Fasting plasma glucose variability in midlife and risk of Parkinson's disease: A nationwide population-based study

Chung, Haegeun; Lee, Joong Sun; Kim, J.A.; Roh, Eun; Lee, Y.-B.; Hong, So Hyeon; Yu, Jae Hong; Kim, N.H.; Yoo, Hye Jin; Seo, Ji A; Kim, S.G.; Baik, Sei Hyun; Choi, Kyung Mook

doi:10.1016/j.diabet.2020.08.006

Cited by 17 publications

(16 citation statements)

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“…Similarly, the ratio of men and women did not account for differential study effects ( P = 0.165). We then considered the cohort studies separately depending on when enrolment to the study occurred relative to the onset of the exposure (T2DM) under the assumption that the effect of survival bias in those with exposure before enrolment (ie, unobserved) would be greatest 35 . In one cohort study, the onset of T2DM was before the period of observation (OR 0.88, 95% CI 0.62–1.25), two of the cohort studies enrolled incident cases of T2DM (pooled OR 1.27, 95% CI 1.15–1.40), and the remaining four cohort studies used a combination of methods to determine the T2DM (pooled OR 1.38, 95% CI 1.24–1.55; Fig.…”

Section: Resultsmentioning

confidence: 99%

“…T2DM represents a continuum ranging from individuals who are unaware of their disease state, to those who are aware but do not comply with management and are uncontrolled, and those who are diet controlled, tablet controlled, or insulin controlled, These factors were not considered in our analyses, and future studies may use biomarkers of glycemic control to further study the effects of T2DM severity on PD 66 . For example, a recent study in non‐diabetic patients reported that midlife variability in glycemic control was associated with future PD 35 . Finally, our analysis does not consider that T2DM might influence markers of PD progression (motor and cognitive scores) independently of a direct effect on PD, and diabetic neuropathy and cognitive impairment due to small vessel disease are also commonly observed in older patients with diabetes.…”

Section: Discussionmentioning

confidence: 99%

“…before enrolment (ie, unobserved) would be greatest. 35 In one cohort study, the onset of T2DM was before the period of observation (OR 0.88, 95% CI 0.62-1.25), two of the cohort studies enrolled incident cases of T2DM (pooled OR 1.27, 95% CI 1.15-1.40), and the remaining four cohort studies used a combination of methods to determine the T2DM (pooled OR 1.38, 95% CI 1.24-1.55; Fig. S3).…”

Section: T2dm and Risk Of Pdmentioning

confidence: 99%

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Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression

et al. 2021

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A BS TRACT: Background: Type 2 diabetes (T2DM) and Parkinson's disease (PD) are prevalent diseases that affect an aging population. Previous systematic reviews and meta-analyses have explored the relationship between diabetes and the risk of PD, but the results have been conflicting. Objective: The objective was to investigate T2DM as a determinant of PD through a meta-analysis of observational and genetic summary data. Methods: A systematic review and meta-analysis of observational studies was undertaken by searching 6 databases. We selected the highest-quality studies investigating the association of T2DM with PD risk and progression. We then used Mendelian randomization (MR) to investigate the causal effects of genetic liability toward T2DM on PD risk and progression, using summary data derived from genome-wide association studies. Results: In the observational part of the study, pooled effect estimates showed that T2DM was associated with an increased risk of PD (odds ratio [OR] 1.21, 95% confidence interval [CI] 1.07-1.36), and there was some evidence that T2DM was associated with faster progression of motor symptoms (standardized mean difference [SMD] 0.55, 95% CI 0.39-0.72) and cognitive decline (SMD −0.92, 95% CI −1.50 to −0.34). Using MR, we found supportive evidence for a causal effect of diabetes on PD risk (inverse-variance weighted method [IVW] OR 1.08, 95% CI 1.02-1.14; P = 0.010) and some evidence of an effect on motor progression (IVW OR 1.10, 95% CI 1.01-1.20; P = 0.032) but not on cognitive progression. Conclusions: Using meta-analyses of traditional observational studies and genetic data, we observed convincing evidence for an effect of T2DM on PD risk and new evidence to support a role in PD progression.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: T2dm and Risk Of Pdmentioning

confidence: 99%

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Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression

et al. 2021

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“…In order to better explain this result, we conducted subgroup analysis and interaction test. According to whether the age is less than 65 years old or not, it can be divided into two parts: less than 65 years old and greater than or equal to 65 years old 27 , 28 . According to WHO standards, BMI is divided into four parts: < 18.5 kg/m 2 , 18.5 ~ 24.9 kg/m 2 , 25.0 ~ 29.9 kg/m 2 and ≥ 30.0 kg/m 2 29 , 30 .…”

Section: Resultsmentioning

confidence: 99%

Association between vitamin D3 levels and insulin resistance: a large sample cross-sectional study

Gong

Luo

et al. 2022

Sci Rep

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Previous studies have shown that vitamin D3 may be a potential factor in insulin resistance, but the relationship between vitamin D3 and insulin resistance still remains controversial. At present, more research is needed to explore the relationship between vitamin D3 and insulin resistance. The samples from 2009 to 2018 in NHANES database were analyzed to Investigate the relationship and the potential mechanism. We performed a cross-sectional study of five periods in the NHANES database. Finally, 9298 participants were selected through strict inclusion and exclusion criteria, Multivariate logistic regression analysis and curve fitting were conducted to explore the relationship between vitamin D3 level and insulin resistance. Moreover, subgroup analysis was used to further prove the association. The results revealed that there was a strong association between vitamin D3 and insulin resistance (OR 0.82, 95% CI 0.72–0.93). However, subgroup analyses indicated that this correlation varied between individuals and races. There was a negative correlation between vitamin D3 level and insulin resistance, which provides a new proof for exploring the influencing factors of insulin resistance. More well-designed studies are still needed to further elaborate on these associations.

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“…In vivo and in vitro studies have also revealed a relationship between glucose fluctuation and endothelial cell/endothelium damage [ 14 – 16 ]. Furthermore, we reported that visit-to-visit variability of FPG may be a significant predictor of chronic cardiometabolic and neurodegenerative diseases, such as type 2 diabetes [ 17 ], cardiovascular disease [ 18 ], non-alcoholic fatty liver disease [ 19 ], and Parkinson’s disease [ 20 ] in the Korean general population without diabetes.…”

Section: Introductionmentioning

confidence: 99%

Implications of fasting plasma glucose variability on the risk of incident peripheral artery disease in a population without diabetes: a nationwide population-based cohort study

Chung

Hwang

Kim

et al. 2022

Cardiovasc Diabetol

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Background Diabetes have been known as a traditional risk factor of developing peripheral artery disease (PAD). However, the study evaluating the impact of long-term glycemic variability on the risk of developing PAD is limited, especially in a general population without diabetes. Methods We included 152,931 individuals without diabetes from the Korean National Health Insurance Service–Health Screening Cohort. Fasting plasma glucose (FPG) variability was measured using coefficient variance (FPG-CV), standard deviation (FPG-SD), and variability independent of the mean (FPG-VIM). Results A total of 16,863 (11.0%) incident cases of PAD were identified during a median follow-up of 8.3 years. Kaplan–Meier curves showed a progressively increasing risk of PAD in the higher quartile group of FPG variability than in the lowest quartile group (log rank P < 0.001). Multivariable Cox proportional hazard analysis showed the hazard ratio for PAD prevalence as 1.11 (95% CI 1.07–1.16, P < 0.001) in the highest FPG-CV quartile than in the lowest FPG-CV quartile after adjusting for confounding variables, including mean FPG. Similar degree of association was shown in the FPG-SD and FPG-VIM. In sensitivity analysis, the association between FPG variability and the risk of developing PAD persisted even after the participants were excluded based on previously diagnosed diseases, including stroke, coronary artery disease, congestive heart failure, chronic kidney disease, or current smokers or drinkers. Subgroup analysis demonstrated that the effects of FPG variability on the risk of PAD were more powerful in subgroups of younger age, regular exercisers, and those with higher income. Conclusions Increased long-term glycemic variability may have a significant prognostic effect for incident PAD in individuals without diabetes. Graphical Abstract

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Fasting plasma glucose variability in midlife and risk of Parkinson's disease: A nationwide population-based study

Cited by 17 publications

References 50 publications

Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression

Type 2 Diabetes as a Determinant of Parkinson's Disease Risk and Progression

Association between vitamin D3 levels and insulin resistance: a large sample cross-sectional study

Implications of fasting plasma glucose variability on the risk of incident peripheral artery disease in a population without diabetes: a nationwide population-based cohort study

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