Factors associated with aseptic loosening after primary total hip arthroplasty: A systematic review and meta-analysis

Abstract

BACKGROUND

Aseptic loosening remains the leading cause of revision in primary total hip arthroplasty (pTHA). However, the literature demonstrates significant variability regarding the relative contributions of different factors.

AIM

To investigate the key determinants of aseptic loosening, we performed a systematic review and meta-analysis.

METHODS

A comprehensive search of PubMed, Web of Science, EMBASE, and the Cochrane Library was conducted, encompassing studies from database inception to January 1, 2025. Meta-analyses were performed to evaluate factors associated with aseptic loosening following pTHA. Inclusion and exclusion criteria were systematically applied at each stage to ensure methodological transparency and reproducibility. Study quality was assessed using standardized categories. Pooled odds ratio (OR) with corresponding 95% confidence interval were calculated with random- or fixed-effects models to generate reliability estimates, and study heterogeneity was visualized using forest plots. Ten factors, categorized into patient-, surgeon-, and device-related domains, were reviewed and meta-analyzed. Funnel plot analysis demonstrated a relatively symmetrical distribution, suggesting minimal publication bias.

RESULTS

A meta-analysis of 20 studies (520789 participants) found a pooled prevalence of 1.96%. Significant risk factors for aseptic loosening after pTHA included elevated body mass index (OR = 1.116, P < 0.001), higher Charlson comorbidity index (OR = 1.378, P < 0.001), prosthesis-related factors (OR = 1.497, P < 0.001), and adverse lifestyles (OR = 2.198, P = 0.037). Protective factors were non-white race (OR = 0.445, P < 0.001) and favorable genetics (OR = 0.723, P < 0.001). Male sex increased risk (OR = 1.232, P = 0.016), while age and anatomy were not significant. Surgical expertise showed a slight protective effect (OR = 1.048, P < 0.001). A comprehensive understanding of the modifiable and non-modifiable factors contributing to aseptic loosening after pTHA requires consideration of patient-related factors, surgical expertise, and prosthesis characteristics.

CONCLUSION

The identification of these factors is critical for risk mitigation. High-risk patients should receive targeted counseling regarding individualized profiles. Further studies are warranted to establish clearer causal relationships and identify additional contributing factors.

Key Words: Factors; Aseptic loosening; Primary total hip arthroplasty; Systematic review; Meta-analysis

Core Tip: Aseptic loosening is the main cause of failure in primary total hip arthroplasty, yet the key risk factors remain unclear. Our meta-analysis of 20 studies clarifies this: Modifiable factors like high body mass index, comorbidity burden, and prosthesis choice significantly increase risk, while surgical expertise is protective. This evidence enables surgeons to identify high-risk patients and prioritize modifiable factors, paving the way for personalized prevention strategies and improved implant longevity.

INTRODUCTION

Primary total hip arthroplasty (pTHA) remains the gold-standard surgical treatment for end-stage hip pathologies, demonstrating consistent efficacy in pain alleviation, functional recovery, and quality-of-life improvement[1]. Despite its clinical success, the increasing volume of pTHA procedures has revealed significant complications that impose considerable healthcare burdens through elevated costs and extended hospitalizations[2,3]. Among these complications, aseptic loosening emerges as the leading cause of late-term implant failure[4,5], though its multifactorial etiology and underlying biological mechanisms require further elucidation. The current evidence base demonstrates substantial heterogeneity in reported risk factors[6,7], underscoring the imperative for systematic evaluation. This knowledge gap necessitates a comprehensive synthesis of existing evidence to inform clinical decision-making and guide future research directions. Through systematic review and meta-analysis, we seek to quantify the factors that associated with aseptic loosening after pTHA.

MATERIALS AND METHODS

Study registration and protocol adherence

The protocol was prospectively registered in the International Prospective Register of Systematic Reviews (registration number CRD42023447688). This systematic review and meta-analysis were conducted in strict accordance with both the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and the Cochrane Handbook for Systematic Reviews of Interventions (version 6.3) reported by Page et al[8].

Search strategies

A systematic literature search was performed to identify studies examining factors for aseptic loosening following pTHA. Four databases (PubMed, EMBASE, Web of Science, and the Cochrane Library) were searched from inception through January 1, 2025. The search strategy incorporated the following key concepts using Boolean operators: Risk factors: ("Risk Factor" OR "Health Correlates" OR "Population at Risk" OR "Risk Score"); implant failure: ("Prosthesis Failure" OR "Aseptic Loosening" OR "Prosthesis Survival" OR "Prosthesis Migration"); surgical procedure: ("Arthroplasty, Replacement, Hip" OR "Total Hip Arthroplasty" OR "Hip Prosthesis Implantation"). Potentially eligible articles were screened based on title or abstract, followed by full-text review. Reference lists of included studies were manually searched to identify additional relevant publications. The complete search syntax is detailed in Supplementary Table 1.

Eligibility criteria

Studies were included if they met the following criterias: (1) Investigations examining factors for aseptic loosening following pTHA; (2) Sample size over 30 participants; (3) Minimum follow-up duration of 1 year; and (4) Reported odds ratio (OR) with corresponding 95% confidence interval (95%CI). Exclusion criteria comprised: (1) Studies involving septic revisions, periprosthetic infections, or revisions for instability or dislocation; (2) Non-original research publications (reviews, commentaries, letters, or case reports); (3) Unavailable full-text articles; and (4) Studies with insufficient data for meta-analysis. Figure 1 illustrates the study selection process, demonstrating the systematic application of these criteria to ensure methodological rigor and reproducibility.

Figure 1  Flow diagram for systematic reviews and meta-analysis.

Data extraction

The study selection process was conducted through independent screening and data extraction by two researchers (Li GQ and Huang Y). All identified records were imported into EndNote X9 (Clarivate Analytics) for duplicate removal. The researchers independently evaluated titles and abstracts for eligibility, with any discrepancies resolved through consensus discussion or arbitration by a third investigator (Zhang J). Using a standardized extraction form, the same researchers collected the following data: Study characteristics: First author, publication year, country, and study design; population details: Sample size, mean age, and follow-up duration; outcome measures: Aseptic loosening rates; statistical analyses: Methods employed and significant risk factors for aseptic loosening. All extracted data were cross-verified, with disagreements resolved by a third reviewer (Zhang J).

Quality assessment

The methodological quality of included cohort studies was evaluated using the Newcastle-Ottawa Scale (NOS), which assesses three domains: (1) Selection; (2) Comparability; and (3) Outcome evaluation, through eight specific criteria. Cross-sectional studies were appraised using the Agency for Healthcare Research and Quality (AHRQ) checklist, consisting of eleven items rated as "yes", "no", or "unclear". Two independent reviewers (Li GQ and Huang Y) conducted the quality assessments, with any discrepancies resolved through consultation with a third author (Zhang J).

Statistical analysis

All outcome metrics and raw data were tabulated in Microsoft Excel (version 16.16.2). Data management, effect size transformation, and calculation of pooled mean effect sizes were performed using comprehensive meta-analysis software (version 3.3.070). OR were pooled and forest plots were generated. The meta-analyses employed the inverse variance method. Heterogeneity was assessed using the I² statistic, with thresholds defined as follows: < 40% (low), 40%-60% (moderate), and > 60% (high). Pooled effect sizes with corresponding 95%CI were calculated. Heterogeneity was further evaluated using the Cochrane Q-statistic and I² statistic. A fixed-effect model was applied when I² ≤ 50% and P > 0.1; otherwise, a random-effects model was used to provide more conservative estimates. In addition, we conducted a sensitivity analysis by sequentially removing individual studies and reperforming the meta-analysis to observe changes in the pooled effect size and heterogeneity. When data extraction or standardized effect size calculation was not feasible, eligible studies were described narratively.

RESULTS

Study selection

A systematic literature search across multiple databases initially identified 2891 potentially eligible studies. In accordance with PRISMA guidelines, we conducted a rigorous multi-stage screening process. During initial screening, we excluded 212 studies focusing on septic revision or periprosthetic joint infection, as these topics were not relevant to our research objectives. Subsequent screening eliminated 1425 studies examining revision procedures for instability or dislocation, which fell outside our study scope. We further excluded 185 review articles (lacking original data), 104 commentaries or editorials (without substantive research content), and 93 case reports (due to small sample sizes and potential bias). Twenty-six studies were excluded because full-text versions were unavailable for thorough assessment. In addition, we excluded 8 studies that were methodologically unsuitable for meta-analysis due to insufficient outcome reporting or study design limitations. Finally, of total 20 studies were included.

General information

The reviewed studies exhibited the following key characteristics: Authorship, country of origin, study design, follow-up duration, sample size, number of aseptic loosening cases and rates, age, and NOS scores (Table 1). These studies featured substantial sample sizes (range: 39-290770; total n = 520789) and extended follow-up periods (range: ≤ 2 years to ≥ 16 years). Conducted between 2005 and 2024 across eleven countries (United Kingdom, United States, Denmark, Italy, Switzerland, Germany, Finland, Sweden, China, Ireland, and France), the majority employed cohort designs (n = 17), with three utilizing case-control methodologies. Participants' mean age predominantly ranged from 60 years to 70 years, with one exception reporting 46.5 years. Aseptic loosening cases varied substantially (4-1260 cases), with rates demonstrating considerable heterogeneity (0.2%-6.57%) with the pooled rate was 1.96%. All studies demonstrated good methodological quality, as evidenced by NOS scores of 6-8. Table 1 comprehensively summarizes these study characteristics, illustrating their diversity in design, population parameters, and outcome measures.

Table 1 The general characteristics of the included studies.

Ref.	Country	Study design	Follow-up (years)	Participations	AL (n)	AL rates, %	Age (years)	NOS
Davis et al[61], 2020	United Kingdom	Cohort study	≥ 7.5	290770	1260	0.43	73.17	7
Amstutz et al[49], 2015	United States	Case-control study	8.58	1375	27	1.96	46.5	7
Benson et al[35], 2022	Denmark	Cohort study	4.4 (1.1-5.9)	53605	479	0.89	NA	7
Bordini et al[15], 2007	Italy	Cohort study	≥ 6	4750	134	2.82	NA	6
Jud et al[42], 2024	Switzerland	Cohort study	NA	2459	14	0.56	63.8 ± 12.7 (18-92)	7
Wagener et al[62], 2024	Germany	Cohort study	2-10	255	255	NA	73	7
Budin et al[34], 2024	United States	Cohort study	2	32811	721	2.19	69.1 ± 8.3	8
Magruder et al[32], 2024	United States	Cohort study	≤ 2	11025	160	1.45	NA	6
Aro et al[13], 2012	Finland	Cohort study	2	39	NA	NA	61.5 (41-78)	6
Electricwala et al[23], 2016	United States	Cohort study	8.7 ± 8.1	257	NA	NA	67 ± 13	7
Münger et al[14], 2006	Sweden	Case-control study	5.3 ± 3.1	50534	725	1.43	63 ± 9.5	8
Layson et al[29], 2024	United States	Cohort study	2	55601	NA	1.91	68-72	7
Clauss et al[58], 2013	Switzerland	Cohort study	≥ 16	156	139	NA	NA	7
Khatod et al[43], 2014	United States	Cohort study	2.2 (1.2-5.1)	36834	635	1.72	65.5 ± 11.7	8
Stelmach et al[45], 2015	Germany	Cohort study	NA	465	234	NA	52.80 ± 12.8	7
Wedemeyer et al[44], 2009	Germany	Cohort study	7.17-8.41	87	87	NA	69.31 ± 10.27	7
Lee et al[56], 2024	China	Cohort study	≥ 1	1995	4	0.2	61.9 ± 13.8	7
Lunn et al[33], 2005	Ireland	Case-control study	5.7 (2-11)	217	101	NA	67.6 ± 9.6	6
Halawi et al[26], 2016	United States	Cohort study	7.6 ± 2.56	426	28	6.57	46.9 ± 7.1	7
Boyer et al[19], 2019	France	Cohort study	5.5	30733	NA	NA	NA	6

AL: Aseptic loosening; NOS: Newcastle Ottawa Scale; NA: Not available.

Statistics methods

The analyzed factors were categorized into three groups: Patient-related factors, surgical-related factors, and implant-related factors (Table 2). Patient-related factors commonly examined across studies included age, sex, and body mass index (BMI). Lifestyle factors encompassed tobacco use, alcohol consumption, and physical activity levels. Comorbidity factors consisted of the Charlson comorbidity index (CCI) such as diabetes mellitus, chronic kidney disease, congestive heart failure, and cerebrovascular disease. Additionally, genetic factors and biomarkers, including polymorphisms, were analyzed. Surgical factors included surgical approach, procedure duration, and surgeon experience. Implant-related factors comprised implant type, fixation method, and component size. Table 2 also summarizes the statistical methods employed in each included study.

Table 2 The statistics information of the included studies.

Ref.	Statistics	Factors
Davis et al[61], 2020	KM survival analysis and Cox regression analyses	Age, sex, head composition, and stem fixation method
Amstutz et al[49], 2015	Multivariate analysis and Cox proportional hazard ratio	Age, gender, BMI, abduction arc, UCLA activity score, center-edge angle, cup abduction, component size, and diagnosis
Benson et al[35], 2022	KM survival analysis and Cox proportional hazards regression model	Age, sex, CCI, fixation type, duration and start of thromboprophylaxis, use of vitamin K antagonists, NOAC, aspirin, and platelet inhibitors
Bordini et al[15], 2007	Multivariate survival analysis and Cox proportional -hazards model	Age, gender, diagnosis, Charnley score, right or left side, surgeon’s skill, and type of components
Jud et al[42], 2024	Multivariate logistic regression analysis	Age, non-steroidal antirheumatics, and nicotine
Wagener et al[62], 2024	KM method and Cox proportional hazards model	Age, sex, BMI, ASA, diagnosis, comorbidities, surgerical approach, duration of surgery, hip type, Dorr type, revision time and type
Budin et al[34], 2024	Multivariable logistic regression	Age, sex, tobacco use, and LOS
Magruder et al[32], 2024	Logistical regression	Age, sex, and CCI
Aro et al[13], 2012	KM and logistic-regression model	Age, BMI, local BMD, T-score of the operated hip, and canal flare index
Electricwala et al[23], 2016	Bonferroni correction for multiple comparisons	BMI
Münger et al[14], 2006	Multivariate conditional logistic regressions	Age, sex, indication for surgery, height, weight, BMI, and mobility level
Layson et al[29], 2024	Multivariate logistic regressions	Age, sex, alcohol abuse, CCI, diabetes, obesity, CKD, history of cancer and tumors, CHF, CVD, RA, liver disease, and tobacco use
Clauss et al[58], 2013	Multivariable Cox regression analysis with stepwise variable selection	Age, sex, primary diagnosis, type of implant, implant material, stem offset, stem size, type of cup, and head diameter
Khatod et al[43], 2014	Multivariate Cox models	Age, gender, race, BMI, diabetes, ASA, implants, techniques, surgeons, and hospital factors
Stelmach et al[45], 2015	Cox regression models	Age, BMI, gender, BCL2-938 polymorphisms
Wedemeyer et al[44], 2009	Cox regression models	Age, gender, weight, height, BMI, BCL2-938C>A and CALCA-1786T>C polymorphisms, defects of acetabular and femoral
Lee et al[56], 2024	Multivariate logistic regression model	Age, stem type, sex, BMI, diagnosis, CCI, stem alignment, and canal fill ratio
Lunn et al[33], 2005	Logistic regression analysis	Age, HFE gene mutations C282Y and H63D genotype
Halawi et al[26], 2016	Multivariate logistic regression	Age, sex, BMI, CCI, diagnosis, approach, prior surgery, head size, articulation
Boyer et al[19], 2019	Cox multivariate regression model	BMI, gender, age, diabetes, weight, height

KM: Kaplan-Meier; BMI: Body mass index; CCI: Charlson Comorbidity Index; NOAC: Non-vitamin K oral anticoagulants; ASA: American Society of Anesthesiologists; LOS: Length of stay; BMD: Bone mineral density; CKD: Chronic kidney disease; CHF: Congestive heart failure; CVD: Cerebrovascular disease; RA: Rheumatoid arthritis.

Quality assessments

The methodological quality was assessed using standardized tools: The NOS for cohort studies and the AHRQ checklist for case-control studies. The NOS evaluation revealed that most cohort studies demonstrated high methodological quality (scores 6-8), with particularly rigorous outcome assessment methods. However, variability was observed in their approaches to confounding factor adjustment and cohort selection (Table 3). Similarly, the AHRQ assessment of three case-control studies indicated inconsistencies, primarily in sampling transparency and the reporting of analysis exclusions (Table 4). The overall high quality of the included studies strengthens the reliability of our findings.

Table 3 Quality assessment of included cohort studies.

Ref.	Selection			Comparability		Outcome			Total
	Item 1	Item 2	Item 3	Item 4	Item 5	Item 6	Item 7	Item 8
Davis et al[61], 2020	1	N/A	1	0	N/A	1	1	1	5
Benson et al[35], 2022	1	1	0	1	1	1	1	1	7
Bordini et al[15], 2007	1	0	N/A	1	1	1	1	1	6
Jud et al[42], 2024	1	1	1	1	N/A	1	1	1	7
Wagener et al[62], 2024	1	0	1	1	N/A	1	1	1	6
Budin et al[34], 2024	1	1	1	1	1	1	1	1	8
Magruder et al[32], 2024	1	1	1	1	N/A	1	1	1	7
Aro et al[13], 2012	1	1	1	1	1	1	0	0	6
Electricwala et al[23], 2016	1	0	0	1	1	1	1	1	6
Layson et al[29], 2024	1	1	N/A	1	1	1	1	1	7
Clauss et al[58], 2013	1	1	1	1	N/A	1	1	1	7
Khatod et al[43], 2014	1	1	1	1	1	1	1	1	8
Stelmach et al[45], 2015	1	1	1	1	N/A	1	1	1	7
Wedemeyer et al[44], 2009	1	0	1	1	N/A	1	1	1	6
Lee et al[56], 2024	1	1	1	1	1	1	1	1	8
Halawi et al[26], 2016	1	N/A	0	1	1	1	1	1	6
Boyer et al[19], 2019	1	1	1	0	1	1	1	1	7

For cohort studies: Selection: Item 1, representativeness of the exposed cohort; item 2, selection of the non-exposed cohort; item 3, ascertainment of exposure; item 4, demonstration that outcome of interest was not present at start of study; Comparability: Item 5, comparability of cohorts on the basis of the design or analysis; Outcome: Item 6, assessment of outcome; item 7, was follow-up long enough for outcomes to occur; item 8, adequacy of follow up of cohorts. N/A: Not applicable.

Table 4 Quality assessment of included case-control studies.

Ref.	Item 1	Item 2	Item 3	Item 4	Item 5	Item 6	Item 7	Item 8	Item 9	Item 10	Item 11
Amstutz et al[49], 2015	Yes	No	No	Yes	Unclear	Yes	No	Yes	Unclear	Unclear	Yes
Münger et al[14], 2006	Yes	Yes	Yes	Yes	Unclear	Yes	No	Yes	Unclear	Yes	Yes
Lunn et al[33], 2005	Yes	Yes	No	Yes	Unclear	Yes	Unclear	Yes	Unclear	Yes	Yes

For case-control studies: Item 1, Define the source of information (survey, record review); item 2, list inclusion and exclusion criteria for exposed and unexposed subjects (cases and controls) or refer to previous publications; item 3, indicate period used for identifying patients; item 4, indicate whether or not subjects were consecutive if not population-based; item 5, indicate if evaluators of subjective components of the study were masked to other aspects of the status of the participants; item 6, describe any assessments undertaken for quality assurance purposes; item 7, explain any patient exclusions from analysis; item 8, describe how confounding was assessed and/or controlled; item 9, if applicable, explain how missing data were handled in the analysis; item 10, summarize patient response rates and completeness of data collection; item 11, clarify what follow-up, if any, was expected and the percentage of patients for which incomplete data or follow-up was obtained.

Meta-analyses summary

The meta-analysis synthesized multiple factors associated with aseptic loosening, presenting pooled OR with 95%CI, heterogeneity measures, and statistical significance (Table 5). The strongest risk factors included BMI, CCI, prosthesis type, and lifestyle factors, all demonstrating significantly elevated OR. Protective factors such as race and genotype showed reduced OR. Non-significant factors including age and anatomical characteristics showed no clinically meaningful associations. Further more, we also performed sensitivity analyses and the results were robust.

Table 5 Summary of meta-analysed factors for the aseptic loosening after primary total hip arthroplasty.

Factors	Pooled OR	LL 95%CI	UL 95%CI	P value	I2	Effects model
Age	0.998	0.911	1.093	0.964	86.033	R
Gender (male)	1.232	1.039	1.460	0.016	87.230	R
BMI	1.116	1.059	1.175	< 0.001	91.057	R
CCI	1.378	1.182	1.606	< 0.001	88.230	R
Lifestyle	2.198	1.048	4.611	0.037	99.140	R
Diagnosis	1.162	1.009	1.338	0.037	61.546	R
Anatomy	1.082	0.876	1.336	0.466	82.582	R
Race	0.445	0.302	0.655	< 0.001	0.000	F
Genetype	0.723	0.617	0.849	< 0.001	0.000	F
Surgical skills	1.048	1.021	1.075	< 0.001	12.78	F
Prosthesis	1.497	1.203	1.864	< 0.001	89.958	R

I² was defined as the proportion of heterogeneity not due to chance or random error. OR: Odds ratio; LL: Lower limit; UL: Upper limit; BMI: Body mass index; CCI: Charlson Comorbidity Index; R: Random-effects model was performed; F: Fixed-effects model was performed.

Age demonstrated no significant correlation (OR = 0.998, 95%CI: 0.911-1.093, P = 0.964) in Figure 2A. The analysis revealed that age was not significantly associated with aseptic loosening (OR = 0.998, 95%CI: 0.911-1.093, P = 0.964), which suggested that age did not play a significant role in influencing the aseptic loosening within the examined population.

Figure 2 The forest plots. A: Shows the odd ratios of age for aseptic loosening; B: Shows the odd ratios of male for aseptic loosening; C: Shows the odd ratios of body mass index for aseptic loosening; D: Shows the odd ratios of Charlson comorbidity index for aseptic loosening; E: Shows the odd ratios of lifestyle for aseptic loosening; F: Shows the odd ratios of diagnosis for aseptic loosening; G: Shows the odd ratios of anatomy for aseptic loosening; H: Shows the odd ratios of surgery skills for aseptic loosening; I: Shows the odd ratios of prosthesis for aseptic loosening.

Male sex was significantly associated with a 23.2% higher risk of the aseptic loosening, as evidenced by an OR of 1.232 (95%CI: 1.039-1.460, P = 0.016) (Figure 2B). This finding suggests that male participants had a notably elevated risk compared to their female counterparts in the study population.

The study revealed a significant association between BMI and aseptic loosening, demonstrating a strong positive relationship (OR = 1.116, 95%CI: 1.059-1.175, P < 0.001). This indicates that each unit increase in BMI was associated with an 11.6% higher risk. Figure 2C further supports a potential causal relationship between elevated BMI levels and increased risk of the aseptic loosening.

Higher comorbidity burden was significantly associated with an elevated risk of the aseptic loosening, as evidenced by an OR of 1.378 (95%CI: 1.182-1.606), indicating a strong relationship between comorbidity burden and increased risk. The Figure 2D suggested that patients with a higher comorbidity burden face a substantially greater likelihood of experiencing the risk of the aseptic loosening.

The study revealed that a specific lifestyle factor was significantly associated with a 2.2-fold elevation in disease risk of aseptic loosening, as evidenced by an OR of 2.198 (95%CI: 1.048-4.611, P = 0.037). Figure 2E shows the OR of lifestyle for aseptic loosening after pTHA. This association suggests that individuals adhering to these lifestyles may face more than double the risk compared to those who do not.

The statistical analysis revealed a marginally significant association between diagnosis and aseptic loosening, with an OR of 1.162 (95%CI: 1.009-1.338, P = 0.037) (Figure 2F). This suggests a modest yet potentially clinically relevant relationship that warrants further investigation. In contrast, anatomical factors demonstrated no statistically significant association (OR = 1.082, 95%CI: 0.876-1.336, P = 0.466) in Figure 2G, indicating these variables may not be meaningful predictors in this context. Notably, non-white race showed substantial protective effects against the risk of aseptic loosening, with significantly reduced odds (OR = 0.445, 95%CI: 0.302-0.655, P < 0.001) (Supplementary Figure 1). This pronounced racial disparity highlights the potential influence of sociodemographic or genetic factors that merit additional exploration in future studies.

The analysis demonstrated that higher levels of surgical skills were associated with a modest but statistically significant reduction in aseptic loosening risk with OR = 0.952, 95%CI: 0.928-0.979, P < 0.001), as illustrated in Figure 2H. This inverse relationship suggests that for each unit increase in surgical proficiency, patients experienced approximately a 4.8% decrease in the likelihood of aseptic loosening. These findings underscore the importance of surgical expertise as a potentially modifiable factor in reducing risk of aseptic loosening.

The selection of prosthesis type demonstrated a substantial influence on clinical outcomes, as evidenced by a statistically significant OR of 1.497 (95%CI: 1.203-1.864, P < 0.001). This robust association, illustrated in Figure 2I, indicates that appropriate prosthesis selection increased the likelihood of positive treatment outcomes by approximately 50%, highlighting the critical importance of individualized implant choice in achieving optimal surgical results. The narrow 95%CI and highly significant P value further strengthen the reliability of these findings, suggesting that prosthesis selection should be carefully considered in clinical decision-making.

DISCUSSION

pTHA demonstrates excellent long-term survivorship, yet aseptic loosening remains a significant complication results from either inadequate initial fixation or progressive mechanical fixation loss[9-11]. Given the varying degrees of association among known factors, a comprehensive understanding of these elements is essential for prevention, necessitating systematic evaluation after pTHA. Our systematic review analyzed aseptic loosening incidence among 520789 pTHA patients, revealing an overall rate of 1.96%. The findings underscore the multifactorial etiologies involving patient characteristics, surgical techniques, and prosthesis selection. Multivariate analysis identified risk factors: Elevated BMI (OR = 1.116, P < 0.001), comorbidities (OR = 1.378, P < 0.001), prosthesis characteristics (OR = 1.497, P < 0.001), and lifestyles (OR = 2.198, P = 0.037). Protective factors included non-white ethnicity (OR = 0.445, P < 0.001) and favorable genetic predisposition (OR = 0.723, P < 0.001). Male showed modest risk elevation (OR = 1.232, P = 0.016), while age and anatomical factors demonstrated no significant association. Surgical experience exhibited a slight protective effect (OR = 1.048, P < 0.001). These results highlight the complex interplay of intrinsic and extrinsic factors in aseptic loosening pathogenesis, emphasizing the need for personalized risk assessment and management strategies.

Patient related factors

Age: Studies suggest that age influences the risk of aseptic loosening[12,13]; however, our study did not identify a significant association (OR = 0.998, 95%CI: 0.911-1.093). Younger patients may face a higher risk of aseptic loosening due to increased activity levels, which can induce greater dynamic mechanical stress at the bone-implant interface, accelerating micromotion, wear, and prosthetic fatigue failure. In contrast, elderly patients typically exhibit a lower risk, attributable to reduced mechanical loading from decreased activity and age-related anatomical changes, such as femoral medullary cavity narrowing, diminished bone remodeling capacity, and osteoporosis-induced alterations in bone-prosthesis congruence. Whereas younger patients are more prone to failure from high-cyclic loading, elderly patients may experience cement-bone interface failures due to poor bone quality. Our findings indicate no significant correlation between age and aseptic loosening risk, suggesting that age alone may not be a determining factor. This discrepancy with prior studies could stem from limited cohort sizes, insufficient raw data, or our inclusion criteria restricting analysis. Future investigations should employ larger cohorts to clarify this potential association. These results highlight the importance of age-specific clinical management: Younger and active patients may benefit from closer surveillance and activity modification, whereas elderly patients with compromised bone quality may require careful preoperative assessment to optimize outcomes.

Gender: Our study reveals sex-specific differences in pTHA outcomes, contrasting with prior research[14-17]. While female patients demonstrated higher aseptic loosening risks potentially attributable to smaller implant sizes (particularly femoral heads), male patients showed significantly greater failure risk (OR = 1.232, 95%CI: 1.039-1.460, P = 0.016), consistent with previous findings from Inacio et al[18]. These discrepancies may stem from variations in study populations, follow-up durations, and failure definitions. Mechanistically, males increased aseptic loosening risk appears multifactorial. As for biomechanical factors, higher cumulative prosthetic loads from greater activity levels and BMI accelerate micromotion-induced osteolysis. As for anatomical considerations, larger femoral heads may paradoxically reduce stability. As for biological differences, gender-specific bone remodeling responses to mechanical stress may occur, potentially influenced by differential bone mineral density (BMD) patterns or protective factors against infectious loosening or differential BMD patterns. These findings underscore the importance of sex-specific surgical approaches, including optimized component sizing, stability enhancement techniques, and personalized activity

BMI: Consistent with previous researches[14,19-23], our meta-analysis demonstrates a significant dose-dependent association between elevated BMI and increased risk of aseptic loosening (OR = 1.116, 95%CI: 1.059-1.175, P < 0.001). Biomechanically, excessive body weight amplifies joint reaction forces, accelerating polyethylene wear and periprosthetic osteolysis through overload, while simultaneously increasing shear stresses at bone-implant interfaces, thereby promoting fretting wear and micromotion. Furthermore, adipose-derived inflammatory cytokines may act synergistically with mechanical factors to exacerbate bone resorption through metabolic pathways. These findings underscore the critical importance of perioperative BMI management in clinical practice. Encourage sustained weight reduction through diet, low-impact exercise, and fall-prevention support to minimize repetitive mechanical stress.

CCI: A higher comorbidity burden, as assessed by the CCI, significantly increased the odds of implant loosening (OR = 1.378, 95%CI: 1.182-1.606, P < 0.001), consistent with prior studies linking CCI to aseptic loosening[21,24-28]. This association may be attributed to multiple mechanisms, including compromised bone quality in patients with osteoporosis or low BMD, which elevates complication risks[13,29-31]. Implant-derived wear debris triggers macrophage activation and periprosthetic osteoclast-mediated bone resorption through an aseptic inflammatory response, driven by cellular and humoral factors. These findings suggest a shared inflammatory pathway underlying implant failure, warranting further experimental investigation. Comorbidities may converge on common pathways, such as poor initial fixation due to osteoporotic bone (increasing micromotion), impaired osseointegration, and chronic inflammation that exacerbates mechanical stress. Additionally, nutritional deficiencies, iron overload, and hyperglycemia may further hinder bone-implant integration[32,33]. Indirect factors, such as urinary tract infection-related bacteremia in incontinent patients, could also contribute to prosthetic failure by seeding the implant interface reported by Budin et al[34], underscoring the complex relationship between systemic health and implant longevity. Preoperative risk assessment and long-term monitoring are essential to mitigate loosening and manage bone resorption progression. Conditions like diabetes, osteoporosis, or chronic inflammatory disorders impair bone remodeling and compromise implant fixation. Optimize metabolic control, manage BMD, and coordinate perioperative care (e.g., endocrinology, rheumatology consults, and urinary infection) to maintain bone quality and reduce the relevant risk[35].

Lifestyle: Multiple studies have established a link between elevated physical activity levels and increased rates of aseptic loosening[36,37], though some reports found no significant association Malik et al[38]. Our study corroborates these findings, demonstrating that smoking combined with high physical activity more than doubles the risk of aseptic loosening (OR = 2.198, 95%CI: 1.048-4.611, P = 0.037). Biomechanical analysis reveals that excessive motion - particularly during dynamic movements requiring extreme hip range of motion - exacerbates prosthetic wear through three key mechanisms: Increased cyclic loading on the acetabular component, especially in edge-loading positions; accelerated polyethylene wear due to sustained impingement during extreme flexion/abduction; and progressive micromotion at the bone-implant interface caused by unbalanced force distribution. These findings highlight the need for activity-specific rehabilitation protocols, particularly for patients engaged in occupations or sports involving extreme hip kinematics. Moreover, smoking further compounds the risk by impairing bone healing, creating a hazardous synergy in which mechanical stressors exceed the prosthesis’s fatigue resistance while compromised osseointegration fails to maintain stable fixation. Therefore, personalized rehabilitation strategies should be implemented. Moreover, promote smoking cessation, moderate alcohol use, and adherence to physiotherapy with progressive strengthening to sustain implant stability.

Diagnosis: Multiple studies have demonstrated the significance of preoperative diagnosis in predicting aseptic loosening[17,39,40]. Our findings corroborate these observations, (OR = 1.162, 95%CI: 1.009-1.338, P = 0.037). Potential underlying mechanisms encompass systemic/Local inflammation, peri-prosthetic microenvironment alterations, disease activity levels, postoperative mobility restrictions, glucocorticoid effects, heightened immune responses, and bone metabolism disturbances. Clinically, these findings underscore the importance of rigorous disease management, appropriate biologic agent use, and routine radiographic surveillance - even in asymptomatic patients - to facilitate early detection of component loosening. Future research should investigate targeted modulation of inflammatory pathways to enhance prosthesis longevity. Crucially, preoperative diagnosis should not singularly guide fixation strategy selection, as bone quality and patient activity levels appear more predictive of loosening risk than the original arthropathy etiology.

Anatomy: Existing research demonstrates that anatomical variations impact implant longevity[41,42]; however, our meta-analysis found no statistically significant association (OR = 1.082, 95%CI: 0.876-1.336, P = 0.466), potentially due to limited studies available for pooled analysis. Nevertheless, three key mechanisms underscore the clinical relevance of anatomical factors in implant loosening: Poor initial fixation - dysplastic acetabuli [low central-endge (CE) angle] or stovepipe femurs, e.g., low canal flare index (CFI) reduce implant-bone contact, increasing micromotion; compromised load transfer - osteoporotic bone exhibits inadequate stress shielding resistance, causing subsidence, while distal fixation dependence in stovepipe femurs promotes proximal bone resorption; and implant-bone mismatch - anatomic stems fail in non-anatomic canals, and excessive stem rigidity elevates fracture risk in osteoporotic bone. Although our quantitative synthesis showed nonsignificant results, we recommend thorough preoperative assessment of CE angle, CFI, and Dorr classification to guide implant selection. High-risk patients (e.g., Dorr C/Low CFI) may benefit from cemented stems or extensively porous-coated designs, while acetabular dysplasia cases might require augmentation (e.g., bone grafts/jumbo cups). Future research should investigate patient-specific implants for extreme anatomies, finite element analyses of load distribution across femoral morphologies, and bone-strengthening therapies to enhance fixation in compromised bone stock.

Ethnicity and polymorphisms: In addition to the factors included in our meta-analysis, ethnicity and genetic polymorphisms were also found to significantly influence aseptic loosening risk. While existing studies report conflicting findings regarding racial disparities between White and Black populations[16,43,44], our analysis demonstrates a protective effect in non-White populations (OR = 0.445, 95%CI: 0.302-0.655, P < 0.001), with Hispanic (HR = 0.49) and Asian (HR = 0.35) patients showing particularly lower risks compared to White patients. These ethnic disparities may arise from variations in femoral geometry, BMD, activity patterns, and genetic predispositions affecting bone remodeling. However, the limited number of studies examining racial factors (only one reporting OR) underscores the need for standardized demographic reporting to minimize bias and enhance comparability. Furthermore, genetic polymorphisms exhibited a significant protective effect (OR = 0.723, 95%CI: 0.617-0.849, P < 0.001)[45,46]. potentially mediated through biological pathways involving macrophage apoptosis regulation (e.g., BCL2 expression) and neuropeptide-mediated osteogenic signaling, which collectively influence wear particle-induced inflammation and bone remodeling. Future studies should systematically report both ethnicity and genetic polymorphisms to better elucidate their roles in aseptic loosening.

Surgical related factors

Research consistently identifies surgical technique and operator expertise as primary determinants of early implant loosening[47-52]. Our study corroborates these findings, demonstrating a significant protective effect against aseptic loosening (OR = 1.048, 95%CI: 1.021-1.075, P < 0.001), where each incremental improvement in technical proficiency corresponded to reduced failure risk. Experienced surgeons achieve superior component positioning, including optimal acetabular inclination and femoral stem alignment, thereby minimizing edge loading and abnormal stress distributions. Next, meticulous bone preparation - particularly preservation of cancellous bone architecture and proper cementation technique - enhances primary stability. Conversely, less experienced operators demonstrate higher rates of technical errors such as varus stem placement, incomplete cement mantles, and inadequate impaction grafting, all of which predispose to early micromotion and compromised osseointegration. These observations highlight the clinical relevance of structured surgical training programs and volume-outcome relationships in pTHA. However, heterogeneity in volume classification methodologies may account for discrepancies across studies. Surgeon fellowship training may require modification to mitigate potential risks.

Prosthesis related factors

Multiple studies have investigated the influence of implant characteristics on aseptic loosening risk[53-61]. Prosthesis-related factors demonstrated statistically significant yet inconsistent effects (OR = 1.497, 95%CI: 1.203-1.864, P < 0.001). The underlying mechanisms may be explained by several factors. First, fixation method selection significantly impacts outcomes: Cementless stems exhibit higher early failure rates than cemented fixation, likely due to differences in proximal load transfer efficiency. Second, bearing surface materials play a crucial role, with ceramic heads demonstrating lower wear rates and consequently reduced particle-induced osteolysis risk compared to metal counterparts. Third, component positioning affects long-term stability; excessive acetabular abduction angles may increase loading in Charnley zones II and III, potentially compromising fixation[62].

While modern designs such as hydroxyapatite-coated stems show enhanced osseointegration, certain configurations like metal-on-metal articulations exhibit time-dependent failure patterns reported by Khatod et al[63]. These findings highlight the critical importance of patient-specific prosthesis selection: Cementless fixation may be preferable for younger patients with adequate bone stock, whereas ceramic bearings suit active individuals. Additionally, precise component alignment is essential for long-term success. Tailor component selection to patient anatomy and bone stock, ensure precise alignment, and favor proven fixation techniques to achieve durable osseointegration. Given the current limitations in high-quality evidence, further research is warranted to elucidate risk factors associated with aseptic loosening in contemporary implant designs, and determine whether modification of identified risk factors can reduce aseptic loosening incidence following pTHA.

Strengths and limitations

This study represents the most up-to-date systematic review and meta-analysis examining factors associated with aseptic loosening following pTHA. A key strength is the inclusion of over 520789 participants, enhancing the statistical power of our findings. Additionally, the diversity of assessed factors-encompassing patient-, surgeon-, and implant-related variables - improves the generalizability of our results. However, several limitations must be acknowledged. First, while meta-analysis cannot establish causal mechanisms, we mitigated potential bias and improved efficiency through multiple imputation techniques. Second, our analysis was restricted to English-language studies from four databases, and methodological quality varied significantly across included studies. Many lacked raw OR data, preventing the calculation of certain relevant factors. Nevertheless, our stringent eligibility criteria ensured a baseline level of evidence, supporting the plausibility of our conclusions. Additionally, some factors (e.g., race) had insufficient data for robust meta-analysis, warranting further investigation. Third, inconsistencies in reported factors across studies may reflect differences in methodology, leading to the exclusion of some relevant but unsuitable data. Fourth, prospective studies on loosening risk factors remain scarce, however, retrospective studies were deemed acceptable for our prognostic analysis.

The differential significance of these variables underscores the complexity of the aseptic loosening and the need for multifactorial analysis. We focused on patient-, surgeon-, and implant-related factors, other contributors may exist. Despite numerous studies examining these variables, insufficient raw data precluded stratified analyses. Furthermore, due to the extensive timeframe and variability in the included studies, we could not thoroughly assess evolving trends in polyethylene processing, sterilization methods, implant design, or surgical techniques. A more precise analysis would require a large, homogenous cohort with uniform implant designs, surgical approaches, and material properties. Given the current evidence, future studies must prioritize methodological rigor to better delineate these risk factors.

CONCLUSION

A thorough understanding of risk factors is essential for clinical decision-making in pTHA, as it enables the identification of patients at increased risk of aseptic loosening. Surgeons should counsel pTHA patients regarding this potential complication. Such awareness facilitates targeted interventions for high-risk individuals, including personalized treatment plans, optimized surgical techniques, and improved prosthetic designs to reduce loosening incidence.