Abstract
Introduction:
Imbalance of adipocytokines has been implicated in endothelial dysfunction in hypertensive disorders of pregnancy (HDP). Adiponectin is an adipocytokine that regulates metabolism, insulin sensitivity, and inflammation, and increased adiponectin levels have been associated with mortality in subjects with cardiovascular diseases. Adiponectin also plays a role in trophoblast invasion during placental development. ADIPOQ gene has polymorphisms that modulate adiponectin levels and are linked to several diseases, including gestational hypertension (GH) and preeclampsia (PE). However, no previous study has examined whether ADIPOQ SNPs and haplotypes affect adiponectin levels in HDP.
Objectives:
We assessed and compared plasma adiponectin levels among healthy pregnant (HP, n = 182), GH (n = 121), and PE (n = 133) women, and examined whether ADIPOQ SNPs rs266729, rs2241766 and rs1501299, and their haplotypes are associated with susceptibility to HDP; and whether these polymorphisms and haplotypes affect adiponectin levels in HP, GH and PE.
Materials and Methods:
Adiponectin concentrations were determined using the Human Adiponectin ELISA kit. Genotypes were determined by Taqman allele discrimination assays. Haplotype frequencies were estimated using Haplo. stats. Multiple logistic/linear regression analyses were adjusted for selected covariates.
Results:
Plasma adiponectin levels were higher in PE than in HP, including when patients were stratified by body mass index. Regarding rs266729 (C>G) SNP, the GG genotype was associated with risk for GH, and the CG genotype may protect against PE. PE patients carrying the CG and GG genotypes showed higher adiponectin levels than their CC counterparts. The ‘G,T,G’ haplotype showed higher adiponectin levels in PE than in HP, and the ‘C,T,T’ haplotype may protect against GH.
Conclusion:
Our novel findings indicate that ADIPOQ polymorphisms and haplotypes may affect circulating adiponectin levels and the susceptibility to GH and PE.
1 Introduction
Hypertensive disorders of pregnancy (HDP) are leading causes of maternal and perinatal mortality and morbidity, and the prevalence of gestational hypertension (GH) and preeclampsia (PE) in worldwide epidemiological studies are 1.8%–4.4% and 0.2%–9.2%, respectively (). PE is characterized as new-onset hypertension defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg occurring after 20 weeks of gestation, which are accompanied by proteinuria or other key organ injury (; ).
Abnormal development of placenta is closely linked to the pathophysiology of PE (). The invasion of cytotrophoblasts into the spiral arteries of the decidua is restricted to the superficial layers, leading to reduced perfusion in the placenta, which is followed by widespread dysfunction of the maternal vascular endothelium (; ). While the mechanisms responsible for PE are not fully clarified, circulating antiangiogenic factors that are released in response to hypoxic/ischemic placental conditions may contribute to the maternal endothelial dysfunction, as reviewed elsewhere ().
Adiponectin, a collagen-like adipocytokine regulates metabolism, insulin sensitivity, and inflammation (), also plays a role in proliferation, differentiation, and invasion of trophoblasts during placental development, and enhances the migration of trophoblastic cells in the decidua by balancing the matrix metalloproteinases/tissue inhibitors of metalloproteinases axis (). Dysregulated adipocytokines, including adiponectin may be associated with endothelial dysfunction in PE (; ; ). Notably, increased adiponectin levels were significantly associated with elevated risk of all-cause and cardiovascular mortality in subjects with cardiovascular diseases (CVD) (), and as an independent predictor of cardiovascular and all-cause mortality in coronary artery disease patients (). Moreover, adiponectin plasma concentrations range from 3 to 30 μg/mL in healthy adults, corresponding to about 0.01% of total plasma proteins (). Once synthesized by cells, it can form low-, medium-, and high–molecular weight complexes (HMW–High Molecular Weight), depending on the post-translational modifications involved, with HMW being the biologically active form of adiponectin and the predominant one in human circulation ().
Several Genome Wide Association Studies (GWAS) have identified single nucleotide polymorphisms (SNPs) in the ADIPOQ gene as significant contributors to the variability in circulating adiponectin levels (). Specifically, SNP rs266729 was associated with cardiovascular and metabolic diseases (; ), and SNP rs1501299 as a low-risk factor for the development of CVD with type 2 diabetes (). Moreover, SNPs rs2241766 and rs1501299 were associated with important clinical manifestations of PE, being the rs1501299 associated with serum adiponectin level (). We have previously found a specific association between the CG genotype of the SNP rs266729 and PE. However, except for one study (), no other previous study has examined whether ADIPOQ SNPs and haplotypes affect circulating adiponectin levels in HDP.
In this study, we aimed at comparing plasma levels of adiponectin in healthy pregnant (HP) women with those found in patients with GH and PE. We then examined whether the ADIPOQ SNPs rs266729, rs2241766 and rs1501299, as well as the haplotypes formed by combination of their alleles affect plasma adiponectin levels in the HP, GH, and PE groups, and whether they are associated with susceptibility to GH and PE.
2 Materials and methods
2.1 Study population
Approval to study human subjects was obtained from the Institutional Review Board at the Ribeirao Preto Medical School, University of Sao Paulo (FMRP-USP), during the execution of Projects approved by the Research Ethics Committee of the Hospital das ClÃnicas of FMRP/USP, and each participant giving signed informed consent. The study enrolled 436 pregnant women at the Department of Obstetrics and Gynecology within the University Hospital of FMRP-USP: 182 healthy pregnant, 121 with GH, and 133 with PE diagnosed according to the American College of Obstetricians and Gynecologists () and the International Society for the Study of Hypertension in Pregnancy ().
GH was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, confirmed by two separate readings at least 6 h apart, after 20 weeks of gestation, which normalized within 12 weeks postpartum. PE was defined as GH along with considerable proteinuria (≥0.3 g per 24 h) and/or thrombocytopenia, pulmonary edema, impaired liver function, renal insufficiency with abnormal lab values, and new-onset headache unresponsive to all forms of management (; ). Individuals with cardiac and renal diseases, diabetes, and those with chronic hypertension, with or without superimposed PE were excluded. Methyldopa was the initial antihypertensive treatment during pregnancy. If the pregnant women did not respond to methyldopa, nifedipine and/or hydralazine were added to achieve targeted blood pressure levels.
Maternal venous blood samples were collected at the time of clinic attendance. Genomic DNA was extracted from the cellular component of 1 mL of whole blood using the salting-out method and stored at −20 °C until analyzed. Plasma samples were obtained after centrifugation of whole blood collected into tubes containing EDTA at 2000 g for 10 min. Those samples were stored at −70 °C until assayed.
2.2 Enzyme immunoassays for adiponectin measurement
Plasma adiponectin concentrations were determined using the Human Adiponectin ELISA kit (Human Adiponectin EZHADP-61K, Millipore, St. Charles, MO, United States), following the manufacturer’s instructions, as previously described ().
2.3 Genotype determination
Genotypes for SNPs −11377C>G (rs266729; c_2412786_10) in the promoter region, 45T>G (rs2241766; c_26426077_10) in exon 2, and 276G>T (rs1501299; c_7497299_10) in intron 2 of the ADIPOQ gene were determined using TaqMan allelic discrimination assays (Applied Biosystems, Foster City, CA, United States). Genotyping was performed in a Real-Time System 7,500 (Applied Biosystems, Foster City, CA, United States). Each PCR was carried out in a total volume of 6.74 mL (3.125 mL of 2 x TaqMan Universal Master Mix and 0.156 mL of primer probe (470 nM and 100 mM, respectively), 3.0 mL of DNA (100 ng/mL) and 0.46 mL purified water-free DNAse/RNAse) placed in 96-well plates. DNA with known genotypes was used as positive control and water as negative control in each experiment.
2.4 Statistical analysis
Demographics, clinical characteristics, and plasma adiponectin levels of HP, and patients with PE or GH were compared by Student’s unpaired t-test, Mann-Whitney U test, one-way ANOVA, or χ2 as appropriate, and reported as mean ± s. e.m.
Distribution of genotypes was assessed for deviation from Hardy–Weinberg equilibrium using Hardy–Weinberg exact tests for each locus in each group using the web version of Genepop available at https://genepop.curtin.edu.au/genepop_op1.html (). A value of P < 0.05 was considered significant. Power calculation was performed using QUANTO version 1.2.4 (). Given the sample size of the study, considering the detectable odds ratio of 0.5 for the minor allele frequency of SNP rs266729, which was found to be significantly associated with PE, the power was 0.929, considering an alpha of 0.05.
The effect of ADIPOQ genotypes on plasma adiponectin levels within each group were compared by analysis of normality (Shapiro-Wilk test), followed by Kruskal-Wallis test (not normally distributed variables) and post-hoc Dunn’s Multiple Comparison test. Haplotype frequencies were estimated using Haplo. stats package in R (v4.3.1), which computes maximum likelihood estimates of haplotype probabilities (). The possible haplotypes including the alleles of the ADIPOQ SNPs rs266729 (C>G), rs2241766 (T>G) and rs1501299 (G>T) considered in the analyses were: ‘C, T, G’, ‘C, T, T’, ‘G, T, G’ and ‘C, G, G’. We have excluded the haplotypes with frequency lower than 5% from the analysis. Differences in haplotype frequencies were tested using χ2 tests, and a value of P < 0.05 was considered significant.
To further examine the effects of ADIPOQ haplotypes on adiponectin levels, we have also performed an additional analysis. We compared ADIPOQ haplotypes distributions in two groups of HP, GH and PE patients: the lower and the upper groups, which included subjects with the lower and upper values of plasma adiponectin levels distribution, respectively.
Linkage disequilibrium (LD) was assessed by calculating D′ using the Haploview software (version 4.2; http://www.broad.mit.edu/mpg/haploview/) (). We used data retrieved from the 1,000 Genomes Phase III study for Europeans (CEU, Utah Residents with Northern and Western European Ancestry), East Asians (CHB, Han Chinese in Beijing, China), and Africans (YRI, Yoruba in Ibadan, Nigeria) (; ).
The comparison of adiponectin levels between groups was done by analysis of normality (Shapiro-Wilk), followed by identification of outliers by using the ROUT method for outlier identification (), and then performance of Kruskal-Wallis test (non-parametric) and post-hoc Dunn’s Multiple Comparison test. The same method was applied for the analysis of adiponectin levels concerning each SNP and their genotypes and haplotypes. Also, we further performed regression analysis for the comparison of adiponectin levels adjusting for all significant covariates, namely, BMI, fasting glucose, ethnicity, and gestational age at sampling.
As an additional analysis, we compared ADIPOQ haplotypes distributions in two groups of HP, GH and PE patients: the lower and the upper groups, which were defined by the median of adiponectin levels on each group, including subjects with the lower and upper values of plasma adiponectin levels distribution, respectively.
Logistic regression analysis was performed using the RStudio integrated development environment (IDE) (https://posit.co/products/open-source/rstudio/) to assess the potential confounding influence of each covariate on the relationship between ADIPOQ genotypes in the PE and GH groups. The variables of clinical importance for GH and PE development were included in the multiple logistic regression models. PE and GH were considered as dependent variables. Genotypes of ADIPOQ SNPs, age, ethnicity, body mass index, primiparity, and gestational age at sampling were considered as independent variables.
3 Results
The clinical and demographic characteristics of all pregnant women enrolled in this study are shown in Table 1. The HP and PE groups demonstrated a similar self-declared race, whereas there was a higher White prevalence in GH when compared with HP. Current smoking percentage and primiparity presented similar values between groups (all P > 0.05). As expected, both PE and GH groups exhibited higher systolic and diastolic blood pressure compared to the HP group (both P < 0.05). It is important to note that most of these patients were on antihypertensive medication. Additionally, patients with GH and PE were older than those in the HP group (P < 0.05). Higher body mass index (BMI) and fasting glucose levels were observed in the GH and PE groups compared to the HP group, while hematocrit and hemoglobin were higher only in PE (all P < 0.05). There was a lower gestational age at delivery (GAD) in the GH and PE groups, along with lower birth weight and gestational age at sampling in PE (all P < 0.05) compared to the HP group. Significant proteinuria was detected in the PE group.
TABLE 1
| Parameters | HP (n = 182) | GH (n = 121) | P valuea | PE (n = 133) | P valuea |
|---|---|---|---|---|---|
| Age (years) | 24.00 (20.00–28.25) | 26.00 (22.00–32.00) | 0.0088 | 27.00 (22.00–32.00) | 0.0051 |
| Race (% white) | 58.24 | 77.69 | 0.0253 | 72.18 | 0.2104 |
| Current smoking (%) | 9.89 | 13.22 | 0.4576 | 6.77 | 0.4163 |
| Primiparity (%) | 46.30 | 41.32 | 0.4680 | 42.11 | 0.4823 |
| BMI (kg/m2) | 27.25 (19.30–45.70) | 33.04 (19.68–56.22) | <0.0001 | 31.60 (21.67–51.85) | <0.0001 |
| Pre-pregnancy BMI (kg/m2) | 21.63 (17.26–39.74) | 29.33 (17.31–49.61) | <0.0001 | 25.13 (16.18–48.41) | 0.0014 |
| Pre-pregnancy BMI (kg/m2) | 21.63 (17.26–39.74) | 29.33 (17.31–49.61) | <0.0001 | 25.13 (16.18–48.41) | 0.0014 |
| SBP (mmHg) | 110.00 (90.00–140.00) | 130.00 (90.00–210.00) | <0.0001 | 140.00 (100.00–200.00) | <0.0001 |
| DBP (mmHg) | 70.00 (50.00–90.00) | 80.00 (58.00–130.00) | <0.0001 | 90.00 (60.00–130.00) | <0.0001 |
| HR (beats/min) | 80.00 (60.00–150.00) | 80.00 (62.00–110.00) | 0.2786 | 80.00 (60.00–120.00) | 0.7653 |
| Fasting glucose (mg/dL) | 73.50 (54.00–111.00) | 78.00 (60.00–117.00) | 0.0391 | 77.00 (60.00–130.00) | 0.0161 |
| Hb (g/dL) | 11.80 (2.30–15.00) | 11.90 (7.30–15.70) | 0.9623 | 12.20 (3.80–15.80) | 0.0103 |
| Hct (%) | 35.25 (6.00–49.00) | 35.70 (22.00–47.00) | 0.9021 | 36.85 (12.50–47.00) | 0.0074 |
| Creatinine (mmol/L) | 0.70 (0.60–0.80) | 0.60 (0.40–1.00) | 0.0231 | 0.70 (0.40–2.90) | 0.4979 |
| Proteinuria (mg/24 h) | ND | 153.00 (20.00–291.00) | ND | 665.50 (50.00–776.20) | ND |
| GAS (weeks) | 37.00 (20.00–41.00) | 37.00 (18.00–43.00) | 0.6079 | 35.00 (14.00–42.00) | <0.0001 |
| GAD (weeks) | 40.00 (36.00–42.00) | 39.00 (35.00–43.00) | 0.0019 | 37.00 (19.00–42.00) | <0.0001 |
| Newborn weight (g) | 3,265 (2,970–4,970) | 3,160 (1,360–4,830) | 0.0458 | 2,883 (1,250–4,235) | <0.0001 |
Clinical and demographic characteristics for all subjects selected for the study.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; GAD, gestational age at delivery; GAS, gestational age at sampling; GH, gestational hypertension; Hb, hemoglobin concentration; Hct, hematocrit; HP, healthy pregnant; HR, heart rate; ND, not determined; PE, preeclampsia; SBP, systolic blood pressure. Values are the mean ± s. e.m.
P < 0.05 vs. healthy pregnant group. Bold values are significant.
Although adiponectin levels were not available for all subjects due to lack of plasma availability, clinical, demographic, and genetic characteristics were collected uniformly for the entire cohort, which is standard practice in observational and genetic association studies. Having complete baseline data for all participants allows us to describe the cohort accurately and confirm that those with and without adiponectin measurements do not differ meaningfully.
The values shown in Figure 1 are for 130 HP women, 42 patients with GH, and 55 patients with PE, the 227 patients that we were able to measure adiponectin levels. The clinical and demographic characteristics for this subset of subjects are shown in Supplementary Table S1. The correlation analysis between ADIPOQ polymorphism and serum adiponectin levels was restricted to the 227 patients.
FIGURE 1
We found significant differences in plasma adiponectin levels between HP and PE (P < 0.05; Figure 1A). We further stratified the subgroups of subjects according to BMI <30 and BMI ≥30 kg/m2, to examine the influence of body fat in circulating adiponectin levels. While we found increased plasma adiponectin in PE compared to HP for the subgroups with BMI <30 kg/m2 (P < 0.05; Figure 1B), no differences were found among HP, GH, and PE groups in the subgroups with BMI ≥30 kg/m2 (P < 0.05; Figure 1C). We further performed a generalized linear regression analysis (Gamma distribution) for the comparison of adiponectin levels adjusting for covariates (Supplementary Table S2). The model showed no significant association between preeclampsia and adiponectin levels (p = 0.1636). BMI, fasting glucose, ethnicity, and gestational age at sampling were also not significant.
The frequencies of ADIPOQ genotypes and alleles are shown in Table 2. The distribution of genotypes for each SNP showed no deviation from Hardy-Weinberg equilibrium (P > 0.05), except for the SNP rs266729 (C>G) in the group of patients with PE (P = 0.0009; Supplementary Material S1). Significant departure from Hardy-Weinberg equilibrium can be expected in relatively small samples of patients over a range of genetic models, and it may occur in case groups in case-control association studies (). Considering codominant distribution, the CG genotype for SNP rs266729 was more common in HP compared to PE, while the GG genotype was more frequent in GH compared with HP (both P < 0.05; Table 2). Conversely, no differences in genotype and allele frequencies among groups were found for SNPs rs2241766 and rs1501299 (all P > 0.05; Table 2). The haplotype distributions are shown in Table 3. The ‘C,T,T’ haplotype was more frequent in HP than in GH (P = 0.0397; Table 3). Conversely, no significant differences were found between HP and PE (all P > 0.05; Table 3).
TABLE 2
| ADIPOQ SNP | HP (n = 182) | GH (n = 121) | OR (95% CI) | P valuea | PE (n = 133) | OR (95% CI) | P-valuea |
|---|---|---|---|---|---|---|---|
| rs266729 | |||||||
| Codominant | |||||||
| CC | 106 (58.24%) | 64 (52.89%) | 1.0000 (Reference) | 84 (63.15%) | 1.0000 (Reference) | ||
| CG | 62 (34.06%) | 37 (30.57%) | 0.9884 (0.5847–1.6370) | >0.9999 | 28 (21.05%) | 0.5699 (0.3310–0.9658) | 0.0378 |
| GG | 14 (7.70%) | 20 (16.52%) | 2.3660 (1.1540–4.8160) | 0.0346 | 21 (15.78%) | 1.8930 (0.8912–4.0140) | 0.0986 |
| Dominant | |||||||
| CC | 106 (58.24%) | 64 (52.89%) | 1.0000 (Reference) | 84 (63.16%) | 1.0000 (Reference) | ||
| CG + GG | 76 (41.76%) | 57 (47.11%) | 1.2420 (0.7721–1.9620) | 0.4083 | 49 (36.84%) | 0.8136 (0.5206–1.2910) | 0.4152 |
| Alleles | |||||||
| C | 274 (75.27%) | 165 (68.18%) | 1.0000 (Reference) | 196 (73.68%) | 1.0000 (Reference) | ||
| G | 90 (24.73%) | 77 (31.82%) | 1.4210 (0.9931–2.0250) | 0.0634 | 70 (26.32%) | 1.0870 (0.7552–1.5520) | 0.7110 |
| rs2241766 | |||||||
| Codominant | |||||||
| TT | 146 (80.22%) | 88 (72.73%) | 1.0000 (Reference) | 101 (75.94%) | 1.0000 (Reference) | ||
| TG | 35 (19.23%) | 30 (24.79%) | 0.7032 (0.4014–1.2210) | 0.2513 | 27 (20.30%) | 0.8968 (0.5066–1.5620) | 0.7734 |
| GG | 1 (0.55%) | 3 (2.48%) | 0.2009 (0.0154–1.3730) | 0.1575 | 5 (3.76%) | 0.1384 (0.0117–1.0330) | 0.0852 |
| Dominant | |||||||
| TT | 146 (80.22%) | 88 (72.73%) | 1.0000 (Reference) | 101 (75.94%) | 1.0000 (Reference) | ||
| TG + GG | 36 (19.78%) | 33 (27.27%) | 0.6575 (0.3847–1.1320) | 0.1616 | 32 (24.06%) | 0.7783 (0.4576–1.3380) | 0.4062 |
| Alleles | |||||||
| T | 327 (89.84%) | 206 (85.12%) | 1.0000 (Reference) | 229 (86.09%) | 1.0000 (Reference) | ||
| G | 37 (10.16%) | 36 (14.88%) | 0.6475 (0.4027–1.0450) | 0.0973 | 37 (13.91%) | 0.7003 (0.4249–1.1550) | 0.1685 |
| rs1501299 | |||||||
| Codominant | |||||||
| GG | 81 (44.51%) | 65 (53.72%) | 1.0000 (Reference) | 67 (50.37%) | 1.0000 (Reference) | ||
| GT | 82 (45.05%) | 47 (38.84%) | 1.4000 (0.8621–2.3040) | 0.1789 | 52 (39.10%) | 1.3040 (0.8181–2.0980) | 0.2801 |
| TT | 19 (10.44%) | 9 (7.44%) | 1.6940 (0.6990–4.1320) | 0.2974 | 14 (10.53%) | 1.1230 (0.5320–2.3120) | 0.8476 |
| Dominant | |||||||
| GG | 81 (44.51%) | 65 (53.72%) | 1.0000 (Reference) | 67 (50.37%) | 1.0000 (Reference) | ||
| GT + TT | 101 (55.49%) | 56 (46.28%) | 1.4470 (0.9055–2.2660) | 0.1278 | 66 (49.63%) | 1.2660 (0.8119–1.980) | 0.3067 |
| Alleles | |||||||
| G | 244 (67.03%) | 177 (73.14%) | 1.0000 (Reference) | 186 (69.92%) | 1.0000 (Reference) | ||
| T | 120 (32.97%) | 65 (26.86%) | 1.3390 (0.9340–1.9230) | 0.1258 | 80 (30.08%) | 1.1430 (0.8102–1.6050) | 0.4883 |
Genotypic and allelic frequencies for ADIPOQ polymorphisms in healthy pregnancy, gestational hypertension and preeclampsia groups.
Abbreviations: CI., confidence interval; OR., odds ratio.
P < 0.05 vs. healthy pregnant group. Significant P values are in bold.
TABLE 3
| ADIPOQ Haplotypes | HP (n = 182) | GH (n = 121) | P valuesa | OR (95% CI) | PE (n = 133) | P valuesa | OR (95% CI) |
|---|---|---|---|---|---|---|---|
| C.T.G | 0.3993 (39.93%) | 0.3574 (35,74%) | 0.3431 | 1.0000 (Reference) | 0.3667 (36,67%) | 0.5205 | 1.0000 (Reference) |
| C.T.T | 0.2709 (27.09%) | 0.1911 (19,11%) | 0.0397 | 0.8132 (0.5145–1.2854) | 0.2659 (26,59%) | 0.6967 | 1.0463 (0.6991–1.5658) |
| G.T.G | 0.1694 (16.94%) | 0.2431 (24,31%) | 0.0699 | 1.4175 (0.8836–2.2742) | 0.1935 (19,35%) | 0.5779 | 1.2370 (0.7776–1.9679) |
| C.G.G | 0.0826 (8.26%) | 0.1225 (12,25%) | 0.1268 | 1.5546 (0.7932–3.0470) | 0.1043 (10,43%) | 0.2856 | 1.3732 (0.7516–2.5088) |
Haplotype frequencies for ADIPOQ polymorphisms in the HP, GH and PE groups. The considered haplotypes were the ones with frequency higher than 5% among groups.
Global-stat = 8.5486. df = 5. P = 0.12848; Global-stat = 3.7515. df = 5. P = 0.58572.
Abbreviations: CI., confidence interval; GH., gestational hypertension; HP., healthy pregnant; OR., odds ratio; PE., preeclampsia.
P < 0.05 vs. healthy pregnant group. Significant P values are in bold.
We next examined the effect of ADIPOQ genotypes and haplotypes on plasma adiponectin levels. We found no difference among groups and within the same group for SNP rs1501299 (all P > 0.05; Figure 2). However, we found higher adiponectin levels in patients with PE carrying the TT genotype of SNP rs2241766 (T>G) when compared with TT carriers of the HP group (P < 0.05; Figure 2), but no differences between genotypes within each group (P > 0.05; Figure 2). Moreover, we found that patients with PE carrying the CG + GG genotypes for SNP rs266729 (C>G) showed higher adiponectin levels than those carrying the CC genotype and CG + GG carriers with HP (both P < 0.05; Figure 2). Regarding haplotypes, we found increased adiponectin levels in patients with PE carrying the ‘G,T,G’ haplotype compared to those with the same haplotype in the HP group (P < 0.05; Figure 3). Conversely, no differences were found when comparing adiponectin levels between GH and PE nor among haplotypes within the same group (all P > 0.05; Figure 3).
FIGURE 2
FIGURE 3
In addition, we compared the distribution of ADIPOQ haplotypes in the subgroups of lower and upper plasma adiponectin levels for each of the study groups, but found no differences in haplotype frequencies for the HP and GH groups (all P > 0.05, Supplementary Tables S3, S4, respectively). For PE, the ‘C,T,T’ haplotype was more frequent in the lower subgroup of adiponectin levels (P = 0.0285; Supplementary Table S4), while the ‘G,T,G’ haplotype was more frequent in the upper group of adiponectin levels (P = 0.0378; Supplementary Table S5).
We further compared analysis of the influence of covariates performed using univariate and multivariate logistic regression models, evaluating the influence of each variable alone and groups of variables in the outcome for GH and PE. Univariate logistic regression showed age and body mass index during pregnancy independently associated with PE and GH compared to HP women (P < 0.05 and OR>1) and gestational age at sampling primiparity associated with GH compared to HP women (P < 0.05 and OR<1; Supplementary Tables S6, S7).
Logistic regression analysis adjusted for independent variables were done for each of the studied SNPs in GH and PE (Supplementary Table S8; Supplementary Table S9, respectively). For the GH group, the GG genotype of the rs266729 (C>G) SNP influences the GH outcome in the model that considers only the maternal age as a covariate (Supplementary Table S8). On the other hand, the same rs266729 SNP, in the PE group, showed influence in the disease outcome concerning the CG genotype for the multivariate model that has BMI during pregnancy and maternal age as covariates (Supplementary Table S8), and the same result was observed for the models considering maternal age or BMI in pregnancy as the only covariate besides the rs266729 (C>G) SNP (Supplementary Table S5).
Finally, we assessed the pairwise LD among the SNPs rs266729 (C>G), rs2241766 (T>G) and rs1501299 (G>T) in all the studied groups. A short segment of higher LD between SNPs rs2241766 and rs1501299 was found in HP (D’ = 1 and (LOD)’≥2) when compared to PE (D’ = 1 and (LOD)’<2) and to GH patients (D’<1 and (LOD)’<2; Supplementary Figure S1). When analyzing LD of these SNPs in the 1000 Genomes Project populations of European (CEU), African (YRI), Chinese (CHB), and Japanese (JPT) subjects, higher LD blocks were found, particularly in Asian populations (CHB and JPT) (D = 1 and LOD≥2) when compared to CEU and YRI populations (Supplementary Figure S2).
4 Discussion
The main findings reported in the present study are: (1) Patients with PE and a BMI <30 exhibited higher plasma adiponectin levels compared to HP women with same BMI range; (2) The CG genotype for SNP rs266729 (C>G) was more frequent in HP women compared to PE patients, while the GG genotype was more frequent in GH patients when compared to HP women; (3) among haplotypes, the ‘C,T,T’ was more frequent in HP than in GH; (4) PE patients carrying the CG + GG genotype for the SNP rs266729 (C>G) had elevated plasma adiponectin concentrations when compared to PE patients with CC genotype and to HP women with the CG + GG genotype, while PE patients carrying the TT genotype for SNP rs2241766 (T>G) presented higher adiponectin levels when compared to the same genotype in HP women; and (5) the ‘G,T,G’ haplotype presented higher adiponectin levels in PE patients when compared to HP women with the same haplotype.
Adiponectin is a protein produced primarily by adipocytes, and has an influence upon metabolism homeostasis, inflammation resolution, and atherosclerosis prevention (; ). During normal pregnancy and in patients with normal BMI, plasma adiponectin levels decrease as the pregnancy progresses, especially after the mid-term gestation (). Although circulating adiponectin may be expected to be lower in PE due to its anti-inflammatory effects (; ), its concentration trends diverge greatly among studies, with some reporting low levels (; ) and others high levels during PE (). Despite these conflicting results, these studies suggest a role for adiponectin in the pathophysiology of PE, and our findings of higher circulating adiponectin levels in PE support this concept. Differences found in the results of these studies may be attributed to characteristics of the included population, including ethnicity, lifestyle, and gestational age at sampling. In addition to these factors, we quantified total adiponectin concentration in plasma. However, adiponectin has three different isoforms, being the high molecular weight (HMW) isoform considered the active form (). Considering this, further studies are needed to clarify these different findings regarding circulating adiponectin levels in PE.
We found one previous association study on PE and GH that investigated the SNPs rs266729, rs2241766, and rs1501299. This study reported that rs266729 was associated with PE () Other studies also found associations for rs2241766 and rs1501299 (; ). rs1501299 was linked to protection against PE and to changes in serum adiponectin levels. However, no causal relationship was identified between genetically predicted serum adiponectin levels and the risk of PE (). However, the present study considered the codominant and dominant models in the association analysis for genotypes of SNPs rs266729, rs2241766 and rs1501299 with HDP. Noteworthy, when considering the codominant model for the genotypic distribution of SNP rs266729, we found the CG genotype to be more frequent in HP, as a possible protective factor against PE, and the GG genotype as a risk factor of GH. Besides the single-locus analysis, our logistic regression models provided evidence that the association of the CG genotype with PE and the GG genotype with GH for the SNP rs266729 occurred even in the presence of BMI and maternal age as confounding variables. Beyond HDP, the G allele of the SNP rs266729 has been associated with an increased risk of type 2 diabetes mellitus and uncontrolled chronic hypertension (; ). While these findings suggest that carriers of the G allele have an unfavorable outcome, in the present study we found that C and G allelic frequencies were statistically similar in PE or GH versus HP. These discordant findings highlight that further replication association studies in different populations are warranted to confirm our observations.
The examination of LD among the three ADIPOQ SNPs for each group revealed that SNP rs266729 is in equilibrium with the other SNPs analyzed in this study, whereas a short segment of higher LD between SNPs rs2241766 and rs1501299 was found in HP when compared to PE and GH. In contrast, when assessing LD of these same SNPs in populations from the 1000 Genomes project, we found high LD blocks, particularly in Asian populations (CHB and JPT). These findings highlight the need to evaluate these SNPs in future studies involving cohorts from diverse ancestries for the validation of the SNP rs266729 as a genetic marker for HDP ().
Regarding the haplotypes, the ‘C,T,T’ haplotype was more frequent in HP than in patients with GH, and may be a possible protective factor against GH. Previous studies focused on haplotypes formed by ADIPOQ SNPs in HDP have found an overrepresentation of the pooled G haplotypes versus the TT haplotype for the rs2241766 and rs1501299, respectively, in PE; however, when four different ADIPOQ SNPs were considered, the association was lost. Nonetheless, the ‘C,T,T’ haplotype has been associated with risk to metabolic syndrome in a cohort of Sudanese patients (), highlighting these ADIPOQ SNPs as possible targets in further studies.
We observed that PE patients carrier of the G allele (GC + GG genotypes) for SNP rs266729 had higher circulating adiponectin levels when compared to PE patients with the CC genotype, suggesting that this SNP may play a potential role in regulating adiponectin levels in PE. Although previous studies have indicated that serum adiponectin levels are lower in patients with the GG genotype than those with TG + TT genotype for SNP rs1501299 in severe PE, we did not find differences in plasma adiponectin levels between carriers of these genotypes in PE. ADIPOQ SNPs have also been shown to affect circulating adiponectin levels in other disease settings. For example, in a subgroup of Chinese subjects, carriers of rs266729 variants were found at high risk of dyslipidemia, atherosclerosis, and coronary artery disease, being the G allele associated with decreased adiponectin (). This may be explained by intrinsic differences between studied populations, but it also could be explained by the allelic and loci heterogeneity (), lifestyle differences between subjects, and also the methodology used to measure circulating adiponectin levels.
According to its 1f score in RegulomeDB and ENCODE gene regulation data (Supplementary Figure S3), the rs266729 may affect the binding of transcription factors in the ADIPOQ promoter region. Moreover, MethPrimer analysis revealed that the C to G change at SNP rs266729 position (CpG site) creates a potential methylation site (data not shown). Since DNA methylation in the promoter region is linked to gene inactivation or reduced expression (), this result contrasts with our finding that carriers of the G allele showed higher adiponectin levels in PE. However, it is important to consider that methylation can be altered in pathological conditions such as HDP (). Thus, future studies need to confirm if this CpG site at the ADIPOQ locus is indeed methylated in PE.
Furthermore, we noted that PE patients carriers of the ‘G,T,G’ haplotype had higher circulating adiponectin levels when compared to HP women with the same haplotype. While previous studies in cardiovascular and metabolic diseases have linked specific ADIPOQ haplotypes to altered circulating adiponectin levels (), their role in HDP remains underexplored. Our findings suggest that, despite inconsistencies across populations, rs266729 SNP may influence plasma adiponectin modulation in PE, potentially reflecting a specific effect distinct from other metabolic or cardiovascular phenotypes.
Finally, the analysis of upper and lower subgroups of plasma adiponectin levels revealed that the ‘G,T,G’ haplotype was more frequent in the subgroup of higher adiponectin levels in PE. However, the ‘G,T,G’ haplotype was not associated with PE or GH in the overall haplotype distribution. As aforementioned, the ‘C,T,T’ haplotype was more frequent in HP than GH. Consistent with this finding, the ‘C,T,T’ haplotype was more frequent in the lower subgroup of adiponectin levels in PE. Taken together, we suggest that this haplotype may be associated with adiponectin levels and the outcome of HDP. Nonetheless, further studies are needed to explore this hypothesis. These findings are also aligned with the proposal that comparing extremes of phenotype distribution improves our abilities to find genetic contributions to phenotypes (; ).
This study has limitations. First, the number of patients included in the study was relatively small, with an even smaller subset having plasma samples available to measure adiponectin levels, which limits the statistical power for this subgroup analysis on both adjusted and unadjusted analysis. Nevertheless, we identified significant associations involving genotypes and haplotypes of ADIPOQ with PE and GH, especially involving SNP rs266729. Furthermore, we did not assess adiponectin levels in placental or adipose tissues, leaving the extent of the SNPs effects at the tissue level unknown. In conclusion, we found that the GG genotype for the ADIPOQ SNP rs266729 (C>G) was associated with risk for GH, whereas the CG genotype and the ‘C,T,T’ haplotype may protect against PE and GH, respectively. In addition, CG and GG genotypes for the same SNP as well as the ‘G,T,G’ haplotype showed higher plasma adiponectin levels in PE subjects.
5 Conclusion
In this study, we provide evidence that the ADIPOQ genotypes and haplotypes studied may affect circulating adiponectin in PE and affect the susceptibility to GH or PE. Regarding rs266729 (C>G) SNP, the GG genotype was associated with risk for GH, and the CG genotype may protect against PE. PE patients carrying the CG and GG genotypes showed higher adiponectin levels than their CC counterparts. Regarding haplotypes formed by three ADIPOQ SNPs, the ‘G,T,G’ haplotype showed higher adiponectin levels in PE than in HP, and the ‘C,T,T’ haplotype may protect against GH. These findings suggest that ADIPOQ polymorphisms influence circulating adiponectin levels and an association with susceptibility for development of GH and PE.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Research Ethics Committee of the Hospital das ClÃnicas of FMRP/USP. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
RF: Writing – review and editing, Investigation, Writing – original draft, Formal Analysis, Data curation, Methodology. DP: Data curation, Formal Analysis, Methodology, Writing – review and editing, Investigation, Writing – original draft. JD: Methodology, Writing – original draft, Investigation, Writing – review and editing. AP: Investigation, Writing – review and editing, Conceptualization, Methodology, Writing – original draft. JR: Investigation, Writing – review and editing, Writing – original draft, Methodology. RC: Writing – original draft, Funding acquisition, Writing – review and editing, Project administration. JT-S: Project administration, Funding acquisition, Writing – original draft, Writing – review and editing. VS: Writing – review and editing, Funding acquisition, Writing – original draft, Project administration, Investigation, Supervision. ML: Methodology, Conceptualization, Supervision, Writing – original draft, Investigation, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study was funded by the São Paulo Research Foundation (grants #2019/07230-8 and #2021/12010-7), and Coordination for the Improvement of Higher Education Personnel (Finance Code 001). DAP was supported by a postdoctoral fellowship from the Universidade Estadual Paulista (Edital PROPE 05/2024, No. Projeto 5471), and a postdoctoral fellowship from the Fundação de Amparo à Pesquisa do Estado de São Paulo (2025/05516-2). JdeOC was supported by a postdoctoral fellowship from the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/CNPq Nº 01/2023, DCR Nº 0007/2023). ACP is supported by the National Institutes of Health (grants #K01HL159032 and #R01HL148191). MRL and VCS are supported by scholarships from the Conselho Nacional de Desenvolvimento CientÃfico e Tecnológico (grants #314486/2023-2 and #308504/2021-6).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2025.1736993/full#supplementary-material
References
1
1000 Genomes Project ConsortiumAbecasisG. R.AltshulerD.AutonA.BrooksL. D.DurbinR. M.et al (2010). A map of human genome variation from population-scale sequencing. Nature467 (7319), 1061–1073. 10.1038/nature09534
2
ACOG Gestational Hypertension and Preeclampsia: ACOG Practice Bulletin Number 222 (2020). Obstet. Gynecol.135 (6), e237–e260. 10.1097/AOG.0000000000003891
3
BarrettJ. C.FryB.MallerJ.DalyM. J. (2005). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics21 (2), 263–265. 10.1093/bioinformatics/bth457
4
BawahA. T.YeboahF. A.NangaS.AliduH.NgalaR. A. (2020). Serum adipocytokines and adiposity as predictive indices of preeclampsia. Clin. Hypertens.26, 19. 10.1186/s40885-020-00152-0
5
CatalanoP. M.HoeghM.MiniumJ.Huston-PresleyL.BernardS.KalhanS.et al (2006). Adiponectin in human pregnancy: implications for regulation of glucose and lipid metabolism. Diabetologia49 (7), 1677–1685. 10.1007/s00125-006-0264-x
6
CeronC. S.LuizonM. R.PaleiA. C. (2023). The potential role of visfatin in mediating vascular dysfunction and hypertension. J. Cardiovasc. Pharmacol.82 (5), 347–349. 10.1097/FJC.0000000000001457
7
ChenX.LiuZ.CuiJ.ChenX.XiongJ.ZhouW. (2022). Circulating adipokine levels and preeclampsia: a bidirectional Mendelian randomization study. Front. Genet.13, 935757. 10.3389/fgene.2022.935757
8
CunhaW. R.AgostiniL. C.Dos MoreiraW. M.M TeixeiraL. F.VitalW. C.da SilvaG. N.et al (2023). Effects of ADIPOQ and NOS3 SNPs/haplotypes on blood pressure control in patients with adherence to antihypertensive therapy. Pharmacogenomics24 (5), 269–281. 10.2217/pgs-2023-0012
9
DengF.LeiJ.QiuJ.ZhaoC.WangX.LiM.et al (2024). DNA methylation landscape in pregnancy-induced hypertension: progress and challenges. Reprod. Biol. Endocrinol.22 (1), 77. 10.1186/s12958-024-01248-0
10
EleuterioN. M.PaleiA. C. T.Rangel MachadoJ. S.Tanus-SantosJ. E.CavalliR. C.SandrimV. C. (2013). Relationship between adiponectin and nitrite in healthy and preeclampsia pregnancies. Clin. Chim. Acta423, 112–115. 10.1016/j.cca.2013.04.027
11
FolkD. M. (2018). Hypertensive disorders of pregnancy: overview and current recommendations. J. Midwifery Womens Health63 (3), 289–300. 10.1111/jmwh.12725
12
FuglsangJ.SkjaerbaekC.FrystykJ.FlyvbjergA.OvesenP. (2006). A longitudinal study of serum adiponectin during normal pregnancy. BJOG113 (1), 110–113. 10.1111/j.1471-0528.2005.00792.x
13
GaudermanW. J. (2002). Sample size requirements for matched case-control studies of gene-environment interaction. Stat. Med.21 (1), 35–50. 10.1002/sim.973
14
GoldsteinB. J.ScaliaR. G.MaX. L. (2009). Protective vascular and myocardial effects of adiponectin. Nat. Clin. Pract. Cardiovasc. Med.6 (1), 27–35. 10.1038/ncpcardio1398
15
GreenbergM. V. C.Bourc'hisD. (2019). The diverse roles of DNA methylation in mammalian development and disease. Nat. Rev. Mol. Cell Biol.20 (10), 590–607. 10.1038/s41580-019-0159-6
16
HendlerI.BlackwellS. C.MehtaS. H.WhittyJ. E.RussellE.SorokinY.et al (2005). The levels of leptin, adiponectin, and resistin in normal weight, overweight, and obese pregnant women with and without preeclampsia. Am. J. Obstet. Gynecol.193 (3 Pt 2), 979–983. 10.1016/j.ajog.2005.06.041
17
KadowakiT.YamauchiT. (2005). Adiponectin and adiponectin receptors. Endocr. Rev.26 (3), 439–451. 10.1210/er.2005-0005
18
LuizonM. R.BeloV. A.PaleiA. C.AmaralL. M.LacchiniR.SandrimV. C.et al (2015). Effects of NAMPT polymorphisms and haplotypes on circulating visfatin/NAMPT levels in hypertensive disorders of pregnancy. Hypertens. Res.38 (5), 361–366. 10.1038/hr.2015.15
19
MachadoJ. S.PaleiA. C. T.AmaralL. M.BuenoA. C.AntoniniS. R.DuarteG.et al (2014). Polymorphisms of the adiponectin gene in gestational hypertension and pre-eclampsia. J. Hum. Hypertens.28 (2), 128–132. 10.1038/jhh.2013.53
20
MageeL. A.BrownM. A.HallD. R.GupteS.HennessyA.KarumanchiS. A.et al (2022). The 2021 International society for the study of hypertension in pregnancy classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens.27, 148–169. 10.1016/j.preghy.2021.09.008
21
MaynardS. E.VenkateshaS.ThadhaniR.KarumanchiS. A. (2005). Soluble Fms-like tyrosine kinase 1 and endothelial dysfunction in the pathogenesis of preeclampsia. Pediatr. Res.57 (5 Pt 2), 1R–7R. 10.1203/01.PDR.0000159567.85157.B7
22
McDonaldE. A.WolfeM. W. (2009). Adiponectin attenuation of endocrine function within human term trophoblast cells. Endocrinology150 (9), 4358–4365. 10.1210/en.2009-0058
23
MeekinsJ. W.PijnenborgR.HanssensM.McFadyenI. R.van AssheA. (1994). A study of placental bed spiral arteries and trophoblast invasion in normal and severe pre-eclamptic pregnancies. Br. J. Obstet. Gynaecol.101 (8), 669–674. 10.1111/j.1471-0528.1994.tb13182.x
24
MetzgerI. F.LuizonM. R.LacchiniR.IshizawaM. H.Tanus-SantosJ. E. (2013). Effects of endothelial nitric oxide synthase tagSNPs haplotypes on nitrite levels in black subjects. Nitric Oxide28, 33–38. 10.1016/j.niox.2012.10.002
25
MiehleK.StepanH.FasshauerM. (2012). Leptin, adiponectin and other adipokines in gestational diabetes mellitus and pre-eclampsia. Clin. Endocrinol. (Oxf)76 (1), 2–11. 10.1111/j.1365-2265.2011.04234.x
26
MoriT.ShinoharaK.WakatsukiA.WatanabeK.FujimakiA. (2010). Adipocytokines and endothelial function in preeclamptic women. Hypertens. Res.33 (3), 250–254. 10.1038/hr.2009.222
27
MosadA. S.ElfadilG. A.GassoumA.ElaminK. M.HusainN. E. O. S. A. (2023). Adiponectin gene polymorphisms and possible susceptibility to metabolic syndrome among the Sudanese population: a case-control Study. Int. J. Endocrinol.2023, 5527963. 10.1155/2023/5527963
28
MotulskyH. J.BrownR. E. (2006). Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinforma.7, 123. 10.1186/1471-2105-7-123
29
NebertD. W. (2000). Extreme discordant phenotype methodology: an intuitive approach to clinical pharmacogenetics. Eur. J. Pharmacol.410 (2-3), 107–120. 10.1016/s0014-2999(00)00809-8
30
NgK. W.ChaturvediN.CotéG. L.FisherS. A.MabbottS. (2024). Biomarkers and point of care screening approaches for the management of preeclampsia. Commun. Med. (Lond)4 (1), 208. 10.1038/s43856-024-00642-4
31
PileggiS.BarleraS.NicolisE.CrociatiL.PietriS.SpecchiaC.et al (2014). Association of ADIPOQ variants and heart failure in an Italian population. Ther. Adv. Cardiovasc. Dis.8 (3), 89–96. 10.1177/1753944714531063
32
RoussetF. (2008). genepop'007: a complete re-implementation of the genepop software for windows and linux. Mol. Ecol. Resour.8 (1), 103–106. 10.1111/j.1471-8286.2007.01931.x
33
SaarelaT.HiltunenM.HelisalmiS.HeinonenS.LaaksoM. (2006). Adiponectin gene haplotype is associated with preeclampsia. Genet. Test.10 (1), 35–39. 10.1089/gte.2006.10.35
34
SarsaniV.BrotmanS. M.XianyongY.Fernandes SilvaL.LaaksoM.SpracklenC. N. (2024). A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin. HGG Adv.5 (1), 100252. 10.1016/j.xhgg.2023.100252
35
SchaidD. J.RowlandC. M.TinesD. E.JacobsonR. M.PolandG. A. (2002). Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am. J. Hum. Genet.70 (2), 425–434. 10.1086/338688
36
SchererP. E.WilliamsS.FoglianoM.BaldiniG.LodishH. F. (1995). A novel serum protein similar to C1q, produced exclusively in adipocytes. J. Biol. Chem.270 (45), 26746–26749. 10.1074/jbc.270.45.26746
37
SmetnevS.KlimushinaM.KutsenkoV.KiselevaA.GumanovaN.KotsA.et al (2019). Associations of SNPs of the ADIPOQ gene with serum Adiponectin levels, unstable angina, and coronary artery disease. Biomolecules9 (10), 537. 10.3390/biom9100537
38
SongY.GaoJ.QuY.WangS.WangX.LiuJ. (2016). Serum levels of leptin, adiponectin and resistin in relation to clinical characteristics in normal pregnancy and preeclampsia. Clin. Chim. Acta458, 133–137. 10.1016/j.cca.2016.04.036
39
SpracklenC. N.IyengarA. K.VadlamudiS.RaulersonC. K.JacksonA. U.BrotmanS. M.et al (2020). Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences. PLoS Genet.16 (9), e1009019. 10.1371/journal.pgen.1009019
40
SpragueA. H.KhalilR. A. (2009). Inflammatory cytokines in vascular dysfunction and vascular disease. Biochem. Pharmacol.78 (6), 539–552. 10.1016/j.bcp.2009.04.029
41
SunK.LiY.WeiC.TongY.ZhengH.GuoY. (2012). Recessive protective effect of ADIPOQ rs1501299 on cardiovascular diseases with type 2 diabetes: a meta-analysis. Mol. Cell Endocrinol.349 (2), 162–169. 10.1016/j.mce.2011.10.001
42
SunP.LiuL.ChenJ.ChenY.ShiL.ImamM. U.et al (2017). The polymorphism of rs266729 in adiponectin gene and type 2 diabetes mellitus: a meta-analysis. Med. Baltim.96 (47), e8745. 10.1097/MD.0000000000008745
43
TomimatsuT.MimuraK.EndoM.KumasawaK.KimuraT. (2017). Pathophysiology of preeclampsia: an angiogenic imbalance and long-lasting systemic vascular dysfunction. Hypertens. Res.40 (4), 305–310. 10.1038/hr.2016.152
44
UmesawaM.KobashiG. (2017). Epidemiology of hypertensive disorders in pregnancy: prevalence, risk factors, predictors and prognosis. Hypertens. Res.40 (3), 213–220. 10.1038/hr.2016.126
45
WangG.WangY.LuoZ. (2022). Effect of adiponectin variant on lipid profile and plasma adiponectin levels: a multicenter systematic review and meta-analysis. Cardiovasc. Ther.2022, 4395266. 10.1155/2022/4395266
46
Wittke-ThompsonJ. K.PluzhnikovA.CoxN. J. (2005). Rational inferences about departures from hardy-weinberg equilibrium. Am. J. Hum. Genet.76 (6), 967–986. 10.1086/430507
47
WuZ. J.ChengY. J.GuW. J.AungL. H. H. (2014). Adiponectin is associated with increased mortality in patients with already established cardiovascular disease: a systematic review and meta-analysis. Metabolism63 (9), 1157–1166. 10.1016/j.metabol.2014.05.001
48
YangL.LiB.ZhaoY.ZhangZ. (2019). Prognostic value of adiponectin level in patients with coronary artery disease: a systematic review and meta-analysis. Lipids Health Dis.18 (1), 227. 10.1186/s12944-019-1168-3
49
YoupengB.WeiX.WeiL.JinJ.HaiyanY.YuanY.et al (2010). Relationships among adiponectin gene polymorphisms, proteinuria and increased blood pressure in the context of placental diseases. Hypertens. Res.33 (10), 1066–1070. 10.1038/hr.2010.134
Summary
Keywords
adiponectin, ADIPOQ, biomarkers, genetic polymorphisms, gestational hypertension, haplotypes, preeclampsia
Citation
Fróes de Castro RN, Pereira DA, de Oliveira Cruz J, Palei AC, Rangel Machado JS, Cavalli RC, Tanus-Santos JE, Sandrim VC and Luizon MR (2026) ADIPOQ polymorphisms and haplotypes affect circulating adiponectin levels and their association with gestational hypertension and preeclampsia. Front. Physiol. 16:1736993. doi: 10.3389/fphys.2025.1736993
Received
31 October 2025
Revised
14 December 2025
Accepted
22 December 2025
Published
12 January 2026
Volume
16 - 2025
Edited by
Francesco Moccia, University of Molise, Italy
Reviewed by
Gayathry Krishnamurthy, University of Tasmania, Australia
Khin Ei Ei Saw, University of Medicine, Taunggyi, Myanmar
Updates
Copyright
© 2026 Fróes de Castro, Pereira, de Oliveira Cruz, Palei, Rangel Machado, Cavalli, Tanus-Santos, Sandrim and Luizon.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Marcelo Rizzatti Luizon, mrluizon@ufmg.br
†These authors have contributed equally to this work and share first authorship
‡ These authors have contributed equally to this work and share senior authorship
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.