• Journal of Korean Biological Nursing Science
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• 제22권1호
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• pp.36-44
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• 2020

Purpose: The purpose of this study was to predict measuring site suited for abdominal skin fold thickness (ASFT) by measuring the distribution of abdominal subcutaneous fat thickness (AScFT) and ascertain the correlations among obesity indicators. Methods: The size of analysis materials was 124 secondary data measured by ultrasonic device, bioelectrical impedance analyzer and caliper. Data were analyzed using t-test, and Pearson's correlation. Results: The average of AScFT was 10.63± 6.79mm with its range 1.39-36.16 mm, and AScFT of female and of central parts were thicker than those of male and outer parts in the abdomen. The average of ASFT was 29.26±12.59 mm. Site 5 on Figure 1 was most similar to the average of AScFT in both sexes. Body mass index (BMI) and waist hip ratio (WHR) were 23.65±3.98 and 0.88±0.05 respectively. The body weight, BMI, WHR, visceral fat, ASFT vs AScFT revealed in significant correlation (r= .29, r= .55, r= .39, r= .33. r= .29). Conclusion: BMI and WHR seem more useful than other obesity indicators, when obesity control is necessary for Type 2 diabetes patients. Site 5 on Figure 1 is most suitable site to measure ASFT.

• International Journal of Advanced Culture Technology
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• 제7권1호
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.

• 대한의용생체공학회:의공학회지
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• 제28권3호
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• pp.355-362
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• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• 동의생리병리학회지
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• 제36권3호
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• pp.94-99
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• 2022

The discovery of biomarkers related to pattern identification (PI), the core diagnostic theory of Korean medicine (KM), is one of the methods that can provide objective and reliable evidence by applying PI to clinical practice. In this study, 40 diabetic patients and 41 healthy control subjects recruited from the Korean medicine clinic were examined to determine the human electrical response related to the deficiency pattern, a representative pattern of diabetes. Qi-Blood-Yin-Yang deficiency pattern scores, which are representative deficiency patterns for diabetes mellitus, were obtained through a questionnaire with verified reliability and validity, and the human electrical response was measured non-invasively using a bioimpedance meter. In ANCOVA analysis using gender as a covariate, the 5 kHz frequency resistance and 5-250 kHz frequency reactance were significantly lower in the diabetic group than in non-diabetic control group. In addition, the multiple regression analysis showed a positive correlation (R2=0.11~0.19) between the Yang deficiency pattern score and resistance value for the diabetic group; the correlation was higher at higher frequencies of 50kHz (R2=0.18) and 250kHz (R2=0.19) compared to 5kHz(R2=0.11). In contrast, there was no such significant association in the control group. It implies that bioimpedance resistance measured at finite frequencies may be useful in predicting Yang deficiency, which is closely related to diabetic complications by reflecting the decrease in body water content and metabolism. In the future, large-scale planned clinical studies will be needed to identify biomarkers associated with different types of PI in diabetes.

• Nutrition Research and Practice
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• 제16권4호
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• pp.450-463
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• 2022

BACKGROUND/OBJECTIVES: Adolescents who skip breakfast have an increased prevalence of chronic diseases. Thus, we aimed to evaluate whether the intake of rice-based breakfast had positive effects on blood glucose indices and to determine the possibility of diabetes prevalence in Korean youths who habitually skip breakfast. SUBJECTS/METHODS: In this randomized parallel-group controlled trial, 81 subjects who were suitable for compliance among 105 middle-and high-school students aged 12-18 years who usually skipped breakfast were included in this study (rice-meal group [RMG], n = 26; wheat-meal group [WMG], n = 29; general-meal group [GMG], n = 26). The RMG and WMG received a rice-based breakfast and a wheat-based breakfast for 12 weeks, respectively. The anthropometric indices, blood glucose indices, and metabolites were measured at baseline and the endpoint, respectively. RESULTS: The mean body weights in the RMG, WMG, and GMG groups at the endpoint were 62.44 kg, 61.80 kg, and 60.28 kg, respectively, and the mean body weights of the WMG and GMG groups at the endpoint were significantly higher than that at baseline (P < 0.05). The levels of fasting insulin and homeostasis model assessment of insulin resistance (HOMA-IR) values were significantly decreased in the RMG group at the endpoint compared to baseline (P < 0.05, P < 0.05, respectively). The levels of tryptophan and tyrosine in the WMG group at the endpoint were significantly higher than that those at baseline (P < 0.01, P < 0.05, respectively). CONCLUSIONS: Rice-based breakfast has positive effects on fasting insulin levels and HOMA-IR in Korean adolescents who skip breakfast. Additionally, it was found that a skipping breakfast could increase the prevalence of diabetes in adolescents who skip breakfast. Therefore, in addition to reducing breakfast skipping, it is vital to develop a rice-based menu that fits teenage preferences to prevent chronic diseases such as diabetes.

• 분석과학
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• 제11권1호
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• pp.47-53
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• 1998

본 연구는 당뇨병 진단방법의 개선을 위하여 채혈을 직접적으로 하지 않고 혈당을 측정할 수 있도록 하기 위하여 근적외선 분광법을 적용하였다. 본 연구를 위하여 근적외선 분광법을 이용하여 1 mg/dL에서 200 mg/dL 사이의 표준 시료 80개 글루코오스 흡수 스팩트럼을 측정하고 이를 정량하여 표준 농도와의 상관관계를 비교하였을 때 1.8 mg/dL 오차범위에서 매우 우수하였다. 그리고 실제 혈액중에 존재할 수 있는 전해질 및 피부에 의한 산란의 영향을 연구하였을 때 모두 2.8 mg/dL 및 3.8 mg/dL의 표준오차를 나타내었다. 특히 실제 피부에 적용하기 위하여 검량곡선에 비직선성을 유발하는 빛의 산란 현상에 관한 모델링을 통하여 정확도를 향상시키는 통계적인 방법을 제시하였다.

• 대한간호학회지
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• 제51권3호
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• pp.280-293
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• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• Genomics & Informatics
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• 제17권4호
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• pp.48.1-48.6
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• 2019

Over the last decade, genome-wide association studies (GWASs) have provided an unprecedented amount of genetic variations that are associated with various phenotypes. However, previous GWAS were mostly conducted in European populations, and these biased results for non-Europeans may result in a significant reduction in risk prediction for non-Europeans. An issue with the early GWAS was the winner's curse problem, which led to misleading results when constructing the polygenic risk scores (PRS). Therefore, more non-European population-based studies are needed to validate reported variants and improve genetic risk assessment across diverse populations. In this study, we validated 422 variants independently associated with glycemic indexes, liver enzymes, and type 2 diabetes in 125,872 samples from a Korean population, and further validated the results by assessing publicly available summary statistics from European GWAS (n = 898,130). Among the 422 independently associated variants, 284, 320, and 361 variants were replicated in Koreans, Europeans, and either one of the two populations. In addition, the effect sizes for Koreans and Europeans were moderately correlated (r = 0.33-0.68). However, 61 variants were not replicated in both Koreans and Europeans. Our findings provide valuable information on effect sizes and statistical significance, which is essential to improve the assessment of disease risk using PRS analysis.

• Genomics & Informatics
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• 제19권4호
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• pp.37.1-37.7
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• 2021

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East Asian T2D summary statistics, and the linkage disequilibrium among the variants (r² < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

• 융합정보논문지
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• 제9권8호
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• pp.248-253
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• 2019

노인성 치매의 전 임상단계인 경도인지장애(MCI)를 조기 진단하고, 조기 개입한다면, 치매의 발병률을 줄일 수 있다. 본 연구는 우리나라 지역사회 노인의 MCI 예측 모형을 개발하고 노년기 인지장애의 예방을 위한 기초자료를 제공하였다. 연구대상은 2012년 Korean Longitudinal Survey of Aging(KLoSA)에 참여한 65세 이상 지역사회 노인 3,240명(남성 1,502명, 여성 1,738명)이다. 결과변수는 MCI유병으로 정의하였고, 설명변수는 성, 연령, 혼인상태, 교육수준, 소득수준, 흡연, 음주, 주1회 이상의 정기적인 운동, 월평균 사회활동 참여시간, 주관적 건강, 고혈압, 당뇨병을 포함하였다. 예측모형의 개발은 Restricted Boltzmann Machine(RBM) 인공신경망을 이용하였다. RMB 인공신경망을 이용하여 우리나라 지역사회 노인의 MCI 예측 모형을 구축한 결과, 유의미한 요인은 연령, 성별, 최종학력, 주관적 건강, 혼인상태, 소득수준, 흡연, 규칙적 운동이었다. 이 결과를 기초로 MCI 고위험군의 특성을 고려한 맞춤형 치매 예방 프로그램의 개발이 요구된다.