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Abstract

Background and Purpose—

Previous results on the association between lipids and stroke were controversial. We investigated the association of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C ), high-density lipoprotein cholesterol (HDL-C), and triglyceride with stroke.

Methods—

Six cohort studies in China with 267 500 participants were included. Cox proportional hazards regression models and restricted cubic spline analyses were used to estimate hazard ratios and 95% CIs and explore linear and nonlinear relationships of lipids and stroke, respectively.

Results—

The median follow-up duration ranged from 6 to 19 years. During 2 295 881 person-years, 8072 people developed stroke. Multivariable adjusted hazard ratios (95% CIs) per 1 mmol/L increase in TC, LDL-C, triglyceride were 1.08 (1.05–1.11), 1.08 (1.04–1.11), 1.07 (1.05-1.09) for ischemic stroke, respectively. Compared with participants with TC 160-199.9 mg/dL, hazard ratios (95% CIs) were 1.43 (1.11–1.85) for hemorrhagic stroke in those with TC <120 mg/dL. Compared with participants with HDL-C 50 to 59.9 mg/dL, hazard ratios (95% CIs) were 1.23 (1.12–1.35), 1.13 (1.04–1.22) for ischemic stroke, and 1.28 (1.10–1.49), 1.17 (1.03–1.33) for hemorrhagic stroke in those with HDL-C <40 and 40 to 49.9 mg/dL, respectively. Restricted cubic spline analyses showed linear relationships of TC and LDL-C, and nonlinear relationships of HDL-C and triglyceride with ischemic stroke (all P<0.001). Hemorrhagic stroke showed linear relationships with TC and HDL-C (P=0.029 and <0.001 respectively), but no relationship with LDL-C and triglyceride (all P>0.05).

Conclusions—

TC, LDL-C, and triglyceride showed positive associations with ischemic stroke. The risk of hemorrhagic stroke was higher when TC was lower than 120 mg/dL. LDL-C and triglyceride showed no association with hemorrhagic stroke. The risks of ischemic and hemorrhagic stroke might be higher when HDL-C was lower than 50 mg/dL.

Introduction

The global prevalence of stroke increased by 21% from 2005 to 2015, affecting 42.4 million people in 2015.1 The contribution of stroke to all-cause deaths did not change in developed countries, while displayed a significant increase in developing countries.2 In China, stroke was the top leading cause of death and disability-adjusted life-years in 2017 estimated with data from Global Burden of Diseases 2017.3 According to National Disease Surveillance Points System in China, 2.4 million new strokes and 1.1 million stroke-related deaths were estimated to occur annually.4
As a modifiable risk factor, total cholesterol (TC) was shown by some studies to be associated with the risk of stroke.5 Stroke-related disability-adjusted life years attributable to high TC (>185 mg/dL) increased by 24% during 1990 to 2013 globally.6 Most studies have found a positive association between TC and ischemic stroke,7,8 whereas an inverse relationship between TC and hemorrhagic stroke was found in others.7–10 In some other studies, TC was not identified to be associated or showed only weak relationships with different subtypes of stroke.11,12 For other lipids components including low-density lipoprotein cholesterol (LDL-C ), high-density lipoprotein cholesterol (HDL-C), and triglyceride, their associations with stroke especially type-specific stroke were also discrepant.9,13–15 Although the burden of stroke was high in China, the association between lipids and stroke has not been well investigated by large prospective studies on Chinese.16–18 To better understand the etiology and prevent stroke, more evidence on the association between lipids and stroke is needed.
We investigated the association of lipid levels with stroke subtype among 267 500 Chinese adults from 6 large prospective cohorts.

Methods

The data that support the findings of this study are available from the corresponding author on reasonable request.

Study Participants

Participants for the current study were from 6 studies: (1) APCSC (Asia Pacific Cohort Studies Collaboration) in China, (2) ChinaMUCA 1992-1994 (China Multicenter Collaborative Study of Cardiovascular Epidemiology 1992-1994), (3) ChinaMUCA 1998, (4) InterASIA (International Collaborative Study of Cardiovascular Disease in Asia), (5) Kailuan study, and (6) CIMIC study (Community Intervention of Metabolic Syndrome in China & Chinese Family Health). The study designs for the 6 studies have been published elsewhere and briefly described in the online-only Data Supplement. Baseline examinations of the APCSC,19 ChinaMUCA 1992-1994,20 ChinaMUCA 1998,21 InterASIA,22 Kailuan study,23 and CIMIC24 were conducted in 1970 to 1998, 1992 to 1994, 1998, 2000 to 2001, 2006 to 2007, and 2007 to 2008, respectively.
Among 292 706 participants from the 6 studies above, 283 918 participants (97.00%) completed the follow-up survey. Of study participants who were followed up, 6328 having atherosclerotic cardiovascular disease at baseline, 1502 participants from InterASIA who were followed up only until 2008 and did not participate in the second follow-up survey from 2012 to 2015, 7971 participants with missing information on lipid levels, sex, or age at baseline, and 617 participants aged <20 years were excluded. Finally, the current analysis was restricted to 267 500 participants aged ≥20 years old (Figure I in the online-only Data Supplement).
APCSC was overseen by an Executive Committee with elected representatives from the countries involved. ChinaMUCA 1992-1994, ChinaMUCA 1998, InterASIA, and CIMIC were approved by the Institutional Review Board at Fuwai Hospital in Beijing. The Kailuan study was approved by the Ethics Committee of the Kailuan General Hospital. Written informed consent was obtained from each participant before data collection.

Baseline Information Collection

Baseline data were collected in examination centers at local health stations, community clinics, or hospitals among the studies. In each cohort from APCSC, sex, age, smoking status, and alcohol consumption were recorded. In the other 5 studies, a standardized and validated questionnaire administered by well-trained staff or research doctors was employed to collect information on socio-demographic characteristics, personal medical history, and lifestyle information. Body weight and height were measured by standardized anthropometric procedures with the participant wearing lightweight clothing and no shoes. For the measurement of blood pressure, 3 or 2 measurements were obtained after a 5-minute rest in a seat, and the mean blood pressure values were used. Overnight fasting venous blood samples were drawn for the measurement of lipid levels (Materials in the online-only Data Supplement).

Follow-Up Event Ascertainment

All the studies from APCSC in our analysis included scheduled follow-up visits or examination of hospital records from 1972 to 1999. All data were checked for completeness and consistency and recoded, when necessary, to maximize comparability across cohorts. Summary reports of data quality were referred to the principal investigators of each collaborating study for review and confirmation. The cohorts from ChinaMUCA 1992-1994, ChinaMUCA 1998, InterASIA, and CIMIC were followed up together with a unified protocol from 2012 to 2015. Before the last follow-up visit for all 4 studies conducted from 2012 to 2015, 14 392 participants in 9 of the 14 clusters in the ChinaMUCA 1992-1994 were followed up biennially from 1996 to 2004, and 27 020 participants from the ChinaMUCA 1998, and InterASIA were followed up once during 2007 to 2008. Study participants or their proxies were interviewed to ascertain disease status and vital information. Hospital records or death certificates were also collected. An end point assessment committee reviewed medical history information and death certificates. Two committee members assessed the end point independently, and discrepancies were discussed with additional committee members. The Kailuan study was followed up until 2015, and disease status was ascertained by surveying each year’s discharge lists from local hospitals, medical records from medical insurance, and death certificates from state vital statistics offices and by contacting participants annually for a history of disease. Causes of death for these studies were coded according to the International Classification of Disease, Ninth Revision or International Classification of Diseases, Tenth Revision.
A stroke event (fatal and nonfatal) was defined as a confirmed diagnosis of stroke including cerebral infarction, subarachnoid or intracerebral hemorrhage, and unspecified stroke or death due to stroke (International Classification of Diseases, Tenth Revision I60 to I69, International Classification of Disease Ninth Revision 430 to 438) during the follow-up period. An ischemic stroke event was defined as a confirmed diagnosis of cerebral infarction or death due to ischemic stroke (International Classification of Diseases, Tenth Revision I63, International Classification of Disease Ninth Revision 433.0 to 434.9). A hemorrhagic stroke event was defined as a confirmed diagnosis of hemorrhagic stroke including intracerebral hemorrhage and subarachnoid hemorrhage or death due to hemorrhagic stroke (International Classification of Diseases, Tenth Revision I60 to I62, International Classification of Disease Ninth Revision 431.0 to 432.9). In APCSC, stroke was verified using record linkage with official sources or on the basis of imaging, lumbar puncture, or autopsy. In the other 5 cohorts, stroke was diagnosed according to the World Health Organization criteria combined with a brain computed tomography, magnetic resonance, or lumbar puncture for confirmation.25

Statistical Analysis

The baseline characteristics of study participants are presented as mean with SD or median values with the interquartile range in parentheses for continuous variables, and as percentages for categorical variables. The baseline characteristics of participants were compared between men and women and between individuals who developed and individuals who did not develop a stroke event during follow-up. Mantel-Haenszel χ2 test was used for categorical variables, and t test was used for continuous variables. Wilcoxon rank-sum test was used for triglyceride which was not normally distributed.
Participants were divided into 5 groups according to TC level (<120, 120–159.9, 160–199.9, 200–239.9, ≥240 mg/dL), LDL-C level (<70, 70–99.9, 100–129.9, 130–159.9, ≥160 mg/dL), HDL-C level (<40, 40–49.9, 50–59.9, 60–69.9, ≥70 mg/dL), or triglyceride level (<50, 50–99.9, 100–149.9, 150–199.9, ≥200 mg/dL). Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and their corresponding 95% CIs of ischemic and hemorrhagic stroke events. The analyses were performed with adjustment for sex, age, smoking status, hypertension, geographic region, alcohol consumption, education level, and body mass index. In addition, we estimated HRs per-unit increase (1 mmol/L) of lipid level.
Restricted cubic spline analyses were used to characterize the dose-response association and explore the potential linear or nonlinear relationship of lipid level with ischemic and hemorrhagic stroke.26 Multivariable adjusted analyses with 4 knots (120, 160, 200, 240 mg/dL for TC, 70, 100, 130, 160 mg/dL for LDL-C, 40, 50, 60, 70 mg/dL for HDL-C, and 50, 100, 150, 200 mg/dL for triglyceride) were used. Test result for nonlinearity was checked first. If test for nonlinearity was not significant, test result for overall association and linearity was checked, with the significant result indicating the linear association.
Statistical analyses were conducted using SAS, version 9.4 (SAS Institute, Inc, Cary, NC). All tests were 2 sided, and P<0.05 was considered statistically significant.

Results

Baseline characteristics of 267 500 study participants are presented in Table 1. The participants were followed up for a total of 2 295 881 person-years, with the median follow-up duration of 6 years, 19 years, 15 years, 13 years, 9 years, and 6 years for the APCSC, ChinaMUCA 1992-1994, ChinaMUCA 1998, InterASIA, Kailuan study, and CIMIC, respectively. A total of 5458 and 2186 study participants developed ischemic and hemorrhagic stroke during follow-up, respectively.
Table 1. Baseline Characteristics of Study Population by Sex and the Development of Stroke Event During Follow-Up
CharacteristicsTotal, (n=267 500)SexP ValueDeveloped Stroke Event During Follow-UpP Value
Men, (n=159 478)Women, (n=108 022)Yes, (n=8072)No, (n=259 428)
Age, mean (SD), y50.40±11.6051.02±11.7549.51±11.31<0.00158.78±10.3950.14±11.53<0.001
Men, n (%)159 478 (59.60)NANANA5751 (71.25)153727 (59.26)<0.001
Northern, n (%)164083 (66.30)107871 (72.09)56212 (57.46)<0.0016171 (79.57)157912 (65.88)<0.001
Body mass index, mean (SD), kg/m223.96±3.6324.07±3.5423.79±3.76<0.00124.79±3.7523.93±3.63<0.001
Current smoker, n (%)84652 (32.00)80938 (51.54)3714 (3.46)<0.0012845 (36.11)81807 (31.91)<0.001
Alcohol users, n (%)73427 (28.40)68772 (44.63)4655 (4.44)<0.0012260 (28.88)71167 (28.34)0.30
≥High school education, n (%)38 834 (16.60)24730 (18.01)14104 (14.61)<0.001700 (9.60)38134 (16.83)<0.001
SBP, mean (SD), mm Hg127.39±21.21128.70±20.57125.46±21.97<0.001144.96±25.09126.84±20.84<0.001
DBP, mean (SD), mm Hg80.31±11.9181.87±11.9278.02±11.51<0.00187.96±13.9480.08±11.76<0.001
Hypertension, n (%)90750 (34.00)58830 (36.98)31920 (29.61)<0.0015351 (66.41)85399 (32.99)<0.001
Lipid levels, mean (SD), mg/dL
 TC182.86±40.71184.34±41.42180.68±39.53<0.001190.44±42.29182.63±40.64<0.001
 LDL-C104.14±34.67105.29±35.39102.52±33.58<0.001109.00±35.31103.98±34.64<0.001
 HDL-C55.15±15.2955.57±15.8254.55±14.47<0.00155.48±16.2455.14±15.25<0.001
 TG, median (interquartile range)111.60 (79.72–164.00)111.80 (79.72–166.80)109.00 (78.00–158.30)<0.001121.60 (86.90–179.30)110.72 (78.83–162.50)<0.001
To convert cholesterol to mmol/L, multiply by 0.0259. To convert triglyceride to mmol/L, multiply by 0.0113.
DBP indicates diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; NA, not applicable; SBP, systolic blood pressure; TC, total cholesterol; and TG, triglycerides.

Association of Lipid Level With Stroke

The multivariable adjusted HRs (95% CIs) per 1 mmol/L increase of TC, LDL-C, HDL-C, and triglyceride for ischemic stroke were 1.08 (1.05–1.11), 1.08 (1.04–1.11), 0.84 (0.78–0.90), and 1.07 (1.05–1.09), respectively (Table 2). Compared with participants with TC 160 to 199.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 0.78 (0.64–0.96), 0.91 (0.84–0.98), 1.07 (1.00–1.14), and 1.15 (1.05–1.27) for ischemic stroke in participants with TC <120, 120 to 159.9, 200 to 239.9, and ≥240 mg/dL, respectively. Compared with participants with LDL-C 100-129.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 0.88 (0.79–0.97), 0.90 (0.84–0.97), 1.03 (0.95–1.13), and 1.11 (0.99–1.25) for ischemic stroke in participants with LDL-C <70, 70 to 99.9, 130 to 159.9, and ≥160 mg/dL. Compared with participants with HDL-C 50 to 59.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 1.23 (1.12–1.35), 1.13 (1.04–1.22), 0.94 (0.86–1.03), and 0.97 (0.89–1.06) for ischemic stroke in participants with HDL-C <40, 40 to 49.9, 60 to 69.9, and ≥70 mg/dL, respectively. Compared with participants with triglyceride 100 to 149.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 0.67 (0.56–0.81), 0.87 (0.81–0.93), 1.04 (0.95–1.13), and 1.15 (1.06–1.25) for ischemic stroke in participants with triglyceride <50, 50 to 99.9, 150 to 199.9, and ≥200 mg/dL, respectively.
Table 2. Hazard Ratios of Ischemic Stroke by 5 Levels of Lipids or Lipids as Continuous Variables
 Person-Years of Follow-UpNo. of EventsRate per 100 000 person-yrsHR (95% CI)P for TrendOne-Unit Increase (1 mmol/L)
Total cholesterol, mg/dL
 <12082 276113137.340.78 (0.64–0.96)*<0.0011.08 (1.05–1.11)
 120–159.9594 460937157.620.91 (0.84–0.98)*
 160–199.9939 0282228237.271.00
 200–239.9508 2421546304.191.07 (1.00–1.14)
 ≥240177 512634357.161.15 (1.05–1.27)*
Low-density lipoprotein cholesterol, mg/dL
 <70236 186528223.550.88 (0.79–0.97)<0.0011.08 (1.04–1.11)
 70–99.9576 8181403243.230.90 (0.84–0.97)*
 100–129.9549 0681682306.341.00
 130–159.9262 054861328.561.03 (0.95–1.13)
 ≥160106 954386360.91.11 (0.99–1.25)
High-density lipoprotein cholesterol, mg/dL
 <40254 072748294.41.23 (1.12–1.35)<0.0010.84 (0.78–0.90)
 40–49.9493 2391377279.181.13 (1.04–1.22)*
 50–59.9547 9421398255.141.00
 60–69.9341 028910266.840.94 (0.86–1.03)
 ≥70271 247863318.160.97 (0.89–1.06)
Triglyceride, mg/dL
 <50104 566143136.760.67 (0.56–0.81)<0.0011.07 (1.05–1.09)
 50–99.9722 0731588219.920.87 (0.81–0.93)
 100–149.9535 9901548288.811.00
 150–199.9249 640810324.471.04 (0.95–1.13)
 ≥200298 4111075360.241.15 (1.06–1.25)
To convert cholesterol to mmol/L, multiply by 0.0259. To convert triglyceride to mmol/L, multiply by 0.0113. HR indicates hazard ratio.
*
P<0.01.
P<0.001.
P<0.05.
The multivariable adjusted HRs (95% CIs) per 1 mmol/L increase of TC and HDL-C for hemorrhagic stroke were 0.95 (0.91–1.00) and 0.79 (0.70–0.89), respectively (Table 3). Compared with participants with TC 160 to 199.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 1.43 (1.11–1.85), 1.12 (0.99–1.27), 0.93 (0.83–1.05), and 0.97 (0.82–1.15) for hemorrhagic stroke in participants with TC <120, 120 to 159.9, 200 to 239.9, and ≥240 mg/dL, respectively. No significant association was found between LDL-C and hemorrhagic stroke. Compared with participants with HDL-C level 50 to 59.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 1.28 (1.10–1.49), 1.17 (1.03–1.33), 1.07 (0.93–1.23), and 0.94 (0.80–1.09) for hemorrhagic stroke in participants with HDL-C <40, 40 to 49.9, 60 to 69.9, and ≥70 mg/dL, respectively. Compared with participants with triglyceride level 100 to 149.9 mg/dL, the multivariable adjusted HRs (95% CIs) were 0.70 (0.51–0.95), 1.03 (0.92–1.16), 1.04 (0.89–1.21), and 0.99 (0.85–1.14) for hemorrhagic stroke in participants with triglyceride <50, 50 to 99.9, 150 to 199.9, and ≥200 mg/dL, respectively.
Table 3. Hazard Ratios of Hemorrhagic Stroke by 5 Levels of Lipids or Lipids as Continuous Variables
 Person-Years of Follow-UpNo. of EventsRate per 100 000 person-yrsHR (95% CI)P for TrendOne-Unit Increase (1 mmol/L)
Total cholesterol, mg/dL
 <12082 43285103.121.43 (1.11–1.85)*0.0020.95 (0.91–1.00)
 120–159.9596 20648781.681.12 (0.99–1.27)
 160–199.9944 24790495.741.00
 200–239.9512 07350999.40.93 (0.83–1.05)
 ≥240179 227201112.150.97 (0.82–1.15)
Low-density lipoprotein cholesterol, mg/dL
 <70237 149250105.420.96 (0.81–1.12)0.800.99 (0.94–1.05)
 70–99.9579 64457699.370.95 (0.85–1.07)
 100–129.9552 832620112.151.00
 130–159.9264 417267100.980.89 (0.77–1.04)
 ≥160108 091123113.790.96 (0.78–1.19)
High-density lipoprotein cholesterol, mg/dL
 <40255 928289112.921.28 (1.10–1.49)*<0.0010.79 (0.70–0.89)
 40–49.9496 627530106.721.17 (1.03–1.33)
 50–59.9551 15052194.531.00
 60–69.9342 785369107.651.07 (0.93–1.23)
 ≥70273 189314114.940.94 (0.80–1.09)
Triglyceride, mg/dL
 <50104 9945855.240.70 (0.51–0.95)0.891.01 (0.98–1.05)
 50–99.9725 42069796.081.03 (0.92–1.16)
 100–149.9539 499581107.691.00
 150–199.9251 577298118.451.04 (0.89–1.21)
 ≥200300 954340112.970.99 (0.85–1.14)
To convert cholesterol to mmol/L, multiply by 0.0259. To convert triglyceride to mmol/L, multiply by 0.0113. HR indicates hazard ratio.
*
P<0.01.
P<0.001.
P<0.05.
The association of triglyceride/HDL-C ratio with ischemic and hemorrhagic stroke was also analyzed (Table I in the online-only Data Supplement). Compared with participants with triglyceride/HDL-C in the third quintile, the multivariable adjusted HRs (95% CIs) were 0.85 (0.78–0.94), 0.85 (0.77–0.93), 1.10 (1.01–1.20), and 1.24 (1.14–1.35) for ischemic stroke, and 0.79 (0.67–0.92), 0.90 (0.78–1.05), 0.94 (0.81–1.09), and 0.98 (0.85–1.14) for hemorrhagic stroke in participants with triglyceride/HDL-C in the first, second, fourth, and fifth quintile, respectively.

Dose-Response Analysis of Lipid Level With Stroke

Multivariable adjusted restricted cubic spline analyses showed linear association of TC and LDL-C with ischemic stroke (all P<0.001; Figure 1A and 1B). Nonlinear relationships of HDL-C and triglyceride with ischemic stroke were observed (all P<0.001; Figure 1C and 1D).
Figure 1. Association of lipids with ischemic stroke in a restricted cubic spline model. Multivariable adjusted hazard ratios (HRs; solid line) with 95% CI (dashed lines) for the association of total cholesterol (TC; A), low-density lipoprotein cholesterol (LDL-C; B), high-density lipoprotein cholesterol (HDL-C; C), and triglyceride (D) with ischemic stroke with TC 180 mg/dL, LDL-C 115 mg/dL, HDL-C 55 mg/dL, and triglyceride 125 mg/dL as the reference value, respectively.
Multivariable adjusted restricted cubic spline analyses showed linear association of TC and HDL-C with hemorrhagic stroke (P=0.029 and <0.001, respectively; Figure 2A and 2C). No such relationships were found for LDL-C (all P>0.05) and triglyceride (all P>0.05) with hemorrhagic stroke (Figure 2B and 2D).
Figure 2. Association of lipids with hemorrhagic stroke in a restricted cubic spline model. Multivariable adjusted hazard ratios (HRs; solid line) with 95% CI (dashed lines) for the association of total cholesterol (TC; A), low-density lipoprotein cholesterol (LDL-C; B), high-density lipoprotein cholesterol (HDL-C; C), and triglyceride (D) with hemorrhagic stroke with TC 180 mg/dL, LDL-C 115 mg/dL, HDL-C 55 mg/dL, and triglyceride 125 mg/dL as the reference value, respectively.

Discussion

In this study, TC, LDL-C, triglyceride, and triglyceride/HDL-C ratio were positively associated with ischemic stroke in Chinese adults from 6 studies. The risk of hemorrhagic stroke was higher when TC was less than 120 mg/dL and lower when triglyceride/HDL-C ratio was in the lowest quintile. Our results showed no association of LDL-C and triglyceride with hemorrhagic stroke. The risks of ischemic and hemorrhagic stroke were higher when HDL-C was less than 50 mg/dL.
Despite sharing atherosclerotic pathogenic origin, the underlying causes of stroke subtypes are different, with ischemic stroke typically caused by blockage of a blood vessel and hemorrhagic stroke caused by either bleeding directly into the brain or into the space between the brain’s membranes.27 Better understanding of the association of lipids with subtype-specific stroke rather than focusing on total stroke would be more enlightening for stroke prevention.
Our results are consistent with most previous studies which showed TC was positively associated with ischemic stroke.16,17 However, several studies conducted among Japanese showed no relationship between TC and ischemic stroke, which could be due to their lower percentage of atherothrombotic infarction and higher percentage of cardioembolic infarction which are both subtypes of cerebral infarction.10 Atherothrombotic infarction and cardioembolic infarction were reported to have positive and negative association with cholesterol level, respectively.10
Our study found LDL-C and triglyceride were positively, and HDL-C was negatively associated with ischemic stroke. These results are similar to those from most previous observational studies.13 Furthermore, our findings are in line with one recent Mendelian randomization study among 188 577 individuals from Global Lipids Genetics Consortium and 16 851 ischemic stroke cases and 32 473 controls from Stroke Genetics Network.14 LDL-C was shown to play a casual role in ischemic stroke and large artery atherosclerosis stroke. However, triglyceride was not found to be casually associated with ischemic stroke or the subtype of ischemic stroke.
For hemorrhagic stroke, its inverse relationship with TC was reported by most studies.8–10,16 Low TC may play a role in promoting arterial media layer smooth muscle cell necrosis and contribute to the development of a fragile cerebrovascular endothelium, leading to the predisposition to hemorrhage.28 We highlighted the increased risk of hemorrhagic stroke when TC is lower than 120 mg/dL in our cohort study with such a large sample size, which provided reliable evidence especially among Asian population. The lowest category of previous studies categorized people with TC lower than 140 or 160 mg/dL but without further analyzing those with TC lower than 120 mg/dL due to the limited number of stroke events.16 Furthermore, low cholesterol was shown as a marker of poor nutrition status such as low dietary consumption of fats and carbohydrates, which was related to stroke risk.29 Since body mass index was included as a covariable in our analyses, the association of TC and hemorrhagic stroke appears to be independent of nutrition or obesity status.
For the relationship of LDL-C, HDL-C, and triglyceride with hemorrhagic stroke, previous observational studies showed inconsistent results.9,13,15,30 We found no relationship between LDL-C and triglyceride with hemorrhagic stroke. The results of nearly half of previous study were similar with our findings,13 whereas the other studies showed negative associations of LDL-C and triglyceride with hemorrhagic stroke.15 In the most recent study conducted among participants from China Kadoorie Biobank, LDL-C was not found to be significantly associated with intracerebral hemorrhage in their Mendelian randomization study (HR, 1.13 [95% CI, 0.91–1.40] per 1 mmol/L lower LDL-C) but showed negative association in their observational study (HR, 1.16 [95% CI, 1.08–1.25] per 1 mmol/L lower LDL-C).31 For HDL-C, most studies did not find any relationship with hemorrhagic stroke.9,15 In our analysis of individual participant data ignoring the studies, the risk of hemorrhagic stroke was higher when HDL-C was less than 50 mg/dL. In one nested case-control study on 12 804 Japanese participants, small- and medium-sized HDL-C, but not large HDL-C were inversely associated with hemorrhagic stroke.30 In another nested case-control study on 4662 Chinese participants from China Kadoorie Biobank, particle and cholesterol concentration of HDL with different diameters were not associated with intracerebral hemorrhage.32 Previous study explored the role of triglyceride/HDL-C for secondary prevention of stroke, showing the negative association of triglyceride/HDL-C with hemorrhagic transformation after acute ischemic stroke or finding triglyceride/HDL of prognostic value for identifying subjects at risk of a secondary stroke.33 Evidence about the association of triglyceride/HDL-C with primary prevention of hemorrhagic stroke is limited. Further studies with examination of particle size and subclass concentration of lipoprotein cholesterol and triglyceride need to be conducted to better explain the role of lipids in the development of different subtypes of stroke.
We found no significant sex interaction except for the associations of TC with hemorrhagic stroke, and HDL-C with ischemic stroke (Pinteraction =0.39, 0.98, <0.001, and 0.07 for TC, LDL-C, HDL-C, and triglyceride with ischemic stroke, and Pinteraction =0.027, 0.10, 0.85, and 0.90 for TC, LDL-C, HDL-C, and triglyceride with hemorrhagic stroke, respectively). In sex-specific analysis, the significantly decreased risk of ischemic stroke when HDL-C was ≥60 mg/dL was found in women but not in men (Table II in the online-only Data Supplement). Several randomized clinical trials did not support the concept that increased HDL-C by highly effective agents such as niacin or fibrates was associated with lower stroke risk.34 The sex difference for the association of HDL-C and ischemic stroke needs to be demonstrated in further studies. The inverse association between TC and hemorrhagic stroke appeared to be mainly due to the association in men. Some studies showed that cerebral microbleeds, the risk of which increased at a lower level of TC, was associated with elevated risk of intracerebral hemorrhagic stroke and was more common in men than women.35,36 Further studies with larger sample size and more stroke events are warranted to examine any differences by sex and other factors.
The current study has several strengths. First, we pooled data from several large-scale and well-conducted prospective cohort studies which included participants quite representative of the Chinese general population. Second, the lipid measurement methods in InterASIA, CIMIC, and 4 clusters of ChinaMUCA passed quality control in the Lipid Standardization Program of the US Centers for Disease Control and Prevention. The lipid levels in other cohorts were also measured with stringent quality control. Third, the high follow-up rate of 97% and the standardized adjudication for both fatal and nonfatal outcomes across these cohorts made the results more reliable.
Several limitations of the present study should be addressed. First, although 4 studies including ChinaMUCA 1992-1994, ChinaMUCA 1998, InterASIA, and CIMIC were followed up together with a unified protocol, the protocols of APCSC and Kailuan study were slightly different. However, the standardized quality control during measurement of variables and verification of outcomes maximized comparability across cohorts. Further adjustment for potential confounding effect of different studies showed similar result (Tables III through VI in the online-only Data Supplement). Second, we did not collect information on usage of lipid-lowering medication at baseline for participants from ChinaMUCA 1992-1994 and ChinaMUCA 1998. However, lipid-lowering treatments were rare at the time these studies were conducted.37 In addition, the sensitivity analyses after excluding participants who were known to be on lipid-lowering medication during follow-up did not change the results substantially (Tables III through VI in the online-only Data Supplement).
In conclusion, our study incorporating 6 large-scale prospective cohort studies elucidated the relationship between lipids and stroke subtypes in Chinese general population. TC, LDL-C, and triglyceride were shown to be positively associated with ischemic stroke. The risk of hemorrhagic stroke in adults might be higher when TC was lower than 120 mg/dL. LDL-C and triglyceride might not be associated with hemorrhagic stroke. Besides, the risk of ischemic, and hemorrhagic stroke might be higher when HDL-C was less than 50 mg/dL. These results provide more evidence for guidelines or health policies of primary prevention of stroke subtypes with the management of lipid level. More prospective studies with measurement of particle size and subclass concentration of lipoprotein cholesterol and triglyceride are warranted to reveal precise associations between lipids and stroke subtypes.

Acknowledgments

We thank the collaborators of cohorts included in our analysis from the Asia Pacific Cohort Studies Collaboration in China and principle investigator of Kailuan Study. Principle collaborators were listed in Materials in the online-only Data Supplement.

Supplemental Material

File (str_stroke-2019-026402_supp1.pdf)

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Received: 22 May 2019
Revision received: 4 September 2019
Accepted: 16 September 2019
Published online: 29 October 2019
Published in print: December 2019

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Keywords

  1. cholesterol
  2. stroke
  3. triglyceride

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Xiaoying Gu, PhD*
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Yunzhi Li, MD, PhD*
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Shuohua Chen, MD
Department of Cardiology, Kailuan Hospital, Hebei United University, Tangshan, China (S.C., S.W.)
Xueli Yang, PhD
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Fangchao Liu, PhD
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Ying Li, MD
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Jianxin Li, MS
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Jie Cao, MS
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Xiaoqing Liu, MD
Division of Epidemiology, Guangdong Provincial People’s Hospital and Cardiovascular Institute, Guangzhou, China (X. Liu)
Jichun Chen, MS
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Chong Shen, PhD
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, China (C.S.)
Ling Yu, MD
Department of Cardiology, Fujian Provincial People’s Hospital, Fuzhou, China (L.Y.)
Jianfeng Huang, MD
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Tai-Hing Lam, MD
School of Public Health, University of Hong Kong, China (T.-H.L.)
Xianghua Fang, MD, PhD
Evidence-based Medical Center, Xuanwu Hospital, Capital Medical University, Beijing, China (X.F.)
Yao He, MD, PhD
Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Chinese PLA General Hospital, Beijing, China (Y.H.)
Xinhua Zhang, MD
Beijing Hypertension League Institute, China (X.Z.).
Xiangfeng Lu, PhD
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)
Shouling Wu, MD
Department of Cardiology, Kailuan Hospital, Hebei United University, Tangshan, China (S.C., S.W.)
Dongfeng Gu, MD, PhD [email protected]
From the Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (X.G., Yunzhi Li, X.Y., F.L., Ying Li, J.L., J. Cao, J. Chen, J.H., X. Lu, D.G.)

Notes

*
Drs Gu and Li contributed equally.
The online-only Data Supplement is available with this article at Supplemental Material.
Correspondence to Dongfeng Gu, MD, PhD, Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 Beilishi Rd, Beijing 100037, China. Email [email protected]

Disclosures

None.

Sources of Funding

This work was supported by grants from the Ministry of Science and Technology of China (2006BAI01A01, 2008BAI52B03, 2011BAI11B03, and 2011BAI09B03), and Chinese Academy of Medical Sciences (2017-I2M-1-004). The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

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