Archives of endocrinology and metabolism · 2026
Relationships between glucose variability with white matter hyperintensity and cerebrovascular abnormalities.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lin Cheng-Chieh, Li Chia-Ing, Liu Chiu-Shong, Lin Chih-Hsueh, Yang Shing-Yu, Li Tsai-Chung
Аннотация
OBJECTIVE: Epidemiological studies have revealed that glucose variability (GV) is a predictor of stroke, cognitive impairment, and dementia in patients with type 2 diabetes mellitus (T2DM). However, evidence on the associations of GV with white matter hyperintensity (WMH) and cerebrovascular abnormalities remains scarce. This study aimed to explore the relationships of GV with WMH and cerebrovascular abnormalities using epidemiological and Mendelian randomization (MR) approaches. The MR approach was used to assess the effects of genetic proxies for GV on MRI outcomes.
SUBJECTS AND METHODS: This cross-sectional study was conducted at a medical center where patients with T2DM were recruited. The measures for fasting plasma glucose (FPG) and HbA1c variability included the standard deviation, coefficient of variation, average real variability (ARV), and variability independent of the mean (VIM). Brain magnetic resonance images were analyzed to assess WMHs and cerebrovascular abnormalities. For MR, instrumental variables were used to assess the causal relationships between glycemic variability and outcome based on two-stage regression analysis.
RESULTS: This study included 2,247 subjects, of whom 1,122 had WMH and 957 had cerebrovascular abnormalities. We found 80 independent single-nucleotide polymorphisms associated with GV but not with WMH or cerebrovascular abnormalities, which were subsequently used as genetic instruments. Genetically increased, unweighted FPG-VIM was linked with WMH (odds ratio 1.17 [95% CI 1.08, 1.27] per standard deviation). All genetically increased, unweighted and weighted GV measures were associated with cerebrovascular abnormalities, except FPG-ARV.
CONCLUSION: Our study provided evidence that genetically predicted GV was associated with WMH and cerebrovascular abnormalities, supporting a potential causal link under MR assumptions.
SUBJECTS AND METHODS: This cross-sectional study was conducted at a medical center where patients with T2DM were recruited. The measures for fasting plasma glucose (FPG) and HbA1c variability included the standard deviation, coefficient of variation, average real variability (ARV), and variability independent of the mean (VIM). Brain magnetic resonance images were analyzed to assess WMHs and cerebrovascular abnormalities. For MR, instrumental variables were used to assess the causal relationships between glycemic variability and outcome based on two-stage regression analysis.
RESULTS: This study included 2,247 subjects, of whom 1,122 had WMH and 957 had cerebrovascular abnormalities. We found 80 independent single-nucleotide polymorphisms associated with GV but not with WMH or cerebrovascular abnormalities, which were subsequently used as genetic instruments. Genetically increased, unweighted FPG-VIM was linked with WMH (odds ratio 1.17 [95% CI 1.08, 1.27] per standard deviation). All genetically increased, unweighted and weighted GV measures were associated with cerebrovascular abnormalities, except FPG-ARV.
CONCLUSION: Our study provided evidence that genetically predicted GV was associated with WMH and cerebrovascular abnormalities, supporting a potential causal link under MR assumptions.