Circulation Journal
Online ISSN : 1347-4820
Print ISSN : 1346-9843
ISSN-L : 1346-9843
Reviews
Traditional Cardiovascular Risk Factors for Incident Atrial Fibrillation
Yoshihiro KokuboChisa Matsumoto
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JOURNALS FREE ACCESS FULL-TEXT HTML

2016 Volume 80 Issue 12 Pages 2415-2422

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Abstract

To prevent atrial fibrillation (AF), it is essential to reduce its risk factors and extend healthy life expectancy as a result. There are few reviews on the AF risk factors. We discuss them and approach the prevention of AF. We briefly review traditional risk factors for incident AF, especially focusing on high blood pressure, overweight/obesity, dyslipidemia, diabetes, tobacco smoking, and excessive drinking. When trying to prevent AF by modifying lifestyle, it is important to comprehensively utilize the risk factors for AF to predict the 10-year as an AF risk score. However, there are only 2 risk scores of AF just for the US population. There are few studies of the AF risk factors in non-Western populations. A risk score for incident AF in non-Westerners is awaited because different race and lifestyles may have different contributions as AF risk factors. An AF risk score in accordance with race could be useful for identifying persons with a high risk of AF in order to encourage them to consult a doctor and encourage lifestyle modifications before the onset of AF. (Circ J 2016; 80: 2415–2422)

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it is a strong risk factor for all-cause mortality and cardiovascular disease (CVD).1,2 During the 20-year period from 1990 to 2010, the global estimated age-adjusted prevalence rates of AF (/1,000 population) increased from 5.7 to 6.0 in men and from 3.6 to 3.7 in women.3 To prevent cardioembolism in patients with AF, new oral anticoagulants are widely used as alternatives to warfarin for anticoagulation in AF patients.4 In addition, for the prevention of AF, it is essential to reduce its risk factors and thus extend healthy life expectancy. However, the fundamental mechanisms that underlie AF are still not clearly understood. In Japan, only a limited number of prospective cohort studies of the risk factors for AF have been conducted. However, the prevalence of AF is also increasing in Japan.5,6 There are few reviews of the AF risk factors, therefore we discuss this topic and the approaches to AF prevention.

Risk Factors for AF

Blood Pressure

Many prospective cohort studies have demonstrated that hypertension is a strong risk factor for total death and CVD.7 Not only hypertension,8,9 but also high-normal blood pressure (BP)10,11 and increasing systolic BP (SBP)12 are risk factors for incident AF (Table 1). The Framingham Heart Study (FHS) showed that a 20-mmHg increase in pulse pressure was associated with a 1.26-fold increased risk of AF.13 In that study, SBP positively associated with AF, but when diastolic BP (DBP) was added to the model, only pulse pressure was associated with AF.

Table 1. Review of Blood Pressure Studies for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
FHS8 M 2,090 55–94 38 264 HT OR=1.5 (1.2–2.0)
W 2,641 298 OR=1.4 (1.1–1.8)
Southern Community
Cohort Study9
M/W 8,836 ≥65 5.7 1,062 HT OR=1.29 (1.07–1.55)
Black OR=1.37 (1.05–1.80)
White OR=1.19 (0.92–1.54)
WHS10 W 34,221 52–65 12.4 644 SBP=130–139 mmHg HR=1.28 (1.00–1.63)
SBP=140–159 mmHg HR=1.56 (1.22–2.01)
SBP=160– mmHg HR=2.74 (1.77–4.22)
DBP=85–89 mmHg HR=1.53 (1.05–2.23)
DBP=90–94 mmHg HR=1.35 (0.82–2.22)
DBP=95– mmHg HR=2.15 (1.21–3.84)
Five governmental
institutions in Oslo11
M 2,014 40–59 35 270 SBP=128–138 mmHg HR=1.98 (1.22–3.27)
SBP=140– mmHg HR=1.84 (1.07–3.19)
DBP=80–86 mmHg HR=1.67 (1.00–2.85)
DBP=88–92 mmHg HR=1.76 (1.01–3.11)
DBP=94– mmHg HR=2.36 (1.38–4.15)
CHS12 M/W 4,844 ≥65 3.28 304 /10 mmHg SBP RR=1.11 (1.05–1.18)
FHS13 M/W 5,331 35– 12 698 /20 mmHg PP HR=1.26 (1.12–1.43)
Suita Study14 M/W 6,906 30–84 12.8 253 Systolic HT HR=1.74 (1.22–2.49)
Diastolic HT HR=1.47 (1.08–1.99)
HT HR=1.58 (1.11–2.26)
PP ≥60 mmHg HR=1.75 (1.17–2.64)

AF, atrial fibrillation; ARIC, Atherosclerosis Risk in Communities; CHS, Cardiovascular Health Study; DBP, diastolic blood pressure; FHS, Framingham Heart Study; HR, hazard ratio; HT, hypertension; M, men; MESA, Multi-Ethnic Study of Atherosclerosis; OR, odds ratio; PP, pulse pressure; RR, relative risk; SBP, systolic blood pressure; W, women; WHS, Women’s Health Study.

In a Japanese prospective cohort study, systolic and diastolic hypertension and high pulse pressure (≥60 mmHg) were suggested to be risk factors for incident AF,14 but after further adjustment for SBP or DBP, the associations of diastolic hypertension and high pulse pressure with incident AF were attenuated.

Elevated pulse pressure is a surrogate measure of increased proximal aortic stiffness,15 which is associated with aging. In the FHS of a cohort without antihypertensive drug use, a linear rise in SBP according to age was observed, but a concurrent early increase in DBP and decline after age 50–60 years of age were also seen,16 which is explained by the concept that increased stiffness of large arteries plays an active role in the pathophysiology of systolic hypertension.15

The underline mechanisms of hypertension that induce atrial structural remodeling remain unclear. Still, 2 mechanisms have been considered: hemodynamic change in the atria and activation of the renin-angiotensin system (RAS). Left ventricular hypertrophy and increased left atrial size are also important mediators of the relationship between BP and incident AF.19 Activating the RAS contributes to the development of AF because angiotensin II induces atrial fibrosis and hypertrophy17,18,20,21 and exerts direct cellular electrophysiological effects on cardiomyocytes.21

Obesity

During the roughly 40 years from 1975 to 2014, the global age-standardized mean body mass index (BMI) increased from 21.7 to 24.2 kg/m2 in men and from 22.1 to 24.4 kg/m2 in women, and the prevalence of obesity increased from 3.2% to 10.8% in men and from 6.4% to 14.9% in women.22 Obesity is an important risk factor for death,23 CVD24 and diabetes mellitus.24

Being overweight14,25,26 or obese2527 is associated with incident AF (Table 2). Each 1 kg/m2 increase in BMI is associated with an approximately 4–5% increased risk of AF. Overweight and obesity are also related to hypertension28 and the sympathetic nervous system.29 In addition, overweight and obesity are associated with metabolic disorders.30

Table 2. Reviews of Obesity for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
The Danish Diet, Cancer, and
Health Study25
M 22,482 50–64 5.7 372 Overweight HR=1.75 (1.35–2.27)
Obesity HR=2.35 (1.70–3.25)
W 25,107 181 Overweight HR=1.39 (0.99–1.94)
Obesity HR=1.99 (1.31–3.02)
Suita Study14 M/W 6,906 30–84 12.8 253 Overweight HR=1.34 (1.01–1.79)
FHS and Framingham Offspring
Study27
M/W 2,384 57±13 13.7 292 Obesity HR=1.52 (1.09–2.13)
W 2,898 234 HR=1.46 (1.03–2.07)
WHS26 W 34,309 55±7 12.9 834 Overweight HR=1.22 (1.02–1.45)
Obesity HR=1.65 (1.36–2.00)

Abbreviations as in Table 1.

A case-control study shows that obesity class 3 (BMI ≥40 kg/m2) is a risk factor for AF. In a hospital-based cohort study, there was an interesting finding that obesity and severe obesity are risk factors for the progression of paroxysmal to permanent AF (hazard ratio (HR)=1.3, 1.5, respectively).31

In the Suita Study conducted in Japan, systolic prehypertensive overweight subjects and normal weight and overweight systolic hypertensive subjects were shown to have 1.7-, 1.7-, and 2.3-fold increased risk of AF, respectively, compared with normal weight normotensive subjects (P for interaction between SBP and BMI=0.04).14 That study also showed that being overweight was independently linked to a 1.35-fold increased risk of AF after adjustment for both SBP and DBP. Both a high SBP and being overweight/obese independently contribute to an increased risk of AF.

Individuals who have a high SBP and are overweight may experience exacerbation of left ventricular hypertrophy32,33 and elevated blood flow volume,34 ventricular diastolic dysfunction,35 and/or left atrial enlargement,36 and consequently a synergistically increased risk of AF.

Blood Lipids

Serum levels of high-density lipoprotein (HDL) and non-HDL cholesterols are inversely and positively established risk factors for coronary artery disease (CAD).37 However, there is limited evidence of an association between plasma lipids and AF, with inconsistent results; in both the Multi-Ethnic Study of Atherosclerosis (MESA) and the FHS, high triglycerides and low HDL-cholesterol were associated with an increased risk of AF.38 Compared with serum levels of HDL-cholesterol <40 mg/dl and triglycerides <150 mg/dl, the adjusted HRs for incident AF were 0.64 for HDL-cholesterol ≥60 mg/dl and 1.60 for triglycerides ≥200 mg/dl. These associations were similar after adjusting for myocardial infarction (MI) and heart failure. However, there was no association between the total and LDL cholesterol levels and incident AF. In the Women’s Health study and the Atherosclerotic Risk in Communities (ARIC) Study, there was an inverse association between total and LDL cholesterol levels and incident AF.39,40 The Cardiovascular Health Study (CHS) also showed that total cholesterol was inversely associated with incident AF.12 A longitudinal study of the results of health examinations in Japan revealed that total, LDL, and non-HDL cholesterol levels were inversely associated with incident AF and that HDL-cholesterol was positively associated with the incidence of AF.41

As we mentioned before (Table 3), the association between lipid levels and incident AF has been studied in several prospective population cohort studies. However, consistent results have not been obtained. Different ethnicities, lifestyles, age ranges, and study designs may partly explain the inconsistencies among the prospective studies. Low levels of HDL-cholesterol may contribute to the risk of incident AF via an increase in the prevalence of CAD and heart failure, which are risk factors for AF.42 HDL-cholesterol has the capacity to inhibit inflammatory mechanisms and oxidant stress,43 which may promote the development of AF.44

Table 3. Reviews of Blood Lipids for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
MESA and FHS38 M/W 7,142 30–87 9.6 480 HDL-C=40–59 mg/dl HR=0.80 (0.64–1.00)
HDL-C ≥60 mg/dl HR=0.66 (0.50–0.91)
TG ≥200 mg/dl HR=1.54 (1.20–1.97)
WHS39 W 23,738 ≥45 16.4 795 Highest quintile of TC HR=0.76 (0.59–0.98)
Highest quintile of LDL-C HR=0.72 (0.56–0.92)
Highest quintile of VLDL-C particle HR=0.78 (0.61–0.99)
Highest quintile of small VLDL-C HR=0.78 (0.62–0.99)
ARIC Study40 M/W 13,969 45–64 18.7 1,433 1-SD increase in LDL-C HR=0.90 (0.85–0.96)
1-SD increase in TC HR=0.89 (0.84–0.95)
CHS12 M/W 4,844 ≥65 3.28 304 1 mmol/L increase in TC HR=0.86 (0.76–0.98)
Niigata Preventive Medicine
Study41
M/W 28,449 59±11 4.5 265 10 mg/dl increase in LDL-C HR=0.92 (0.88–0.96)
LDL-C ≥140 mg/dl HR=0.69 (0.52–0.92)
HDL-C <40 mg/dl HR=1.58 (1.00–2.48)

HDL-C, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; VLDL, very low-density lipoprotein cholesterol. Other abbreviations as in Table 1.

Diabetes Mellitus

The worldwide age-standardized diabetes prevalence increased between 1980 and 2014 from 4.3% to 9.0% in men and from 5.0% to 7.9% in women.45 Type 2 diabetes mellitus (T2DM) is a risk factor for all-cause death, CVD, cancer, and Alzheimer disease. The prevention of T2DM contributes to healthy life expectancy.

In the several prospective cohort studies that have examined the relationship between T2DM and incident AF, there are inconsistent results (Table 4). The FHS showed that in men and women, T2DM presents a respective 1.7- and 2.1-fold increased risk of incident AF.8 In the Malmö Diet and Cancer study, individuals with a history of diabetes and those with diabetes showed an increased risk of AF.46 In other studies, diabetes and an intermediate occurrence of diabetes did not correlate with the risk of AF.25,47 A longitudinal study of the results of health examinations in Japan revealed that impaired fasting glucose was associated with increased risk of AF.48 A meta-analysis revealed that T2DM presents a weak but statistically significant increased risk of incident AF (relative risk, 1.34).49

Table 4. Reviews of Diabetes for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
FHS8 M 2,090 55–94 38 264 DM OR=1.7 (1.2–2.3)
W 2,641 298 OR=2.1 (1.5–2.8)
Malmo Diet and Cancer Study46 M   44–73     History of DM HR=1.39 (1.02–1.42)
W HR=1.67 (1.15–2.43)
Niigata Preventive Medicine
Study48
M/W 28,449 59±11 4.5 265 IFG (≥110 mg/dl) HR=1.44 (1.09–1.90)
IFG (≥100 mg/dl) HR=1.35 (1.06–1.73)

DM, diabetes mellitus; IFG, impaired fasting glucose. Other abbreviations as in Table 1.

The mechanism of the association between T2DM and AF is unclear. T2DM is a risk factor for hypertension, obesity, and MI, and T2DM could increase the risk of AF by increasing those risk factors.

Tobacco Smoking

Smoking is a modifiable risk factor for CVD. Two prospective studies showed that smoking is a risk factor for incident AF (Table 5),50,51 but other studies showed no association.8,52 In the ARIC Study, the multivariable-adjusted HRs for AF were 1.3 in former smokers and 2.1 in current smokers, compared with never-smokers.50 In the highest tertile of accumulated amount of smoking (>675 cigarette-years), the incidence of AF was 2.1-fold greater than in those who never smoked.

Table 5. Reviews of Tobacco Smoking for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
ARIC study50 M/W 15,329 45–64 13.1 876 Current smoking HR=1.32 (1.10–1.57)
Former smoking HR=2.05 (1.71–2.47)
Cigarette-years=300–675 HR=1.60 (1.30–1.95)
Cigarette-years=676– HR=2.10 (1.74–2.53)
Former cigarette-years=800– HR=1.89 (1.47–2.42)
Current cigarette-years <800 HR=1.85 (1.49–2.30)
Current cigarette-years ≥800 HR=2.31 (1.83–2.92)
Rotterdam Study51 M/W 5,668 ≥55 7.2 371 Current smoking HR=1.84 (1.54–2.19)
Former smoking HR=1.19 (0.99–1.42)

Abbreviations as in Table 1.

The Rotterdam Study indicated that both current smokers and former smokers had a 1.5-fold increased risk of AF compared with never-smokers.51 A meta-analysis revealed that current smokers and former smokers had a 1.4- and 1.2-fold increased risk of AF, respectively, compared with never-smokers.52 That analysis also showed that 6.7% and 1.4% of the worldwide total risk of AF in men and women, respectively, was attributable to smoking.52

Some acute effect of tobacco smoking may play a role in the increased risk of AF. Cigarettes and cigars contain nicotine, which increases the heart rate, BP,53 and plasma catecholamines,54 and nicotine acts as a potent inhibitor of cardiac A-type K+ channels, whose action contributes to the ability of nicotine to affect cardiac electrophysiology and induce arrhythmias.55 Chronic nicotine intake also contributes to the development of atrial fibrosis and atrial arrhythmias,56 and it is a risk factor for MI, pulmonary disease, and heart failure.57

Alcohol Consumption

In prospective studies of Western populations, excessive alcohol intake (60–69 g ethanol/day) has been associated with a 45–46% increased risk of AF in men (Table 6), but no association between moderate alcohol consumption (≤36–39 g ethanol/day) and the risk of AF was observed in women.58,59 In the Women’s Health Study, alcohol consumption <2 drinks/day was not associated with an increased risk of incident AF.60 Heavier consumption (≥2 drinks/day) was associated with a trivial but statistically significant increased risk of AF. A Japanese cohort study showed that the adjusted HR (95% confidence interval [CI]) for incident AF in drinkers (>69 g/day) was 2.90 (1.61–5.23).61 In a meta-analysis, a linear regression model showed that the pooled estimate for a 10 g ethanol/day increment was 1.08 (95% CI, 1.05–1.10).

Table 6. Reviews of Alcohol Drinking for Incident AF
Population Sex n Age
(years)
Follow-up
(years)
AF
(n)
Variables Results
Danish Diet, Cancer, and
Health Study58
M 22,528 56 5.7 374 Highest quintile
alcohol=68.7±22.8 g/day
HR=1.46 (1.05–2.04)
W 25,421 56 182 Highest quintile
alcohol=38.8±14.8 g/day
HR=1.14 (0.70–1.85)
The Copenhagen City
Heart Study59
M 7,588   16.3 548 ≥35drinks/week HR=1.63 (1.15–2.31)
W 8,827 18.8 523 ≥21drinks/week HR=1.07 (0.65–1.75)
WHS60 W 34,715 ≥45 12.4 653 ≥2drinks/day HR=1.60 (1.13–2.25)
Circulatory Risk in
Communities Study61
M/W 8,602 30–80 6.4 296 Ethanol=46–69 g/day HR=1.36 (0.79–2.35)
M Ethanol >69 g/day HR=2.90 (1.61–5.23)
Ethanol >69 g/day HR=3.14 (1.58–6.24)

Abbreviations as in Table 1.

Three potential reasons why excessive drinking is associated with incident AF are as follows. (1) Long-term excessive alcohol consumption could affect atrial structure and size as a direct cardiotoxin, which was shown in a rat experiment,62 and it may also have direct proarrhythmic effects.63 (2) Excessive drinking is linked to an increased risk of hypertension,64 which is a risk factor for AF.10,11,14

Other Risk Factors

As well as the traditional risk factors for incident AF, there are several other risk factors (Table 7), but further studies of these factors are awaited. Killip class >I in acute MI,65 congestive heart failure, valve disease,8 reduced kidney function, presence of albuminuria,66 birth weight,67 presence of parental AF,68 obstructive sleep apnea,69 non-alcoholic fatty liver disease,70 and a history of non-life-threatening cancer71 may be also associated with incident AF.

Table 7. Reviews of Other Risk Factors for Incident AF
Risk factors Study name/population Sex No. of
subjects
Age
(years)
Results
Coronary artery
disease
AMI patients (Meta-analysis)65 M/W 10,053   OR=2.29 (1.96–2.67) *Killip class >I vs. Killip
class=I
Heart failure FHS8 M 2,090 55–94 M: OR=4.5 (3.1–6.6), W: OR=5.9 (4.2–8.4)
W 2,641
Valve disease FHS8 M 2,090 55–94 M: OR=1.8 (1.2–2.5), W: OR=3.4 (2.5–4.5)
W 2,641
Chronic kidney
disease
ARIC Study66 M/W 10,328 45–64 eGFR=60–89: HR=1.3 (1.1–1.6)
eGFR=30–59: HR=1.6 (1.3–2.1)
eGFR=15–29: HR=3.2 (2.0–5.0)
Macroalbuminuria (ACR ≥300 and 30–299 mg/g);
HR=3.2 (2.3–4.5) and 2.0 (1.6–2.4)
Sleep apnea
syndrome
Patients attending a sleep clinic69 M/W 6,841 48.3±12.5 AHI >5/h: HR=1.55 (1.21–2.00)
Non-alcoholic fatty
liver disease
OPERA (Oulu Project Elucidating
Risk of Atherosclerosis)70
  958   OR=1.88 (1.03–3.45)
Cancer Reasons for Geographic And
Racial Differences Stroke71
M/W 15,428 66±8.9 OR=1.19 (1.02–1.38)
Impaired-media
thickness
ARIC study73 M/W 13,907   cIMT: per 1-SD: HR=1.12 (1.08–1.16)
MESA study M/W 6,640 Carotid plaque: HR=1.30 (1.19–1.42)
Rotterdam study M/W 5,220  
Arterial stiffness ARIC study73 M/W 13,907   PWV: HR per 1-SD increment=1.15 (1.03–1.29)
MESA study M/W 6,640  
Rotterdam study M/W 5,220  
Left ventricular
diastolic function
FHS72 M/W 942 75 Per 1-SD increment in VTI E/A: HR=1.28
(1.02–1.59)
Family history Framingham Offspring Study68 M/W 2,243 ≤30 At least 1 parent with history of AF: OR=1.85
(1.12–3.06)
Low birth weight WHS67 W 27,982 >45 years Birth weight categories (3.9–4.5 and >4.5 kg):
HR=1.70 (1.23–2.37) and 1.71 (1.12–2.61) (P
for linear trend=0.002)
Exercise Swedish Mammography Cohort77 W 36,513 49–83 Leisure-time exercise: RR=0.85 (0.75–0.95) for
≥4 h/week vs. <1 h/week)
  Walking/bicycling: RR=0.81 (0.72–0.92) for
≥40 min/day vs. almost never
Physicians’ Health Study79 M 16,921   With increasing frequency of vigorous exercise
(0, 1, 1–2, 3–4, 5–7 days/week) RR=1.0
(referent), 0.90, 1.09, 1.04, and 1.20 (P=0.04)
  Men <50 years of age (1.0, 0.94, 1.20, 1.05,
1.74, P<0.01) and joggers (1.0, 0.91, 1.03, 1.30,
1.53, P<0.01)
Uric acid Meta-analysis74 M/W 138,306   High UA: RR=1.67 (1.23–2.27)
Fish intake FHS78 M/W 4,815 ≥65 Intake 1–4times/week HR=0.72 (0.58–0.91)
Intake >5times/week HR=0.69 (0.52–0.91)
Sleep duration Physicians’ Health Study80 M 18,755 67.7±8.6 HR=1.13 (1.00–1.27) for greatest category of
sleep duration
B-type natriuretic
peptide
FHS75 M/W 120 58.4±9.7 Log-transformed BNP/1-SD: HR=1.62
(1.41–1.85)
Troponin T ARIC Study76 M/W 10,584 62.7 1-SD difference in log transformed troponin T:
HR=1.16 (1.10–1.23)

ACR, albumin-to-creatinine ratio; AHI, apnea/hypopnea index; AMI, acute myocardial infarction; cIMT, carotid intima-media thickness; eGFR, estimated glomerular filtration rate; PMV, pulse wave velocity; RR, relative risk. Other abbreviations as in Table 1.

Increased velocity-time integrals E/A,72 high carotid intima-media thickness, high arterial stiffness,73 urinary acid,74 B-type natriuretic peptide,75 and high-sensitivity cTnT levels76 are markers of increased risk of AF. As for lifestyle factors, physical activity77 and fish intake78 are inversely associated with a risk of AF. However, heavy exercise was associated with an increased risk of incident AF in young men and joggers.79 Long sleep duration is associated with a modestly elevated risk of AF among US men.80

AF Risk Score

It is very important that a scoring system based on community-based cohorts is used to predict an individual’s absolute risk of incident AF occurring within the next 10 years, based on the individual’s cardiovascular risk factors identified in primary care. However, only 2 studies that examined a risk score of incident AF are currently available. The ARIC study demonstrated that 56.5% of incident AF could be attributed to common cardiovascular risk factors, including hypertension, obesity, T2DM, and smoking.81 The Framingham study used a risk score for incident AF that included the factors of age, sex, BMI, SBP, treatment for hypertension, PR interval, clinically significant cardiac murmur, and congestive heart failure; the additional incorporation of echocardiographic measurements only slightly improved the predictive ability of this risk score.42 Recently, in a Japanese general urban population, a risk score for incident AF has been developed to predict individuals’ absolute risk of incident AF.82

Conclusions

We briefly reviewed the primary traditional risk factors for incident AF, including high BP, obesity, dyslipidemia, diabetes, tobacco smoking, excessive drinking, and other risk factors. Prevention of AF in clinical practice and conducting lifestyle modification programs to prevent AF require clinicians to utilize these risk factors comprehensively to predict an individual’s 10-year risk of AF. For that reason, it is essential to have a clinical risk score for AF that can accurately predict the risk. However, there are only 2 risk scores for AF, both of which were designed for the US population. Development of a risk score for incident AF in non-Westerners, especially in the Suita Study, is awaited.

Disclosures

None.

Sources of Funding

This work was supported by the Intramural Research Fund of the National Cerebral and Cardiovascular Center, by the Japan Agency for Medical Research and development, AMED (15 gk0210001 h0101), the Practical Research Project for Life-Style related Diseases including Cardiovascular Diseases and Diabetes Mellitus from Japan Agency for Medical Research and Development, AMED (15656344), and by a Grant-in-Aid for Scientific Research (B, No. 16H05252) and Challenging Exploratory Research (No. 16K15365) in Japan.

References
 
© 2016 THE JAPANESE CIRCULATION SOCIETY
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