RESUMO
Background: The association of moderate and severe dementia with low body mass index (BMI) is well described, but weight decline seems to also occur in individuals with preclinical neuropathologies. Considering that up to one-fifth of individuals with normal cognition meet the criteria for a dementia-related neuropathological diagnosis, autopsy studies are key to detecting preclinical neurodegenerative and cerebrovascular diseases that could be underlying weight changes. Objective: We investigated the association between dementia-related brain lesions and BMI and evaluated whether the cognitive function was a mediator of this association. Methods: In 1,170 participants, sociodemographic data, clinical history, and cognitive post-mortem evaluation were assessed with an informant. Neuropathological evaluation was performed in all cases. Linear regression models were used to investigate the association between neuropathological lesions (exposure variable) and BMI (outcome) adjusted for demographic, clinical, and cognitive variables in the whole sample, and in only those with normal cognition. Corrections for multiple comparisons were performed. In addition, a mediation analysis was performed to investigate the direct and indirect effects of cognitive abilities on the association between neuropathology and BMI. Results: Individuals with lower BMI had a higher burden of neuropathological lesions and poorer cognitive abilities. Only neurofibrillary tangles (NFT) and neuropathological comorbidity were associated with low BMI, while other neurodegenerative and cerebrovascular lesions were not. NFT were indirectly associated with BMI through cognitive abilities, and also directly, even in participants with normal cognition. Conclusions: Neurofibrillary tangles were directly associated with low BMI even in individuals with preclinical Alzheimer's disease.
Assuntos
Índice de Massa Corporal , Cognição , Humanos , Feminino , Masculino , Idoso , Cognição/fisiologia , Idoso de 80 Anos ou mais , Encéfalo/patologia , Emaranhados Neurofibrilares/patologia , Demência/patologia , Demência/epidemiologia , Testes Neuropsicológicos , AutopsiaRESUMO
OBJECTIVE: To identify more severe COVID-19 presentations. METHODS: Consecutive intensive care unit-admitted patients were subjected to a stepwise clustering method. RESULTS: Data from 147 patients who were on average 56 ± 16 years old with a Simplified Acute Physiological Score 3 of 72 ± 18, of which 103 (70%) needed mechanical ventilation and 46 (31%) died in the intensive care unit, were analyzed. From the clustering algorithm, two well-defined groups were found based on maximal heart rate [Cluster A: 104 (95%CI 99 - 109) beats per minute versus Cluster B: 159 (95%CI 155 - 163) beats per minute], maximal respiratory rate [Cluster A: 33 (95%CI 31 - 35) breaths per minute versus Cluster B: 50 (95%CI 47 - 53) breaths per minute], and maximal body temperature [Cluster A: 37.4 (95%CI 37.1 - 37.7)°C versus Cluster B: 39.3 (95%CI 39.1 - 39.5)°C] during the intensive care unit stay, as well as the oxygen partial pressure in the blood over the oxygen inspiratory fraction at intensive care unit admission [Cluster A: 116 (95%CI 99 - 133) mmHg versus Cluster B: 78 (95%CI 63 - 93) mmHg]. Subphenotypes were distinct in inflammation profiles, organ dysfunction, organ support, intensive care unit length of stay, and intensive care unit mortality (with a ratio of 4.2 between the groups). CONCLUSION: Our findings, based on common clinical data, revealed two distinct subphenotypes with different disease courses. These results could help health professionals allocate resources and select patients for testing novel therapies.
OBJETIVO: Identificar apresentações mais graves de COVID-19. MÉTODOS: Pacientes consecutivamente admitidos à unidade de terapia intensiva foram submetidos à análise de clusters por meio de método de explorações sequenciais. RESULTADOS: Analisamos os dados de 147 pacientes, com média de idade de 56 ± 16 anos e Simplified Acute Physiological Score 3 de 72 ± 18, dos quais 103 (70%) demandaram ventilação mecânica e 46 (31%) morreram na unidade de terapia intensiva. A partir do algoritmo de análise de clusters, identificaram-se dois grupos bem definidos, com base na frequência cardíaca máxima [Grupo A: 104 (IC95% 99 - 109) batimentos por minuto versus Grupo B: 159 (IC95% 155 - 163) batimentos por minuto], frequência respiratória máxima [Grupo A: 33 (IC95% 31 - 35) respirações por minuto versus Grupo B: 50 (IC95% 47 - 53) respirações por minuto] e na temperatura corpórea máxima [Grupo A: 37,4 (IC95% 37,1 - 37,7)ºC versus Grupo B: 39,3 (IC95% 39,1 - 39,5)ºC] durante o tempo de permanência na unidade de terapia intensiva, assim como a proporção entre a pressão parcial de oxigênio no sangue e a fração inspirada de oxigênio quando da admissão à unidade de terapia intensiva [Grupo A: 116 (IC95% 99 - 133) mmHg versus Grupo B: 78 (IC95% 63 - 93) mmHg]. Os subfenótipos foram distintos em termos de perfis inflamatórios, disfunções orgânicas, terapias de suporte, tempo de permanência na unidade de terapia intensiva e mortalidade na unidade de terapia intensiva (com proporção de 4,2 entre os grupos). CONCLUSÃO: Nossos achados, baseados em dados clínicos universalmente disponíveis, revelaram dois subfenótipos distintos, com diferentes evoluções de doença. Estes resultados podem ajudar os profissionais de saúde na alocação de recursos e seleção de pacientes para teste de novas terapias.