Evidence map›Paper›PMID 30820851›Full record

ArticleSleep & breathing = Schlaf & Atmung2019

The association between obstructive sleep apnea syndrome and metabolic syndrome: a confirmatory factor analysis.

Fan Wang, Xiaolu Xiong, Huajun Xu, Hengye Huang, Yue Shi, Xinyi Li, Yingjun Qian, Jianyin Zou, Hongliang Yi, Jian Guan and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

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

  1. Pooled it
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  6. Application Value of STOP-Bang Questionnaire in Predicting Abnormal Metabolites.Diabetes, metabolic syndrome and obesity : targets and therapy · 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

11 authors.

Fan Wang *Department of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.
Xiaolu Xiong *Department of Endocrinology, Drum Tower Hospital, Affiliated to Nanjing University Medical School, Nanjing, 210008, China.
Huajun XuDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China. sunnydayxu2010@163.com.
Hengye HuangDepartment of Epidemiology, School of Public Health, Shanghai Jiao Tong University, 225 South Chongqing Road, Shanghai, 200020, China. huanghy1107@qq.com.
Yue ShiDepartment of Epidemiology, School of Public Health, Shanghai Jiao Tong University, 225 South Chongqing Road, Shanghai, 200020, China.
Xinyi LiDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.
Yingjun QianDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.
Jianyin ZouDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.
Hongliang YiDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.
Jian GuanDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China. guanjian0606@sina.com.
Shankai YinDepartment of Otolaryngology Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Yishan Road 600, Shanghai, 200233, China.

Funding

National Key R&D Program of China 2017YFC0112500National Natural Science Foundation of China 81770987, 81700896, 81701306, 81770988School of Medicine, Shanghai Jiao Tong University DLY201502Shanghai Municipal Education Commission 2017-01-07-00-02-E00047Shanghai Sailing Program 17YF1414300Shanghai Shen Kang Hospital Development Center SHDC 12014240Shanghai Shen Kang Hospital Development Center SHDC12015101
6 · The paper itself

Abstract

backgroundGrowing evidence suggests an independent relationship between obstructive sleep apnea syndrome (OSAS) and metabolic syndrome (MS). Patients with OSAS always show clustering of metabolic components. However, the understanding of interplay between OSAS and metabolic components is still lacking.

methodsParticipants were consecutively enrolled from our sleep center during the period 2009-2013. Anthropometric variables, metabolic indicators, and sleep parameters were collected from all participants. The factor structure for MS in OSAS and non-OSAS was examined by confirmatory factor analysis.

resultsThe OSAS and non-OSAS demonstrated clustering of metabolic components. MS in patients with OSAS was strongly associated with insulin resistance (standardized factor loading = 0.93, p < 0.001), obesity (loading = 0.92, p < 0.001), and the lipid profile (loading = 0.72, p < 0.001). Furthermore, insulin resistance was correlated with obesity and lipid profile (r = 0.86, p < 0.001; r = 0.68, p < 0.001, respectively). Obesity and lipid profile were also highly correlated in OSAS (r = 0.66, p < 0.001). In non-OSAS, MS was strongly associated with insulin resistance, obesity, and lipid profile (loading = 0.95, p < 0.001; loading = 0.74, p < 0.001; loading = 0.68, p < 0.001, respectively). Insulin resistance was most strongly associated with fasting insulin (loading = 0.65, p < 0.001). Lipid profile was most strongly associated with TG (loading = 0.88, p < 0.001). Obesity was most strongly associated with BMI (loading = 0.80, p < 0.001).

conclusionsOSAS is more prone to show clustering of metabolic components compared with non-OSAS. In particular, insulin resistance, obesity, and the lipid profile were independently and strongly correlated with MS in OSAS.

Indexed as

Insulin ResistanceAdultBody Mass IndexFemaleHumansLeptinMaleMetabolic SyndromeMiddle AgedObesityPolysomnographyRisk FactorsSleep Apnea, ObstructiveLeptinConfirmatory factor analysisInsulin resistanceMetabolic syndromeObesityObstructive sleep apnea syndrome

Identifiers

PMID30820851

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.