Evidence map›Paper›PMID 39709342›Full record

ArticleBMC geriatrics2024

A correlation study between blood glucose fluctuation and chronic pain in the older people with type 2 diabetes mellitus.

Qinyi Huang, Mengjie Cui, Yangliu Yin, Cancan Hui, Yuwei Cheng, Shuqian Wang, Xiujuan Hu, Xiaorong Yin, Xiaoxiang Zhang, Xinyu Sun and 3 more

Abstract read
In one paragraph

Article in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Trial
  2. Review
  3. Observational
  4. 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

13 authors.

Qinyi HuangGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Mengjie CuiGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Yangliu YinGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Cancan HuiGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Yuwei ChengGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Shuqian WangGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Xiujuan HuGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Xiaorong YinGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Xiaoxiang ZhangGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Xinyu SunGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Tiantian TangGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Lang LangGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China.
Yan SunGeriatric Endocrinology, Department of Geriatric Endocrinology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Shushan District, Hefei City, Anhui Province, China. syan956@163.com.

Funding

National Natural Science Foundation of China 82072134Natural Science Key Fund of Anhui Provincial Department of Education 2023AH053309
6 · The paper itself

Abstract

objectivesTo investigate the correlation between blood glucose fluctuation parameters and other clinical data with chronic pain in older patients ( ≧ 60 years) with type 2 diabetes mellitus (T2DM), as well as evaluating the predictive value of risk of these parameters for chronic pain.

methodsClinical data were collected from 60 older patients with T2DM undergoing chronic pain who were hospitalized in the Department of Geriatric Endocrinology at the First Affiliated Hospital of Anhui Medical University. Pain scores using the numeric rating scale (NRS) were administered to all study participants by a dedicated person. Based on their pain scores, patients were categorized into two groups: mild pain group (NRS ≤ 5, n = 28) and severe pain group (NRS > 5, n = 32). Blood glucose levels were continuously monitored using the Continuous Glucose Monitoring System (CGMS). Spearman correlation analysis was performed to investigate the correlation between pain scores and blood glucose fluctuation parameters, as well as other clinical data of concern. Comparing general clinical information and relevant data recorded by CGMS between the two groups. Binary logistic regression was used to identify factors influencing the severity of chronic pain in old patients with T2DM combined with chronic pain. Additionally, the predictive value of Mean Amplitude of Glycemic Excursions (MAGE), Coefficient of Variation (CV), and Time in Range (TIR) for chronic pain severity was assessed using Receiver Operating Characteristic (ROC) curve analysis.

resultsSpearman correlation analysis revealed positive correlations between pain scores and the following variables: gender, age, duration of diabetes, duration of pain, MAGE, CV, mean blood glucose (MBG), standard deviation (SD), Mean of Daily Differences (MODD), and the highest glucose level. Conversely, pain scores were negatively correlated with red blood cell (RBC) count, hemoglobin (Hb), estimated glomerular filtration rate (eGFR). There were statistically significant differences in gender, age, disease duration, pain duration, Hb, eGFR, MAGE, CV, TIR, MBG, SD, MODD, and highest blood glucose values between the two groups. The gender, age, duration of diabetes, duration of pain, Hb, eGFR, MAGE, TIR, CV, MBG, SD, and MODD were identified as the risk factors for the severity of chronic pain in older T2DM patients by using binary logistic regression analysis. ROC curve analysis showed that the area under the curve for MAGE was 0.741 (sensitivity: 53.1%, specificity: 89.3%), for CV it was 0.668 (sensitivity: 40.6%, specificity: 89.3%), and for TIR it was 0.763 (sensitivity: 67.9%, specificity: 84%).

conclusionThe chronic pain is strongly correlated with blood glucose fluctuation parameters in older patients with T2DM. This work shows that those indicators of blood glucose fluctuations can be used for predicting chronic pain level in older T2DM patients, providing a potential methodology for rapid evaluation of chronic pain. CLINICAL TRIAL NUMBER: ChiCTR1800019107.

Indexed as

Blood GlucoseChronic PainDiabetes Mellitus, Type 2AgedAged, 80 and overFemaleHumansMaleMiddle AgedPain MeasurementBlood Glucoseblood glucose fluctuationchronic paincontinuous glucose monitoringcorrelationGeriatricstype 2 diabetes

Identifiers

PMID39709342
PMCPMC11662845

What Socratic holds

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LicenceCC BY-NC-ND
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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.