Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

doi:10.21203/rs.3.rs-2712115/v2

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Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

https://doi.org/10.21203/rs.3.rs-2712115/v2

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Background: The move away from investigating mental disorders as whole using sum scores to the analysis of symptom-level interactions using network analysis has provided new insights into comorbidity.The current study explored the dynamic interactions between depressive and anxiety symptoms in older Chinese adults with diabetes mellitus (DM) and identified central and bridge symptoms in the depression-anxiety network to provide targets for prevention and intervention into depression and anxiety.

Methods: This study used a cross-sectional design with data from the 2017–2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A regularized partial correlation network for depression and anxiety was estimated based on self-reported scales completed by 1685 older adults with DM aged 65 years or older. Expected influence (EI) and bridge expected influence(BEI) indices were calculated for each symptom.

Results: The prevalences of depression and anxiety in our sample were 52.9% and 12.8%, respectively. The comorbidity rate of depression and anxiety was 11.5%. The six edges with the strongest regularized partial correlations were between symptoms from the same disorder. “Feeling blue/depressed”, “Nervousness or anxiety”, “Uncontrollable worry”, “Trouble relaxing”, and “Worry too much” had the highest EI values. “Nervousness or anxiety” and “Everything was an effort” exhibited the highest BEI values.

Conclusion: Central and bridge symptoms were highlighted in this study. Targeting these symptoms may be effective in preventing the comorbidity of depressive and anxiety symptoms and facilitate interventions in older Chinese adults with DM who are at risk for or suffer from depressive and anxiety symptoms.

depression

anxiety

network analysis

patients with diabetes

older adults

Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia and caused by both genetic and environmental factors [1]. The number of adults with DM worldwide is increasing rapidly; according to the International Diabetes Federation (IDF) report, this number has currently reached 537 million and is expected to increase to 643 million by 2030 [2]. China has the largest number of diabetes patients in the world, with about 140 million patients in 2021 [3]. With the increasingly serious problem of population aging in China, the proportion of diabetes patients over 65 years old is increasing, and older adults have become the primary demographic of diabetes patients [4].

DM is a chronic non-communicable disease that seriously threatens mental health. The incidence of depression and anxiety in the diabetic population is much higher than that of the population at large [5, 6]. In particular, the complex condition of older adults, the decline in their physical function and immune systems, long-term monitoring of blood glucose and diet control, the increased economic pressure resulting from long-term drug treatment, and reduced emotional support from their family members, all make older adults with DM susceptible to comorbidity with depression and/or anxiety [7, 8]. DM interacts bidirectionally with depression and anxiety. On the one hand, as mentioned above, DM increases the prevalence of depression and anxiety; on the other hand, depression and anxiety can be independent risk factors for the occurrence and development of DM and are known to predict the incidence of later DM [9, 10].

The comorbidity of depression and anxiety is also common in older adults with diabetes. It has been reported that approximately 30% of those with major depressive disorder (MDD) and roughly 50% of those with general anxiety disorder (GAD) meet the criteria for a dual MDD/GAD diagnosis in a sample of diabetic patients with mean age of 57.8 years [6]. The presence of either depression or anxiety often increases the risk of developing the other [11], and the comorbidity of depression and anxiety has more severe symptoms, less effective treatment, and poorer prognosis than either disorder alone [12]. Furthermore, diabetic patients with depression and anxiety also have an increased risk of diabetes complications, have a poor prognosis and poor blood glucose control and have lower quality of life [13-15]. Therefore, the comorbidity of depression and anxiety in older adults with DM is an important research topic.

Most prior studies on comorbid depression and anxiety have been based on latent variable models, using the total score of the measurement items for evaluation of the overall severity. However, such an approach ignores the interactions between individual symptoms and masks the heterogeneity of the various symptoms [16, 17]. The pervasive use of sum-scores has hampered progress in key research fields such as the search for more effective targets for anxiety and depression [18]. Therefore, to better understand the comorbidity of depression and anxiety in older adults with DM and make possible more targeted interventions, we need to adopt a more fine-grained research methodology such as the analysis of individual symptoms and their interactions.

Network analysis is an emerging, data-driven approach that provides a new perspective for understanding psychopathology and comorbidity. It permits the structure of mental disorders and the interactions between individual symptoms to be investigated and visualized [19-21]. Network analysis is based on the assumption that psychiatric disorders emerge from causal interactions between various symptoms, and different symptoms may actively reinforce or inhibit other symptoms, rather than simply viewing symptoms as reflective of underlying latent variables [20, 21]. The comorbid effect of anxiety and depression means that the specific symptoms of one psychiatric disorder will increase the risk developing the other. It is both reasonable and feasible, therefore, to regard them as a complex network comprising the interactions of different symptoms [22, 23]. The symptom network is composed of two basic elements: the nodes representing symptoms and the edges representing the partial correlations between them [24]. Network analysis helps identify specific relationships between the individual symptoms of anxiety and depression. A centrality index can be calculated to quantify the influence of individual symptoms in the network, and determine critical central symptoms that are more likely to activate other symptoms and play major roles in the onset and/or maintenance of the mental disorders [25]. Network analysis also calculates a bridge centrality index to identify important bridge symptoms that can facilitate the contagion of one disorder to another, leading to the development and maintenance of comorbidity [26].

Prior studies have used network analysis to explore the comorbidity of anxiety and depression in different populations, such as epilepsy patients [27, 28], the functionally impaired elderly [29], nursing students [30, 31], people with MDD [32], and people with anxiety disorder [33, 34]. One study based on network analysis examined diabetes distress and depressive and anxiety symptoms in middle-aged Canadians [35]. However, to date, depressive and anxiety symptoms in older Chinese adults with DM have not been studied using network analysis. Thus, despite the high prevalence of comorbid depression and anxiety in older adults with diabetes, all of which seriously affect their mental health and quality of life, these comorbidities of psychiatric disorders have still not received due attention. Considering the data-driven nature of network analysis, the examination of different study populations with various clinical features of depression and anxiety can lead to heterogeneous results. Additionally, the symptoms are influenced by sociocultural factors [36]. Hence, findings based on other samples are not necessarily applicable to older Chinese adults with DM, and studies are warranted to investigate the comorbidity of depression and anxiety in this population.

The current study is the first to use network analysis to construct a symptom-level network of depression and anxiety in older Chinese adults with DM. We aimed to explore the dynamic interrelationships between depressive and anxiety symptoms. We also aimed to identify central symptoms and bridge symptoms as a way to provide targets for the prevention and intervention into anxiety and depression for older Chinese adults with DM.

2.1. Study design and participants

This study used a cross-sectional design based on data from the 2017–2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). The CLHLS is a nationally representative, population-based, ongoing survey focusing on older adults in mainland China. Following the baseline survey in 1998, the CLHLS has conducted seven waves of serial follow-up surveys in 2000, 2002, 2005, 2008–2009, 2011–2012, 2014, and 2017–2018. Due to the need for a representative sample, the CLHLS adopted a multi-stage disproportionate and targeted random sampling method [37]. The participants were older adults (aged 65 years and above) and their children (aged 35–64 years) selected from about half of the counties and cities in 23 provinces, municipalities, and autonomous regions across China. More details about the CLHLS can be found elsewhere [38-40]. The 2017–2018 wave of the CLHLS included 15,874 older adults aged 65 years and over [41]. Following the example of previous studies [42, 43], older adults were defined in this study as those aged more than 65 years old. The inclusion criteria for this study were: (1) validated age ≥ 65, (2) DM diagnosed by a hospital, and (3) complete data on all depressive and anxiety items. Participants with missing values or answers of “not able to answer”, “don’t know”, or “not applicable” in any scale items of interest were excluded. Finally, a total of 1685 older adults were included in the current study.

2.2. Measures

2.2.1. 10-item Center for Epidemiologic Studies Depression Scale (CESD-10)

The CESD-10 is a self-reported scale used to measure depressive symptoms, that has been validated in Chinese older adults [44, 45]. It comprises 10 items, each of which is rated on a 4-point Likert-type scale ranging from 0 = never to 3 = always [46]. The total score on the CESD-10 can range from 0 to 30, with higher scores representing more severe symptoms of depression. In accordance with previous studies [45-47], a cutoff score of 10 was used to indicate the possible presence of depression. The Cronbach's α coefficient of this scale was 0.79 in the current study, indicating good internal consistency.

2.2.2. Seven-Item Generalized Anxiety Disorder Scale (GAD-7)

The GAD-7 is a reliable self-reported scale used to assess the frequency of the most important diagnostic symptoms of GAD over the previous two weeks [48]. It comprises seven items, each of which is rated on a 4-point Likert-type scale ranging from 0 = not at all to 3 = nearly every day. The total score for the GAD-7 can range from 0 to 21. The higher the GAD-7 score, the more severe the symptoms of anxiety. Cutoff scores of 5, 10, and 15 are taken to represent mild, moderate, and severe levels of anxiety [48, 49]. The Cronbach’s α coefficient of the GAD-7 was 0.91 in our sample, indicating excellent internal consistency.

2.3. Statistical analysis

2.3.1. Network estimation

The program RStudio was used to construct the network structure and calculate the expected influence (EI) and bridge expected influence (BEI) of each node. The R package qgraph was used to build and visualize the depression-anxiety network [50]. The network was estimated via the Gaussian graphical model (GGM), which is an undirected network [51]. We constructed the network based on nonparametric Spearman rho correlations because items were ordinal. In the constructed network, nodes represented symptoms and were divided into the depression community and the anxiety community; each edge represented the partial correlation between two nodes, with the confounding effects of all other nodes in the network eliminated by statistical controls. To regularize the network, a combination of least absolute shrinkage and selection operator (LASSO) and the extended Bayesian information criterion (EBIC) was adopted to shrink all the edges and attenuate small correlations to zero [52-54]. We set the EBIC hyperparameter to 0.5 to determine the optimal network model, thereby creating a sparse and interpretable network.

2.3.2. Centrality and bridge centrality estimation

The R packages qgraph and networktools were used to calculate the centrality index (i.e., EI) and bridge centrality index (i.e., BEI) of each node to determine important central and bridge nodes, respectively [26, 50]. Node EI is the sum weights of all edges directly connected to a given node [55]. A higher EI value indicates the node is more associated with other nodes and exerts more influence on the entire network. Node BEI is the sum of the weights of all edges directly linking a given node to nodes in another community [26]. A higher BEI value suggests the node contributes more to comorbidity and presents a higher risk of contagion from the current community to another community.

2.3.3. Network accuracy and stability estimation

R package bootnet was used to estimate the robustness of the network by testing the accuracy of edge weights and the stability of the centrality and bridge centrality indices [24], in order to ensure the accuracy and replicability of the network analysis. The accuracy of the edge weights was assessed by computing the 95% confidence interval (CI) using non-parametric bootstrapping (1000 bootstrapped samples); the narrower the CI, the more accurate the edge weight estimation [56]. The edge weight difference of node pairs was also tested by parametric bootstrapping (α = 0.05, 1000 bootstrapped samples). The stabilities of EI and BEI were assessed by case-dropping bootstrapping (1000 bootstrapped samples). We quantified stability using the correlation stability coefficient (CS-coefficient). The CS-coefficient should not be below 0.25 and should preferably be above 0.5, which represents ideal stability [24]. Subsequently, the differences between node EIs and BEIs were tested by parametric bootstrapping (α = 0.05, 1000 bootstrapped samples).

3.1. Descriptive statistics

The mean age of the included participants (N = 1685) was 80.53 ± 9.98 years (mean ± standard deviation); of them, 748 (44.4%) were males, 758 (45%) were urban residents, 872 (51.8%) were married and living with their spouses. The prevalences of depression (defined as CESD-10 total score ≥ 10) and anxiety (defined as GAD-7 total score ≥ 5) in the present sample were 52.9% and 12.8%, respectively. Additionally, the comorbidity rate of depression and anxiety was 11.5%. Table 1 shows the abbreviations, mean scores, standard deviations, EIs (raw values), and BEIs (raw values) for each symptom of depression and anxiety in the present network.

3.2. Network structure

Figure 1 shows the network structure of depression and anxiety symptoms. The network comprised 17 nodes and was estimated with 62.5% (85 of 136) non-zero edges. All edges had positive weights. The six strongest edges that exhibited relatively strong regularized partial correlations were identified. Four of these were in the depression community, those being the edges between CESD2 “Difficulty with concentrating” and CESD4 “Everything was an effort” (weight = 0.26), between CSED8 “Loneliness” and CSED9 “Inability to get going” (weight = 0.30), between CSED1 “Feeling bothered” and CSED3 “Feeling blue/depressed” (weight = 0.33), and between CSED5 “Hopelessness” and CSED7 “Lack of happiness” (weight = 0.39). The other two were in the anxiety community, those being the edges between GAD2 “Uncontrollable worry” and GAD3 “Worry too much” (weight = 0.29) and between GAD5 “Restlessness” and GAD6 “Easily annoyed/irritated” (weight = 0.30). Although weaker, there were several bridge edges linking depression and anxiety. GAD1 “Nervousness or anxiety” was positively connected to: CESD10 “Sleep disturbances” (weight = 0.10), CESD3 “Feeling blue/depressed”, CESD4 “Everything was an effort”, CESD5 “Hopelessness”, and CESD6 “Feeling nervous/fearful” (weights = 0.05 from CESD3 to CESD6). CESD4 “Everything was an effort” was also positively associated with GAD3 “Worry too much” (weight = 0.06). Supplementary Table 1 gives all the edge weights within the depression-anxiety network.

3.3. Central symptoms and bridge symptoms

Figure 2 shows the EI indices of each node to assess their relative importance in the network. The five nodes with the highest EIs were CESD3 “Feeling blue/depressed” (EI = 1.12), GAD1 “Nervousness or anxiety” (EI = 1.05), GAD2 “Uncontrollable worry” (EI = 1.05), GAD4 “Trouble relaxing” (EI = 1.04), and GAD3 “Worry too much” (EI = 1.02), indicating that those were the most important central symptoms. Figure 3 shows the raw BEI values of each node. Within the depression community, the node CESD4 “Everything was an effort” had the highest BEI value (BEI = 0.13); within the anxiety community, the node GAD1 “Nervousness or anxiety” had the highest BEI value (BEI = 0.38). This revealed that these two nodes represent critical bridge symptoms.

3.4. Network accuracy and stability

As shown in Supplementary Figure 1, the bootstrapped 95% CI was narrow, suggesting that the estimation of edge weights was accurate and stable. Supplementary Figure 2 presents the bootstrapped difference test results for the edge weights, indicating that the weights of the six strongest edges were significantly higher than those of 88.1% - 98.8% of the other nodes. The CS-coefficients of EI and BEI were both 0.75, suggesting that the estimations of EI and BEI were both adequately stable (see Figures 4 and 5). The bootstrapped difference test for node EIs showed that the EI values of the five central nodes were significantly higher than those of 68.8% - 75% of the other nodes (see Supplementary Figure 3). Supplementary Figure 4 illustrates the bootstrapped difference test for node BEIs, indicating that the BEI values of the two bridge nodes were significantly higher than those of 56.3% - 100% of other nodes.

To the best of our knowledge, this is the first study to use network analysis to investigate symptom-level interactions between depression and anxiety in a group of older Chinese adults with DM. We found some important connections between individual symptoms. We also identified several influential central and bridge symptoms. These findings may facilitate our understanding of the dynamic interplay of individual symptoms in depression and anxiety, shed light on the pathological mechanisms that underly the development and maintenance of the comorbidity of depression and anxiety, and provide better insights into intervention and treatment strategies.

The strongest edges appeared within each mental disorder community rather than connecting the depressive and anxiety symptom communities. This is consistent with many previous studies that have used network analysis to examine the comorbidity of depression and anxiety and found that the strongest edges were between symptoms from the same disorder [27, 30-32, 57-60]. The results identified four strong connections within the depressive community. A prior study used network analysis to examine insomnia and depressive symptoms (measured by CESD-10) and the strong edges identified within the depressive symptom community are consistent with our findings [61]. Additionally, in the anxiety community, the finding that strong edges existed between GAD2 “Uncontrollable worry” and GAD3 “Worry too much” and between GAD5 “Restlessness” and GAD6 “Easily annoyed/irritated” is consistent with prior network analysis studies of comorbid depressive and anxiety symptoms [30, 31, 57, 60]. Together, these findings were expected because, from a theoretical perspective, the associated symptoms interacted closely with each other to induce mental disorders [20, 62].

According to the network theory of psychopathology, EI may be a crucial way to identify influential central symptoms. Nodes with high EI are thought to be critical central symptoms that contribute to the development and maintenance of mental disorders [20, 55]. By activating other symptoms in the network, these central symptoms are thought to trigger and maintain the other symptoms and, by extension, the psychopathological networks. The results of this study showed that CESD-3 “Feeling blue/depressed” was the most central symptom, indicating its central role in the depression-anxiety network for Chinese older adults with DM. Similarly, “Depressed or sad mood” was found to be a central symptom, as has been previously reported in network analyses of depressive and anxiety symptoms in adolescents [63], psychiatric samples (mood, anxiety, personality, and psychotic disorders) [57], nursing students [31], and patients diagnosed with both depression and anxiety disorders [64]. Our findings are also consistent with other network analysis studies of depressed outpatients and shift workers [17, 61]. Therefore, the central symptom of “Feeling blue/depressed” may be consistent across different populations; nonetheless, it should be examined further. Moreover, our findings provide supporting evidence for the requirement that depressed mood is one of the two symptoms for the diagnosis of depressive disorders according to the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) [65]. Depressive mood is also a required feature in the diagnosis of depressive disorders according to the International Classification of Diseases (ICD-11) [66]. Additionally, depressed or sad mood has been reported to be important for the prediction of MDD and increases the incidence of MDD [67], which is in line with our finding. Furthermore, prior studies have revealed that DM is associated with increased risk of incident depressed mood in 70-to 79-year-old adults with DM [68], and diabetes, diabetes complications, and diabetes-related distress have all been found to be positively related to depressive symptoms in patients with DM [69, 70]. These lines of evidence support our finding that “Feeling blue/depressed” was critical to the development and maintenance of the depression-anxiety network in older adults with DM.

The symptom GAD2 “Uncontrollable worry” was another predominant central symptom that emerged in the depression-anxiety network, suggesting that it may also contribute to the activation of other symptoms and the maintenance of the depression-anxiety network in older adults with DM. This is consistent with prior studies that have identified “Uncontrollable worry” as a central node in the network of depression and anxiety in different populations [31, 57, 59, 60, 64]. In addition, we also found that GAD1 “Nervousness or anxiety”, GAD4 “Trouble relaxing”, and GAD3 “Worry too much” were also high in EI and were thus established as central symptoms. These findings are partially consistent with prior studies that have shown that “Trouble relaxing” (or “Unable to relax”) and “Excessive worry” (or “Too much worry”) had high centrality indices for the populations studied [28, 57, 59, 60, 64]. However, GAD1 “Nervousness or anxiety” was not found to be a central symptom in prior studies. This inconsistency may be the result of the use of different study samples, e.g., older adults with DM in our study versus a psychiatric sample in Beard et al.’s study [57], patients with epilepsy in Gauld et al.’s study [28], and patients with both depression and an anxiety disorder in Kaiser et al.’s study [64]. The characteristics of older adults with DM differ from those of individuals with neurological or psychiatric disorders. Serious diabetes complications such as macrovascular complications (e.g., cardiovascular disease) and microvascular complications (e.g., kidney disease and diabetic retinopathy and neuropathy) [71], as well as diabetes-related distress such as hypoglycemia induced by insulin treatment [72, 73], are all intractable problems for patients with DM. In particular, the potential pathophysiology of DM in older adults is worsened because of the adverse effects of aging on metabolic regulation; aging effects can interact with diabetes to accelerate the progression of diabetic complications [74]. Additionally, older adults are more susceptible to hypoglycemia and its consequences such as falls and consequent fractures, cardiovascular events, and mortality [75-77]. Therefore, older adults with DM can be prone to feel nervous, anxious, or on edge (i.e., GAD1 “Nervousness or anxiety”). This distinguishes them from other populations and warrants further validation in future studies.

Patients with comorbid depressive and anxiety symptoms tend to respond poorly to treatment, have a longer duration of illness, and experience poor prognoses [12]. The results of this study found GAD1 “Nervousness or anxiety” and CESD4 “Everything was an effort” to be critical bridge symptoms, indicating their roles in the development and maintenance of concurrent depression and anxiety in older adults with DM. Bridge symptoms facilitate the spreading of activation of one mental disorder to another, thereby contributing to the contagion between disorders, providing a new perspective for explaining comorbidity [26]. GAD1 “Nervousness or anxiety” was identified as the bridge symptom, indicating the role of anxiety in the development of depression. This finding is consistent with prior research that found “Nervousness” to be the bridge symptom between depression and anxiety for nursing students and pregnant women [31, 78]. Our results showed that GAD1 “Nervousness or anxiety” was positively linked to many anxiety symptoms, such as CESD10 “Sleep disturbances” and CESD3 “Feeling blue/depressed”. This is in agreement with prior studies that showed that the edges between “Nervousness or anxiety” and “sad mood” and between “Nervousness or anxiety” and “Sleep difficulties” are bridge pathways between depression and anxiety [29, 57]. Similarly, we found the influential bridge symptom within the depression community to be CESD4 “Everything was an effort”, suggesting its important role in contagion from depression to anxiety. Although we adopted a cross-sectional design and thus causality cannot be inferred from our study, our findings provide preliminary insights into the hallmark bridge symptoms facilitating the comorbidity of depression and anxiety. Moreover, our findings are in accordance with prior longitudinal studies that demonstrated that anxiety and depression are reciprocal risk factors for one another: that is to say, anxiety symptoms can lead to depressive symptoms and vice versa [11, 79-82].

The prominent central and bridge symptoms that were identified in the depression-anxiety network have potential clinical implications. According to the theory of psychopathological network, interventions targeting important central symptoms may have the greatest effect in destroying the overall network and reducing the severity of the network as a whole, facilitating treatment and intervention [19, 55]. This study thus provides guidance for intervention strategies in that targeting the symptoms “Feeling blue/depressed”, “Nervousness or anxiety”, “Uncontrollable worry”, “Trouble relaxing”, and “Worry too much” may be conducive to the prevention and treatment of depression and anxiety. Similarly, deactivating important bridge symptoms can disrupt the connections between comorbid mental disorders and prevent the contagion of one disorder to another, thereby reducing comorbidity [26]. Based on the results of the present study, the bridge symptoms “Nervousness or anxiety” and “Everything was an effort” are recommended as intervention targets for the prevention and reduction of the comorbidity of depression and anxiety disorders. Cognitive behavior therapy (CBT) is an effective treatment that is commonly used in the prevention and intervention into depressive and anxiety symptoms in patients with DM [83-85]. Our findings indicate that CBT strategies (e.g., cognitive restructuring and behavioral activation) focusing on these central symptoms and bridge symptoms may be of benefit for the prevention and treatment of depression and anxiety and reduction of the emergence of comorbidity in older adults with DM, although this needs further empirical research.

Despite its important implications, this study also has several limitations that should be noted. First, due to the cross-sectional design of our study, we could not infer the direction of causality between depression and anxiety. For insights into the temporal relationships, longitudinal research is needed. Second, the depressive and anxiety symptoms were measured using self-reported scales, which may induce recall bias. This reminds us to interpret the results cautiously. Third, the findings may have limited generalizability. Our sample focused on older Chinese adults with DM, and it is not known how generalizable our findings are to other populations. The applicability of our results to other populations with DM or older adults with clinically diagnosed depression and/or anxiety requires replication. Fourth, the network did not include covariates or confounders such as diabetes complications, individuals’ personality traits, and biological factors. Future studies should take them into consideration. Finally, the network structure constructed in this study only reflects group effects, meaning that it cannot capture idiographic individual-level processes of depression and anxiety.

This study presents the first application of symptom-level network analysis to investigate the depressive and anxiety symptoms of older Chinese adults with DM. The results revealed that “Feeling blue/depressed”, “Nervousness or anxiety”, “Uncontrollable worry”, “Trouble relaxing”, and “Worry too much” are the most central symptoms and that “Nervousness or anxiety” and “Everything was an effort” are the key bridge symptoms within the depression-anxiety network. These identified symptoms may be potentially effective targets for the prevention of depression and anxiety among at-risk older adults with DM and inform treatment strategies for those who are currently suffering from depression and anxiety.

DM: diabetes mellitus; CLHLS: Chinese Longitudinal Healthy Longevity Survey; EI: expected influence; BEI: bridge expected influence; IDF: International Diabetes Federation; MDD: major depressive disorder; GAD: general anxiety disorder; CESD-10: 10-item Center for Epidemiologic Studies Depression Scale; GAD-7: Seven-Item Generalized Anxiety Disorder Scale; GGM: Gaussian graphical model; LASSO: least absolute shrinkage and selection operator; EBIC: extended Bayesian information criterion; CI: confidence interval; CS: correlation stability;

Ethics approval and consent to participate

All methods were carried out in accordance with the Declaration of Helsinki and with relevant guidelines and regulations. Written informed consent was obtained from all participants included in this study. The ethical approvals of the CLHLS study were obtained from the Biomedical Ethics Committee, Peking University (IRB00001052–13074), and the Institutional Review Board, Duke University (Pro00062871). The CLHLS is publicly accessible.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available at Peking University Open Research Data Platform: https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/WBO7LK.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by PLA Military General Program (22BJZ12).

Author Contributions

Zhihua Guo: Conceptualization, Writing - original draft. Yi Cui: Formal analysis, Writing - original draft. Yijun Li: Formal analysis, Visualization. Hongliang Lu: Investigation, Methodology, Visualization. Quanhui Liu: Investigation, Methodology. Xia Zhu: Funding acquisition, Supervision, Writing - Review & Editing, Project administration.

Acknowledgements

The authors thank MogoEdit (https://www.mogoedit.com) for its English editing during the preparation of this manuscript.

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Table 1 Abbreviations, mean scores, standard deviations, EIs (raw values), and BEIs (raw values) for each symptom in the depression-anxiety network

Symptoms	Abb	M	SD	EI	BEI
Depression symptoms (CESD-10)
Feeling bothered	CESD1	0.79	0.58	0.90	0.05
Difficulty with concentrating	CESD2	1.02	0.70	0.68	0.06
Feeling blue/depressed	CESD3	0.73	0.58	1.12	0.09
Everything was an effort	CESD4	1.06	0.73	0.85	0.13
Hopelessness	CESD5	1.29	0.88	0.66	0.05
Feeling nervous/fearful	CESD6	0.65	0.60	0.84	0.08
Lack of happiness	CESD7	1.45	0.92	0.71	0.02
Loneliness	CESD8	0.64	0.65	0.92	0.04
Inability to get going	CESD9	0.50	0.60	0.90	0.10
Sleep disturbances	CESD10	1.36	0.77	0.39	0.13
Anxiety symptoms (GAD-7)
Nervousness or anxiety	GAD1	0.32	0.56	1.05	0.38
Uncontrollable worry	GAD2	0.23	0.51	1.05	0.05
Worry too much	GAD3	0.28	0.54	1.02	0.11
Trouble relaxing	GAD4	0.20	0.47	1.04	0.09
Restlessness	GAD5	0.16	0.42	0.92	0.01
Easily annoyed/irritated	GAD6	0.20	0.47	0.89	0.09
Afraid something terrible might happen	GAD7	0.12	0.38	0.65	0.03

Abb, abbreviation; M, mean; SD, standard deviation; EI, expected influence; BEI, bridge expected influence; CESD-10, 10-item Center for Epidemiologic Studies Depression Scale; GAD-7, seven-item Generalized Anxiety Disorder scale.

No competing interests reported.

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Network analysis of depressive and anxiety symptoms in older Chinese adults with diabetes mellitus

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