Key result
Body-mass index associations with mortality may be biased by time-varying confounders like systolic blood pressure, requiring causal methods such as G-estimation for appropriate adjustment.
Causal methods such as G-estimation should be used to handle time-varying confounding when analyzing the relationship between BMI, blood pressure, and cardiovascular mortality.
In their prospective cohort study (July, 2018),1Gajalakshmi V Lacey B Kanimozhi V Sherliker P Peto R Lewington S Body-mass index, blood pressure, and cause-specific mortality in India: a prospective cohort study of 500 810 adults.Lancet Glob Health. 2018; 6: e787-e794Summary Full Text Full Text PDF PubMed Scopus (32) Google Scholar Vendhan Gajalakshmi and colleagues investigate the associations between body-mass index and blood pressure with specific vascular mortality in India. Their results suggest that systolic blood pressure was strongly associated with stroke, cardiac, and vascular mortality. Gajalakshmi and colleagues adjusted the association between body-mass index and mortality for age, sex, education, socioeconomic status, smoking status, alcohol consumption, and systolic blood pressure, and reported that a body-mass index of 15–30 kg/m2 was inversely associated with both cardiac and stroke mortality. However, this result contradicts previous findings.2Shakiba M Mansournia MA Salari A Soori H Mansournia N Kaufman JS Accounting for time-varying confounding in the relation between obesity and coronary heart disease: analysis with G-estimation: the ARIC study.Am J Epidemiol. 2018; 187: 1319-1326Crossref PubMed Scopus (32) Google Scholar, 3Flegal KM Kit BK Orpana H Graubard BI Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis.JAMA. 2013; 309: 71-82Crossref PubMed Scopus (2596) Google Scholar, 4Prospective Studies CollaborationBody-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies.Lancet. 2009; 373: 1083-1096Summary Full Text Full Text PDF PubMed Scopus (3219) Google Scholar To explain this discrepancy, in the study by Gajalakshmi and colleagues, several covariates including systolic blood pressure, smoking status, and alcohol consumption are time-varying confounders for the effect of body-mass index on mortality. Furthermore, these time-varying confounders can be affected by previous body-mass index measurements resulting in time-varying confounding that is affected by previous exposure.5Mansournia MA Etminan M Danaei G Kaufman JS Collins G Handling time varying confounding in observational research.BMJ. 2017; 359: j4587Crossref PubMed Scopus (175) Google Scholar Thus, feedback occurs between exposure and confounders. For example, previous systolic blood pressure can act as a strong confounder for the effect of current body-mass index since hypertensive patients might decide to lose weight; this is also clearly affected by a patient's previous body-mass index. Gajalakshmi and colleagues adjusted for these confounders using Cox regression, which has been found to result in biased effect estimates in the presence of time-varying confounding affected by previous exposure.5Mansournia MA Etminan M Danaei G Kaufman JS Collins G Handling time varying confounding in observational research.BMJ. 2017; 359: j4587Crossref PubMed Scopus (175) Google Scholar Causal methods, including the inverse probability of treatment weighting, g-estimation, and parametric g-formulae have been proposed to appropriately adjust for time-varying confounders.2Shakiba M Mansournia MA Salari A Soori H Mansournia N Kaufman JS Accounting for time-varying confounding in the relation between obesity and coronary heart disease: analysis with G-estimation: the ARIC study.Am J Epidemiol. 2018; 187: 1319-1326Crossref PubMed Scopus (32) Google Scholar, 5Mansournia MA Etminan M Danaei G Kaufman JS Collins G Handling time varying confounding in observational research.BMJ. 2017; 359: j4587Crossref PubMed Scopus (175) Google Scholar, 6Tilling K Sterne JAC Szklo M Estimating the effect of cardiovascular risk factors on all-cause mortality and incidence of coronary heart disease using G-estimation: the Atherosclerosis Risk in Communities Study.Am J Epidemiol. 2002; 155: 710-718Crossref PubMed Scopus (31) Google Scholar, 7Mansournia MA Danaei G Forouzanfar MH et al.Effect of physical activity on functional performance and knee pain in patients with osteoarthritis: analysis with marginal structural models.Epidemiology. 2012; 23: 631-640Crossref PubMed Scopus (67) Google Scholar, 8Mansournia MA Altman DG Inverse probability weighting.BMJ. 2016; 352: i189Crossref PubMed Scopus (254) Google Scholar We recommend the use of causal methods to adjust for time-varying confounders when cohort data with repeated measurements on the exposure and confounders are available. We declare no competing interests. Body-mass index, blood pressure, and cause-specific mortality in India: a prospective cohort study of 500 810 adultsIn this South Asian population, BMI was little associated with vascular mortality, even though increased BMI is associated with increased systolic blood pressure, which in turn is associated with increased vascular mortality. Hence, some close correlates of below-average BMI must have important adverse effects, which could be of relevance in all populations.
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Doosti‐Irani et al. (2018) conducted a letter in cause-specific mortality (n=500,810). Body-mass index was evaluated on mortality. Body-mass index associations with mortality may be biased by time-varying confounders like systolic blood pressure, requiring causal methods such as G-estimation for appropriate adjustment.
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