Background
Puerto Ricans have higher odds of peripheral artery disease (PAD) compared with Mexican Americans. Limited studies have examined relationships between clinical risk assessment scores with PAD assessments.

Methods
Using 2004-2015 data from the Boston Puerto Rican Health Study (BPRHS) (n = 370-583), cross-sectional, 5-y change and patterns of change in Framingham Risk Score (FRS) and allostatic load (AL) with ankle brachial index (ABI) at 5-y follow-up was assessed among Puerto Rican adults (45-75 y). Analysis were conducted in 2020. FRS and AL were calculated at baseline, 2-y and 5-y follow-up. Multivariable linear regression models examined cross-sectional and 5-y changes in FRS and AL with ABI at 5-y. Latent growth mixture modeling identified trajectories of FRS and AL over 5-y, and multivariable linear regression models were used to test associations between trajectory groups at 5-y.

Results
Greater FRS at 5-y and increases in FRS from baseline were associated with lower ABI at 5-y (β = -0.149, p = 0.010; β = -0.171, p = 0.038, respectively). AL was not associated with ABI in cross-sectional or change analyses. Participants in low-ascending (vs. no change) FRS trajectory, and participants in moderate-ascending (vs. low-ascending) AL trajectory, had lower 5-y ABI (β = -0.025, p = 0.044; β = -0.016, p = 0.023, respectively).

Conclusions
FRS was a better overall predictor of ABI, compared with AL. FRS may be a clinically feasible measure of PAD risk in Puerto Ricans, an understudied population. Additional research examining relationships between FRS and AL and development of PAD is warranted.

Please read the READ ME.txt:

There are six data sets were used in this analysis, please use the data structure and dictionary workbook as a guide to target the corresponding datasets and models/Usage.

You can find following information of a data set in the metadata workbook: name, Usage, size, observations, number of variables, and variable list, which contains variable name and lable, and identifier tag(yes/no).

Due to only on more then three identifiers are allowed to be included in one data set. We removed Height(cn), Weight(kg), Body mass index (kg/m2), and Waist circumference(cm) from the original data set abi_table1. This action doesn't affect the analysis, since these variable weren't used in any of the models.