Engaging the ASEAN Diaspora: Type 2 Diabetes Prevalence, Pathophysiology, and Unique Risk Factors among Filipino Migrants in the United States

Lessons Learned from Immigrant Health Cohorts: A Review of the Evidence and Implications for Policy and Practice in Addressing Health Inequities among Asian Americans, Native Hawaiians, and Pacific Islanders

The health of Asian Americans, Native Hawaiians, and Pacific Islanders (AANHPI) is uniquely impacted by structural and social determinants of health (SSDH) shaped by immigration policies and colonization practices, patterns of settlement, and racism. These SSDH also create vast heterogeneity in disease risks across the AANHPI population, with some ethnic groups having high disease burden, often masked with aggregated data. Longitudinal cohort studies are an invaluable tool to identify risk factors of disease, and epidemiologic cohort studies among AANHPI populations have led to seminal discoveries of disease risk factors. This review summarizes the limited but growing literature, with a focus on SSDH factors, from seven longitudinal cohort studies with substantial AANHPI samples. We also discuss key information gaps and recommendations for the next generation of AANHPI cohorts, including oversampling AANHPI ethnic groups; measuring and innovating on measurements of SSDH; emphasizing the involvement of scholars from diverse disciplines; and, most critically, engaging community members to ensure relevancy for public health, policy, and clinical impact.

Filipino Americans' Social and Cultural Experiences of Type 2 Diabetes Management: Cultural Paradox, Ownership, and Success Definition

BACKGROUND

Although type 2 diabetes mellitus (T2DM) disproportionately affects Filipino Americans, they have not received much attention in the literature. Focusing on how Filipino Americans' social and cultural contextual experiences affect their self-management is critical. This study examined T2DM self-management among Filipino Americans by describing their sociocultural experiences, strategies, and significance of self-management.

METHOD

An interpretive descriptive qualitative design was used. Data were analyzed using thematic analysis. The study followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.

RESULTS

Filipino Americans (n = 19) with T2DM were interviewed. Three themes emerged: (a) cultural paradox of being Filipino American, (b) movement from invisibility to ownership of T2DM, and (c) definition of successful management of T2DM.

CONCLUSION

Results contribute to a greater understanding of Filipino Americans' T2DM self-management experiences. Implications include the provision of culturally congruent health care, being aware of Filipino Americans' sociocultural experiences, and involvement of family/community.

Experiences of Filipino Americans with Type 2 Diabetes during COVID-19: A Qualitative Study

Little is known about the experiences of Filipino Americans with type 2 diabetes regarding their self-management during the early phase of the COVID-19 pandemic. We conducted a qualitative research study using semistructured interviews. In total, 19 interviews were recorded, transcribed, and analyzed by 4 independent coders. We situated our understanding of these results using three concepts from an indigenous Filipino knowledge system called Sikolohiyang Pilipino: Kapwa (shared identity), Bahala Na (determination), and Pakikibaka (spaces of resistance). The following three main themes emerged: (1) stressors of the pandemic, (2) coping behaviors (with two subthemes: emotional and lifestyle-focused responses), and (3) diabetes self-management outcomes. Participants experienced stresses, anxiety, and loneliness during the pandemic magnified by the complexities of self-management. Although many admitted the pandemic brought challenges, including burnout, they coped by using existing resources-support from family, friends, the use of technology, and various emotional coping mechanisms. Many said that they made few diabetes self-management changes.

Social Determinants of Health, Cardiovascular Risk Factors, and Atherosclerotic Cardiovascular Disease in Individuals of Vietnamese Origin

In 2022, the Vietnamese population in the United States (US) comprises 2.2 million individuals, and Vietnam ranks as the sixth most frequent country of origin among immigrants in the US. The American Heart Association and the National Institutes of Health have called for research to define the burden of cardiovascular risk factors, cardiovascular disease, and their determinants across Asian American subgroups, including Vietnamese Americans. Despite these calls, Vietnamese Americans remain remarkably overlooked in cardiovascular research in the US. Studies in Vietnam, small cross-sectional surveys in the US, and research using US mortality data point to a high prevalence of hypertension and tobacco use among men and a high incidence of gestational diabetes among women. Moreover, Vietnamese Americans have one of the highest rates of cerebrovascular mortality in the country. Adverse social determinants of health-including frequent language barriers, limited health literacy, and low average income-have been suggested as important factors that contribute to cardiovascular risk in this group. In this narrative review, we summarize the existing knowledge in this space, highlight the distinct characteristics of cardiac risk in both Vietnamese and Vietnamese American individuals, discuss upstream determinants, and identify key knowledge gaps. We then outline several proposed interventions and emphasize the need for further studies in this underrepresented population. Our aim is to increase awareness of the significant burden of risk factors and cardiovascular disease shouldered by this large-but thus far overlooked-population in the US, boost research in this space, and help inform tailored, effective preventive interventions.

Prediction of Weight Loss in Filipino Americans to Decrease Risk for Type 2 Diabetes: Using Multi-Dimensional Data (Preprint)

BACKGROUND

Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy.

OBJECTIVE

The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D.

METHODS

The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss.

RESULTS

Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]).

CONCLUSIONS

Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

Heterogeneity in cardio-metabolic risk factors and atherosclerotic cardiovascular disease among Asian groups in the United States

OBJECTIVE

The Asian American population in the U.S. comprises various, ethnically diverse subgroups. Traditionally, this population has been studied as a single, aggregated group, potentially masking differences in risk among subgroups. Analyses using disaggregated data can help better characterize the health needs of different Asian subpopulations and inform targeted, effective public health interventions. We assessed the prevalence of cardiovascular disease (CVD) risk factors and atherosclerotic CVD (ASCVD) and their associations with socioeconomic factors among Chinese, Asian Indian, Filipino and Other Asian subjects, compared with non-Hispanic White (NHW) subjects in the U.S.

METHODS

: Cross-sectional study using data from 298,286 adults from the National Health Interview Survey (NHIS) from 2007 to 2018. We utilized chi-squared tests to compare characteristics across subgroups. Weighted proportions and unadjusted and adjusted logistic regression models were utilized to examine the associations between Asian subgroups, self-reported CVD risk factors and self-reported ASCVD, as well as between socioeconomic factors within each Asian subgroup.

RESULTS

: Asian Indian subjects had the highest prevalence of diabetes (12.5%), while Filipino subjects had the highest prevalence of hyperlipidemia (27.7%), hypertension (29.8%) and obesity (19.8%). Despite this, the prevalence of self-reported ASCVD was lower in all Asian groups compared with NHWs. Chinese subjects had the lowest odds of having each of the CVD risk factors assessed.

CONCLUSION

: We found considerable heterogeneity in the distribution of risk factors as well as ASCVD among Asian subgroups in the US. Compared with health system or community-based reports, the prevalence of risk factors and ASCVD may be underestimated in some Asian NHIS subgroups. There is an urgent need for efforts to improve recruitment of Asian participants of heterogeneous socioeconomic backgrounds in national surveys, as well as to perform a thorough assessment of risk factors and disease in this population, not relying solely on self-report.