Glycemic variability and subsequent malignancies among the population without diabetes

Kobayashi, Daiki; Noto, Hiroshi; Takahashi, Osamu; Shimbo, Takuro

doi:10.1016/j.diabres.2019.107987

Cited by 12 publications

(12 citation statements)

References 38 publications

(36 reference statements)

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“…It has been shown that a higher GV is accompanied by an unfavorable metabolic profile and is associated with an increased risk of developing complications and mortality in patients with DM [ 37 – 39 ]. Abundant evidence has indicated that increased long-term GV, assessed by long-term fluctuations of HbA1c or FPG, can predict the risk of CVD [ 14 , 40 – 44 ] in patients with T2DM, the prognosis of acute lung diseases in patients with DM [ 45 ], and malignancies in the general population [ 46 ]. Increased GV after transcatheter aortic valve implantation is associated with an increased risk of major complications within 30 days [ 47 ], and reducing GV may represent a new therapeutic strategy for preventing the development of heart failure with preserved ejection fraction in patients with T2DM [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study

Tang

Luo

et al. 2020

Cardiovasc Diabetol

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Background Glycemic variability (GV) confers a risk of cardiovascular events. In this study, we aimed to investigate whether long-term GV has an impact on coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM). Methods A total of 396 patients with T2DM who had coronary computed tomography angiography and laboratory data available at baseline and for follow-up evaluations [median 2.3 (1.8–3.1) years] were included. Fasting plasma glucose (FPG) was measured every 1–3 months, and HbA1c was measured quarterly. The coefficient of variation (CV) of HbA1c and FPG were calculated as measures of GV. Quantitative assessment of coronary plaques was performed by measuring the annual change and progression rate of total plaque volume (TPV). Significant progression was defined as annual TPV progression ≥ 15%. Multivariable regression analyses were used to assess the effects of GV on atherosclerosis progression. Results In the 396 patients, the annual change in TPV was 12.35 ± 14.23 mm3, and annual progression rate was 13.36 ± 12.69%. There were 143 (36.11%) patients with significant progression, and they had a significantly higher CV-HbA1c (P < 0.001) and CV-FPG (P < 0.001) than those without significant progression. In multivariable regression analyses, both CV-HbA1c and CV-FPG were independent predictors of annual change in TPV [CV-HbA1c: β = 0.241 (0.019–0.462), P = 0.034; CV-FPG: β = 0.265 (0.060–0.465), P = 0.012], annual TPV progression [CV-HbA1c: β = 0.214 (0.023–0.405), P = 0.029; CV-FPG: β = 0.218 (0.037–0.399), P = 0.019], and significant atherosclerosis progression [CV-HbA1c: odds ratio [OR] = 1.367 (1.149–1.650), P = 0.010; CV-FPG: OR = 1.321 (1.127–1.634), P = 0.013]. Conclusions Long-term GV is associated with accelerated progression of coronary atherosclerosis independent of conventional risk factors in patients with T2DM. Trial registration ClinicalTrials.gov (NCT02587741), October 27, 2015; retrospectively registered

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Section: Discussionmentioning

confidence: 99%

Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study

Tang

Luo

et al. 2020

Cardiovasc Diabetol

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“…Glycemic variability has been associated with increased risk of adverse events (i.e., infections, non-relapse mortality; Hammer et al, 2009) and lower remission rates and reduced survival (Kuhlman et al, 2019) in patients with hematological malignancies. Recent studies have also found an increased risk for cancer among individuals with higher levels of glycemic variability, suggesting that glycemic variability also may be associated with tumorigenesis (Kobayashi et al, 2020;Saito et al, 2019). The level of glycemic variability that increases the risk for adverse events in patients with solid tumor cancers, such as colon cancer, is currently unknown; therefore, evaluating glycemic variability in this population over time is a first step.…”

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confidence: 99%

Glycemic Variability Within 1 Year Following Surgery for Stage II–III Colon Cancer

Mandolfo

Berger

Struwe

et al. 2021

Biological Research For Nursing

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Objective: To examine glycemic variability within 1 month and 1 year following surgery among adult patients, with and without Type 2 Diabetes (T2D), treated for stage II-III colon cancer. Method: A retrospective analysis of electronic health record data was conducted. Glycemic variability (i.e., standard deviation [SD] and coefficient of variation [CV] of > 2 blood glucose measures) was assessed within 1 month and within 1 year following colon surgery. Chi-square (χ2), Fisher’s exact, and Mann-Whitney U tests were used for the analyses. Results: Among the sample of 165 patients with stage II–III colon cancer, those with T2D had higher glycemic variability compared to patients without T2D ( p < .001), with values within 1 month following surgery (SD = 44.69 mg/dL, CV = 27.4%) vs (SD = 20.55 mg/dL, CV = 17.53%); and within 1 year following surgery (SD = 45.04 mg/dL, CV = 29.04%) vs (SD = 21.36 mg/dL, CV = 18.6%). Associations were found between lower body mass index and higher glycemic variability (i.e., SD [r = −.413, p < .05] and CV [r = −.481, p < .01]) within 1 month following surgery in patients with T2D. Higher preoperative glucose was associated with higher glycemic variability (i.e., SD r = .448, p < .01) within 1 year in patients with T2D. Demographic and clinical characteristics were weakly associated with glycemic variability in patients without T2D. Conclusions: Patients with stage II–III colon cancer with T2D experienced higher glycemic variability within 1 month and within 1 year following surgery compared to those without T2D. Associations between glycemic variability and demographic and clinical characteristics differed by T2D status. Further research in prospective studies is warranted.

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“…Both hyperglycemia and glucose variability have been associated with various cancers, including hepatocellular carcinoma [ 15 ] and gastric cancer [ 22 ]. In one study that analyzed cancer by organ system, glucose variability was associated with cancers of the digestive, respiratory, and intrathoracic systems as well as of genital organs [ 23 ]. Oscillating glucose has more deleterious effects than a constant glucose level on endothelial function and oxidative stress [ 24 ], which may aggravate carcinogenesis.…”

Section: Discussionmentioning

confidence: 99%

Association of Metabolic Parameter Variability with Esophageal Cancer Risk: A Nationwide Population-Based Study

Lee

Han

Yoo

et al. 2022

JPM

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Introduction: Certain metabolic parameters increase the risk of esophageal cancer. This study investigated the association between the variability in metabolic parameters and esophageal cancer incidence using large nationally representative data. Methods: Using the health checkup and claims data provided by the Korean National Health Insurance Service (NHIS), we included 8,376,233 subjects who underwent NHIS-provided health checkups between 2009 and 2010 (index year) and two or more health checkups within five years before the index year. Hazard ratios (HRs) and 95% confidence intervals (CIs) for esophageal cancer were obtained using Cox proportional hazards models according to the quartiles of variability of each metabolic parameter: fasting blood glucose (FBG), weight, systolic blood pressure (SBP), and total cholesterol (TC) as well as a cumulative number of high-variability parameters. Results: A total of 6,455 cases of esophageal cancer occurred during a mean (±SD) follow-up of 8.8 (±1.1) years. The following metabolic parameters were used, with an adjusted HR and 95% CI: FBG (1.11, 1.03–1.18), weight (1.15, 1.07–1.23), SBP (1.08, 1.01–1.16), and TC (1.23, 1.15–1.32). The risk of esophageal cancer was higher in the highest quartile of variability than the lower quartiles. The risk of esophageal cancer gradually increased with a greater number of high-variability parameters: 1.08 (1.02–1.15), 1.22 (1.14–1.31), and 1.33 (1.21–1.46) for 1, 2, and 3–4 high-variability parameters (vs. none). Conclusions: A high variability of metabolic parameters was associated with an increased esophageal cancer risk. Further studies are needed to replicate our findings in other populations.

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Glycemic variability and subsequent malignancies among the population without diabetes

Cited by 12 publications

References 38 publications

Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study

Impact of long-term glucose variability on coronary atherosclerosis progression in patients with type 2 diabetes: a 2.3 year follow-up study

Glycemic Variability Within 1 Year Following Surgery for Stage II–III Colon Cancer

Association of Metabolic Parameter Variability with Esophageal Cancer Risk: A Nationwide Population-Based Study

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