Stroke accounts for approximately 10% of global mortality (1), and ranks as the second most common cause of death (2). Obesity and metabolic syndrome (Mets), which often co-exist, are associated with an increased risk of stroke (3,4). However, quite a few studies reported that obesity might confer a beneficial effect on individuals with stroke (5-7). The complexity of the association between body mass index (BMI) and stroke may be presumably attributed to the heterogeneity of obese phenotypes: unhealthy or healthy metabolic status. A significant proportion of subjects with obesity do not develop Mets. On the contrary, Mets can be present in normal weight individuals (8). Therefore, it has been increasingly essential to distinguish between obese individuals at a high risk of obesity-related metabolic diseases and metabolically “healthy” obese individuals. Correspondingly, the population can be divided into 6 subtypes according to BMI and metabolic status: metabolically healthy normal weight (MH-NW), metabolically healthy overweight (MH-OW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MU-NW), metabolically unhealthy overweight (MU-OW), and metabolically unhealthy obese (MUO) (8).
Obese individuals without metabolic complications, such as elevated blood pressure, elevated triglycerides, reduced high-density lipoprotein cholesterol (HDL-C) and diabetes, are known as MHO individuals. Despite the excessive weight, MHO displays a beneficial hormonal profile, a favorable metabolic profile characterized by higher insulin sensitivity, a favorable immune profile, and reduced inflammation (8,9). Some studies have confirmed that MHO has a protective effect and does not increase the risk of cardiovascular diseases (CVDs) and mortality, especially when compared with at-risk obesity (10,11). Several other studies have shown that compared with MH-NW individuals, the MHO group has higher incidences of CVD, cancer, and mortality (12-14). However, whether MHO is associated with an excess risk of stroke remains a subject of debate due to lack of evidence. Previous studies on the effects of the BMI-metabolic status phenotypes on stroke morbidity have produced conflicting results (15,16).
It remains unclear whether MHO is associated with an excess risk of stroke. Thus, we conducted this meta-analysis which only included prospective cohorts to explore the associations of stroke risk with the BMI-metabolic status phenotypes, especially the MHO subtype. We present the following article in accordance with the PRISMA reporting checklist (available at http://dx.doi.org/10.21037/atm-20-4387).
Search strategy and selection criteria
We complied with the recommendations made by the Meta-Analysis of Observational Studies in Epidemiology Group, the 2009 guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (17,18). PubMed, Embase and Cochrane Library were thoroughly searched from December 1946 to January 2019. The following scientific search strategy was used: (“stroke” OR “cerebrovascular” OR “cerebral” OR “transient ischemic attack” OR “TIA” OR “hemorrhage” OR “hemorrhagic”) AND (“Metabolic*” OR “metabolism”) AND (“obese” OR “obesity” OR “overweight” OR “BMI” OR “body mass index” OR “waist circumstance”). We also manually searched the biographies of eligible studies and related meta-analyses to find possible missing studies.
We included studies that simultaneously met the following criteria: (I) they were conducted in adults; (II) they were prospective cohort studies; (III) participants were stroke-free at baseline; (IV) according to BMI, they categorized participants into three groups, normal weight (BMI <25 kg/m2), overweight (BMI ≥25 and <30 kg/m2), and obesity (BMI ≥30 kg/m2), or into two groups, normal weight (BMI <25 kg/m2) and abnormal weight (BMI ≥25 kg/m2). A threshold of 24 kg/m2 was set for Asians; (V) they further classified participants according to healthy or unhealthy metabolic status; (VI) they defined MHO as individuals with less than two common metabolic complications of obesity, such as elevated blood pressure, high triglycerides, decreased HDL-C and diabetes. Studies which met one of the following criteria were excluded: (I) the article was not published in English; (II) the normal weight was defined as BMI <30 kg/m2; (III) stroke was not treated as a single outcome. We only included the updated study for overlapping studies. In addition, studies were excluded if their risk ratios (RRs) and 95% confidence intervals (95% CIs) were not accessible even after contacting the authors.
Data extraction and quality assessment
The following detailed information was extracted carefully from each study, including authors’ name, publication dates, follow-up periods, countries, cohort sources, ages at baseline, gender ratios, sample sizes, case numbers, BMI categories, definitions of metabolic status, BMI-metabolic status phenotypes, adjusted confounders, outcome events, effect sizes, and 95% CIs. For studies without reported risk estimates, raw data, if available, were used to calculate effect sizes. Details of all included studies are shown in Table 1.
Quality assessments of all potentially eligible studies were conducted using the Newcastle-Ottawa Scale (NOS) (Table S1) (19), which contains 8 items categorized into 3 domains: selection, comparability, and exposure. No studies were excluded due to their low quality scores. Data extraction and quality evaluation were conducted by two independent authors, and any disagreement was resolved by consensus with a third author.
Overall RRs were calculated to assess the risk for MH-OW, MHO, MU-NW, MU-OW, and MUO phenotypes using MH-NW participants as the reference group. Due to the significant heterogeneity in subject characteristics across studies, effect sizes and 95% CIs were calculated using a random effects model (20). We used Cochrane Q test to assess heterogeneity among studies (20,21). Furthermore, we performed sensitivity analyses after excluding one study at a time to assess the reliability and sensitivity of the results. Subgroup analyses according to follow-up duration were conducted to detect the possible sources of heterogeneity (21). Finally, publication bias was adopted to check up if the pooled values were impacted by part of the studies’ positive results using Egger’s test. When statistically significant bias was found, the trim and fill method was used to adjust it. All statistical tests used a significance level of P<0.05. Statistical analyses were performed using R 3.6.1.
Figure 1 shows the flowchart of the study selection. We identified 28,416 studies through electronic searches. Eighty-three studies that may be eligible for inclusion were identified after reviewing titles and abstracts. Eight prospective cohort studies met the inclusion criteria after reviewing the full text (15,16,22-27).
Characteristics of included studies
Table 1 summarizes the characteristics of studies. Among the included studies, five used Adult Treatment Panel III (ATP III) criteria including elevated blood pressure, higher triglycerides, reduced HDL-C and diabetes, and the other three used medical histories of these diseases to define the metabolic status. The sample size of the studies varied greatly, ranging from 7,122 participants to 3,495,777 participants. The included studies had an average follow-up time of 11.4 years, and the population had a mean age of 47.9 years. The quality scores of these studies according to the NOS are summarized in Table S1. All studies included in this meta-analysis got at least seven stars, indicating that the quality is acceptable.
Effects of BMI categories on metabolically healthy population
In an overall analysis of eight studies involving 4,256,888 participants (15,16,22-27), metabolically healthy individuals with BMI ≥25 kg/m2 had a higher risk of stroke compared with MH-NW individuals (RR =1.09, 95% CI: 0.99–1.19) (Figure 2A) with non-significant heterogeneity (I2=36%, P=0.14).
In a pooled analysis of five studies involving 3,633,467 participants (16,22-25), no significant association was found between MH-OW and stroke (RR =1.02, 95% CI: 0.84–1.23) (Figure 2B) with moderate heterogeneity across studies measured by a random-effects model (I2=57%, P=0.05). Sensitivity analyses were conducted to explore the sources of heterogeneity; the pooled effect did not vary substantially after excluding any single study (Figure S1). We pooled studies with over 10 years of follow-up to explore the sources of heterogeneity and explore whether the length of follow-up will have an impact on the outcome. The results showed that the stroke risk in MH-OW patients was similar to that in MH-NW patients, both for long-term follow-up (RR =0.99, 95% CI: 0.56–1.77, I2=65%, P=0.06) and short-term follow-up (RR =1.00, 95% CI: 0.86–1.16, I2=28%, P=0.24) (Figure S2), suggesting follow-up duration was not a source of heterogeneity.
In a pooled analysis of five studies involving 3,633,467 participants (16,22-25), a significant association between MHO and a higher risk of stroke was found with non-significant heterogeneity (RR =1.17, 95% CI: 1.11–1.23, I2=0%, P=0.67) (Figure 2C). We performed subgroup analyses according to follow-up duration (more than 10 years or less than 10 years). Compared with the MH-NW phenotype, the pooled RRs of MHO phenotype were extremely higher irrespective of follow-up duration, with their RRs of 1.33 (95% CI: 1.04–1.72, I2=0%, P=0.54) for analysis of studies with over 10 years of follow-up (Figure S3A) and 1.16 (95% CI: 1.10–1.23, and I2=0%, P=0.91) for analysis of studies with less than 10 years of follow-up (Figure S3B) respectively. The results of the funnel plot and Egger test showed that publication biases weren’t found in MHO subgroups (P=0.632) (Figure S4). The credibility of result was moderate using GRADEpro GDT (Figure S5).
Effects of BMI categories on metabolically unhealthy individuals
As expected, all groups with metabolically unhealthy status had a similarly increased risk of stroke. In a pooled analysis of eight studies (15,16,22-27), the MU-NW group had an increased risk of stroke compared to the MH-NW group (RR =1.83, 95% CI: 1.57–2.14) (Figure 3A) with obvious heterogeneity across studies (I2=86%, P<0.01). In a pooled analysis of 5 studies, a significant association was found between MU-OW phenotype and a higher risk of stroke (RR =1.93, 95% CI: 1.44–2.58, I2=88%, P<0.01) (Figure 3B), and a similar result was found for MUO phenotype (RR =2.00, 95% CI: 1.40–2.87) (Figure 3C) with obvious heterogeneity across studies (I2=91%, P<0.01). Subsequently, sensitivity analyses were conducted to explore the sources of heterogeneity. Finally, the study of THIN (16) fully explained the heterogeneity (Figure S1). After carefully comparing this study with other studies, we found that this study only used the medical history provided by the participants rather than rigorous laboratory tests to determine whether participants had metabolic complications of obesity, such as elevated blood pressure, high triglycerides, decreased HDL-C and diabetes. After excluding this study, the heterogeneities in MU-NW, MU-OW and MUO phenotypes were reduced to 52%, 0%, and 30%, respectively. The pooled effects did not vary substantially after excluding this single study (MU-NW: RR =1.95, 95% CI: 1.71–2.22; MU-OW: RR =2.23, 95% CI: 1.95–2.54; MUO: RR =2.30, 95% CI: 1.73–3.06) (Table S2).
This meta-analysis of prospective studies summarized the inconsistent results from previous studies investigating the associations between BMI-metabolic status phenotypes and stroke risk. There are two critical findings. First, compared with MH-NW individuals, MHO individuals were at an increased risk of stroke. Second, all three unhealthy metabolic status phenotypes showed increased risks, which are not affected by the weight status.
Results suggested that MHO participants were at a higher risk of stroke. And further analyses suggested that regardless of follow-up duration, the MHO phenotype did associate with a higher risk of stroke. MH-OW participants were not at an increased risk of stroke compared with MH-NW individuals, although statistical significance was almost reached. However, the results have to be interpreted with caution, as moderate heterogeneity was observed in these analyses. As expected, the three unhealthy metabolic status phenotypes showed higher risks, which are not affected by the weight status.
Obesity has been considered as an indicator of poor health. However, a concept of “obesity paradox” has recently emerged, which is used to describe the unexpected improvement in prognosis and reduction in mortality found in several diseases among patients with higher body weight (28-30). A meta-analysis showed that MHO population had a higher risk of cardiovascular events (stroke was considered as one of the outcomes) over the long term, which indicated that the duration of follow-up is a critical element in evaluating low-risk populations for future events (31). Partially consistent with previous studies, our analyses found, even not considering the effects of follow-up, the risk of stroke in MHO individuals was still higher than that of MH-NW individuals, suggesting that there is no “healthy” pattern of obesity. Some previous reports only assessed MHO individuals over short term (32,33) or compared these individuals with controls without cardiovascular risk (32), which may lead to the “obesity paradox”. Our study once again refuted the view that high BMI may be harmless.
In addition, special attention should be paid to MU-HW individuals. Although the risk of stroke in the MHO group was increased, it was still lower than that of MU-NW participants, suggesting intermediate risk. Indeed, the MU-HW group had a similar risk compared with MU-OW and MUO groups. A possible explanation is that even without excess weight, this group may represent the most severe subtype in the phenotypic profile of individuals genetically predisposed to cardiovascular disease, so they have unfavorable metabolic characteristics. The study reported that compared with the MH-NW group, MU-HW group had the highest weighted differences in LDL cholesterol and glucose levels (even higher than their overweight and obese peers with unhealthy metabolism) (31).
Previous meta-analyses evaluating the association between BMI and the risk of stroke have not considered the presence of metabolic factors (5,34,35). The advantage of this study is that the large sample size can well reflect BMI and metabolic status, which allows us to determine reliable risk estimates related to the six BMI-metabolism categories.
It is worth noting that this study has some limitations. First, most of studies used BMI to define obesity. Although waist circumference might more accurately depict an individual’s visceral obesity (36), waist circumference values were not available from the database. What’s more, weight gain is an essential risk factor, often accompanied by the progression of abnormal metabolic status. However, there were no studies reporting data on BMI changes during follow-up time. It is possible that BMI changes, rather than weight status at the time of data collection, impact stroke risk more profoundly (16). Besides, the study has shown that non-Hispanic black population had a higher rate of MHO, suggesting that ethnic background may be an important factor in determining MHO (37). However, due to the lack of information from original studies, we did not carry out detailed studies of different races.
In the future, detailed studies should be conducted for individuals of different ethnic backgrounds. The dynamic changes of participants’ BMI, waist circumference and other indicators should be regularly followed up in more in-depth research.
In conclusion, this meta-analysis of prospective studies showed that obese individuals with metabolic phenotypes considered to be ‘healthy’ still had a higher risk of stroke compared with MH-NW individuals. Existing prospective evidence does not indicate that healthy obesity is a harmless condition. Weight reduction and improvements in health are needed and they are beneficial to reducing the risk of stroke for MHO individuals.
Funding: This study was supported by grants from the Taishan Scholar Program of Shandong Province (ts201511109 and tsqn20161079) and Qingdao Key Health Discipline Development Fund, Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01) and ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.
Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at http://dx.doi.org/10.21037/atm-20-4387
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-4387). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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