As healthcare improves and the population's life expectancy increases, the proportion of the older adults with two or more chronic diseases (multimorbidity) is steadily increasing. Previous studies have shown that after the age of 60 years old, multimorbidity is the norm rather than the exception (Calderón-Larrañaga et al., 2017). It is estimated that the proportion of older adults in the world will increase from approximately 12 % to 22 % between 2015 and 2050 (“Ageing and health”, 2022), while the prevalence of multimorbidity in the population over the age of 60 years ranges between 55 % and 98 %(Marengoni et al., 2011). Multimorbidity has been recognized as a growing global challenge, placing an increased burden of disease treatment on individuals, families, health systems, and societies, and leading to higher socio-economic costs (Skou et al., 2022).

Many studies have shown that multimorbidity among older adults is associated with a greater risk of depressive disorders and cognitive impairment (Du et al., 2024; Tong et al., 2021). Depressive disorders and/or cognitive impairment have been found in one third of older adults with multimorbidity (Quiñones et al., 2018). Individuals with multimorbidity have twice the risk of depression than individuals with a single chronic illness and three times the risk of individuals with no chronic physical illness (Read et al., 2017). At a theoretical level, the Psychological and Biological Pathways Model (Schulberg et al., 2000) proposes that physical illness leads to decreased ability about maintaining important aspects of life and loss of independence, which combined with neurochemical and neuroanatomical changes associated with the illness, may lead to depressive symptoms or cognitive-behavioral abilities reduction. Meanwhile, a large number of population-based studies have shown that multimorbidity is associated with accelerated cognitive decline, mild cognitive impairment, and dementia (Grande et al., 2021; Jin et al., 2023). Multimorbidity at baseline significantly increases the risk of dementia, particularly for individuals aged 60 to 70 years old (Veronese et al., 2023). Diabetes and cardiovascular disease are often linked to potential cognitive decline and an increased risk of dementia. Stroke or cerebrovascular disease, cancer, Parkinson's disease, dyslipidemia, and hypertension are also associated with a higher risk of cognitive impairment (Lyall et al., 2017; Xing et al., 2024; Xue et al., 2019). Older adults with multimorbidity have more complex health conditions, putting them at a higher risk of developing cognitive decline. When depression or cognitive impairment arises in older adults with multimorbidity, it can further diminish their self-management capabilities and treatment adherence, leading to an increased burden of disease management and perpetuating a vicious cycle.

Depressive disorders and cognitive impairment have been shown to be highly correlated (Rock et al., 2014). Depression-related vascular disease, inflammation, and increased glucocorticoid production can diminish cognitive reserve and accelerated cognitive decline (Salwierz et al., 2023). Individuals with depressive disorders face a notably higher risk of developing cognitive impairment compared to those without (Zhou et al., 2024). Cognitive impairment has also been identified as a risk factor for dementia and depressive disorders (Mirza et al., 2017). Nearly one-third of individuals with cognitive impairment also experience depressive disorders (Mayor, 2016). Persistent cognitive difficulties can lead to various negative outcomes such as psychosocial impairment, poor quality of life, declining social relationships, and increased feelings of depression. As cognitive function declines, older adults may experience feelings of helplessness and isolation (Burholt et al., 2017), which can increase the risk of depression. Therefore, there appears to be a link between depressive disorders and cognitive performance, with individuals exhibiting symptoms of one disorder being at higher risk of developing the other. It is estimated that the proportion of older adults with both cognitive impairment and depressive disorders will rise over the next two decades (Kingston et al., 2018). Enhancing cognitive function may lower the risk of depressive relapse, while reducing depressive relapse may decrease the likelihood of accumulating cognitive dysfunction (Porter and Douglas, 2019). Thus, there may exist a potential symptomatic association between depressive symptoms and cognitive performance in older adults with multimorbidity. Investigating this association could facilitate the identification of early signs of depression or cognitive decline. Furthermore, early detection and management of these risks through targeted interventions may enhance the quality of life and overall health outcomes for older adults facing multimorbidity.

In contrast to the traditional view of mental disorders as underlying entities that cause symptoms, the network approach emphasizes associations between the symptoms themselves (Borsboom and Cramer, 2013). Currently, network analysis is widely used to explore the relationships between variables, especially the associations between symptoms of different psychiatric disorders. For example, network analysis methods have been used to study the associations between loneliness, depression and anxiety among college students, as well as to examine the interrelationships between depressive symptoms and anxiety symptoms among disabled older adults (Yang et al., 2023; Zhang et al., 2023). These studies have all found that different psychiatric problems are highly correlated with each other and that several key symptoms act as central hubs in the network. According to the network theory of mental illness (Borsboom, 2017), the core symptoms in the symptom network of mental illness play an important role in the development of mental illness. When the intensity of a symptom changes, the symptoms linked to it may also change (Borsboom and Cramer, 2013). Meanwhile, bridging symptoms connect different psychiatric disorders within the symptom networks of mental disorders, and when a bridging symptom changes, it may affect the symptoms linked to it in multiple psychiatric disorders simultaneously. Intervention on core and bridging symptoms in the symptom network can help to change the structure of the psychiatric disorder network and thus impede the development of psychiatric disorders (Borsboom, 2017; Borsboom and Cramer, 2013). Therefore, the use of network analysis to identify core and bridging symptoms in mental illness networks is valuable for pinpointing intervention targets and enhancing intervention effects.

Although depressive disorders and cognitive impairment often co-occur, few studies have utilized network analyses to investigate the relationship between depressive symptoms and cognitive performance in older adults. No studies have been found that specifically focus on the association between depressive symptoms and cognitive performance in older adults with multimorbidity. Therefore, this study utilized data from the China Health and Retirement Longitudinal Survey to conduct a network analysis exploring the relationship between depressive symptoms and cognitive performance among older adults aged 60 years and older with multimorbidity. The study aimed to identify core symptoms and bridging symptoms in the network, as well as to compare differences based on gender and age. The goal is to provide a foundation of key symptoms for intervention, ultimately reducing the potential burden on patients with multimorbidity.