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Record W2967036405 · doi:10.1161/jaha.118.010870

Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese

2019· article· en· W2967036405 on OpenAlexaff
Jia Liu, Lap Ah Tse, Zhiguang Liu, Sumathy Rangarajan, Bo Hu, Yin Lu, Darryl P. Leong, Wei Li, Bing Liu, Chun‐Ming Chen, Jin Guo, Hongye Zhang, Hui Chen, Jian Bo, Jian Li, Juan Li, Jun Yang, Kean Wang, Li Zhang, Qing Deng, Ren Bing, Tao Chen, Tao Xu, Wei Wang, Wenhua Zhao, Xiaohong Chang, Xiaoru Cheng, Xinye He, Xixin Hou, Xingyu Wang, Xiulin Bai, Zhao Xiuwen, Xu Liu, Xuan Jia, Yang Wang, Yi Sun, Yi Zhai, Di Chen, Hui Jin, Jiwen Tian, Yumin Ma, Yindong Li, Chao He, Kai You, Songjian Zhang, Xiuzhen Tian, Xu Xu, Jinling Di, Mei Wang, Qiang Zhou, Aiying Han, Minzhi Cao, Weiping Jiang, Deren Qiang, Jing Qin, Shan Qian, Suyi Shi, Yihong Zhou, Zhengrong Liu, Ming Wan, Jinhua Tang, Yongzhen Mo, Rongwen Bian, Qinglin Lou, Lihua Hu, Shuwei Xiong, Yan Zhong, Ning Li, Xincheng Tang, Shuli Ye, Chunyi Li, Yujin Li, Qiuyang Wang, Xiaoli Fu, Baoxia Guo, Huilian Feng, Lihui Xu, Haibin Ma, Ruiqi Wu, Yali Wang, Hongze Liu, Yurong Ma, Bo Yuan, Qian Zhao, Guofan Xu, Hui He, Jiankang Liu, Xin Wang, Ming Chen, Wenqing Deng, Zhendong Liu, Hua Zhang, Shangwen Sun, Shujian Wang, Yingkin Zhao, Yutao Diao, Xuezheng Shi, Chuanrui Wei, Jufang Wang, Guoqin Liu, Cuiying Wu, Guilan Ma, Wei Hua, Junying Wang, Xiongfei Bao, Yue Tang, Yahong Zhi, Ailing Wang, Huijuan Wang, Jianna Liu, Q H Liu, Rong Wang, Aideer Aili, Ayoufumiti Wula, A Bu-la, Dongmei Yang, Wen Qian, Yize Xiao, Qingping Shi, Ying Shao, Kehua Li, Wuba Bai, Huaxing Liu, Shunyun Yang

Bibliographic record

VenueJournal of the American Heart Association · 2019
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersWellcome Trust
KeywordsMedicineWaistDyslipidemiaBody mass indexAnthropometryInternal medicineWaist-to-height ratioCircumferenceReceiver operating characteristicPopulationWaist–hip ratioLogistic regressionObesityEnvironmental health

Abstract

fetched live from OpenAlex

Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases (CVDs) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity-related anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE-China (Prospective Urban and Rural Epidemiology) study in China. All participants' anthropometric parameters, CVDs, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity-related anthropometric indices to the cardiometabolic risk factors and CVDs. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVDs in all models (P<0.001). Compared with other anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, and A Body Shape Index), waist-to-height ratio had significantly higher areas under the curve (AUCs) for predicting dyslipidemia (AUCs: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia (AUCs: 0.595, sensitivity: 60%, specificity: 45%), and CVDs (AUCs: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure (AUCs: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVDs than that of any other traditional obesity-related indices. Conclusions Waist-to-height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVDs, while waist circumference had a better prediction for abnormal blood pressure.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.278
Teacher spread0.266 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations116
Published2019
Admission routes1
Has abstractyes

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