Evidence map›Paper›PMID 42740863›Full record

ArticlePreventive medicine reports2026

Risk factors for incident type 2 diabetes among adults with impaired fasting glucose: a comparison of three regression models accounting for competing mortality in Liuzhou, Guangxi, China.

Zhenzhen Lu, Chaoyun Li, Qi Li, Jun Lv, Canqing Yu, Dianjianyi Sun, Wei Mao

Abstract read
In one paragraph

Article in Preventive medicine reports, 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
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Zhenzhen LuGuangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning 530028, PR China.
Chaoyun LiGuangxi Zhuang Autonomous Region Health Statistics Information Center, Nanning 530028, PR China.
Qi LiSchool of Information and Management, Guangxi Medical University, Nanning 530021, PR China.
Jun LvDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, PR China.
Canqing YuDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, PR China.
Dianjianyi SunDepartment of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, PR China.
Wei MaoGuangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning 530028, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Impaired fasting glucose (IFG) is a prediabetic precursor of type 2 diabetes (T2DM). Few long-term cohorts with substantial competing mortality compare risk estimates from logistic, Cox and Fine-Gray regression. This study aimed to examine cross-model discrepancies and demonstrate model-dependent bias in IFG risk inference. Methods: We followed 913 Chinese IFG adults recruited in Liuzhou, Guangxi (2004-2008) for up to 20 years until 31 December 2024 (median 17.83 years). Three regression models were used to identify fully adjusted T2DM predictors. Results: Fasting glucose, body fat percentage and older age were consistent positive predictors; female sex was persistently protective. Body mass index (BMI) ≥28 kg/m Conclusions: Statistical model choice alters risk factor conclusions in IFG cohorts with high competing mortality. Joint interpretation of complementary regression methods is recommended.

Indexed as

Anthropometric biomarkersCompeting risk analysisCox regressionImpaired fasting glucoseLogistic regressionType 2 diabetes

Identifiers

PMID42740863
PMCPMC13572089

What OpenQuestion holds

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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.