Success Rate of Dental Implants in Patients with Autoimmune Disorders: A Systematic Review and Meta-Analysis

ABSTRACT

Aim:

Autoimmune disorders may affect bone metabolism and tissue healing, potentially influencing dental implant outcomes. This systematic review and meta-analysis aimed to assess implant survival and success in patients with autoimmune diseases, with particular focus on marginal bone loss (MBL) and bleeding on probing (BOP).

Materials and Methods:

A comprehensive search of seven databases (PubMed, MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and CINAHL) was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Fourteen observational studies including 2657 participants were included. Risk of bias was assessed using ROBINS-I and Newcastle–Ottawa scale. Meta-analyses were performed using a random-effects model.

Results:

Implant survival rates ranged from 76.4% to 100% across autoimmune conditions. No significant differences in MBL were observed for patients with type 1 diabetes or Sjögren’s syndrome compared to controls, while rheumatoid arthritis (RA) patients showed significantly increased MBL [MD = 1.60 mm; 95% confidence intervals (CI) = 1.13–2.07; P < 0.00001]. RA patients also had lower odds of BOP (OR = 0.13; 95% CI = 0.05–0.36; P < 0.0001). Overall, autoimmune conditions were associated with reduced BOP (OR = 0.24; 95% CI = 0.06–0.91; P = 0.04).

Conclusion:

Dental implants demonstrate favorable survival in autoimmune patients, although RA and diabetes may increase MBL risk. Tailored treatment planning and follow-up are essential. Further high-quality studies are needed to confirm disease-specific risks.

INTRODUCTION

Dental implants are the gold standard for replacing missing teeth because they provide patients with functional, cosmetic, and psychological benefits. The high success and survival rates are mostly due to advancements in surgical techniques, implant material, and prosthetic design.[1] However, such good results are influenced by a variety of systemic health factors. Autoimmune disorders represent an important category of systemic conditions that may impact implant outcomes: these are characterized by dysregulation of the immune system, resulting in attacks on the body’s own tissues.[2] Autoimmune disorders include RA, systemic lupus erythematosus (SLE), Sjögren’s syndrome (SS), multiple sclerosis, and inflammatory bowel diseases, all of which are distinguished by chronic inflammation and abnormal immune responses.[3] Such disorders may have a significant impact on bone metabolism, tissue repair, and immunological competence. These are essential for the integration of dental implants with bone and their long-term viability.[4]

Osseointegration is the direct structural and functional bond that formed between the living bone and the implant surface of a load-bearing implant. It is the leading determinant of implant stability and success.[5] For osseointegration to occur, a perfect biological interface must be developed, allowing bone biology to interact harmoniously with the implant surface. Autoimmune illnesses frequently result in systemic inflammation and increased bone turnover, as well as the use of immunosuppressive medicines, which can impair bone healing and regeneration.[3,6]

The impact of autoimmune disorders and treatments, such as corticosteroids, disease-modifying anti-rheumatic drugs (DMARDs), and biologics, on bone metabolism and tissue repair processes affects the predictability of dental implant results.[7] Corticosteroids, for example, are highly successful at reducing inflammation, but they impede osteoblast function and enhance osteoclast-mediated bone resorption. This holds true for immunosuppressive medicines, which hinder the host’s capacity to establish an effective healing response and may interfere with dental implant integration.[8,9] These pharmacologic agents, especially corticosteroids, DMARDs, and biologics, modulate immune and inflammatory pathways essential for osseointegration. Corticosteroids impair bone regeneration and healing, DMARDs may suppress angiogenesis and tissue repair, and biologic agents such as TNF-α inhibitors alter cytokine-driven bone turnover. Their cumulative effects can compromise implant stability, healing capacity, and long-term success, making it crucial to interpret implant outcomes in the context of these therapies.[7,8,9]

The increasing prevalence of autoimmune disorders and growing demand for dental implant placement in elderly populations necessitate comprehension of the possible influence of these conditions on implant success.[10] Impaired wound healing, peri-implantitis, and the inability to develop or sustain osseointegration are some of the causes. Patient susceptibility may be attributed to underlying immune system dysregulation and altered inflammatory response in autoimmune disorders. [5,9]Additionally, osteopenia and systemic bone loss are frequently associated with autoimmune disorders, which may complicate achieving implant success, particularly when combined with insufficient bone volume at the implant site.[11] Nevertheless, a myriad of reports and single studies on the success rate of dental implants in patients affected by autoimmune disorders have been published during recent years, and its results are still very heterogeneous, so no conclusive evidence has been established yet. As a result, variation in study design, variations in type and severity of autoimmune illness, patient medication history, and implant-related characteristics have been highlighted as the reasons for lack of consensus in this literature.[11,12]

While prior systematic reviews, by Górski et al.[13], Esimekara et al.[14], and Duttenhoefer et al.,[15] have explored the impact of systemic autoimmune or immunocompromised conditions on dental implant outcomes, a definitive consensus remains elusive due to heterogeneity in study designs, populations, and outcome reporting. Górski’s narrative review was limited to oral lichen planus (OLP) and did not include a meta-analysis,[13] while Esimekara provided a critical review without quantitative synthesis or subgroup comparison.[14] Duttenhoefer’s meta-analysis included a broader immunocompromised population such as transplant recipients and human immunodeficiency virus-positive patients without disease-specific analysis or evaluation of soft-tissue outcomes.[15] Moreover, these earlier reviews lacked comprehensive subgroup analyses (e.g., RA, SS, and OLP) and detailed assessments of secondary clinical outcomes like marginal bone loss (MBL)and bleeding on probing (BOP).

The present review addresses these gaps by providing an updated, focused synthesis of the literature through a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic review and meta-analysis. It incorporates subgroup-specific comparisons across autoimmune disorders and analyzes both implant survival and peri-implant parameters such as MBL and BOP. The objective is to determine whether autoimmune conditions influence implant success and to identify specific disease-related or treatment-related factors that may affect outcomes.

MATERIALS AND METHODS

PROTOCOL AND REGISTRATION

This systematic review was conducted in accordance with the PRISMA statement.[16] The study protocol was registered in the international prospective register of systematic reviews (PROSPERO) It can be accessed from https://www.crd.york.ac.uk/PROSPERO/view/CRD420250656073 with identifier PROSPERO 2025 CRD420250656073. The Institutional Review Board of the King Abdullah International Medical Research Center approved the study, as indicated by the reference number NRC23R/681/11; as it was a retrospective study, ethical approval was not required.

ELIGIBILITY CRITERIA

The PECO protocol was designed for review based on PRISMA guidelines.[16] The “P” (Population): Adult patients (≥18 years) with clinically diagnosed autoimmune disorders [e.g., RA, OLP, SS, and type 1 diabetes mellitus (T1DM)] who underwent dental implant treatment. The “E” (Exposure): stands for presence of autoimmune disease including but not limited to RA, OLP, SS, T1DM, SLE, and connective tissue disorders (CTDs), regardless of immunosuppressive therapy, medication regimen, or implant type. The “C” (Comparison): Systemically healthy individuals receiving dental implants under similar clinical conditions. The “O” (Outcomes): Referred to implant survival and success, as well as secondary outcomes such as MBL and BOP. Table 1 depicts inclusion and exclusion criteria.

Table 1.

Selection criteria devised for the review

Criteria	Inclusion	Exclusion
Population	Patients diagnosed with autoimmune disorders undergoing dental implant treatment	Patients without autoimmune disorders or those undergoing non-implant dental treatments
Age	Adults (≥18 years)	Patients younger than 18 years
Type of study	Randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies	Narrative reviews, editorials, case reports, conference abstracts, or expert opinions
Language	Articles published in English	Articles not available in English
Outcome measures	Success rate of dental implants, including implant survival, stability, and osseointegration	Studies without clear outcome data regarding dental implant success

DATABASE SEARCH STRATEGY

The search protocol looked at seven electronic databases: PubMed, MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and CINAHL. Combining Boolean operators, MeSH terms, and free-text keywords resulted in a robust search method. The Boolean operators AND, OR, and NOT were employed in structuring to mix keywords and MeSH terms for optimal results of the search process. A search strategy specific to each database was developed, customized according to indexing practices unique to each database, Table 2 depicts different search databases showing a combination of MeSH terms and key words. First, duplicate publications were eliminated. One author evaluated titles for eligibility, while abstracts and full-text publications were subsequently assessed independently by two authors. Title and abstract screenings were conducted via the online screening tool Rayyan (Qatar Computing Research Institute; HBKU, Doha, Qatar). Studies that did not meet inclusion criteria were excluded. Two reviewers resolved their disagreements through discussion. In absence of a consensus, disputes were adjudicated by consulting a third author. The degree of agreement between the two reviewers following abstract and full-text screening was assessed using Cohen’s kappa (κ) coefficient.

Table 2.

MeSH phrases utilized across databases

Database	Search string
PubMed	(“Dental Implants”[MeSH] AND (“Autoimmune Diseases”[MeSH] OR “Rheumatoid Arthritis”[MeSH] OR “Systemic Lupus Erythematosus”[MeSH]) AND (“Implant Survival” OR “Osseointegration” OR “Implant Failure”)) AND (English[lang] AND “1990/01/01”[PDAT]: “2023/12/31”[PDAT])
MEDLINE	(“Dental Implants” AND (“Autoimmune Disorders” OR “Inflammatory Systemic Disease”) AND (“Implant Stability” OR “Implant Survival Rate”) AND “Bone Healing”) NOT “Case Reports”
Embase	(“dental implant”/exp AND (“autoimmune disease”/exp OR “sjogren syndrome”/exp OR “multiple sclerosis”/exp) AND (“implant success” OR “osseointegration”) AND ([article] AND [english] AND [adult]))
Scopus	TITLE-ABS-KEY((“Dental Implant” AND (“Autoimmune Disorder” OR “Systemic Lupus” OR “Inflammatory Disease”)) AND (“Osseointegration” OR “Success Rate”)) AND (LIMIT-TO (LANGUAGE, “English”))
Web of science	(TS=(“Dental Implant”) AND TS=(“Autoimmune Disease” OR “Rheumatoid Arthritis” OR “Systemic Inflammatory Condition”) AND TS=(“Implant Success” OR “Implant Stability”)) NOT TS=(“Narrative Review” OR “Case Study”)
Cochrane library	((“Dental Implant*” OR “Implant Dentistry”) AND (“Autoimmune Disease*” OR “Chronic Inflammatory Condition*”) AND (“Success Rate” OR “Osseointegration”)) in Title, Abstract, Keywords
CINAHL	(MH “Dental Implants” AND (MH “Autoimmune Diseases” OR “Chronic Immune Condition”) AND (“Implant Survival” OR “Bone Regeneration”)) AND Limiters - Published Date: 19900101-20231231; English Language

VARIABLE EXTRACTION STRATEGY

Among the salient items of information derived were characteristics of study, including author, year of publication, country, and study type; patient characteristics, such as age and gender; type of autoimmune disease the patient had; whether the patient was on drug therapy; implant characteristics, including the type of implant, location for implant placement, number of implants; and the character of the outcomes, including the survival rate of the implant. Data extraction was done using standardized forms to avoid deviation by the reviewers from uniformity. Since data extraction is prone to errors and mismatches, the extracted data were verified by two separate reviewers, and all disagreements were resolved either through consensus or consultation with the third reviewer.

QUALITY AND BIAS ASSESSMENT

The ROBINS-I tool[17] and Newcastle–Ottawa scale[18] were used to assess the risk of bias in non-randomized studies in the meta-analysis. Independent assessment of all included studies was performed by two authors; however, if consensus was not reached, disagreements were resolved by consulting the third author.

STATISTICAL ANALYSIS PROTOCOL

For this meta-analysis, the protocol for review was done with RevMan 5 (v 5.4.1) by aggregating rates of MBL and BOP across various autoimmune disorders in affected individuals compared with healthy controls. To incorporate study heterogeneity expected due to differences in autoimmune disorders, patient characteristics, and treatment protocols, an inverse-variance RE model was used. Continuous outcomes like MBL were represented through mean difference (MD), while dichotomous outcomes, as in the case of BOP, were represented through odds ratio (OR). The precision of pooled effect was assessed using 95% CI. Funnel plot and Egger’s test were not performed for outcomes with fewer than 10 studies, as per Cochrane guidelines, due to limited reliability.

RESULTS

STUDY SELECTION PROCESS

The database search yielded 411 records, but no records from the registers Figure 1 depicts the methodology of screening. The process began by removing duplicated records prior to screening 373 records. During the screening, 27 reports were not available in full text. The screening required retrieval of 346 reports. Twenty-nine of them were unavailable for retrieval, leaving 317 reports for consideration in eligibility assessment. A total of 204 publications were omitted from appraisal for various reasons, including 55 off-topic, 48 that did not meet the PECO criteria, 39 animal studies, 47 literature reviews, 56 case reports, and 58 scoping reviews. Finally, 14 papers[19,20,21,22,23,24,25,26,27,28,29,30,31,32] passed the eligibility requirements for inclusion in the review.

Figure 1.

Overview of the different phases in the article selection process for the review according to PRISMA guidelines. Flow diagram outlines the different phases of study selection according to PRISMA guidelines. It illustrates the number of records identified, screened, assessed for eligibility, and included in the final systematic review after applying inclusion and exclusion criteria

DEMOGRAPHIC VARIABLES OBSERVED

Table 3 summarizes demographic characteristics of the studies reviewed. Sample sizes varied considerably, ranging from a minimum of eight participants in Isidor et al.[27] to as large as 721 participants in Malo et al.[30] and reflect considerable heterogeneity in study populations. The mean age of participants also varies from one study to another, ranging from 45.43 years in Alenazi et al.[20] to 72 years in Bertl et al.[24] Most of the studies had a mixed gender ratio, with females showing remarkable preponderance in several studies, such as Anitua et al.[22] 3 males to 20 females; and Hernandez et al.[26] 4 males to 14 females.

Table 3.

Demographic characteristics of the studies assessed

Study ID	Year	Study design	Sample size	Mean age (in years)	Male: Female ratio	Follow-up period
Aboushelib et al.[19]	2016	Clinical management protocol	23	52.4	12:11	3 years
Alenazi et al.[20]	2021	Case–control	42	45.43	16:26	39–44 months
Alsaadi et al.[21]	2008	Prospective	283	56.2	96:187	1–6 months
Anitua et al.[22]	2017	Retrospective cohort	23	58	03:20	68 months
Ayele et al.[23]	2023	Retrospective clinical	180	60.3	198:151	36–115 months
Bertl et al.[24]	2019	Retrospective cohort	444	72	192:252	11.5 years
Hasanoglu et al.[25]	2019	Retrospective multicenter	199	53.6	84:115	6–104 months
Hernandez et al.[26]	2011	Prospective-controlled	36	53.7	04:14	53.5 months
Isidor et al.[27]	1999	Prospective cohort	8	61.5	00:08	4 years
Krennmair et al.[28]	2010	Retrospective clinical	34	58.1	00:34	47.6 months
Maarse et al.[29]	2022	Prospective multicenter cohort	34	61.5	17:17	18 months
Malo et al.[30]	2015	Retrospective clinical	721	51	299:422	3–17 years (average 8 years)
Mozzati et al.[31]	2021	Retrospective	99	55.42	45:54	63.06 months
Petsinis et al.[32]	2017	Retrospective	31	57.2	08:23	71 months

In terms of the follow-up duration, the studies ranged from Alsaadi et al.,[21] with the shortest follow-up period of 1–6 months, to Bertl et al.,[24] with the maximum period of 11.5 years. Most of the studies were mid-point follow-ups, such as Hasanoglu et al.[25] at 6–104 months and Mozzati et al.[31] at 63.06 months. Differences in follow-up times may have influenced reported results, thus shaping the observed patterns in implant success and survival.

A range of retrospective and prospective methodologies were adapted in study designs. Most of them are retrospective in nature, and three examples of such studies are Ayele et al.,[23] Bertl et al.,[24] and Mozzati et al.[31] Only a few studies used prospective or prospective-controlled designs, as demonstrated by Alsaadi et al.[21] and Hernandez et al.,[26] which are generally more robust in terms of bias reduction. Alenazi et al.[20] employed a case–control design to compare instances of affected persons to a pool of healthy controls.

IMPLANT-ASSOCIATED OUTCOMES ASSESSMENT

Table 4 depicts different autoimmune disorders and various implant parameters. The survival rate was usually higher, namely, from a minimum of 76.4% in Aboushelib et al.[19] who studied a patient with an active OLP to 100% in Hernandez et al.[26] and Krennmair et al.[28] who had patients suffering from OLP and RA, respectively. Studies in RA, including those by Alenazi et al.[20] and Petsinis et al.[32], showed almost a good implant survival over 97%.

Table 4.

Please reduce the last page by adjust the figures. The tableImplant-related characteristics assessed across the selected papers

Study ID	Type of autoimmune disorder	Implant survival rate (%)	Implant success rate (%)	Marginal bone loss (in millimeters)	Peri-implant probing depth (in millimeters)	Incidence of peri-implantitis (%)	Conclusion assessed
Aboushelib et al.[19]	Active oral lichen planus (OLP)	76.4	70	Initial reduction, then stable for three years	Not reported	76% failure of initially placed implants	High failure rate observed initially; corticosteroids and laser therapy improved outcomes.
Alenazi et al.[20]	Rheumatoid arthritis (RA) with/without connective tissue disorder (CTD)	97	Not specified	Significantly higher in RA with CTD group	Higher in RA with CTD group	Higher in RA with CTD group	RA with CTD patients had increased inflammation and peri-implant complications compared to RA without CTD and healthy individuals.
Alsaadi et al.[21]	Various autoimmune disorders (e.g., RA and Crohn’s disease)	98.1	94	Not specified	Not specified	1.90%	Implant failure rate associated with various systemic and local factors, including autoimmune conditions.
Anitua et al.[22]	OLP	98.5	96.7	0.96 mm mesially, 0.99 mm distally	Not specified	1.50%	Stable long-term outcomes for short implants in patients with OLP.
Ayele et al.[23]	Diabetes Mellitus (T1DM and T2DM)	96.2	Not specified	Greater in diabetic patients, up to 3.34 mm	Six sites averaged	Not specified	Greater MBL in diabetic patients, particularly T1DM
Bertl et al.[24]	Diabetes, osteoporosis, RA	98.1	Not specified	0.7 mm after one year	Not specified	Not reported	Age-related increase in EIL; minimal impact of systemic diseases on implant success
Hasanoglu et al.[25]	Type II diabetes mellitus	95.9	90	<0.2 mm annually	Not specified	73.9	High survival rate but smoking and history of periodontitis were risk factors
Hernandez et al.[26]	OLP	100	100	Not specified	3.4 ± 1.04	10.7	OLP does not significantly affect implant success; high rate of peri-implant mucositis.
Isidor et al.[27]	Primary and secondary Sjögren’s syndrome (SS)	98.1	95.2	0.7 mm after 1 year	Not specified	Not reported	Sjogren’s patients benefit from implants, with moderate bone loss observed.
Krennmair et al.[28]	RA with/without connective tissue disease	100	93.8	2.1 mm for RA; 3.1 mm for RA + CTD	2.8 mm for RA; 3.2 mm for RA+CTD	Higher incidence in RA+CTD group	Higher bone resorption in RA+CTD patients; RA without CTD showed favorable outcomes
Maarse et al[29]	Primary and secondary SS	100	Not specified	1.10 ± 1.04	Measured at six sites	Not specified	Implant performance in SS patients comparable to controls
Malo et al[30]	Multiple disorders (e.g., diabetes, hepatitis, and rheumatological)	94.6	83.5	1.18 mm (1 year), 1.56 mm (5 years), 1.47 mm (10 years)	Not reported	11.90%	Implant rehabilitations in patients with systemic disorders or smoking habits are feasible with good outcomes, but different impacts exist depending on the type of disorder.
Mozzati et al[31]	Diabetes, osteoporosis, systemic lupus erythematosus (SLE), and RA	94.6	Not specified	0.45 ± 0.12	Not reported	None reported	Calcium ions-modified surface implants were effective in medically compromised patients
Petsinis et al[32]	RA, asthma, emphysema, and SLE	99	97	Not reported	Higher in RA with CTD group	One implant failed due to peri-implantitis (1%)	RA with CTD patients had increased inflammation and peri-implant complications compared to

According to Table 4, the success of implants varied depending on the type of autoimmune disease affected. The survival rate was frequently greater, ranging from 76.4% in Aboushelib et al.[19] who evaluated a patient with active OLP, to 100% in Hernandez et al.[26] and Krennmair et al.[28], who studied patients with OLP and RA, respectively. Alenazi et al.[20] and Petsinis et al.[32] demonstrated nearly good implant survival over 97% in RA studies.

However, success rates for implants were variable, and several studies did not provide specific values. Aboushelib et al.[19] documented a success rate of 70%. Anitua et al.[22] and Hernandez et al.[26] reported success rates of 96.7% and 100%, respectively. Hasanoglu et al.[25] conducted a study involving patients with type II diabetes mellitus who underwent implant placement, reporting a success rate of 90%. Krennmair et al.[28] reported a 93.8% success rate for implants in patients with RA and connective tissue diseases.

MBL levels varied from low to moderate depending on the autoimmune disease. Anitua et al.[22] measured MBL at 0.96 mm mesially and 0.99 mm distally in patients with OLP. In contrast, Krennmair et al.[28] reported MBL values ranging from 2.1 mm to 3.1 mm in RA with connective tissue disease. Research indicates that MBL levels were significantly elevated in diabetics, reaching up to 3.34 mm, suggesting an increased risk for metabolic disorders in these patients. Peri-implant probing depth was not recorded in all studies.

In another study, Hernandez et al.[26] measured the probing depth of 3.4 mm ± 1.04 mm, whereas probing depths measured by Krennmair et al.[28] were 2.8 mm in the patients with RA and 3.2 mm in additional CTD. Furthermore, there was variation in the frequency of peri-implantitis as Hernandez et al.[26] reported an incidence of 10.7%, while Alsaadi et al.[21] reported an extremely low incidence of 1.9%. Aboushelib et al.[19] found a significant risk of peri-implantitis, which led to a 76% failure rate for the initially placed implants.

QUALITY ASSESSMENT OBSERVATIONS

Figure 2 shows the quality assessment using the ROBINS-I method, which examined seven categories of bias across the included papers. Both Aboushelib et al.[19] and Hernandez et al.[26] found a modest risk of bias across multiple categories. Both studies ranked confounding bias (D1) as “moderate,” probably because full control for all confounders is impossible in the actual world. Bias owing to participant selection (D2) and intervention classification (D3) was minimal, indicating that research populations well-defined and interventions were correctly classified. Bias due to departures from intended interventions was minor in both investigations, demonstrating proper adherence to the intervention protocols. However, two trials had a moderate risk of bias due to missing data (D5), which may have influenced result completeness. The bias in outcome measurement (D6) and result selection (D7) was graded as minimal, indicating accurate outcome measures and transparent reporting.

Figure 2.

Quality assessment performed across the included studies using the ROBINS-I tool. It summarizes the ROBINS-I quality assessment for non-randomized studies. Both included studies showed moderate risk of bias in confounding and missing data domains, while selection, intervention classification, and outcome measurement domains were rated low risk

Figure 3 depicts NOS utilized to analyze bias in included studies by evaluating selection bias (D1), performance bias (D2), attrition bias (D3), detection bias (D4), and reporting bias (D5). The quality domains varied across investigations. Selection bias (D1) was mostly rated as “unclear” by Alenazi et al.[20], Hasanoglu et al.[25], and Maarse et al.[29] This may result in a problem with unclear participant selection. Alsaadi et al[21], Anitua et al.[22], and Ayele et al.[23] all reported “high” performance bias (D2). This could indicate an issue with blinding or inconsistent administration of therapies. Attrition bias (D3) was generally low across studies, indicating minimal loss to follow-up or accurately reported dropout rates. Detection bias (D4) was inconsistently rated; for example, Isidor et al.[27] and Krennmair et al.[28] did not clearly score this domain, indicating inconsistency in result measurement methodologies. Reporting bias (D5) was similarly low across all trials, indicating that outcomes were well reported without selective reporting.

Figure 3.

Quality assessment performed across the included studies using the Newcastle-Ottawa scale tool. It shows the NOS-based risk of bias evaluation across included observational studies. Common issues included unclear selection bias and high-performance bias. However, most studies showed low attrition and reporting bias, indicating overall methodological robustness

MARGINAL BONE LOSS AND BLEEDING ON PROBING STATISTICS OBSERVED

Figure 4 depicts a forest plot demonstrating the MD in MBL rates between cases and controls for patients with autoimmune disorders. There were three subgroups: T1DM, SS, and RA. For T1DM [23], MD was 0.05 mm, with a 95% confidence interval of −−0.07 to 0.17. The MD effect was not statistically significant with Z = 0.80 and P = 0.42, and MBL rates were the same in diabetic patients and healthy volunteers. The SS subgroup [29] had an insignificant MD of −0.03mm (95% CI: −0.53 to 0.47) and no significant change in MBL (Z = 0.12, P = 0.91) between the case and control groups. However, in the RA subgroup [20], MBL was significantly greater in the case group compared to controls, with an MD of 1.60 mm (95% CI = 1.13 to 2.07) and overall significant effect (Z = 6.69, P < 0.0001). As a result, people with RA had higher MBL levels than those without the condition.

Figure 4.

Forest plot showing mean difference (MD) in marginal bone loss (MBL) across autoimmune groups (case group) vs healthy controls. The forest plot shows MDs in MBL across subgroups of autoimmune patients vs healthy controls. MBL was significantly higher in rheumatoid arthritis (RA) patients, but not significantly different in diabetes or Sjögren’s syndrome

The forest plot in Figure 5 depicted the OR for BOP seen across several autoimmune illnesses between affected patients (case group) and healthy controls. In the RA subgroup [20], OR for BOP was 0.13 (95% CI = 0.05 to 0.36), with an overall statistically significant effect (Z = 3.91, P < 0.0001). As a result, patients with RA had a lower BOP compared to controls. The OR of OLP [26] was 0.51 with a nonsignificant effect; Z = 1.00, P = 0.32, indicating no significant difference in BOP between the case and control groups. The overall pooled OR was 0.24 (95% CI = 0.06 to 0.91), with an overall significance effect (Z = 2.10, P = 0.04), indicating that people suffering from autoimmune disorders tended to have higher odds of developing BOP as compared to healthy controls. However, there is moderate heterogeneity at the level of subgroups (I² = 61%). Funnel plot and Egger’s test were not conducted as only four studies were included, which is below the threshold for reliable interpretation.

Figure 5.

Forest plot showing odds ratio (OR) for bleeding on probing (BOP) in autoimmune groups (case group) vs healthy controls. The forest plot displays ORs for BOP between autoimmune patients and controls. RA patients showed significantly reduced BOP, while oral lichen planus patients showed no significant difference. The overall pooled OR was 0.24 (P = 0.04), with moderate heterogeneity

Figure 6 displays implant survival rates across autoimmune conditions, with most exceeding 94%, highlighting disease-specific risk differences. Figure 7 shows average MBL, with higher values in RA and diabetes, further emphasizing variability in peri-implant outcomes by condition.

Figure 6.

Bar chart of implant survival rate across autoimmune disorders. It shows a bar chart comparing implant survival percentages among different autoimmune conditions. The survival rates remained above 94% in most groups. This visual emphasizes disease-specific risk variability for peri-implant outcomes

Figure 7.

Bar chart of MBL across autoimmune disorders. It shows a bar chart comparing average (MBL in mm) among different autoimmune conditions. RA and diabetes showed higher MBL values. This visual emphasizes disease-specific risk variability for peri-implant outcomes

DISCUSSION

A collective comparison of included studies showed high implant survival in autoimmune patients, though complications varied by condition and patient characteristics. The present findings corroborate those of previous systematic reviews, Górski et al.[13], Esimekara et al.[14], and Duttenhoefer et al.[15], in supporting the general feasibility of implant placement in autoimmune or immunocompromised populations. This review builds on earlier work by adding updated data, subgroup meta-analyses (RA, SS, and OLP), and additional outcomes (MBL and BOP), offering a clearer view of peri-implant health and disease-specific risks.

Alsaadi et al.[21], Petsinis et al.[32], and Bertl et al.[24] reported high implant survival rates despite autoimmune diseases, with no difference in the overall survival. However, Aboushelib et al.[19] discovered a substantial failure rate in initial cases of active OLP. Hernandez et al.[26] and Anitua et al.[22] reported favorable results for OLP patients, implying that differences in treatment approaches can influence outcomes. The wide CI in the OLP subgroup indicate imprecision from small samples and study variability, warranting cautious interpretation.

Alenazi et al.[20] and Krennmair et al.[28] reported elevated levels of inflammation and secondary complications in patients with RA and CTD, comparing with more favorable outcomes reported by Isidor et al.[27] in cases of SS. This indicates that disorders marked by systemic inflammation, such as those associated with RA and connective tissue diseases, pose a greater risk than SS.

Ayele et al.[23] and Hasanoglu et al.[25] indicated that MBL levels were elevated in diabetic patients, highlighting the influence of metabolic control and smoking, in addition to periodontal disease, on implant success. The findings of Mozzati et al.[31] showed that additive implant surfaces had a strong effect on outcomes in autoimmune patients, which is consistent with results of Anitua et al.[22], and high success rates may be achieved by further modifications of implant surfaces.

The survival rate of dental implants in patients with OLP is no different than that of the general population, which was a conclusion drawn by the review of Gorski et al.[13]; survival rates of dental implants in our review in patients with OLP were up to 100%. However, Gorski et al.[13] report that patients with erosive OLP or desquamative gingivitis are more prone to peri-implant diseases, whereas the present review suggested that patients with OLP exhibit different susceptibility, which defines that the subtype of condition plays an important role in the occurrence of implant complications.

Esimekara et al.[14] reported that, consistent with their case series, implant survival rates among autoimmune patients were comparable to those in general population, ranging from 76.4% to 100%. Two reviewers indicated that the complication rate was elevated in patients exhibiting more severe manifestations, including secondary SS and erosive OLP. The augmented MBL identified in our review of T1DM patients aligns with observations of Esimekara et al.[14] regarding secondary autoimmune conditions, suggesting that the health of an implant is partially contingent upon metabolic regulation. MBL >1.5 mm in the first year may signal early peri-implantitis, requiring closer follow-up or early intervention.

Hyldahl et al.[33] documented implant survival rates between 50% and 100% in patients with autoimmune diseases, contingent on the follow-up duration. Our review corroborates these findings, albeit with a more restricted range of 76.4%–100%. Similarly, Hyldahl et al.[33] noted absence of data regarding effects of immunosuppressive drugs, presenting a comparable challenge for us in discerning the specific influence of various management strategies on implant success.

According to Sarafidou et al.[34] implant survival is reported to be mostly in the range of 94.6%–100% in cases of OLP, SS, and lupus erythematosus. Our study demonstrated favorable implant survival rates in patients with autoimmune illnesses, thereby supporting this hypothesis. Both their and our reviews highlighted the importance of long-term follow-up and adequate oral hygiene as critical factors for successful implant outcomes. RA and diabetes were of particular concern as the onset of autoimmune oral diseases did not adversely affect the survival of dental implants when patients maintained proper oral hygiene. This aligns with our review, which indicated that while these conditions, along with diabetes, pose increased risks for MBL and peri-implantitis, they do not lead to reduced survival rates when managed appropriately.

Duttanhoefer et al.[15] reported an average implant survival rate of 88.75% for autoimmune disorders and found no significant effect of immunocompromised conditions on implant survival. This is consistent with our findings, which showed that most autoimmune diseases did not significantly reduce implant survival. However, our research found that patients with RA and diabetes had greater MBL, whereas Duttenhoefer et al.[15] appeared to have offered an overall perspective without comparing specific autoimmune diseases. The disparity emphasizes the importance of performing disease-specific analysis when evaluating the results of implants in immunocompromised patients. Reduced BOP in RA patients may result from NSAIDs and corticosteroids, which inhibit pro-inflammatory substances such as COX-2, IL-1, IL-2, IL-6, and TNF-α. Anti-rheumatic medications like methotrexate and sulfasalazine diminish inflammation further. Methotrexate and prednisolone reduce gingivitis by reducing IL-1β, IL-6, and free radical activity.[35] The “two-hit” concept, where anaerobic bacteria in the gingiva induce bone-resorptive cytokines and enzymes, may increase MBL in RA. Second, RA causes systemic inflammation, leading to increased CRP, IL-6, and TNF-α levels. These boost periodontal immune responses and tissue damage, producing further bone loss.[36]

Ambrosio et al.[37] addressed the effects of systemic disorders and medicines, pointing out that some drugs, such as PPIs and SSRIs, may have a deleterious impact on osseointegration. Although our literature analysis did not look at the effects of drugs in depth, it did cover the subject of using customized therapeutic approaches in autoimmune patients to ensure success with implant-based treatments. Both evaluations emphasize the importance of carefully considering the effects of systemic factors on implant durability.

Conventional removable dentures are a major challenge to patients with connective tissue diseases, mainly SS and systemic scleroderma. SS can cause extreme sensitivity and dryness on mucosa, which causes pain and complicates the usage of dentures.[12,34] Systemic scleroderma can lead to conditions like microstomia and sclerodactyly,[8,11] which complicate maintenance of oral hygiene and render use of removable dentures impractical.[37] Implant-supported fixed prostheses provide significant advantages for patients with OLP, SS, and systemic scleroderma by alleviating mucosal stress compared to conventional dentures, potentially enhancing the quality of life for individuals with these conditions.[38,39,40,41] In systemic scleroderma, implant-supported fixed rehabilitation is likely to enhance the quality of life.[35] The functional and quality of life advantages provided by implant-supported prostheses may surpass associated risks, and a marginally reduced implant survival rate could be deemed an acceptable compromise for certain patient groups.[41]

Findings of this review have meaningful clinical implications for implant treatment in patients with autoimmune disorders. Observed trends such as increased MBL in RA and type 1 diabetes and higher BOP in OLP suggest autoimmune patients may benefit from customized treatment planning, including careful site selection, bone grafting where needed, and use of surfaces that enhance osseointegration. Given the higher biological risk profile, a more rigorous recall protocol (e.g., every 3–4 months in the first year) is advisable to monitor for early peri-implant inflammation. Additionally, prosthetic design should consider reduced occlusal loading, use of fixed rather than removable prostheses, and easy-clean contours to reduce plaque accumulation. Tailoring implant therapy in this way may help mitigate systemic and local risk factors associated with autoimmune disease and improve long-term outcomes.

Limitations

The main limitation of this review was significant heterogeneity in study design, sample size, follow-up, and outcome reporting, contributing to result variability. Differences in MBL and BOP likely stem from varied patient populations, treatments, and autoimmune conditions. Many studies lacked data on implant success and probing depths, limiting the comparability. Additional variability arose from corticosteroid/DMARD use and implant surface types. Subgroup analysis by medication, smoking, or implant system was not feasible due to inconsistent reporting, and stratification by immunosuppressive therapy was hindered by limited pharmacological data.

CONCLUSION

Overall, dental implants appear feasible for patients with autoimmune diseases, with survival rates comparable to those of healthy controls. However, success varied by condition RA, and T1DM patients showed higher MBL and peri-implantitis risk. Managing these cases requires tailored treatment plans. This underscores the need for standardized research to define implant success more clearly in this population.

CONFLICTS OF INTEREST

There are no conflicts of interest.

AUTHOR CONTRIBUTIONS

Conceptualization: RC, FJ, and AP. Investigation: AA, RC, AP, and SS. Formal analysis: RC, SM, AA, and SS. Validation: FJ, SM, AP, and AA. Project administration: RC, AP, and FJ. Funding acquisition: None. Writing–original draft: RC, SM, AA, and AP. Writing–review and editing: FJ, AP, RC, SM, AA, and SS.

ETHICAL POLICY AND INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

PATIENT DECLARATION OF CONSENT

Not applicable.

DATA AVAILABILITY STATEMENT

Not applicable.

Funding Statement

Nil.