Benfotiamine and Cognitive Decline in Alzheimer’s Disease: Results of a Randomized Placebo-Controlled Phase IIa Clinical Trial

Background: In preclinical models, benfotiamine efficiently ameliorates the clinical and biological pathologies that define Alzheimer’s disease (AD) including impaired cognition, amyloid-β plaques, neurofibrillary tangles, diminished glucose metabolism, oxidative stress, increased advanced glycation end products (AGE), and inflammation. Objective:To collect preliminary data on feasibility, safety, and efficacy in individuals with amnestic mild cognitive impairment (aMCI) or mild dementia due to AD in a placebo-controlled trial of benfotiamine. Methods:A twelve-month treatment with benfotiamine tested whether clinical decline would be delayed in the benfotiamine group compared to the placebo group. The primary clinical outcome was the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog). Secondary outcomes were the clinical dementia rating (CDR) score and fluorodeoxyglucose (FDG) uptake, measured with brain positron emission tomography (PET). Blood AGE were examined as an exploratory outcome. Results:Participants were treated with benfotiamine (34) or placebo (36). Benfotiamine treatment was safe. The increase in ADAS-Cog was 43% lower in the benfotiamine group than in the placebo group, indicating less cognitive decline, and this effect was nearly statistically significant (p = 0.125). Worsening in CDR was 77% lower (p = 0.034) in the benfotiamine group compared to the placebo group, and this effect was stronger in the APOE ɛ4 non-carriers. Benfotiamine significantly reduced increases in AGE (p = 0.044), and this effect was stronger in the APOE ɛ4 non-carriers. Exploratory analysis derivation of an FDG PET pattern score showed a treatment effect at one year (p = 0.002). Conclusion:Oral benfotiamine is safe and potentially efficacious in improving cognitive outcomes among persons with MCI and mild AD

Domains of Chronic Stress, Lifestyle Factors, and Allostatic Load in Middle-Aged Mexican-American Women

Abstract available at publisher's website

Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

Background: Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases. Methods: We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically ‘favourable adiposity’ (FA) and ‘unfavourable adiposity’ (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases. Results: MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism. Conclusions: Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthyDiabetes UK (17/0005594); Medical Research Council (MR/T002239/1)l; World Cancer Research Fund (IIG_2019_2009); Medical Research Council (MC_UU_00011/1); Diabetes UK (17/0005587); Cancer Research UK (C18281/A29019)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Increased Cardiovascular Risk Associated with Reduced Kidney Function

BACKGROUND: Individuals with chronic kidney disease (CKD) are at substantial risk for cardiovascular mortality, but the risk associated with specific glomerular filtration rates (GFRs) is unknown. The objective of this study was to investigate the relationship between level of kidney function and the risk of cardiovascular mortality in a diverse population. METHODS AND RESULTS: This was a nonconcurrent cohort study of 34,982 ambulatory patients. Kidney function was entered into the model as a time-dependent variable to minimize misclassification and allow for improved estimate of the effect of decreasing GFR on cardiovascular mortality. The adjusted hazard ratio for cardiovascular mortality was 1.00 (95% CI 0.93-1.06) with an estimated GFR (eGFR) of 45-59; 1.77 (95% CI 1.65-1.89) with an eGFR 30-44; 3.75 (95% CI 3.47-4.06) with an eGFR 15-29, and 3.83 (95% CI 3.40-4.33) with an eGFR \u3c15. CONCLUSION: We demonstrate a graded risk of cardiovascular mortality with decreasing GFR, with a marked increase with an eGFR \u3c45 ml/min/1.73 m(2). These data also suggest that the availability of eGFR to physicians has had little impact on reducing the cardiovascular risk facing individuals with CKD. Our findings further highlight the public health significance of CKD and the importance of its early identification and management to reduce cardiovascular mortality

TDCS does not enhance the effects of robot-assisted gait training in patients with subacute stroke

Background: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique, which can modulate cortical excitability and combined with rehabilitation therapies may improve motor recovery after stroke. Objective: Our aim was to study the feasibility of a 4-week robotic gait training protocol combined with tDCS, and to study tDCS to the leg versus hand motor cortex or sham to improve walking ability in patients after a subacute stroke. Methods: Forty-nine subacute stroke patients underwent 20 daily sessions (5 days a week for 4 weeks) of robotic gait training combined with tDCS. Patients were assigned either to the tDCSleg group (n = 9), receiving 2mA anodal tDCS over the motor cortex leg representation (vertex), or an active control group (n = 17) receiving anodal tDCS over the hand motor cortex area (tDCShand). In addition, we studied 23 matched patients in a control group receiving gait training without tDCS (notDCS). Study outcomes included gait speed (10-meter walking test), and quality of gait, using the Functional Ambulatory Category (FAC) before and after the 4-week training period. Results: Only one patient did not complete the treatment because he presented a minor side-effect. Patients in all three groups showed a significantly improvement in gait speed and FAC. The tDCSleg group did not perform better than the tDCShand or notDCS group. Conclusion: Combined tDCS and robotic training is a safe and feasible procedure in subacute stroke patients. However, adding tDCS to robot-assisted gait training shows no benefit over robotic gait training alone