J-curve relation between daytime nap duration and type 2 diabetes or metabolic syndrome: A dose-response meta-analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12-02

AUTHORS

Tomohide Yamada, Nobuhiro Shojima, Toshimasa Yamauchi, Takashi Kadowaki

ABSTRACT

Adequate sleep is important for good health, but it is not always easy to achieve because of social factors. Daytime napping is widely prevalent around the world. We performed a meta-analysis to investigate the association between napping (or excessive daytime sleepiness: EDS) and the risk of type 2 diabetes or metabolic syndrome, and to quantify the potential dose-response relation using cubic spline models. Electronic databases were searched for articles published up to 2016, with 288,883 Asian and Western subjects. Pooled analysis revealed that a long nap (≥60 min/day) and EDS were each significantly associated with an increased risk of type 2 diabetes versus no nap or no EDS (odds ratio 1.46 (95% CI 1.23–1.74, p < 0.01) for a long nap and 2.00 (1.58–2.53) for EDS). In contrast, a short nap (<60 min/day) was not associated with diabetes (p = 0.75). Dose-response meta-analysis showed a J-curve relation between nap time and the risk of diabetes or metabolic syndrome, with no effect of napping up to about 40 minutes/day, followed by a sharp increase in risk at longer nap times. In summary, longer napping is associated with an increased risk of metabolic disease. Further studies are needed to confirm the benefit of a short nap. More... »

PAGES

38075

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep38075

DOI

http://dx.doi.org/10.1038/srep38075

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043530669

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27909305


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": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Diabetes Mellitus, Type 2", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolic Syndrome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Odds Ratio", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk Factors", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sleep", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yamada", 
        "givenName": "Tomohide", 
        "id": "sg:person.01177257735.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01177257735.35"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shojima", 
        "givenName": "Nobuhiro", 
        "id": "sg:person.01257321237.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257321237.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yamauchi", 
        "givenName": "Toshimasa", 
        "id": "sg:person.012027672272.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012027672272.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kadowaki", 
        "givenName": "Takashi", 
        "id": "sg:person.01353472360.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353472360.41"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/srep21480", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014170842", 
          "https://doi.org/10.1038/srep21480"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12889-015-1521-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022054398", 
          "https://doi.org/10.1186/s12889-015-1521-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11892-009-0082-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048718019", 
          "https://doi.org/10.1007/s11892-009-0082-x"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-12-02", 
    "datePublishedReg": "2016-12-02", 
    "description": "Adequate sleep is important for good health, but it is not always easy to achieve because of social factors. Daytime napping is widely prevalent around the world. We performed a meta-analysis to investigate the association between napping (or excessive daytime sleepiness: EDS) and the risk of type 2 diabetes or metabolic syndrome, and to quantify the potential dose-response relation using cubic spline models. Electronic databases were searched for articles published up to 2016, with 288,883 Asian and Western subjects. Pooled analysis revealed that a long nap (\u226560\u2009min/day) and EDS were each significantly associated with an increased risk of type 2 diabetes versus no nap or no EDS (odds ratio 1.46 (95% CI 1.23\u20131.74, p\u2009<\u20090.01) for a long nap and 2.00 (1.58\u20132.53) for EDS). In contrast, a short nap (<60\u2009min/day) was not associated with diabetes (p\u2009=\u20090.75). Dose-response meta-analysis showed a J-curve relation between nap time and the risk of diabetes or metabolic syndrome, with no effect of napping up to about 40\u2009minutes/day, followed by a sharp increase in risk at longer nap times. In summary, longer napping is associated with an increased risk of metabolic disease. Further studies are needed to confirm the benefit of a short nap.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/srep38075", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6129108", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5868540", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5928148", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "keywords": [
      "type 2 diabetes", 
      "metabolic syndrome", 
      "J-curve relation", 
      "nap time", 
      "potential dose-response relation", 
      "risk of diabetes", 
      "minutes/day", 
      "short nap", 
      "daytime nap duration", 
      "dose-response relation", 
      "cubic spline models", 
      "pooled analysis", 
      "nap duration", 
      "daytime napping", 
      "diabetes", 
      "metabolic diseases", 
      "adequate sleep", 
      "electronic databases", 
      "syndrome", 
      "good health", 
      "longer naps", 
      "Further studies", 
      "risk", 
      "napping", 
      "nap", 
      "spline model", 
      "Western subjects", 
      "social factors", 
      "disease", 
      "sleep", 
      "association", 
      "health", 
      "duration", 
      "subjects", 
      "ED", 
      "days", 
      "factors", 
      "database", 
      "summary", 
      "study", 
      "benefits", 
      "time", 
      "increase", 
      "contrast", 
      "effect", 
      "sharp increase", 
      "relation", 
      "analysis", 
      "article", 
      "model", 
      "world", 
      "EDS"
    ], 
    "name": "J-curve relation between daytime nap duration and type 2 diabetes or metabolic syndrome: A dose-response meta-analysis", 
    "pagination": "38075", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043530669"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/srep38075"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "27909305"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/srep38075", 
      "https://app.dimensions.ai/details/publication/pub.1043530669"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:42", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_702.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/srep38075"
  }
]
 

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.10.1038/srep38075'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/srep38075'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/srep38075'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/srep38075'


 

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

183 TRIPLES      21 PREDICATES      87 URIs      76 LITERALS      15 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/srep38075 schema:about N5db67a160eb04982ae05f2f78621de58
2 N8b827c06226644ffa72d4eb7a6723a01
3 Nacda341d97a4422ebb01f03f328cbb9f
4 Nae5058087a394bf8afbdda853f8921e1
5 Nc189becb7d5b43349832a743c22bf325
6 Nc729c970061c4f5cbd38632e59e182b9
7 Nc837c2da084042c1b0e7a0240a6dce0c
8 Ne25f2be19e2c494d9c614b3adec29cab
9 anzsrc-for:11
10 anzsrc-for:1117
11 schema:author Ndb50c5fb061e4675b55d4a1363c97796
12 schema:citation sg:pub.10.1007/s11892-009-0082-x
13 sg:pub.10.1038/srep21480
14 sg:pub.10.1186/s12889-015-1521-z
15 schema:datePublished 2016-12-02
16 schema:datePublishedReg 2016-12-02
17 schema:description Adequate sleep is important for good health, but it is not always easy to achieve because of social factors. Daytime napping is widely prevalent around the world. We performed a meta-analysis to investigate the association between napping (or excessive daytime sleepiness: EDS) and the risk of type 2 diabetes or metabolic syndrome, and to quantify the potential dose-response relation using cubic spline models. Electronic databases were searched for articles published up to 2016, with 288,883 Asian and Western subjects. Pooled analysis revealed that a long nap (≥60 min/day) and EDS were each significantly associated with an increased risk of type 2 diabetes versus no nap or no EDS (odds ratio 1.46 (95% CI 1.23–1.74, p < 0.01) for a long nap and 2.00 (1.58–2.53) for EDS). In contrast, a short nap (<60 min/day) was not associated with diabetes (p = 0.75). Dose-response meta-analysis showed a J-curve relation between nap time and the risk of diabetes or metabolic syndrome, with no effect of napping up to about 40 minutes/day, followed by a sharp increase in risk at longer nap times. In summary, longer napping is associated with an increased risk of metabolic disease. Further studies are needed to confirm the benefit of a short nap.
18 schema:genre article
19 schema:isAccessibleForFree true
20 schema:isPartOf N908e12c2c477490a9173851522c579c9
21 Nf2ca8c3271a54ee4a407489d18707ce9
22 sg:journal.1045337
23 schema:keywords ED
24 EDS
25 Further studies
26 J-curve relation
27 Western subjects
28 adequate sleep
29 analysis
30 article
31 association
32 benefits
33 contrast
34 cubic spline models
35 database
36 days
37 daytime nap duration
38 daytime napping
39 diabetes
40 disease
41 dose-response relation
42 duration
43 effect
44 electronic databases
45 factors
46 good health
47 health
48 increase
49 longer naps
50 metabolic diseases
51 metabolic syndrome
52 minutes/day
53 model
54 nap
55 nap duration
56 nap time
57 napping
58 pooled analysis
59 potential dose-response relation
60 relation
61 risk
62 risk of diabetes
63 sharp increase
64 short nap
65 sleep
66 social factors
67 spline model
68 study
69 subjects
70 summary
71 syndrome
72 time
73 type 2 diabetes
74 world
75 schema:name J-curve relation between daytime nap duration and type 2 diabetes or metabolic syndrome: A dose-response meta-analysis
76 schema:pagination 38075
77 schema:productId N2b2329c9dc5c45b5b04ad46036b05a35
78 N857a9321194f4ecd938e39f0ee64016a
79 Ndd6830985e1349cf9a0efcaea3897e04
80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043530669
81 https://doi.org/10.1038/srep38075
82 schema:sdDatePublished 2022-10-01T06:42
83 schema:sdLicense https://scigraph.springernature.com/explorer/license/
84 schema:sdPublisher N82728ae751144090acbc09d41421b1b3
85 schema:url https://doi.org/10.1038/srep38075
86 sgo:license sg:explorer/license/
87 sgo:sdDataset articles
88 rdf:type schema:ScholarlyArticle
89 N2b2329c9dc5c45b5b04ad46036b05a35 schema:name doi
90 schema:value 10.1038/srep38075
91 rdf:type schema:PropertyValue
92 N422bd6ccd4de4f0f9061c5da9d56fa2f rdf:first sg:person.01353472360.41
93 rdf:rest rdf:nil
94 N5db67a160eb04982ae05f2f78621de58 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Odds Ratio
96 rdf:type schema:DefinedTerm
97 N6fb6f6f0b70c4f07bba4a00bc3ec5376 rdf:first sg:person.012027672272.37
98 rdf:rest N422bd6ccd4de4f0f9061c5da9d56fa2f
99 N82728ae751144090acbc09d41421b1b3 schema:name Springer Nature - SN SciGraph project
100 rdf:type schema:Organization
101 N857a9321194f4ecd938e39f0ee64016a schema:name pubmed_id
102 schema:value 27909305
103 rdf:type schema:PropertyValue
104 N8b827c06226644ffa72d4eb7a6723a01 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Risk Factors
106 rdf:type schema:DefinedTerm
107 N908e12c2c477490a9173851522c579c9 schema:volumeNumber 6
108 rdf:type schema:PublicationVolume
109 Nacda341d97a4422ebb01f03f328cbb9f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Diabetes Mellitus, Type 2
111 rdf:type schema:DefinedTerm
112 Nae5058087a394bf8afbdda853f8921e1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
113 schema:name Humans
114 rdf:type schema:DefinedTerm
115 Nc189becb7d5b43349832a743c22bf325 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Sleep
117 rdf:type schema:DefinedTerm
118 Nc47b0354aaa3459ca34aa56e1f7adba0 rdf:first sg:person.01257321237.22
119 rdf:rest N6fb6f6f0b70c4f07bba4a00bc3ec5376
120 Nc729c970061c4f5cbd38632e59e182b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Middle Aged
122 rdf:type schema:DefinedTerm
123 Nc837c2da084042c1b0e7a0240a6dce0c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Aged
125 rdf:type schema:DefinedTerm
126 Ndb50c5fb061e4675b55d4a1363c97796 rdf:first sg:person.01177257735.35
127 rdf:rest Nc47b0354aaa3459ca34aa56e1f7adba0
128 Ndd6830985e1349cf9a0efcaea3897e04 schema:name dimensions_id
129 schema:value pub.1043530669
130 rdf:type schema:PropertyValue
131 Ne25f2be19e2c494d9c614b3adec29cab schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
132 schema:name Metabolic Syndrome
133 rdf:type schema:DefinedTerm
134 Nf2ca8c3271a54ee4a407489d18707ce9 schema:issueNumber 1
135 rdf:type schema:PublicationIssue
136 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
137 schema:name Medical and Health Sciences
138 rdf:type schema:DefinedTerm
139 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
140 schema:name Public Health and Health Services
141 rdf:type schema:DefinedTerm
142 sg:grant.5868540 http://pending.schema.org/fundedItem sg:pub.10.1038/srep38075
143 rdf:type schema:MonetaryGrant
144 sg:grant.5928148 http://pending.schema.org/fundedItem sg:pub.10.1038/srep38075
145 rdf:type schema:MonetaryGrant
146 sg:grant.6129108 http://pending.schema.org/fundedItem sg:pub.10.1038/srep38075
147 rdf:type schema:MonetaryGrant
148 sg:journal.1045337 schema:issn 2045-2322
149 schema:name Scientific Reports
150 schema:publisher Springer Nature
151 rdf:type schema:Periodical
152 sg:person.01177257735.35 schema:affiliation grid-institutes:grid.26999.3d
153 schema:familyName Yamada
154 schema:givenName Tomohide
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01177257735.35
156 rdf:type schema:Person
157 sg:person.012027672272.37 schema:affiliation grid-institutes:grid.26999.3d
158 schema:familyName Yamauchi
159 schema:givenName Toshimasa
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012027672272.37
161 rdf:type schema:Person
162 sg:person.01257321237.22 schema:affiliation grid-institutes:grid.26999.3d
163 schema:familyName Shojima
164 schema:givenName Nobuhiro
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257321237.22
166 rdf:type schema:Person
167 sg:person.01353472360.41 schema:affiliation grid-institutes:grid.26999.3d
168 schema:familyName Kadowaki
169 schema:givenName Takashi
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01353472360.41
171 rdf:type schema:Person
172 sg:pub.10.1007/s11892-009-0082-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1048718019
173 https://doi.org/10.1007/s11892-009-0082-x
174 rdf:type schema:CreativeWork
175 sg:pub.10.1038/srep21480 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014170842
176 https://doi.org/10.1038/srep21480
177 rdf:type schema:CreativeWork
178 sg:pub.10.1186/s12889-015-1521-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1022054398
179 https://doi.org/10.1186/s12889-015-1521-z
180 rdf:type schema:CreativeWork
181 grid-institutes:grid.26999.3d schema:alternateName Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan
182 schema:name Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Japan
183 rdf:type schema:Organization
 




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


...