Left Ventricular Hypertrophy is a predictor of cardiovascular events in elderly hypertensives: hypertension in the the very elderly trial (HYVET)

Objective: We assessed the prognostic value of electrocardiographic left ventricular hypertrophy (LVH) using Sokolow-Lyon (SL-LVH), Cornell Voltage (CV-LVH) or Cornell Product (CP-LVH) Criteria in 3043 hypertensive people aged 80 years and over enrolled in the Hypertension in the Very Elderly Trial. Methods: Multivariate Cox proportional hazard models were used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for all-cause mortality, cardiovascular diseases, stroke and heart failure in participants with and without LVH at baseline. The mean follow-up was 2.1 years. Results: LVH identified by CV- or CP-LVH Criteria was associated with a 1.6 to 1.9-fold risk of cardiovascular disease and stroke. The presence of CP-LVH was associated with an increased risk of heart failure (HR 2.38, 95% CL 1.16-4.86). In gender specific analyses, CV-LVH (HR 1.94, 95%Cl 1.06-3.55) and CP-LVH (HR 2.36, 95% CI 1.25-4.45) were associated with an increased risk of stroke in women and of heart failure in men, CV-LVH (HR 6.47, 95 % Cl 1.41-29.79) and CP-LVH (10.63, 95Cl % 3.58-31.57), respectively. There was no significant increase in the risk of any outcomes associated with SL LVH. LVH identified by these three methods was not a significant predictor of all-cause mortality. Conclusions: Use of Cornell Voltage and Cornell Product criteria for LVH predicted the risk of cardiovascular disease and stroke. Only Cornell Product was associated with an increased the risk of heart failure. This was particularly the case in men. The identification of electrocardiographic LVH proved to be important in very elderly hypertensive people

Fracture risk and the use of a diuretic (indapamide sr) ± perindopril: a substudy of the Hypertension in the Very Elderly Trial (HYVET)

BACKGROUND: The Hypertension in the Very Elderly Trial (HYVET) is a placebo controlled double blind trial of treating hypertension with indapamide Slow Release (SR) ± perindopril in subjects over the age of 80 years. The primary endpoints are stroke (fatal and non fatal). In view of the fact that thiazide diuretics and indapamide reduce urinary calcium and may increase bone mineral density, a fracture sub study was designed to investigate whether or not the trial anti-hypertensive treatment will reduce the fracture rate in very elderly hypertensive subjects. METHODS: In the trial considerable care is taken to ascertain any fractures and to identify risk factors for fracture, such as falls, co-morbidity, drug treatment, smoking and drinking habits, levels of activity, biochemical abnormalities, cardiac irregularities, impaired cognitive function and symptoms of orthostatic hypotension. POTENTIAL RESULTS: The trial is expected to provide 10,500 patient years of follow-up. Given a fracture rate of 40/1000 patient years and a 20% difference in fracture rate, the power of the sub study is 58% to detect this difference at the 5% level of significance. The corresponding power for a reduction of 25% is 78%. CONCLUSION: The trial is well under way, expected to complete in 2009, and on target to detect, if present, the above differences in fracture rate

Left ventricular hypertrophy and incident cognitive decline in older adults with hypertension

The association between raised blood pressure and increased risk of subsequent cognitive decline is well known. Left ventricular hypertrophy (LVH), as a marker of hypertensive target organ damage, may help identify those at risk of cognitive decline. We assessed whether LVH was associated with subsequent cognitive decline or dementia in hypertensive participants aged ≥80 years in the randomized, placebo-controlled Hypertension in the Very Elderly Trial. LVH was assessed using 12-lead electrocardiography (ECG) based on the Cornell Product (CP-LVH), Sokolow-Lyon (SL-LVH), and Cornell Voltage (CV-LVH) criteria. The Mini-Mental State Examination (MMSE) was used to assess cognitive function at baseline and annually. A fall in MMSE to 3 points were defined as cognitive decline and triggered dementia screening (Diagnostic Statistical Manual IV). Death was defined as a competing event. Fine-Gray regression models were used to examine the relationship between baseline LVH and cognitive outcomes. There were 2645 in the analytical sample, including 201 (7.6%) with CP-LVH, 225 (8.5%) SL-LVH and 251 (9.5%) CV-LVH. CP-LVH was associated with increased risk of cognitive decline, subdistribution hazard ratio (sHR)1.3 (95% confidence interval (CI) 1.01-1.67) in multivariate analyses. SL-LVH and CV-LVH were not associated with cognitive decline (sHR1.06 (95% CI 0.82-1.37) and sHR1.13 (95% CI 0.89-1.43), respectively). LVH was not associated with dementia. LVH may be related to subsequent cognitive decline, but evidence was inconsistent depending on ECG criterion and there were no associations with incident dementia. Additional work is needed to understand the relationships between blood pressure, LVH assessment and cognition. [Abstract copyright: © 2022. The Author(s).

The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement

INTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study. METHODS: ADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition. RESULTS: Multimodal analyses will provide insight into AD pathophysiology and disease progression. DISCUSSION: ADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments

Haemoglobin, anaemia, dementia and cognitive decline in the elderly, a systematic review

<p>Abstract</p> <p>Background</p> <p>Anaemia may increase risk of dementia or cognitive decline. There is also evidence that high haemoglobin levels increase risk of stroke, and consequently possible cognitive impairment. The elderly are more at risk of developing dementia and are also more likely to suffer from anaemia, although there is relatively little longitudinal literature addressing this association.</p> <p>Methods</p> <p>To evaluate the evidence for any relationship between incident cognitive decline or dementia in the elderly and anaemia or haemoglobin level, we conducted a systematic review and meta-analyses of peer reviewed publications. Medline, Embase and PsychInfo were searched for English language publications between 1996 and 2006. Criteria for inclusion were longitudinal studies of subjects aged ≥65, with primary outcomes of incident dementia or cognitive decline. Other designs were excluded.</p> <p>Results</p> <p>Three papers were identified and only two were able to be combined into a meta-analysis. The pooled hazard ratio for these two studies was 1.94 (95 percent confidence intervals of 1.32–2.87) showing a significantly increased risk of incident dementia with anaemia. It was not possible to investigate the effect of higher levels of haemoglobin.</p> <p>Conclusion</p> <p>Anaemia is one factor to bear in mind when evaluating risk of incident dementia. However, there are few data available and the studies were methodologically varied so a cautionary note needs to be sounded and our primary recommendation is that further robust research be carried out.</p

Impact of the Alzheimer's Disease Neuroimaging Initiative, 2004 to 2014

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. METHODS: We searched for ADNI publications using established methods. RESULTS: ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. DISCUSSION: ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials

Autosomal dominant and sporadic late onset Alzheimer's disease share a common in vivo pathophysiology

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease