Nonlinear relationship between serum uric acid and body mass index: a cross-sectional study of a general population in coastal China View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-11-25

AUTHORS

Hui Zhou, Zhen Liu, Zhong Chao, Yeqing Chao, Lidan Ma, Xiaoyu Cheng, Yangang Wang, Changgui Li, Ying Chen

ABSTRACT

BackgroundConflicting evidence exists on the relationship between body mass index (BMI) and serum uric acid (SUA). Therefore, we aimed to evaluate the SUA–BMI relationship in a large-scale epidemiological survey in coastal China.MethodsThis survey was conducted among the general population in the coastal region of China from September 2014 to January 2015. SUA Levels were measured by the automatic Sysmex Chemix-180 biochemical analyzer.ResultsA total of 6098 men (BMI: 24.58 ± 3.74 kg/m2) and 7941 women (24.56 ± 3.64 kg/m2) were included in this study. A stronger positive BMI-SUA association was found for men than women (all P-values < 0.05). The piecewise linear spline models indicated a U-shaped relationship of SUA-BMI association for both men and women; and the lowest turning points were at 19.12 kg/m2 for men and 21.3 kg/m2 for women. When BMIs were lower than the nadir point, each 1 kg/m2 increase in BMI related to a 7.74-fold (95% CI − 14.73, − 0.75) reduction for men and 2.70-fold reduction (− 4.47, − 0.94) for women in SUA levels. Once the BMI was higher than the nadir point, each 1 kg/m2 increase in BMI was related to a 5.10-fold (4.44, 5.77) increment for men and 3.93-fold increment (3.42, 4.43) for women in SUA levels. The regression coefficient differences between the two stages were 12.84 (5.66, 20.03) for men and 6.63 (4.65, 8.61) for women.ConclusionsA U-shaped relationship between BMI and SUA was found for both men and women; the association was stronger for men than women. More... »

PAGES

389

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12967-019-02142-9

DOI

http://dx.doi.org/10.1186/s12967-019-02142-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1122878407

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31767029


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Body Mass Index", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "China", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cross-Sectional Studies", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Ecosystem", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Multivariate Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Nonlinear Dynamics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Regression Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Uric Acid", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Heze Medical College, Heze, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.452430.4", 
          "name": [
            "Heze Medical College, Heze, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhou", 
        "givenName": "Hui", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Zhen", 
        "id": "sg:person.0636602311.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636602311.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chao", 
        "givenName": "Zhong", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Heze Medical College, Heze, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.452430.4", 
          "name": [
            "Heze Medical College, Heze, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chao", 
        "givenName": "Yeqing", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Lidan", 
        "id": "sg:person.016416130433.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416130433.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cheng", 
        "givenName": "Xiaoyu", 
        "id": "sg:person.013430226433.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013430226433.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wang", 
        "givenName": "Yangang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Changgui", 
        "id": "sg:person.0676564741.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676564741.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China", 
          "id": "http://www.grid.ac/institutes/grid.412521.1", 
          "name": [
            "Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Ying", 
        "id": "sg:person.0670070367.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0670070367.45"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/srep40009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034129300", 
          "https://doi.org/10.1038/srep40009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12944-018-0934-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110812506", 
          "https://doi.org/10.1186/s12944-018-0934-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12199-015-0473-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016553343", 
          "https://doi.org/10.1007/s12199-015-0473-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11926-017-0688-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091354043", 
          "https://doi.org/10.1007/s11926-017-0688-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/ar2519", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049059733", 
          "https://doi.org/10.1186/ar2519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-02640-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085537773", 
          "https://doi.org/10.1038/s41598-017-02640-0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-11-25", 
    "datePublishedReg": "2019-11-25", 
    "description": "BackgroundConflicting evidence exists on the relationship between body mass index (BMI) and serum uric acid (SUA). Therefore, we aimed to evaluate the SUA\u2013BMI relationship in a large-scale epidemiological survey in coastal China.MethodsThis survey was conducted among the general population in the coastal region of China from September 2014 to January 2015. SUA Levels were measured by the automatic Sysmex Chemix-180 biochemical analyzer.ResultsA total of 6098 men (BMI: 24.58\u2009\u00b1\u20093.74\u00a0kg/m2) and 7941 women (24.56\u2009\u00b1\u20093.64\u00a0kg/m2) were included in this study. A stronger positive BMI-SUA association was found for men than women (all P-values\u2009<\u20090.05). The piecewise linear spline models indicated a U-shaped relationship of SUA-BMI association for both men and women; and the lowest turning points were at 19.12\u00a0kg/m2 for men and 21.3\u00a0kg/m2 for women. When BMIs were lower than the nadir point, each 1\u00a0kg/m2 increase in BMI related to a 7.74-fold (95% CI \u2212\u200914.73, \u2212\u20090.75) reduction for men and 2.70-fold reduction (\u2212\u20094.47, \u2212\u20090.94) for women in SUA levels. Once the BMI was higher than the nadir point, each 1\u00a0kg/m2 increase in BMI was related to a 5.10-fold (4.44, 5.77) increment for men and 3.93-fold increment (3.42, 4.43) for women in SUA levels. The regression coefficient differences between the two stages were 12.84 (5.66, 20.03) for men and 6.63 (4.65, 8.61) for women.ConclusionsA U-shaped relationship between BMI and SUA was found for both men and women; the association was stronger for men than women.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12967-019-02142-9", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8353586", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1032886", 
        "issn": [
          "1479-5876"
        ], 
        "name": "Journal of Translational Medicine", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "17"
      }
    ], 
    "keywords": [
      "body mass index", 
      "serum uric acid", 
      "SUA levels", 
      "mass index", 
      "general population", 
      "m2 increase", 
      "uric acid", 
      "cross-sectional study", 
      "large-scale epidemiological surveys", 
      "linear spline models", 
      "ResultsA total", 
      "MethodsThis survey", 
      "epidemiological survey", 
      "biochemical analyzer", 
      "women", 
      "men", 
      "association", 
      "spline model", 
      "population", 
      "levels", 
      "index", 
      "total", 
      "study", 
      "increase", 
      "reduction", 
      "m2", 
      "survey", 
      "acid", 
      "relationship", 
      "evidence", 
      "differences", 
      "stage", 
      "point", 
      "increment", 
      "China", 
      "region", 
      "nadir point", 
      "analyzer", 
      "model", 
      "nonlinear relationship", 
      "coefficient difference", 
      "coastal regions", 
      "coastal China"
    ], 
    "name": "Nonlinear relationship between serum uric acid and body mass index: a cross-sectional study of a general population in coastal China", 
    "pagination": "389", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1122878407"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12967-019-02142-9"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "31767029"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12967-019-02142-9", 
      "https://app.dimensions.ai/details/publication/pub.1122878407"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_817.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12967-019-02142-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12967-019-02142-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12967-019-02142-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12967-019-02142-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12967-019-02142-9'


 

This table displays all metadata directly associated to this object as RDF triples.

234 TRIPLES      21 PREDICATES      86 URIs      72 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12967-019-02142-9 schema:about N0c22fe4bfa9c4cf0b5010618f3f01ab5
2 N3a37d4cff09d470182c0fd34c9494660
3 N405a4b9f41ad4128a32cd39e87a5fe9b
4 N4fbd0fe38d1d4fc0be936244baf5e11c
5 N80ae7341b53b4827883711f83b538097
6 N84f6c5713dc742a1a914cb87be1d9ae2
7 N99a087ad12184acabf213d123f194c82
8 Nc397130c045f447db33a97b9ae3bec0a
9 Nd19d16c5f1c44ea0a79ec10240c20405
10 Nec757a4160ed4e73828d77a9dbe7a871
11 Nf68221630dbd4a619e933044b0edfa03
12 Nfcb542d982164536bbee0532295560ae
13 anzsrc-for:11
14 anzsrc-for:1117
15 schema:author N5ae12a61a16445769b80105fabb23774
16 schema:citation sg:pub.10.1007/s11926-017-0688-y
17 sg:pub.10.1007/s12199-015-0473-3
18 sg:pub.10.1038/s41598-017-02640-0
19 sg:pub.10.1038/srep40009
20 sg:pub.10.1186/ar2519
21 sg:pub.10.1186/s12944-018-0934-y
22 schema:datePublished 2019-11-25
23 schema:datePublishedReg 2019-11-25
24 schema:description BackgroundConflicting evidence exists on the relationship between body mass index (BMI) and serum uric acid (SUA). Therefore, we aimed to evaluate the SUA–BMI relationship in a large-scale epidemiological survey in coastal China.MethodsThis survey was conducted among the general population in the coastal region of China from September 2014 to January 2015. SUA Levels were measured by the automatic Sysmex Chemix-180 biochemical analyzer.ResultsA total of 6098 men (BMI: 24.58 ± 3.74 kg/m2) and 7941 women (24.56 ± 3.64 kg/m2) were included in this study. A stronger positive BMI-SUA association was found for men than women (all P-values < 0.05). The piecewise linear spline models indicated a U-shaped relationship of SUA-BMI association for both men and women; and the lowest turning points were at 19.12 kg/m2 for men and 21.3 kg/m2 for women. When BMIs were lower than the nadir point, each 1 kg/m2 increase in BMI related to a 7.74-fold (95% CI − 14.73, − 0.75) reduction for men and 2.70-fold reduction (− 4.47, − 0.94) for women in SUA levels. Once the BMI was higher than the nadir point, each 1 kg/m2 increase in BMI was related to a 5.10-fold (4.44, 5.77) increment for men and 3.93-fold increment (3.42, 4.43) for women in SUA levels. The regression coefficient differences between the two stages were 12.84 (5.66, 20.03) for men and 6.63 (4.65, 8.61) for women.ConclusionsA U-shaped relationship between BMI and SUA was found for both men and women; the association was stronger for men than women.
25 schema:genre article
26 schema:isAccessibleForFree true
27 schema:isPartOf N7043d4950ea040bca768dfcde0126689
28 N9e30fcba8e6946a1978f774beb0d70f5
29 sg:journal.1032886
30 schema:keywords China
31 MethodsThis survey
32 ResultsA total
33 SUA levels
34 acid
35 analyzer
36 association
37 biochemical analyzer
38 body mass index
39 coastal China
40 coastal regions
41 coefficient difference
42 cross-sectional study
43 differences
44 epidemiological survey
45 evidence
46 general population
47 increase
48 increment
49 index
50 large-scale epidemiological surveys
51 levels
52 linear spline models
53 m2
54 m2 increase
55 mass index
56 men
57 model
58 nadir point
59 nonlinear relationship
60 point
61 population
62 reduction
63 region
64 relationship
65 serum uric acid
66 spline model
67 stage
68 study
69 survey
70 total
71 uric acid
72 women
73 schema:name Nonlinear relationship between serum uric acid and body mass index: a cross-sectional study of a general population in coastal China
74 schema:pagination 389
75 schema:productId N1ba0dea675b448f0905056450fe81fbb
76 N3b5c357e64d44d9c9ef4391b0ca6200c
77 Ne03270e8b51643b9a0559f836a70b8e4
78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122878407
79 https://doi.org/10.1186/s12967-019-02142-9
80 schema:sdDatePublished 2022-11-24T21:05
81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
82 schema:sdPublisher Nb47452cb1bac4f92871daa828116664d
83 schema:url https://doi.org/10.1186/s12967-019-02142-9
84 sgo:license sg:explorer/license/
85 sgo:sdDataset articles
86 rdf:type schema:ScholarlyArticle
87 N00effbd33fa74b85892666c7d740d8c0 rdf:first sg:person.0676564741.16
88 rdf:rest Nb260826c37f5402c9b0ed035553ce1e4
89 N0c22fe4bfa9c4cf0b5010618f3f01ab5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Multivariate Analysis
91 rdf:type schema:DefinedTerm
92 N15e631bfc08244b889864c5862302db8 rdf:first sg:person.0636602311.22
93 rdf:rest N9be748ffbaad4c3daca192b04af0ca35
94 N1ba0dea675b448f0905056450fe81fbb schema:name doi
95 schema:value 10.1186/s12967-019-02142-9
96 rdf:type schema:PropertyValue
97 N2036c12f35744a39b274706c334e0b91 rdf:first N312bbff14470416d9e646dda2432bda3
98 rdf:rest N913c676ee06546e58aacc4018225ffa3
99 N312bbff14470416d9e646dda2432bda3 schema:affiliation grid-institutes:grid.452430.4
100 schema:familyName Chao
101 schema:givenName Yeqing
102 rdf:type schema:Person
103 N3a37d4cff09d470182c0fd34c9494660 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
104 schema:name Male
105 rdf:type schema:DefinedTerm
106 N3b5c357e64d44d9c9ef4391b0ca6200c schema:name pubmed_id
107 schema:value 31767029
108 rdf:type schema:PropertyValue
109 N405a4b9f41ad4128a32cd39e87a5fe9b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Humans
111 rdf:type schema:DefinedTerm
112 N4fbd0fe38d1d4fc0be936244baf5e11c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Cross-Sectional Studies
114 rdf:type schema:DefinedTerm
115 N5ae12a61a16445769b80105fabb23774 rdf:first N795b9600af44481e8d47d3ebee64adeb
116 rdf:rest N15e631bfc08244b889864c5862302db8
117 N6bd69c88ef9b48ea9c255deb426302e6 schema:affiliation grid-institutes:grid.412521.1
118 schema:familyName Chao
119 schema:givenName Zhong
120 rdf:type schema:Person
121 N7043d4950ea040bca768dfcde0126689 schema:issueNumber 1
122 rdf:type schema:PublicationIssue
123 N795b9600af44481e8d47d3ebee64adeb schema:affiliation grid-institutes:grid.452430.4
124 schema:familyName Zhou
125 schema:givenName Hui
126 rdf:type schema:Person
127 N80ae7341b53b4827883711f83b538097 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Female
129 rdf:type schema:DefinedTerm
130 N84f6c5713dc742a1a914cb87be1d9ae2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Uric Acid
132 rdf:type schema:DefinedTerm
133 N8d06aec5db8c476fb1b19a869ba24e33 schema:affiliation grid-institutes:grid.412521.1
134 schema:familyName Wang
135 schema:givenName Yangang
136 rdf:type schema:Person
137 N913c676ee06546e58aacc4018225ffa3 rdf:first sg:person.016416130433.46
138 rdf:rest Nab9018ae8f1742b7b1c46e4f102b9f6a
139 N99a087ad12184acabf213d123f194c82 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name China
141 rdf:type schema:DefinedTerm
142 N9be748ffbaad4c3daca192b04af0ca35 rdf:first N6bd69c88ef9b48ea9c255deb426302e6
143 rdf:rest N2036c12f35744a39b274706c334e0b91
144 N9e30fcba8e6946a1978f774beb0d70f5 schema:volumeNumber 17
145 rdf:type schema:PublicationVolume
146 N9ee0410a71dd4f978bb1e6ac7428adf5 rdf:first N8d06aec5db8c476fb1b19a869ba24e33
147 rdf:rest N00effbd33fa74b85892666c7d740d8c0
148 Nab9018ae8f1742b7b1c46e4f102b9f6a rdf:first sg:person.013430226433.74
149 rdf:rest N9ee0410a71dd4f978bb1e6ac7428adf5
150 Nb260826c37f5402c9b0ed035553ce1e4 rdf:first sg:person.0670070367.45
151 rdf:rest rdf:nil
152 Nb47452cb1bac4f92871daa828116664d schema:name Springer Nature - SN SciGraph project
153 rdf:type schema:Organization
154 Nc397130c045f447db33a97b9ae3bec0a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Regression Analysis
156 rdf:type schema:DefinedTerm
157 Nd19d16c5f1c44ea0a79ec10240c20405 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Ecosystem
159 rdf:type schema:DefinedTerm
160 Ne03270e8b51643b9a0559f836a70b8e4 schema:name dimensions_id
161 schema:value pub.1122878407
162 rdf:type schema:PropertyValue
163 Nec757a4160ed4e73828d77a9dbe7a871 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
164 schema:name Body Mass Index
165 rdf:type schema:DefinedTerm
166 Nf68221630dbd4a619e933044b0edfa03 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Middle Aged
168 rdf:type schema:DefinedTerm
169 Nfcb542d982164536bbee0532295560ae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
170 schema:name Nonlinear Dynamics
171 rdf:type schema:DefinedTerm
172 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
173 schema:name Medical and Health Sciences
174 rdf:type schema:DefinedTerm
175 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
176 schema:name Public Health and Health Services
177 rdf:type schema:DefinedTerm
178 sg:grant.8353586 http://pending.schema.org/fundedItem sg:pub.10.1186/s12967-019-02142-9
179 rdf:type schema:MonetaryGrant
180 sg:journal.1032886 schema:issn 1479-5876
181 schema:name Journal of Translational Medicine
182 schema:publisher Springer Nature
183 rdf:type schema:Periodical
184 sg:person.013430226433.74 schema:affiliation grid-institutes:grid.412521.1
185 schema:familyName Cheng
186 schema:givenName Xiaoyu
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013430226433.74
188 rdf:type schema:Person
189 sg:person.016416130433.46 schema:affiliation grid-institutes:grid.412521.1
190 schema:familyName Ma
191 schema:givenName Lidan
192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016416130433.46
193 rdf:type schema:Person
194 sg:person.0636602311.22 schema:affiliation grid-institutes:grid.412521.1
195 schema:familyName Liu
196 schema:givenName Zhen
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0636602311.22
198 rdf:type schema:Person
199 sg:person.0670070367.45 schema:affiliation grid-institutes:grid.412521.1
200 schema:familyName Chen
201 schema:givenName Ying
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0670070367.45
203 rdf:type schema:Person
204 sg:person.0676564741.16 schema:affiliation grid-institutes:grid.412521.1
205 schema:familyName Li
206 schema:givenName Changgui
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0676564741.16
208 rdf:type schema:Person
209 sg:pub.10.1007/s11926-017-0688-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1091354043
210 https://doi.org/10.1007/s11926-017-0688-y
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s12199-015-0473-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016553343
213 https://doi.org/10.1007/s12199-015-0473-3
214 rdf:type schema:CreativeWork
215 sg:pub.10.1038/s41598-017-02640-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085537773
216 https://doi.org/10.1038/s41598-017-02640-0
217 rdf:type schema:CreativeWork
218 sg:pub.10.1038/srep40009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034129300
219 https://doi.org/10.1038/srep40009
220 rdf:type schema:CreativeWork
221 sg:pub.10.1186/ar2519 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049059733
222 https://doi.org/10.1186/ar2519
223 rdf:type schema:CreativeWork
224 sg:pub.10.1186/s12944-018-0934-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1110812506
225 https://doi.org/10.1186/s12944-018-0934-y
226 rdf:type schema:CreativeWork
227 grid-institutes:grid.412521.1 schema:alternateName Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China
228 Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
229 schema:name Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, 266003, Qingdao, Shandong, China
230 Shandong Provincial Key Laboratory of Metabolic Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China
231 rdf:type schema:Organization
232 grid-institutes:grid.452430.4 schema:alternateName Heze Medical College, Heze, Shandong, China
233 schema:name Heze Medical College, Heze, Shandong, China
234 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...