Background: Recent meta-analyses suggest the use of technology-based interventions as a treatment option for obesity in adulthood. Similar meta-analytic approaches for children are scarce. Objective: The aim of this meta-analysis is to examine the effect of technology-based interventions on overweight and obesity treatment in children and adolescents. Methods: A systematic literature search was performed using MEDLINE (PubMed), Scopus, and Cochrane Library for randomized clinical trials to identify interventional studies published between January 2000 and February 2021. Results: In total, 9 manuscripts from 8 clinical trials of 582 children or adolescents were considered eligible. BMI, BMI z-score, and other BMI-related baseline metrics during and after intervention were considered as primary outcomes. In 7 of 8 studies, a technology-based intervention was applied in addition to conventional care. Of the 8 studies, 6 studies were conducted in the United States, 1 in Australia, and 1 in northwestern Europe. In total, 5 studies included adolescents, whereas the rest addressed children aged 9 to 12 years. Intervention duration ranged from 3 to 24 months. Significant differences between groups in BMI metric changes were reported by 5 of the 8 studies. Pooled analysis revealed an overall significant decrease in BMI metrics in the intervention group (standardized mean difference –0.61, 95% CI –1.10 to –0.13; P=.01). Subgroup analysis revealed that significance was lost in case of no parental involvement (standardized mean difference –0.36, 95% CI –0.83 to 0.11; P=.14). The small number of clinical trials found, the varying study quality, and the study heterogeneity are some limitations of this review. Conclusions: The studies reported herein describe functional and acceptable technology-based approaches, in addition to conventional treatments, to enhance weight loss in young populations.

Many lifestyle factors have been linked to cognitive function but little is known about their combined effect. An overall lifestyle pattern for people living in the Mediterranean basin has been proposed, including diet, but also physical activity, sleep and daily living activities with social/intellectual aspects. We aimed to examine the associations between a combination of these lifestyle factors and detailed cognitive performance. A total of 1716 participants from the Hellenic Longitudinal Investigation of Ageing and Diet (HELIAD), a population-based study of participants ≥65 years, were included in this analysis. Lifestyle factors were evaluated using standard, validated questionnaires and a Total Lifestyle Index (TLI) was constructed. Cognitive outcomes included mild cognitive impairment (MCI) diagnosis, a composite z-score (either continuous or with a threshold at the 25th percentile) and z-scores for five cognitive domains. A higher TLI was associated with 65% reduced odds for MCI in the non-demented individuals and 43% reduced odds for low global cognition when MCI participants were excluded, a risk reduction equivalent to 9 and 2.7 fewer years of ageing, respectively. Each lifestyle factor was differentially associated with domain-specific cognitive performance. Our results suggest that a TLI, more so than single lifestyle parameters, may be related to cognitive performance.

Aim: Although there is some evidence of the relationships between sleep duration/quality and nutrient and/or food intake, the associations between sleep and dietary patterns have been poorly explored. The aim of the present study was to evaluate sleep duration and quality in relation to adherence to the Mediterranean diet (MeDi), and to investigate the sex‐ and age‐specific associations in a population‐representative cohort of older adults.Methods: Participants from the Hellenic Longitudinal Investigation of Aging and Diet were included. The sample consisted of 1639 adults aged ≥65 years. Sleep duration and quality were assessed through a self‐report questionnaire, whereas adherence to the MeDi was evaluated by an a priori score.Results: Sleep quality was positively associated with the MeDi in the unadjusted and the adjusted model (age, sex, depression, years of education, body mass index, level of physical activity and total energy intake were added as covariates). In contrast, sleep duration was not associated with MeDi adherence either in the unadjusted or the adjusted models. In relation to the age‐related associations, sleep quality was positively associated with MeDi adherence in those aged ≤75 years, and not in those aged >75 years. Associations between sleep and MeDi did not differ between men and women.Conclusions: The present results suggest that sleep quality is associated with MeDi adherence in older adults; there are also age‐specific associations between sleep quality and the MeDi. Although additional studies are required, improvements in diet quality should be considered in the context of sleep management interventions in older individuals. Geriatr Gerontol Int 2018; 18: 1543–1548.

Cognitive impairment is a rapidly growing public health problem. As there is no curative treatment for dementia, the proactive management of modifiable risk factors and the identification of early biomarkers indicative of the cognitive decline are of great importance. Although nutrition is one of the most extensively studied lifestyle factor in relation to cognitive health, its association with brain magnetic resonance imaging (MRI) biomarkers is not well established. In the present work, we review available studies relating dietary or nutrient patterns with brain MRI biomarkers in dementia-free adults. Greater adherence to the Mediterranean diet has been associated with the preservation of structural connectivity and less brain atrophy in adults without dementia. In addition, specific nutrient patterns, characterized by a high intake of antioxidant vitamins, polyphenols and unsaturated fatty acids, have been related to larger brain volume. Although the results are encouraging regarding the role of dietary and nutrient patterns on imaging biomarkers, more well-designed observational longitudinal studies and clinical trials are needed in order to confirm potentially causal relationships and better understand underlying mechanisms.

Background and Objectives: This article presents data from the ongoing Aiginition Longitudinal Biomarker Investigation of Neurodegeneration study (ALBION) regarding baseline clinical characterizations and CSF biomarker profiles, as well as preliminary longitudinal data on clinical progression. Materials and Methods: As of March 2022, 138 participants who either were cognitively normal (CN, n = 99) or had a diagnosis of mild cognitive impairment (MCI, n = 39) had been recruited at the specialist cognitive disorders outpatient clinic at Aiginition Hospital. Clinical characteristics at baseline were provided. These patients were followed annually to determine progression from CN to MCI or even dementia. CSF biomarker data (amyloid β1-42, phosphorylated tau at threonine 181, and total tau) collected using automated Elecsys^® assays (Roche Diagnostics) were available for 74 patients. These patients were further sorted based on the AT(N) classification model, as determined by CSF Aβ42 (A), CSF pTau (T), and CSF tTau (N). Results: Of the 49 CN patients with CSF biomarker data, 21 (43%) were classified as exhibiting “Alzheimer’s pathologic change” (A+Τ– (Ν)−) and 6 (12%) as having “Alzheimer’s disease” (A+T–(N)+, A+T+(N)–, or A+T+(N)+). Of the 25 MCI patients, 8 (32%) displayed “Alzheimer’s pathologic change”, and 6 (24%) had “Alzheimer’s disease”. A total of 66 individuals had a mean follow-up of 2.1 years (SD = 0.9, min = 0.8, max = 3.9), and 15 of those individuals (22%) showed a clinical progression (defined as a worsening clinical classification, i.e., from CN to MCI or dementia or from MCI to dementia). Overall, participants with the “AD continuum” AT(N) biomarker profile (i.e., A+T–(N)–, A+T–(N)+, A+T+(N)–, and A+T+(N)+) were more likely to clinically progress (p = 0.04). Conclusions: A CSF “AD continuum” AT(N) biomarker profile is associated with an increased risk of future clinical decline in CN or MCI subjects.

Emerging evidence suggests a potential role of gut dysbiosis in neurodegenerative disorders and, in particular, Alzheimer’s disease (AD) pathology and cognitive decline. However, the role of gut microbiome in the early prodromal stages of AD and particularly in mild cognitive impairment (MCI) remains understudied and has been mostly explored in Asian populations with no representation of European populations. To address this research gap in the literature and to suggest novel microbiome features associated with MCI, we conducted a cross-sectional study in a European population sample and profiled gut microbiota in 99 individuals without dementia through 16s ribosomal RNA (rRNA) sequencing. Individuals were categorized by cognitive status based on standard clinical criteria to cognitively normal (n = 49) or individuals with MCI (n = 50). Differential abundance through Microbiome Multivariable Associations with Linear model (MaAsLin2) and elastic net logistic regression analyses were used to identify gut microbiome features associated with MCI. MCI group was older than the CN group and age was used as covariate in the differential abundance analysis. No differences in alpha and beta diversity were found between the two groups (p > 0.05). At false discovery rate (FDR) < 0.05, we identified specific genera associated with MCI, mostly linked to short chain fatty acids (SCFAs) production (e.g., Candidatus_Soleaferrea q = 0.027, MaAsLin2 coefficient = 1.65, Sellimonas q = 0.017, MaAsLin2 coefficient = −4.45), while we highlight nominal (p < 0.05, q > 0.05) correlations of genera (e.g., Hydrogenoanaerobacterium, Subdoligranulum) with metrics of cognitive assessment. Microbiota was shown to have a fairly good discriminative capacity for MCI status (area under the curve AUC = 0.77), with Rothia genus found as the top predictor for MCI (beta coefficient [95% confidence intervals] = 0.224 [0.216–0.233]). Overall, our findings add to current knowledge reporting gut microbiome alterations in MCI by suggesting novel associated microbiome features; however, larger scale longitudinal studies are needed to further elucidate the underlying biological pathways linked to the disease.