Accounting for Time-Varying Confounding in the Relationship Between Obesity and Coronary Heart Disease: Analysis With G-Estimation

Abstract

In longitudinal studies, standard analysis may yield biased estimates of exposure effect in the presence of time-varying confounders that are also intermediate variables. We aimed to quantify the relationship between obesity and coronary heart disease (CHD) by appropriately adjusting for time-varying confounders. This study was performed in a subset of participants from the Atherosclerosis Risk in Communities (ARIC) Study (1987–2010), a US study designed to investigate risk factors for atherosclerosis. General obesity was defined as body mass index (weight (kg)/height (m)²) ≥30, and abdominal obesity (AOB) was defined according to either waist circumference (≥102 cm in men and ≥88 cm in women) or waist:hip ratio (≥0.9 in men and ≥0.85 in women). The association of obesity with CHD was estimated by G-estimation and compared with results from accelerated failure-time models using 3 specifications. The first model, which adjusted for baseline covariates, excluding metabolic mediators of obesity, showed increased risk of CHD for all obesity measures. Further adjustment for metabolic mediators in the second model and time-varying variables in the third model produced negligible changes in the hazard ratios. The hazard ratios estimated by G-estimation were 1.15 (95% confidence interval (CI): 0.83, 1.47) for general obesity, 1.65 (95% CI: 1.35, 1.92) for AOB based on waist circumference, and 1.38 (95% CI: 1.13, 1.99) for AOB based on waist:hip ratio, suggesting that AOB increased the risk of CHD. The G-estimated hazard ratios for both measures were further from the null than those derived from standard models.

Coronary heart disease (CHD) is the leading cause of death and disease burden throughout the world (1, 2). Despite the large body of evidence regarding the association of obesity with various adverse health outcomes, its relationship with the risk of CHD remains controversial. One source of discrepancy between studies is the type of anthropometric index used to define obesity status as either general obesity (GOB), which is based on body mass index (BMI), or abdominal obesity (AOB), which is based on waist circumference or waist:hip ratio (3–9). The other sources of heterogeneity are the type of analysis and the covariates used for adjustment in the model. Although most observational studies have shown an association between obesity and CHD, the association has decreased or disappeared after adjustment for metabolic mediators of obesity, such as hypertension, diabetes mellitus, and lipid levels (10–13). For estimation of the total association between obesity and CHD, standard regression methods model CHD as a function of baseline obesity and other covariates. However, in longitudinal studies, there may be time-varying covariates whose postbaseline values predict both future exposure and outcome within strata defined by baseline covariates and prior obesity. These covariates are referred to as time-varying confounders, and standard regression methods cannot correctly adjust for them when they are affected by previous exposure (14–17). Adjustment for such confounders-mediators with standard regression analysis eliminates part of the effect of obesity operating through those variables and may also introduce collider-stratification bias (18–21). In such settings, G-estimation of a structural nested accelerated failure-time model overcomes this issue by adequately adjusting for time-varying confounders (16, 22–29). In this study, we aimed to quantify the relationships of GOB and AOB with the risk of CHD, using G-estimation of a structural nested failure-time model, and to compare the results with those of standard regression methods.

METHODS

Study population and outcome

This study was performed in a subset of participants from the Atherosclerosis Risk in Communities (ARIC) Study. Details on study procedures and outcome ascertainment have been published previously (30). Briefly, the ARIC Study is an ongoing prospective cohort study of 15,792 participants aged 45–64 years residing in 4 US communities, designed to investigate risk factors for atherosclerosis. The study included 4 repeated examinations that started in 1987–1989 (visit 0), followed by 3 examinations at 3-year intervals (visits 1–3). At each examination, all participants were given an interviewer-administered questionnaire and underwent an extensive physical examination. Outcome events were ascertained by annual questionnaires administered through telephone interviews, 3-year follow-up examinations, and community-wide surveillance procedures. In this study, outcome events ascertained through December 31, 2010, were included, and incident CHD events were defined as definite or probable myocardial infarction or fatal CHD according to the ARIC Study criteria. To adjust for lagged confounders, we included a total of 12,902 subjects who had complete data at visits 0 and 1 in our analysis.

Exposure

In this study, the main exposure of interest was obesity status. Three anthropometric measures were used to define GOB and AOB. Trained and certified technicians took anthropometric measurements, including height and the circumferences of the waist (umbilical level) and hip (maximum buttock) to the nearest centimeter. Weight was measured using a zeroed daily scale that was calibrated quarterly. All anthropometric indices were measured in fasting participants wearing standard hospital scrub suits. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. GOB was defined as BMI ≥30. AOB was defined on the basis of 2 anthropometric measures: waist circumference ≥102 cm in men and ≥88 cm in women and waist:hip ratio ≥0.9 in men and ≥0.85 in women (31).

Confounders

The potential confounders that were measured only at the baseline visit included age, sex, race (black or white), educational level (basic (≤11 years), intermediate (12–16 years), or advanced (17–21 years)), calorie intake, percentage of daily energy intake from saturated fat, physical activity level (hours/week) at work and during leisure time, and study center (Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; or Minneapolis, Minnesota). The covariates measured at all visits, including the baseline visit, were hypertension (systolic blood pressure ≥90 or diastolic blood pressure ≥140 mm Hg or use of any medication for high blood pressure); diabetes mellitus (blood glucose concentration ≥200 mg/dL and fasting blood glucose concentration ≥126 mg/dL or use of any medication for diabetes); plasma lipid concentrations, including levels of cholesterol, high-density lipoprotein cholesterol, and triglyceride; smoking status (defined as currently smoking cigarettes) and drinking status (defined as currently drinking alcoholic beverages); and occurrence of stroke. Lipid profile was categorized on the basis of cutpoints from the Adult Treatment Panel III guidelines (32): 240 mg/dL for total cholesterol, 200 mg/dL for triglyceride, and 40 mg/dL for high-density lipoprotein cholesterol.

Causal diagram and notations

Figure 1 depicts the causal structure of the relationship between obesity and CHD (33–35). In this graph, there are 4 visits labeled by K = 0, K = 1, K = 2, and K = 3. The main exposure of interest is obesity status. A_k denotes obesity status as a dichotomous variable (0, 1), and L_k corresponds to a vector of time-varying covariates at visit k. A₀ is obesity status at the baseline visit, and L₀ includes variables measured only at the baseline visit and baseline values of time-varying variables. U_k corresponds to unmeasured risk factors at visit k, such as comorbidity. The arrows from A_k to L_k suggest that time-varying covariates are affected by previous exposure. Adjusting for such variables using conventional regression methods may lead to biased effect estimates. The overbars are used to denote the history of time-varying variables through visit K—for example, ${\overline{L}}_k=\left[L_0,L_1,L_2,\dots ,L_k\right]$. The outcome of interest is CHD or death from CHD and is denoted by Y₁, Y₂, and Y₃ for each follow-up visit. A subject’s observed failure time is denoted by T, and counterfactual failure time under the exposure histories $\overline{A}=\overline{a}$ is denoted by $T_{\overline{a}}$. In particular, the counterfactual failure time if the subject was never exposed throughout the follow-up period is denoted by $T_{\overline{0}}$.

Figure 1.

Causal diagram for the effect of obesity (A) on coronary heart disease (CHD) or death from CHD (Y) in the Atherosclerosis Risk in Communities Study.

Statistical analysis

To quantify the relationship between obesity and CHD, G-estimation of a structural nested accelerated failure-time (SNAFT) model was used (16, 21–27, 29, 36). G-estimation is one of Robins’ G-methods for adjustment of time-varying confounders affected by previous exposure (37). We used the following SNAFT model:

$$T_{\overline{0}}=\sum _{k=1}^3\exp (\mathrm{\psi }*A_k)\mathrm{\Delta }{t}_k,$$

where $\mathrm{\Delta }{t}_k$ is the time between visit k and the earliest of the next visit, an outcome event, or the end of 2010. The SNAFT model implies that exp(−ψ*) is the ratio of survival time if the participant is continuously exposed to survival time if the participant is unexposed throughout. G-estimation is a 2-step iterative process. At the first step, the value of counterfactual failure time is calculated using the SNAFT model mentioned above based on $T_{\overline{A}}$, observed exposure history A_k, and a candidate value of ψ for ψ*:

$$T(\mathrm{\psi })=\sum _{k=1}^3\exp (\mathrm{\psi }{A}_k)\mathrm{\Delta }{t}_k.$$

The T(ψ) under the true value of ψ = ψ* is $T_{\overline{0}}$, that is, the counterfactual failure time that would have been observed had the individual never been exposed throughout the study. In the second step, the probability of obesity status at each visit K was modeled as a function of previous obesity and all fixed and time-varying covariates, as well as T(ψ), using the following pooled logistic regression model:

$$\begin{array}{c}\mathrm{logit}(\mathrm{Pr}(A_k=1))={\mathrm{\beta }}_{0k}+{\mathrm{\beta }}_1{A}_0+{\mathrm{\beta }}_2{A}_{k-1}+{\mathrm{\beta }}_3{L}_0\hfill \\ +{\mathrm{\beta }}_4{L}_{k-1}+{\mathrm{\beta }\prime }_5{L}_k+{\mathrm{\beta }}_{6}T(\mathrm{\psi }).\hfill \end{array}$$

The G-estimate of the ψ* parameter is the value of ψ for which the coefficient of counterfactual failure time (β₆) would be zero (or equivalently P = 1), meaning that counterfactual survival time is independent of observed exposure (obesity) given past exposure and covariate history. Thus, the 2 steps mentioned above are iterated for different values of ψ until the G-estimate is found.

The 95% confidence interval for ψ* was determined by bootstrapping using 1,000 resamples. To convert the survival time ratio to a hazard ratio, we assumed a Weibull distribution for survival time. Then, using the G-estimate of ψ $(\hat{\mathrm{\psi }})$ and the shape parameter (p) of the Weibull distribution, the hazard ratio comparing always obese to never obese was calculated as follows (21, 23):

$$\mathrm{HR}=\exp (\hat{\mathrm{\psi }}\times P).$$

The variable T(ψ) can be derived using the SNAFT model above for participants who experience the events. To account for censoring by the end of follow-up, the value of T(ψ) was replaced by ∆(ψ), a function of T(ψ) and C (the time from visit 1 to the predefined end of follow-up of December 31, 2010) that takes the value 1 if the subject’s survival time would have been observed under any possible exposure regime: ∆(ψ) is 1 if T(ψ) < C(ψ) and ∆(ψ) is 0 if T(ψ) ≥ C(ψ), where for negative values of ψ, C(ψ) = C* exp(ψ) and for positive values of ψ, C_i(ψ) = C (21). Thus, ∆(ψ) is zero for all participants who are censored by the administrative end of follow-up, and it may also be zero for some of those who did experience an event. There are 2 key points about ∆(ψ). First, like T(ψ), ∆(ψ) is independent of observed exposure given past exposure and covariate history, as it is a function of T(ψ) (and C, which is a baseline covariate). Second, unlike T(ψ), ∆(ψ) can be computed for all subjects (21, 26).

We adjusted for selection bias due to competing risks (i.e., death from causes other than CHD) and loss to follow-up by applying visit-specific weights to uncensored individuals equal to the inverse of the conditional probability of being uncensored from that visit to the time of the subject’s last visit before an outcome (CHD or death from CHD) or the administrative end of follow-up, whichever came first, given past exposure and covariate history. The probabilities were estimated using the pooled logistic regression model for censoring due to competing risks and loss to follow-up, with covariates and obesity from the current visit, the previous visit, and the baseline visit as predictors. The weights were stabilized by including in the numerator the same probability as that included in the denominator, but without conditioning on past exposure and covariate history. G-estimation was then applied to the pseudo-population created by inverse-probability-of-censoring weighting (38).

We also compared the results from G-estimation with those obtained from standard regression analysis. To do this, we estimated the associations of GOB and AOB with risk of CHD using Weibull accelerated failure-time models with 3 different modeling strategies to adjust for confounding factors. The first model adjusted for A₀ (i.e., obesity status at baseline) and L₀, excluding metabolic mediators of obesity. The second model adjusted for the variables in the first model plus baseline values for hypertension, use of antihypertensive medication, diabetes, and lipid profiles as metabolic mediators of obesity (12). The third model was a standard time-dependent survival model and adjusted for A₀, L₀, A_k−1, L_k−1, A_k, and L_K. As for G-estimation, all 3 standard analyses started at visit 1 (the second visit) and were performed using Stata, version 13 (StataCorp LP, College Station, Texas). G-estimation was conducted using the SNAFTM macro (24, 26) in SAS, version 9.2 (SAS Institute, Inc., Cary, North Carolina).

RESULTS

Of the 13,580 participants in the ARIC Study with no history of symptomatic CHD, 678 subjects with missing data at baseline were excluded. The 12,902 included subjects were followed for a total of 242,161 person-years (median duration of follow-up, 21.2 years), during which 1,481 new cases of CHD occurred. The incidence rate of CHD was 61.1 per 10,000 person-years (95% confidence interval (CI): 58.1, 64.3). Table 1 shows the baseline characteristics of study participants according to GOB status. Obese participants were more likely to be female, to be black, and to have lower education and annual family income, and they were less likely to have health insurance compared with nonobese individuals. The prevalence of drinking and smoking were lower in obese participants than nonobese participants. In contrast, obese participants had higher levels of hypertension, antihypertensive medication use, diabetes mellitus, total cholesterol, and triglyceride than nonobese individuals and lower high-density lipoprotein cholesterol than the nonobese. Nonobese participants had higher physical activity levels during sport and leisure time and lower percentages of daily energy intake from saturated fat compared with obese individuals.

Table 1.

Baseline Characteristics of 12,902 Participants According to General Obesity Status, Atherosclerosis Risk in Communities Study, 1987–2010

Characteristic	Obese (n = 3,424)			Nonobese (n = 9,478)			P Value
	No. of Persons	%	Mean (SD)	No. of Persons	%	Mean (SD)
Age, years			54 (5.7)			54 (5.8)	0.377
Female sex	2,125	62		5,019	53		0.001
Black race	1,332	39		1,991	21		0.001
Educational level
Basic (≤11 years)	1,011	29		1,854	20		0.001
Intermediate (12–16 years)	1,359	40		3,932	41		0.001
Advanced (17–21 years)	1,046	31		3,685	39		0.001
Family income, dollars/year
<16,000	982	31		1,637	18		0.001
16,000–50,000	1,643	51		4,729	53		0.001
>50,000	573	18		2,608	29
Possession of health insurance	2,961	86		8,727	92		0.001
Family history of CVD	1,886	55		5,240	55		0.105
Current alcohol drinking	1,555	45		5,882	62		0.001
Current tobacco smoking	659	19		2,728	29		0.001
Hypertensiona	1,717	50		2,585	27		0.001
Antihypertensive medication use	1,297	38		1,755	18		0.001
Diabetes mellitusb	729	21		690	7.3		0.001
Physical activity level, hours/week
Work			2.21 (0.98)			2.19 (0.92)	0.717
Sports			2.28 (0.72)			2.50 (0.81)	0.001
Leisure time			2.27 (0.58)			2.40 (0.56)	0.001
Total energy intake, kcal/day			1,615 (615)			1,625 (607)	0.748
Saturated fatty acid, % of kcal/day			12.2 (2.9)			11.9 (3.0)	0.001
Lipid profilec
High total cholesterol level (>240 mg/dL)	924	27		2,227	23		0.001
High triglyceride level (>200 mg/dL)	618	18		1,050	11		0.001
Low HDL cholesterol level (<40 mg/dL)	1,141	33		2,127	22		0.001

Abbreviations: CVD, cardiovascular disease; HDL, high-density lipoprotein; SD, standard deviation.

^a Hypertension was defined as systolic blood pressure ≥90 or diastolic blood pressure ≥140 mm Hg or use of any medication for high blood pressure.

^b Diabetes mellitus was defined as blood glucose concentration ≥200 mg/dL and fasting blood glucose concentration ≥126 mg/dL or use of any medication for diabetes.

^c Lipid profile was categorized on the basis of cutpoints from the Adult Treatment Panel III guidelines (32).

During follow-up, 2,998 subjects died from causes other than CHD, and 5,456 subjects were lost to follow-up. The mean value of stabilized censoring weights was 0.997 (standard deviation, 0.34; range, 0.43–5.98).

Table 2 shows the value of ψ and the corresponding survival time ratio as exp(−ψ) for the associations of GOB and AOB with CHD. The hypothesized value for estimating ψ ranged between −1.5 to 1.5. The point estimate of exp(−ψ) for GOB was 0.89, suggesting an 11% decrease in G-estimated survival time to CHD (95% CI: 0.75, 1.14). AOB based on waist circumference and AOB based on waist:hip ratio decreased G-estimated survival time to CHD by 33% (95% CI: 21, 41) and 22% (95% CI: 9, 43), respectively.

Table 2.

Estimates of the Causal Effects of General and Abdominal Obesity on Risk of Coronary Heart Disease Using G-Estimation^a, Atherosclerosis Risk in Communities Study, 1987–2010

Obesity Status	G-Estimate of ψb	95% CIc	Survival Time Ratio	95% CIc
General obesityd	0.11	−0.13, 0.28	0.89	0.75, 1.14
Abdominal obesity
WCe	0.39	0.23, 0.51	0.67	0.59, 0.79
WHRf	0.25	0.09, 0.56	0.78	0.57, 0.91

Abbreviations: CI, confidence interval; WC, waist circumference; WHR, waist:hip ratio.

^a Adjusted for study visit (indicator variable), age, sex, race, educational level, physical activity, calorie intake, and percentage of daily energy intake from saturated fat at baseline, and for baseline and current tobacco smoking status, alcohol drinking status, hypertension, diabetes mellitus, antihypertensive medication use, high cholesterol level (level >240 mg/dL), high triglyceride level (>200 mg/dL), low high-density lipoprotein cholesterol level (<40 mg/dL), and occurrence of stroke.

^b ψ is the causal parameter in the structural nested accelerated failure-time model.

^c Bootstrap-based 95% CI.

^d General obesity was defined as body mass index (weight (kg)/height (m)²) ≥30.

^e Abdominal obesity was defined as WC ≥102 cm in men and ≥88 cm in women.

^f Abdominal obesity was defined as WHR ≥0.9 in men and ≥0.85 in women.

Figure 2 shows the hazard ratios estimated by G-estimation in comparison with 3 standard modeling strategies for all obesity measures. G-estimation suggested that obesity based on BMI, waist:hip ratio, and waist circumference increased the hazard of CHD by 15%, 38%, and 65%, respectively, though the 95% confidence interval for the BMI hazard ratio included the null value (Figure 2). The results from the first standard model adjusting for baseline covariates, excluding metabolic mediators of obesity, showed an increase in the hazard of CHD for all measures of obesity. Further adjustment for metabolic mediators of obesity resulted in a decrease in hazard ratios to 0.95 (95% CI: 0.83, 1.08) for GOB, 1.04 (95% CI: 0.88, 1.14) for AOB based on waist circumference, and 1.04 (95% CI: 0.86, 1.25) for AOB based on waist:hip ratio. The third model, which adjusted for baseline, lagged, and current values of time-varying covariates, showed reduced risk of CHD associated with all obesity measures, though all 95% confidence intervals included the null value. The hazard ratios from G-estimation were higher than those from standard time-dependent survival models and suggested increased risks of CHD for general obesity (hazard ratio (HR) = 1.15, 95% confidence interval (CI): 0.83, 1.47), waist circumference (HR = 1.65, 95% CI: 1.35, 1.92), and waist:hip ratio (HR = 1.38, 95% CI: 1.13, 1.99) (Figure 2).

Figure 2.

Hazard ratios for coronary heart disease (CHD) or CHD mortality according to general obesity and abdominal obesity in the Atherosclerosis Risk in Communities Study, 1987–2010. Hazard ratios were based on G-estimation as well as on 3 standard modeling strategies. The vertical axis is on the log scale. ●, standard model adjusted with no metabolic mediators; ■, standard model adjusted with metabolic mediators; ▲, standard time-varying adjusted model; ♦, G-estimation. Bars, 95% confidence intervals.

DISCUSSION

In the present study, the association of 3 different obesity measures with risk of CHD was assessed using the G-estimation method. AOB decreased the time to CHD by 33% based on waist circumference and 22% based on waist:hip ratio. Previous studies attempting to address time-varying confounding in the association between obesity and cardiovascular events had inconsistent results (29, 39–43). In 2 Mendelian randomization studies using genetic score as an instrument, no association of BMI with CHD was found (40, 41). The effect of obesity was also assessed through weight-loss strategies as an intervention in randomized trials or using the parametric G-formula in observational studies (39, 42, 43). The findings were inconclusive in that weight loss was associated with improvement in cardiovascular disease risk factors but no association was found with risk of cardiovascular events. In a previous study by Tilling et al. (29) using G-estimation, no evidence of increased CHD risk was found with high BMI. However, in previous studies that used causal modeling approaches, no measures of AOB were used. Therefore, to our knowledge, this is the first study that has examined the G-estimated relationship between AOB (based on 2 different measures) and risk of CHD. It has been suggested that visceral fat captured by AOB, in contrast to peripheral or subcutaneous fat, is proatherogenic and that central distribution of adipose tissue confers a greater risk of cardiovascular disease than total amount of adiposity (3, 5, 44–47).

In accordance with most previous observational studies (10–12, 48–50), the results of the current study using different modeling strategies showed that the higher risk associated with obesity in standard models adjusting for baseline variables disappeared after adjustment for metabolic mediators of obesity. This was due to overadjustment bias introduced by conditioning on variables that lie on the causal pathway between obesity and CHD. More importantly, adjusting for time-varying confounders using G-estimation showed an increase in risk for all measures of AOB in comparison with the standard time-dependent accelerated failure-time model, suggesting that the association of obesity with CHD might be biased by adjustment for time-varying confounders through standard methods.

While standard regression methods provide estimates that are conditional on the confounders, G-methods, including G-estimation, produce marginal estimates. Marginal and conditional estimates are not directly comparable, because of 1) biases caused by conditioning on a variable affected by exposure and 2) noncollapsibility of certain effect measures (19, 51). Assuming correct model specification, we suspect that the main reason for the divergence between the results of G-estimation and those of the corresponding conventional model (model 3) is the bias induced by conditioning on the time-varying confounders affected by the previous exposure, as the outcome is uncommon and thus noncollapsibility of the hazard ratio is not an issue (52). It is notable that the estimates from a standard analysis without adjustment for metabolic mediators were roughly similar to the estimates derived from the G-estimation method. It was only when adjusting for metabolic mediators (with or without accounting for their changing values over time) that the standard regressions gave null results (Figure 2). This pattern of results is consistent with the interpretation that in this application there was essentially no direct effect, and that confounding of the total effect by the metabolic mediators was not an important source of bias. Another explanation for this pattern is that there is an unmeasured shared cause of the metabolic mediator(s) and the outcome, which would cause collider stratification bias in the adjusted regression.

The validity of inference from G-estimation relies on several assumptions (15, 53). The first assumption, which is untestable, is comparability of exposed and unexposed subjects at any visit k given the previous exposure and confounder history. We used many time-varying and time-fixed confounders to justify the assumption of exchangeability between obese and nonobese individuals, but there might still have been some other, unmeasured confounders, such as diet quality and use of statins. In the ARIC Study, as in other interval cohorts, confounders are not measured at the times of exposure change and, in fact, concurrent or lagged time-varying confounders are the mismeasured versions of the true values of the confounders. Moreover, residual confounding bias can arise from using dichotomous instead of continuous measures of time-varying confounders such as blood pressure and serum lipid levels, as well as measurement errors in crudely self-reported confounders (e.g., physical activity, smoking, and alcohol consumption). The second assumption is specifying a suitable model for linking observed failure time and exposure history with counterfactual failure time. Similar to the work done by Witteman et al. (21), we used the current value of exposure (i.e., obesity) status and assumed that obesity at visit t represented obesity during the interval (t, t + 1). Another assumption for causal methods is positivity—that is, both obese and nonobese individuals are observed within every stratum of confounders. However, unlike inverse-probability-of-treatment weighting, G-estimation can result in asymptotically unbiased estimates even under violation of positivity if one is willing to rely on extrapolation to the empty cells, assuming that confounders are not effect modifiers (24, 53). The final assumption that is dubious in our study is well-defined intervention or consistency. This assumption states that for each subject, the counterfactual outcome under the observed value of exposure is equal to the observed outcome. Since there are different ways in which an obese subject could have been nonobese (more exercise, less food intake, bariatric surgery, etc.) that may correspond to different counterfactual outcomes, the consistency assumption cannot be considered credible. In fact, this assumption is best achieved for well-defined experiments and medical treatments. Therefore, interpretation of the causal effect in an observational study for metabolic or biomarker exposures such as obesity is not straightforward and should be made cautiously (54, 55).

In summary, our study used G-estimation to quantify the relationship between obesity and risk of CHD by appropriately adjusting for time-varying confounding affected by previous obesity. Shorter survival time to CHD was associated with higher levels of AOB, whether measured as waist circumference or as waist:hip ratio. This highlights the importance of considering AOB rather than BMI as a risk factor for CHD or CHD mortality.

Abbreviations

AOB

abdominal obesity

ARIC

Atherosclerosis Risk in Communities

BMI

body mass index

CHD

coronary heart disease

CI

confidence interval

GOB

general obesity

HR

hazard ratio

SNAFT

structural nested accelerated failure time