Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 25Coffee research and impactsBone health and osteoporosis researchVitamin D Research Studies

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Association between caffeine intake and lumbar spine bone mineral density in adults aged 20–49: A cross-sectional study.

Open access
Wang, Gaoxiang, Fang, Ze-Bin, Liu, De-Liang, Chu, Shu-Fang, Li, Hui-Lin, Zhao, Heng-Xia·2022·Frontiers in Endocrinology

Abstract

Background: Many epidemiological studies have investigated the connection between coffee intake and bone mineral density (BMD), but the results are controversial. This study aimed to assess the association between caffeine consumption and lumbar BMD in adults aged 20–49. Methods: From a cross-sectional study based on a large sample of the National Health and Nutrition Examination Survey 2011–2018. After controlling for confounders, the weighted multivariate linear regression model was created and stratified by age, gender, and race for subgroup analysis. In addition, we simultaneously stratified analysis by age and sex and divided caffeine intake into quartiles to assess the association between coffee intake and BMD. Results: Caffeine intake was not significantly linked with lumbar BMD in this study of 7041 adults. In subgroup studies stratified by age, there was a significant correlation between lumbar BMD and caffeine consumption in participants aged 30–39 and 40–49. In females, there was a positive correlation between lumbar BMD and coffee consumption stratified by gender. When evaluated by race, the association between lumbar BMD and caffeine intake was independent of race. Consequently, when stratifying for age, sex, and coffee intake quartiles, a significant positive correlation was discovered between the fourth coffee intake quartile and lumbar BMD in females aged 30–39. In addition, a negative correlation was discovered between coffee consumption and lumbar BMD in males aged 40–49. Conclusions: Our research indicates that drinking coffee may benefit 30–39 women’s lumbar BMD, but it may adversely affect men aged 40–49.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fendo.2022.1008275

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Flowchart of participants selection.
Figure 1 Flowchart of participants selection.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 28 references

Cited by

See all 25

Metadata

Authors
Wang, G., Fang, Z.-B., Liu, D.-L., Chu, S.-F., Li, H.-L., & Zhao, H.-X.
Affiliations
  • Nanjing University of Chinese Medicine (CN)
  • Guangzhou University of Chinese Medicine (CN)
  • Shenzhen Traditional Chinese Medicine Hospital (CN)
Corresponding author
  • Chu, Shu-Fang
  • Li, Hui-Lin
Venue
Frontiers in Endocrinology
Published
2022-10-17
Volume
13
Pages
1008275-1008275
ISSN
1664-2392 (ISSN-L)
OpenAlex
W4306698757
Cited by
23
References
28
Citations by year
2026: 1 · 2025: 9 · 2024: 8 · 2023: 5
FWCI
3.29
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (82104759)
  • Natural Science Foundation of Guangdong Province (NO.2019A1515110108)
  • Shenzhen Municipal Science and Technology Innovation Council (JCYJ20180302173821841, JCYJ20170817094838619)
Topics
  • Health Sciences › Medicine › Pharmacology (medical) › Coffee research and impacts
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone health and osteoporosis research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Vitamin D Research Studies
Keywords
caffeine consumption, coffee consumption, lumbar spine bone mineral density, bone health, sex differences, age differences, racial differences, young and middle-aged adults
MeSH terms
Adult, Caffeine, Coffee, Cross-Sectional Studies, Female, Humans, Male, Nutrition Surveys, Absorptiometry, Photon, Bone Density

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.