Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach

3PM-guided innovation in treatments of severe alcohol-associated hepatitis utilizing fecal microbiota transplantation

Rationale

Severe alcohol-associated hepatitis (SAH) is the most critical, acute, inflammatory phenotype within the alcohol-associated liver disease (ALD) spectrum, characterized by high 30- and 90-day mortality. Since several decades, corticosteroids (CS) are the only approved pharmacotherapy offering highly limited survival benefits. Contextually, there is an evident demand for 3PM innovation in the area meeting patients' needs and improving individual outcomes. Fecal microbiota transplantation (FMT) has emerged as one of the new potential therapeutic options. In this study, we aimed to address the crucial 3PM domains in order to assess (i) the impact of FMT on mortality in SAH patients beyond CS, (ii) to identify factors associated with the outcome to be improved (iii) the prediction of futility, (iv) prevention of suboptimal individual outcomes linked to increased mortality, and (v) personalized allocation of therapy.

Methods

We conducted a prospective study (NCT04758806) in adult patients with SAH who were non-responders (NR) to or non-eligible (NE) for CS between January 2018 and August 2022. The intervention consisted of five 100 ml of FMT, prepared from 30 g stool from an unrelated healthy donor and frozen at - 80 °C, administered daily to the upper gastrointestinal (GI) tract. We evaluated the impact of FMT on 30- and 90-day mortality which we compared to the control group selected by the propensity score matching and treated by the standard of care; the control group was derived from the RH7 registry of patients hospitalized at the liver unit (NCT04767945). We have also scrutinized the FMT outcome against established and potential prognostic factors for SAH - such as the model for end-stage liver disease (MELD), Maddrey Discriminant Function (MDF), acute-on-chronic liver failure (ACLF), Liver Frailty Index (LFI), hepatic venous-portal pressure gradient (HVPG) and Alcoholic Hepatitis Histologic Score (AHHS) - to see if the 3PM method assigns them a new dimension in predicting response to therapy, prevention of suboptimal individual outcomes, and personalized patient management.

Results

We enrolled 44 patients with SAH (NR or NE) on an intention-to-treat basis; we analyzed 33 patients per protocol for associated factors (after an additional 11 being excluded for receiving less than 5 doses of FMT), and 31 patients by propensity score matching for corresponding individual outcomes, respectively. The mean age was 49.6 years, 11 patients (33.3%) were females. The median MELD score was 29, and ACLF of any degree had 27 patients (81.8%). FMT improved 30-day mortality (p = 0.0204) and non-significantly improved 90-day mortality (p = 0.4386). Univariate analysis identified MELD ≥ 30, MDF ≥ 90, and ACLF grade > 1 as significant predictors of 30-day mortality, (p = 0.031; p = 0.014; p = 0.034). Survival was not associated with baseline LFI, HVPG, or AHHS.

Conclusions and recommendations in the framework of 3PM

In the most difficult-to-treat sub-cohort of patients with SAH (i.e., NR/NE), FMT improved 30-day mortality. Factors associated with benefit included MELD ≤ 30, MDF ≤ 90, and ACLF < 2. These results support the potential of gut microbiome as a therapeutic target in the context of 3PM research and vice versa - to use 3PM methodology as the expedient unifying template for microbiome research. The results allow for immediate impact on the innovative concepts of (i) personalized phenotyping and stratification of the disease for the clinical research and practice, (ii) multilevel predictive diagnosis related to personalized/precise treatment allocation including evidence-based (ii) prevention of futile and sub-optimally effective therapy, as well as (iii) targeted prevention of poor individual outcomes in patients with SAH. Moreover, our results add to the existing evidence with the potential to generate new research along the SAH's pathogenetic pathways such as diverse individual susceptibility to alcohol toxicity, host-specific mitochondrial function and systemic inflammation, and the role of gut dysbiosis thereof.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00381-5.

Current situation and influencing factors of oral frailty for community-dwelling older adults in the northeastern border areas of China: A cross-sectional study

OBJECTIVES

Unique lifestyle and cultural factors in China may lead to distinct patterns of risk factors for oral frailty among older adults, especially in regions inhabited by northeastern border minority groups.

METHODS

From July to November 2023, a convenience sampling method was employed to select older adults from three communities in Yanji City as the subjects. Data were collected by a set of questionnaires.

RESULTS

A total of 478 older adults were included, revealing a prevalence rate of 71.6 % for oral frailty. Factors influencing were found to include age, ethnicity, gender, income, number of chronic diseases, body mass index, drinking, physical frailty, sleep disorders, and attitudes towards aging (p < 0.05).

CONCLUSIONS

There is a higher prevalence of oral frailty. It is crucial to prioritize the oral health issues of older adults with high-risk factors and implement targeted intervention measures to reduce and control the occurrence and progression of oral frailty.

Targeting DNA methylation and demethylation in diabetic foot ulcers

BACKGROUND

Poor wound healing is a significant complication of diabetes, which is commonly caused by neuropathy, trauma, deformities, plantar hypertension and peripheral arterial disease. Diabetic foot ulcers (DFU) are difficult to heal, which makes patients susceptible to infections and can ultimately conduce to limb amputation or even death in severe cases. An increasing number of studies have found that epigenetic alterations are strongly associated with poor wound healing in diabetes.

AIM OF REVIEW

This work provides significant insights into the development of therapeutics for improving chronic diabetic wound healing, particularly by targeting and regulating DNA methylation and demethylation in DFU. Key scientific concepts of review: DNA methylation and demethylation play an important part in diabetic wound healing, via regulating corresponding signaling pathways in different breeds of cells, including macrophages, vascular endothelial cells and keratinocytes. In this review, we describe the four main phases of wound healing and their abnormality in diabetic patients. Furthermore, we provided an in-depth summary and discussion on how DNA methylation and demethylation regulate diabetic wound healing in different types of cells; and gave a brief summary on recent advances in applying cellular reprogramming techniques for improving diabetic wound healing.

The "new normal" of hygiene measures at the end of the COVID-19 epidemic: a survey among French dentists

OBJECTIVES

The COVID-19 epidemic upset the standards in terms of hygiene and protection in the dental office, bringing additional precautions for dentists. The objective of our study was to draw the "new normal" of hygiene measures at the end of the COVID-19 epidemic.

MATERIALS AND METHODS

A self-administered questionnaire about transitional recommendations for oral care in the context of the COVID-19 epidemic was published online in private groups dedicated to French dentists.

RESULTS

The 246 respondents understood the reasons behind those recommendations, since 10 out of 11 measures reached a mean score greater than 2.5 on a 0 (not at all) to 4 (absolutely) scale when it came to determining whether the measure made the practitioner feel safe and ensured patient safety. Besides, more of the respondents intended to maintain the measures than they were to apply them before the epidemic.

CONCLUSIONS

The COVID-19 epidemic reshaped the relationship to hygiene and protection measures in the context of dental practices. The "new normal" of hygiene measures at the end of the COVID-19 epidemic will probably involve more protective measures than before.

CLINICAL RELEVANCE

These results constitute interesting avenues for public health deliberation, which would make it possible to best adapt future health recommendations in order to define the "new normal" of hygiene measures in dental practices at the end of the COVID-19 epidemic. Therefore, it could have an impact on all practitioners in their clinical activities.

COVID-19 lessons to protect populations against future pandemics by implementing PPPM principles in healthcare

The coronavirus disease 2019 (COVID-19) pandemic has continued for more than 3 years, placing a huge burden on society worldwide. Although the World Health Organization (WHO) has declared an end to COVID-19 as a Public Health Emergency of International Concern (PHEIC), it is still considered a global threat. Previously, there has been a long debate as to whether the COVID-19 emergency will eventually end or transform into a more common infectious disease from a PHEIC, and how should countries respond to similar pandemics in the future more time-efficiently and cost-effectively. We reviewed the past, middle and current situation of COVID-19 based on bibliometric analysis and epidemiological data. Thereby, the necessity is indicated to change the paradigm from reactive healthcare services to predictive, preventive and personalised medicine (PPPM) approach, in order to effectively protect populations against COVID-19 and any future pandemics. Corresponding measures are detailed in the article including the involvement of multi-professional expertise, application of artificial intelligence, rapid diagnostics and patient stratification, and effective protection, amongst other to be considered by advanced health policy.

Clarifying the effect of gut microbiota on allergic conjunctivitis risk is instrumental for predictive, preventive, and personalized medicine: a Mendelian randomization analysis

Background

Allergic conjunctivitis is an ocular immune disease which affects the conjunctiva, eyelids, and cornea. Growing evidence implicates the gut microbiota in balancing and modulating immunity response, and in the pathogenesis of allergic disease. As a result, gut microbial imbalance could be a useful indicator for allergic conjunctivitis. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of gut microbial imbalance in the development of allergic conjunctivitis could provide a window of opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

Working hypothesis and methodology

In our study, we hypothesized that individuals with microbial dysbiosis may be more susceptible to allergic conjunctivitis due to an increased inflammatory response. To verify the working hypothesis, our analysis selected genetic variants linked with gut microbiota features (N = 18,340) and allergic conjunctivitis (4513 cases, 649,376 controls) from genome-wide association studies. The inverse-variance weighted (IVW) estimate, Mendelian randomization (MR)-Egger, weighted median estimator, maximum likelihood estimator (MLE), and MR robust adjusted profile score (MR.RAPS) were employed to analyze the impact of gut microbiota on the risk of allergic conjunctivitis and identify allergic conjunctivitis-related gut microbes. Ultimately, these findings may enable the identification of individuals at risk of allergic conjunctivitis through screening of gut microbial imbalances, and allow for new targeted prevention and personalized treatment strategies.

Results

Genetic liability to Ruminococcaceae_UCG_002 (OR, 0.83; 95% CI, 0.70-0.99; P = 4.04×10^-2), Holdemanella (OR, 0.78; 95% CI, 0.64-0.96; P = 2.04×10^-2), Catenibacterium (OR, 0.69; 95% CI, 0.56-0.86; P = 1.09×10^-3), Senegalimassilia (OR, 0.71; 95% CI, 0.55-0.93; P = 1.23×10^-2) genus were associated with a low risk of allergic conjunctivitis with IVW. Besides, we found suggestive associations of a genetic-driven increase in the Oscillospira (OR, 1.41; 95% CI, 1.00-2.00; P = 4.63×10^-2) genus with a higher risk of allergic conjunctivitis. Moreover, MLE and MR.RAPS show consistent results with IVW after further validation and strengthened confidence in the true causal associations. No heterogeneity and pleiotropy was detected.

Conclusions

Our study suggests that gut microbiota may play a causal role in the development of allergic conjunctivitis and provides new insights into the microbiota-mediated mechanism of the disease. Gut microbiota may serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in allergic conjunctivitis, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00321-9.

Multi-omics and immune cells' profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

Background

Intensive care unit admission (ICUA) triage has been urgent need for solving the shortage of ICU beds, during the coronavirus disease 2019 (COVID-19) surge. In silico analysis and integrated machine learning (ML) approach, based on multi-omics and immune cells (ICs) profiling, might provide solutions for this issue in the framework of predictive, preventive, and personalized medicine (PPPM).

Methods

Multi-omics was used to screen the synchronous differentially expressed protein-coding genes (SDEpcGs), and an integrated ML approach to develop and validate a nomogram for prediction of ICUA. Finally, the independent risk factor (IRF) with ICs profiling of the ICUA was identified.

Results

Colony-stimulating factor 1 receptor (CSF1R) and peptidase inhibitor 16 (PI16) were identified as SDEpcGs, and each fold change (FC_ij) of CSF1R and PI16 was selected to develop and validate a nomogram to predict ICUA. The area under curve (AUC) of the nomogram was 0.872 (95% confidence interval (CI): 0.707 to 0.950) on the training set, and 0.822 (95% CI: 0.659 to 0.917) on the testing set. CSF1R was identified as an IRF of ICUA, expressed in and positively correlated with monocytes which had a lower fraction in COVID-19 ICU patients.

Conclusion

The nomogram and monocytes could provide added value to ICUA prediction and targeted prevention, which are cost-effective platform for personalized medicine of COVID-19 patients. The log₂fold change (log₂FC) of the fraction of monocytes could be monitored simply and economically in primary care, and the nomogram offered an accurate prediction for secondary care in the framework of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00317-5.

Consequences of COVID-19 and Its Variants: Understanding the Physical, Oral, and Psychological Impact

The highly infectious severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) caused the coronavirus disease 2019 (COVID-19) pandemic, which affects the lives of people worldwide in a variety of unprecedented ways [...].

Radiographic Examination before Dental Extraction from Dentists' Perspective

Background

It is generally agreed that radiographic examination is important before dental extraction. It provides information about the roots and the surrounding tissues. In terms of practice, it does not seem to be a universally implemented protocol regarding the use of dental radiology before dental extraction. Besides, the type of radiographic technique is not specified. Some references prefer periapical dental radiographs. Others prefer orthopantomography), or even cone beam computed tomography Delpachitra et al. (2021) [1]. In terms of the dental practice, it is not clear whether there is a universally adopted protocol regarding the use of dental radiographs before dental extraction. Aim of the study. To assess dental professionals' perspective toward radiographic examination before conventional dental extraction.

Materials and Methods

A Google form questionnaire was circulated to different dental professionals using mainly ResearchGate, in addition to different social media platforms.

Results

One hundred and forty-five dentists participated in the questionnaire. The respondents were divided according to the country of current practice: national (Iraqi), regional (Middle Eastern), and international participants. Out of 144 respondents, 51.4% percent of the participants were international, while 40.3% were Iraqis, and 8.3% were from the Middle East. The need for dental radiography in all dental extraction procedures was reported in the majority of responses (n = 86). Only 11 dentists think there is no necessity for radiographic examination before conventional extraction. The chi-square test showed a highly significant relationship between the country of current practice and the need for X-ray examination for conventional dental extraction (P < 0.01). Seventy-six dentists prefer periapical radiographs. Thirty-five preferred orthopantomography. A highly significant relationship was found between the country of practice and the X-ray technique (P < 0.01).

Conclusion

The study showed that there is no universally adopted protocol regarding the use of dental radiography before dental extraction. The country of practice appears to govern the dentists' decisions regarding the need for an X-ray and the type of radiography prior to dental extraction. Periapical radiographs for posterior teeth seem to be the preferable choice before dental extraction.

Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up

BACKGROUND

Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery.

METHODS

We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing.

RESULTS

The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later.

CONCLUSIONS

This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare.

Dental Research Data Availability and Quality According to the FAIR Principles

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable-for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.

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.

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021

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.

Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers

Background

The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records.

Objective

This study presents the application of machine learning techniques to these records to predict the blood culture's outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes.

Methods

Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture.

Results

The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93.

Conclusions

Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers' importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.

Targeting phytoprotection in the COVID-19-induced lung damage and associated systemic effects-the evidence-based 3PM proposition to mitigate individual risks

The risks related to the COVID-19 are multi-faceted including but by far not restricted to the following: direct health risks by poorly understood effects of COVID-19 infection, overloaded capacities of healthcare units, restricted and slowed down care of patients with non-communicable disorders such as cancer, neurologic and cardiovascular pathologies, among others; social risks-restricted and broken social contacts, isolation, professional disruption, explosion of aggression in the society, violence in the familial environment; mental risks-loneliness, helplessness, defenceless, depressions; and economic risks-slowed down industrial productivity, broken delivery chains, unemployment, bankrupted SMEs, inflation, decreased capacity of the state to perform socially important programs and to support socio-economically weak subgroups in the population. Directly or indirectly, the above listed risks will get reflected in a healthcare occupation and workload which is a tremendous long-term challenge for the healthcare capacity and robustness. The article does not pretend to provide solutions for all kind of health risks. However, it aims to present the scientific evidence of great clinical utility for primary, secondary, and tertiary care to protect affected individuals in a cost-effective manner. To this end, due to pronounced antimicrobial, antioxidant, anti-inflammatory, and antiviral properties, naturally occurring plant substances are capable to protect affected individuals against COVID-19-associated life-threatening complications such as lung damage. Furthermore, they can be highly effective, if being applied to secondary and tertiary care of noncommunicable diseases under pandemic condition. Thus, the stratification of patients evaluating specific health conditions such as sleep quality, periodontitis, smoking, chronic inflammation and diseases, metabolic disorders and obesity, vascular dysfunction, and cancers would enable effective managemenet of COVID-19-associated complications in primary, secondary, and tertiary care in the context of predictive, preventive, and personalized medicine (3PM).

Sleep quality and COVID-19 outcomes: the evidence-based lessons in the framework of predictive, preventive and personalised (3P) medicine

Sleep quality and duration play a pivotal role in maintaining physical and mental health. In turn, sleep shortage, deprivation and disorders are per evidence the risk factors and facilitators of a broad spectrum of disorders, amongst others including depression, stroke, chronic inflammation, cancers, immune defence insufficiency and individual predisposition to infection diseases with poor outcomes, for example, related to the COVID-19 pandemic. Keeping in mind that COVID-19-related global infection distribution is neither the first nor the last pandemic severely affecting societies around the globe to the costs of human lives accompanied with enormous economic burden, lessons by predictive, preventive and personalised (3P) medical approach are essential to learn and to follow being better prepared to defend against global pandemics. To this end, under extreme conditions such as the current COVID-19 pandemic, the reciprocal interrelationship between the sleep quality and individual outcomes becomes evident, namely, at the levels of disease predisposition, severe versus mild disease progression, development of disease complications, poor outcomes and related mortality for both - population and healthcare givers. The latter is the prominent example clearly demonstrating the causality of severe outcomes, when the long-lasting work overload and shift work rhythm evidently lead to the sleep shortage and/or deprivation that in turn causes immune response insufficiency and strong predisposition to the acute infection with complications. This article highlights and provides an in-depth analysis of the concerted risk factors related to the sleep disturbances under the COVID-19 pandemic followed by the evidence-based recommendations in the framework of predictive, preventive and personalised medical approach.