Introduction into PPPM as a new paradigm of public health service: an integrative view

Mass spectrometry-based analysis of eccrine sweat supports predictive, preventive and personalised medicine in a cohort of breast cancer patients in Austria

Objective

Metabolomics measurements of eccrine sweat may provide novel and relevant biomedical information to support predictive, preventive and personalised medicine (3PM). However, only limited data is available regarding metabolic alterations accompanying chemotherapy of breast cancer patients related to residual cancer burden (RCB) or therapy response. Here, we have applied Metabo-Tip, a non-invasive metabolomics assay based on the analysis of eccrine sweat from the fingertips, to investigate the feasibility of such an approach, especially with respect to drug monitoring, assessing lifestyle parameters and stratification of breast cancer patients.

Methods

Eccrine sweat samples were collected from breast cancer patients (n = 9) during the first cycle of neoadjuvant chemotherapy at four time points in this proof-of-concept study at a Tertiary University Hospital. Metabolites in eccrine sweat were analysed using mass spectrometry. Blood plasma samples from the same timepoints were also collected and analysed using a validated targeted metabolomics kit, in addition to proteomics and fatty acids/oxylipin analysis.

Results

A total of 247 exogenous small molecules and endogenous metabolites were identified in eccrine sweat of the breast cancer patients. Cyclophosphamide and ondansetron were successfully detected and monitored in eccrine sweat of individual patients and accurately reflected the administration schedule. The non-essential amino acids asparagine, serine and proline, as well as ornithine were significantly regulated in eccrine sweat and blood plasma over the therapy cycle. However, their distinct time-dependent profiles indicated compartment-specific distributions. Indeed, the metabolite composition of eccrine sweat seems to largely resemble the composition of the interstitial fluid. Plasma proteins and fatty acids/oxylipins were not affected by the first treatment cycle. Individual smoking habit was revealed by the simultaneous detection of nicotine and its primary metabolite cotinine in eccrine sweat. Stratification according to RCB revealed pronounced differences in the metabolic composition of eccrine sweat in these patients at baseline, e.g., essential amino acids, possibly due to the systemic contribution of breast cancer and its impact on metabolic turnover.

Conclusion

Mass spectrometry-based analysis of metabolites from eccrine sweat of breast cancer patients successfully qualified lifestyle parameters for risk assessment and allowed us to monitor drug treatment and systemic response to therapy. Moreover, eccrine sweat revealed a potentially predictive metabolic pattern stratifying patients by the extent of the metabolic activity of breast cancer tissue at baseline. Eccrine sweat is derived from the otherwise hardly accessible interstitial fluid and, thus, opens up a new dimension for biomonitoring of breast cancer in secondary and tertiary care. The simple sample collection without the need for trained personnel could also enable decentralised long-term biomonitoring to assess stable disease or disease progression. Eccrine sweat analysis may indeed significantly advance 3PM for the benefit of breast cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-025-00396-6.

Causal relationship between gut Prevotellaceae and risk of sepsis: a two-sample Mendelian randomization and clinical retrospective study in the framework of predictive, preventive, and personalized medicine

Objective

Gut microbiota is closely related to sepsis. Recent studies have suggested that Prevotellaceae could be associated with intestinal inflammation; however, the causal relationship between Prevotellaceae and sepsis remains uncertain. From the perspective of predictive, preventive, and personalized medicine (PPPM), exploring the causal relationship between gut Prevotellaceae and sepsis could provide opportunity for targeted prevention and personalized treatment.

Methods

The genome-wide association study (GWAS) summary-level data of Prevotellaceae (N = 7738) and sepsis were obtained from the Dutch Microbiome Project and the UK Biobank (sepsis, 1380 cases; 429,985 controls). MR analysis was conducted to estimate the associations between Prevotellaceae and sepsis risk. The 16S rRNA sequencing analysis was conducted to calculate the relative abundance of Prevotellaceae in sepsis patients to explore the relationship between Prevotellaceae relative abundance and the 28-day mortality.

Results

Genetic liability to f__Prevotellaceae (OR, 1.91; CI, 1.35-2.71; p = 0.0003) was associated with a high risk of sepsis with inverse-variance weighted (IVW). The median Prevotellaceae relative abundance in non-survivors was significantly higher than in survivors (2.34% vs 0.17%, p < 0.001). Multivariate analysis confirmed that Prevotellaceae relative abundance (OR, 1.10; CI, 1.03-1.22; p = 0.027) was an independent factor of 28-day mortality in sepsis patients. ROC curve analysis indicated that Prevotellaceae relative abundance (AUC: 0.787, 95% CI: 0.671-0.902, p = 0.0003) could predict the prognosis of sepsis patients.

Conclusion

Our results revealed that Prevotellaceae was causally associated with sepsis and affected the prognosis of sepsis patients. These findings may provide insights to clinicians on developing improved sepsis PPPM strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00340-6.

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

Microsatellite instability assessment is instrumental for Predictive, Preventive and Personalised Medicine: status quo and outlook

A form of genomic alteration called microsatellite instability (MSI) occurs in a class of tandem repeats (TRs) called microsatellites (MSs) or short tandem repeats (STRs) due to the failure of a post-replicative DNA mismatch repair (MMR) system. Traditionally, the strategies for determining MSI events have been low-throughput procedures that typically require assessment of tumours as well as healthy samples. On the other hand, recent large-scale pan-tumour studies have consistently highlighted the potential of massively parallel sequencing (MPS) on the MSI scale. As a result of recent innovations, minimally invasive methods show a high potential to be integrated into the clinical routine and delivery of adapted medical care to all patients. Along with advances in sequencing technologies and their ever-increasing cost-effectiveness, they may bring about a new era of Predictive, Preventive and Personalised Medicine (3PM). In this paper, we offered a comprehensive analysis of high-throughput strategies and computational tools for the calling and assessment of MSI events, including whole-genome, whole-exome and targeted sequencing approaches. We also discussed in detail the detection of MSI status by current MPS blood-based methods and we hypothesised how they may contribute to the shift from conventional medicine to predictive diagnosis, targeted prevention and personalised medical services. Increasing the efficacy of patient stratification based on MSI status is crucial for tailored decision-making. Contextually, this paper highlights drawbacks both at the technical level and those embedded deeper in cellular/molecular processes and future applications in routine clinical testing.

Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine

BACKGROUND

The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM).

METHODS

The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment.

RESULTS

The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/.

CONCLUSION

The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-022-00305-1.

CRISPR-Cas systems for diagnosing infectious diseases

Infectious diseases are a global health problem affecting billions of people. Developing rapid and sensitive diagnostic tools is key for successful patient management and curbing disease spread. Currently available diagnostics are very specific and sensitive but time-consuming and require expensive laboratory settings and well-trained personnel; thus, they are not available in resource-limited areas, for the purposes of large-scale screenings and in case of outbreaks and epidemics. Developing new, rapid, and affordable point-of-care diagnostic assays is urgently needed. This review focuses on CRISPR-based technologies and their perspectives to become platforms for point-of-care nucleic acid detection methods and as deployable diagnostic platforms that could help to identify and curb outbreaks and emerging epidemics. We describe the mechanisms and function of different classes and types of CRISPR-Cas systems, including pros and cons for developing molecular diagnostic tests and applications of each type to detect a wide range of infectious agents. Many Cas proteins (Cas3, Cas9, Cas12, Cas13, Cas14 etc.) have been leveraged to create highly accurate and sensitive diagnostic tools combined with technologies of signal amplification and fluorescent, potentiometric, colorimetric, lateral flow assay detection and other. In particular, the most advanced platforms -- SHERLOCK/v2, DETECTR, CARMEN or CRISPR-Chip -- enable detection of attomolar amounts of pathogenic nucleic acids with specificity comparable to that of PCR but with minimal technical settings. Further developing CRISPR-based diagnostic tools promises to dramatically transform molecular diagnostics, making them easily affordable and accessible virtually anywhere in the world. The burden of socially significant diseases, frequent outbreaks, recent epidemics (MERS, SARS and the ongoing COVID-19) and outbreaks of zoonotic viruses (African Swine Fever Virus etc.) urgently need the developing and distribution of express-diagnostic tools. Recently devised CRISPR-technologies represent the unprecedented opportunity to reshape epidemiological surveillance and molecular diagnostics.

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine

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.

Prevalence and correlates of hypertension in a semi-rural population of Southern India

While elevated blood pressure is a recognized risk factor for cardiovascular disease, the prevalence of hypertension still remains unclear for most populations. A door-to-door survey was conducted using modified WHO STEPS questionnaire in a group of villages under the Thavanampalle Mandal of Chittoor District in the state of Andhra Pradesh of South India. Data were collated and analyzed for 16,636 individuals (62.3% females and 37.7% males) above 15 years of age. Overall, prevalence of hypertension (as per JNC-7 classification) was found to be 27.0% (95% CI, 26.3, 27.7) in the surveyed community with 56.7% of the total hypertensives being diagnosed for the first time during the survey. An additional 39.1% had their blood pressure readings in the prehypertensive range. Among the known Hypertensives on treatment only 46.2% had a blood pressure recording within acceptable limits, with 31.2% in the prehypertensive range and only 15.0% in the normal range. Systolic blood pressure (SBP) of the surveyed population showed a continuous linear increase with age, but diastolic blood pressure (DBP) peaked and started reducing in early fifth decade in males. Male gender, increasing age, higher body mass index (BMI), increased waist-hip ratio, increased body weight, family history of hypertension, death of spouse, and diabetes were found to be positively correlated with hypertension. Risk factors of alcohol intake, use of ground nut/palm oil, and family history of diabetes lost their independent predictive ability for hypertension on multivariate logistic regression analysis. The level of physical activity was also not found to be a significant predictor of hypertension in the study population.

Next generation sequencing applications for breast cancer research

For some time, cancer has not been thought of as a disease, but as a multifaceted, heterogeneous complex of genotypic and phenotypic manifestations leading to tumorigenesis. Due to recent technological progress, the outcome of cancer patients can be greatly improved by introducing in clinical practice the advantages brought about by the development of next generation sequencing techniques. Biomedical suppliers have come up with various applications which medical researchers can use to characterize a patient’s disease from molecular and genetic point of view in order to provide caregivers with rapid and relevant information to guide them in choosing the most appropriate course of treatment, with maximum efficiency and minimal side effects. Breast cancer, whose incidence has risen dramatically, is a good candidate for these novel diagnosis and therapeutic approaches, particularly when referring to specific sequencing panels which are designed to detect germline or somatic mutations in genes that are involved in breast cancer tumorigenesis and progression. Benchtop next generation sequencing machines are becoming a more common presence in the clinical setting, empowering physicians to better treat their patients, by offering early diagnosis alternatives, targeted remedies, and bringing medicine a step closer to achieving its ultimate goal, personalized therapy.