Evidence map›Paper›PMID 26629099›Full record

ArticleInternational journal of clinical and experimental medicine2015

Associations between frequency of coffee consumption and osteoporosis in Chinese postmenopausal women.

Pei Yang, Xiu-Zhen Zhang, Keqin Zhang, Zihui Tang

Abstract read
In one paragraph

Article in International journal of clinical and experimental medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 21% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 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

4 authors at 2 institutions in 1 country.

Pei YangDepartment of Tumor Internal Medicine, The Tumor Hospital Affiliated to Central South China University Changsha, Hunan, China.
Xiu-Zhen ZhangDepartment of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine China.
Keqin ZhangDepartment of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine China.
Zihui TangDepartment of Endocrinology and Metabolism, Shanghai Tongji Hospital, Tongji University School of Medicine China.
Tongji University · CNHunan Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study was aimed at estimating the associations between coffee intake and osteoporosis (OP) in Chinese postmenopausal women.

methodsWe conducted a large-scale, community-based, cross-sectional study to investigate the associations by using self-report questionnaire to access frequency of coffee intake. The total of 1817 participants was available to data analysis in this study. Multiple regression models controlling for confounding factors to include frequency of coffee intake variable were performed to investigate the relationships for OP.

resultsPositive correlations between frequency of meat food intake and T-score were reported (β = 0.216, P value < 0.001). Multiple regression analysis indicated that the frequency of meat food intake was significantly associated with OP (P < 0.05 for model 1 and model 2). The postmenopausal women with high frequency of meat food intake had a lower prevalence of OP.

conclusionThe findings indicated that frequency of coffee intake was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women not preferring coffee habits.

Indexed as

associationChinese postmenopausal womencoffee intakeFrequencyosteoporosis

Identifiers

PMID26629099
PMCPMC4658988
OpenAlexW2194038525

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.