Evidence map›Paper›PMID 38562131›Full record

ArticleJournal of the Endocrine Society2024

MODY Probability Calculator Is Suitable for MODY Screening in China: A Population-based Study.

Jing Zhao, Yan Chen, Fuhui Ma, Hua Shu, Li Zheng, Yang Liu, Xia Li, Tao Xu, Zhiguang Zhou, Kaixin Zhou

Open access · goldAbstract read
In one paragraph

Article in Journal of the Endocrine Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.0field-weighted citation impact, top 5% of its field
1 · What the graph read from it

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 registry

The 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 literature

Who cites it

9 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Jing ZhaoCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China.ORCID https://orcid.org/0009-0003-6344-8830
Yan ChenNational Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
Fuhui MaDepartment of Endocrinology and Metabolic Diseases, People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Research Center for Diabetes, Urumqi, 830001, China.
Hua ShuDepartment of Endocrinology and Metabolism, Tianjin Medical University General Hospital, Heping District, Tianjin, 300052, China.
Li ZhengKey Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
Yang LiuCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China.
Xia LiNational Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.ORCID https://orcid.org/0000-0001-8665-7983
Tao XuGuangzhou Laboratory, Guangdong Province, Guangdong 510005, China.
Zhiguang ZhouNational Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.ORCID https://orcid.org/0000-0002-0374-1838
Kaixin ZhouGuangzhou Laboratory, Guangdong Province, Guangdong 510005, China.ORCID https://orcid.org/0000-0002-4751-7248
Central South University · CNGuangzhou Experimental Station · CNUniversity of Chinese Academy of Sciences · CNChinese Academy of Sciences · CNPeople's Hospital of Xinjiang Uygur Autonomous Region · CNTianjin Medical University General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Context: Selecting appropriate individuals for genetic testing is essential due to the optimal treatment for maturity-onset diabetes of the young (MODY). However, how to effectively screen for MODY in China remains unclear. Objective: To validate the performance of current screening strategies in selecting patients with MODY based on a nationwide type 2 diabetes cohort. Methods: A panel of 14 MODY genes was analyzed from 1911 type 2 diabetes patients who were ages 15 to 35 years. Variants were evaluated according to the American College of Medical Genetics and Genomics guidelines. Based on this cohort, we simulated the 2 most frequently used screening strategies, including the traditional MODY criteria and the MODY probability calculator (MPC), to assess their ability to select patients with MODY. Results: From a total of 1911 participants, 42 participants harbored pathogenic/likely pathogenic variants. The performance of the traditional criteria was sensitivity: 19.0%, specificity: 72.9%, positive predictive value (PPV): 1.6%, and missing rate: 81.0%. The optimal cut-off for MPC was 40.7%. Based on this cut-off value, the performance was sensitivity: 54.8%, specificity: 81.0%, PPV: 6.1%, and missing rate: 45.2%. Moreover, hemoglobin A1c, insulin treatment, and family history of diabetes have poor discrimination between MODY and young-onset type 2 diabetes. Conclusion: The MPC is better than traditional criteria in terms of both sensitivity and PPV. To ensure more MODY patients benefit from optimal treatment, we therefore suggest that routine genetic testing be performed on all type 2 diabetes patients who are between the ages of 15 and35 years and have MPC probability value over 40.7%.

Indexed as

genetic testingMODY probability calculatorMODY screeningnext-generation sequencingsensitivity

Identifiers

PMID38562131
PMCPMC10983078
OpenAlexW4392764265

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.