Screening for diabetes with HbA1c: Test performance of HbA1c compared to fasting plasma glucose among Chinese, Malay and Indian community ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Wei-Yen Lim, Stefan Ma, Derrick Heng, E. Shyong Tai, Chin Meng Khoo, Tze Ping Loh

ABSTRACT

The prevalence of diabetes in Singapore is high. Screening to facilitate early detection and intervention has been shown to be cost-effective. Current clinical practice guidelines in Singapore recommend screening with fasting plasma glucose (FPG), followed by an oral glucose tolerance test (OGTT) in those with FPG 6.1-6.9 mmol/L. Glycated haemoglobin A1c (HbA1c) has robust stability at ambient temperature, and can be performed on non-fasted capillary blood samples, making it an attractive potential alternative for screening. However, limitations of HbA1c include differential performance in different races, and its performance as a screening test has not been well characterized in Asian populations. This study compares HbA1c and FPG as diabetes screening modalities in 3540 community-dwelling Singapore residents of Chinese, Malay and Indian race to detect diabetes mellitus diagnosed based on blood glucose (FPG ≥ 7.0 mmol/L, 2 hr OGTT ≥ 11.1 mmol/L). The area under the receiver-operating-characteristic curve (AUC) was higher for FPG compared to HbA1c in the overall population and age, race and age-race strata, but these differences were not statistically significant. HbA1c > = 7.0% identified 95% of individuals with diabetes mellitus, and the remainder had impaired glucose tolerance (IGT). HbA1c cut-off at 6.1% had better sensitivity (0.825) to FPG at 6.1 mmol/L. The positive predictive value of HbA1c at 6.1% was 40-50% in different age-race combinations with a negative predictive value of about 98%. If follow-up screening with FPG is used, a lower cut-off at 5.6 mmol/L is appropriate in identifying people with pre-diabetes, as about 85% of people with HbA1c 6.1-6.9% and FPG 5.6-6.9 mmol/L had IFG/IGT or diabetes in the study sample. HbA1c is an appropriate alternative to FPG as a first-step screening test, and the combination of Hba1c > = 6.1% and FPG > = 5.6 mmol/L would improve the identification of individuals with diabetes mellitus and prediabetes. More... »

PAGES

12419

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-29998-z

DOI

http://dx.doi.org/10.1038/s41598-018-29998-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106160299

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30127499


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/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Tan Tock Seng Hospital", 
          "id": "https://www.grid.ac/institutes/grid.240988.f", 
          "name": [
            "Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lim", 
        "givenName": "Wei-Yen", 
        "id": "sg:person.01013433637.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01013433637.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ministry of Health", 
          "id": "https://www.grid.ac/institutes/grid.415698.7", 
          "name": [
            "Epidemiology & Disease Control Division, Ministry of Health, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Stefan", 
        "id": "sg:person.01244625416.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01244625416.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ministry of Health", 
          "id": "https://www.grid.ac/institutes/grid.415698.7", 
          "name": [
            "Public Health Group, Ministry of Health, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heng", 
        "givenName": "Derrick", 
        "id": "sg:person.015501727204.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015501727204.52"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412106.0", 
          "name": [
            "Department of Medicine, National University Hospital, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tai", 
        "givenName": "E. Shyong", 
        "id": "sg:person.012151130377.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012151130377.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University Hospital", 
          "id": "https://www.grid.ac/institutes/grid.412106.0", 
          "name": [
            "Department of Medicine, National University Hospital, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Khoo", 
        "givenName": "Chin Meng", 
        "id": "sg:person.0740327705.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0740327705.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National University of Singapore", 
          "id": "https://www.grid.ac/institutes/grid.4280.e", 
          "name": [
            "Department Laboratory Medicine, National University Hospital, Singapore, Singapore", 
            "Biomedical Institute for Global Health Research and Technology, National University of Singapore, Singapore, Singapore"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Loh", 
        "givenName": "Tze Ping", 
        "id": "sg:person.01145636767.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145636767.72"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1371/journal.pone.0079767", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004082524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1464-5491.2012.03599.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006287385"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc09-1763", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019980025"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/jdi.12498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024785400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1744-8603-9-11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029333991", 
          "https://doi.org/10.1186/1744-8603-9-11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1503/cmaj.120732", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030957798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1503/cmaj.120732", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030957798"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dc10-1546", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032208135"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cca.2012.10.043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033262309"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.7143/jhep.41.533", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049704907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1464-5491.2007.02106.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049943654"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/diacare.27.7.1728", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052321602"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1046/j.1464-5491.2000.00382.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052569235"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2015/932057", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053250964"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1210/jc.2014-2498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064295307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078942597", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pmed.1002383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091560606"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41598-017-14172-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092252323", 
          "https://doi.org/10.1038/s41598-017-14172-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2337/dci18-0007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101699617"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "The prevalence of diabetes in Singapore is high. Screening to facilitate early detection and intervention has been shown to be cost-effective. Current clinical practice guidelines in Singapore recommend screening with fasting plasma glucose (FPG), followed by an oral glucose tolerance test (OGTT) in those with FPG 6.1-6.9\u2009mmol/L. Glycated haemoglobin A1c (HbA1c) has robust stability at ambient temperature, and can be performed on non-fasted capillary blood samples, making it an attractive potential alternative for screening. However, limitations of HbA1c include differential performance in different races, and its performance as a screening test has not been well characterized in Asian populations. This study compares HbA1c and FPG as diabetes screening modalities in 3540 community-dwelling Singapore residents of Chinese, Malay and Indian race to detect diabetes mellitus diagnosed based on blood glucose (FPG\u2009\u2265\u20097.0\u2009mmol/L, 2\u2009hr OGTT\u2009\u2265\u200911.1\u2009mmol/L). The area under the receiver-operating-characteristic curve (AUC) was higher for FPG compared to HbA1c in the overall population and age, race and age-race strata, but these differences were not statistically significant. HbA1c >\u2009=\u20097.0% identified 95% of individuals with diabetes mellitus, and the remainder had impaired glucose tolerance (IGT). HbA1c cut-off at 6.1% had better sensitivity (0.825) to FPG at 6.1\u2009mmol/L. The positive predictive value of HbA1c at 6.1% was 40-50% in different age-race combinations with a negative predictive value of about 98%. If follow-up screening with FPG is used, a lower cut-off at 5.6\u2009mmol/L is appropriate in identifying people with pre-diabetes, as about 85% of people with HbA1c 6.1-6.9% and FPG 5.6-6.9\u2009mmol/L had IFG/IGT or diabetes in the study sample. HbA1c is an appropriate alternative to FPG as a first-step screening test, and the combination of Hba1c\u2009>\u2009=\u20096.1% and FPG\u2009>\u2009=\u20095.6\u2009mmol/L would improve the identification of individuals with diabetes mellitus and prediabetes.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41598-018-29998-z", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "name": "Screening for diabetes with HbA1c: Test performance of HbA1c compared to fasting plasma glucose among Chinese, Malay and Indian community residents in Singapore", 
    "pagination": "12419", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "1f6a1840ccd9699e1d8aa809d13a685769c2b3678aa370766711d9a5bdad7883"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30127499"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101563288"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41598-018-29998-z"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1106160299"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41598-018-29998-z", 
      "https://app.dimensions.ai/details/publication/pub.1106160299"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:11", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000605.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41598-018-29998-z"
  }
]
 

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/s41598-018-29998-z'

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/s41598-018-29998-z'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-29998-z'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-29998-z'


 

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

169 TRIPLES      21 PREDICATES      47 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41598-018-29998-z schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N65e8663b2a204edca58719cc692aa43d
4 schema:citation sg:pub.10.1038/s41598-017-14172-8
5 sg:pub.10.1186/1744-8603-9-11
6 https://app.dimensions.ai/details/publication/pub.1078942597
7 https://doi.org/10.1016/j.cca.2012.10.043
8 https://doi.org/10.1046/j.1464-5491.2000.00382.x
9 https://doi.org/10.1111/j.1464-5491.2007.02106.x
10 https://doi.org/10.1111/j.1464-5491.2012.03599.x
11 https://doi.org/10.1111/jdi.12498
12 https://doi.org/10.1155/2015/932057
13 https://doi.org/10.1210/jc.2014-2498
14 https://doi.org/10.1371/journal.pmed.1002383
15 https://doi.org/10.1371/journal.pone.0079767
16 https://doi.org/10.1503/cmaj.120732
17 https://doi.org/10.2337/dc09-1763
18 https://doi.org/10.2337/dc10-1546
19 https://doi.org/10.2337/dci18-0007
20 https://doi.org/10.2337/diacare.27.7.1728
21 https://doi.org/10.7143/jhep.41.533
22 schema:datePublished 2018-12
23 schema:datePublishedReg 2018-12-01
24 schema:description The prevalence of diabetes in Singapore is high. Screening to facilitate early detection and intervention has been shown to be cost-effective. Current clinical practice guidelines in Singapore recommend screening with fasting plasma glucose (FPG), followed by an oral glucose tolerance test (OGTT) in those with FPG 6.1-6.9 mmol/L. Glycated haemoglobin A1c (HbA1c) has robust stability at ambient temperature, and can be performed on non-fasted capillary blood samples, making it an attractive potential alternative for screening. However, limitations of HbA1c include differential performance in different races, and its performance as a screening test has not been well characterized in Asian populations. This study compares HbA1c and FPG as diabetes screening modalities in 3540 community-dwelling Singapore residents of Chinese, Malay and Indian race to detect diabetes mellitus diagnosed based on blood glucose (FPG ≥ 7.0 mmol/L, 2 hr OGTT ≥ 11.1 mmol/L). The area under the receiver-operating-characteristic curve (AUC) was higher for FPG compared to HbA1c in the overall population and age, race and age-race strata, but these differences were not statistically significant. HbA1c > = 7.0% identified 95% of individuals with diabetes mellitus, and the remainder had impaired glucose tolerance (IGT). HbA1c cut-off at 6.1% had better sensitivity (0.825) to FPG at 6.1 mmol/L. The positive predictive value of HbA1c at 6.1% was 40-50% in different age-race combinations with a negative predictive value of about 98%. If follow-up screening with FPG is used, a lower cut-off at 5.6 mmol/L is appropriate in identifying people with pre-diabetes, as about 85% of people with HbA1c 6.1-6.9% and FPG 5.6-6.9 mmol/L had IFG/IGT or diabetes in the study sample. HbA1c is an appropriate alternative to FPG as a first-step screening test, and the combination of Hba1c > = 6.1% and FPG > = 5.6 mmol/L would improve the identification of individuals with diabetes mellitus and prediabetes.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree true
28 schema:isPartOf N9da74cf30c3240be87da53d0df9a17a2
29 Nd1627467ee5b418ab47c5da9138ee4b9
30 sg:journal.1045337
31 schema:name Screening for diabetes with HbA1c: Test performance of HbA1c compared to fasting plasma glucose among Chinese, Malay and Indian community residents in Singapore
32 schema:pagination 12419
33 schema:productId N0ca44d0c97b54dfd8ebae6741442907d
34 N11b24159c3314397a5d221a5ec5a5471
35 N3822e82486b5413ca96e7f3d58da9e2b
36 N4bf91ac9482042a5b4b7cbf54724e200
37 N98e41a0dbaa44f9a917c2946655a3b26
38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1106160299
39 https://doi.org/10.1038/s41598-018-29998-z
40 schema:sdDatePublished 2019-04-10T20:11
41 schema:sdLicense https://scigraph.springernature.com/explorer/license/
42 schema:sdPublisher N5114558d1d314482b0d474942faee0a6
43 schema:url https://www.nature.com/articles/s41598-018-29998-z
44 sgo:license sg:explorer/license/
45 sgo:sdDataset articles
46 rdf:type schema:ScholarlyArticle
47 N0ca44d0c97b54dfd8ebae6741442907d schema:name readcube_id
48 schema:value 1f6a1840ccd9699e1d8aa809d13a685769c2b3678aa370766711d9a5bdad7883
49 rdf:type schema:PropertyValue
50 N11b24159c3314397a5d221a5ec5a5471 schema:name dimensions_id
51 schema:value pub.1106160299
52 rdf:type schema:PropertyValue
53 N3822e82486b5413ca96e7f3d58da9e2b schema:name doi
54 schema:value 10.1038/s41598-018-29998-z
55 rdf:type schema:PropertyValue
56 N412bce2339f540cbb0353ca79cd13c37 rdf:first sg:person.01244625416.94
57 rdf:rest N43bf01ea7da148bd91d17966504d97e5
58 N43bf01ea7da148bd91d17966504d97e5 rdf:first sg:person.015501727204.52
59 rdf:rest N8937f28509d740dfaf8596a0aef67f0e
60 N4bf91ac9482042a5b4b7cbf54724e200 schema:name nlm_unique_id
61 schema:value 101563288
62 rdf:type schema:PropertyValue
63 N5114558d1d314482b0d474942faee0a6 schema:name Springer Nature - SN SciGraph project
64 rdf:type schema:Organization
65 N553784fe2b3545e7b1cc18f19738e1d8 rdf:first sg:person.0740327705.02
66 rdf:rest N761410ea08ef41dda82c2c5322f4792d
67 N65e8663b2a204edca58719cc692aa43d rdf:first sg:person.01013433637.16
68 rdf:rest N412bce2339f540cbb0353ca79cd13c37
69 N761410ea08ef41dda82c2c5322f4792d rdf:first sg:person.01145636767.72
70 rdf:rest rdf:nil
71 N8937f28509d740dfaf8596a0aef67f0e rdf:first sg:person.012151130377.17
72 rdf:rest N553784fe2b3545e7b1cc18f19738e1d8
73 N98e41a0dbaa44f9a917c2946655a3b26 schema:name pubmed_id
74 schema:value 30127499
75 rdf:type schema:PropertyValue
76 N9da74cf30c3240be87da53d0df9a17a2 schema:volumeNumber 8
77 rdf:type schema:PublicationVolume
78 Nd1627467ee5b418ab47c5da9138ee4b9 schema:issueNumber 1
79 rdf:type schema:PublicationIssue
80 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
81 schema:name Medical and Health Sciences
82 rdf:type schema:DefinedTerm
83 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
84 schema:name Clinical Sciences
85 rdf:type schema:DefinedTerm
86 sg:journal.1045337 schema:issn 2045-2322
87 schema:name Scientific Reports
88 rdf:type schema:Periodical
89 sg:person.01013433637.16 schema:affiliation https://www.grid.ac/institutes/grid.240988.f
90 schema:familyName Lim
91 schema:givenName Wei-Yen
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01013433637.16
93 rdf:type schema:Person
94 sg:person.01145636767.72 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
95 schema:familyName Loh
96 schema:givenName Tze Ping
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01145636767.72
98 rdf:type schema:Person
99 sg:person.012151130377.17 schema:affiliation https://www.grid.ac/institutes/grid.412106.0
100 schema:familyName Tai
101 schema:givenName E. Shyong
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012151130377.17
103 rdf:type schema:Person
104 sg:person.01244625416.94 schema:affiliation https://www.grid.ac/institutes/grid.415698.7
105 schema:familyName Ma
106 schema:givenName Stefan
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01244625416.94
108 rdf:type schema:Person
109 sg:person.015501727204.52 schema:affiliation https://www.grid.ac/institutes/grid.415698.7
110 schema:familyName Heng
111 schema:givenName Derrick
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015501727204.52
113 rdf:type schema:Person
114 sg:person.0740327705.02 schema:affiliation https://www.grid.ac/institutes/grid.412106.0
115 schema:familyName Khoo
116 schema:givenName Chin Meng
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0740327705.02
118 rdf:type schema:Person
119 sg:pub.10.1038/s41598-017-14172-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092252323
120 https://doi.org/10.1038/s41598-017-14172-8
121 rdf:type schema:CreativeWork
122 sg:pub.10.1186/1744-8603-9-11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029333991
123 https://doi.org/10.1186/1744-8603-9-11
124 rdf:type schema:CreativeWork
125 https://app.dimensions.ai/details/publication/pub.1078942597 schema:CreativeWork
126 https://doi.org/10.1016/j.cca.2012.10.043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033262309
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1046/j.1464-5491.2000.00382.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052569235
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1111/j.1464-5491.2007.02106.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1049943654
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1111/j.1464-5491.2012.03599.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006287385
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1111/jdi.12498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024785400
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1155/2015/932057 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053250964
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1210/jc.2014-2498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064295307
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1371/journal.pmed.1002383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091560606
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1371/journal.pone.0079767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004082524
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1503/cmaj.120732 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030957798
145 rdf:type schema:CreativeWork
146 https://doi.org/10.2337/dc09-1763 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019980025
147 rdf:type schema:CreativeWork
148 https://doi.org/10.2337/dc10-1546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032208135
149 rdf:type schema:CreativeWork
150 https://doi.org/10.2337/dci18-0007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101699617
151 rdf:type schema:CreativeWork
152 https://doi.org/10.2337/diacare.27.7.1728 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052321602
153 rdf:type schema:CreativeWork
154 https://doi.org/10.7143/jhep.41.533 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049704907
155 rdf:type schema:CreativeWork
156 https://www.grid.ac/institutes/grid.240988.f schema:alternateName Tan Tock Seng Hospital
157 schema:name Department of Clinical Epidemiology, Tan Tock Seng Hospital, Singapore, Singapore
158 rdf:type schema:Organization
159 https://www.grid.ac/institutes/grid.412106.0 schema:alternateName National University Hospital
160 schema:name Department of Medicine, National University Hospital, Singapore, Singapore
161 rdf:type schema:Organization
162 https://www.grid.ac/institutes/grid.415698.7 schema:alternateName Ministry of Health
163 schema:name Epidemiology & Disease Control Division, Ministry of Health, Singapore, Singapore
164 Public Health Group, Ministry of Health, Singapore, Singapore
165 rdf:type schema:Organization
166 https://www.grid.ac/institutes/grid.4280.e schema:alternateName National University of Singapore
167 schema:name Biomedical Institute for Global Health Research and Technology, National University of Singapore, Singapore, Singapore
168 Department Laboratory Medicine, National University Hospital, Singapore, Singapore
169 rdf:type schema:Organization
 




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


...