TY - JOUR
T1 - Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank
T2 - a phenome-wide and polygenic risk study
AU - Eating Disorders Working Group of the Psychiatric Genomics Consortium
AU - Xu, Jiayi
AU - Johnson, Jessica S.
AU - Signer, Rebecca
AU - Birgegård, Andreas
AU - Jordan, Jennifer
AU - Kennedy, Martin A.
AU - Landén, Mikael
AU - Maguire, Sarah L.
AU - Martin, Nicholas G.
AU - Mortensen, Preben Bo
AU - Petersen, Liselotte V.
AU - Thornton, Laura M.
AU - Bulik, Cynthia M.
AU - Huckins, Laura M.
N1 - Funding Information:
JX, JSJ, RS, JJ, and LMH are supported by the Klarman Family Foundation. JX is supported by the Seaver Foundation. LMH is supported by the Seaver Centre for Autism Research and Treatment, NIMH (R01MH118278; R01MH124839) and the US National Institute of Environmental Health Sciences (R01ES033630). CMB, JJ, MAK, NGM, and LVP are supported by NIMH (R01MH120170). CMB and LVP are supported by the Lundbeck Foundation (R276–2018–4581). CMB is also supported by NIMH (R01MH124871; R01MH119084; R01MH118278), the Brain and Behavior Research Foundation Distinguished Investigator Grant, and the Swedish Research Council (Vetenskapsrådet, 538–2013–8864). LVP is supported by the Novo Nordisk Foundation (20OC0064993). ML is supported by the Swedish state, the Swedish Brain Foundation (FO2020–0261), and the Swedish Research Council (2018–02653). SLM is supported by the Warman Foundation. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai under award number S10OD018522 from the Office of Research Infrastructure of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Furthermore, this work was supported through the resources and staff expertise provided by the Charles Bronfman Institute for Personalized Medicine and the BioMe Biobank Program at the Icahn School of Medicine at Mount Sinai. We would also like to thank the participants in the UK Biobank and the scientists involved in the construction of the UK Biobank resource. The replication analyses in this study have been done using the UK Biobank Resource under Paul F O'Reilly's application (number 18177). Additionally, we would like to thank Shing Wan Choi in the Department of Genetics and Genomic Sciences at Icahn School of Medicine at Mount Sinai for his input on the PRSice software and PRS analyses, as well as the Huckins Lab members for their feedback on this project during lab meetings. Last, but not least, we would like to thank one of our co-authors, Jessica S Johnson, who kindly offered to create a beautiful cover image for this manuscript on weight trajectory.
Funding Information:
JX, JSJ, RS, JJ, and LMH are supported by the Klarman Family Foundation. JX is supported by the Seaver Foundation. LMH is supported by the Seaver Centre for Autism Research and Treatment, NIMH (R01MH118278; R01MH124839) and the US National Institute of Environmental Health Sciences (R01ES033630). CMB, JJ, MAK, NGM, and LVP are supported by NIMH (R01MH120170). CMB and LVP are supported by the Lundbeck Foundation (R276–2018–4581). CMB is also supported by NIMH (R01MH124871; R01MH119084; R01MH118278), the Brain and Behavior Research Foundation Distinguished Investigator Grant, and the Swedish Research Council (Vetenskapsrådet, 538–2013–8864). LVP is supported by the Novo Nordisk Foundation (20OC0064993). ML is supported by the Swedish state, the Swedish Brain Foundation (FO2020–0261), and the Swedish Research Council (2018–02653). SLM is supported by the Warman Foundation. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai under award number S10OD018522 from the Office of Research Infrastructure of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Furthermore, this work was supported through the resources and staff expertise provided by the Charles Bronfman Institute for Personalized Medicine and the BioMe Biobank Program at the Icahn School of Medicine at Mount Sinai. We would also like to thank the participants in the UK Biobank and the scientists involved in the construction of the UK Biobank resource. The replication analyses in this study have been done using the UK Biobank Resource under Paul F O'Reilly's application (number 18177). Additionally, we would like to thank Shing Wan Choi in the Department of Genetics and Genomic Sciences at Icahn School of Medicine at Mount Sinai for his input on the PRSice software and PRS analyses, as well as the Huckins Lab members for their feedback on this project during lab meetings. Last, but not least, we would like to thank one of our co-authors, Jessica S Johnson, who kindly offered to create a beautiful cover image for this manuscript on weight trajectory.
Publisher Copyright:
© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
PY - 2022/8
Y1 - 2022/8
N2 - Background: Weight trajectories might reflect individual health status. In this study, we aimed to examine the clinical and genetic associations of adult weight trajectories using electronic health records (EHRs) in the BioMe Biobank. Methods: We constructed four weight trajectories based on a-priori definitions of weight changes (5% or 10%) using annual weight in EHRs (stable weight, weight gain, weight loss, and weight cycle); the final weight dataset included 21 487 participants with 162 783 annual weight measures. To confirm accurate assignment of weight trajectories, we manually reviewed weight trajectory plots for 100 random individuals. We then did a hypothesis-free phenome-wide association study (PheWAS) to identify diseases associated with each weight trajectory. Next, we estimated the single-nucleotide polymorphism-based heritability (hSNP2) of weight trajectories using GCTA-GREML, and we did a hypothesis-driven analysis of anorexia nervosa and depression polygenic risk scores (PRS) on these weight trajectories, given both diseases are associated with weight changes. We extended our analyses to the UK Biobank to replicate findings from a patient population to a generally healthy population. Findings: We found high concordance between manually assigned weight trajectories and those assigned by the algorithm (accuracy ≥98%). Stable weight was consistently associated with lower disease risks among those passing Bonferroni-corrected p value in our PheWAS (p≤4·4 × 10–5). Additionally, we identified an association between depression and weight cycle (odds ratio [OR] 1·42, 95% CI 1·31–1·55, p≤7·7 × 10–16). The adult weight trajectories were heritable (using 5% weight change as the cutoff: hSNP2 of 2·1%, 95% CI 0·9–3·3, for stable weight; 4·1%, 1·4–6·8, for weight gain; 5·5%, 2·8–8·2, for weight loss; and 4·7%, 2·3–7·1%, for weight cycle). Anorexia nervosa PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (OR1SD 1·16, 95% CI 1·07–1·26, per 1 SD higher PRS, p=0·011), and the association was not attenuated by obesity PRS. No association was found between depression PRS and weight trajectories after permutation tests. All main findings were replicated in the UK Biobank (p<0·05). Interpretation: Our findings suggest the importance of considering weight from a longitudinal aspect for its association with health and highlight a crucial role of weight management during disease development and progression. Funding: Klarman Family Foundation, US National Institute of Mental Health (NIMH).
AB - Background: Weight trajectories might reflect individual health status. In this study, we aimed to examine the clinical and genetic associations of adult weight trajectories using electronic health records (EHRs) in the BioMe Biobank. Methods: We constructed four weight trajectories based on a-priori definitions of weight changes (5% or 10%) using annual weight in EHRs (stable weight, weight gain, weight loss, and weight cycle); the final weight dataset included 21 487 participants with 162 783 annual weight measures. To confirm accurate assignment of weight trajectories, we manually reviewed weight trajectory plots for 100 random individuals. We then did a hypothesis-free phenome-wide association study (PheWAS) to identify diseases associated with each weight trajectory. Next, we estimated the single-nucleotide polymorphism-based heritability (hSNP2) of weight trajectories using GCTA-GREML, and we did a hypothesis-driven analysis of anorexia nervosa and depression polygenic risk scores (PRS) on these weight trajectories, given both diseases are associated with weight changes. We extended our analyses to the UK Biobank to replicate findings from a patient population to a generally healthy population. Findings: We found high concordance between manually assigned weight trajectories and those assigned by the algorithm (accuracy ≥98%). Stable weight was consistently associated with lower disease risks among those passing Bonferroni-corrected p value in our PheWAS (p≤4·4 × 10–5). Additionally, we identified an association between depression and weight cycle (odds ratio [OR] 1·42, 95% CI 1·31–1·55, p≤7·7 × 10–16). The adult weight trajectories were heritable (using 5% weight change as the cutoff: hSNP2 of 2·1%, 95% CI 0·9–3·3, for stable weight; 4·1%, 1·4–6·8, for weight gain; 5·5%, 2·8–8·2, for weight loss; and 4·7%, 2·3–7·1%, for weight cycle). Anorexia nervosa PRS was positively associated with weight loss trajectory among individuals without eating disorder diagnoses (OR1SD 1·16, 95% CI 1·07–1·26, per 1 SD higher PRS, p=0·011), and the association was not attenuated by obesity PRS. No association was found between depression PRS and weight trajectories after permutation tests. All main findings were replicated in the UK Biobank (p<0·05). Interpretation: Our findings suggest the importance of considering weight from a longitudinal aspect for its association with health and highlight a crucial role of weight management during disease development and progression. Funding: Klarman Family Foundation, US National Institute of Mental Health (NIMH).
UR - http://www.scopus.com/inward/record.url?scp=85134736180&partnerID=8YFLogxK
U2 - 10.1016/S2589-7500(22)00099-1
DO - 10.1016/S2589-7500(22)00099-1
M3 - Article
C2 - 35780037
AN - SCOPUS:85134736180
VL - 4
SP - e604-e614
JO - The Lancet Digital Health
JF - The Lancet Digital Health
SN - 2589-7500
IS - 8
ER -