Hyeong Jun Ahn

Context: Although thyroid hormones regulate metabolism, the relevance of thyroid hormone sensitivity indices in distinguishing metabolic phenotypes across age groups remains unclear. Objective: We evaluated the association between thyroid sensitivity indices and metabolic phenotypes, including metabolically healthy and unhealthy individuals with obesity (MHO, MUO) and normal weight (MHNW, MUNW), focusing on age-specific differences. Methods: Data from participants aged 18 years and older in the National Health and Nutrition Examination Survey (NHANES) 2007 to 2012 were analyzed. Thyroid sensitivity indices, including the Thyroid Feedback Quantile-based Index (TFQI), Thyrotropin Index (TSHI), and Thyrotroph Thyroxine Resistance Index (TT4RI), were calculated. Multivariable regression and piecewise regression analyses were performed to examine associations between metabolic phenotypes and thyroid sensitivity indices, stratified by age groups (<65 and ≥65 years). Results: In the total population, the MUO group exhibited significantly higher values for TSHI (P = .035) compared to the MHNW group, while there were borderline and no significant differences for TT4RI (P = .093) and TFQI (P = .134), respectively. Among younger adults (<65 years), MUO showed the highest values for TSHI (β = 0.122; P = .006), TT4RI (β = 2.006; P = .010), and TFQI (β = 0.058; P = .018), with significant linear and quadratic trends (P < .05). No significant associations were observed in older adults (aged ≥65 years). Conclusion: Our findings highlight the importance of thyroid sensitivity indices in understanding metabolic health, particularly among younger adults. Incorporating these indices into clinical assessments may enhance metabolic phenotype stratification and inform targeted management of obesity.

Insect-borne viruses may account for a significant proportion of non-malaria and non-bacterial febrile illnesses in Liberia. Although the presence of many arthropod vectors has been documented, the collective burden of arbovirus infections and baseline pre-existing immunity remains enigmatic. Our goal was to determine the seroprevalence of arbovirus exposure across the country using a resource-sparing, multiplex immunoassay to determine IgG responses to immunodominant antigens. 532 human serum samples, from healthy adults, collected from 10 counties across Liberia, were measured for IgG reactivity against antigens of eight common flavi-, alpha-, and orthobunya/nairoviruses suspected to be present in West Africa. Approximately 32.5% of our samples were reactive to alphavirus (CHIKV) E2, ~7% were reactive separately to West Nile (WNV) and Zika virus (ZIKV) NS1, while 4.3 and 3.2% were reactive to Rift Valley Fever virus (RVFV) N and Dengue virus-2 (DENV-2) NS1, respectively. Altogether, 21.6% of our samples were reactive to ≥1 flavivirus NS1s. Of the CHIKV E2 reactive samples, 8.5% were also reactive to at least one flavivirus NS1, and six samples were concurrently reactive to antigens of all three arbovirus groups, suggesting a high burden of multiple arbovirus infections for some participants. These insights suggest the presence of these four arbovirus families in Liberia with low and moderate rates of flavi- and alphavirus infections, respectively, in healthy adults. Further confirmational investigation, such as mosquito surveillance or other serological tests, is warranted and should be conducted before initiating additional flavivirus vaccination campaigns. The findings of these studies can help guide healthcare resource mobilization, vector control, and animal husbandry practices.

Native Hawaiians were a healthy and robust population who developed a sophisticated food system that was dismantled by colonization. Currently, Native Hawaiians face pervasive health disparities due to the limited access to healthy foods and lifestyles. This study pilot tested a family-based community-driven intervention called MALAMA, which teaches families to build and use a backyard aquaponics system to grow their own food. A total of 21 participants from 10 families completed a three-month curriculum that included a series of hands-on workshops. Participant attendance was recorded and participants completed a behavioral health questionnaire as well as provided clinical indicators at three time points. They also attended a focus group at the end of the curriculum. There was a high level of engagement and no participant attrition. Fruit consumption among all participants significantly increased and there were favorable trends in blood pressure and fish and vegetable consumption. No significant differences were found in the other clinical indicators. Participants found MALAMA to be highly culturally acceptable and identified multiple benefits. Community-driven solutions, such as MALAMA, may be a promising approach to addressing pervasive health disparities and promoting health equity in minority and Indigenous communities.

In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Our analysis utilized laboratory parameters relevant to thyroid function or metabolic dysregulation in addition to demographic features, aiming to uncover potential associations between thyroid function and metabolic phenotypes by various machine learning methods. Multinomial Logistic Regression performed best to identify the relationship between thyroid function and metabolic phenotypes, achieving an area under receiver operating characteristic curve (AUROC) of 0.818, followed closely by Neural Network (AUROC: 0.814). Following the above, the performance of Random Forest, Boosted Trees, and K Nearest Neighbors was inferior to the first two methods (AUROC 0.811, 0.811, and 0.786, respectively). In Random Forest, homeostatic model assessment for insulin resistance, serum uric acid, serum albumin, gamma glutamyl transferase, and triiodothyronine/thyroxine ratio were positioned in the upper ranks of variable importance. These results highlight the potential of machine learning in understanding complex relationships in health data. However, it’s important to note that model performance may vary depending on data characteristics and specific requirements. Furthermore, we emphasize the significance of accounting for sampling weights in complex survey data analysis and the potential benefits of incorporating additional variables to enhance model accuracy and insights. Future research can explore advanced methodologies combining machine learning, sample weights, and expanded variable sets to further advance survey data analysis.

Concussions are caused by physical trauma to the head, face, or neck and can be sustained while surfing, increasing the risk of drowning. The purpose of this pilot study was to establish a preliminary assessment of concussion knowledge in a group of adult recreational surfers. Using the standardized Concussion Knowledge Index, an anonymous survey was conducted with 55 surfers. The Concussion Knowledge Index and similar statistical measures were used in a previous study of adult soccer players in England. Data from these 2 groups were compared. The preliminary data suggests that the group of adult surfers demonstrate more concussion knowledge than the group of adult soccer players. Further study into surfers’ knowledge of concussion with a larger sample size could increase the clinical utility and generalizability of this study.

All-payer, population-level hospital discharge data have been used to identify health disparities across racial/ethnic and other demographic groups. However, researchers are often unable to identify unique patients in the data sets if a unique patient identifier is not provided. The lack of the unique patient identifier can result in biased estimates of research outcomes using discharge data. This could then mislead the researchers, public, or policy-makers who utilize such biased results. This study examined estimation bias of health disparities due to rehospitalizations considering diabetes-related preventable hospitalizations using 6 years of state-level data from Hawai'i Health Information Corporation. Different analyses methods showed different probabilities of having multiple visits by age, race/ethnicity and payer subgroups. Charge analysis results also showed that ignoring the multiple visits could result in significance error. For a patient with multiple hospitalizations, rehospitalizations are often dependent upon the discharge status of previous visits, and the independence assumption of the multiple visits may not be appropriate. Ignoring the multiple visits in population-level analyses could result in severe health disparities significance errors. In this hospitalization charge analysis, the Chinese group was not significantly different than the White group (relative risk ratio - RR: [95% CI]: 0.93 [0.80, 1.08]), while the difference was signficant (RR [95% CI]: 0.86 [0.77,0.96]) when the multiple visits were ignored.