Diabetes is associated with cognitive decline as well as the development of dementia. Although mean blood glucose levels are typically used to assess the status of diabetic patients, glucose variability is also involved in the manifestation of macro- and microvascular complications in this population. Thus, the present study sought to determine whether visit-to-visit glucose variability contributes to cognitive decline in patients with type 2 diabetes. The present study assessed 68 patients with type 2 diabetes using several validated neuropsychological measures. All patients had no cerebrovascular disease, history of hypoglycemia, psychiatric conditions, or other medical illnesses. Standard deviations (SDs) and coefficients of variance (CVs) of the patients’ blood glucose (after fasting and 2 hours postprandial; FBS and PP2), and glycated hemoglobin (HbA1c) values were used as indices of glucose variability. The cognitive outcome parameters were transformed with z-scores and entered into a multiple linear regression model that included educational status, age, sex, vascular risk factors, and mean glucose parameters as covariates. The mean age of the total patient population was 70.9 years; 46 (67.6%) of the patients were men, and the median follow-up duration at our endocrinology outpatient clinic was 4.8 years. The mean FBS and PP2 glucose levels of the patients were 132 mg/dL and 199 mg/dL, respectively, and the mean HbA1c level was 8.0%. A univariable analysis revealed that only the PP2 value was associated with the Mini-Mental State Examination (MMSE) score, and multivariable analysis revealed that a high SD and/or CV for PP2 glucose were associated with low scores on the Rey Complex Figure Copy test and/or the Verbal Learning Test. Additionally, a high SD and a higher CV for HbA1c level were significantly associated with low MMSE and Digit Span test scores even after adjusting for mean HbA1c values. The present data indicate that a greater degree of visit-to-visit glucose variability influenced specific types of cognitive function in type 2 diabetic patients independently of mean blood glucose levels. Future studies should focus on whether reductions in glycemic variability will improve the cognitive decline observed in type 2 diabetic patients.

Many studies have identified various vestibular symptoms and laboratory abnormalities in migraineurs. Although the vestibular tests may be abnormal, the changes may exist without vestibular symptoms. To date, vestibular-evoked myogenic potential (VEMP) has been the easiest and simplest test for measuring vestibular function in clinical practice. Cervical VEMP (cVEMP) represents a vestibulo-collic reflex, whereas ocular VEMP (oVEMP) reflects a vestibulo-ocular pathway. Therefore, we determined whether ocular and rectified cervical VEMPs differed in patients with migraine or tension type headache (TTH) and compared the results to controls with no accompanying vestibular symptoms. The present study included 38 females with migraine without aura, 30 with episodic TTH, and 50 healthy controls without vestibular symptoms. oVEMP and cVEMP using a blood pressure manometer were recorded during a headache-free period. From the VEMP graphs, latency and amplitude parameters were analyzed, especially following EMG rectification in cVEMP. With respect to oVEMP, the migraine group exhibited significantly longer mean latencies of bilateral n1 and left p1 than the other groups (p < 0.05). Amplitudes of n1-p1 were lower than in other groups, but the difference did not reach statistical significance. In regards to cVEMP, p13 and n23 latencies and amplitudes after rectification did not differ significantly among groups. An abnormal interictal oVEMP profile was associated with subclinical vestibular dysfunction in migraineurs, suggesting pathology within the vestibulo-ocular reflex. oVEMP is a more reliable measure than cVEMP to evaluate vestibular function in migraineurs, although results from the two tests in patients with migraine are complementary.

The trigeminal nociceptive system plays a pivotal role in the pathophysiology of migraines. The present study investigated whether there are differences between patients with episodic migraine (EM) and patients with chronic migraine (CM) in trigeminal pain processing at the brainstem and cortical levels using the nociceptive blink reflex (nBR) and pain-related evoked potentials (PREP). This study assessed 68 female migraineurs (38 EM patients and 30 CM patients) and 40 age-matched controls using simultaneous recordings of nBR and PREP during the interictal period. In terms of the nBR, EM patients displayed significantly decreased latencies and larger amplitudes and area-under-the-curve (AUC) values for the R2 component, whereas CM patients showed significantly prolonged latencies and smaller amplitudes and AUC values for the R2 component (p < 0.05). In terms of PREP, both the EM and CM patients had decreased latencies (N1, P1), with larger amplitude compared with the controls (p < 0.05), which indicates facilitation at the cortical level. Additionally, the amplitude and AUC values of the R2 component exhibited a negative correlation, whereas the latency of the R2 component for the nBR showed a positive correlation, with the frequency of headaches in migraineurs (p < 0.01). In the present study, the facilitation in the trigeminal nociceptive pathway of the EM group suggests the occurrence of migraine-specific hyperexcitability. Additionally, the suppression of R2 at the brainstem level in the CM group may relate to impaired or dysfunctional descending pain modulation. These findings suggest that there are adaptive or maladaptive responses due to the chronification of migraine attacks.

Background: Some patients with mild cognitive impairment (MCI) experience gait disturbances. However, there are few reports on the relationship between gait disturbance and cognitive function in patients with MCI. Therefore, we investigated the neural correlates of gait characteristics related to cognitive dysfunction. Methods: Eighty patients diagnosed with MCI from three dementia centers in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of 0.5 or higher, with a memory domain score of 0.5 or greater. The patients were classified as having either higher or lower MMSE and the groups were based on their Mini Mental Status Examination z-scores. Multiple logistic regression analysis was performed to examine the association between the gait characteristics and cognitive impairment. Analyses included variables such as age, sex, years of education, number of comorbidities, body mass index, and height. Results: Gait velocity, step count, step length, heel-to-heel base support, swing and stance phase duration, and support time were associated with cognitive function. A decrease in gray matter volume in the right pericalcarine area was associated with gait characteristics related to cognitive dysfunction. An increase in the curvature of gray matter in the right entorhinal, right lateral orbitofrontal, right cuneus, and right and left pars opercularis areas was also associated with gait characteristics related to cognitive dysfunction. Conclusion: Since gait impairment is an important factor in determining activities of daily living in patients with mild cognitive impairment, the evaluation of gait and cognitive functions in patients with mild cognitive impairment is important.

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.