Evidence map›Paper›PMID 39959277›Full record

ArticleWorld journal of diabetes2025

Association between body mass index and lumbar spine volumetric bone mineral density in diabetic and non-diabetic patients.

Fang Lv, Xiao-Ling Cai, Xiu-Ying Zhang, Xiang-Hai Zhou, Xue-Yao Han, Yu-Feng Li, Li-Nong Ji

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In one paragraph

Article in World journal of diabetes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Fang LvDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xiao-Ling CaiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xiu-Ying ZhangDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xiang-Hai ZhouDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xue-Yao HanDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Yu-Feng LiDepartment of Endocrinology, Beijing Friendship Hospital Pinggu Campus, Beijing 101200, China.
Li-Nong JiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China. jiln@bjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe association between body mass index (BMI) and bone mineral density (BMD) has shown inconsistent results, varying by sex and skeletal site. Despite normal or elevated bone mass, individuals with type 2 diabetes have an increased risk of hip and vertebral fractures.

aimTo assess lumbar spine trabecular volumetric BMD (vBMD) across different BMI categories in individuals with and without diabetes.

methodsThis cross-sectional study included 966 men over 50 years old and 1001 postmenopausal women from the Pinggu Metabolic Disease Study. The vBMD of lumbar vertebrae 2 through 4 was measured using quantitative computed tomography. Total adipose tissue, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and lumbar skeletal muscle area were also quantified.

resultsIn men with obesity (

conclusionThe relationship between BMI and trabecular vBMD differs in individuals with and without diabetes. Overweight and obese men with diabetes exhibit higher vBMD.

Indexed as

Body mass indexDiabetesLumbar spine volumetric bone mineral densityObesity and overweightQuantitative computed tomography

Identifiers

PMID39959277
PMCPMC11718480

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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.