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1.
Annals of Laboratory Medicine ; JOUR(2):137-144, 43.
Article in English | MEDLINE | ID: covidwho-2089751

ABSTRACT

While the coronavirus disease 2019 pandemic is ongoing, monkeypox has been rapidly spreading in non-endemic countries since May 2022. Accurate and rapid laboratory tests are essential for identifying and controlling monkeypox. Korean Society for Laboratory Medicine and the Korea Disease Prevention and Control Agency have proposed guidelines for diagnosing monkeypox in clinical laboratories in Korea. These guidelines cover the type of tests, selection of specimens, collection of specimens, diagnostic methods, interpretation of test results, and biosafety. Molecular tests are recommended as confirmatory tests. Skin lesion specimens are recommended for testing in the symptomatic stage, and the collection of both blood and oropharyngeal swabs is recommended in the presymptomatic or prodromal stage.

2.
International Journal on Informatics Visualization ; 6(3):676-680, 2022.
Article in English | Scopus | ID: covidwho-2081478

ABSTRACT

Although COVID-19 has severely affected the global economy, information technology (IT) employees managed to perform most of their work from home. Telecommuting and remote work have promoted a demand for IT services in various market sectors, including retail, entertainment, education, and healthcare. Consequently, computer and information experts are also in demand. However, producing IT, experts is difficult during a pandemic owing to limitations, such as the reduced enrollment of international students. Therefore, researching increasing software productivity is essential;this study proposes a code similarity determination model that utilizes augmented data filtering and ensemble strategies. This algorithm is the first automated development system for increasing software productivity that addresses the current situation—a worldwide shortage of software dramatically improves performance in various downstream natural language processing tasks (NLP). Unlike general-purpose pre-trained language models (PLMs), CodeBERT and GraphCodeBERT are PLMs that have learned both natural and programming languages. Hence, they are suitable as code similarity determination models. The data filtering process consists of three steps: (1) deduplication of data, (2) deletion of intersection, and (3) an exhaustive search. The best mating (BM) 25 and length normalization of BM25 (BM25L) algorithms were used to construct positive and negative pairs. The performance of the model was evaluated using the 5-fold cross-validation ensemble technique. Experiments demonstrate the effectiveness of the proposed method quantitatively. Moreover, we expect this method to be optimal for increasing software productivity in various NLP tasks. © 2022, Politeknik Negeri Padang. All rights reserved.

3.
Journal of ornithology ; : 1-12, 2022.
Article in English | EuropePMC | ID: covidwho-2073210

ABSTRACT

Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the ‘Naturblick’ app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10–1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions. Supplementary Information The online version contains supplementary material available at 10.1007/s10336-022-02018-8.

4.
Journal of Interprofessional Education & Practice ; : 100552, 2022.
Article in English | ScienceDirect | ID: covidwho-2061978

ABSTRACT

Ideal patient care involves interprofessional collaboration and therefore emphasizes the importance of communicating how roles and responsibilities differ to create a team environment critical for providing optimal patient care. In light of the ongoing opioid epidemic associated with chronic pain, this interprofessional simulation focused on utilizing an interprofessional team approach to recognize the biopsychosocial and pharmacologic aspects of chronic pain management through creation of a patient-centered care plan using a virtual platform. Virtual IPE events can be performed by institutions with limited access to other healthcare disciplines for remote learning opportunities and can be adapted to develop comprehensive strategies to evaluate effectiveness of learning interventions among varied disciplines. Participants from the Schools of Medicine, Nursing, Pharmacy, and Health Related Professions, including occupational and physical therapy, participated in the virtual simulation event. The format was chosen to adhere to current COVID-19 safety guidelines and facilitate easier scheduling between disciplines. The event included individual pre-work through an online learning management system leading to a 2-h virtual simulation event. Interprofessional Education Collaborative, or IPEC developed competencies focused on communication and teamwork to establish activity objectives. International Association for the Study of Pain, or IASP, pain curriculum outlines provided additional objectives and guided presented information on best practice approaches for interprofessional pain management. Objectives were evaluated through peer team feedback, peer discipline feedback, and assessment of the comprehensive team care plan that consisted of pharmacologic and nonpharmacologic pain management strategies. Programmatic review demonstrated students were able to have effective communication that led to a holistic patient care plan at the end of this activity.

5.
JAMIA Open ; 5(4): ooac079, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2051472

ABSTRACT

Objective: COVID-19 accelerated telehealth use to ensure care delivery, but there is limited data on the patient perspective. This study aimed to examine telehealth visit uptake before and during COVID-19 and correlates of patient satisfaction and interest in future telehealth visits. Materials and Methods: This was a cross-sectional observational study between October 2019 and April 2020. Participants included patients who completed satisfaction surveys following telehealth visits. Results: A total of 8930 patients completed the satisfaction survey using 4-point Likert Scales. Multivariable, hierarchical, cumulative logit models were constructed to examine correlates of satisfaction with quality of care and interest in future telehealth visits. Most patients were satisfied with the patient portal, video quality, and instructions (92.7%-96.8%). Almost half reported saving 1-2 h (46.9%). Correlates positively associated with quality of care and interest in future telehealth visits were ease of patient portal (odds ratio [OR], 1.43, 95% confidence interval [CI], 1.30-1.58; OR, 1.56, 95% CI, 1.41-1.73, respectively), video quality (OR, 1.62, 95% CI, 1.50-1.75; OR, 1.26, 95% CI, 1.16-1.37, respectively), instructions (OR, 5.62, 95% CI, 5.05-6.26; OR, 1.80, 95% CI, 1.62-2.01, respectively), and time saved (>4 h: OR, 1.69, 95%,CI, 1.22-2.34; OR, 3.49, 95% CI, 2.47-4.93, respectively). Being seen after the COVID-19 surge in telehealth (OR, 0.76, 95% CI, 0.63-0.93) or by providers with higher visit volume (OR, 0.71, 95% CI, 0.60-0.85) was associated with lower interest in future telehealth visits. Conclusions: Patients expressed relatively high satisfaction levels with telehealth. Better technical quality, quality of instructions, and greater time saved were associated with higher satisfaction ratings. To maintain interest in future telehealth use and improve the patient experience, we must enhance the quality of telehealth delivery platforms and instructions provided to patients.

6.
129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2045881
7.
Journal of General Internal Medicine ; 37:S321-S322, 2022.
Article in English | EMBASE | ID: covidwho-1995613

ABSTRACT

BACKGROUND: California is the most populous state in the United States (US), with 40 million residents and a global economy that would be the 5th largest. California is also known for dramatic disparities in wealth and healthwith some of the richest and poorest communities in the world just a few miles apart. As such, the traumas of the Coronavirus-19 disease (COVID-19) pandemic have fallen starkly and unevenly across this state. An equitable and just pandemic response calls for a “distributive approach” to close the gaps on these disparate COVID-19 experiences. The National Institutes of Health (NIH) responded in such a way-with the Community Engagement Alliance (CEAL) as an NIH platform for real-time communityengaged COVID-19 strategies. The NIH CEAL asked for the development of state teams to engage communities, and California was one of the first states to answer this call. STOP COVID-19 CA was established in September 2021 to advance equity in COVID-19 research, clinical practice, and public health for California's most under-resourced racial/ethnic minority groups. This study evaluates the early impacts of the Alliance, from the perspective of its participating sites and partnered community-based organizations (CBOs). METHODS: 11 university sites (and their 68 affiliated CBOs) were sent a qualtrics survey in August 2021. We requested at least one academic/CBO response from each of the 11 sites. We conducted a mixed methods evaluation of the responses: analysis of monthly acitivity reports from sites (9/2020-8/ 2021) and summary of their perceptions regarding impact. RESULTS: We received responses from 17 academic investigators and 17 CBOs. In the aggregate, STOP COVID-19 CA partnerships reported >18,000 surveys and 40 focus groups and reached an estimated 25,000 vulnerable Californians in >500 COVID-19 town halls and vaccine events. In the survey, academic and CBOs emphasized that the Alliance expanded community networks and broadened access to culturally specific COVID-19 messaging and vaccine outreach strategies. They noted accelerated knowledge sharing by learning from the successes and challenges of other sites' COVID-19 initiatives. Academic partners described leveraging the STOP COVID-19 CA network as a platform to reach local, state, and federal policymakers. CBOs expressed concerns about bureaucracy delaying funding for timesensitive COVID-19 CBOs-driven initiatives. Both groups also highlighted the potential for the Sustainability of this Alliance and the need for flexible resources to address the health disparities, conditions, and social determinants of health that predispose their communities to high rates and poor outcomes from COVID-19. CONCLUSIONS: STOP COVID-19 CA represents a new and potentially sustainable community engagement model for addressing disparities in multiethnic/multicultural and geographically dispersed communities.

8.
Cancer Research ; 82(12), 2022.
Article in English | EMBASE | ID: covidwho-1986508

ABSTRACT

Purpose: Especially with the COVID19 pandemic, the necessity of technology-based interventions using computers and mobile devices has increased in cancer survivorship management including symptom management. However, little is known about the effectiveness of a technology-based intervention in improving symptom experience of racial/ethnic minorities including Asian American breast cancer survivors. The purpose of this study was to examine the efficacy of a technologybased intervention in improving symptom experience of Asian American breast cancer survivors. Methods: This study was conducted as a part of an ongoing randomized clinical trial among 199 Asian American breast cancer survivors. The technology-based intervention included three subethnic specific social media sites, interactive online educational sessions, and online resources. Both groups (intervention and control groups) used the American Cancer Society's website on breast cancer, and only the intervention group used the technology-based intervention. Only the data collected using the questionnaire on background characteristics and health/disease status and the Memorial Symptom Assessment Scale-Short Form (MSAS) were analyzed for this study. The data were analyzed using separate intent-to-treat growth curve models. Results: While both groups reported decreases in symptom scores from the pre-test to post 3- months (p<.01), the intervention group had larger decreases in symptom scores compared with the control group (p<.01). There existed significant group∗time interactive effects on the Global Distress Index (β = - 0.234), the Physical Symptom Distress scores (β = -0.266), the psychological symptom distress scores (β = - 0.212c), the total number of symptoms (β = -0.261), and the total symptom distress scores ( β = -0.261). Conclusions: The findings of this study clearly indicated symptom improvement among Asian American breast cancer survivors by a technology-based intervention using computers and mobile devices. Further studies with diverse racial/ethnic minorities are warranted to confirm the effectiveness of technology-based interventions in improving symptom experience of cancer survivors across different types of cancer.

9.
ERJ Open Res ; 7(3)2021 Jul.
Article in English | MEDLINE | ID: covidwho-1338098

ABSTRACT

INTRODUCTION: In primary ciliary dyskinesia (PCD) impaired mucociliary clearance leads to recurrent airway infections and progressive lung destruction, and concern over chronic airway infection and patient-to-patient transmission is considerable. So far, there has been no defined consensus on how to control infection across centres caring for patients with PCD. Within the BEAT-PCD network, COST Action and ERS CRC together with the ERN-Lung PCD core a first initiative has now been taken towards creating such a consensus statement. METHODS: A multidisciplinary international PCD expert panel was set up to create a consensus statement for infection prevention and control (IP&C) for PCD, covering diagnostic microbiology, infection prevention for specific pathogens considered indicated for treatment and segregation aspects. Using a modified Delphi process, consensus to a statement demanded at least 80% agreement within the PCD expert panel group. Patient organisation representatives were involved throughout the process. RESULTS: We present a consensus statement on 20 IP&C statements for PCD including suggested actions for microbiological identification, indications for treatment of Pseudomonas aeruginosa, Burkholderia cepacia and nontuberculous mycobacteria and suggested segregation aspects aimed to minimise patient-to-patient transmission of infections whether in-hospital, in PCD clinics or wards, or out of hospital at meetings between people with PCD. The statement also includes segregation aspects adapted to the current coronavirus disease 2019 (COVID-19) pandemic. CONCLUSION: The first ever international consensus statement on IP&C intended specifically for PCD is presented and is targeted at clinicians managing paediatric and adult patients with PCD, microbiologists, patient organisations and not least the patients and their families.

10.
Kuwait Medical Journal ; 54(2):283-286, 2022.
Article in English | EMBASE | ID: covidwho-1976249

ABSTRACT

BA.2, nicknamed “stealth omicron”, is one of three known subvariants of Omicron. BA.2 differs from BA.1 (the original Omicron variant) in some mutations, including in the spike protein. This mutation makes it more difficult to identify as Omicron subvariant on several tests, and BA.2 is more infectious and more likely to infect vaccinated people compared to BA.1. BA.2 has been detected in at least 40 countries and in all continents except Antarctica, and the World Health Organization is continuing to monitor its spread, while BA.2 is beginning to replace the original Omicron strain in many countries. This commentary provides an overview of current knowledge and unknowns about this new variant and summarizes the important study findings for the purpose of informing experts.

11.
Jpn J Infect Dis ; : JJID.2022.081-JJID.2022.081, 2022.
Article in English | J-STAGE | ID: covidwho-1969764

ABSTRACT

Community Treatment Centers (CTC) have been set up in South Korea to quarantine and treat COVID-19 patients with mild symptoms. Such CTCs have shown to be successful in terms of management and operation. However, recent incidences of patient deaths at CTCs have brought about concerns and the need to re-examine the administration of CTCs. The following issues include some of the problems of CTCs: failure to monitor patients, recognize emergency situations, and rapidly transfer patients to hospitals, and also the increased fatigue of medical staff. It is necessary to enhance patient safety measures at CTCs by setting up a stronger patient monitoring system, a swifter hospital transfer process and a faster response to emergency situations.

12.
Neurology ; 98(18 SUPPL), 2022.
Article in English | EMBASE | ID: covidwho-1925321

ABSTRACT

Objective: The purpose of this study was to investigate the changes in the neuropsychiatric symptoms of patients with dementia during the pandemic through the complete enumeration telephone survey in the caregivers of dementia patients who were registered at the database of Yangcheon Dementia Reassurance Center (YDRC) for Dementia. Background: It has been reported that the social isolation imposed by COVID-19 pandemic can have a major impact on the mental health of dementia patients. Design/Methods: There were a total of 2080 dementia patients on the database of YDRC who were registered as having dementia from 2008 ∼2020. From Mar to April in 2021, the telephone survey was conducted in all the caregivers with dementia on the database of YDRC. We asked whether the neuropsychiatric symptoms of patients were aggravated during COVID 19 and if they were, which neuropsychiatric symptoms were aggravated among the 12 symptoms of neuropsychiatric inventory (NPI): delusions, hallucinations, agitation/aggression, depression/dysphoria, anxiety, elation/euphoria, apathy/indifference, disinhibition, irritability/lability, aberrant motor behavior, disturbances of sleep, and disturbances of appetite/eating. Results: Among 2080 patients with dementia (mean age: 81.2 years, 66.8 % females), a total of 1038 caregivers of patients with dementia responded to the telephone survey. Among 1038 patients, 274 patients (26.4%) were reported to have at least one and more aggravating neuropsychiatric symptoms, especially depression/dysphoria (44.5 %), disturbances of sleep (9.5 %), and delusion (9.1 %). Conclusions: Given that patients'neuropsychiatric worsening is commonly associated with greater burden of the caregiver's, a more preemptive strategy to manage aggravating the neuropsychiatric symptoms from the Community Dementia Reassurance can help reduce difficulties of caregivers in this pandemic situation.

13.
IEEE ACCESS ; 10:62282-62291, 2022.
Article in English | Web of Science | ID: covidwho-1909181

ABSTRACT

In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The analysis of a dataset from the East Asian region with a focus on data from the Philippines revealed that the hazard of time to death was associated with the symptoms and background variables of patients. Machine learning algorithms, i.e., dimensionality reduction and boosting, were used along with conventional Cox regression. Machine learning algorithms solved the diverging problem observed when using traditional Cox regression and improved performance by maximizing the concordance index (C-index). Logistic principal component analysis for dimensionality reduction was significantly efficient in addressing the collinearity problem. In addition, to address the nonlinear pattern, a higher C-index was achieved using extreme gradient boosting (XGBoost). The results of the analysis showed that the symptoms were statistically significant for the hazard rate. Among the symptoms, respiratory and pneumonia symptoms resulted in the highest hazard level, which can help in the preliminary identification of high-risk patients. Among various background variables, the influence of age, chronic disease, and their interaction were identified as significant. The use of XGBoost revealed that the hazards were minimized during middle age and increased for younger and older people without any chronic diseases, with only the elderly having a higher risk of chronic disease. These results imply that patients with respiratory and pneumonia symptoms or older patients should be given medical attention.

14.
35th Conference on Neural Information Processing Systems, NeurIPS 2021 ; 29:24617-24630, 2021.
Article in English | Scopus | ID: covidwho-1898090

ABSTRACT

Federated learning, which shares the weights of the neural network across clients, is gaining attention in the healthcare sector as it enables training on a large corpus of decentralized data while maintaining data privacy. For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals. Unfortunately, the exchange of the weights quickly consumes the network bandwidth if highly expressive network architecture is employed. So-called split learning partially solves this problem by dividing a neural network into a client and a server part, so that the client part of the network takes up less extensive computation resources and bandwidth. However, it is not clear how to find the optimal split without sacrificing the overall network performance. To amalgamate these methods and thereby maximize their distinct strengths, here we show that the Vision Transformer, a recently developed deep learning architecture with straightforward decomposable configuration, is ideally suitable for split learning without sacrificing performance. Even under the non-independent and identically distributed data distribution which emulates a real collaboration between hospitals using CXR datasets from multiple sources, the proposed framework was able to attain performance comparable to data-centralized training. In addition, the proposed framework along with heterogeneous multi-task clients also improves individual task performances including the diagnosis of COVID-19, eliminating the need for sharing large weights with innumerable parameters. Our results affirm the suitability of Transformer for collaborative learning in medical imaging and pave the way forward for future real-world implementations. © 2021 Neural information processing systems foundation. All rights reserved.

15.
American Journal of Respiratory and Critical Care Medicine ; 205:1, 2022.
Article in English | English Web of Science | ID: covidwho-1880450
16.
Frontiers in Marine Science ; 9:13, 2022.
Article in English | Web of Science | ID: covidwho-1869379

ABSTRACT

Unprecedented retardation of spring water temperature rising during the 2020 pandemic year was identified in the Socheongcho Ocean Research Station within the northeastern basin of the Yellow Sea: an exceptionally high water temperature anomaly in March and a contrasted low-temperature anomaly in May. The slowest temperature evolution was principally caused by the significant increase in latent heat releases in April (117% higher than the climatology of 1982-2019). Strong northwesterly winds generated these exceptional heat fluxes associated with the dipole-like atmospheric circulation pattern over Siberia and the East Sea (Japan Sea). Besides, warm winter water facilitated the enhanced release of latent heat fluxes as a precondition. The oceanic heat redistribution partially supported the cold anomaly in the surface layer up to the middle of May through positive feedback between the low surface temperature and the active entrainment associated with tidal turbulent mixing. The resultant low temperature at the surface weakened the vertical stratification, both impeding the activation of phytoplankton's photosynthesis albeit under the eutrophic surface layer, consequently resulting in the delayed and suppressed spring bloom during 2020. Since such extreme events are anticipated to occur more frequently under global warming, our results highlight the importance of continuously monitoring multi-disciplinary environmental conditions, climate extremes, and their impact on the Yellow Sea marine ecosystem.

17.
Engineering Economics ; 33(2):161-173, 2022.
Article in English | Scopus | ID: covidwho-1847593

ABSTRACT

We analyze the impact of financial crises on major stock markets from 2000 to the COVID-19 pandemic using Fourier series. Analyzing the behaviors of the spectra obtained from monthly returns of their indices, we identify three global financial crises from 2000 to 2015, with different characteristics. In addition, applying Z-test and the color-contour plotting method to monthly propagations of the spectra of major frequencies from the monthly returns of each index, we analyze the developments in each market around the crises by comparing patterns in the color-contour plots. Using recent status analysis, we identify an instability around 2016 close to a real crisis;starting in 2020, the markets, which had already recovered from this instability have generated abnormal signals of an approaching crisis. Applying Z-test and color-contour plotting to monthly propagations from the recent status, we show that recent developments in major markets might be more serious than those occurring around previous financial crises. © 2022, Kauno Technologijos Universitetas. All rights reserved.

18.
2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831817

ABSTRACT

Since the first advent of SARS-CoV-2 in December 2019, Coronavirus disease (COVID-19) is still affecting the world. In the pandemic situation of the novel infectious disease, early detection of COVID-19 infection and severity for febrile respiratory patients is critical for efficient management of the medical system delivery system with limited medical personnel and facilities. Thus, we propose early triage exploiting data-driven strategical methods and machine learning techniques using the data of 5,628 admitted patients provided by Korea Central Disease Control Headquarters and 50 confirmed cases in Korea University Ansan Hospital. We proved validity of our data-driven strategies with machine learning models accuracy by doing 200 experiments and find out the features that affect COVID-19 through various feature selection in each medical inspection step. As a result, Stage 5 shows the results of blood test could affect to classify critical and severe cases obtaining precision of 0.2, 0.03 higher than without blood test results. But Stage 3 without blood test results achieved the highest accuracy of 0.88 showing possibility of early triage system without blood test. In conclusion, our triage system, based on data-driven strategies and machine learning techniques, can help in early detection and triage of COVID-19 patients. © 2022 IEEE.

19.
Journal of Korean Academy of Psychiatric and Mental Health Nursing ; 30(4):390-399, 2021.
Article in Korean | Scopus | ID: covidwho-1753984

ABSTRACT

Purpose: The aim of this study was to determine burnout and its related factors among nurses working at a tertiary hospital, who had experienced caring for patients with confirmed and suspected coronavirus disease 2019 (COVID-19), including those with severe and critical conditions during the outbreak. Methods: Responses of 129 nurses, who worked in a tertiary hospital in Daegu, which was designated as a special control area for infectious disease in Korea, were analyzed. Data were collected from November 1, 2020 to December 14, 2020 using self-report questionnaires. Analysis was performed using t-test, one-way ANOVA, Pearson correlation coefficients, and stepwise multiple regression. Results: Burnout did not show any statistically significant differences in age, sex, marital status, total length of clinical experience, and the department at the time of caring for COVID-19 patients. Compassion fatigue, stress, depression, and anxiety were positively related with burnout, and compassion satisfaction was negatively related with burnout. In regression analysis, compassion satisfaction, compassion fatigue, and stress were confirmed as the predictive factors of burnout. Conclusion: The study results suggest that compassion satisfaction, compassion fatigue, and stress could play an important role in reducing burnout among tertiary hospital nurses during infectious disease outbreaks. © 2021 The Korean Academy of Psychiatric and Mental Health Nursing.

20.
Nutr Metab (Lond) ; 19(1): 15, 2022 Mar 03.
Article in English | MEDLINE | ID: covidwho-1745441

ABSTRACT

BACKGROUND: Diets high in saturated fatty acids (SFAs) and greater abdominal obesity are both associated with raised low-density lipoprotein cholesterol (LDL-C) concentrations, an independent cardiovascular disease (CVD) risk marker. Although reducing SFA intake is a public health strategy for CVD prevention, the role of body fat distribution on the relationship between SFA and LDL-C is unclear. Therefore, our objective was to investigate whether the association between dietary SFAs and LDL-C concentrations is related to body composition. METHODS: In the BODYCON (impact of physiological and lifestyle factors on body composition) study, 409 adults [mean age 42 ± 16 years and median BMI of 23.5 (21.5-25.9) kg/m2] underwent a measure of body composition by dual energy x-ray absorptiometry, assessment of habitual dietary intake using a 4-day weighed food diary and physical activity level using a tri-axial accelerometer. Blood pressure was measured, and a fasting blood sample was collected to determine cardiometabolic disease risk markers. Correlations between body composition, circulating risk markers and dietary macronutrients were assessed prior to multivariate regression analysis. The effect of increasing intakes of dietary SFA on outcome measures was assessed using ANCOVA after adjusting for covariates. RESULTS: Abdominal visceral adipose tissue (VAT) mass was moderately positively correlated with total cholesterol (TC), LDL-C, systolic blood pressure (SBP), diastolic blood pressure and HOMA-IR (rs = 0.25-0.44, p < 0.01). In multiple regression analysis, 18.3% of the variability in LDL-C was explained by SFA intake [% total energy (TE)], abdominal VAT mass, carbohydrate%TE and fat%TE intakes. When data were stratified according to increasing SFA%TE intakes, fasting TC, LDL-C and non-high-density lipoprotein-cholesterol were higher in Q4 compared with Q2 (p ≤ 0.03). SBP was higher in Q4 versus Q3 (p = 0.01). Android lean mass was also higher in Q3 versus Q1 (p = 0.02). Other anthropometric and CVD risk markers were not different across quartile groups. CONCLUSIONS: Although dietary SFA was found to explain 9% of the variability in LDL-C, stratification of data according to quartiles of SFA intake did not reveal a dose-dependent relationship with LDL-C concentration. Furthermore, this association appeared to be independent of abdominal obesity in this cohort. Clinical Trail registration: Trial registration: clinicaltrials.gov as NCT02658539. Registered 20 January 2016, https://clinicaltrials.gov/ct2/show/NCT02658539 .

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