Evidence map›Paper›PMID 36424968›Full record

ArticleFrontiers in public health2022

Association between trajectories of fasting plasma glucose and risk of osteoporosis in non-diabetic and diabetic populations.

Ping Wang, Yuanfeng Zhang, Ruiqi Shan, Jing Wu, Sailimai Man, Yuhan Deng, Jun Lv, Xiaona Wang, Jianchun Yin, Yi Ning and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

4 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

12 authors at 2 institutions in 1 country.

Ping WangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Yuanfeng ZhangDepartment of Medical Innovation, China Science and Technology Development Center for Chinese Medicine, Beijing, China.
Ruiqi ShanDepartment of Evidence Based Medicine, Meinian Institute of Health, Beijing, China.
Jing WuDepartment of Evidence Based Medicine, Meinian Institute of Health, Beijing, China.
Sailimai ManDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Yuhan DengDepartment of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Xiaona WangBeijing MJ Health Screening Center Co., Ltd., Beijing, China.
Jianchun YinBeijing MJ Health Screening Center Co., Ltd., Beijing, China.
Yi NingDepartment of Evidence Based Medicine, Meinian Institute of Health, Beijing, China.
Bo WangDepartment of Evidence Based Medicine, Meinian Institute of Health, Beijing, China.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
Peking University · CNHainan Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Previous studies based on a single measure of fasting plasma glucose (FPG) showed an inconsistent conclusion about the association between FPG and osteoporosis risk. Not accounting for time-varying and cumulative average of FPG over time could bias the true relation between FPG and osteoporosis. Our study aims to investigate the association between the trajectories of FPG and osteoporosis risk for non-diabetic and diabetic populations. Methods: A total of 18,313 participants who attended physical examinations during 2008-2018 were included. They were free of osteoporosis at their first physical examination and followed until their last physical examination before December 31, 2018. We recorded their incidence of osteoporosis and at least three FPG values during follow-up. Their longitudinal FPG trajectories were identified by the latent class growth analysis model based on the changes in FPG. Multivariable logistic regression models were used to analyze the association between the trajectories of FPG and osteoporosis diagnosed in the follow-up physical examination in both non-diabetics and diabetics. Results: There were 752 incident osteoporosis among 16,966 non-diabetic participants, and 57 incident osteoporosis among 1,347 diabetic participants. Among non-diabetics, the elevated-increasing FPG trajectory was negatively associated with osteoporosis risk in women (odds ratio (OR), 0.62; 95% confidence interval (CI), 0.43-0.88). Premenopausal women with elevated-increasing FPG trajectory had lower osteoporosis risk than those women with normal-stable FPG trajectory (OR, 0.41; 95% CI, 0.20-0.88), while this association was insignificant in postmenopausal women. Among diabetics, those whose longitudinal FPG is kept at a very high level had the highest risk of osteoporosis (OR, 3.09; 95% CI, 1.16-8.22), whereas those whose FPG starts with the high level and keeps on increasing did not exhibit a significantly increased risk (OR, 1.75; 95% CI, 0.81-3.76) compared with those who keep stable moderate-high level of FPG, except in men (OR, 2.49; 95% CI, 1.02-6.12). Conclusion: Distinct trajectories of FPG are associated with differential risk of osteoporosis in non-diabetic and diabetic populations. Controlling a proper FPG level in different populations is necessary for osteoporosis prevention.

Indexed as

Diabetes MellitusOsteoporosisBlood GlucoseFastingFemaleHumansIncidenceMaleBlood Glucosediabetesfasting plasma glucose levelsosteoporosisphysical examinationtrajectory analyses

Identifiers

PMID36424968
PMCPMC9679646
OpenAlexW4308591128

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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.