Might the haemoglobin, albumin, lymphocyte, and platelet (HALP) score predict prefrailty and frailty in older adults without cancer: a cross-sectional study?

Might the haemoglobin, albumin, lymphocyte, and platelet (HALP) score predict prefrailty and frailty in older adults without cancer: a cross-sectional study? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Might the haemoglobin, albumin, lymphocyte, and platelet (HALP) score predict prefrailty and frailty in older adults without cancer: a cross-sectional study? Suleyman Emre KOCYIGIT, Ali KIRIK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6540533/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Frailty is associated with both malnutrition and systemic inflammation. Aims: This study investigated the relationship between the haemoglobin, albumin, lymphocyte, and platelet (HALP) score and frailty stages in older adults. Methods: In total, 439 patients admitted to our geriatrics outpatient clinic between January 2023 and June 2024 were retrospectively analysed. All patients underwent a comprehensive geriatric assessment, and frailty was assessed according to the Fried frailty phenotype. Patients were categorised into three groups: frail, prefrail, and robust. HALP score was calculated and compared between this three groups. Results: The mean age of all patients was 76.12 ± 6.74 years, and 71.3% were female. Age, the frequencies of female sex, dementia, recurrent falls, geriatric depression, polypharmacy, malnutrition, and probable sarcopenia were higher in the frail group than in the prefrail and robust groups (p < 0.05). The median HALP score was lower in the frail and prefrail groups than in the robust group (34.9 vs. 48.3, p < 0.001 and 38.6 vs. 48.3, p = 0.005, respectively). In the logistic regression analysis, after adjusting for confounding factors, the significance remained for the frail and prefrail groups compared with the robust group (p < 0.05). Conclusion: The HALP score may be an predictor of prefrail and frail older adults, with potential utility in clinical practice. frailty inflammation nutrition aging geriatric syndrome Key Messages 1- HALP score may be a reliable and useful marker of frailty within the framework of a comprehensive geriatric assessment. 2- HALP score might be predictor for prefrail and frail older patients 3- HALP score may indicate for inflammation with prefrail and frail older patients. Introduction Frailty is an important geriatric syndrome. Its frequency increases with age, leading older individuals to become more vulnerable to stressors as a result of age-associated declines in reserve and function across multiple physiological systems [ 1 , 2 ]. Frailty is associated with adverse outcomes such as falls, hospitalisation, disability, and mortality in older adults [ 2 ]. It is also stated that frail individuals have lower income and more comorbidities than non-frail older adults [ 3 ]. In this respect, screening for frailty is important in geriatric practice, and several assessment tools are used for frailty screening. The most commonly used of these is the Fried frailty scale. According to the Fried frailty phenotype, frailty is described as a clinical syndrome in which three or more of the five following criteria are present: unintentional weight loss, exhaustion, low grip strength, slow walking speed, and low physical activity [ 4 ]. Patients are defined as prefrail when one or two of these criteria are met. In the literature, the FRAIL and FRIED scales are frequently used in frailty screening, and scoring systems that can predict frailty in accordance with these scales have also been developed [ 5 ]. The pathophysiology of frailty is quite complex, affecting multiple organs and systems. Changes in the immune system and inflammation are frequently emphasised in the pathophysiology of frailty [ 1 ]. In addition to inflammation, the pathophysiology of frailty includes mitochondrial dysfunction, deregulated nutrient sensing, cellular senescence, oxidative stress, and neuroendocrine dysregulation [ 6 ]. Chronic inflammation may lead to frailty by increasing growth factor inhibition and catabolism in response to noninfectious triggers [ 6 ]. There is also a clear relationship between the nutritional status and frailty [ 7 ]. The haemoglobin, albumin, lymphocyte, and platelet (HALP) score is a simple and clinically suitable marker that reflects the combination of inflammation and the nutritional status [ 8 ]. It stands out as a new biomarker for predicting poor clinical outcomes in various diseases. It has also been suggested to be associated with certain malignancies, as well as cardiovascular mortality [ 8 ]. However, there are no studies in the literature to determine whether the HALP score is associated with frailty. Inflammatory markers may be helpful in predicting frailty. There is no study in the literature that the HALP score, an inflammatory marker, predicts frailty. The aim of our retrospective and observational study was to determine whether there is a relationship between the HALP score and frailty status. Materials and Methods Study design This cross-sectional observational study included 439 older adults who presented to the geriatrics outpatient clinic of Balikesir University Hospital between January 2023 and June 2024. Comprehensive Geriatric Assessment (CGA), which included a frailty scale, was performed. Inclusion criteria Patients aged >65 years who agreed to participate in the study, underwent a CGA, and did not meet the exclusion criteria were included in the study. Exclusion criteria The exclusion criteria were severe anemia (haemoglobin level of <7 g/dL); critical cardiac valve disease; acute or chronic kidney failure (stage 4 and 5); decompensated cardiac and/or hepatic failure; malignancy; severe peripheral vascular stenosis and/or coronary artery stenosis; acute episodes of rheumatological or connective tissue diseases; active infection; a history of cerebrovascular disease, myocardial infarction, or lower extremity fracture in the last month; findings of acute dehyration; electrolyte imbalance such as hyponatremia, hypernatremia or hypercalcemia; acute haemorrhage;; immobility due to severe osteoarthritis or neuromuscular disease; and acute mental changes [7]. Patient characteristics Age, sex, education level, and comorbidities including hypertension, diabetes mellitus, cardiovascular disease, chronic pulmonary disease, and Parkinson’s disease), were examined. Geriatric syndromes, including polypharmacy (>5 medications), dementia, probable sarcopenia, geriatric depression, urinary incontinence, recurrent falls in the last year, essential tremor, and malnutrition, were also examined and obtained from patient file records. The diagnoses of major neurocognitive disorder or dementia were made according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria [9]. Probable sarcopenia was diagnosed according to the European Working Group on Sarcopenia in Older People 2 criteria [10]. The nutritional status was assessed by the Mini-Nutritional Assessment-Short Form (MNA-SF) [11]. Patients were grouped as malnourished or at risk of malnutrition (0–11 points) or well-nourished (12–14 points) according to MNA-SF. Patients underwent a CGA that included the Yesavage’s Geriatric Depression Scale (YGDS), Tinetti Performance Oriented Mobility Assessment, timed up-and-go test, Basic Activities of Daily Living Index, and Instrumental Activities of Daily Living [7,12]. Laboratory findings Patients' blood samples were routinely taken as fasting blood at 08:00 in the morning. The serum haemoglobin level, platelet count, lymphocyte count, glucose level, albumin level, estimated glomerular filtration rate (eGFR), vitamin D level, folate level, ferritin level, thyroid-stimulating hormone level, and vitamin B12 level were obtained from the patients’ laboratory records. All these biochemical tests were performed on a Diagnostic Modular Systems auto-analyser (Roche E170 and P-800). Serum 25-hydroxyvitamin D was measured by radioimmunoassay [7]. HALP score The HALP score was calculated using the following formula: [haemoglobin (g/L) × albumin (g/L) × lymphocytes (/L)] / platelets (/L) [13]. Frailty phenotype Frailty was assessed using Fried’s physical frailty scale [4]. The components of frailty are weakness, slowness, low level of physical activity, exhaustion, and weight loss [3]. The patients were divided into three groups according to their frailty scores: robust (0 points), prefrail (1–2 points), or frail (3–5 points). Statistical analysis Categorical variables were expressed as percentages (%) and continuous variables were presented as mean±standard deviation. The patients were divided into three groups according to their frailty status: robust, prefrail, and frail. Continuous or categorical variables of each group were compared between dichotomous groups within these three categories. In categorical vairables, the chi-square test was used for comparison between groups. Continuous variables were first evaluated using the Kolmogorov–Smirnov test and measuring the homoscedasticity to assess compliance with normal distribution. Because not all continuous variables were normally distributed, the Mann–Whitney U test, a non-parametric test, was used for the comparison between groups, and these variables are presented as. An ANOVA test was performed for comparisons across the three groups. Binary logistic regression analysis was performed to assess the association between the prefrail and frail groups with the robust group using modelling. Model 0 was unadjusted for contributing factors. Model 1 was adjusted for age and sex. Model 2 was adjusted for Model 1 plus comorbidities and geriatric syndromes. Odds ratios (ORs) were calculated with 95% confidence intervals (CIs). A p-value of <0.05 was considered statistically significant. All statistical analyses were conducted using IBM SPSS Statistics (Version 22.0. Armonk, NY: IBM Corp.). Ethical issues The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the School of Medicine, Balikesir University in Balikesir, Turkey (2024/10/01). Results In total, 439 patients were evaluated. Their mean age was 76.12 ± 6.74 years, and 71.3% were female. When the patients were divided into three groups—frail, prefrail, and robust—and examined, age and sex were found to be statistically significantly higher in the frail group than in the robust and prefrail groups (p 0.05). Among the comorbid diseases, Parkinson’s disease was observed more frequently in the frail group than in the other two groups (p < 0.05). The frequencies of geriatric depression, dementia, recurrent falls, probable sarcopenia, polypharmacy, malnutrition, and essential tremor were also found to be higher in the frail group than in the robust and prefrail groups (p < 0.05). Additionally, the frequency of essential tremor, geriatric depression, malnutrition, and probable sarcopenia was statistically higher in the prefrail group than in the robust group (p < 0.05). The haemoglobin, albumin, and eGFR levels were found to be lower in the frail group than in the other two groups (p < 0.05). Mobility tests, daily living activity scores, and geriatric depression scales were significantly lower in the frail and prefrail groups than in the robust group (p < 0.05) (Table 1). The mean HALP scores in the frail group were significantly lower than those in the robust and prefrail groups (p < 0.001 and p = 0.001, respectively). Additionally, the HALP score in the prefrail group was lower than in the robust group (p = 0.005) (Table 2). In the binary logistic regression analysis, when the HALP score was adjusted for demographic characteristics, comorbidities, geriatric syndromes, and laboratory findings in frail patients, a statistically significant difference was observed compared to the robust group (β = −0.041, OR = 0.95, 95% CI = 0.93–0.95, p = 0.006 according to Model 3). Similarly, after adjusting for the same confounding factors in the prefrail group, significance was recorded compared with the robust group (β = −0.024, OR = 0.97, 95% CI = 0.95–0.99, p = 0.033 according to Model 3) (Table 3). Decreased HALP score was associated with prefrail and frail older patient compared to robust one. Discussion In the present study, the HALP score was lower in both frail and prefrail patients than in healthy controls. Even at the prefrail stage, the HALP score was found to be lower, independent of demographic characteristics, comorbidities, geriatric syndromes, and laboratory findings. The frequency of anaemia, defined as a low red blood cell count or haemoglobin level, increases with age [ 14 ]. Anaemia in older adults can be categorised into these groups: nutritional anaemia, anaemia due to bleeding, chronic disease, haematological malignancy, hereditary anemia and unexplained anaemia. Age-related inflammation is associated with anaemia in the elderly, and ageing processes such as genomic instability, reactive oxygen species in mitochondria, proinflammatory cytokines, adverse environmental factors, and chronic diseases contribute to inflammation [ 15 ]. Additionally, malnutrition, eating disorders, and loss of appetite can lead to anaemia in older adults. Anaemia in this population is associated with increased hospitalisation, falls, dementia, and functional dependence; decreased quality of life; and development of frailty [ 16 ]. Furthermore, the literature has highlighted a strong potential relationship between haemoglobin concentration and frailty [ 16 ]. Albumin levels are influenced by patients’ nutritional status and metabolic demands [ 17 ]. Low albumin levels are seen in patients with a poor nutritional status and inflammation [ 18 ]; hence, albumin is considered a negative acute-phase reactant. Increased proinflammatory cytokines, particularly in the context of low albumin levels, are known to significantly contribute to the development of cancer cachexia [ 17 ]. As a result, it is unsurprising that muscle strength or muscle mass may also be affected in these patients. Inadequate nutritional intake raises the risk of oxidative stress, chronic disease, impaired immune response, osteoporosis, fracture risk, peripheral arterial disease, and frailty in older adults [ 19 ]. Furthermore, malnutrition shares common pathophysiological mechanisms with frailty. The literature also highlights the importance of nutritional support in slowing or preventing frailty [ 19 ]. Lymphocytes and platelets are essential mediators of immune function. Decreased lymphocyte and increased platelet levels indicate impaired immunity and an increased risk of infection [ 20 ]. Lymphocytes play a key role in the initiation and progression of atherosclerosis pathogenesis [ 8 ]. For example, lymphopaenia has been associated with inflammation, malnutrition, peripheral congestion, and sympathetic activation in individuals with heart failure [ 21 ], while lymphocytes are crucial for immunosurveillance, tumour detection, and destruction in patients with cancer [ 17 ]. Additionally, a decreased lymphocyte count may be a determinant of unfavourable outcomes in systemic inflammatory diseases [ 22 ]. Total lymphocyte count decreases with age due to immunosenescence, making older adults more susceptible to infections [ 23 ]. Thus, changes in lymphocyte counts may be particularly significant in frail individuals. A recent review indicated that low lymphocyte counts may be associated with frailty and its severity [ 23 ]. Platelets play an essential role in the progression of both acute and chronic diseases, particularly cardiovascular diseases, and the platelet count generally decreases with age [ 24 ]. However, the effect of age on platelet function or molecular changes remains unclear [ 24 ]. In examining the relationship between frailty and platelets, a study with a small sample size showed greater platelet aggregation and activation in frail individuals [ 25 ]. Additionally, platelet oxidative stress is thought to increase the risk of cardiovascular disease in frail individuals [ 26 ]. The HALP score is a simple, inexpensive, and reliable marker reflecting the combination of inflammation and the nutritional status [ 27 ]. It is a relatively new marker used in assessing clinical outcomes and disease progression across various conditions. The importance of the HALP score in predicting prognosis was first highlighted in patients with gastric carcinoma [ 13 ]. Since then, the HALP score has gained attention as a mortality marker in numerous malignancies and in patients with stroke [ 8 ]. A higher HALP score has been associated with lower mortality in patients with solid tumours or acute ischaemic stroke [ 21 ], whereas a low HALP score is reported to be linked to a poorer immunonutritional status [ 20 ]. In summary, the association of the HALP score with inflammatory conditions is well established. Thus, the lower HALP score observed in frail individuals in our study suggests that malnutrition and inflammation play a significant role in the aetiopathogenesis of frailty [ 6 , 28 ]. There are currently limited studies on the effectiveness of the HALP score in predicting geriatric syndromes in community-dwelling older adults. In a study conducted with specific patient groups, the HALP score was reported as a risk factor for post-stroke cognitive impairment [ 22 ]. Another study found a low HALP score to be associated with sarcopenia in patients with intrahepatic cholangiocarcinoma [ 29 ]. In the present study, our findings suggest that the HALP score may be a predictor for prefrailty and frailty according to FRIED criteria, particularly in individuals without malignancy, marking the first such evidence in the literature. The pathophysiological mechanism of frailty is still unclear. However, aging or low-grade inflammatory status, is an important condition in the development of frailty [ 30 ]. In addition, some changes at the genetic, epigenetic, cellular and system levels in mouse models may play an important role in the development of frailty [ 31 ]. At the genetic level, deficiencies in the genes Nrf2 - which has an important role in the inflammatory pathway - and IL-10 - known as anti-inflammatory cytokine - accelerated frailty in mouse models [ 31 ]. In addition, chronic inflammation also plays a pivotal role in the development of frailty [ 6 ]. Especially, the production of proinflammatory cytokines, of which IL-6 is at the forefront, and chemokines that upregulate IL-6 play an important role in frailty [ 31 ]. In addition, oxidative stress, mitochondrial dysfunction and neuroendocrine dysregulation, in addition to inflammation, also contribute to the anorexia of aging, indicating the relationship between frailty and malnutrition [ 31 , 32 ]. Therefore, the early predictive power of the HALP score may be important due to these related mechanisms. It is known that inflammaging, which is defined as chronic and low-grade inflammation with age, increases the risk of mortality and morbidity [ 33 ]. It is stated that CRP and IL-6 are at the forefront in inflammation in the elderly [ 33 ]. It is known that inflammation is also associated with malnutrition in older adults [ 34 ]. It is even emphasized that nutritional assessment can predict frailty [ 35 ]. As a result, frailty and malnutrition have common pathophysiological mechanisms and are closely related to each other. Since the HALP score also provides information about nutritional assessment, the low HALP score in frail older individuals supports the relationship with malnutrition as well as inflammation. Our study has certain strengths. It is the first to demonstrate an association between frailty and low HALP scores in geriatric syndromes. Additionally, a lower HALP score was observed at the prefrail stage compared with healthy older adults, suggesting that it may serve as an early marker. However, there are also some limitations to our study. First, it was retrospective in design. Second, the study's cross-sectional nature limits the ability to infer causality. Additionally, several factors such as hydration status, which can influence hemoglobin and albumin levels, might confound the HALP score. Third, our results cannot be associated with the general population because the exclusion criteria included conditions that are common in the elderly, such as anemia or chronic kidney disease. Fourth, we could not administer the neurocognitive test including MMSE because permission was not obtained. Conclusions the HALP score appears to be a simple, effective, and inexpensive marker for the early screening and recognition of frailty, a significant geriatric syndrome in clinical practice. Nevertheless, further high-quality studies with larger sample sizes are needed in this area. Declarations Acknowledgements None Author contributions SEK – Conceptualization, Investigation, Methodology, Data curation, Writing – Original Draft, Writing – Review & Editing; AK – Conceptualization; Writing – Review & Editing Funding None Data availability The datasets generated and/or analyzed during the current study are not publicly available as policy does not allow sharing of patient’s data. Datasets are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to the study. All procedures were approved by the Ethics Committee of Balikesir University (Date: 03/12/2024 - Protocol number: 2024/208). Consent for publication Not applicable. Competing Interest The authors declare no conflicts of interest. References Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K (2013) Frailty in elderly people. Lancet 381(9868):752-62. https://doi.org/10.1016/S0140-6736(12)62167-9 Doody P, Lord JM, Greig CA, Whittaker AC (2023) Frailty: Pathophysiology, Theoretical and Operational Definition(s), Impact, Prevalence, Management and Prevention, in an Increasingly Economically Developed and Ageing World. 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Front Endocrinol (Lausanne) 15:1339921. https://doi.org/10.3389/fendo.2024.1339921 Ni Lochlainn M, Cox NJ, Wilson T et al (2021) Nutrition and Frailty: Opportunities for Prevention and Treatment. Nutrients 13(7):2349. https://doi.org/10.3390/nu13072349 Toshida K, Itoh S, Nakayama Y et al (2023) Preoperative HALP score is a prognostic factor for intrahepatic cholangiocarcinoma patients undergoing curative hepatic resection: association with sarcopenia and immune microenvironment. Int J Clin Oncol 28(8):1082-1091. https://doi.org/10.1007/s10147-023-02358-2 Bleve A, Motta F, Durante B, Pandolfo C, Selmi C, Sica A (2023) Immunosenescence, Inflammaging, and Frailty: Role of Myeloid Cells in Age-Related Diseases. Clin Rev Allergy Immunol 64(2):123-144. https://doi.org/10.1007/s12016-021-08909-7 Liu P, Li Y, Ma L (2022) Frailty in rodents: Models, underlying mechanisms, and management. Ageing Res Rev 79:101659. https://doi.org/10.1016/j.arr.2022.101659 Park C, Ko FC. The Science of Frailty: Sex Differences (2021) Clin Geriatr Med 37(4):625-638. https://doi.org/10.1016/j.cger.2021.05.008. Franceschi C, Campisi J (2014) Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci 69 Suppl 1:S4-9. https://doi.org/10.1093/gerona/glu057 Stumpf F, Keller B, Gressies C, Schuetz P (2023) Inflammation and Nutrition: Friend or Foe? Nutrients 15(5):1159. https://doi.org/10.3390/nu15051159 Soysal P, Isik AT, Arik F, Kalan U, Eyvaz A, Veronese N (2019) Validity of the Mini-Nutritional Assessment Scale for Evaluating Frailty Status in Older Adults. J Am Med Dir Assoc 20(2):183-187. https://doi.org/10.1016/j.jamda.2018.07.016. Tables Table 1. Comparisons for demographic features, comorbidities, geriatric syndromes, laboratory findings and comprehensive geriatric assessment parameters in terms of frailty status Robust n=104 Prefrail n= 177 Frail n= 158 p all groups p robust à frail p robust à prefrail p prefrail à frail DEMOGRAPHIC FEATURES Age (mean±sd) 73.2±5.2 75.1±6.6 78.4±6.7 <0.001 <0.001 0.074 0.001 Gender (female;%) 64.2 67.2 78.0 0.031 0.028 0.652 0.024 Education year (mean±sd) 6.80±3.82 5.50±3.90 4.26±3.23 <0.001 <0.001 0.089 0.208 COMORBITIES AND GERIATRIC SYNDROMES (%) Hypertension 79.1 70.6 71.2 0.386 0.212 0.183 0.907 Cardiovascular disease 26.9 19.2 23.2 0.332 0.547 0.192 0.363 Chronic Lung Disease 12.4 18.6 14.9 0.207 0.416 0.258 0.519 Diabetes Mellitus 31.3 44.6 41.2 0.170 0.156 0.060 0.443 Parkinson Disease 1.5 2.8 16.4 <0.001 <0.001 0.549 <0.001 Dementia 9.0 16.9 44.9 <0.001 <0.001 0.116 <0.001 Recurrent falls (in a year) 28.4 36.2 59.9 <0.001 <0.001 0.251 <0.001 Essential tremor 14.9 30.5 33.3 0.012 0.004 0.014 0.569 Urinary Incontinence 46.3 55.9 62.1 0.148 0.060 0.177 0.275 Geriatric Depression 26.9 41.2 67.8 <0.001 <0.001 0.038 <0.001 Polypharmacy 58.2 64.4 74.6 0.024 0.013 0.372 0.038 Malnutrition 6.0 23.2 61.0 <0.001 <0.001 0.002 <0.001 Probable Sarcopenia 3.0 34.5 79.5 <0.001 <0.001 <0.001 <0.001 LABORATORY FINDINGS (mean±sd) Hemoglobin (g/dL) 12.91±1.52 12.63±1.49 12.15±1.81 <0.001 0.001 0.020 0.034 Glucose (mg/dL) 111.22±31.28 124.27±52.96 121.54±45.87 0.154 0.132 0.567 0.983 eGFR (ml/min/1.73 m 2 ) 68.87±14.04 70.92±17.67 63.14±19.48 <0.001 0.047 0.463 0.004 Albumin (g/dL) 4.25±0.24 4.14±0.31 4.09±2.42 0.789 <0.001 0.225 <0.001 TSH (mIU/L) 1.67±1.07 1.71±126 2.36±6.09 0.263 0.895 0.717 0.940 Vitamin B12 (ng/L) 329.91±248.77 354.74±276.50 430.07±352.03 0.119 0.118 0.605 0.084 Folate (mcg/L) 9.50±3.98 8.98±4.55 8.94±4.98 0.722 0.098 0.544 0.852 25-hydroxy vitamin D (mcg/L) 24.28±16.78 21.25±13.09 21.37±14.87 0.308 0.116 0.364 0.888 Ferritin (mcg/L) 38.93±41.88 55.33±95.18 94.48±68.63 0.012 0.033 0.795 0.127 COMPREHENSIVE GERIATRIC ASSESSMENT PARAMETERS (mean±sd) Tinetti-POMA 27.13±1.63 25.48±3.29 17.98±6.92 <0.001 <0.001 0.001 <0.001 TUG duration (sec) 11.99±2.67 15.31±5.61 35.31±30.01 <0.001 <0.001 <0.001 <0.001 Barthel Index 95.44±5.05 91.66±7.09 74.37±20.34 <0.001 <0.001 0.013 <0.001 Lawton-Brody Index 20.82±2.08 18.81±3.02 12.11±6.00 <0.001 <0.001 0.005 <0.001 Yesevage GDS 2.69±2.57 4.10±3.08 6.29±3.73 <0.001 <0.001 0.017 <0.001 eGFR: estimated glomerular filtration rate; GDS: geriatric depression scale; POMA: performance-oriented mobility assessment; sd: standard deviation; TSH: thyroid stimulating hormone; TUG: timed up and go *p all groups : comparison for between frail, prefrail and robust group Table 2. Comparison for HALP score within robust, prefrail and frail groups Table 3. Examining the relationship between HALP score in frail and prefrail groups compared to robust group in logistic regression analysis FRAIL vs ROBUST PREFRAIL vs ROBUST HALP score β OR 95 % CI p β OR 95 % CI p Model 0 -0.031 0.96 0.95-0.98 <0.001 -0.020 0.98 0.96-0.99 0.007 Model 1 -0.028 0.97 0.95-0.98 <0.001 -0.019 0.98 0.96-0.99 0.011 Model 2 -0.041 0.95 0.93-0.98 0.006 -0.016 0.98 0.96-0.99 0.042 CI: confidence interval; OR: odds ratio Model 0- Unadjusted Model 1- Adjusted for age and gender Model 2- Model 1 plus the presence of Parkinson’s disease, dementia, recurrent falls, essential tremor, geriatric depression, polypharmacy, malnutrition, probable sarcopenia Additional Declarations No competing interests reported. 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Its frequency increases with age, leading older individuals to become more vulnerable to stressors as a result of age-associated declines in reserve and function across multiple physiological systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Frailty is associated with adverse outcomes such as falls, hospitalisation, disability, and mortality in older adults [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is also stated that frail individuals have lower income and more comorbidities than non-frail older adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this respect, screening for frailty is important in geriatric practice, and several assessment tools are used for frailty screening. The most commonly used of these is the Fried frailty scale. According to the Fried frailty phenotype, frailty is described as a clinical syndrome in which three or more of the five following criteria are present: unintentional weight loss, exhaustion, low grip strength, slow walking speed, and low physical activity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients are defined as prefrail when one or two of these criteria are met. In the literature, the FRAIL and FRIED scales are frequently used in frailty screening, and scoring systems that can predict frailty in accordance with these scales have also been developed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The pathophysiology of frailty is quite complex, affecting multiple organs and systems. Changes in the immune system and inflammation are frequently emphasised in the pathophysiology of frailty [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to inflammation, the pathophysiology of frailty includes mitochondrial dysfunction, deregulated nutrient sensing, cellular senescence, oxidative stress, and neuroendocrine dysregulation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Chronic inflammation may lead to frailty by increasing growth factor inhibition and catabolism in response to noninfectious triggers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. There is also a clear relationship between the nutritional status and frailty [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe haemoglobin, albumin, lymphocyte, and platelet (HALP) score is a simple and clinically suitable marker that reflects the combination of inflammation and the nutritional status [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It stands out as a new biomarker for predicting poor clinical outcomes in various diseases. It has also been suggested to be associated with certain malignancies, as well as cardiovascular mortality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there are no studies in the literature to determine whether the HALP score is associated with frailty.\u003c/p\u003e \u003cp\u003eInflammatory markers may be helpful in predicting frailty. There is no study in the literature that the HALP score, an inflammatory marker, predicts frailty. The aim of our retrospective and observational study was to determine whether there is a relationship between the HALP score and frailty status.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional observational study included 439 older adults who presented to the geriatrics outpatient clinic of Balikesir University Hospital between January 2023 and June 2024. Comprehensive Geriatric Assessment (CGA), which included a frailty scale, was performed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInclusion criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients aged \u0026gt;65 years who agreed to participate in the study, underwent a CGA, and did not meet the exclusion criteria were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExclusion criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were severe anemia (haemoglobin level of \u0026lt;7 g/dL); critical cardiac valve disease; acute or chronic kidney failure (stage 4 and 5); decompensated cardiac and/or hepatic failure; malignancy; severe peripheral vascular stenosis and/or coronary artery stenosis; acute episodes of rheumatological or connective tissue diseases; active infection; a history of cerebrovascular disease, myocardial infarction, or lower extremity fracture in the last month; findings of acute dehyration; electrolyte imbalance such as hyponatremia, hypernatremia or hypercalcemia; acute haemorrhage;; immobility due to severe osteoarthritis or neuromuscular disease; and acute mental changes [7].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAge, sex, education level, and comorbidities including hypertension, diabetes mellitus, cardiovascular disease, chronic pulmonary disease, and Parkinson\u0026rsquo;s disease), were examined. Geriatric syndromes, including polypharmacy (\u0026gt;5 medications), dementia, probable sarcopenia, geriatric depression, urinary incontinence, recurrent falls in the last year, essential tremor, and malnutrition, were also examined and obtained from patient file records. The diagnoses of major neurocognitive disorder or dementia were made according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria [9]. Probable sarcopenia was diagnosed according to the European Working Group on Sarcopenia in Older People 2 criteria [10]. The nutritional status was assessed by the Mini-Nutritional Assessment-Short Form (MNA-SF) [11]. Patients were grouped as malnourished or at risk of malnutrition (0\u0026ndash;11 points) or well-nourished (12\u0026ndash;14 points) according to MNA-SF. Patients underwent a CGA that included the Yesavage\u0026rsquo;s Geriatric Depression Scale (YGDS), Tinetti Performance Oriented Mobility Assessment, timed up-and-go test, Basic Activities of Daily Living Index, and Instrumental Activities of Daily Living [7,12]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLaboratory findings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients\u0026apos; blood samples were routinely taken as fasting blood at 08:00 in the morning. The serum haemoglobin level, platelet count, lymphocyte count, glucose level, albumin level, estimated glomerular filtration rate (eGFR), vitamin D level, folate level, ferritin level, thyroid-stimulating hormone level, and vitamin B12 level were obtained from the patients\u0026rsquo; laboratory records. All these biochemical tests were performed on a Diagnostic Modular Systems auto-analyser (Roche E170 and P-800). Serum 25-hydroxyvitamin D was measured by radioimmunoassay [7].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHALP score\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe HALP score was calculated using the following formula: [haemoglobin (g/L) \u0026times; albumin (g/L) \u0026times; lymphocytes (/L)] / platelets (/L) [13].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFrailty phenotype\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrailty was assessed using Fried\u0026rsquo;s physical frailty scale [4]. The components of frailty are weakness, slowness, low level of physical activity, exhaustion, and weight loss [3]. The patients were divided into three groups according to their frailty scores: robust (0 points), prefrail (1\u0026ndash;2 points), or frail (3\u0026ndash;5 points).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were expressed as percentages (%) and continuous variables were presented as mean\u0026plusmn;standard deviation. The patients were divided into three groups according to their frailty status: robust, prefrail, and frail. Continuous or categorical variables of each group were compared between dichotomous groups within these three categories. In categorical vairables, the chi-square test was used for comparison between groups. Continuous variables were first evaluated using the Kolmogorov\u0026ndash;Smirnov test and measuring the homoscedasticity to assess compliance with normal distribution. Because not all continuous variables were normally distributed, the Mann\u0026ndash;Whitney U test, a non-parametric test, was used for the comparison between groups, and these variables are presented as. An ANOVA test was performed for comparisons across the three groups. Binary logistic regression analysis was performed to assess the association between the prefrail and frail groups with the robust group using modelling. Model 0 was unadjusted for contributing factors. Model 1 was adjusted for age and sex. Model 2 was adjusted for Model 1 plus comorbidities and geriatric syndromes. Odds ratios (ORs) were calculated with 95% confidence intervals (CIs). A p-value of \u0026lt;0.05 was considered statistically significant. All statistical analyses were conducted using IBM SPSS Statistics (Version 22.0. Armonk, NY: IBM Corp.).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical issues\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the School of Medicine, Balikesir University in Balikesir, Turkey (2024/10/01).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 439 patients were evaluated. Their mean age was 76.12 \u0026plusmn; 6.74 years, and 71.3% were female. When the patients were divided into three groups\u0026mdash;frail, prefrail, and robust\u0026mdash;and examined, age and sex were found to be statistically significantly higher in the frail group than in the robust and prefrail groups (p \u0026lt; 0.05). There was no difference between the prefrail and robust groups in terms of demographic characteristics (p \u0026gt; 0.05). Among the comorbid diseases, Parkinson\u0026rsquo;s disease was observed more frequently in the frail group than in the other two groups (p \u0026lt; 0.05). The frequencies of geriatric depression, dementia, recurrent falls, probable sarcopenia, polypharmacy, malnutrition, and essential tremor were also found to be higher in the frail group than in the robust and prefrail groups (p \u0026lt; 0.05). Additionally, the frequency of essential tremor, geriatric depression, malnutrition, and probable sarcopenia was statistically higher in the prefrail group than in the robust group (p \u0026lt; 0.05). The haemoglobin, albumin, and eGFR levels were found to be lower in the frail group than in the other two groups (p \u0026lt; 0.05). Mobility tests, daily living activity scores, and geriatric depression scales were significantly lower in the frail and prefrail groups than in the robust group (p \u0026lt; 0.05) (Table 1).\u003c/p\u003e\n\u003cp\u003eThe mean HALP scores in the frail group were significantly lower than those in the robust and prefrail groups (p \u0026lt; 0.001 and p = 0.001, respectively). Additionally, the HALP score in the prefrail group was lower than in the robust group (p = 0.005) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the binary logistic regression analysis, when the HALP score was adjusted for demographic characteristics, comorbidities, geriatric syndromes, and laboratory findings in frail patients, a statistically significant difference was observed compared to the robust group (\u0026beta; = \u0026minus;0.041, OR = 0.95, 95% CI = 0.93\u0026ndash;0.95, p = 0.006 according to Model 3). Similarly, after adjusting for the same confounding factors in the prefrail group, significance was recorded compared with the robust group (\u0026beta; = \u0026minus;0.024, OR = 0.97, 95% CI = 0.95\u0026ndash;0.99, p = 0.033 according to Model 3) (Table 3). Decreased HALP score was associated with prefrail and frail older patient compared to robust one.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the HALP score was lower in both frail and prefrail patients than in healthy controls. Even at the prefrail stage, the HALP score was found to be lower, independent of demographic characteristics, comorbidities, geriatric syndromes, and laboratory findings.\u003c/p\u003e \u003cp\u003eThe frequency of anaemia, defined as a low red blood cell count or haemoglobin level, increases with age [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Anaemia in older adults can be categorised into these groups: nutritional anaemia, anaemia due to bleeding, chronic disease, haematological malignancy, hereditary anemia and unexplained anaemia. Age-related inflammation is associated with anaemia in the elderly, and ageing processes such as genomic instability, reactive oxygen species in mitochondria, proinflammatory cytokines, adverse environmental factors, and chronic diseases contribute to inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, malnutrition, eating disorders, and loss of appetite can lead to anaemia in older adults. Anaemia in this population is associated with increased hospitalisation, falls, dementia, and functional dependence; decreased quality of life; and development of frailty [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, the literature has highlighted a strong potential relationship between haemoglobin concentration and frailty [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlbumin levels are influenced by patients\u0026rsquo; nutritional status and metabolic demands [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Low albumin levels are seen in patients with a poor nutritional status and inflammation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; hence, albumin is considered a negative acute-phase reactant. Increased proinflammatory cytokines, particularly in the context of low albumin levels, are known to significantly contribute to the development of cancer cachexia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As a result, it is unsurprising that muscle strength or muscle mass may also be affected in these patients. Inadequate nutritional intake raises the risk of oxidative stress, chronic disease, impaired immune response, osteoporosis, fracture risk, peripheral arterial disease, and frailty in older adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, malnutrition shares common pathophysiological mechanisms with frailty. The literature also highlights the importance of nutritional support in slowing or preventing frailty [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLymphocytes and platelets are essential mediators of immune function. Decreased lymphocyte and increased platelet levels indicate impaired immunity and an increased risk of infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Lymphocytes play a key role in the initiation and progression of atherosclerosis pathogenesis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, lymphopaenia has been associated with inflammation, malnutrition, peripheral congestion, and sympathetic activation in individuals with heart failure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while lymphocytes are crucial for immunosurveillance, tumour detection, and destruction in patients with cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, a decreased lymphocyte count may be a determinant of unfavourable outcomes in systemic inflammatory diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Total lymphocyte count decreases with age due to immunosenescence, making older adults more susceptible to infections [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, changes in lymphocyte counts may be particularly significant in frail individuals. A recent review indicated that low lymphocyte counts may be associated with frailty and its severity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlatelets play an essential role in the progression of both acute and chronic diseases, particularly cardiovascular diseases, and the platelet count generally decreases with age [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the effect of age on platelet function or molecular changes remains unclear [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In examining the relationship between frailty and platelets, a study with a small sample size showed greater platelet aggregation and activation in frail individuals [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, platelet oxidative stress is thought to increase the risk of cardiovascular disease in frail individuals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe HALP score is a simple, inexpensive, and reliable marker reflecting the combination of inflammation and the nutritional status [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is a relatively new marker used in assessing clinical outcomes and disease progression across various conditions. The importance of the HALP score in predicting prognosis was first highlighted in patients with gastric carcinoma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Since then, the HALP score has gained attention as a mortality marker in numerous malignancies and in patients with stroke [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A higher HALP score has been associated with lower mortality in patients with solid tumours or acute ischaemic stroke [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], whereas a low HALP score is reported to be linked to a poorer immunonutritional status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In summary, the association of the HALP score with inflammatory conditions is well established. Thus, the lower HALP score observed in frail individuals in our study suggests that malnutrition and inflammation play a significant role in the aetiopathogenesis of frailty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are currently limited studies on the effectiveness of the HALP score in predicting geriatric syndromes in community-dwelling older adults. In a study conducted with specific patient groups, the HALP score was reported as a risk factor for post-stroke cognitive impairment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Another study found a low HALP score to be associated with sarcopenia in patients with intrahepatic cholangiocarcinoma [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the present study, our findings suggest that the HALP score may be a predictor for prefrailty and frailty according to FRIED criteria, particularly in individuals without malignancy, marking the first such evidence in the literature.\u003c/p\u003e \u003cp\u003eThe pathophysiological mechanism of frailty is still unclear. However, aging or low-grade inflammatory status, is an important condition in the development of frailty [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, some changes at the genetic, epigenetic, cellular and system levels in mouse models may play an important role in the development of frailty [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. At the genetic level, deficiencies in the genes Nrf2 - which has an important role in the inflammatory pathway - and IL-10 - known as anti-inflammatory cytokine - accelerated frailty in mouse models [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, chronic inflammation also plays a pivotal role in the development of frailty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Especially, the production of proinflammatory cytokines, of which IL-6 is at the forefront, and chemokines that upregulate IL-6 play an important role in frailty [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, oxidative stress, mitochondrial dysfunction and neuroendocrine dysregulation, in addition to inflammation, also contribute to the anorexia of aging, indicating the relationship between frailty and malnutrition [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, the early predictive power of the HALP score may be important due to these related mechanisms.\u003c/p\u003e \u003cp\u003eIt is known that inflammaging, which is defined as chronic and low-grade inflammation with age, increases the risk of mortality and morbidity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is stated that CRP and IL-6 are at the forefront in inflammation in the elderly [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is known that inflammation is also associated with malnutrition in older adults [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is even emphasized that nutritional assessment can predict frailty [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As a result, frailty and malnutrition have common pathophysiological mechanisms and are closely related to each other. Since the HALP score also provides information about nutritional assessment, the low HALP score in frail older individuals supports the relationship with malnutrition as well as inflammation.\u003c/p\u003e \u003cp\u003eOur study has certain strengths. It is the first to demonstrate an association between frailty and low HALP scores in geriatric syndromes. Additionally, a lower HALP score was observed at the prefrail stage compared with healthy older adults, suggesting that it may serve as an early marker. However, there are also some limitations to our study. First, it was retrospective in design. Second, the study's cross-sectional nature limits the ability to infer causality. Additionally, several factors such as hydration status, which can influence hemoglobin and albumin levels, might confound the HALP score. Third, our results cannot be associated with the general population because the exclusion criteria included conditions that are common in the elderly, such as anemia or chronic kidney disease. Fourth, we could not administer the neurocognitive test including MMSE because permission was not obtained.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ethe HALP score appears to be a simple, effective, and inexpensive marker for the early screening and recognition of frailty, a significant geriatric syndrome in clinical practice. Nevertheless, further high-quality studies with larger sample sizes are needed in this area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSEK \u0026ndash; Conceptualization, Investigation, Methodology, Data curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing; AK \u0026ndash; Conceptualization; Writing \u0026ndash; Review \u0026amp; Editing\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available as policy does not allow sharing of patient\u0026rsquo;s data. Datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to the study. All procedures were approved by the Ethics Committee of Balikesir University (Date: 03/12/2024 - Protocol number: 2024/208).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eClegg A, Young J, Iliffe S, Rikkert MO, Rockwood K (2013) Frailty in elderly people. Lancet 381(9868):752-62. https://doi.org/10.1016/S0140-6736(12)62167-9\u003c/li\u003e\n\u003cli\u003eDoody P, Lord JM, Greig CA, Whittaker AC (2023) Frailty: Pathophysiology, Theoretical and Operational Definition(s), Impact, Prevalence, Management and Prevention, in an Increasingly Economically Developed and Ageing World. Gerontology 69(8):927-945. https://doi.org/10.1159/000528561\u003c/li\u003e\n\u003cli\u003eAznar-Tortonda V, Palaz\u0026oacute;n-Bru A, Gil-Guill\u0026eacute;n VF (2020) Using the FRAIL scale to compare pre-existing demographic lifestyle and medical risk factors between non-frail, pre-frail and frail older adults accessing primary health care: a cross-sectional study. PeerJ 8:e10380. https://doi.org/10.7717/peerj.10380\u003c/li\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J et al (2001) Frailty in older adults: evidence for a phenotype. 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Blood 131(5):505-514. https://doi.org/10.1182/blood-2017-07-746446\u003c/li\u003e\n\u003cli\u003eWacka E, Nicikowski J, Jarmuzek P, Zembron-Lacny A (2024) Anemia and Its Connections to Inflammation in Older Adults: A Review. J Clin Med 13(7):2049. https://doi.org/10.3390/jcm13072049\u003c/li\u003e\n\u003cli\u003eSteinmeyer Z, Delpierre C, Soriano G, et al (2020) Hemoglobin concentration; a pathway to frailty. BMC Geriatr 20(1):202. https://doi.org/10.1186/s12877-020-01597-6\u003c/li\u003e\n\u003cli\u003eFarag CM, Antar R, Akosman S, Ng M, Whalen MJ (2023) What is hemoglobin, albumin, lymphocyte, platelet (HALP) score? A comprehensive literature review of HALP\u0026apos;s prognostic ability in different cancer types. Oncotarget 14:153-172. https://doi.org/10.18632/oncotarget.28367\u003c/li\u003e\n\u003cli\u003eEckart A, Struja T, Kutz A, et al (2020) Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. Am J Med 133(6):713-722.e7. https://doi.org/10.1016/j.amjmed.2019.10.031\u003c/li\u003e\n\u003cli\u003eLorenzo-L\u0026oacute;pez L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodr\u0026iacute;guez-Villamil JL, Mill\u0026aacute;n-Calenti JC (2017) Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatr 17(1):108. https://doi.org/10.1186/s12877-017-0496-2\u003c/li\u003e\n\u003cli\u003eAntar R, Farag C, Xu V, Drouaud A, Gordon O, Whalen MJ (2023) Evaluating the baseline hemoglobin, albumin, lymphocyte, and platelet (HALP) score in the United States adult population and comorbidities: an analysis of the NHANES. Front Nutr 10:1206958. https://doi.org/10.3389/fnut.2023.1206958\u003c/li\u003e\n\u003cli\u003eLiu L, Gong B, Wang W, Xu K, Wang K, Song G (2024) Association between haemoglobin, albumin, lymphocytes, and platelets and mortality in patients with heart failure. ESC Heart Fail 11(2):1051-1060. https://doi.org/10.1002/ehf2.14662\u003c/li\u003e\n\u003cli\u003eZuo L, Dong Y, Liao X, et al. Low HALP (Hemoglobin, Albumin, Lymphocyte, and Platelet) Score Increases the Risk of Post-Stroke Cognitive Impairment: A Multicenter Cohort Study (2024) Clin Interv Aging 19:81-92. doi: 10.2147/CIA.S432885\u003c/li\u003e\n\u003cli\u003eNavarro-Mart\u0026iacute;nez R, Cauli O (2021) Lymphocytes as a Biomarker of Frailty Syndrome: A Scoping Review. Diseases 9(3):53. doi: 10.3390/diseases9030053\u003c/li\u003e\n\u003cli\u003eJones CI. Platelet function and ageing (2016) Mamm Genome 27(7-8):358-66. https://doi.org/10.1007/s00335-016-9629-8\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez B, Fuentes E, Palomo I, Alarc\u0026oacute;n M (2019) Increased platelet function during frailty. Exp Hematol 77:12-25.e2. https://doi.org/10.1016/j.exphem.2019.08.006\u003c/li\u003e\n\u003cli\u003eFuentes F, Palomo I, Fuentes E (2017) Platelet oxidative stress as a novel target of cardiovascular risk in frail older people. Vascul Pharmacol 93-95:14-19. https://doi.org/10.1016/j.vph.2017.07.003\u003c/li\u003e\n\u003cli\u003eYuan Y, Liang X, He M, Wu Y, Jiang X (2024) Haemoglobin, albumin, lymphocyte, and platelet score as an independent predictor for renal prognosis in IgA nephropathy. Front Endocrinol (Lausanne) 15:1339921. https://doi.org/10.3389/fendo.2024.1339921\u003c/li\u003e\n\u003cli\u003eNi Lochlainn M, Cox NJ, Wilson T et al (2021) Nutrition and Frailty: Opportunities for Prevention and Treatment. Nutrients 13(7):2349. https://doi.org/10.3390/nu13072349\u003c/li\u003e\n\u003cli\u003eToshida K, Itoh S, Nakayama Y et al (2023) Preoperative HALP score is a prognostic factor for intrahepatic cholangiocarcinoma patients undergoing curative hepatic resection: association with sarcopenia and immune microenvironment. Int J Clin Oncol 28(8):1082-1091. https://doi.org/10.1007/s10147-023-02358-2\u003c/li\u003e\n\u003cli\u003eBleve A, Motta F, Durante B, Pandolfo C, Selmi C, Sica A (2023) Immunosenescence, Inflammaging, and Frailty: Role of Myeloid Cells in Age-Related Diseases. Clin Rev Allergy Immunol 64(2):123-144. https://doi.org/10.1007/s12016-021-08909-7\u003c/li\u003e\n\u003cli\u003eLiu P, Li Y, Ma L (2022) Frailty in rodents: Models, underlying mechanisms, and management. Ageing Res Rev 79:101659. https://doi.org/10.1016/j.arr.2022.101659\u003c/li\u003e\n\u003cli\u003ePark C, Ko FC. The Science of Frailty: Sex Differences (2021) Clin Geriatr Med 37(4):625-638. https://doi.org/10.1016/j.cger.2021.05.008.\u003c/li\u003e\n\u003cli\u003eFranceschi C, Campisi J (2014) Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci 69 Suppl 1:S4-9. https://doi.org/10.1093/gerona/glu057\u003c/li\u003e\n\u003cli\u003eStumpf F, Keller B, Gressies C, Schuetz P (2023) Inflammation and Nutrition: Friend or Foe? Nutrients 15(5):1159. https://doi.org/10.3390/nu15051159\u003c/li\u003e\n\u003cli\u003eSoysal P, Isik AT, Arik F, Kalan U, Eyvaz A, Veronese N (2019) Validity of the Mini-Nutritional Assessment Scale for Evaluating Frailty Status in Older Adults. J Am Med Dir Assoc 20(2):183-187. https://doi.org/10.1016/j.jamda.2018.07.016.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Comparisons for demographic features, comorbidities, geriatric syndromes, laboratory findings and comprehensive geriatric assessment parameters in terms of frailty status\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"746\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eRobust\u003c/p\u003e\n \u003cp\u003en=104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePrefrail\u003c/p\u003e\n \u003cp\u003en= 177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003cp\u003en= 158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003ep\u003csub\u003eall groups\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003ep\u003csub\u003erobust\u003c/sub\u003e\u003csub\u003e\u0026agrave;\u003c/sub\u003e\u003csub\u003efrail\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ep\u003csub\u003erobust\u003c/sub\u003e\u003csub\u003e\u0026agrave;\u003c/sub\u003e\u003csub\u003eprefrail\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep\u003csub\u003eprefrail\u003c/sub\u003e\u003csub\u003e\u0026agrave;\u003c/sub\u003e\u003csub\u003efrail\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 746px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDEMOGRAPHIC FEATURES\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eAge (mean\u0026plusmn;sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e73.2\u0026plusmn;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e75.1\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e78.4\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eGender (female;%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e64.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e67.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eEducation year (mean\u0026plusmn;sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6.80\u0026plusmn;3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e5.50\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.26\u0026plusmn;3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 746px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOMORBITIES AND GERIATRIC SYNDROMES (%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eChronic Lung Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e44.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eParkinson Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e44.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eRecurrent falls (in a year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eEssential tremor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eUrinary Incontinence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e62.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eGeriatric Depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003ePolypharmacy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eMalnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eProbable Sarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 746px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLABORATORY FINDINGS (mean\u0026plusmn;sd)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.91\u0026plusmn;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.63\u0026plusmn;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.15\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e111.22\u0026plusmn;31.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e124.27\u0026plusmn;52.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e121.54\u0026plusmn;45.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e68.87\u0026plusmn;14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e70.92\u0026plusmn;17.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e63.14\u0026plusmn;19.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.25\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.14\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.09\u0026plusmn;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eTSH (mIU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.67\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e1.71\u0026plusmn;126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.36\u0026plusmn;6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eVitamin B12 (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e329.91\u0026plusmn;248.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e354.74\u0026plusmn;276.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e430.07\u0026plusmn;352.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eFolate (mcg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e9.50\u0026plusmn;3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.98\u0026plusmn;4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e8.94\u0026plusmn;4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003e25-hydroxy vitamin D (mcg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e24.28\u0026plusmn;16.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e21.25\u0026plusmn;13.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e21.37\u0026plusmn;14.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eFerritin (mcg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e38.93\u0026plusmn;41.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55.33\u0026plusmn;95.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e94.48\u0026plusmn;68.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 746px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOMPREHENSIVE GERIATRIC ASSESSMENT PARAMETERS (mean\u0026plusmn;sd)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eTinetti-POMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27.13\u0026plusmn;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e25.48\u0026plusmn;3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e17.98\u0026plusmn;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eTUG duration (sec)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e11.99\u0026plusmn;2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e15.31\u0026plusmn;5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e35.31\u0026plusmn;30.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eBarthel Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e95.44\u0026plusmn;5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e91.66\u0026plusmn;7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e74.37\u0026plusmn;20.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eLawton-Brody Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20.82\u0026plusmn;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e18.81\u0026plusmn;3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e12.11\u0026plusmn;6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 185px;\"\u003e\n \u003cp\u003eYesevage GDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e2.69\u0026plusmn;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e4.10\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e6.29\u0026plusmn;3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate; GDS: geriatric depression scale; POMA: performance-oriented mobility assessment; sd: standard deviation; TSH: thyroid stimulating hormone; TUG: timed up and go\u003c/p\u003e\n\u003cp\u003e*p\u003csub\u003eall groups\u003c/sub\u003e: comparison for between frail, prefrail and robust group\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison for HALP score within robust, prefrail and frail groups\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"656\" height=\"537\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Examining the relationship between HALP score in frail and prefrail groups compared to robust group in logistic regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"746\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRAIL vs ROBUST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePREFRAIL vs ROBUST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eHALP\u003c/p\u003e\n \u003cp\u003escore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cem\u003e95 % CI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026beta;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e95 % CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eModel 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.95-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.96-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.95-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.96-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.93-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.96-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI: confidence interval; OR: odds ratio\u003c/p\u003e\n\u003cp\u003eModel 0- Unadjusted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1- Adjusted for age and gender\u003c/p\u003e\n\u003cp\u003eModel 2- Model 1 plus the presence of Parkinson\u0026rsquo;s disease, dementia, recurrent falls, essential tremor, geriatric depression, polypharmacy, malnutrition, probable sarcopenia\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"frailty, inflammation, nutrition, aging, geriatric syndrome","lastPublishedDoi":"10.21203/rs.3.rs-6540533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6540533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFrailty is associated with both malnutrition and systemic inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e This study investigated the relationship between the haemoglobin, albumin, lymphocyte, and platelet (HALP) score and frailty stages in older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn total, 439 patients admitted to our geriatrics outpatient clinic between January 2023 and June 2024 were retrospectively analysed. All patients underwent a comprehensive geriatric assessment, and frailty was assessed according to the Fried frailty phenotype. Patients were categorised into three groups: frail, prefrail, and robust. HALP score was calculated and compared between this three groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The mean age of all patients was 76.12 ± 6.74 years, and 71.3% were female. Age, the frequencies of female sex, dementia, recurrent falls, geriatric depression, polypharmacy, malnutrition, and probable sarcopenia were higher in the frail group than in the prefrail and robust groups (p \u0026lt; 0.05). The median HALP score was lower in the frail and prefrail groups than in the robust group (34.9 vs. 48.3, p \u0026lt; 0.001 and 38.6 vs. 48.3, p = 0.005, respectively). In the logistic regression analysis, after adjusting for confounding factors, the significance remained for the frail and prefrail groups compared with the robust group (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The HALP score may be an predictor of prefrail and frail older adults, with potential utility in clinical practice.\u003c/p\u003e","manuscriptTitle":"Might the haemoglobin, albumin, lymphocyte, and platelet (HALP) score predict prefrailty and frailty in older adults without cancer: a cross-sectional study?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 12:22:20","doi":"10.21203/rs.3.rs-6540533/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8502857c-9c62-4545-96a8-2e8de997b487","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:41:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 12:22:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6540533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6540533","identity":"rs-6540533","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}