Estimates and Forecasts on the Burden of Prediabetes and Diabetes in Adult and Elderly Population in Turkiye.

AIMS: Diabetes mellitus is a chronic disease that limits the quality and duration of life. We aimed to estimate the impact of demographic change on the burden of prediabetes and diabetes between 2010 and 2021, and the projections to 2030 and 2045 in Turkiye. MATERIALS AND METHODS: Prediabetes and diabetes estimates were calculated by direct standardization method using age- and sex-specific prevalence data from the previous 'Turkish Epidemiology Survey of Diabetes, Hypertension, Obesity and Endocrine Disease' (TURDEP-II) as reference. The 2010-2021 population demographics were obtained from TurkStat. Comparative age-adjusted diabetes prevalence was estimated using the standard population models of world and Europe. RESULTS: Estimates depicted that the population (20-84 years) of any degree of glucose intolerance in Turkiye increased by over 5.7 million (diabetes: 2.4 million and prediabetes: 3.3 million) from 2010 to 2021. While the increase in prediabetes and diabetes prevalence was 24.3% and 35.2% in overall population, corresponding increase were 46.5% and 51.3% in the elderly. Estimated prevalence of prediabetes and diabetes in 2021 was significantly higher in women than in men (prediabetes: 32.6% vs. 25.2%; diabetes: 17.1% vs. 14.2%). The comparative age-adjusted diabetes prevalence to the European population model was higher than that of the world population model (19.4% vs. 15.0%). According to the projections the prevalence of diabetes will reach 17.5% in 2030 and 19.2% in 2045. CONCLUSION: Assuming age- and sex-specific diabetes prevalence of TURDEP-II survey remained constant, this study revealed that the number of people with diabetes in the general population (particularly in the elderly) in the last 11 years in Turkiye has increased in parallel with the population growth and aging; it will continue to grow over the coming decades. This means the burden of diabetes on the social, economic and health services will remain to increase. The fact suggests that there is an urgent need for re-organization of care as well as to develop and implement a country-specific prevention program to reduce this burden.

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell.

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

Understanding signaling in yeast: Insights from network analysis.

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.