Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

Prevalence and secular trends in premetabolic syndrome in the United States: Findings from 1999-2020 nationally representative data of adults

PURPOSE

Although Premetabolic syndrome (PeMetSyn) is a precursor for metabolic syndrome (MetSyn), its prevalence and trends are unknown. This study examined the prevalence and trends in PreMetSyn and its association with sociodemographic risk factors in American adults.

METHODS

The 1999-2000 to 2017-2020 United States National Health and Nutritional Health Surveys (NHANES) data were used. PreMetSyn was defined as co-occurrence two cardiometabolic risk factors consisting of abdominal obesity, elevated triglycerides, reduced HDL-C, elevated blood pressure, and fasting plasma glucose. We calculated sex-specific overall prevalence of PreMetSyn and by race/ethnicity, age, education, poverty, and body mass index (BMI) categories. Sex-specific logistic regression models were used to test the association between sociodemographic risk factors and PreMetSyn.

RESULTS

From 1999 - 2000 to 2017-2020, the age-adjusted overall prevalence of PreMetSyn increased by 155.4% (from 9.2% to 23.5%) in men and by 131.3% (from 11.2% to 25.9%) in women. Increases in prevalence of PreMetSyn were observed by race/ethnicity, age, education, poverty and BMI levels in men and women from 1999-2000 to 2017-2020. Survey cycle, race/ethnicity, age, education, poverty-income ratio, and BMI were independently associated with greater odds of PreMetSyn in males and females. During this period, the co-occurrence of abdominal obesity and elevated blood pressure was the most common comorbidity and increased from 20.6% to 30.8% in men and from 27.8% to 36.1% in women.

CONCLUSIONS

This nationally representative study indicates a rapid increase from 1999-2000 to 2017-2020 in the proportion of American adults who meet the criteria for PreMetSyn. Early identification of subjects with PreMetSyn in the U.S. is a public health priority for initiating effective strategies to prevent the development of MetSyn and its associated chronic diseases.

Yuragi biomarker concept for evaluating human induced pluripotent stem cells using heterogeneity-based Raman finger-printing

Considering the fundamental mechanism causing singularity phenomena, we performed the following abduction: Assuming that a multicellular system is driven by spontaneous fluctuation of each cell and dynamic interaction of the cells, state transition of the system would be experimentally predictable from cellular heterogeneity. This study evaluates the abductive hypothesis by analyzing cellular heterogeneity to distinguish pre-state of state transition of differentiating cells with Raman spectroscopy and human induced pluripotent stem cells (hiPSCs) technique. Herein, we investigated the time development of cellular heterogeneity in Raman spectra during cardiomyogenesis of six hiPSC lines and tested two types of analyses for heterogeneity. As expected, some spectral peaks, possibly attributed to glycogen, correctively exhibited higher heterogeneity, prior to intensity changes of the spectrum in the both analyses in the all cell-lines tested. The combination of spectral data and heterogeneity-based analysis will be an approach to the arrival of biology that uses not only signal intensity but also heterogeneity as a biological index.

Anticipating regime shifts by mixing early warning signals from different nodes

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis

Multiple myeloma (MM) is a cancer of plasma cells. Normal (NL) cells are considered to pass through a precancerous state, such as monoclonal gammopathy of undetermined significance (MGUS), before transitioning to MM. In the present study, we acquired Raman spectra at three stages-834 NL, 711 MGUS, and 970 MM spectra-and applied the dynamical network biomarker (DNB) theory to these spectra. The DNB analysis identified MGUS as the unstable pre-disease state of MM and extracted Raman shifts at 1149 and 1527-1530 cm-1 as DNB variables. The distribution of DNB scores for each patient showed a significant difference between the mean values for MGUS and MM patients. Furthermore, an energy landscape (EL) analysis showed that the NL and MM stages were likely to become stable states. Raman spectroscopy, the DNB theory, and, complementarily, the EL analysis will be applicable to the identification of the pre-disease state in clinical samples.

Appropriate sleep duration modifying the association of insulin resistance and hepatic steatosis is varied in different status of metabolic disturbances among adults from the United States, NHANES 2017-March 2020

Steatosis is the hepatic manifestation of metabolic syndrome (MetS) and its developing is closely associated with insulin resistance. Shortened sleep has adverse effects on hepatic steatosis and the underlying mechanism remains unknown. We conceived to evaluate whether sleep duration was a lifestyle factor modifying the association between insulin resistance and hepatic steatosis and whether it was varied in different status of metabolic disturbances. We performed a cross-sectional analysis on 2264 adults of United States representing a population of 138,319,512 with MetS or pre-MetS from National Health and Nutrition Examination Survey (NHANES) 2017-March 2020. Participants underwent hepatic transient elastography and laboratory tests. The sleep duration was obtained from interviews. Results showed that insulin resistance was significantly associated with hepatic steatosis among participants with metabolic disturbances (OR = 1.85, 95% CI: 1.30-2.65). Significant moderation of sleep duration on the association between insulin resistance and hepatic steatosis was observed when sleep duration was dichotomized by 6.5- (P = 0.042) or 9.5-hour (P = 0.031). The risk of hepatic steatosis associated with insulin resistance was increased when sleep duration was ≤ 6.5 h and > 9.5 h. Furthermore, the moderation effect of 6.5-hour sleeping was only significant among participants with pre-MetS while that of 9.5-hour sleeping was only significant among participants with MetS. In conclusion, insufficient or excessive sleep increased the risk of hepatic steatosis associated with insulin resistance. Appropriate sleep duration was advocated and varied in different status of metabolic disturbances. Ensuring adequate sleep should be highlighted before MetS occurs and excessive sleep should be prevented for participants with MetS.

Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation

Cassava storage roots (SR) are an important source of food energy and raw material for a wide range of applications. Understanding SR initiation and the associated regulation is critical to boosting tuber yield in cassava. Decades of transcriptome studies have identified key regulators relevant to SR formation, transcriptional regulation and sugar metabolism. However, there remain uncertainties over the roles of the regulators in modulating the onset of SR development owing to the limitation of the widely applied differential gene expression analysis. Here, we aimed to investigate the regulation underlying the transition from fibrous (FR) to SR based on Dynamic Network Biomarker (DNB) analysis. Gene expression analysis during cassava root initiation showed the transition period to SR happened in FR during 8 weeks after planting (FR8). Ninety-nine DNB genes associated with SR initiation and development were identified. Interestingly, the role of trehalose metabolism, especially trehalase1 (TRE1), in modulating metabolites abundance and coordinating regulatory signaling and carbon substrate availability via the connection of transcriptional regulation and sugar metabolism was highlighted. The results agree with the associated DNB characters of TRE1 reported in other transcriptome studies of cassava SR initiation and Attre1 loss of function in literature. The findings help fill the knowledge gap regarding the regulation underlying cassava SR initiation.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Phytochemicals and Vitamin D for a Healthy Life and Prevention of Diseases

A variety of phytocompounds contained in medical plants have been used as medication, including Kampo (traditional Japanese) medicine. Phytochemicals are one category of the chemical compounds mainly known as antioxidants, and recently, their anti-inflammatory effects in preventing chronic inflammation have received much attention. Here, we present a narrative review of the health-promotion and disease-prevention effects of phytochemicals, including polyphenols, the latter of which are abundant in onions, oranges, tea, soybeans, turmeric, cacao, and grapes, along with the synergetic effects of vitamin D. A phenomenon currently gaining popularity in Japan is finding non-disease conditions, so-called ME-BYO (mibyou) and treating them before they develop into illnesses. In addition to lifestyle-related diseases such as metabolic syndrome and obesity, dementia and frailty, commonly found in the elderly, are included as underlying conditions. These conditions are typically induced by chronic inflammation and might result in multiple organ failure or cancer if left untreated. Maintaining gut microbiota is important for suppressing (recently increasing) intestinal disorders and for upregulating immunity. During the COVID-19 pandemic, the interest in phytochemicals and vitamin D for disease prevention increased, as viral and bacterial infection to the lung causes fatal inflammation, and chronic inflammation induces pulmonary fibrosis. Furthermore, sepsis is a disorder inducing severe organ failure by the infection of microbes, with a high mortality ratio in non-coronary ICUs. However, antimicrobial peptides (AMPs) working using natural immunity suppress sepsis at the early stage. The intake of phytochemicals and vitamin D enhances anti-inflammatory effects, upregulates immunity, and reduces the risk of chronic disorders by means of keeping healthy gut microbiota. Evidence acquired during the COVID-19 pandemic revealed that daily improvement and prevention of underlying conditions, in terms of lifestyle-related diseases, is very important because they increase the risk of infectious diseases. This narrative review discusses the importance of the intake of phytochemicals and vitamin D for a healthy lifestyle and the prevention of ME-BYO, non-disease conditions.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

Identifying the critical state of cancers by single-sample Markov flow entropy

Background

The progression of complex diseases sometimes undergoes a drastic critical transition, at which the biological system abruptly shifts from a relatively healthy state (before-transition stage) to a disease state (after-transition stage). Searching for such a critical transition or critical state is crucial to provide timely and effective scientific treatment to patients. However, in most conditions where only a small sample size of clinical data is available, resulting in failure when detecting the critical states of complex diseases, particularly only single-sample data.

Methods

In this study, different from traditional methods that require multiple samples at each time, a model-free computational method, single-sample Markov flow entropy (sMFE), provides a solution to the identification problem of critical states/pre-disease states of complex diseases, solely based on a single-sample. Our proposed method was employed to characterize the dynamic changes of complex diseases from the perspective of network entropy.

Results

The proposed approach was verified by unmistakably identifying the critical state just before the occurrence of disease deterioration for four tumor datasets from The Cancer Genome Atlas (TCGA) database. In addition, two new prognostic biomarkers, optimistic sMFE (O-sMFE) and pessimistic sMFE (P-sMFE) biomarkers, were identified by our method and enable the prognosis evaluation of tumors.

Conclusions

The proposed method has shown its capability to accurately detect pre-disease states of four cancers and provide two novel prognostic biomarkers, O-sMFE and P-sMFE biomarkers, to facilitate the personalized prognosis of patients. This is a remarkable achievement that could have a major impact on the diagnosis and treatment of complex diseases.

Tisochrysis lutea F&M-M36 Mitigates Risk Factors of Metabolic Syndrome and Promotes Visceral Fat Browning through β3-Adrenergic Receptor/UCP1 Signaling

Pre-metabolic syndrome (pre-MetS) may represent the best transition phase to start treatments aimed at reducing cardiometabolic risk factors of MetS. In this study, we investigated the effects of the marine microalga Tisochrysis lutea F&M-M36 (T. lutea) on cardiometabolic components of pre-MetS and its underlying mechanisms. Rats were fed a standard (5% fat) or a high-fat diet (20% fat) supplemented or not with 5% of T. lutea or fenofibrate (100 mg/Kg) for 3 months. Like fenofibrate, T. lutea decreased blood triglycerides (p < 0.01) and glucose levels (p < 0.01), increased fecal lipid excretion (p < 0.05) and adiponectin (p < 0.001) without affecting weight gain. Unlike fenofibrate, T. lutea did not increase liver weight and steatosis, reduced renal fat (p < 0.05), diastolic (p < 0.05) and mean arterial pressure (p < 0.05). In visceral adipose tissue (VAT), T. lutea, but not fenofibrate, increased the β3-adrenergic receptor (β3ADR) (p < 0.05) and Uncoupling protein 1 (UCP-1) (p < 0.001) while both induced glucagon-like peptide-1 receptor (GLP1R) protein expression (p < 0.001) and decreased interleukin (IL)-6 and IL-1β gene expression (p < 0.05). Pathway analysis on VAT whole-gene expression profiles showed that T. lutea up-regulated energy-metabolism-related genes and down-regulated inflammatory and autophagy pathways. The multitarget activity of T. lutea suggests that this microalga could be useful in mitigating risk factors of MetS.

Application of the Dynamical Network Biomarker Theory to Raman Spectra

The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.

The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression

The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods. In this study, a novel computational method, called single-sample network module biomarkers (sNMB), is presented to predict the pre-deterioration stage or critical point using only a single sample. Specifically, the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples. Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets, including acute lung injury, stomach adenocarcinoma, esophageal carcinoma, and rectum adenocarcinoma. In addition, it provides signaling biomarkers for further practical application, which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.

KAMPOmics: A framework for multidisciplinary and comprehensive research on Japanese traditional medicine

Traditional Japanese medicines, known as "Kampo medicines", are pharmaceutical-grade multi-herbal treatments that are integrated within the modern medical system in Japan. Although basic and clinical research including placebo-controlled double-blind trials is attempting to clarify their effectiveness and mechanisms of action, such studies are seriously limited due to the multi-targeted, multi-component "long-tail" properties of Kampo medicines, which are fundamentally different from modern western therapeutics. However, recent progress in high-throughput analytical technology, coupled with an exponential increase in biomedical information on various levels from molecular biology to clinical "big" data, is enabling us to commence a multidisciplinary and comprehensive investigation of Kampo medicines based on multi-omics, bio-informatics, and systems biology. In addition to deriving an inclusive understanding of the benefits and mechanisms of Kampo medicines, "KAMPOmics" may lead to the development of new principles to control and treat diseases in a systems-oriented manner. Furthermore, elucidation of "sho" and "mibyo" - classical concepts of Kampo, which loosely approximate to the notions of "precise medicine" and "pre-symptomatic aberration", respectively - may contribute to the development of patient-oriented medicine, an area attracting enormous growth and interest in contemporary medicine.

White matter fiber-specific degeneration in older adults with metabolic syndrome

OBJECTIVE

Metabolic syndrome (MetS) is defined as a complex of interrelated risk factors for type 2 diabetes and cardiovascular disease, including glucose intolerance, abdominal obesity, hypertension, and dyslipidemia. Studies using diffusion tensor imaging (DTI) have reported white matter (WM) microstructural abnormalities in MetS. However, interpretation of DTI metrics is limited primarily due to the challenges of modeling complex WM structures. The present study used fixel-based analysis (FBA) to assess the effect of MetS on the fiber tract-specific WM microstructure in older adults and its relationship with MetS-related measurements and cognitive and locomotor functions to better understand the pathophysiology of MetS.

METHODS

Fixel-based metrics, including microstructural fiber density (FD), macrostructural fiber-bundle cross-section (FC), and a combination of FD and FC (FDC), were evaluated in 16 healthy controls (no components of MetS; four men; mean age, 71.31 ± 5.06 years), 57 individuals with premetabolic syndrome (preMetS; one or two components of MetS; 29 men; mean age, 72.44 ± 5.82 years), and 46 individuals with MetS (three to five components of MetS; 27 men; mean age, 72.15 ± 4.97 years) using whole-brain exploratory FBA. Tract of interest (TOI) analysis was then performed using TractSeg across 14 selected WM tracts previously associated with MetS. The associations between fixel-based metrics and MetS-related measurements, neuropsychological, and locomotor function tests were also analyzed in individuals with preMetS and MetS combined. In addition, tensor-based metrics (i.e., fractional anisotropy [FA] and mean diffusivity [MD]) were compared among the groups using tract-based spatial statistics (TBSS) analysis.

RESULTS

In whole-brain FBA, individuals with MetS showed significantly lower FD, FC, and FDC compared with healthy controls in WM areas, such as the splenium of the corpus callosum (CC), corticospinal tract (CST), middle cerebellar peduncle (MCP), and superior cerebellar peduncle (SCP). Meanwhile, in fixel-based TOI, significantly reduced FD was observed in individuals with preMetS and MetS in the anterior thalamic radiation, CST, SCP, and splenium of the CC compared with healthy controls, with relatively greater effect sizes observed in individuals with MetS. Compared with healthy controls, significantly reduced FC and FDC were only demonstrated in individuals with MetS, including regions with loss of FD, inferior cerebellar peduncle, inferior fronto-occipital fasciculus, MCP, and superior longitudinal fasciculus part I. Furthermore, negative correlations were observed between FD and Brinkman index of cigarette consumption cumulative amount and between FC or FDC and the Trail Making Test (parts B-A), which is a measure of executive function, waist circumference, or low-density lipoprotein cholesterol. Finally, TBSS analysis revealed that FA and MD were not significantly different among all groups.

CONCLUSIONS

The FBA results demonstrate that substantial axonal loss and atrophy in individuals with MetS and early axonal loss without fiber-bundle morphological changes in those with preMetS within the WM tracts are crucial to cognitive and motor function. FBA also clarified the association between executive dysfunction, abdominal obesity, hyper-low-density lipoprotein cholesterolemia, smoking habit, and compromised WM neural tissue microstructure in MetS.

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

The dynamic network biomarker (DNB) method has advanced since it was first proposed. This review discusses advances in the DNB method that can identify the dynamic change in the expression signature related to the critical time point of disease progression by utilizing different kinds of transcriptome data. The DNB method is good at identifying potential biomarkers for cancer and other disease development processes that are represented by a limited molecular profile change between the normal and critical stages. We highlight that the cancer tipping point or premalignant state has been widely discovered for different types of cancer by using the DNB method that utilizes bulk or single-cell RNA sequencing data. This method could also be applied to other dynamic research studies and help identify early warning signals, such as the prediction of a pre-outbreak of COVID-19. We also discuss how the identification of reliable biomarkers of cancer and the development of new methods can be utilized for early detection and intervention and provide insights into emerging paths of the widespread biomarker candidate pool for further validation and disease/health management.

Dynamical network biomarkers: Theory and applications

This paper reviews theory of DNB (Dynamical Network Biomarkers) and its applications including both modern medicine and traditional medicine. We show that omics data such as gene/protein expression profiles can be effectively used to detect pre-disease states before critical transitions from healthy states to disease states by using the DNB theory. The DNB theory with big biological data is expected to lead to ultra-early precision and preventive medicine.

Childhood Obesity-Related Mechanisms: MicroRNome and Transcriptome Changes in a Nested Case-Control Study

Childhood obesity could contribute to adulthood obesity, leading to adverse health outcomes in adults. However, the mechanisms for how obesity is developed are still unclear. To determine the epigenome-wide and genome-wide expression changes related with childhood obesity, we compared microRNome and transcriptome levels as well as leptin protein levels in whole bloods of 12 obese and 24 normal children aged 6 years. miR-328-3p, miR-1301-3p, miR-4685-3p, and miR-6803-3p were negatively associated with all obesity indicators. The four miRNAs were also associated with 3948 mRNAs, and separate 475 mRNAs (185 among 3948 mRNAs) were associated with all obesity indicators. The 2533 mRNAs (64.2%) among the 3948 mRNAs and 286 mRNAs (60.2%) among the 475 mRNAs were confirmed as targets of the four miRNAs in public databases through miRWalk 2.0. Leptin protein was associated with miR-6803-3p negatively and all obesity indicators positively. Using DAVID bioinformatics resources 6.8, top three pathways for obesity-related gene set were metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. The top three obesity-related disease classes were metabolic, cardiovascular, and chemdependency. Our results support that childhood obesity could be developed through miRNAs-related epigenetic mechanism and, further, these obesity-related epigenetic changes could control the pathways related with the development of various diseases.

Pre-Metabolic Syndrome and Incidence of Type 2 Diabetes and Hypertension: From the Korean Genome and Epidemiology Study

The aim of this study was to investigate the prevalence of premetabolic syndrome (pre-MetSyn) and its components and to longitudinally examine their association with new-onset type 2 diabetes (T2D) or hypertension. A total of 4037 men and 4400 women aged 40 to 69 years were selected from the Korean Genome and Epidemiology Study, observed from 2001 to 2014. Pre-MetSyn was defined as the presence of one or two components of MetSyn (B, elevated blood pressure; G, elevated glucose; H, low HDL-cholesterol; T, elevated triglycerides; W, increased waist circumference). The prevalence of pre-MetSyn was higher than that of non-MetSyn and MetSyn in both men and women. In multivariate Cox regression analyses, G, T, G+T, W+G, B+G, B+T, W+T, B+H, and H+T in men and G, T, G+H, B+T, and H+T in women were significantly associated with new-onset T2D. B, W, B+H, B+T, W+H, and W+T in men and B, B+T, B+H, B+W, and W+H in women were significantly associated with new-onset hypertension. The prevalence of pre-MetSyn components and their associations with new-onset T2D or hypertension differed according to sex and disease. Our results suggest that specific phenotypes of pre-MetSyn may be important factors for predicting and preventing the development of T2D and hypertension.

Suppression of Dynamical Network Biomarker Signals at the Predisease State (Mibyou) before Metabolic Syndrome in Mice by a Traditional Japanese Medicine (Kampo Formula) Bofutsushosan

Due to the increasing incidence of metabolic syndrome, the development of new therapeutic strategies is urgently required. One promising approach is to focus on the predisease state (so-called Mibyou in traditional Japanese medicine) before metabolic syndrome as a preemptive medical target. We recently succeeded in detecting a predisease state before metabolic syndrome using a mathematical theory called the dynamical network biomarker (DNB) theory. The detected predisease state was characterized by 147 DNB genes among a total of 24,217 genes in TSOD (Tsumura-Suzuki Obese Diabetes) mice, a well-accepted model of metabolic syndrome, at 5 weeks of age. The timing of the predisease state was much earlier than the onset of metabolic syndrome in TSOD mice reported to be at approximately 8-12 weeks of age. In the present study, we investigated whether the predisease state in TSOD mice can be inhibited by the oral administration of a Kampo formula, bofutsushosan (BTS), which is usually used to treat obese patients with metabolic syndrome in Japan, from 3 to 7 weeks of age. We found the comprehensive suppression of the early warning signals of the DNB genes by BTS at 5 weeks of age and later. Specifically, the standard deviations of 134 genes among the 147 DNB genes decreased at 5 weeks of age as compared to the nontreatment control group, and 80 of them showed more than 50% reduction. In addition, at 7 weeks of age, the body weight and blood glucose level were significantly lower in the BTS-treated group than in the nontreatment control group. The results of our study suggest a novel mechanism of BTS; it suppressed fluctuations of the DNB genes at the predisease state before metabolic syndrome and thus prevented the subsequent transition to metabolic syndrome. In conclusion, this study demonstrated the preventive and preemptive effects of a Kampo formula on Mibyou before metabolic syndrome for the first time based on scientific evaluation.

Single-Sample Node Entropy for Molecular Transition in Pre-deterioration Stage of Cancer

A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with a priori established protein interaction network. Using this model, critical stages were successfully detected in simulation data and four TCGA datasets, indicating its sensitivity and robustness. Besides, compared with the results of the differential analysis, our results showed that most of dynamic network biomarkers identified by node entropy, such as NKD2 or DAAM1, located in upstream in many important cancer-related signaling pathways regulated intergenic signaling within pathways. We also identified some novel prognostic biomarkers such as PER2, TNFSF4, MMP13 and ENO4 using node entropy rather than expression level. More importantly, we found the switch of non-specific pathways related to DNA damage repairing was the main driven force for cancer progression. In conclusion, we have successfully developed a dynamic node entropy model based on single case data to find out tipping point and possible mechanism for cancer progression. These findings may provide new target genes in therapeutic intervention tactics.