The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

Background and Objectives: As the Korean population ages fast, it is estimated that the people with hypertension, especially female patients, will increase rapidly. However, there are few data comparing the size of female and male hypertensive patients in the Korean population. Thus we assessed sex-specific trends in the prevalence and the number of people with hypertension. Methods: We analyzed data for 128,949 adults aged ≥20 years with valid blood pressure measurements from the 1998 to 2018 Korea National Health and Nutrition Examination Survey (KNHANES). The prevalence and the absolute number of hypertension were estimated with taking into the sampling weights separately for women and men. Results: Overall prevalence of hypertension is higher in men than in women. But, in older adults, women show higher prevalence and the number of people with hypertension. Between 1998 and 2018, prevalence of hypertension increased from 61.8% to 65.9% in elderly (age 65+) women, and from 49.0% to 59.4% in elderly men. During the same period, the number of elderly women with hypertension increased from 1.18 to 2.70 million, while the number of elderly men with hypertension increased from 0.57 to 1.78 million. Among hypertensive patients, undiagnosed hypertension and diagnosed-but-untreated hypertension were more common in men, while treated-but-uncontrolled hypertension were more common in women. Conclusion The fast-growing number of elderly women with hypertension will be an important public health challenge for the Korean society to solve in order to reduce the burden of cardiovascular disease.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body’s response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome’s role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome’s function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

BACKGROUND: Cardiac enzymes such as creatine kinase-MB, troponin I, and brain natriuretic peptide (BNP) are thought to be useful prognostic factors in patients with acute ischemic stroke. This study investigated the efficacy of cardiac biomarkers as prognostic factors. METHODS: We reviewed patients with acute ischemic stroke whose cardiac biomarkers had been measured and who were admitted to our hospital between January 2012 and December 2013. The cardiac biomarkers were measured within 24 hours after admission to the emergency room. We evaluated the clinical characteristics and compared the outcomes of the patients based on their cardiac biomarkers. RESULTS: The following cardiac biomarkers were measured in 219 patients with acute ischemic stroke: creatine kinase-MB (n=218), troponin I (n=219), and BNP (n=143). Statistically significant differences were observed in older age (68.77+/-12.42 vs. 74.59+/-6.68, p<0.05), insula involvement (30.5% vs. 59.1%, p<0.01), and higher BNP (259.75+/-422.65 vs. 667.06+/-1093.22, p<0.01). CONCLUSIONS: These results suggest that measuring all cardiac biomarkers may be not effective in determining the prognosis of acute ischemic stroke. However, BNP may be a superior to troponin I in predicting the prognosis.
Biomarkers ; Cerebral Infarction ; Creatine ; Emergency Service, Hospital ; Humans ; Natriuretic Peptide, Brain ; Prognosis ; Stroke* ; Troponin I

BACKGROUND: Cardiac enzymes such as creatine kinase-MB, troponin I, and brain natriuretic peptide (BNP) are thought to be useful prognostic factors in patients with acute ischemic stroke. This study investigated the efficacy of cardiac biomarkers as prognostic factors. METHODS: We reviewed patients with acute ischemic stroke whose cardiac biomarkers had been measured and who were admitted to our hospital between January 2012 and December 2013. The cardiac biomarkers were measured within 24 hours after admission to the emergency room. We evaluated the clinical characteristics and compared the outcomes of the patients based on their cardiac biomarkers. RESULTS: The following cardiac biomarkers were measured in 219 patients with acute ischemic stroke: creatine kinase-MB (n=218), troponin I (n=219), and BNP (n=143). Statistically significant differences were observed in older age (68.77+/-12.42 vs. 74.59+/-6.68, p<0.05), insula involvement (30.5% vs. 59.1%, p<0.01), and higher BNP (259.75+/-422.65 vs. 667.06+/-1093.22, p<0.01). CONCLUSIONS: These results suggest that measuring all cardiac biomarkers may be not effective in determining the prognosis of acute ischemic stroke. However, BNP may be a superior to troponin I in predicting the prognosis.
Biomarkers ; Cerebral Infarction ; Creatine ; Emergency Service, Hospital ; Humans ; Natriuretic Peptide, Brain ; Prognosis ; Stroke* ; Troponin I

OBJECTIVES: We estimated the population prevalence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including unreported infections, through a Korea Seroprevalence Study of Monitoring of SARS-CoV-2 Antibody Retention and Transmission (K-SEROSMART) in 258 communities throughout Korea. METHODS: In August 2022, a survey was conducted among 10,000 household members aged 5 years and older, in households selected through two stage probability random sampling. During face-to-face household interviews, participants self-reported their health status, COVID-19 diagnosis and vaccination history, and general characteristics. Subsequently, participants visited a community health center or medical clinic for blood sampling. Blood samples were analyzed for the presence of antibodies to spike proteins (anti-S) and antibodies to nucleocapsid proteins (anti-N) SARS-CoV-2 proteins using an electrochemiluminescence immunoassay. To estimate the population prevalence, the PROC SURVEYMEANS statistical procedure was employed, with weighting to reflect demographic data from July 2022. RESULTS: In total, 9,945 individuals from 5,041 households were surveyed across 258 communities, representing all basic local governments in Korea. The overall population-adjusted prevalence rates of anti-S and anti-N were 97.6% and 57.1%, respectively. Since the Korea Disease Control and Prevention Agency has reported a cumulative incidence of confirmed cases of 37.8% through July 31, 2022, the proportion of unreported infections among all COVID-19 infection was suggested to be 33.9%. CONCLUSIONS The K-SEROSMART represents the first nationwide, community-based seroepidemiologic survey of COVID-19, confirming that most individuals possess antibodies to SARS-CoV-2 and that a significant number of unreported cases existed. Furthermore, this study lays the foundation for a surveillance system to continuously monitor transmission at the community level and the response to COVID-19.