Evidence mapPaperPMID 33947391Full record

Observational studyBMC endocrine disorders2021

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

Sharen Lee, Jiandong Zhou, Wing Tak Wong, Tong Liu, William K K Wu, Ian Chi Kei Wong, Qingpeng Zhang, Gary Tse

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC endocrine disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 6 pooled it
12.0field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 6 syntheses or guidelines pooled it, 96 citations in OpenAlex.

  1. Pooled it
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  10. Observational
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  17. HbAAlzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
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  19. Lipid Swings Provoke Vascular Inflammation.Endocrinology and metabolism (Seoul, Korea) · 2024
    Article
  20. Review
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

8 authors at 4 institutions in 3 countries.

Sharen Lee *Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China.
Jiandong Zhou *School of Data Science, City University of Hong Kong, Hong Kong, China.
Wing Tak WongSchool of Life Sciences, Chinese University of Hong Kong, Hong Kong, China.
Tong LiuTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211, China.
William K K WuLi Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
Ian Chi Kei WongDepartment of Pharmacology and Pharmacy, University of Hong Kong, Pokfulam, Hong Kong, China.
Qingpeng ZhangSchool of Data Science, City University of Hong Kong, Hong Kong, China. qingpeng.zhang@cityu.edu.hk.
Gary TseSchool of Life Sciences, Chinese University of Hong Kong, Hong Kong, China. g.tse@surrey.ac.uk.
Chinese University of Hong Kong · CNCity University of Hong Kong · HKUniversity College London · GBSecond Hospital of Tianjin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRecent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes.

methodsThis retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications.

resultThe study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p <  0.05). Significant association was found between hypoglycemic frequency (p <  0.0001), HbA1c (p <  0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR).

conclusionRaised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies.

Indexed as

Machine LearningAdultAgedBlood GlucoseComputer SimulationDiabetes ComplicationsDiabetes MellitusFemaleGlycated HemoglobinHong KongHumansLipidsMaleMiddle AgedPrognosisRetrospective StudiesBlood GlucoseGlycated HemoglobinLipids

Identifiers

PMID33947391
PMCPMC8097996
OpenAlexW3158961545

What Socratic holds

Textmetadata
LicenceCC BY
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.