Evidence map›Paper›PMID 38903624›Full record

ArticleFrontiers in nutrition2024

Association between weight-adjusted-waist index and cognitive decline in US elderly participants.

Jiaxin Li, Jian Sun, Yishuo Zhang, Bo Zhang, Liya Zhou

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–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

16 citing papers in PubMed.

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  10. [Association between weight-adjusted waist index and pain: A cross-sectional study].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025
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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

5 authors.

Jiaxin LiChangchun University of Chinese Medicine, Changchun, China.
Jian SunChangchun University of Chinese Medicine, Changchun, China.
Yishuo ZhangChangchun University of Chinese Medicine, Changchun, China.
Bo ZhangChangchun University of Chinese Medicine, Changchun, China.
Liya ZhouChangchun University of Chinese Medicine, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the association between the weight-adjusted-waist index (WWI) and cognitive decline in elderly Americans from 2011 to 2014. Methods: A cross-sectional study was conducted on 2,762 elderly participants from the National Health and Nutrition Examination (NHANES) between 2011 and 2014. WWI was calculated by dividing waist circumference (cm) by the square root of body weight (kg). Participants assessed their cognitive functions using tests such as the DSST, AFT, and CERAD W-L. In this research, multiple logistic regression, HIA, limited cubic spline (RCS), and threshold effect analysis methods were utilized to explore the relationship between cognitive decline and WWI. Results: The study involved 2,762 participants aged 60 years and older, comprising 1,353 males (49%) and 1,409 females (51%), with a median age of 69.3 years (standard deviation = 6.7). The analysis revealed that the risk of cognitive decline was positively associated with the WWI. Fully adjusted models indicated significant correlations with the CERAD W-L [odds ratio (OR) = 1.24, 95% confidence interval (CI) = 1.06-1.46, Conclusion: Our data have shown a significant positive association between the WWI and a higher risk of cognitive decline in older Americans, with a J-shaped non-linear relationship between WWI and DSST. In addition, our findings indicate that WWI was associated with greater cognitive decline than other markers of obesity.

Indexed as

cognitive functioncross-sectional studyNHANESobesityweight-adjusted-waist index

Identifiers

PMID38903624
PMCPMC11187255

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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.