The relationship between body mass index and recurrence risk of stroke: a systematic review and meta‑analysis

doi:10.21203/rs.3.rs-3756784/v1

Background The relationship between body mass index (BMI) and recurrence risk of stroke is unclear, and a systematic review and meta-analysis on the relationship between BMI and recurrence risk of stroke needs to be updated.

Objective To explore the relationship between BMI and the risk of stroke recurrence.

Methods We searched databases including the Web of Science, Cochrane Library, Embase, PubMed, CBM, VIP, WanFang Database and CNKI, from inception to August 2023, to collect literature on BMI and the risk of stroke recurrence. After two researchers independently screened the literature, extracted the literature data and assessed the quality of the literature included in the study, a meta-analysis was conducted using Stata 16.0 software to examine the relationship between BMI and the risk of stroke recurrence.

Results A total of 17 studies were included, involving 166309 patients. In terms of stroke recurrence risk, compared with normal weight patients, underweight patients (RR=1.45, 95% confidence interval [CI] 1.19-1.78) had a higher risk of stroke recurrence, while overweight (RR = 0.91, 95% CI 0.87-0.96) and obese patients (RR =0.88, 95% CI 0.79-0.98) had a lower risk of stroke recurrence.

Conclusion BMI is associated with stroke recurrence. Underweight (BMI<18.5 kg/m2) is a risk factor for stroke recurrence, while overweight (25 kg/m2≤BMI≤29.9 kg/m2) and obesity (BMI≥30 kg/m2) are protective factors for stroke recurrence. Overweight and obesity may be beneficial for secondary prevention in stroke patients.

Registration number CRD42023447236

Body mass index

Stroke

Recurrence

Meta-analysis

Stroke is defined as an episode of acute neurological dysfunction presumed to be caused by ischemia or hemorrhage, persisting for more than 24 hours or until death[1]. Stroke is characterized by high incidence rate, high disability rate, high mortality, high recurrence rate and high economic burden[2]. The Global Burden of Diseases 2019 (GBD) released stroke remained the second-leading cause of death and the third-leading cause of death and disability globally. From 1990 to 2019, the burden of stroke significantly increased, with a 70.0% increase in stroke incidence, a 43.0% increase in stroke deaths, and a 102.0% increase in stroke prevalence[3]. It is estimated that the global losses caused by stroke exceed 891 billion US dollars, accounting for 1.12% of global GDP[4]. However, stroke recurrence is the main cause of long-term disability and increased mortality in patients[5].

Recurrent stroke is defined as the appearance of new neurological deficits after a clinically stable period, lasting for more than 24 hours, and confirmed by CT or MRI to be attributed to new ischemic or hemorrhagic lesions[6]. The risk of stroke recurrence at 30 days, 1 year, 5 years, 10 years, and 12 years after the first stroke was 11.1%, 26.4%, 39.2%, and 39.7%, respectively[7, 8]. Compared with the first stroke, the stroke recurrence is more disabling, fatal, and costly, with a 43% higher risk of death and prolonged length of stay, which seriously affects the life quality of patients[9, 10], increasing the burden on caregivers and the economic burden on families, society, and the country. Therefore, reducing the risk of stroke recurrence is very important and has the significance of reducing the burden on society, the country, and families.

There are many risk factors for stroke recurrence, including both modifiable (e.g., physical exercise, BMI, diet, smoking, drinking, comorbid conditions) and nonmodifiable risk factors (e.g., age, sex, race)[11]. About 90% of strokes are caused by the presence of modifiable risk factors, and the regulation of core health behaviors could avert about 75% of this burden[12, 13]. Thus, the key to reducing stroke recurrence risk is to identify modifiable risk factors.

There is overall agreement in the scientific and clinical community that obesity is one of the major public health problems and is increasingly recognized as an important risk factor for recurrent stroke[14–18]. However, BMI is not only an important indicator for determining the degree of obesity and thinness in the human body[19], but also a cheap and convenient method for screening weight categories, providing a simple and feasible intervention measure for stroke recurrence.

Recent studies[20, 21] reported that the stroke obesity paradox may even be extended to include the risk of stroke recurrence. One study[22] has reported that obesity is an independent risk factor of recurrence at 36 months, but the other study[23] indicated that obese patients had a lower risk and underweight patients had a higher risk of recurrent stroke, so the relationship between BMI and recurrence risk of stroke is unclear. So far, only one study[24] has included relevant cohort studies before December 15, 2015 to conduct a systematic evaluation and meta-analysis of the relationship between BMI and stroke recurrence, which needs to be updated. Therefore, we performed a systematic review and meta-analysis to examine the relationship between BMI and the risk of stroke recurrence, intending to find controllable factors of stroke recurrence to reduce its recurrence risk, thus improving the quality of life of patients and reducing the economic burden of family, society and country.

The present study was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-2009) guidelines[25]. The detailed study protocol can be obtained on the Prospero website.

Data sources and search strategy

Comprehensive literature searching was conducted in PubMed, Embase, Web of Science, Cochrane Library, VIP, WanFang Database, China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature (CBM), from inception to August 2023. Additionally, reference lists included in the identified articles were manually searched to identify additional relevant publications. We performed the search strategy through a combination of Mesh terms and free words. The specific search strategies can be found in the appendix, and the PubMed search strategy is provided as an example, the search details are provided in Box 1.

Study selection

Two investigators were asked to perform literature screening independently. Literature retrieved from the initial database search was screened for duplicates and excluded before other exclusion procedures. Full-text articles were retrieved when at least one reviewer decided that an abstract was eligible for inclusion. Each publication was assessed independently by both investigators for final study inclusion. Disagreements were resolved by discussion.

The inclusion criteria were as follows: ① Case-control, cross-sectional, or cohort studies; ② Study subjects were patients with a definite diagnosis of stroke; ③ The study analyzed the relationship between BMI and recurrence risk of stroke; ④ The BMI grouping criteria were based on WHO standards. ⑤ Stroke recurrence confirmed by CT or MRI. The exclusion criteria were as follows: ① Case reports, reviews, letters and comments; ② The language of the publication was other than English or Chinese; ③ Studies contained incomplete data.

Data extraction and quality assessment

Relevant data from the included studies was extracted by two independent authors. Any disagreements were resolved by discussion. The following information was recorded: first author name, publication year, study location, study design, sample size, gender, age, BMI classification, number of recurrence risk of stroke cases in different BMI groups, and overall number of recurrence risk of stroke cases. All extracted data was stored in the Microsoft Excel file format. The risk of bias assessment of all included studies was conducted using the Newcastle-Ottawa Scale[26, 27]. We regarded total scores of 0–3, 4–6, and 7–9 as low, moderate, and high quality, respectively. Any disagreements were resolved by discussion.

Statistical analysis

The results were quantitatively analyzed using Stata 16.0, using RRs and 95% CIs as measures of effect size. We tested heterogeneity among studies using Cochrane’s Q statistic. The degree of heterogeneity was assessed using the I² statistic, with I² values of 25%, 50%, and 75% being considered to indicate low, moderate, and high heterogeneity, respectively. RRs and 95% CIs were calculated using a random-effects model when Cochrane’s Q statistic detected significant heterogeneity; otherwise, a fixed-effects model was used. P < 0.05 was the threshold for statistical significance.

Literature search

A total of 3962 relevant literature were obtained through a preliminary search, of which 614 duplicate pieces were removed. Among the remaining 3048 pieces of literature, 141 were excluded based on the total and abstract. By reading the full text, 17 studies[20–23, 28–40] met the inclusion criteria and were included. See Fig. 1 for the detailed process.

Study characteristics

We summarized the basic characteristics of the 17 included studies. 17 studies (10 prospective cohort studies, 4 longitudinal studies, 3 case-control studies) were published between 1997 and 2023 from 8 countries (the United States, Denmark, China, Japan, Korea, Athens, Danish, and Germany). The sample sizes of the 17 literature ranged from 100 to 30912 and contained 166309 patients. The specific results are shown in Table 1.

Risk of bias in included studies

The quality of the included studies was evaluated by the NOS scale, with a score of 5–8, indicating that 12 studies were high quality and 5 studies were moderate quality. See Table 2 for the specific evaluation results.

Table 1 The basic characteristics of the included studies.

Study	Year	Country	Sample size	Male（%）	Age（year）	BMI classify(kg/m²)	Number of recurrence of stroke in each group	Total number of recurrence of stroke
Chung[28]	2023	Korea	1584	59.2%	Female：71.9 Male：67.6	≥25 <25	298 489	787
Ju[29]	2022	Korea	30912	65.7	18-74	<18.5 18.5- 24.9	80 1183	1263
Yan C[30]	2021	China	100	60	67.43±3.53	＜18.5 ≥18.5	2 3	5
Hou[31]	2021	China	12964	65.17	64.83±12.2	<25 ≥25	609 286	895
Kumral[32]	2021	Turkey	9285	56	66.2±12.6	<30 ≥30	1355 843	2198
Zhang^[22]	2019	China	2524	67.2	62.71 ±11.50	<30 ≥30	468 78	546
Kawase[33]	2017	Japan	1206	63	72.5 ± 11.4	<18.5 18.5-24.9 ≥25	15 129 52	196
Jingguang W[34]	2016	China	100	77	67.2±13.5	<25 ≥25	8 7	15
Andersen[35]	2015	Denmark	29326	52	72.3	<18.5 18.5-24.9 25.0-29.9 ≥30	75 848 705 286	1914
Zhao[36]	2015	China	2360	61	Female：66.4 Male：62.7	<30 ≥30	——	668
Aiming. J[37]	2014	China	510	51.18	≥18	<25 ≥25	77 121	198
Andersen[23]	2013	Denmark	28382	54.6	71.5±13.2	<18.5 18.5-24.9 25.0-29.9 ≥ 30.0	293 2480 1870 928	5571
Doehner[38]	2013	German	1521	53.5	59-84	<18.5 18.5- 24.9 25-30 30-34.9 ≥35	1 37 34 6 4	82
Chen[39]	2012	China	620	54.5	72 ± 10.9	≥25 <25	84 16	100
Ovbiagele[40]	2011	America	20246	64	66.1±8.6	<25 25-29.9 ≥30	600 778 427	1805
Vemmos[20]	2011	Denmark	2785	62	18-103	<25 25.0-29.9 ≥30	67 65 15	147
Olsen[21]	2008	Greece	21884	52	≥40	<18.5 18.5–24.9 25.0–29.9 30.0–34.9 ＞35	271 2100 1516 548 166	4601

Table 2 Risk of bias assessment for included studies

References	Year	Selection of study populations				Comparability of the group whether to control for confounders	Result measurement			Total score
References	Year	Representativeness of the exposure group	Selection of the non exposing group	Determination of exposure factors	Demonstration that outcome of interest was not present at start of study		Independent evaluation by blind method	Follow-up is long enough	Adequacy of follow-up	Total score
Chung[28]	2023	1	1	1	1	2	0	0	0	6
Ju[29]	2022	1	1	0	0	2	1	0	0	5
Yan C[30]	2021	1	1	1	0	2	1	0	1	7
Hou[31]	2021	1	1	1	0	2	1	0	1	7
Kumral[32]	2021	1	1	1	0	2	1	1	1	8
Zhang[22]	2019	1	1	1	0	2	1	0	1	7
Kawase[33]	2017	1	1	1	0	2	1	0	1	7
Jingguang W[34]	2016	1	1	1	1	2	1	1	1	9
Andersen[35]	2015	1	1	1	0	2	0	0	1	7
Zhao[36]	2015	1	1	1	0	2	1	0	1	7
Aiming. J[37]	2014	1	1	1	1	2	1	1	1	9
Andersen[23]	2013	1	1	1	0	2	1	1	1	8
Doehner[38]	2013	1	1	1	0	2	0	0	1	6
Chen[39]	2012	1	1	1	0	2	1	0	0	6
Ovbiagele[40]	2011	1	1	1	0	2	0	0	1	6
Vemmos[20]	2011	1	1	1	0	2	1	0	1	7
Olsen[21]	2008	1	1	1	0	2	1	1	1	8

Table 3 The result of meta-analysis

	Number of included studies	Pooled effect size		Heterogeneity test
		HR/RR	95%CI	I² (%) P value
Underweight	7	1.45 1.19-1.78		62.0 0.000
Overweight	6	0.91 0.87-0.96		0.0 0.000
Obese	9	0.88 0.79-0.98		63.3 0.017

The result of the meta-analysis

All 17 studies reported the relationship between BMI and recurrence risk of stroke, but only 12[20–23, 29, 30, 32, 33, 35, 36, 38, 40] were included in the meta-analysis. 3[31, 37, 39] out of the remaining 5 studies[28, 31, 34, 37, 39] showed a correlation between BMI and stroke recurrence, while 2 studies[28, 34] showed no correlation between BMI and stroke recurrence. Among 12 studies, the normal BMI category in each study was used as a reference group, and we then compared it to other groups with different BMI ranges (underweight versus normal BMI, overweight versus normal BMI, and obese versus normal BMI). The result of the meta-analysis is presented in Table 3.

Effect of underweight and normal BMI categories on recurrence risk of stroke

7 studies[21, 23, 29, 30, 33, 35, 38] examined the association between underweight and recurrence risk of stroke. Results of the meta-analysis showed that the risk of stroke recurrence in the underweight BMI group (BMI < 18.5 kg/m²) was 1.45 times higher compared to the normal BMI group (RR = 1.45, 95% CI 1.19–1.78, I² = 62.0%, P = 0.000).

Effect of overweight and normal BMI categories on recurrence risk of stroke

6 studies[20, 21, 23, 35, 38, 40] examined the association between overweight and the recurrence risk of stroke. Results of the meta-analysis showed that the risk of recurrent stroke decreased in the overweight BMI group (25 kg/m² ≤ BMI ≤ 29.9 kg/m²) by 0.91 times that in the normal BMI group (RR = 0.91, 95% CI 0.87–0.96, I² = 0%, P = 0.000).

Effect of obese and normal BMI categories on recurrence risk of stroke

9 studies[20–23, 32, 35, 36, 38, 40] examined the association between obesity and the recurrence risk of stroke. Results of the meta-analysis showed that the risk of recurrent stroke decreased in the obese BMI group (BMI ≥ 30 kg/m²) by 0.88 times that in the normal BMI group (RR = 0.88, 95% CI 0.79–0.98, I² = 63.3%, P = 0.017).

In the present study, we explored the relationship between BMI and the risk of stroke recurrence. The results indicate that BMI is associated with stroke recurrence, and obese and overweight patients have a higher risk of stroke recurrence compared to underweight patients. Our study further suggests that the 'obesity paradox' is possible.

Underweight is a risk factor for recurrent stroke, and the risk of recurrent stroke is increased in underweight patients by 1.45 times that of the normal BMI group. This may be due to BMI and the onset of stroke, which lead to various kinetics, such as a catabolic/anabolic imbalance and tissue wasting of both fat and muscle tissue[41], and form a vicious cycle that worsens the nutritional status[42], subsequently causing a poor outcome. On the one hand, it may be that patients with underweight, whether malnourished or not, have mild low-grade inflammation, and chronic inflammation may play a crucial role in the relationship between underweight and atherosclerosis[43]. One study suggests that underweight is associated with impaired endothelial function, leading to a decrease in the bioavailability of nitric oxide, which can regulate vascular tension, and inhibit platelet aggregation and adhesion[44]. On the other hand, low body weight can lead to a decrease in muscle mass, which is associated with excessive inflammation[45], insulin resistance, and various metabolic disorders[46], which may lead to myocardial infarction and stroke. Greater muscle mass is associated with better exercise ability and cardiovascular health, which may lead to a reduced risk of cardiovascular disease[47]. In addition, lower BMI can serve as an alternative marker for patient frailty, including malnutrition and non-cardiovascular comorbidities, and frailty itself increases the risk of stroke[48, 49].

Overweight and obesity are protective factors for stroke recurrence, with a reduced risk of stroke recurrence in overweight and obese patients of 0.91 and 0.88 times that in the normal BMI group, respectively. This conclusion is consistent with the "obesity paradox" found in previous studies[20, 23, 50, 51]. This may be related to overweight or obese patients having more metabolic reserves compared with normal weight patients, which helps them better cope with unbalanced catabolic status and impaired metabolic efficiency caused by stroke[52, 53]. Furthermore, a soluble tissue necrosis factor receptor produced by adipose tissue can neutralize the deleterious effects of tumors on the development and progression of stroke[54].

However, one study[32] suggests that obesity is associated with an increased risk of recurrent stroke compared to non-obese individuals, which may be related to several mechanisms in obese people, such as stress-related neuroendocrine autonomic nerve activation, proinflammatory cytokines, an increased load of oxygen-free radicals, and systemic hormone imbalance[41, 55]. Obesity can significantly aggravate the occurrence and development of atherosclerosis. On the one hand, adipocytes produce leptin, plasminogen activator inhibitor 1 (PAI-1), adiponectin, tumor necrosis factor (TNF-α) and other physiologically active substances that are directly involved in the formation of inflammation and atherosclerosis. On the other hand, obesity is one of the important risk factors for diabetes, hypertension and hyperlipidemia, which will promote arteriosclerosis and may increase the risk of stroke recurrence[56].

Although we have explained the relationship between obesity and stroke recurrence, there is still controversy, possibly due to the heterogeneity of the study subjects, differences in sample size, and inconsistent BMI criteria. Further research is needed to explain the potential mechanisms leading to the obesity paradox in stroke recurrence patients.

Strengths and limitations

Several strengths exist in this study. Firstly, this is one of the few studies that have conducted a systematic evaluation and a meta-analysis of the relationship between BMI and stroke recurrence. Secondly, this study gave a detailed explanation of the relationship between BMI and stroke recurrence in different, while the previous study mainly elaborated the relationship between one or several BMI categories and stroke recurrence. In addition, the recent new original studies on the relationship between BMI and stroke recurrence were included and most of the included studies had a large sample size. Finally, the literature included in this study was of good quality, with high hierarchy of evidence. 12 studies were of high quality, and 5 were of medium quality.

However, there are still some limitations that need to be addressed in this study. Firstly, the retrieval language was confined to English or Chinese; thus, publications in other languages may have been missed. Secondly, the small number of literature included in this study affects the reliability of the conclusion. Finally, the included subjects were mostly older and fewer younger, which may lead to biased results.

To sum up, the current research results show that BMI is related to stroke recurrence. Underweight (BMI < 18.5 kg/m²) is a risk factor for stroke recurrence, while overweight (25 kg/m² ≤ BMI ≤ 29.9 kg/m²) and obesity (BMI ≥ 30 kg/m²) are protective factors for stroke recurrence. These findings inform clinical decision-making and secondary prevention of stroke and suggest that appropriate weight management, including nutrition and rehabilitation, is particularly important for stroke patients.

Author Contribution

QX Qian and YT Zhao: Conceptualisation, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. YX Ma: Conceptualisation, Methodology, Writing–review & editing, Supervision, Project administration.All authors read and approved the final manuscript.

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Box 1 is available in the Supplementary Files section.

No competing interests reported.

Box1.docx

The relationship between body mass index and recurrence risk of stroke: a systematic review and meta‑analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data sources and search strategy

Study selection

Data extraction and quality assessment

Statistical analysis

Results

Literature search

Study characteristics

Risk of bias in included studies

The result of the meta-analysis

Effect of underweight and normal BMI categories on recurrence risk of stroke

Effect of overweight and normal BMI categories on recurrence risk of stroke

Effect of obese and normal BMI categories on recurrence risk of stroke

Discussion

Strengths and limitations

Conclusion

Declarations

Author Contribution

References

Box 1

Additional Declarations

Supplementary Files

Status:

Version 1