PERSONALIZED PERIODONTICS

Comprehensive Theory Examination Notes

(Postgraduate Periodontology - University Examiner Level)

1. INTRODUCTION AND CONCEPTUAL BACKGROUND

• Periodontology and implant dentistry are an integral part of overall connected health and personalized care in the era of precision medicine. (Newman & Carranza's Clinical Periodontology and Implantology, 14th ed., Chapter 1)
• The contemporary clinical protocol that uses only surrogate outcomes of periodontitis (e.g., probing depths) is geared toward detecting periodontal diseases that have already occurred and does not offer any guidance for preventive strategies. The current workflow also lacks the ability to predict the degree of responsiveness to a proposed therapy. (Newman & Carranza 14th ed., Ch. 1)
• The concept of personalized or individualized medicine is not new. Clinicians and patients have long recognized that a "one shoe fits all" approach to treatment is unrealistic. And yet clinical trial outcomes and many practice guidelines are designed around data based on analysis of a mean response to treatment in which a population of patients, defined by study inclusion criteria, is expected to react to treatment in the same way. As accumulating data confirms, such an approach is biologically naive. (Genomics, Personalized Medicine and Oral Disease - Sonis, 2015)
• With the coalescing advances in science and technology, we are fast approaching a point at which identification of risk and best treatment at the patient level is both clinically and economically feasible. (Sonis, 2015)

2. TERMINOLOGY AND DEFINITIONS

2.1 Key Definitions

2.2 KEY FACT - Distinction Between Precision and Personalized Medicine

"Although the terms precision medicine and personalized medicine are used interchangeably, it is important to note that there is a distinction between the two. Precision medicine encompasses personalized medicine, and it harnesses clinical and biologic information to stratify patients and to guide clinical decision-making."

(Newman & Carranza's Clinical Periodontology and Implantology, 14th ed., Chapter 1 - KEY FACT Box)

3. THE FOUR Ps OF PRECISION HEALTH CARE (CORNERSTONES)

PRECISION HEALTH CARE - THE "4Ps" FRAMEWORK
(Newman & Carranza 14th ed., Chapter 1)

                         PRECISION HEALTH CARE
                                   |
          +-----------+-----------+-----------+
          |           |           |           |
     PREDICTIVE  PREVENTIVE  PERSONALIZED  PARTICIPATORY
          |           |           |           |
   Predict degree  See provider  Personalized  Patient engagement
   of responsive-  BEFORE        treatment    via biosensors,
   ness to therapy  disease       plan based   teledentistry,
   using biologic   initiates    on biologic   smart toothbrushes,
   information                   makeup        mobile apps

• Predictive: The clinician can assemble a more accurate diagnosis and prognosis; the treatment plan is far more predictable.
• Preventive: Patients see the health care provider before the initiation of disease - feasible by knowing the patient's risk profile based on biologic makeup.
• Personalized: Development of a more personalized treatment plan for the patient that takes into account individual host inflammatory characteristics.
• Participatory: Oral hygiene aids such as brushes or intraoral appliances, fitted with biosensors, can detect plaque levels or biomarker analytes in saliva and can provide real time notifications to patients regarding their oral and systemic health. (Newman 14th ed., Ch. 1)

4. COMPONENTS OF PRECISION/PERSONALIZED PERIODONTICS

FLOWCHART: Precision Periodontics Model of Care

PRECISION PERIODONTICS - WORKFLOW (Fig. 1.4 - Newman 14th ed.)
                                   |
              +--------------------+--------------------+
              |                                         |
    PATIENT (with biosensors                 MODERN CLINICIAN
     in oral cavity + mobile                (trained with digital
      reminder applications)                 technology: haptics,
              |                              digital simulation,
              |                              wearables)
              +--------------------+
                     |
             PATIENT-CLINICIAN INTERACTION
                     |
     +---------------+--------------+----------------+
     |               |              |                |
 Patient's       Patient's      Digital           Scientific
 Genetic         History,       Assets            Evidence Base
 Information     Clinical &     (EHR, 2D & 3D    with Dental
                 Radiographic   Imaging,          Patient-Reported
                 Examination    AI and ML)        Outcomes (dPROs)
     |               |              |                |
     +---------------+--------------+----------------+
                     |
           +---------+---------+
           |         |         |
      Responsiveness  Risk for  Improved
      to Therapy      Future    Accuracy in
                     Disease   Diagnosis
           |         |         |
           +---------+---------+
                     |
              PERSONALIZED TREATMENT PLAN
                     |
        +------------+------------+
        |            |            |
   Predictive   Personalized   Participatory
                Frequency for  (Home Monitoring
                Maintenance    with Biosensors,
                of Results     Teledentistry,
                               Smart Toothbrushes)

4.1 Biologic Information

A. Microbiologic and Host Response Information

• Maintaining the host-microorganism balance and homeostasis is a prerequisite for maintaining periodontal health.
• Comprehensively deciphering the host response and microbiome, at the patient level, will be critical to stratify patients for precise diagnosis and effective treatment.
• Periodontitis is an inflammatory disease initiated by microorganisms, primarily bacteria. However, tissue damage in periodontitis is primarily mediated by the host response, with a smaller contribution by bacteria.
• The presence of periodontal pathogens is required but not sufficient for disease initiation.
• Historically, several bacterial species - Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia - were known as major periodontal pathogens.
• Keystone pathogens, such as P. gingivalis, with a relatively low abundance in dental plaque, can cause dysbiosis, which can instigate periodontal inflammation.
• More recently, viruses, bacteriophages, and fungi are being identified (through novel technologies such as 16S sequencing, shotgun sequencing, and metagenomics) to be associated with periodontitis. (Newman 14th ed., Ch. 1)

B. Genetic Information

• Specific genetic mutations are associated with disorders and diseases.
• The single gene-single disorder ("candidate gene") approach to study the impact of genetic factors on diseases is no longer effective due to complexity of associations and interactions between a large number of genes.
• By using advanced informatics technology, a conceptual framework can be developed to systematically link all genetic disorders (disease phenome) with all disease-associated genes (disease genome), resulting in a global view or "diseasome" that comprises comprehensive known disorder/disease gene associations. (Newman 14th ed., Ch. 1)

4.2 Biomarkers in Periodontitis

• GCF is a physiologic fluid and inflammatory exudate originating from the gingival vascular plexus and exists in the gingival sulcus.
• GCF is composed of molecules that originate from the blood, host tissues, and bacteria, including electrolytes, metabolites, cytokines, antibodies, bacterial antigens, and enzymes.
• GCF can be easily collected using special paper strips or micropipette; its volume increases when periodontal tissues are inflamed.
• The concentration of specific molecules (biomarkers) from GCF can be assessed by techniques such as enzyme-linked immunosorbent assay (ELISA), proteomics, or metabolomics.
• The molecular profiles of GCF can be clustered with molecular profiles of serum and saliva, subgingival microbial profile data, and/or clinical signs to increase the overall diagnostic accuracy, which will lead to a more precise and predictable treatment plan. (Newman 14th ed., Ch. 1)

5. OMICS TECHNOLOGIES IN PERSONALIZED PERIODONTICS

FLOWCHART: Omics Technologies

OMICS TECHNOLOGIES
        |
        +---> GENOMICS         (genes and their function)
        |
        +---> TRANSCRIPTOMICS  (organism's transcriptome = sum of all RNA transcripts)
        |
        +---> PROTEOMICS       (quantifying and studying structure/function of proteins)
        |
        +---> LIPIDOMICS       (lipid structure and function)
        |
        +---> METABOLOMICS     (cellular metabolites)

5.1 Next-Generation Sequencing (NGS)

• NGS uses massively parallel sequencing technology that allows for the generation and detection of millions of short sequencing reads in a quick single machine run.
• As compared with conventional Sanger sequencing, NGS provides a more affordable and efficient approach to acquire genomic information, making it more practical for clinical applications.
• 16S ribosomal RNA (rRNA) sequencing, using NGS technology, is a cost-effective technique to profile the microbiome of environmental and clinical samples based on variation in the bacterial 16S rRNA gene.
• 16S rRNA sequencing can identify bacterial species and measure their relative abundance in plaque samples collected from patients with periodontitis.
• The presence of specific bacterial species and microbiome profiles are associated with periodontitis severity and progression. (Newman 14th ed., Ch. 1)

6. BIOSENSORS IN PERSONALIZED PERIODONTICS

• A biosensor is an analytic device that detects analytes with specific biologic activities.
• A typical biosensor has two fundamental units:
  1. A "bioreceptor" - such as an enzyme, an antibody, or DNA, responsible for selective recognition of the target analyte.
  2. A "physicochemical transducer" - which can be an electrochemical, optical, or mechanical transducer, converting the detected activity into a signal.
• The signal is then exported and transformed into clinically meaningful results.
• A highly sensitive peptide-graphene nanosensor can be placed on tooth enamel to detect salivary bacteria at single-cell levels.
• A biosensor with antibodies coated on a small chip can detect and quantify salivary matrix metalloproteinase-8 (MMP-8), a biomarker associated with periodontitis.
• Biosensors can offer either real time biologic data collection or can be used for point-of-care testing. (Newman 14th ed., Ch. 1)

7. ELECTRONIC HEALTH RECORDS (EHRs) IN PERSONALIZED PERIODONTICS

• An EHR is a digital version of the traditional patient paper chart.
• EHRs can save more information and provide standardization of patient records.
• EHR systems provide reminders, warning messages about medical conditions including drug allergies, and can help perform risk assessment for oral and systemic diseases.
• The "Health Information Technology for Economic and Clinical Health (HITECH) Act" was enacted in 2009 in the United States, listing "meaningful use" of interoperable EHRs as a critical national goal.
• "Meaningful Use" = use of certified EHR technology in a meaningful manner, ensuring effective electronic exchange of health information to improve the quality of care.
• The Consortium for Oral Health Research and Informatics (COHRI) was convened in February 2007 by dental schools to facilitate advancement of education, research, and patient care outcomes.
• BigMouth is an oral health database developed from partially deidentified EHR data contributed by COHRI member institutions for clinical and research purposes.
• Example of EHR use for precision periodontics: Cross-sectional studies have shown that certain clinical thresholds, such as the presence of 26% or greater teeth with deep pockets (≥5 mm) or four or more missing teeth, could accurately identify greater than 70% of patients with prediabetes. (Newman 14th ed., Ch. 1)

8. ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML)

• AI has improved the accuracy of diagnostic interpretation of clinical and radiographic images.
• Deep learning (DL) program can be used to diagnose periodontal disease - to detect bone levels, cementoenamel junctions, and tooth/implant morphology.
• With the assistance of AI and ML in EHRs, clinicians will be able to assemble a more accurate diagnosis and prognosis.
• AI assists in integrating biologic information (microbiome, genetic, environmental, immune status) to provide optimum treatment and prevention guidance. (Newman 14th ed., Ch. 1)

9. INTERNET OF MEDICAL THINGS (IoMT) AND WEARABLES

• Many wearable or portable electronic devices (smartphones, smartwatches) that are used in day-to-day lives can collect and transfer data effectively.
• These devices are developed based on the concept of the "Internet of Things" (IoT), which is about connecting and exchanging data between a variety of devices and systems over the Internet.
• The Internet of Medical Things (IoMT) is a cloud network-based advanced technology that is essentially IoT applied to the medical field, allowing for collection and active monitoring of patients' health status for disease prevention.
• Examples of consumer wearables: Accelerometer, altimeter, digital camera, electrocardiogram, electromyograph, electroencephalogram, electrodermograph, smartwatches, GPS, oximeter, sensors embedded in clothing. (Newman 14th ed., Ch. 1)

10. TELEDENTISTRY

• Teledentistry is an effective way to remotely interact with patients by videoconferencing to provide dental consultations and instructions.
• Can be a good medium to reach patients in rural areas, nursing facilities, or during a pandemic.
• The COVID-19 pandemic has brought to light the importance and usefulness of teledentistry.
• Teledentistry offers an alternative approach to screen for oral diseases and potentially help patients manage oral diseases when immediate dental care is unavailable. (Newman 14th ed., Ch. 1)

11. PATIENT-REPORTED OUTCOMES (PROs) IN PERSONALIZED PERIODONTICS

• One critical aspect of personalized medicine is that each person perceives disease burden differently. Disease-related morbidity has an experiential component that cannot be captured by clinical disease measures.
• In periodontal practice, treatment effects are routinely based on clinician-measured surrogate outcomes (probing depth and attachment levels), which are not easily communicated to or perceived by dental patients.
• This generates a communication gap in the dentist-patient relationship that ultimately affects care.

"Examples of dental patient-reported outcome measures (PROMs) include how a patient feels and functions and the overall patient-reported satisfaction, phonetics, chewing comfort, stability, cleanability, and esthetics. PROMs must be reported directly by the patient without interpretation by anyone else, including the dentist."

(Newman & Carranza 14th ed., Ch. 1 - KEY FACT Box)

• Tailored treatment plans go beyond precision dentistry (which considers individual host inflammatory characteristics) to encompass behavioral characteristics and individually perceived treatment needs.
• Dental Patient-Reported Outcomes (dPROs) are true outcomes of disease and have gained significant momentum to better capture disease burden and treatment effects. (Newman 14th ed., Ch. 1)

12. GENETICS AND EPIGENETICS IN PERSONALIZED PERIODONTICS

12.1 Genetic Basis

• It is now widely accepted that differences among individuals who are at risk of the development of most diseases have a substantial inherited component.
• Factors in the environment (diet, smoking, preventive care, exposure to pathogens) interact with each person's genetic predisposition to determine health outcomes.
• Most cases of periodontitis appear to fit the complex gene and environment model - inherited variation in DNA has a role roughly equal to that of the environment in determining who remains periodontally healthy versus who is affected. (Newman 14th ed., Ch. 9)

12.2 Genomic Advances

• Genome-wide association studies (GWAS - pronounced "geewas") and next-generation DNA sequencing techniques are now being used in periodontology research.
• For these strategies to be successful, they must be combined with further improvement in research definitions of periodontal disease and much larger sample sizes than have been used in the majority of previous genetic studies.
• Knowledge of which specific genes are most important remains extremely limited and virtually nothing is known about the role that inherited genetic differences play in determining how patients respond to alternative treatments. (Newman 14th ed., Ch. 9)

12.3 Pharmacogenomics and Individualized Dentistry

• The gene may distinguish between different subtypes of disease etiology or pathogenesis, or it might code for an enzyme, transporter, or receptor important for the metabolism of a therapeutic drug.
• Without awareness of the genotype-treatment relationship, clinicians would be forced to use a trial-and-error approach, causing unnecessary expense and not providing optimal quality of care. (Newman 14th ed., Ch. 9)

12.4 IL-1 Genetic Testing (Commercially Available Test)

• A test based on inherited genetic variation at the interleukin-1 alpha (IL-1α) and IL-1β cytokine genes was proposed as being able to predict the risk, progression, and severity of periodontitis.
• The test has been commercialized and gone through multiple versions: PST, PerioPredict, and today's version, ILUSTRA.
• Reviews of the IL-1 gene polymorphisms indicate that genetic variation at these loci may be associated with at most, a modest effect on disease risk.
• Huynh-Ba et al. (2007) concluded that "there is insufficient evidence to establish if a positive IL-1 genotype status contributes to progression of periodontitis or treatment outcomes."
• A meta-analysis of available data suggested that IL-1 polymorphisms have a small (odds ratio ≈1.5) but significant effect on periodontitis risk.
• First major study to develop guidelines for individualized treatment involved 5117 adults aged 34-55 years, followed retrospectively for 16 years, evaluating whether a subset of high-risk patients benefit from two versus one preventive visit per year. Risk factors tested: smoking, diabetes, and IL-1 composite genotype.
• An independent reanalysis showed that of the three factors, only diabetes and smoking had statistically significant effects on tooth extraction risk.
• Therefore, there is insufficient evidence to demonstrate the scientific validity or clinical utility of the IL-1 genetic test as a periodontitis risk prediction tool. (Newman 14th ed., Ch. 9)

12.5 Candidate Genes Possibly Related to Risk of Chronic or Aggressive Periodontitis

12.6 Epigenetics in Personalized Periodontics

• Epigenetics involves changes in gene function that are heritable through mitosis but do not involve a change in DNA sequence.
• Two major epigenetic mechanisms:
  1. DNA methylation - controlled by DNA methyl transferase enzymes (DNMTs); when methylation occurs in a gene promoter, it typically suppresses transcription of that gene.
  2. Histone acetylation - promotes transcription; controlled by histone acetyltransferases (add acetyl groups to lysine residues) and histone deacetylases (remove acetyl groups).
• Epigenetics factors play an important role in chronic inflammatory conditions by allowing microbial persistence to play a role in the infectious mechanism, resulting in pathogen interference with the host genome.
• Periodontal pathogens containing lipopolysaccharides can be a cause of epigenetic modifications affecting expression of inflammatory mediators in periodontal tissue.
• Low-grade chronic inflammation in periodontal disease has been shown to promote DNA methylation.
• Periodontal disease is closely linked with diabetes mellitus complications, which are related to epigenetic changes. (Newman 14th ed., Ch. 9)

• Histone modifications occur to the core structure of DNA
• Nucleosome positioning changes
• Non-coding RNA
• Acetylation or methylation modifies cellular responses by altering the production of transcription factors and subsequent gene expression.
• Abnormal epigenetic patterns can be identified in a range of diseases from asthma and chronic obstructive pulmonary disease to cancers.
• Epigenetics may have an important role in periodontal disease and oral cancer. (Genomics, Personalized Medicine and Oral Disease - Sonis, 2015)

13. PERSONALIZED MEDICINE AND GENOMICS IN ORAL DISEASE

13.1 Advantages of Individualized Treatment for All Stakeholders

1. Determination of disease risk
2. Assessment of a patient's likelihood to respond (or not respond) to a specific medication
3. Appraisal of the probability of toxicities associated with treatment choices
4. An accurate probabilistic determination enabling a hierarchical guide to intervention
5. Especially valuable when best therapy is based on preventive strategies (Sonis, 2015)

13.2 The "Ideal Individualized Approach"

IDEAL INDIVIDUALIZED APPROACH (Sonis 2015)
              |
     SCREEN PATIENTS FOR:
              |
    +----+----+----+
    |         |    |
 Risk of    Response  Risk of
 Disease    to each   treatment-related
            treatment  TOXICITY
    option
              |
    GENERATE PRIORITIZED LIST:
    Top option = highest efficacy + lowest toxicity risk + best price

13.3 Clinical Reality of Genomics in Oral Disease

• Commercially available genetic tests now:
  • Predict the behavior of certain breast cancers
  • Help establish effective doses of Coumadin
  • Determine toxic potential of certain cancer drugs
  • Identify patients at risk for periodontitis (Sonis, 2015)
• Genomic-based results can provide a powerful snapshot of an individual's state of health and disease and lead the way for diagnostic solutions in the field of personalized oral medicine. (Sonis, 2015)

14. PERSONALIZED PHAGE THERAPY

• A survey of the oral bacteriophage composition revealed a stable and highly personalized population with a significant portion belonging to the Caudovirales order.
• Subgingival plaque taken from inflamed periodontal pockets has a reduction in bacteriophage richness and diversity within Caudovirales families.
• Dominance of Siphoviridae temperate bacteriophages in health is replaced by a surge in Myoviridae lytic bacteriophages in subgingival plaque during active periodontal disease.
• Once bacteriophage databases are further developed, tailoring a bacteriophage cocktail to match a patient's specific bacterial infection will make personalized phage therapy a potential reality.
• Treatment of periodontitis with phage therapy may first appear as adjunctive therapy to local or systemic antibiotics and non-surgical treatment procedures that include the debridement of dental plaque. (Newman 14th ed., Ch. 27)

15. DYNAMIC TREATMENT REGIMENS (DTRs) AND CLINICAL TRIAL DESIGN

• In traditional clinical protocols, the next decision in treatment sequence is made at a given point in therapy - this is the principle behind the concept of Dynamic Treatment Regimens (DTRs).
• Traditional clinical trials in periodontics will not allow researchers to identify DTRs or validate interventions that have to be adapted based on patient responses, called "adaptive interventions."
• Sequential Multiple Assignment Randomized Trial (SMART) clinical trials with the help of advanced statistical techniques will allow researchers to identify DTRs and adaptive treatment interventions in a given patient population.
• Multiple nodes of key clinical decision-making steps along with specific outcomes embedded within the SMART designs make it an ideal design. (Newman 14th ed., Ch. 1)

16. PERIODONTAL RISK ASSESSMENT AND PERSONALIZED TREATMENT

"The ultimate goal of performing a periodontal risk assessment is to develop a more personalized treatment plan for a specific patient, taking into account the periodontal risk profile of that patient. Once an at-risk patient is identified and a diagnosis is made, the treatment plan may be modified accordingly."

(Newman & Carranza 14th ed., Ch. 40 - Clinical Correlation Box)

17. PERSONALIZED PERIODONTAL TREATMENT PLAN

• A thorough and diligent "information gathering" process is an absolute prerequisite for the clinician to formulate a comprehensive treatment plan that takes into account not just local factors in the oral cavity but also:
  • Systemic health status
  • Psychosocial and behavioral factors
  • Molecular markers of health and disease
• The information-gathering process now expands to these domains in order to render preventive and/or therapeutic precision health care.
• Digitalization of diagnostic information (periodontal charting, radiographs, intraoral scans) allows for the effective integration of AI and machine-learning capabilities that can aid in decision-making.
• 3D imaging like CBCT, with its ability to link intraoral scanning images to CBCT images, has greatly expanded the diagnostic and treatment planning possibilities.
• The previous information is utilized to diagnose periodontal diseases and conditions and to generate a personalized treatment plan for a patient. (Newman 14th ed., Ch. 42)

18. GENETIC TESTING AND PREDICTIVE ABILITY

18.1 Predictive Ability of Genetic Tests

• Accurate genetic testing may become possible for the juvenile and post-adolescent (early-onset) forms of aggressive periodontitis, which are the most severe phenotypes and have a particularly early age of onset, particularly if the genetic susceptibility factors are fully identified.
• In contrast, late-onset diseases such as chronic periodontitis, with moderate and variable phenotypes and a high risk in the population, have many underlying low-risk variants, which may interact with each other and with other non-genetic risk factors in many different ways.
• Increased and decreased risks due to very complex multidimensional interactions are still beyond our statistical and computational scope in predictive testing models. (Lindhe, 6th ed., Ch. 15)

18.2 Future Vision

• The vision remains that, on the basis of comprehensive data that encompass the complete DNA sequence, biochemical data, and information on environmental and behavioral factors over a large time period in the life of a patient, this information can be turned into computational models that represent the patient and his/her health status and will enable the physician to devise a personalized therapy for the consenting patient. (Lindhe, 6th ed.)

19. PERIODONTAL EDUCATION FOR PRECISION PRACTICE

• Educators will be required to adapt and update the curriculum accordingly to prepare clinicians for precision-based future practice.
• The Commission on Dental Accreditation requires academic institutions to integrate technologies in a meaningful way in training for both dental students and postgraduate residents.
• Release of the new Integrated National Board Dental Examination (August 2020) in the United States, with a focus on case-based assessment, is consistent with the ongoing shift to a more person-centered approach.
• A case-based component has been piloted in the American Academy of Periodontology's In-Service examination (2021).
• Virtual reality (VR) is an advanced computer-generated technology that offers realistic experience by three-dimensional visual rendering; students can wear a VR headset to interact with virtual patients or perform virtual procedures. (Newman 14th ed., Ch. 1)

20. COMPARISON TABLE: VIEWPOINTS FROM DIFFERENT REFERENCES

21. RECENT TERMINOLOGY CHANGES AND UPDATES

22. SUMMARY FLOWCHART: PERSONALIZED PERIODONTICS - INTEGRATIVE MODEL

PERSONALIZED PERIODONTICS - COMPREHENSIVE INTEGRATIVE MODEL
                 (From uploaded references, synthesized)

              PATIENT-SPECIFIC BIOLOGIC INFORMATION
                           |
       +-------------------+-------------------+
       |                   |                   |
  MICROBIOME          GENETIC/             BIOMARKERS
  ANALYSIS            EPIGENETIC           (GCF, Saliva,
  (16S rRNA,          INFORMATION          Serum: IL-1β,
  Metagenomics)       (GWAS, NGS,          IL-6, CRP,
                      IL-1 polymorphisms,  MMP-8, RANK-L,
                      Epigenetic changes)  TNF-α)
       |                   |                   |
       +-------------------+-------------------+
                           |
                 INTEGRATED WITH DIGITAL TOOLS
                           |
       +-------------------+-------------------+
       |                   |                   |
     EHR            AI/ML ANALYSIS         IoMT/WEARABLES
  (BigMouth,        (Deep learning         (Biosensors,
  COHRI, HITECH)    for radiographs,       Smartwatches,
                    diagnosis)             Teledentistry)
       |                   |                   |
       +-------------------+-------------------+
                           |
                PATIENT-REPORTED OUTCOMES (dPROs)
                (Phonetics, Chewing, Esthetics, Satisfaction)
                           |
                           |
                 PERSONALIZED TREATMENT PLAN
                           |
          +----------------+----------------+
          |                |                |
    RISK STRATIFICATION  TARGETED        PERSONALIZED
    (Predictive)         THERAPY         MAINTENANCE
                         (Personalized)  FREQUENCY
                                         (Participatory)
          |                |                |
    PHAGE THERAPY     PHARMACOGENOMICS  SMART DESIGN
    (Personalized     GUIDED            CLINICAL TRIALS
    bacteriophage     ANTIBIOTICS       (DTRs, SMART)
    cocktails)        (Preventive)

23. KEY EXAMINER KEYWORDS - MASTER LIST

1. Precision medicine vs. personalized medicine - distinction
2. The 4Ps - Predictive, Preventive, Personalized, Participatory
3. Omics technologies - genomics, transcriptomics, proteomics, lipidomics, metabolomics
4. Next-Generation Sequencing (NGS)
5. 16S rRNA sequencing
6. Genome-Wide Association Studies (GWAS)
7. Keystone pathogens and dysbiosis
8. Biomarkers - GCF, saliva, serum; ELISA, proteomics, metabolomics
9. Biosensor - bioreceptor + physicochemical transducer
10. Peptide-graphene nanosensor (single-cell level bacterial detection)
11. MMP-8 (salivary biomarker for periodontitis)
12. Electronic Health Records (EHR) - HITECH Act, COHRI, BigMouth
13. Artificial Intelligence (AI) and Machine Learning (ML)
14. Deep learning (DL) - periodontal bone loss detection
15. Internet of Things (IoT) / Internet of Medical Things (IoMT)
16. Teledentistry
17. Dental Patient-Reported Outcomes (dPROs) / PROMs
18. IL-1 genetic test - PST / PerioPredict / ILUSTRA - insufficient evidence
19. Diseasome - disease phenome + disease genome
20. Candidate gene approach - no longer effective
21. DNA methylation and histone acetylation - epigenetic mechanisms
22. Dynamic Treatment Regimens (DTRs)
23. Sequential Multiple Assignment Randomized Trial (SMART)
24. Personalized phage therapy - Caudovirales, Siphoviridae, Myoviridae
25. CRISPR/Cas system
26. Virtual Reality (VR) in dental education
27. Huynh-Ba et al. (2007) - IL-1 genotype insufficient evidence

24. REFERENCES (FROM UPLOADED DOCUMENTS)

1. Newman MG, Takei HH, Klokkevold PR, Carranza FA. Newman and Carranza's Clinical Periodontology and Implantology. 14th ed. Elsevier; 2023. Chapter 1: Clinical Periodontology and Implantology in the Era of Precision Medicine; Chapter 9: Precision Dentistry: Genetics and Epigenetics of Periodontitis; Chapter 27: Oral Bacteriophage Ecology; Chapter 40: Periodontal Risk Assessment; Chapter 42: Periodontal Treatment Plan.
2. Sonis ST (Ed.). Genomics, Personalized Medicine and Oral Disease. Springer International Publishing; 2015. Chapter 1: Fundamentals of Genetics and Genomics; Chapter: Current and Evolving Technologies.
3. Lang NP, Lindhe J (Eds.). Clinical Periodontology and Implant Dentistry. 6th ed. Wiley-Blackwell; 2015. Chapter 15: Genetics of Periodontitis.
4. Carranza FA. Carranza's Clinical Periodontology. 10th ed. Saunders; 2006. (Personalized oral hygiene instruction - referenced in context of biofilm control.)
5. Razzouk S, Termatchi O. Host genome, epigenome and oral microbiome interactions: toward personalized periodontal therapy. J Periodontol. 2013;84:1266. (cited in Newman 14th ed., Ch. 1, Ref. 76)
6. Ginsburg GS, Willard HF. Genomic and personalized medicine: foundations and applications. Transl Res. 2009;154:277. (cited in Newman 14th ed., Ch. 9, Ref. 26)
7. McKenzie and Pretty. Connected toothbrushes: bridging the gap to personalized oral health. Am J Dent. 2018;31(3):115-120. (cited in Newman 14th ed., Ch. 1, Ref. 67)

Summary of Coverage

• Newman & Carranza 14th ed. - Most content-rich source; provided Ch. 1 (Precision Medicine era, 4Ps, biosensors, AI, EHR, IoMT, dPROs, SMART trials), Ch. 9 (genetics/epigenetics, IL-1 testing, GWAS, pharmacogenomics), Ch. 27 (personalized phage therapy), Ch. 40 (risk assessment), and Ch. 42 (personalized treatment plan)
• Genomics, Personalized Medicine and Oral Disease (Sonis 2015) - Provided the philosophical framework of personalized medicine, advantages for stakeholders, the "ideal individualized approach," genomic technologies (NGS platforms comparison table), and the reality of clinical genomics translation
• Clinical Periodontology and Implant Dentistry, Lindhe 6th ed. - Provided the genetic basis of periodontitis, predictive ability of genetic tests, and future vision of computational patient models
• Carranza 10th ed., Periodontics Medicine Surgery Implants, Essentials of Periodontology (Reddy), Misch 4th ed. - Searched; these references did not contain substantive dedicated content on personalized periodontics beyond brief mentions of "personalized oral hygiene instruction"