Evidence map›Paper›PMID 38989799›Full record

ArticleJournal of diabetes investigation2024

A novel predictive model for optimizing diabetes screening in older adults.

Yushuang Lin, Ya Shen, Rongbo He, Quan Wang, Hongbin Deng, Shujunyan Cheng, Yu Liu, Yimin Li, Xiang Lu, Zhengkai Shen

Abstract read
In one paragraph

Article in Journal of diabetes investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

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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

1 citing paper in PubMed.

  1. Review
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.

Yushuang Lin *Department of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.ORCID https://orcid.org/0000-0002-5009-4151
Ya Shen *Department of Integrated Service and Management, Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China.
Rongbo HeDepartment of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Quan WangDepartment of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Hongbin DengMedical School of Nanjing University, Nanjing, Jiangsu Province, China.
Shujunyan ChengHealth Management Center, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Yu LiuDepartment of Endocrinology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Yimin LiDepartment of Cardiology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Xiang LuDepartment of Geriatrics, Sir Run Run Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.
Zhengkai ShenDepartment of Integrated Service and Management, Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels. MATERIALS AND

methodsThis study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis.

resultsNine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility.

conclusionsWe provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

Indexed as

Blood GlucoseMass ScreeningNomogramsAgedAged, 80 and overDiabetes MellitusDiabetes Mellitus, Type 2FemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsROC CurveBlood GlucoseBlood glucoseDiabetes mellitusNomograms

Identifiers

PMID38989799
PMCPMC11442884

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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.