Evidence map›Paper›PMID 37265697›Full record

ArticleFrontiers in endocrinology2023

Relationship between lipid accumulation product and new-onset diabetes in the Japanese population: a retrospective cohort study.

Ting Liu, Weilin Lu, Xiaofang Zhao, Tianci Yao, Bei Song, Haohui Fan, Guangyu Gao, Chengyun Liu

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In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 8% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Ting LiuDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Weilin LuDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaofang ZhaoDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tianci YaoDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bei SongDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Haohui FanDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Guangyu GaoDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chengyun LiuDepartment of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Huazhong University of Science and Technology · CNUnion Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetes has become a global public health problem. Obesity has been established as a risk factor for diabetes. However, it remains unclear which of the obesity indicators (BMI, WC, WhtR, ABSI, BRI, LAP, VAI) is more appropriate for monitoring diabetes. Therefore, the objective of this investigation is to compare the strength of the association of these indicators and diabetes and reveal the relationship between LAP and diabetes. Methods: 15,252 people took part in this research. LAP was quartered and COX proportional risk model was applied to explore the relationship between LAP and new-onset diabetes. Smooth curve fitting was employed to investigate the non-linear link between LAP and diabetes mellitus. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the aforementioned indicators for diabetes. Results: After adjusting for confounding factors, multiple linear regression analysis showed that each unit increase in LAP was associated with a 76.8% increase in the risk of developing diabetes (HR=1.768, 95% CI: 1.139 to 2.746, P=0.011). In addition, LAP predicted new-onset diabetes better than other indicators, and the AUC was the largest [HR: 0.713, 95% CI: 0.6806-0.7454, P<0.001, in women; HR: 0.7922, 95% CI: 0.7396-0.8447; P<0.001, in men]. When LAP was used as a lone predictor, its AUC area was largest both men and women. However, after adding classical predictors (FPG, HbA1c, SBP, exercise, age) to the model, the LAP is better than the ABSI, but not better than the other indicators when compared in pairs. Conclusions: High levels of LAP correlate very strongly with diabetes and are an important risk factor for diabetes, especially in women, those with fatty liver and current smokers. LAP was superior to other indicators when screening for diabetes susceptibility using a single indicator of obesity, both in men and in women. However, when obesity indicators were added to the model together with classical predictors, LAP did not show a significant advantage over other indicators, except ABSI.

Indexed as

Diabetes MellitusLipid Accumulation ProductAnthropometryBody Mass IndexEast Asian PeopleFemaleHumansMaleObesityRetrospective Studiesdiabeteslipid accumulation productnonlinearityobesityretrospective

Identifiers

PMID37265697
PMCPMC10230034
OpenAlexW4376875516

What Socratic holds

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LicenceCC BY
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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 Socratic graph.