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With over 14 million people living above 3,500 m, the study of acclimatization and adaptation to high altitude in human populations is of increasing importance, where exposure to high altitude (HA) imposes a blood oxygenation and acid–base challenge. A sustained and augmented hypoxic ventilatory response protects oxygenation through ventilatory acclimatization, but elicits hypocapnia and respiratory alkalosis. A subsequent renally mediated compensatory metabolic acidosis corrects pH toward baseline values, with a high degree of interindividual variability. Differential renal compensation between acclimatizing lowlanders (LL) and Tibetan highlanders (TH; Sherpa) with ascent was previously unknown. We assessed ventilatory and renal acclimatization between unacclimatized LL and TH during incremental ascent from 1,400 m to 4,300 m in age- and sex-matched groups of 15-LL (8F) and 14-TH (7F) of confirmed Tibetan ancestry. We compared respiratory and renally mediated blood acid–base acclimatization (PCO₂, [HCO₃⁻], pH) in both groups before (1,400 m) and following day 8 to 9 of incremental ascent to 4,300 m. We found that following ascent to 4,300 m, LL had significantly lower PCO₂(P<0.0001) and [HCO₃⁻] (P<0.0001), and higher pH (P= 0.0037) than 1,400 m, suggesting respiratory alkalosis and only partial renal compensation. Conversely, TH had significantly lower PCO₂(P< 0.0001) and [HCO₃⁻] (P< 0.0001), but unchanged pH (P= 0.1), suggesting full renal compensation, with significantly lower PCO₂(P= 0.01), [HCO₃⁻] (P< 0.0001) and pH (P= 0.005) than LL at 4,300 m. This demonstration of differential integrative respiratory–renal responses between acclimatizing LL and TH may indicate selective pressure on TH, and highlights the important role of the kidneys in acclimatization. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.