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Association of Genome-Wide Polygenic Risk Score for Body Mass Index With Cardiometabolic Health From Childhood Through Midlife

Originally publishedhttps://doi.org/10.1161/CIRCGEN.121.003375Circulation: Genomic and Precision Medicine. 2022;15

Abstract

Background:

Genetic information may help to identify individuals in childhood who are at increased risk for cardiometabolic disease.

Methods:

We included 1201 BHS (Bogalusa Heart Study) participants (832 White participants and 369 Black participants) who were followed up to 42.3 years, starting at a mean age of 9.8 years. A validated genome-wide polygenic risk score (PRS) was tested for association with midlife body mass index (BMI), fasting plasma glucose, and systolic blood pressure using multiple linear regression models. Cox proportional hazards models tested associations of the PRS with incident obesity, diabetes, and hypertension. All analyses were conducted according to race and adjusted for baseline age, sex, ancestry, and BMI.

Results:

The constructed PRS was significantly and modestly correlated with midlife BMI in both White and Black participants, with correlation coefficients of 0.27 (P=1.94×10−8) and 0.16 (P=5.50×10−3), respectively. In White participants, per SD increase of PRS was associated with an average 1.29 kg/m2 higher BMI (P=4.44×10−9), 2.82 mg/dL higher fasting plasma glucose (P=1.17×10−3), and 1.09 mm Hg higher systolic blood pressure (P=3.57×10−2) at midlife. The PRS also conferred a 26% higher increased risk of obesity (P=3.50×10−6) in White participants. In addition, the variance in midlife BMI explained increased from 0.1973 to 0.2293 when PRS was added to the model including age, sex, principal components, and baseline BMI (P<0.0001). No associations were observed in Black participants.

Conclusions:

Adiposity-related genetic information independently predicted cardiometabolic health in White BHS participants. Null associations observed in Black BHS participants highlight the urgent need for PRS development in multi-ancestry populations.

Obesity is a common condition affecting ≈711.4 million individuals worldwide.1 As the fourth leading risk factor for mortality, high body mass index (BMI) was responsible for 4.0 million deaths in 2015, mostly because of cardiovascular diseases.2 Genetic determinants are known to play an important role in adiposity phenotypes, with genetic factors estimated to explain 30% to 40% of the variance in BMI based on whole genome sequencing data.3 While studies have identified rare mutations of large effects,4 for most individuals, the genetic basis of obesity is polygenic with common variants of small effects explaining the significant proportion of its estimated heritability.4,5 Polygenic risk scores (PRSs) comprised of common variants reaching genome-wide significance in large-scale genome-wide association studies have been widely utilized to predict risk of numerous complex conditions.6–11 Recent studies have shown that incorporating single nucleotide polymorphisms below traditional genome-wide significance thresholds can further improve prediction and increase the proportion of variance explained by genetic factors.12–14 Hence, genome-wide PRSs, including up to millions of single nucleotide polymorphisms and constructed based on genome-wide association studies summary statistics and linkage disequilibrium information provide a novel and powerful approach for examining the aggregate effects of common variants on complex phenotypes.15–18

Recently, Khera et al19 derived a PRS for BMI and documented its strong association with incident severe obesity, as well as cardiometabolic diseases in midlife. While this seminal research demonstrated the predictive power of an obesity PRS, the utility of this tool beyond measured BMI in childhood for predicting adulthood cardiometabolic phenotypes was not assessed nor was its performance in non-European ancestry populations. Establishing an association of an adiposity PRS independent of clinically available measures could have important implications. Genetic information may help to identify individuals in childhood who might be at increased risk for obesity and related conditions and could benefit most from primordial prevention strategies. In the current study, we examined the independent associations of the PRS with anthropometric, glycemic, and hemodynamic end points among a biracial sample of BHS (Bogalusa Heart Study) participants who were followed up from childhood in nearly 4 decades of subsequent examinations.

Methods

Detailed methods are available in the Supplemental Material. The data that support the findings of this study are available from the corresponding author upon reasonable request. Informed consent was obtained from all BHS participants, and the studies were approved by the Institutional Review Board of the Tulane University Health Sciences Center.

Results

Characteristics of BHS study participants are shown in Table 1. Median follow-up time from baseline to the last study visit was similar across race groups, 36 years in Black participants, and 37 years in White participants. Compared with Black participants, White participants were more likely to be male and have higher PRS. At baseline, White participants had higher average fasting plasma glucose. At the most recently completed study visit, White participants had lower average BMI and systolic blood pressure (SBP) compared with Black participants. They were also less likely to have obesity, hypertension, diabetes, and take glucose and blood pressure lowering medications compared with Black participants.

Table 1. Characteristics of BHS Participants (N=1201)

White participants (N=832)Black participants (N=369)P Value
Male, n (%)375 (45.1)143 (38.8)0.04
Time, median (IQR)37.1 (8.88)36.2 (9.99)0.55
Polygenic risk score, mean (SD)37.8 (0.13)37.3 (0.11)<0.0001
Childhood*
 Age, mean (SD)9.94 (3.28)9.56 (2.98)0.05
 Body mass index, mean (SD)17.8 (3.47)17.6 (3.75)0.34
 Systolic blood pressure, mean (SD)100 (9.83)99.1 (10.69)0.08
 Fasting plasma glucose, mean (SD)84.1 (8.64)81.0 (7.43)0.005
 Obesity, n (%)91 (11.0)37 (10.1)0.64
 Hypertension, n (%)46 (5.55)16 (4.34)0.38
 Diabetes, n (%)0 (0.00)0 (0.00)NA
 Antihypertension medication, n (%)0 (0.00)0 (0.00)NA
 Antidiabetic medication, n (%)0 (0.00)0 (0.00)NA
Midlife
 Age, mean (SD)45.4 (6.99)44.6 (7.38)0.07
 Body mass index, mean (SD)30.2 (6.93)32.8 (9.15)<0.0001
 Systolic blood pressure, mean (SD)118 (14.0)126 (19.7)<0.0001
 Fasting plasma glucose, mean (SD)98.8 (30.2)102 (37.9)0.19
 Obesity, n (%)361(43.4)206 (55.8)<0.0001
 Hypertension, n (%)462 (55.6)265 (71.8)<0.0001
 Diabetes, n (%)105 (12.8)71 (19.7)0.002
 Antihypertension medication, n (%)198 (23.8)144 (39.0)<0.0001
 Antidiabetic medication, n (%)71 (8.94)55 (15.5)0.001

BHS indicates Bogalusa Heart Study; and IQR, interquartile range.

* Baseline study visit.

† Most recently completed study visit.

Associations of PRS With Continuous Cardiometabolic Phenotypes

The constructed PRS was significantly correlated with childhood BMI in both White and Black participants, with correlation coefficients of 0.19 (P=1.94×10−8) and 0.14 (P=5.50×10−3), respectively (Figure [A] and [B], respectively). The correlation was substantially increased by midlife (last-visit) among White participants (r=0.27; P=1.62×10−15; Figure [C]) and modestly increased among Black participants (r=0.16; P=2.59×10−3; Figure [D]).

Figure.

Figure. The correlations between continuous polygenic risk score (PRS) and body mass index (BMI) in childhood (baseline) and midlife (last completed follow-up visit). A, The correlations between continuous PRS and childhood BMI in White participants. B, The correlations between continuous PRS and childhood BMI in Black participants. C, The correlations between continuous PRS and midlife BMI in White participants. D, The correlations between continuous PRS and midlife BMI in Black participants.

Table 2 presented the association between continuous PRS and midlife BMI as well as the variance in midlife BMI explained by different models. Among White participants, each SD increase in PRS associated with a 1.85 kg/m2 higher average midlife BMI (P=6.7×10−15) in model unadjusted for childhood BMI. After adjustment for childhood BMI, higher PRS conferred 1.29 kg/m2 higher BMI (P=4.44×10−9). Compared with the base model, which included age, sex, and principal components, adding continuous PRS or (and) baseline BMI significantly increased the variance explained (P<0.0001). In addition, the variance explained increased from 0.1973 to 0.2293 when PRS was added to the model including age, sex, baseline BMI, and principal components (P<0.0001). Among Black participants, each SD increase in PRS associated with a 1.21 kg/m2 higher average midlife BMI (P=1.68×10−2); however, the association was not significant after adjustment of childhood BMI. PRS and baseline BMI explained more variance compared with the base model; however, PRS failed to add more predictive value beyond important clinical predictors.

Table 2. Variance in Midlife body mass index (BMI) explained by the continuous polygenic risk score and their association.

ModelWhite participantsBlack participants
PRS effect size (per SD increase)PRS effect size (per SD increase)
BetaSEPModel R2BetaSEPModel R2
Model 0NANANA0.0065NANANA0.0424
Model 11.850.236.69×10−150.0761*1.210.501.68×10−20.0551
Model 2NANANA0.1973*NANANA0.2046*
Model 31.290.224.44×10−90.2293*§0.720.461.24×10−10.2077*

Model 0: BMI=age, sex, PCs; Model 1: BMI=age, sex, PCs, PRS; Model 2: BMI=age, sex, PCs, baseline BMI; Model 3: BMI=age, sex, PCs, PRS, baseline BMI. BMI indicates body mass index; PC, principal component; and PRS, polygenic risk score.

* Significant improvement in R2 compared with model 0 (P<0.0001).

† Significant improvement in R2 compared with model 0 (P<0.05).

‡ Significant improvement in R2 compared with model 1 (P<0.0001).

§ significant improvement in R2 compared with model 2 (P<0.0001).

Significant positive associations between continuous PRS and midlife fasting plasma glucose and SBP were also observed in White participants (Table 3). Each SD increase in PRS associated with a 3.07 mg/dL higher midlife fasting plasma glucose (P=2.8×10−4), and 1.43 mm Hg higher midlife SBP (P=5.03×10−3) in models unadjusted for childhood BMI. After adjustment for childhood BMI, higher PRS conferred 2.82 mg/dL higher fasting plasma glucose (P=1.17×10−3), and 1.09 mm Hg higher SBP (P=3.57×10−2). No significant findings were observed for midlife fasting plasma glucose and SBP among Black participants.

Table 3. Association per SD Increase in the Continuous Polygenic Risk Score With Midlife FPG and SBP

Model 1*Model 2
Beta (SE)P ValueBeta (SE)P Value
White participants
 FPG3.07 (0.84)2.79×10−42.82 (0.87)1.17×10−3
 SBP1.43 (0.51)5.03×10−31.09 (0.52)3.57×10−2
Black participants
 FPG−0.45 (1.63)7.83×10−1−1.05 (1.65)5.24×10−1
 SBP−0.22 (1.22)8.55×10−1−0.47 (1.24)7.04×10−1

BMI indicates body mass index; FPG, fasting plasma glucose; and SBP, systolic blood pressure.

* Adjusted for age, sex, and ancestry principal components.

† Adjusted for covariables in model 1 plus baseline BMI.

‡ Beta estimates reflect effect sizes per SD increase in polygenic risk score.

Similar to findings of the continuous PRS analyses, significant dose-response associations between PRS quartile and midlife cardiometabolic phenotypes were observed among White participants (Figure S2). Compared with the lowest quartile, the highest quartile of PRS associated with a 5.1 kg/m2 higher average midlife BMI (P for linear trend [Plinear]=9.7×10−14), 8.6 mg/dL higher midlife fasting plasma glucose (Plinear=1.2×10−3), and 5.0 mm Hg higher midlife SBP (Plinear=3.1×10−3) in models unadjusted for childhood BMI. After adjustment for childhood BMI, the observed associations were only modestly attenuated. In contrast, no associations between PRS quartile and cardiometabolic phenotypes were observed among Black participants.

Results of sensitivity analyses excluding participants taking antihypertension medication were similar to those of the main analyses (Table S2).

Association of PRS With Incidence of Cardiometabolic Conditions

Table 4 shows the association between continuous PRS and the incidence of obesity, diabetes and hypertension. Among White participants, each SD increase in PRS conferred a 45% higher risk of obesity, 38% higher risk of diabetes, and 9% higher risk of hypertension before adjustment for childhood BMI (P=1.32×10−14, 4.71×10−3 and 3.85×10−2, respectively). After adjustment for childhood BMI, higher PRS remained significantly associated with higher incidence of obesity (hazard ratio=1.26, P=3.50×10−6). The associations between PRS and incident diabetes and hypertension were similar but nonsignificant. Among Black participants, per SD increase of PRS conferred a 20% higher risk of obesity before adjustment for childhood BMI (P=1.49×10−2); however, the association became nonsignificant after considering childhood BMI. No significant findings were observed for diabetes and hypertension among Black participants.

Table 4. Association per SD Increase in the Continuous Polygenic Risk Score and Incidence of Cardiometabolic Disease

Model 1*Model 2
HR (95% CI)P ValueHR (95% CI)P Value
White participants
 Obesity1.45 (1.32–1.60)1.32×10−141.26 (1.14–1.38)3.50×10−6
 Diabetes1.38 (1.10–1.72)4.71×10−31.23 (0.97–1.56)8.14×10−2
 Hypertension1.09 (1.00–1.18)3.85×10−21.05 (0.97–1.14)2.17×10−1
Black participants
 Obesity1.20 (1.04–1.39)1.49×10−21.11 (0.96–1.29)1.52×10−1
 Diabetes1.03 (0.78–1.38)8.17×10−11.01 (0.76–1.33)9.74×10−1
 Hypertension0.96 (0.85–1.09)5.28×10−10.94 (0.83–1.07)3.44×10−1

HR indicates hazard ratio.

* Adjusted for sex, birth year, and ancestry principal components.

† Adjusted for covariables in model 1 plus baseline BMI.

‡ Hazard ratios reflect effect sizes per SD increase in polygenic risk score.

As expected, a positive, graded association between PRS quartiles and incidence of cardiometabolic conditions was observed among White participants before adjustment for childhood BMI (Figure S3). Compared with the lowest quartile, the highest quartile of PRS conferred a 3.0-fold increased risk of obesity, 2.1-fold increased risk of diabetes, and 1.3-fold increased risk of hypertension (Plinear=4.96×10−13, 1.51×10−2, and 3.05×10−2, respectively). After adjusting for childhood BMI, the PRS remained significantly associated with incident obesity while nonsignificant trend was observed for diabetes and hypertension. Among Black participants, the highest quartile of PRS conferred 1.5-fold increased risks of obesity before adjustment for childhood BMI (Plinear=1.73×10−2). No significant findings were observed for diabetes and hypertension among Black participants.

Sensitivity analysis results were consistent with main results for both ancestry groups (see Table S3 and Figure S4).

Discussion

We are the first to report consistent, independent associations of an adiposity PRS with cardiometabolic health in over 30 years of longitudinal study. In White participants, per SD increase of PRS was associated with an average 1.29 kg/m2 higher BMI (P=4.44×10−9), 2.82 mg/dL higher fasting plasma glucose (P=1.17×10−3), and 1.09 mm Hg higher SBP (P=3.57×10−2) at midlife. The PRS also conferred a 26% higher increased risk of obesity (P=3.50×10−6) in White participants. Furthermore, PRS modestly but significantly improved the variance in midlife BMI explained beyond age, sex and baseline BMI. Similar findings were observed in analyses of the ordinal PRS measure. In contrast to findings in White participants, neither continuous PRS nor ordinal PRS predicted cardiometabolic health in Black BHS participants independently of measured BMI. In total, our findings highlight the potential utility of genetic information for identifying high risk subgroups who may benefit from targeted early intervention strategies to preserve cardiometabolic health across the lifespan. Furthermore, these results underline the urgent need for PRS development in diverse populations.

Among White participants of the BHS, we observed consistent, positive associations of an adiposity PRS with mid-life BMI, and incidence of obesity independent of childhood BMI. While previous studies have reported associations of obesity-related PRSs with anthropometric traits, few has leveraged longitudinal data spanning from childhood through middle-age to demonstrate an association independently of childhood BMI.6,19–26 Recently, Murthy et al26 assessed the predictive value of the same PRS among participants of the CARDIA (Coronary Artery Risk Development in Young Adults) Study. Similar to our study, they observed substantial increases in variance explained when adding PRS to a base model that did not adjust for baseline BMI. When baseline BMI was included in the model, variance explained increased by <2% with the addition of PRS among CARDIA participants. In our study, addition of the PRS to a model including baseline BMI did not increase variance explained in Black participants. In White participants, however, we observed modest but significant 3.2% increases in variance explained when adding PRS to the model. The larger variance explained in White participants in the BHS compared to CARDIA may be due to the longer average follow-up in the BHS (37 years in the BHS compared with 25 years in CARDIA), the earlier age of the baseline measure (when variability in measured BMI is smaller), or differences in the base models, which, in CARDIA, included parental history of overweight. In total, these findings implicate genetic information as a relevant tool in the primordial prevention of obesity and point to the potential utility of lifestyle intervention strategies to prevent early weight gain among those at the highest genetic risk.

Among White BHS children, higher PRS for BMI also independently associated with higher mean fasting plasma glucose and SBP in midlife. The PRS did not predict incident diabetes and hypertension after adjusting for childhood BMI in our relatively small sample. However, the observed hazard ratios of 1.59 and 1.19 for diabetes and hypertension, respectively, were similar to the respective and significant odds ratios of 1.70 and 1.35 observed in UK Biobank adults when comparing the highest to lowest categories of PRS.19 Overall, our findings provide additional genomic support of the well-established, etiologic role of adiposity in dysglycemia and elevated blood pressure.27–30 More importantly, these findings demonstrate that genetic information may help to identify individuals at highest risk for obesity-related cardiometabolic conditions well before they manifest clinically.

In Black participants, the associations between PRS and cardiometabolic phenotypes were generally null. The smaller sample size of Black BHS participants is unlikely to explain this discrepancy since effect sizes also appeared attenuated in this subgroup. The null associations are more likely to reflect the derivation of the PRS from predominantly European populations, which have well known differences in linkage disequilibrium structure when compared with populations of African ancestry.31,32 Indeed, lower correlations of the PRS with measured BMI in Black BHS participants compared with White BHS participants suggest that the PRS served as a better lifetime proxy for elevated BMI in the latter subgroup. Another noted discrepancy between Black and White BHS participants were differences in the correlation between the PRS and BMI over time. While correlations of the PRS with measured BMI substantially increased by midlife compared with childhood in White participants (r=0.27 and r=0.19, respectively), with midlife correlations consistent to those reported previously by Khera et al,19 the correlation in Black participants was only very modestly increased in midlife compared with childhood (r=0.16 compared with r=0.14, respectively). This is likely a statistical anomaly reflecting the PRS as a weak instrumental variable in Black participants rather than an indication of true differences in the relative contributions of gene and environment with increasing age across race groups. Given the utility of the PRS in White participants and the unusual correlations observed in Black participants, our results highlight the critical need for the development and further evaluation of PRSs in non-European populations.

The current study has several strengths. The BHS is a rich resource of carefully collected information on cardiometabolic phenotypes longitudinally measured from early childhood through midlife, providing a unique opportunity to prospectively examine the relationship of PRS with these traits across the lifespan. These data also provided an important opportunity to assess the relevance of the PRS in the longitudinal cardiometabolic health independent of measured BMI at childhood, allowing for the evaluation of the utility of genetic information in the primordial prevention of not only obesity but related cardiometabolic disease. Furthermore, our PRS, constructed using a validated tool comprising 2.1 M single nucleotide polymorphisms, substantially enhanced power to assess associations of genetically elevated BMI compared with previous studies limited to only genome-wide significant variants.6,20–24 However, certain limitations should also be mentioned. Time of obesity, diabetes, and hypertension events were defined by the age at the study visit when these conditions were first identified, which likely somewhat underestimates the time-to-event for these end points. Given the relatively short intervals between study visits (≈4 years on average), we think any resulting bias should be minimal and in a direction that is toward the null. In addition, the relatively small sample size of the BHS limited power to detect associations of our adiposity PRS with development of cardiometabolic disease through midlife. Furthermore, the correlations between the constructed PRS and midlife BMI were modest. However, the PRS still increased the variance explained even beyond baseline BMI. Also, the lower correlation of the PRS with measured BMI in Black BHS participants, likely reflecting its derivation in a European ancestry population, limited our ability to evaluate the relevance of genetic information for informing cardiometabolic health through midlife in these individuals. Future studies in larger sample sizes using multi-ancestry PRSs are warranted.

We present some of the first evidence that adiposity-related genetic information can predict cardiometabolic health in midlife independently of measured childhood BMI. Among White BHS participants, higher PRS independently associated with increased adiposity, glycemia, and blood pressure in nearly 4 decades of follow-up. In contrast, the PRS generally demonstrated null associations with cardiometabolic phenotypes among Black BHS participants, a finding most likely reflecting the derivation of the PRS in a European sample. Given the rapidly decreasing costs of genome-wide genotyping, genetic information could represent a valuable and cost-effective tool for the early identification of high-risk populations that might benefit from targeted lifestyle interventions to prevent cardiometabolic disease. However, without urgent work to develop appropriate PRSs in non-European ancestry populations, implementation of such a tool could lead to further disparities in already more vulnerable populations.

Article Information

Acknowledgments

We are grateful for the contribution of all staff members who were involved in conducting the Bogalusa Heart Study. We extend our gratitude to the participants of Bogalusa Heart Study, many of whom have diligently participated since they were children. The authors’ responsibilities were as follows—Dr Kelly designed research; Dr Shi analyzed data; Dr Shi and Kelly wrote the article; Drs Khera and Kelly designed the methodology; Dr Chen, X. Sun, Drs Bazzano, He, A.C. Razavi, Drs Li, Qi, and Khera provided critical revisions of the article; Dr Chen, X. Sun, Drs Bazzano, He, A.C. Razavi, Drs Li, Qi, and Kelly conducted the research; Dr Bazzano, He, Qi, and Kelly performed funding acquisition; and all authors read and approved the final article.

Supplemental Material

Supplemental Methods

Tables S1–S3

Figures S1–S4

References15,33–44

Nonstandard Abbreviations and Acronyms

BHS

Bogalusa Heart Study

BMI

body mass index

CARDIA

Coronary Artery Risk Development in Young Adults

FPG

fasting plasma glucose

PC

principal component

PRS

polygenic risk score

SBP

systolic blood pressure

Disclosures Dr Khera has served as a scientific advisor to Sanofi, Medicines Company, Maze Therapeutics, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, Color, and Columbia University (NIH); received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). All other authors report no conflicts of interest.

Footnotes

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.121.003375.

For Sources of Funding and Disclosures, see page 332.

Correspondence to: Tanika N. Kelly, PhD, MPH, Tulane Center for Public Health Genomics, 1440 Canal St, Ste 2000, New Orleans, LA 70112. Email

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