Alleviating the burden of rare diseases requires research into new diagnostic and therapeutic strategies. We undertook a systematic review to identify and compare the impact of stand-alone registries, registries with biobanks, and rare disease biobanks on research outcomes in rare diseases. A systematic review and meta-aggregation was conducted using the preferred reporting items for systematic reviews and meta-analyses (the PRISMA statement). English language publications were sourced from PubMed, Medline, Scopus, and Web of Science. Original research papers that reported clinical, epidemiological, basic or translational research findings derived from data contained in stand-alone registries, registries with biobanks, and rare disease biobanks were considered. Articles selected for inclusion were assessed using the critical appraisal instruments by JBI-QARI. Each article was read in its entirety and findings were extracted using the online data extraction software from JBI-QARI. Thirty studies including 28 rare disease resources were included in the review. Of those, 14 registries were not associated to biobank infrastructure, 9 registries were associated with biobank infrastructure, and 6 were rare disease biobank resources. Stand-alone registries had the capacity to uncover the natural history of disease and contributed to evidence-based practice. When annexed to biobank infrastructure, registries could also identify and validate biomarkers, uncover novel genes, elucidate pathogenesis at the Omics level, and develop new therapeutic strategies. Rare disease biobanks in this review had similar capacity for biological investigations, but in addition, had far greater sample numbers and higher quality laboratory techniques for quality assurance processes. We examined the research outcomes of three specific populations: stand-alone registries, registries with biobanks, and stand-alone rare disease biobanks and demonstrated that there are key differences among these resources. These differences are a function of the resources' design, aims, and objectives, with each resource having a distinctive and important role in contributing to the body of knowledge for rare disease research. Whilst stand-alone registries had the capacity to uncover the natural history of disease, develop best practice, replace clinical trials, and improve patient outcomes, they were limited in their capacity to conduct basic research. The role of basic research in rare disease research is vital; scientists must first understand the pathways of disease before they can develop appropriate interventions. Rare disease biobanks, on the other hand (particularly larger biobanks), had the key infrastructure required to conduct basic research, making novel Omics discoveries, identify and validate biomarkers, uncover novel genes, and develop new therapeutic strategies. However, these stand-alone rare disease biobanks did not collect comprehensive data or impact on clinical observations like a rare disease registry. Rare disease research is important not only for rare diseases, but also for also common diseases. For example, research of low-density lipoprotein (LDL)-receptors in the rare disease known as familial hypercholesterolemia led to the discovery of statins, a drug therapy that is now used routinely to prevent heart disease. Rare diseases are still under-researched worldwide. This review made the important observation that registries with biobanks had the function of both stand-alone registries (the capacity to collect comprehensive clinical and epidemiological data) and stand-alone rare disease biobanks (the ability to contribute to Omics research). We found registries with biobanks offer a unique, practical, cost-effective, and impactful solution for rare disease research. Linkage of stand-alone registries to rare disease biobanks will provide the appropriate resources required for the effective translation of basic research into clinical practice. Furthermore, facilitators such as collaboration, engagement, blended recruitment, pro-active marketing, broad consent, and "virtual biobank" online catalogues will, if utilised, add to the success of these resources. These important observations can serve to direct future rare diseases research efforts, ultimately improve patient outcomes and alleviate the significant burden associated with rare disease for clinicians, hospitals, society, and most importantly, the patients and their families.
Suboptimal Health Status (SHS) is the physical state between health and disease. This study aimed to fill in the knowledge gap by investigating the prevalence of SHS and psychological symptoms among unpaid carers and to identify SHS-risk factors from the perspective of predictive, preventive and personalised medicine (PPPM).
Suboptimal health status (SHS) is a reversible stage between health and illness that is characterized by health complaints, low energy, general weakness, and chronic fatigue. The Suboptimal Health Status Questionnaire-25 (SHSQ-25) has been validated in three major populations (African, Asian, and Caucasian) and is internationally recognized as a reliable and robust tool for health estimation in general populations. This study focused on the development of K-SHSQ-25, a Korean version of the SHSQ-25, from its English version.The SHSQ-25 was translated from English to Korean according to international guidelines set forth by the World Health Organization (WHO) for health instrument translation between different languages. A subsequent cross-sectional survey involved 460 healthy South Korean participants (aged 18-83 years; 65.4% females) to answer the 25 questions focusing on the health perspectives of 5 domains, 1) fatigue, 2) cardiovascular health, 3) digestive tract, 4) immune system and 5) mental health. The K-SHSQ-25 was further validated using tests for reliability, internal consistency, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA).The version of K-SHSQ-25 achieved linguistic, cultural, and conceptual equivalence to the English version. The intraclass correlation coefficient (ICC) of test-retest reliability for individual items ranged from 0.88 to 0.99. Reliability estimates based on internal consistency reached a Cronbach's α of 0.953; the Cronbach's α for each domain ranged from 0.76 to 0.94. Regarding construct validity, the EFA of the K-SHSQ-25 generally replicated the multidimensional structure (fatigue, cardiovascular, digestive, immune system, and mental health) and 25 questions. The CFA revealed that the root mean square error of approximation (RMSEA), goodness-of-fit index (GFI) and adjusted goodness of fit index (AGFI) were excellent (RMSEA = 0.069<0.08, GFI = 0.929>0.90, AGFI = 0.907>0.90). The five domains of the K-SHSQ-25 showed significant correlations with each other (r = 0.59-0.81, P<0.001). The cut-off point of K-SHSQ-25 for SHS was determined as an SHS score of 25. The prevalence of SHS in this study was 60.0% (276/460), with 47.8% (76/159) for males and 58.5% for females (176/301).Our results indicate that the Korean version of SHSQ-25, K-SHSQ-25, is a transcultural equivalent, robust, valid, and reliable assessment tool for evaluating SHS in the Korean-speaking population.
The use of the emerging “omics” technologies for large scale population screening is promising in terms of predictive, preventive and personalized medicine. For Parkinson's disease, it is essential that an accurate diagnosis is obtained and disease progression can be monitored. Immunoglobulin G (IgG) has the ability to exert both anti-inflammatory and pro-inflammatory effects, and the N-glycosylation of the fragment crystallizable portion of IgG is involved in this process. This study aimed to determine whether the IgG glycome could be a candidate biomarker for Parkinson's disease. Ninety-four community-based individuals with Parkinson's disease and a sex-, age- and ethnically-matched cohort of 102 individuals with mixed phenotypes, representative of a “normally” aged Caucasian controls, were investigated. Plasma IgG glycans were analyzed by ultra-performance liquid chromatography. Overall, seven glycan peaks and 11 derived traits had statistically significant differences (P < 8.06 × 10−4) between Parkinson's disease cases and healthy controls. Out of the seven significantly different glycan peaks, four were selected by Akaike's Information Criterion to be included in the logistic regression model, with a sensitivity of 87.2% and a specificity of 92.2%. The study suggested that there may be a reduced capacity for the IgG to inhibit Fcγ-RIIIa binding, which would allow an increased ability for the IgG to cause antibody-dependent cell cytotoxicity and a possible state of low-grade inflammation in individuals with Parkinson's disease.
Type 2 diabetes mellitus (T2DM) is a complex, pandemic disease contributing towards the global burden of health issues. To date, there are no simple clinical tests for the early detection of T2DM.To identify potential peptide biomarkers for such applications, 406 sera of T2DM patients (n = 206) and healthy controls (n = 200) are analyzed by using MALDI-TOF MS with a cross-sectional case-control design.Six peptides (peaks m/z 1452.9, 1692.8, 1946.0, 2115.1, 2211.0 and 4053.6) are identified as candidate biomarkers for T2DM. A diagnostic model constructed with six peptides is able to discriminate T2DM patients from healthy controls, with an accuracy of 82.20%, sensitivity of 82.50%, and specificity of 77.80% in the validation set. Peptide peaks m/z 1452.9 and 1692.8 are identified as fragments of the complement C3f, while peptide peaks m/z 1946.0, 2115.1, and 2211.0 are identified as the fragments of kininogen 1 isoform 1 precursor.This study reinforces proteomic analyses as a potential technique for defining significant clinical peptide biomarkers, providing a simple and convenient diagnostic model for T2DM in clinical examination.
First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.
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