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Genetic risk score enhances the risk prediction of severe obesity in adult survivors of childhood cancer

Nature Medicine volume 28, pages 1590–1598 (2022)Cite this article

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Abstract

Adult survivors of childhood cancer have high rates of obesity, which, in combination with the cardiotoxic effects of specific cancer therapies, places them at high risk for cardiovascular morbidity. Here we show the contribution of genetic risk scores (GRSs) to increase prediction of those survivors of childhood cancer who are at risk for severe obesity (body mass index ≥40 kg m−2) as an adult. Among 2,548 individuals of European ancestry from the St. Jude Lifetime Cohort Study who were 5-year survivors of childhood cancer, the GRS was found to be associated with 53-fold-higher odds of severe obesity. Addition of GRSs to risk prediction models based on cancer treatment exposures and lifestyle factors significantly improved model prediction (area under the curve increased from 0.68 to 0.75, resulting in the identification of 4.3-times more high-risk survivors), which was independently validated in 6,064 individuals from the Childhood Cancer Survivor Study. Genetic predictors improve identification of patients who could benefit from heightened surveillance and interventions to mitigate the risk of severe obesity and associated cardio-metabolic complications.

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Fig. 1: BMI and its categories in survivors of childhood cancer and community controls.
Fig. 2: Genetic risk prediction of obesity and severe obesity in survivors of childhood cancer.

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Data availability

Aligned binary files for all the SJLIFE survivors and a subset of CCSS survivors whose childhood cancer was diagnosed between 1987 and 1999 and the joint genotype calls are accessible through the St. Jude Cloud (https://stjude.cloud). For CCSS survivors diagnosed between 1970 and 1986, genome-wide genotype data are available through the database of Genotypes and Phenotypes (accession number phs001327.v2.p1). Relevant phenotype data are available through the St. Jude Cloud for SJLIFE survivors and through https://ccss.stjude.org/ for CCSS survivors. Variant-level information used to calculate various GRSs examined in this study are available from previously published studies, including Turcot et al.16 (GRSrare), Khera et al.18 (GRScommon), Monda et al.61 (GRSAFR_8 and GRSAFR_179), Ng et al.62 (GRSAFR_genomewide), Vogelezang et al.22 (GRSchildhood_bmi), Bradfield et al.23 (GRSchildhood_obesity), Pulit et al.21 (GRSwhradjBMI) and Yengo et al.24 (GRS941). All data generated or analyzed during the study are included in the published article (and its supplementary files).

Code availability

In-house scripts used to calculate AUC values and their CIs using 1,000 bootstrap iterations are available to download from https://github.com/sapkotagrp/NatMed2022_SevereObesity.

References

  1. Robison, L. L. & Hudson, M. M. Survivors of childhood and adolescent cancer: life-long risks and responsibilities. Nat. Rev. Cancer 14, 61–70 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Garmey, E. G. et al. Longitudinal changes in obesity and body mass index among adult survivors of childhood acute lymphoblastic leukemia: a report from the Childhood Cancer Survivor Study. J. Clin. Oncol. 26, 4639–4645 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Janiszewski, P. M. et al. Abdominal obesity, liver fat, and muscle composition in survivors of childhood acute lymphoblastic leukemia. J. Clin. Endocr. Metab. 92, 3816–3821 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Oeffinger, K. C. et al. Obesity in adult survivors of childhood acute lymphoblastic leukemia: a report from the childhood cancer survivor study. J. Clin. Oncol. 21, 1359–1365 (2003).

    Article  PubMed  Google Scholar 

  5. Razzouk, B. I. et al. Obesity in survivors of childhood acute lymphoblastic leukemia and lymphoma. J. Clin. Oncol. 25, 1183–1189 (2007).

    Article  PubMed  Google Scholar 

  6. Wilson, C. L. et al. Genetic and clinical factors associated with obesity among adult survivors of childhood cancer: a report from the St. Jude Lifetime Cohort. Cancer 121, 2262–2270 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Armstrong, G. T. et al. Modifiable risk factors and major cardiac events among adult survivors of childhood cancer. J. Clin. Oncol. 31, 3673–3680 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Armstrong, G. T. et al. Reduction in late mortality among 5-year survivors of childhood cancer. N. Engl. J. Med. 374, 833–842 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chow, E. J., Pihoker, C., Hunt, K., Wilkinson, K. & Friedman, D. L. Obesity and hypertension among children after treatment for acute lymphoblastic leukemia. Cancer 110, 2313–2320 (2007).

    Article  PubMed  Google Scholar 

  10. Reilly, J. J., Blacklock, C. J., Dale, E., Donaldson, M. & Gibson, B. E. S. Resting metabolic rate and obesity in childhood acute lymphoblastic leukaemia. Int. J. Obesity 20, 1130–1132 (1996).

    CAS  Google Scholar 

  11. Warner, J. T., Bell, W., Webb, D. K. H. & Gregory, J. W. Daily energy expenditure and physical activity in survivors of childhood malignancy. Pediatr. Res. 43, 607–613 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Poirier, P. et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss—an update of the 1997 American Heart Association Scientific Statement on obesity and heart disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 113, 898–918 (2006).

    Article  PubMed  Google Scholar 

  13. Ndumele, C. E. et al. Obesity and subtypes of incident cardiovascular disease. J. Am. Heart Assoc. 5, e003921 (2016).

  14. Flegal, K. M., Kit, B. K., Orpana, H. & Graubard, B. I. Association of all-cause mortality with overweight and obesity using standard body mass index categories a systematic review and meta-analysis. JAMA 309, 71–82 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Turcot, V. et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat. Genet. 50, 26–41 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Khera, A. V. et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177, 587–596 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Green, D. M. et al. Risk factors for obesity in adult survivors of childhood cancer: a report from the Childhood Cancer Survivor Study. J. Clin. Oncol. 30, 246–255 (2012).

    Article  PubMed  Google Scholar 

  20. Meacham, L. R. et al. Body mass index in long-term adult survivors of childhood cancer—a report of the Childhood Cancer Survivor Study. Cancer 103, 1730–1739 (2005).

    Article  PubMed  Google Scholar 

  21. Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. Vogelezang, S. et al. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. PLoS Genet. 16, e1008718 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bradfield, J. P. et al. A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity. Hum. Mol. Genet. 28, 3327–3338 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in similar to 700 000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chow, E. J. et al. Prediction of ischemic heart disease and stroke in survivors of childhood cancer. J. Clin. Oncol. 36, 44–52 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Chow, E. J. et al. Individual prediction of heart failure among childhood cancer survivors. J. Clin. Oncol. 33, 394–U326 (2015).

    Article  PubMed  Google Scholar 

  27. Sapkota, Y. et al. Whole-genome sequencing of childhood cancer survivors treated with cranial radiation therapy identifies 5p15.33 locus for stroke: a report from the St. Jude Lifetime Cohort Study. Clin. Cancer Res. 25, 6700–6708 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sapkota, Y. et al. Genetic variants associated with therapy-related cardiomyopathy among childhood cancer survivors of African ancestry. Cancer Res. 81, 2556–2565 (2021).

  29. Sapkota, Y. et al. Genome-wide association study in irradiated childhood cancer survivors identifies HTR2A for subsequent basal cell carcinoma. J. Invest. Dermatol. 139, 2042–2045 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sapkota, Y. et al. A novel locus predicts spermatogenic recovery among childhood cancer survivors exposed to alkylating agents. Cancer Res. 80, 3755–3764 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang, Z. et al. Genetic risk for subsequent neoplasms among long-term survivors of childhood cancer. J. Clin. Oncol. 36, 2078–2087 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sapkota, Y. et al. Contribution of polygenic risk to hypertension among long-term survivors of childhood cancer. JACC CardioOncol. 3, 76–84 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Qi, Q.B. et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ 348, g1610 (2014).

  34. Qi, Q. B. et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 367, 1387–1396 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tyrrell, J. et al. Gene–obesogenic environment interactions in the UK Biobank study. Int. J. Epidemiol. 46, 559–575 (2017).

    PubMed  PubMed Central  Google Scholar 

  36. Krul, A. J., Daanen, H. A. M. & Choi, H. Self-reported and measured weight, height and body mass index (BMI) in Italy, the Netherlands and North America. Eur. J. Public Health 21, 414–419 (2011).

    Article  PubMed  Google Scholar 

  37. Wang, Y., Rimm, E. B., Stampfer, M. J., Willett, W. C. & Hu, F. B. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am. J. Clin. Nutr. 81, 555–563 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Collaborators, G. B. D. O. et al. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377, 13–27 (2017).

    Article  Google Scholar 

  39. Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pharoah, P. D. P., Antoniou, A. C., Easton, D. F. & Ponder, B. A. J. Polygenes, risk prediction, and targeted prevention of breast cancer. N. Engl. J. Med. 358, 2796–2803 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Marston, N. A. et al. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. Circulation 141, 616–623 (2020).

    Article  PubMed  Google Scholar 

  44. Damask, A. et al. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation 141, 624–636 (2020).

    Article  PubMed  Google Scholar 

  45. Yanovski, S. Z. & Yanovski, J. A. Long-term drug treatment for obesity: a systematic and clinical review. JAMA 311, 74–86 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wadden, T. A., Webb, V. L., Moran, C. H. & Bailer, B. A. Lifestyle modification for obesity: new developments in diet, physical activity, and behavior therapy. Circulation 125, 1157–1170 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Howell, C. R. et al. Cohort profile: the St. Jude Lifetime Cohort Study (SJLIFE) for paediatric cancer survivors. Int. J. Epidemiol. 50, 39–49 (2021).

    Article  PubMed  Google Scholar 

  49. Hudson, M. M. et al. Prospective medical assessment of adults surviving childhood cancer: study design, cohort characteristics, and feasibility of the St. Jude Lifetime Cohort study. Pediatr. Blood Cancer 56, 825–836 (2011).

    Article  PubMed  Google Scholar 

  50. Bhakta, N. et al. The cumulative burden of surviving childhood cancer: an initial report from the St Jude Lifetime Cohort Study (SJLIFE). Lancet 390, 2569–2582 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Leisenring, W. M. et al. Pediatric cancer survivorship research: experience of the Childhood Cancer Survivor Study. J. Clin. Oncol. 27, 2319–2327 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Robison, L. L. et al. Study design and cohort characteristics of the childhood cancer survivor study: a multi-institutional collaborative project. Med. Pediatr. Oncol. 38, 229–239 (2002).

    Article  PubMed  Google Scholar 

  53. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  57. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Morton, L. M. et al. Genome-wide association study to identify susceptibility loci that modify radiation-related risk for breast cancer after childhood cancer. J. Natl Cancer Inst. 109, djx058 (2017).

  59. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  Google Scholar 

  61. Monda, K. L. et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat. Genet. 45, 690–696 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ng, M. C. Y. et al. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet. 13, e1006719 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults—the evidence report. National Institutes of Health. Obes. Res. 6, 51S–209S (1998).

  64. Petersen, R., Pan, L. P. & Blanck, H. M. Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action. Prev. Chronic Dis. 16, E46 (2019).

  65. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  66. Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Ogden, C. L. et al. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 295, 1549–1555 (2006).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The St. Jude Lifetime Cohort (U01 CA195547: M.M.H. and K.K.N.) and the Childhood Cancer Survivor Study (U24 CA55727; G.T.A.) are supported by the National Cancer Institute at the National Institutes of Health and the Cancer Center Support CORE grant (CA21765: C. Roberts). The CCSS original cohort genotyping was supported by the Intramural Research Program of the National Cancer Institute, National Institutes of Health. This work is also supported by R01 CA261898 (Y.S.) and R01 CA216354 (Y.Y. and J.Z.) from the National Cancer Institute at the National Institutes of Health and the American Lebanese Syrian Associated Charities in Memphis, Tennessee.

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Authors and Affiliations

  1. St. Jude Children’s Research Hospital, Memphis, TN, USA

    Yadav Sapkota, Stephanie B. Dixon, Carmen L. Wilson, Zhaoming Wang, Jinghui Zhang, Gregory T. Armstrong, Leslie L. Robison, Melissa M. Hudson, Angela Delaney & Yutaka Yasui

  2. School of Public Health, University of Alberta, Edmonton, AB, Canada

    Weiyu Qiu

  3. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Wendy Leisenring & Eric J. Chow

  4. University of Alabama at Birmingham, Birmingham, AL, USA

    Smita Bhatia

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  1. Yadav Sapkota
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Contributions

Y.S. and Y.Y. designed the study. S.B.D., C.L.W., Z.W., J.Z., W.L., E.J.C., S.B., G.T.A., L.L.R., M.M.H. and A.D. assisted in, or provided support for, data collection and recruitment of study participants. Y.S. and Y.Y. developed the statistical analysis plan. Y.S., W.Q. and Y.Y. performed the statistical analyses. Y.S. drafted the manuscript. All authors contributed to data interpretation and manuscript revision and approved the final version for publication.

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Correspondence to Yadav Sapkota or Yutaka Yasui.

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Nature Medicine thanks Struan Grant and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Table 1 Demographic and clinical characteristics of African American survivors of childhood cancer in the St. Jude Lifetime Cohort
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Extended Data Table 2 Distribution of childhood cancer diagnoses by BMI categories among European American survivors in the St. Jude Lifetime Cohort and the Childhood Cancer Survivor Study
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Extended Data Table 3 Distribution of childhood cancer diagnoses by BMI categories among African American survivors in the St. Jude Lifetime Cohort
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Extended Data Table 4 Multivariable polytomous logistic regression model for obesity among European American survivors of childhood cancer in the St. Jude Lifetime Cohort, using the GRSrare_v2 based on the same 12 rare/low-frequency variants used for the analysis in the Childhood Cancer Survivor Study
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Extended Data Table 5 AUC estimates for predicting overweight or higher, obesity or higher and severe obesity among European American and African American survivors
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Extended Data Table 6 AUC estimates for predicting overweight or higher, obesity or higher and severe obesity among survivors of childhood cancer, further including age at cancer diagnosis in the prediction models
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Extended Data Table 7 Multivariable polytomous logistic regression model for obesity among European American survivors of childhood cancer in the St. Jude Lifetime Cohort, further including the genetic risk score for waist-to-hip ratio adjusted for BMI
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Extended Data Table 8 Multivariable polytomous logistic regression model for obesity among European American survivors of childhood cancer in the St. Jude Lifetime Cohort, further including the genetic risk score for childhood BMI
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Extended Data Table 9 Multivariable polytomous logistic regression model for obesity among European American survivors of childhood cancer in the St. Jude Lifetime Cohort, further including the genetic risk score for childhood obesity
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Extended Data Table 10 Multivariable polytomous logistic regression model for obesity among European American survivors of childhood cancer in the St. Jude Lifetime Cohort, further including the genetic risk scores for waist-to-hip ratio adjusted for BMI, childhood BMI and childhood obesity
Full size table

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Sapkota, Y., Qiu, W., Dixon, S.B. et al. Genetic risk score enhances the risk prediction of severe obesity in adult survivors of childhood cancer. Nat Med 28, 1590–1598 (2022). https://doi.org/10.1038/s41591-022-01902-3

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