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Introduction: Longitudinal psychopathological predictors of relapse in alcohol use disorder are unclear. Methods: Relapses, sociodemographic and psychopathological risk factors were assessed in 171 alcohol use disorder outpatients within a 1‐year follow up. Impulsivity and alexithymia were evaluated using the Barratt Impulsiveness Scale and the Toronto Alexithymia Scale, respectively. Results: At endpoint, 39% of patients maintained abstinence, 30.9% relapsed at ≤1 month from detoxification (early), 30.1% at >1 month (subsequent). Baseline Barratt Impulsiveness Scale score was predictive of early versus subsequent relapse (odds ratio 1.12, p = 0.005) and versus abstinence (odds ratio 1.17, p < 0.001). Toronto Alexithymia Scale score was a risk factor for subsequent versus early relapse (odds ratio 1.13, p = 0.003) and versus abstinence (odds ratio 1.21, p < 0.001). Discussion and Conclusions: Impulsivity predicted relapse within the first 4‐weeks; alexithymia showed delayed effects. Time‐varying effects of specific relapse factors emphasise the need for preliminary careful assessment and personalised interventions to promote long‐term abstinence. [ABSTRACT FROM AUTHOR]

Background: Organic cation transporter 2 (OCT2) is a renal carrier transporter protein found in the basolateral membrane of proximal epithelial cells, which facilitates active secretion of Metformin. The genetic polymorphism of OCT2 influences the pharmacodynamic and pharmacokinetic effect of Metformin in type 2 diabetes mellitus (T2DM) patients. This is also mainly associated with frequencies of the associated risk allele in a particular population. Objective: The purpose of the study is to determine the impact of OCT2 genetic polymorphism on Metformin pharmacodynamics (PD) and pharmacokinetics (PK). Method of study: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for performing the research. Following databases were used to conduct the search: PubMed/MEDLINE, Google Scholar, and the Cochrane Library. Relevant studies were retrieved and literatures were appraised for methodology, demographic characteristics, relevant SNPs, genetic intervention trials, and outcomes. Results: Based on the data collected, 13 OCT2 Single nucleotide polymorphisms (SNPs) were identified across various ethnic groups. There were significant differences between the frequency distribution of shared alleles and impact of thirteen SNPs on Metformin. Among the thirteen OCT2 variants studied, rs316019 variant produced the most diverse responses in population by showing positive and negative impact on PK & PD of Metformin. Discussion and conclusion: Each population's OCT2 polymorphism had a distinct effect on Metformin responsiveness. The findings of this study could bring significant benefits to patients with OCT2 genetic polymorphism if individualised T2DM therapy is introduced. Patient-centered treatment would improve the Metformin efficacy leading to new research in personalised medicine. [ABSTRACT FROM AUTHOR]

Sequencing of tumour tissue with comprehensive gene panels is increasingly used to guide treatment in precision oncology. Analysis of tumour–normal pairs allows in contrast to tumour-only assessment direct discrimination between somatic and germline alterations, which might have important implications not only for the patients but also their families. We performed tumour normal sequencing with a large gene panel in 1048 patients with advanced cancer to support treatment decision. Sequencing results were correlated with clinical and family data. We identified 156 likely pathogenic or pathogenic (LP/P) germline variants in cancer predisposition genes (CPGs) in 144 cases (13.7%). Of all patients, 8.8% had a LP/P variant in autosomal-dominant cancer predisposition genes (AD-CPGs), most of them being genes with high or moderate penetrance (ATM , BRCA2, CHEK2 and BRCA1). In 48 cases, the P/LP variant matched the expected tumour spectrum. A second variant in tumour tissue was found in 31 patients with AD-CPG variants. Low frequency mutations in either TP53 , ATM or DNMT3A in the normal sample indicated clonal haematopoiesis in five cases. Tumour–normal testing for personalised treatment identifies germline LP/P variants in a relevant proportion of patients with cancer. The majority of them would not have been referred to genetic counselling based on family history. Indirect functional readouts of tumour–normal sequencing can provide novel links between CPGs and unexpected cancers. The interpretation of increasingly complex datasets in precision oncology is challenging and concepts of interdisciplinary personalised cancer prevention are needed to support patients and their families. • Significant amount of relevant germline variants is detected by blood-tumour testing. • Carriers do not always fulfil clinical criteria for cancer predisposition. • Blood–Tumour testing identifies novel links between cancer predisposition genes and cancer types. • Family-specific screening strategies are required when cancer predisposition genes are found in atypical tumours. [ABSTRACT FROM AUTHOR]

Background: Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Methods: These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations. Results: Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine. Conclusions: Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future. [ABSTRACT FROM AUTHOR]

Bronchiectasis is a heterogenous disease with multiple aetiologies resulting in inflammation and dilatation of the airways with associated mucus production and chronic respiratory infection. The condition is being recognised ever more frequently as the availability of computed tomography increases. It is associated with significant morbidity and healthcare-related costs. With new understanding of the disease process, varying endotypes, identification of underlying causes and treatable traits, the management of bronchiectasis can be increasingly personalised. [ABSTRACT FROM AUTHOR]

Adolescent and young adult (AYA) patients with cancer are poorly recruited to molecularly targeted trials and have not witnessed the advances in cancer treatment and survival seen in other age groups. We report here a pan-European proof-of-concept study to identify actionable alterations in some of the worst prognosis AYA cancers: bone and soft tissue sarcomas. Patients aged 12–29 years with newly diagnosed or recurrent, intermediate or high-grade bone and soft tissue sarcomas were recruited from six European countries. Pathological diagnoses were centrally reviewed. Formalin-fixed tissues were analysed by whole exome sequencing, methylation profiling and RNA sequencing and were discussed in a multidisciplinary, international molecular tumour board. Of 71 patients recruited, 48 (median 20 years, range 12–28) met eligibility criteria. Central pathological review confirmed, modified and re-classified the diagnosis in 41, 3, and 4 cases, respectively. Median turnaround time to discussion at molecular tumour board was 8.4 weeks. whole exome sequencing (n = 48), methylation profiling (n = 44, 85%) and RNA sequencing (n = 24, 50%) led to therapeutic recommendations for 81% patients, including 4 with germ line alterations. The most common were for agents targeted towards tyrosine kinases (n = 20 recommendations), DNA repair (n = 18) and the PI3K/mTOR/AKT pathway (n = 15). Recommendations were generally based on weak evidence such as activity in a different tumour type (n = 68, 61%), reflecting the dearth of relevant molecular clinical trial data in the same tumour type. We demonstrate here that comprehensive molecular profiling of AYA patients' samples is feasible and deliverable in a European programme. • AYA cancers have been neglected by molecular profiling initiatives. • Most AYA sarcomas of adequate sample quality have identifiable actionable mutations. • Clinical evidence to support personalised treatment for AYAs is weak or absent. • International molecular profiling is feasible and necessary to fill research gaps. • More effort is needed to enable AYA patients' access to molecularly focused trials. [ABSTRACT FROM AUTHOR]

Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set. Results: We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug–target interactions were found among the upregulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets. [ABSTRACT FROM AUTHOR]

The challenges in making animal models of complex human epilepsy phenotypes with varied aetiology highlights the need to develop alternative disease models that can address the limitations of animal models by effectively recapitulating human pathophysiology. The advances in stem cell technology provide an opportunity to use human iPSCs to make disease-in-a-dish models. The focus of this review is to report the current information and progress in the generation of epileptic patient-specific iPSCs lines, isogenic control cell lines, and neuronal models. These in vitro models can be used to study the underlying pathological mechanisms of epilepsies, anti-seizure medication resistance, and can also be used for drug testing and drug screening with their isogenic control cell lines. [ABSTRACT FROM AUTHOR]

Beyond the core features of Alzheimer’s disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput “omics” comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD. [ABSTRACT FROM AUTHOR]

Background: For patients with therapy-refractory persistent spinal pain syndrome type II (PSPS-T2), spinal cord stimulation (SCS) may serve as an effective minimally invasive treatment. Despite the evidence that SCS can improve return to work (RTW), only 9.5 to 14% of patients implanted with SCS are effectively capable of returning to work. Thus, it seems that current post-operative interventions are not effective for achieving RTW after SCS implantation in clinical practice. The current objective is to examine whether a personalised biopsychosocial rehabilitation programme specifically targeting RTW alters the work ability in PSPS-T2 patients after SCS implantation compared to usual care.Methods: A two-arm, parallel-group multicentre randomised controlled trial will be conducted including 112 patients who will be randomised (1:1) to either (a) a personalised biopsychosocial RTW rehabilitation programme of 14 weeks or (b) a usual care arm, both with a follow-up period until 12 months after the intervention. The primary outcome is work ability. The secondary outcomes are work status and participation, pain intensity, health-related quality of life, physical activity and functional disability, functional capacities, sleep quality, kinesiophobia, self-management, anxiety, depression and healthcare expenditure.Discussion: Within the OPERA project, we propose a multidisciplinary personalised biopsychosocial rehabilitation programme specifically targeting RTW for patients implanted with SCS, to tackle the high socio-economic burden of patients that are not re-entering the labour market. The awareness is growing that the burden of PSPS-T2 on our society is expected to increase over time due to the annual increase of spinal surgeries. However, innovative and methodologically rigorous trials exploring the potential to decrease the socio-economic burden when patients initiate a trajectory with SCS are essentially lacking.Trial Registration: ClinicalTrials.gov NCT05269212. Registered on 7 March 2022. [ABSTRACT FROM AUTHOR]

In this paper, we view health strategies in the field of personalised medicine as performative actions which articulate the development desired for a given society and demonstrate and affirm values and visions for that society. Based on a qualitative study in Denmark, we show how political actors and policy documents articulate visions for personalised medicine through a distinct sociotechnical imaginary. This sociotechnical imaginary mobilises the distinction between non-profit public and for-profit private organisations, placing personalised medicine in the public domain as the self-evidently desirable future for Danish health care In the Danish case, the issue of implementing personalised medicine becomes an opportunity to revisit the vision for the welfare state and verify public institutions as a shared space for state and citizens. While Danish political actors see personalised medicine as transformative, they articulate this transformation as being steered by public actors, at the same time downplaying the role of private companies. Where public health studies have focused on how political investments into personalised medicine may marginalise specific groups and populations, the Danish case shows that also powerful transnational players may be excluded from political discourse. It teaches us not to treat personalised medicine as one phenomenon, but to pay direct attention to the local contexts in which it operates and shapes what 'public' and 'health' become. [ABSTRACT FROM AUTHOR]

Summary: Mandibular advancement device (MAD) treatment outcome for obstructive sleep apnea (OSA) is variable and patient dependent. A global, clinically applicable predictive model is lacking. Our aim was to combine characteristics obtained during drug‐induced sleep endoscopy (DISE), awake nasendoscopy, and computed tomography scan‐based computational fluid dynamic (CFD) measurements in one multifactorial model, to explain MAD treatment outcome. A total of 100 patients with OSA were prospectively recruited and treated with a MAD at fixed 75% protrusion. In all, 72 underwent CFD analysis, DISE, and awake nasendoscopy at baseline in a blinded fashion and completed a 3‐month follow‐up polysomnography with a MAD. Treatment response was defined as a reduction in the apnea–hypopnea index (AHI) of ≥50% and deterioration as an increase of ≥10% during MAD treatment. To cope with missing data, multiple imputation with predictive mean matching was used. Multivariate logistic regression, adjusting for body mass index and baseline AHI, was used to combine all potential predictor variables. The strongest impact concerning odds ratios (ORs) was present for complete concentric palatal collapse (CCCp) during DISE on deterioration (OR 28.88, 95% confidence interval [CI] 1.18–704.35; p = 0.0391), followed by a C‐shape versus an oval shape of the soft palate during wakefulness (OR 8.54, 95% CI 1.09–67.23; p = 0.0416) and tongue base collapse during DISE on response (OR 3.29, 95% CI 1.02–10.64; p = 0.0464). Both logistic regression models exhibited excellent and fair predictive accuracy. Our findings suggest DISE to be the most robust examination associated with MAD treatment outcome, with tongue base collapse as a predictor for successful MAD treatment and CCCp as an adverse DISE phenotype. [ABSTRACT FROM AUTHOR]

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Abstract Background Using participatory methods to engage end-users in the development and design of eHealth is important to understand and incorporate their needs and context. Within participatory research, recent social distancing practice has forced a transition to digital communication platforms, a setting that warrants deeper understanding. The aim of this study was to describe the experiences of, and evaluate a digital co-creation process for developing an eHealth tool for people with chronic obstructive pulmonary disease (COPD). Methods The co-creation was guided by Participatory appreciative action and reflection, where a convenience sample (n = 17), including persons with COPD, health care professionals, relatives and a patient organization representative participated in six digital workshops. User instructions, technical equipment, and skilled support were provided if necessary. Workshops centred around different topics, with pre-recorded films, digital lectures and home assignments to up-skill participants. Process validity, experiences and ownership in the co-creation process were evaluated by repeated respondent validation, member checking, questionnaires and by assessing attendance. Data was analysed quantitatively or qualitatively as appropriate. Results The co-creators were in general satisfied with the digital format of the workshops. Mean attendance and perceived engagement in workshops was high and the experience described as enjoyable. Engagement was facilitated by up-skilling activities and discussions in small groups. Few had used digital communication previously, and feelings ranging from excitement to concern were expressed initially. Technical issues, mainly audio related, were resolved with support. At completion, skills using equipment and digital platform surpassed expectations. Few disadvantages with the digital format were identified, and advantages included reduced travel, time efficiency and reduced infection risk. Conclusions Experiences of digital co-creation were overwhelmingly positive, despite initial barriers related to computer naivety and use of digital equipment and platforms. The high level of satisfaction, engagement, attendance rates, and agreement between individual and group views suggests that a digital co-creation process is a feasible method. Several important success factors were identified, such as the provision of information and education on discussion topics in advance of workshops, as well as the smaller group discussions during workshops. The knowledge gained herein will be useful for future digital co-creation processes.

Psychedelics have emerged as promising therapeutics for several psychiatric disorders. Hypotheses around their mechanisms have revolved around their partial agonism at the serotonin 2 A receptor, leading to enhanced neuroplasticity and brain connectivity changes that underlie positive mindset shifts. However, these accounts fail to recognise that the gut microbiota, acting via the gut-brain axis, may also have a role in mediating the positive effects of psychedelics on behaviour. In this review, we present existing evidence that the composition of the gut microbiota may be responsive to psychedelic drugs, and in turn, that the effect of psychedelics could be modulated by microbial metabolism. We discuss various alternative mechanistic models and emphasize the importance of incorporating hypotheses that address the contributions of the microbiome in future research. Awareness of the microbial contribution to psychedelic action has the potential to significantly shape clinical practice, for example, by allowing personalised psychedelic therapies based on the heterogeneity of the gut microbiota. [Display omitted] • Psychedelics share antimicrobial properties with serotonergic antidepressants. • The gut microbiota can control metabolism of psychedelics in the host. • Microbes can act as mediators and modulators of psychedelics' behavioural effects. • Microbial heterogeneity could map to psychedelic responses for precision medicine. [ABSTRACT FROM AUTHOR]

IntroductionFolates, the main actors in one-carbon (C1) metabolism, are involved in synthesising monoamines and maintaining genomic stability. Previous studies support the association between C1 metabolism and schizophrenia. The main purpose of this study was to assess the prevalence of plasma folate, and/or vitamin B12 deficiencies and hyperhomocysteinemia in young patients with psychotic disorders.MethodsWe included young inpatients (15–30 years old) with psychosis between 2014 and 2017 from Sainte-Anne Hospital in Paris. Plasma folate, vitamin B12 deficiency and homocysteinemia dosages were done at admission. Clinical data were extracted retrospectively, and patients diagnosed with a first-episode psychosis (FEP), schizophrenia, schizoaffective disorder, or persistent delusional disorder were retained for the analysis.ResultsAmong the 334 inpatients, 188 (56%) had C1 dosages available (135 males; 53 females). From the 188 patients, 32% had a C1 abnormality. This abnormality reached 38% of FEP patients. The most frequent abnormality was folate deficiency: 21% of all patients and 27% of FEP. Lower levels of folates were found in males compared to females (p = 0.02) and were correlated with more severe disorder, as assessed by Clinical Global Impression – Severity (CGI-S; p = 0.009). Antipsychotic dosage was positively associated with B12 levels (p = 0.013) and negatively with homocysteinemia (p = 0.034).ConclusionOne-carbon metabolism anomalies in young patients with psychotic disorders are highly prevalent, reaching almost half of the patients with FEP. Potential protective effects from females and antipsychotics have emerged. These results spotlight the need for new therapeutic prospects, such as folate supplementation, to achieve personalised medical approaches to the early stages of psychotic disorders.

Eva &Ccaron;ešková,1– 4 Petr Šilhán1,2 1Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic; 2Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic; 3Department of Psychiatry, University Hospital Brno, Brno, Czech Republic; 4Department of Psychiatry, Faculty of Medicine, Masaryk University, Brno, Czech RepublicCorrespondence: Petr ŠilhánDepartment of Psychiatry, University Hospital Ostrava, 17. listopadu 1790, Ostrava, 708 52, Czech RepublicTel +420 597 373 145Fax +420 597 373 314Email petr.silhan@fno.czAbstract: Personalised medicine aims to find an individualized approach for each particular patient. Most factors used in current psychiatry, however, depend on the assessment made by the individual clinician and lack a higher degree of reliability. Precision medicine bases decisions on quantifiable indicators available thanks to the tremendous progress in science and technology facilitating the acquisition, processing and analysis of huge amounts of data. So far, psychiatry has not been benefiting enough from the advanced diagnostic technologies; nevertheless, we are witnessing the dawn of the era of precision psychiatry, starting with the gathering of sufficient amounts of data and its analysis by the means of artificial intelligence and machine learning. First results of this approach in psychiatry are available, which facilitate diagnosis assessment, course prediction, and appropriate treatment choice. These processes are often so complex and difficult to understand that they may resemble a “black box”, which can slow down the acceptance of the results of this approach in clinical practice. Still, bringing precision medicine including psychiatry to standard clinical practice is a big challenge that can result in a completely new and transformative concept of health care. Such extensive changes naturally have both their supporters and opponents. This paper aims to familiarize clinically oriented physicians with precision psychiatry and to attract their attention to its recent developments. We cover the theoretical basis of precision medicine, its specifics in psychiatry, and provide examples of its use in the field of diagnostic assessment, course prediction, and appropriate treatment planning.Keywords: personalised medicine, personalised psychiatry, precision medicine, precision psychiatry

Abstract Background Currently, numerous antihypertensive drugs from different pharmacological classes are available; however, blood pressure control is achieved in only less than a third of patients treated for hypertension. Moreover, providing optimal and personalised treatment for hypertension is challenging. Therefore, in this study, we propose a ‘drug-related attributes’ sensitive spectrum. This novel concept can assist clinicians in selecting an optimal antihypertensive drug and improve blood pressure control after examining the attributes of a patient. Methods We collected clinical data on attributes related to hypertension and its therapy of inpatients from West China Hospital who received metoprolol therapy and constructed the sensitive spectrum using data-visualisation tools. Results Our analysis revealed that haematocrit, haemoglobin, serum creatinine, serum cystatin C, serum urea, age, sex, systolic pressure, diastolic pressure, pulse pressure, and heart rate are metoprolol-related attributes. Conclusion Our study showed that all metoprolol-related attributes identified are reasonable and helpful in improving the personalisation of metoprolol therapy. The proposed drug-related attributes spectrum can help personalise antihypertensive medication. Moreover, data-visualisation tools can be effectively used to mine the drug-related attributes sensitive spectrum.

Beyond the core features of Alzheimer’s disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput “omics” comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.

Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field. [ABSTRACT FROM AUTHOR]

Ovarian cancer is a commonly diagnosed malignancy in women. When diagnosed at an early stage, survival outcomes are favourable for the vast majority, with up to 90% of ovarian cancer patients being free of disease at 5 years follow-up. Unfortunately, ovarian cancer is typically diagnosed at an advanced stage due to the majority of patients remaining asymptomatic until the cancer has metastasised, resulting in poor outcomes for the majority. While the molecular era has facilitated the subclassification of the disease into distinct clinical subtypes, ovarian cancer remains managed and treated as a single disease entity. MicroRNAs (miRNAs) are small (19–25 nucleotides), endogenous molecules which are integral to regulating gene expression. Aberrant miRNA expression profiles have been described in several cancers, and have been implicated to be useful biomarkers which may aid cancer diagnostics and treatment. Several preliminary studies have identified candidate tumour suppressor and oncogenic miRNAs which may be involved in the development and progression of ovarian cancer, highlighting their candidacy as oncological biomarkers; understanding the mechanisms by which these miRNAs regulate the key processes involved in oncogenesis can improve our overall understanding of cancer development and identify novel biomarkers and therapeutic targets. This review highlights the potential role of miRNAs which may be utilised to aid diagnosis, estimate prognosis and enhance therapeutic strategies in the management of primary ovarian cancer. [ABSTRACT FROM AUTHOR]

3D printing in the pharmaceutical and healthcare settings is expanding rapidly, such as the rapid prototyping of orthotics, dental retainers, drug-loaded implants, and pharmaceutical solid oral dosage forms. Through 3D printing, we have the capability to precisely control dose, release kinetics, and several aesthetic features of dosage forms such as colour, shape, and texture. Additionally, polypills can be created with combinations of medications in one solid dosage form at completely customisable strengths that would be extremely difficult to obtain commercially. As the technology and formulations developed through 3D printing are expanding, the development of new hybrid materials to obtain superior formulations are also gaining momentum. In this review we collate data on the importance of developing hybrid formulations of polymers, drugs and excipients necessary to produce reliable and high-quality 3D printed dosage forms with a special emphasis on fused deposition modelling (FDM). FDM technology is one of the most widely used forms of 3D printing and has demonstrated compatibility with unique polymer-based hybrids to allow for enhanced drug delivery, protection of thermolabile drugs, modifiable release kinetics, and more. The data collated covers different categories of hybrids as well as the methods used to fabricate them, and their respective effects on the properties of 3D printed solid oral dosage forms. Therefore, this review will provide an overview of upcoming and emerging trends in pharmaceutical 3D printing formulation compositions. [Display omitted] [ABSTRACT FROM AUTHOR]

Gestational diabetes mellitus (GDM) is the most prevalent pregnancy-related endocrinopathy, affecting up to 25% of pregnancies worldwide. Pregnant individuals who develop GDM have an increased risk of complications during pregnancy and birth, as well as future development of type 2 diabetes mellitus and CVD. This increased risk is subsequently passed along to the offspring, perpetuating a cycle of metabolic dysfunction across generations. GDM prevention strategies have had mixed results for many years, but more recent systematic reviews and meta-analyses have suggested potential new avenues of prevention. The objective of this review is to summarise the literature examining the efficacy of lifestyle interventions for the prevention of GDM and to uncover if specific individual-level characteristics influence this outcome. Based on the present literature, we determined that future trials should be designed to understand if initiation of lifestyle intervention in the preconception period is more effective to reduce GDM. Furthermore, trials initiated during pregnancy should be developed through the lens of precision prevention. That is, trials should tailor intervention approaches based on individual-level risk defined by the presence of modifiable and non-modifiable risk factors. Finally, future interventions might also benefit from just-in-time adaptive intervention designs, which allow for interventions to be modified in real-time based on objective assessments of an individual's response. [ABSTRACT FROM AUTHOR]

First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future. [ABSTRACT FROM AUTHOR]

Rapid advances in technology and data science have the potential to improve the precision of preventive and therapeutic interventions, and enable the right treatment to be recommended, at the right time, to the right person. There are well-described examples of successful precision medicine approaches for monogenic conditions such as specific diets for phenylketonuria, and sulfonylurea treatments for certain types of MODY. However, the majority of chronic diseases are polygenic, and it is unlikely that the research strategies used for monogenic diseases will deliver similar changes to practice for polygenic traits. Type 2 diabetes, for example, is a multifactorial, heterogeneous, polygenic palette of metabolic disorders. In this non-systematic review I highlight limitations of the evidence, and the challenges that need to be overcome prior to implementation of precision medicine in the prevention and management of type 2 diabetes. Most precision medicine approaches are spuriously precise, overly complex and too narrowly focused on predicting blood glucose levels with a limited set of characteristics of individuals rather than the whole person and their context. Overall, the evidence to date is insufficient to justify widespread implementation of precision medicine approaches into routine clinical practice for type 2 diabetes. We need to retain a degree of humility and healthy scepticism when evaluating novel strategies, and to demand that existing evidence thresholds are exceeded prior to implementation. [ABSTRACT FROM AUTHOR]

Integration of genomic and other data has begun to stratify type 2 diabetes in prognostically meaningful ways, but this has yet to impact on mainstream diabetes practice. The subgroup of diabetes caused by single gene defects thus provides the best example to date of the vision of 'precision diabetes'. Monogenic diabetes may be divided into primary pancreatic beta cell failure, and primary insulin resistance. In both groups, clear examples of genotype-selective responses to therapy have been advanced. The benign trajectory of diabetes due to pathogenic GCK mutations, and the sulfonylurea-hyperresponsiveness conferred by activating KCNJ11 or ABCC8 mutations, or loss-of-function HNF1A or HNF4A mutations, often decisively guide clinical management. In monogenic insulin-resistant diabetes, subcutaneous leptin therapy is beneficial in some severe lipodystrophy. Increasing evidence also supports use of 'obesity therapies' in lipodystrophic people even without obesity. In beta cell diabetes the main challenge is now implementation of the precision diabetes vision at scale. In monogenic insulin-resistant diabetes genotype-specific benefits are proven in far fewer patients to date, although further genotype-targeted therapies are being evaluated. The conceptual paradigm established by the insulin-resistant subgroup with 'adipose failure' may have a wider influence on precision therapy for common type 2 diabetes, however. For all forms of monogenic diabetes, population-wide genome sequencing is currently forcing reappraisal of the importance assigned to pathogenic mutations when gene sequencing is uncoupled from prior suspicion of monogenic diabetes. [ABSTRACT FROM AUTHOR]

It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level. [ABSTRACT FROM AUTHOR]

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes. [ABSTRACT FROM AUTHOR]

The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice. [ABSTRACT FROM AUTHOR]

This narrative review aims to examine the value of addressing mental disorders as part of the care of people with type 1 and type 2 diabetes in terms of four components of precision medicine. First, we review the empirical literature on the role of common mental disorders in the development and outcomes of diabetes (precision prevention and prognostics). We then review interventions that can address mental disorders in individuals with diabetes or at risk of diabetes (precision treatment) and highlight recent studies that have used novel methods to individualise interventions, in person and through applications, based on mental disorders. Additionally, we discuss the use of detailed assessment of mental disorders using, for example, mobile health technologies (precision monitoring). Finally, we discuss future directions in research and practice and challenges to addressing mental disorders as a factor in precision medicine for diabetes. This review shows that several mental disorders are associated with a higher risk of type 2 diabetes and its complications, while there is suggestive evidence indicating that treating some mental disorders could contribute to the prevention of diabetes and improve diabetes outcomes. Using technologically enabled solutions to identify mental disorders could help individuals who stand to benefit from particular treatments. However, there are considerable gaps in knowledge and several challenges to be met before we can stratify treatment recommendations based on mental disorders. Overall, this review demonstrates that addressing mental disorders as a facet of precision medicine could have considerable value for routine diabetes care and has the potential to improve diabetes outcomes. [ABSTRACT FROM AUTHOR]

Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium–glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future. [ABSTRACT FROM AUTHOR]

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making. [ABSTRACT FROM AUTHOR]

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course. [ABSTRACT FROM AUTHOR]