Prognostic Value of Osteoporosis in Elderly Patients with Stable Coronary Artery Disease Undergoing Percutaneous Coronary Intervention

Prognostic Value of Osteoporosis in Elderly Patients with Stable Coronary Artery Disease Undergoing Percutaneous Coronary Intervention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of Osteoporosis in Elderly Patients with Stable Coronary Artery Disease Undergoing Percutaneous Coronary Intervention Wenwen Xu¹, Jianning Li, Jun Zhou, Guoxin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7030982/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To investigate the impact of osteoporosis on the prognosis of elderly patients with stable coronary artery disease (CAD) after percutaneous coronary intervention (PCI). Methods This study included 215 patients diagnosed with stable CAD, who were divided into an osteoporosis group (n = 92) and a non-osteoporosis group (n = 123) based on their bone mineral density (BMD) T-scores. Clinical characteristics between the two groups were compared. Multivariate Cox regression analysis was used to assess the impact of osteoporosis on major adverse cardiovascular events (MACE). Kaplan-Meier curves were used for survival analysis, and Pearson correlation analysis was performed to examine the relationship between bone metabolism markers and MACE. Results The study showed that patients in the osteoporosis group were older and had a higher proportion of females. The osteoporosis group had significantly higher levels of bone metabolism markers (osteocalcin, PICP, PINP), a higher proportion of three-vessel disease, and a higher incidence of MACE compared to the non-osteoporosis group ( P < 0.05). Multivariate Cox regression analysis revealed that osteoporosis was an independent risk factor for MACE (HR = 1.80, 95% CI: 1.08–2.98). Kaplan-Meier curves demonstrated a higher incidence of MACE in the osteoporosis group compared to the non-osteoporosis group (Log-Rank χ 2 = 14.20, P < 0.001). Pearson correlation analysis found that BMD was negatively correlated with MACE (r=-0.328, P < 0.001), while osteocalcin, PICP, and PINP were positively correlated with MACE (r = 0.415, 0.394, 0.367, respectively, all P < 0.001). Conclusion Elderly patients with stable CAD and osteoporosis have an increased risk of MACE after PCI. Decreased bone mineral density and abnormal bone metabolism markers can serve as predictors of poor prognosis in these patients. Osteoporosis Coronary artery disease Percutaneous coronary intervention Prognosis Figures Figure 1 Introduction The treatment of stable coronary artery disease (CAD) typically involves two approaches: (1) anti-anginal drug therapy; (2) vascular revascularization, namely percutaneous coronary intervention (PCI) [ 1 ] . Forty years ago, PCI was introduced to alleviate angina in patients with stable CAD [ 2 ] . Today, over 500,000 PCI procedures are performed annually worldwide to treat this condition [ 3 ] . Studies show that PCI significantly reduces the incidence of angina and markedly improves patients’ quality of life [ 4 ] . In cases of acute coronary syndrome, PCI also reduces mortality and the incidence of subsequent myocardial infarction. Despite the significant symptom relief reported with PCI, its prognosis remains associated with numerous lifestyle factors and patient comorbidities [ 5 ] . Research indicates that age is a critical factor affecting post-PCI outcomes, with an approximately 8% increase in mortality for each additional year of age. Patients with a left ventricular ejection fraction below 50% exhibit significantly higher post-PCI mortality [ 6 – 8 ] . Additionally, diabetes, anemia, and smoking have been identified as independent risk factors for adverse post-PCI outcomes. Given the widespread use of PCI, identifying factors influencing its prognosis is crucial for improving long-term patient outcomes. Osteoporosis is a multifactorial metabolic bone disease characterized by reduced bone mass and deteriorated bone microarchitecture, leading to increased bone fragility and fracture risk [ 9 ] . Its pathogenesis involves an imbalance in bone homeostasis maintained by bone formation and resorption [ 10 ] . According to the World Health Organization’s bone density classification, the categories are: normal bone mass (T-score ≥ -1), osteopenia (T-score >-2.5 and < -1), osteoporosis (T-score ≤ -2.5), and severe osteoporosis (T-score ≤ -2.5 with fragility fractures) [ 11 ] . Globally, the prevalence of osteoporosis in the elderly is 21.7%, with the highest rate in Asia (24.3%), followed by Europe (16.7%) and the United States (11.5%). Recent clinical studies suggest that lower bone mineral density (BMD) is associated with an increased risk of myocardial infarction, independent of gender [ 12 ] . Epidemiological studies also report that reduced BMD is linked to higher incidence and mortality rates of stroke and heart failure [ 13 ] . However, no studies have yet explored the association between osteoporosis and post-PCI prognosis. This study analyzes patients with stable CAD treated with PCI, grouping them by osteoporosis status, to examine the correlation between prognosis and various bone mass parameters, as well as the impact of osteoporosis on prognosis. It aims to provide a new perspective for clinical post-PCI monitoring and individualized care. Methods 1.1 Study Design and Population This retrospective cohort study aimed to investigate the impact of osteoporosis on the prognosis of elderly patients with stable coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). A total of 215 patients with stable CAD, admitted to the Cardiology Department of Qixia District Hospital, were enrolled. Based on bone mineral density (BMD) T-scores, patients were divided into an osteoporosis group (n = 92) and a non-osteoporosis group (n = 123). Clinical characteristics and the impact of osteoporosis on major adverse cardiovascular events (MACE) were compared between the groups. 1.2 Inclusion and Exclusion Criteria Inclusion Criteria (1) Diagnosed with stable CAD and underwent PCI; (2) Age ≥ 65 years; (3) Preoperative BMD measured by dual-energy X-ray absorptiometry, with osteoporosis status determined by T-score. Exclusion Criteria (1) Patients with other cardiac conditions (e.g., acute coronary syndrome, heart failure); (2) Severe comorbidities such as end-stage renal disease or cancer; (3) Patients with incomplete follow-up data. 1.3 Data Collection (1) Clinical Characteristics : Age, gender, smoking history, diabetes history, prior cardiovascular disease history, and medication history (e.g., antiplatelet drugs). (2) Bone Metabolism Markers : BMD, osteocalcin, procollagen type I carboxy-terminal propeptide (PICP), and N-terminal propeptide of type I procollagen (PINP). (3) MACE : Recorded during follow-up, including myocardial infarction, stroke, death, and coronary re-intervention. 1.4 Statistical Analysis Continuous variables (e.g., age, bone metabolism markers) were compared using independent samples t-tests. Categorical variables were compared using chi-square tests. Cox proportional hazards regression was used to analyze the independent impact of osteoporosis on MACE, adjusting for potential confounders such as age, gender, diabetes, hypertension, and triple-vessel disease. Kaplan-Meier survival curves were used to assess the relationship between osteoporosis and MACE incidence, with log-rank tests to compare survival differences. Pearson correlation analysis was used to explore correlations between BMD, bone metabolism markers (e.g., osteocalcin, PICP, PINP), and MACE occurrence. Results A total of 215 patients with stable coronary artery disease were enrolled and divided into osteoporosis group (n = 92) and non-osteoporosis group (n = 123) based on bone mineral density T-scores. Patients in the osteoporosis group were significantly older, had a higher proportion of females, and lower body mass index compared to the non-osteoporosis group (P < 0.05). No significant differences were observed in the prevalence of comorbidities between the two groups. The osteoporosis group demonstrated significantly higher levels of bone metabolism markers, a greater proportion of three-vessel disease, and higher incidence of MACE compared to the non-osteoporosis group (P < 0.001). The differences were statistically significant (Table 1 ). Multivariate Cox regression analysis revealed that after adjusting for confounding factors, osteoporosis (HR = 1.80, 95% CI: 1.08–2.98), age, and number of diseased vessels were independent risk factors for MACE (Table 2 ). Kaplan-Meier survival curve analysis showed that patients in the osteoporosis group had a significantly higher incidence of MACE compared to the non-osteoporosis group (Log-Rank χ² = 14.20, P < 0.001) (Fig. 1 ). To investigate the correlation between BMD, osteocalcin, PICP, PINP, and MACE, Pearson correlation analysis was performed. BMD showed a moderate negative correlation with MACE (r = -0.328, P < 0.001), indicating that lower BMD was associated with higher MACE incidence. Osteocalcin, PICP, and PINP all showed moderate positive correlations with MACE (r = 0.415, 0.394, and 0.367, respectively, all P < 0.001), suggesting that higher levels of these bone metabolism markers were associated with increased MACE incidence (Table 3 ). Table 1 Baseline Clinical Characteristics of Study Population Variable Osteoporosis Group (n = 92) Non-osteoporosis Group (n = 123) P value Age (years) 76.53 ± 8.27 73.82 ± 7.64 0.013 Sex, n (%) 0.014 Male 50 (54.35) 86 (69.92) Female 42 (45.65) 37 (30.08) BMI (kg/m²) 22.84 ± 3.52 24.18 ± 3.79 0.006 Smoking, n (%) 0.05 Yes 31 (33.70) 58 (47.15) No 61 (66.30) 65 (52.85) Hypertension, n (%) 0.973 Yes 66 (71.74) 88 (71.54) No 26 (28.26) 35 (28.46) Diabetes mellitus, n (%) 0.34 Yes 35 (38.04) 39 (31.71) No 57 (61.96) 84 (68.29) Stroke, n (%) 0.432 Yes 18 (19.57) 19 (15.45) No 84 (80.43) 104 (84.55) Atrial fibrillation, n (%) 0.324 Yes 13 (14.13) 12 (9.76) No 79 (85.87) 111 (90.24) SBP (mmHg) 138.62 ± 20.47 135.13 ± 19.85 0.21 DBP (mmHg) 76.18 ± 12.15 78.54 ± 12.63 0.18 BMD (T-score) -3.12 ± 0.43 -1.48 ± 0.61 < 0.001 Osteocalcin (µg/L) 28.64 ± 10.25 16.79 ± 6.48 < 0.001 PICP (µg/L) 132.47 ± 45.58 92.34 ± 28.41 < 0.001 PINP (µg/L) 62.76 ± 20.13 45.59 ± 16.24 < 0.001 Creatinine (µmol/L) 88.43 ± 25.27 82.65 ± 20.84 0.07 BUN (mmol/L) 6.81 ± 2.47 6.23 ± 2.18 0.065 Hemoglobin (g/L) 126.48 ± 18.57 132.81 ± 16.52 0.009 FBG (mmol/L) 5.79 ± 1.58 5.62 ± 1.41 0.33 TC (mmol/L) 4.42 ± 1.19 4.18 ± 1.13 0.201 TG (mmol/L) 1.57 ± 0.96 1.73 ± 1.12 0.495 LDL-C (mmol/L) 2.58 ± 0.97 2.53 ± 0.92 0.443 HDL-C (mmol/L) 1.16 ± 0.32 1.13 ± 0.29 0.014 LVEF (%) 60.84 ± 8.46 62.53 ± 9.17 0.167 Number of diseased vessels, n (%) < 0.001 Single vessel 27 (29.35) 60 (48.78) Two vessels 35 (38.04) 48 (39.02) Three vessels 30 (32.61) 15 (12.20) MACE, n (%) 36 (39.13) 26 (21.14) < 0.001 Table 2 Multivariate Cox Regression Analysis of Factors Affecting MACE Occurrence Variable β SE Wald P value HR (95% CI) Age 0.152 0.062 5.256 0.02 1.16 (1.02–1.33) Sex 0.302 0.235 1.425 0.158 1.35 (0.81–2.26) BMI -0.205 0.092 4.565 0.033 0.81 (0.67–0.99) Smoking history -0.198 0.138 1.862 0.145 0.82 (0.60–1.12) Number of diseased vessels 0.426 0.228 7.852 0.006 1.53 (1.12–2.09) Osteoporosis 0.586 0.242 6.425 0.01 1.80 (1.08–2.98) Multivariate Cox proportional hazards regression model was used for analysis, with variables entered based on P < 0.05. Table 3 Pearson Correlation Analysis Between Bone Metabolism Markers and MACE Marker Correlation coefficient (r) P value BMD -0.328 < 0.001 Osteocalcin 0.415 < 0.001 PICP 0.394 < 0.001 PINP 0.367 < 0.001 Disscussion CAD is the leading cause of mortality worldwide, with its risk factors extensively studied by researchers and clinicians [ 14 , 15 ] . PCI, rather than balloon angioplasty, is the preferred reperfusion strategy for STEMI, as it reduces the need for additional revascularization and may lower MI incidence [ 16 ] . For stable CAD, studies show PCI does not reduce overall adverse outcomes but remains a common choice for symptom relief and improved quality of life [ 17 ] . Consequently, research on PCI prognosis is increasingly prioritized. Age significantly impacts PCI outcomes; in randomized trials, STEMI patients aged ≥ 75 years (n = 977) undergoing PPCI had lower ST-segment resolution and more complications compared to younger patients [ 18 ] . For each decade increase in age, the adjusted 90-day mortality risk doubles [ 19 ] . Studies also report 30-day and 90-day mortality rates of 10.8% and 13.1%, respectively, in patients aged ≥ 75, with a high 90-day composite outcome (congestive heart failure, shock, or death [22.8%]) [ 20 ] . Thus, prognostic monitoring for elderly CAD patients is a critical clinical need. Recent literature indicates BMD is closely linked to cardiovascular disease (CVD) risk factors. Iseri et al [ 21 ] . found lower femoral BMD in patients with higher Framingham cardiovascular risk scores [ 22 ] . Patients with myocardial perfusion abnormalities or impaired left ventricular ejection fraction also exhibit significantly reduced BMD [ 23 ] . Coronary artery calcification, a hallmark of CAD, is negatively correlated with BMD, as noted by Wiegandt [ 24 – 26 ] . Beyond BMD, higher cortical bone status and bone strength are associated with lower major adverse cardiovascular event (MACE) risk after adjusting for confounders. Studies also report associations between BMD and CVD outcomes. A UK Biobank prospective cohort study found osteoporosis strongly linked to cardiovascular mortality in men [ 27 ] . A Japanese chronic heart failure cohort study reported significantly higher adverse event rates (hospitalization or death) in osteoporosis patients (HR = 2.40, 95% CI: 1.36–4.22). Bisphosphonates, first-line osteoporosis treatments, were shown in a recent Chinese study to significantly reduce all-cause mortality risk in patients with acute coronary syndrome or ischemic stroke [ 13 ] . CVD and osteoporosis commonly coexist in the elderly, but whether they merely coexist or interact remains debated. The bone-vascular axis concept suggests bone density or metabolism abnormalities are linked to CVD risk, yet the specific cellular and molecular mechanisms are unclear [ 28 , 29 ] . Recent research highlights several factors: First, osteoporosis and CVD share common risk factors, genetic and pathological mechanisms, and causal relationships, leading to mutual influence. Second, vascular calcification is a key factor explaining their association [ 30 ] . Vascular calcification is an active, complex process, particularly with aging, where calcium is lost from bones and deposited in the cardiovascular system, triggering diseases [ 31 ] . Specifically, with bone loss, vascular smooth muscle cells transform into osteoblast-like phenotypes via increased matrix metalloproteinase-2 levels and RunX promoter activation, causing vascular calcification, increased stiffness, and altered cardiovascular hemodynamics. Another factor is low-grade inflammation, which catalyzes BMD reduction and plays a critical role in atherosclerotic vascular disease pathogenesis [ 32 ] . Ongoing clinical trials are investigating inflammation [ 33 ] . Lastly, individuals with poor bone health are often frail with reduced physical activity, particularly those with fractures, whose prolonged immobility significantly increases CVD risk. This retrospective study found osteoporosis to be an independent risk factor for MACE (HR = 1.80, 95% CI: 1.08–2.98), supporting an independent association between osteoporosis and CVD, suggesting higher cardiovascular event risk post-PCI in osteoporosis patients. Given their close relationship, future research could explore whether osteoporosis treatment improves cardiovascular prognosis and if anti-osteoporosis therapy can serve as an adjunctive CVD treatment. Limitations include: (1) retrospective design with potential data collection bias; (2) single-center data lacking broad external validation; (3) the specific mechanisms linking osteoporosis and CVD require further basic research. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Qixia District Hospital, Nanjing. All participants provided written informed consent before enrollment. The study protocol, including data collection procedures and imaging assessments, was reviewed and approved by the institutional review board. All research procedures complied with institutional guidelines for the protection of human research subjects. All participants or their legal guardians provided written informed consent before enrollment. Consent for publication Not applicable. Competing Interests The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Clinical trial number Not applicable. Funding This research was supported by the Nanjing City Health Science and Technology Development Special Fund Project (Grant Numbers: YKK24233, YKK23218). Author Contribution Wenwen Xu and Jianning Li wrote the main manuscript text. Jun Zhou and Guoxin Zhang prepared figures . All authors reviewed the manuscript. Acknowledgement We acknowledge the dedicated staff at Qixia District Hospital, Nanjing, for their assistance with patient recruitment, data collection, and neurological assessments. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical considerations but are available from the corresponding author on reasonable request and with appropriate institutional approvals. 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08:47:00","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90534,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7030982/v1/92405b3249cf06cc8222485d.html"},{"id":95805979,"identity":"a958b0ca-9914-40b4-838b-18e5cc1ee451","added_by":"auto","created_at":"2025-11-13 08:47:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181236,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for MACE events in two groups of patients\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7030982/v1/00948c8dbdcf02dc17c80952.png"},{"id":95819012,"identity":"5a73c882-4b2a-410c-bf25-11959ff31e79","added_by":"auto","created_at":"2025-11-13 10:37:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":904142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030982/v1/85353501-307d-4f3b-852d-0445b6e3572f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Osteoporosis in Elderly Patients with Stable Coronary Artery Disease Undergoing Percutaneous Coronary Intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe treatment of stable coronary artery disease (CAD) typically involves two approaches: (1) anti-anginal drug therapy; (2) vascular revascularization, namely percutaneous coronary intervention (PCI)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Forty years ago, PCI was introduced to alleviate angina in patients with stable CAD\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Today, over 500,000 PCI procedures are performed annually worldwide to treat this condition\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Studies show that PCI significantly reduces the incidence of angina and markedly improves patients\u0026rsquo; quality of life\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In cases of acute coronary syndrome, PCI also reduces mortality and the incidence of subsequent myocardial infarction.\u003c/p\u003e\u003cp\u003eDespite the significant symptom relief reported with PCI, its prognosis remains associated with numerous lifestyle factors and patient comorbidities\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Research indicates that age is a critical factor affecting post-PCI outcomes, with an approximately 8% increase in mortality for each additional year of age. Patients with a left ventricular ejection fraction below 50% exhibit significantly higher post-PCI mortality\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Additionally, diabetes, anemia, and smoking have been identified as independent risk factors for adverse post-PCI outcomes. Given the widespread use of PCI, identifying factors influencing its prognosis is crucial for improving long-term patient outcomes.\u003c/p\u003e\u003cp\u003eOsteoporosis is a multifactorial metabolic bone disease characterized by reduced bone mass and deteriorated bone microarchitecture, leading to increased bone fragility and fracture risk\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Its pathogenesis involves an imbalance in bone homeostasis maintained by bone formation and resorption\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. According to the World Health Organization\u0026rsquo;s bone density classification, the categories are: normal bone mass (T-score \u0026ge; -1), osteopenia (T-score \u0026gt;-2.5 and \u0026lt; -1), osteoporosis (T-score \u0026le; -2.5), and severe osteoporosis (T-score \u0026le; -2.5 with fragility fractures)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Globally, the prevalence of osteoporosis in the elderly is 21.7%, with the highest rate in Asia (24.3%), followed by Europe (16.7%) and the United States (11.5%). Recent clinical studies suggest that lower bone mineral density (BMD) is associated with an increased risk of myocardial infarction, independent of gender\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Epidemiological studies also report that reduced BMD is linked to higher incidence and mortality rates of stroke and heart failure\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, no studies have yet explored the association between osteoporosis and post-PCI prognosis. This study analyzes patients with stable CAD treated with PCI, grouping them by osteoporosis status, to examine the correlation between prognosis and various bone mass parameters, as well as the impact of osteoporosis on prognosis. It aims to provide a new perspective for clinical post-PCI monitoring and individualized care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Study Design and Population\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study aimed to investigate the impact of osteoporosis on the prognosis of elderly patients with stable coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). A total of 215 patients with stable CAD, admitted to the Cardiology Department of Qixia District Hospital, were enrolled. Based on bone mineral density (BMD) T-scores, patients were divided into an osteoporosis group (n\u0026thinsp;=\u0026thinsp;92) and a non-osteoporosis group (n\u0026thinsp;=\u0026thinsp;123). Clinical characteristics and the impact of osteoporosis on major adverse cardiovascular events (MACE) were compared between the groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003cp\u003e(1) Diagnosed with stable CAD and underwent PCI; (2) Age\u0026thinsp;\u0026ge;\u0026thinsp;65 years; (3) Preoperative BMD measured by dual-energy X-ray absorptiometry, with osteoporosis status determined by T-score.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003cp\u003e(1) Patients with other cardiac conditions (e.g., acute coronary syndrome, heart failure); (2) Severe comorbidities such as end-stage renal disease or cancer; (3) Patients with incomplete follow-up data.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Data Collection\u003c/h2\u003e\u003cp\u003e(1) \u003cb\u003eClinical Characteristics\u003c/b\u003e: Age, gender, smoking history, diabetes history, prior cardiovascular disease history, and medication history (e.g., antiplatelet drugs).\u003c/p\u003e\u003cp\u003e(2) \u003cb\u003eBone Metabolism Markers\u003c/b\u003e: BMD, osteocalcin, procollagen type I carboxy-terminal propeptide (PICP), and N-terminal propeptide of type I procollagen (PINP).\u003c/p\u003e\u003cp\u003e(3) \u003cb\u003eMACE\u003c/b\u003e: Recorded during follow-up, including myocardial infarction, stroke, death, and coronary re-intervention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables (e.g., age, bone metabolism markers) were compared using independent samples t-tests. Categorical variables were compared using chi-square tests. Cox proportional hazards regression was used to analyze the independent impact of osteoporosis on MACE, adjusting for potential confounders such as age, gender, diabetes, hypertension, and triple-vessel disease. Kaplan-Meier survival curves were used to assess the relationship between osteoporosis and MACE incidence, with log-rank tests to compare survival differences. Pearson correlation analysis was used to explore correlations between BMD, bone metabolism markers (e.g., osteocalcin, PICP, PINP), and MACE occurrence.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 215 patients with stable coronary artery disease were enrolled and divided into osteoporosis group (n\u0026thinsp;=\u0026thinsp;92) and non-osteoporosis group (n\u0026thinsp;=\u0026thinsp;123) based on bone mineral density T-scores. Patients in the osteoporosis group were significantly older, had a higher proportion of females, and lower body mass index compared to the non-osteoporosis group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in the prevalence of comorbidities between the two groups. The osteoporosis group demonstrated significantly higher levels of bone metabolism markers, a greater proportion of three-vessel disease, and higher incidence of MACE compared to the non-osteoporosis group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The differences were statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultivariate Cox regression analysis revealed that after adjusting for confounding factors, osteoporosis (HR\u0026thinsp;=\u0026thinsp;1.80, 95% CI: 1.08\u0026ndash;2.98), age, and number of diseased vessels were independent risk factors for MACE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Kaplan-Meier survival curve analysis showed that patients in the osteoporosis group had a significantly higher incidence of MACE compared to the non-osteoporosis group (Log-Rank χ\u0026sup2; = 14.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo investigate the correlation between BMD, osteocalcin, PICP, PINP, and MACE, Pearson correlation analysis was performed. BMD showed a moderate negative correlation with MACE (r = -0.328, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that lower BMD was associated with higher MACE incidence. Osteocalcin, PICP, and PINP all showed moderate positive correlations with MACE (r\u0026thinsp;=\u0026thinsp;0.415, 0.394, and 0.367, respectively, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that higher levels of these bone metabolism markers were associated with increased MACE incidence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Clinical Characteristics of Study Population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteoporosis Group (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-osteoporosis Group (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.82\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (54.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (69.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (45.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (30.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (33.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (47.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (66.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (52.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (71.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (71.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (28.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (28.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (38.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (31.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (61.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (68.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (19.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (15.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (80.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (84.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial fibrillation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (14.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (9.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (85.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (90.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.62\u0026thinsp;\u0026plusmn;\u0026thinsp;20.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.13\u0026thinsp;\u0026plusmn;\u0026thinsp;19.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.18\u0026thinsp;\u0026plusmn;\u0026thinsp;12.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.54\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMD (T-score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsteocalcin (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.64\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.79\u0026thinsp;\u0026plusmn;\u0026thinsp;6.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePICP (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132.47\u0026thinsp;\u0026plusmn;\u0026thinsp;45.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.34\u0026thinsp;\u0026plusmn;\u0026thinsp;28.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePINP (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.76\u0026thinsp;\u0026plusmn;\u0026thinsp;20.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.59\u0026thinsp;\u0026plusmn;\u0026thinsp;16.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.43\u0026thinsp;\u0026plusmn;\u0026thinsp;25.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.65\u0026thinsp;\u0026plusmn;\u0026thinsp;20.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126.48\u0026thinsp;\u0026plusmn;\u0026thinsp;18.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132.81\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of diseased vessels, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle vessel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (29.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (48.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo vessels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (38.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (39.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree vessels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (32.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (12.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMACE, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (39.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (21.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox Regression Analysis of Factors Affecting MACE Occurrence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.16 (1.02\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.35 (0.81\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81 (0.67\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.82 (0.60\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of diseased vessels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.53 (1.12\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.80 (1.08\u0026ndash;2.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultivariate Cox proportional hazards regression model was used for analysis, with variables entered based on P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson Correlation Analysis Between Bone Metabolism Markers and MACE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation coefficient (r)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsteocalcin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePICP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePINP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Disscussion","content":"\u003cp\u003eCAD is the leading cause of mortality worldwide, with its risk factors extensively studied by researchers and clinicians\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. PCI, rather than balloon angioplasty, is the preferred reperfusion strategy for STEMI, as it reduces the need for additional revascularization and may lower MI incidence\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. For stable CAD, studies show PCI does not reduce overall adverse outcomes but remains a common choice for symptom relief and improved quality of life\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Consequently, research on PCI prognosis is increasingly prioritized. Age significantly impacts PCI outcomes; in randomized trials, STEMI patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years (n\u0026thinsp;=\u0026thinsp;977) undergoing PPCI had lower ST-segment resolution and more complications compared to younger patients\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. For each decade increase in age, the adjusted 90-day mortality risk doubles\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Studies also report 30-day and 90-day mortality rates of 10.8% and 13.1%, respectively, in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75, with a high 90-day composite outcome (congestive heart failure, shock, or death [22.8%])\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Thus, prognostic monitoring for elderly CAD patients is a critical clinical need.\u003c/p\u003e\u003cp\u003eRecent literature indicates BMD is closely linked to cardiovascular disease (CVD) risk factors. Iseri et al\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. found lower femoral BMD in patients with higher Framingham cardiovascular risk scores\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Patients with myocardial perfusion abnormalities or impaired left ventricular ejection fraction also exhibit significantly reduced BMD\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Coronary artery calcification, a hallmark of CAD, is negatively correlated with BMD, as noted by Wiegandt\u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Beyond BMD, higher cortical bone status and bone strength are associated with lower major adverse cardiovascular event (MACE) risk after adjusting for confounders. Studies also report associations between BMD and CVD outcomes. A UK Biobank prospective cohort study found osteoporosis strongly linked to cardiovascular mortality in men\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A Japanese chronic heart failure cohort study reported significantly higher adverse event rates (hospitalization or death) in osteoporosis patients (HR\u0026thinsp;=\u0026thinsp;2.40, 95% CI: 1.36\u0026ndash;4.22). Bisphosphonates, first-line osteoporosis treatments, were shown in a recent Chinese study to significantly reduce all-cause mortality risk in patients with acute coronary syndrome or ischemic stroke\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCVD and osteoporosis commonly coexist in the elderly, but whether they merely coexist or interact remains debated. The bone-vascular axis concept suggests bone density or metabolism abnormalities are linked to CVD risk, yet the specific cellular and molecular mechanisms are unclear\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Recent research highlights several factors: First, osteoporosis and CVD share common risk factors, genetic and pathological mechanisms, and causal relationships, leading to mutual influence. Second, vascular calcification is a key factor explaining their association\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Vascular calcification is an active, complex process, particularly with aging, where calcium is lost from bones and deposited in the cardiovascular system, triggering diseases\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Specifically, with bone loss, vascular smooth muscle cells transform into osteoblast-like phenotypes via increased matrix metalloproteinase-2 levels and RunX promoter activation, causing vascular calcification, increased stiffness, and altered cardiovascular hemodynamics. Another factor is low-grade inflammation, which catalyzes BMD reduction and plays a critical role in atherosclerotic vascular disease pathogenesis\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Ongoing clinical trials are investigating inflammation\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Lastly, individuals with poor bone health are often frail with reduced physical activity, particularly those with fractures, whose prolonged immobility significantly increases CVD risk.\u003c/p\u003e\u003cp\u003eThis retrospective study found osteoporosis to be an independent risk factor for MACE (HR\u0026thinsp;=\u0026thinsp;1.80, 95% CI: 1.08\u0026ndash;2.98), supporting an independent association between osteoporosis and CVD, suggesting higher cardiovascular event risk post-PCI in osteoporosis patients. Given their close relationship, future research could explore whether osteoporosis treatment improves cardiovascular prognosis and if anti-osteoporosis therapy can serve as an adjunctive CVD treatment. Limitations include: (1) retrospective design with potential data collection bias; (2) single-center data lacking broad external validation; (3) the specific mechanisms linking osteoporosis and CVD require further basic research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Qixia District Hospital, Nanjing. All participants provided written informed consent before enrollment. The study protocol, including data collection procedures and imaging assessments, was reviewed and approved by the institutional review board. All research procedures complied with institutional guidelines for the protection of human research subjects. All participants or their legal guardians provided written informed consent before enrollment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the Nanjing City Health Science and Technology Development Special Fund Project (Grant Numbers: YKK24233, YKK23218).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWenwen Xu and Jianning Li wrote the main manuscript text. Jun Zhou and Guoxin Zhang prepared figures . All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the dedicated staff at Qixia District Hospital, Nanjing, for their assistance with patient recruitment, data collection, and neurological assessments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to privacy and ethical considerations but are available from the corresponding author on reasonable request and with appropriate institutional approvals. Data sharing will be considered in accordance with institutional policies and participant consent agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNowbar AN, Howard JP, Finegold JA, et al. 2014 global geographic analysis of mortality from ischaemic heart disease by country, age and income: statistics from World Health Organisation and United Nations. Int J Cardiol. 2014;174(2):293\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRandomised trial of cholesterol lowering. in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet. 1994;344(8934):1383\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Lamee RK, Nowbar AN, Francis DP. Percutaneous coronary intervention for stable coronary artery disease. 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Am Heart J. 2011;161(4):664\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStone GW, Grines CL, Cox DA, et al. Comparison of angioplasty with stenting, with or without abciximab, in acute myocardial infarction. N Engl J Med. 2002;346(13):957\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKala P, Kanovsky J, Rokyta R, et al. Age-related treatment strategy and long-term outcome in acute myocardial infarction patients in the PCI era. BMC Cardiovasc Disord. 2012;12:31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrines CL, Cox DA, Stone GW, et al. Coronary angioplasty with or without stent implantation for acute myocardial infarction. Stent Primary Angioplasty in Myocardial Infarction Study Group. N Engl J Med. 1999;341(26):1949\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGharacholou SM, Lopes RD, Alexander KP, et al. 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The relationship between volumetric thoracic bone mineral density and coronary calcification in men and women - results from the Copenhagen General Population Study. Bone. 2019;121:116\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-G\u0026oacute;mez I, Gray SR, Ho FK, et al. Osteoporosis and Its Association With Cardiovascular Disease, Respiratory Disease, and Cancer: Findings From the UK Biobank Prospective Cohort Study. Mayo Clin Proc. 2022;97(1):110\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaCroix AZ, Jackson RD, Aragaki A, et al. OPG and sRANKL serum levels and incident hip fracture in postmenopausal Caucasian women in the Women's Health Initiative Observational Study. Bone. 2013;56(2):474\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallegari A, Coons ML, Ricks JL, et al. 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AME Med J 2018;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTank\u0026oacute; LB, Christiansen C, Cox DA, et al. Relationship between osteoporosis and cardiovascular disease in postmenopausal women. J Bone Min Res. 2005;20(11):1912\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteoporosis, Coronary artery disease, Percutaneous coronary intervention, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7030982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7030982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo investigate the impact of osteoporosis on the prognosis of elderly patients with stable coronary artery disease (CAD) after percutaneous coronary intervention (PCI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study included 215 patients diagnosed with stable CAD, who were divided into an osteoporosis group (n\u0026thinsp;=\u0026thinsp;92) and a non-osteoporosis group (n\u0026thinsp;=\u0026thinsp;123) based on their bone mineral density (BMD) T-scores. Clinical characteristics between the two groups were compared. Multivariate Cox regression analysis was used to assess the impact of osteoporosis on major adverse cardiovascular events (MACE). Kaplan-Meier curves were used for survival analysis, and Pearson correlation analysis was performed to examine the relationship between bone metabolism markers and MACE.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study showed that patients in the osteoporosis group were older and had a higher proportion of females. The osteoporosis group had significantly higher levels of bone metabolism markers (osteocalcin, PICP, PINP), a higher proportion of three-vessel disease, and a higher incidence of MACE compared to the non-osteoporosis group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate Cox regression analysis revealed that osteoporosis was an independent risk factor for MACE (HR\u0026thinsp;=\u0026thinsp;1.80, 95% CI: 1.08\u0026ndash;2.98). Kaplan-Meier curves demonstrated a higher incidence of MACE in the osteoporosis group compared to the non-osteoporosis group (Log-Rank χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Pearson correlation analysis found that BMD was negatively correlated with MACE (r=-0.328, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while osteocalcin, PICP, and PINP were positively correlated with MACE (r\u0026thinsp;=\u0026thinsp;0.415, 0.394, 0.367, respectively, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElderly patients with stable CAD and osteoporosis have an increased risk of MACE after PCI. Decreased bone mineral density and abnormal bone metabolism markers can serve as predictors of poor prognosis in these patients.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Osteoporosis in Elderly Patients with Stable Coronary Artery Disease Undergoing Percutaneous Coronary Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:47:16","doi":"10.21203/rs.3.rs-7030982/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-31T12:26:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T16:13:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T12:30:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T12:28:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-07-02T15:29:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79ce7e1f-8de1-4db8-aefe-aa09b9a94bc2","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T07:47:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 07:47:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7030982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7030982","identity":"rs-7030982","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}