Diabetes, hemoglobin A1c, and cognitive performance in older adults: is there any impact of frailty? Evidence from the ELSI-Brazil study.

The aim of this study was to evaluate the association between diabetes and cognitive performance in a nationally representative study in Brazil. We also aimed to investigate the interaction between frailty and diabetes on cognitive performance. A cross-sectional analysis of the Brazilian Longitudinal Study of Aging (ELSI-Brazil) baseline data that included adults aged 50 years and older was conducted. Linear regression models were used to study the association between diabetes and cognitive performance. A total of 8,149 participants were included, and a subgroup analysis was performed in 1,768 with hemoglobin A1c data. Diabetes and hemoglobin A1c levels were not associated with cognitive performance. Interaction of hemoglobin A1c levels with frailty status was found on global cognitive z-score (P-value for interaction=0.038). These results suggested an association between higher hemoglobin A1c levels and lower cognitive performance only in non-frail participants. Additionally, undiagnosed diabetes with higher hemoglobin A1c levels was associated with both poor global cognitive (ß=-0.36; 95%CI: -0.62; -0.10, P=0.008) and semantic verbal fluency performance (ß=-0.47; 95%CI: -0.73; -0.21, P=0.001). In conclusion, higher hemoglobin A1c levels were associated with lower cognitive performance among non-frail participants. Higher hemoglobin A1c levels without a previous diagnosis of diabetes were also related to poor cognitive performance. Future longitudinal analyses of the ELSI-Brazil study will provide further information on the role of frailty in the association of diabetes and glycemic control with cognitive decline.

Diabetes, hemoglobin A1c, and cognitive performance in older adults: is there any impact of frailty? Evidence from the ELSI-Brazil study

Abstract The aim of this study was to evaluate the association between diabetes and cognitive performance in a nationally representative study in Brazil. We also aimed to investigate the interaction between frailty and diabetes on cognitive performance. A cross-sectional analysis of the Brazilian Longitudinal Study of Aging (ELSI-Brazil) baseline data that included adults aged 50 years and older was conducted. Linear regression models were used to study the association between diabetes and cognitive performance. A total of 8,149 participants were included, and a subgroup analysis was performed in 1,768 with hemoglobin A1c data. Diabetes and hemoglobin A1c levels were not associated with cognitive performance. Interaction of hemoglobin A1c levels with frailty status was found on global cognitive z-score (P-value for interaction=0.038). These results suggested an association between higher hemoglobin A1c levels and lower cognitive performance only in non-frail participants. Additionally, undiagnosed diabetes with higher hemoglobin A1c levels was associated with both poor global cognitive (β=-0.36; 95%CI: -0.62; -0.10, P=0.008) and semantic verbal fluency performance (β=-0.47; 95%CI: -0.73; -0.21, P=0.001). In conclusion, higher hemoglobin A1c levels were associated with lower cognitive performance among non-frail participants. Higher hemoglobin A1c levels without a previous diagnosis of diabetes were also related to poor cognitive performance. Future longitudinal analyses of the ELSI-Brazil study will provide further information on the role of frailty in the association of diabetes and glycemic control with cognitive decline.

Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study.

The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants' mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.

Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study

The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants' mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.

Association of sleep disturbances with sarcopenia and its defining components: the ELSA-Brasil study.

Sarcopenia and sleep problems share common physiopathology. We aimed to investigate the association of sleep disturbances with sarcopenia and its defining components in Brazilian middle-aged and older adults. In this cross-sectional analysis of the second wave of the ELSA-Brasil study, we included data from 7948 participants aged 50 years and older. Muscle mass was evaluated by bioelectrical impedance analysis and muscle strength by hand-grip strength. Sarcopenia was defined according to the Foundation for the National Institutes of Health criteria. Sleep duration and insomnia complaint were self-reported. Short sleep duration was considered as ≤6 h/night and long sleep duration as >8 h/night. High risk of obstructive sleep apnea (OSA) was assessed using the STOP-Bang questionnaire. Possible confounders included socio-demographic characteristics, lifestyle, clinical comorbidities, and use of sedatives and hypnotics. The frequencies of sarcopenia, low muscle mass, and low muscle strength were 1.6, 21.1, and 4.1%, respectively. After adjustment for possible confounders, high risk of OSA was associated with low muscle mass (OR=2.17, 95%CI: 1.92-2.45). Among obese participants, high risk of OSA was associated with low muscle strength (OR=1.68, 95%CI: 1.07-2.64). However, neither short nor long sleep duration or frequent insomnia complaint were associated with sarcopenia or its defining components. In conclusion, high risk of OSA was associated with low muscle mass in the whole sample and with low muscle strength among obese participants. Future studies are needed to clarify the temporal relationship between both conditions.

Ideal vascular health and cognitive performance in the Brazilian Longitudinal Study of Adult Health.

BACKGROUND AND PURPOSE: Most evidence for the association between ideal vascular health (IVH) and cognitive performance comes from high income countries. The aim was to investigate this association in the Brazilian Longitudinal Study of Adult Health. METHODS: Cognition was assessed using the word list, verbal fluency and trail making tests. The IVH score included ideal metrics for body mass index, smoking, physical activity, diet, blood pressure, fasting glucose and total cholesterol. Poor, intermediate and optimal health were characterized in those presenting 0-2, 3-4, 5-7 ideal metrics, respectively. To determine the association between IVH score and cognitive performance, linear regression models adjusted for age, sex, education, race, alcohol use, depression and thyroid function were used. RESULTS: In 12 271 participants, the mean age was 51.3 ± 8.9 years, 54% were women, 57% White and 53% had poor vascular health. Participants with intermediate (ß = 0.064, 95% confidence interval 0.033; 0.096) and optimal health (ß = 0.108, 95% confidence interval 0.052; 0.164) had better global cognitive Z-scores. In addition, interactions of IVH score with age, education and race were found, suggesting a better cognitive performance with higher IVH in older adults, Black/Brown participants and those with lower levels of education. CONCLUSION: Ideal vascular health was associated with better cognitive performance. Older, Black/Brown and low-educated participants had better cognition in the presence of higher IVH scores.

Association of sleep disturbances with sarcopenia and its defining components: the ELSA-Brasil study

Sarcopenia and sleep problems share common physiopathology. We aimed to investigate the association of sleep disturbances with sarcopenia and its defining components in Brazilian middle-aged and older adults. In this cross-sectional analysis of the second wave of the ELSA-Brasil study, we included data from 7948 participants aged 50 years and older. Muscle mass was evaluated by bioelectrical impedance analysis and muscle strength by hand-grip strength. Sarcopenia was defined according to the Foundation for the National Institutes of Health criteria. Sleep duration and insomnia complaint were self-reported. Short sleep duration was considered as ≤6 h/night and long sleep duration as >8 h/night. High risk of obstructive sleep apnea (OSA) was assessed using the STOP-Bang questionnaire. Possible confounders included socio-demographic characteristics, lifestyle, clinical comorbidities, and use of sedatives and hypnotics. The frequencies of sarcopenia, low muscle mass, and low muscle strength were 1.6, 21.1, and 4.1%, respectively. After adjustment for possible confounders, high risk of OSA was associated with low muscle mass (OR=2.17, 95%CI: 1.92-2.45). Among obese participants, high risk of OSA was associated with low muscle strength (OR=1.68, 95%CI: 1.07-2.64). However, neither short nor long sleep duration or frequent insomnia complaint were associated with sarcopenia or its defining components. In conclusion, high risk of OSA was associated with low muscle mass in the whole sample and with low muscle strength among obese participants. Future studies are needed to clarify the temporal relationship between both conditions.

Association between ideal cardiovascular health and depression incidence: a longitudinal analysis of ELSA-Brasil.

OBJECTIVE: We investigated whether ideal cardiovascular health (ICH), a metric proposed by the American Heart Association, predicts depression development. METHODS: Cohort analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Adults with no current depression and other common mental disorders, cardiovascular diseases, and antidepressant drug use at baseline had their ICH (composite score of smoking, dietary habits, body mass index, blood pressure, fasting glucose, cholesterol, and physical activity) assessed and classified into poor, intermediate, and optimal. Depression was assessed using the Clinical Interview Schedule-Revised (CIS-R). Poisson regression models, adjusted for sociodemographic factors and alcohol consumption, were employed. Stratified analyses were performed for age and sex. RESULTS: We included 9214 participants (mean age 52 ± 9 years, 48.6% women). Overall depression incidence at 3.8-year follow-up was 1.5%. Intermediate and poor ICH significantly increased the risk rate (RR) of developing depression (2.48 [95%CI 1.06-5.78] and 3 [1.28-7.03], respectively) at a 3.8-year follow-up. Higher ICH scores decreased the rate of depression development (RR = 0.84 [0.73-0.96] per metric). Stratified analyses were significant for women and adults < 55 years old. CONCLUSIONS: Poor cardiovascular health tripled depression risk at follow-up in otherwise healthy adults. Ameliorating cardiovascular health might decrease depression risk development.

Hemodynamic and metabolic profile in offspring of malignant hypertensive parents.

Malignant hypertension is a serious form of arterial hypertension in which the physiopathological mechanisms include increased activity of the sympathetic nervous system, renin angiotensin system, and endothelium dysfunction. Family history of hypertension is an important predictive factor for hypertension and is associated with metabolic and hemodynamic abnormalities. Studies of these abnormalities in malignant hypertensive offspring have not yet been published. Therefore, we studied 42 offspring of malignant hypertensive parents (OMH group: age, 22+/-7 years; 23 male subjects; 27 white) and 35 offspring of normotensive parents (ONT group: age, 21+/-4 years; 23 male subjects; 25 white). All subjects had blood pressure <140/90 mm Hg. We evaluated body mass index; office blood pressure; 24-hour ambulatory and continuous beat-to-beat blood pressure monitoring (Finapres); biochemical analysis, including total cholesterol and fractions, triglycerides, glucose, and insulin; and hormonal analysis, including plasma renin activity, aldosterone, and catecholamines. The subjects were also submitted to cold pressure test and handgrip measurements. The body mass index was significantly higher in the OMH group (24+/-5 kg/m(2)) than in the ONT group (22+/-4 kg/m(2)). The OMH group showed significantly higher blood pressure and heart rate in office and Finapres measurements (P<0.05). In 24-hour ambulatory monitoring, the OMH group presented higher 24-hour blood pressure and heart rate, higher blood pressure during the night, and higher heart rate variability during the day compared with those of the ONT group. They also presented lower HDL cholesterol, higher levels of plasma insulin and norepinephrine, and higher insulin-to-glucose ratio (P<0.05) than the ONT group. There were no differences in the other biochemical parameters measured. In conclusion, OMH subjects show early hemodynamic, neurohumoral, and metabolic alterations that are typical of hypertensive metabolic syndrome.