Evidence map›Paper›PMID 34758092›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2022

Albumin-Corrected Fructosamine Predicts All-Cause and Non-CVD Mortality Among the Very Elderly Aged 80 Years or Older Without Diabetes.

Jinhui Zhou, Yuebin Lv, Feng Zhao, Yuan Wei, Xiang Gao, Chen Chen, Feng Lu, Yingchun Liu, Chengcheng Li, Jiaonan Wang and 7 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
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

7 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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

17 authors at 4 institutions in 2 countries.

Jinhui ZhouChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuebin LvChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Feng ZhaoChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yuan WeiChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiang GaoDepartment of Nutritional Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA.ORCID 0000-0003-2617-6509
Chen ChenChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-8412-2966
Feng LuChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Yingchun LiuChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Chengcheng LiChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Jiaonan WangChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiaochang ZhangDivision of Non-communicable Disease and Healthy Ageing Management, Chinese Center for Disease Control and Prevention, Beijing, China.
Heng GuChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhaoxue YinDivision of Non-communicable Disease and Healthy Ageing Management, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhaojin CaoChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Virginia B KrausDuke Molecular Physiology Institute and Division of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
Chen MaoDivision of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China.
Xiaoming ShiChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.
Chinese Center For Disease Control and Prevention · CNDuke University · USPennsylvania State University · USSouthern Medical University · CN

Funding

Resource Core 3 - Metabolomics CoreP30AG028716 · NIA · DUKE UNIVERSITY · PI CATHLEEN S COLON-EMERIC, Susan Nicole Hastings · 2006 to 2026
$24.6M
THE RANGE OF MALE-FEMALE MORTALITY DIFFERENCESP01AG031719 · NIA · DUKE UNIVERSITY · PI CHRISTENSEN, KAARE · 2009 to 2020
$10.2M
Claude D. Pepper Older Americans Independence Centers 5P30 AG028716National Natural Science Foundation of China 82025030NIA NIH HHS 2P01AG031719NIA NIH HHS P01 AG031719NIA NIH HHS P30 AG028716
6 · The paper itself

Abstract

backgroundSeveral guidelines have suggested alternative glycemic markers for hemoglobin A1c among older adults with limited life expectancy or multiple coexisting chronic illnesses. We evaluated associations between fructosamine, albumin-corrected fructosamine (AlbF), fasting plasma glucose (FPG), and mortality in the diabetic and nondiabetic subpopulations, and compared which marker better predicts mortality among participants aged 80 and older.

methodsIncluded were 2 238 subjects from the Healthy Ageing and Biomarkers Cohort Study (2012-2018) and 207 participants had diabetes at baseline. Multivariable Cox proportional hazards regression models investigated the associations of fructosamine, AlbF, FPG, and all-cause, cardiovascular disease (CVD), and non-CVD mortality in the diabetic and nondiabetic subpopulations. Restricted cubic splines explored potential nonlinear relations. C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) evaluated the additive value of different glycemic markers to predict mortality.

resultsOverall, 1 191 deaths were documented during 6 793 person-years of follow-up. In the linear model, per unit increases of fructosamine, AlbF, and FPG were associated with a higher risk of mortality in nondiabetic participants, with hazard ratios of 1.02 (1.00, 1.05), 1.27 (1.14, 1.42), and 1.04 (0.98, 1.11) for all-cause mortality, and 1.04 (1.00, 1.07), 1.38 (1.19, 1.59), and 1.10 (1.01, 1.19) for non-CVD mortality, respectively. Comparisons indicated that AlbF better predicts all-cause and non-CVD mortality in nondiabetic participants with significant improvement in IDI and NRI.

conclusionsHigher concentrations of fructosamine, AlbF, and FPG were associated with a higher risk of all-cause or non-CVD mortality among the very elderly where AlbF may constitute an alternative prospective glycemic predictor of mortality.

Indexed as

AlbuminsDiabetes MellitusFructosamineMortalityAgedAged, 80 and overBiomarkersBlood GlucoseCardiovascular DiseasesCohort StudiesGlycated HemoglobinHumansProspective StudiesRisk FactorsAlbuminsBiomarkersBlood GlucoseFructosamineGlycated HemoglobinCardiovascularDiabetesGeriatric endocrinologyGlycemic markerLongevity

Identifiers

PMID34758092
PMCPMC9373969
OpenAlexW3213011979

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.