BackgroundThe consumption of dairy products may influence the risk of type 2 diabetes mellitus (T2DM), but inconsistent findings have been reported. Moreover, large variation in the types of dairy intake has not yet been fully explored.Methods and ResultsWe conducted a systematic review and meta-analysis to clarify the dose–response association of dairy products intake and T2DM risk. We searched PubMed, EMBASE and Scopus for studies of dairy products intake and T2DM risk published up to the end of October 2012. Random-effects models were used to estimate summary relative risk (RR) statistics. Dose-response relations were evaluated using data from different dairy products in each study. We included 14 articles of cohort studies that reported RR estimates and 95% confidence intervals (95% CIs) of T2DM with dairy products intake. We found an inverse linear association of consumption of total dairy products (13 studies), low-fat dairy products (8 studies), cheese (7 studies) and yogurt (7 studies) and risk of T2DM. The pooled RRs were 0.94 (95% CI 0.91–0.97) and 0.88 (0.84–0.93) for 200 g/day total and low-fat dairy consumption, respectively. The pooled RRs were 0.80 (0.69–0.93) and 0.91 (0.82–1.00) for 30 g/d cheese and 50 g/d yogurt consumption, respectively. We also found a nonlinear association of total and low-fat dairy intake and T2DM risk, and the inverse association appeared to be strongest within 200 g/d intake.ConclusionA modest increase in daily intake of dairy products such as low fat dairy, cheese and yogurt may contribute to the prevention of T2DM, which needs confirmation in randomized controlled trials.

Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD.Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization.Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients.Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.

Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally on standard neuropsychological tests. SCD with the presence of AD pathology is the transitional stage 2 of Alzheimer’s continuum, earlier than the prodromal stage, mild cognitive impairment (MCI), which seems to be the best target to research AD. In this study, we aimed to detect the transformational patterns of the intrinsic brain activity as the disease burden got heavy.Method: In this study, we enrolled 44 SCD, 55 amnestic MCI (aMCI), 47 AD dementia (d-AD) patients and 57 normal controls (NC) in total. A machine learning classification was utilized to detect identification accuracies between groups by using ALFF, fALFF, and fusing ALFF with fALFF features. Then, we measured the amplitude of the low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) levels in three frequency bands (classic: 0.01–0.1 Hz; slow-5: 0.01–0.027 Hz; and slow-4: 0.027–0.073 Hz) and compared alterations in patients with NC.Results: In the machine learning verification, the identification accuracy of SCD, aMCI, d-AD from NC was higher when fused ALFF and fALFF features (76.44, 81.94, and 91.83%, respectively) than only using ALFF or fALFF features. Several brain regions showed significant differences in ALFF/fALFF within these bands among four groups: brain regions presented decreasing trend of values, including the Cingulum_Mid_R (aal), bilateral inferior cerebellum lobe, bilateral precuneus, and the Cingulum_Ant_R (aal); increasing trend of values were detected in the Hippocampus_L (aal), Frontal_Mid_Orb_R (aal), Frontal_Sup_R (aal) and Paracentral_Lobule_R (aal) as disease progressed. The normalized ALFF/fALFF values of these features were significantly correlated with the neuropsychological test scores.Conclusion: This study revealed gradual disturbances in intrinsic brain activity as the disease progressed: the normal objective performance in SCD may be dependent on compensation; as disease advanced, the cognitive function gradually impaired and decompensated in aMCI, severer in d-AD. Our results indicated that the ALFF and fALFF may help detect the underlying pathological mechanism in AD continuum.Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02353884 and NCT02225964.

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Aim To investigate the factors associated with the duration of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) RNA shedding in patients with coronavirus disease 2019 (COVID‐19). Methods A retrospective cohort of COVID‐19 patients admitted to a designated hospital in Beijing was analyzed to study the factors affecting the duration of viral shedding. Results The median duration of viral shedding was 11 days (IQR, 8‐14.3 days) as measured from illness onset. Univariate regression analysis showed that disease severity, corticosteroid therapy, fever (temperature>38.5℃), and time from onset to hospitalization were associated with prolonged duration of viral shedding ( p <0.05). Multivariate regression analysis showed that fever (temperature>38.5℃) (OR 5.1, 95%CI: 1.5‐18.1), corticosteroid therapy (OR 6.3, 95%CI: 1.5‐27.8), and time from onset to hospitalization (OR 1.8, 95%CI: 1.19‐2.7) were associated with increased odds of prolonged duration of viral shedding. Conclusions Corticosteroid treatment, fever (temperature>38.5℃), and longer time from onset to hospitalization were associated with prolonged viral shedding in COVID‐19 patients. This article is protected by copyright. All rights reserved.