BackgroundThere are no disease-modifying treatments for dementia. There is also no consensus on disease modifying outcomes. We aimed to produce the first evidence-based consensus on core outcome measures for trials of disease modification in mild-to-moderate dementia.Methods and findingsWe defined disease-modification interventions as those aiming to change the underlying pathology. We systematically searched electronic databases and previous systematic reviews for published and ongoing trials of disease-modifying treatments in mild-to-moderate dementia. We included 149/22,918 of the references found; with 81 outcome measures from 125 trials. Trials involved participants with Alzheimer’s disease (AD) alone (n = 111), or AD and mild cognitive impairment (n = 8) and three vascular dementia. We divided outcomes by the domain measured (cognition, activities of daily living, biological markers, neuropsychiatric symptoms, quality of life, global). We calculated the number of trials and of participants using each outcome. We detailed psychometric properties of each outcome. We sought the views of people living with dementia and family carers in three cities through Alzheimer’s society focus groups. Attendees at a consensus conference (experts in dementia research, disease-modification and harmonisation measures) decided on the core set of outcomes using these results. Recommended core outcomes were cognition as the fundamental deficit in dementia and to indicate disease modification, serial structural MRIs. Cognition should be measured by Mini Mental State Examination or Alzheimer's Disease Assessment Scale-Cognitive Subscale. MRIs would be optional for patients. We also made recommendations for measuring important, but non-core domains which may not change despite disease modification.LimitationsMost trials were about AD. Specific instruments may be superseded. We searched one database for psychometric properties.InterpretationThis is the first review to identify the 81 outcome measures the research community uses for disease-modifying trials in mild-to-moderate dementia. Our recommendations will facilitate designing, comparing and meta-analysing disease modification trials in mild-to-moderate dementia, increasing their value.Trial registrationPROSPERO no. CRD42015027346.

Background: There is currently no disease-modifying treatment available to halt or delay the progression of the disease pathology in dementia. An agreed core set of the best-available and most appropriate outcomes for disease modification would facilitate the design of trials and ensure consistency across disease modification trials, as well as making results comparable and meta-analysable in future trials. Objectives: To agree a set of core outcomes for disease modification trials for mild to moderate dementia with the UK dementia research community and patient and public involvement (PPI). Data sources: We included disease modification trials with quantitative outcomes of efficacy from (1) references from related systematic reviews in workstream 1; (2) searches of the Cochrane Dementia and Cognitive Improvement Group study register, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Latin American and Caribbean Health Sciences Literature and PsycINFO on 11 December 2015, and clinical trial registries [International Standard Randomised Controlled Trial Number (ISRCTN) and clinicaltrials.gov] on 22 and 29 January 2016; and (3) hand-searches of reference lists of relevant systematic reviews from database searches. Review methods: The project consisted of four workstreams. (1) We obtained related core outcome sets and work from co-applicants. (2) We systematically reviewed published and ongoing disease modification trials to identify the outcomes used in different domains. We extracted outcomes used in each trial, recording how many used each outcome and with how many participants. We divided outcomes into the domains measured and searched for validation data. (3) We consulted with PPI participants about recommended outcomes. (4) We presented all the synthesised information at a conference attended by the wider body of National Institute for Health Research (NIHR) dementia researchers to reach consensus on a core set of outcomes. Results: We included 149 papers from the 22,918 papers screened, referring to 125 individual trials. Eighty-one outcomes were used across trials, including 72 scales [31 cognitive, 12 activities of daily living (ADLs), 10 global, 16 neuropsychiatric and three quality of life] and nine biological techniques. We consulted with 18 people for PPI. The conference decided that only cognition and biological markers are core measures of disease modification. Cognition should be measured by the Mini Mental State Examination (MMSE) or the Alzheimer’s Disease Assessment Scale – Cognitive subscale (ADAS-Cog), and brain changes through structural magnetic resonance imaging (MRI) in a subset of participants. All other domains are important but not core. We recommend using the Neuropsychiatric Inventory for neuropsychiatric symptoms: the Disability Assessment for Dementia for ADLs, the Dementia Quality of Life Measure for quality of life and the Clinical Dementia Rating scale to measure dementia globally. Limitations: Most of the trials inclu...

Context Individuals with type 1 diabetes mellitus (T1DM) have alterations in brain activity that have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain’s resting state activity remains unclear. Objective To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware). Design Observational study. Setting Academic medical center. Participants 27 individuals with T1DM and 12 HC volunteers participated in the study. Intervention All participants underwent blood oxygenation level dependent (BOLD) resting state functional magnetic brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dL)–hypoglycemic (60 mg/dL) clamp. Outcome Changes in resting state functional connectivity. Results Using 2 separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia between HC, T1DM-Aware, and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016). Conclusion These findings provide evidence that individuals with T1DM have changes in the brain’s resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes among individuals with T1DM.

The impact of hypoglycemia awareness (HA) on brain resting state connectivity remains unclear. To examine the impact of HA on the default mode network (DMN), a functional network in the brain that is active during wakeful rest and associated with introspection, 12 T1DM individuals with hypo unawareness (T1DM-UW) (by Clarke score) (7F/5M, age 44±12 years, BMI 26.4±4.2 kg/m2, HbA1c 7.1±0.7) and 15 T1DM-Aware (AW) (10F/5M, age 30±7, BMI 24.5±3.1, HbA1C 7.1±0.9) individuals underwent resting state BOLD fMRI scanning to assess the DMN during a two-step euglycemic (Eu)-hypoglycemic (Hypo) clamp (90-60 mg/dl). T1DM-AW individuals exhibited a hypoglycemia-induced decrease in DMN connectivity (P<0.001). In contrast, T1DM-UW individuals showed no difference in connectivity between conditions. Moreover, the degree of DMN inhibition correlated inversely with plasma cortisol (r=-0.432, p=0.031), norepinephrine (r=-0.40, p=0.038) and positively with hypoglycemia symptom scores (r=0.40, p=0.037). These findings suggest that, unlike T1DM-AW individuals, hypoglycemia fails to elicit changes in the DMN amongst T1DM-UA individuals. Furthermore, the change in DMN connectivity is associated with measures of physiological and interoceptive responses to stress. These findings highlight the need for future studies to investigate whether avoidance of hypoglycemia can restore brain connectivity patterns. Disclosure D. Groskreutz: None. J. Hwang: None. D. Seo: None. C. Lacadie: None. L. Parikh: None. R. Belfort-DeAguiar: Research Support; Self; GlaxoSmithKline plc.. D. Scheinost: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; QuintilesIMS, MannKind Corporation. Research Support; Self; Regeneron Pharmaceuticals, Inc.. Other Relationship; Self; ICON plc..

While studies have investigated changes in brain connectivity following diet and weight loss, it has not yet been explored whether baseline brain connectivity can be used to predict weight loss during diet or to identify individual subjects for which a particular diet may be effective. The following study uses Connectome-based predictive modeling (CPM), a data-driven analysis approach for producing predictive models of individual brain-behavior relationships from brain connectivity data using cross-validation, to help identify individual participants who will lose weight after an 8-week low calorie diet. 16 Healthy OB subjects (10F/6M, age 44.4±8 years, BMI 32.7±2) and 9 T2DM subjects (5F/4M, age 48±9, BMI 33.9±2) underwent functional MRI (fMRI) during a hyperglycemic-euglycemic clamp. Blood-oxygen-level-dependent (BOLD) brain activity was assessed while subjects viewed food (high-calorie, low-calorie) pictures and non-food images. The study was repeated after 8 weeks of a reduced calorie diet. CPM was applied to BOLD activity during hyperglycemia and euglycemia. Analyses show that models built on individual subject brain connectivity matrices averaged between hyperglycemia and euglycemia before an 8-week low calorie diet are able to predict 50.2% and 53.4% of the variance in BMI and weight loss (kg), respectively (p<0.05). While models built on brain connectivity matrices during euglycemia (similar to the post-prandial state), but not hyperglycemia predicted 42.2% of the variance in BMI change (p=0.017). These results suggest the possibility that individual brain connectivity signatures may be used to identify subjects for whom a low calorie diet might be an effective weight loss strategy, while also revealing the relevant functional connections associated with weight loss success. Disclosure D. Groskreutz: None. W. Lam: None. C. Lacadie: None. A. Elshafie: None. J.J. Hwang: None. D. Seo: None. M. Savoye: None. R. Sinha: None. T. Constable: None. R. Sherwin: Other Relationship; Self; ICON plc., IQVIA, MannKind Corporation. R. Belfort-DeAguiar: Research Support; Self; Silver Palate Kitchens, Inc. Funding National Institutes of Health