Introduction
Multimorbidity, defined as the coexistence of two or more chronic diseases, is a significant public health concern affecting approximately 33% of the global adult population (Chowdhury et al., Reference Chowdhury, Chandra Das, Sunna, Beyene and Hossain2023). In particular, physical–mental multimorbidity, which refers to the co-occurrence of physical and mental disorders, has emerged as a global public health challenge. For instance, a nationally representative study conducted in Korea found that 68% of adults with a mental disorder had at least one physical disorder (Kim et al., Reference Kim, Chang, Bae, Cho, Lee, Kim and Cho2016), while in Singapore, 50.6% of adults with mental disorders also suffered from a physical disease (Chong et al., Reference Chong, Abdin, Nan, Vaingankar and Subramaniam2012). Individuals with multimorbidity experience lower health-related quality of life (Makovski et al. Reference Makovski, Schmitz, Zeegers, Stranges and van den Akker2019), reduced functional abilities (Calderón-Larrañaga et al., Reference Calderón-Larrañaga, Vetrano, Ferrucci, Mercer, Marengoni, Onder, Eriksdotter and Fratiglioni2019), greater treatment burdens (Morris et al., Reference Morris, Roderick, Harris, Yao, Crowe, Phillips, Duncan and Fraser2021) and higher risks of premature mortality (Feng et al., Reference Feng, Sarma, Seubsman, Sleigh and Kelly2023).
A growing body of evidence has demonstrated a strong correlation between mental disorders and physical diseases (Sartorius, Reference Sartorius2018). Individuals with one to five physical diseases have a 2.55- to 3.89-fold greater risk of depression as compared to those without physical diseases (Smith et al., Reference Smith, Shin, Butler, Barnett, Oh, Jacob, Kostev, Veronese, Soysal, Tully, López Sánchez and Koyanagi2022). Furthermore, the relative risks of physical diseases and physical multimorbidity are 27% and 31% higher, respectively, for individuals with mental disorders as compared to the general population (Wei et al., Reference Wei, Wang, Deng, Cohen, Luo, Liu and Ran2022). However, most of these studies primarily relied on basic descriptive analyses and traditional logistic regression models, which are limited in the ability to capture the underlying clustering of diseases. Consequently, prior studies failed to fully explore the complex interrelationships among different diseases.
In recent years, network analysis has emerged as an innovative method for multimorbidity research, offering deeper insights into the complex interrelationships among different diseases (Hidalgo et al., Reference Hidalgo, Blumm, Barabási and Christakis2009; Jones et al., Reference Jones, Cocker, Jose, Charleston and Neil2021). In the context of multimorbidity networks, nodes represent individual diseases, while the edges between them reflect the degree of co-occurrence or association between these diseases. The weight of the edges reflects the strength of the association between diseases, with the strength typically calculated based on large-scale clinical data, epidemiological studies or genetic analysis results. Through edge weights, we can intuitively assess which diseases are more strongly associated and which have relatively weaker associations. This method not only assesses diseases aggregation but also identifies patterns of multimorbidity (Ferris et al., Reference Ferris, Fiedeldey, Kim, Clemens, Irvine, Hosseini, Smolina and Wister2023), providing valuable implications for disease prevention and treatment. Several studies have used network analysis to clarify patterns of physical multimorbidity, such as cardiometabolic, respiratory/cancer and musculoskeletal conditions, across different populations (Bao et al., Reference Bao, Lu, Wang, Zhang, Song, Gu, Ma, Su, Wang, Shang, Zhu, Zhai, He, Li, Liu, Fairley, Yang and Zhang2023; Chen et al., Reference Chen, Yu, Yin, Shan, Xing, Min, Ding, Fei and Li2023, Reference Chen, Zhang, Zhang, Lin, Deng, Hou, Li and Gao2024). In the field of mental disorders, network analyses have focused on symptom networks, where the reinforcement between symptoms helps sustain the disorder and highlights key characteristics (Borsboom, Reference Borsboom2017; Borsboom and Cramer, Reference Borsboom and Cramer2013). Notably, research on physical–mental multimorbidity networks has predominantly been hospital-based, relying on electronic medical record data to investigate disease co-occurrence patterns, although a recent review reported that studies based on these data consistently identify two primary multimorbidity patterns (mental health and cardiovascular diseases) alongside three further patterns (musculoskeletal, respiratory and gastrointestinal diseases) (Beridze et al., Reference Beridze, Abbadi, Ars, Remelli, Vetrano, Trevisan, Pérez, López-Rodríguez and Calderón-Larrañaga2024a).
Despite these advancements, research on physical–mental multimorbidity remains limited, particularly in developing countries, which often lack diagnostic data on mental disorders. To the best of our knowledge, no study has yet explored the complex patterns of multimorbidity based on mental disorders and physical diseases in Chinese adults. This gap is critical, as China, with its large and aging population, is experiencing a rapid increase in both mental and physical health problems. Adults are particularly vulnerable due to changing lifestyles, stress and growing mental health challenges. Understanding physical–mental multimorbidity in this group is essential for developing early interventions and prevention strategies. Given the existing evidence from other populations, we hypothesized that the physical–mental multimorbidity network structure and multimorbidity patterns in the general population may resemble those observed in hospital-based studies.
The aims of this cross-sectional survey of community residents in northeast China were to (1) construct a complex multimorbidity network of the general population to provide a comprehensive description of the characteristics between physical diseases and mental disorders, and (2) identify and analyse complex multimorbidity patterns for future prevention and treatment strategies.
Methods
Study area and participants
This study conducted a cross-sectional survey of mental disorders in Liaoning Province, China, from November 2004 to April 2005. Liaoning is the most populous and economically important province in northeast China. The locations of study areas and selection of participants are described in a previous study (Pan et al., Reference Pan, Jiang and Yang2006). A multistage probability sampling design was employed for the study, with selections at each stage based on probability proportionality. For the first stage, the province was divided into urban and rural clusters. Reflecting the levels of economic development, three cities (Shenyang, Anshan and Fuxin) and three rural counties (Dawa, Qingyuan and Zhangwu) were selected. For each selected city and county, four districts or townships were randomly chosen. From these, five streets or villages were selected, and from every selected street or village, 60 households were randomly chosen for inclusion. All adults aged 18–65 years were included in the study. Participants were provided with a detailed description of the study and gave written informed consent prior to participation. Exclusion criteria were as follows: individuals with severe cognitive disabilities that would prevent them from understanding the study procedures and providing informed consent were excluded. Additionally, those with severe physical disabilities that would impede their ability to complete the study assessments were not included. This approach ensured that the study sample was composed of participants who could fully engage with the research process and provide valid data. Trained interviewers conducted face-to-face interviews with each participant to ensure thorough and systematic data collection. In this study, physical–mental multimorbidity was defined as the co-occurrence of at least one mental disorder and one physical disease.
Demographic characteristics included age (18–24, 25–34, 35–44, 45–54, 55–65), sex (male, female), residence, marital status, education and income. The residence was categorized as ‘urban’ and ‘rural’. For marital status, the responses ‘divorced’ and ‘widowed’ accounted for 1.9% and 2.0%, respectively, and were combined into the ‘divorced/widowed’ group. Education was categorized as ‘primary school’, ‘junior middle school’, ‘senior middle school’ or ‘college/university’. Income level was classified as ‘high’, ‘middle’ or ‘low’.
Assessment of mental disorders
The Chinese version of the Composite International Diagnostic Interview 1.0 was administered to all eligible subjects by trained interviewers. All diagnosed lifetime mental disorders were identified using the International Classification of Diseases (10th revision) and the Diagnostic and Statistical Manual, Third Edition, of the American Psychiatric Association. The mental disorder groups in the survey included mood disorders (major depression disorder and dysphoric mood), anxiety disorders (social anxiety disorders, agoraphobia and specific phobias) and alcohol use disorders. Notably, the diagnostic process did not employ a hierarchical rule system; thus, an individual can meet the diagnostic criteria for any particular disorder regardless of another concurrent disorder. Only diseases with a prevalence ≥1.0% were included in the analysis. All diseases were defined as binary variables (present or absent).
Assessment of physical diseases
Physical diseases were assessed based on self-reporting and only those with a prevalence ≥1.0% were included. In accordance with the International Classification of Diseases (10th revision), physical diseases included diabetes mellitus, hypertensive diseases, ischemic heart diseases, other forms of heart disease, cerebrovascular diseases, chronic lower respiratory diseases, diseases of the oesophagus, stomach and duodenum, arthropathies, dorsopathies and soft tissue disorders. All diseases were defined as binary variables (present or absent).
The decision to include diseases with a prevalence ≥1.0% was based on the aim to focus on conditions that are more likely to have a significant impact on public health, as diseases with lower prevalence may be less relevant to overall disease burden. This threshold has been used in previous studies to ensure a focus on the most common conditions while avoiding the inclusion of rare diseases that may not contribute meaningfully to the analysis of multimorbidity patterns (Zhang et al., Reference Zhang, He, Yao, Jing, Sun, Lu, Xue, Qi, Cui, Cao and Ning2023).
Statistical analysis
Frequencies, percentages and cross-tabulations were used for descriptive analysis. The chi-square test was applied to measure differences in the prevalence of diseases and physical–mental multimorbidity between men and women. A matrix heat map was generated of the absolute number of comorbid pairs of each type.
Network analysis was conducted to identify the complex relationships among 16 physical and mental morbidities. A multimorbidity network was constructed using nodes and edges, where each node represents a distinct disease entity. The size of each node is proportional to the prevalence of the disease, as a visual cue of disease frequency. The edges between nodes represent associations between pairs of diseases, with the thickness of each edge indicating the strength of coexistence. In this study, the strength of coexistence refers to the observed-to-expected ratio (OER) of all possible disease pairs. This metric has been widely used in previous research as an indicator of comorbidity strength (Lee and Park, Reference Lee and Park2021). Specifically, OER is calculated as follows: for each disease pair i and j, OER represents the ratio of the observed probability of co-occurrence of diseases i and j to the expected probability of co-occurrence under the assumption of independence between the diseases. Mathematically, it is expressed as follows:
\begin{equation*}OE{R_{ij}} = \frac{{{P_{ij}}}}{{{P_i}{P_j}}}\end{equation*}An OER > 1 indicates that the pair of diseases co-occur more frequently than expected by chance, suggesting a significant non-random association and a close relationship between the two diseases. Conversely, an OER < 1suggests that the diseases are more likely to be independent of each other. Importantly, the relationships between disease pairs were considered non-directional.
Network analysis was selected due to its ability to capture and visualize the complex, non-linear interrelationships among diseases, which is not possible using traditional statistical methods. This approach allows for a deeper understanding of disease co-occurrence patterns and their interconnections. Network analysis also provides a visual representation of the relative importance of each disease in the overall system, which is particularly useful for identifying key intervention targets. The method has been widely used in multimorbidity research (Fallah et al., Reference Fallah, Hong, Wang, Humphreys, Parsons, Walden, Street, Charest-Morin, Cheng, Cheung and Noonan2023; Woodman et al., Reference Woodman, Koczwara and Mangoni2023).
The centrality of nodes was assessed using several network centrality metrics, including degree, strength, closeness and betweenness. Degree centrality is the most fundamental attribute of a node in a network and defined as the total number of direct links to other nodes. A higher degree centrality indicates a greater number of direct connections to other diseases, suggesting that the disease plays a central role in the comorbidity network. Diseases with high degree centrality may be pivotal in the development or progression of other conditions, potentially acting as primary drivers of multimorbidity. Strength centrality is determined by the sum of all connection strengths of a particular node, reflecting the intensity of direct links between the disease and other diseases. A disease with high strength centrality indicates that its relationships with other diseases are not only numerous but also strong, suggesting a substantial impact on the overall health condition of individuals. Closeness centrality is calculated as the reciprocal of the sum of the shortest path lengths between the node and all other nodes, indicating how quickly a node can be reached from other nodes. A high closeness centrality means that the disease can quickly spread or influence other diseases in the network, implying that it plays a key role in the transmission or mutual reinforcement of comorbidities. Finally, betweenness centrality measures the number of times a node lies on the shortest path between other nodes, indicating the importance of the disease in facilitating interactions within the network. Diseases with high betweenness centrality act as bridges, connecting otherwise disconnected disease clusters. Such diseases might be critical in understanding the interrelations between different comorbidity patterns, potentially serving as key intervention targets for reducing the burden of multimorbidity.
The Louvain algorithm in the R package ‘igraph’ (https://r.igraph.org/) was used to detect multimorbidity patterns within the network (Blondel et al., Reference Blondel, Guillaume, Lambiotte and Lefebvre2008). This algorithm detects communities by maximizing modularity, which measures how strongly connected the nodes are within a group compared to between groups. In our study, it was used to identify clusters of diseases that frequently co-occur, helping to reveal patterns of multimorbidity in the population. Each disease can belong to different multimorbidity patterns. The characteristics of the participants are presented in the identified multimorbidity patterns. The chi-square test was used to analyse the distribution differences of different multimorbidity patterns among different demographic characteristics. The physical–mental multimorbidity network was visualized using a force-directed layout, which positions nodes based on their relationships and connection strengths. In this layout, highly connected diseases are placed at the centre, highlighting their central role within the network.
A probability (p) value <0.05 was considered statistically significant. All descriptive statistical analyses were performed using IBM SPSS Statistics for Windows (version 26.0; IBM Corporation, Armonk, NY, USA). The physical–mental multimorbidity network was estimated and visualized using the RStudio (version 4.3; https://posit.co/download/rstudio-desktop/) packages ‘igraph’ and ‘qgraph’.
Results
A total of 15,516 people were planned to be surveyed in urban and rural areas, but 13,358 people were actually surveyed, including 6,610 men (49.50%) and 6,748 women (50.50%), as detailed in Table 1. The response rate was 86.09%, the loss-to-follow-up rate was 12.30% and the refusal rate was 1.61%. Of the participants, 11.3%, 19.9%, 28.1%, 27.0% and 13.7% were aged 18–24, 25–34, 35–44, 45–54 and 55–65 years, respectively. The majority of participants (58.7%) resided in rural areas, and 84.5% were married. In regard to education levels, 25.9%, 47.0%, 16.9% and 10.2% were educated at the primary school, middle school, secondary school and college levels, respectively.
Table 1. General characteristics of the study participants
Of the 16 evaluated diseases (Table 2), the 3 most common physical diseases were dorsopathies (3.8%), hypertensive diseases (3.2%) and other forms of heart disease (2.6%). The most prevalent mental disorders were specific phobia (3.8%), agoraphobia (3.0%) and major depressive disorder (2.6%). Among diseases with statistically significant differences in prevalence, only alcohol use disorder exhibited a higher prevalence in men than women (4.6% vs. 0.2%, respectively, p < 0.01, Table 1). The occurrence of physical–mental multimorbidity was 3.7%, with a higher prevalence observed among women than men (4.2% vs. 3.3%, respectively, p < 0.01). The frequency of morbidities of all diseases and the number of associated morbidities is presented in Table S1. The prevalence of comorbidity was higher for all mental disorders than physical diseases, with the exception of alcohol use disorders. The highest mean number of associated morbidities was observed for dysphoric mood (2.87), social anxiety disorder (2.78) and major depressive disorder (2.54).
Table 2. Prevalence of diseases and multimorbidity stratified by sex
Figure S1 illustrates the absolute numbers of comorbidity between various diseases. It is evident that comorbidity was generally higher among mental disorders than physical diseases. Among mental disorders, the highest comorbidity was observed between major depressive disorder and dysthymia, followed by agoraphobia and specific phobias, and social phobia and specific phobias. For physical diseases, the most common comorbid pair was hypertension and other types of heart disease.
Figure 1. Physical–mental multimorbidity for 16 physical and mental diseases.
A physical–mental multimorbidity network of the 16 included diseases is presented in Figure. 1. The multimorbidity network consisted of 16 diseases (nodes), with each node representing a specific disease. Blue nodes indicate mental disorders and orange nodes indicate physical diseases, with 40 links identifying the comorbidity relationships between diseases pairs. The strongest connections were between major depression and dysphoric mood, social anxiety disorder and specific phobia, and social anxiety disorder and agoraphobia with edge weights of 4.31, 2.31 and 2.08, respectively. The centrality measure of nodes (degree, betweenness, closeness and strength) showed that other forms of heart disease and dysphoric mood were the most central hub diseases in the network (Table 3). Overall, the network was divided into two domains: physical diseases and mental disorders. Intra-domain associations were markedly stronger than inter-domain associations. Within the mental disorders domain, the most strength of coexistence was between major depression (B1) and dysphoric mood (B2), followed by associations among social anxiety disorder (B3), agoraphobia (B4) and specific phobias (B5). In the physical diseases domain, the strongest associations were observed between hypertensive diseases (C2) and cerebrovascular diseases (C5). The two domains were primarily bridged by other forms of heart diseases (C4) and dysphoric mood (B2), both located in the centre of the network.
Table 3. Results of node centrality in the multimorbidity network
Four clusters were identified in the physical–mental multimorbidity network (Figure. 2). The affective-addictive cluster (27.6%) was formed by major depression, dysphoric mood and alcohol use disorder. The anxiety cluster (25.1%) was composed of social anxiety disorder, agoraphobia and specific phobia. The cardiometabolic cluster (25.4%) included diabetes mellitus, hypertensive diseases, ischemic heart diseases and cerebrovascular diseases. The gastro-musculoskeletal-respiratory cluster (46.4%) consisted of chronic lower respiratory diseases, other forms of heart disease, diseases of the oesophagus, stomach and duodenum, arthropathies, dorsopathies and soft tissue disorders. Men were more likely to be included in the affective-addictive cluster (67.7%), while women were more likely to be included in the anxiety, cardiometabolic and gastro-musculoskeletal-respiratory clusters (69.0%, 57.6% and 55.7% respectively) in Table 4. Participants aged 35–54 years accounted for the highest proportions of all multimorbidity clusters. Participants from urban areas accounted for the highest proportion in the cardiometabolic cluster (59.5%).
Table 4. Comparison of demographic characteristics among different multimorbidity pattern
Discussion
To the best of our knowledge, this is the first population-based study to employ multimorbidity network analysis to explore the relationships between diagnosed mental disorders and physical diseases in Chinese adults. Physical–mental multimorbidity was observed in 3.7% of the participants. The prevalence of physical–mental multimorbidity was higher in women than men (4.2% vs. 3.3%). The physical–mental multimorbidity network was divided into two domains: mental disorders and physical diseases. Four multimorbidity patterns were identified: affective-addictive, anxiety, cardiometabolic and gastro-musculoskeletal-respiratory.
In this study, the prevalence of physical–mental multimorbidity was slightly lower than the prevalence (5.7%) among residents of the United States at the county level (age, 0–64 years) (Bobo et al., Reference Bobo, Yawn, St Sauver, Grossardt, Boyd and Rocca2016) and among individuals from Singapore aged ≥18 years (6.1%) based on a nationwide cross-sectional survey (Chong et al., Reference Chong, Abdin, Nan, Vaingankar and Subramaniam2012). This difference may be due to the sampled populations, the number of included conditions and the study methods. The prevalence of multimorbidity was higher for women than men, consistent with prior studies (Barnett et al., Reference Barnett, Mercer, Norbury, Watt, Wyke and Guthrie2012; Bobo et al., Reference Bobo, Yawn, St Sauver, Grossardt, Boyd and Rocca2016).
In line with previous studies (Dong et al., Reference Dong, Feng, Sun, Chen and Zhao2021; Isvoranu et al., Reference Isvoranu, Abdin, Chong, Vaingankar, Borsboom and Subramaniam2021), the physical–mental multimorbidity network was distinctly divided into two domains: mental disorders and physical diseases. Associations within each domain, particularly the mental domain, were stronger than those between domains. A large-scale multimorbidity study based on the UK Biobank revealed that significant comorbidity within physiological systems may be due to the presence of more shared genes (Dong et al., Reference Dong, Feng, Sun, Chen and Zhao2021). In addition, stronger comorbidity associations within the mental disorders domain can be explained by the fact that mental disorders often involve multiple symptoms, and causal interactions among these symptoms can lead to co-occurrence of mental disorders (Borsboom, Reference Borsboom2017; Isvoranu et al., Reference Isvoranu, Abdin, Chong, Vaingankar, Borsboom and Subramaniam2021). In addition, the complexity of diagnosing the mental disorders (Insel TR, 2014; Maj, Reference Maj2011), along with stigma and feeling of shame, often delay seeking help (Clement et al., Reference Clement, Schauman, Graham, Maggioni, Evans-Lacko, Bezborodovs, Morgan, Rüsch, Brown and Thornicroft2015; Corrigan et al., Reference Corrigan, Druss and Perlick2014), resulting in the co-occurrence of mental disorders by the time other illnesses are diagnosed.
The centrality measures in our multimorbidity network provide valuable insights for both prevention and treatment. Other forms of heart diseases (C4), as a highly central node, highlight the importance of targeting shared risk factors that influence multiple diseases. By focusing on such key nodes, prevention efforts can address several comorbidities simultaneously. This network approach also suggests that integrated treatment strategies, based on centrality, could improve outcomes by considering the interconnectedness of diseases. Personalized care, tailored to individual network characteristics, offers potential for more effective management of multimorbidity.
Identifying hub diseases is crucial for understanding the structure of multimorbidity networks because these diseases bridge the gap between physical and mental disorders. Diseases with high centrality values are considered hub diseases because they are central to the connectivity and flow of the network. These diseases demonstrate strongly associated with multiple other conditions, underscoring their role as key components of multimorbidity while reflecting complex interconnection patterns among diseases. Specifically, other forms of heart disease are frequently linked with various physical conditions such as hypertension and diabetes, as well as mental disorders like depression and anxiety. Similarly, dysphoric mood is centrally positioned in the mental health domain, often co-occurring with other mood disorders and physical diseases, particularly those with chronic pain or cardiovascular conditions.
Figure 2. Identification of multimorbidity communities using Louvain algorithms.
Consistent with expectations, the four multimorbidity patterns identified in this study have been previous reported, although the proportions differed. The gastro-musculoskeletal-respiratory pattern, characterized by chronic lower respiratory diseases, other forms of heart disease, gastrointestinal diseases, stomach and duodenum, arthritis, dorsopathies and soft tissue disorders, was the most common in the present study but has been less reported in previous studies. Similar to the present study, several primary care-based studies of multimorbidity patterns identified two distinct patterns: musculoskeletal and respiratory (Diaz et al., Reference Diaz, Poblador-Pou, Gimeno-Feliu, Calderón-Larrañaga, Kumar and Prados-Torres2015; Roso-Llorach et al., Reference Roso-Llorach, Violán, Foguet-Boreu, Rodriguez-Blanco, Pons-Vigués, Pujol-Ribera and Valderas2018; Zhu et al., Reference Zhu, Edwards, Mant, Payne and Kiddle2020). Coexistence may be due to the high prevalence of gastrointestinal diseases, arthritis and respiratory diseases in China (‘Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019’, Reference Ferris, Fiedeldey, Kim, Clemens, Irvine, Hosseini, Smolina and Wister2020). Additionally, treatment and management of one disease can contribute to the onset of another. Non-steroidal anti-inflammatory drugs are commonly used to treat arthritis, but long-term use can lead to the formation of gastric ulcers (Henry et al., Reference Henry, Lim, Garcia Rodriguez, Perez Gutthann, Carson, Griffin, Savage, Logan, Moride, Hawkey, Hill and Fries1996), while inhaled corticosteroids used for managing chronic respiratory diseases can increase the risk of osteoporosis (Casaburi, Reference Casaburi2001).
The affective-addictive pattern included major depression, dysphoric mood and alcohol use disorder, while the anxiety pattern was characterized by social anxiety disorder, agoraphobia and specific phobia. These two multimorbidity patterns are consistent with the findings of previous studies (Beridze et al., Reference Beridze, Abbadi, Ars, Remelli, Vetrano, Trevisan, Pérez, López-Rodríguez and Calderón-Larrañaga2024; Roso-Llorach et al., Reference Roso-Llorach, Violán, Foguet-Boreu, Rodriguez-Blanco, Pons-Vigués, Pujol-Ribera and Valderas2018). Comorbidity among mental disorders was common. A comprehensive analysis of 145,990 adults from 27 countries found that each previous psychiatric disorder was associated with an increased risk of first-onset of other subsequent psychiatric disorders (McGrath et al., Reference McGrath, Lim, Plana-Ripoll, Holtz, Agerbo, Momen, Mortensen, Pedersen, Abdulmalik, Aguilar-Gaxiola, Al-Hamzawi, Alonso, Bromet, Bruffaerts, Bunting, de Almeida, de Girolamo, De Vries, Florescu, Gureje, Haro, Harris, Hu, Karam, Kawakami, Kiejna, Kovess-Masfety, Lee, Mneimneh, Navarro-Mateu, Orozco, Posada-Villa, Roest, Saha, Scott, Stagnaro, Stein, Torres, Viana, Ziv, Kessler and de Jonge2020). The mechanisms underlying this pattern are likely multifactorial, including share neurobiological origins (Xie et al., Reference Xie, Xiang, Shen, Peng, Kang, Li, Cheng, He, Bobou, Broulidakis, van Noort, Zhang, Robinson, Vaidya, Winterer, Zhang, King, Banaschewski, Barker, Bokde, Bromberg, Büchel, Flor, Grigis, Garavan, Gowland, Heinz, Ittermann, Lemaître, Martinot, Martinot, Nees, Orfanos, Paus, Poustka, Fröhner, Schmidt, Sinclair, Smolka, Stringaris, Walter, Whelan, Desrivières, Sahakian, Robbins, Schumann, Jia and Feng2023) as well as psychosocial factors, such as adverse childhood experiences (Kessler et al., Reference Kessler, McLaughlin, Green, Gruber, Sampson, Zaslavsky, Aguilar-Gaxiola, Alhamzawi, Alonso, Angermeyer, Benjet, Bromet, Chatterji, de Girolamo, Demyttenaere, Fayyad, Florescu, Gal, Gureje, Haro, Hu, Karam, Kawakami, Lee, Lépine, Ormel, Posada-Villa, Sagar, Tsang, Ustün, Vassilev, Viana and Williams2010), in addition to social and economic inequalities (Patel et al., Reference Patel, Saxena, Lund, Thornicroft, Baingana, Bolton, Chisholm, Collins, Cooper, Eaton, Herrman, Herzallah, Huang, Jordans, Kleinman, Medina-Mora, Morgan, Niaz, Omigbodun, Prince, Rahman, Saraceno, Sarkar, De Silva, Singh, Stein, Sunkel and Unützer2018). Therefore, early identification and intervention are crucial to reduce the comorbidity of mental disorders. These findings also highlight the importance of prioritizing mental healthcare on par with physical healthcare.
As one of most common patterns in prior reports (Ioakeim-Skoufa et al., Reference Ioakeim-Skoufa, Poblador-Plou, Carmona-Pírez, Díez-Manglano, Navickas, Gimeno-Feliu, González-Rubio, Jureviciene, Dambrauskas, Prados-Torres and Gimeno-Miguel2020; Prados-Torres et al., Reference Prados-Torres, Calderón-Larrañaga, Hancco-Saavedra, Poblador-Plou and van den Akker2014) and the present study, the cardiometabolic pattern included diabetes mellitus, hypertensive diseases, ischemic heart diseases and cerebrovascular diseases. Hypertension is an established risk factor for other cardiovascular and renal diseases, which plays a crucial role in the progression of these conditions (Kokubo and Iwashima, Reference Kokubo and Iwashima2015). As the leading cause of death in China, cardiovascular diseases impose a heavy burden on healthcare systems (NCCD-WC, Reference Smith, Shin, Butler, Barnett, Oh, Jacob, Kostev, Veronese, Soysal, Tully, López Sánchez and Koyanagi2023). The presence of the cardiometabolic pattern emphasized the urgent need for targeted and comprehensive management approaches.
Comparisons of the demographics of the different multimorbidity patterns found that the mood-addiction cluster was more prevalent in men. The Chinese Mental Health Survey reported a 12-month prevalence of alcohol use disorder in 3.5% of men, compared to 0.1% of women (Huang et al., Reference Huang, Wang, Wang, Liu, Yu, Yan, Yu, Kou, Xu, Lu, Wang, He, Xu, He, Li, Guo, Tian, Xu, Xu, Ma, Wang, Wang, Yan, Wang, Xiao, Zhou, Li, Tan, Zhang, Ma, Li, Ding, Geng, Jia, Shi, Wang, Zhang, Du, Du and Wu2019). Conversely, women were more likely to belong to the anxiety, cardiometabolic and gastro-musculoskeletal-respiratory clusters, possibly due to the higher prevalence of cardiovascular disease, anxiety and overall multimorbidity (McLean et al., Reference McLean, Asnaani, Litz and Hofmann2011; Chowdhury et al., Reference Chowdhury, Chandra Das, Sunna, Beyene and Hossain2023). Furthermore, the role of oestrogen in cardiovascular regulation, as well as societal expectations, may lead to higher levels of stress and anxiety among women. Urban populations accounted for a higher proportion of the cardiometabolic cluster, possibly due to a sedentary lifestyle and increased consumption of high-calorie foods among, which may contribute to the increased prevalence of chronic diseases, such as diabetes, hypertension and heart disease, in these areas (Ekelund et al., Reference Ekelund, Steene-Johannessen, Brown, Fagerland, Owen, Powell, Bauman and Lee2016; Gong et al., Reference Gong, Liang, Carlton, Jiang, Wu, Wang and Remais2012).
In conclusion, this study underscores the intricate relationships between physical and mental health in Chinese adults, highlighting the significant prevalence of physical–mental multimorbidity and its associated patterns. The identification of distinct multimorbidity patterns not only contributes to the understanding of disease co-occurrence but also emphasizes the need for integrated healthcare approaches that address both physical and mental health.
There were several limitations to this study that should be addressed. First, the absence of mental disorders clustering with other physical diseases may be attributed to the low prevalence of these disorders in this sample. Future research should consider increasing the sample size to better explore the specific multimorbidity network characteristics between sexes, urban vs. rural settings and different age groups, thereby providing more targeted health interventions. Second, the reliance on self-reported physical diseases may have introduced reporting bias, potentially leading to underestimation of the actual prevalence. Additionally, the cross-sectional design of this study limited the ability to establish temporal sequences and causal relationships between mental disorders and physical diseases. Finally, these findings were primarily based on a population in northeast China, which may affect the generalizability and replicability of the results due to geographic specificity. Future studies could benefit from utilizing multicentre and multi-geographic samples to offer a more comprehensive understanding of the patterns and determinants of physical–mental multimorbidity.
Conclusion
For the first time, a network of physical and mental multimorbidity in the adults residing in northeast China was established and identified four distinct patterns: emotional-addictive, anxiety, modern metabolic and digestive-respiratory-musculoskeletal. Consistent with expectations, the physical and mental multimorbidity network characteristics and patterns of the general population were similar to those of a hospital-based population, although the multimorbidity patterns accounted for different proportions. Men were more likely to have an affective-addictive pattern, and psychiatric disorders were more prevalent in rural areas. These findings suggest the need to consider giving equal priority to mental and somatic disorders in multiple disease prevention and to develop management strategies for specific populations.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S2045796025000204.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Author contributions
Conceptualization: QHW, LL and YXL; Methodology: QHW, LL, XY and HJM; Software: QHW and LL; Validation:XYL and WS; Formal Analysis: XY, HL and SYH; Investigation: YXL, HJM, HL, and SYH; Resources: GWP and LJY; Data Curation: XY and LL; Writing – Original Draft Preparation: QHW, XY and HL; Writing – Review & Editing: QHW and GWP; Visualization: QHW and XY; Supervision: GWP; Project Administration: LL; Funding Acquisition: GWP. Wei Sun can also be contacted for correspondence [email protected].
Financial support
This study was supported by Liaoning Provincial Science-Technology Department (grant No. 2004225001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests
None to declare.
Ethical standards
This cross-sectional study was conducted in accordance with the Declaration of Helsinki on ethical principles for medical research involving human subjects. The ethics committee of Liaoning Provincial Center for Disease Control and Prevention (LNCDCP) approved the study. All subjects gave written informed consent after the study objectives were explained, and all subjects were free to withdraw at any time without giving any reason.
Open access
