RESUMO
BACKGROUND: Patterns of cognitive change and modifiable factors for cognitive decline versus stable cognitive trajectories have rarely been described in lower-educated older adults. OBJECTIVES: We aimed to identify long-term trajectories of cognitive functioning and possible factors associated with cognitive decline. DESIGN AND PARTICIPANTS: We used data from 1,042 adults aged ≥ 60 participating in the Health, Welfare and Aging Study (SABE), São Paulo, Brazil, without cognitive impairment at baseline. Data were collected across four waves (2000-2015). Group-based trajectory modelling was used to identify cognitive trajectories. Associations with socioeconomic variables, childhood background, lifestyle, and cardiovascular risk factors were explored using weighted multinomial logistic regressions. MEASUREMENTS: The abbreviated Mini-Mental State Examination was used to measure cognition. RESULTS: Three cognitive trajectories were identified: stable (n= 754, 68.6%), mild-decline (n= 183, 20.8%), and strong-decline (n= 105, 10.7%). At baseline, respondents in the strong-decline group were more likely to be older than those with stable and mild-decline trajectories. Furthermore, participants in both the mild and strong-decline groups were more likely to have no schooling, be divorced/separated, receive less than 4 monthly wages, and be underweight (BMI < 18.5) compared to the stable group. Finally, the mild-decline group was more likely to have lived in rural areas during childhood than participants located in a stable trajectory. CONCLUSIONS: Our findings suggest that interventions to reduce cognitive decline for low-educated older adults might include strategies addressing inequalities and improving modifiable risk factor burden.
Assuntos
Disfunção Cognitiva , Escolaridade , Fatores de Proteção , Humanos , Masculino , Disfunção Cognitiva/epidemiologia , Feminino , Brasil/epidemiologia , Idoso , Fatores de Risco , Seguimentos , Pessoa de Meia-Idade , Fatores Socioeconômicos , Idoso de 80 Anos ou mais , Testes de Estado Mental e DemênciaRESUMO
OBJECTIVE: To identify gestational weight gain trajectories and their association with demographic, obstetric, and anthropometric characteristics. METHOD: Information of 599 pregnant women undergoing prenatal care in public health units was collected through questionnaires and medical records, resulting in 4,272 measures of body weight. Group-based trajectory models were applied to identify patterns of gestational weight gain. RESULTS: The average gestational weight gain at 40 weeks was 14.1 kg (95%CI: 13.2, 15.0). Three different trajectory groups were identified: low (9.9%), intermediate (66.4%), and high (23.7%). Pregnant women who smoked and those classified above IOM's weight gain recommendations were more likely to be classified in the group with a high weight gain trajectory; while those with pre-pregnancy overweight or obesity, and with greater parity were more likely to be categorized in the low weight gain trajectory. After adjusting for other variables in the model, parity remained significantly associated with gestational weight gain (ß: 0.88; 95%CI: 0.27, 0.73). In the multilevel model, weight gain remained different among the categories of group-based trajectory, even after adjustment for other investigated variables for intermediate (ß: 2.94; 95%CI: 2.32, 3.58) and high (ß: 5.88; 95%CI: 5.09, 6.66) gestational weight gain trajectory groups. CONCLUSIONS: Behavioral, obstetric, and anthropometric characteristics determined the belonging to groups with a high or low trajectory of gestational weight gain. The findings contribute to a better understanding of patterns of gestational weight gain in middle-income women groups.
RESUMO
OBJECTIVE: To identify body mass index (BMI) trajectories from birth to age 10 years and to assess their association with child and parental characteristics and with adult nutritional status and metabolic risk factors. STUDY DESIGN: Retrospective cohort study with 1188 subjects aged 20-60 years. Childhood growth was assessed using measured weight and height data collected retrospectively from health booklets, which also provided information on gestational age, birth weight, and early nutrition. Height, weight, waist circumference, fasting blood glucose, lipids profile, and blood pressure were measured at adulthood. Participants self-reported parental silhouette based on a 9-figural scale. Group-based modeling was applied to identify BMI trajectories. Associations were assessed using ANOVA and multiple logistic regression. RESULTS: Five growth trajectories following or crossing BMI percentiles emerged: stable-25th (15.3% of the sample), stable-50th (35.9%), stable-75th (28.0%), ascending-75th (19.2%), and ascending-obesity (1.6%). Overall, associated factors from early life were mother's corpulence (higher in the ascending-obesity group), gestational age (higher in the stable-50th, stable-75th, and in the ascending-obesity groups), and birth weight (higher in the ascending-obesity group) (all P < .05). Childhood trajectories were associated with adult BMI and waist circumference (higher in the stable-75th and in the ascending groups) (all P < .0001). CONCLUSIONS: This study shows heterogeneity in patterns of growth trajectories. Specific trajectories were associated with greater BMI and waist circumference during adulthood. Monitoring growth trajectories may improve chronic disease prevention.