RESUMO
Diabetes mellitus (DM) is a complex and multifactorial disease characterised by high blood glucose. Type 2 Diabetes (T2D), the most frequent clinical condition accounting for about 90% of all DM cases worldwide, is a chronic disease with slow development usually affecting middle-aged or elderly individuals. T2D represents a significant problem of public health today because its incidence is constantly growing among both children and adults. It is also estimated that underdiagnosis prevalence would strongly further increase the real incidence of the disease, with about half of T2D patients being undiagnosed. Therefore, it is important to increase diagnosis accuracy. The current interest in RNA molecules (both protein- and non-protein-coding) as potential biomarkers for diagnosis, prognosis, and treatment lies in the ease and low cost of isolation and quantification with basic molecular biology techniques. In the present study, we analysed the transcriptome in serum samples collected from T2D patients and unaffected individuals to identify potential RNA-based biomarkers. Microarray-based profiling and subsequent validation using Real-Time PCR identified an uncharacterised long non-coding RNA (lncRNA) transcribed from the ASAP1 locus as a potential diagnostic biomarker. ROC curve analysis showed that a molecular signature including the lncRNA and the clinicopathological parameters of T2D patients as well as unaffected individuals showed a better diagnostic performance compared with the glycated haemoglobin test (HbA1c). This result suggests that the application of this biomarker in clinical practice would help to improve the diagnosis, and therefore the clinical management, of T2D patients. The proposed biomarker would be useful in the context of predictive, preventive, and personalised medicine (3PM/PPPM).
Assuntos
Diabetes Mellitus Tipo 2 , Hiperglicemia , RNA Longo não Codificante , Adulto , Idoso , Criança , Humanos , Pessoa de Meia-Idade , Diabetes Mellitus Tipo 2/genética , Saúde Pública , RNA Longo não Codificante/genéticaRESUMO
Background: The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns. Methods: The cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30-80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations. Results: We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions. Conclusions: We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage. Supplementary information: The online version contains supplementary material available at 10.1007/s13167-023-00344-2.
RESUMO
An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.
RESUMO
Severe durable changes may occur to the DNA structure caused by exogenous and endogenous risk factors initiating the process of carcinogenesis. By evidence, a large portion of malignancies have been demonstrated as being preventable. Moreover, the targeted prevention of cancer onset is possible, due to unique properties of plant bioactive compounds. Although genoprotective effects of phytochemicals have been well documented, there is an evident lack of articles which would systematically present the spectrum of anticancer effects by phytochemicals, plant extracts, and plant-derived diet applicable to stratified patient groups at the level of targeted primary (cancer development) and secondary (cancer progression and metastatic disease) prevention. Consequently, clinical implementation of knowledge accumulated in the area is still highly restricted. To stimulate coherent co-development of the dedicated plant bioactive compound investigation on one hand and comprehensive cancer preventive strategies on the other hand, the current paper highlights and deeply analyses relevant evidence available in the area. Key molecular mechanisms are presented to detail genoprotective and anticancer activities of plants and phytochemicals. Clinical implementation is discussed. Based on the presented evidence, advanced chemopreventive strategies in the context of 3P medicine are considered.