Background: Postmenopausal osteoporosis (PMOP) is a bone metabolism disorder involving systematic inflammation activation. Blood routine examination is easily available in clinical practice and contains abundant information reflecting the systematic inflammation level. Thus, it is attractive to achieve early diagnosis of PMOP and predict osteoporotic fracture risk just based on the biomarkers in blood routine examination.
Methods: A multi-centric prospective cohort study was designed and enrolled postmenopausal women from two independent institutions. All participants underwent the dual-energy X-ray absorptiometry (DEXA) scanning for diagnosing PMOP. Blood routine examination was conducted, and the key inflammatory biomarkers such as neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) were calculated. PMOP patients were followed up to observe osteoporotic fracture and identify the related risk predictors.
Results: A total of 92 participants out of 238 enrolled postmenopausal women were diagnosed with PMOP, with a prevalence of 38.66%. The main risk factors identified for PMOP included older age (OR = 2.06, 95% CI = 1.14-3.72), longer menopause duration (OR = 3.14, 95% CI = 2.06-4.79), higher NLR (OR = 2.11, 95% CI = 1.37-3.25), and higher SII (OR = 3.02, 95% CI = 1.98-4.61). Besides age and menopause duration, SII ≥834.89 was newly identified as a prominent risk factor for discriminating osteoporotic fracture risk in PMOP patients (HR = 3.66, 95% CI = 1.249-10.71).
Conclusion: As an easy and economical biomarker calculated from blood routine examination, SII not only acts as a good risk predictor for PMOP diagnosis but also well discriminates the osteoporotic fracture risk, which deserves further investigation and application in clinical practice.
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http://dx.doi.org/10.1002/jcla.23016 | DOI Listing |
Chin J Traumatol
December 2024
Department of Orthopaedics, Xinhua Hospital of Zhejiang Province, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310003, China.
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View Article and Find Full Text PDFJ Robot Surg
January 2025
Department of Orthopaedics, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, Jiangsu, China.
The rising incidence of osteoporotic vertebral compression fractures (OVCF) has increased the demand for precise treatments like robot-assisted percutaneous vertebroplasty (PVP), especially for conditions like Kümmell's disease that require high surgical accuracy. However, the traditional tracer fixation method has certain limitations. This study aimed to compare the safety and clinical efficacy of a modified tracer fixation technique with the traditional fixation method in robot-assisted percutaneous vertebroplasty (PVP) for Kümmell's disease.
View Article and Find Full Text PDFPain Res Manag
January 2025
Biostatistics Unit DRCI, University Hospital, Clermont-Ferrand, France.
The neuropathic characteristics of pain occurring after an osteoporosis (OP)-related fracture are often under-recognized. The aim of this pilot study is to identify, in patients suffering from pain localized on the site of a previous osteoporotic fracture, the presence of neuropathic characteristics, their medical management, and their impact on quality of life. This pilot cross-sectional study on consecutive patients in University Hospital, Rheumatology Department, Clermont-Ferrand, France, was approved by the Ethics Committee (IRB number 2023-CF34).
View Article and Find Full Text PDFTunis Med
December 2024
Orthopaedic Department, Fattouma Bourguiba Hospital, Monastir Faculty of Medicine, University of Monastir, Tunisia.
Objectives: To evaluate survival after osteoporotic fractures of the upper femur and determine its associated factors.
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PLOS Digit Health
January 2025
Department of Geriatric Medicine, The Canberra Hospital, ACT Health, Canberra, Australia.
Osteoporotic hip fractures (HFs) in the elderly are a pertinent issue in healthcare, particularly in developed countries such as Australia. Estimating prognosis following admission remains a key challenge. Current predictive tools require numerous patient input features including those unavailable early in admission.
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