Walkable urban zip codes and specific chronic diseases are associated with Sarcopenia

Walkable urban zip codes and specific chronic diseases are associated with Sarcopenia | 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 Walkable urban zip codes and specific chronic diseases are associated with Sarcopenia Laura F. Goodfield, Amy Tsurumi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5751166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Sarcopenia is an aging-related disease characterized by muscle mass loss and wasting, and is among the most significant causes of frailty among the elderly. Despite it being a detrimental condition, it only recently, as of October 1, 2016, received the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code, M62. 84, thus being recognized as a distinct disease. This study evaluated patient characteristics for sarcopenia using hospital data from the first six years since the adoption of the ICD-10-CM code, and its associations with residential walkability and comorbidities. Methods: The Mass General Brigham Research Patient Data Registry was used to query for cases with ICD-10 code, M62.84 between October 1, 2016 and October 1, 2022. For each case, a control was identified matched on sex, age and race. The inclusion criteria were patients at the Massachusetts General Hospital or Brigham and Women’s Hospital, with Massachusetts residential zip codes, excluding employees. The main association of interest was whether Walk Score was associated with sarcopenia. Additional factors evaluated were marital status and aging-related chronic disease comorbidities. Univariable and multivariable logistic regression analyses were performed to compute odds ratios. Results: The final analysis included 685 subjects (343 sarcopenia cases and 342 controls). The mean ± standard deviation of age of sarcopenia diagnosis record was 77.53 ± 11.47. Univariable logistic regression analysis found highly walkable neighborhoods of ‘very walkable’ (OR [95% CI]: 1.734 [1.197-2.522], p=0.0037) and ‘walker’s paradise’ (OR [95% CI]: 2.958 [1.664-5.455], p=0.0003) to be associated with sarcopenia. In the multivariable logistic regression model adjusted for sex, race, ethnicity, median household income and marital status, ‘very walkable’ (OR [95% CI]: 1.648 [1.069-2.551], p=0.0242) and ‘walker’s paradise’ (OR [95% CI]: 2.624 [1.404-5.078], p=0.0031) remained to be associated. The multivariable logistic regression model additionally including chronic diseases found that ‘walker’s paradise’ (OR [95% CI]: 2.898 [1.497-5.788], p=0.0020) to be associated. Diabetes (OR [95% CI]: 1.655 [1.122-2.453], p=0.0115), dementia, (OR [95% CI]: 5.154 [3.291-8.270], p=2.71x10 -12 ) and chronic obstructive pulmonary disease (OR [95% CI]: 2.497 [1.558-4.066], p=0.0002) were associated with sarcopenia, while cancer and cardiovascular diseases with stroke were not. Conclusions: Increased residential walkability is associated with increased odds of sarcopenia. Diabetes, dementia and COPD are significant comorbidities, while cancer and stroke were not found to be significantly associated. Future longitudinal studies evaluating lifetime residential walkability and comorbidities in relation to sarcopenia would aid in further elucidating how those factors can impact sarcopenia outcome. Figures Figure 1 Figure 2 Introduction Sarcopenia is an aging-related disease characterized by muscle mass loss and wasting, and is among the most significant causes of frailty among the elderly. Despite it being a detrimental condition, it only recently (as of October 1, 2016), received the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code, M62. 84, thus being recognized as a distinct disease [1,2]. The estimated worldwide prevalence of sarcopenia is 10-16%, although diagnostic criteria vary, highlighting challenges [3–7]. Diseases with a similar clinical presentation of muscle loss exist, including cachexia, which differs from sarcopenia, as it can occur at any age and is driven by an underlying illness, such as cancer or anorexia [8]. Sarcopenia is associated with multiple adverse health outcomes, ranging from heightened susceptibility to falls and fractures, to increased mortality and different comorbidities [9–12]. Strategies to mitigate muscle loss among the elderly population include increased protein intake, resistance/weight training, and physical activity, including walking [13–20]. Walkability refers to design aspects of a neighborhood that are conducive to residents using walking as a viable mode of transportation to access greenspace, sidewalks, nearby amenities, and other design elements [21–25]. While neighborhood walkability encompasses a variety of factors, a commonly used index is the Walk Score, which employs a proprietary algorithm using data from sources such as the U.S. Census, Google and Open Street Map, to measure the walkability of a given area on a scale of 0-100, according to its proximity to essential amenities, including schools, grocery stores, transit stations, retail, among others. Several studies have established a correlation between health outcomes and high neighborhood walkability, particularly with lower body mass index (BMI), reduced rates of chronic disease and increased physical activity [26–32]. It is unclear whether this positive relationship between high neighborhood walkability and beneficial health outcomes remains valid for sarcopenia prevention. One study in Taiwan showed a non-linear relationship between Walk Score and self-reported sarcopenia risk (SARC-F), with reduced odds of sarcopenia in ‘car-dependent’ or ‘very walkable’ neighborhoods, and increased odds of sarcopenia in ‘somewhat walkable’ neighborhoods, after adjusting for comorbidities and behavioral patterns [33]. While the mechanisms of this non-linear association remain unclear, the authors speculated that those living in ‘walkable,’ yet areas with heavy vehicle traffic, may be disincentivized to walk due to safety concerns. This study aims to further characterize the association between Walk Score and sarcopenia by using a data source from the United States, a geographic location where this relationship has not yet been assessed. Moreover, considering that sarcopenia is an aging-related disease, and that ample previous studies have reported that married status is strongly associated with reduced all-cause mortality among elderly adults [34–38], simultaneously evaluating marital status is expected to be important. Assessing the impact of aging-related chronic diseases as comorbidities is also critical. Taken together, the objective of this study is to provide updated descriptive statistics of demographic and socioeconomic characteristics of sarcopenia patients in the hospital setting using its recently adopted ICD-10 code, as well as evaluate the potential association of various factors, including residential walkability, marital status and comorbidities. Providing updated analysis results for the initial six years since the first ICD codification is novel, and results on associated factors are expected to provide further insights into potential protective effects and risk factors for sarcopenia. Methods Data Source and Study Population The Mass General Brigham (MGB) Research Patient Data Registry (RPDR), a centralized data repository across the MGB hospital system, was used to identify sarcopenia cases (by recorded incidence of ICD-10-CM diagnosis code, M62. 84) from the Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (BWH) between October 1, 2016 and October 1, 2022. For each sarcopenia case, a healthy control with routine annual wellness visit data (procedure code G0438 or G0439) was identified using the RPDR query function, matched on age at 10-year intervals, legal sex, and race. The search was limited to Massachusetts zip codes and MGB employees were excluded from the query. Zip codes associated with PO Boxes were then excluded from the analysis, as they cannot provide accurate Walk Score nor median household income data (n=2 sarcopenia and n=3 control subjects were excluded). The final analysis included 343 cases and 342 controls. The analysis of data using this database was approved by the MGB IRB protocol, 2023P001330. Study Variables Walk Score and walkability range category allocations Walk Scores were generated from patient address zip codes in the RPDR, using Walkscore.com data. Walk Score can change over time based on the addition or removal of amenities, thus, zip code to Walk Score allocation as of September 8, 2023 was employed. Zip code 02370 did not have a Walk Score allocated according to Walkscore.com, however, the town encompassed by 02370, Rockland, Massachusetts, had a Walk Score of 74 allocated (n=5). Walkability range categories were assigned according to the Walkscore.com methodology, where Walk Score of 0-49 were categorized as ‘car-dependent’; 50-69 as ‘somewhat walkable’; 70-89 as ‘very walkable’; and 90-100 as ‘walker’s paradise.’ Demographic and socioeconomic variables Due to potential disclosure risk, any age ≥89 years were recoded as 89. Race and ethnicity data indicated as ‘declined’ or ‘unavailable,’ were re-labeled as ‘unknown.’ Median household income by zip code was assigned according to data reported by the US Census Bureau 2021 American Community Survey. Clinical and diagnostic variables Body mass index (BMI) categories were defined according to the common Centers for Disease Control and Prevention (CDC) cut-offs of BMI ≤18.5 as ‘underweight’; 18.5 to <25 as ‘healthy’; 25 to <30 as ‘overweight’; and ≥30 as ‘obese.’ For cases, the most recently recorded BMI from physical exam data before sarcopenia diagnosis was used, and for controls, the most recent data generally was used. There were 291 sarcopenia and 315 control subjects with BMI data available, and analyses on BMI categories were performed among only these subjects. For assessment of other diagnoses, ICD-10-CM code, R64 was used for cachexia, and codes used for comorbidities (cancer, chronic obstructive pulmonary disease (COPD), dementia, type 2 diabetes, cardiovascular disease (CVD), and CVD including stroke and heart attack) are listed in Supplementary Table S1 . Statistical Analysis R version 4.3.2 was used and analyses were performed in the base “stats” package. For continuous variables, mean ± standard deviation (SD), and two-sample equal variance unpaired t-test p-values were calculated. For categorical variables, the total number (n) and proportion (%), and Chi-square test, or for any expected count below five, Fisher’s exact test p-values, were calculated. Bonferroni correction was applied to Chi-square test p-values for making comparisons between cases and controls within each of the four walkability range categories. For constructing the multivariable logistic regression models, the main effect of walkability range category was adjusted for median household income, legal sex, race, and ethnicity, as well as marriage status and comorbidities. Given the small numbers, ‘Native American or Alaska Native’ (n=1) and ‘two or more races’ (n=5) were combined with the ‘other’ category. No patients were found for the ‘Native Hawaiian and Other Pacific Islander’ category. For marriage status, ‘divorced’ (n=51) and ‘legally separated’ (n=9) subjects were combined as ‘divorced/legally separated’; ‘partner’ (n=5) and ‘married’ (n=313) were combined as ‘married/partnered’; and ‘unknown’ (n=16), ‘unknown/missing’ (n=1) and ‘other’ (n=4) were combined as ‘other/unknown.’ Results Patient characteristics A total of 343 sarcopenia cases and 342 age, sex, and race-matched controls were included in the analysis. Cases had a mean age of 77.5 years at the time of their first sarcopenia ICD-10-CM M62.84 record. The majority of cases were female (n=201 (58.6%)), White (n=260 (75.8%)), and non-Hispanic (n=303 (88.3%)) ( Table 1 ). Sarcopenia cases on average resided in more walkable zip codes by Walk Score (mean ± SD: 56.3 ± 29.5 among sarcopenia cases versus 49.5± 27.1 among controls, p=0.002). Walkability range categories differed significantly between sarcopenia patients and controls for ‘somewhat walkable’ (n (%): 49 (14.3%) among cases versus 83 (24.3%) among controls, p=0.0052), ‘very walkable’ (n (%): 107 (31.2%) among cases versus 73 (21.3%) among controls, p=0.0179), and ‘walker's paradise’ (n (%): 45 (13.1%) among cases versus 18 (5.3%) among controls, p=0.0025), while ‘car-dependent’ areas (n (%): 142 (41.4%) among cases versus 168 (49.1%) among controls, p=0.2029) did not significantly differ by patient type ( Figure 1 ). Cases also resided within zip codes where the median household income is lower ($105,569.30 for cases versus $112,554.50 for controls, p=0.0322) ( Table 1 ). BMI categories were also evaluated, and while more sarcopenia patients were found to be underweight or healthy compared to controls, more control subjects were found to be overweight or obese ( Table 1 ). Higher walkability range is associated with increased odds of sarcopenia in both univariable and multivariable logistic regression models adjusted for demographic and socioeconomic factors Univariable logistic regression model results showed that higher walkability range categories of ‘very walkable’ (OR [95% CI]: 1.734 [1.197 - 2.522], p=0.0037) and ‘walker’s paradise’ (OR [95% CI]: 2.958 [1.664 - 5.455], p=0.0003) were significantly associated with higher odds of sarcopenia, compared to ‘car-dependent’ as the reference ( Table 2 ). A multivariable model, adjusted for demographic and socioeconomic factors (sex, race, ethnicity, and median household income), also showed that the ‘very walkable’ (aOR [95% CI]: 1.840 [1.206 - 2.823], p=0.0049) and ‘walker’s paradise’ (aOR [95% CI]: 3.025 [1.639- 5.793], p=0.0006) areas remained to be significantly associated. Marital status was additionally included in the multivariable model adjusted for legal sex, race, ethnicity, and median household. Those with marital status of ‘single’ (aOR 1.981 [1.227, 3.226], p=0.0055) and ‘widowed’ (aOR 1.840 [1.215, 2.799], p=0.0041) had higher odds of sarcopenia, compared to the ‘married/partnered’ reference group. In this model, higher walkability range categories of ‘very walkable’ (OR [95% CI]: 1.648 [1.069 - 2.551], p=0.0242) and ‘walker’s paradise’ (OR [95% CI]: 2.624 [1.404 - 5.078], p=0.0031) also remained significantly associated with higher odds of sarcopenia ( Table 2 ). Sarcopenia cases have increased incidence of comorbidities compared to controls A higher total number of other grouped ICD-10-CM diagnosis codes were found for sarcopenia cases compared to controls (median [25th-75th quantiles]: 82 [53-108.5] for cases versus 44 [25-72] for controls, p<0.0001) ( Figure 2A ). Given the similarity in the clinical manifestation of sarcopenia and cachexia, the frequency of cachexia records (ICD-10-CM of R64) were also compared between cases and controls (n (%): 41 (12.0%) among cases versus 8 (2.3%) among controls, p<0.00001) ( Figure 2B ). When evaluating the temporal relationship between recorded sarcopenia and cachexia diagnosis code dates, 9 had cachexia diagnosis before their age of initial sarcopenia diagnosis, 24 patients had cachexia diagnosis at the same age as sarcopenia diagnosis, and 8 patients had cachexia diagnosis after their first age at sarcopenia diagnosis ( Figure 2B ). Other aging-related chronic disease comorbidities were also evaluated, including type 2 diabetes, cancer, dementia, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD) only, or CVD and stroke combined (as stroke can be an acute event). For all comorbidities except cancer, significantly more incidence was found among sarcopenia cases compared to controls overall ( Figure 2C-H ). When assessing the timing of these comorbidities relative to the first sarcopenia record, diabetes, cancer, COPD and CVD alone or CVD and stroke diagnoses were more frequently found after sarcopenia, while the first record of dementia most frequently occurred at the same age as the first sarcopenia record ( Figure 2C-H ). High walkability is associated with sarcopenia in multivariable logistic regression models adjusted for demographic and socioeconomic factors, as well as comorbidities and BMI Various multivariable models were constructed adding comorbidities (diabetes, cancer, dementia, COPD, CVD without or with stroke) recorded before or at the same age as sarcopenia, in addition to demographic and socioeconomic factors and marital status ( Table 3 , Supplementary Table S2 ). The model with demographic and socioeconomic factors, marital status and comorbidities with CVD and stroke showed that ‘somewhat walkable’ (OR [95% CI]: 0.548 [0.338 - 0.880], p=0.0137) and ‘walker’s paradise’ (OR [95% CI]: 2.898 [1.497 - 5.788], p=0.0020) areas, and marital status of only ‘single’ (aOR 1.942 [1.163, 3.268], p=0.0116) were associated with sarcopenia ( Table 3 ). Using a subset of patients with BMI data available, a multivariable model was constructed including all the previous demographic, socioeconomic and comorbidity covariates with BMI categories – this model showed that ‘somewhat walkable’ (OR [95% CI]: 0.558 [0.325 - 0.943], p=0.0312), and ‘walker’s paradise’ (OR [95% CI]: 3.434 [1.685 - 7.247], p=0.0009) were significantly associated with sarcopenia, while none of the marital status categories remain associated ( Table 3 ). Models without considering stroke yielded similar results as the models considering both CVD and stroke, in which increasing walkability range categories showed progressively increased odds of sarcopenia, and ‘walker’s paradise’ remained significantly associated in all models ( Supplementary Table S2 ). For marital status, only the ‘single’ category was significantly associated in the model without BMI categories, and none of the marital status categories remained significantly associated with sarcopenia when BMI categories were added to the model. Specific comorbidities before or at the age of sarcopenia recording appear to be risk factors Each of the models including chronic disease comorbidities consistently showed that type 2 diabetes, dementia and COPD recorded before or at the same age as sarcopenia diagnosis record are associated with increased odds of sarcopenia, whereas cancer and CVD and stroke are not ( Table 3 , Supplementary Table S2 ). In a multivariable logistic regression model including all the previous demographic, socioeconomic and comorbidity covariates, diabetes showed a significant association without BMI categories in the model (OR [95% CI]: 1.634 [1.106 - 2.425], p=0.0141), or with them (OR [95% CI]: 2.095 [1.372 - 3.222], p=0.0007) ( Table 3 ). The magnitude and strength of this association was especially large for dementia, both without including BMI categories (OR [95% CI]: 5.154 [3.291 - 8.270], p=2.71x10 -12 ), or with them (OR [95% CI]: 5.066 [3.141 - 8.378], p=8.1x10 -11 ) ( Table 3 ). Similarly, COPD was associated with increased sarcopenia incidence, both without BMI (OR [95% CI]: 2.478 [1.545 - 4.037], p=0.0002), or with them (OR [95% CI]: 1.949 [1.171 - 3.282], p=0.0110) ( Table 3 ). Similar effects for these covariates were also found when omitting strokes in the assessment ( Supplementary Table S2 ). Discussion This study explored a previously uncharacterized role of walkability of urban areas in Massachusetts, United States, in sarcopenia. Contrary to the positive correlations between high Walk Score and health outcomes previously found in the literature [26–28,31,32], this study suggests a positive correlation between Walk Score and sarcopenia may exist. A previous study in Taiwan reported a reduced odds of sarcopenia in ‘car-dependent’ neighborhoods, similarly to our results, but it also showed a non-linear relationship, in which, reduced odds of sarcopenia were also found for ‘very walkable’ neighborhoods, and increased odds in ‘somewhat walkable’ neighborhoods, after adjusting for comorbidities and behavioral patterns [33]. While the underlying mechanisms of this association are not fully understood, there are a few possible explanations. The objective of this study was to evaluate the first years of the use of the newly adopted ICD-10 code for sarcopenia; however, this system is primarily used for insurance and billing codification, and thus relatively unreliable for accurate diagnosis or detecting the timing of the disease onset. Additionally, ICD-10 may not fully encapsulate a patient’s medical history, as well as not capture the heterogeneity in clinical presentation. Taken together, future prospective studies using clinical monitoring and longitudinal assessment of residential walkability and comorbidity diagnoses as patients age, is expected to improve the understanding of factors related to sarcopenia incidence. Although ample previous literature suggests that increased physical activity is catalyzed by high residential walkability, it is possible that the effect of increased walkability may not be sufficient to prevent nor reverse the clinical loss of muscle mass, potentially leading to sarcopenia. Increased physical activity in walkable areas can be attributed to an increase in incidental walking as a means of transportation, replacing car travel. It is possible that aging-related muscle loss as seen in sarcopenia may require additional effort to increase exercise, such as by including weight-bearing activities and resistance training [14–19]. This study considered Walk Score as an indicator for walking as an incidental exercise, and additional data on walking behavior directly, as well as other types of exercise is expected to provide further insights. It is also possible that sarcopenia patients, who had a higher average number of comorbidities compared to their control counterparts, moved into more urban areas with a higher Walk Score from more rural areas seeking increased healthcare access. The protective effect of marital status has been shown for all-cause mortality in previous studies [34–38], and in this study, the ‘single’ status showed increased odds of sarcopenia compared to the ‘married/partnered’ reference category, which is consistent such results, although this effect remained only significant in multivariable models that did not include BMI categories. The number of subjects in each of the marital status categories were relatively small, or this may suggest that physical health conditions may represent more important predictors of sarcopenia compared to marital status. Accordingly, additional studies to simultaneously evaluate marital status and various comorbidities would be informative. Each of the multivariable models constructed consistently showed type 2 diabetes, dementia and COPD diagnosis before or at the same age as sarcopenia diagnosis as significant risk factors for sarcopenia. Ample studies have shown that type 2 diabetes is a major comorbidity for sarcopenia [39–43], and suggestions have been made that screening for “diabetic sarcopenia” may improve detection [41], or that that sarcopenia related to diabetes is a distinct condition [42]. In this study, type 2 diabetes was identified as a risk factor as expected, although simultaneously, higher BMI categories were found to be associated with decreased odds of sarcopenia. This discrepancy may be due to reduced BMI as a reflection of muscle loss and limitations in the use of BMI for specific groups of patients, such as patients with various conditions of frailty. Dementia has also been associated with sarcopenia in previous studies, and while most studies have suggested that sarcopenia is a risk factor for dementia [44–49], there is also a suggestion of a bidirectional interaction between sarcopenia and dementia [50]. Of note, the magnitude and strength of the effect was particularly large for dementia as a covariate in this study, highlighting the importance of future studies to elucidate the underlying mechanisms. The association between sarcopenia and COPD has been particularly well-established by multiple studies [51–60], although the directionality remains elusive. In this study, the majority of first diabetes and COPD diagnoses were found after sarcopenia diagnosis, although many also occurred at the same age. In contrast, the majority of initial dementia diagnoses were found at the same age as sarcopenia diagnosis, although many were also found after sarcopenia diagnosis. Additional prospective studies to understand the directionality of each of these associations are crucial. Limitations Study participation was restricted by a Massachusetts zip code, potentially limiting the generalizability of the results obtained, to other states. Only zip codes, rather than exact addresses, were available, limiting the granularity of the Walk Score analyzed. Zip codes provided in the MGB RPDR database may not represent a comprehensive history of address, income, and walkability. Additionally, Walk Score can change over time and cannot be calculated retrospectively. Walk Score was used as an indicator of walkability, as data on walking behavior itself was not available, as well as information on other forms of exercise. The use of ICD-10 codes for clinical assessments may not provide accurate diagnosis and timing of disease onset. Analyses were limited by low sample sizes in some racial categories, as MGB services a relatively less demographically diverse population compared to other hospitals in Massachusetts. Household income is a more accurate indicator of socioeconomic status, however median household income as reported by the US Census Bureau 2021 American Community Survey was used as the closest proxy available. Future Directions A large multi-site prospective cohort study in a diverse setting with clinical monitoring would allow for an improved assessment of factors related to sarcopenia incidence. Evaluating walkability using exact addresses of confirmed long-term residences would generate more meaningful results. Additional data on the amount and intensity of walking, and other forms of exercise would also be advantageous. Declarations Ethics approval and consent to participate: This study used existing electronic health data from the Mass General Brigham (MGB) Research Patient Data Repository (RPDR). The analysis of data using this database was approved by the MGB IRB protocol, 2023P001330. Consent for publication: Not applicable Availability of data and material: This study used data obtained from the Mass General Brigham (MGB) Research Patient Data Repository (RPDR), which cannot be shared without institutional approval. Competing interests: The authors have no disclosures to report. Funding: None. Authors' contributions: L.F.G and A.T. conceptualized the study, performed the analysis, wrote the main manuscript, prepared the figures, and reviewed the manuscript. Acknowledgments: The authors thank Dr. Shawn Murphy, Dr. Henry Chueh, and the Mass General Brigham Health Care Research Patient Data Registry group for facilitating use of their database. References Anker SD, Morley JE, von Haehling S. Welcome to the ICD-10 code for sarcopenia. J Cachexia Sarcopenia Muscle 2016;7:512–4. https://doi.org/10.1002/jcsm.12147. Cao L, Morley JE. Sarcopenia Is Recognized as an Independent Condition by an International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) Code. J Am Med Dir Assoc 2016;17:675–7. https://doi.org/10.1016/j.jamda.2016.06.001. Yuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 2023;144:155533. https://doi.org/10.1016/j.metabol.2023.155533. Carvalho do Nascimento PR, Bilodeau M, Poitras S. How do we define and measure sarcopenia? A meta-analysis of observational studies. Age Ageing 2021;50:1906–13. https://doi.org/10.1093/ageing/afab148. Shafiee G, Keshtkar A, Soltani A, Ahadi Z, Larijani B, Heshmat R. Prevalence of sarcopenia in the world: a systematic review and meta- analysis of general population studies. J Diabetes Metab Disord 2017;16:21. https://doi.org/10.1186/s40200-017-0302-x. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412–23. https://doi.org/10.1093/ageing/afq034. Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, et al. Sarcopenia: Alternative Definitions and Associations with Lower Extremity Function. J Am Geriatr Soc 2003;51:1602–9. https://doi.org/10.1046/j.1532-5415.2003.51534.x. Rolland Y, Van Kan GA, Gillette-Guyonnet S, Vellas B. Cachexia versus sarcopenia. Curr Opin Clin Nutr Metab Care 2011;14:15. https://doi.org/10.1097/MCO.0b013e328340c2c2. Xu J, Wan CS, Ktoris K, Reijnierse EM, Maier AB. Sarcopenia Is Associated with Mortality in Adults: A Systematic Review and Meta-Analysis. Gerontology 2022;68:361–76. https://doi.org/10.1159/000517099. Tournadre A, Vial G, Capel F, Soubrier M, Boirie Y. Sarcopenia. Joint Bone Spine 2019;86:309–14. https://doi.org/10.1016/j.jbspin.2018.08.001. Pacifico J, Geerlings MAJ, Reijnierse EM, Phassouliotis C, Lim WK, Maier AB. Prevalence of sarcopenia as a comorbid disease: A systematic review and meta-analysis. Exp Gerontol 2020;131:110801. https://doi.org/10.1016/j.exger.2019.110801. Shaw SC, Dennison EM, Cooper C. Epidemiology of Sarcopenia: Determinants Throughout the Lifecourse. Calcif Tissue Int 2017;101:229–47. https://doi.org/10.1007/s00223-017-0277-0. Rondanelli M, Cereda E, Klersy C, Faliva MA, Peroni G, Nichetti M, et al. Improving rehabilitation in sarcopenia: a randomized-controlled trial utilizing a muscle-targeted food for special medical purposes. J Cachexia Sarcopenia Muscle 2020;11:1535–47. https://doi.org/10.1002/jcsm.12532. Beaudart C, Dawson A, Shaw SC, Harvey NC, Kanis JA, Binkley N, et al. Nutrition and physical activity in the prevention and treatment of sarcopenia: systematic review. Osteoporos Int 2017;28:1817–33. https://doi.org/10.1007/s00198-017-3980-9. Vlietstra L, Hendrickx W, Waters DL. Exercise interventions in healthy older adults with sarcopenia: A systematic review and meta-analysis. Australas J Ageing 2018;37:169–83. https://doi.org/10.1111/ajag.12521. Emanuele M, Riccardo C, Matteo T, Matteo C, Mauro DB, Cherubini A, et al. Physical activity and exercise as countermeasures to physical frailty and sarcopenia. Aging Clin Exp Res 2017;29:35–42. https://doi.org/10.1007/s40520-016-0705-4. Rogeri PS, Zanella R, Martins GL, Garcia MDA, Leite G, Lugaresi R, et al. Strategies to Prevent Sarcopenia in the Aging Process: Role of Protein Intake and Exercise. Nutrients 2022;14:52. https://doi.org/10.3390/nu14010052. Clark BC, Clark LA, Law TD. Resistance Exercise to Prevent and Manage Sarcopenia and Dynapenia. Annu Rev Gerontol Geriatr 2016;36:205–28. https://doi.org/10.1891/0198-8794.36.205. Shen Y, Shi Q, Nong K, Li S, Yue J, Huang J, et al. Exercise for sarcopenia in older people: A systematic review and network meta-analysis. J Cachexia Sarcopenia Muscle 2023;14:1199–211. https://doi.org/10.1002/jcsm.13225. Kim HK, Suzuki T, Saito K, Yoshida H, Kobayashi H, Kato H, et al. Effects of exercise and amino acid supplementation on body composition and physical function in community-dwelling elderly Japanese sarcopenic women: a randomized controlled trial. J Am Geriatr Soc 2012;60:16–23. https://doi.org/10.1111/j.1532-5415.2011.03776.x. Richardson AS, Troxel WM, Ghosh-Dastidar MB, Beckman R, Hunter GP, DeSantis AS, et al. One size doesn’t fit all: cross-sectional associations between neighborhood walkability, crime and physical activity depends on age and sex of residents. BMC Public Health 2017;17:97. https://doi.org/10.1186/s12889-016-3959-z. Twardzik E, Judd S, Bennett A, Hooker S, Howard V, Hutto B, et al. Walk Score and objectively measured physical activity within a national cohort. J Epidemiol Community Health 2019;73:549–56. https://doi.org/10.1136/jech-2017-210245. Thielman J, Manson H, Chiu M, Copes R, Rosella LC. Residents of highly walkable neighbourhoods in Canadian urban areas do substantially more physical activity: a cross-sectional analysis. CMAJ Open 2016;4:E720–8. https://doi.org/10.9778/cmajo.20160068. Kim EJ, Jin S. Walk Score and Neighborhood Walkability: A Case Study of Daegu, South Korea. Int J Environ Res Public Health 2023;20:4246. https://doi.org/10.3390/ijerph20054246. Carr LJ, Dunsiger SI, Marcus BH. Walk score TM as a global estimate of neighborhood walkability. Am J Prev Med 2010;39:460–3. https://doi.org/10.1016/j.amepre.2010.07.007. Villanueva K, Knuiman M, Nathan A, Giles-Corti B, Christian H, Foster S, et al. The impact of neighborhood walkability on walking: does it differ across adult life stage and does neighborhood buffer size matter? Health Place 2014;25:43–6. https://doi.org/10.1016/j.healthplace.2013.10.005. Keats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJB. Associations between Neighborhood Walkability, Physical Activity, and Chronic Disease in Nova Scotian Adults: An Atlantic PATH Cohort Study. Int J Environ Res Public Health 2020;17:8643. https://doi.org/10.3390/ijerph17228643. Lang I-M, Antonakos CL, Judd SE, Colabianchi N. A longitudinal examination of objective neighborhood walkability, body mass index, and waist circumference: the REasons for Geographic And Racial Differences in Stroke study. Int J Behav Nutr Phys Act 2022;19:17. https://doi.org/10.1186/s12966-022-01247-7. Wang ML, Narcisse M-R, McElfish PA. Higher walkability associated with increased physical activity and reduced obesity among United States adults. Obesity 2023;31:553–64. https://doi.org/10.1002/oby.23634. Barnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E, on behalf of the Council on Environment and Physical Activity (CEPA) – Older Adults working group. Built environmental correlates of older adults’ total physical activity and walking: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2017;14:103. https://doi.org/10.1186/s12966-017-0558-z. Van Holle V, Van Cauwenberg J, Van Dyck D, Deforche B, Van de Weghe N, De Bourdeaudhuij I. Relationship between neighborhood walkability and older adults’ physical activity: results from the Belgian Environmental Physical Activity Study in Seniors (BEPAS Seniors). Int J Behav Nutr Phys Act 2014;11:110. https://doi.org/10.1186/s12966-014-0110-3. Sallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain KL, et al. Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med 1982 2009;68:1285–93. https://doi.org/10.1016/j.socscimed.2009.01.017. Park J-H, Lai T-F, Chang C-S, Huang W-C, Cho JS, Liao Y. A Nonlinear Association between Neighborhood Walkability and Risks of Sarcopenia in Older Adults. J Nutr Health Aging 2021;25:618–23. https://doi.org/10.1007/s12603-021-1588-4. Rendall MS, Weden MM, Favreault MM, Waldron H. The Protective Effect of Marriage for Survival: A Review and Update. Demography 2011;48:481–506. https://doi.org/10.1007/s13524-011-0032-5. Wang L, Yi Z. Marital status and all-cause mortality rate in older adults: a population-based prospective cohort study. BMC Geriatr 2023;23:214. https://doi.org/10.1186/s12877-023-03880-8. Johnson NJ, Backlund E, Sorlie PD, Loveless CA. Marital Status and Mortality: The National Longitudinal Mortality Study. Ann Epidemiol 2000;10:224–38. https://doi.org/10.1016/S1047-2797(99)00052-6. Robards J, Evandrou M, Falkingham J, Vlachantoni A. Marital status, health and mortality. Maturitas 2012;73:295–9. https://doi.org/10.1016/j.maturitas.2012.08.007. Manzoli L, Villari P, M Pirone G, Boccia A. Marital status and mortality in the elderly: A systematic review and meta-analysis. Soc Sci Med 2007;64:77–94. https://doi.org/10.1016/j.socscimed.2006.08.031. Chen H, Huang X, Dong M, Wen S, Zhou L, Yuan X. The Association Between Sarcopenia and Diabetes: From Pathophysiology Mechanism to Therapeutic Strategy. Diabetes Metab Syndr Obes 2023;16:1541. https://doi.org/10.2147/DMSO.S410834. Mesinovic J, Fyfe JJ, Talevski J, Wheeler MJ, Leung GKW, George ES, et al. Type 2 Diabetes Mellitus and Sarcopenia as Comorbid Chronic Diseases in Older Adults: Established and Emerging Treatments and Therapies. Diabetes Metab J 2023;47:719–42. https://doi.org/10.4093/dmj.2023.0112. de Luis Román D, Gómez JC, García-Almeida JM, Vallo FG, Rolo GG, Gómez JJL, et al. Diabetic Sarcopenia. A proposed muscle screening protocol in people with diabetes. Rev Endocr Metab Disord 2024;25:651–61. https://doi.org/10.1007/s11154-023-09871-9. Liu Z, Guo Y, Zheng C. Type 2 diabetes mellitus related sarcopenia: a type of muscle loss distinct from sarcopenia and disuse muscle atrophy. Front Endocrinol 2024;15. https://doi.org/10.3389/fendo.2024.1375610. Sravya SL, Swain J, Sahoo AK, Mangaraj S, Kanwar J, Jadhao P, et al. Sarcopenia in Type 2 Diabetes Mellitus: Study of the Modifiable Risk Factors Involved. J Clin Med 2023;12:5499. https://doi.org/10.3390/jcm12175499. Lin A, Wang T, Li C, Pu F, Abdelrahman Z, Jin M, et al. Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study. Metabolites 2023;13:245. https://doi.org/10.3390/metabo13020245. Arosio B, Calvani R, Ferri E, Coelho-Junior HJ, Carandina A, Campanelli F, et al. Sarcopenia and Cognitive Decline in Older Adults: Targeting the Muscle–Brain Axis. Nutrients 2023;15:1853. https://doi.org/10.3390/nu15081853. Kim S, Wang S-M, Kang DW, Um YH, Yoon HM, Lee S, et al. Development of a prediction model for cognitive impairment of sarcopenia using multimodal neuroimaging in non-demented older adults. Alzheimers Dement 2024;20:4868–78. https://doi.org/10.1002/alz.14054. Beeri MS, Leugrans SE, Delbono O, Bennett DA, Buchman AS. Sarcopenia is associated with incident Alzheimer’s dementia, mild cognitive impairment, and cognitive decline. J Am Geriatr Soc 2021;69:1826–35. https://doi.org/10.1111/jgs.17206. Kim J, Suh S-I, Park YJ, Kang M, Chung SJ, Lee ES, et al. Sarcopenia is a predictor for Alzheimer’s continuum and related clinical outcomes. Sci Rep 2024;14:21074. https://doi.org/10.1038/s41598-024-62918-y. Maniscalco L, Veronese N, Ragusa FS, Vernuccio L, Dominguez LJ, Smith L, et al. Sarcopenia using muscle mass prediction model and cognitive impairment: A longitudinal analysis from the English longitudinal study on ageing. Arch Gerontol Geriatr 2024;117:105160. https://doi.org/10.1016/j.archger.2023.105160. Lu C, Liu W, Cang X, Sun X, Wang X, Wang C, et al. The bidirectional associations between sarcopenia-related traits and cognitive performance. Sci Rep 2024;14:7591. https://doi.org/10.1038/s41598-024-58416-w. Benz E, Trajanoska K, Lahousse L, Schoufour JD, Terzikhan N, Roos ED, et al. Sarcopenia in COPD: a systematic review and meta-analysis. Eur Respir Rev 2019;28. https://doi.org/10.1183/16000617.0049-2019. Zhou J, Liu Y, Yang F, Jing M, Zhong X, Wang Y, et al. Risk Factors of Sarcopenia in COPD Patients: A Meta-Analysis. Int J Chron Obstruct Pulmon Dis 2024;19:1613–22. https://doi.org/10.2147/COPD.S456451. Yu Z, He J, Chen Y, Zhou Z, Wang L. Chronic obstructive pulmonary disease as a risk factor for sarcopenia: A systematic review and meta-analysis. PLOS ONE 2024;19:e0300730. https://doi.org/10.1371/journal.pone.0300730. Sepúlveda-Loyola W, Osadnik C, Phu S, Morita AA, Duque G, Probst VS. Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2020;11:1164–76. https://doi.org/10.1002/jcsm.12600. Zhao X, Su R, Hu R, Chen Y, Xu X, Yuan Y, et al. Sarcopenia index as a predictor of clinical outcomes among older adult patients with acute exacerbation of chronic obstructive pulmonary disease: a cross-sectional study. BMC Geriatr 2023;23:89. https://doi.org/10.1186/s12877-023-03784-7. Kim SH, Hong CH, Shin M-J, Kim KU, Park TS, Park JY, et al. Prevalence and clinical characteristics of Sarcopenia in older adult patients with stable chronic obstructive pulmonary disease: a cross-sectional and follow-up study. BMC Pulm Med 2024;24:219. https://doi.org/10.1186/s12890-024-03034-5. Perrot L, Greil A, Boirie Y, Farigon N, Mulliez A, Costes F, et al. Prevalence of sarcopenia and malnutrition during acute exacerbation of COPD and after 6 months recovery. Eur J Clin Nutr 2020;74:1556–64. https://doi.org/10.1038/s41430-020-0623-6. Limpawattana P, Inthasuwan P, Putraveephong S, Boonsawat W, Theerakulpisut D, Sawanyawisuth K. Sarcopenia in chronic obstructive pulmonary disease: A study of prevalence and associated factors in the Southeast Asian population. Chron Respir Dis 2018;15:250–7. https://doi.org/10.1177/1479972317743759. Huang W-J, Ko C-Y. Systematic review and meta-analysis of nutrient supplements for treating sarcopenia in people with chronic obstructive pulmonary disease. Aging Clin Exp Res 2024;36:69. https://doi.org/10.1007/s40520-024-02722-w. Chua JR, Tee ML. Association of sarcopenia with osteoporosis in patients with chronic obstructive pulmonary disease. Osteoporos Sarcopenia 2020;6:129–32. https://doi.org/10.1016/j.afos.2020.07.004. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files MainTables.docx SupplementaryMaterials.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 15 Feb, 2025 Editor invited by journal 03 Jan, 2025 Editor assigned by journal 03 Jan, 2025 Submission checks completed at journal 03 Jan, 2025 First submitted to journal 02 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5751166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469335325,"identity":"90bf84bf-f172-4ae4-9fcb-6d002409c2cf","order_by":0,"name":"Laura F. Goodfield","email":"","orcid":"","institution":"Milken Institute School of Public Health, George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"F.","lastName":"Goodfield","suffix":""},{"id":469335327,"identity":"f55efa71-8b83-46b1-96ad-7834c912e24e","order_by":1,"name":"Amy Tsurumi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBADHn4GBjYGxgYIT4KBgRm3WjaoFskGUrUwGBwgVgv//PZn0pVt22SMrx2/9oBxh12+wfGzD28wVFgnNuDQInGMx0zybNttHrPbOeUGjGeSLTecSTe2YDiTjlMLwzEeNslGiJY0CcY2ZgPJhjQ2IOMwTi3yx9ifgbUYzwZrqTeQ7H8G1PIPtxaDYwxmYC0G0unHQIYb8EuAbGnArcXwWI6xZcO52zwSt3PYJBLbjgO1PGO2SDiWboxLi9zh4w9vNpTdtuefnf5M4mNbtQEbfxrjjQ811rI4vY8APAYMCTB2Am5lyID9AXHqRsEoGAWjYMQBAI7MVA1NaEPGAAAAAElFTkSuQmCC","orcid":"","institution":"Massachusetts General Hospital, Harvard Medical School","correspondingAuthor":true,"prefix":"","firstName":"Amy","middleName":"","lastName":"Tsurumi","suffix":""}],"badges":[],"createdAt":"2025-01-02 10:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5751166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5751166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97422242,"identity":"205203ed-68e6-40d8-bf38-b03f828174d7","added_by":"auto","created_at":"2025-12-04 08:41:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201843,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the proportions of controls versus sarcopenia patients across walkability range categories. Chi-squared test p-values with Bonferroni correction are shown.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5751166/v1/33896093d535d4a03fdd386e.jpg"},{"id":97667797,"identity":"9368ffb8-2351-4cd0-91c5-ff00f8290320","added_by":"auto","created_at":"2025-12-08 09:24:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":289327,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Boxplot depicting the median number of 3-character grouped ICD-10-CM codes, as well as the distribution of (B) cachexia, and various comorbidities, including (C) diabetes, (D) cancer, (E) dementia, (F) chronic obstructive pulmonary disease (COPD), (G) cardiovascular diseases (CVD), and (H) CVD and stroke, comparing sarcopenia and control patients. For sarcopenia patients, the timing of age before, same year or after the first sarcopenia diagnosis recording is also shown to the right.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5751166/v1/65af95402e10bd868bf69db1.jpg"},{"id":97677441,"identity":"6c7027d7-f115-443f-ab1f-9b582ac94646","added_by":"auto","created_at":"2025-12-08 09:53:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1365672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5751166/v1/b7b058db-e717-402c-ad5d-6082a86655a0.pdf"},{"id":97422243,"identity":"d949f3e7-ca83-44e0-9518-3fc03fb6bfc8","added_by":"auto","created_at":"2025-12-04 08:41:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29507,"visible":true,"origin":"","legend":"","description":"","filename":"MainTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5751166/v1/fee0d12f7f7e223fb444fd8b.docx"},{"id":97666623,"identity":"c4d25f82-c983-4f88-8b9f-328b08296e8d","added_by":"auto","created_at":"2025-12-08 09:21:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":230272,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5751166/v1/2b27e3c5d4c9f686618b4642.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Walkable urban zip codes and specific chronic diseases are associated with Sarcopenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is an aging-related disease characterized by muscle mass loss and wasting, and is among the most significant causes of frailty among the elderly. Despite it being a detrimental condition, it only recently (as of October 1, 2016), received the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code, M62. 84, thus being recognized as a distinct disease\u0026nbsp;[1,2]. The estimated worldwide prevalence of sarcopenia is 10-16%, although diagnostic criteria vary, highlighting challenges\u0026nbsp;[3–7]. Diseases with a similar clinical presentation of muscle loss exist, including cachexia, which differs from sarcopenia, as it can occur at any age and is driven by an underlying illness, such as cancer or anorexia\u0026nbsp;[8]. Sarcopenia is associated with multiple adverse health outcomes, ranging from heightened susceptibility to falls and fractures, to increased mortality and different comorbidities\u0026nbsp;[9–12].\u003c/p\u003e\n\u003cp\u003eStrategies to mitigate muscle loss among the elderly population include increased protein intake, resistance/weight training, and physical activity, including walking\u0026nbsp;[13–20]. Walkability refers to design aspects of a neighborhood that are conducive to residents using walking as a viable mode of transportation to access greenspace, sidewalks, nearby amenities, and other design elements\u0026nbsp;[21–25]. While neighborhood walkability encompasses a variety of factors, a commonly used index is the Walk Score, which employs a proprietary algorithm using data from sources such as the U.S. Census, Google and Open Street Map, to measure the walkability of a given area on a scale of 0-100, according to its proximity to essential amenities, including schools, grocery stores, transit stations, retail, among others.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral studies have established a correlation between health outcomes and high neighborhood walkability, particularly with lower body mass index (BMI), reduced rates of chronic disease and increased physical activity\u0026nbsp;[26–32]. It is unclear whether this positive relationship between high neighborhood walkability and beneficial health outcomes remains valid for sarcopenia prevention. One study in Taiwan showed a non-linear relationship between Walk Score and self-reported sarcopenia risk (SARC-F), with reduced odds of sarcopenia in ‘car-dependent’ or ‘very walkable’ neighborhoods, and increased odds of sarcopenia in ‘somewhat walkable’ neighborhoods, after adjusting for comorbidities and behavioral patterns\u0026nbsp;[33]. While the mechanisms of this non-linear association remain unclear, the authors speculated that those living in ‘walkable,’ yet areas with heavy vehicle traffic, may be disincentivized to walk due to safety concerns. This study aims to further characterize the association between Walk Score and sarcopenia by using a data source from the United States, a geographic location where this relationship has not yet been assessed. Moreover, considering that sarcopenia is an aging-related disease, and that ample previous studies have reported that married status is strongly associated with reduced all-cause mortality among elderly adults\u0026nbsp;[34–38], simultaneously evaluating marital status is expected to be important. Assessing the impact of aging-related chronic diseases as comorbidities is also critical.\u003c/p\u003e\n\u003cp\u003eTaken together, the objective of this study is to provide updated descriptive statistics of demographic and socioeconomic characteristics of sarcopenia patients in the hospital setting using its recently adopted ICD-10 code, as well as evaluate the potential association of various factors, including residential walkability, marital status and comorbidities. Providing updated analysis results for the initial six years since the first ICD codification is novel, and results on associated factors are expected to provide further insights into potential protective effects and risk factors for sarcopenia.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Source and Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mass General Brigham (MGB) Research Patient Data Registry (RPDR), a centralized data repository across the MGB hospital system, was used to identify sarcopenia cases (by recorded incidence of ICD-10-CM diagnosis code, M62. 84) from the Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (BWH) between October 1, 2016 and October 1, 2022. For each sarcopenia case, a healthy control with routine annual wellness visit data (procedure code G0438 or G0439) was identified using the RPDR query function, matched on age at 10-year intervals, legal sex, and race. The search was limited to Massachusetts zip codes and MGB employees were excluded from the query. Zip codes associated with PO Boxes were then excluded from the analysis, as they cannot provide accurate Walk Score nor median household income data (n=2 sarcopenia and n=3 control subjects were excluded). The final analysis included 343 cases and 342 controls. The analysis of data using this database was approved by the MGB IRB protocol, 2023P001330.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWalk Score and walkability range category allocations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWalk Scores were generated from patient address zip codes in the RPDR, using Walkscore.com data. Walk Score can change over time based on the addition or removal of amenities, thus, zip code to Walk Score allocation as of September 8, 2023 was employed. Zip code 02370 did not have a Walk Score allocated according to Walkscore.com, however, the town encompassed by 02370, Rockland, Massachusetts, had a Walk Score of 74 allocated (n=5). Walkability range categories were assigned according to the Walkscore.com methodology, where Walk Score of 0-49 were categorized as ‘car-dependent’; 50-69 as ‘somewhat walkable’; 70-89 as ‘very walkable’; and 90-100 as ‘walker’s paradise.’\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic and socioeconomic variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to potential disclosure risk, any age ≥89 years were recoded as 89. Race and ethnicity data indicated as ‘declined’ or ‘unavailable,’ were re-labeled as ‘unknown.’ Median household income by zip code was assigned according to data reported by the US Census Bureau 2021 American Community Survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and diagnostic variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI) categories were defined according to the common Centers for Disease Control and Prevention (CDC) cut-offs of BMI ≤18.5 as ‘underweight’; 18.5 to \u0026lt;25 as ‘healthy’; 25 to \u0026lt;30 as ‘overweight’; and ≥30 as ‘obese.’ For cases, the most recently recorded BMI from physical exam data before sarcopenia diagnosis was used, and for controls, the most recent data generally was used. There were 291 sarcopenia and 315 control subjects with BMI data available, and analyses on BMI categories were performed among only these subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor assessment of other diagnoses, ICD-10-CM code, R64 was used for cachexia, and codes used for comorbidities (cancer, chronic obstructive pulmonary disease (COPD), dementia, type 2 diabetes, cardiovascular disease (CVD), and CVD including stroke and heart attack) are listed in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR version 4.3.2 was used and analyses were performed in the base “stats” package. For continuous variables, mean ± standard deviation (SD), and two-sample equal variance unpaired t-test p-values were calculated. For categorical variables, the total number (n) and proportion (%), and Chi-square test, or for any expected count below five, Fisher’s exact test p-values, were calculated. Bonferroni correction was applied to Chi-square test p-values for making comparisons between cases and controls within each of the four walkability range categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor constructing the multivariable logistic regression models, the main effect of walkability range category was adjusted for median household income, legal sex, race, and ethnicity, as well as marriage status and comorbidities. Given the small numbers, ‘Native American or Alaska Native’ (n=1) and ‘two or more races’ (n=5) were combined with the ‘other’ category. No patients were found for the ‘Native Hawaiian and Other Pacific Islander’ category. For marriage status, ‘divorced’ (n=51) and ‘legally separated’ (n=9) subjects were combined as ‘divorced/legally separated’; ‘partner’ (n=5) and ‘married’ (n=313) were combined as ‘married/partnered’; and ‘unknown’ (n=16), ‘unknown/missing’ (n=1) and ‘other’ (n=4) were combined as ‘other/unknown.’\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 343 sarcopenia cases and 342 age, sex, and race-matched controls were included in the analysis. Cases had a mean age of 77.5 years at the time of their first sarcopenia ICD-10-CM M62.84 record. The majority of cases were female (n=201 (58.6%)), White (n=260 (75.8%)), and non-Hispanic (n=303 (88.3%)) (\u003cstrong\u003eTable 1\u003c/strong\u003e). Sarcopenia cases on average resided in more walkable zip codes by Walk Score (mean ± SD: 56.3 ± 29.5 among sarcopenia cases versus 49.5± 27.1 among controls, p=0.002). Walkability range categories differed significantly between sarcopenia patients and controls for ‘somewhat walkable’ (n (%): 49 (14.3%) among cases versus 83 (24.3%) among controls, p=0.0052), ‘very walkable’ (n (%): 107 (31.2%) among cases versus 73 (21.3%) among controls, p=0.0179), and ‘walker's paradise’ (n (%): 45 (13.1%) among cases versus 18 (5.3%) among controls, p=0.0025), while ‘car-dependent’ areas (n (%): 142 (41.4%) among cases versus 168 (49.1%) among controls, p=0.2029) did not significantly differ by patient type (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Cases also resided within zip codes where the median household income is lower ($105,569.30 for cases versus $112,554.50 for controls, p=0.0322) (\u003cstrong\u003eTable 1\u003c/strong\u003e). BMI categories were also evaluated, and while more sarcopenia patients were found to be underweight or healthy compared to controls, more control subjects were found to be overweight or obese (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigher walkability range is associated with increased odds of sarcopenia in both univariable and multivariable logistic regression models adjusted for demographic and socioeconomic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression model results showed that higher walkability range categories of ‘very walkable’ (OR [95% CI]: 1.734 [1.197 - 2.522], p=0.0037) and ‘walker’s paradise’ (OR [95% CI]: 2.958 [1.664 - 5.455], p=0.0003) were significantly associated with higher odds of sarcopenia, compared to ‘car-dependent’ as the reference (\u003cstrong\u003eTable 2\u003c/strong\u003e). A multivariable model, adjusted for demographic and socioeconomic factors (sex, race, ethnicity, and median household income), also showed that the ‘very walkable’ (aOR [95% CI]: 1.840 [1.206 - 2.823], p=0.0049) and ‘walker’s paradise’ (aOR [95% CI]: 3.025 [1.639- 5.793], p=0.0006) areas remained to be significantly associated. Marital status was additionally included in the multivariable model adjusted for legal sex, race, ethnicity, and median household. Those with marital status of ‘single’ (aOR 1.981 [1.227, 3.226], p=0.0055) and ‘widowed’ (aOR 1.840 [1.215, 2.799], p=0.0041) had higher odds of sarcopenia, compared to the ‘married/partnered’ reference group. In this model, higher walkability range categories of ‘very walkable’ (OR [95% CI]: 1.648 [1.069 - 2.551], p=0.0242) and ‘walker’s paradise’ (OR [95% CI]: 2.624 [1.404 - 5.078], p=0.0031) also remained significantly associated with higher odds of sarcopenia (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSarcopenia cases have increased incidence of comorbidities compared to controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA higher total number of other grouped ICD-10-CM diagnosis codes were found for sarcopenia cases compared to controls (median [25th-75th quantiles]: 82 [53-108.5] for cases versus 44 [25-72] for controls, p\u0026lt;0.0001) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Given the similarity in the clinical manifestation of sarcopenia and cachexia, the frequency of cachexia records (ICD-10-CM of R64) were also compared between cases and controls (n (%): 41 (12.0%) among cases versus 8 (2.3%) among controls, p\u0026lt;0.00001) (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). When evaluating the temporal relationship between recorded sarcopenia and cachexia diagnosis code dates, 9 had cachexia diagnosis before their age of initial sarcopenia diagnosis, 24 patients had cachexia diagnosis at the same age as sarcopenia diagnosis, and 8 patients had cachexia diagnosis after their first age at sarcopenia diagnosis (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOther aging-related chronic disease comorbidities were also evaluated, including type 2 diabetes, cancer, dementia, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD) only, or CVD and stroke combined (as stroke can be an acute event). For all comorbidities except cancer, significantly more incidence was found among sarcopenia cases compared to controls overall (\u003cstrong\u003eFigure 2C-H\u003c/strong\u003e). When assessing the timing of these comorbidities relative to the first sarcopenia record, diabetes, cancer, COPD and CVD alone or CVD and stroke diagnoses were more frequently found after sarcopenia, while the first record of dementia most frequently occurred at the same age as the first sarcopenia record (\u003cstrong\u003eFigure 2C-H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh walkability is associated with sarcopenia in multivariable logistic regression models adjusted for demographic and socioeconomic factors, as well as comorbidities and BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious multivariable models were constructed adding comorbidities (diabetes, cancer, dementia, COPD, CVD without or with stroke) recorded before or at the same age as sarcopenia, in addition to demographic and socioeconomic factors and marital status (\u003cstrong\u003eTable 3\u003c/strong\u003e, \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e). The model with demographic and socioeconomic factors, marital status and comorbidities with CVD and stroke showed that ‘somewhat walkable’ (OR [95% CI]: 0.548 [0.338 - 0.880], p=0.0137) and ‘walker’s paradise’ (OR [95% CI]: 2.898 [1.497 - 5.788], p=0.0020) areas, and marital status of only ‘single’ (aOR 1.942 [1.163, 3.268], \u0026nbsp;p=0.0116) were associated with sarcopenia (\u003cstrong\u003eTable 3\u003c/strong\u003e). Using a subset of patients with BMI data available, a multivariable model was constructed including\u0026nbsp;all the previous\u0026nbsp;demographic, socioeconomic and comorbidity\u0026nbsp;covariates with BMI categories – this model showed that ‘somewhat walkable’ (OR [95% CI]: 0.558 [0.325 - 0.943], p=0.0312), and ‘walker’s paradise’ (OR [95% CI]: 3.434 [1.685 - 7.247], p=0.0009)\u0026nbsp;were significantly associated with sarcopenia, while none of the marital status categories remain associated (\u003cstrong\u003eTable 3\u003c/strong\u003e). Models without considering stroke yielded similar results as the models considering both CVD and stroke, in which increasing walkability range categories showed progressively increased odds of sarcopenia, and ‘walker’s paradise’ remained significantly associated in all models (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e). For marital status, only the ‘single’ category was significantly associated in the model without BMI categories, and none of the marital status categories remained significantly associated with sarcopenia when BMI categories were added to the model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific comorbidities before or at the age of sarcopenia recording appear to be risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach of the models\u0026nbsp;including chronic disease comorbidities\u0026nbsp;consistently\u0026nbsp;showed\u0026nbsp;that type 2 diabetes, dementia and COPD recorded before or at the same age as sarcopenia diagnosis record are associated with increased odds of sarcopenia, whereas cancer and CVD and stroke are not (\u003cstrong\u003eTable 3\u003c/strong\u003e, \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e).\u0026nbsp;In a multivariable logistic regression model\u0026nbsp;including\u0026nbsp;all the previous\u0026nbsp;demographic, socioeconomic and comorbidity\u0026nbsp;covariates, diabetes showed a significant association without BMI categories in the model\u0026nbsp;(OR [95% CI]: 1.634 [1.106 - 2.425], p=0.0141), or with them (OR [95% CI]: 2.095 [1.372 - 3.222], p=0.0007) (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;The magnitude and strength of this association was especially large for dementia,\u0026nbsp;both without including BMI categories (OR [95% CI]: 5.154 [3.291 - 8.270], p=2.71x10\u003csup\u003e-12\u003c/sup\u003e), or with them (OR [95% CI]: 5.066 [3.141 - 8.378], p=8.1x10\u003csup\u003e-11\u003c/sup\u003e) (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;Similarly,\u0026nbsp;COPD\u0026nbsp;was associated with increased sarcopenia incidence, both without BMI\u0026nbsp;(OR [95% CI]: 2.478 [1.545 - 4.037], p=0.0002), or with them (OR [95% CI]: 1.949 [1.171 - 3.282], p=0.0110) (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;Similar effects\u0026nbsp;for these covariates\u0026nbsp;were\u0026nbsp;also\u0026nbsp;found when omitting strokes in the assessment (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored a previously uncharacterized role of walkability of urban areas in Massachusetts, United States, in sarcopenia. Contrary to the positive correlations between high Walk Score and health outcomes previously found in the literature\u0026nbsp;[26–28,31,32], this study suggests a positive correlation between Walk Score and sarcopenia\u0026nbsp;may exist. A previous study in Taiwan reported a reduced odds of sarcopenia in ‘car-dependent’ neighborhoods, similarly to our results, but\u0026nbsp;it also\u0026nbsp;showed a non-linear relationship, in which, reduced\u0026nbsp;odds of sarcopenia\u0026nbsp;were also found for\u0026nbsp;‘very\u0026nbsp;walkable’ neighborhoods, and increased odds\u0026nbsp;in ‘somewhat walkable’ neighborhoods,\u0026nbsp;after adjusting for comorbidities and behavioral patterns\u0026nbsp;[33].\u0026nbsp;While the underlying mechanisms of this association are not fully understood, there are a few possible explanations.\u0026nbsp;The objective of this study was to evaluate\u0026nbsp;the\u0026nbsp;first years of the use of the\u0026nbsp;newly adopted ICD-10 code\u0026nbsp;for sarcopenia; however,\u0026nbsp;this system is primarily used for insurance and billing codification, and\u0026nbsp;thus\u0026nbsp;relatively unreliable for accurate diagnosis or detecting the timing of the disease onset. Additionally, ICD-10 may not fully encapsulate a patient’s medical history, as well as not capture the heterogeneity in clinical presentation. Taken together, future prospective studies using clinical monitoring and longitudinal assessment of residential walkability\u0026nbsp;and comorbidity diagnoses\u0026nbsp;as patients age, is expected to improve the understanding of factors related to sarcopenia incidence.\u003c/p\u003e\n\u003cp\u003eAlthough ample previous literature suggests that increased physical activity is catalyzed by high residential walkability, it is possible that the effect of increased walkability may not be sufficient to prevent nor reverse the clinical loss of muscle mass, potentially leading to sarcopenia. Increased physical activity in walkable areas can be attributed to an increase in incidental walking as a means of transportation, replacing car travel. It is possible that aging-related muscle loss as seen in sarcopenia may require additional effort to increase exercise, such as by including weight-bearing activities and resistance training\u0026nbsp;[14–19]. This study considered Walk Score as an indicator for walking as an incidental exercise, and additional data on walking behavior directly, as well as other types of exercise is expected to provide further insights.\u0026nbsp;It is\u0026nbsp;also\u0026nbsp;possible that sarcopenia patients, who had a higher average number of comorbidities compared to their control counterparts, moved into more urban areas with a higher Walk Score from more rural areas seeking increased healthcare access.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The protective effect of marital status has been shown for all-cause mortality in previous studies\u0026nbsp;[34–38], and in this study, the ‘single’ status showed increased odds of sarcopenia compared to the ‘married/partnered’ reference category,\u0026nbsp;which is consistent such results, although this effect remained only significant\u0026nbsp;in multivariable models that did not include BMI categories.\u0026nbsp;The number of subjects in each of the marital status categories were relatively small, or this may suggest that physical health conditions may represent more important predictors of sarcopenia compared to marital status. Accordingly, additional studies to simultaneously evaluate marital status and various comorbidities would be informative.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach of the multivariable models constructed consistently showed\u0026nbsp;type 2\u0026nbsp;diabetes, dementia and COPD diagnosis before or at the same age as sarcopenia diagnosis as significant risk factors for sarcopenia. Ample studies have shown that type 2 diabetes is a major comorbidity for sarcopenia\u0026nbsp;[39–43], and suggestions have been made that screening for “diabetic sarcopenia” may improve detection\u0026nbsp;[41], or that that sarcopenia related to diabetes is a distinct condition\u0026nbsp;[42]. In this study, type 2 diabetes was identified as a risk factor as expected,\u0026nbsp;although\u0026nbsp;simultaneously, higher BMI categories were found to be associated with decreased odds of sarcopenia. This discrepancy may be due to reduced BMI as a reflection of muscle loss and limitations in the use of BMI for specific groups of patients, such as patients with various conditions of frailty. Dementia has also been associated with sarcopenia in previous studies, and while most studies have suggested that sarcopenia is a risk factor for dementia\u0026nbsp;[44–49], there is also a suggestion of a bidirectional interaction between sarcopenia and dementia\u0026nbsp;[50]. Of note, the magnitude and strength of the effect was particularly large for dementia as a covariate in this study, highlighting the importance of future studies to elucidate the underlying mechanisms. The association between sarcopenia and COPD has been\u0026nbsp;particularly\u0026nbsp;well-established by multiple studies\u0026nbsp;[51–60], although the directionality remains elusive. In\u0026nbsp;this\u0026nbsp;study, the majority of first diabetes and COPD diagnoses were found after sarcopenia diagnosis, although many also occurred at the same age. In contrast, the majority of initial dementia diagnoses were found at the same age as sarcopenia diagnosis, although many were also found after sarcopenia diagnosis. Additional prospective studies to understand the directionality of each of these associations are crucial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy participation was restricted by a Massachusetts zip code, potentially limiting the generalizability of the results obtained, to other states. Only zip codes, rather than exact addresses, were available, limiting the granularity of the Walk Score analyzed. Zip codes provided in the MGB RPDR database may not represent a comprehensive history of address, income, and walkability. Additionally, Walk Score can change over time and cannot be calculated retrospectively. Walk Score was used as an indicator of walkability, as data on walking behavior itself was not available, as well as information on other forms of exercise. The use of ICD-10 codes for clinical assessments may not provide accurate diagnosis and timing of disease onset. Analyses were limited by low sample sizes in some racial categories, as MGB services a relatively less demographically diverse population compared to other hospitals in Massachusetts. Household income is a more accurate indicator of socioeconomic status, however median household income as reported by the US Census Bureau 2021 American Community Survey was used as the closest proxy available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; A large multi-site prospective cohort study in a diverse setting with clinical monitoring would allow for an improved assessment of factors related to sarcopenia incidence. Evaluating walkability using exact addresses of confirmed long-term residences would generate more meaningful results. Additional data on the amount and intensity of walking, and other forms of exercise would also be advantageous.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study used existing electronic health data from the Mass General Brigham (MGB) Research Patient Data Repository (RPDR). The analysis of data using this database was approved by the MGB IRB protocol, 2023P001330.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThis study used data obtained from the Mass General Brigham (MGB) Research Patient Data Repository (RPDR), which cannot be shared without institutional approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors have no disclosures to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eL.F.G and A.T. conceptualized the study, performed the analysis, wrote the main manuscript, prepared the figures, and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors thank Dr. Shawn Murphy, Dr. Henry Chueh, and the Mass General Brigham Health Care Research Patient Data Registry group for facilitating use of their database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnker SD, Morley JE, von Haehling S. Welcome to the ICD-10 code for sarcopenia. J Cachexia Sarcopenia Muscle 2016;7:512\u0026ndash;4. https://doi.org/10.1002/jcsm.12147.\u003c/li\u003e\n\u003cli\u003eCao L, Morley JE. Sarcopenia Is Recognized as an Independent Condition by an International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) Code. J Am Med Dir Assoc 2016;17:675\u0026ndash;7. https://doi.org/10.1016/j.jamda.2016.06.001.\u003c/li\u003e\n\u003cli\u003eYuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism 2023;144:155533. https://doi.org/10.1016/j.metabol.2023.155533.\u003c/li\u003e\n\u003cli\u003eCarvalho do Nascimento PR, Bilodeau M, Poitras S. How do we define and measure sarcopenia? A meta-analysis of observational studies. Age Ageing 2021;50:1906\u0026ndash;13. https://doi.org/10.1093/ageing/afab148.\u003c/li\u003e\n\u003cli\u003eShafiee G, Keshtkar A, Soltani A, Ahadi Z, Larijani B, Heshmat R. Prevalence of sarcopenia in the world: a systematic review and meta- analysis of general population studies. J Diabetes Metab Disord 2017;16:21. https://doi.org/10.1186/s40200-017-0302-x.\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412\u0026ndash;23. https://doi.org/10.1093/ageing/afq034.\u003c/li\u003e\n\u003cli\u003eNewman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, et al. Sarcopenia: Alternative Definitions and Associations with Lower Extremity Function. J Am Geriatr Soc 2003;51:1602\u0026ndash;9. https://doi.org/10.1046/j.1532-5415.2003.51534.x.\u003c/li\u003e\n\u003cli\u003eRolland Y, Van Kan GA, Gillette-Guyonnet S, Vellas B. Cachexia versus sarcopenia. Curr Opin Clin Nutr Metab Care 2011;14:15. https://doi.org/10.1097/MCO.0b013e328340c2c2.\u003c/li\u003e\n\u003cli\u003eXu J, Wan CS, Ktoris K, Reijnierse EM, Maier AB. Sarcopenia Is Associated with Mortality in Adults: A Systematic Review and Meta-Analysis. Gerontology 2022;68:361\u0026ndash;76. https://doi.org/10.1159/000517099.\u003c/li\u003e\n\u003cli\u003eTournadre A, Vial G, Capel F, Soubrier M, Boirie Y. Sarcopenia. Joint Bone Spine 2019;86:309\u0026ndash;14. https://doi.org/10.1016/j.jbspin.2018.08.001.\u003c/li\u003e\n\u003cli\u003ePacifico J, Geerlings MAJ, Reijnierse EM, Phassouliotis C, Lim WK, Maier AB. Prevalence of sarcopenia as a comorbid disease: A systematic review and meta-analysis. Exp Gerontol 2020;131:110801. https://doi.org/10.1016/j.exger.2019.110801.\u003c/li\u003e\n\u003cli\u003eShaw SC, Dennison EM, Cooper C. Epidemiology of Sarcopenia: Determinants Throughout the Lifecourse. Calcif Tissue Int 2017;101:229\u0026ndash;47. https://doi.org/10.1007/s00223-017-0277-0.\u003c/li\u003e\n\u003cli\u003eRondanelli M, Cereda E, Klersy C, Faliva MA, Peroni G, Nichetti M, et al. Improving rehabilitation in sarcopenia: a randomized-controlled trial utilizing a muscle-targeted food for special medical purposes. J Cachexia Sarcopenia Muscle 2020;11:1535\u0026ndash;47. https://doi.org/10.1002/jcsm.12532.\u003c/li\u003e\n\u003cli\u003eBeaudart C, Dawson A, Shaw SC, Harvey NC, Kanis JA, Binkley N, et al. Nutrition and physical activity in the prevention and treatment of sarcopenia: systematic review. Osteoporos Int 2017;28:1817\u0026ndash;33. https://doi.org/10.1007/s00198-017-3980-9.\u003c/li\u003e\n\u003cli\u003eVlietstra L, Hendrickx W, Waters DL. Exercise interventions in healthy older adults with sarcopenia: A systematic review and meta-analysis. Australas J Ageing 2018;37:169\u0026ndash;83. https://doi.org/10.1111/ajag.12521.\u003c/li\u003e\n\u003cli\u003eEmanuele M, Riccardo C, Matteo T, Matteo C, Mauro DB, Cherubini A, et al. Physical activity and exercise as countermeasures to physical frailty and sarcopenia. Aging Clin Exp Res 2017;29:35\u0026ndash;42. https://doi.org/10.1007/s40520-016-0705-4.\u003c/li\u003e\n\u003cli\u003eRogeri PS, Zanella R, Martins GL, Garcia MDA, Leite G, Lugaresi R, et al. Strategies to Prevent Sarcopenia in the Aging Process: Role of Protein Intake and Exercise. Nutrients 2022;14:52. https://doi.org/10.3390/nu14010052.\u003c/li\u003e\n\u003cli\u003eClark BC, Clark LA, Law TD. Resistance Exercise to Prevent and Manage Sarcopenia and Dynapenia. Annu Rev Gerontol Geriatr 2016;36:205\u0026ndash;28. https://doi.org/10.1891/0198-8794.36.205.\u003c/li\u003e\n\u003cli\u003eShen Y, Shi Q, Nong K, Li S, Yue J, Huang J, et al. Exercise for sarcopenia in older people: A systematic review and network meta-analysis. J Cachexia Sarcopenia Muscle 2023;14:1199\u0026ndash;211. https://doi.org/10.1002/jcsm.13225.\u003c/li\u003e\n\u003cli\u003eKim HK, Suzuki T, Saito K, Yoshida H, Kobayashi H, Kato H, et al. Effects of exercise and amino acid supplementation on body composition and physical function in community-dwelling elderly Japanese sarcopenic women: a randomized controlled trial. J Am Geriatr Soc 2012;60:16\u0026ndash;23. https://doi.org/10.1111/j.1532-5415.2011.03776.x.\u003c/li\u003e\n\u003cli\u003eRichardson AS, Troxel WM, Ghosh-Dastidar MB, Beckman R, Hunter GP, DeSantis AS, et al. One size doesn\u0026rsquo;t fit all: cross-sectional associations between neighborhood walkability, crime and physical activity depends on age and sex of residents. BMC Public Health 2017;17:97. https://doi.org/10.1186/s12889-016-3959-z.\u003c/li\u003e\n\u003cli\u003eTwardzik E, Judd S, Bennett A, Hooker S, Howard V, Hutto B, et al. Walk Score and objectively measured physical activity within a national cohort. J Epidemiol Community Health 2019;73:549\u0026ndash;56. https://doi.org/10.1136/jech-2017-210245.\u003c/li\u003e\n\u003cli\u003eThielman J, Manson H, Chiu M, Copes R, Rosella LC. Residents of highly walkable neighbourhoods in Canadian urban areas do substantially more physical activity: a cross-sectional analysis. CMAJ Open 2016;4:E720\u0026ndash;8. https://doi.org/10.9778/cmajo.20160068.\u003c/li\u003e\n\u003cli\u003eKim EJ, Jin S. Walk Score and Neighborhood Walkability: A Case Study of Daegu, South Korea. Int J Environ Res Public Health 2023;20:4246. https://doi.org/10.3390/ijerph20054246.\u003c/li\u003e\n\u003cli\u003eCarr LJ, Dunsiger SI, Marcus BH. Walk score\u003csup\u003eTM\u003c/sup\u003e as a global estimate of neighborhood walkability. Am J Prev Med 2010;39:460\u0026ndash;3. https://doi.org/10.1016/j.amepre.2010.07.007.\u003c/li\u003e\n\u003cli\u003eVillanueva K, Knuiman M, Nathan A, Giles-Corti B, Christian H, Foster S, et al. The impact of neighborhood walkability on walking: does it differ across adult life stage and does neighborhood buffer size matter? Health Place 2014;25:43\u0026ndash;6. https://doi.org/10.1016/j.healthplace.2013.10.005.\u003c/li\u003e\n\u003cli\u003eKeats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJB. Associations between Neighborhood Walkability, Physical Activity, and Chronic Disease in Nova Scotian Adults: An Atlantic PATH Cohort Study. Int J Environ Res Public Health 2020;17:8643. https://doi.org/10.3390/ijerph17228643.\u003c/li\u003e\n\u003cli\u003eLang I-M, Antonakos CL, Judd SE, Colabianchi N. A longitudinal examination of objective neighborhood walkability, body mass index, and waist circumference: the REasons for Geographic And Racial Differences in Stroke study. Int J Behav Nutr Phys Act 2022;19:17. https://doi.org/10.1186/s12966-022-01247-7.\u003c/li\u003e\n\u003cli\u003eWang ML, Narcisse M-R, McElfish PA. Higher walkability associated with increased physical activity and reduced obesity among United States adults. Obesity 2023;31:553\u0026ndash;64. https://doi.org/10.1002/oby.23634.\u003c/li\u003e\n\u003cli\u003eBarnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E, on behalf of the Council on Environment and Physical Activity (CEPA) \u0026ndash; Older Adults working group. Built environmental correlates of older adults\u0026rsquo; total physical activity and walking: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2017;14:103. https://doi.org/10.1186/s12966-017-0558-z.\u003c/li\u003e\n\u003cli\u003eVan Holle V, Van Cauwenberg J, Van Dyck D, Deforche B, Van de Weghe N, De Bourdeaudhuij I. Relationship between neighborhood walkability and older adults\u0026rsquo; physical activity: results from the Belgian Environmental Physical Activity Study in Seniors (BEPAS Seniors). Int J Behav Nutr Phys Act 2014;11:110. https://doi.org/10.1186/s12966-014-0110-3.\u003c/li\u003e\n\u003cli\u003eSallis JF, Saelens BE, Frank LD, Conway TL, Slymen DJ, Cain KL, et al. Neighborhood built environment and income: examining multiple health outcomes. Soc Sci Med 1982 2009;68:1285\u0026ndash;93. https://doi.org/10.1016/j.socscimed.2009.01.017.\u003c/li\u003e\n\u003cli\u003ePark J-H, Lai T-F, Chang C-S, Huang W-C, Cho JS, Liao Y. A Nonlinear Association between Neighborhood Walkability and Risks of Sarcopenia in Older Adults. J Nutr Health Aging 2021;25:618\u0026ndash;23. https://doi.org/10.1007/s12603-021-1588-4.\u003c/li\u003e\n\u003cli\u003eRendall MS, Weden MM, Favreault MM, Waldron H. The Protective Effect of Marriage for Survival: A Review and Update. Demography 2011;48:481\u0026ndash;506. https://doi.org/10.1007/s13524-011-0032-5.\u003c/li\u003e\n\u003cli\u003eWang L, Yi Z. Marital status and all-cause mortality rate in older adults: a population-based prospective cohort study. BMC Geriatr 2023;23:214. https://doi.org/10.1186/s12877-023-03880-8.\u003c/li\u003e\n\u003cli\u003eJohnson NJ, Backlund E, Sorlie PD, Loveless CA. Marital Status and Mortality: The National Longitudinal Mortality Study. Ann Epidemiol 2000;10:224\u0026ndash;38. https://doi.org/10.1016/S1047-2797(99)00052-6.\u003c/li\u003e\n\u003cli\u003eRobards J, Evandrou M, Falkingham J, Vlachantoni A. Marital status, health and mortality. Maturitas 2012;73:295\u0026ndash;9. https://doi.org/10.1016/j.maturitas.2012.08.007.\u003c/li\u003e\n\u003cli\u003eManzoli L, Villari P, M Pirone G, Boccia A. Marital status and mortality in the elderly: A systematic review and meta-analysis. Soc Sci Med 2007;64:77\u0026ndash;94. https://doi.org/10.1016/j.socscimed.2006.08.031.\u003c/li\u003e\n\u003cli\u003eChen H, Huang X, Dong M, Wen S, Zhou L, Yuan X. The Association Between Sarcopenia and Diabetes: From Pathophysiology Mechanism to Therapeutic Strategy. Diabetes Metab Syndr Obes 2023;16:1541. https://doi.org/10.2147/DMSO.S410834.\u003c/li\u003e\n\u003cli\u003eMesinovic J, Fyfe JJ, Talevski J, Wheeler MJ, Leung GKW, George ES, et al. Type 2 Diabetes Mellitus and Sarcopenia as Comorbid Chronic Diseases in Older Adults: Established and Emerging Treatments and Therapies. Diabetes Metab J 2023;47:719\u0026ndash;42. https://doi.org/10.4093/dmj.2023.0112.\u003c/li\u003e\n\u003cli\u003ede Luis Rom\u0026aacute;n D, G\u0026oacute;mez JC, Garc\u0026iacute;a-Almeida JM, Vallo FG, Rolo GG, G\u0026oacute;mez JJL, et al. Diabetic Sarcopenia. A proposed muscle screening protocol in people with diabetes. Rev Endocr Metab Disord 2024;25:651\u0026ndash;61. https://doi.org/10.1007/s11154-023-09871-9.\u003c/li\u003e\n\u003cli\u003eLiu Z, Guo Y, Zheng C. Type 2 diabetes mellitus related sarcopenia: a type of muscle loss distinct from sarcopenia and disuse muscle atrophy. Front Endocrinol 2024;15. https://doi.org/10.3389/fendo.2024.1375610.\u003c/li\u003e\n\u003cli\u003eSravya SL, Swain J, Sahoo AK, Mangaraj S, Kanwar J, Jadhao P, et al. Sarcopenia in Type 2 Diabetes Mellitus: Study of the Modifiable Risk Factors Involved. J Clin Med 2023;12:5499. https://doi.org/10.3390/jcm12175499.\u003c/li\u003e\n\u003cli\u003eLin A, Wang T, Li C, Pu F, Abdelrahman Z, Jin M, et al. Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study. Metabolites 2023;13:245. https://doi.org/10.3390/metabo13020245.\u003c/li\u003e\n\u003cli\u003eArosio B, Calvani R, Ferri E, Coelho-Junior HJ, Carandina A, Campanelli F, et al. Sarcopenia and Cognitive Decline in Older Adults: Targeting the Muscle\u0026ndash;Brain Axis. Nutrients 2023;15:1853. https://doi.org/10.3390/nu15081853.\u003c/li\u003e\n\u003cli\u003eKim S, Wang S-M, Kang DW, Um YH, Yoon HM, Lee S, et al. Development of a prediction model for cognitive impairment of sarcopenia using multimodal neuroimaging in non-demented older adults. Alzheimers Dement 2024;20:4868\u0026ndash;78. https://doi.org/10.1002/alz.14054.\u003c/li\u003e\n\u003cli\u003eBeeri MS, Leugrans SE, Delbono O, Bennett DA, Buchman AS. Sarcopenia is associated with incident Alzheimer\u0026rsquo;s dementia, mild cognitive impairment, and cognitive decline. J Am Geriatr Soc 2021;69:1826\u0026ndash;35. https://doi.org/10.1111/jgs.17206.\u003c/li\u003e\n\u003cli\u003eKim J, Suh S-I, Park YJ, Kang M, Chung SJ, Lee ES, et al. Sarcopenia is a predictor for Alzheimer\u0026rsquo;s continuum and related clinical outcomes. Sci Rep 2024;14:21074. https://doi.org/10.1038/s41598-024-62918-y.\u003c/li\u003e\n\u003cli\u003eManiscalco L, Veronese N, Ragusa FS, Vernuccio L, Dominguez LJ, Smith L, et al. Sarcopenia using muscle mass prediction model and cognitive impairment: A longitudinal analysis from the English longitudinal study on ageing. Arch Gerontol Geriatr 2024;117:105160. https://doi.org/10.1016/j.archger.2023.105160.\u003c/li\u003e\n\u003cli\u003eLu C, Liu W, Cang X, Sun X, Wang X, Wang C, et al. The bidirectional associations between sarcopenia-related traits and cognitive performance. Sci Rep 2024;14:7591. https://doi.org/10.1038/s41598-024-58416-w.\u003c/li\u003e\n\u003cli\u003eBenz E, Trajanoska K, Lahousse L, Schoufour JD, Terzikhan N, Roos ED, et al. Sarcopenia in COPD: a systematic review and meta-analysis. Eur Respir Rev 2019;28. https://doi.org/10.1183/16000617.0049-2019.\u003c/li\u003e\n\u003cli\u003eZhou J, Liu Y, Yang F, Jing M, Zhong X, Wang Y, et al. Risk Factors of Sarcopenia in COPD Patients: A Meta-Analysis. Int J Chron Obstruct Pulmon Dis 2024;19:1613\u0026ndash;22. https://doi.org/10.2147/COPD.S456451.\u003c/li\u003e\n\u003cli\u003eYu Z, He J, Chen Y, Zhou Z, Wang L. Chronic obstructive pulmonary disease as a risk factor for sarcopenia: A systematic review and meta-analysis. PLOS ONE 2024;19:e0300730. https://doi.org/10.1371/journal.pone.0300730.\u003c/li\u003e\n\u003cli\u003eSep\u0026uacute;lveda-Loyola W, Osadnik C, Phu S, Morita AA, Duque G, Probst VS. Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2020;11:1164\u0026ndash;76. https://doi.org/10.1002/jcsm.12600.\u003c/li\u003e\n\u003cli\u003eZhao X, Su R, Hu R, Chen Y, Xu X, Yuan Y, et al. Sarcopenia index as a predictor of clinical outcomes among older adult patients with acute exacerbation of chronic obstructive pulmonary disease: a cross-sectional study. BMC Geriatr 2023;23:89. https://doi.org/10.1186/s12877-023-03784-7.\u003c/li\u003e\n\u003cli\u003eKim SH, Hong CH, Shin M-J, Kim KU, Park TS, Park JY, et al. Prevalence and clinical characteristics of Sarcopenia in older adult patients with stable chronic obstructive pulmonary disease: a cross-sectional and follow-up study. BMC Pulm Med 2024;24:219. https://doi.org/10.1186/s12890-024-03034-5.\u003c/li\u003e\n\u003cli\u003ePerrot L, Greil A, Boirie Y, Farigon N, Mulliez A, Costes F, et al. Prevalence of sarcopenia and malnutrition during acute exacerbation of COPD and after 6 months recovery. Eur J Clin Nutr 2020;74:1556\u0026ndash;64. https://doi.org/10.1038/s41430-020-0623-6.\u003c/li\u003e\n\u003cli\u003eLimpawattana P, Inthasuwan P, Putraveephong S, Boonsawat W, Theerakulpisut D, Sawanyawisuth K. Sarcopenia in chronic obstructive pulmonary disease: A study of prevalence and associated factors in the Southeast Asian population. Chron Respir Dis 2018;15:250\u0026ndash;7. https://doi.org/10.1177/1479972317743759.\u003c/li\u003e\n\u003cli\u003eHuang W-J, Ko C-Y. Systematic review and meta-analysis of nutrient supplements for treating sarcopenia in people with chronic obstructive pulmonary disease. Aging Clin Exp Res 2024;36:69. https://doi.org/10.1007/s40520-024-02722-w.\u003c/li\u003e\n\u003cli\u003eChua JR, Tee ML. Association of sarcopenia with osteoporosis in patients with chronic obstructive pulmonary disease. Osteoporos Sarcopenia 2020;6:129\u0026ndash;32. https://doi.org/10.1016/j.afos.2020.07.004.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5751166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5751166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSarcopenia is an aging-related disease characterized by muscle mass loss and wasting, and is among the most significant causes of frailty among the elderly. Despite it being a detrimental condition, it only recently, as of October 1, 2016, received the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code, M62. 84, thus being recognized as a distinct disease. This study evaluated patient characteristics for sarcopenia using hospital data from the first six years since the adoption of the ICD-10-CM code, and its associations with residential walkability and comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The Mass General Brigham Research Patient Data Registry was used to query for cases with ICD-10 code, M62.84 between October 1, 2016 and October 1, 2022. For each case, a control was identified matched on sex, age and race. The inclusion criteria were patients at the Massachusetts General Hospital or Brigham and Women’s Hospital, with Massachusetts residential zip codes, excluding employees. The main association of interest was whether Walk Score was associated with sarcopenia. Additional factors evaluated were marital status and aging-related chronic disease comorbidities. Univariable and multivariable logistic regression analyses were performed to compute odds ratios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe final analysis included 685 subjects (343 sarcopenia cases and 342 controls). The mean ± standard deviation of age of sarcopenia diagnosis record was 77.53 ± 11.47. Univariable logistic regression analysis found highly walkable neighborhoods of ‘very walkable’ (OR [95% CI]: 1.734 [1.197-2.522], p=0.0037) and ‘walker’s paradise’ (OR [95% CI]: 2.958 [1.664-5.455], p=0.0003) to be associated with sarcopenia. In the multivariable logistic regression model adjusted for sex, race, ethnicity, median household income and marital status, ‘very walkable’ (OR [95% CI]: 1.648 [1.069-2.551], p=0.0242) and ‘walker’s paradise’ (OR [95% CI]: 2.624 [1.404-5.078], p=0.0031) remained to be associated. The multivariable logistic regression model additionally including chronic diseases found that ‘walker’s paradise’ (OR [95% CI]: 2.898 [1.497-5.788], p=0.0020) to be associated. Diabetes (OR [95% CI]: 1.655 [1.122-2.453], p=0.0115), dementia, (OR [95% CI]: 5.154 [3.291-8.270], p=2.71x10\u003csup\u003e-12\u003c/sup\u003e) and chronic obstructive pulmonary disease (OR [95% CI]: 2.497 [1.558-4.066], p=0.0002) were associated with sarcopenia, while cancer and cardiovascular diseases with stroke were not.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Increased residential walkability is associated with increased odds of sarcopenia. Diabetes, dementia and COPD are significant comorbidities, while cancer and stroke were not found to be significantly associated. Future longitudinal studies evaluating lifetime residential walkability and comorbidities in relation to sarcopenia would aid in further elucidating how those factors can impact sarcopenia outcome.\u003c/p\u003e","manuscriptTitle":"Walkable urban zip codes and specific chronic diseases are associated with Sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 08:41:25","doi":"10.21203/rs.3.rs-5751166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-02-15T16:44:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-03T10:03:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-03T07:21:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-03T07:18:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-01-02T10:50:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cc91c2f-d81e-4db9-af36-7a74934273e2","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-04T08:41:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 08:41:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5751166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5751166","identity":"rs-5751166","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}