Evidence map›Paper›PMID 36733801›Full record

ArticleFrontiers in endocrinology2022

Incidence and long-term specific mortality trends of metabolic syndrome in the United States.

Weiya Li, Xinfan Qiu, Huan Ma, Qingshan Geng

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Weiya LiGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Xinfan QiuGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Huan MaGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Qingshan GengGuangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Metabolic syndrome (MetS) is extremely prevalent and related to severe diseases and death. This study aims to investigate the incidence and mortality trends among MetS over the past few decades. The gender and age differences of MetS are also explored. Patients and methods: Adults with MetS were screened in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014. The mortality data were also acquired. Then we assessed the incidence and mortality trends of MetS in the United States. Results: Our study included 14171 participants with a mean age of 46.8 ± 19.3 years, of whom 7354 (51.9%) were women. Among them, 4789 participants were subsequently diagnosed with MetS. From 1999 to 2014, the overall trend of MetS incidence increased (from 27.6 to 32.3%; adjusted odds ratios [aOR], 1.71; 95% confidence interval [CI], 1.42-2.05; Conclusion: From 1999 to 2014, the incidence of MetS in U.S. adults significantly increased overall, while the mortality rate of MetS had a considerable downward trend. Both trends showed marked gender differences, being more prevalent and at lower risk in women compared with men. It is important to identify the factors that will curb the incidence of MetS and decrease mortality, especially in male patients.

Indexed as

Metabolic SyndromeAdultAgedFemaleHumansIncidenceMaleMiddle AgedNutrition SurveysProportional Hazards ModelsSex FactorsUnited Statesincidencemetabolic syndromemortalityNHANEStrend

Identifiers

PMID36733801
PMCPMC9886893

What OpenQuestion holds

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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.