Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery
The location and type of adipose tissue is an important factor in metabolic syndrome. A database of picture archiving and communication system (PACS) derived abdominal computerized tomography (CT) images from a large health care provider, Geisinger, was used for large-scale research of the relations...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Obesity |
| Online Access: | http://dx.doi.org/10.1155/2018/3253096 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832563986200002560 |
|---|---|
| author | Elliot D. K. Cha Yogasudha Veturi Chirag Agarwal Aalpen Patel Mohammad R. Arbabshirani Sarah A. Pendergrass |
| author_facet | Elliot D. K. Cha Yogasudha Veturi Chirag Agarwal Aalpen Patel Mohammad R. Arbabshirani Sarah A. Pendergrass |
| author_sort | Elliot D. K. Cha |
| collection | DOAJ |
| description | The location and type of adipose tissue is an important factor in metabolic syndrome. A database of picture archiving and communication system (PACS) derived abdominal computerized tomography (CT) images from a large health care provider, Geisinger, was used for large-scale research of the relationship of volume of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with obesity-related diseases and clinical laboratory measures. Using a “greedy snake” algorithm and 2,545 CT images from the Geisinger PACS, we measured levels of VAT, SAT, total adipose tissue (TAT), and adipose ratio volumes. Sex-combined and sex-stratified association testing was done between adipose measures and 1,233 disease diagnoses and 37 clinical laboratory measures. A genome-wide association study (GWAS) for adipose measures was also performed. SAT was strongly associated with obesity and morbid obesity. VAT levels were strongly associated with type 2 diabetes-related diagnoses (p = 1.5 × 10−58), obstructive sleep apnea (p = 7.7 × 10−37), high-density lipoprotein (HDL) levels (p = 1.42 × 10−36), triglyceride levels (p = 1.44 × 10−43), and white blood cell (WBC) counts (p = 7.37 × 10−9). Sex-stratified tests revealed stronger associations among women, indicating the increased influence of VAT on obesity-related disease outcomes particularly among women. The GWAS identified some suggestive associations. This study supports the utility of pursuing future clinical and genetic discoveries with existing imaging data-derived adipose tissue measures deployed at a larger scale. |
| format | Article |
| id | doaj-art-59bf9ca374b64a89b0e471337d8a2165 |
| institution | Kabale University |
| issn | 2090-0708 2090-0716 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Obesity |
| spelling | doaj-art-59bf9ca374b64a89b0e471337d8a21652025-02-03T01:12:08ZengWileyJournal of Obesity2090-07082090-07162018-01-01201810.1155/2018/32530963253096Using Adipose Measures from Health Care Provider-Based Imaging Data for DiscoveryElliot D. K. Cha0Yogasudha Veturi1Chirag Agarwal2Aalpen Patel3Mohammad R. Arbabshirani4Sarah A. Pendergrass5Biomedical and Translational Informatics Institute, Geisinger Research, Danville, PA, USABiomedical and Translational Informatics Institute, Geisinger Research, Danville, PA, USADepartment of Imaging Science and Innovation, Geisinger Research, Danville, PA, USADepartment of Imaging Science and Innovation, Geisinger Research, Danville, PA, USABiomedical and Translational Informatics Institute, Geisinger Research, Danville, PA, USABiomedical and Translational Informatics Institute, Geisinger Research, Danville, PA, USAThe location and type of adipose tissue is an important factor in metabolic syndrome. A database of picture archiving and communication system (PACS) derived abdominal computerized tomography (CT) images from a large health care provider, Geisinger, was used for large-scale research of the relationship of volume of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with obesity-related diseases and clinical laboratory measures. Using a “greedy snake” algorithm and 2,545 CT images from the Geisinger PACS, we measured levels of VAT, SAT, total adipose tissue (TAT), and adipose ratio volumes. Sex-combined and sex-stratified association testing was done between adipose measures and 1,233 disease diagnoses and 37 clinical laboratory measures. A genome-wide association study (GWAS) for adipose measures was also performed. SAT was strongly associated with obesity and morbid obesity. VAT levels were strongly associated with type 2 diabetes-related diagnoses (p = 1.5 × 10−58), obstructive sleep apnea (p = 7.7 × 10−37), high-density lipoprotein (HDL) levels (p = 1.42 × 10−36), triglyceride levels (p = 1.44 × 10−43), and white blood cell (WBC) counts (p = 7.37 × 10−9). Sex-stratified tests revealed stronger associations among women, indicating the increased influence of VAT on obesity-related disease outcomes particularly among women. The GWAS identified some suggestive associations. This study supports the utility of pursuing future clinical and genetic discoveries with existing imaging data-derived adipose tissue measures deployed at a larger scale.http://dx.doi.org/10.1155/2018/3253096 |
| spellingShingle | Elliot D. K. Cha Yogasudha Veturi Chirag Agarwal Aalpen Patel Mohammad R. Arbabshirani Sarah A. Pendergrass Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery Journal of Obesity |
| title | Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery |
| title_full | Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery |
| title_fullStr | Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery |
| title_full_unstemmed | Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery |
| title_short | Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery |
| title_sort | using adipose measures from health care provider based imaging data for discovery |
| url | http://dx.doi.org/10.1155/2018/3253096 |
| work_keys_str_mv | AT elliotdkcha usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery AT yogasudhaveturi usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery AT chiragagarwal usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery AT aalpenpatel usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery AT mohammadrarbabshirani usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery AT sarahapendergrass usingadiposemeasuresfromhealthcareproviderbasedimagingdatafordiscovery |