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Osteosarcopenic obesity and its components—osteoporosis, sarcopenia, and obesity—are associated with blood cell count-derived inflammation indices in older Chinese people

Abstract

Background

The aim of this study was to investigate the associations of osteosarcopenic obesity (OSO) and its components with complete blood cell count-derived inflammation indices.

Methods

In this cross-sectional study, data of 648 participants aged ≥60 years (men/women: 232/416, mean age: 67.21 ± 6.40 years) were collected from January 2018 to December 2020. Areal bone mineral density and body fat percentage were used to define osteopenia/osteoporosis and obesity, respectively. The criteria of the 2019 Asian Working Group for Sarcopenia were used to diagnose sarcopenia. Based on the number of these conditions, participants were divided into four groups: OSO/0, OSO/1, OSO/2, and OSO/3. Logistic regression analysis was conducted to identify associations between blood cell count-derived inflammation indices and the number of disorders with abnormal body composition.

Results

Systemic inflammation response index (SIRI), white blood cells, neutrophil-to-lymphocyte ratio (NLR), aggregate inflammation systemic index (AISI), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) showed statistically significant differences among the four groups (P < 0.05). Unlike in the OSO/0 group, in all other groups, AISI, SIRI, PLR, and NLR were significantly associated with increased likelihood of having multiple disorders with abnormal body composition after adjustment for confounders (P < 0.0001 for all). However, LMR showed an inverse correlation with the number of these conditions (P < 0.05).

Conclusion

Higher SIRI, AISI, NLR, and PLR values and lower LMR values are closely associated with OSO and its individual components—osteoporosis, sarcopenia, and obesity—in older adults, suggesting that the value of these indices in the evaluation of OSO warrants further investigation.

Peer Review reports

Background

Osteoporosis, obesity, and sarcopenia are major global public health concerns [1]. A combination of two or three of these conditions usually results in comorbid conditions that pose an immense economic burden on older people and society [2, 3]. These three conditions have multifactorial etiologies and are believed to have some shared pathophysiological mechanisms involved in their development [3]. Given their close association with one another, a new clinical entity, osteosarcopenic obesity (OSO), has been proposed [4, 5]. As the age increases, body fat gets redistributed to the visceral organs and often infiltrates into muscle and bone tissues, potentially leading to sarcopenia and osteoporosis [6, 7]. Therefore, conventional evaluation tools often underestimate obesity in older adults [5]. Ethnicity also influences the distribution of body fat, particularly ectopic and visceral fat [8]. A study with a 5–10-year follow-up in community-dwelling adults demonstrated that sarcopenic obesity was associated with a higher risk for osteoporosis than obesity alone, indicating the presence of an interaction among the three conditions [9].

A hallmark of aging is a chronic state of low-grade systemic inflammation, also known as inflammaging, which has been shown to underlie the pathogenesis of various chronic disorders and comorbidities, including those comprising osteoporosis, obesity, and sarcopenia, ultimately contributing to frailty and disability [10, 11]. Inflammaging is usually accompanied by increased levels of proinflammatory mediators and their markers, such as interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), and interleukin-6 (IL-6), reflecting destructive and degenerative processes in tissues [12]. As a major etiological factor for osteoporosis, estrogen deficiency favors the release of proinflammatory cytokines, particularly TNF-α, leading to preosteoclast production and bone resorption [13, 14]. In individuals with obesity, hypertrophied adipocytes secrete proinflammatory cytokines, which in turn promote the accumulation of proinflammatory immune cells [15]. Meanwhile, there is extracellular and intracellular lipid deposition, which replaces muscle fibers and damages myocyte contractile function [16]. Taken together, these mechanisms increase the risk of OSO [17].

Numerous peripheral blood biomarkers of inflammation, including IL-1β, TNF-α, CRP, and IL-6, have been used to monitor disease states and assess treatment outcomes [18,19,20]. Blood cell count-derived inflammation indices can be easily quantified using simple blood tests, which are a more economical and readily available alternative for the detection of chronic low-grade systemic inflammation and evaluation of chronic conditions. The cell types these indices are based on play critical roles in the host’s innate and adaptive immune responses to changes in the internal and external environments [21,22,23,24,25]. Some of the indices, such as the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), aggregate inflammation systemic index (AISI), and systemic inflammation response index (SIRI), have been used as inflammatory markers for characterizing many diseases, including osteoporosis, obesity, and sarcopenia [26,27,28,29,30,31,32,33]. Nevertheless, to our knowledge, their value in assessing OSO has not been investigated. In this study, we sought to establish the profiles of NLR, lymphocyte-to-monocyte ratio (LMR), PLR, AISI, and SIRI in patients with OSO and those with osteoporosis, obesity and/or sarcopenia.

Methods

Participants

This was a cross-sectional study involving 648 older adults (416 women, 232 men; age ≥ 60 years). Participants were randomly recruited from among people receiving annual check-ups at the Physical Examination Center of The Second Affiliated Hospital of Harbin Medical University between January 2018 and December 2020. This study was approved by the ethics committee of The Second Affiliated Hospital of Harbin Medical University (Ethics approval No: KY2018–262), and written informed consent was obtained from all participants. The exclusion criteria were as follows: (1) refusal to give or legally incapable of giving informed consent; (2) body composition and bone density could not be accurately estimated due to lower limb injuries; (3) history of serious metabolic or endocrine diseases or fractures; and (4) recent or ongoing use of medications affecting bone metabolism, such as thiazolidinediones, glucocorticoids, statins, and antiepileptic medications.

Demographics, health-related behaviors, and health conditions

The following information was collected from in-person interviews: age, sex, education level (below university vs. university or higher), monthly household income (< ¥5000 vs. ≥¥5000), smoking, alcohol consumption, regular exercise, sunlight exposure (< 1 h/d vs. ≥1 h/d), and comorbidities (coronary heart disease, hypertension, stroke, diabetes, and fatty liver disease). A person was considered a smoker if the average number of cigarettes smoked per day in the past six months was ≥1. Alcohol consumption was defined as drinking at least once a week in the past six months [34]. Regular exercise was defined as exercise ≥5 times/week [35], with each exercise routine lasting at least 30 min.

Physical and skeletal muscle measurements

An altimeter was used to measure the height of participants while they stood barefoot. The waist, calf, and hip circumferences of the participants were determined using a tape measure. For diastolic blood pressure and systolic blood pressure measurements, participants were asked to rest in a seated position for 5 min before their blood pressure values were acquired using an OMRON HEM-1000 electronic sphygmomanometer (OMRON Healthcare, Japan) by trained staff.

Dual-energy X-ray absorptiometry (Discovery-WI; Hologic Inc., Bedford-MA, USA) was used to estimate body composition. Data on total lean mass (kg), appendicular lean mass (kg), and total fat mass (in kg and %) were collected. Hand grip strength (HGS) was measured using a hand dynamometer (Jamar Hand Dynamometer, Sammons Preston, Inc., Bolingbrook, IL). Gait speed was calculated from a 4-m walk at normal pace and was expressed in meters/second (m/s). Areal bone mineral density (aBMD) was measured at the femur and the lumbar spine 1–4 (L1–L4).

Laboratory tests

All laboratory tests were conducted at the Department of Laboratory Medicine, The Second Affiliated Hospital, Harbin Medical University. Fasting venous blood was drawn in the morning, and samples were processed on the same day. Standardized laboratory tests were performed for white blood cell counts, neutrophil counts, lymphocyte counts, monocyte counts, platelet counts, plasma glucose, albumin, plasma lipids, blood urea nitrogen (BUN), creatinine (Cr), and uric acid (UA).

Diagnostic criteria for osteopenia, obesity, sarcopenia and their combinations

We defined sarcopenia as low skeletal muscle mass with low HGS or low gait speed (GS) or both on the basis of the recommended diagnostic algorithm of the 2019 consensus of the Asian Working Group for Sarcopenia (AWGS2019) [36]; we used AWGS2019 cutoff values to assess the participants. The appendicular skeletal mass index was calculated as the appendicular skeletal muscle mass/height2 (kg/m2). In line with the AWGS2019 recommendations, low skeletal muscle mass was evaluated using height-adjusted skeletal muscle mass from dual-energy X-ray absorptiometry (DXA), with cutoff values of 5.4 kg/m2 for women and 7.0 kg/m2 for men. AWGS2019 recommendations for low muscle strength are HGS values < 18.0 kg for women and < 28.0 kg for men [36]; a GS of < 1.0 m/s indicates reduced physical performance [36].

The WHO criteria were used for the diagnosis of osteopenia and osteoporosis. Osteopenia was defined as having an aBMD T-score between − 1.0 and − 2.5 SD and osteoporosis as having an aBMD T score of ≤ − 2.5 SD at the femoral neck, hip, and lumbar spine [5, 37]. The standard cutoff points recommended by the American Council on Exercise (ACE) were used to assess adiposity [38]. Obesity was defined as percent body fat (PBF) ≥32% for women and ≥ 25% for men [38].

Participants were divided into four groups on the basis of whether they had none, one, two, or all of the three conditions, namely obesity, osteoporosis, and sarcopenia: the OSO/0 group (none of the three conditions), the OSO/1 group (one condition), the OSO/2 group (two conditions), and the OSO/3 group (all three conditions).

Calculation of inflammatory indices

AISI was calculated by multiplying the neutrophil count, monocyte count, and platelet count and dividing the result by the lymphocyte count [23]. SIRI was calculated by multiplying the neutrophil count and monocyte count and dividing the result by the lymphocyte count. LMR represented the ratio of the lymphocyte count over the absolute monocyte count. PLR is the ratio of the platelet count over the lymphocyte count, and NLR is the ratio of the neutrophil count over the lymphocyte count.

Statistical analysis

All statistical analyses were performed using SAS 9.4 version (SAS Institute Inc., Cary, NC, USA). For baseline data, continuous variables were expressed as the mean ± SD or frequency (%), and categorical variables were expressed as an absolute number and percentage (%). We used the chi-square test (for categorical variables) or the Kruskal–Wallis test with the Bonferroni correction (for continuous variables) to examine differences between different groups. Where applicable, Spearman’s r or Pearson’s r was used to detect the correlations of inflammation indices with the parameters of OSO (GS, ASM, PBF, HGS, aBMDfemur, and aBMDL1-L4). Logistic regression analysis was conducted to estimate the risk of osteoporosis, obesity, and/or sarcopenia based on blood cell-derived inflammation indices. First, univariate analysis was used to identify potential risk factors. Then, multivariate logistic regression analysis was performed to control for confounders. After adjustment for confounders, the odds ratios (ORs) and their 95% confidence intervals (CIs) for risk factors were calculated. All tests were two-tailed, with the statistical significance level set at P < 0.05.

Results

The mean age of the 648 participants (64.2% women) was 67.21 ± 6.40 years; among these participants, 8.18% (53) belonged to the OSO/0 group, 30.25% (196) to the OSO/1 group, 45.06% (292) to the OSO/2 group, and 16.51% (107) to the OSO/3 group. Overall, 70.99% (460) had osteopenia or osteoporosis, 72.53% (470) had obesity, and 26.39% (171) had sarcopenia.

Osteoporosis, obesity, sarcopenia, or any combination of these disorders was positively correlated with age (P < 0.001), coronary heart disease (P = 0.001), PBF (P < 0.001), and total cholesterol (TCH, P = 0.001) and low-density lipoprotein (LDL, P = 0.019) levels but negatively correlated with alcohol consumption, regular exercise, duration of sunlight exposure, creatinine, aBMDL1-L4, aBMDtotal hip, aBMDfemoral neck, appendicular skeletal mass index (ASMI), HGS, and GS (P < 0.001 for all; Table 1).

Table 1 General characteristics of participants with different numbers of osteoporosis, obesity, and sarcopenia

The relationships of inflammatory indices with osteoporosis, obesity, sarcopenia, and their combinations are shown in Table 2. The Kruskal–Wallis test with the Bonferroni correction revealed statistically significant differences in white blood cells (WBC), SIRI, AISI, NLR, PLR, and LMR among the four groups (P < 0.05). Higher numbers of these disorders were associated with higher AISI, SIRI, and NLR values and lower LMR values (P < 0.001 for each index). Paired comparisons between groups showed that the OSO/3 group had higher AISI, SIRI, and NLR values than any other group (P < 0.05). WBC counts were lower in the OSO/0 group than in any other group. Furthermore, SIRI, AISI, NLR, and PLR values were higher and LMR values were lower in the OSO/1, OSO/2, and OSO/3 groups than in the OSO/0 group (P < 0.05 for each index).

Table 2 Relationships between inflammatory indices and numbers of disorders with body composition

Correlations of the inflammatory indices with ASMI, HGS, GS, PBF, aBMDL1-L4, aBMDtotal hip and aBMDfemoral neck were identified using Spearman’s and Pearson’s analyses (Table 3). Overall, no clear correlation patterns were identified between most of the inflammatory indices and parameters used for the assessment of osteoporosis, obesity, and sarcopenia in different groups. The WBC count was not correlated with any of the parameters in any of the groups. SIRI, AISI, and PLR were correlated with some of the parameters, but the correlations did not become stronger with increasing numbers of disorders. NLR was correlated with aBMDtotal hip (r = 0.153, P = 0.032) and aBMDfemoral neck (r = 0.167, P = 0.019) in the OSO/1group, whereas LMR was correlated with ASMI (r = − 0.250, P = 0.009) and HGS (r = − 0.202, P = 0.037) in the OSO/3 group.

Table 3 Correlations of inflammation indices with common parameters for osteoporosis, obesity, and sarcopenia assessment

Using data in the OSO/0 group as normal reference values, logistic regression analyses were performed to evaluate the potential of the inflammatory indices as indicators of the risk of osteoporosis, obesity, sarcopenia, or their combinations. After adjustment for confounding factors, SIRI, AISI, NLR, and PLR were found to be positively correlated with the presence of one or more of the following conditions: osteoporosis, obesity, and sarcopenia (P < 0.001 for OSO/1, OSO/2 and OSO/3); however, LMR was negatively correlated with OSO/1, OSO/2, and OSO/3 (P < 0.05). In addition, WBC was associated with OSO/3 (OR = 1.410, 95% CI: 1.111–1.789; Table 4).

Table 4 Multivariate logistic regression analysis on the predictive value of inflammation indices for osteoporosis, obesity, sarcopenia assessment and their combinations

Discussion

This study was intended to assess the value of several common inflammatory markers, namely, SIRI, WBC, NLR, AISI, LMR, and PLR, in screening patients with osteoporosis, obesity, sarcopenia, or their combinations. Lower LMR values and higher AISI, SIRI, and NLR values were associated with a higher number of these disorders. Participants belonging to the OSO/3 group had higher AISI, SIRI, and NLR values than those belonging to the OSO/1 or OSO/2 group. After adjustment for confounding factors, these inflammatory markers, except for WBC, were independently correlated with these disorders. Furthermore, AISI, SIRI, PLR, NLR, and LMR were moderately associated with HGS, ASMI, PBF, and GS, which are often used to evaluate sarcopenia and body composition. Our study was the first to use these markers to characterize comorbidities comprising osteoporosis, obesity, and sarcopenia, and we generated promising leads for further validation of these markers in the assessment of the above disorders.

Osteoporosis, obesity, and sarcopenia have been studied extensively as individual conditions, and strong evidence has been uncovered for the roles of inflammatory cytokines, such as IL-6, IL-1, and TNF-α, in their pathogenesis [39,40,41]. Osteoporosis represents an imbalance in bone metabolism wherein bone absorption exceeds bone formation [42]. Its development requires the participation of inflammatory cytokines, which promote the formation of osteoclasts from its precursors through a series of signal transduction pathways [43,44,45,46]. Higher circulating levels of proinflammatory cytokines have been found to be associated with a lower aBMD, accelerated bone loss, and increased fracture risk in older adults [47,48,49]. Obesity is not only a major feature of inflammation but also a contributor to a chronic inflammatory state [50]. Increased levels of TNF-α, IL-6, IL-1β, and other cytokines are found in adipose tissues of obese animals and humans [51]. The mechanisms responsible for sarcopenia are poorly understood because of lack of appropriate animal models [52]. However, loss of muscle mass, muscle strength, and physical performance are generally accompanied by increased circulating levels of proinflammatory cytokines [53]. These markers are often not included in the diagnostic criteria for these disorders.

Another reason behind choosing these inflammatory markers in the present study was that although they have been increasingly used for evaluating chronic inflammatory diseases and monitoring cancer therapy, their value in osteoporosis, obesity, and sarcopenia remains to be firmly established because of the limited number of studies [54, 55]. NLR has been reported to be negatively associated with aBMD; however, this is not reflected in our results [56]. It has also been suggested as an indicator of the risk of postmenopausal osteopenia in women [57]. Since obesity-related inflammation with adipose tissue remodeling occurs predominantly in individuals with visceral adiposity, which causes monocyte/macrophage infiltration and changes in lymphocyte subtypes, biomarkers based on these cell types may very well reflect these dynamic processes [32, 58]. Indeed, a study of metabolic syndrome in middle-aged and older adults in Taiwan showed that PBF, obesity, hip circumference, waist circumference, and waist-to-hip ratio had a significant positive correlation with NLR and C-reactive protein levels [59]. Sarcopenia and systemic inflammatory indices have been used together as indicators for outcomes of cancer treatment [60]. The potential of these indices as tools for the assessment of sarcopenia remains unclear.

Our results showed that some of the demographic parameters (age, sex, and monthly household income), health-related habits (alcohol consumption, regular exercise, and sunlight exposure), laboratory test results (TG, TCH, LDL, HDL, UA, and Cr levels), and medical conditions (coronary heart disease, diabetes, and fatty liver) were associated with the prevalence of osteoporosis, obesity, sarcopenia, and their combinations. As shown in previous studies, these disorders are more likely to occur in aging individuals with unhealthy habits, metabolic abnormalities, or chronic diseases [2, 61]. It is particularly interesting that Cr and UA levels were lower in groups with abnormal body composition. Cr is often used to estimate renal function, but as a metabolite from skeletal muscle, its levels are dependent on muscle mass under steady renal function [62]. However, Cr levels may also be affected by sex and age [63]. Women and older adults typically have relatively low muscle mass and therefore a low output of Cr [64, 65]. As we had large proportions of women and older adults in the study and sarcopenia is more likely to be found in people with a multiple disorders with abnormal body composition, the findings of Cr largely reflect these characteristics. UA is the end-product of purine metabolism, and its effects on muscle function are far from unequivocal. Serum UA levels are positively associated with levels of proinflammatory cytokines, such as TNF-α and IL-6 [66]. Conversely, UA is a powerful scavenger of reactive oxygen species, which possess proinflammatory properties [67]. In our study, UA levels were lower in the OSO/2 and OSO/3 groups than in the OSO/1 group, suggesting a positive correlation with aBMD and muscle mass [68,69,70,71].

This study had some limitations. First, we only collected cross-sectional data, and therefore, we were unable to establish any causal relationships between blood cell count-derived inflammation indices and OSO. Second, as a single-center study with a moderate sample size, some potential biases may not have been completely eliminated. Third, there were nearly twice as many women as men in the study, and the sex distribution was clearly not representative of that of the general population. Additionally, as there were no standard PBF cutoffs available for the Chinese population, we used values recommended by the ACE, which may have underestimated obesity due to ethnic differences. Finally, this study explored the association of OSO and its components with blood cell count-derived inflammation indices in older Chinese adults. Considering that lifestyle is an important etiological factor for these disorders, our findings need confirmation from studies on populations in other countries. The value of these inflammation indices as assessment tools and indicators of treatment efficacy for OSO will become clearer when larger-scale and prospective data are available.

Conclusions

After accounting for potential confounding factors, we found the blood cell count-derived inflammation indices SIRI, NLR, PLR, AISI, and LMR to be associated with increased prevalence of OSO. These findings suggest that the value of these indices in the evaluation of these disorders warrants further investigation.

Availability of data and materials

The datasets generated and analyzed during the present study are available from the corresponding author on reasonable request.

Abbreviations

ACE:

The American Council on Exercise

AISI:

Aggregate inflammation systemic index

ASMI:

Appendicular skeletal mass index

AWGS:

The Asian Working Group for Sarcopenia

BUN:

Blood urea nitrogen

aBMD:

Bone mineral density

CI:

Confidence interval

Cr:

Creatinine

HDL:

High-density lipoprotein cholesterol

HGS:

Handgrip strength

GS:

Gait speed

LDL:

Low-density lipoprotein cholesterol

LMR:

Lymphocyte-to-monocyte ratio

NLR:

Neutrophil-to-lymphocyte ratio

OR:

Odds ratios

OSO:

Osteosarcopenic obesity

PBF:

Percentage body fat

PLR:

Platelet-to-lymphocyte ratio

SIRI:

Systemic inflammation response index

TCH:

Total cholesterol

TG:

Triglycerides

UA:

Uric acid

WBC:

White blood cell

References

  1. Pang BWJ, Wee SL, Chen KK, et al. Coexistence of osteoporosis, sarcopenia and obesity in community-dwelling adults - the Yishun study. Osteoporos Sarcopenia. 2021;7:17–23.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ormsbee MJ, Prado CM, Ilich JZ, et al. Osteosarcopenic obesity: the role of bone, muscle, and fat on health. J Cachexia Sarcopenia Muscle. 2014;5:183–92.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ilich JZ, Kelly OJ, Inglis JE. Osteosarcopenic obesity syndrome: what is it and how can it be identified and diagnosed? Curr Gerontol Geriatr Res. 2016;2016:7325973.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bauer JM, Cruz-Jentoft AJ, Fielding RA, et al. Is there enough evidence for osteosarcopenic obesity as a distinct entity? A critical literature review. Calcif Tissue Int. 2019;105:109–24.

    Article  CAS  PubMed  Google Scholar 

  5. Kelly OJ, Gilman JC, Boschiero D, Ilich JZ. Osteosarcopenic obesity: current knowledge, revised identification criteria and treatment principles. Nutrients. 2019;11:747.

    Article  CAS  PubMed Central  Google Scholar 

  6. Lee SH, Lee JY, Lim KH, Lee YS, Koh JM. Associations between plasma growth and differentiation factor-15 with aging phenotypes in muscle, adipose tissue, and bone. Calcif Tissue Int. 2022;110:236–43.

    Article  CAS  PubMed  Google Scholar 

  7. JafariNasabian P, Inglis JE, Reilly W, Kelly OJ, Ilich JZ. Aging human body: changes in bone, muscle and body fat with consequent changes in nutrient intake. J Endocrinol. 2017;234:R37–51.

    Article  CAS  PubMed  Google Scholar 

  8. Azzolino D, Cesari M. Obesity and COVID-19. Front Endocrinol (Lausanne). 2020;11:581356.

    Article  Google Scholar 

  9. Scott D, Chandrasekara SD, Laslett LL, Cicuttini F, Ebeling PR, Jones G. Associations of sarcopenic obesity and dynapenic obesity with bone mineral density and incident fractures over 5-10 years in community-dwelling older adults. Calcif Tissue Int. 2016;99:30–42.

    Article  CAS  PubMed  Google Scholar 

  10. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14:576–90.

    Article  CAS  PubMed  Google Scholar 

  11. Franceschi C, Garagnani P, Vitale G, Capri M, Salvioli S. Inflammaging and 'Garb-aging'. Trends Endocrinol Metab. 2017;28:199–212.

    Article  CAS  PubMed  Google Scholar 

  12. Koelman L, Pivovarova-Ramich O, Pfeiffer AFH, Grune T, Aleksandrova K. Cytokines for evaluation of chronic inflammatory status in ageing research: reliability and phenotypic characterisation. Immun Ageing. 2019;16:11.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fulop T, Larbi A, Pawelec G, et al. Immunology of aging: the birth of inflammaging. Clin Rev Allergy Immunol. 2021;1-14.

  14. Riggs BL, Khosla S, Melton LJ 3rd. Sex steroids and the construction and conservation of the adult skeleton. Endocr Rev. 2002;23:279–302.

    Article  CAS  PubMed  Google Scholar 

  15. Makki K, Froguel P, Wolowczuk I. Adipose tissue in obesity-related inflammation and insulin resistance: cells, cytokines, and chemokines. ISRN Inflamm. 2013;2013:139239.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bilski J, Pierzchalski P, Szczepanik M, Bonior J, Zoladz JA. Multifactorial mechanism of sarcopenia and Sarcopenic obesity. Role of physical exercise, microbiota and Myokines. Cells. 2022:11:160.

  17. Livshits G, Kalinkovich A. Inflammaging as a common ground for the development and maintenance of sarcopenia, obesity, cardiomyopathy and dysbiosis. Ageing Res Rev. 2019;56:100980.

    Article  CAS  PubMed  Google Scholar 

  18. Reuben DB, Cheh AI, Harris TB, et al. Peripheral blood markers of inflammation predict mortality and functional decline in high-functioning community-dwelling older persons. J Am Geriatr Soc. 2002;50:638–44.

    Article  PubMed  Google Scholar 

  19. Karantanos T, Karanika S, Seth B, Gignac G. The absolute lymphocyte count can predict the overall survival of patients with non-small cell lung cancer on nivolumab: a clinical study. Clin Transl Oncol. 2019;21:206–12.

    Article  CAS  PubMed  Google Scholar 

  20. Giovannini S, Onder G, Liperoti R, et al. Interleukin-6, C-reactive protein, and tumor necrosis factor-alpha as predictors of mortality in frail, community-living elderly individuals. J Am Geriatr Soc. 2011;59:1679–85.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Karhausen J, Choi HW, Maddipati KR, et al. Platelets trigger perivascular mast cell degranulation to cause inflammatory responses and tissue injury. Sci Adv. 2020;6(eaay6314).

  22. Nathan C. Neutrophils and immunity: challenges and opportunities. Nat Rev Immunol. 2006;6:173–82.

    Article  CAS  PubMed  Google Scholar 

  23. Zinellu A, Paliogiannis P, Sotgiu E, et al. Blood cell count derived inflammation indexes in patients with idiopathic pulmonary fibrosis. Lung. 2020;198:821–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kruger P, Saffarzadeh M, Weber AN, et al. Neutrophils: between host defence, immune modulation, and tissue injury. PLoS Pathog. 2015;11:e1004651.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Francesca P, Elisabetta T, Anna R, et al. Neutrophil-to-lymphocyte ratio, Mediterranean diet, and bone health in coeliac disease patients: a pilot study. Oxidative Med Cell Longev. 2019;7384193.

  26. Paliogiannis P, Satta R, Deligia G, et al. Associations between the neutrophil-to-lymphocyte and the platelet-to-lymphocyte ratios and the presence and severity of psoriasis: a systematic review and meta-analysis. Clin Exp Med. 2019;19:37–45.

    Article  PubMed  Google Scholar 

  27. Erre GL, Paliogiannis P, Castagna F, et al. Meta-analysis of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio in rheumatoid arthritis. Eur J Clin Investig. 2019;49:e13037.

    Article  Google Scholar 

  28. Pinna A, Porcu T, D'Amico-Ricci G, et al. Complete blood cell count-derived inflammation biomarkers in men with age-related macular degeneration. Ocul Immunol Inflamm. 2019;27:932–6.

    Article  PubMed  Google Scholar 

  29. Pillay J, Kamp VM, van Hoffen E, et al. A subset of neutrophils in human systemic inflammation inhibits T cell responses through mac-1. J Clin Invest. 2012;122:327–36.

    Article  CAS  PubMed  Google Scholar 

  30. Faria SS, Fernandes PC Jr, Silva MJ, et al. The neutrophil-to-lymphocyte ratio: a narrative review. Ecancermedicalscience. 2016;10:702.

    PubMed  PubMed Central  Google Scholar 

  31. Rodríguez-Rodríguez E, López-Sobaler AM, Ortega RM, Delgado-Losada ML, López-Parra AM, Aparicio A. Association between neutrophil-to-lymphocyte ratio with abdominal obesity and healthy eating index in a representative older Spanish population. Nutrients. 2020;12:855.

    Article  PubMed Central  Google Scholar 

  32. Lolmède K, Duffaut C, Zakaroff-Girard A, Bouloumié A. Immune cells in adipose tissue: key players in metabolic disorders. Diabetes Metab. 2011;37:283–90.

    Article  PubMed  Google Scholar 

  33. Imtiaz F, Shafique K, Mirza SS, Ayoob Z, Vart P, Rao S. Neutrophil lymphocyte ratio as a measure of systemic inflammation in prevalent chronic diseases in Asian population. Int Arch Med. 2012;5:2.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Li RD, Tang YH, Wang HL, Yang D, Sun LJ, Li W. The SMYD3 VNTR 3/3 polymorphism confers an increased risk and poor prognosis of hepatocellular carcinoma in a Chinese population. Pathol Res Pract. 2018;214:625–30.

    Article  CAS  PubMed  Google Scholar 

  35. Djoussé L, Driver JA, Gaziano JM. Relation between modifiable lifestyle factors and lifetime risk of heart failure. Jama. 2009;302:394–400.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chen LK, Woo J, Assantachai P, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21:300–307.e302.

    Article  PubMed  Google Scholar 

  37. Knapp KM, Blake GM, Spector TD, Fogelman I. Can the WHO definition of osteoporosis be applied to multi-site axial transmission quantitative ultrasound? Osteoporos Int. 2004;15:367–74.

    Article  CAS  PubMed  Google Scholar 

  38. Choi B, Steiss D, Garcia-Rivas J, et al. Comparison of body mass index with waist circumference and skinfold-based percent body fat in firefighters: adiposity classification and associations with cardiovascular disease risk factors. Int Arch Occup Environ Health. 2016;89:435–48.

    Article  PubMed  Google Scholar 

  39. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Santoro A, Guidarelli G, Ostan R, et al. Gender-specific association of body composition with inflammatory and adipose-related markers in healthy elderly Europeans from the NU-AGE study. Eur Radiol. 2019;29:4968–79.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kalinkovich A, Livshits G. Sarcopenic obesity or obese sarcopenia: a cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Res Rev. 2017;35:200–21.

    Article  CAS  PubMed  Google Scholar 

  42. Cheng CH, Chen LR, Chen KH. Osteoporosis due to hormone imbalance: an overview of the effects of estrogen deficiency and glucocorticoid overuse on bone turnover. Int J Mol Sci. 2022;23.

  43. Krabbe KS, Pedersen M, Bruunsgaard H. Inflammatory mediators in the elderly. Exp Gerontol. 2004;39:687–99.

    Article  CAS  PubMed  Google Scholar 

  44. Franceschi C, Bonafè M, Valensin S, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2000;908:244–54.

    Article  CAS  PubMed  Google Scholar 

  45. Chen Y, Liu S, Leng SX. Chronic low-grade inflammatory phenotype (clip) and senescent immune dysregulation. Clin Ther. 2019;41:400–9.

    Article  PubMed  Google Scholar 

  46. Loi F, Córdova LA, Pajarinen J, Lin TH, Yao Z, Goodman SB. Inflammation, fracture and bone repair. Bone. 2016;86:119–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cauley JA, Danielson ME, Boudreau RM, et al. Inflammatory markers and incident fracture risk in older men and women: the health aging and body composition study. J Bone Miner Res. 2007;22:1088–95.

    Article  CAS  PubMed  Google Scholar 

  48. Ding C, Parameswaran V, Udayan R, Burgess J, Jones G. Circulating levels of inflammatory markers predict change in bone mineral density and resorption in older adults: a longitudinal study. J Clin Endocrinol Metab. 2008;93:1952–8.

    Article  CAS  PubMed  Google Scholar 

  49. Ishii S, Cauley JA, Greendale GA, et al. C-reactive protein, bone strength, and nine-year fracture risk: data from the study of Women's health across the nation (SWAN). J Bone Miner Res. 2013;28:1688–98.

    Article  CAS  PubMed  Google Scholar 

  50. Kim J, Nam JH. Insight into the relationship between obesity-induced low-level chronic inflammation and COVID-19 infection. Int J Obes. 2020;44:1541–2.

    Article  CAS  Google Scholar 

  51. Wang T, He C. Pro-inflammatory cytokines: the link between obesity and osteoarthritis. Cytokine Growth Factor Rev. 2018;44:38–50.

    Article  PubMed  Google Scholar 

  52. Wiedmer P, Jung T, Castro JP, et al. Sarcopenia - molecular mechanisms and open questions. Ageing Res Rev. 2021;65:101200.

    Article  CAS  PubMed  Google Scholar 

  53. Tuttle CSL, Thang LAN, Maier AB. Markers of inflammation and their association with muscle strength and mass: a systematic review and meta-analysis. Ageing Res Rev. 2020;64:101185.

    Article  CAS  PubMed  Google Scholar 

  54. Sunbul M, Gerin F, Durmus E, et al. Neutrophil to lymphocyte and platelet to lymphocyte ratio in patients with dipper versus non-dipper hypertension. Clin Exp Hypertens. 2014;36:217–21.

    Article  PubMed  Google Scholar 

  55. Huszno J, Kolosza Z. Prognostic value of the neutrophil-lymphocyte, platelet-lymphocyte and monocyte-lymphocyte ratio in breast cancer patients. Oncol Lett. 2019;18:6275–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Yilmaz H, Uyfun M, Yilmaz TS, et al. Neutrophil-lymphocyte ratio may be superior to C-reactive protein for predicting the occurrence of postmenopausal osteoporosis. Endocr Regul. 2014;48:25–33.

    Article  CAS  PubMed  Google Scholar 

  57. Lee SH, Ryu SY, Park J, Shin MH, Han MA, Choi SW. The relationship of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio with bone mineral density in korean postmenopausal women. Chonnam Med J. 2019;55:150–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105:141–50.

    Article  CAS  PubMed  Google Scholar 

  59. Syauqy A, Hsu CY, Rau HH, Chao JC. Association of dietary patterns with components of metabolic syndrome and inflammation among middle-aged and older adults with metabolic syndrome in Taiwan. Nutrients. 2018;10.

  60. Feliciano EMC, Kroenke CH, Meyerhardt JA, et al. Association of Systemic Inflammation and Sarcopenia with Survival in nonmetastatic colorectal Cancer: results from the C SCANS study. JAMA Oncol. 2017;3:e172319.

    Article  PubMed  Google Scholar 

  61. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14:513–37.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Patel SS, Molnar MZ, Tayek JA, et al. Serum creatinine as a marker of muscle mass in chronic kidney disease: results of a cross-sectional study and review of literature. J Cachexia Sarcopenia Muscle. 2013;4:19–29.

    Article  PubMed  Google Scholar 

  63. Nishino T, Endo S, Miyano H, et al. Reference serum creatinine levels according to sex, age, and height in children with Down syndrome. Eur J Pediatr. 2021;180:2977–83.

    Article  CAS  PubMed  Google Scholar 

  64. Thongprayoon C, Cheungpasitporn W, Kashani K. Serum creatinine level, a surrogate of muscle mass, predicts mortality in critically ill patients. J Thorac Dis. 2016;8:E305–11.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Nitipir C, Orlov-Slavu C, Alecu L, et al. Possible influence of weight gain and creatinine levels in predicting response to nivolumab: a multicenter analysis. Metabolites. 2020;10.

  66. Lyngdoh T, Marques-Vidal P, Paccaud F, et al. Elevated serum uric acid is associated with high circulating inflammatory cytokines in the population-based Colaus study. PLoS One. 2011;6:e19901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Boardman NT, Falck AT, Lund T, et al. Human concentrations of uric acid scavenges adaptive and maladaptive reactive oxygen species in isolated rat hearts subjected to ischemic stress. Can J Physiol Pharmacol. 2020;98:139–46.

    Article  CAS  PubMed  Google Scholar 

  68. Beavers KM, Beavers DP, Serra MC, Bowden RG, Wilson RL. Low relative skeletal muscle mass indicative of sarcopenia is associated with elevations in serum uric acid levels: findings from NHANES III. J Nutr Health Aging. 2009;13:177–82.

    Article  CAS  PubMed  Google Scholar 

  69. Dong XW, Tian HY, He J, Wang C, Qiu R, Chen YM. Elevated serum uric acid is associated with greater bone mineral density and skeletal muscle mass in middle-aged and older adults. PLoS One. 2016;11:e0154692.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lee J, Hong YS, Park SH, Kang KY. High serum uric acid level is associated with greater handgrip strength in the aged population. Arthritis Res Ther. 2019;21:73.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Molino-Lova R, Sofi F, Pasquini G, et al. Higher uric acid serum levels are associated with better muscle function in the oldest old: results from the Mugello study. Eur J Intern Med. 2017;41:39–43.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

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This work was supported by a grant from the Scientific Research Projects of Heilongjiang Provincial Health Commission (2018164).

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YZN and QHW designed/performed most of the investigation and data analysis and wrote the manuscript; ZQY participated in the implementation of the study. HY, LHS and JHW contributed to data interpretation and analysis. All of the authors have read and approved the manuscript.

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Correspondence to Qun-hong Wu.

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This study was approved by the ethics committee of The Second Affiliated Hospital of Harbin Medical University (Ethics approval No: KY2018–262). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all participants.

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Nie, Yz., Yan, Zq., Yin, H. et al. Osteosarcopenic obesity and its components—osteoporosis, sarcopenia, and obesity—are associated with blood cell count-derived inflammation indices in older Chinese people. BMC Geriatr 22, 532 (2022). https://doi.org/10.1186/s12877-022-03225-x

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Keywords

  • Inflammation indices
  • Osteosarcopenic obesity
  • Osteoporosis
  • Sarcopenia
  • Obesity