Type 2 Diabetes Among Filipino American Adults in the Multiethnic Cohort

Raquinio, Phyllis; Maskarinec, Gertraud; Cruz, Rica Dela; Setiawan, Veronica Wendy; Kristal, Bruce S.; Wilkens, Lynne R.; Marchand, Loı̈c Le

doi:10.5888/pcd18.210240

Cited by 10 publications

(15 citation statements)

References 33 publications

(82 reference statements)

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“…32 Moreover, the discussion of type 2 diabetes among Asian American populations in aggregate neglects the varying prevalence of type 2 diabetes within subgroups, which ranges from 12.6% among Asian Indian populations to 5.6% among Chinese populations. 33 In addition, the relative lack of mentions of Asian Indian populations further reflects the perspective centered on populations considered to be East Asian and marginalization of other minoritized Asian American subgroups often encompassed by the aggregated discussion of Asian American populations. 2,30 Aggregation in this way serves to foster clinicians' lack of knowledge of Asian American health and the risk of underdiagnosing disease.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, disaggregation of Asian American medical school applicants, who are often considered overrepresented, has previously revealed a lack of representation in various subgroups, such as Indonesian, Cambodian, and Japanese populations . Moreover, the discussion of type 2 diabetes among Asian American populations in aggregate neglects the varying prevalence of type 2 diabetes within subgroups, which ranges from 12.6% among Asian Indian populations to 5.6% among Chinese populations . In addition, the relative lack of mentions of Asian Indian populations further reflects the perspective centered on populations considered to be East Asian and marginalization of other minoritized Asian American subgroups often encompassed by the aggregated discussion of Asian American populations .…”

Section: Discussionmentioning

confidence: 99%

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Representation of Asian American Populations in Medical School Curricula

et al. 2022

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ImportanceDespite being one of the fastest-growing populations in the US, the Asian American population is often misrepresented in and omitted from health research and policy debate. There is a current lack of understanding of how Asian American populations are portrayed in medical school curricula.ObjectiveTo assess how Asian American populations and their subgroups are represented in medical school curricula.Design, Setting, and ParticipantsIn this qualitative study, the content of 632 lectures from all 19 courses of the preclinical curriculum at a single US institution from the academic year 2020 to 2021 was analyzed to identify and characterize unique mentions of race and ethnicity as well as granular ethnicity. Among the 632 lectures, we identified 256 nonrepetitive, unique mentions of race and ethnicity or granular ethnicity. These unique mentions were coded and analyzed for emerging patterns of use.Main Outcomes and MeasuresStudy outcomes included (1) the frequency of specific racial and ethnic categories mentioned in the curriculum, (2) the relative proportion of mentions of race and ethnicity that involved or included Asian American data by courses and context, and (3) key themes representing emerging patterns found from qualitative analysis of curriculum content for mentions of Asian American populations or lack thereof.ResultsAmong the 632 lectures, 256 nonrepetitive mentions of race and ethnicity or granular ethnicity were identified; of these, Asian American populations and/or their subgroups were mentioned in 79 of the instances (30.9%). The most common terms used to denote Asian American populations were Asian, with 36 mentions (45.6%); followed by Japanese, with 10 mentions (12.7%); and Chinese, with 8 mentions (10.1%). Overall, there were 26 mentions (10.2%) of American Indian or Alaska Native populations, 12 mentions (4.7%) of Asian and Pacific Islander or Asian American and Pacific Islander populations, 67 mentions (26.2%) of Asian or Asian American populations, 143 mentions (55.9%) of Black or African American populations, 62 mentions (24.2%) of Hispanic or Latino populations, 4 mentions (1.6%) of Native Hawaiian or Pacific Islander populations, and 154 mentions (60.2%) of White populations. During the analysis of the curriculum for representation of Asian American populations, the following 5 key themes emerged from the data: (1) omission, (2) aggregation, (3) inconsistent categorization, (4) misidentification of granular ethnicity, and (5) association of race and ethnicity with disease.Conclusions and RelevanceThis qualitative study suggests that the curriculum from a single US medical school largely mirrors the inappropriate use of race and ethnicity found in published health literature and clinical guidelines. Solutions with long-term results will require collaboration among diverse groups of interest to adopt inclusive research programs and design. Such solutions could better equip students in combating race-based medicine and could promote community outreach programs built based on trust.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Representation of Asian American Populations in Medical School Curricula

et al. 2022

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“…Cultural and societal influences on diet may put certain populations and groups at higher risk of T2D. For example, certain racial and ethnic groups, including Filipino Americans, have been found to be more susceptible to developing T2D, with an estimated 2.5-fold higher T2D incidence compared to White adults [ 7 ]. Filipino American diets include a mix of carbohydrates and proteins like rice, vegetables, and meat [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, certain racial and ethnic groups, including Filipino Americans, have been found to be more susceptible to developing T2D, with an estimated 2.5-fold higher T2D incidence compared to White adults [ 7 ]. Filipino American diets include a mix of carbohydrates and proteins like rice, vegetables, and meat [ 7 ]. These diets are associated with an overall increase in caloric and fat intake compared to the historical diets of Filipinos living in the Philippines [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

Chang¹,

Fukuoka²,

Aouizerat³

et al. 2023

JMIR Diabetes

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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

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“…These ethnic groups have a high prevalence of diet-related health outcomes such as obesity [23]. Non-Communicable Diseases (NCDs), including Type 2 Diabetes Mellitus (T2DM), are disparately more prevalent amongst these populations than in the general US adult population [24][25][26]. Childhood obesity is reported to affect as much as 13.6% of NHPI and Filipino children aged 2-8 years, which was the highest prevalence second only to American Indians and Alaskan Natives [23].…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Analysis of a Caregivers’ Experience of Complementary Feeding in a Population of Native Hawaiian, Other Pacific Islander and Filipino Infants: The Timing of the Introduction of Complementary Foods, and the Role of Transgenerational Experience

et al. 2022

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The aim of this study was to investigate caregivers’ experiences of complementary feeding (CF) among the Native Hawaiian and Other Pacific Islander (NHPI), and Filipino populations. Research focused on the timing of CF commencement, and the influence of transgenerational experience on feeding practices. The experiences and practices of those who fed human milk exclusively (HME), were compared to those who included infant formula (F&HM). Caregivers of a subset of 32 infants who were participating in a larger longitudinal study relating to CF and diet diversity, took part in voluntary in-depth interviews relating to CF practices. Interviews were recorded and transcribed. Two researchers analyzed interview transcripts. Interrater reliability and saturation were established. Institutional Review Board exemption was confirmed prior to study commencement. Interviews with 29 caregivers of infants were included in this study. Only infants of the F&HM group had an early introduction to complementary foods (<4 months of age). Caregivers reported receiving conflicting advice from healthcare professionals (HCPs) in relation to timing of the introduction of complementary foods. Nonetheless, the majority of caregivers reported following the advice of HCPs. Extended family (including grandparents) played less of a role in infant feeding, compared to previous generations. While transgenerational practices were valued and included, ultimately, the perceived health and safety of the practice for infants influenced decisions.

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Type 2 Diabetes Among Filipino American Adults in the Multiethnic Cohort

Cited by 10 publications

References 33 publications

Representation of Asian American Populations in Medical School Curricula

Representation of Asian American Populations in Medical School Curricula

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis

A Qualitative Analysis of a Caregivers’ Experience of Complementary Feeding in a Population of Native Hawaiian, Other Pacific Islander and Filipino Infants: The Timing of the Introduction of Complementary Foods, and the Role of Transgenerational Experience

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