Multiethnic community cardio-metabolic risk factor screening in Northeast Florida: The VIDASANA Project

Abstract

Background

Cardiometabolic risk disproportionately affects racially and ethnically diverse United States populations. Few community-based studies have examined the intersection of metabolic syndrome (MS), functional capacity, and cardiovascular disease (CVD) risk across multiple groups within a single region.

Objective

To evaluate MS prevalence, functional capacity, and 10-year CVD risk in adults from diverse communities in Northeast Florida.

Methods

A total of 345 adults were screened between 2017 and 2019 in a community-based, cross-sectional study. MS was defined by Adult Treatment Panel III criteria (≥3 of 5 thresholds). Functional capacity and 10-year CVD risk were assessed using Duke Activity Status Index (DASI), and Framingham Risk Score (FRS). Group differences were evaluated using appropriate univariate tests, and multivariable logistic regression identified independent predictors of MS (p < 0.05).

Results

MS prevalence was highest among Hispanic (H) subjects (47%), followed by non-Hispanic Black (NHB, 35%) and Southeast Asian participants (SEA, 33%). Adjusted odds of MS were higher in H (OR 7.3, 95% CI 1.8–28.6) and SEA participants (OR 5.2, 95% CI 1.2–22.2) compared to non-Hispanic White (NHW) subjects. DASI scores were significantly lower in women and minority (H and SEA) populations. MS further reduced functional capacity in H (p = 0.011) and SEA participants (p = 0.038). Median FRS differed by race/ethnicity (p = 0.0013); with NHB subjects showing the highest median risk (9.75%).

Conclusions

This study identified marked disparities in MS prevalence, functional capacity, and cardiovascular risk across racial and ethnic groups with greater burden among minoritized populations across all domains. Findings support the need for more inclusive, culturally informed prevention strategies for diverse populations.

Highlights

• •Hispanic and Southeast Asian adults had the highest adjusted odds of metabolic syndrome.
• •Functional capacity (DASI) was significantly lower in minority (Hispanic and Southeast Asian participants) groups and women.
• •Metabolic syndrome further reduced DASI scores in Hispanic and Southeast Asian participants.
• •Non-Hispanic Black adults exhibited the highest median 10-year cardiovascular risk.
• •Findings reveal substantial ethnic disparities and support culturally tailored prevention.

1. Introduction

Metabolic syndrome (MS), a cluster of interrelated risk factors including central adiposity, dyslipidemia, elevated blood pressure, and impaired glucose metabolism, is a well-established precursor to cardiovascular disease (CVD) [1], [2], [3]. In the United States (U.S.), the prevalence of MS continues to rise, increasing from 41.8% in 2017–2018 to 45.9% in the 2017–2020 cycle [4]. This burden is not equally distributed; racially and ethnically minoritized populations experience disproportionately higher prevalence and differential clustering of MS components compared with the White population [5], [6]. These disparities, shaped by genetic, environmental, and structural determinants of health, contribute to inequities in cardiovascular outcomes [7], [8].

A contributing factor to persistent disparities is the historical underrepresentation of minoritized populations in cardiovascular research, clinical trials, and preventive screening initiatives. Although national guidelines acknowledge variation in baseline cardiovascular risk across racial and ethnic groups, evidence has often been insufficient to support tailored, population-specific recommendations [9]. Similarly, widely used risk prediction models such as the Framingham Risk Score (FRS) were developed and validated predominantly in Non- Hispanic White (NHW) and Non-Hispanic Black (NHB) cohorts. While these tools provide valuable estimates of 10-year risk, concerns regarding generalizability to other populations, including Hispanic (H) and Southeast Asian (SEA) subgroups, remain [10]. Such limitations may affect risk classification and subsequent clinical decision making.

Beyond traditional risk factors, functional capacity, an individual's ability to perform activities of daily living, has emerged as an independent predictor of cardiovascular morbidity and mortality [11]. The Duke Activity Status Index (DASI) provides a patient-centered measure of functional status and may capture social and environmental influences not fully reflected in conventional risk models [12]. However, functional capacity is infrequently incorporated into community-level cardiovascular screening frameworks, limiting understanding of its distribution across diverse populations.

To address these evidence gaps, the VIDASANA (“Healthy Life”) project was designed to evaluate three complementary domains: MS prevalence (per Adult Treatment Panel III [ATP III] criteria), functional capacity (via DASI), and 10-year cardiovascular risk (via FRS) within a geographically defined, multiethnic community cohort in Northeast Florida. Adults were recruited from neighborhoods with large NHB, H, and SEA populations. By integrating metabolic, functional, and risk-estimation measures within a single framework, this study seeks to characterize cardiometabolic risk heterogeneity across racial and ethnic groups and inform culturally responsive prevention strategies aimed at reducing cardiovascular health disparities.

2. Methods

2.1. Study design and setting

The VIDASANA project was a community-based, cross-sectional study conducted from 2017 to 2019 by the University of Florida, Jacksonville Women's Heart Program. The study aimed to assess cardiometabolic risk profiles among four self-identified ethnic groups: H (originating from South/Central America or the Caribbean), NHB, SEA, and NHW individuals, living in the Jacksonville metropolitan area. Study protocols were approved by the University of Florida Institutional Review Board (IRB-01).

Community health fairs were held at four local centers strategically located in zip codes with high concentrations of the respective ethnic populations, identified using census data and community outreach partnerships. Recruitment was done through bilingual flyers and community networks. Recruitment targeted communities to reflect the ethnic diversity of the Jacksonville metropolitan area (regionally comprised of approximately 49% NHW, 28.5% NHB, 11.3% H, and 4.75% SEA populations).

2.2. Study population

Adults aged 18 to 80 years who self-identified as belonging to one of the four specified ethnic groups were eligible to participate. Inclusion required willingness to provide informed consent, share medical history, complete a questionnaire, and undergo fingerstick blood sampling after an overnight fast.

Individuals were excluded from the study if they were not fasting at the time of screening or if they had incomplete health data, or declined to complete any required assessments, such as questionnaires or anthropometric measurements.

2.3. Data collection

All data was collected onsite by a single trained member of the study team who attended all screening events to ensure standardized measurement procedures and reduce inter-observer variability. The personnel received appropriate training in anthropometric measurement, questionnaire administration, and equipment use.

2.4. Blood pressure measurement

Blood pressure (BP) was measured using a validated automated BP device with the participant seated, feet flat on the floor, back supported, and arm positioned at heart level after a five-minute rest. At least two measurements were taken per participant, spaced 1–2 min apart. The average of these readings was used for analysis. If initial readings varied by more than 10 mmHg for systolic or diastolic blood pressure, additional measurements were taken until readings stabilized within this range. Appropriate cuff sizes were used for all participants.

2.5. Anthropometric and laboratory assessments

Participants' height, weight, and waist circumference were measured according to standardized protocols. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m²). Fasting lipid profile and glucose levels were obtained from capillary blood using the Cholestech LDX® System (Abbott), a Clinical Laboratory Improvement Amendments (CLIA) -waived, U.S. Food and Drug Administration (FDA) -approved point-of-care device.

2.6. Classification

MS was defined according to the National Cholesterol Education Program's Adult Treatment Panel III (NCEP ATP III) 2001 criteria, requiring the presence of three or more of the following five risk factors: (1) elevated waist circumference (>102 cm in men or > 88 cm in women); (2) elevated triglycerides (≥150 mg/dL) or on drug treatment for high triglycerides; (3) reduced HDL cholesterol (<40 mg/dL in men or < 50 mg/dL in women) or on drug treatment for low HDL—C; (4) elevated BP (systolic ≥130 mmHg or diastolic ≥85 mmHg) or on antihypertensive drug therapy; and (5) elevated fasting glucose (≥110 mg/dL) or on drug treatment for high glucose [13].

2.7. Functional capacity

Functional capacity was evaluated using the 12-item DASI, a validated self-reported questionnaire administered onsite in English or Spanish. Scores range from 0 to 58, with higher scores indicating greater functional fitness [14].

2.8. CVD risk calculation

The Framingham 10-year cardiovascular risk score (FRS) was calculated for each participant using established sex-specific algorithms incorporating age, total cholesterol, HDL cholesterol, systolic BP, antihypertensive medication use, smoking status, and diabetes status. The original FRS point-based algorithm was applied without race-specific adjustments [15].

2.9. Statistical analysis

Data was analyzed using Statistical Analysis System (SAS) software (SAS Institute, Cary, NC, Windows Version 9.4). Descriptive statistics summarized participant characteristics. Categorical variables were compared using Chi-square tests or Fisher's Exact tests. Continuous variables were assessed for normality; normally distributed variables are presented as mean (standard deviation), and skewed variables as median (interquartile range). Group comparisons used ANOVA or Kruskal–Wallis tests, as appropriate. Multivariable logistic regression identified predictors of MS, adjusting for race/ethnicity, BMI, and hypertension treatment (excluding variables used in the MS definition). Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. A two-tailed p-value of <0.05 was considered statistically significant. BMI was included as a covariate to account for general adiposity, which is a strong confounder for MS independent of central adiposity as measured by waist circumference.

3. Results

A total of 460 adults underwent community health screenings for the study. Of these, 115 were excluded, leaving a sample of 345 participants who met all inclusion criteria. The primary reasons for exclusion were not fasting (n = 58), incomplete records (n = 37), and failure to complete the full screening protocol (n = 20). The final race/ethnic distribution of the sample was 131H (38.0%), 129 NHB (37.4%), 52 SEA (15.1%), and 33 NHW (9.6%). Significant differences across race/ethnicity groups in demographic and clinical characteristics are shown in Table 1.

Table 1.

Baseline characteristics of study participants by race/ethnicity: Demographic and clinical characteristics of Hispanic, Black, Non- Hispanic White, and Southeast Asian participants, including sex distribution, age, body mass index (BMI), family history of cardiovascular disease (CVD), prevalence and treatment of hypertension (HTN) and hyperlipidemia (HLD), and presence of metabolic syndrome. Data is presented as percentages for categorical variables and mean ± standard deviation (SD) for continuous variables. P-values reflect comparisons across all race/ethnicity groups. All tests are Fisher's Exact tests, unless otherwise specified by * Kruskal-Wallis tests.

Characteristic	Hispanic (n = 131)	Black (n = 129)	White (n = 33)	Southeast Asian (n = 52)	P-value
Sex, Female (%)	73	83	67	50	<0.001
Age (Mean ± SD)	49.7 ± 14.6	57.7 ± 14.5	49.4 ± 13.3	46.4 ± 13.1	<0.001*
BMI (Mean ± SD)	31.2 ± 8.2	29.8 ± 7.1	27.4 ± 6.2	26.4 ± 4.6	<0.001*
Family History of CVD (%)	51	39	61	59	0.032
Hypertension (%)	28	51	12	26	<0.001
HTN Treatment (%)	21	47	6	23	<0.001
Hyperlipidemia (%)	33	41	13	21	0.005
HLD Treatment (%)	15	26	3	6	<0.001
Metabolic Syndrome (%)	47	35	21	33	0.003

3.1. MS criteria

The distribution of individual MS components varied by ethnicity, as illustrated in Fig. 1, with H participants more likely to meet triglyceride and HDL criteria and NHB participants more likely to meet the waist circumference and glucose criteria. MS prevalence was highest among H individuals (47%), followed by NHB (35%), SEA (33%), and NHW participants (21%). H participants had 7.3-fold greater odds of MS compared to NHW individuals (Odds-Ratio [OR] 7.3, Confidence Interval [CI 1.8–28.6]) and 3.3-fold higher odds than NHB individuals (OR 3.3, CI 1.66–6.4). SEA individuals had lower MS prevalence but significantly higher odds than NHW individuals (OR 5.2, CI 1.2–22.2). Female sex was associated with increased MS likelihood (OR 1.5), though not significant in multivariable analysis.

Fig. 1.

Distribution of metabolic syndrome components by ethnic/racial group – Bar chart comparing the prevalence of systolic/diastolic blood pressure (SBP/DBP), waist circumference (WC), fasting blood sugar (FBS), high-density lipoprotein (HDL), and triglycerides (TG) among Hispanic, Black, White, and Asian adults. Asterisks indicate statistically significant differences between groups (P < 0.05).

3.2. Functional capacity (DASI)

Mean DASI scores were significantly lower in H and SEA groups. Women had significantly lower DASI scores than men (49.9 vs. 54.4, p = 0.001). MS was associated with lower DASI scores overall (p < 0.001).

Among H and SEA participants, MS presence was associated with further reduced functional capacity (p = 0.011 and p = 0.038, respectively), but no such effect was observed in NHB and NHW participants (Table 2).

Table 2.

Duke Activity Status Index (DASI) scores by race/ethnicity, sex, and metabolic syndrome status: Mean DASI scores with 95% confidence intervals (CI) for participants with and without metabolic syndrome (MS), stratified by race/ethnicity and sex. P-values indicate the statistical significance of differences between MS and non-MS groups within each category.

Group	With MS (Mean DASI [95% CI])	Without MS (Mean DASI [95% CI])	P-value
Hispanic	47.7 (44.7–50.8)	54.9 (52.3–57.5)	0.011
Southeast Asian	44.1 (39.0–49.2)	54.1 (50.5–57.7)	0.038
Black	53.8 (50.2–57.4)	51.5 (49.0–54.0)	0.968
Non- Hispanic White	53.8 (50.2–57.4)	58.0 (53.7–62.2)	0.986
Sex – Female	45.2 (42.1–48.3)	53.8 (51.9–55.7)	<0.001
Sex – Male	54.5 (50.6–58.5)	55.4 (52.7–58.2)	0.983

3.3. Framingham 10-year risk score

Median 10-year CVD risk, based on the FRS, differed significantly across racial and ethnic groups (p = 0.001). NHB participants had the highest median risk (9.75%), followed by H (5.6%), SEA (5.3%), and NHW participants (4.1%) (Table 3).

Table 3.

Framingham Risk Score by ethnic group: Distribution of 10-year Framingham Risk Scores (FRS) among Hispanic, Non-Hispanic Black, Non-Hispanic White, and Southeast Asian participants. Data are presented as median (interquartile range) along with descriptive statistics. The P–values from the Kruskal–Wallis test indicate a statistically significant difference in FRS distributions across groups.

Variable	Group	N	Mean	Std dev	Min	1st quartile	*Median	3rd quartile	Max	P-value
FRS Risk Score	Hispanic	111	9.1	9.4	0.3	2.7	5.6	13.2	50.6	<0.05
	Black	106	13.7	13.4	0.5	4.4	9.7	17.8	68.1
	White	28	6.8	7.0	0.9	2.5	4.1	7.7	29.0
	Asian	48	8.9	11.2	0.6	2.2	5.2	9.1	50.8

3.4. Multivariable model

Significant predictors of MS included BMI (p < 0.001), race/ethnicity (p = 0.001), and HTN treatment (p < 0.001). Age and sex were not significant after adjustment (Table 4).

Table 4.

Significant predictors of metabolic syndrome from multivariate logistic regression analysis: Results of multivariate analysis assessing the association between demographic and clinical variables and the presence of metabolic syndrome. Variables with statistically significant associations included race, body mass index (BMI), and hypertension (HTN) treatment. DF = degrees of freedom; BMI = body mass index; CVD = cardiovascular disease; HTN = hypertension.

Effect	DF	Wald Chi-Square	p-value
Race	3	16.2	0.001
Sex	1	1.6	0.205
Age	1	0.6	0.408
BMI	1	13.3	<0.001
Family History of CVD	1	0.5	0.464
Alcohol Use	1	0.4	0.508
HTN Treatment	1	16.3	<0.001

A summary of these findings across MS prevalence, DASI scores, and Framingham cardiovascular risk by ethnicity is provided in Fig. 3.

Fig. 3.

Cardiometabolic risk factors and functional capacity by ethnic group – Bar charts showing metabolic syndrome prevalence (%), Framingham Risk Score, Duke Activity Status Index (DASI) scores (functional capacity), and odds ratios for metabolic syndrome among Hispanic, Black, Asian, and White participants.

4. Discussion

The present community-based, cross-sectional study provides evidence of significant race- and ethnicity-based disparities in the cardiometabolic risk profile among the diverse communities in Northeast Florida. MS prevalence was highest among H participants, with increased odds observed in H, NHB and SEA groups compared to NHW participants. Women and minority populations demonstrated lower functional capacity, as assessed by DASI, and the presence of MS further reduced the DASI scores in H and SEA participants. Median 10-year CVD risk, as estimated by the FRS, also differed significantly across groups, with NHB individuals exhibiting the highest predicted risk. These findings highlight meaningful disparities that likely reflect social, behavioral and structural determinants of health rather than inherent biological differences (Fig. 2).

Fig. 2.

Mean functional capacity by metabolic syndrome status across ethnic groups – Bar chart showing mean Duke Activity Status Index (DASI) scores in participants with and without metabolic syndrome (MS) among White, Black, Hispanic, and Southeast Asian (SEA) groups. Error bars represent standard deviations. P-values indicate the significance of differences between MS and No MS groups.

MS prevalence varies across regions and populations in the U.S. Gurka et al. reported substantial variation ethnic variation, with higher frequency among H adults, particularly in the southern U.S. [16]. Other national studies have identified NHB adults as having the highest prevalence, followed by H and NHB groups [17], while Liang et al. observed higher prevalence among NHW participants compared with H participants [4]. In contrast, our regional data demonstrated the lowest prevalence among NHW participants and disproportionate burden among H, NHB and SEA groups. Such divergence from national trends likely reflects regional demographic, environmental, and socioeconomic factors. Variability across studies may also relate to differing MS definitions; the International Diabetes Federation (IDF) criteria often yield higher prevalence estimates than ATP III criteria, particularly among H men [18]. Our use of ATP III criteria ensured internal consistency across this multiethnic cohort.

H participants demonstrated the highest likelihood of developing MS, consistent with literature linking increased susceptible to psychological stressors, lifestyle factors, and limited access to preventive care [19]. Lower utilization of antihypertensive and lipid-modifying therapies among H adults and persistent dyslipidemia among NHB adults may further contribute to risk disparities [17]. Lower educational attainment in some minority populations may amplify these vulnerabilities [20]. Although genetic overlap exists across racial and ethnic groups, differences in inflammatory pathways, insulin resistance, adiposity distribution, and lifestyle exposures have been described [21]. SEA participants, despite lower overall prevalence, exhibited higher odds of MS compared with NHW participants, possibly reflecting central adiposity and characteristic metabolic phenotypes, including increased cardiometabolic risk at lower body mass index thresholds described in Asian populations [22]. NHB participants demonstrated intermediate MS but the highest FRS, consistent with earlier hypertension onset, vascular dysfunction, and persistent structural inequities affecting healthcare access [23], [24]. These findings underscore factors that long-term CVD burden may not parallel MS prevalence alone. Notably, race related variation in FRS-derived risk may influence therapeutic decisions, including statin initiation.

Distinct clustering of MS components was also observed. H participants more frequently exhibited elevated BP, triglycerides, and lower HDL—C, whereas NHB participants more often showed increased waist circumference and fasting glucose. These patterns differ from NHANES findings [17], [25], [26] and further emphasize how regional and socioeconomic context may shape metabolic risk [27]. Such variation reinforces the importance of locally informed prevention strategies.

Functional capacity measured by DASI, was lower among minority groups and women, consistent with prior reports of racial, ethnic, and sex-based disparities in physical function [28], [29], [30]. Contributing factors include neighborhood environment, (e.g., walkability, safety), socioeconomic constraints, comorbid burden, and cultural relevance of self-reported physical activity measures [30], [31], [32]. Certain DASI items (e.g., golf or recreational sports) may underestimate functional status in lower-income or minority groups. Importantly, MS further reduced DASI scores among H and SEA participants, compounding vulnerability in groups already at elevated cardiometabolic risk [33].

Strengths of this study include the integrated assessment of MS, functional capacity (via DASI), and 10-year CVD risk (via FRS) within a diverse, community-based cohort. This multidimensional framework incorporates functional capacity-often overlooked in preventive cardiology research and includes underrepresented populations such SEA participants and H women. Use of standardized ATP III criteria enhance comparability with national datasets while preserving internal consistency.

This study has several limitations. The cross-sectional design precludes causal inference.

Small subgroup sizes, particularly among SEA and NHW particularly, limit statistical precision; therefore, interpretation should emphasize directionality and overall patterns rather than exact magnitude. Voluntary participation may introduce selection bias, and reliance on self-reported DASI may contribute to reporting bias. ATP III waist circumference thresholds may underestimate MS risk in SEA populations, and FRS, derived primarily from NHW and NHB cohorts, may misestimate absolute risk in H and SEA participants [34]. Other commonly used estimators, including the ASCVD/Pooled Cohort Equations, were developed in similarly limited populations and may incompletely represent disaggregated H and SEA subgroups. In this cross-sectional screening study, FRS was used as a consistent comparative metric rather than definitive risk adjudication. Subgroup-specific calibration of multiple tools warrants evaluation in larger longitudinal cohorts.

MS, DASI, and estimated CV risk by FRS, are clinically interconnected; however, this cross-sectional design was not intended to evaluate mediation or hierarchical interaction effects. Subgroup-stratified MS–FRS or DASI–FRS modeling would likely produce unstable estimates given modest sample sizes. Accordingly, these domains were interpreted descriptively rather than mechanistically. Longitudinal studies are needed to clarify potential modifying relationships.

Although income data were not collected, recruitment by targeted zip codes provided approximate socioeconomic homogeneity.

Future research should employ longitudinal designs to clarify the temporal relationships among MS, functional capacity, and CVD risk. Larger, adequately powered studies, including underrepresented ethnic groups, are essential. Development of culturally adapted risk assessment tools incorporating objective functional measures may improve identification of at-risk individuals and inform community-level interventions addressing structural barriers to cardiovascular health.

5. Clinical and public health implications

Community-based screening models integrating metabolic, functional, and risk-estimation domains can identify preventable cardiometabolic burden in minoritized communities. Lower DASI scores among women and minority groups suggest functional limitations that may reflect environmental constraints and unmet preventive or rehabilitative needs. These findings support locally tailored prevention strategies, including culturally adapted lifestyle interventions, optimized and BP and lipid management, and community partnerships aimed at reducing structural barriers to CV health.

6. Conclusion and implications

This community-based study identified significant disparities in MS, functional capacity, and cardiovascular risk across racially and ethnically diverse populations in Northeast Florida. Minority participants showed a disproportionately higher cardiometabolic burden, reflected in higher MS prevalence, reduced DASI-measured functional capacity, and a higher 10-year CVD risk. While tools such as DASI and FRS captured these differences, their limited cultural applicability underscores the need for contextually adapted instruments that more accurately assess risk and function across diverse groups.

The clustering and consequences of MS differ by ethnicity and sex, reinforcing the importance of tailored prevention strategies. Integrating community-based screening models like VIDASANA into preventive cardiology could enhance early detection and equitable risk reduction. Future studies should incorporate longitudinal follow-up, objective assessments of functional capacity, and validation of risk tools in underrepresented groups. Advancing cardiovascular equity will require prevention frameworks that are inclusive, culturally grounded, and informed by the live realities of the communities they serve.

Glossary

MS

Metabolic Syndrome

CVD

Cardiovascular Disease

NHANES

National Health and Nutrition Examination Survey

NHW

Non- Hispanic White

NHB

Non-Hispanic Black

SEA

Southeast Asian

ATP III

Adult Treatment Panel III

FRS

Framingham Risk Score

HDL

High-Density Lipoprotein

BMI

Body Mass Index

BP

Blood Pressure

DASI

Duke Activity Status Index

OR

Odds Ratio

CI

Confidence Interval

IDF

International Diabetes Federation

Abbreviation

JIS

Joint Interim Statement

CLIA

Clinical Laboratory Improvement Amendments

FDA

Food and Drug Administration

IRB

Institutional Review Board

H

Hispanic

Central Illustration.

Summary of the VIDASANA Project, a community-based cardiometabolic screening initiative in Northeast Florida, demonstrating racial and ethnic disparities in metabolic syndrome prevalence, functional capacity (Duke Activity Status Index), and estimated 10-year cardiovascular risk (Framingham Risk Score).

Abbreviations: H = Hispanic; NHB = Non-Hispanic Black; SEA = Southeast Asian; NHW = Non-Hispanic White; DASI = Duke Activity Status Index; MS = Metabolic Syndrome; CVD = cardiovascular disease.

CRediT authorship contribution statement

Gladys Velarde: Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Project administration, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Katia Bravo-Jaimes: Writing – review & editing, Investigation, Formal analysis, Data curation. Grishma Sharma: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. Maedeh Ganji: Project administration, Methodology, Investigation, Formal analysis. Carmen Smotherman: Validation, Software, Methodology, Formal analysis, Data curation. Catherine Klein: Visualization, Supervision, Resources, Project administration. Khadeeja Esmail: Supervision, Project administration. Jose Rivas: Supervision, Resources, Project administration.

Ethical statement

The VIDASANA Project was conducted in accordance with the ethical standards of the Declaration of Helsinki. The study protocol was reviewed and approved by the University of Florida Institutional Review Board (IRB-01). Written informed consent was obtained from all participants prior to enrollment. All data were de-identified prior to analysis to ensure participant confidentiality.

Source of funding

University of Florida College of Medicine – Jacksonville, supported through a Dean's Grant.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The de-identified participant data and data dictionary for this study will be made available to qualified researchers upon request. Interested parties should submit a formal proposal outlining their research plan and intended use of the data to the corresponding author at gladys.velarde@jax.ufl.edu. Requests will be reviewed by the study's steering committee to ensure scientific validity and adherence to ethical principles. A signed data use agreement will be required prior to the release of any data to protect patient confidentiality.