In one paragraphArticle in JMIR diabetes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
12 authors.
Hanze Du *Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0003-0067-4884 Shuaihua Song *Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0009-0000-9852-2226 Xiao ZhaiKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0002-0648-067X Junchi LiuKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0009-0002-9567-0869 Yue JiangKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0003-0547-2595 Yaoda HuDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0000-0002-3713-3208 Huijing HeDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0000-0002-2922-752X Huijuan ZhuKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0001-5172-6870 Shi ChenKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0002-6473-5511 Guangliang ShanDepartment of Epidemiology and Statistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.ORCID http://orcid.org/0000-0002-4535-8941 Hui PanKey Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifu Garden, Beijing, China, 86 (010) 69155685.ORCID http://orcid.org/0000-0003-2413-0646 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
Background: The triglyceride-glucose (TyG) index has demonstrated promising predictive capability in clinical studies, but its distribution characteristics across different age and sex groups in the Chinese population have not been fully characterized. Objective: This study aimed to describe the population-based distribution of the TyG index. Methods: A total of 4621 participants aged 20-80 years from the China National Health Survey were included in this study. The TyG index was calculated from fasting blood glucose and triglycerides. The age- and sex-specific distribution values of the TyG index were obtained using the percentiles method. Results: Males had higher BMI (23.32, SD 2.63 vs 22.72, SD 2.60 kg/m2), triglyceride (133.78, SD 78.07 vs 111.58, SD 61.18 mg/dL), fasting glucose (97.05, SD 11.95 vs 94.34, SD 10.46 mg/dL), and TyG index values (8.63, SD 0.54 vs 8.45, SD 0.50) than females. The TyG index of males reached its peak value at approximately 40 years of age. The lower limit percentile values for females exceeded that of males around age 50. After the age of 60, the upper limit of the distribution values in females was higher than in males. Conclusions: This study characterized the age- and sex-specific distribution of the TyG index among Chinese adults aged 20-80 years. The results of this study contribute to a more precise assessment of glucolipid metabolism.
Indexed as
age- and sex-specific distributionchina national health surveychinese adultsglucolipid metabolismtriglyceride-glucose index
Identifiers
PMID42201745
PMCPMC13215049
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