2200.2260.330−0.2230.663
Intercept−0.7050.7530.349−2.1810.772
Quality1.444 *0.4880.0030.4882.400
Intercept−2.140.9380.022−3.982−0.305
Continent2.07 *0.528<0.0011.0323.104
BilirubinIntercept1.1890.234<0.0010.7301.647
Study design−0.384 *0.1340.004−0.647−0.122
Intercept1.2370.294<0.0010.6601.814
Maternal age−0.304 *0.1290.019−0.558−0.050
Intercept0.8560.2770.0020.3121.399
BMI−0.1420.1450.328−0.4270.143
Intercept0.59000.2480.0170.1041.075
Gestation age 0.01510.1350.911−0.2500.280
Intercept−0.6070.3380.073−1.2690.056
Quality0.761 *0.206<0.0010.3581.164
Intercept−0.08080.6620.903−1.3791.217
Continent0.38150.3540.282−0.3131.076
BMI: body mass index; AST: aspartate aminotransferase; ALT: alanine aminotransferase; ALP: alkaline phosphatase; CI: confidence interval; se: standard error; *: shows statistically significant effect.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Strauss, K.-L.E.; Phoswa, W.N.; Mokgalaboni, K. Pre-Eclampsia-Induced Maternal Liver Dysfunction: Systematic Review, Meta-Analysis and Meta-Regression of Observation Studies. Life 2026, 16, 223. https://doi.org/10.3390/life16020223

AMA Style

Strauss K-LE, Phoswa WN, Mokgalaboni K. Pre-Eclampsia-Induced Maternal Liver Dysfunction: Systematic Review, Meta-Analysis and Meta-Regression of Observation Studies. Life. 2026; 16(2):223. https://doi.org/10.3390/life16020223

Chicago/Turabian Style

Strauss, Kay-Lee E., Wendy N. Phoswa, and Kabelo Mokgalaboni. 2026. "Pre-Eclampsia-Induced Maternal Liver Dysfunction: Systematic Review, Meta-Analysis and Meta-Regression of Observation Studies" Life 16, no. 2: 223. https://doi.org/10.3390/life16020223

APA Style

Strauss, K.-L. E., Phoswa, W. N., & Mokgalaboni, K. (2026). Pre-Eclampsia-Induced Maternal Liver Dysfunction: Systematic Review, Meta-Analysis and Meta-Regression of Observation Studies. Life, 16(2), 223. https://doi.org/10.3390/life16020223

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop