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Record W3107557740 · doi:10.1016/j.metabol.2020.154456

CT-derived abdominal adiposity: Distributions and better predictive ability than BMI in a nationwide study of 59,429 adults in China

2020· article· en· W3107557740 on OpenAlex
Qiang Zeng, Ling Wang, Sheng-Yong Dong, Xiaojuan Zha, Limei Ran, Yongli Li, Shuang Chen, Jianbo Gao, Shaolin Li, Yong Lü, Yuqin Zhang, Xigang Xiao, Yuehua Li, Xiao Ma, Xiangyang Gong, Wei Chen, Ying‐Ying Yang, Xia Du, Bairu Chen, Yinru Lv, Yan Wu, Guobin Hong, Yaling Pan, Jun Jiao, Yan Yan, Huijuan Qi, Jian Zhai, Kai Li, Kaiping Zhao, Jing Wu, Shiwei Liu, Glen M. Blake, Haihong Fu, Xiaoxia Fu, Zhiping Guo, Isabelle Lemieux, Jean‐Pierre Després, Xiaoguang Cheng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetabolism · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversité Laval
FundersNatural Science Foundation of Beijing Municipality
KeywordsChinaMedicineDemographyInternal medicineGerontologyGeography

Abstract

fetched live from OpenAlex

BackgroundAlthough abdominal adiposity is associated with an altered cardiometabolic risk profile, the specific contribution of abdominal adipose tissue distribution remains not fully understood. Computed tomography (CT) is a well-established and precise method to measure abdominal adipose tissue distribution. The present study investigated abdominal adiposity assessed by CT in a large-scale Chinese population.MethodA total of 59,429 adults who underwent a low dose chest CT for lung cancer screening at one of 13 health checkup centers throughout China were evaluated. Abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas were measured at the center of the 2nd lumbar vertebra with Mindways quantitative CT software using the existing CT dataset without any additional radiation exposure. The ratio of visceral to total adipose tissue (TAT) areas (VAT/TAT ratio) was calculated and expressed as a percentage. Anthropometric indices including body mass index (BMI) and waist circumference were also obtained.ResultsBMI, waist circumference, VAT area, SAT area, and the VAT/TAT ratio were 25.0 ± 3.0 kg/m2, 90 ± 8 cm, 194 ± 77 cm2, 85 ± 41 cm2, and 69.5 ± 9.1%, respectively, in men and 23.3 ± 3.1 kg/m2, 79 ± 8 cm, 120 ± 57 cm2, 123 ± 53 cm2, and 48.9 ± 9.7% in women. With increasing age, VAT area and the VAT/TAT ratio increased in both sexes whereas SAT area decreased in men (P < 0.001 for all). After adjustment for BMI and waist circumference, older individuals showed higher VAT area and higher VAT/TAT ratio than younger subjects (P < 0.001 for all). Adjusted VAT areas in participants aged 75 or older was 45 cm2 (95% confidence interval [CI]: 41 cm2, 50 cm2) higher in men and 43 cm2 (95% CI: 37 cm2, 49 cm2) higher in women compared with participants aged 31–44 years. Additionally, differences in VAT area across age groups increased as BMI or waist circumference increased. VAT and SAT areas, but not the VAT/TAT ratio, were positively associated with BMI and waist circumference in every age group.ConclusionIn a nationwide study conducted in China, distributions of CT-derived measures of visceral and subcutaneous adiposity were found to vary significantly between sex and age groups. Our study also revealed that the proportion of VAT (an important driver of cardiometabolic risk) could not be predicted from BMI in a Chinese population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.232
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it