Evidence map›Paper›PMID 35807807›Full record

ArticleNutrients2022

Associations of Nutritional, Environmental, and Metabolic Biomarkers with Diabetes-Related Mortality in U.S. Adults: The Third National Health and Nutrition Examination Surveys between 1988-1994 and 2016.

Xi Zhang, Shirin Ardeshirrouhanifard, Jing Li, Mingyue Li, Hongji Dai, Yiqing Song

Open access · goldAbstract read
In one paragraph

Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

6 authors at 3 institutions in 2 countries.

Xi ZhangClinical Research Unit, Department of Pediatrics, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Shirin ArdeshirrouhanifardDepartment of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, USA.
Jing LiDepartment of Biostatistics, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, USA.
Mingyue LiDepartment of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, USA.
Hongji DaiDepartment of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin 300060, China.ORCID 0000-0002-3009-5536
Yiqing SongDepartment of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN 46202, USA.
Indiana University – Purdue University Indianapolis · USShanghai Jiao Tong University · CNTianjin Medical University Cancer Institute and Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Nutritional, environmental, and metabolic status may play a role in affecting the progression and prognosis of type 2 diabetes. However, results in identifying prognostic biomarkers among diabetic patients have been inconsistent and inconclusive. We aimed to evaluate the associations of nutritional, environmental, and metabolic status with disease progression and prognosis among diabetic patients. Methods: In a nationally representative sample in the NHANES III (The Third National Health and Nutrition Examination Survey, 1988−1994), we analyzed available data on 44 biomarkers among 2113 diabetic patients aged 20 to 90 years (mean age: 58.2 years) with mortality data followed up through 2016. A panel of 44 biomarkers from blood and urine specimens available from NHANES III were included in this study and the main outcomes as well as the measures are mortalities from all-causes. We performed weighted logistic regression analyses after controlling potential confounders. To assess incremental prognostic values of promising biomarkers beyond traditional risk factors, we compared c-statistics of the adjusted models with and without biomarkers, separately. Results: In total, 1387 (65.2%) deaths were documented between 1988 and 2016. We observed an increased risk of all-cause mortality associated with higher levels of serum C-reactive protein (p for trend = 0.0004), thyroid stimulating hormone (p for trend = 0.04), lactate dehydrogenase (p for trend = 0.02), gamma glutamyl transferase (p for trend = 0.02), and plasma fibrinogen (p for trend = 0.03), and urine albumin (p for trend < 0.0001). In contrast, higher levels of serum sodium (p for trend = 0.005), alpha carotene (p for trend = 0.006), and albumin (p for trend = 0.005) were associated with a decreased risk of all-cause mortality. In addition, these significant associations were not modified by age, sex, or race. Inclusion of thyroid stimulating hormone (p = 0.03), fibrinogen (p = 0.01), and urine albumin (p < 0.0001), separately, modestly improved the discriminatory ability for predicting all-cause mortality among diabetic patients. Conclusions: Our nationwide study findings provide strong evidence that some nutritional, environmental, and metabolic biomarkers were significant predictors of all-cause mortality among diabetic patients and may have potential clinical value for improving stratification of mortality risk.

Indexed as

Diabetes Mellitus, Type 2AdultBiomarkersC-Reactive ProteinFibrinogenHumansMiddle AgedNutrition SurveysThyrotropinBiomarkersC-Reactive ProteinFibrinogenThyrotropinall-cause mortalitydiabetesenvironmental biomarkermetabolic biomarkersnutritional biomarker

Identifiers

PMID35807807
PMCPMC9268621
OpenAlexW4283589801

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.