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Neurofilament light chain (NfL) is a novel biomarker of neurodegenerative diseases. It is detectable in the peripheral blood, allowing low-invasive assessment of early signs of neurodegeneration. The level of NfL gradually increases with age; however, what other factors affect it remains unclear. The present study examined the association between blood NfL level and renal function among healthy participants undergoing a health check (n = 43, serum NfL) and patients with diabetes mellitus (n = 188, plasma NfL). All participants were 60 years of age or older; none were diagnosed with dementia. In each group, levels of blood NfL and serum creatinine significantly correlated (coefficient r = 0.50, 0.56). These associations remained statistically significant even after adjustment for age, sex, and body mass index. These findings indicate that blood NfL level might be partially affected by renal function. We recommend measuring renal function for a more precise evaluation of neuroaxonal damage, in particular, among older adults.

Eating is one of the most important daily activities in managing patients with dementia. Although various eating disturbance occur as dementia progresses, to our knowledge, most of the studies focused on a part of eating disturbance such as swallowing and appetite. There have been few comprehensive studies including eating habits and food preference in patients with Alzheimer's disease (AD). The aims of this study were to investigate almost all eating disturbance and to examine the relationship of eating disturbance to dementia stage in AD. A total of 220 patients with AD and 30 normal elderly (NE) subjects were recruited. Eating disturbance was assessed by a comprehensive questionnaire that had been previously validated. Potential relationships between the characteristics of eating disturbance and dementia stage as classified by the Clinical Dementia Rating (CDR) were assessed. Overall, 81.4% of patients with AD showed some eating and swallowing disturbance, whereas only 26.7% of the NE subjects had such a disturbance. Even in an early stage, patients with AD had many types of eating disturbance; \"Appetite change\" was shown in nearly half of the mild AD patients (49.5%). In the moderate stage, the scores of \"change of eating habits and food preference\" were highest, and in the severe stage \"swallowing disturbance\" became critical. In AD, the relationship of dementia stage to eating disturbance differs according to the type of eating disturbance. The relationships between various eating disturbance and the severity of dementia should be considered.

Background Hypersensitivity to antipsychotic drugs is one of the supportive features of dementia with Lewy bodies, and side effects to drugs other than antipsychotics are also known to occur frequently. We experienced a case of dementia with Lewy bodies in which hallucinations and delusions repeatedly appeared and disappeared after administration and discontinuation of mirogabalin and pregabalin. Case presentation The patient, a woman in her late 70s, developed hallucinations and delusional misidentification of places and persons immediately after receiving a prescription of mirogabalin (15 mg daily) for neuropathic pain. After discontinuation of mirogabalin, her hallucinatory delusions improved but remained. Mild dementia and mild parkinsonism were associated, cognitive fluctuations were evident, and dopamine-transporter scintigraphy showed bilateral striatal uptake reduction. Residual psychosis resolved with donepezil. Later, when the pain worsened, pregabalin (25 mg daily) was administered, and the psychosis recurred and resolved with discontinuation. Conclusions Although pregabalin-induced psychosis has been reported at higher doses (300–450 mg daily), it has not been reported at doses as low as those used in this patient. Gabapentinoids may cause psychosis in patients with dementia with Lewy bodies even at low doses, likely due to hypersensitivity to gabapentinoids in DLB.

Cognitive frailty (CF) is a clinical condition defined by the presence of both mild cognitive impairment (MCI) and physical frailty (PF). Elderly with CF are at greater risk of dementia than those with MCI or PF alone, but there are few known clinical or neuroimaging features to reliably distinguish CF from PF or MCI. We therefore conducted a population-based cross-sectional study of community elderly combining physical, cognitive, neuropsychiatric, and multisequence magnetic resonance imaging (MRI) evaluations. The MRI evaluation parameters included white matter (WM) lesion volumes, perivascular and deep subcortical WM lesion grades, lacunar infarct prevalence, microbleed number, and regional medial temporal lobe (MTL) volumes. Participants were divided into 4 groups according to the presence or absence of MCI and PF—(1) no MCI, PF (n = 27); (2) no PF, MCI (n = 119); (3) CF (MCI + PF) (n = 21), (4) normal controls (n = 716). Unique features of CF included shorter one-leg standing time; severe depressive symptoms; and MRI signs of significantly more WM lesions, lacunar infarcts, small-vessel disease lesions, microbleeds, and reduced MTL volumes. These unique deficits suggest that interventions for CF prevention and treatment should focus on motor skills, depressive symptoms, and vascular disease risk factor control.

To characterize Parkinson’s disease, abnormal phase-amplitude coupling is assessed in the cortico-basal circuit using invasive recordings. It is unknown whether the same phenomenon might be found in regions other than the cortico-basal ganglia circuit. We hypothesized that using magnetoencephalography to assess phase-amplitude coupling in the whole brain can characterize Parkinson’s disease. We recorded resting-state magnetoencephalographic signals in patients with Parkinson’s disease and in healthy age- and sex-matched participants. We compared whole-brain signals from the two groups, evaluating the power spectra of 3 frequency bands (alpha, 8–12 Hz; beta, 13–25 Hz; gamma, 50–100 Hz) and the coupling between gamma amplitude and alpha or beta phases. Patients with Parkinson’s disease showed significant beta–gamma phase-amplitude coupling that was widely distributed in the sensorimotor, occipital, and temporal cortices; healthy participants showed such coupling only in parts of the somatosensory and temporal cortices. Moreover, beta- and gamma-band power differed significantly between participants in the two groups ( P  < 0.05). Finally, beta–gamma phase-amplitude coupling in the sensorimotor cortices correlated significantly with motor symptoms of Parkinson’s disease ( P  < 0.05); beta- and gamma-band power did not. We thus demonstrated that beta–gamma phase-amplitude coupling in the resting state characterizes Parkinson’s disease.

Associations between delirium and postoperative adverse events in cardiovascular surgery have been reported and the preoperative identification of high-risk patients of delirium is needed to implement focused interventions. We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium. Patients aged ≥ 40 years who underwent cardiovascular surgery at a single hospital were prospectively enrolled. Preoperative and intraoperative factors were assessed. Each patient was evaluated for postoperative delirium 7 days after surgery. We developed machine learning models using the Bernoulli naive Bayes, Support vector machine, Random forest, Extra-trees, and XGBoost algorithms. Stratified fivefold cross-validation was performed for each developed model. Of the 87 patients, 24 (27.6%) developed postoperative delirium. Age, use of psychotropic drugs, cognitive function (Mini-Cog < 4), index of activities of daily living (Barthel Index < 100), history of stroke or cerebral hemorrhage, and eGFR (estimated glomerular filtration rate) < 60 were selected to develop delirium prediction models. The Extra-trees model had the best area under the receiver operating characteristic curve (0.76 [standard deviation 0.11]; sensitivity: 0.63; specificity: 0.78). XGBoost showed the highest sensitivity (AUROC, 0.75 [0.07]; sensitivity: 0.67; specificity: 0.79). Machine learning algorithms could predict post-cardiovascular delirium using preoperative data. Trial registration : UMIN-CTR (ID; UMIN000049390).

Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer’s disease. We previously developed a novel dual-task system with high accuracy for differentiating patients with neurocognitive disorders from healthy controls. In this study, we aimed to elucidate whether the output value obtained through artificial intelligence assumptions has clinical meaning other than diagnosis labelling. This is a retrospective cross-sectional study. Patients with Alzheimer’s disease dementia, dementia with Lewy bodies, or mild cognitive impairment who participated in our previous dual-task experiment and completed all routine neuropsychological assessments at our hospital within one year of the experimental date were eligible for inclusion in the neurocognitive disorders group. Participants in the healthy control group were recruited from community-dwelling older adults. The correlation between the output value, “y-value”, and each neuropsychological test: Mini-Mental State Examination (MMSE), Addenbrook’s Cognitive Examination, Logical Memory tests, Frontal Assessment Battery, and digit span were assessed by Pearson’s correlation coefficient. We also evaluated the correlation between the MMSE and those neurocognitive tests. To elucidate the diagnostic availability of the dual-task system and the MMSE on this dataset, we conducted a receiver operating characteristic analysis. We enrolled 97 participants in the neurocognitive disorders group: 42 with Alzheimer’s disease dementia, 11 with dementia with Lewy bodies, and 44 with mild cognitive impairment. Additionally, 249 participants were included in the healthy control group. Although the y-value showed significant correlations with several tests, the MMSE demonstrated much stronger significant correlations with a broader range of cognitive tests. Meanwhile, its sensitivity and specificity were 0.969 and 0.912, respectively, and the area under the curve was 0.981, which was higher than the 0.934 of the MMSE. Our new AI-driven dual-task system has a high ability to predict neurocognitive disorders. However, the clinical significance of its output values is limited to screening for neurocognitive disorders and does not extend to estimating cognitive function. When using this system in clinical practice, it is essential to understand its limitations and select the appropriate usage scenarios.

Background Understanding the treatment needs of patients with dementia with Lewy bodies (DLB) is essential to develop treatment strategies. We examined the treatment needs of patients with DLB and their caregivers and the extent to which the attending physicians understand these treatment needs. Methods This was a cross-sectional, observational study conducted using questionnaires for patients, caregivers, and physicians. The study participants included patients, their caregivers, and their attending physicians who were experts in DLB. Fifty-two symptoms that are frequent and clinically important in DLB were pre-selected and classified into seven symptom domains. Treatment needs of patients and caregivers were defined as “symptom that causes them most distress,” and the frequency of each answer was tabulated. To assess the physician’s understanding of the treatment needs of patients and caregivers, patient–physician and caregiver–physician concordance rates for each answer regarding treatment needs were calculated according to symptom domains. Results In total, 263 pairs of patients–caregivers and 38 physicians were surveyed. The mean age of patients was 79.3 years, and their mean total score on the Mini-Mental State Examination was 20.9. Thirty-five and 38 symptoms were selected as symptoms causing patients and caregivers most distress, respectively. Memory impairment was most frequently selected for the treatment needs of patients, followed by constipation and bradykinesia. Memory impairment was also most frequently selected by caregivers, followed by visual hallucinations. For the symptom domain that causes patients or caregivers most distress, only about half of the patient–physician pairs (46.9%) and caregiver–physician pairs (50.8%) were matched. Logistic regression analysis identified that concordance rates for treatment needs between patient–physician and caregiver–physician were lower when autonomic dysfunction and sleep-related disorders were selected as the symptom domains that cause most distress. Conclusion There was considerable variability in the treatment needs of patients with DLB and their caregivers. Attending physicians had difficulty understanding the top treatment needs of their patients and caregivers, despite their expertise in DLB, because of various clinical manifestations. Attending physicians should pay more attention to autonomic dysfunction and sleep-related disorders in the treatment of DLB. Trial registration UMIN Clinical Trials Registry, UMIN000041844. Registered on 23 September 2020