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| PMID |
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| TITLE |
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| Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study. |
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| ABSTRACT |
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| BACKGROUND |
NlmCategory: 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 |
NlmCategory: METHODS |
| 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. 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 (h ) 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 |
NlmCategory: RESULTS |
| 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. 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 (h ) 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. 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 ). 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 ). The adult weight trajectories were heritable (using 5% weight change as the cutoff: h 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 (OR 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 |
NlmCategory: CONCLUSIONS |
| 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. 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 (h ) 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. 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 ). 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 ). The adult weight trajectories were heritable (using 5% weight change as the cutoff: h 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 (OR 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). 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 |
NlmCategory: 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. 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 (h ) 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. 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 ). 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 ). The adult weight trajectories were heritable (using 5% weight change as the cutoff: h 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 (OR 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). 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. Klarman Family Foundation, US National Institute of Mental Health (NIMH). |
| Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved. |
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| DATE PUBLISHED |
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| HISTORY |
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| PUBSTATUS |
PUBSTATUSDATE |
| received |
2021/10/04 |
| revised |
2022/04/19 |
| accepted |
2022/05/06 |
| pubmed |
2022/07/03 06:00 |
| medline |
2022/07/27 06:00 |
| entrez |
2022/07/02 22:06 |
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| AUTHORS |
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| NAME |
COLLECTIVENAME |
LASTNAME |
FORENAME |
INITIALS |
AFFILIATION |
AFFILIATIONINFO |
| Xu J |
|
Xu |
Jiayi |
J |
|
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. |
| Johnson JS |
|
Johnson |
Jessica S |
JS |
|
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. |
| Signer R |
|
Signer |
Rebecca |
R |
|
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. |
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Eating Disorders Working Group of the Psychiatric Genomics Consortium |
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| Birgegård A |
|
Birgegård |
Andreas |
A |
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Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. |
| Jordan J |
|
Jordan |
Jennifer |
J |
|
Department of Psychological Medicine, University of Otago, Christchurch, New Zealand; Canterbury District Health Board, Christchurch, New Zealand. |
| Kennedy MA |
|
Kennedy |
Martin A |
MA |
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Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand. |
| Landén M |
|
Landén |
Mikael |
M |
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Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden. |
| Maguire SL |
|
Maguire |
Sarah L |
SL |
|
InsideOut Institute, Charles Perkins Centre, The University of Sydney, Camperdown, Sydney, NSW, Australia. |
| Martin NG |
|
Martin |
Nicholas G |
NG |
|
Genetics & Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. |
| Mortensen PB |
|
Mortensen |
Preben Bo |
PB |
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The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark; National Centre for Register-Based Research, Aarhus BSS, and Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark. |
| Petersen LV |
|
Petersen |
Liselotte V |
LV |
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The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark; National Centre for Register-Based Research, Aarhus BSS, and Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark. |
| Thornton LM |
|
Thornton |
Laura M |
LM |
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Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. |
| Bulik CM |
|
Bulik |
Cynthia M |
CM |
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Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. |
| Huckins LM |
|
Huckins |
Laura M |
LM |
|
Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mental Illness Research, Education and Clinical Centers, James J Peters Department of Veterans Affairs Medical Center, Bronx, NY, USA. Electronic address: laura.huckins@mssm.edu. |
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| INVESTIGATORS |
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| JOURNAL |
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| VOLUME: 4 |
| ISSUE: 8 |
| TITLE: The Lancet. Digital health |
| ISOABBREVIATION: Lancet Digit Health |
| YEAR: 2022 |
| MONTH: Aug |
| DAY: |
| MEDLINEDATE: |
| SEASON: |
| CITEDMEDIUM: Internet |
| ISSN: 2589-7500 |
| ISSNTYPE: Electronic |
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| MEDLINE JOURNAL |
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| MEDLINETA: Lancet Digit Health |
| COUNTRY: England |
| ISSNLINKING: 2589-7500 |
| NLMUNIQUEID: 101751302 |
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| PUBLICATION TYPE |
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| PUBLICATIONTYPE TEXT |
| Journal Article |
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| COMMENTS AND CORRECTIONS |
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| GRANTS |
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| GENERAL NOTE |
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| KEYWORDS |
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| MESH HEADINGS |
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| DESCRIPTORNAME |
QUALIFIERNAME |
| Adult |
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| Biological Specimen Banks |
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| Body-Weight Trajectory |
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| Electronic Health Records |
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| Genome-Wide Association Study |
methods |
| Humans |
methods |
| Multifactorial Inheritance |
methods |
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| SUPPLEMENTARY MESH |
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| GENE SYMBOLS |
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| CHEMICALS |
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| OTHER ID's |
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