Dataset Information

Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes.

ABSTRACT: Glycemic variability (GV) is a risk factor for depression in patients with diabetes. However, whether it is also a predictor of incident depression in people without diabetes remains unclear. We aimed to investigate the association between visit-to-visit variability in fasting serum glucose (FSG) levels and the incidence of depression among Koreans without diabetes. This retrospective cohort study included data of people without diabetes who did not have depression at baseline and had at least three FSG measurements (n = 264,480) extracted from the 2002-2007 Korean National Health Insurance Service-National Health Screening Cohort. GV was calculated as the average successive variability of FSG. Among 264,480 participants, 198,267 were observed during 2008-2013 and their hazard ratios (HR) of incident depression were calculated. Participants with the highest GV showed a higher risk of depression in fully adjusted models than those with the lowest GV (HR, 1.09; 95% CI, 1.02-1.16). The risk of incident depression heightened with increasing GV (p for trend < 0.001). Greater visit-to-visit GV may be associated with the risk of developing depression in people without diabetes. Conversely, maintaining steady FSG levels may reduce the risk of incident depression in people without diabetes.

SUBMITTER: Kim HJ

PROVIDER: S-EPMC9636237 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes.

Kim Hye Jun HJ Kim Sung Min SM Lee Gyeongsil G Choi Seulggie S Son Joung Sik JS Oh Yun Hwan YH Choi Soo Jung SJ Jeong Seogsong S Park Sang Min SM

Scientific reports 20221104 1

Glycemic variability (GV) is a risk factor for depression in patients with diabetes. However, whether it is also a predictor of incident depression in people without diabetes remains unclear. We aimed to investigate the association between visit-to-visit variability in fasting serum glucose (FSG) levels and the incidence of depression among Koreans without diabetes. This retrospective cohort study included data of people without diabetes who did not have depression at baseline and had at least t ...[more]

PMID: 36333430

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Project description:BackgroundAtrial fibrillation (AF) is prevalent in patients with type 2 diabetes mellitus (T2DM). Glycemic variability (GV) is associated with risk of micro- and macrovascular diseases. However, whether the GV can increase the risk of AF remains unknown.MethodsThe cohort study used a database from National Taiwan University Hospital, a tertiary medical center in Taiwan. Between 2014 and 2019, a total of 27,246 adult patients with T2DM were enrolled for analysis. Each individual was assessed to determine the coefficients of variability of fasting glucose (FGCV) and HbA1c variability score (HVS). The GV parameters were categorized into quartiles. Multivariate Cox regression models were employed to estimate the relationship between the GV parameters and the risk of AF, transient ischemic accident (TIA)/ischemic stroke and mortality in patients with T2DM.ResultsThe incidence rates of AF and TIA/ischemic stroke were 21.31 and 13.71 per 1000 person-year respectively. The medium follow-up period was 70.7 months. In Cox regression model with full adjustment, the highest quartile of FGCV was not associated with increased risk of AF [Hazard ratio (HR): 1.12, 95% confidence interval (CI) 0.96-1.29, p = 0.148] or TIA/ischemic stroke (HR: 1.04, 95% CI 0.83-1.31, p = 0.736), but was associated with increased risk of total mortality (HR: 1.33, 95% CI 1.12-1.58, p < 0.001) and non-cardiac mortality (HR: 1.41, 95% CI 1.15-1.71, p < 0.001). The highest HVS was significantly associated with increased risk of AF (HR: 1.29, 95% CI 1.12-1.50, p < 0.001), total mortality (HR: 2.43, 95% CI 2.03-2.90, p < 0.001), cardiac mortality (HR: 1.50, 95% CI 1.06-2.14, p = 0.024) and non-cardiac mortality (HR: 2.80, 95% CI 2.28-3.44, p < 0.001) but was not associated with TIA/ischemic stroke (HR: 0.98, 95% CI 0.78-1.23, p = 0.846). The Kaplan-Meier analysis showed significantly higher risk of AF, cardiac and non-cardiac mortality according to the magnitude of GV (log-rank test, p < 0.001).ConclusionsOur data demonstrate that high GV is independently associated with the development of new-onset AF in patients with T2DM. The benefit of maintaining stable glycemic levels to improve clinical outcomes warrants further studies.

Project description:Background Peripheral arterial disease (PAD) is a severe complication in patients with type 2 diabetes. Glycemic variability (GV) is associated with increased risks of developing microvascular and macrovascular diseases. However, few studies have focused on the association between GV and PAD. Methods and Results This cohort study used a database maintained by the National Taiwan University Hospital, a tertiary medical center in Taiwan. For each individual, GV parameters were calculated, including fasting glucose coefficient of variability (FGCV) and hemoglobin A1c variability score (HVS). Multivariate Cox regression models were constructed to estimate the relationships between GV parameters and composite scores for major adverse limb events (MALEs) and major adverse cardiovascular events (MACEs). Between 2014 and 2019, a total of 45 436 adult patients with prevalent type 2 diabetes were enrolled for analysis, and GV was assessed during a median follow-up of 64.4 months. The average number of visits and time periods were 13.38 and 157.87 days for the HVS group and 14.27 and 146.59 days for the FGCV group, respectively. The incidence rates for cardiac mortality, PAD, and critical limb ischemia (CLI) were 5.38, 20.11, and 2.41 per 1000 person-years in the FGCV group and 5.35, 20.32, and 2.50 per 1000 person-years in HVS group, respectively. In the Cox regression model with full adjustment, the highest FGCV quartile was associated with significantly increased risks of MALEs (hazard ratio [HR], 1.57 [95% CI, 1.40-1.76]; P<0.001) and MACEs (HR, 1.40 [95% CI, 1.25-1.56]; P<0.001). Similarly, the highest HVS quartile was associated with significantly increased risks of MALEs (HR, 1.44 [95% CI, 1.28-1.62]; P<0.001) and MACEs (HR, 1.28 [95% CI, 1.14-1.43]; P<0.001). The highest FGCV and HVS quartiles were both associated with the development of PAD and CLI (FGCV: PAD [HR, 1.57; P<0.001], CLI [HR, 2.19; P<0.001]; HVS: PAD [HR, 1.44; P<0.001], CLI [HR, 1.67; P=0.003]). The Kaplan-Meier analysis showed significantly higher risks of MALEs and MACEs with increasing GV magnitude (log-rank P<0.001). Conclusions Among individuals with diabetes, increased GV is independently associated with the development of MALEs, including PAD and CLI, and MACEs. The benefit of maintaining stable glycemic levels for improving clinical outcomes warrants further studies.

Project description:Background and aimsThe aim of this study is to investigate the association between visit-to-visit variability in fasting plasma glucose (FPG) and the risk of digestive cancers among individuals with and without diabetes.MethodsUsing data from Kailuan cohort, a prospective population-based study, individuals who had at least two measurements of FPG between 2006 and 2012 without prior cancer were included in this study. Four indexes of variability were used, including standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average successive variability (ARV). Cox regression was used to evaluate the relationship between the quartiles of FPG variability and digestive cancers.ResultsA total of 98,161 individuals were studied. Over a mean follow-up of 9.32 ± 0.81 years, 1103 individuals developed incident digestive cancer (1.21 per 1000 person-years). Compared to the individuals in the lowest quartile, those in the highest quartile of FPG variability by SD had 38.7% higher risk of developing overall digestive cancers after adjusting for the significant confounders (hazard ratio, 1.387; 95% confidence interval, 1.160-1.659; P = 0.0003). Higher FPG variability was associated with significantly higher risks of colorectal cancer (fully adjusted HR 1.432, 95% CI [1.073-1.912], P = 0.015) and pancreatic cancer (fully adjusted HR 2.105, 95% CI [1.024-4.329], P = 0.043), but not liver cancer (fully adjusted HR 1.427, 95% CI [0.973-2.092], P = 0.069) or esophageal and gastric cancer (fully adjusted HR 1.139, 95% CI [0.776-1.670], P = 0.506). Subgroup analyses showed that individuals who were younger (<65 years), male, and those without diabetes experienced a predominantly higher risk of developing digestive cancers. Similar results were observed when using CV, VIM, and ARV.ConclusionsFPG variability was significantly associated with increasing risk of digestive cancers, especially for pancreatic and colorectal cancer. Our study suggested a potential role of FPG variability in risk stratification of digestive cancers. Approaches that reduce FPG variability may lower the risks of incident digestive cancers among the general population. This trial is registered with ChiCTR-TNRC-11001489.

Project description:BackgroundIdentification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.MethodsA retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models.ResultsA total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748-0.758 and 0.774-0.787) and CKD models (C-indices: 0.734-0.750 and 0.724-0.740) had modestly better performance than CVD models (C-indices: 0.703-0.730 and 0.698-0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance.ConclusionsWe found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.

Project description:AimEvidence for contribution of basal and postprandial glucose increment, and glycemic variability to glycated hemoglobin (HbA1c) among adults with type 1 diabetes (T1D) is limited. This study aimed to capture glycemic fluctuation patterns and quantify contributions of these factors to HbA1c levels among adults with T1D.MethodsHbA1c, continuous glucose monitoring (CGM), and diet diaries were collected and pooled from two clinical trials. Available data sets were divided into HbA1c quartiles: group 1 (≤6.7%), group 2 (6.7%-7.3%), group 3 (7.3%-7.8%), and group 4 (≥7.8%). Area under curve above 110 mg/dL (AUC>110mg/dL ) in 24-h profile was defined as overall hyperglycemia and stratified with postprandial hyperglycemia (PHG, AUC>110mg/dL in 3-h period after meals) and basal hyperglycemia (BHG, AUC>110mg/dL in remaining period). Linear regression analysis was used to estimate the proportion of variance in HbA1c explained by BHG, preprandial glucose, PHG, glycemic variability, and non-glycemic factors (age, body mass index, hemoglobin, and duration).ResultsA total of 169 550 glucose data in 2409 meals recorded from 102 patients (male/female, 34/68) were included. Age and duration were 35.2 ± 12.6 and 8.9 (2.9, 13.0) years, with 51.0% using pumps. Overall, BHG was four times higher than PHG (p all <.05) and between-group comparisons showed BHG exhibited a progressive increase (group 1 vs. 2, 3, 4, p = .053, .086, .006) with fasting contribution of 76.1%, 82.6%, 81.5%, and 84.3% from group 1 to 4. The increment was not significant among groups 2, 3, and 4 (p > .05). Factors included in analysis explained a total of 74% of the variance in HbA1c, in which BHG accounted for 32.1% of variance whereas PHG accounted for 24.4%. In group with HbA1c >7.3%, BHG accounted for a higher percentage with 33.8% of the variance in HbA1c.ConclusionsIn our study, basal hyperglycemia better predicts overall glycemic control than postprandial hyperglycemia among adults with T1D. The relative contribution of basal hyperglycemia increased gradually with HbA1c increasing and predominant strategy for insulin titration among T1D is different among different levels of glycemic control.

Dataset Information

Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes.

Publications

Association between visit-to-visit fasting glycemic variability and depression: a retrospective cohort study in a representative Korean population without diabetes.

Similar Datasets

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