A Simulation of the Effects of Diffusion on Hyperpolarized [1-13C]-Pyruvate Signal Evolution.

OBJECTIVE: Hyperpolarized [1-13C]-pyruvate magnetic resonance imaging is an emerging metabolic imaging method that offers unprecedented spatiotemporal resolution for monitoring tumor metabolism in vivo. To establish robust imaging biomarkers of metabolism, we must characterize phenomena that may modulate the apparent pyruvate-to-lactate conversion rate (kPL). Here, we investigate the potential effect of diffusion on pyruvate-to-lactate conversion, as failure to account for diffusion in pharmacokinetic analysis may obscure true intracellular chemical conversion rates. METHODS: Changes in hyperpolarized pyruvate and lactate signal were calculated using a finite-difference time domain simulation of a two-dimensional tissue model. Signal evolution curves with intracellular kPL values from 0.02 to 1.00 s-1 were analyzed using spatially invariant one-compartment and two-compartment pharmacokinetic models. A second spatially variant simulation incorporating compartmental instantaneous mixing was fit with the same one-compartment model. RESULTS: When fitting with the one-compartment model, apparent kPL underestimated intracellular kPL by approximately 50% at an intracellular kPL of 0.02 s-1. This underestimation increased for larger kPL values. However, fitting the instantaneous mixing curves showed that diffusion accounted for only a small part of this underestimation. Fitting with the two-compartment model yielded more accurate intracellular kPL values. SIGNIFICANCE: This work suggests diffusion is not a significant rate-limiting factor in pyruvate-to-lactate conversion given that our model assumptions hold true. In higher order models, diffusion effects may be accounted for by a term characterizing metabolite transport. Pharmacokinetic models used to analyze hyperpolarized pyruvate signal evolution should focus on carefully selecting the analytical model for fitting rather than accounting for diffusion effects.

Associations of Demographic, Socioeconomic, and Cognitive Characteristics With Mobile Health Access: MESA (Multi-Ethnic Study of Atherosclerosis).

Background Mobile health (mHealth) has an emerging role in the prevention of cardiovascular disease. This study evaluated possible inequities in mHealth access in older adults. Methods and Results mHealth access was assessed from 2019 to 2020 in MESA (Multi-Ethnic Study of Atherosclerosis) telephone surveys of 2796 participants aged 62 to 102 years. A multivariable logistic regression model adjusted for general health status assessed associations of mHealth access measures with relevant demographic, socioeconomic, and cognitive characteristics. There were lower odds of all access measures with older age (odds ratios [ORs], 0.37-0.59 per 10 years) and annual income <$50 000 (versus ≥$50 000 ORs, 0.55-0.62), and higher odds with higher Cognitive Abilities Screening Instrument Score (ORs, 1.22-1.29 per 5 points). Men (versus women) had higher odds of internet access (OR, 1.32 [95% CI,1.05-1.66]) and computing device ownership (OR, 1.31 [95% CI, 1.05-1.63]) but lower fitness tracker ownership odds (OR, 0.70 [95% CI, 0.49-0.89]). For internet access and computing device ownership, we saw lower odds for Hispanic participants (versus White participants OR, 0.61 [95% CI, 0.44-0.85]; OR, 0.69 [95% CI, 0.50-0.95]) and less than a high school education (versus bachelor's degree or higher OR, 0.27 [95% CI, 0.18-0.40]; OR, 0.32 [95% CI, 0.28-0.62]). For internet access, lower odds were seen for Black participants (versus White participants OR, 0.64 [95% CI, 0.47-0.86]) and other health insurance (versus health maintenance organization/private OR, 0.59 [95% CI, 0.47-0.74]). Chinese participants (versus White participants) had lower internet access odds (OR, 0.63 [95% CI, 0.44-0.91]) but higher computing device ownership odds (OR, 1.87 [95% CI, 1.28-2.77]). Conclusions Among older-age adults, mHealth access varied by major demographic, socioeconomic, and cognitive characteristics, suggesting a digital divide. Novel mHealth interventions should consider individual access barriers. Registration URL: https://www.clinicaltrials.gov/; Unique identifier: NCT00005487.