The Mexican Cognitive Aging Ancillary Study (Mex-Cog): Study Design and Methods

Objective: Describe the protocol sample and instruments of the Cognitive Aging Ancillary Study in Mexico (Mex-Cog). The study performs an in-depth cognitive assessment in a subsample of older adults of the ongoing Mexican Health and Aging Study (MHAS). The Mex-Cog is part of the Harmonized Cognitive Assessment Protocol (HCAP) design to facilitate cross-national comparisons of the prevalence and trends of dementia in aging populations around the world, funded by the National Institute on Aging (NIA). Methods: The study protocol consists of a cognitive assessment instrument for the target subject and an informant questionnaire. All cognitive measures were selected and adapted by a team of experts from different ongoing studies following criteria to warrant reliable and comparable cognitive instruments. The informant questionnaire is from the 10/66 Dementia Study in Mexico. Results: A total of 2,265 subjects aged 55-104 years participated, representing a 70% response rate. Validity analyses showed the adequacy of the content validity, proper quality-control procedures that sustained data integrity, high reliability, and internal structure. Conclusions: The Mex-Cog study provides in-depth cognitive data that enhances the study of cognitive aging in two ways. First, linking to MHAS longitudinal data on cognition, health, genetics, biomarkers, economic resources, health care, family arrangements, and psychosocial factors expands the scope of information on cognitive impairment and dementia among Mexican adults. Second, harmonization with other similar studies around the globe promotes cross-national studies on cognition with comparable data. Mex-Cog data is publicly available at no cost to researchers

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Socioeconomic Status and Longitudinal Lung Function of Healthy Mexican Children

<div><p>Introduction</p><p>Our aim was to estimate the longitudinal effect of Socioeconomic status (SES) on lung function growth of Mexican children and adolescents.</p><p>Materials and Methods</p><p>A cohort of Mexican children in third grade of primary school was followed with spirometry twice a year for 6 years through secondary school. Multilevel mixed-effects lineal models were fitted for the spirometric variables of 2,641 respiratory-healthy Mexican children. Monthly family income (in 2002 U.S. dollars [USD]) and parents’ years completed at school were used as proxies of SES.</p><p>Results</p><p>Individuals with higher SES tended to have greater height for age, and smaller sitting height/standing height and crude lung function. For each 1-year increase of parents’ schooling, Forced expiratory volume in 1 sec (FEV₁) and Forced vital capacity (FVC) increased 8.5 (0.4%) and 10.6 mL (0.4%), respectively (<i>p</i> <0.05) when models were adjusted for gender. Impact of education on lung function was reduced drastically or abolished on adjusting by anthropometric variables and ozone.</p><p>Conclusions</p><p>Higher parental schooling and higher monthly family income were associated with higher lung function in healthy Mexican children, with the majority of the effect likely due to the increase in height-for-age.</p></div

Socioeconomics status (SES) and ozone exposure of the girls studied (means and Standard deviation [SD]).

<p>*2002 U.S. dollars (USD): Exchange rate, $9.66 Mexican pesos per USD</p><p>**Previous 6 months of the daily O₃ 8-hour mean (parts per billion [ppb] from 10 a.m. to 6 p.m.).</p><p>Socioeconomics status (SES) and ozone exposure of the girls studied (means and Standard deviation [SD]).</p

Longitudinal models for spirometric variables and Socioeconomic status (SES), both genders taken together.

<p>^¶Natural logarithm of income in 2002 U.S. dollars (USD)</p><p>^δPrevious 6 months of the daily O₃ 8-hour mean (parts per billion [ppb] 10 a.m. to 6 p.m.)</p><p>^§AIC: Akaike information criterion.</p><p>***<i>p</i> <0.01</p><p>**<i>p</i> <0.05</p><p>*<i>p</i> <0.1.</p><p>Longitudinal models for spirometric variables and Socioeconomic status (SES), both genders taken together.</p

Relationship between height-for-age Z-score and spirometric, sitting height/standing height, and income.

<p>(A) Lung function (FVC and FEV₁), (B) sitting height/standing height, and (C) monthly family income as a function of height for age as Z-score (horizontal axis). Line and shadows represent linear or quadratic regression and 95% Confidence intervals (95% CI) of the regression models. Symbols are means of terciles for height-for-age as described in the text, showing good fit of the models.</p

Characteristics of the children studied by tertile of parents’ schooling.

<p>*Measured in phase 4 only</p><p>**Z-score was calculated with equation published by Martínez-Briseño et al. (13)</p><p>^§Previous 6 months of the daily O₃ 8-hour mean (parts per billion [ppb] from 10 a.m. to 6 p.m.).</p><p>Characteristics of the children studied by tertile of parents’ schooling.</p