Abstract
Background
Chronic inflammation and lipid metabolism are closely associated with osteoporotic fractures. Previous studies have reported associations between Apolipoprotein A1 (ApoA1) and inflammatory markers in specific inflammatory diseases. Nevertheless, the association between ApoA1 and the Systemic Inflammatory Response Index (SIRI) has been scarcely investigated in populations with osteoporotic fractures. We therefore conducted this cross-sectional study to examine this association.
Methods
In this cross-sectional study, 2067 patients with osteoporotic fractures (OPF) needing surgical intervention at Kunshan Hospital affiliated with Jiangsu University from January 2017 to July 2022 were enrolled. The relationship between serum ApoA1 concentrations and SIRI was evaluated and described using linear regression models and smooth curve fitting. A two-piecewise linear regression model was employed to identify the inflection point. Additionally, stratified and univariate analyses were conducted.
Results
After accounting for potential confounders, serum ApoA1 concentrations were discovered to have an inverse association with SIRI (β = -0.822, 95% CI (-1.491, -0.153), P = 0.016). Moreover, the smoothed plot revealed a non-linear relationship between serum ApoA1 concentrations and SIRI, with the inflection point observed at ApoA1 levels of 1.25 g/L. The magnitude of effect and 95% CI on either side of the inflection point were -2.258 (-3.541, -0.974) and 0.512 (-0.706, 1.729), respectively. In addition, further subgroup analyses reinforced the consistency of the association between ApoA1 concentrations and SIRI.
Conclusions
Our study revealed a threshold effect between serum ApoA1 levels and SIRI in patients with OPF, demonstrating a negative correlation between ApoA1 and SIRI within a specific range. However, further research is warranted to expand and validate these findings.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12891-025-09460-8.
Keywords: Apolipoprotein A1, Systemic inflammation response index, Osteoporotic fractures
Background
Osteoporotic fractures (OPF), also known as fragility fractures, are characterized by fractures that happen in the presence of established osteoporosis or from minimal trauma, such as falls from a standing height, lifting, or fractures occurring spontaneously without any significant injury [1, 2]. The incidence of OPF is rising worldwide due to the increasing elderly population [3]. In China, the prevalence of osteoporosis (OP) among middle-aged and elderly residents, adjusted for age, is reported to be 33.49% [4], with fragility fractures being the most significant complication linked to osteoporosis [5]. It is anticipated that by 2050, the yearly occurrence of fractures related to osteoporosis will hit 5.99 million, leading to an estimated expense of 25.43 billion USD [6]. These alarming figures highlight the growing challenge of OPF as a critical public health concern, profoundly affecting individuals’ lives.
Apolipoprotein A1 (ApoA1) is a major constituent of high-density lipoprotein (HDL), making up roughly 70% of its composition. It plays a role in functions including immune regulation, modulation of oxidative stress, and lipid metabolism [7]. Low levels of ApoA1 can lead to weakened immune responses, increased infection risk, and the development of cardiovascular diseases [8]. The systemic inflammatory response index (SIRI) is a newly introduced biomarker first proposed by Qi et al. to predict the prognosis of pancreatic cancer [9]. It is determined using the counts of three types of peripheral blood cells and serves to evaluate the status of systemic inflammatory and immune response. Recent studies have identified a strong link between SIRI and conditions such as cancer [10, 11], and ischemic stroke [12], where elevated SIRI values are frequently correlated with poorer outcomes and reduced survival rates. It has been reported that SIRI could potentially be used to detect and prevent bone loss in postmenopausal women [13]. This implies that SIRI might hold significant potential for application in the field of bone metabolism.
In the development of osteoporosis, factors such as inflammation, immune system abnormalities, and abnormal lipid levels are crucial [14–16]. Despite the traditionally recognized anti-inflammatory properties of ApoA1, recent evidence has revealed complex and paradoxical relationships between ApoA1 and bone metabolism. A recent large population-based cross-sectional study by Sun et al., utilizing data from the National Health and Nutrition Examination Survey (NHANES), reported that higher ApoA1 levels were paradoxically associated with increased osteoporosis risk [17]. This unexpected finding suggests that ApoA1 may have context-dependent roles in bone-related pathophysiology that differ from its general anti-inflammatory properties, which may challenge the conventional understanding of ApoA1’s protective effects and inspire the necessity for further investigation in specific populations, particularly those individuals with osteoporotic fractures. Additional studies have also explored the inverse relationship between apoA1 and blood inflammatory markers such as C-reactive protein (CRP) and Tumor Necrosis Factor α (TNF-α) in individuals with coronary heart disease [18]. Given the current limited knowledge about the separate link between ApoA1 levels and SIRI, and considering the paradoxical associations observed between ApoA1 and bone health, the primary objective of this study was to investigate the connection between serum ApoA1 levels and SIRI in individuals with osteoporotic fractures. The aim was to deepen our understanding of the clinical relevance and possible uses of this relationship in the realm of OPF.
Materials and methods
Study framework and subjects
This research employed a retrospective observational approach to examine the connection between serum apoA1 concentrations and SIRI among Chinese individuals with OPF. The research was carried out at the Kunshan Hospital affiliated with Jiangsu University, which caters to a community of more than 3,000,000 residents in Kunshan City, Jiangsu Province, China. Digital medical records were collected for all patients admitted with an OPF between January 1, 2017, and July 29, 2022. At the outset, the study included 3,358 patients aged 50 and above who had osteoporotic fractures necessitating hospitalization for surgery. The criteria for including OP were as follows: (1) a diagnosis of OP determined by a T-Score of -2.5 or less, even if there are no major bone fractures; and (2) the presence of bone instability and fractures without any other metabolic bone diseases, along with a normal bone density (T-Score) [19]. Patients lacking complete data on ApoA1, neutrophil count, monocyte count, or lymphocyte count were excluded from the study. Moreover, to minimize the impact of patients’ specific conditions at the time on the association between ApoA1 and SIRI under investigation, individuals with inflammation, infection, shock, recent administration of antibiotics, statins, or glucocorticoids, as well as those with missing covariate data, were also excluded from the study. Medication usage definitions were based on prior studies [20–22], with statin usage defined as having taken at least one type of statin in the past three months, antibiotic usage defined as having used antibiotics in the past 30 days, and glucocorticoid use defined as receiving oral or intravenous glucocorticoids within the previous 180 days. In the end, a total of 2067 patients incorporated into this analysis (Fig. 1). The research received approval from the Ethics Committee at our hospital (Approval No: 2021-06-015-K01) and adhered to the principles outlined in the Helsinki Declaration. All analyses were performed by individuals who were unaware of the patients’ identities.
Fig. 1.
Study flow chart
Dependent variable and independent variable
In this research, serum ApoA1 levels were assessed as the independent variable through an electro-chemiluminescence immunoassay conducted on the LABOSPECT 008AS platform (Hitachi High-Tech Co., Tokyo, Japan). The SIRI, calculated as the multiplication of neutrophil count and monocyte count divided by lymphocyte count, was calculated as the dependent variable [9]. The measurement of neutrophils, monocytes, and lymphocytes was conducted using nuclear staining and flow cytometry with a Sysmex XN-10 (B4) hematology analyzer. All data were gathered by skilled technicians following consistent protocols and utilizing the same instrument. Standard quality control procedures were conducted on the device before participant checks each day.
Covariates analyses
This study analyzed several potential covariates, including age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA) score [23], Charlson Comorbidity Index (CCI) [24], hypertension, diabetes, platelet count, smoking, drinking, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). Smoking is defined as having smoked currently or in the past 12 months. Alcohol use is characterized as having consumed alcohol at least once a week over the previous 12 months. Blood samples were collected from patients after an 8-hour fasting period, and all clinical variables were measured within three days of hospital admission.
Statistical analyses
Population demographics, laboratory measurements, and clinical data were presented as averages with standard deviation (SD) or medians with interquartile ranges (25th and 75th percentiles) for continuous variables. Categorical data were presented as counts with percentages. For categorical data, univariate analysis was conducted using Pearson’s chi-square test or Fisher’s exact test. Continuous variables were analyzed with either the independent sample t-test or the Mann-Whitney U test, depending on their distribution (normal or non-normal, respectively). The association between characteristics of osteoporotic fracture patients and SIRI was also examined through univariate analysis.
This research explored the direct connection among ApoA1 and SIRI in OPF patients. Following similar studies [17, 25], this was achieved by employing the Generalized Estimating Equation (GEE) approach, with suitable adjustments made for confounding variables. The models produced were fully adjusted (Model 4), partially adjusted (Model 2 and 3), and not adjusted (Model 1). Model 1 was the unadjusted version; Model 2 included adjustments for gender, age, BMI, primary diagnosis, ASA, and CCI; Model 3 incorporated additional adjustments for hypertension, diabetes, smoking, and drinking; and Model 4 further included adjustments for platelet count, ALT and AST.
Generalized Additive Models (GAM) are employed to identify potential nonlinear relationships. If a notable association is detected, a two-piecewise linear regression model is utilized to determine breakpoint effects within the smooth curve. An algorithmic approach using maximum likelihood models is applied to iteratively determine the inflection points of these distinct ratio curves [26]. After categorizing patients based on specific covariates, further analyses were conducted to assess the robustness of the study results and to compare differences among various patient categories. Interactions and adjustments within subgroups were evaluated using likelihood ratio tests (LRT).
All analyses were conducted using R packages (http://www.R-project.org, The R Foundation) and EmpowerStats software (http://www.empowerstats.com, X&Y Solutions, Inc, MA, USA) with a significance level set at a two-sided P < 0.05.
Results
Patient characteristics
Table 1 presents an overview of the initial characteristics of 2067 OPF patients admitted from January 2017 to July 2022, categorized by their ApoA1 quartiles. The average age of these patients was 69.06 ± 11.00 years. Among them, 67.59% were men and 32.41% were women. The mean ApoA1 level was 1.22 ± 0.24 g/L, and the average SIRI was 3.22 ± 3.75. Patients were categorized into quartiles based on their ApoA1 levels: <1.05 g/L,1.06–1.19 g/L,1.20–1.34 g/L, and 1.35–2.49 g/L. Notable differences were found between these quartiles in ALT, SIRI, platelet count, and gender.
Table 1.
Patient characteristics based on ApoA1 quartiles
| Characteristics | Total | Mean ± SD / N (%)/Median(Q1,Q3) | P-value | |||
|---|---|---|---|---|---|---|
Q1( 1.05 g/L) |
Q2(1.06–1.19 g/L) | Q3(1.20–1.34 g/L) | Q4(1.35–2.49 g/L) | |||
| N | 2067 | 500 | 532 | 513 | 522 | |
| Age, years | 69.06 ± 11.00 | 69.99 ± 11.32 | 69.10 ± 10.99 | 68.65 ± 10.45 | 68.51 ± 11.20 | 0.133 |
| BMI, kg/m2 | 22.99 ± 3.43 | 22.83 ± 3.46 | 23.02 ± 3.43 | 23.23 ± 3.46 | 22.88 ± 3.35 | 0.235 |
| ALT, U/L | 18(13,25) | 17(13,24) | 18(13,26) | 18(14,25) | 19(14,25) | 0.428 |
| AST, U/L | 21(18,27) | 21(17,27) | 21(18,27) | 21(18,26) | 22(18,27) | 0.897 |
| SIRI | 2.16(1.20,3.86) | 2.58(1.45,4.26) | 2.20(1.27,3.87) | 1.89(1.07,3.32) | 2.08(1.11,3.72) | 0.017 |
| Platelet count, × 109/L | 176.25 ± 61.68 | 172.03 ± 68.21 | 177.15 ± 61.29 | 174.84 ± 61.11 | 180.76 ± 55.61 | 0.137 |
| Gender, N (%) | < 0.001 | |||||
| female | 1397 (67.59%) | 363 (72.60%) | 382 (71.80%) | 347 (67.64%) | 305 (58.43%) | |
| male | 670 (32.41%) | 137 (27.40%) | 150 (28.20%) | 166 (32.36%) | 217 (41.57%) | |
| Primary diagnosis, N (%) | 0.933 | |||||
| Thoracic vertebral fracture | 343 (16.59%) | 89 (17.80%) | 85 (15.98%) | 87 (16.96%) | 82 (15.71%) | |
| Lumbar fracture | 609 (29.46%) | 144 (28.80%) | 154 (28.95%) | 163 (31.77%) | 148 (28.35%) | |
| Humerus fracture | 96 (4.64%) | 20 (4.00%) | 25 (4.70%) | 26 (5.07%) | 25 (4.79%) | |
| Radial fracture | 265 (12.82%) | 67 (13.40%) | 74 (13.91%) | 61 (11.89%) | 63 (12.07%) | |
| Femoral fracture | 754 (36.48%) | 180 (36.00%) | 194 (36.47%) | 176 (34.31%) | 204 (39.08%) | |
| CCI score categorical | 0.752 | |||||
| 0 | 1868 (90.37%) | 449 (89.80%) | 483 (90.79%) | 464 (90.45%) | 472 (90.42%) | |
| 1 | 158 (7.64%) | 40 (8.00%) | 43 (8.08%) | 36 (7.02%) | 39 (7.47%) | |
| ≥ 2 | 41 (1.98%) | 11 (2.20%) | 6 (1.13%) | 13 (2.53%) | 11 (2.11%) | |
| ASA score categorical | 0.648 | |||||
| 1 | 178 (8.61%) | 42 (8.40%) | 48 (9.02%) | 36 (7.02%) | 52 (9.96%) | |
| 2 | 1414 (68.41%) | 335 (67.00%) | 361 (67.86%) | 361 (70.37%) | 357 (68.39%) | |
| ≥ 3 | 475 (22.98%) | 123 (24.60%) | 123 (23.12%) | 116 (22.61%) | 113 (21.65%) | |
| Smoking, N (%) | 0.253 | |||||
| No | 1917 (92.74%) | 462 (92.40%) | 502 (94.36%) | 477 (92.98%) | 476 (91.19%) | |
| Yes | 150 (7.26%) | 38 (7.60%) | 30 (5.64%) | 36 (7.02%) | 46 (8.81%) | |
| Drinking, N (%) | 0.521 | |||||
| No | 1969 (95.26%) | 475 (95.00%) | 513 (96.43%) | 487 (94.93%) | 494 (94.64%) | |
| Yes | 98 (4.74%) | 25 (5.00%) | 19 (3.57%) | 26 (5.07%) | 28 (5.36%) | |
| Hypertension, N (%) | 0.997 | |||||
| NO | 1792 (86.70%) | 432 (86.40%) | 462 (86.84%) | 445 (86.74%) | 453 (86.78%) | |
| Yes | 275 (13.30%) | 68 (13.60%) | 70 (13.16%) | 68 (13.26%) | 69 (13.22%) | |
| Diabetes, N (%) | 0.318 | |||||
| No | 1992 (96.37%) | 478 (95.60%) | 519 (97.56%) | 495 (96.49%) | 500 (95.79%) | |
| Yes | 75 (3.63%) | 22 (4.40%) | 13 (2.44%) | 18 (3.51%) | 22 (4.21%) | |
Abbreviations ApoA1 Apolipoprotein A1, SD standard deviation, Q1 first quartile, Q2 second quartile, Q3 third quartile, Q4 fourth quartile, BMI body mass index, ALT alanine aminotransferase, AST aspartate aminotransferase, SIRI systemic inflammation response index, CCI Charlson comorbidity index, ASA American Society of Anesthesiologists
Univariate analyses of factors associated with SIRI
In the univariate analysis presented in Table 2, a negative correlation exists between the BMI and levels of ApoA1 with the SIRI. The ALT and AST levels exhibited a positive relationship with SIRI. Furthermore, patients with a CCI score of 1 had lower SIRI levels compared to those with a score of 0.
Table 2.
Univariate analysis for SIRI
| Characteristics | Mean ± SD / N (%) | βa (95% CI) | P-value | P-value for trend |
|---|---|---|---|---|
| Age, years | 69.06 ± 11.00 | 0.005 (-0.010, 0.020) | 0.511 | |
| BMI, kg/m2 | 23.00 ± 3.43 | -0.053(-0.101, -0.006) | 0.027 | |
| ALT, U/L | 22.42 ± 20.79 | 0.011 (0.003, 0.019) | 0.005 | |
| AST, U/L | 24.81 ± 15.11 | 0.044 (0.033, 0.054) | < 0.001 | |
| Platelet count, × 109/L | 176.25 ± 61.69 | 0.002 (-0.001, 0.004) | 0.230 | |
| Gender | ||||
| female | 1397 (67.59%) | Reference | ||
| male | 670 (32.41%) | 0.327 (-0.018, 0.673) | 0.064 | |
| Primary diagnosis, N (%) | ||||
| Thoracic vertebral fracture | 343 (16.59%) | Reference | 0.746 | |
| Lumbar fracture | 609 (29.46%) | -0.116 (-0.613, 0.381) | 0.647 | |
| Humerus fracture | 96 (4.64) | -0.066 (-0.917, 0.784) | 0.879 | |
| Radial fracture | 265 (12.82%) | 0.050 (-0.552, 0.652) | 0.871 | |
| Femoral fracture | 754 (36.48%) | -0.006 (-0.486, 0.473) | 0.980 | |
| CCI score, N (%) | ||||
| 0 | 1868 (90.37%) | Reference | 0.574 | |
| 1 | 158 (7.64%) | -0.832 (-1.440, -0.223) | 0.007 | |
| ≥ 2 | 41 (1.98%) | 1.04 (-0.124, 2.195) | 0.080 | |
| ASA, N (%) | ||||
| 1 | 178 (8.61%) | Reference | 0.257 | |
| 2 | 1414 (68.41%) | -0.172 (-0.758, 0.413) | 0.564 | |
| ≥ 3 | 475 (22.98%) | -0.345 (-0.991, 0.302) | 0.297 | |
| Hypertension, N (%) | ||||
| No | 1792 (86.70%) | Reference | ||
| Yes | 275 (13.30%) | -0.405 (-0.882, 0.071) | 0.100 | |
| Diabetes, N (%) | ||||
| No | 1992 (96.37%) | Reference | ||
| Yes | 75 (3.63%) | -0.533 (-1.399, 0.332) | 0.228 | |
| Smoking, N (%) | ||||
| No | 1917 (92.74%) | Reference | ||
| Yes | 150 (7.26%) | 0.391 (-0.233, 1.015) | 0.219 | |
| Drinking, N (%) | ||||
| No | 1969 (95.26%) | Reference | ||
| Yes | 98 (4.74%) | 0.391 (-0.371, 1.152) | 0.315 | |
| ApoA1b, g/L | 1.22 ± 0.24 | -0.777 (-1.455, -0.099) | 0.025 | |
| ApoA1 quartile, N (%) | ||||
| Q1(< 1.05 g/L) | 500 (24.19%) | Reference | 0.005 | |
| Q2(1.06–1.19 g/L) | 532 (25.74%) | -0.381 (-0.839, 0.076) 0.103 | ||
| Q3(1.20–1.34 g/L) | 513 (24.82%) | -0.695 (-1.157, -0.233) 0.003 | ||
| Q4(1.35–2.49 g/L) | 522 (25.25%) | -0.595 (-1.055, -0.135) 0.011 | ||
Abbreviations SIRI systemic inflammation response index, SD standard deviation, CI confidence interval, ALT alanine aminotransferase, AST aspartate aminotransferase, BMI body mass index, CCI Charlson comorbidity index, ASA American Society of Anesthesiologists, ApoA1 Apolipoprotein A1
a Dependent variable SIRI, as a result of univariate analyses for SIRI
b For continuous variables
Exploration of the association between apoA1 levels and SIRI
Linear regression models were used to evaluate the association between ApoA1 and SIRI. The results for four distinct models are presented in Table 3. In Model 1, which had no adjustments, a significant association was found between these variables. (β = -0.777, 95% CI -1.455, -0.099, P = 0.025). Model 2, which accounted for age, gender, BMI, primary diagnosis, ASA, and CCI indicators, exhibited a comparable correlation (β = -0.861, 95% CI -1.543, -0.179, P = 0.013). Additionally, Model 3, which further adjusted for hypertension, diabetes, smoking, and drinking in addition to the variables in Model 2, demonstrated a notable negative correlation (β = -0.855, 95% CI -1.538, -0.173, P = 0.014). Despite further adjustments for platelet count, ALT, and AST in the fully adjusted Model 4, the correlation continued to be statistically significant (β = -0.822, 95% CI: -1.491, -0.153, P = 0.016).
Table 3.
Association between ApoA1 concentrations and SIRI across various models (N = 2067)
| Model 1a | Model 2b | Model 3c | Model4d | |||||
|---|---|---|---|---|---|---|---|---|
| β(95%CI) | P-value | β(95%CI) | P-value | β(95%CI) | P-value | β(95%CI) | P-value | |
| ApoA1 Per 1 g/L increment | -0.777 (-1.455, -0.099) | 0.025 | -0.861 (-1.543, -0.179) | 0.013 | -0.855 (-1.538, -0.173) | 0.014 | -0.822 (-1.491, -0.153) | 0.016 |
| ApoA1 quartile | ||||||||
| Q1( ≤1.050 g/L) | Reference | Reference | Reference | Reference | ||||
| Q2(1.060–1.190 g/L) | -0.381 (-0.839, 0.076) | 0.103 | -0.359 (-0.816, 0.097) | 0.123 | -0.352 (-0.810, 0.106) | 0.132 | -0.298 (-0.747, 0.151) | 0.193 |
| Q3(1.200–1.340 g/L) | -0.695 (-1.157, -0.233) | 0.003 | -0.692 (-1.154, -0.230) | 0.003 | -0.686 (-1.148, -0.224) | 0.004 | -0.640 (-1.093, -0.188) | 0.006 |
| Q4(1.350–2.490 g/L) | -0.595 (-1.055, -0.135) | 0.011 | -0.649 (-1.111, -0.188) | 0.006 | -0.641 (-1.104, -0.179) | 0.007 | -0.610 (-1.063, -0.156) | 0.008 |
| P-value for trend | 0.005 | 0.002 | 0.003 | 0.003 | ||||
Abbreviations ApoA1 Apolipoprotein A1, SIRI systemic inflammation response index, CI confidence interval, Q1 first quartile, Q2 second quartile, Q3 third quartile, Q4 fourth quartile, ALT alanine aminotransferase, AST aspartate aminotransferase, BMI body mass index, CCI Charlson comorbidity index, ASA American Society of Anesthesiologists
a: No adjustment
b: adjusted for: gender, age, BMI, primary diagnosis, ASA, CCI
c: adjusted for: gender, age, BMI, primary diagnosis, ASA, CCI, hypertension, diabetes, smoking, drinking
d: adjusted for: gender, age, BMI, primary diagnosis, ASA, CCI, hypertension, diabetes, smoking, drinking, platelet count, ALT, AST
In addition, we performed a sensitivity analysis by categorizing ApoA1 into quartiles. In the fully adjusted Model 4, the pattern of the association between ApoA1 and SIRI remained unchaged (P for the trend was 0.003). To validate the robustness of our findings, we conducted subgroup analyses across various categorical variables, and no significant interaction effects were observed (Table S1).
Spline smoothing plot and threshold analyses
In this research, we examined the non-linear association between ApoA1 and SIRI, given that both variables were continuous. Figure 2 illustrates the findings from our analysis. We identified a threshold non-linear relationship between ApoA1 and SIRI within the study, even after adjusting for gender, age, BMI, primary diagnosis, ASA, CCI, hypertension, diabetes, smoking, drinking, platelet count, ALT, and AST. We used a two-piecewise linear modeling approach to identify the turning point, which we determined to be 1.25 g/L for ApoA1. On the right side of the turning point, the effect size is 0.512, with a 95% CI ranging from − 0.706 to 1.729, and P of 0.410, indicating no significant correlation between ApoA1 and SIRI. However, a negative correlation was observed between ApoA1 and SIRI to the left of the turning point. The determined effect magnitude was − 2.258, with a 95% CI ranging from − 3.541 to -0.974, and the associated P was 0.001 (Table 4).
Fig. 2.
The adjusted smoothed curves illustrate the association between serum ApoA1 levels and the SIRI. A generalized additive model identified a threshold non-linear association between serum ApoA1 levels and SIRI in patients with OPF. The upper and lower blue curves denote the boundaries of the 95% confidence interval, whereas the central red curve elucidates the association between ApoA1 levels and SIRI. The model has been adjusted for variables including gender, age, BMI, primary diagnosis, ASA, CCI, hypertension, diabetes, smoking, drinking, platelet count, ALT, and AST. In Model 4, the red curve indicates an inflection point (K) at 1.25. ApoA1 Apolipoprotein A1, SIRI systemic inflammation response index, OPF osteoporotic fractures, CI confidence interval, ALT alanine aminotransferase, AST aspartate aminotransferase, BMI body mass index, CCI Charlson comorbidity index, ASA American Society of Anesthesiologists
Table 4.
Threshold effect analysis of the association between ApoA1 and SIRI
| Model4a | ||
|---|---|---|
| β (95%CI) | P-value | |
| Model Ab | ||
| One line effect | -0.822 (-1.491, -0.153) | 0.016 |
| Mode Bc | ||
| ApoA1 turning point (K), g/L | 1.25 | |
| < K | -2.258 (-3.541, -0.974) | 0.001 |
| > K | 0.512 (-0.706, 1.729) | 0.410 |
| Slope 2–Slope 1 | 2.769 (0.656, 4.882) | 0.010 |
| LRT testd | 0.010 | |
Abbreviations ApoA1 Apolipoprotein A1, SIRI systemic inflammation response index, CI confidence interval, ALT alanine aminotransferase, AST aspartate aminotransferase, BMI body mass index, CCI Charlson comorbidity index, ASA American Society of Anesthesiologists
aAdjusted for Gender; age; BMI; primary diagnosis; ASA; CCI, hypertension, diabetes, smoking, drinking, platelet count, ALT, AST
bLinear analysis, P-value < 0.05 indicates a linear relationship
cNon-linear analysis
dLRT, likelihood ratio test, P-value < 0.05 means Model II is significantly different from Model I, which indicates a non-linear relationship
Discussion
This research involved a retrospective cross-sectional examination of 2067 surgical patients with osteoporotic fractures to explore the relationship between serum ApoA1 concentrations and the inflammatory marker SIRI. To our knowledge, here are limited studies examining the correlation between these two factors. Our main finding was that, after controlling for confounding variables, there was a non-linear association between ApoA1 concentrations and SIRI among Chinese individuals with osteoporotic fractures in Kunshan. The correlation between ApoA1 and SIRI varied on either side of the inflection point. Specifically, ApoA1 exhibited a negative correlation with SIRI when ApoA1 levels were under 1.25 g/L, whereas no significant association was found when ApoA1 levels surpassed 1.25 g/L. This finding implies that ApoA1 may have a protective role in patients with osteoporotic fractures under specific conditions.
Disruptions in lipid metabolism are strongly linked to systemic inflammation, and inflammation triggered by lipid metabolism disturbances can accelerate the advancement of cardiovascular diseases [27, 28]. ApoA1 plays a crucial role in maintaining lipid metabolism balance and overall health. It participates in reverse cholesterol transport, helping to move cholesterol from peripheral tissues and macrophages to the liver, which enhances cholesterol excretion and helps prevent atherosclerosis [28]. In recent years, there has been growing recognition of the role of chronic inflammation in the development and progression of various diseases, such as diabetes [29], cancer [30] and Alzheimer’s disease [31]. Simultaneously, a growing body of research is increasingly uncovering the anti-inflammatory effects of ApoA1, leading researchers to focus more on its beneficial roles in certain inflammatory diseases [32, 33]. SIRI, as a reliable marker of inflammation, has demonstrated predictive value for a range of diseases. For example, Zhu et al. [34] observed that SIRI could provide more accurate prognostic assessment compared to CRP in individuals with chronic heart failure. Recent research in orthopedics has identified a negative relationship between SIRI and bone mineral density at certain locations in hypertensive individuals. Additionally, SIRI may signal a higher risk of osteoporotic and hip fractures [35]. As a blood-based indicator, SIRI represents the overall inflammatory condition of the body. It can be readily measured in clinical environments. Our study has pinpointed a turning point in the association between ApoA1 and SIRI, deepening our comprehension of the connection between lipids and inflammation.
Several studies have investigated the anti-inflammatory mechanisms of ApoA1. Inflammation is marked by the accumulation of inflammatory cells like neutrophils and macrophages, along with the release of various inflammatory mediators [36–38]. Early studies using lipopolysaccharide-induced inflammation in animals have shown that overexpression of ApoA1 can reduce the release of inflammatory factors such as TNF-α, Interleukin-6 (IL-6), and IL-1β [39], underscoring ApoA1’s potential in modulating inflammatory responses. At the cellular level, ApoA1 and HDL have been found to inhibit the activation, adhesion, spreading, and migration of neutrophils [40]. In the context of intracellular signaling, HDL has been found to induce the activating transcription factor 3 in macrophages, which subsequently downregulates the expression of pro-inflammatory cytokines triggered by toll-like receptors [41]. Macrophages are central to inflammation, and ApoA1 is recognized for its ability to inhibit macrophage activation through three key mechanisms. First, ApoA1 promotes the removal of excess cholesterol from cells by facilitating its efflux via the ATP-binding cassette transporter A1 (ABCA1) [42]. Furthermore, ApoA1 can engage with the ABCA1, activating the JAK2/ STAT3 signaling pathway, which in turn decreases the production of pro-inflammatory cytokines [43], Zhang et al. also demonstrated that ApoA1 inhibits the degradation of ABCA1, which leads to the removal of Toll-like receptor 4 (TLR4) from lipid rafts [44]. These studies enhance our understanding of how ApoA1 regulates inflammation at the molecular level. Recent research has indicated that an ApoA1-like peptide, ELK-2A2K2E, when activated by ABCA1, facilitates the degradation of TNF-α, IL-6, and Monocyte Chemoattractant Protein-1 (MCP-1) mRNA via the JAK2-STAT3-TTP signaling pathway, thus mitigating inflammation [45]. Moreover, the apolipoprotein A-I mimetic peptide (D-4 F) inhibits epithelial-mesenchymal transition within the TGF-β/Smad pathway, leading to decreased levels of inflammatory factors and oxidative stress [46]. Consequently, ApoA1 regulates inflammation by directly or indirectly inhibiting the production of inflammatory factors and suppressing the activation of inflammatory cells.
Previous studies examing serum samples from colorectal cancer patients have reported a significant negative correlation between APOA1 levels and systemic inflammatory markers, including CRP, IL-8 levels, and blood neutrophil counts [47]. A recent cross-sectional study involving patients with nosocomial coronary artery disease reported a negative correlation between ApoA1 levels with inflammatory markers such as CRP, high-sensitivity CRP, and TNF-α. However, no significant correlations were observed between ApoA1 and HDL levels with IL-6 and IL-8 [18]. These disparate findings related to the association between serum ApoA1 concentrations and specific inflammatory markers might be attributed to variations in the study populations. In our study population, the patients had recently experienced fractures. Bone healing involves three overlapping stages: inflammation, osteogenesis, and bone remodeling [48]. During the inflammatory phase following a fracture, neutrophils, macrophages, lymphocytes, and other cells in the hematoma facilitate the accumulation of inflammatory cells by secreting pro-inflammatory cytokines such as TNF-α, IL-1, and IL-6 [49, 50]. A study has also indicated that levels of anti-inflammatory cytokines IL-10, IL-19, IL-27, IL-28a, and IL-11 are higher in hematomas following fractures compared to peripheral blood [51]. In our investigation, we postulated that the anti-inflammatory effect of ApoA1 might be concentration-dependent when its levels are below 1.25 g/L. At lower ApoA1 levels, its anti-inflammatory properties might show a negative correlation with the SIRI. However, at elevated ApoA1 levels, the suppressive effect of ApoA1 on inflammatory responses might have reached its maximum. Due to the complex regulation of pro-inflammatory and anti-inflammatory factors related to osteoporosis and fractures, the relationship between SIRI and ApoA1 may not appear significant in this context.
Inflammation is a key factor in the process of osteoporosis [52]. In their clinical investigation on fibrous dysplasia-related bone disease, Lam et al. uncovered a negative correlation between IL-8 and femoral neck bone density, thus highlighting the involvement of IL-8 in inflammation-induced bone loss [53]. Hrmer suggested that IL-6 interaction with precursor cells promotes osteoclastogenesis, which may lead to increased bone resorption [54]. TNF-α can directly stimulate osteoclast formation by inducing receptor activator of nuclear factor kappa-B ligand expression in osteoblasts [55], highlighting its critical role in inflammation-driven bone resorption. An imbalance between bone formation and resorption, influenced by inflammatory factors, can initiate the onset of osteoporosis [56]. Our study unveiled a correlation between lower ApoA1 levels and higher systemic immune-inflammation index in osteoporotic fracture patients when ApoA1 levels were below 1.25 g/L. Therefore, using the SIRI derived from blood cell counts could help determine the need for lipid profile assessments in patients with osteoporotic fractures, highlighting a potential clinical application of our research.
This is among the limited research exploring the connection between serum ApoA1 concentrations and SIRI. In patients with osteoporotic fractures, ApoA1 shows an inverse relationship with SIRI within a particular range of ApoA1 concentrations, with a threshold of 1.25 g/L established by our study. This discovery offers fresh perspectives on how we understand and manage osteoporosis. On one hand, in inflammatory conditions like osteoporosis, ApoA1 might positively influence the control of blood inflammation levels in patients with osteoporotic fractures in specific situations. Conversely, a reduction in SIRI within a certain range may correspond with an increase in ApoA1, providing a new viewpoint on lipid metabolism and transport. Furthermore, osteoporotic fracture patients with elevated SIRI levels may require further lipid examinations. Increased SIRI levels might potentially have adverse effects on surgical risks and outcomes [57]. Clinically, regulating ApoA1 in patients with ApoA1 levels below 1.25 g/L may help reduce SIRI, providing a valuable suggestion for perioperative clinical interventions targeting SIRI. These insights will expand the pool of beneficial information related to the prevention and treatment of osteoporotic fractures.
Our study presents several advantages. For one, Certain potential confounders, such as recent statin medication usage, were excluded prior to the analysis. Additionally, the association between ApoA1 levels and SIRI was meticulously analyzed utilizing four distinct generalized linear models that controlled for a range of different potential confounding variables, while also incorporating GAM to elucidate the nonlinear relationships between these two variables. Thirdly, our study is among the few clinical investigations to indicate an anti-inflammatory role of ApoA1 and to determine a threshold for this effect. However, several limitations of this study need to be recognized. First, given the cross-sectional study design, our investigation cannot establish a causal relationship between serum ApoA1 levels and SIRI, but rather demonstrates their association without determining the underlying causal mechanisms. Second, while we excluded certain populations that might influence study outcomes and accounted for potential confounding factors, it remains challenging to comprehensively control for all unmeasured confounding variables, including patients’ nutritional status, dietary patterns, and physical activity levels, which may affect baseline ApoA1 concentrations and systemic inflammatory states. Third, study participants were predominantly recruited from a single medical center focusing on osteoporotic fracture patients, thereby limiting the generalizability of our findings to broader regional populations. Fourth, as an exploratory investigation, our observations may require validation through larger-scale, multicenter population studies. Fifth, our study lacks comprehensive data on other established inflammatory markers, such as C-reactive protein (CRP), interleukin-6, and tumor necrosis factor-α, which precludes direct comparison with SIRI and limits our ability to contextualize the inflammatory profile within the broader spectrum of inflammatory biomarkers in osteoporotic fracture patients. It is important to consider these limitations when interpreting the findings of this study and to encourage further research in order to validate and expand upon our results.
Conclusions
In the present investigation, we observed a correlation between serum ApoA1 and SIRI in osteoporotic fracture patients and identified a potential threshold effect. Our findings suggest that when ApoA1 levels fall below approximately 1.25 g/L, an inverse relationship may exist between ApoA1 and SIRI, indicating that ApoA1 appears to be associated with systemic inflammatory indices within a specific range in the osteoporotic fracture population. To our knowledge, this represents one of the limited studies exploring this particular relationship. Nevertheless, future investigations involving larger and more diverse patient cohorts remain essential to validate and further elucidate our preliminary observations.
Supplementary Material
Acknowledgements
None.
Abbreviations
- ApoA1
Apolipoprotein A1
- SIRI
Systemic inflammation response index
- OPF
Osteoporotic fractures
- OP
Osteoporosis
- HDL
High-density lipoprotein
- CRP
C-reactive protein
- TNF-α
Tumor necrosis factor α
- CI
Confidence interval
- ALT
Alanine aminotransferase
- AST
Aspartate aminotransferase
- BMI
Body mass index
- CCI
Charlson comorbidity index
- ASA
American society of anesthesiologists
- SD
Standard deviations
- GEE
Generalized estimating equations
- VIF
Variance inflation factor
- GAM
Generalized additive model
- LRT
Likelihood ratio test
- IL
Interleukin
- ABCA1
ATP-binding cassette transporter A1
- TLR4
Toll-like receptor 4
Authors’ contributions
Study design: KL, LC and YQG. Study conduct: CL and KL. Data collection: GJL, SHG, JQL, YQG, KL. Data analysis: GJL and KL. Data interpretation: GJL SHG and JQL. Drafting manuscript: GJL. Revising manuscript content: YQG and KL. Approving final version of manuscript: CL, KL and YQG. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Funding
The study was supported by National Natural Science Foundation of China (CN) (82172441), Suzhou City Major Disease Multicenter Clinical Research Project (CN) (DZXYJ202312), Special Funding for Jiangsu Province Science and Technology Plan (Key Research and Development Program for Social Development) (CN) (BE2023737), Medical Education Collaborative Innovation Fund of Jiangsu University (JDY2022013), Kunshan Key Research and Development Program Project (CN) (KS2126) and Gusu Health Talent Plan Scientific Research Project (CN) (GSWS2022109).
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This is a retrospective observational study. We received ethical approval from the Affiliated Kunshan Hospital of Jiangsu University (approval No. 2021-06-015-K01), and was compliant with the Declaration of Helsinki. As this was an observational study and data were gathered anonymously, written informed consent was not required for these analyses.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.