Brain health in diverse settings : How age, demographics and cognition shape brain function

Peer reviewe

Brain clocks capture diversity and disparities in aging and dementia

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.</p

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Peer reviewe

Central nervous system involvement in systemic lupus erythematosus: data from the Spanish Society of Rheumatology Lupus Register (RELESSER)

Objectives: To analyze the prevalence, incidence, survival and contribution on mortality of major central nervous system (CNS) involvement in systemic lupus erythematosus (SLE). Methods: Patients fulfilling the SLE 1997 ACR classification criteria from the multicentre, retrospective RELESSER-TRANS (Spanish Society of Rheumatology Lupus Register) were included. Prevalence, incidence and survival rates of major CNS neuropsychiatric (NP)-SLE as a group and the individual NP manifestations cere-brovascular disease (CVD), seizure, psychosis, organic brain syndrome and transverse myelitis were calculated. Furthermore, the contribution of these manifestations on mortality was analysed in Cox regression models adjusted for confounders. Results: A total of 3591 SLE patients were included. Of them, 412 (11.5%) developed a total of 522 major CNS NP-SLE manifestations. 61 patients (12%) with major CNS NP-SLE died. The annual mortality rate for patients with and without ever major CNS NP-SLE was 10.8% vs 3.8%, respectively. Individually, CVD (14%) and organic brain syndrome (15.5%) showed the highest mortality rates. The 10% mortality rate for patients with and without ever major CNS NP-SLE was reached after 12.3 vs 22.8 years, respectively. CVD (9.8 years) and organic brain syndrome (7.1 years) reached the 10% mortality rate earlier than other major CNS NP-SLE manifestations. Major CNS NP-SLE (HR 1.85, 1.29-2.67) and more specifically CVD (HR 2.17, 1.41-3.33) and organic brain syndrome (HR 2.11, 1.19-3.74) accounted as independent prognostic factors for poor survival. Conclusion: The presentation of major CNS NP-SLE during the disease course contributes to a higher mortality, which may differ depending on the individual NP manifestation. CVD and organic brain syndrome are associated with the highest mortality rates.Pathophysiology and treatment of rheumatic disease

Cut-offs and response criteria for the Hospital Universitario la Princesa Index (HUPI) and their comparison to widely-used indices of disease activity in rheumatoid arthritis

Objective To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Methods Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. Results The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if =2, low disease activity if >2 and =5), moderate if >5 and <9 and high if =9. HUPI''s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28''s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. Conclusions The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure &lt; 100 mmHg (n = 1127), estimated glomerular filtration rate &lt; 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Structural inequality and temporal brain dynamics across diverse samples

Structural income inequality — the uneven income distribution across regions or countries — could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries=10; healthy individuals=1,394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey, and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analyzed. Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterized by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporo-posterior regions. These findings might challenge conventional neuroscience approaches that tend to overemphasize the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations

Brain health in diverse settings : how age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1,298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function

Latin America: situation and preparedness facing the multi-country human monkeypox outbreak

Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigación Biomedicina. Pereira, Risaralda, Colombia / Universidad Científica del Sur. Master of Clinical Epidemiology and Biostatistics. Lima, Peru / Latin American network of Monkeypox Virus Research. Pereira, Risaralda, ColombiaUniversity of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina.Hospital Britanico de Buenos Aires. Servicio de Infectología. Buenos Aires, Argentina.University of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina / Hospital de Enfermedades Infecciosas F. J. Muniz. Buenos Aires, Argentina.University of Buenos Aires. Cátedra de Enfermedades Infecciosas. Buenos Aires, Argentina / Hospital de Enfermedades Infecciosas F. J. Muniz. Buenos Aires, Argentina.Hospital Clínico Viedma. Cochabamba, Bolivia.Gobierno Autonomo Municipal de Cochabamba. Secretaría de Salud. Centros de Salud de Primer Nivel. Direction. Cochabamba, Bolivia.Franz Tamayo University. National Research Coordination. La Paz, Bolivia.Paulista State University Júlio de Mesquita Filho. Botucatu Medical School. Infectious Diseases Department. São Paulo, SP, Brazil / Brazilian Society for Infectious Diseases. Sãao Paulo, SP, Brazil.Universidade de São Paulo. Faculdade de Saúde Pública. Departamento de Epidemiologia. São Paulo, SP, Brazil.Institute of Infectious Diseases Emilio Ribas. São Paulo, Brazil.Ministério da Saúde. Secretaria de Ciência, Tecnologia, Inovação e Insumos Estratégicos. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Centro de Referencia de Salud Dr. Salvador Allende Gossens. Policlínico Neurología. Unidad Procedimientos. Santiago de Chile, Chile.Pontificia Universidad Católica de Chile. School of Medicine. Department of Pediatric Infectious Diseases and Immunology. Santiago de Chile, Chile.Universidad Austral de Chile. Facultad de Medicina. Instituto de Salud Publica. Valdivia, Chile.Ministerio de Salud. Hospital de San Fernando. San Fernando, VI Region, Chile.Fundación Universitaria Autónoma de las Américas. Faculty of Medicine. Grupo de Investigación Biomedicina. Pereira, Risaralda, Colombia.Universidad Nacional de Colombia. Department of Pediatrics. Bogota, DC, Colombia / Hospital Pediatrico La Misericordia. Division of Infectious Diseases. Bogota, DC, Colombia.Hemera Unidad de Infectología IPS SAS. Bogota, Colombia.Hospital San Vicente Fundacion. Rionegro, Antioquia, Colombia.Clinica Imbanaco Grupo Quironsalud. Cali, Colombia / Universidad Santiago de Cali. Cali, Colombia / Clinica de Occidente. Cali, Colombia / Clinica Sebastian de Belalcazar. Valle del Cauca, Colombia.National Institute of Gastroenterology. Epidemiology Unit. La Habana, CubaHospital Salvador Bienvenido Gautier. Santo Domingo, Dominican Republic.Pontificia Universidad Catolica Madre y Maestra. Santiago, Dominican Republic.International University of Ecuador. School of Medicine. Quito, Ecuador.Universidad Tecnica de Ambato. Ambato, Ecuador.Hospital Roosevelt. Guatemala City, Guatemala.Universidad Nacional Autonoma de Honduras. Faculty of Medical Sciences. School of Medical. Unit of Scientific Research. Tegucigalpa, Honduras.Hospital Infantil de Mexico. Federico Gomez, Mexico City, Mexico.Hospital General de Tijuana. Departamento de Infectología. Tijuana, Mexico.Hospital General de Tijuana. Departamento de Infectología. Tijuana, Mexico.Asociacion de Microbiólogos y Químicos Clínicos de Nicaragua. Managua, Nicaragua.Hospital Santo Tomas. Medicine Department-Infectious Diseases Service. Panama City, Panama / Instituto Oncologico Nacional. Panama city, Panama.University of Arizona College of Medicine-Phoenix. Division of Endocrinology. Department of Medicine. Phoenix, AZ, USA / Indian School Rd. Phoenix, AZ, USA.Dirección Nacional de Vigilancia Sanitaria. Dirección de Investigación. Asunción, Paraguay.Universidad Nacional de Asuncion. Faculty of Medical Sciences. Division of Dermatology. Asuncion, Paraguay.Instituto Nacional de Salud del Nino San Borja. Infectious Diseases Division. Lima, Peru / Universidad Privada de Tacna. Facultad de Ciencias de la Salud. Tacna, Peru.Universidad San Juan Bautista. Lima, Peru.Universidad San Ignacio de Loyola. Vicerrectorado de Investigación. Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud. Lima, Peru.Hospital Evangelico de Montevideo. Montevideo, Uruguay.Icahn School of Medicine at Mount Sinai. Molecular and Cell-based Medicine. Department of Pathology. Molecular Microbiology Laboratory. New York, USA / Universidad del Rosario. Facultad de Ciencias Naturales. Centro de Investigaciones en Microbiología y Biotecnología-UR. Bogota, Colombia.Hospital Evangélico de Montevideo. Montevideo, Uruguay / Venezuelan Science Incubator and the Zoonosis and Emerging Pathogens Regional Collaborative Network. Infectious Diseases Research Branch. Cabudare, Lara, Venezuela.Universidad Central de Venezuela. Faculty of Medicine. Caracas, Venezuela.Universidad Central de Venezuela. Faculty of Medicine. Caracas, Venezuela / Biomedical Research and Therapeutic Vaccines Institute. Ciudad Bolivar, Venezuela.Universidad Central de Venezuela. Tropical Medicine Institute, Infectious Diseases Section. Caracas, Venezuela.Instituto Conmemorativo Gorgas de Estudios de la Salud. Clinical Research Department. Investigador SNI Senacyt Panama. Panama City, Panama