Objective: Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques. Methods: We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2. Results: Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D. Conclusions: Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.

Background: One critical premise of disease‐related biomarkers is the definition of the counterpart normality. Contrary to pre‐clinical models that can be carefully tailored according to scientific need, heterogeneity and uncontrollability is the essence of humans in health studies. Fully characterization of consistent parameters that define the normal population is the basis to individual differences normalization irrelevant to a given disease process. Self claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases or diseases at their early stage such as cancer, diabetes and hypertension.Methods: This paper exemplifies the characterization of the suboptimal health status (SHS) which represents a new public health problem in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome and chronic fatigue. We applied clinical informatics approaches and developed a questionnaire for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross‐sectional study of 3,405 individuals.Results: The final questionnaire congregated into a score (SHSQ‐25) which could significantly distinguish among several abnormal conditions.Conclusion: SHSQ‐25 could be used as a translational medicine instrument for health measuring in the general population.

The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS).

Suboptimal health status (SHS) is a physical state between health and disease, characterized by the perception of health complaints, general weakness, chronic fatigue and low energy levels. SHS is proposed by the ancient concept of traditional Chinese medicine (TCM) from the perspective of preservative, predictive and personalized (precision) medicine. We previously created the suboptimal health status questionnaire 25 (SHSQ-25), a novel instrument to measure SHS, validated in various populations. SHSQ-25 thus affords a window of opportunity for early detection and intervention, contributing to the reduction of chronic disease burdens. To investigate the causative effect of SHS in non-communicable chronic diseases (NCD), we initiated the China suboptimal health cohort study (COACS), a longitudinal study starting from 2013. Phase I of the study involved a cross-sectional survey aimed at identifying the risk/protective factors associated with SHS; and Phase II: a longitudinal yearly follow-up study investigating how SHS contributes to the incidence and pattern of NCD. (1) Cross-sectional survey: in total, 4313 participants (53.8 % women) aged from 18 to 65 years were included in the cohort. The prevalence of SHS was 9.0 % using SHS score of 35 as threshold. Women showed a significantly higher prevalence of SHS (10.6 % in the female vs. 7.2 % in the male, P < 0.001). Risk factors for chronic diseases such as socioeconomic status, marital status, highest education completed, physical activity, salt intake, blood pressure and triglycerides differed significantly between subjects of SHS (SHS score ≥35) and those of ideal health (SHS score <35). (2) Follow up: the primary and secondary outcomes will be monitored from 2015 to 2024. The sex-specific difference in prevalence of SHS might partly explain the gender difference of incidence of certain chronic diseases. The COACS will enable a thorough characterization of SHS and establish a cohort that will be used for longitudinal analyses of the interaction between the genetic, lifestyle and environmental factors that contribute to the onset and etiology of targeted chronic diseases. The study together with the designed prospective cohort provides a chance to characterize and evaluate the effect of SHS systemically, and it thus generates an unprecedented opportunity for the early detection and prevention of chronic disease.

Background: Many studies have investigated microRNAs (miRNAs) in the detection of type 2 diabetes mellitus (T2DM). Herein, the dysregulated direction of stress‐related miRNAs used as biomarkers of T2DM are summarized and analyzed.Methods: PubMed, EMBASE, ISI Web of Science, and three Chinese databases were searched for case–control miRNA profiling studies about T2DM. A meta‐analysis under a random effect was performed. Subgroup analysis was conducted based on different tissues and species. Sensitivity analysis was conducted to confirm the robustness among studies. The effect size was pooled using ln odds ratios (ORs), 95% confidence intervals (95% CIs), and P‐values.Results: The present meta‐analysis included 39 case–control studies with a total of 494 miRNAs. Only 33 miRNAs were reported in three or more studies and, of these, 18 were inconsistent in their direction of dysregulation. Two significantly dysregulated miRNAs (let‐7 g and miR‐155) were identified in the meta‐analysis. Four miRNAs (miR‐142‐3p, miR‐155, miR‐21, and miR‐34c‐5p) were dysregulated in patients with T2DM, whereas five miRNAs (miR‐146a, miR‐199a‐3p, miR‐200b, miR‐29b and miR‐30e) were dysregulated in animal models of diabetes. In addition, two dysregulated miRNAs (miR‐146a and miR‐21) were highly cornea specific and heart specific. In sensitivity analysis, only miR‐155 was still significantly dysregulated after removing studies with small sample sizes.Conclusions: The present meta‐analysis revealed that 16 stress‐related miRNAs were significantly dysregulated in T2DM. MiR‐148b, miR‐223, miR‐130a, miR‐19a, miR‐26b and miR‐27b were selected as potential circulating biomarkers of T2DM. In addition, miR‐146a and miR‐21 were identified as potential tissue biomarkers of T2DM.

Suboptimal health status (SHS) is recognized as a subclinical, reversible stage of chronic disease. Association has been confirmed between SHS and cardiova