Low glomerular filtration rate and risk of stroke: meta-analysis (original) (raw)

Meng Lee, visiting scholar and instructor,1,3 Jeffrey L Saver, director and professor,1 Kuo-Hsuan Chang, instructor,4 Hung-Wei Liao, director,5 Shen-Chih Chang, epidemiologist,6 and Bruce Ovbiagele, associate professor1,2

Meng Lee

1Stroke Center, 710 Westwood Plaza, University of California, Los Angeles, CA 90095, USA

3Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang Gung University College of Medicine, Taiwan

Jeffrey L Saver

1Stroke Center, 710 Westwood Plaza, University of California, Los Angeles, CA 90095, USA

Kuo-Hsuan Chang

4Department of Neurology, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine

Hung-Wei Liao

5Ching-Ten Clinic, Taiwan

Shen-Chih Chang

6Department of Epidemiology, School of Public Health, University of California

Bruce Ovbiagele

1Stroke Center, 710 Westwood Plaza, University of California, Los Angeles, CA 90095, USA

2Department of Neurology, University of California, Los Angeles

1Stroke Center, 710 Westwood Plaza, University of California, Los Angeles, CA 90095, USA

2Department of Neurology, University of California, Los Angeles

3Department of Neurology, Chang Gung Memorial Hospital at Chiayi, Chang Gung University College of Medicine, Taiwan

4Department of Neurology, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine

5Ching-Ten Clinic, Taiwan

6Department of Epidemiology, School of Public Health, University of California

Supplementary Materials

Search strategy

GUID: 0E96B467-DE20-4BC3-9AD8-2F7AF664781F

Funnel plots

GUID: CE08EBE0-6579-4284-A88E-FAF1EF29D905

Abstract

Objective To qualitatively and quantitatively investigate the link between a low estimated glomerular filtration rate (eGFR) at baseline and risk of future stroke.

Design Systematic review and meta-analysis of prospective studies.

Data sources PubMed (1966-October 2009) and Embase (1947-October 2009).

Selection criteria Inclusion criteria were studies that prospectively collected data within cohort studies or clinical trials, estimated glomerular filtration rate at baseline using the modification of diet in renal disease or Cockcroft-Gault equations, assessed incident stroke, had a follow-up of at least one year, and reported quantitative estimates of multivariate adjusted relative risk and 95% confidence interval for stroke associated with an eGFR of 60-90 ml/min/1.73 m2 or <60 ml/min/1.73 m2.

Data abstraction Two investigators independently abstracted data from eligible studies. Estimates were combined using a random effects model. Heterogeneity was assessed by P value of χ2 statistics and I2. Publication bias was assessed by visual examination of funnel plots.

Results 21 articles derived from 33 prospective studies: 14 articles assessed eGFR <60 ml/min/1.73 m2 and seven assessed eGRF at both <60 ml/min/1.73 m2 and 60-90 ml/min/1.73 m2 for a total of 284 672 participants (follow-up 3.2-15 years) with 7863 stroke events. Incident stroke risk increased among participants with an eGFR <60 ml/min/1.73 m2 (relative risk 1.43, 95% confidence interval 1.31 to 1.57; P<0.001) but not among those with an eGFR of 60-90 ml/min/1.73 m2 (1.07, 0.98 to 1.17; P=0.15). Significant heterogeneity existed between estimates among patients with an eGFR <60 ml/min/1.73 m2 (P<0.001). In subgroup analyses among participants with an eGFR <60 ml/min/1.73 m2, heterogeneity was significant in Asians compared with non-Asians (1.96, 1.73 to 2.23 v 1.25, 1.16 to 1.35; P<0.001), and those with an eGFR of 40-60 ml/min/1.73 m2 v <40 ml/min/1.73 m2 (1.28, 1.04 to 1.56 v 1.77, 1.32 to 2.38; P<0.01).

Conclusions A baseline eGFR <60 ml/min/1.73 m2 was independently related to incident stroke across a variety of participants and study designs. Prompt and appropriate implementation of established strategies for reduction of vascular risk in people with know renal insufficiency may prevent future strokes.

Introduction

Chronic kidney disease and cardiovascular disease are major public health problems worldwide and often share the same pathophysiological mechanisms.1 Indeed, the prevalence of traditional cardiovascular risk factors can be high in those with impaired kidney function,2 and most patients with an estimated glomerular filtration rate (eGFR) lower than 60 ml/min/1.73 m2 die of cardiovascular causes and not progression to end stage renal disease.3 As such, averting future vascular events in patients with a low eGFR should be a primary goal.4

A systematic review of observational studies showed that a reduced eGFR was associated with an increased risk of coronary heart disease,5 and a recent meta-analysis showed that a low eGFR was linked to all cause and cardiovascular mortality in the general population.6 The effect of reduced eGFR on incident stroke, however, has not been well delineated in a qualitative or quantitative manner using the totality of published data. As stroke is a leading cause of mortality and morbidity worldwide, and several strategies, such as blood pressure control and use of statins and aspirin, may reduce subsequent cardiovascular disease in patients with chronic kidney disease, it is important to identify people at potential high risk, then appropriate therapy can be applied.7 8 We carried out a systematic review and meta-analysis to determine whether a link exists between reduced eGFR and incident stroke and the magnitude of any relation.

Methods

The search strategy was done according to the recommendations of the Meta-analysis of Observational Studies in Epidemiology.9 We searched PubMed (1966 to October 2009) and Embase (1947 to October 2009) using the search strategy “glomerular filtration rate” OR “renal disease” OR “chronic kidney disease” AND “stroke” OR “cerebrovascular disease” OR “cerebrovascular attack” OR “cerebral infarct” OR “intracranial hemorrhage” AND “prospective” OR “cohort” OR “observational” OR “post hoc” (see web extra fig 1). We restricted the search to studies in humans. No language restrictions were applied. Further information was retrieved through a manual search of references from recent reviews and relevant published original studies.

Study selection and data abstraction

We included studies that prospectively collected data within cohort studies or clinical trials, used the modification of diet in renal disease or Cockcroft-Gault equations to estimate glomerular filtration rate at baseline, assessed incident stroke, had a follow-up of at least one year, and reported quantitative estimates of the multivariate adjusted relative risk and 95% confidence interval for stroke associated with an eGFR of 60-90 ml/min/1.73 m2 or <60 ml/min/1.73 m2, or both. We excluded studies that had a cross sectional, case-control, or retrospective cohort study; that had mostly participants with end stage renal disease (by history of dialysis or an eGFR <15 ml/min/1.73 m2) or kidney transplant; that only reported unadjusted or age and sex adjusted relative risk; that did not report 95% confidence intervals; and that were duplicated. Studies that used slightly varying eGFR intervals were included if they were otherwise comparable. Two investigators (ML and K-HC) independently abstracted data from eligible studies. Discrepancies were resolved by discussion with a third investigator (BO) and by referencing the original report.

Study quality

We assessed the quality of eligible studies. Assessment was based on guidelines developed by the US Preventive Task Force as well as the modified checklist used in previous studies.10 11 12 We assessed eight characteristics: prospective study design, maintenance of comparable groups, adjustment of potential confounders, documented loss of follow-up rate, assessor of outcome blinded to exposure status, clear definition of exposures (eGFR) and outcomes (stroke), temporality (eGFR measured at baseline, not at time of outcomes assessment), and follow-up of at least one year. Studies were graded as good quality if they met at least seven of eight criteria, fair if they met four to six, and poor if they met fewer than four.

Statistical analysis

For data analysis we used multivariate adjusted outcome data (expressed as relative risks and 95% confidence intervals). When articles provided estimates based on both the modification of diet in renal disease and the Cockcroft-Gault equations, we used estimates from the more informative, expert recommended modification of diet in renal disease equation4 for primary analysis. In each study we converted these values by using their natural logarithms, and we calculated the standard errors from these logarithmic numbers and their corresponding 95% confidence intervals. For the statistical analysis we combined log relative risks and standard errors using the inverse variance approach. We used a random effect model and explored for sources of inconsistency (I2) and heterogeneity. A fixed effect model was also used for comparison with the random effects model on the overall risk estimate. Reported P values were two sided, with significance set at less than 0.05. Heterogeneity was assessed by P value of χ2 statistics and I2, which describes the percentage of variability in the effect estimates that is due to heterogeneity rather than to chance.13 14 Based on the suggestion of the Cochrane Collaboration we regarded heterogeneity as possibly unimportant when the I2 value was less than 40% and considerable when more than 75%.15 RevMan 5 was used for the meta-analysis of observational studies.16 17

The leading outcomes of interest were relative risks of incident stroke in patients with an eGFR of 60-90 ml/min/1.73 m2 and <60 ml/min/1.73 m2. Publication bias was assessed by visual examination of funnel plots. Subgroup analyses for eGFRs <60 ml/min/1.73 m2 were done according to normal references (studies using an eGFR >60 ml/min/1.73 m2 as the normal reference versus studies using >90 ml/min/1.73 m2 as normal), study population type (general or hypertension only versus established cardiovascular disease or high cardiovascular risk at entry), study design (ordinary cohorts versus secondary analysis of clinical trials), ethnicity (Asians v non-Asians), follow-up (<7 years _v_ ≥7 years), number of participants (<10 000 _v_ ≥10 000), equation used to determine eGFR (modification of diet in renal disease _v_ Cockcroft-Gault), end points (fatal _v_ fatal plus non-fatal stroke), stroke subtype (ischaemic _v_ haemorrhagic stroke), sex (men _v_ women), degree of eGFR impairment (eGFR 40-60 ml/min/1.73 m2 or nearest equivalent _v_ <40 ml/min/1.73 m2 or nearest equivalent), level of adjustment (age and sex adjusted _v_ multivariate adjusted), and study quality (good _v_ fair). We also explored the interaction between eGFR and albuminuria by using as a reference those groups with an eGFR of >60 ml/min/1.73 m2 without albuminuria.

Results

The literature review identified 83 full articles for detailed assessment, of which 53 were excluded for having no multivariate adjusted stroke estimate, six for being duplicated studies, and three for having a retrospective cohort design. Our final primary analysis included 21 articles derived from 33 prospective studies18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38: 14 articles assessed eGFR <60 ml/min/1.73 m2 only and seven assessed both <60 ml/min/1.73 m2 and 60-90 ml/min/1.73 m2 (fig 1). The table shows the characteristics of the included studies. Overall, 284 672 participants had a total of 7863 stroke events. Among the 21 articles, one contained 10 community cohorts from Japan30 and the other four community cohorts from the United States.36 Participants were derived from ordinary cohorts in 13 articles and clinical trials in eight. On a scale of 8 the overall quality of studies was good (median score 7, range 5-8). Follow-up ranged from 3.222 to 15 years.20 Glomerular filtration rate was estimated by the modification of diet in renal disease equation in 15 articles and by the Cockcroft-Gault equation in six. Nineteen articles reported fatal plus non-fatal stroke as a primary end point, whereas two reported fatal stroke as a primary end point.20 24 One study used thromboembolic events as a primary end point, but ischaemic stroke constituted over 94% of total thromboembolic events.23 Transient ischaemic attacks were only included as end points in three studies.22 26 34

An external file that holds a picture, illustration, etc. Object name is leem753731.f1_default.jpg

Fig 1 Flow of study selection

Characteristics of included studies

Study, country Study population Equation to calculate eGFR eGFR groups (ml/min/1.73 m2) No of participants % men Mean (SD) or median (range) age (years) No of strokes Follow-up (years) End points Adjusted variables Study quality
Bax 2008, Netherlands18 Atherosclerotic vascular disease or cardiovascular risk factors at entry Modification of diet in renal disease >90 (reference); 60-90; <60 602; 2097; 517 83; 77; 64 54 (10); 60 (10); 67 (8) 15; 59; 38 3.3 All stroke Age, sex, body mass index, hypertension, coronary heart disease, cerebral disease, peripheral artery disease, abdominal aortic aneurysm, diabetes mellitus, smoking, and use of angiotensin converting enzyme inhibitors and angiotensin II antagonists Fair
Bos 2007, Netherlands19 General, no stroke at entry Cockcroft-Gault ≥60 (reference); <60 2652; 2285 40 69 (62 to 77) 586 10.2 All stroke (ischaemic and haemorrhagic recorded separately) Age, sex, and propensity score (systolic blood pressure, diastolic blood pressure, antihypertensive drug use, left ventricular hypertrophy, diuretic use, pack years of smoking, diabetes mellitus, cholesterol level, high density lipoprotein level, carotid intima media thickness, uric acid, C reactive protein, previous myocardial infarction, previous atrial fibrillation, waist to hip ratio, antithrombotic drug use, lipid lowering drug use) Good
Cheng 2008, Taiwan20 General Modification of diet in renal disease >90 (reference); 60 to 90; <60 4190; 11 583; 1253 63; 80; 87 56 (5); 57 (5); 61 (6) 29; 88; 35 15 Fatal stroke (ischaemic and haemorrhagic recorded separately) Age, sex, body mass index, smoking status (current, former, never), total cholesterol level, haemoglobin concentration, diabetes mellitus, systolic blood pressure, history of hypertension, and prevalent cardiovascular disease Fair
Deo 2008, USA21 General, no stroke at entry Modification of diet in renal disease ≥60 (reference); <60 2340; 632 49 74 (70 to 79) 126; 37 6 All stroke Race, age, sex, site, body mass index, alcohol use, current smoking status, diabetes mellitus, hypertension, aspirin use, diuretic use, angiotensin converting enzyme inhibitors use, β blocker use, statin use, low density lipoprotein and high density lipoprotein cholesterol level, plasminogen activator inhibitor, C reactive protein, albumin, interleukin-6, and tumour necrosis factor α Fair
Ford 2009, Ireland, Scotland, and Netherlands22 Pre-existing vascular disease or increased risk of such disease, secondary analysis of clinical trial Modification of diet in renal disease ≥60 (reference); 50-60; 40-50; 20-40 2702; 1641; 1104; 349 58; 48; 33; 26 75 (3); 75 (3); 76 (3); 77 (3) 190; 120; 74; 31 3.2 All stroke and transient ischaemic attacks Randomised treatment; country; sex; current smoking status; age; histories of hypertension, diabetes mellitus, and vascular disease; levels of low density lipoprotein cholesterol and high density lipoprotein cholesterol; systolic and diastolic blood pressure; glucose level; body mass index; and C reactive protein Good
Go 2009, USA23 Atrial fibrillation at entry Modification of diet in renal disease ≥60 (reference); 45 to 59; <45 7690; 2499; 1338 60; 48; 52 72 (64 to 78); 76 (70 to 82); 78 (73 to 83) 637 8 Thromboembolic events, 94% were ischaemic stroke Age, sex, race/ethnicity, educational attainment, annual income status, previous ischaemic stroke, heart failure, diabetes mellitus, hypertension, and coronary artery disease Good
Irie 2006, Japan24 General, men; general, women Modification of diet in renal disease Men: ≥100 (reference), 60 to 99, <60. Women: ≥100 (reference), 60 to 99, <60 Men: 7082, 23 858, 824. Women: 10 554, 48 041, 2073 Men: 100 for all groups. Women: 0 for all groups 61 Men: 84, 363, 44. Women: 53, 365, 76 10 Fatal stroke Age, hypertension category, cigarette smoking, alcohol intake, diabetes mellitus, sex-specific fifths of serum total cholesterol level, serum high density lipoprotein cholesterol level, body mass index, and urinary protein Fair
Kokubo 2009, Japan25 General Modification of diet in renal disease ≥90 (reference); 60 to 89; 50 to 59; <50 2415; 2452; 387; 124 47 56 65; 99; 36; 13 11.7 All stroke (ischaemic and haemorrhagic recorded separately) Age, sex, body mass index, smoking, alcohol consumption, and present illness (hypertension, diabetes mellitus, and hypercholesterolaemia) Good
Koren- Morag 2006, Israel26 Coronary heart disease but not stroke at entry, secondary analysis of clinical trial Modification of diet in renal disease and Cockcroft-Gault >60 (reference); ≤60 5345; 1340 91; 79 58 (7); 65 (4) 207; 80 4.8 to 8.1 Ischaemic stroke and transient ischaemic attacks Age, sex, systolic blood pressure, diabetes mellitus, level of triglycerides, high density lipoprotein level, New York Heart Association functional class, body mass index, peripheral artery disease, current smoking status, antiplatelets, antihypertensive and lipid modifying drugs Good
Kurth 2009, USA27 General, female health professionals, no cardiovascular disease at entry, secondary analysis of clinical trial Modification of diet in renal disease ≥90 (reference); 75 to 89; 60 to 74; <60 14 979; 8073; 3572; 1315 0 for all groups 54 (0.1); 55 (0.1); 57 (0.1); 57 (0.2) 197; 111; 50; 31 12 All stroke Age, systolic blood pressure, antihypertensive treatment, smoking, body mass index, alcohol, exercise, total cholesterol level, C reactive protein, use of hormone replacement therapy, diabetes mellitus, and assigned treatments Good
Nakayama 2007, Japan28 General Cockcroft-Gault >70 (reference); 40 to 70; <40 555; 1246; 176 42; 35; 35 55 (9); 65 (7); 76 (7) 15; 77; 20 7.8 All stroke Age, sex, systolic blood pressure, body mass index, smoking status, use of antihypertensive drugs, history of cardiovascular disease, hypercholesterolaemia, and diabetes mellitus Good
Nickolas 2008, USA29 General, not stroke at entry Cockcroft-Gault ≥60 (reference); 15 to 59 2353; 945 37 63 201 6.5 All stroke Age, sex, education, hypertension, low density lipoprotein cholesterol level, diabetes mellitus, prevalent cardiac disease, smoking, and alcohol consumption Good
Ninomiya 2008, Japan30 General, data from 10 community-based cohort studies Modification of diet in renal disease ≥90 (reference); 60 to 89; <60 7206; 14 003; 1875 39; 56; 5 58 (12) 84; 404; 104 7.4 All stroke Age, sex, cohort, systolic blood pressure, diabetes mellitus, total cholesterol level, body mass index, and current smoking status Fair
Perkovic 2007, multicountries38 Stroke, secondary analysis of clinical trial Cockcroft-Gault ≥60 (reference); <60 4314; 1757 75; 55 61 (9); 70 (8) 460; 264 4 All stroke Age, sex, smoking status, diabetes mellitus, systolic blood pressure, body mass index, active versus placebo therapy, and single versus dual agent therapy Good
Perticone 2009, Italy31 Postmenopausal women, no cardiovascular disease or diabetes mellitus at entry Modification of diet in renal disease ≥60 (reference); <60 1071; 429 0; 0 53 (6); 53 (6) 41; 24 6 All stroke Age, smoking (former or never smokers, current smokers), cholesterol level, systolic blood pressure, fasting glucose level, body mass index, menopause, and metabolic syndrome Fair
Ruilope 2001, Multicountries32 Hypertension cohort, secondary analysis of clinical trial Cockcroft-Gault >60 (eference); ≤60 15 770; 2821 57; 30 60 (7); 68 (7) 211; 77 3.8 All stroke Achieved diastolic and systolic blood pressure, age, gender, smoking habits, previous cardiovascular disease, diabetes mellitus, and total cholesterol Good
Ruilope 2007, multicountries33 Hypertension cohort, secondary analysis of clinical trial Modification of diet in renal disease and Cockcroft-Gault ≥60 (reference); <60 9214; 5999 67; 44 65 (8); 70 (8) 603 4.6 All stroke Age, sex, coronary heart disease, and left ventricular hypertrophy Good
Shilipak 2001, USA34 Postmenopausal women with coronary heart disease, secondary analysis of clinical trial Cockcroft-Gault >60 (reference); 40 to 60; <40 1306; 1135; 322 0 for all groups 66 (7) 70; 93; 51 4.1 All stroke and transient ischaemic attacks Age; race; hypertension; diabetes mellitus; tobacco use; previous coronary artery bypass surgery; body mass index; waist to hip ratio; levels of low density lipoprotein cholesterol, high density lipoprotein cholesterol, triglyceride, and lipoprotein(a); physical activity; lipid lowering drug use; diuretic use; and atrial fibrillation Good
Tonelli 2006, USA and Canada35 History of myocardial infarction, secondary analysis of clinical trial Modification of diet in renal disease ≥60 (reference); <60 2839; 707 89; 75 58 (50 to 64); 65 (59 to 70) 71; 28 5 All stroke Age, sex, ethnic origin, smoking status, diabetes mellitus, waist to hip circumference ratio, fasting glucose level, haemoglobin concentration, albumin, low density lipoprotein and high density lipoprotein cholesterol levels, triglyceride levels, systolic and diastolic blood pressure, country of treatment (US v Canada), left ventricular ejection fraction, and use of drugs (β blockers, angiotensin converting enzyme inhibitors, aspirin, or pravastatin) Good
Weiner 2004, USA36 Combined four population studies (Atherosclerosis Risk in Community Study, Cardiovascular Health Study, Framingham Heart Study, and Framingham Offspring Study) Modification of diet in renal disease ≥60 (reference); 15 to 59 20 970; 1664 44; 33 56 (11); 68 (11) 587; 125 10 All stroke Age, sex, hypertension, diabetes mellitus, systolic blood pressure, body mass index, total and high density lipoprotein cholesterol level, current smoking status, current alcohol use, left ventricular hypertrophy, high school graduation status, and race Fair
Yang 2008, China37 Diabetic population without stroke at entry Modification of diet in renal disease ≥115 (reference); 60 to 114.9; <60 6969 46 57 314 5.4 Ischaemic stroke Age; sex; systolic and diastolic blood pressure; haemoglobin A1c; body mass index; haemoglobin concentration; white blood cell count; levels of high density lipoprotein, low density lipoprotein, total cholesterol, and triglyceride; and drug use (blood pressure lowering, cholesterol lowering, insulin, antiplatelet, angiotensin converting enzyme inhibitor, and angiotensin II antagonist) Fair

eGFR=estimated glomerular filtration rate.

Main outcome

Pooling results from the random effects model showed that incident stroke increased among patients with an eGFR <60 ml/min/1.73 m2 (relative risk 1.43, 95% confidence interval 1.31 to 1.57, P<0.001; fig 2). The risk of incident stroke did not, however, increase significantly among patients with an eGFR of 60-90 ml/min/1.73 m2 (1.07, 0.98 to 1.17, P=0.15; fig 2). Significant heterogeneity existed between estimates among patients with an eGFR <60 ml/min/1.73 m2 (P<0.001, I2=69%) but not among those with an eGFR of 60-90 ml/min/1.73 m2 (P=0.06, I2=38%). The estimates were similar between the fixed effects model and random effect model.

An external file that holds a picture, illustration, etc. Object name is leem753731.f2_default.jpg

Fig 2 Risk ratio for association of estimated glomerular filtration rate (eGFR) and risk of stroke in prospective cohort studies. *Subgroups of estimates with eGFR <60 ml/min/1.73 m2. †Subgroups of estimates with eGFR 60-90 ml/min/1.73 m2

Subgroup analyses

An eGFR <60 ml/min/1.73 m2 was associated with an increased risk of subsequent stroke in all subgroups when estimates were stratified by eGFR reference group, study population type, study design, ethnicity, duration of follow-up, number of participants, equation used to determine eGFR, end points, sex, stroke subtype, different eGFR intervals <60 ml/min/1.73 m2, study quality, and level of adjustment (fig 3). Significant heterogeneity between pooled analyses were noted for studies using an eGFR >60 ml/min/1.73 m2 as the normal reference compared with studies using >90 ml/min/1.73 m2 as normal (1.29, 1.18 to 1.41 v 1.82, 1.53 to 2.16, P for heterogeneity among subgroups <0.001), cohort studies compared with clinical trials (1.59, 1.40 to 1.81 v 1.25, 1.13 to 1.38, P<0.01), Asians compared with non-Asians (1.96, 1.73 to 2.23 v 1.26, 1.16 to 1.35, P<0.001), fatal compared with fatal plus non-fatal stroke (1.97, 1.63 to 2.38 v 1.38, 1.26 to 1.51, P<0.001), eGFR 40-60 ml/min/1.73 m2 v <40 ml/min/1.73 m2 (1.28, 1.04 to 1.56 v 1.77, 1.32 to 2.38, P<0.01), and good study quality compared with fair study quality (1.35, 1.23 to 1.49 v 1.62, 1.33 to 1.97, P=0.01).

An external file that holds a picture, illustration, etc. Object name is leem753731.f3_default.jpg

Fig 3 Subgroup analyses for comparison between studies reporting associations of estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 with risk of stroke

A total of 11 studies reported adjusted estimates of the strength of the association, first by age and sex then by other known cardiovascular risk factors—for example, blood pressure, smoking, lipids levels, diabetes. The overall age and sex adjusted summary estimate was 1.64 (95% confidence interval 1.45 to 1.85), which after further adjustment of other known cardiovascular risk factors was reduced to 1.45 (1.26 to 1.68; P for heterogeneity among subgroups 0.01).

Otherwise no obvious heterogeneity found between baseline risk populations (general or hypertension only v high cardiovascular risk), duration of follow-up, number of participants, equation used to determine eGFR, stroke subtypes, and sex. Based on the few papers that provided information on the interaction between proteinuria and eGFR, proteinuria did not substantially increase the risk of stroke in patients with an eGFR of <60 or >60 ml/min/1.73 m2 (fig 4).

An external file that holds a picture, illustration, etc. Object name is leem753731.f4_default.jpg

Fig 4 Interaction between estimated glomerular filtration rate (eGFR) and albuminuria, using groups with eGFR >60 ml/min/1.73 m2 without albuminuria as reference

Publication bias

The funnel plots showed no major asymmetry except for a small degree of publication bias, with a slight under-representation of small studies showing neutral or unexpected protective effects (see web extra fig 2).

Discussion

In this meta-analysis of 21 articles derived from 33 prospective studies of generally good quality, among over 280 000 people experiencing almost 8000 stroke events, we found that patients with a baseline estimated glomerular filtration rate (eGFR) of <60 ml/min/1.73 m2 had a risk of future stroke that was 43% greater than those with a normal baseline eGFR. This relation was consistent across diverse population subgroups—that is, those with or without traditional cardiovascular risk factors. The size and inclusion of only prospectively collected data strengthened the robustness of our findings, as selection bias, recall bias, and reverse causality were unlikely. In addition, all studies included in our meta-analysis reported a multivariate adjusted relative risk, which probably mitigated the possibility of known confounding influencing our results.

We used subgroup analyses to assess the varying influence of several factors on the association between eGFR <60 ml/min/1.73 m2 and risk of stroke. The magnitude of risk was larger when studies used an eGFR >90 ml/min/1.73 m2 as reference compared with >60 ml/min/1.73 m2, which raised the possibility that an eGFR of 60-90 ml/min/1.73 m2 may increase the risk of stroke compared with an eGFR >90 ml/min/1.73 m2. Our formal meta-analysis did not, however, show significantly increased risk of incident stroke among patients with an eGFR of 60-90 ml/min/1.73 m2. The explanation could be that such a rate is not sensitive enough as a marker of kidney disease to discriminate risk of stroke. We did, however, find a possible dose-response relation between eGFR and stroke at levels <60 ml/min/1.73 m2—that is, the risk of stroke was significantly greater for eGFR <40 ml/min/1.73 m2 than for levels of 40-60 ml/min/1.73 m2.

A meta-analysis based on observational studies cannot prove causality. However, based on these results it may not be unreasonable to regard the presence of a low eGFR as a marker for increased risk of stroke, prompting optimal application of established vascular risk reduction strategies such as control of blood pressure, statin use, and antiplatelet therapy.7

Interestingly we found that Asian people with a low baseline eGFR seemed to be at higher risk of future stroke. Indeed, in Asian populations, hypertension is a major risk factor of both stroke and death from renal causes,39 40 chronic kidney disease further increases the association of blood pressure with stroke,25 and meta-analysis showed that the risk of stroke associated with hypertension is consistently and significantly greater in Chinese than in white people.41 Furthermore, it has been suggested that Asian people tend to develop hypertension at earlier ages than other races,42 and it is conceivable that a longer history of hypertension may cause more profound damage of end organs and vessels thereby leading to a higher likelihood of vascular events within a given study period. A systematic review that linked reduced eGFR with increased risk of coronary heart disease was only among participants in Western countries and so did not have the means of exploring this issue.5 Although most of the studies we analysed adjusted for hypertension or blood pressure, none adjusted for the duration of hypertension, thereby limiting the extent to which we could fully adjust for hypertension as a confounder. As such, this potential disparity between races will need to be more comprehensively explored in future studies.

We also observed that the effect of reduced eGFR was more profound on the risk of fatal stroke than on all strokes, which probably points to the association of compromised kidney function with risk factors for generally poor clinical outcomes such as oxidative stress, widespread inflammation, electrolyte derangements, procoagulation, and presence of uraemic toxins.3 In fact, kidney disease even of mild severity has been shown to be an independent predictor of poorer clinical outcomes among people with stroke, including higher risk of all cause mortality and cardiovascular mortality.43 44 Also of note, the presence of albuminuria did not substantially further increase the risk of stroke among patients with a baseline eGFR of <60 or >60 ml/min/1.73 m2. Our result should be interpreted with caution, however, as it was based on just three studies and the rate of albuminuria is low in people without diabetes. A recent meta-analysis showed that compared with people without albuminuria or a low eGFR, those with either condition had a higher risk of cardiovascular death and those with both conditions had the highest risk of cardiovascular death.6 Furthermore, meta-analyses have shown that albuminuria was independently associated with a higher risk of stroke even when the included studies had adjusted for eGFR or serum creatinine level.45 46

Limitations of this meta-analysis

Limitations of this meta-analysis must be considered. Firstly, meta-analyses may be biased if the literature search fails to identify all relevant studies or the selection criteria for including a study are applied in a subjective manner. To minimise these risks we carried out thorough searches across different databases using explicit criteria for study selection, data abstraction, and data analysis. Secondly, compared with studies of good quality, those of fair quality showed a stronger association between reduced eGFR and stroke. When we restricted analysis to good quality studies, the estimate of association slightly decreased. Thirdly, a large amount of heterogeneity was observed in the results of the various studies. Although subanalyses were done to identify this, heterogeneity persisted in many subgroups, suggesting that other factors might explain this result. Meta-regression by average baseline eGFR and other variables could have been a better way of exploring potential sources of heterogeneity. However, most included articles did not provide average baseline eGFR in each eGFR category, which prevented us from exploring further. In those studies that provided both age and sex adjusted and multivariate unadjusted estimates, the association between reduced eGFR and stroke was slightly, but significantly, attenuated after further adjustment. Such an attenuation in effect size suggests that residual confounding may have remained and that the summary result presented here may be a slight overestimation of the true magnitude of the association between reduced eGFR and risk of stroke. Despite these limitations, the results of this systematic review represent the most precise and accurate estimate of the strength of the relation between reduced eGFR and incident stroke currently available.

Implications

Our formal meta-analysis found a significant association between eGFR <60 ml/min/1.73 m2 and increased incident stroke across various populations, after adjustment for established cardiovascular risk factors. None the less, these results possibly underestimated the magnitude of this relation because a reduced eGFR often simultaneously exists with several traditional and novel vascular risk factors. Of major public health interest were our findings that Asian patients with a low eGFR were at higher risk for stroke than their non-Asian counterparts, that below an eGFR level of 60 ml/min/1.73 m2 a dose-response relation with risk of stroke might exist, and that fatal strokes were especially associated with low baseline eGFR.

At this juncture, a low baseline eGFR should be seen simply as a risk marker. Established evidence based strategies already proved to mitigate vascular risk, such as reduction of blood pressure, when promptly and appropriately applied are likely to avert future strokes in people with renal insufficiency. Specific patient subgroups with a low eGFR, such as people of Asian race, may particularly benefit.

What is already known on this topic

What this study adds

Web Extra. Extra material supplied by the author

Notes

We thank Yueh Lee for retrieving the papers.

Contributors: ML and BO conceived the study. ML, S-CC, and BO design the inclusion and exclusion criteria. ML, and K-HC participated in the study search and data collection and extraction. ML did the statistical analysis with guidance from JLS, S-CC, and BO. ML wrote the first draft of the report, and JLS, H-WL, and BO did the major revision and made comments. All other authors commented on the draft and approved the final version. ML and BO had full access to all the data and had the final decision to submit for publication. They are guarantors.

Funding: ML was supported by a grant from Chang Gung Memorial Hospital, Taiwan (CMRPG 660311, Taiwan). JLS was supported by the specialised programme on translational research in acute stroke (SPOTRIAS) award (P50 NS044378) from the National Institutes of Health, and BO was supported by University of California, Los Angeles-Resource Centers for Minority Aging Research under National Institutes of Health/National Institutes on Aging grant No P30-AG021684. The sponsors played no role in the study design, data collection and analysis, or decision to submit the article for publication.

Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any company for the submitted work; no financial relationships with any companies that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: No additional data available.

Notes

Cite this as: BMJ 2010;341:c4249

References

1. Rosamond W, Flegal K, Friday G, Furie K, Go A, Greenlund K, et al. Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2007;115:e69-171. [PubMed] [Google Scholar]

2. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003;108:2154-69. [PubMed] [Google Scholar]

3. Schiffrin EL, Lipman ML, Mann JF. Chronic kidney disease: effects on the cardiovascular system. Circulation 2007;116:85-97. [PubMed] [Google Scholar]

4. Brosius FC, Hostetter TH, Kelepouris E, Mitsnefes MM, Moe SM, Moore MA, et al. Detection of chronic kidney disease in patients with or at increased risk of cardiovascular disease: a science advisory from the American Heart Association Kidney and Cardiovascular Disease Council; the Councils on High Blood Pressure Research, Cardiovascular Disease in the Young, and Epidemiology and Prevention; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: developed in collaboration with the National Kidney Foundation. Circulation 2006;114:1083-7. [PubMed] [Google Scholar]

5. Di Angelantonio E, Danesh J, Eiriksdottir G, Gudnason V. Renal function and risk of coronary heart disease in general populations: new prospective study and systematic review. PLoS Med 2007;4:e270. [PMC free article] [PubMed] [Google Scholar]

6. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073-81. [PMC free article] [PubMed] [Google Scholar]

7. James MT, Hemmelgarn BR, Tonelli M. Early recognition and prevention of chronic kidney disease. Lancet 2010;375:1296-309. [PubMed] [Google Scholar]

8. Strippoli GF, Navaneethan SD, Johnson DW, Perkovic V, Pellegrini F, Nicolucci A, et al. Effects of statins in patients with chronic kidney disease: meta-analysis and meta-regression of randomised controlled trials. BMJ 2008;336:645-51. [PMC free article] [PubMed] [Google Scholar]

9. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008-12. [PubMed] [Google Scholar]

10. Harris RP, Helfand M, Woolf SH, Lohr KN, Mulrow CD, Teutsch SM, et al. Current methods of the US Preventive Services Task Force: a review of the process. Am J Prev Med 2001;20:21-35. [PubMed] [Google Scholar]

11. Loosemore M, Knowles CH, Whyte GP. Amateur boxing and risk of chronic traumatic brain injury: systematic review of observational studies. BMJ 2007;335:809. [PMC free article] [PubMed] [Google Scholar]

12. Smith GL, Lichtman JH, Bracken MB, Shlipak MG, Phillips CO, DiCapua P, et al. Renal impairment and outcomes in heart failure: systematic review and meta-analysis. J Am Coll Cardiol 2006;47:1987-96. [PubMed] [Google Scholar]

13. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60. [PMC free article] [PubMed] [Google Scholar]

14. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539-58. [PubMed] [Google Scholar]

15. Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions. Cochrane Collaboration, 2008.

16. Ho WK, Hankey GJ, Quinlan DJ, Eikelboom JW. Risk of recurrent venous thromboembolism in patients with common thrombophilia: a systematic review. Arch Intern Med 2006;166:729-36. [PubMed] [Google Scholar]

17. Critchley JA, Capewell S. Mortality risk reduction associated with smoking cessation in patients with coronary heart disease: a systematic review. JAMA 2003;290:86-97. [PubMed] [Google Scholar]

18. Bax L, Algra A, Mali WP, Edlinger M, Beutler JJ, van der Graaf Y. Renal function as a risk indicator for cardiovascular events in 3216 patients with manifest arterial disease. Atherosclerosis 2008;200:184-90. [PubMed] [Google Scholar]

19. Bos MJ, Koudstaal PJ, Hofman A, Breteler MM. Decreased glomerular filtration rate is a risk factor for hemorrhagic but not for ischemic stroke: the Rotterdam Study. Stroke 2007;38:3127-32. [PubMed] [Google Scholar]

20. Cheng TY, Wen SF, Astor BC, Tao XG, Samet JM, Wen CP. Mortality risks for all causes and cardiovascular diseases and reduced GFR in a middle-aged working population in Taiwan. Am J Kidney Dis 2008;52:1051-60. [PubMed] [Google Scholar]

21. Deo R, Fyr CL, Fried LF, Newman AB, Harris TB, Angleman S, et al. Kidney dysfunction and fatal cardiovascular disease—an association independent of atherosclerotic events: results from the Health, Aging, and Body Composition (Health ABC) study. Am Heart J 2008;155:62-8. [PubMed] [Google Scholar]

22. Ford I, Bezlyak V, Stott DJ, Sattar N, Packard CJ, Perry I, et al. Reduced glomerular filtration rate and its association with clinical outcome in older patients at risk of vascular events: secondary analysis. PLoS Med 2009;6:e16. [PMC free article] [PubMed] [Google Scholar]

23. Go AS, Fang MC, Udaltsova N, Chang Y, Pomernacki NK, Borowsky L, et al. Impact of proteinuria and glomerular filtration rate on risk of thromboembolism in atrial fibrillation: the anticoagulation and risk factors in atrial fibrillation (ATRIA) study. Circulation 2009;119:1363-9. [PMC free article] [PubMed] [Google Scholar]

24. Irie F, Iso H, Sairenchi T, Fukasawa N, Yamagishi K, Ikehara S, et al. The relationships of proteinuria, serum creatinine, glomerular filtration rate with cardiovascular disease mortality in Japanese general population. Kidney Int 2006;69:1264-71. [PubMed] [Google Scholar]

25. Kokubo Y, Nakamura S, Okamura T, Yoshimasa Y, Makino H, Watanabe M, et al. Relationship between blood pressure category and incidence of stroke and myocardial infarction in an urban Japanese population with and without chronic kidney disease: the Suita Study. Stroke 2009;40:2674-9. [PubMed] [Google Scholar]

26. Koren-Morag N, Goldbourt U, Tanne D. Renal dysfunction and risk of ischemic stroke or TIA in patients with cardiovascular disease. Neurology 2006;67:224-8. [PubMed] [Google Scholar]

27. Kurth T, de Jong PE, Cook NR, Buring JE, Ridker PM. Kidney function and risk of cardiovascular disease and mortality in women: a prospective cohort study. BMJ 2009;338:b2392. [PMC free article] [PubMed] [Google Scholar]

28. Nakayama M, Metoki H, Terawaki H, Ohkubo T, Kikuya M, Sato T, et al. Kidney dysfunction as a risk factor for first symptomatic stroke events in a general Japanese population—the Ohasama study. Nephrol Dial Transplant 2007;22:1910-5. [PubMed] [Google Scholar]

29. Nickolas TL, Khatri M, Boden-Albala B, Kiryluk K, Luo X, Gervasi-Franklin P, et al. The association between kidney disease and cardiovascular risk in a multiethnic cohort: findings from the Northern Manhattan Study (NOMAS). Stroke 2008;39:2876-9. [PMC free article] [PubMed] [Google Scholar]

30. Ninomiya T, Kiyohara Y, Tokuda Y, Doi Y, Arima H, Harada A, et al. Impact of kidney disease and blood pressure on the development of cardiovascular disease: an overview from the Japan Arteriosclerosis Longitudinal Study. Circulation 2008;118:2694-701. [PubMed] [Google Scholar]

31. Perticone F, Sciacqua A, Maio R, Perticone M, Laino I, Bruni R, et al. Renal function predicts cardiovascular outcomes in southern Italian postmenopausal women. Eur J Cardiovasc Prev Rehabil 2009;16:481-6. [PubMed] [Google Scholar]

32. Ruilope LM, Salvetti A, Jamerson K, Hansson L, Warnold I, Wedel H, et al. Renal function and intensive lowering of blood pressure in hypertensive participants of the hypertension optimal treatment (HOT) study. J Am Soc Nephrol 2001;12:218-25. [PubMed] [Google Scholar]

33. Ruilope LM, Zanchetti A, Julius S, McInnes GT, Segura J, Stolt P, et al. Prediction of cardiovascular outcome by estimated glomerular filtration rate and estimated creatinine clearance in the high-risk hypertension population of the VALUE trial. J Hypertens 2007;25:1473-9. [PubMed] [Google Scholar]

34. Shlipak MG, Simon JA, Grady D, Lin F, Wenger NK, Furberg CD. Renal insufficiency and cardiovascular events in postmenopausal women with coronary heart disease. J Am Coll Cardiol 2001;38:705-11. [PubMed] [Google Scholar]

35. Tonelli M, Jose P, Curhan G, Sacks F, Braunwald E, Pfeffer M. Proteinuria, impaired kidney function, and adverse outcomes in people with coronary disease: analysis of a previously conducted randomised trial. BMJ 2006;332:1426. [PMC free article] [PubMed] [Google Scholar]

36. Weiner DE, Tighiouart H, Amin MG, Stark PC, MacLeod B, Griffith JL, et al. Chronic kidney disease as a risk factor for cardiovascular disease and all-cause mortality: a pooled analysis of community-based studies. J Am Soc Nephrol 2004;15:1307-15. [PubMed] [Google Scholar]

37. Yang X, So WY, Ma RC, Ko GT, Kong AP, Ho CS, et al. Thresholds of risk factors for ischemic stroke in type 2 diabetic patients with and without albuminuria: a non-linear approach. Clin Neurol Neurosurg 2008;110:701-9. [PubMed] [Google Scholar]

38. Perkovic V, Ninomiya T, Arima H, Gallagher M, Jardine M, Cass A, et al. Chronic kidney disease, cardiovascular events, and the effects of perindopril-based blood pressure lowering: data from the PROGRESS study. J Am Soc Nephrol 2007;18:2766-72. [PubMed] [Google Scholar]

39. Nakamura K, Barzi F, Lam TH, Huxley R, Feigin VL, Ueshima H, et al. Cigarette smoking, systolic blood pressure, and cardiovascular diseases in the Asia-Pacific region. Stroke 2008;39:1694-702. [PubMed] [Google Scholar]

40. O’Seaghdha CM, Perkovic V, Lam TH, McGinn S, Barzi F, Gu DF, et al. Blood pressure is a major risk factor for renal death: an analysis of 560 352 participants from the Asia-Pacific region. Hypertension 2009;54:509-15. [PubMed] [Google Scholar]

41. Zhang XF, Attia J, D’Este C, Ma XY. The relationship between higher blood pressure and ischaemic, haemorrhagic stroke among Chinese and Caucasians: meta-analysis. Eur J Cardiovasc Prev Rehabil 2006;13:429-37. [PubMed] [Google Scholar]

42. Hohn AR, Dwyer KM, Dwyer JH. Blood pressure in youth from four ethnic groups: the Pasadena Prevention Project. J Pediatr 1994;125:368-73. [PubMed] [Google Scholar]

43. Ovbiagele B, Sanossian N, Liebeskind DS, Kim D, Ali LK, Pineda S, et al. Indices of kidney dysfunction and discharge outcomes in hospitalized stroke patients without known renal disease. Cerebrovasc Dis 2009;28:582-8. [PubMed] [Google Scholar]

44. Ani C, Ovbiagele B. Relation of baseline presence and severity of renal disease to long-term mortality in persons with known stroke. J Neurol Sci 2010;288:123-8. [PubMed] [Google Scholar]

45. Ninomiya T, Perkovic V, Verdon C, Barzi F, Cass A, Gallagher M, et al. Proteinuria and stroke: a meta-analysis of cohort studies. Am J Kidney Dis 2009;53:417-25. [PubMed] [Google Scholar]

46. Lee M, Saver JL, Chang KC, Ovbiagele B. Level of albuminuria and risk of stroke: systematic review and meta-analysis. Cerebrovasc Dis 2010. (in press). [PMC free article] [PubMed]


Articles from The BMJ are provided here courtesy of BMJ Publishing Group