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. 2024 Mar 12;210(7):890–899. doi: 10.1164/rccm.202308-1384OC

Genetically Predicted Body Mass Index and Mortality in Chronic Obstructive Pulmonary Disease

Jingzhou Zhang 1,2, Matthew Moll 2,3,4, Brian D Hobbs 2,3, Per Bakke 5, Elizabeth A Regan 6, Hanfei Xu 7, Josée Dupuis 7,8, Joe W Chiles 9, Merry-Lynn N McDonald 9,10,11, Miguel J Divo 3, Edwin K Silverman 2,3, Bartolome R Celli 3, George T O’Connor 1,12, Michael H Cho 2,3,✉
PMCID: PMC11506912  PMID: 38471013

Abstract

Rationale

Body mass index (BMI) is associated with chronic obstructive pulmonary disease (COPD) mortality, but the underlying mechanisms are unclear. The effect of genetic variants aggregated into a polygenic score may elucidate the causal mechanisms and predict risk.

Objectives

To examine the associations of genetically predicted BMI with all-cause and cause-specific mortality in COPD.

Methods

We developed a polygenic score (PGS) for BMI (PGSBMI) and tested for associations of the PGSBMI with all-cause, respiratory, and cardiovascular mortality in participants with COPD from the COPDGene (Genetic Epidemiology of COPD), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points), and Framingham Heart studies. We calculated the difference between measured BMI and PGS-predicted BMI (BMIdiff) and categorized participants into groups of discordantly low (BMIdiff <20th percentile), concordant (BMIdiff between the 20th and 80th percentiles), and discordantly high (BMIdiff >80th percentile) BMI. We applied Cox models, examined potential nonlinear associations of the PGSBMI and BMIdiff with mortality, and summarized results with meta-analysis.

Measurements and Main Results

We observed significant nonlinear associations of measured BMI and BMIdiff, but not PGSBMI, with all-cause mortality. In meta-analyses, a one–standard deviation increase in the PGSBMI was associated with an increased hazard for cardiovascular mortality (hazard ratio [HR], 1.29; 95% confidence interval [CI], 1.12–1.49), but not for respiratory or all-cause mortality. Compared with participants with concordant measured and genetically predicted BMI, those with discordantly low BMI had higher risks for all-cause mortality (HR, 1.57; 95% CI, 1.41–1.74) and respiratory death (HR, 2.01; 95% CI, 1.61–2.51).

Conclusions

In people with COPD, a higher genetically predicted BMI is associated with higher cardiovascular mortality but not respiratory mortality. Individuals with a discordantly low BMI have higher all-cause and respiratory mortality rates than those with a concordant BMI.

Keywords: cardiovascular mortality, BMI, COPD, respiratory mortality, weight loss

At a Glance Commentary

Scientific Knowledge on the Subject

Low and extremely high body mass index (BMI) are associated with increased mortality in chronic obstructive pulmonary disease (COPD), but the mechanisms underlying these associations are unclear. Genetic association studies allow the genetic prediction of BMI, which may help elucidate the biologic basis of these associations.

What This Study Adds to the Field

A higher genetically predicted BMI was associated with an increased risk for cardiovascular death, but not respiratory death. A discordantly low (measured lower than genetically predicted) BMI was associated with an increased risk for all-cause and respiratory mortality, adjusted for COPD severity, and may have potential prognostic value among persons with COPD.

Chronic obstructive pulmonary disease (COPD), a complex respiratory disorder characterized by persistent airflow obstruction and respiratory symptoms, is a highly prevalent condition and a leading cause of mortality worldwide (1, 2). Respiratory failure is considered the leading cause of death in people with advanced COPD (3). In individuals with less severe COPD, comorbid conditions such as cardiovascular diseases are major contributors to morbidity and mortality (4). Thus, all-cause and cause-specific mortality offer distinct and complementary information in the risk stratification and prognostic assessment of patients with COPD.

Body mass index (BMI), a readily available and widely used marker of adiposity, has a nonlinear relationship with COPD mortality. In large multiple-study analyses of participants worldwide, an inverse association of measured BMI with respiratory and COPD mortality was found in the BMI range of 15–25 kg/m2 (5, 6). Similarly, in people with COPD, low measured BMI has consistently been associated with an increased risk for all-cause, respiratory, and cardiovascular mortality compared with normal weight (7–9). Accordingly, low measured BMI has been incorporated into COPD mortality prediction tools such as the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index (10–12). At the other end of the BMI spectrum, overweight and mild to moderate obesity is associated with lower or no additional mortality risk in COPD compared with normal weight, which is commonly referred to as the obesity paradox (9). Extreme obesity in persons with COPD, however, has been reported to be associated with increased mortality mainly driven by cardiovascular death (8, 9). The mechanisms underlying these associations between BMI and COPD mortality are unclear. Severe COPD is often associated with weight loss and cachexia, and it is unclear whether low measured BMI is causally related to COPD mortality or serves as a surrogate for other aspects of disease severity. Elucidating the underlying mechanisms linking BMI and COPD mortality may clarify the potential benefits of interventions targeting BMI in people with COPD.

BMI is determined by genetic and environmental factors (13). BMI has an estimated heritability of 30–40%, with common genetic variants explaining more than half of the heritability based on large genome-wide association studies (GWASs) (14, 15). In investigations of etiology, genetic variants are less susceptible to unmeasured confounding because they are present from birth. Although the effect of an individual genetic variant is usually small for complex traits such as BMI, aggregating effects of numerous variants across the entire genome into a polygenic score (PGS) improves predictive performance for the corresponding phenotype (16, 17). In this study, we hypothesized that: 1) genetically predicted BMI is associated with COPD mortality and has differential effects on respiratory and cardiovascular death and 2) the difference between measured BMI and genetically predicted BMI is associated with COPD mortality. We tested our hypotheses in individuals with COPD from cohorts enriched in smokers and a community-based cohort. Some of the results of this study have been previously reported in the form of an abstract (18).

Methods

Study Populations

We included participants with COPD from the COPDGene (Genetic Epidemiology of COPD) study (non-Hispanic White [NHW] and African American [AA] participants) (19), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points) study (20), and Framingham Heart Study (FHS) (21, 22), which have been previously described. In the COPDGene study, participants were enrolled from 2008 to 2011, and we used the updated all-cause mortality released through August 2022 and cause-specific mortality through October 2018. In the ECLIPSE study, cause-specific mortality at 3-year follow-up and all-cause mortality at 8-year follow-up were available (23). In the FHS, all-cause and cardiovascular mortality released through the end of 2018 were used. We defined COPD as a FEV1/FVC ratio lower than 0.7 and an FEV1% predicted <80% (i.e., Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage 2–4) using postbronchodilator spirometry (prebronchodilator spirometry was used in the FHS). For comparison, we also included FHS participants without COPD (defined by an FEV1/FVC ratio ⩾0.7 and FEV1% predicted ⩾80%). All studies were approved by the institutional review boards at each participating institution, and all participants provided written informed consent. See online supplement for further details of each cohort, including a flow chart of sample selection (Figure E1 in the online supplement) and genotyping information.

Development of a BMI PGS

To obtain a summative measure of genetically predicted BMI, we developed a PGS of BMI (PGSBMI) using weights generated from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank GWAS meta-analysis summary statistics (∼700,000 predominantly general-population participants of European ancestry) using lassosum (version 0.4.5) (15, 24). We calculated a PGSBMI for each participant as a weighted sum of variant effects using weights generated by lassosum and the dosage of BMI-increasing alleles for each variant. See online supplement for further details about the PGSBMI, including a link to the code package.

Statistical Analysis

We standardized the PGSBMI of each cohort to scores with a mean of 0 and a standard deviation (SD) of 1. We estimated the proportion of phenotypic variance explained by the PGSBMI using R2 measures. We calculated R2 in the FHS using a subsample of unrelated participants by randomly selecting one participant from each family.

We used a Cox regression model to examine the association between the PGSBMI and all-cause mortality. To account for competing risks, we used cause-specific hazard models to test for associations between the PGSBMI and respiratory and cardiovascular mortality. To determine whether to model the PGSBMI as a continuous or categorical variable, we examined a potential nonmonotonic association of the PGSBMI with mortality using penalized splines.

To obtain the difference between measured BMI and BMI predicted by the PGSBMI, we fitted a linear model to regress BMI on the PGSBMI and calculated regression residuals (hereafter referred to as BMIdiff, in kg/m2). We did not calculate BMIdiff for COPDGene AA participants given the low phenotypic variance explained by the PGSBMI in AA individuals. We used penalized splines to examine a potential nonlinear association of BMIdiff with all-cause mortality. Based on the spline analysis, we then categorized participants into three groups based on BMIdiff values: 1) BMIdiff lower than the 20th percentile, i.e., discordantly low BMI; 2) BMIdiff between the 20th and 80th percentiles, i.e., concordant measured and PGS-predicted BMI; and 3) BMIdiff greater than the 80th percentile, i.e., discordantly high BMI. We used the Kaplan-Meier method and Cox regression to examine the associations of the BMI concordance group with all-cause mortality. In additional analyses, we stratified on GOLD stage, adjusted for emphysema severity, and adjusted for a recent change in measured BMI over a history of as long as 5 years. We used cause-specific hazard models to test for associations between the BMI concordance group and cause-specific mortality.

To determine if the BMIdiff has an added prognostic value to measured BMI, we examined the association between the BMI concordance group and all-cause mortality adjusting for measured BMI (as a continuous variable with spline terms) in all participants and participants with a normal BMI as defined by the BODE index (21–25 kg/m2). To determine if the BMI concordance group adds prognostic value beyond the BODE index, which was available in COPDGene and ECLIPSE, we examined the association between the BMI concordance variable and all-cause mortality controlling for the BODE index.

We adjusted for age, sex, smoking status, pack-years of smoking, principal components of genetic ancestry, and FEV1% predicted for Cox models. We performed meta-analyses using the inverse variance-weighted average method to examine the combined effect estimates. We performed analyses using R version 4.1.0. See online supplement for further details of the statistical analysis.

Results

Baseline Characteristics

We included 2,811 NHW COPDGene participants, 753 AA COPDGene participants, 1,708 ECLIPSE participants, and 751 FHS participants who had GOLD stage 2–4 COPD (Table 1). Mean participant ages ranged from 59.3 to 64.6 years among the cohorts. The COPDGene NHW and AA cohorts and the FHS cohort had approximately equal proportions of male and female participants, whereas ECLIPSE had predominantly male participants (67.2%). On average, the ECLIPSE participants had a lower BMI and more severe airflow obstruction compared with the other cohorts. The FHS participants had less of a cumulative smoking history, less severe airflow obstruction, and longer follow-up time compared with the smoker-enriched cohorts. During follow-up, 1,313 NHW COPDGene participants (46.7%), 323 AA COPDGene participants (42.9%), 557 ECLIPSE participants (32.6%), and 323 FHS participants (43.0%) died. The characteristics of participants by BMI concordance group among the cohorts are shown in Table E1.

Table 1.

Baseline Characteristics of Participants in Each Study Cohort

Cohort COPDGene NHW (n = 2,811) COPDGene AA (n = 753) ECLIPSE (n = 1,708) FHS (n = 751)
Age, yr 64.6 ± 8.2 59.3 ± 8.2 63.7 ± 7.1 59.8 ± 9.8
Male sex 1,564 (55.6%) 412 (54.7%) 1,148 (67.2%) 371 (49.4%)
Smoking status        
 Ever-smoker 2,811 (100%) 753 (100%) 1,708 (100%) 597 (79.5%)
 Current smoker 978 (34.8%) 445 (59.1%) 586 (34.3%) 236 (31.4%)
Pack-years of smoking* 56.3 ± 28.0 42.8 ± 23.3 50.5 ± 27.4 29.5 ± 26.5
BMI, kg/m2 28.1 ± 6.1 27.9 ± 6.9 26.7 ± 5.6 28.2 ± 5.6
FEV1 % predicted 49.6 ± 18.0 51.5 ± 17.9 47.4 ± 15.5 68.8 ± 10.5
Years of follow-up 9.0 (5.0–12.0) 7.8 (4.0–11.6) 7.1 (3.0–8.0) 16.1 (10.8–22.4)
Death from all causes 1,313 (46.7%) 323 (42.9%) 557 (32.6%) 323 (43.0%)
Respiratory death† 323 (50.0%) 70 (49.0%) 68 (38.2%) —
Cardiovascular death† 84 (13.0%) 25 (17.5%) 26 (14.6%) 72 (22.3%)
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Definition of abbreviations: AA = African American; BMI = body mass index; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; NHW = non-Hispanic White.

Continuous variables are presented as mean ± standard deviation except years of follow-up, which is presented as median (first to third quartile).

*

Among ever-smokers.

†

Among participants who died of a known cause. Respiratory death was not captured in the FHS.

BMI PGS

The PGSBMI, containing approximately 439,000 genetic variants, accounted for 9.9% of the BMI variation in NHW COPDGene participants, 1.0% in AA COPDGene participants, 7.5% in ECLIPSE participants, and 15.8% in FHS participants (based on a subsample of 390 unrelated participants). A 1-SD increase of the PGSBMI was associated with increases of BMI of 1.96 kg/m2 (P = 3.8 × 10−67) in NHW COPDGene participants, 1.21 kg/m2 (P = 0.0029) in AA COPDGene participants, 1.57 kg/m2 (P = 6.3 × 10−32) in ECLIPSE participants, and 2.45 kg/m2 (P = 2.5 × 10−39) in FHS participants.

PGSBMI and Mortality

We observed a nonlinear association between BMI and all-cause mortality in our cohorts (P < 0.001 for nonlinearity for NHW COPDGene, ECLIPSE, and FHS; P = 0.060 for AA COPDGene). However, we did not observe a significant nonlinear association of the PGSBMI with all-cause mortality (P ⩾ 0.16 for nonlinearity), respiratory mortality (P ⩾ 0.32 for nonlinearity), or cardiovascular mortality (P ⩾ 0.11 for nonlinearity) (Figure 1). The results of the association analysis between the PGSBMI and all-cause mortality, respiratory mortality, and cardiovascular mortality in each cohort are shown in Figure 2. For all cohorts combined, a 1-SD increase of the PGSBMI was associated with a higher risk for cardiovascular death (HR, 1.29; P = 5.6 × 10−4), but not associated with respiratory death (HR, 1.01; P = 0.80). There was a suggestive positive association of the PGSBMI with all-cause mortality (HR per SD of PGS, 1.04; P = 0.052). We did not observe significant heterogeneity in the mortality associations of the PGSBMI among cohorts (I2 = 0%, Q-statistic P > 0.58 for all outcomes).

Figure 1.

Figure 1.

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Plots of partial residuals of measured body mass index and body mass index polygenic score in Cox models with penalized splines for all-cause, respiratory, and cardiovascular mortality. Solid lines represent fitted values of partial residuals; dashed lines represent standard errors of fitted values. Respiratory death was not captured in the Framingham Heart Study. AA = African American; BMI = body mass index; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; NHW = non-Hispanic White; PGS = polygenic score.

Figure 2.

Figure 2.

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Associations of the body mass index polygenic score with all-cause, respiratory, and cardiovascular mortality. AA = African American; BMI = body mass index; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; HR = hazard ratio; NHW = non-Hispanic White; PGS = polygenic score; SD = standard deviation.

Non–PGSBMI-predicted BMI and Mortality

Similar to the association between BMI and mortality, we observed a significant nonlinear (i.e., U-shaped) relationship between BMIdiff (difference between measured and PGS-predicted BMI) and all-cause mortality in NHW COPDGene, ECLIPSE, and FHS participants (Figure 3). After categorizing individuals by concordance between measured and PGS-predicted BMI, we observed a significant difference in overall survival probability among the groups based on the log-rank test in NHW COPDGene, ECLIPSE, and FHS participants (Figure 4). The results of the multivariable Cox regression analysis of the BMI concordance group with all-cause, respiratory, and cardiovascular mortality in each cohort are shown in Figure 5. For the combined cohorts, compared with participants who had concordant measured and PGS-predicted BMI, those with a discordantly low BMI had a 57% increase in the hazard of all-cause death (HR, 1.57; P = 1.2 × 10−17), a 101% increase in the hazard of respiratory death (HR, 2.01; P = 7.3 × 10−10), and a suggestive increase in the risk of cardiovascular death (HR, 1.31; 95% confidence interval, 0.87–1.98; P = 0.20). In contrast, compared with participants with concordant BMI, those with a discordantly high BMI had a 60% increase in the hazard of cardiovascular death (HR, 1.60; P = 0.0071) and a suggestive increase in the risk for all-cause mortality (HR, 1.09; 95% confidence interval, 0.97–1.23, P = 0.13), but no significant difference in the risk of respiratory mortality.

Figure 3.

Figure 3.

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Plots of partial residuals of the difference between measured and polygenic score–predicted BMI (in kg/m2) in Cox models with penalized splines for all-cause mortality. Solid lines represent fitted values of partial residuals; dashed lines represent standard errors of fitted values. BMIdiff = difference between measured and polygenic score–predicted BMI; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; NHW = non-Hispanic White.

Figure 4.

Figure 4.

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Survival probability for all-cause mortality and scatter plots of participants among groups based on the concordance between measured and polygenic score–predicted body mass index (BMI). Participants with a discordantly low BMI (difference between measured and PGS-predicted BMI [BMIdiff] <20th percentile) are shown in red; those with a concordant BMI (BMIdiff within 20th to 80th percentiles) are shown in blue, and those with a discordantly high BMI (BMIdiff >80th percentile) are shown in yellow. BMI = body mass index; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; NHW = non-Hispanic White; PGS = polygenic score.

Figure 5.

Figure 5.

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Associations of body mass index (BMI) concordance group with all-cause, respiratory, and cardiovascular mortality. A discordantly low BMI indicates a difference between measured and polygenic score–predicted BMI (BMIdiff) lower than the 20th percentile, a concordant BMI indicates a BMIdiff within the 20th to the 80th percentile, and a discordantly high BMI indicates a BMIdiff higher than the 80th percentile. The Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints study was not included in the analysis comparing cardiovascular mortality risk between participants of discordantly low BMI versus concordant BMI because of the limited number of events (n = 1) in those with a discordantly low BMI. CI = confidence interval; COPDGene = Genetic Epidemiology of COPD study; ECLIPSE = Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints [study]; FHS = Framingham Heart Study; HR = hazard ratio; NHW = non-Hispanic White.

The association between discordantly low BMI and increased all-cause mortality remained when stratified by GOLD stage (Table E2) or adjusted for baseline emphysema severity or a recent change in measured BMI (Tables E3 and E4).

For the meta-analysis including all the participants across the entire range of BMI, adjusting for measured BMI, those with a discordantly low BMI still had a higher risk for all-cause mortality (HR, 1.32; P = 1.8 × 10−4) compared with those with a concordant BMI (Table E5). Among individuals with a normal measured BMI, 262 NHW COPDGene participants (41.5%), 127 ECLIPSE participants (27.4%), and 91 FHS participants (44.0%) had a discordantly low BMI and the remainder had concordant BMIs; adjusting for measured BMI, those with a discordantly low BMI had an increased risk for all-cause mortality in the NHW COPDGene cohort (HR, 1.61; P = 1.9 × 10−4) and the FHS cohort (HR, 1.66; P = 0.026) compared with individuals with a concordant BMI, but not in the ECLIPSE cohort (HR, 0.69; P = 0.080).

After adjusting for the BODE index, for the NHW COPDGene and ECLIPSE participants combined, participants with a discordantly low BMI still had a higher risk for all-cause mortality (HR, 1.40; P = 8.4 × 10−9) compared with those with a concordant BMI (Table E6). See the online supplement for further details of the study results.

Results in FHS Participants without COPD

Of 3,008 FHS participants without COPD, there was a significant association of the PGSBMI (per 1-SD increase) with all-cause death (HR, 1.10; P = 0.021) and a suggestive association with cardiovascular death (HR, 1.15; P = 0.11). Compared with participants with concordant measured and PGS-predicted BMIs, those with a discordantly high BMI, but not those with a discordantly low BMI, had a higher risk for all-cause and cardiovascular mortality. See the online supplement for further details of the study results.

Discussion

In this multicohort analysis of adults with COPD, a higher PGSBMI was associated with an increased risk for cardiovascular death, but not with respiratory death. In addition, the difference between measured BMI and PGS-predicted BMI (i.e., BMIdiff) was associated with mortality. Compared with participants with concordant measured and genetically predicted BMIs, those with a discordantly low BMI had a substantially increased risk for respiratory and all-cause mortality. In contrast, among a community sample of adults without COPD, a discordantly low BMI was not associated with increased all-cause or cardiovascular mortality.

For cardiovascular mortality, our finding of a positive association of genetically predicted BMI is consistent with: 1) epidemiological evidence of a trend toward increased risk of cardiovascular mortality with increased measured BMI in overweight and obese individuals with COPD (8, 9) and 2) the association of higher genetically predicted BMI with increased risk for cardiovascular diseases and mortality in general-population samples (25, 26). This finding implies a causal effect of BMI on cardiovascular death in COPD. We also found that individuals with discordantly high BMI had a higher risk for cardiovascular mortality compared with those with concordant BMI, which might be explained by an effect of BMI- and/or obesity-associated factors on cardiovascular death (27, 28).

In contrast, we did not find an association between genetically predicted BMI and respiratory mortality in persons with COPD. This finding does not support a causal effect of the genetic determinants of BMI in a general population on respiratory mortality. Further, and similar to prior studies, we observed a nonlinear association between measured BMI and all-cause mortality in these cohorts and did not observe a significant nonmonotonic association between PGSBMI and all-cause or cause-specific mortality. This is consistent with large population-based Mendelian randomization analyses that suggest a linear relationship between genetically predicted BMI and mortality in a wide BMI range (26, 29). Taken together, our findings suggest that the association between low measured BMI and increased COPD mortality could be due to genetic factors separate from those associated with BMI in the general population, as well as nongenetic effects. Consistent with this hypothesis, we found that individuals with discordantly low BMI had a substantially higher risk for all-cause and respiratory mortality compared with those with concordant measured and genetically predicted BMI.

The association between measured BMI and mortality not explained by the PGSBMI is likely due to environmental factors, the relationship between environmental factors and genetics, and genetic effects specific to weight loss and cachexia. Cigarette smokers tend to weigh less and have a higher risk for mortality compared with nonsmokers (30, 31). In population-based epidemiological and Mendelian randomization analyses, the effects of BMI on mortality differed by smoking status (32, 33), which suggests that smoking-related effects or biases could explain the relationship between low measured BMI and mortality to a certain degree (34). Weight loss and cachexia commonly occur in severe COPD and are associated with reduced survival (35–37). We found a significant association between discordantly low BMI and increased risk for all-cause death after adjusting for the severity of airflow obstruction and emphysema, suggesting that the association between discordantly low BMI and mortality was not explained by COPD severity. Moreover, the GWASs used to build the PGSBMI were not designed to specifically capture genetic determinants of low BMI in disease. We did not observe an increased mortality risk in the discordantly low BMI group among FHS participants without COPD, suggesting that the mechanisms contributing to a discordantly low BMI are specific to COPD. The underlying factors accounting for non–PGS-predicted BMI and its association with mortality in COPD warrant further research.

Previous studies showed that: 1) weight gain was associated with improved survival in patients with severe COPD and low measured BMI, but not in patients with mild to moderate COPD or severe COPD and high BMI (36); and, 2) among patients with advanced COPD who received oral nutritional therapy, a small proportion experienced weight gain in response, which was associated with improved survival (38). It is unclear whether interventions specifically targeting BMI provide universal benefits in terms of mortality in individuals with COPD. In randomized controlled trials, short-term nutritional supplementation was associated with improved fatigue, dyspnea, and maintenance or improvement of weight, although these studies were not designed to identify effects on mortality (39–41). Our findings do not imply that interventions targeting BMI such as nutrition supplementation are without benefit in some patients with COPD; however, the complex physiology of weight loss and cachexia in COPD likely merit individualized and multifactorial management (42). On the contrary, our findings suggest that individuals with COPD who have a high BMI may benefit from weight management to improve cardiovascular mortality, which needs to be confirmed by prospective interventional studies.

BMI is affected by genetic and environmental factors and is a dynamic measure over a person’s lifetime. Although the genetic variants influencing BMI in infancy and early childhood appear to differ from those influencing adult BMI (43, 44), data from a birth cohort and the UK Biobank indicate that PGSs for adult BMI show a consistent association with BMI across the adult age range (25, 45). In our study, we developed a PGSBMI from GWAS meta-analysis of the GIANT consortium and UK Biobank, which covers a full spectrum of adult age, and applied this PGS to our middle-aged and older cohort participants. Given the previously reported relatively consistent association of PGSBMI with BMI across the age range of our study participants, we have not modeled the PGSBMI and PGS-predicted BMI as time-dependent variables and have instead estimated an average effect across the age range.

A recent study demonstrated that it was possible to detect somatotype trajectories in patients with COPD (46). In that study, people with trajectories of maintaining a low measured BMI or starting to lose weight during midlife had more severe COPD compared with the other trajectories with higher measured BMI. These findings suggest various pathways to low measured BMI in a life course in individuals with COPD. In our study, the association between discordantly low BMI and increased all-cause mortality risk remained after accounting for a recent change in measured BMI over a period as long as 5 years, suggesting a potential prognostic role of the BMIdiff even for individuals with a known recent weight change history.

Low measured BMI (e.g., a BMI ⩽21 kg/m2 on the BODE index) is currently used as a prognostic marker in COPD. In our cohorts, most participants who had a low measured BMI had a discordantly low BMI with respect to their genetically predicted BMI. However, a substantial proportion of participants with normal measured BMI were in the discordantly low BMI group and would not be identified as having increased mortality risk based on low measured BMI. Although patients with COPD who have low measured BMI are often suspected to have experienced weight loss and cachexia, unintentional weight loss has been associated with mortality across the BMI spectrum (47). We observed in COPDGene and FHS cohorts that individuals with a discordantly low BMI had a higher all-cause mortality risk compared with those with a concordant BMI even if the measured BMI was considered normal. Although the findings in these cohorts suggest that the identification of individuals with a discordantly low BMI could improve sensitivity beyond the measured BMI in the prognostication of people with COPD, the effect in the ECLIPSE study was not statistically significant, and, in fact, was in the opposite direction. Further, compared with participants with a concordant BMI, those with a discordantly low BMI had worse survival after adjusting for the BODE index. Additional research is necessary to ascertain the role of the BMIdiff in the risk prediction of COPD mortality.

Of note, the variance of BMI explained by the PGS varied among cohorts. The performance of the PGS was better in the FHS than in the NHW COPDGene and ECLIPSE cohorts. The FHS contributed to the sample of the BMI GWAS from which our PGS was derived, and the performance of the PGS might be overestimated in the FHS as a result of potential overfitting, even though FHS accounted for only approximately 1% of the participants in the BMI GWAS (15). In addition, the BMI in community-based FHS participants may be less affected by COPD severity, smoking, and environmental exposures compared with the smoker-enriched COPDGene and ECLIPSE participants. The PGS in the AA COPDGene cohort did not perform as well as in the NHW cohorts, which is consistent with previously observed poor transferability of PGSs across ancestry and strengthens the importance of conducting multiancestry GWASs and constructing multiancestry PRSs in the future (48–50).

Strengths of our study include the analysis of cohorts of relatively large sample sizes and long follow-up times, along with the use of a rigorous PGS, which altogether increased the power of our analyses. Standardized and reliable methods were used to ascertain cause-specific mortality in our cohorts (51). In addition, we were able to replicate our findings in the multinational and multicenter smoker-enriched samples and a U.S. community-dwelling sample of individuals with COPD.

Limitations of our study include the use of a PGS that comprised effects of common genetic variants from a general population predominantly of European ancestry, precluding the ability to accurately assess the effects on non-European ancestry, rare variants, and variants influencing BMI only in a specific disease state (e.g., variants associated with very low BMI in severe COPD) (52). Further studies are needed to examine the causal effect of extremely low BMI on COPD mortality. Prebronchodilator spirometry was used to define COPD in the FHS; however, we included participants with moderate to very severe airflow obstruction and age older than 40 years to mitigate the misclassification of COPD. Also, respiratory mortality was not captured in the FHS. We did not include body composition measures and were not able to examine the effect of body composition on COPD mortality. Further, we did not include other “omics” data beyond the genomics of BMI in our analyses (53). Future studies integrating multiomics data may deepen our understanding of the pathobiology linking BMI and COPD mortality and facilitate the development of useful clinical biomarkers.

In conclusion, in individuals with COPD from smoker-enriched cohorts and a population-based cohort, a higher genetically predicted BMI is associated with an increased risk for cardiovascular death, but not respiratory death. Compared with people with concordant measured and genetically predicted BMI, those with a discordantly low BMI have an increased risk for all-cause and respiratory mortality, and those with a discordantly high BMI have an increased risk for cardiovascular mortality. Future research is needed to further unravel the mechanisms underlying the association between low measured BMI and increased mortality in COPD.

Supplemental Materials

Online Data Supplement
rccm.202308-1384OC.docx (537.3KB, docx)
DOI: 10.1164/rccm.202308-1384OC

Footnotes

Supported by National Heart, Lung, and Blood Institute grant K08HL159318 (M.M.), NIH grants U01 HL089856 and R01 HL147148 and an Alpha-1 Foundation grant (B.D.H.); NIH grant T32 HL105346 (J.W.C.); NIH grant R01 HL153460 (M.L.M.); NIH grants R01 HL152728, R01 HL147148, U01 HL089856, R01 HL133135, and P01 HL114501 (E.K.S.); and NIH grants R01 HL137927, R01 HL135142, R01 HL147148, and U01 HL089856 (M.H.C.). COPDGene was supported by NHLBI awards U01 HL089897 and U01 HL089856. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion. The Framingham Heart Study is conducted and supported by the NHLBI in collaboration with Boston University (contracts N01-HC-25195, HHSN268201500001I, and 75N92019D00031).

Author Contributions: J.Z., G.T.O’C., and M.H.C. conceptualized and designed the study. J.Z., M.M., and H.X. performed data analysis. J.Z., G.T.O’C., and M.H.C. drafted and edited the initial version of the manuscript. All authors contributed to the data interpretation and critical revision of the manuscript, and approved the final version of the manuscript.

A data supplement for this article is available via the Supplements tab at the top of the online article.

Originally Published in Press as DOI: 10.1164/rccm.202308-1384OC on March 12, 2024

Author disclosures are available with the text of this article at www.atsjournals.org.

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