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The impact of social relationships on the risk of stroke and post-stroke mortality: a systematic review and meta-analysis

BMC Public Health volume 24, Article number: 2403 (2024) Cite this article

Abstract

Background

The association between poor social relationships and post-stroke mortality remains uncertain, and the evidence regarding the relationship between poor social relationships and the risk of stroke is inconsistent. In this meta-analysis, we aim to elucidate the evidence concerning the risk of stroke and post-stroke mortality among individuals experiencing a poor social relationships, including social isolation, limited social networks, lack of social support, and loneliness.

Methods

A thorough search of PubMed, Embase, and the Cochrane Library databases to systematically identify pertinent studies. Data extraction was independently performed by two researchers. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using either a random-effects or fixed-effects model. Sensitivity analyses were conducted to evaluate the reliability of the results. Random-effects meta-regression was performed to explore the sources of heterogeneity in stroke risk estimates between studies. Assessment for potential publication bias was carried out using Egger’s and Begg’s tests.

Results

Nineteen studies were included, originating from 4 continents and 12 countries worldwide. A total of 1,675,707 participants contributed to this meta-analysis. Pooled analyses under the random effect model revealed a significant association between poor social relationships and the risk of stroke (OR = 1.30; 95%CI: 1.17–1.44), as well as increased risks for post-stroke mortality (OR = 1.36; 95%CI: 1.07–1.73). Subgroup analyses demonstrated associations between limited social network (OR = 1.52; 95%CI = 1.04–2.21), loneliness (OR = 1.31; 95%CI = 1.13–1.51), and lack of social support (OR = 1.66; 95%CI = 1.04–2.63) with stroke risk. The meta-regression explained 75.21% of the differences in reported stroke risk between studies. Random-effect meta-regression results indicate that the heterogeneity in the estimated risk of stroke may originate from the continent and publication year of the included studies.

Conclusion

Social isolation, limited social networks, lack of social support, and feelings of loneliness have emerged as distinct risk factors contributing to both the onset and subsequent mortality following a stroke. It is imperative for public health policies to prioritize the multifaceted influence of social relationships and loneliness in stroke prevention and post-stroke care.

Trial registration

The protocol was registered on May 1, 2024, on the Prospero International Prospective System with registration number CRD42024531036.

Peer Review reports

Introduction

Stroke remains a prominent cause of both mortality and disability on a global scale [1, 2], imposing substantial burdens on individuals, families, and healthcare systems alike [3]. ramifications of stroke surpass 1.6% of the global GDP [4], with incidence rates of associated conditions steadily climbing due to population aging [5]. While traditional risk factors such as hypertension, diabetes, and smoking have been extensively explored and documented [6], emerging evidence underscores the significance of social relationships in both the onset and aftermath of stroke events [7].

Within the realm of social relationship science, various dimensions warrant consideration, including social isolation, social support, social networks, and loneliness [8,9,10]. Social isolation denotes the objective absence of social interactions or living alone [11], whereas social support encompasses the diverse functions and provisions offered by an individual’s social connections, including emotional care, instrumental assistance, and informational guidance [12, 13]. Social networks encapsulate the structure and composition of an individual’s social relationships, encompassing both the breadth and diversity of their social ties [14]. Conversely, loneliness constitutes a subjective psychological state characterized by feelings of perceived social isolation or dissatisfaction with one’s social relationships [15]. In alignment with the recommendations set forth by the National Committee on Health and Medical Issues pertaining to Social Isolation and Loneliness among Elderly Populations in the United States, the term “social relationships” is employed herein to encompass social isolation, social support, social networks, and loneliness [16].

Previous meta-analyses have suggested that individuals experiencing social isolation, lack of social support, and loneliness may confront an increased risk of stroke [17, 18] and all-cause mortality [19]. However, the association between these factors and post-stroke mortality remains largely unexplored. Moreover, significant inconsistencies and limitations exist within previous findings. For instance, Valtorta’s study [17] amalgamated social isolation with various social factors such as social networks and social support, thereby failing to specifically address the role of social isolation. Similarly, Freak-Poli’s research [18] was limited to Australia and New Zealand, with a restricted number of studies included.

Consequently, there exists a pressing need for a comprehensive synthesis of available evidence through a systematic review and meta-analysis to elucidate the influence of social isolation, lack of social support, limited social network, and loneliness on both the risk of stroke and post-stroke mortality.

Methods

Our study followed the directives delineated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [20]. The protocol has been properly recorded on the International prospective register of systematic reviews, Prospero, under the registration number CRD42024531036.

Data sources

This study conducted searches across multiple databases including PubMed, Embase, and the Cochrane Library, up to March 20, 2024. Language restrictions were not applied during the search process, employing a blend of medical subject headings (Mesh) and keywords. The search terms comprised (“Social Isolation” OR “Social Support” OR “Social Networking” OR “Loneliness”), (“Stroke” OR “Mortality”). Additionally, we conducted a review of previous meta-analyses [17, 18] to identify any additional studies that might meet the inclusion criteria. Comprehensive details regarding the search methodologies are available in Supplementary Table S1, S2 and S3.

Eligibility criteria

All included studies met the following criteria: (1) all observational studies, including cohort studies, case-control studies based on cohort trials, and cross-sectional studies; (2) the association between poor social relationships and the risk of stroke or the risk of mortality after stroke had to be examined; (3) the exposure variable comprises poor social relationships factors, encompassing challenges in social isolation, limited social network, lack of social support, loneliness and related factors; (4) the outcome is defined as stroke and mortality after stroke; (5) the studies included must provide comprehensive risk estimates, including Hazard Ratios (HR), Relative Risks (RR), or Odds Ratios (OR), accompanied by their respective 95% confidence intervals (CIs). In instances where this data was lacking, direct correspondence was established with the authors to ensure the acquisition of precise risk estimates.

The exclusion criteria: (1) duplicated publications; (2) letters, conference abstracts, comments and reviews; (3) the objects of observation are non-healthy groups; (4) studies that used the same database for repeated analysis or conducted repeated investigations on different aspects of the same population. (5) no interest outcome.

Study selection

Two independent assessors, MMX and SXM, conducted a meticulous review of the literature based on predefined inclusion and exclusion criteria. Initially, a combination of automated processes and manual screening was employed to eliminate duplicate publications. Subsequently, titles and abstracts underwent careful scrutiny to exclude literature irrelevant to the research topic. Finally, the remaining publications underwent comprehensive evaluation, involving full-text retrieval and thorough examination, with strict adherence to the inclusion and exclusion criteria to filter out publications that did not meet the specified standards. Any discrepancies between assessors were resolved through consultation with a third reviewer, XYM, throughout the literature selection process.

Data extraction

Two independent assessors (MMX and SXM) conducted data extraction, carefully scrutinizing all included studies and cross-verifying them to ensure the accuracy of information retrieval. Preliminary extraction of the following data from the articles was performed, including authorship, publication year, country, sample characteristics, study type, exposure variables, and outcome variables [21]. Exposure variables included social isolation, limited social networks, lack of social support, and loneliness. The extracted basic information was organized into tabular form to describe the specific characteristics of the included studies. In cases where data were incomplete or unclear, communication was established with the authors of the respective studies to obtain necessary details. When necessary, a third reviewer, XYM, was involved to resolve any disagreements.

Risk of bias

To evaluate the literature quality of cohort studies and case-control studies, we employed the Newcastle-Ottawa Quality Rating Scale (NOS) [22]. This scale assesses studies based on Selection, Comparability, and Outcome criteria. Each study was assigned up to nine stars by the NOS, reflecting the rigor of its design and reporting. Higher star ratings indicate higher quality. The assessment process involved two independent reviewers. For cross-sectional studies, we utilized the Agency for Healthcare Research and Quality Quality Assessment Tool (AHRQ) [23], which comprises 11 items focusing on aspects like research questions, design, data sources and methods, results, and conclusions. To ensure consistency and reliability in our evaluation, any disagreements between assessors (MMX and SXM) were resolved through discussion or by consulting a third-party reviewer, XYM.

Statistical analysis

Adjusted odds ratios (OR) and 95% confidence intervals (CI) were used to assess the association of all-cause social isolation with stroke and post-stroke mortality. Heterogeneity was assessed using the χ² test and I² values [24]. A fixed-effects model was applied if P > 0.1 and I²≤50%, while a random-effects model was used if I²>50% [25]. Sensitivity analyses systematically excluded individual studies to validate overall effects [26]. Visual inspection of funnel plots and statistical assessment with Egger’s and Begg’s tests were used to evaluate publication bias [27, 28]. Subgroup analyses based on continent, follow-up duration, study type, and exposure subtype of the poor social relationships were conducted to explore variations. The exposure subtypes of the poor social relationships included social isolation, limited social networks, lack of social support, and loneliness. Random-effects univariable and multivariable meta-regression analyses were performed to investigate potential sources of heterogeneity and to assess the impact of moderators, including region, follow-up duration, exposure subtype, study type, and year of publication. Stata statistical software (version 14.0) was used to perform all analyses [29, 30].

Results

Study selection

A total of 7,478 studies were retrieved from the database and 872 duplicates were removed. A further 6,606 studies were excluded by reading the title and abstract. The full texts of the remaining 70 studies have been downloaded and thoroughly examined. However, one article was not retrievable due to its outdated publication year. After a thorough review of the full texts, 12 studies [31,32,33,34,35,36,37,38,39,40,41,42] met the inclusion and exclusion criteria. Additionally, 7 studies [43,44,45,46,47,48,49] were sourced from previously published meta-analyses. Ultimately, 19 studies [31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] that met the inclusion and exclusion criteria were included in this meta-analysis. The flow chart for literature screening is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram of study screening

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Characteristics of included studies

A total of 19 studies were included in this meta-analysis, 14 of which were cohort studies [31, 32, 35, 36, 38, 41,42,43, 45,46,47,48,49], 1 studies [40] were case-control studies and 4 studies were cross-sectional studies [33, 34, 37, 39]; with the publication ranging from 1992 to 2024. A total of 1,675,707 people participated in the study, ranging from 100 to 938,558 people. All studies geographically spanned 4 continents and 12 countries, with 4 studies [35, 38, 40, 44] from Europe, 7 studies [31, 32, 34, 37, 39, 42, 45] from Asia, 2 studies [36, 46] from Oceania, and 6 studies [33, 41, 43, 47,48,49] from the Americas. The follow-up periods spanned from a minimum of 4 months to a maximum of 30 years. Self-reported questionnaires emerged as the predominant method for diagnosing stroke and post-stroke mortality, with a total of 11 studies [31,32,33,34, 37, 39, 41, 44, 46, 47, 49] employing this approach. A total of 4 studies [31, 32, 34, 37] measured poor social relationships using the one item of the Centre for Epidemiological Studies Depression Scale (CES-D) [50], 2 studies [33, 38] used the Revised UCLA Loneliness Scale [51], 2 studies [36, 43] used the Revised Lubben Social Network Scale (LSNS) [52], 1 study [43] used the Modified version of the Interpersonal Support Evaluation List Short Form [53], and 1 study [46] used the Duke Social Support Index (DSSI) [54]. The different measurement methods and details of social relationships in all single studies are presented in Supplementary Table S4. Additionally, all studies utilized self-reported questionnaires to diagnose the lack of social relationship factors. All studies, which adjusted for various confounding factors with slight variations across different investigations, presented adjusted risk estimates denoted by HR, OR, or RR, with consistent adjustments for sex and age across all research. The basic characteristics of the included studies are shown in Table 1.

Table 1 Basic characteristics of the included studies
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Quality assessment

Among all the studies included in the meta-analysis, the NOS score of 11 cohort studies and case-control studies included in the Meta-analysis is greater than 7, which indicates the high quality of the included studies. Five studies received a score of 6, indicating moderate quality. The total NOS score of each study is shown in Table 1, and the detailed NOS scores of each part for selection, comparability, exposure, and outcome are shown in Supplementary Table S5. Four cross-sectional studies were included, with detailed AHRQ assessment scores provided in Supplementary Table S6. The specific items of the AHRQ assessment tool are outlined in Supplementary Table S7.

Risk of stroke

A total of 16 studies [31,32,33,34,35,36,37,38,39, 43,44,45,46,47,48,49] among 19 studies in this Meta-analysis investigated the impact of poor social relationships on the risk of stroke. The pooled analyses with a random effect model showed that poor social relationships were associated with a high risk of stroke (OR = 1.30; 95%CI: 1.17–1.44; I 2 = 73.0%; P = 0.001). The sensitive analysis illustrates a robust result, as presented in Supplementary Figure S1. Figure 2 gives the forest plot of the association between poor social relationships and the risk of stroke.

Fig. 2
figure 2

Poor social relationships and risk of Stroke. Abbreviations: ES, effect size; CI, confidence intervals

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Risk of post-stroke mortality

A total of 8 studies [35, 38, 40,41,42, 45, 48, 49] that met inclusion and exclusion criteria examined the impact of social isolation, lack of social support, limited social network, and loneliness on the risk of post-stroke mortality. A positive outcome in pool analysis confirms that poor social relationships are associated with a high risk of post-stroke mortality (OR = 1.36; 95%CI: 1.07–1.73; I 2 = 81.6%; P = 0.011). As a result of the sensitive analysis displayed in Supplementary Figure S2, indicates that the pooled analysis of the poor social relationships and risk of post stroke mortality is robust. Figure 3 demonstrates the forest plot of poor social relationships and the risk of post-stroke mortality.

Fig. 3
figure 3

Poor social relationships and risk of post stroke mortality. Abbreviations: ES, effect size; CI, confidence intervals

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Meta-regression

The meta-regression explained 75.21% of the differences in reported stroke risk between studies (R 2 = 75.21%). The results of random-effects univariate and multivariate meta-regression analyses showed that the continent (β = 0.27; 95%CI: 0.12–0.42; P = 0.002) and publication year of the included studies (β = 0.02; 95%CI: 0.01–0.04; P = 0.041) were the main sources of heterogeneity in the estimated risk of stroke. The details of meta-regression results with the risk of stroke are depicted in Supplementary Table S8. We refrained from doing a meta-regression analysis with post-stroke mortality due to the small number of studies included.

Subgroup analysis

Subgroup analysis was conducted on the studies examining the association between poor social relationships and the risk of stroke (Table 2). Subgroups were delineated according to exposure subtype, continents, follow-up years, and study types. The pooled results of 4 studies [35, 38, 40, 44] from Europe showed that poor social relationships increased the risk of stroke (OR = 1.10;95%CI = 1.01–1.19; I 2 = 49.3%; P = 0.028), as did the pooled results of 7 studies [31, 32, 34, 37, 39, 42, 45] from Asia (OR = 1.27; 95%CI = 1.06–1.52; I 2 = 76.1%; P = 0.01). In addition, 6 studies [33, 41, 43, 47,48,49] from the Americas also confirmed that poor social relationships are associated with an increased risk of stroke (OR = 1.45; 95%CI = 1.16–1.82; I 2 = 26.1%; P = 0.001), so as the pooled analyses of 2 studies [36, 46] from Oceania (OR = 2.78; 95%CI = 1.78–4.32; I 2 = 0%; P = 0.001). Under the follow-up time subgroup, the results of the pooled analyses illustrate that follow-up time > 10 years [43, 44, 47, 49] (OR = 1.44; 95%CI = 1.01–2.06; I 2 = 49.6%; P = 0.042), and the follow-up time ≤ 10 years [18, 31,32,33,34,35, 37,38,39, 45, 46, 48] (OR = 1.29; 95%CI = 1.15–1.43; I 2 = 77.1%; P = 0.001). Subgroups according to study types showed that both cohort studies [31, 32, 35, 36, 38, 43,44,45,46,47,48,49] (OR = 1.30; 95%CI = 1.15–1.48; I 2 = 75.7%; P = 0.001) and cross-sectional studies [33, 34, 37, 39] (OR = 1.31; 95%CI = 1.12–1.52; I 2 = 21.5%; P = 0.001) indicated an increased risk of stroke associated with poor social relationships. Subgroups underlying Exposure subtypes, 4 studies [36, 38, 43,44,45, 47,48,49] examined the effectiveness of the link between social isolation and risk of stroke. Pooled analysis findings point to no statistically significant association between social isolation and risk of stroke (OR = 1.10; 95%CI = 0.87–1.37; I 2 = 49.8%; P = 0.428). As a part of exposure subtypes, 4 studies investigated the relationship between social network and stroke (OR = 1.52; 95%CI = 1.04–2.21; I 2 = 55.7%; P = 0.003). Apart from this, 7 studies [31,32,33,34,35, 37, 38] research on the relationship between loneliness and risk of stroke. After pool analysis, the result suggests that loneliness is linked to the risk of developing stroke (OR = 1.31; 95%CI = 1.13–1.51; I 2 = 83.5%; P = 0.001). Additionally, pool analysis under 4 studies [36, 43, 45, 46] also showed that lack of social support was also associated with the risk of stroke (OR = 1.66; 95%CI = 1.04–2.63; I 2 = 74.9%; P = 0.033).

Table 2 Subgroup analysis for the risk of stroke in patients with poor social relationships
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Publication bias

A visual inspection of the funnel plots (Fig. 4) revealed a publication bias in the studies investigating the relationship between poor social relationships and the risk of stroke. Furthermore, validation through Egger’s test (P = 0.025) and Begg’s test (P = 0.014) confirmed the publication bias. We employed the Trim and Fill method to address potential publication bias. After four iterations using the Linear method, the Stata software estimated that five studies were missing. After incorporating data from 5 virtual studies, a meta-analysis was conducted on all studies again, and the results showed a heterogeneity test (Q = 88.584, P = 0.001). A random effects model was used to obtain the pooled results (OR = 1.224; 95% CI: 1.103–1.358). The trim and fill funnel plot was showed in Supplementary Figure S3. On the contrary, the funnel plot (Fig. 5), Egger’s test (P = 0.174) and Begg’s tests (P = 0.074) under the studies which examined the impact of poor social relationships on post-stroke mortality didn’t find evidence for publication bias. Trim and fill funnel plot displayed in Supplementary Figure S4.

Fig. 4
figure 4

The funnel plot depict the relationship between poor social relationships and risk of stroke

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Fig. 5
figure 5

The funnel plot reveal the impact of poor social relationships on post stroke mortality

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Discussion

Main findings

Our comprehensive analysis revealed that poor social relationships are significantly associated with an elevated risk of stroke and post-stroke mortality. Specifically, the risk of stroke increased by 1.30-fold, and the risk of post-stroke mortality by 1.36-fold.

Comparison with previous studies

This meta-analysis represents a pioneering effort in investigating the association between poor social relationships and post-stroke mortality risk. Distinguishing itself, we conducted a detailed subgroup analysis, delineating the impact of various social factors such as social isolation, social support, and social networks on stroke risk. Our comprehensive study incorporated the latest and largest observational studies, spanning cohort studies, case-control studies, and cross-sectional studies from four continents and 12 countries across varying income levels.

Building upon previous research, our findings align with a prior study [17] that demonstrated social isolation and loneliness heighten the risk of stroke. Notably, our meta-analysis addresses a gap in the literature by disentangling these social factors, which were sometimes conflated in earlier studies. This refinement enhances the clarity and specificity of our results. It’s worth noting that the previous study [17] included only 8 studies, potentially limiting its statistical power.

In contrast, the study by Freak-Poli et al. [18] yielded divergent results. However, it’s essential to recognize the limitations of their study, chiefly the restricted geographic scope limited to Australia and New Zealand. This regional constraint may not adequately capture the diverse socio-cultural contexts that influence the relationship between social factors and stroke risk on a global scale.

Interpretation of findings

Our meta-analysis delves into how the poor social relationships affect stroke risk and post-stroke mortality, highlighting the nuanced relationships between different types of social deficits. Given the growing recognition of poor social relationships as a public health crisis, it’s crucial to grasp its implications for stroke outcomes. Our findings underscore the pivotal role of the poor social relationships in both stroke incidence and survival afterward, emphasizing the need for public health policies that prioritize addressing these factors in stroke prevention and post-stroke care.

Stroke prevention

Numerous studies have underscored the association between inadequate social relationships and heightened susceptibility to a range of chronic diseases and unhealthy lifestyle choices, encompassing cardiovascular diseases [55], hypertension [56], atherosclerosis [57], type 2 diabetes [58, 59], sedentary behavior, reduced physical activity [60], smoking [61] and insomnia [62]. Collectively, these factors have been shown to amplify the risk of stroke.

Furthermore, inflammation emerges as a pivotal mediator in the relationship between social isolation and stroke occurrence [63]. Socially isolated individuals exhibit markedly elevated levels of C-reactive protein (CRP), nearly double that of their socially integrated counterparts [64]. In murine models subjected to recurrent social stressors, there is a notable accumulation of Ly6C^hi monocytes in key tissues such as the bone marrow, spleen, blood, and brain [65, 66]. This selective expansion of the Ly6C^hi monocyte subset precipitates a pro-inflammatory milieu in both animal models and humans [65, 67]. Prolonged inflammation constitutes a crucial mechanism underlying atherosclerosis in individuals experiencing social isolation [68].

In the human body, loneliness can also exert profound effects on the hypothalamic pituitary adrenal axis (HPA), resulting in elevated levels of catecholamines and cortisol in the bloodstream [69, 70]. Excessive cortisol disrupts the delicate balance within the body, exerting detrimental effects on various physiological processes, including glucose metabolism, cell apoptosis, immunity, reproductive and cardiovascular systems, as well as inflammation [70]. Furthermore, heightened glucocorticoid levels can exacerbate the development of atherosclerosis and hypertension [71].

Public health policies should focus on building strong social support networks to alleviate feelings of isolation. This strategy can lower rates of chronic diseases, discourage unhealthy behaviors, prevent long-term inflammation, and stabilize the HPA axis, potentially reducing the risk of stroke.

Post-stroke care

Brain-derived neurotrophic factor (BDNF) represents another crucial regulatory factor in this context. In a rat model that underwent 8 weeks of social isolation, decreased BDNF concentrations were observed in key brain regions such as the hippocampus and prefrontal cortex, as well as in blood and saliva [72]. Several studies have shown that decreased BDNF levels are closely associated with poor prognosis [73,74,75]. This means that social support and positive social interactions are essential for maintaining healthy neurological function.

Another pivotal determinant is treatment adherence. A comprehensive meta-analysis focusing on patient social support and medication adherence revealed a significant reduction in treatment adherence associated with increased social isolation, loneliness, and inadequate social support, ultimately culminating in unfavorable health outcomes [76]. Therefore, enhancing social interaction in the care of post-stroke patients may help increase BDNF levels and improve compliance with rehabilitation therapy, thereby reducing the risk of stroke-related mortality and improving overall health outcomes.

Interpretation of subgroup analysis

The subgroup analysis of this meta-analysis suggests that while social isolation alone may not significantly influence stroke risk, the presence of lack of social support and limited social networks may indeed contribute to a heightened risk of stroke. For a long time, social isolation has been regarded as equivalent to loneliness [16, 77,78,79]. However, in social psychology, loneliness is only a subjective personal feeling [80]. People with less social activity may not necessarily feel lonely, and people with more social activity may feel lonely [16]. This suggests that the quality and quantity of social interactions, rather than mere isolation, play a crucial role in shaping stroke outcomes. The conclusion of subgroup analysis according to region shows that Americans who suffer from social loneliness have a higher risk of stroke. This may be related to the national background of the United States which worships individualism [81]. Social economic status, unmarried status, and education level are all related to social segregation [82]. This may be the reason for the inconsistent levels of stroke risk among subgroups on different continents. Research samples with longer follow-up times are generally older at the end of the study, and the risk of social isolation in older individuals is much higher than in younger individuals [80]. This may give a reason why subgroups with different follow-up times have different levels of stroke risk. The subgroup analysis results of different research types show that both cohort studies and cross-sectional studies show a association between social isolation and higher stroke risk. This consistency suggests that social isolation may indeed be an important factor in stroke risk, and different types of research have little impact on this conclusion.

Future research and limitations

The poor social relationships, which has emerged as a public health epidemic, is quietly undergoing a shift in prevention strategies. Given the impact of the poor social relationships on stroke and its prognostic outcomes. Future research should investigate the mechanisms through which social isolation, limited social networks, lack of social support, and loneliness influence both stroke risk and the risk of post-stroke mortality, specifically examining how these social relationship factors impact stroke risk and recovery processes. Moreover, longitudinal studies are necessary to provide clearer causal insights and assist in crafting tailored interventions aimed at specific social shortcomings.

Despite the insights gleaned, several limitations warrant consideration. Different measurement methods on social relationships and demographic differences included in the study may lead to significant heterogeneity. Although our meta-regression speculates on some sources of heterogeneity, we cannot consider all factors. Also, it is important to note the publication bias. To minimize the impact of publication bias, we implemented measures such as funnel plot analysis and the Trim and Fill method. Despite these efforts, the influence of publication bias cannot be entirely ruled out. Therefore, our results should be viewed with caution.

Conclusion

This meta-analysis demonstrates a significant association between social isolation, lack of social support, limited social network, loneliness, and an elevated risk of stroke and post-stroke mortality. These findings highlight the importance of early identification and intervention targeting these poor social relationships factors after stroke.

Availability of data and materials

The datasets used during the study are available from the public databases and corresponding author on reasonable request.

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Funding

The research was financially supported by the National Natural Science Foundation of China under grant number 81303011 and 81973618, China University Industry -Academia-Research Innovation Fund–Huatong Guokang Medical Research Project (2023HT033) and Construction of the Double First-Class Traditional Chinese Medicine Discipline Project in Henan Province–Cultivating the Innovation Ability of Graduate Students (HSEP-DFCTCM-2023-8-44, HSEP-DFCTCM-2023-8-27).

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Authors and Affiliations

  1. Encephalopathy Hospital, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China

    Mingxian Meng, Rui Lan, Shirui Zhu, Deyu Miao & Xiaoming Shen

  2. The First Clinical Medical College, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China

    Mingxian Meng

  3. School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China

    Zheng Ma

  4. State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China

    Hangning Zhou

  5. Institute of Clinical Basic Medicine, China Academy of Chinese Medical Sciences, Beijing, China

    Yanming Xie

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  1. Mingxian Meng
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  7. Deyu Miao
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  8. Xiaoming Shen
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Contributions

Mingxian Meng, Zheng Ma and Xiaoming Shen design the study and wrote the paper; Yanming Xie were pivotal in supervision and editing process. Rui Lan, Deyu Miao and Shirui Zhu played crucial roles in data acquisition. Mingxian Meng analyzed the raw data, Hangning Zhou revised the manuscript.

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Meng, M., Ma, Z., Zhou, H. et al. The impact of social relationships on the risk of stroke and post-stroke mortality: a systematic review and meta-analysis. BMC Public Health 24, 2403 (2024). https://doi.org/10.1186/s12889-024-19835-6

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