Association of blood neutrophil lymphocyte ratio in the patients with postmenopausal osteoporosis

Huang, Chenshu; Li, Shaolin

doi:10.12669/pjms.323.10292

Cited by 51 publications

(59 citation statements)

References 13 publications

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“…Öztürk et al [19] found that NLR is an independent marker for osteoporosis and suggested that inflammation is present during bone remodeling [19]. Huang and Li [20] demonstrated a significant association between BMD and NLR in osteoporotic postmenopausal women [20]. In our study, there was no significant relationship between low BMD and NLR in patients with hemophilia.…”

Section: Discussioncontrasting

confidence: 43%

“…We found no significant differences in CBC, ESR, CRP, liver and renal functions, ALP, Alb, P, total bilirubin, and fasting blood glucose between the two groups. A few studies reported an association of NLR, PLR, and MPV with osteoporosis [19][20][21][22]. However, no previous study evaluated the association between low BMD and NLR, PLR and MPV in hemophiliacs.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, several recent studies have found a significant association between neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and mean platelet volume (MPV), with low BMD [19][20][21][22]. However, this association has not been evaluated in hemophilic patients.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of bone mineral density (BMD) and indicators of bone turnover in patients with hemophilia

Dağlı

Kutlucan

Abuşoğlu

et al. 2018

Bosn J of Basic Med Sci

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A decrease in bone mass is observed in hemophilic patients. The aim of this study was to evaluate bone mineral density (BMD), parathyroid hormone (PTH), 25-hydroxy vitamin D (vitamin D), and a bone formation and resorption marker, procollagen type I N-terminal propeptide (PINP) and urinary N-terminal telopeptide (uNTX) respectively, in hemophilic patients and healthy controls. Laboratory parameters related to the pathogenesis of bone loss such as neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) were also evaluated. Thirty-five men over 18 years of age, with severe hemophilia (A and B) and receiving secondary prophylaxis, were included in the study. The same number of age-, sex-, and ethnicity-matched healthy controls were evaluated. Anthropometric, biochemical, and hormonal parameters were determined in both groups. No significant difference in anthropometric parameters was found between the two groups. The BMD was low in 34% of hemophilic patients. Vitamin D, calcium, and free testosterone levels were significantly lower (p < 0.001, p = 0.011, p < 0.001, respectively), while PTH, PINP, and activated partial thromboplastin time (aPTT) levels were significantly higher (p < 0.014, p = 0.043, p < 0.001, respectively), in hemophilic patients compared to controls. There was no significant difference between the two groups in NLR, PLR, phosphorus, thyroid-stimulating hormone, and uNTX level. The reduction of bone mass in hemophilic patients may be evaluated using the markers of bone formation and resorption, enabling early detection and timely treatment.

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Section: Discussioncontrasting

confidence: 43%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of bone mineral density (BMD) and indicators of bone turnover in patients with hemophilia

Dağlı

Kutlucan

Abuşoğlu

et al. 2018

Bosn J of Basic Med Sci

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“…For instance, some emerging studies suggested blood neutrophil‐to‐lymphocyte ratio (NLR). As a simple peripheral blood index which could reflect the systemic inflammatory level, NLR can well discriminate PMOP among postmenopausal women, even being superior to C reaction protein . However, to date, scarce study further explored if there are more optimal biomarkers other than NLR in blood routine examination for diagnosing PMOP, and if there are any blood biomarkers could predict fracture risk among PMOP patients.…”

Section: Introductionmentioning

confidence: 99%

Systemic immune‐inflammation index acts as a novel diagnostic biomarker for postmenopausal osteoporosis and could predict the risk of osteoporotic fracture

Fang

Zhang

Wang

et al. 2019

Clinical Laboratory Analysis

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Abbreviations: BMD, bone mineral density; BMI, body mass index; CI, confidence interval; DEXA, dual-energy X-ray absorptiometry; HR, hazard ratio; LMR, lymphocyte-to-monocyte ratio; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; NLR, neutrophil-to-lymphocyte ratio; OR, odds ratio; PDW, platelet distribution width; PLR, platelet-to-lymphocyte ratio; PMOP, postmenopausal osteoporosis; RDW, red blood cell distribution width; SII, systemic immune-inflammation index. AbstractBackground: 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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“…The precise function of ALB and its role in OP are unclear. However, a previous study found that the expression of ALB differed significantly between a normal group and an OP group (P<0.05), and others have linked oxidative status with bone alterations (27)(28)(29). PHLPP2 is involved in the regulation of protein kinase B (Akt) and protein kinase C signaling, and mediates dephosphorylation in the C-terminal domain hydrophobic motif of members of the AGC serine/threonine protein kinase family.…”

Section: Discussionmentioning

confidence: 99%

Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach

Chai

Tan

et al. 2018

Oncol Lett

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Osteoporosis (OP) is an age-related disease, and osteoporotic fracture is one of the major causes of disability and mortality in elderly patients (>70 years old). As the pathogenesis and molecular mechanism of OP remain unclear, the identification of disease biomarkers is important for guiding research and providing therapeutic targets. In the present study, core genes and microRNAs (miRNAs) associated with OP were identified. Differentially expressed genes (DEGs) between human mesenchymal stem cell specimens from normal osseous tissues and OP tissues were detected using the GEO2R tool of the Gene Expression Omnibus database and Morpheus. Network topological parameters were determined using NetworkAnalyzer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, and ClueGO. Cytoscape with the Search Tool for the Retrieval of Interacting Genes and Molecular Complex Detection plug-in was used to visualize protein-protein interactions (PPIs). Additionally, miRNA-gene regulatory modules were predicted using CyTargetLinker in order to guide future research. In total, 915 DEGs were identified, including 774 upregulated and 141 downregulated genes. Enriched GO terms and pathways were determined, including 'nervous system development', 'regulation of molecular function', 'glutamatergic synapse pathway' and 'pathways in cancer'. The node degrees of DEGs followed power-law distributions. A PPI network with 541 nodes and 1,431 edges was obtained. Overall, 3 important modules were identified from the PPI network. The following 10 genes were identified as core genes based on high degrees of connectivity: Albumin, PH domain leucine-rich repeat-containing protein phosphatase 2 (PHLPP2), DNA topoisomerase 2-α, kininogen 1 (KNG1), interleukin 2 (IL2), leucine-rich repeats and guanylate kinase domain containing, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG), leptin, transferrin and RNA polymerase II subunit A (POLR2A). Additionally, 15 miRNA-target interactions were obtained using CyTargetLinker. Overall, 7 miRNAs co-regulated IL2, 3 regulated PHLPP2, 3 regulated KNG1, 1 regulated PIK3CG and 1 modulated POLR2A. These results indicate potential biomarkers in the pathogenesis of OP and therapeutic targets.

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Association of blood neutrophil lymphocyte ratio in the patients with postmenopausal osteoporosis

Cited by 51 publications

References 13 publications

Evaluation of bone mineral density (BMD) and indicators of bone turnover in patients with hemophilia

Evaluation of bone mineral density (BMD) and indicators of bone turnover in patients with hemophilia

Systemic immune‐inflammation index acts as a novel diagnostic biomarker for postmenopausal osteoporosis and could predict the risk of osteoporotic fracture

Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach

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