Association Between Platelet, White Blood Cell Count, Platelet To White Blood Cell Ratio and Sarcopenia in Community-Dwelling Older Adults: Focus On Bushehr Elderly Health (BEH) Program




Sarcopenia is a progressive age-related skeletal muscle disorder associated with harmful impacts on health. The present study aimed to investigate the relation between sarcopenia, platelet (PLT), white blood cell (WBC), and PLT to WBC ratio (PWR) due to the importance of early sarcopenia diagnosis.


This cross-sectional study was conducted based on the second stage of the Bushehr Elderly Health (BEH) Program. Sarcopenia was defined based on the revised edition of the European Working Group on Sarcopenia in Older People (EWGSOP2) in accordance with the Iranian cut-off point. Univariate and adjusted multivariate logistic regression and linear regression were used to evaluate the associations.


The prevalence of sarcopenia among participants was 35.73 %. PLT count and PWR were statistically higher in severe sarcopenic participants, while no differences were seen in WBC. In crude analysis, sarcopenia was not associated with quartiles of PLT, WBC, and PWR, while after adjusting for age, marital status, and sex, the association was seen in the fourth quartile of PLT and PWR [OR (95%CI) = 1.40 (1.08 to 1.81), p-value = 0.009 for PLT; OR (95%CI) = 1.55 (1.20 to 2.00), p-value = 0.001 for PWR]. This association remained significant in the fully adjusted model [OR (95%CI) = 1.92 (1.25 to 2.95), p-value = 0.003 for PLT; OR (95%CI) = 1.64 (1.06 to 2.52), p-value = 0.024 for PWR]. Among sarcopenia parameters, PLT count was more likely to be associated with handgrip strength and muscle mass. After stratifying the participants by gender, sarcopenia parameters were no longer statistically significant in men.


This study showed that PLT and PWR were associated with sarcopenia after considering confounding factors, while this association was not seen in WBC. Moreover, results showed that gender had an important impact on sarcopenia parameters.


Given the increase in the older adults population, the age-related diseases have proportionately raised [1]. On the other hand, aging has been shown to be associated with a decrease in muscle mass and muscle strength. It is estimated that each person, after age 30, approximately loses 0.1 to 0.5% muscle mass per year, which will escalate after age 65 [2, 3]. This age-related decline in muscle mass is defined as sarcopenia [4]. Sarcopenia is prevalent among older adults; however, its prevalence varies among older adults in various parts of the world[5]. Various mechanisms have been described for age-related muscle mass decline or sarcopenia, including insulin resistance, nutrition, age-related sex hormone, oxidative stress, neuromuscular dysfunction, endocrine abnormality, physical inactivity [68], but lately, studies have emphasized the key role of chronic inflammation in age-related muscle mass decrease [9]. It has been confirmed that aging is associated with a reduction in the regulation of pro-inflammatory cytokines [10]. This dysregulation is associated with decreased muscle mass and strength by interfering with muscle synthesis and catabolism [11, 12].

WBC, PLT, and platelet to white blood cell ratio count are broad and affordable disease indicators used in clinical settings. WBC is a standardized and stable marker that measures inflammation[13].

PLTs, a significant part of blood, have been shown that alongside the hemostasis function contribute to subclinical inflammation and oxidative stress[14]. Studies have shown that PLT activity increases in inflammatory diseases, and it has been confirmed that PLTs can indicate an inflammatory state [15]. Moreover, in vivo studies have demonstrated that age-related elevated TNF-α increases PLT activity, while anti-TNF-α administration declines PLT activity [16].

Previous studies have investigated the effectiveness of WBC and PLT count as possible markers of sarcopenia [17, 18]. In this regard, some studies have shown a significant association between elevated PLT count and sarcopenia [18, 19], while other studies from china health and aging trend, and community-dwelling older adults did not find a significant association between PLT and sarcopenia [20, 21].

Given the controversial results in previous studies and lack of sufficient information in eastern Mediterranean countries, and drawing on data from the BEH program, in this study, it was attempted to investigate whether PLT, WBC, and PWR are associated with sarcopenia. In addition, early diagnosis of sarcopenia with inexpensive markers like CBC seems to be essential for early detection, prevention and treatment of sarcopenia.


Research design and participants

This cross-sectional study was conducted based on the second stage of the Bushehr elderly health (BEH) program. The methodology of the BEH program has previously been reported in detail [22, 23]. The BEH program is a prospective cohort study in Bushehr, south of Iran, targeting a population of 60 and over. Among 3297 who were selected through multistage stratified random sampling, 3000 were accepted to participate in the first phase of the study (participants rate was 91%). The first stage was conducted from March 2013 to October 2014, and the second phase, focusing on musculoskeletal and cognitive outcomes, started in 2015 with 2368 who were following the first stage (response rate was 81%).

Measurement of laboratory parameters:

Venous blood samples were collected from the participants following 8–12 h of fasting condition. Red blood cell count (RBC), hemoglobin (Hgb), WBC, PLT were assessed by an automated hematology analyzer, Medonic CA620 (Menarini Diagnostic Srl, Florence, Italy). Blood urea nitrogen (BUN), creatinine (Cr), uric acid, alkaline phosphatase (Alk-p), fasting plasma glucose (FPG), and lipid profile were assessed by an auto-analyzer using commercial kits (ParsAzmun, Karaj, Iran). Hemoglobin A1c (HbA1c) was measured by the CERA-STAT system (CERAGEMMEDISYS,chungcheongnam-do, Korea).

Measures and definition of sarcopenia:

Sarcopenia was defined based on the current revised edition of the European Working Group on sarcopenia in Older People (EWGSOP2), issued recently and defined as having low muscle mass and low muscle strength; it is also characterized as severe if the previous criteria were extant with poor physical performance. Dual x-ray absorptiometry (DXA, Discovery WI, Hologic, Bedford, Virginia, USA) was used to measure fat mass and muscle mass with minimal radiation exposure. Appendicular skeletal muscle mass (ASM) for each participant was calculated as the sum of upper and lower limb muscle mass. The skeletal muscle mass index (SMI) was defined as ASM/height2 (kg/m2). According to previous studies, the cut-off point for low muscle mass was defined as SMI< 7.0 kg/mfor men and < 5.4 kg/m2 for women in the Iranian population[24]. Muscle strength was assessed based on handgrip strength and chair stand measures. Handgrip strength was measured three times for each hand using a digital dynamometer. The handgrip strength threshold was 26 kg for men and 18 kg for women[24]. In this study, the chair stand test was used to assess the lower extremity muscle strength[25]. For the measuring chair stand test, participants were asked to keep their arms folded across their chest; then, if participants could perform the first test, they were asked to stand up and sit down five times without using arms. Time was recorded for each participant from the initial sitting to the final standing position, and the cut-off point was defined as chair stand test time > 15 seconds.   Physical performance was evaluated by short physical performance battery (SPPB) and usual gait speed. SPPB is a group of tests evaluating physical performance by combining the result of the chair stand, gait speed, and balance tests described elsewhere[23]. For measuring the usual gait speed, participants were asked to walk for 4.57 m at a normal pace twice; then, the fastest record was used. Poor physical performance was defined as SPPB ≤ 8 point score or gait speed ≤ 0.8 m/s[25].

Other variables: 

Metabolic syndrome (MetS) was defined according to the revised edition of national cholesterol education program adult treatment panel III  (NCEP-ATP III)[26], and cognitive function was assessed using the mini-mental state examination (MMSE), mini-cog, and categorical verbal fluency test (CFT), which have been described in the previous study [27]. The chronic diseases included liver disease, lung disease, cardiovascular disease, thyroid diseases, osteoarthritis (OA), rheumatoid arthritis (RA), which were defined as self-reported or medication use. Chronic renal failure was defined as glomerular filtration rate (GFR) below 60; hypertension (HTN) as medication use, systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg), and diabetes mellitus (DM), as HbA1C≥6.5, FPG ≥ 126 mg/dl or taking anti-diabetic medication).  Use of Anti-inflammatory medication was defined as the implementation of non-steroidal anti-inflammatory drugs (NSAID), azathioprine, mesalazine, sulfasalazine, methotrexate, mycophenolate mofetil, corticosteroids, colchicine, and tacrolimus. Use of anti-PLT medication was defined as the use of aspirin (ASA), clopidogrel, and dipyridamole. The use of anti-hyperlipidemic medication comprised the implementation of statins (atorvastatin, lovastatin, and simvastatin) or fibrates (gemfibrozil and fenofibrate). The use of HTN medication was characterized by the implementation of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), alpha-blocker medications, beta-blockers, calcium channel blockers (CCBs), diuretic medications, and nitrates medications.  Other covariates included age (years), gender (male/female), marital status (single, married, divorced, and widow), and smoking, which included no history of smoking, smoking regularly if the participant had a history of smoking at least one cigarette per day in a week, and the lower rate known as smoking occasionally. Still, as other covariates, body mass index (BMI ) was calculated by dividing weight (kg) to height squared (m2); waist to hip circumference ratio (WHR), which was calculated by dividing waist circumference (WC) to hip circumference  (HC), and disability was assessed by instrumental activities of daily living (IADL) using Lawton scale questionnaires, translated into Persian[28].

Statistical analysis:

The normality of all variables was assessed by the Kolmogorov–Smirnov test. PLT, WBC and PWR were divided into four quartiles as fellows: Q1 ≤ 220, 220 <Q2 < 259, 259≤ Q3≤ 300, and Q4> 300 (103 / for PLT; Q1 ≤ 6.1, 6.1 <Q2 < 7.3, 7.3≤ Q3≤ 8.4, and Q4> 8.4 (103/ for WBC, and Q1 ≤29.46, 29.46< Q2 < 36, 36≤ Q3≤ 43.28, and Q4> 43.28 for PWR. Categorical variables were presented by the frequency and percentage, and the mean and standard deviation (SD) were used for continuous variables. Differences in quartiles were evaluated by running one-way analysis of variance (ANOVA) and chi-square (X2) for continuous variables and categorical variables, respectively. Multivariable linear and logistic regression analyses were used to evaluate the association between PLT, sarcopenia, and sarcopenia parameters. Covariates that had a significant clinical and pathophysiological association with desired outcomes were first assessed by univariate regression models; then, statistically significant covariates were used in multivariate logistic regression analyses. Covariates were adjusted as: model 1= age, marital, and gender; model 2= model 1 + smoking, metabolic syndrome, cognitive disorder, and the number of chronic diseases; model 3 = model2 + Anti-inflammatory medications, anti-PLT medications, anti-diabetic medications, anti-hyperlipidemic medications, HTN medications, IADL, waist to hip ratio (WHR), and BMI; model 4= model 3 + laboratory parameters (HGB, WBC, HbA1c, HDL-cholesterol, ALK-P, TG, uric acid, and creatinine). Stata MP (version 15) was used, and a two-sided p-value of <0.05 was taken as statistically significant for all analyses. P-values for trends were obtained from adjusted models by assigning quartiles as continuous variables. 


Of 2,368 who were included in this study, 1,223 participants (51.65%) were female. The mean age of the participants was 69.34 ± 6.33, and the prevalence of sarcopenia among participants was 35.73 %. According to Table 1, there are significant differences in PLT count and PWR between sarcopenic and nonsarcopenic participants, while no statistical differences are seen in WBC. Participants with severe sarcopenia have a higher prevalence of metabolic syndrome, cognitive disorder, and they are more likely to use anti-inflammatory, anti-hyperlipidemic, and anti-HTN medication than participants with mild sarcopenia.

Table 2 demonstrates multivariate logistic regression analysis between sarcopenia, PLT, WBC, and PWR. Considering the lowest quartile (Q1) as a reference, in unadjusted analysis, sarcopenia is not associated with PLT, WBC, and PWR. Although WBC is not associated with sarcopenia in any analysis models, the association between PLT, PWR, and sarcopenia appears after adjusting covariates. According to the analysis, the association between sarcopenia and the fourth quartile is seen after adjusting age, gender, marital status (model 1) [OR (95%CI) = 1.40 (1.08 to 1.81), p-value =0.009 for PLT; OR (95%CI) =1.55 (1.20 to 2.00), p-value =0.001 for PWR]. This association between PLT, PWR, and sarcopenia in the fourth quartile is steadily getting stronger as other covariates are considered in the analysis [in the fully-adjusted model (model 4): OR (95%CI) =1.92 (1.25 to 2.95), p-value =0.003 for PLT; OR (95%CI) =1.70 (1.11 to 2.60), p-value =0.013 for PWR].

Multivariate linear regression between PLT and sarcopenia parameters is illustrated in Table 3. Results show that after fully adjusted analysis (model 4), only the association between ASM and handgrip remains statistically significant, which means that PLTs are more likely to affect handgrip strength (β = -.0120) and ASM (β = -.0070) compared to gait speed (β = -.0001) and SPPBβ =-.0006). However, after stratified analysis based on gender, none of the sarcopenia parameters are statistically associated with PLTs in men (all p-values > 0.05), while in women, handgrip remains statistically significant even after adjusting for age, marital, smoking, metabolic syndrome, chronic diseases, and cognitive disorder (model 2), and ASM remains statistically significant in the fully adjusted model (model 4). 

Figures 1 and 2 show the association between PLT, PWR, and age based on gender differences by fractional polynomial plots. As figures illustrate, by increasing age, PWR escalates; however, PLT slightly declines in women over 90 years old.


This study showed that sarcopenia association with PLT count and PWR appeared in the primary adjustment, which remained significant even after controlling for potential confounders. This association was not seen between WBC and sarcopenia. Moreover, among sarcopenia measures, a prominent effect of PLTs was only seen in ASM and handgrip. However, considering gender separately, the association between PLT and sarcopenia parameters remained statistically significant only in women. 

Previous findings revealed that the biological effect of aging might be caused by oxidative stress and mitochondrial dysfunction[29]. The aging process is associated with chronic low-grade inflammation, which is called Inflamm-aging[30]. It has been demonstrated that Inflamm-aging plays a prominent role in the pathogenesis number of age-related chronic diseases such as atherosclerosis, insulin resistance,  sarcopenia, frailty, and disability [31]. For instance, obesity and body fat mass, which have been explained to be associated with sarcopenia, have been shown to be positively correlated with PLT activity [19]. 

In our study, severe sarcopenia participants had a higher amount of PLT and PWR than other groups. After adjusting the model, the effect of PLT on sarcopenia showed a meaningful effect; however, the pathway of this effect is not clearly understood. 

This association might be explained by endothelial dysfunction. Endothelial dysfunction is identified as an imbalance between vasodilatory and vasoconstrictive actions that cause a reduction in vasodilation activity of vessels. The integrity of endothelium, different receptors, and flow-mediated stimuli can affect the production and release of endothelium-derived relaxing factors (EDRF) such as nitric oxide [32].  EDRF causes vasodilation by activated cellular cascade such as soluble guanylate cyclase and subsequently raises cyclic guanylate in vascular smooth muscle. EDRF can play an anti-inflammatory role by inhibiting PLT aggregation and adhesion. Moreover, PLTs are initiators of vascular inflammation and remodeling and might cause oxygen and nutrients supply disturbance in muscle cells by interfering in microcirculation and microvascular endothelium[33, 34]. Another possible mechanism is that PLTs might accelerate bone marrow hematopoietic stem cell proliferation and affect the differentiation of human  CD34-positive cells into foam cells, which has been shown to play a key role in the pathophysiology of atherosclerotic and small vessels diseases such as cerebral microbleeds (CMBs)  [34, 35]. CMBs have been shown to be associated with cognitive impairment, physical frailty, and low handgrip strength, independent of other confounding factors. This hypothesis might be confirmed by a higher prevalence of cognitive impairment in severe sarcopenic participants.

White blood cell count is a strong indicator of low-grade inflammation and also might interfere with muscle metabolism by producing pro-inflammatory cytokines like IL-6 [20, 21]. However, our study did not find any significant association between WBC count and sarcopenia; even PLT and PWR have been shown to have a significant association.

Effect of PLTs and WBC count can be simultaneously assessed by  PLT to-white blood cell ratio (PWR)[36]. The interaction between PLTs and WBC has been introduced in the pathogenesis of several diseases (e.g., cerebrovascular infarction). PLTs have been shown to affect other blood cells by releasing chemokines and membrane ligands and also play as a bridge in white blood cells-PLT aggregates (LPAs) in the periphery[37]. Therefore, PWR can represent the degree of inflammation, and a significant association of PWR may show the prominent effect of PLT than WBC on sarcopenia.

PLT count has been shown to decrease with aging in different older adults population. It has been demonstrated that despite the stable number of PLTs in middle-aged people, the PLT count declines by approximately 10% after age 60, which is more prominent in men[38]. Previous studies have shown that women have almost 15% higher PLT count than men, showing gender differences in PLT count in older adults [38, 39]. However, our study showed that PLT count might decrease slightly after 60 in women. Although the PLT count in men is lower than women, we see the gradually escalating change in PLT count after age 90 in men. However, only less than 0.6% of our population is older than 90 years; therefore, we cannot interpret this increasing trend accurately.  PLT count decreased in the older adults might be explained by the fact that hematopoietic stem cell reserve reduces with aging[40]. Although the drop in PLT count is observed, PLT activity seems to increase with aging. It shows that the level of cytokines released by activated PLTs such as PLT factor 4 (PF4), which affects muscle mass, increases in the older adults population[33, 41]. The effect of age-related inflammation might explain this increase. In addition, it has been revealed that platelet response to inflammatory cytokines increases by aging, leading to platelet hyperactivity in older adults [42].

After stratifying participants based on gender in this study, none of the sarcopenia parameters had a statistically significant association with PLT in men. These results were consistent with previous studies emphasizing gender-related differences in sarcopenia and sarcopenia parameters[43, 44]. However, the differences might be related to the age-related decline in sex hormones and the other physiological pathway, especially in menopausal women, who have a higher decline rate than men[45]. It has been confirmed that the decline in estrogen levels in postmenopausal women is associated with a decrease in muscle mass. This association could be explained by the regulatory effects of estrogen on pro-inflammatory cytokines and the direct protective effect of estrogen on muscles, which decreases in menopausal women. Gender-related variations in the distribution of fat mass might also be attributed to these differences. Furthermore, in developing countries such as Iran, women have been shown to be frailer, making them more susceptible to sarcopenia than men[46]. Higher prevalence of frailty in women might be due to the fact that a significant number of the women’s population in lower-middle-income (LMICs) and developing countries, have a lower socioeconomic status than men, which might affect the quality of their nutrition intake, chance to participate in regular exercise, and access to healthcare services. 

Moreover, after adjusting medications (e.g., anti-inflammatory, anti-PLT, HTN, anti-hyperlipidemic, and anti-diabetic medication) in women, results demonstrated that the association between PLT, ASM, and handgrip strength decreased, so that it was no longer statistically significant in the handgrip strength. These medications might prevent sarcopenia and might directly reverse this process. According to Landi et al., individuals who used non-steroidal anti-inflammatory drugs (NSAID) had almost 80% lower risk of sarcopenia compared with non-NSAID users, even after considering potential confounders[47]. Furthermore, anti-diabetic agents such as metformin play a protective role against sarcopenia through increased insulin sensitivity and glucose hemostasis. Aghili et al.’s study showed that those who received metformin had a lower risk for sarcopenia, which was notably true in women [48, 49].

This study was conducted using reliable data and a fully validated protocol with a large number of participants representing the Iranian older adults population. In this study, sarcopenia was defined by revised edition of the European Working Group on Sarcopenia in Older People (EWGSOP2). Moreover, in this study, the relationship between PLT and sarcopenia was adjusted by a number of covariates such as sociodemographic factors, metabolic syndrome, cognitive disorder, and medications. None the less, this study had a number of potential limitations one of which was the cross-sectional nature of the study since musculoskeletal outcomes were measured only in the second stage of the BEH program, and thus, the cause-effect relationships between PLT, PWR, and sarcopenia could not be recognized. In addition, other inflammatory cytokines and markers (e.g., TNF-a, interleukin- 6, C-reactive-protein), mean PLT volume (MPV), and nutritional status were not presented in this study.


In conclusion, this study showed that PLT and PWR were associated with sarcopenia after considering confounding factors, muscle mass, and muscle strength, whereas WBC was not significantly connected with sarcopenia. Moreover, based on the results, women showed a significant association with PLT levels and sarcopenia with their components of it. We suggest that regular PLT screening will be effective alongside other inflammatory markers such as TNF-a, CRP, and ESR. Regular follow-ups of CBC consisting PLT, WBC, Hgb in the elderly population might be helpful in pre-diagnosis of sarcopenia in the early stage. However, further longitudinal studies with different inflammatory cytokines are needed to confirm the connection between the inflammatory markers and sarcopenia and whether anti-inflammatory medication can prevent sarcopenia from happening.


BEH: Bushehr elderly health;  SBP: Systolic blood pressure; DBP: diastolic blood pressure; BMI: Body mass index; WHR: waist to hip ratio; IADL: Instrumental activities of daily living; ASM: Appendicular skeletal muscle mass; SMI: skeletal muscle mass index; SPPB: Short Physical Performance Battery; HTN: Hypertension; HDL: high-density lipoproteins; LDL: low-density lipoproteins; TG: triglycerides; Hgb: Hemoglobin; RBC: Red blood cells; PLT: platelet; WBC: White blood cells; PWR: PLT to WBC ratio; BUN: blood urea nitrogen; ALK-P: Alkaline phosphatase; HbA1c: hemoglobin A1c; EDRF: Endothelium-derived relaxing factor.


Ethics approval and consent to participate

The Ethics Committee of Bushehr University of Medical Sciences granted ethical permission for this study (Ref. No. B-91–14-2) in compliance with the Helsinki Declaration and national guidelines for research ethics. Before research enrollment, all participants gave their informed consent after being informed about procedures involved in the study. Participation was entirely optional, and any participant could withdraw consent at any moment with no repercussions.

Consent for publication

Not applicable.

Availability of data and materials 

The datasets used during the current study are available from the corresponding author, AO ([email protected]) or IN ([email protected]) upon reasonable request.

Competing interests

The authors declare no competing interests.


The Persian Gulf Biomedical Sciences Research Institute, affiliated with Bushehr (Port) University of Medical Sciences (BPUMS), and the Endocrinology and Metabolism Research Institute, affiliated with Tehran University of Medical Sciences, both contributed funding for the BEH Program. This study project was designed and carried out with the help of researchers from both research institutions.

Authors’ contributions

MM, MGH, and ES conceived the study and performed data analysis and interpretation. AF, MEM, MGH drafted the manuscript and participated in interpretation, study design, and conduct and helped draft the manuscript and interpretation. IN, MK, GSH, KA, BL, AHD, and AO participated in the study design and interpretation of the findings. All authors reviewed and approved the submitted manuscript.


The authors would like to express their appreciation to all medical research center employees at Bushehr University of Medical Sciences (BUMS) and Tehran University of Medical Sciences (TUMS) for their dedication and collaboration. The authors would also like to convey their heartfelt appreciation to the Persian Gulf Martyrs Hospital's Clinical Research Development Center and the Persian Gulf Tropical Medicine Research Center. We would like to thank all participants for their participation in the study.


  1. Livshits G, Kalinkovich A: Inflammaging as a common ground for the development and maintenance of sarcopenia, obesity, cardiomyopathy and dysbiosis. Ageing research reviews 2019, 56:100980.
  2. Liguori I, Russo G, Aran L, Bulli G, Curcio F, Della-Morte D, Gargiulo G, Testa G, Cacciatore F, Bonaduce D: Sarcopenia: assessment of disease burden and strategies to improve outcomes. Clinical interventions in aging 2018, 13:913.
  3. Tsukasaki K, Matsui Y, Arai H, Harada A, Tomida M, Takemura M, Otsuka R, Ando F, Shimokata H: Association of muscle strength and gait speed with cross-sectional muscle area determined by mid-thigh computed tomography—A comparison with skeletal muscle mass measured by dual-energy X-ray absorptiometry. The Journal of frailty & aging 2020, 9(2):82-89.
  4. Rosenberg IH: Sarcopenia: origins and clinical relevance. The Journal of nutrition 1997, 127(5):990S-991S.
  5. Papadopoulou S, Tsintavis P, Potsaki G, Papandreou D: Differences in the prevalence of sarcopenia in community-dwelling, nursing home and hospitalized individuals. A systematic review and meta-analysis. The journal of nutrition, health & aging 2020, 24(1):83-90.
  6. Kim JA, Choi KM: Sarcopenia and fatty liver disease. Hepatology international 2019, 13(6):674-687.
  7. Malafarina V, Úriz-Otano F, Iniesta R, Gil-Guerrero L: Sarcopenia in the elderly: diagnosis, physiopathology and treatment. Maturitas 2012, 71(2):109-114.
  8. Tay L, Leung B, Wee S, Tay K, Ali N, Chan M, Lim W: Association of nutrition and immune-endocrine dysfunction with muscle mass and performance in cognitively impaired older adults. Archives of gerontology and geriatrics 2018, 75:20-27.
  9. Bano G, Trevisan C, Carraro S, Solmi M, Luchini C, Stubbs B, Manzato E, Sergi G, Veronese N: Inflammation and sarcopenia: A systematic review and meta-analysis. Maturitas 2017, 96:10-15.
  10. Grosicki GJ, Barrett BB, Englund DA, Liu C, Travison TG, Cederholm T, Koochek A, von Berens Å, Gustafsson T, Benard T et al: Circulating Interleukin-6 Is Associated with Skeletal Muscle Strength, Quality, and Functional Adaptation with Exercise Training in Mobility-Limited Older Adults. The Journal of frailty & aging 2020, 9(1):57-63.
  11. Grosicki GJ, Barrett B, Englund D, Liu C, Travison T, Cederholm T, Koochek A, von Berens Å, Gustafsson T, Benard T: Circulating Interleukin-6 Is Associated with Skeletal Muscle Strength, Quality, and Functional Adaptation with Exercise Training in Mobility-Limited Older Adults. The Journal of frailty & aging 2020, 9(1):57-63.
  12. Schaap LA, Pluijm SM, Deeg DJ, Harris TB, Kritchevsky SB, Newman AB, Colbert LH, Pahor M, Rubin SM, Tylavsky FA: Higher inflammatory marker levels in older persons: associations with 5-year change in muscle mass and muscle strength. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 2009, 64(11):1183-1189.
  13. Kounis NG, Soufras GD, Tsigkas G, Hahalis G: White blood cell counts, leukocyte ratios, and eosinophils as inflammatory markers in patients with coronary artery disease. Clinical and Applied Thrombosis/Hemostasis 2015, 21(2):139-143.
  14. Jesri A, Okonofua EC, Egan BM: Platelet and white blood cell counts are elevated in patients with the metabolic syndrome. The Journal of Clinical Hypertension 2005, 7(12):705-711.
  15. Yazici S, Yazici M, Erer B, Erer B, Calik Y, Ozhan H, Ataoglu S: The platelet indices in patients with rheumatoid arthritis: mean platelet volume reflects disease activity. Platelets 2010, 21(2):122-125.
  16. Davizon-Castillo P, McMahon B, Aguila S, Bark D, Ashworth K, Allawzi A, Campbell RA, Montenont E, Nemkov T, D'Alessandro A et al: TNF-α-driven inflammation and mitochondrial dysfunction define the platelet hyperreactivity of aging. Blood 2019, 134(9):727-740.
  17. Chung T-H, Shim J-Y, Lee Y-J: Association between leukocyte count and sarcopenia in postmenopausal women: The Korean National Health and Nutrition Examination Survey. Maturitas 2016, 84:89-93.
  18. Park W-J, Jung D-H, Lee J-W, Shim J-Y, Kwon Y-J: Association of platelet count with sarcopenic obesity in postmenopausal women: A nationwide population-based study. Clinica Chimica Acta 2018, 477:113-118.
  19.  Lee HS, Koh I-H, Kim H-S, Kwon Y-J: Platelet and white blood cell count are independently associated with sarcopenia: A nationwide population-based study. Thrombosis research 2019, 183:36-44.
  20. Zhao W-y, Zhang Y, Hou L-s, Xia X, Ge M-l, Liu X-L, Yue J-R, Dong B-r: The association between systemic inflammatory markers and sarcopenia: Results from the West China Health and Aging Trend Study (WCHAT). Archives of gerontology and geriatrics 2021, 92:104262.
  21. Tang T, Xie L, Tan L, Hu X, Yang M: Inflammatory indexes are not associated with sarcopenia in Chinese community-dwelling older people: a cross-sectional study. BMC geriatrics 2020, 20(1):1-9.
  22. Ostovar A, Nabipour I, Larijani B, Heshmat R, Darabi H, Vahdat K, Ravanipour M, Mehrdad N, Raeisi A, Heidari G: Bushehr elderly health (BEH) Programme, phase I (cardiovascular system). BMJ open 2015, 5(12):e009597.
  23. Shafiee G, Ostovar A, Heshmat R, Darabi H, Sharifi F, Raeisi A, Mehrdad N, Shadman Z, Razi F, Amini MR: Bushehr Elderly Health (BEH) programme: study protocol and design of musculoskeletal system and cognitive function (stage II). BMJ open 2017, 7(8):e013606.
  24. Shafiee G, Heshmat R, Ostovar A, Khatami F, Fahimfar N, Arzaghi SM, Gharibzadeh S, Hanaei S, Nabipour I, Larijani B: Comparison of EWGSOP-1and EWGSOP-2 diagnostic criteria on prevalence of and risk factors for sarcopenia among Iranian older people: the Bushehr Elderly Health (BEH) program. Journal of diabetes and metabolic disorders 2020, 19(2):727-734.
  25. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA: Sarcopenia: revised European consensus on definition and diagnosis. Age and ageing 2019, 48(1):16-31.
  26. Ervin RB: Prevalence of Metabolic Syndrome Among Adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index; United States, 2003-2006. 2009.
  27. Marzban M, Nabipour I, Farhadi A, Ostovar A, Larijani B, Darabi AH, Shabankari E, Gholizade M: Association between anemia, physical performance and cognitive function in Iranian elderly people: evidence from Bushehr Elderly Health (BEH) program. BMC geriatrics 2021, 21(1):1-17.
  28. Khalagi K, Ansarifar A, Fahimfar N, Sanjari M, Gharibzdeh S, Sharifi F, Shafiee G, Heshmat R, Nabipour I, Larijani B: Cardio-metabolic and socio-demographic risk factors associated with dependency in basic and instrumental activities of daily living among older Iranian adults: Bushehr elderly health program. BMC geriatrics 2021, 21(1):1-9.
  29. Ji LL: Redox signaling in skeletal muscle: role of aging and exercise. Advances in physiology education 2015, 39(4):352-359.
  30. Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A: Inflammaging: a new immune–metabolic viewpoint for age-related diseases. Nature Reviews Endocrinology 2018, 14(10):576-590.
  31. Franceschi C, Garagnani P, Vitale G, Capri M, Salvioli S: Inflammaging and ‘Garb-aging’. Trends in Endocrinology & Metabolism 2017, 28(3):199-212.
  32. Zuchi C, Tritto I, Carluccio E, Mattei C, Cattadori G, Ambrosio G: Role of endothelial dysfunction in heart failure. Heart failure reviews 2020, 25(1):21-30.
  33. Vajen T, Benedikter BJ, Heinzmann AC, Vasina EM, Henskens Y, Parsons M, Maguire PB, Stassen FR, Heemskerk JW, Schurgers LJ: Platelet extracellular vesicles induce a pro-inflammatory smooth muscle cell phenotype. Journal of extracellular vesicles 2017, 6(1):1322454.
  34. Daub K, Langer H, Seizer P, Stellos K, May AE, Goyal P, Bigalke B, Schönberger T, Geisler T, Siegel‐Axel D: Platelets induce differentiation of human CD34+ progenitor cells into foam cells and endothelial cells. The FASEB journal 2006, 20(14):2559-2561.
  35. Zou J, Yuan C, Wu C, Cao C, Yang H: The effects of platelet-rich plasma on the osteogenic induction of bone marrow mesenchymal stem cells. Connective tissue research 2014, 55(4):304-309.
  36. Chen Z, Huang Y, Li S, Lin J, Liu W, Ding Z, Li X, Chen Y, Pang W, Yang D: Platelet-to-white blood cell ratio: a prognostic predictor for 90-day outcomes in ischemic stroke patients with intravenous thrombolysis. Journal of Stroke and Cerebrovascular Diseases 2016, 25(10):2430-2438.
  37. Amalia L, Dalimonthe NZ: Clinical significance of Platelet-to-white blood cell ratio (PWR) and National Institute of health stroke scale (NIHSS) in acute ischemic stroke. Heliyon 2020, 6(10):e05033.
  38. Biino G, Santimone I, Minelli C, Sorice R, Frongia B, Traglia M, Ulivi S, Di Castelnuovo A, Gögele M, Nutile T: Age-and sex-related variations in platelet count in Italy: a proposal of reference ranges based on 40987 subjects' data. PloS one 2013, 8(1):e54289.
  39. Segal JB, Moliterno AR: Platelet counts differ by sex, ethnicity, and age in the United States. Annals of epidemiology 2006, 16(2):123-130.
  40. Montenont E, Rondina MT, Campbell RA: Altered functions of platelets during aging. Current opinion in hematology 2019, 26(5):336-342.
  41. Bastyr III EJ, Kadrofske MM, Vinik AI: Platelet activity and phosphoinositide turnover increase with advancing age. The American journal of medicine 1990, 88(6):601-606.
  42. Hearps AC, Martin GE, Angelovich TA, Cheng WJ, Maisa A, Landay AL, Jaworowski A, Crowe SM: Aging is associated with chronic innate immune activation and dysregulation of monocyte phenotype and function. Aging cell 2012, 11(5):867-875.
  43. Gao L, Jiang J, Yang M, Hao Q, Luo L, Dong B: Prevalence of sarcopenia and associated factors in Chinese community-dwelling elderly: comparison between rural and urban areas. Journal of the American Medical Directors Association 2015, 16(11):1003. e1001-1003. e1006.
  44. Argyriadou S, Vlachonikolis I, Melisopoulou H, Katachanakis K, Lionis C: In what extent anemia coexists with cognitive impairment in elderly: a cross-sectional study in Greece. BMC family practice 2001, 2(1):1-6.
  45. Xing Y, Wei C, Chu C, Zhou A, Li F, Wu L, Song H, Zuo X, Wang F, Qin W: Stage-specific gender differences in cognitive and neuropsychiatric manifestations of vascular dementia. American Journal of Alzheimer's Disease & Other Dementias® 2012, 27(6):433-438.
  46. Biritwum RB, Minicuci N, Yawson AE, Theou O, Mensah GP, Naidoo N, Wu F, Guo Y, Zheng Y, Jiang Y et al: Prevalence of and factors associated with frailty and disability in older adults from China, Ghana, India, Mexico, Russia and South Africa. Maturitas 2016, 91:8-18.
  47. Landi F, Marzetti E, Liperoti R, Pahor M, Russo A, Martone AM, Colloca G, Capoluongo E, Bernabei R: Nonsteroidal anti-inflammatory drug (NSAID) use and sarcopenia in older people: results from the ilSIRENTE study. J Am Med Dir Assoc 2013, 14(8):626.e629-613.
  48. Aghili R, Malek M, Valojerdi AE, Banazadeh Z, Najafi L, Khamseh ME: Body composition in adults with newly diagnosed type 2 diabetes: effects of metformin. Journal of Diabetes & Metabolic Disorders 2014, 13(1):1-8.
  49. Chen F, Xu S, Wang Y, Chen F, Cao L, Liu T, Huang T, Wei Q, Ma G, Zhao Y: Risk Factors for Sarcopenia in the Elderly with Type 2 Diabetes Mellitus and the Effect of Metformin. Journal of Diabetes Research 2020, 2020.


Table 1: Characteristics of the study participants based on severity of sarcopenia in BEH program





(n= 2,368)




Severe sarcopenia

(n= 454)


p-value a

No sarcopenia

(n= 1,522)

Mild sarcopenia

(n= 392)

Demographic & Clinical 

Age (years)

69.30 ± 6.33

67.99 ± 5.48

69.58 ± 6.05

73.44 ± 7.35


Sex (Female), n (%)

1,223 (51.65)

836 (54.93)

132 ( 33.67)

255 ( 56.17)


Marital status, n (%)


19 ( 0.80)

10 (0.66)

2 (0.51)

7 (1.54 )



1,824 (77.03)

1,189 (78.12)

331 (84.44)

304 (66.96)


20 (0.84)

10 (0.66)

6 ( 1.53)

4 (0.88)


505 (21.33)

313 ( 20.57)

53 ( 13.52)

139 (30.62)



1,523 (83.41)

994 (83.95)

233 (76.90)

296 (87.32)


Yes, occasionally

30 (1.64)

15 (1.27)

10 (3.30)

5 (1.47)

Yes, regularly

273 (14.95)

175 (14.78)

60 (19.80)

38 (11.21)

Cognitive disorder, n (%)

1,397 (59.45)

860 (57.03)

212 (54.36)

325 (71.90)


Metabolic syndrome, n (%)

1,179 (49.81)

863 (56.70)

127 (32.40)

189 (41.72)


Chronic disease


244 (10.30)

152 (9.99)

55 (14.03)

37 (8.15)



317 (13.39)

199 (13.07)

53 (13.52)

65 (14.32)

Two or more

1,807 (76.31)

1,171 (76.94)

284 (72.45)

352 (77.53)

SBP (mm Hg)

139.57± 19.32

140.08± 18.83


140.57 ± 19.98


DBP (mm Hg)

81.49 ± 8.64

82.10± 8.40

80.48 ± 8.74

80.29 ± 9.15


BMI (kg/m2)

27.33 ± 4.63

29.05 ± 4.44

24.19 ± 3.07

24.28 ± 3.16


Waist circumference (cm)

98.39 ± 11.67

101.81± 10.98

92.33 ± 10.13

92.16 ± 10.42



.89 ± .12

.90 ± .15

.89 ± .06

.88 ± .07


IADL (dependent), n (%)

1,201 (56.31)

751 (53.84)

164 46.99)

286 (73.52)


Sarcopenia parameters

ASM (kg)

15.89 ± 3.63

16.77 ± 3.52

15.24 ± 3.13

13.51 ± 3.15


SMI (kg/m2)

6.23 ± .98

6.58 ± .90

5.78 ± .77

5.45 ± .76


Total body fat mass (%)

37.57 ± 8.11

38.71 ± 8.11

34.00 ± 7.58

36.83 ± 7.57


Gait speed (m/s)

.84 ± .30

.87 ± .31

1.05 ± .17

.59 ± .17



9.38 ± 1.73

9.62 ± 1.72

9.61 ± 1.04

8.22 ± 1.86


Mean hand grip (kg)


22.22 ± 9.20

23.83 ± 9.64

22.17 ± 7.61

16.88 ± 6.59



Anti-inflammatory medication, n (%)

1,171 (60.27)

771 ( 60.71)

157 (53.22)

243 (64.29)


AntiPLT medication, n (%)

995 ( 51.21)

661 ( 52.05)

137 (46.44)

197 (  52.12)


Antihyperlipidemia medication, n (%)

807 (41.53)

563 (44.33)

111 (37.63)

133 (35.19)


Anti-HTN medication, n (%)

1,186 ( 61.04)

794 ( 62.52)

153 ( 51.86)

239 ( 63.23)


DM medication, n (%)

591 ( 30.42)

390 ( 30.71)

83 ( 28.14)

118 (  31.22)


Total cholesterol

182.13 ± 44.20

182.54± 44.14

181.38 ± 44.38

181.39 ± 44.33


Biochemical parameters

HDL-cholesterol (mg/dl)

45.96 ± 11.20

45.67 ± 10.95

45.90 ± 10.99

46.99 ± 12.16


LDL- cholesterol

109.40 ± 37.75

109.07± 38.39

110.28 ± 36.88

109.74 ± 36.38



135.69 ± 70.27

140.45± 69.57

127.92± 63.16

126.45 ± 76.77



14.50 ± 1.73

14.56 ± 1.72

14.57 ± 1.65

14.21 ± 1.84


RBC (10^6)

5.01± .63

5.03 ± .62

5.03 ± .62

4.91±  .69


PLT (10^3)

262.52± 66.02

262.10± 65.23

256.61± 67.75

269.02± 66.75


WBC (10^3)

7.36 ± 2.19

7.43 ± 2.36

7.16 ± 1.82

7.30 ± 1.84


PLT to WBC ratio

37.28 ± 11.34

36.94 ± 11.16

37.22± 11.18

38.48 ± 11.99



14.98 ± 5.70

14.59 ± 5.42

15.26± 5.31

16.07 ± 6.73



1.10 ± .36

1.08 ± .35

1.12 ± .31

1.13 ± .44


Uric acid

5.17 ± 1.30

5.21 ± 1.28

5.14 ± 1.26

5.08 ± 1.39



220.32 ± 75.80

222.23± 77.74

212.07 ± 74.28

221.05 ± 69.98



5.67 ± 1.56

5.69± 1.54

5.57± 1.47

5.66± 1.67


P-values for continuous variables and categorical variables were assessed using ANOVA and Chi-square, respectively.

Note: BEH: Bushehr elderly health; SBP: Systolic blood pressure; DBP: diastolic blood pressure; BMI: Body mass index; WHR: waist to hip ratio; IADL: Instrumental activities of daily living; ASM: Appendicular skeletal muscle mass; SMI: skeletal muscle mass index; SPPB: Short Physical Performance Battery; HTN: Hypertension; HDL: high-density lipoproteins; LDL: low-density lipoproteins; TG: triglycerides; Hgb: Hemoglobin; RBC: Red blood cells; PLT: platelet ; WBC: White blood cells; PWR: PLT to WBC ratio; BUN: blood urea nitrogen; ALK-P: Alkaline phosphatase; HbA1c: hemoglobin A1c.

Table 2: Associations between PLT, WBC, PWR and sarcopenia parameters in the  BEH program 












Analytic Model

Q1 220


220 Q2 < 259






P-value for trend

OR (95% CI)


OR (95% CI)


OR (95% CI)


OR (95% CI)



1 (reference)


.86(.68 to 1.10)


.91 (.72 to       1.14)


1.07(.84to  1.34)



Model 1



1.00 (.77 to     1.28)


1.14 (.88 to       1.46)


1.40 (1.08 to 1.81)



Model 2



1.16 (.86 to 1.56)


1.25 (.93 to 1.67)


1.64 (1.21 to 2.21)



Model 3



1.25 (.83 to 1.89)


1.50( 1.00 to          2.25)


1.92 (1.26 to       2.93)



Model 4



1.25 (.83 to 1.90


1.49 (.99to  2.24


1.92 (1.25 to 2.95)





Analytic Model

Q1 6.1


6.1 Q2 < 7.3






P-value for trend

OR (95% CI)


OR (95% CI)


OR (95% CI)


OR (95% CI)



1 (reference)


.87 (.69 to 1.09)


.88 (.69 to        1.11)


.81 (.64 to       1.03)



Model 1



.87 (.68 to 1.11)


.91(.71    to 1.16)


.82 (.64 to       1.05)



Model 2



.87 (.66 to 1.15)


.92(.69 to       1.23)


.95 (.70 to       1.27)



Model 3



.89 (.60 to 1.31)


1.14(.77to       1.70)


1.12 (.75 to     1.66)



Model 4



.91 (.62 to 1.35)


1.21 (.81 to      1.81)


1.17 (.78 to 1.75)





Analytic Model

Q1 29.46


29.46Q2 < 36






P-value for trend

OR (95% CI)


OR (95% CI)


OR (95% CI)


OR (95% CI)



1 (reference)


1.07 (.84 to     1.36)


1.05 (.82 to      1.33)


1.24 (.98 to 1.57)



Model 1



1.12 (.87 to 1.44)


1.19 (.92 to     1.538)


1.55 (1.20 to     2.00)



Model 2



1.15 ( .86 to     1.56)


1.34 (.99 to 1.81)


1.65( 1.22 to      2.23)



Model 3



1.01 (.67 to 1.52)


1.36 (.90 to      2.04)


1.70 (1.11 to     2.60)



Model 4



1.00 ( .66 to       1.51)


1.36 (.91 to        2.06)


1.64 (1.06 to 2.52)



Multivariate logistic regression was used for analysis

Model 1 adjusted for age, marital, and sex

Model 2 adjusted for Model 1+   smoking, metabolic syndrome, cognitive disorder, and the number of chronic diseases a

Model 3 adjusted for Model 2 + anti-inflammatory medications, anti- platelet medications, anti-diabetic medications, anti-hyperlipidemic medications, HTN medications, IADL, WHR, and BMI

Model 4 adjusted for Model 3 + Hgb, HbA1c, HDL_C, ALK-P, TG, uric acid, and creatinine

Chronic diseases included:  liver diseases, lung diseases, cardiovascular disease, Hypertension, , diabetes mellitus ,thyroid diseases, osteoarthritis, and rheumatoid arthritis

Note: BEH: Bushehr elderly health; PLT: platelet; WBC: White blood cells; PWR: PLT to WBC ratio; HTN: Hypertension; IADL: Instrumental activities of daily living; WHR: waist to hip ratio; BMI: Body mass index; Hgb: Hemoglobin; HbA1c: hemoglobin A1c; HDL: high-density lipoproteins; ALK-P: Alkaline phosphatase; TG: triglycerides.


Table 3: The relationship between PLT and sarcopenia parameters in the Bushehr Health (BEH) Program 

Outcome variable

Analytic del




β (95% CI)


β (95% CI)


β (95% CI)





-0.0135 (-0.0157 to -.0114)

< 0.001

-0.0006(-0.0006 to 0.0019)


-0.0034 (-0.0053 to -0.0015)

< 0.001

Model 1

-0.0118( -0.0138 to -0.0098)

< 0.001

-0.0015(-0.0039 to 0.0008)


-0.0034 (-0.0052 to -0.0015)

< 0.001

Model 2

-0.0110 ( -0.0133 to -0.0088)

< 0.001

-0.0022 (-0.0048 to 0.0003)


-0.0044(-0.0065 to -0.0023)

< 0.001

Model 3

-.0105(-.0127 to -.0082)

< 0.001

-.0023(-.0049 to .0002)


-.0034(-.0052 to     -.0017)

< 0.001

Model 4

-.0070(-.0091 to  -.0050)

< 0.001

-.0017 (-.0043 to     .0008)


-.0032 (-.0050 to-.0014)

< 0.001



-0.0300 ( -0.0354 to -0.0245)

< 0.001

0.0002 (-0.0072 to 0.0077)


-0.0047 (-0.0091 to -0.0003)


Model 1

-0.0262 (-.0311 to -0.0212)

< 0.001

-.0030 (-.0096 to .0036)


-.0050(-.0089 to -.0010)


Model 2

-.0219 (-.0273 to -.0164)

< 0.001

-.0037 (-.0111 to .0037)


-.0053 (-.0099 to -.0007)


Model 3

-.0194(-.0253 to -.0135)

< 0.001

-.0050(-.0142 to .0040)


-.0039(-.0090 to .0010)


Model 4

-.0120 (-.0178 to  -.0062)

< 0.001

-.0038 (-.0129 to      .0053)


-.0032 ( -.0084 to     .0019)


gait  speed


-.0004 (-.0006 to -.0003)

< 0.001

-.0000 (-.0002 to .0002)


-.0000 (-.0003 to .0001)


Model 1

-.0004 (-.0006 to -.0002)

< 0.001

-.0001 (-.0003 to .0001)


-.0000 (-.0002 to .0001)


Model 2

-.0002 (-.0004 to -.0001)


-.0000 (-.0002 to .0002)


-.0000(-.0003 to.0001)


Model 3

-.0001 (-.0003 to .0000)


-.0000(-.0003 to .0002)


.0000(-.0001 to .0003)


Model 4

-.0001 (-.0003 to      .0000)


-.0000 (-.0003 to .0002)


.0000 (-.0002 to .0003)




-.0024 (-.0035 to -.0013)

< 0.001

.0001 (-.0013 to.0016)


-.0010 (-.0027 to .0006


Model 1

-.0022 (-.0032 to -.0012)

< 0.001

-.0002 (-.0016 to .0011)


-.0012 (-.0028 to .0003)


Model 2

-.00195 (-.0031 to -.0007)


-.0000 (-.0016 to .0015)


-.0018 (-.0036 to .0000)


Model 3

-.0010(-.0024 to .0003)


.0006(-.0013 to .0025)


-.0008(-.0029 to      .0012)


Model 4

-.0006(-.0021 to .0007)


.0006 (-.0013 to .0026)


-.0009(-.0031 to .0011


Multivariate linear regression was used for analysis

Model 1 adjusted for age, marital

Model 2 adjusted for Model 1+   smoking, metabolic syndrome, cognitive disorder, and the number of chronic diseases b

Model 3 adjusted for Model 2 + anti-inflammatory medications, antiPLT medications, anti-diabetic medications, anti-hyperlipidemic medications, HTN medications, IADL, WHR, and BMI

Model 4 adjusted for Model 3 + Hgb, HbA1c, HDL-cholesterol, ALK-P, TG, Uric Acid, and Creatinine

a PLT concentration was used as an independent variable

b Chronic diseases included:  liver diseases, lung diseases, cardiovascular disease, Hypertension, diabetes mellitus, thyroid diseases, osteoarthritis, and rheumatoid arthritis

Note: BEH: Bushehr elderly health; ASM: Appendicular skeletal muscle mass; SPPB: Short Physical Performance Battery; HTN: Hypertension; IADL: Instrumental activities of daily living; WHR: waist to hip ratio; BMI: Body mass index; Hgb: Hemoglobin; WBC: White blood cells; HbA1c: hemoglobin A1c; HDL: high-density lipoproteins; ALK-P: Alkaline phosphatase; TG: triglycerides.