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Cardiovascular Disease

Risk of Cardiovascular Disease from Cumulative Cigarette Use and the Impact of Smoking Intensity

Lubin, Jay H.; Couper, David; Lutsey, Pamela L.; Woodward, Mark; Yatsuya, Hiroshi; Huxley, Rachel R.

Author Information
doi: 10.1097/EDE.0000000000000437


Although cigarette smoking prevalence has declined in most Western countries, it remains high in many countries, and consequently smoking remains an important risk factor for cardiovascular disease (CVD; WHO Report on The Global Tobacco Epidemic, 2013; available at Investigators have consistently reported that relative risks (RRs) for CVD by cigarettes/day exhibit a concave pattern, implying that the RR per cigarette/day decreases with greater cigarettes/day.1–3 Several reviews have discussed possible mechanisms that link smoking to CVD, including the nonlinear effects.2–9 However, the precise smoking rate-dependent biologic mechanisms responsible for the concave pattern remain uncertain.

Cigarettes/day represents a metric of exposure rate (the time weighted average over the period of active consumption), and thus is not a quantitative measure of cigarette smoke exposure. Furthermore, it is well recognized that analyses based on cigarettes/day alone, or indeed smoking duration alone or pack-years alone, provide an incomplete characterization of smoking-related disease risk. Consequently, a comprehensive description of risks by smoking requires a more complete representation of exposure. Starting with cigarettes/day, duration, and/or pack-years, investigators often adjust one metric for another or cross-tabulate RRs for two factors with never-smokers as the referent group. Less frequently, investigators use a single comprehensive smoking index that simultaneously incorporates multiple smoking-related components.10,11 The typical approach computes the joint RRs with cigarettes/day and smoking duration, although this choice leads to problems of interpretation.12–14 For example, in a simple log-linear RR model with cigarettes/day and duration, the cigarettes/day parameter represents the ln(RR) per cigarette/day with duration held fixed. Because duration is fixed, RRs for increasing cigarettes/day necessarily embed increasing total pack-years. For example, for 30 years of smoking, a comparison of RRs at 20 and 30 cigarettes/day reflects not only different smoking rates but also different total exposures (i.e., 30 and 45 pack-years, respectively, or nearly 110,000 [≈15 × 20 × 365.25] additional cigarettes). Similarly, at fixed cigarettes/day, RRs at two different durations of smoking include effects of increasing pack-years. Hence, the cigarettes/day and duration parameters are not interpretable as distinct, unrelated effects.

We analyze RRs for pack-years and cigarettes/day, which allows a more direct interpretation of parameters, in which smoking rate serves to modify RR trends with pack-years. In this approach, cigarettes/day represents the relative influence of exposure accrual on the RR for a given pack-years (i.e., the differential in the RRs at a fixed pack-years when delivered at lower smoking rates for longer durations or higher rates for shorter durations). This approach reinterprets the nonlinear RRs for cigarettes/day as a “smoking rate effectiveness factor” or “delivery rate effect.” For example, for individuals who smoked 20 cigarettes/day for 50 years or 30 cigarettes/day for 33.3 years or 50 cigarettes/day for 20 years, the analysis below estimates RRs of 2.2, 1.9, and 1.7, respectively, even though exposure for all individuals was 50 pack-years (365,000 cigarettes).

Our analysis considers two issues: (1) the joint RRs by pack-years and cigarettes/day for CVD, coronary heart disease (CHD), and stroke; and (2) the modification of smoking-related RRs for CVD by smoking-related behaviors, age started smoking, extent of inhalation, years since cessation, and additional use of cigars and pipes.


Study Design

The Atherosclerosis Risk in Communities Study (ARIC) is a large prospective cohort study conducted in four areas of the US: Forsyth County, NC; Jackson, MS; Washington County, MD; and the northwest suburbs of Minneapolis, MN. Enrollment occurred between 1987 and 1989 using a probability-based sample of adults aged 45–64 years. Study details have been provided previously.15–18 Study personnel collected a wide variety of data from clinical examinations and from personal interviews at baseline enrollment and at three clinic visits: 1990–1992 (visit 2), 1993–1995 (visit 3), and 1996–1998 (visit 4). Annual telephone calls collected information from participants or their surrogates on vital status, hospital visits, and other factors.

For this analyses, we followed those participants without a pre-enrollment history of CHD or stroke through the earliest date of CVD incidence, loss to follow-up, death or December 31, 2008.18 We ascertained outcome information on CVD through annual telephone interviews and surveillance of hospital discharge records in the study areas and death certificates. Events were validated by examination of hospital records, death certificates and, when available, autopsy records, with outcomes classified according to ARIC Study criteria.18 CVD encompassed CHD and stroke. CHD included a validated, definite or probable hospitalized myocardial infarction, a definite CHD death, an unrecognized myocardial infarction defined by electrocardiographic reading or coronary revascularization.17 A stroke event comprised a validated, definite or probable hospitalized ischemic or hemorrhagic stroke.

Questionnaires administered at baseline and subsequent clinic visits provided information on smoking status and cigarettes/day, while the annual telephone contacts provided additional information on smoking status. For cigarettes/day, we used baseline information only. This choice likely had minimal effect, since few individuals modified their cigarettes/day. At baseline, there were 42% never-smokers, 32% former smokers, and 26% current smokers, half of whom stopped smoking by the end of 2008. Among current and former smokers at baseline who had follow-up smoking information from one or more clinic visits, only 15% and 8%, respectively, changed their smoking status. In addition, for continuing smokers, the mean cigarettes/day during follow-up was on average within 1–4 cigarettes/day of their baseline value. In addition, smoking-years from baseline through visit 4 represented a relatively limited percentage of the lifetime years of consumption. Thus, cigarettes/day at baseline provided a good estimate of the time weighted average cigarettes/day throughout follow-up. The clinic questionnaires and the yearly telephone contacts yielded time-dependent information on smoking status, which enabled time-dependent calculation for duration of smoking, pack-years, and time since smoking cessation. For the analyses, we defined former smokers as those who last smoked one or more years prior.

Study personnel obtained three blood pressure (BP) measurements using a random-zero sphygmomanometer with the participant seated, with BP determined as the mean of the last two values. We defined hypertension as systolic BP ≥140 mmHg, diastolic BP ≥90 mmHg, or current use of antihypertensive medication. We conducted a fasting blood collection and measured glucose and plasma total cholesterol by standard enzymatic methods. We designated diabetes at baseline as a self-reported history of, or treatment for, diabetes, a fasting glucose level of 126 mg/dl or greater, or a casual blood glucose level of 200 mg/dl or greater.18

The initial dataset included 14,878 subjects and 3,603 CVD events. We excluded 751 participants with missing data, including 212 CVD events, leaving 14,127 participants with 3,391 CVD events and 232,002 person-years of follow-up. Exclusions resulted from missing information for smoking (247 subjects and 59 CVD cases), lipid measurements (228 subjects and 64 cases), body mass index (BMI) and diagnosed diabetes mellitus (43 subjects and 15 cases), alcohol use (78 subjects and 24 cases), and other variables (155 subjects and 50 cases). There were 2,321 non-CVD deaths during follow-up, with 1,241 cancers (including 352 lung, 97 breast, 80 pancreas, 80 colon, and 65 prostate cancer deaths), 468 diseases of the circulatory system (including 72 atherosclerosis, 34 hypertensive disease, 27 congestive heart failure, and 23 aortic aneurisms), 236 respiratory diseases (including 140 chronic obstructive pulmonary disease deaths), and 376 other causes. In addition, there were 297 (2%) participants with missing or unknown status which were censored at last contact.

Data Structure

We used Poisson regression to estimate RRs, with data summarized in a multidimensional person-years table. The cross-classification variables included attained age (<54, 54–55,…, 78–79, ≥80), calendar period (<1990, 1990–1994, 1995–1999, 2000–2004, 2005–2009), birth year (<1930, 1930–1934, 1935–1939, ≥1940), study site, sex, race (White, African-America, Asian-American, other), BMI (<25.0, 25.0–29.9, 30.0–34.9, ≥50 kg/m2), alcohol intake in g-ethanol/week (<40, 40–107, ≥108), diagnosed high BP, diagnosed diabetes mellitus, total cholesterol (<5.2, 5.2–6.1, ≥6.2 mmol/L), ever use of cigars/pipes, education (<12 years, high school/vocational school and college, graduate, or professional school), cigarettes smoked per day (0, 1–4, 5–9, …, 45–49, ≥50), pack-years (0, 1–9, 10–19, 20–24,…, 55–59, ≥60), years since last smoked (<1, 1–4, 5–9, 10–19, ≥20), age first smoked (<16, 16–17, 18–19, ≥20), and inhalation (never/seldom, moderately, deeply). We used fine categorizations for the primary analytic variables (age, calendar year, cigarettes/day, pack-year, etc.). For other variables, we selected broader categories that both covered the full range of values and allowed for sufficient numbers of CVD cases. For each cell, we accrued person-time, counted disease events, and computed person-years weighted means for continuous variables.

Competing risks could influence results since those who accrued longer follow-up or were lighter smokers may be more likely to incur CVD events, while heavier smokers were more likely to be selectively removed from follow-up due to other diseases, in particular lung cancer. We, therefore, conducted analyses using competing risks methodology that incorporated multiple outcomes, including incident CVD, lung cancer, other selected smoking-related cancers (esophagus, larynx, oropharynx, bladder, kidney, stomach, colon, rectum, and pancreas) and mortality from all other causes.19,20 For these analyses, we had to restrict follow-up, since detailed cancer incidence data were available only through December 31, 2006. Consequently, we first compared results for the full follow-up with results for the restricted follow-up, which then served as a basis for comparison of results under competing risks methods.

Relative Risk Models

We modeled disease rate as

, where z and α were vectors of adjustment variables and parameters, respectively. For categories of cigarettes/day, smoking duration or pack-years, denoted s, we modeled RR(s) using indicator variables and the standard exponential form. We computed joint RRs for the cross-tabulation of pack-years and cigarettes/day, relative to never-smokers, and observed that RR trends with pack-years were approximately linear within each category of cigarettes/day. Since a linear slope fully described the pack-years-related RRs within cigarettes/day categories, the goal was to characterize the linear trends and their variations with cigarettes/day.

Our models were based on continuous pack-years, denoted as d. We started with a linear model

where β was the slope parameter (i.e., the excess RR/pack-year). However, since linearity occurred only within cigarettes/day categories, we extended Equation 1 for S categories of cigarettes/day, s = 1,…,S:

where ds equaled d within category s and zero otherwise and β1,…, βS were slope parameters.

The slope measured the strength of CVD and pack-years association relative to never-smokers within each cigarette/day category, while variations among the slope parameters (β’s) reflected the influence of smoking rate on the strength of association. We modeled variations of the slope with continuous cigarettes/day (n) using

where β g(n) defined the linear slope at n cigarettes/day. We explored various forms for ln[g(.)] including restricted cubic splines and various parametric forms using n, n2, ln(n), and ln(n).2 The simple power function

, with

, fitted the data well and resulted in the minimum Akaike Information Criterion,21 suggesting it was the preferred form. None of various parametric extensions, including

, significantly improved fit.

We evaluated the interactions of several potential smoking-related effect modifiers by extending Equation (3) for categories of a factor (e.g., years since smoking cessation). For categorical factor x with levels f = 1,…, F, we fitted

where βfdgf (n) replaced β d g(n). The difference in the deviances of models 3 and 4 provided a likelihood ratio test of no effect modification.

Adjustment variables (z) included study site (four levels), sex, birth cohort (year of birth categories <1930, 1930–1934, 1935–1939, 1940–1945), race, BMI, years of schooling, g-ethanol/week alcohol consumption (never and tertiles based on cases), high BP, diabetes mellitus, and total cholesterol. We adjusted for attained age by including four continuous variables, age, and its natural logarithm for males and for females. We included an indicator variable for never-smokers who used cigars or pipes. For cigarette smokers, we did not define a cigarette-equivalence for cigar and pipe use due to data limitations and the potential for increased misclassification, but rather evaluated cigar or pipe use as an effect modifier.

Analyses used the Epicure software package.22

The institutional review board of each participating university and the Office of Human Subjects Research Protections of the National Institutes of Health approved the study protocol, and all participants provided informed consent. All authors have no declared conflicts of interest.


Marginal and Adjusted Relative Risks for Cigarette Smoking Variables

RRs with each smoking-related metric—cigarettes/day, duration of cigarette smoking, and pack-years—increased then leveled at higher categories (Table 1). However, a single smoking variable did not fully characterize risk, as model fit improved with inclusion of a second smoking variable (P < 0.01). After adjustment for cigarettes/day or for duration, RRs by pack-years (representing 1–9 cigarettes/day or ≥50 years duration, footnoted columns c and d, respectively) continued to exhibit an increasing trend. After adjustment for pack-years, RRs decreased with cigarettes/day and, correspondingly, increased with duration of smoking, indicating a stronger pack-years association at lower cigarettes/day and longer durations.

Numbers of Cardiovascular Disease Events, P-yrs at Risk, RRs with 95% CI by Pack-years of Cigarette Smoking and Cigarettes Smoked Per Day

Joint Relative Risks for Pack-years and Cigarettes/Day

RRs increased with pack-years, but departed from linearity (P < 0.01; Fig. 1, upper left panel, dash line). For the joint RRs, relative to never-smokers, RRs by pack-years increased within each cigarettes/day category (Fig. 1 and eAppendix, eTable 1; Trends were consistent with linearity within each category, except the 10–19 cigarettes/day category (P = 0.01), where the difference between never-smokers and the lowest pack-years category induced nonlinearity (dot line, among smokers P = 0.25 for the test of nonlinearity). The figure highlights the variation in the slopes. The excess RR/pack-year estimates for the five cigarettes/day categories were 0.046, 0.031 (0.065 after adjustment for never/ever smoker), 0.024, 0.017, and 0.011, respectively, revealing a declining strength of association with increasing cigarettes/day (P < 0.01 for the test of γ = 0 in Eq. 3).

Relative risks of cardiovascular disease for categories of pack-years of cigarette smoking (solid symbol) relative to never-smokers for all data and within categories of CPD and fitted models, including linear (solid line), linear-exponential (dash line), and linear adjusted for ever-smoked cigarettes (dot line). All results adjusted for age, birth year, sex, and other factors (see text). CPD indicates cigarettes per day.

The plot of the slope estimates by mean cigarettes/day revealed a deceasing strength of association across the full range of cigarettes/day and were well described by Equation 3 (Fig. 2). For <10 cigarettes/day smokers, there was substantial uncertainty in the excess RR/pack-year estimate, due to a limited range for pack-years (the 25th to 75th percentile interval was 2.6 to 9.6 pack-years). Splitting the category into 1–4 and 5–9 cigarettes/day resulted in excess RR/pack-year estimates of 0.097 and 0.038, respectively, indicting a continued strengthening of the association at lower smoking rates (Fig. 2, open symbol). The nonlinear excess RR/pack-year pattern with cigarettes/day represented an “inverse smoking rate effect,” suggesting that for equal pack-years, smoking fewer cigarettes/day for longer duration was more deleterious than smoking more cigarettes/day for shorter duration.

Estimated ERR/PKY for cardiovascular disease within categories of cigarettes/day (solid symbol), with the lowest category further divided into 1–4 and 5–9 cigarettes/day (open symbol), and fitted models for continuous pack-years and cigarettes/day. All results adjusted for age, birth year, sex, and other factors (see text). ERR/PKY indicates excess relative risk per pack-year.

Smoking Risks for Coronary Heart Disease and Stroke

There were 2,705 CHD and 1,011 stoke events. As in Table 1, RRs by pack-years (Fig. 3, upper panels) and by cigarettes/day increased for CHD and for stroke (Table 2). The RRs by cigarettes/day appeared slightly larger for CHD, but homogeneity of the RRs by type of outcome was not rejected (P = 0.15). With adjustment for cigarettes/day, RRs by pack-years increased for both outcomes. The inverse smoking rate pattern occurred for both CHD and stroke, although stroke appeared to exhibit a greater rate of decline. The plotted estimates of excess RR/pack-year by mean cigarettes/day and the fitted Equation 3 (Fig. 3, lower panels) revealed homogeneity of the inverse smoking rate effect by outcome (P = 0.57).

Numbers of Events, RRs with 95% CI by Pack-years of Cigarette Smoking and Cigarettes Smoked Per Day for Coronary Heart Disease and Stroke
For coronary heart disease (left panels) and stroke (right panels), relative risks for categories of pack-years of cigarette smoking (solid symbol) relative to never-smokers with fitted linear (solid line) and linear-exponential (dash line) model (upper panels) and estimated excess relative risk/pack-year within categories of cigarettes/day (solid symbol) and fitted models for continuous pack-years and cigarettes/day (solid line; lower panels). All results adjusted for age, birth year, sex, and other factors (see text).

Effect Modification by Smoking-related Factors

For age started smoking, depth of inhalation, and additional use of cigars/pipes, the estimated RR by pack-years increased within each category of these variables (Table 3). The RR trends with pack-years were roughly similar within each level. As in Figure 2, the excess RR/pack-year estimates declined smoothly with cigarettes/day within each level of age started smoking, inhalation and use of cigars/pipes (eAppendix eFigures 1–3, with eTable 2 providing parameter estimates; The fitted excess RR/pack-year estimates at 20 cigarettes/day from Equation 4 were similar across levels, and hypothesis tests did not reject homogeneous by age started smoking (P = 0.50), method of inhalation (P = 0.74), or additional use of cigars/pipes (P = 0.79; Table 3).

Estimated RRs for Cardiovascular Disease by Pack-years with Never Cigarette Smokers as Referent and the Fitted ERR/PKY at 20 CPD Within Level of Smoking-related Modifiersa

In contrast, RR trends with pack-years significantly diminished with years since cessation of smoking (P < 0.01; Fig. 4, left panels). The fitted excess RR/pack-year estimates at 20 cigarettes/day declined with cessation starting 5 years and more after cessation (P < 0.01; Table 3). Although pack-years-related risks declined with cessation, the inverse smoking rate patterns for each cessation category were similar (Fig. 4, right panels).

For years since cessation of smoking, relative risks for cardiovascular disease by categories of pack-years of cigarette smoking (solid symbol) relative to never-smokers with fitted linear (solid line) and linear-exponential (dash line) models (left panels) and estimated excess relative risk/pack-year within categories of cigarettes/day (solid symbol) and fitted models for continuous pack-years and cigarettes/day (solid line; right panels). All results adjusted for age, birth year, sex, and other factors (see text).

Smoking and CVD Under a Competing Risks Model

For the competing risks analysis, follow-up time accrued through 2006. We omitted 803 participants with a pre- enrollment diagnosis of cancer, leaving 13,324 participants, with 200,347 person-years and 4,945 total events, including 2,638 CVD cases, 350 lung cancers, 684 other smoking-related cancers (including 112 bladder, 280 colon and rectum, and 114 stomach cancers), and 1,273 deaths from other causes (including 401 other cancers, 363 diseases of the circulatory system and 147 diseases of the respiratory system). The eAppendix presents detailed results ( Competing risks adjustment had minimal impact on results. The decreasing smoking rate patterns, that is, estimates of γ in Equation 3, with 95% confidence interval were −0.70 (−0.87, −0.52) for the full data analysis in Figure 3, −0.71 (−0.90, −0.53) for the restricted follow-up through 2006 and −0.75 (−0.96, −0.55) in the competing risks analysis, while the estimated RRs for 50 pack-years accrued at a rate of 20 cigarettes/day were 2.1 (2.0, 2.4), 2.2 (1.2, 2.5), and 2.1 (1.9, 2.4), respectively.


In our data, RRs for CVD increased for each smoking metric: cigarettes/day, smoking duration, and pack-years. In addition, there was a concave pattern for the RRs by cigarettes/day, indicating that the RR per cigarette/day decreased with greater cigarettes/day.1–3 However, any interpretation must acknowledge that cigarettes/day represents an exposure rate metric and not a quantitative measure of exposure to cigarette smoke, and that no single metric, cigarettes/day, smoking duration, or pack-years, fully characterizes smoking-related risks. Using joint RRs for pack-years and cigarettes/day, the concave RR pattern with cigarettes/day was consistent with cigarettes/day modifying a linear RR association for CVD and pack-years, i.e., the strength of association declined with smoking rate. Our analyses reaffirmed pack-years as the preeminent smoking-related risk factor for CVD, but also demonstrated that the manner of accrual of pack-years influenced CVD risk levels. There was an inverse smoking rate pattern, whereby smoking fewer cigarettes/day for longer duration was more deleterious than smoking more cigarettes/day for shorter duration. For 50 pack-years (365,000 cigarettes), the estimated RR of CVD was 2.1 if exposure accrued at 20 cigarettes/day and 1.6 if exposure accrued at 50 cigarettes/day. This inverse rate pattern was quantitatively comparable for CHD and stroke.

The observed inverse rate pattern may be due to smoking rate-dependent pathophysiologic mechanisms of CVD risks. Several reviews have described possible mechanisms for smoking-related CVD, and in particular the nonlinear RRs for cigarettes/day.1–3,5–9 Factors that link cigarette smoking to CVD which may contribute to the nonlinear cigarette/day association include nicotine stimulation with enhanced oxygen demand and vasoconstriction, carbon monoxide-induced hemodynamic effects, increased inflammation arising from reduced antioxidant compounds, particulates and other constituents of tobacco smoke, insulin sensitivity, and alterations in lipid profiles.4,8,9 For example, carbon monoxide, a combustion product of cigarette smoke, has an affinity for hemoglobin and exhibits smoking rate-dependent effects. Among current smokers, the ratio of serum carboxyhemoglobin to cigarettes/day decreased with greater smoking intensity.23 Polycyclic aromatic hydrocarbons (PAHs), including benzo[a]pyrene, result from incomplete combustion of tobacco and other organic products, and are associated with increased CVD risk.9 PAH exposure can activate the aryl hydrocarbon receptor pathway and thereby induce a vascular inflammatory response, including the progression of atherosclerosis.9,24–26 Investigators reported that DNA adduct levels per unit PAH exposure were higher in environmentally exposed individuals than in workers exposed at occupational levels.27,28

Cigarette smoking may also influence risk through intensity-dependent variations in its impact on nontobacco CVD risk factors, although evidence for this is circumstantial. Law and Wald3 suggested that inflammation-induced platelet aggregation dominates at low smoking intensities while other mechanisms (e.g., increased fibrinogen, reduced high-density lipoprotein cholesterol, and increased carboxyhemoglobin) dominate at higher intensities.

Exposure bias may also have contributed to the inverse smoking rate pattern, whereby heavy smokers inhaled less vigorously following nicotine satiation, resulting in “reduced potency,” with the reported cigarettes/day increasingly overestimating internal exposure. Using cotinine as a biomarker of smoking rate, studies have reported that cotinine levels increased approximately linearly through about 15–20 cigarettes/day.23,29–38 However, trends at higher smoking rates have been complex. In some studies, cotinine concentrations increased with cigarettes/day, then leveled and even declined.29,30,32,37 Other studies have reported trends that continued to increase without substantial diminution30,33,35,36,39,40 or with only modest diminution.23,29–31,34,37 In our data, the inverse smoking rate pattern occurred across the full range of cigarettes/day, and consequently was incompatible with any presumed inhalation bias. A sensitivity analysis in conjunction with a smoking and lung cancer study used urinary cotinine to adjust cigarettes/day and found that the cotinine-adjusted estimates of excess RR/pack-year within smoking rate categories indeed increased, since reported cigarettes/day reflected an overestimate. Nonetheless, the shape of the inverse smoking rate effect was unaffected, since the cotinine-adjusted cigarettes/day at higher rates correspondingly represented lower adjusted rates,41 again suggesting inhalation bias did not greatly influence our results.

Competing risks may have impacted our observed inverse cigarettes/day effects, whereby those who accrued longer follow-up or were lighter smokers may have been more likely to have incurred CVD events, while heavier smokers were selectively removed from follow-up due to other diseases, in particular lung cancer. We conducted competing risks analyses that incorporated incident CVD, lung cancer, other selected smoking-related cancers and mortality from all other causes.19,20 However, we found that consideration of competing risks had no appreciable effect on the results which suggested that any potential bias from competing risk considerations was minimal. This absence of impact was likely due to the relatively small numbers of cancer events, particularly lung cancer, compared with CVD events and to the relatively small number of current smokers (27.4% at enrollment), who had the highest smoking-related risk.

Our results must be interpreted with caution. This analysis is the first to evaluate the effects of exposure accrual, comparing the relative impact on the strength of the association of pack-years and CVD for smoking fewer cigarettes/day for longer duration with more cigarettes/day for shorter duration. At low smoking rates, there was substantial uncertainty due to the limited range of pack-years, and additional analyses are needed to determine whether the inverse smoking rate pattern continues for even lower rate smokers, as in our data, or flattens or decreases. Our analyses did not additionally adjust for exposure to environmental tobacco smoke, either in never-smokers or in smokers, and this may have underestimated smoking risks. In our data, the RR in never-smokers for exposure to any environmental tobacco smoke was of modest magnitude (RR = 1.1 with 95% confidence interval 1.0, 1.3), and additional adjustment for any environmental tobacco smoke exposure had minimal impact on the smoking rate patterns.

Finally, inference for the inverse smoking rate effect arose from the characterization of pack-year trends within categories of cigarettes/day. A comparable analysis could have evolved from the joint RRs of pack-years and duration of smoking, and indeed the RR trends for pack-years increased in strength with increasing duration of smoking. However, this approach was more complex because trends in RRs with pack-years within categories of duration deviated from simple linear relationships and therefore there was no set of linear slope estimates that fully characterize smoking-related RRs.

In summary, the ARIC data confirmed the deleterious consequences of cigarette smoking on CVD risk. While pack-years represented the principal determinant of smoking-related CVD risk, results demonstrated that the manner of exposure accrual had consequences for smoking-related risks. Across the full range of cigarettes/day, smoking fewer cigarettes/day for longer durations was more deleterious than smoking more cigarettes/day for shorter durations. The precise reasons for this inverse smoking intensity pattern remain uncertain.


The authors thank the staff and participants of the Atherosclerosis Risk in Communities (ARIC) study for their important contributions.


1. Benowitz NL.. Cigarette smoking and cardiovascular disease: pathophysiology and implications for treatment. Prog Cardiovasc Dis. 2003;46:91–111
2. Burns DM.. Epidemiology of smoking-induced cardiovascular disease. Prog Cardiovasc Dis. 2003;46:11–29
3. Law MR, Wald NJ.. Environmental tobacco smoke and ischemic heart disease. Prog Cardiovasc Dis. 2003;46:31–38
4. Benowitz NL, Pomerleau OF, Pomerleau CS, Jacob P III. Nicotine metabolite ratio as a predictor of cigarette consumption. Nicotine Tob Res. 2003;5:621–624
5. Csordas A, Bernhard D.. The biology behind the atherothrombotic effects of cigarette smoke. Nat Rev Cardiol. 2013;10:219–230
6. Pant R, Marok R, Klein LW.. Pathophysiology of coronary vascular remodeling: relationship with traditional risk factors for coronary artery disease. Cardiol Rev. 2014;22:13–16
7. Ambrose JA, Barua RS.. The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol. 2004;43:1731–1737
8. U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. 2010 Washington, DC: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Superintendent of Documents, U.S. Government Printing Office
9. U.S. Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress: A Report of the Surgeon General. 2014 Atlanta, GA U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Superintendent of Documents. Washington, DC: U.S. Government Printing Office
10. Dietrich T, Hoffmann K.. A comprehensive index for the modeling of smoking history in periodontal research. J Dent Res. 2004;83:859–863
11. Leffondré K, Abrahamowicz M, Xiao Y, Siemiatycki J.. Modelling smoking history using a comprehensive smoking index: application to lung cancer. Stat Med. 2006;25:4132–4146
12. Peto J.. That the effects of smoking should be measured in pack-years: misconceptions 4. Br J Cancer. 2012;107:406–407
13. Lubin JH, Caporaso NE.. Misunderstandings in the misconception on the use of pack-years in analysis of smoking. Br J Cancer. 2013;108:1218–1220
14. Thomas DC.. Invited commentary: is it time to retire the “pack-years” variable? Maybe not! Am J Epidemiol. 2014;179:299–302
15. ARIC Investigators. . The atherosclerosis risk in communities (ARIC) study: design and objectives. Am J Epidemiol. 1989;129:687–702
16. Rosamond WD, Chambless LE, Heiss G, et al. Twenty-two-year trends in incidence of myocardial infarction, coronary heart disease mortality, and case fatality in 4 US communities, 1987-2008. Circulation. 2012;125:1848–1857
17. White AD, Folsom AR, Chambless LE, et al. Community surveillance of coronary heart disease in the atherosclerosis risk in communities (ARIC) study: methods and initial two years’ experience. J Clin Epidemiol. 1996;49:223–233
18. Huxley RR, Yatsuya H, Lutsey PL, Woodward M, Alonso A, Folsom AR.. Impact of age at smoking initiation, dosage, and time since quitting on cardiovascular disease in African Americans and whites: the atherosclerosis risk in communities study. Am J Epidemiol. 2012;175:816–826
19. Lunn M, McNeil D.. Applying Cox regression to competing risks. Biometrics. 1995;51:524–532
20. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. 2015 New York, NY Wiley
21. Akaike H.. A new look at the statistical model identification. IEEE Trans Automatic Control. 1974;19:716–723
22. Preston DL, Lubin JH, Pierce DA, McConney ME. Epicure User’s Guide. 2006 Seattle, WA HiroSoft International Corporation
23. Law MR, Morris JK, Watt HC, Wald NJ.. The dose-response relationship between cigarette consumption, biochemical markers and risk of lung cancer. Br J Cancer. 1997;75:1690–1693
24. Wu D, Nishimura N, Kuo V, et al. Activation of aryl hydrocarbon receptor induces vascular inflammation and promotes atherosclerosis in apolipoprotein E−/− mice. Arterioscler Thromb. 2011;31:1260–1237
25. Lewtas J.. Air pollution combustion emissions: characterization of causative agents and mechanisms associated with cancer, reproductive, and cardiovascular effects. Mutat Res. 2007;636:95–133
26. Alshaarawy O, Zhu M, Ducatman A, Conway B, Andrew ME.. Polycyclic aromatic hydrocarbon biomarkers and serum markers of inflammation. A positive association that is more evident in men. Environ Res. 2013;126:98–104
27. Phillips DH.. Smoking-related DNA and protein adducts in human tissues. Carcinogenesis. 2002;23:1979–2004
28. Lewtas J, Walsh D, Williams R, Dobiás L.. Air pollution exposure-DNA adduct dosimetry in humans and rodents: evidence for non-linearity at high doses. Mutat Res. 1997;378:51–63
29. Joseph AM, Hecht SS, Murphy SE, et al. Relationships between cigarette consumption and biomarkers of tobacco toxin exposure. Cancer Epidemiol Biomarkers Prev. 2005;14:2963–2968
30. Blackford AL, Yang G, Hernandez-Avila M, et al. Cotinine concentration in smokers from different countries: relationship with amount smoked and cigarette type. Cancer Epidemiol Biomarkers Prev. 2006;15:1799–1804
31. Campuzano JC, Hernandez-Avila M, Jaakkola MS, et al. Determinants of salivary cotinine levels among current smokers in Mexico. Nicotine Tob Res. 2004;6:997–1008
32. Etter JF, Perneger TV.. Measurement of self reported active exposure to cigarette smoke. J Epidemiol Community Health. 2001;55:674–680
33. Lewis SJ, Cherry NM, McL Niven R, Barber PV, Wilde K, Povey AC.. Cotinine levels and self-reported smoking status in patients attending a bronchoscopy clinic. Biomarkers. 2003;8:218–228
34. O’Connor RJ, Giovino GA, Kozlowski LT, et al. Changes in nicotine intake and cigarette use over time in two nationally representative cross-sectional samples of smokers. Am J Epidemiol. 2006;164:750–759
35. Olivieri M, Poli A, Zuccaro P, et al. Tobacco smoke exposure and serum cotinine in a random sample of adults living in Verona, Italy. Arch Environ Health. 2002;57:355–359
36. Richie JP Jr, Carmella SG, Muscat JE, Scott DG, Akerkar SA, Hecht SS.. Differences in the urinary metabolites of the tobacco-specific lung carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone in black and white smokers. Cancer Epidemiol Biomarkers Prev. 1997;6:783–790
37. Lubin JH, Caporaso N, Hatsukami DK, Joseph AM, Hecht SS.. The association of a tobacco-specific biomarker and cigarette consumption and its dependence on host characteristics. Cancer Epidemiol Biomarkers Prev. 2007;16:1852–1857
38. Woodward M, Tunstall-Pedoe H, Smith WC, Tavendale R.. Smoking characteristics and inhalation biochemistry in the Scottish population. J Clin Epidemiol. 1991;44:1405–1410
39. Xia Y, Bernert JT, Jain RB, Ashley DL, Pirkle JL.. Tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) in smokers in the United States: NHANES 2007-2008. Biomarkers. 2011;16:112–119
40. Nagano T, Shimizu M, Kiyotani K, et al. Biomonitoring of urinary cotinine concentrations associated with plasma levels of nicotine metabolites after daily cigarette smoking in a male Japanese population. Int J Environ Res Public Health. 2010;7:2953–2964
41. Lubin JH, Caporaso N, Wichmann HE, Schaffrath-Rosario A, Alavanja MC.. Cigarette smoking and lung cancer: modeling effect modification of total exposure and intensity. Epidemiology. 2007;18:639–648

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