Multimorbidity Networks in Older Adults：Structural Characteristics and Socioecological Determinants

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The aim of this study was to investigate the characteristics of the network structure of multiple chronic diseases among the elderly population in China and its socioecological determinants. Methods We analyzed cross-sectional data from wave 7 of the 23 provinces Survey of Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2018. Results The older multimorbidity network displayed distinct structural features. Hypertension has the highest incidence rate and is the most central disease in the network. Tuberculosis, epilepsy, and bedsores had the highest network effective sizes and efficiency. However, tuberculosis, bedsores, and dyslipidemia had the lowest constraint and hierarchy. The multimorbidity network for old adults could be divided into eight subgroups, with varying degrees associations of inter-group and intra-group diseases. The regression coefficients for male gender and mildew odor in the home were positive, while the coefficient for sleep duration > 7 hours was negative. The significance tests showed that all coefficients had p-values less than 0.05. Conclusions The multimorbidity network for older adults exhibits typical network structure characteristics, which are influenced by factors such as gender, family environment, and sleep time. These findings highlight the need for collaborative efforts from families, health care system, and policymakers to improve quality of life in the older adultswith multimorbidity. multimorbidity older adults network characteristics socioecological determinants Figures Figure 1 Figure 2 Figure 3 Introduction Multimorbidity, defined as the co-occurrence of two or more chronic conditions within an individual[ 1 ], has emerged as a critical public health challenge in aging populations. Epidemiological studies reveal a striking age-dependent progression: while 15–20% of children exhibit early multimorbidity patterns[ 2 ], prevalence escalates exponentially after middle age, affecting over 60% of adults ≥ 65 years in high-income countries[ 3 ]. This complex health phenomenon transcends simple disease aggregation, manifesting as dynamic networks where conditions interact through shared pathophysiological pathways, treatment cascades, and psychosocial mediators[ 4 ]. The resultant health burden is multiplicative rather than additive - multimorbid older adults face 2.3-fold higher risks of functional decline[ 5 ], 4.1 times greater hospitalization rates[ 6 ], and 38% increased mortality compared to single-disease counterparts[ 7 ], imposing substantial strain on healthcare systems and caregivers[ 8 – 10 ]. Current research paradigms exhibit critical gaps requiring urgent attention. First, < 1% of multimorbidity studies employ network science approaches despite their unique capacity to model disease-disease interactions and identify critical network nodes[ 11 ]. Traditional pattern-based classifications (e.g., cardiometabolic or musculoskeletal clusters[ 12 – 14 ]) often overlook the strength, directionality, and temporal evolution of inter-disease connections. Second, the structural topology of geriatric multimorbidity networks remains poorly characterized. Key network metrics - including centrality (influence of specific diseases), structural holes (information brokerage potential), and eigenvector centrality (connections to influential nodes) - could reveal whether conditions like hypertension act as network bridges connecting distinct disease clusters, or if conditions such as diabetes exhibit "club effects" preferentially co-occurring within specific subsystems[ 15 ]. Third, while socioeconomic status (SES), environmental exposures, and lifestyle factors are implicated in multimorbidity risk[ 16 – 18 ], their differential impacts on network structure remain unexplored. Advanced relational analysis methods like Quadratic Assignment Procedure (QAP) regression, which accounts for network autocorrelation, could disentangle how macro-level determinants shape disease network architecture. This study addresses these knowledge gaps through three innovative approaches: 1) Constructing China's first nationally representative multimorbidity network map for adults ≥ 60 using data from the China Health and Retirement Longitudinal Study (CHARLS, n = 17,708); 2) Applying social network analysis metrics to quantify node centrality, network density, and cohesive subgroups; 3) Implementing QAP regression to identify socioecological determinants of network structure. Our findings will advance multimorbidity science by identifying critical bridge conditions requiring targeted interventions, disease clusters benefiting from integrated care pathways, socioecological drivers of network complexity. Guided by the Socioecological Model and Complex Systems Theory[ 19 , 20 ], we conceptualize multimorbidity as an emergent property of dynamic interactions across five levels: 1) Individual levels include genetic predispositions, epigenetic modifications. 2) Microsystem levels include health behaviors, treatment adherence. 3) Exosystem levels include healthcare access, pollution exposure. 4) Macrosystem levels include urban-rural disparities, pension policies. 5) Chronosystem levels include life-course accumulation of risk factors. This multilevel perspective enables identification of leverage points for network-level interventions, moving beyond current single-disease management paradigms. For clinical practice, network centrality metrics could prioritize conditions warranting intensified monitoring, while cohesive subgroup analysis may inform comorbidity screening protocols. Policy-wise, quantifying SES-network associations could guide resource allocation to regions with vulnerable network structures. Methods Study design and population This cross-sectional analysis utilized data from the seventh wave (2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a nationally representative cohort employing multistage stratified cluster sampling. The survey encompasses 23 provinces (covering 85% of China’s population), with inter-wave attrition rates ranging from 8.3–20.6%. Detailed methodological validation and quality assessments of the CLHLS have been previously documented[ 21 – 24 ]. The study protocol received ethical approval from Peking University’s Research Ethics Committee (IRB00001052-13074), and all participants provided written informed consent. From the initial sample of 15,874 adults aged ≥ 65 years, we applied the following exclusion criteria sequentially. 1) Exclusion of 5,080 participants with fewer than two chronic conditions. 2) Further exclusion of 3,041 individuals with missing chronic disease diagnoses or covariates. Finally, this yielded a final analytical sample of 7,753 older adults with validated multimorbidity profiles. Assessment of multimorbidity Twenty five chronic diseases were measured in the CLHLS, which included hypertension, diabetes, heart disease, stroke or CVD (cardiovascular disease), lung disease (including bronchitis, emphysema, pneumonia, asthma), tuberculosis, cataract, glaucoma, cancer, prostate tumor, gastric or duodenal ulcer, Parkinson’s disease, bedsore, arthritis, dementia, epilepsy, cholecystitis or cholelith disease, dyslipidemia, rheumatism or rheumatoid disease, chronic nephritis, mammary gland hyperplasia, uterine tumor, prostatic hyperplasia, hepatitis, and suffering. All diseases were measured dichotomously. Participants were given a score of “0” if they answered “no”, and “1” if they answered “yes”. Covariate Based on previous studies[ 25 – 28 ], covariate information covers demographic factors, psychological health conditions, health behaviors, and environmental factors. In the CLHLS questionnaire, demographic factors included age and gender with “0” coding “female” and “1” “male”. Psychological health conditions included self-reported quality of life, self-reported health (“very bad”, “bad”, “so so”, “good”, and “very good”), and depression (“rarely or never”, “seldom”, “sometimes”, “often”, and “always”), with “0” coding “very bad”, “bad”, “rarely or never”, “seldom”, and “1” coding “so so”, “good”, “very good”, “sometimes”, “often”, and “always”. Environmental factors included mildew odor in the home (with “0” coding “no” and “1” “yes”), ventilation of kitchen (with “0” coding “no ventilation measures”, “1” “lampblack exhauster”, “ventilating fan” and “natural windowing wentilation”), improving air quality (with “0” coding “no” and “1” “yes”), and cooking with charcoal (with “0” coding “no” and “1” “yes”), and health behaviors included sleep time with “0” coding “sleep time ≤ 7 hours, “1” “sleep time > 7 hours”, and living alone (with “1” coding “With household members”, “0” “alone” and “in an institution”). Network Analysis Methodology The multimorbidity network was constructed using Jaccard similarity coefficients to quantify disease co-occurrence patterns in older adults' binary classification data [ 29 ]. This weighted network was subsequently analyzed through an Ising model framework[ 30 , 31 ], which integrated logistic regression with goodness-of-fit measures to identify significant node relationships. Within this network paradigm, nodes represented individual diseases. Edges denoted disease associations, with visual properties encoding topological features:1) Node size proportional to degree centrality. 2) Edge thickness reflecting association strength. Network Characterization We evaluated both centrality metrics (degree, strength, eigenvector) and structural hole indices (effective size, efficiency, constraint, hierarchy) to assess node importance and brokerage potential [ 32 ]. Cohesive subgroup analysis[ 33 ] complemented these measures by identifying disease clusters with heightened interconnection probabilities. QAP Correlation Analysis Quadratic Assignment Procedure (QAP) analyses [ 34 ] examined network-environment relationships through three sequential steps: 1) QAP Correlation: Tested associations between multimorbidity matrices and characteristic matrices (demographic, psychological, behavioral, environmental). 2) QAP Regression: Modeled significant characteristic matrices (p < 0.05) as predictors of multimorbidity patterns. 3) Visualization: Network representations of significant associations accompanied by correlation heatmaps using a three-color scheme: Green spectrum and orange spectrum indicate significant positive correlations. Green has a stronger correlation than orange, in which the darker the color is, the stronger the correlation is. Red indicates non-significant associations. Analytical Workflow In the data analysis, 1) Descriptive Statistics and Jaccard Similarity were conducted by using:SPSS 25.0. 2) Network Construction and Visualization were achieved by using using UCINET 6. 3) Ising Model Implementation is conducted by using MATLAB R2016a (with node shrinkage). 4) Topological Analysis is achieved by using UCINET 6 (structural metrics) .5) Microsoft Excel was used to conduct the Heatmap Generation. Results Descriptive statistics A total of 7,753 participants were included in this study, with an average age of 85.46 years. The study comprised 4,372 females (56.4%) and 3,381 males (43.6%). The most prevalent diseases among the participants were hypertension, heart disease, and cataract, accounting for 39.4%, 19.5%, and 12.9% of cases, respectively. In contrast, the least frequently reported conditions were epilepsy and mammary gland hyperplasia, each representing only 0.3% of the total cases(Table 1). Network centrality analysis The network of multimorbidity among older adults, as estimated by the Ising model, was shown in Fig. 1. This network was organized around the complex of hypertension, diabetes, heart disease, stroke or CVD, lung disease, and tuberculosis all displaying high values of degree centrality, strength and eigenvector values (Fig. 1, Table 2). Network Structural hole and cohesive subgroup analysis We analyzed the structural hole of the multimorbidity network among older adults. Tuberculosis, epilepsy, and bedsores displayed high values of EffSize and efficiency. Constraint and Hierarchy for these diseases were also slightly higher than other diseases (Table 2). According to network cohesive subgroups analysis, the subgroup density matrix R-squared was 0.764 for the older adults's multimorbidity network. The network was divided into eight subgroups. Subgroup 1 comprised seven diseases (hypertension, heart disease, diabetes, stroke or CVD, lung disease, dyslipidemia, cataract, and suffering), exhibiting an internal density of 0.1. Subgroup 2(arthritic, gastric or duodenal ulcer, rheumatism or rheumatoid disease, cholecystitis or cholelith disease) and subgroup 8(mammary gland hyperplasia, chronic nephritis, hepatitis, uterine tumor) included four diseases, exhibiting an internal density of 0.086, 0.094 respectively. Subgroup 3(dementia, glaucoma), subgroup 4(prostate tumor, prostatic hyperplasia), subgroup 5 (parkinson, tuberculosis), and subgroup 7(epilepsy, bedsore) included two diseases, exhibiting an internal density of 0.025, 0.374, 0.034, 0039 respectively. While subgroup 6 (Cancer) included only one disease, exhibiting an internal density of 0. Moreover, the highest inter-subgroup connectivity were between Subgroups 1 and 2, with density of 0.069 (Table 3, Fig. 2). Table 2 Structural hole measures and centrality measures of multimorbidity network of older people degree strength eigenvector EffSize Efficiency Constraint Hierarchy hypertension 1.575 0.017 0.296 14.761 0.590 0.336 0.531 diabetes 1.527 0.011 0.255 15.281 0.611 0.340 0.570 heart disease 1.515 0.014 0.323 14.245 0.570 0.511 0.496 stroke or CVD 1.423 0.011 0.268 14.856 0.594 0.325 0.549 lung disease 1.398 0.010 0.229 15.545 0.622 0.340 0.596 tuberculosis 1.388 0.011 0.303 18.632 0.745 0.288 0.458 cataract 1.369 0.001 0.071 14.351 0.574 0.599 0.500 glaucoma 1.307 0.003 0.128 16.541 0.662 0.392 0.748 cancer 1.217 0.002 0.091 17.797 0.712 0.450 0.818 prostate tumor 1.127 0.007 0.277 14.559 0.582 0.339 0.563 gastric or duodenal ulcer 1.059 0.005 0.193 15.861 0.634 0.341 0.638 Parkinson 1.057 0.001 0.066 18.258 0.730 0.490 0.856 bedsore 1.033 0.001 0.053 18.310 0.732 0.307 0.464 arthritis 0.900 0.010 0.297 14.412 0.576 0.503 0.507 dementia 0.771 0.002 0.098 17.737 0.709 0.440 0.811 epilepsy 0.769 0.001 0.050 18.610 0.744 0.317 0.548 cholecystitis or cholelith disease 0.720 0.005 0.194 15.768 0.631 0.333 0.633 dyslipidemia 0.681 0.008 0.266 14.738 0.590 0.300 0.532 rheumatism or rheumatoid disease 0.663 0.006 0.206 15.549 0.622 0.349 0.628 chronic nephritis 0.616 0.002 0.129 16.436 0.657 0.366 0.703 mammary gland hyperplasia 0.593 0.001 0.073 17.228 0.689 0.462 0.759 uterine tumor 0.507 0.001 0.072 17.550 0.702 0.472 0.778 prostatic hyperplasia 0.504 0.006 0.263 14.716 0.589 0.342 0.579 hepatitis 0.487 0.001 0.073 17.292 0.692 0.446 0.777 suffering 0.470 0.006 0.153 17.265 0.691 0.401 0.725 EffSize: the effective size of the network Network correlation and regression analysis The correlation coefficients and significance levels between each matrix were obtained by using the QAP test. The correlation coefficients were visualized using a heatmap. The QAP test showed that the correlation coefficient between males, unimproving air quality, unventilation of the kitchen, and multimorbidity among older adults were 0.924, 0.799, 0.791(Fig. 3). Heatmap showed that cell of the correlation coefficient of male was green, cells of depression, no depression and females were red, and other cells were orange (Fig. 3). QAP regression analyses showed that the standardization regression coefficient of males, mildew oder in home, and sleep time > 7 hours was 0.802, 0.451, -0.368, in which significance level was less than 0.10(P < 0.10. The adjusted R2 is 0.887. (Table 4). Discussion Main finding of this study Four main findings emerged from this study. Firstly, the older multimorbidity network displayed distinct structural features, with pronounced disease correlations. Hypertension emerged as the most central diseases, significantly influencing the network's overall structure. Secondly, tuberculosis, epilepsy, bedsores, and dyslipidemia held structural hole advantages within the network, positioning them as the most influential diseases in shaping network dynamics. Thirdly, the network could be categorized into eight subgroups, each exhibiting varying degrees of inter-group and intra-group disease associations, highlighting the complexity of multimorbidity patterns. Lastly, gender, sleep duration, and household environment were identified as the primary factors influencing the structure and dynamics of the older multimorbidity network. What is already known on this topic Our findings are supported by numerous previous studies. Emerging evidence has identified distinct multimorbidity clusters across diverse populations, with studies revealing 3–5 phenotypic patterns, including cardiometabolic, respiratory-cancer, musculoskeletal, and neuropsychiatric configurations[ 35 – 38 ]. Hypertension remains the most prevalent chronic condition, exhibiting strong syndemic links with metabolic comorbidities such as dyslipidemia, obesity, and type 2 diabetes[ 39 ]. Despite its prevalence, critical gaps persist in understanding hypertension's trans-systemic pathophysiological linkages, particularly its associations with pulmonary disorders,cataracts, and chronic pain syndromes. The multimorbidity landscape is shaped by multiple determinants, including biological sex differences, environmental exposures (e.g., particulate matter air pollution), psychosocial stressors, modifiable lifestyle factors[ 40 – 42 ]. Notably, U-shaped sleep duration distributions exhibit differential risk profiles across the lifespan. Studies indicate that both ≤ 5h and ≥ 9h sleep durations are associated with incident multimorbidity in older adults (60–70 years), but not in middle-aged populations[ 42 ]. Gender disparities persist, with females demonstrating significantly higher susceptibility to multisystem diseases, while tobacco use emerges as a potent multisystem stressor across populations[ 41 ]. What this study adds This study systematically analyze the network structure of multimorbidity in older adults, employing advanced network analysis techniques to uncover complex disease interrelationships. Our findings reveal that hypertension is the most central diseases in the multimorbidity network. Hypertension demonstrated strong associations not only with diabetes, cardiovascular diseases (e.g., stroke, coronary heart disease) but also with cross-system comorbidities such as pulmonary diseases, cataracts, and chronic pain. These associations may reflect unique mechanisms in China’s aging population, offering new targets for intervention and resource allocation. We identify tuberculosis, epilepsy, bedsores, and dyslipidemia as diseases with structural hole advantages, indicating their roles as critical mediators across disease clusters, providing a novel perspective on disease influence and control. These insights extend the concept of structural holes in social networks to the field of multimorbidity, offering a theoretical framework for future research. Our findings also reveal subgroup heterogeneity of multimorbidity network in older adults. Subgroup 4 exhibits the strongest disease clustering between benign prostatic hyperplasia and prostate cancer, reflecting shared androgen-driven pathophysiological mechanisms[ 43 ]. The isolated cancer node in Subgroup 6 reflect underdiagnosis of comorbidities and psychosocial care shortcomings in oncology care[ 44 ]. High connectivity between Subgroups 1 and 2 suggests interactions between cardiometabolic diseases and musculoskeletal pain. Additionally, Male gender (β=+0.80) and household mildew odor (β=+0.45) significantly may increase network complexity through biological (e.g., androgen-driven IL-1β expression) [ 45 ] and environmental pathways (e.g., mycotoxins activating chronic inflammation) [ 46 , 47 ]. Sleep duration > 7 hours shows protective effects (β= -0.36) for multimorbidity, may mediate by immune system modulation, metabolic homeostasis, neurological and cognitive benefits, psychological resilience, and cellular repair mechanisms[ 48 – 53 ]. Practically, our findings can inform the design of targeted prevention and treatment strategies, potentially improving health outcomes for older adults. This study also highlights the need for longitudinal research to explore the temporal dynamics of multimorbidity networks and their response to interventions Limitations of this study The present study has several limitations that warrant consideration. 1)Selection Bias and Unmeasured Confounding Factors: The observational nature of the study introduces potential selection bias, particularly regarding disease types, specific etiology, and unmeasured confounding factors such as geographical and economic conditions. These limitations restrict a comprehensive analysis of the relationship between multimorbidity in older adults and the study outcomes.2)Data Source Constraints: The study relies on data records from specific institutional patient registries, which do not include information on multimorbidity in older patients with acute illnesses. Additionally, the registered data is subject to institutional review, potentially omitting multimorbidity cases that occurred prior to the start date of patient registration. This may result in missed diagnoses of physical conditions and injuries in older adults with multimorbidity. 3)Limited Scope of Chronic Diseases: Only a subset of chronic diseases was included in the study sample, which may lead to an underestimation of the physical health burden in older adults with multimorbidity. This is particularly relevant for individuals with mental disorders, intellectual disabilities, and cognitive impairments, whose conditions may not have been fully captured. 4)Outdated Data: The study uses data from 2018, which may not reflect the latest changes in health-related outcomes among older adults with multimorbidity. Further research utilizing more recent data is essential to determine whether the observed patterns of multimorbidity persist over time. Conclusion Our study reveals that multimorbidity in older adults is characterized by intricate and diverse interrelationships among various chronic diseases, and shaped by a gender, household environment and sleep duration. We can intervene in the following ways: 1) Primary Prevention: Bridge Diseases: Launch tuberculosis screening campaigns aligned with World Tuberculosis Day. Environmental Mitigation: Promote housing mildew removal subsidies. 2) Clinical Integration: Establish "Cardio-Metabolic-Pain" integrated clinics. Develop cancer comorbidity alert systems merging pathology and complication databases. 3) Policy Innovations: Incorporate multimorbidity network metrics into the “National Chronic Disease Comprehensive Prevention and Control Demonstration Zone” evaluation framework. Reform insurance reimbursement to incentivize interdisciplinary care. Declarations Author Contribution All authors read and approved the final manuscript and made contributions to the study conception or design. H Ch participated in the conceptualization, funding acquisition, visualization and writing (original draft, review and editing) L H participated in the conceptualization, project administration, supervision and validation. W W carried out formal analysis and development or design of methodology. T L participated in the investigation and provision of study materials. Declaration of competing interests ： The authors declare no conflict of interest. Funding statement ： This work was supported by the Clinical Nursing Research Fund Program of Second Xiangya Hospital of Central South Univesity (2022-HLKY-15), Science Fund Program for Health Commission of Hunan Province (20200113), Youth Fund of Natural Science Foundation of Hunan Province (2019JJ50865). Ethics approval and informed consent statements ：The Chinese Longitudinal Healthy Longevity Survey (CLHLS) is publicly available, and all procedures involving research study participants were approved by the biomedical ethics committee of Peking University (IRB00001052–24713074). Data availability statement ： The datasets generated or analyzed during this study are vailable in the [CLHLS] repository, [ CLHLS_2018_cross_sectional_dataset_15874.rar]. CLHLS Website: https://opendata. pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK Consent for publication ： Consent for publication is not applicable in this study, because there is not any individual person’s data. Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Images statement All the images in the article are our own. References WHO. Multimorbidity: Technical Series on Safer Primary Care. Geneva, 2016. 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Identifying multimorbidity clusters among Brazilian older adults using network analysis: Findings and perspectives. PLoS One. 2022 Jul 20;17(7):e0271639. doi: 10.1371/journal.pone.0271639. PMID: 35857809; PMCID: PMC9299350. Larvin H, Kang J, Aggarwal VR, Pavitt S, Wu J. Systemic Multimorbidity Clusters in People with Periodontitis. J Dent Res. 2022 Oct;101(11):1335-1342. doi: 10.1177/00220345221098910. Epub 2022 Jun 9. PMID: 35678074; PMCID: PMC9516606. Tran TN, Lee S, Oh CM, Cho H. Multimorbidity patterns by health-related quality of life status in older adults: an association rules and network analysis utilizing the Korea National Health and Nutrition Examination Survey. Epidemiol Health. 2022;44:e2022113. doi: 10.4178/epih.e2022113. Epub 2022 Nov 29. PMID: 36470261; PMCID: PMC10185967. Kuan V, Denaxas S, Patalay P, et al. Multimorbidity Mechanism and Therapeutic Research Collaborative (MMTRC). Identifying and visualizing multimorbidity and comorbidity patterns in patients in the English National Health Service: a population-based study. Lancet Digit Health. 2023 Jan;5(1):e16-e27. doi: 10.1016/S2589-7500(22)00187-X. Epub 2022 Nov 29. PMID: 36460578. Ramos-Vera C, Saintila J, O'Diana AG, Calizaya-Milla YE. Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system. Front Public Health. 2022 Sep 20;10:981944. doi: 10.3389/fpubh.2022.981944. PMID: 36203679; PMCID: PMC9530468. Chen W, Wang X, Chen J, et al. Household air pollution, adherence to a healthy lifestyle, and risk of cardiometabolic multimorbidity: Results from the China health and retirement longitudinal study. Sci Total Environ. 2023 Jan 10;855:158896. doi: 10.1016/j.scitotenv.2022.158896. Epub 2022 Sep 20. PMID: 36150596. Tazzeo C, Zucchelli A, Vetrano DL, et al. Risk factors for multimorbidity in adulthood: A systematic review. Ageing Res Rev. 2023 Nov;91:102039. doi: 10.1016/j.arr.2023.102039. Epub 2023 Aug 28. PMID: 37647994. Sabia S, Dugravot A, Léger D, Ben Hassen C, Kivimaki M, Singh-Manoux A. Association of sleep duration at age 50, 60, and 70 years with risk of multimorbidity in the UK: 25-year follow-up of the Whitehall II cohort study. PLoS Med. 2022 Oct 18;19(10):e1004109. doi: 10.1371/journal.pmed.1004109. PMID: 36256607; PMCID: PMC9578599. Cao D, Sun R, Peng L, et al. Immune Cell Proinflammatory Microenvironment and Androgen-Related Metabolic Regulation During Benign Prostatic Hyperplasia in Aging. Front Immunol. 2022 Mar 21;13:842008. doi: 10.3389/fimmu.2022.842008. PMID: 35386711; PMCID: PMC8977548. Raphael MJ, Gupta S, Wei X, et al. Long-Term Mental Health Service Utilization Among Survivors of Testicular Cancer: A Population-Based Cohort Study. J Clin Oncol. 2021 Mar 1;39(7):779-786. doi: 10.1200/JCO.20.02298. Epub 2021 Jan 28. PMID: 33507821. Wang D, Cheng C, Chen X,et al. IL-1β Is an Androgen-Responsive Target in Macrophages for Immunotherapy of Prostate Cancer. Adv Sci (Weinh). 2023 Jun;10(17):e2206889. doi: 10.1002/advs.202206889. Epub 2023 Apr 24. PMID: 37092583; PMCID: PMC10265092. Pan H, Hu T, He Y, et al. Curcumin attenuates aflatoxin B1-induced ileum injury in ducks by inhibiting NLRP3 inflammasome and regulating TLR4/NF-κB signaling pathway. Mycotoxin Res. 2024 May;40(2):255-268. doi: 10.1007/s12550-024-00524-7. Epub 2024 Feb 24. PMID: 38400893. Deng Y, Chen H, Wu Y, Yuan J, Shi Q, Tong P, Gao J. Aflatoxin B1 can aggravate BALB/c mice allergy to ovalbumin through changing their Th2 cells immune responses. Toxicon. 2023 Jun 1;228:107121. doi: 10.1016/j.toxicon.2023.107121. Epub 2023 Apr 14. PMID: 37062343. Sabia S, Dugravot A, Léger D, et al. Association of sleep duration at age 50, 60, and 70 years with risk of multimorbidity in the UK: 25-year follow-up of the Whitehall II cohort study. PLoS Med. 2022 Oct 18;19(10):e1004109. doi: 10.1371/journal.pmed.1004109. PMID: 36256607; PMCID: PMC9578599. Sousa MEP, Gonzatti MB, Fernandes ER, et al. Invariant Natural Killer T cells resilience to paradoxical sleep deprivation-associated stress. Brain Behav Immun. 2020 Nov;90:208-215. doi: 10.1016/j.bbi.2020.08.018. Epub 2020 Aug 19. PMID: 32827702. Zuraikat FM, Laferrère B, Cheng B, et al. Chronic Insufficient Sleep in Women Impairs Insulin Sensitivity Independent of Adiposity Changes: Results of a Randomized Trial. Diabetes Care. 2024 Jan 1;47(1):117-125. doi: 10.2337/dc23-1156. PMID: 37955852; PMCID: PMC10733650. Ju YS, Ooms SJ, Sutphen C, et al. Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain. 2017 Aug 1;140(8):2104-2111. doi: 10.1093/brain/awx148. PMID: 28899014; PMCID: PMC5790144. van Dalfsen JH, Markus CR. The influence of sleep on human hypothalamic-pituitary-adrenal (HPA) axis reactivity: A systematic review. Sleep Med Rev. 2018 Jun;39:187-194. doi: 10.1016/j.smrv.2017.10.002. Epub 2017 Oct 18. PMID: 29126903. Zada D, Sela Y, Matosevich N, et al. Parp1 promotes sleep, which enhances DNA repair in neurons. Mol Cell. 2021 Dec 16;81(24):4979-4993.e7. doi: 10.1016/j.molcel.2021.10.026. Epub 2021 Nov 18. PMID: 34798058; PMCID: PMC8688325. Table 1,3,4 Table 1,3,4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table134.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 25 Sep, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 29 May, 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-6777465","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470411990,"identity":"c91b1871-de12-4fbc-9682-b4cc76f13596","order_by":0,"name":"huaying chen","email":"","orcid":"","institution":"Clinical Nursing Teaching and Research Section of Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"huaying","middleName":"","lastName":"chen","suffix":""},{"id":470411991,"identity":"17b73b20-c3af-4893-a627-cadfdc415251","order_by":1,"name":"Weihong Wang","email":"","orcid":"","institution":"Nursing College of Hunan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Wang","suffix":""},{"id":470411992,"identity":"4416d531-92a8-492a-9ec7-ebed2bce4082","order_by":2,"name":"Ting Luo","email":"","orcid":"","institution":"Clinical Nursing Teaching and Research Section of Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Luo","suffix":""},{"id":470411993,"identity":"a4deb441-e58a-442f-b237-4f50f2257694","order_by":3,"name":"Lingzhi Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACAzBZIAEkeBgOfEAWxK/FAKLl4AwStDCAtTDzEKPFXCL52cMvBhZ58v5nDx62qalLbGBv3ibBUHMHpxbLGWnmxjIGEsWGB84lHM45djixgedYmQTDsWe4HXYjwUxawkAicWNjj8Hh3IYDiQ0SOWYSjA2H8WhJ/wbR0sxjcNiyAegw+TeEtOSYSX4AapnPBtTC2MAMtIWHgJYzb8qkgYGcuIGHx+Bgz7HDxm08acUWCcfwaDmevk3yR0Vd4vz+M8YfftTUyfazH95440MNbi0gAI4OgwNQHhuISMCrgYGB8QeQkG8goGoUjIJRMApGLgAAIo1U2q66FGcAAAAASUVORK5CYII=","orcid":"","institution":"Clinical Nursing Teaching and Research Section of Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Lingzhi","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-05-29 14:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6777465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6777465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84578430,"identity":"d87bc9d3-46ca-47b2-9914-5ec31cb42b81","added_by":"auto","created_at":"2025-06-13 17:41:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":335391,"visible":true,"origin":"","legend":"\u003cp\u003eMultimorbidity network of older people. The network represents the relationships between 25chronic diseases in the older people. In the diagram disease nodesrepresent degree centrality. The node size is proportional to degree centrality size. Lines between nodes (edges) represent correlations .The edge thickness is proportional to the strength of the association between disease nodes.cvd: cardiovascular disease.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6777465/v1/0188a9272f4b5cb74a82b9f9.png"},{"id":84578676,"identity":"a3a4fa1e-b7a2-4157-b24f-081b4ab4f0eb","added_by":"auto","created_at":"2025-06-13 17:49:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":324650,"visible":true,"origin":"","legend":"\u003cp\u003eCohesive subgroups tree plot of multimorbidity network of older people\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6777465/v1/7ad3a4a49e0fd286d0d14d93.png"},{"id":84579459,"identity":"4a3709ad-7a8e-4063-9fa2-b24c88a13663","added_by":"auto","created_at":"2025-06-13 18:05:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1644356,"visible":true,"origin":"","legend":"\u003cp\u003eQAP correlation analysis heat map of multimorbidity network of older people\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6777465/v1/45260da0471f8bb40db95a1c.png"},{"id":84579819,"identity":"5922cda2-14a6-4d4b-8d65-39f038e0699a","added_by":"auto","created_at":"2025-06-13 18:13:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2632752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6777465/v1/be3c45b7-5fbe-423a-9e57-f865ecf735b6.pdf"},{"id":84578431,"identity":"ed6cd647-b8cf-40bb-aa63-d6f5cef0ae75","added_by":"auto","created_at":"2025-06-13 17:41:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45613,"visible":true,"origin":"","legend":"","description":"","filename":"Table134.docx","url":"https://assets-eu.researchsquare.com/files/rs-6777465/v1/4aff54d0f78f45c6eab35b57.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimorbidity Networks in Older Adults：Structural Characteristics and Socioecological Determinants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity, defined as the co-occurrence of two or more chronic conditions within an individual[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], has emerged as a critical public health challenge in aging populations. Epidemiological studies reveal a striking age-dependent progression: while 15\u0026ndash;20% of children exhibit early multimorbidity patterns[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], prevalence escalates exponentially after middle age, affecting over 60% of adults\u0026thinsp;\u0026ge;\u0026thinsp;65 years in high-income countries[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This complex health phenomenon transcends simple disease aggregation, manifesting as dynamic networks where conditions interact through shared pathophysiological pathways, treatment cascades, and psychosocial mediators[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The resultant health burden is multiplicative rather than additive - multimorbid older adults face 2.3-fold higher risks of functional decline[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], 4.1 times greater hospitalization rates[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and 38% increased mortality compared to single-disease counterparts[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], imposing substantial strain on healthcare systems and caregivers[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research paradigms exhibit critical gaps requiring urgent attention. First, \u0026lt;\u0026thinsp;1% of multimorbidity studies employ network science approaches despite their unique capacity to model disease-disease interactions and identify critical network nodes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Traditional pattern-based classifications (e.g., cardiometabolic or musculoskeletal clusters[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]) often overlook the strength, directionality, and temporal evolution of inter-disease connections. Second, the structural topology of geriatric multimorbidity networks remains poorly characterized. Key network metrics - including centrality (influence of specific diseases), structural holes (information brokerage potential), and eigenvector centrality (connections to influential nodes) - could reveal whether conditions like hypertension act as network bridges connecting distinct disease clusters, or if conditions such as diabetes exhibit \"club effects\" preferentially co-occurring within specific subsystems[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Third, while socioeconomic status (SES), environmental exposures, and lifestyle factors are implicated in multimorbidity risk[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], their differential impacts on network structure remain unexplored. Advanced relational analysis methods like Quadratic Assignment Procedure (QAP) regression, which accounts for network autocorrelation, could disentangle how macro-level determinants shape disease network architecture.\u003c/p\u003e \u003cp\u003eThis study addresses these knowledge gaps through three innovative approaches: 1) Constructing China's first nationally representative multimorbidity network map for adults\u0026thinsp;\u0026ge;\u0026thinsp;60 using data from the China Health and Retirement Longitudinal Study (CHARLS, n\u0026thinsp;=\u0026thinsp;17,708); 2) Applying social network analysis metrics to quantify node centrality, network density, and cohesive subgroups; 3) Implementing QAP regression to identify socioecological determinants of network structure. Our findings will advance multimorbidity science by identifying critical bridge conditions requiring targeted interventions, disease clusters benefiting from integrated care pathways, socioecological drivers of network complexity.\u003c/p\u003e \u003cp\u003eGuided by the Socioecological Model and Complex Systems Theory[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we conceptualize multimorbidity as an emergent property of dynamic interactions across five levels: 1) Individual levels include genetic predispositions, epigenetic modifications. 2) Microsystem levels include health behaviors, treatment adherence. 3) Exosystem levels include healthcare access, pollution exposure. 4) Macrosystem levels include urban-rural disparities, pension policies. 5) Chronosystem levels include life-course accumulation of risk factors.\u003c/p\u003e \u003cp\u003eThis multilevel perspective enables identification of leverage points for network-level interventions, moving beyond current single-disease management paradigms. For clinical practice, network centrality metrics could prioritize conditions warranting intensified monitoring, while cohesive subgroup analysis may inform comorbidity screening protocols. Policy-wise, quantifying SES-network associations could guide resource allocation to regions with vulnerable network structures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis cross-sectional analysis utilized data from the seventh wave (2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a nationally representative cohort employing multistage stratified cluster sampling. The survey encompasses 23 provinces (covering 85% of China\u0026rsquo;s population), with inter-wave attrition rates ranging from 8.3\u0026ndash;20.6%. Detailed methodological validation and quality assessments of the CLHLS have been previously documented[\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study protocol received ethical approval from Peking University\u0026rsquo;s Research Ethics Committee (IRB00001052-13074), and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003eFrom the initial sample of 15,874 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, we applied the following exclusion criteria sequentially. 1) Exclusion of 5,080 participants with fewer than two chronic conditions. 2) Further exclusion of 3,041 individuals with missing chronic disease diagnoses or covariates. Finally, this yielded a final analytical sample of 7,753 older adults with validated multimorbidity profiles.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of multimorbidity\u003c/h3\u003e\n\u003cp\u003eTwenty five chronic diseases were measured in the CLHLS, which included hypertension, diabetes, heart disease, stroke or CVD (cardiovascular disease), lung disease (including bronchitis, emphysema, pneumonia, asthma), tuberculosis, cataract, glaucoma, cancer, prostate tumor, gastric or duodenal ulcer, Parkinson\u0026rsquo;s disease, bedsore, arthritis, dementia, epilepsy, cholecystitis or cholelith disease, dyslipidemia, rheumatism or rheumatoid disease, chronic nephritis, mammary gland hyperplasia, uterine tumor, prostatic hyperplasia, hepatitis, and suffering. All diseases were measured dichotomously. Participants were given a score of \u0026ldquo;0\u0026rdquo; if they answered \u0026ldquo;no\u0026rdquo;, and \u0026ldquo;1\u0026rdquo; if they answered \u0026ldquo;yes\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eCovariate\u003c/h3\u003e\n\u003cp\u003eBased on previous studies[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], covariate information covers demographic factors, psychological health conditions, health behaviors, and environmental factors. In the CLHLS questionnaire, demographic factors included age and gender with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;female\u0026rdquo; and \u0026ldquo;1\u0026rdquo; \u0026ldquo;male\u0026rdquo;. Psychological health conditions included self-reported quality of life, self-reported health (\u0026ldquo;very bad\u0026rdquo;, \u0026ldquo;bad\u0026rdquo;, \u0026ldquo;so so\u0026rdquo;, \u0026ldquo;good\u0026rdquo;, and \u0026ldquo;very good\u0026rdquo;), and depression (\u0026ldquo;rarely or never\u0026rdquo;, \u0026ldquo;seldom\u0026rdquo;, \u0026ldquo;sometimes\u0026rdquo;, \u0026ldquo;often\u0026rdquo;, and \u0026ldquo;always\u0026rdquo;), with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;very bad\u0026rdquo;, \u0026ldquo;bad\u0026rdquo;, \u0026ldquo;rarely or never\u0026rdquo;, \u0026ldquo;seldom\u0026rdquo;, and \u0026ldquo;1\u0026rdquo; coding \u0026ldquo;so so\u0026rdquo;, \u0026ldquo;good\u0026rdquo;, \u0026ldquo;very good\u0026rdquo;, \u0026ldquo;sometimes\u0026rdquo;, \u0026ldquo;often\u0026rdquo;, and \u0026ldquo;always\u0026rdquo;. Environmental factors included mildew odor in the home (with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;no\u0026rdquo; and \u0026ldquo;1\u0026rdquo; \u0026ldquo;yes\u0026rdquo;), ventilation of kitchen (with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;no ventilation measures\u0026rdquo;, \u0026ldquo;1\u0026rdquo; \u0026ldquo;lampblack exhauster\u0026rdquo;, \u0026ldquo;ventilating fan\u0026rdquo; and \u0026ldquo;natural windowing wentilation\u0026rdquo;), improving air quality (with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;no\u0026rdquo; and \u0026ldquo;1\u0026rdquo; \u0026ldquo;yes\u0026rdquo;), and cooking with charcoal (with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;no\u0026rdquo; and \u0026ldquo;1\u0026rdquo; \u0026ldquo;yes\u0026rdquo;), and health behaviors included sleep time with \u0026ldquo;0\u0026rdquo; coding \u0026ldquo;sleep time\u0026thinsp;\u0026le;\u0026thinsp;7 hours, \u0026ldquo;1\u0026rdquo; \u0026ldquo;sleep time\u0026thinsp;\u0026gt;\u0026thinsp;7 hours\u0026rdquo;, and living alone (with \u0026ldquo;1\u0026rdquo; coding \u0026ldquo;With household members\u0026rdquo;, \u0026ldquo;0\u0026rdquo; \u0026ldquo;alone\u0026rdquo; and \u0026ldquo;in an institution\u0026rdquo;).\u003c/p\u003e\n\u003ch3\u003eNetwork Analysis Methodology\u003c/h3\u003e\n\u003cp\u003eThe multimorbidity network was constructed using Jaccard similarity coefficients to quantify disease co-occurrence patterns in older adults' binary classification data [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This weighted network was subsequently analyzed through an Ising model framework[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which integrated logistic regression with goodness-of-fit measures to identify significant node relationships. Within this network paradigm, nodes represented individual diseases. Edges denoted disease associations, with visual properties encoding topological features:1) Node size proportional to degree centrality. 2) Edge thickness reflecting association strength.\u003c/p\u003e\n\u003ch3\u003eNetwork Characterization\u003c/h3\u003e\n\u003cp\u003eWe evaluated both centrality metrics (degree, strength, eigenvector) and structural hole indices (effective size, efficiency, constraint, hierarchy) to assess node importance and brokerage potential [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Cohesive subgroup analysis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] complemented these measures by identifying disease clusters with heightened interconnection probabilities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQAP Correlation Analysis\u003c/h2\u003e \u003cp\u003eQuadratic Assignment Procedure (QAP) analyses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] examined network-environment relationships through three sequential steps: 1) QAP Correlation: Tested associations between multimorbidity matrices and characteristic matrices (demographic, psychological, behavioral, environmental). 2) QAP Regression: Modeled significant characteristic matrices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as predictors of multimorbidity patterns. 3) Visualization: Network representations of significant associations accompanied by correlation heatmaps using a three-color scheme: Green spectrum and orange spectrum indicate significant positive correlations. Green has a stronger correlation than orange, in which the darker the color is, the stronger the correlation is. Red indicates non-significant associations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalytical Workflow\u003c/h3\u003e\n\u003cp\u003eIn the data analysis, 1) Descriptive Statistics and Jaccard Similarity were conducted by using:SPSS 25.0. 2) Network Construction and Visualization were achieved by using using UCINET 6. 3) Ising Model Implementation is conducted by using MATLAB R2016a (with node shrinkage). 4) Topological Analysis is achieved by using UCINET 6 (structural metrics) .5) Microsoft Excel was used to conduct the Heatmap Generation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n \u003cp\u003eA total of 7,753 participants were included in this study, with an average age of 85.46 years. The study comprised 4,372 females (56.4%) and 3,381 males (43.6%). The most prevalent diseases among the participants were hypertension, heart disease, and cataract, accounting for 39.4%, 19.5%, and 12.9% of cases, respectively. In contrast, the least frequently reported conditions were epilepsy and mammary gland hyperplasia, each representing only 0.3% of the total cases(Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eNetwork centrality analysis\u003c/h2\u003e\n \u003cp\u003eThe network of multimorbidity among older adults, as estimated by the Ising model, was shown in Fig. 1. This network was organized around the complex of hypertension, diabetes, heart disease, stroke or CVD, lung disease, and tuberculosis all displaying high values of degree centrality, strength and eigenvector values (Fig. 1, Table 2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eNetwork Structural hole and cohesive subgroup analysis\u003c/h2\u003e\n \u003cp\u003eWe analyzed the structural hole of the multimorbidity network among older adults. Tuberculosis, epilepsy, and bedsores displayed high values of EffSize and efficiency. Constraint and Hierarchy for these diseases were also slightly higher than other diseases (Table 2).\u003c/p\u003e\n \u003cp\u003eAccording to network cohesive subgroups analysis, the subgroup density matrix R-squared was 0.764 for the older adults's multimorbidity network. The network was divided into eight subgroups. Subgroup 1 comprised seven diseases (hypertension, heart disease, diabetes, stroke or CVD, lung disease, dyslipidemia, cataract, and suffering), exhibiting an internal density of 0.1. Subgroup 2(arthritic, gastric or duodenal ulcer, rheumatism or rheumatoid disease, cholecystitis or cholelith disease) and subgroup 8(mammary gland hyperplasia, chronic nephritis, hepatitis, uterine tumor) included four diseases, exhibiting an internal density of 0.086, 0.094 respectively. Subgroup 3(dementia, glaucoma), subgroup 4(prostate tumor, prostatic hyperplasia), subgroup 5 (parkinson, tuberculosis), and subgroup 7(epilepsy, bedsore) included two diseases, exhibiting an internal density of 0.025, 0.374, 0.034, 0039 respectively. While subgroup 6 (Cancer) included only one disease, exhibiting an internal density of 0. Moreover, the highest inter-subgroup connectivity were between Subgroups 1 and 2, with density of 0.069 (Table 3, Fig. 2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eStructural hole measures and centrality measures of multimorbidity network of older people\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003edegree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003estrength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeigenvector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEffSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eEfficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eConstraint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHierarchy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eheart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003estroke or CVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etuberculosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecataract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eglaucoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprostate tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003egastric or duodenal ulcer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParkinson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ebedsore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003earthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eepilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003echolecystitis or cholelith disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003erheumatism or rheumatoid disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003echronic nephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emammary gland hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003euterine tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprostatic hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esuffering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eEffSize: the effective size of the network\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eNetwork correlation and regression analysis\u003c/h2\u003e\n \u003cp\u003eThe correlation coefficients and significance levels between each matrix were obtained by using the QAP test. The correlation coefficients were visualized using a heatmap. The QAP test showed that the correlation coefficient between males, unimproving air quality, unventilation of the kitchen, and multimorbidity among older adults were 0.924, 0.799, 0.791(Fig. 3). Heatmap showed that cell of the correlation coefficient of male was green, cells of depression, no depression and females were red, and other cells were orange (Fig. 3). QAP regression analyses showed that the standardization regression coefficient of males, mildew oder in home, and sleep time \u0026gt; 7 hours was 0.802, 0.451, -0.368, in which significance level was less than 0.10(P \u0026lt; 0.10. The adjusted R2 is 0.887. (Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMain finding of this study\u003c/h2\u003e \u003cp\u003eFour main findings emerged from this study. Firstly, the older multimorbidity network displayed distinct structural features, with pronounced disease correlations. Hypertension emerged as the most central diseases, significantly influencing the network's overall structure. Secondly, tuberculosis, epilepsy, bedsores, and dyslipidemia held structural hole advantages within the network, positioning them as the most influential diseases in shaping network dynamics. Thirdly, the network could be categorized into eight subgroups, each exhibiting varying degrees of inter-group and intra-group disease associations, highlighting the complexity of multimorbidity patterns. Lastly, gender, sleep duration, and household environment were identified as the primary factors influencing the structure and dynamics of the older multimorbidity network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWhat is already known on this topic\u003c/h2\u003e \u003cp\u003eOur findings are supported by numerous previous studies. Emerging evidence has identified distinct multimorbidity clusters across diverse populations, with studies revealing 3\u0026ndash;5 phenotypic patterns, including cardiometabolic, respiratory-cancer, musculoskeletal, and neuropsychiatric configurations[\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hypertension remains the most prevalent chronic condition, exhibiting strong syndemic links with metabolic comorbidities such as dyslipidemia, obesity, and type 2 diabetes[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Despite its prevalence, critical gaps persist in understanding hypertension's trans-systemic pathophysiological linkages, particularly its associations with pulmonary disorders,cataracts, and chronic pain syndromes. The multimorbidity landscape is shaped by multiple determinants, including biological sex differences, environmental exposures (e.g., particulate matter air pollution), psychosocial stressors, modifiable lifestyle factors[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Notably, U-shaped sleep duration distributions exhibit differential risk profiles across the lifespan. Studies indicate that both \u0026le;\u0026thinsp;5h and \u0026ge;\u0026thinsp;9h sleep durations are associated with incident multimorbidity in older adults (60\u0026ndash;70 years), but not in middle-aged populations[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Gender disparities persist, with females demonstrating significantly higher susceptibility to multisystem diseases, while tobacco use emerges as a potent multisystem stressor across populations[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWhat this study adds\u003c/h2\u003e \u003cp\u003eThis study systematically analyze the network structure of multimorbidity in older adults, employing advanced network analysis techniques to uncover complex disease interrelationships. Our findings reveal that hypertension is the most central diseases in the multimorbidity network. Hypertension demonstrated strong associations not only with diabetes, cardiovascular diseases (e.g., stroke, coronary heart disease) but also with cross-system comorbidities such as pulmonary diseases, cataracts, and chronic pain. These associations may reflect unique mechanisms in China\u0026rsquo;s aging population, offering new targets for intervention and resource allocation.\u003c/p\u003e \u003cp\u003eWe identify tuberculosis, epilepsy, bedsores, and dyslipidemia as diseases with structural hole advantages, indicating their roles as critical mediators across disease clusters, providing a novel perspective on disease influence and control. These insights extend the concept of structural holes in social networks to the field of multimorbidity, offering a theoretical framework for future research. Our findings also reveal subgroup heterogeneity of multimorbidity network in older adults. Subgroup 4 exhibits the strongest disease clustering between benign prostatic hyperplasia and prostate cancer, reflecting shared androgen-driven pathophysiological mechanisms[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The isolated cancer node in Subgroup 6 reflect underdiagnosis of comorbidities and psychosocial care shortcomings in oncology care[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. High connectivity between Subgroups 1 and 2 suggests interactions between cardiometabolic diseases and musculoskeletal pain.\u003c/p\u003e \u003cp\u003eAdditionally, Male gender (β=+0.80) and household mildew odor (β=+0.45) significantly may increase network complexity through biological (e.g., androgen-driven IL-1β expression) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and environmental pathways (e.g., mycotoxins activating chronic inflammation) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Sleep duration\u0026thinsp;\u0026gt;\u0026thinsp;7 hours shows protective effects (β= -0.36) for multimorbidity, may mediate by immune system modulation, metabolic homeostasis, neurological and cognitive benefits, psychological resilience, and cellular repair mechanisms[\u003cspan additionalcitationids=\"CR49 CR50 CR51 CR52\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Practically, our findings can inform the design of targeted prevention and treatment strategies, potentially improving health outcomes for older adults. This study also highlights the need for longitudinal research to explore the temporal dynamics of multimorbidity networks and their response to interventions\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of this study\u003c/h2\u003e \u003cp\u003eThe present study has several limitations that warrant consideration. 1)Selection Bias and Unmeasured Confounding Factors: The observational nature of the study introduces potential selection bias, particularly regarding disease types, specific etiology, and unmeasured confounding factors such as geographical and economic conditions. These limitations restrict a comprehensive analysis of the relationship between multimorbidity in older adults and the study outcomes.2)Data Source Constraints: The study relies on data records from specific institutional patient registries, which do not include information on multimorbidity in older patients with acute illnesses. Additionally, the registered data is subject to institutional review, potentially omitting multimorbidity cases that occurred prior to the start date of patient registration. This may result in missed diagnoses of physical conditions and injuries in older adults with multimorbidity. 3)Limited Scope of Chronic Diseases: Only a subset of chronic diseases was included in the study sample, which may lead to an underestimation of the physical health burden in older adults with multimorbidity. This is particularly relevant for individuals with mental disorders, intellectual disabilities, and cognitive impairments, whose conditions may not have been fully captured. 4)Outdated Data: The study uses data from 2018, which may not reflect the latest changes in health-related outcomes among older adults with multimorbidity. Further research utilizing more recent data is essential to determine whether the observed patterns of multimorbidity persist over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study reveals that multimorbidity in older adults is characterized by intricate and diverse interrelationships among various chronic diseases, and shaped by a gender, household environment and sleep duration. We can intervene in the following ways: 1) Primary Prevention: Bridge Diseases: Launch tuberculosis screening campaigns aligned with World Tuberculosis Day. Environmental Mitigation: Promote housing mildew removal subsidies. 2) Clinical Integration: Establish \"Cardio-Metabolic-Pain\" integrated clinics. Develop cancer comorbidity alert systems merging pathology and complication databases. 3) Policy Innovations: Incorporate multimorbidity network metrics into the \u0026ldquo;National Chronic Disease Comprehensive Prevention and Control Demonstration Zone\u0026rdquo; evaluation framework. Reform insurance reimbursement to incentivize interdisciplinary care.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript and made contributions to the study conception or design.\u003c/p\u003e\n\u003cp\u003eH Ch participated in the conceptualization, funding acquisition, visualization and writing (original draft, review and editing)\u003c/p\u003e\n\u003cp\u003eL H participated in the conceptualization, project administration, supervision and validation.\u003c/p\u003e\n\u003cp\u003eW W carried out formal analysis and development or design of methodology.\u003c/p\u003e\n\u003cp\u003eT L participated in the investigation and provision of study materials.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis work was supported by the Clinical Nursing Research Fund Program of Second Xiangya Hospital of Central South Univesity (2022-HLKY-15), Science Fund Program for Health Commission of Hunan Province (20200113), Youth Fund of Natural Science Foundation of Hunan Province (2019JJ50865).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and informed consent statements\u003c/strong\u003e：The Chinese Longitudinal Healthy Longevity Survey (CLHLS) is publicly available, and all procedures involving research study participants were approved by the biomedical ethics committee of Peking University (IRB00001052\u0026ndash;24713074).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe datasets generated or analyzed during this study are vailable in the [CLHLS] repository, [ CLHLS_2018_cross_sectional_dataset_15874.rar]. CLHLS Website: https://opendata. pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eConsent for publication is not applicable in this study, because there is not any individual person\u0026rsquo;s data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePatient and public involvement:\u0026nbsp;\u003c/strong\u003ePatients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.\u003c/p\u003e\u003cp\u003eImages statement\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All the images in the article are our own.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Multimorbidity: Technical Series on Safer Primary Care. Geneva, 2016.\u003c/li\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, et al. 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PMID: 34798058; PMCID: PMC8688325.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1,3,4 ","content":"\u003cp\u003eTable 1,3,4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"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-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multimorbidity, older adults, network characteristics, socioecological determinants","lastPublishedDoi":"10.21203/rs.3.rs-6777465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6777465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eResearch of multimorbidity network for older adults (\u0026ge;\u0026thinsp;65years) based on nationwide is concerningly scare. The aim of this study was to investigate the characteristics of the network structure of multiple chronic diseases among the elderly population in China and its socioecological determinants.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed cross-sectional data from wave 7 of the 23 provinces Survey of Chinese Longitudinal Healthy Longevity Survey (CLHLS) in 2018.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe older multimorbidity network displayed distinct structural features. Hypertension has the highest incidence rate and is the most central disease in the network. Tuberculosis, epilepsy, and bedsores had the highest network effective sizes and efficiency. However, tuberculosis, bedsores, and dyslipidemia had the lowest constraint and hierarchy. The multimorbidity network for old adults could be divided into eight subgroups, with varying degrees associations of inter-group and intra-group diseases. The regression coefficients for male gender and mildew odor in the home were positive, while the coefficient for sleep duration\u0026thinsp;\u0026gt;\u0026thinsp;7 hours was negative. The significance tests showed that all coefficients had p-values less than 0.05.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe multimorbidity network for older adults exhibits typical network structure characteristics, which are influenced by factors such as gender, family environment, and sleep time. These findings highlight the need for collaborative efforts from families, health care system, and policymakers to improve quality of life in the older adultswith multimorbidity.\u003c/p\u003e","manuscriptTitle":"Multimorbidity Networks in Older Adults：Structural Characteristics and Socioecological Determinants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 17:41:11","doi":"10.21203/rs.3.rs-6777465/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2025-09-26T03:32:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"62265729016388449458088921509555063894","date":"2025-06-12T13:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T13:08:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T08:36:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T08:34:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-05-29T14:14:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"511e4b9a-9382-4cee-a717-d33f9da6927a","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T17:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 17:41:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6777465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6777465","identity":"rs-6777465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}