Abstract
Aim:
Menopause associated estrogen deficiency affects bone metabolism and may increase peri implant bone resorption. Genetic polymorphisms in inflammatory and bone related genes may further influence implant outcomes. This study evaluated the association between gene polymorphisms and peri implant early crestal bone loss (ECBL) in premenopausal and postmenopausal females.
Setting and Design:
This was a prospective observational clinical study conducted at a tertiary care government institution.
Materials and Methods:
The study included 146 female patients (73 premenopausal and 73 postmenopausal), each receiving a single mandibular posterior dental implant. Menopausal status was confirmed using medical history and serum estradiol quantification. A standardized two stage protocol was followed for implant placement. Cone beam computed tomography based measurements of ECBL were performed at baseline and 6 months. Genotyping of interleukin 1 alpha (IL1A 889), interleukin 1 beta (IL1B 3954) and IL1B 511, IL6 572, collagen type I alpha 1 (COL1A1), and bone gamma carboxyglutamate protein (BGP) (osteocalcin) polymorphisms was performed using real time PCR.
Statistical Analysis Used:
Data were analyzed using IBM SPSS Statistics software (version 26.0). Chi-square test, unpaired t-test, Mann–Whitney U test, and univariate binary logistic regression analysis were applied.
Results:
Final analysis included 141 women (69 premenopausal and 72 postmenopausal). Postmenopausal females had significantly higher body mass index and lower bone mineral density (P < 0.05). Mean ECBL was significantly greater in postmenopausal females (1.20 ± 0.78 mm) than in premenopausal females (0.65 ± 0.51 mm; P < 0.001), with critical ECBL (>0.5 mm) observed in 84.7% and 44.9% of participants, respectively. No genetic associations were identified in premenopausal females. In postmenopausal females, IL1A 889 AG (odds ratio (OR) = 7.75; P = 0.044) and IL6 572 CC genotypes (OR = 8.96; P = 0.045) were significantly associated with critical ECBL.
Conclusion:
Postmenopausal females exhibited significantly greater ECBL around dental implants, and IL1A 889 and IL6 572 polymorphisms showed a significant association with increased ECBL. These findings highlighted the influence of IL1A 889 and IL6 572 gene polymorphisms on early peri implant bone remodeling in postmenopausal females.
Keywords: Dental implants, early crestal bone loss, genetic polymorphisms, menopause
INTRODUCTION
Hormonal status, particularly in women, plays a crucial role in bone metabolism.[1] The transition to menopause involves a marked decline in estrogen levels, which contribute significantly to maintaining bone homeostasis. Estrogen suppresses osteoclast-mediated bone resorption while promoting osteoblastic activity, thereby preserving bone mass. In postmenopausal women, this hormonal shift results in reduced bone mineral density (BMD), systemic bone loss, and a higher risk of osteoporosis.[2] Importantly, this decline in bone quality and quantity is not limited to skeletal sites but may also affect oral and maxillofacial structures, including peri-implant bone.[3,4,5]
While dental implants are a predictable treatment option for tooth replacement, their long-term success depends critically on the preservation of peri-implant bone, particularly the crestal bone.[6] Although minor bone remodeling is expected postplacement, early or excessive crestal bone loss (CBL) can indicate underlying biological or systemic risk factors.[7] Recent evidence indicates that postmenopausal women are more susceptible to peri-implant bone resorption,[8,9] and lower estradiol levels have been linked to increased alveolar ridge resorption, higher rates of prosthetic complications, and reduced prosthesis satisfaction, highlighting the need to account for menopausal status during implant treatment planning.[10,11]
Several factors, including surgical technique, implant design, loading protocol, bone quality, and systemic health, have been associated with CBL.[12] Nevertheless, individual variation in bone loss persists even when these variables are controlled, suggesting a potential genetic component in early peri-implant bone remodeling.[13,14] Single-nucleotide polymorphisms (SNPs) in genes regulating inflammation and bone metabolism, such as interleukin-1 alpha (IL1A), interleukin-1 beta (IL1B), interleukin-6 (IL6), collagen type I alpha 1 (COL1A1), and bone gamma-carboxyglutamate protein (BGP), also known as osteocalcin, may influence the host response to implants.[15,16,17] These proteins are central to the inflammatory cascade and bone remodeling, both of which are vital to implant healing and osseointegration.
In postmenopausal women, estrogen deficiency may synergize with pro-inflammatory gene variants, intensifying peri-implant bone resorption. Notably, the IL1B-511 (rs16944) AA genotype and IL6-572 (rs1800796) GG genotype have been associated with elevated cytokine levels and increased early crestal bone loss.[16,18] The interaction between hormonal status and genetic predisposition may therefore compound the risk of implant failure in this population.
Despite growing interest in these mechanisms, few studies have examined the combined relationship between menopausal status and genetic polymorphisms in relation to crestal bone changes following implant placement. A deeper understanding of this interplay is crucial for identifying high-risk individuals and optimizing personalized implant protocols, especially for postmenopausal women. Therefore, this study aimed to evaluate the association between IL1A, IL1B, IL6, COL1A1, and BGP gene polymorphisms and early crestal bone loss around submerged implants in premenopausal and postmenopausal women. The null hypothesis stated that there would be no significant association between gene polymorphisms and peri-implant early crestal bone loss (ECBL) in premenopausal and postmenopausal women.
MATERIALS AND METHODS
This prospective observational clinical study was carried out in the Department of Prosthodontics in collaboration with the Departments of Molecular Biology and Obstetrics and Gynecology, at a tertiary care government institution. Ethical approval was obtained from the Institutional Ethics Committee (reg. no. ECR/262/Ins/UP/2013) before initiation of the study, which was conducted in accordance with the Declaration of Helsinki, 2000, and Good Clinical Practice standards. Informed written consent was secured from all participants following a detailed explanation of the study purpose, methodology, and possible risks.
Female participants aged 18–65 years with a single missing mandibular posterior tooth scheduled for implant placement were recruited in this study. The sample size was determined using the formula n = 2 (Zα/2 + Zβ)2 σ2/δ2, with calculations performed using G*Power software (version 3.1, Düsseldorf, North Rhine-Westphalia, Germany). Based on Wagner et al.,[10] assuming α = 0.05 and a power = 90% with an anticipated follow-up loss of 10%, a total of 73 participants were required in each group. Participants were selected according to predefined criteria into two groups: Group I: 73 premenopausal females, and Group II: 73 postmenopausal females. Inclusion criteria for the study included healed extraction sites (a minimum of 6 months postextraction), adequate bone volume (≥11 mm height, ≥8 mm width), mucosal thickness of ≥2 mm, a minimum of 2 mm of keratinized gingiva at the site of implant placement, nonsmokers, and individuals with good oral hygiene. Participants with any systemic condition influencing bone metabolism (for example, osteoporosis, uncontrolled diabetes, or hyperparathyroidism), a history of radiotherapy, chemotherapy, bisphosphonate use, or those currently undergoing hormone replacement therapy were excluded from the study. Individuals with genetic disorders, active infection or pathology at the implant site, and those with poor oral hygiene were also excluded to control confounding variables [Figure 1].
Figure 1.
Study flow chart summarizing methodology
Menopausal status was determined primarily by clinical history and confirmed using serum estradiol levels, in accordance with international recommendations for research settings. Participants were classified as premenopausal if serum estradiol levels were ≥ 30 pg/mL, and as postmenopausal if levels were < 30 pg/mL. To minimize hormonal variation, blood samples from premenopausal females were collected during the mid-menstrual cycle, when estradiol levels are relatively stable; this was not required for postmenopausal females, as their hormone levels remain consistently low with no cyclical variation. Estradiol quantification was carried out using an automated chemiluminescence analyzer (Elecsys 2010; Roche Diagnostics, Germany), which is a validated method for hormonal profiling.
Demographic and clinical variables were recorded for both groups, which included age, body mass index (BMI), bone quality at implant sites,[19] and BMD with corresponding T-scores of the right femoral neck (evaluated using dual-energy X-ray absorptiometry). According to World Health Organization (WHO) recommendations, T-score-based classification divided participants into: osteoporosis (≤–2.5), osteopenia (between − 1.0 and − 2.5), and normal (≥–1.0).[20,21]
Dental implants (Myriad Plus; Equinox Medical Technologies, Netherlands) were placed using a standardized two-stage surgical protocol.[22] Following local anesthesia, a mid-crestal incision with crestal flap elevation was performed. Implant osteotomies were prepared according to the manufacturer’s drilling protocol under copious irrigation. In accordance with bone availability and the manufacturer’s guidelines, implants with a diameter of 4.5 mm and lengths ranging from 9.5–11 mm were inserted with a primary stability of ≥30 Ncm. No bone augmentation or regenerative procedures were performed at the time of placement to avoid variability in healing outcomes. Cover screws were placed, and the flap was sutured with 3-0 silk sutures. A healing period of 6 months was allowed before second-stage surgery and prosthetic loading. After the healing period, implants were restored with zirconia crowns using a delayed loading protocol following implant protected occlusion.
Cone-beam computed tomography (CBCT) scans were obtained at two time points: immediately following implant placement (baseline) and at 6 months postoperatively, prior to second-stage surgery. The digital imaging and communication in medicine files were analyzed using CS 3D Imaging Software (Carestream Dental), and measurements were made in four planes (buccal, lingual, mesial, and distal) [Figure 2]. Two blinded examiners independently evaluated the images for the position of the first bone-to-implant contact. Measurements were taken from the implant platform to the alveolar crest at each site.[23,24] In cases of discrepancy exceeding 0.1 mm between observers, a consensus was reached through joint evaluation. Inter-observer reliability was evaluated using intraclass correlation coefficients (ICC), revealing excellent agreement (ICC > 0.90). The mean difference in bone levels across the four surfaces from baseline to 6 months was used to assess early crestal bone loss (ECBL), which was defined as the primary outcome measure. For each participant, the mean bone loss was considered for the analysis.
Figure 2.
Bone level measurement around a dental implant. (a) Buccal and lingual side, (b) Mesial and distal side
Peripheral venous blood samples (5 mL) were collected and preserved in EDTA vials for genomic evaluation. Genomic deoxyribonucleic acid extraction was carried out using a commercially available isolation kit (QIamp DNA Mini Kit; Qiagen, Hilden, Germany), and its concentration and purity were assessed using 0.8% agarose gel electrophoresis, along with spectrophotometric analysis (Quawell; Quawell Technology Inc) and fluorimetric evaluation (QbitBR; Agilent). Samples with an A260/A280 ratio of ≥1.8 were considered suitable for polymerase chain reaction (PCR) analysis. SNP genotyping for the rs1800587, rs16944, rs1143634, rs1800796, rs1800012, and rs1800247 was performed using TaqMan real-time PCR assays (Applied Biosystems; Thermo Fisher Scientific), employing allele-specific minor groove binder (MGB) probes labeled with FAM and VIC dyes for allelic discrimination in a 40×TaqMan SNP genotyping assay mix (Applied Biosystems; Thermo Fisher Scientific) and 20 ng of specific genomic DNA. The universal thermal cycling was performed according to the recommended protocol (Applied Biosystems; Thermo Fisher Scientific) with each sample analyzed in triplicate along with negative controls to ensure genotyping accuracy and reproducibility. The raw data generated using the step one plus real-time PCR system (Applied Biosystems; Thermo Fisher Scientific) were analyzed using a TaqMan genotyper software program (Applied Biosystems; Thermo Fisher Scientific) to determine allele sequences. The genotype call was evaluated using a threshold quality value = 0.94).[25]
All data, including demographic characteristics, CBCT bone measurements, and genotyping results, were compiled and analyzed using IBM SPSS Statistics software, v26.0 (IBM Corp., Armonk, NY, USA).[26] Continuous variables were expressed as mean ± standard deviation. Intergroup comparisons were performed using the unpaired t-test for normally distributed variables and the Mann–Whitney U test for nonnormally distributed variables. ECBL was dichotomized into two categories: ≤0.5 mm (noncritical bone loss) and >0.5 mm (critical bone loss). Univariate binary logistic regression analysis was employed to evaluate the association between genetic polymorphisms and the two ECBL categories in premenopausal and postmenopausal groups. A P ≤ 0.05 was considered statistically significant.
RESULTS
The study population consisted of Indian females seeking implant therapy for single posterior mandibular tooth replacement. A total of five participants were lost to follow-up (four did not come back, and one implant failure). Finally, 141 participants were included in the analysis, comprising 69 participants in Group I and 72 participants in Group II. The mean age was 33.72 ± 11.66 years in the premenopausal group and 56.49 ± 6.77 years in the postmenopausal group (P < 0.001). Postmenopausal females demonstrated significantly elevated BMI values (26.61 ± 3.88 kg/m2) compared with premenopausal females (22.14 ± 4.52 kg/m2; P < 0.001). BMD values were lower in the postmenopausal group (0.86 ± 0.12 g/cm2) than in the premenopausal group (0.92 ± 0.13 g/cm2; P = 0.018). Although postmenopausal females exhibited lower mean T-scores (−1.16 ± 0.92) than premenopausal females (−0.76 ± 0.91), this difference was not statistical significance (P = 0.057). Based on the WHO classification, low BMD (osteopenia or osteoporosis) was observed in 50.7% of postmenopausal and 44.9% of premenopausal females, with no statistically significant difference (P = 0.494). Most implants were placed in type II bone in both groups (89.9% in premenopausal and 76.1% in postmenopausal women). Type III bone was more frequently observed among postmenopausal females (21.1% vs. 8.7%), although this variation was not statistically significant (P = 0.095) [Table 1].
Table 1.
Basic characteristics of participants of Group I and Group II
| Variables | Group I- premenopausal (n=69, n (%) | Group II- postmenopausal (n=72), n (%) | t/Z/χ2 | P |
|---|---|---|---|---|
| Age (years), mean±SD | 33.72±11.66 | 56.49±6.77 | −14.18 | <0.001* |
| Sex - female | 69 (100) | 72 (100) | NA | |
| BMI (kg/m2), mean±SD | 22.14±4.52 | 26.61±3.88 | −6.29 | <0.001* |
| T-score, mean±SD | −0.76±0.91 | −1.16±0.92 | −1.91 | 0.057‡ |
| BMD (g/cm2), mean±SD | 0.92±0.13 | 0.86±0.12 | −2.37 | 0.018‡ |
| BMD category | 0.478 | |||
| Low BMD (osteopenia+osteoporosis) | 31 (44.9) | 36 (50.7) | 0.494† | |
| Normal BMD | 38 (55.1) | 35 (49.3) | ||
| Bone quality | ||||
| I | 1 (1.4) | 2 (2.8) | 4.71 | 0.095† |
| II | 62 (89.9) | 54 (76.1) | ||
| III | 6 (8.7) | 15 (21.1) | ||
| ECBL (mm) | 0.65±0.51 | 1.20±0.78 | −4.78 | <0.001‡ |
*Applied unpaired t-test, †Chi square test, ‡Applied Mann–Whitney U-test. BMI: Body mass index, BMD: Bone mineral density, ECBL: Early crestal bone loss, SD: Standard deviation, NA: Not available
Mean ECBL was significantly greater in postmenopausal females (1.20 ± 0.78 mm) compared with premenopausal females (0.65 ± 0.51 mm; P < 0.001). When ECBL was dichotomized using a clinically relevant threshold of ≤0.5 mm and >0.5 mm, 61 postmenopausal females (84.7%) exhibited ECBL >0.5 mm, compared with 31 premenopausal women (44.9%), indicating a significantly higher prevalence of critical ECBL in the postmenopausal group [Tables 2 and 3]. The genotypic distributions for all studied genes, IL1A-889, IL1B-511, IL1B-3954, IL6-572, COL1A1, and BGP polymorphisms were evaluated. All loci confirmed to the Hardy–Weinberg equilibrium (P > 0.05), except BGP, which showed deviation (χ2 = 9.83; P = 0.002) [Table 2].
Table 2.
Hardy-Weinberg equilibrium analysis of the studied genes
| Gene | Genotype | n (%) | Hardy-Weinberg equilibrium χ2 | P |
|---|---|---|---|---|
| IL1A-889 | AA | 25 (17.9) | 2.97 | 0.085 |
| AG | 56 (40.0) | |||
| GG | 59 (42.1) | |||
| IL1B-511 | AA | 41 (29.3) | 2.65 | 0.104 |
| GA | 78 (55.7) | |||
| GG | 21 (15.0) | |||
| IL1B-3954 | CC | 63 (45.0) | 3.84 | 0.050 |
| CT | 69 (49.3) | |||
| TT | 8 (5.7) | |||
| IL6-572 | CC | 19 (13.6) | 3.58 | 0.058 |
| CG | 51 (36.4) | |||
| GG | 70 (50.0) | |||
| COL1A1 | AC | 32 (22.9) | 2.33 | 0.127 |
| CC | 108 (77.1) | |||
| BGP | CC | 19 (13.6) | 9.83 | 0.002 |
| CT | 42 (30.0) | |||
| TT | 79 (56.4) |
IL: Interleukin, COL1A1: Collagen type 1 alpha 1, BGP: Bone gamma-carboxyglutamate protein
Table 3.
Association of early crestal bone loss with gene frequency of different genetic polymorphisms in premenopausal females (Group I)
| Group I | P | Univariate binary logistic regression | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Gene | Genotype | ECBL | OR (95% CI) | P | ||
|
| ||||||
| ≤0.5, n (%) | >0.5, n(%) | |||||
| IL1A-889 | AA | 6 (15.8) | 10 (32.3) | 0.157 | Reference | |
| AG | 12 (31.6) | 11 (35.5) | 0.55 (0.15–2.02) | 0.368 | ||
| GG | 20 (52.6) | 10 (32.3) | 0.30 (0.085–1.06) | 0.062 | ||
| IL1B-511 | AA | 9 (23.7) | 6 (19.4) | 0.593 | Reference | |
| GA | 20 (52.6) | 20 (64.5) | 1.50 (0.45–5.0) | 0.509 | ||
| GG | 9 (23.7) | 5 (16.1) | 0.833 (0.185–3.75) | 0.812 | ||
| IL1B-3954 | CC | 22 (57.9) | 20 (64.5) | 0.575 | Reference | |
| CT | 16 (42.1) | 11 (35.5) | 0.756 (0.28–2.01) | 0.575 | ||
| IL6-572 | CC | 6 (15.8) | 4 (12.9) | 0.944 | 0.98 (0.34–2.83) | 0.971 |
| GG | 12 (31.6) | 10 (32.3) | 0.784 (0.189–3.247) | 0.738 | ||
| CG | 20 (52.6) | 17 (54.8) | Reference | |||
| COL1A1 | AC | 8 (21.1) | 7 (22.6) | 0.878 | 1.09 (0.35–3.45) | 0.878 |
| CC | 30 (78.9) | 24 (77.4) | Reference | |||
| BGP | CC | 5 (13.2) | 3 (9.7) | 0.850 | 0.743 (0.159–3.47) | 0.706 |
| CT | 7 (18.4) | 7 (22.6) | 1.24 (0.38–4.09) | 0.726 | ||
| TT | 26 (68.4) | 21 (67.7) | Reference | |||
ECBL: Early crestal bone loss, OR: Odds ratio, IL: Interleukin, COL1A1: Collagen type 1 alpha 1, BGP: Bone gamma-carboxyglutamate protein, CI: Confidence interval
Among premenopausal females, no statistically significant associations were observed between ECBL (>0.5 mm) and any of the evaluated genetic polymorphisms on univariate binary logistic regression analysis (P > 0.05 for all comparisons). Although the IL1A-889 GG genotype showed a trend toward lower odds of critical ECBL compared to the AA genotype (OR = 0.30; 95% confidence interval [CI]: 0.085–1.06), this association was not statistically significance (P = 0.062) [Table 3].
In postmenopausal females, the IL1A-889 gene polymorphism demonstrated a statistically significant association with critical ECBL (>0.5 mm). Participants carrying the AG genotype exhibited 7.75-fold higher odds of critical ECBL (>0.5 mm) compared to those with the AA genotype (OR = 7.75; 95% CI: 1.06–56.77; P = 0.044). The GG genotype did not show a statistically significant association (P = 0.309). A significant association was also observed for the IL6-572 polymorphism, where individuals with the CC genotype had significantly higher odds of critical ECBL compared to the CG genotype (OR = 8.96; 95% CI: 1.05–76.74; P = 0.045). No significant association was observed for the GG genotype (P = 0.408). No statistically significant associations were identified between ECBL and IL1B-511, IL1B-3954, COL1A1, or BGP polymorphisms in the postmenopausal group (P > 0.05) [Table 4].
Table 4.
Association of early crestal bone loss with gene frequency of different genetic polymorphisms in postmenopausal females (Group II)
| Group II | P | Univariate binary logistic regression | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| Gene | Genotype | ECBL | ||||
|
| ||||||
| ≤0.5, n (%) | >0.5, n (%) | OR (95% CI) | P | |||
| IL1A-889 | AA | 3 (30.0) | 6 (9.8) | 0.093 | Reference | |
| AG | 2 (20.0) | 31 (50.8) | 7.75 (1.06–56.77) | 0.044 | ||
| GG | 5 (50.0) | 24 (39.3) | 2.40 (0.44–12.98) | 0.309 | ||
| IL1B-511 | AA | 4 (40.0) | 22 (36.1) | 0.969 | Reference | |
| GA | 5 (50.0) | 33 (54.1) | 1.20 (0.29–4.97) | 0.801 | ||
| GG | 1 (10.0) | 6 (9.8) | 1.09 (0.10–11.67) | 0.943 | ||
| IL1B-3954 | CC | 3 (30.0) | 18 (29.5) | 0.462 | Reference | |
| CT | 7 (70.0) | 35 (57.4) | 0.83 (0.19–3.61) | 0.808 | ||
| TT | 0 | 8 (13.1) | 00 | 1.000 | ||
| IL6-572 | CC | 1 (10.0) | 8 (13.1) | 0.061 | 8.96 (1.05–76.74) | 0.045 |
| GG | 1 (10.0) | 28 (45.9) | 2.56 (0.28–23.72) | 0.408 | ||
| CG | 8 (80.0) | 25 (41.0) | Reference | |||
| COL1A1 | AC | 1 (10.0) | 16 (26.2) | 0.265 | 3.2 (0.38–27.29) | 0.287 |
| CC | 9 (90.0) | 45 (73.8) | Reference | |||
| BGP | CC | 2 (20.0) | 9 (14.8) | 0.896 | 0.64 (0.10–4.11) | 0.641 |
| CT | 4 (40.0) | 24 (39.3) | 0.86 (0.19–3.80) | 0.839 | ||
| TT | 4 (40.0) | 28 (45.9) | Reference | |||
ECBL: Early crestal bone loss, OR: Odds ratio, IL: Interleukin, COL1A1: Collagen type 1 alpha 1, BGP: Bone gamma-carboxyglutamate protein, CI: Confidence interval
DISCUSSION
The present investigation evaluated the association between menopausal status, selected genetic polymorphisms, and early crestal bone loss (ECBL) around submerged dental implants, with ECBL defined as the primary biological outcome. The findings partially reject the null hypothesis, demonstrating that menopausal status and certain genetic variants are associated with increased early peri-implant bone loss.
Postmenopausal females demonstrated significantly greater mean ECBL compared with premenopausal females, and a substantially higher proportion exhibited clinically relevant bone loss (>0.5 mm) within the first 6 months following implant placement (85.9% vs. 44.9%). These results align with the observations of Kumar et al.[27] Menopause represents a universal physiological transition associated with profound hormonal changes that affect multiple endocrine and metabolic pathways. The role of estrogen in regulating bone turnover is well established and has been linked to increased osteoclast activity, decreased BMD, and systemic skeletal fragility.[1,2] Postmenopausal osteoporosis developing within 10–15 years following menopause represents the most common subtype of primary osteoporosis. In this condition, the rate of trabecular bone loss is 2–3 times higher than normal, resulting in accelerated bone resorption, altered parathyroid hormone function, and impaired bone regeneration.[8]
Few studies have reported menopause related changes in maxillary and mandibular bone architecture. Stein et al. observed significant differences in trabecular bone volume fraction, thickness, number, and their separation in the maxillary tuberosity and incisive foramen regions between premenopausal and postmenopausal women.[3] Similar findings have been reported in the mandible as well.[4,28] Despite these changes, dental implant placement in postmenopausal women with osteopenia or osteoporosis is not considered contraindicated.[29] However, several longitudinal studies have reported associations between menopausal status and compromised implant outcomes, with postmenopausal women demonstrating reduced marginal bone support and higher complication rates.[3,5,10,30] The greater early bone loss in the postmenopausal cohort may predispose them to long-term complications, emphasizing the need for closer monitoring and tailored maintenance strategies.
Genetic analysis revealed that the IL1A-889 polymorphism was significantly associated with critical ECBL (>0.5 mm) in postmenopausal females, with carriers of the AG genotype exhibiting markedly higher odds of bone loss compared with AA genotype carriers. This association was not observed in premenopausal women, suggesting that the phenotypic expression of IL1A variants may be modulated by estrogen deficiency. IL-1 is a key mediator of inflammatory bone resorption, and enhanced inflammatory responsiveness in estrogen-deficient states may amplify its effects at the bone–implant interface.[25]
A statistically significant association was also observed between the IL6-572 CC genotype and critical ECBL in postmenopausal women. IL-6 plays a central role in osteoclast differentiation and activation, and increased IL-6 signaling has been linked to both systemic osteoporosis and localized bone loss.[25,30,31] The absence of this association in premenopausal women further supports the concept that hormonal status modulates the biological impact of inflammatory gene polymorphisms, rather than indicating a direct gene–hormone interaction.
In contrast, no significant associations were identified between ECBL and IL1B-511, IL1B-3954, COL1A1, or BGP polymorphisms in either group. Although genotype distributions differed between premenopausal and postmenopausal women for certain loci, these differences did not translate into consistent effects on early bone loss. Notably, the BGP polymorphisms deviated from the Hardy–Weinberg equilibrium, and therefore, findings related to this locus should be interpreted cautiously. Possible explanations include population stratification, sampling variability, or selection bias, which may have influenced genotype distribution in the present cohort.
Age and body mass index differed significantly between the study groups and are recognized as independent modifiers of bone metabolism. Although age-related skeletal changes overlap with menopausal effects, the greater ECBL observed in postmenopausal women is biologically plausible given the decline in estrogen-mediated suppression of bone resorption during this phase. In addition, the higher BMI and lower mean BMD observed in postmenopausal participants may have contributed to the increased susceptibility to early peri-implant bone loss, underscoring the multifactorial nature of peri-implant bone remodeling.[32] However, systemic inflammatory markers such as C-reactive protein, interleukins, or tumor necrosis factor-α were not assessed, and their absence may partially explain inter-individual variability in peri-implant bone loss.
Overall, the findings represent associative rather than causal relationships between ECBL, menopausal status, and selected genetic polymorphisms. From a clinical perspective, the findings highlight the importance of risk counseling in postmenopausal women undergoing implant therapy, particularly those with higher BMI and specific inflammatory gene polymorphisms. Such patients may benefit from more frequent maintenance visits and radiographic monitoring during the early healing phase to detect excessive crestal bone loss. However, given the short follow-up period and the observational nature of the study, routine genetic screening or modification of implant protocols based solely on genetic status cannot be recommended and should currently be regarded as investigational.
This study is strengthened by its prospective design, the use of CBCT for precise three-dimensional assessment of peri-implant bone changes, and the incorporation of molecular genetic analysis. Nevertheless, several limitations warrant consideration. The duration of follow-up was limited to 6 months and captured only early remodeling changes, and multivariable regression analysis could not be performed due to limited sample size and the low number of outcome events in relation to the number of predictor variables. Therefore, the findings primarily reflect short-term peri-implant bone dynamics and should be interpreted cautiously when extrapolating to long-term implant success and clinical outcomes. Longer-term prospective studies are required to determine whether these genetic and hormonal associations persist and influence implant survival. Although the sample size was adequate for the primary outcome, larger multicenter studies are necessary to validate genotype associations across diverse populations. In addition, potential confounders such as dietary calcium intake, Vitamin D status, and systemic inflammatory markers were not evaluated, and variations in these factors, particularly in postmenopausal women, may have influenced osteointegration and early bone healing.
Future investigations should be directed toward the longitudinal effects of gene–hormone interactions on implant outcomes and evaluate whether pharmacologic modulation of bone turnover, such as selective estrogen receptor modulators or anti-resorptive therapies, can mitigate peri-implant bone loss in genetically susceptible postmenopausal women. Incorporating genetic profiling into pre-implant risk assessment may ultimately contribute to precision dentistry, enabling improved risk stratification and individualized treatment planning.
CONCLUSION
Postmenopausal females exhibited significantly greater early crestal bone loss compared with premenopausal females evaluated around submerged dental implants. IL1A-889 AG and IL6-572 CC genotypes were associated with higher early crestal bone loss exclusively in postmenopausal females, suggesting that estrogen deficiency may modulate genetic susceptibility to peri-implant bone remodeling.
These findings underscore the combined influence of hormonal status and genetic factors on early peri-implant bone changes. Further long-term studies are required to confirm these associations and to elucidate the clinical relevance of gene–hormone interactions in implant dentistry.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Assisted by Anusandhan National Research Foundation (ANRF).
REFERENCES
- 1.Friedlander AH. The physiology, medical management and oral implications of menopause. J Am Dent Assoc. 2002;133:73–81. doi: 10.14219/jada.archive.2002.0025. [DOI] [PubMed] [Google Scholar]
- 2.August M, Chung K, Chang Y, Glowacki J. Influence of estrogen status on endosseous implant osseointegration. J Oral Maxillofac Surg. 2001;59:1285–9. doi: 10.1053/joms.2001.27515. [DOI] [PubMed] [Google Scholar]
- 3.De Oliveira TR, Dos Santos LG, de Sales TH. Effects of menopause on maxillary bones and dental implant survival. Res Soc Dev. 2021;10:13. [Google Scholar]
- 4.Levit M, Finn T, Sachadava S, Matsumura S, Shah J, Cantos A, et al. Menopause-associated changes in mandibular bone microarchitecture are site-specific. J Oral Maxillofac Surg. 2024;82:485–93. doi: 10.1016/j.joms.2024.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Munakata M, Tachikawa N, Honda E, Shiota M, Kasugai S. Influence of menopause on mandibular bone quantity and quality in Japanese women receiving dental implants. Arch Osteoporos. 2011;6:51–7. doi: 10.1007/s11657-011-0058-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Insua A, Galindo-Moreno P, Miron RJ, Wang HL, Monje A. Emerging factors affecting peri-implant bone metabolism. Periodontol 2000. 2024;94:27–78. doi: 10.1111/prd.12532. [DOI] [PubMed] [Google Scholar]
- 7.Hjalmarsson L, Gheisarifar M, Jemt T. A systematic review of survival of single implants as presented in longitudinal studies with a follow-up of at least 10 years. Eur J Oral Implantol. 2016;9(Suppl 1):S155–62. [PubMed] [Google Scholar]
- 8.Baxter JC, Fattore L. Osteoporosis and osseointegration of implants. J Prosthodont. 1993;2:120–5. doi: 10.1111/j.1532-849x.1993.tb00393.x. [DOI] [PubMed] [Google Scholar]
- 9.Seifert-Klauss V, Fillenberg S, Schneider H, Luppa P, Mueller D, Kiechle M. Bone loss in premenopausal, perimenopausal and postmenopausal women: Results of a prospective observational study over 9 years. Climacteric. 2012;15:433–40. doi: 10.3109/13697137.2012.658110. [DOI] [PubMed] [Google Scholar]
- 10.Wagner F, Schuder K, Hof M, Heuberer S, Seemann R, Dvorak G. Does osteoporosis influence the marginal peri-implant bone level in female patients? A cross-sectional study in a matched collective. Clin Implant Dent Relat Res. 2017;19:616–23. doi: 10.1111/cid.12493. [DOI] [PubMed] [Google Scholar]
- 11.Gaetti-Jardim EC, Santiago-Junior JF, Goiato MC, Pellizer EP, Magro-Filho O, Jardim Junior EG. Dental implants in patients with osteoporosis: A clinical reality? J Craniofac Surg. 2011;22:1111–3. doi: 10.1097/SCS.0b013e3182108ec9. [DOI] [PubMed] [Google Scholar]
- 12.Al Amri MD. Crestal bone loss around submerged and nonsubmerged dental implants: A systematic review. J Prosthet Dent. 2016;115:564–70.e1. doi: 10.1016/j.prosdent.2015.11.002. [DOI] [PubMed] [Google Scholar]
- 13.Jeffcoat MK, Chesnut CH., 3rd Systemic osteoporosis and oral bone loss: Evidence shows increased risk factors. J Am Dent Assoc. 1993;124:49–56. doi: 10.14219/jada.archive.1993.0225. [DOI] [PubMed] [Google Scholar]
- 14.Yu F, Huang X, Miao J, Guo L, Tao D. Association between osteoprotegerin genetic variants and osteoporosis in Chinese postmenopausal women. Endocr J. 2013;60:1303–7. doi: 10.1507/endocrj.ej13-0308. [DOI] [PubMed] [Google Scholar]
- 15.Santiago Junior JF, Biguetti CC, Matsumoto MA, Abu Halawa Kudo G, Parra da Silva RB, Pinto Saraiva P, et al. Can genetic factors compromise the success of dental implants? A systematic review and meta-analysis. Genes (Basel) 2018;9:444. doi: 10.3390/genes9090444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Mohammadi H, Roochi MM, Sadeghi M, Garajei A, Heidar H, Meybodi AA, et al. Association between interleukin-1 polymorphisms and susceptibility to dental peri-implant disease: A meta-analysis. Pathogens. 2021;10:1600. doi: 10.3390/pathogens10121600. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Agrawal KK, Singh N, Chand P, Singh SV, Solanki N, Garg RK, et al. Associations among gene polymorphisms, crestal bone loss, and bone mineral density in patients receiving dental implants. J Taibah Univ Med Sci. 2024;19:313–20. doi: 10.1016/j.jtumed.2023.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hong SJ, Kang SW, Kim SK, Kim YS, Ban JY. Lack of association between interleukin-1β gene polymorphism (rs16944) and chronic periodontitis: From a case-control studies to an updated meta-analysis. Dis Markers 2018. 2018:8287026. doi: 10.1155/2018/8287026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lekholm U, Zarb GA. Patient selection and preparation. In: Branemark PI, Zarb GA, Albrektsson T, editors. Tissue Integrated Prostheses: Osseointegration in Clinical Dentistry. Chicago: Quintessence Publishing Company; 1985. pp. 199–209. [Google Scholar]
- 20.Nakamura K, Tsugawa N, Saito T, Ishikawa M, Tsuchiya Y, Hyodo K, et al. Vitamin D status, bone mass, and bone metabolism in home-dwelling postmenopausal Japanese women: Yokogoshi study. Bone. 2008;42:271–7. doi: 10.1016/j.bone.2007.09.056. [DOI] [PubMed] [Google Scholar]
- 21.Garg MK, Kharb S. Dual energy X-ray absorptiometry: Pitfalls in measurement and interpretation of bone mineral density. Indian J Endocrinol Metab. 2013;17:203–10. doi: 10.4103/2230-8210.109659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Agrawal KK, Rao J, Anwar M, Singh K, Himanshu D. Flapless versus flapped implant insertion in patients with controlled type 2 diabetes subjected to delayed loading: 1-year follow-up results from a randomised controlled trial. Eur J Oral Implantol. 2017;10:403–13. [PubMed] [Google Scholar]
- 23.Kehl M, Swierkot K, Mengel R. Three-dimensional measurement of bone loss at implants in patients with periodontal disease. J Periodontol. 2011;82:689–99. doi: 10.1902/jop.2010.100318. [DOI] [PubMed] [Google Scholar]
- 24.Gröbe A, Semmusch J, Schöllchen M, Hanken H, Hahn M, Eichhorn W, et al. Accuracy of bone measurements in the vicinity of titanium implants in CBCT data sets: A comparison of radiological and histological findings in minipigs. Biomed Res Int 2017. 2017:3848207. doi: 10.1155/2017/3848207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Agrawal KK, Chand P, Singh SV, Singh N, Gupta P, Garg RK, et al. Association of interleukin-1, interleukin-6, collagen type I alpha 1, and osteocalcin gene polymorphisms with early crestal bone loss around submerged dental implants: A nested case control study. J Prosthet Dent. 2023;129:425–32. doi: 10.1016/j.prosdent.2021.05.023. [DOI] [PubMed] [Google Scholar]
- 26.IBM Corp. Armonk, NY: IBM Corp; 2019. IBM SPSS Statistics for Windows. Version 26.0. [Google Scholar]
- 27.Kumar D, Hassan S, Ratre MS, Jain S, Gupta RK, Agnihotri NP. Association between age of menopause and thickness of crestal cortical bone at dental implant site: A cross-sectional observational study. J Pharm Bioallied Sci. 2025;17:S1316–8. doi: 10.4103/jpbs.jpbs_2010_24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Walker MD, Liu XS, Zhou B, Agarwal S, Liu G, McMahon DJ, et al. Premenopausal and postmenopausal differences in bone microstructure and mechanical competence in Chinese-American and white women. J Bone Miner Res. 2013;28:1308–18. doi: 10.1002/jbmr.1860. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Zhang CY, DeBaz C, Bhandal G, Alli F, Buencamino Francisco MC, Thacker HL, et al. Buccal bone thickness in the esthetic zone of postmenopausal women: A CBCT analysis. Implant Dent. 2016;25:478–84. doi: 10.1097/ID.0000000000000405. [DOI] [PubMed] [Google Scholar]
- 30.Shao MY, Huang P, Cheng R, Hu T. Interleukin-6 polymorphisms modify the risk of periodontitis: A systematic review and meta-analysis. J Zhejiang Univ Sci B. 2009;10:920–7. doi: 10.1631/jzus.B0920279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Mazurek-Mochol M, Bonsmann T, Mochol M, Poniewierska-Baran A, Pawlik A. The role of interleukin 6 in periodontitis and its complications. Int J Mol Sci. 2024;25:2146. doi: 10.3390/ijms25042146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Avery D, Morandini L, Sheakley L, Alajmi A, Bergey L, Donahue HJ, et al. Obesity prolongs the pro-inflammatory response and attenuates bone healing on titanium implants. Acta Biomater. 2025;192:473–86. doi: 10.1016/j.actbio.2024.11.040. [DOI] [PMC free article] [PubMed] [Google Scholar]