Abstract
Background and Purpose
Ischemic stroke is associated with the metabolic syndrome (MetS) as diagnosed using dichotomous criteria; however, these criteria exhibit racial/ethnic discrepancies. Our goal was to assess whether ischemic stroke risk extended over the spectrum of worsening MetS severity using a sex- and race/ethnicity-specific MetS-severity z-score (MetS-Z).
Methods
We used Cox-proportional hazards models to assess the relationship between baseline MetS-Z and incident ischemic stroke among participants of the Atherosclerosis Risk in Communities study and Jackson Heart Study who were free from diabetes, coronary heart disease or stroke at baseline, evaluating 13,141 white and black individuals with mean follow-up of 18.6 years.
Results
We found that risk of ischemic stroke increased consistently with MetS severity, with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.35,2.27) for those >75th percentile compared to those <25th percentile. This risk was highest for white females (HR=2.63 CI 1.70,4.07) though without significant interaction by sex and race. Relationships between stroke and all the individual components of MetS were only noted for white females, though again without sex-race interactions. HR’s for systolic blood pressure and stroke were significant among all sex/racial subgroups.
Conclusion
Ischemic stroke risk increased over the spectrum of MetS severity in the absence of baseline diabetes, further implicating potential etiologic risks from processes underlying MetS. Individuals with elevated MetS severity should be counselled toward lifestyle modification to lower ischemic stroke risk.
Keywords: Ischemic Stroke, Metabolic Syndrome, Cardiovascular Disease, Risk Factors
Stroke prevalence in the U.S. is projected to reach 3.9% over the coming decade.1 One pertinent risk factor is the metabolic syndrome (MetS), a cluster of cardiovascular indices, including central obesity, high blood pressure (BP), low HDL, elevated triglycerides and elevated fasting glucose. Using criteria such as the Adult-Treatment-Panel-III (ATP-III), the presence of MetS is associated with a relative risk of 1.70 for future stroke,2 including in the absence of glucose abnormalities.3
Nevertheless, these dichotomous MetS criteria have limitations including racial/ethnic discrepancies, with black individuals classified as having MetS at low prevalence despite having higher prevalence of diabetes, cardiovascular mortality and stroke.1,4 Therefore, we developed a continuous MetS-severity Z-score that is specific to sex and race/ethnicity.5 This score revealed continuous risk for coronary-heart-disease (CHD)6–8 and diabetes.8–10 We hypothesized that among a cohort without baseline diabetes 1) there would be an increased risk for ischemic stroke across the spectrum of MetS-severity and 2) these risks would be similar between blacks and whites and between females and males.
METHODS
Cohorts and exclusion criteria
The Atherosclerosis Risk in Communities (ARIC) followed 15,792 mostly white and black participants ages 45–64 years from 1987–2011 across four U.S. centers.11 Data from these studies are available through collaboration with the respective studies and through the NHLBI BioLINCC. We utilized data from Visits 1&2, and data on adjudicated stroke outcomes collected through 2011.
The Jackson Heart Study (JHS) began as an extension of black participants in the Jackson, MS site of ARIC, utilizing similar methodologies, following 5,301 participants ages 21–95 years, current data from 2000–2016.12 For 1,626 participants initially followed as part of ARIC, we used data from ARIC not JHS.
ARIC and JHS were approved by IRB’s of participating institutions, and participants provided written informed consent.
After combining the two cohorts (n=19,026), we excluded participants with baseline CHD, stroke, or diabetes (which is associated with MetS and already a known risk factor for stroke1) and participants with non-fasting labs or missing baseline on MetS components and/or outcomes, leaving 13,141 individuals (Supplementary Figure 1).
Adjudicated incident ischemic stroke data were ascertained using standard ARIC and JHS protocols through hospital discharge codes and stroke-attributed deaths.12 Follow-up time was the minimum number of days between baseline visit and either the first stroke, death from other causes, or last contact.11,12
MetS components were measured9 and ATP-III-MetS was defined9 as described previously.
Baseline MetS-severity Z–scores were calculated for participants using sex- and race-based formulas. As described previously,5 these scores were derived using a factor analysis approach for the 5 traditional ATP-III-MetS components to determine the weighted contribution of each component to a latent MetS “factor” on a sex- and race/ethnicity-specific basis using data from adults 20–64 years-old from the National Health and Nutrition Examination Survey (NHANES), with categorization into six sub-groups based on sex and race/ethnicity (male and female for non-Hispanic-white, non-Hispanic-black and Hispanic). Factor loadings from the previous derivation analysis for the MetS components in each sub-group are shown in Supplementary Table 1.5 The resulting MetS-severity-scores are Z-scores (normally-distributed and ranging from theoretical negative to positive infinity with mean=0 and SD=1) of relative MetS-severity on a sex- and race/ethnicity-specific basis (https://metscalc.org). These scores correlate with other CHD risk factors5 and long-term risk for CHD6–8 and diabetes.8–10
Statistics
We used Cox-proportional-hazard models accounting for competing risk of death to determine the ability of MetS-severity to discriminate future stroke, as described previously.6 To explore potential non-linear associations and for increased clinical interpretability, we categorized MetS-severity into quartiles as defined by z-score values. All models adjusted for age and included ARIC/JHS sites as strata. We examined hazard ratios (HR’s) by race and sex, while testing for interactions using a Wald chi-square test to assess whether the relationship between MetS-Z and stroke differed by sub-group.
RESULTS
We analyzed 13,141 ARIC/JHS participants without baseline diabetes, who had 709 strokes over a mean follow-up time of 18.6 years (Table 1). Cumulative stroke incidence rose with increasing MetS-Z percentile, being 9.9% by 25 years for those >75th percentile of MetS-Z, vs. 5.0% for those <25th percentile.
Table 1.
Demographics
| Overall | No Incident Stroke | Incident Stroke | ||||
| n | % | n | % | n | % | |
| ARIC | 11,004 | 83.7 | 10,323 | 83.0 | 681 | 97.1 |
| JHS | 2,137 | 16.3 | 2,109 | 17.0 | 28 | 2.9 |
| Total: | 13,141 | 12,432 | 709 | |||
| Sex: | ||||||
| Male | 5,665 | 43.1 | 5,302 | 42.7 | 363 | 51.2 |
| Female | 7,476 | 56.9 | 7,130 | 57.3 | 346 | 48.8 |
| Race: | ||||||
| White | 8,626 | 65.6 | 8,159 | 65.6 | 467 | 65.9 |
| Black | 4,515 | 34.4 | 4,273 | 34.4 | 242 | 34.1 |
| Education: | ||||||
| < High School | 2,329 | 17.7 | 2,126 | 17.1 | 203 | 28.6 |
| High School | 4,010 | 30.5 | 3,810 | 30.7 | 200 | 28.2 |
| Vocational | 1,734 | 13.2 | 1,661 | 13.4 | 73 | 10.3 |
| College | 3,514 | 26.7 | 3,347 | 26.9 | 167 | 23.6 |
| Graduate/Professional | 1,538 | 11.7 | 1,473 | 11.9 | 65 | 9.2 |
| Missing | 16 | 0.1 | 15 | 0.1 | 1 | 0.1 |
| Income: | ||||||
| < 5k | 395 | 3.0 | 350 | 2.8 | 45 | 6.4 |
| 5–12k | 965 | 7.3 | 909 | 7.3 | 56 | 7.9 |
| 12–25k | 2,492 | 19.0 | 2,295 | 18.5 | 197 | 27.8 |
| 25–50k | 4,638 | 35.3 | 4,388 | 35.3 | 250 | 35.3 |
| 50k + | 3,719 | 28.3 | 3,597 | 28.9 | 122 | 17.2 |
| Missing | 932 | 7.1 | 893 | 7.2 | 39 | 5.5 |
| Mean | SD | Mean | SD | Mean | SD | |
| Baseline Age (years) | 52.95 | 7.11 | 52.77 | 7.14 | 56.07 | 5.89 |
| MetS Z Baseline | 0.04 | 0.76 | 0.03 | 0.76 | 0.25 | 0.75 |
| MetS Z Quartiles: | n | % | n | % | n | % |
| MetS Z Q1: < 25th | 2,360 | 18.0 | 2,275 | 18.3 | 85 | 12.0 |
| MetS Z Q2: 20–50th | 4,027 | 30.6 | 3,842 | 30.9 | 185 | 26.1 |
| MetS Z Q3: 50–75th | 4,012 | 30.5 | 3,784 | 30.4 | 228 | 32.2 |
| MetS Z Q4: >75th | 2,742 | 20.9 | 2,531 | 20.4 | 211 | 29.8 |
Table 2 displays HR’s of ischemic stroke, overall and by sex and race, according to ATP-III-MetS, MetS-Z and individual MetS components quartiles. Compared to individuals with MetS-Z <25th percentile, HR for stroke was higher for those between the 25th-50th (HR 1.33, 95% confidence interval [CI] 1.03,1.72) and 50th-75th percentiles (HR 1.75, CI 1.35,2.27). Above the 75th percentile, this HR was significant for white females (HR 2.63, CI 1.70,4.07) but not for black females and white males. However, no interactions between MetS-Z and race or sex were statistically significant (α=0.05).
Table 2.
Analysis of Time to Stroke (No Baseline CHD, stroke or diabetes)*
| n** | Model by Sex and Race | |||||
|---|---|---|---|---|---|---|
| Hazard Ratio (95% CI) | Hazard Ratio (95% CI) |
|||||
| White Males | White Females | Black Males | Black Females | |||
| ATP-III MetS Model | ||||||
| ATP-III MetS (n=4174) | 304 (7.3%) | 1.41 (1.22, 1.65) | 1.38 (1.07, 1.78) | 1.51 (1.15, 1.97) | 1.19 (0.82, 1.74) | 1.68 (1.17, 2.42) |
| No MetS (n=8967) | 405 (4.5%) | Ref | Ref | Ref | Ref | Ref |
| MetS Severity Model | ||||||
| 4th Quartile (n=2742) | 211 (7.7%) | 1.70 (1.32, 2.20) | 1.16 (0.72, 1.86) | 2.63 (1.70, 4.08) | 1.78 (0.96, 3.28) | 1.22 (0.68, 2.19) |
| 3rd Quartile (n=4012) | 228 (5.7%) | 1.32 (1.03, 1.70) | 1.08 (0.68, 1.72) | 1.57 (1.01, 2.43) | 1.27 (0.72, 2.25) | 0.97 (0.54, 1.76) |
| 2nd Quartile (n=4027) | 185 (4.6%) | 1.14 (0.88, 1.47) | 0.79 (0.47, 1.31) | 1.51 (0.98, 2.31) | 1.22 (0.69, 2.16) | 0.85 (0.46, 1.57) |
| 1st Quartile (n=2360) | 85 (3.6%) | Ref | Ref | Ref | Ref | Ref |
| Waist Circumference Model | ||||||
| 4th Quartile (n=2768) | 169 (6.1%) | 1.31 (1.04, 1.65) | 1.33 (0.77, 2.28) | 1.19 (0.80, 1.77) | 1.56 (0.86, 2.80) | 1.07 (0.68, 1.69) |
| 3rd Quartile (n=3580) | 220 (6.2%) | 1.30 (1.05, 1.61) | 1.33 (0.80, 2.23) | 1.62 (1.15, 2.28) | 1.20 (0.68, 2.12) | 0.66 (0.39, 1.13) |
| 2nd Quartile (n=3527) | 181 (5.1%) | 1.12 (0.90, 1.40) | 1.11 (0.65, 1.89) | 1.05 (0.74, 1.49) | 1.57 (0.91, 2.71) | 0.68 (0.39, 1.18) |
| 1st Quartile (n=3266) | 139 (4.3%) | Ref | Ref | Ref | Ref | Ref |
| HDL Model | ||||||
| 4th Quartile (n=2966) | 136 (4.6%) | 0.61 (0.49, 0.76) | 1.06 (0.67, 1.67) | 0.53 (0.36, 0.77) | 0.74 (0.42, 1.26) | 0.93 (0.50, 1.74) |
| 3rd Quartile (n=2746) | 142 (5.2%) | 0.74 (0.60, 0.90) | 0.64 (0.41, 1.00) | 0.66 (0.45, 0.98) | 0.98 (0.62, 1.57) | 1.22 (0.65, 2.28) |
| 2nd Quartile (n=3717) | 186 (5.0%) | 0.75 (0.62, 0.91) | 0.65 (0.48, 0.89) | 0.72 (0.49, 1.06) | 0.69 (0.43, 1.09) | 1.63 (0.89, 3.01) |
| 1st Quartile (n=3712) | 245 (6.6%) | Ref | Ref | Ref | Ref | Ref |
| Systolic BP Model | ||||||
| 4th Quartile (n=3004) | 276 (9.2%) | 2.66 (2.09, 3.38) | 2.35 (1.55, 3.56) | 2.33 (1.63, 3.32) | 4.50 (1.81, 11.19) | 4.37 (1.75, 10.90) |
| 3rd Quartile (n=2797) | 161 (5.8%) | 1.82 (1.42, 2.35) | 1.99 (1.31, 3.02) | 1.44 (0.97, 2.16) | 3.02 (1.17, 7.81) | 2.65 (1.02, 6.90) |
| 2nd Quartile (n=3747) | 173 (4.6%) | 1.59 (1.24, 2.04) | 1.74 (1.16, 2.61) | 1.30 (0.89, 1.88) | 2.07 (0.77, 5.55) | 2.59 (0.99, 6.80) |
| 1st Quartile (n=3593) | 99 (2.8%) | Ref | Ref | Ref | Ref | Ref |
| Triglyceride Severity Model | ||||||
| 4th Quartile (n=3139) | 188 (6.0%) | 1.25 (1.00, 1.55) | 1.11 (0.75, 1.66) | 1.66 (1.09, 2.53) | 1.15 (0.68, 1.95) | 0.98 (0.55, 1.77) |
| 3rd Quartile (n=3191) | 221 (6.9%) | 1.46 (1.18, 1.81) | 1.33 (0.89, 1.99) | 2.10 (1.40, 3.16) | 1.32 (0.83, 2.11) | 1.05 (0.66, 1.68) |
| 2nd Quartile (n=3352) | 158 (4.7%) | 1.01 (0.80, 1.27) | 0.95 (0.61, 1.47) | 1.25 (0.81, 1.94) | 0.96 (0.58, 1.58) | 0.92 (0.58, 1.46) |
| 1st Quartile (n=3459) | 142 (4.1%) | Ref | Ref | Ref | Ref | Ref |
| Glucose Severity Model | ||||||
| 4th Quartile (n=3078) | 221 (7.2%) | 1.39 (1.10, 1.76) | 0.99 (0.64, 1.52) | 1.71 (1.14, 2.58) | 1.42 (0.82, 2.46) | 1.06 (0.62, 1.81) |
| 3rd Quartile (n=3263) | 207 (6.3%) | 1.33 (1.05, 1.68) | 0.91 (0.58, 1.41) | 1.60 (1.08, 2.39) | 1.23 (0.70, 2.17) | 1.31 (0.77, 2.22) |
| 2nd Quartile (n=3489) | 169 (4.8%) | 1.09 (0.85, 1.39) | 0.68 (0.42, 1.10) | 1.17 (0.78, 1.76) | 1.27 (0.73, 2.23) | 1.34 (0.80, 2.25) |
| 1st Quartile (n=3311) | 112 (3.4%) | Ref | Ref | Ref | Ref | Ref |
Presence of ATP-III-MetS was associated with higher HR for incident stroke (HR 1.43, CI 1.22,1.65); among the individual sex- and race subgroups, HR’s varied from black males at 1.19 (CI 0.82,1.74) to black females at 1.68 (CI 1.17,2.42) without significant sex-race interactions.
DISCUSSION
While the presence of MetS by traditional criteria had been shown to be associated with risk of future ischemic stroke, it was not clear whether this relationship was due to the presence of individual risk factors such as diabetes and whether the relationship varied over the full spectrum of MetS-severity.2,3,13 Using a race/ethnicity-specific MetS-severity Z-score, we found that risk for ischemic stroke rose steadily with increasing severity of MetS and in the absence of baseline diabetes. These findings support links between ischemic stroke and MetS-severity, though the etiology behind these links remains unknown—including whether this relationship is due to the presence of multiple individual risk factors together or to common pathways that drive the associations between MetS components.
It is not clear why the MetS-Z – stroke risk was strongest for white females—although with a lack of significant interaction by race/sex, these differences could have occurred by chance alone. Nevertheless, these results are notable enough that they may warrant further study into race and sex differences. Overall, stroke incidence is higher among females compared to males and among blacks compared to whites.1 Prior studies had found that the MetS-stroke relationship was stronger overall among women without differences by race,2 despite known racial/ethnic discrepancies between the prevalence of ATP-III MetS and MetS-related disease, with black men having a low prevalence of MetS but high prevalence of diabetes, CVD mortality and stroke. Using this race/ethnicity-specific MetS-Z-score, we had previously found that for CHD risk there were no differences in MetS-Z-related risk by sex or race6—suggesting any current racial/ethnic differences for this score are unique for ischemic stroke risk. Though uncertain, the cause for these racial/ethnic differences between MetS-Z and ischemic stroke could relate to a greater amount of non-MetS related stroke risk in certain sex and race groups due to factors such as essential hypertension and smoking.1
While BP and glucose strongly correlate with stroke risk, the factor loadings for these components toward MetS-Z are low5—meaning that elevations in other components contribute more to MetS-severity. It is thus more likely that high MetS-Z levels in the current analysis were due to elevations in factors such as waist circumference13 and triglycerides,14 themselves both associated with stroke risk.
The relationship between ATP-III-MetS and stroke (HR 1.44) was slightly lower than that seen from the same cohorts for MetS and CHD (HR 1.64).6 However, the gap between highest and lowest quartile of MetS-severity was much smaller for risk of stroke (HR=1.76) compared to CHD (HR=4.20).
Study limitations included the older nature of ARIC’s data (original recruitment 1987–1989), with some follow-up time pre-dating the current obesity epidemic and many current treatments. We also lacked comprehensive assessment of subsequent diabetes diagnosis, which likely contributed to stroke risk.13 For example, in our prior analysis, the risk for new diabetes in the highest MetS-Z quartile (relative to the lowest) was 17.4 over a median follow-up of 7.8 years, with diabetes developing in 29% of those in the highest quartile.9 Nevertheless, ascertainment of new diabetes is often missed in clinical settings as well, and thus baseline MetS-Z remains a potentially important identifier of individuals with risk for future ischemic stroke.
In conclusion, we demonstrated novel continuous associations that extend across the spectrum of MetS-severity in the absence of baseline diabetes and risk of future ischemic stroke. These risks provide guidance to healthcare providers in counseling patients with elevated MetS-severity toward reducing their risk.
Supplementary Material
Figure 1:
Time to Stroke by Metabolic Syndrome Severity Quartile.
ACKNOWLEDGEMENTS
Funding:
1R01HL120960 (MJG, MDD). JHS is supported by contracts HHSN268201300046C, HHSN268201300047C,HHSN268201300048C,HHSN268201300049C, HHSN268201300050C from NHLBI and NIMHD. ARIC is supported by NHLBI contracts HHSN268201100005C,HHSN268201100006C,HHSN268201100007C, HHSN268201100008C,HHSN268201100009C,HHSN268201100010C, HHSN268201100011C,HHSN268201100012C.
Footnotes
Disclosures:None
REFERENCES
- 1.Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2019 update: A report from the american heart association. Circulation. 2019;139:e56–e66 [DOI] [PubMed] [Google Scholar]
- 2.Li X, Lin H, Fu X, Lin W, Li M, Zeng X, et al. Metabolic syndrome and stroke: A meta-analysis of prospective cohort studies. J Clin Neurosci. 2017;40:34–38 [DOI] [PubMed] [Google Scholar]
- 3.Liu J, Grundy SM, Wang W, Smith SC, Vega GL, Wu Z, et al. Ten-year risk of cardiovascular incidence related to diabetes, prediabetes, and the metabolic syndrome. Am Heart J. 2007;153:552–558 [DOI] [PubMed] [Google Scholar]
- 4.Walker SE, Gurka MJ, Oliver MN, Johns DW, DeBoer MD. Racial/ethnic discrepancies in the metabolic syndrome begin in childhood and persist after adjustment for environmental factors. Nutrition Metabolism and Cardiovascular Diseases. 2012;22:141–148 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Gurka MJ, Lilly CL, Norman OM, DeBoer MD. An examination of sex and racial/ethnic differences in the metabolic syndrome among adults: A confirmatory factor analysis and a resulting continuous severity score. Metabolism. 2014;63:218–225 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.DeBoer MD, Gurka MJ, Hill Golden S, Musani SK, Sims M, Vishnu A, et al. Independent associations between metabolic syndrome severity & future coronary heart disease by sex and race. J. Am. Coll. Card 2017;69:1204–1205 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.DeBoer MD, Gurka MJ, Woo JG, Morrison JA. Severity of metabolic syndrome as a predictor of cardiovascular disease between childhood and adulthood: The princeton lipid research cohort study. J. Amer. Coll. Card 2015;66:755–757 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.DeBoer MD, Gurka MJ, Morrison JA, Woo JG. Inter-relationships between the severity of metabolic syndrome, insulin and adiponectin and their relationship to future type 2 diabetes and cardiovascular disease. Int J Obes (Lond). 2016;40:1353–1359 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gurka MJ, Golden SH, Musani SK, Sims M, Vishnu A, Guo Y, et al. Independent associations between a metabolic syndrome severity score and future diabetes by sex and race: The atherosclerosis risk in communities study and jackson heart study. Diabetologia. 2017;60:1261–1270 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.DeBoer MD, Gurka MJ, Woo JG, Morrison JA. Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: The princeton lipid research cohort study. Diabetologia. 2015;58:2745–2752 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Jones SA, Gottesman RF, Shahar E, Wruck L, Rosamond WD. Validity of hospital discharge diagnosis codes for stroke: The atherosclerosis risk in communities study. Stroke. 2014;45:3219–3225 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Keku E, Rosamond W, Taylor HA, Garrison R, Wyatt SB, Richard M, et al. Cardiovascular disease event classification in the jackson heart study: Methods and procedures. Ethn Dis. 2005;15:S6–62-70 [PubMed] [Google Scholar]
- 13.Air EL, Kissela BM. Diabetes, the metabolic syndrome, and ischemic stroke: Epidemiology and possible mechanisms. Diabetes Care. 2007;30:3131–3140 [DOI] [PubMed] [Google Scholar]
- 14.Freiberg JJ, Tybjaerg-Hansen A, Jensen JS, Nordestgaard BG. Nonfasting triglycerides and risk of ischemic stroke in the general population. JAMA. 2008;300:2142–2152 [DOI] [PubMed] [Google Scholar]
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