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Progressive Model ofChronic Pancreatitis 319

A. “At Risk”

B. “AP-RAP”

C. “Early CP” D. “Established CP” E. “End-Stage CP”

Downloaded from https://onlinelibrary.wiley.com/doi/ by Universität Bern, Wiley Online Library on [22/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

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Injury

Susceptibility

factors

(asymptomatic)

YearsDays Months Months to years Remainder of life

Figure38.1 Progressive model of chronic pancreatitis development. This model is based on the sentinel acute pancreatitis event (SAPE)

hypotheses where progression to CP begins with AP (stage B). Genetic and environmental risk/protective factors are present at all stages. Stage A is asymptomatic, with the effects of underlying disorders being adequately compensated. Stage B represents acute pancreatitis, which can be the first event (SAPE) or RAP, with SAPE sensitizing the pancreas to RAP. Stage C follows AP events where there is resolution or progression, depending on ongoing injury or stress. Stage D defines “irreversible” damage, as currently required for diagnosing CP as a disease. Note that risk/protective factors in different cell types are not surrogates of each other so the immune response (e.g., fibrosis) is not a strict surrogate of acinar cell function, islet cell function, nervous system response, or oncogenesis (e.g., failed DNA injury repair). This stage is hypothetically reversible with new therapies. Stage D is endmore radical surgical treatments. Source: Whitcomb etal. 2016[11]/with permission of Elsevier.

SAPE,

then RAP

susceptibility to

recurrence

biomarkers

biomarkers are present in a subject change over time, as seen in all progressive disorders. The stages are useful for classifying the patient’s disease state and trajectory, as well as tracking severity indicators. All etiologies of chronic pancreatitis go through progressive stages, although in individual cases acute pancreatitis may not be seen. Etiologies are also important since the likelihood of progressing from one stage to the next is strongly affected by etiology.

Resolve

Injury or

stress

Immune dysregulation

Acinar dysfunction

Islet dysfunction

Pathologic pain

Metaplasia

Therapeutic approaches

stage where cell function fails requiring replacement therapy or

Progression

pathways

Fibrosis/sclerosis

Exocrine insufficiency

DM (T3c)

Pain syndrome

PDAC

Symptomatic and

supportive treatment

distinct from Established CP in that the features are reversible. The patient may have symptoms of one or more underlying disorders, but has not met criteria for chronic pancreatitis as a disease.

Currently, Stage C can only be diagnosed in retrospect because the biomarkers are nonspecific and progression is uncertain using traditional definitions and an imaging approach to CP diagnosis. Other names for this stage include “possible CP” and “probable CP.” In contrast, a precision medicine approach defines the essence of the

Stage A. At Risk. This stage includes all patients with risks for chronic pancreatitis who have no signs or symptoms of diseases and who have not had an episode of acute pancreatitis. The patient may have dysfunction of some physiologic processes, but the body is able to adapt

underlying process with a probability estimate of progression. Furthermore, the genetics can be used to determine and, in concert with specific biomarkers, help diagnose a clinical disorder that, if left untreated, will progress to a disease state.

and compensate for the dysfunction without signs and symptoms of disease.

Stage D. Established CP. Established CP includes patients who have characteristic features of chronic pan-

Stage B. AP-

RAP. This stage includes all subjects with at

least one episode of acute pancreatitis (AP; designated as the sentinel AP event, SAPE; see below) or recurrent acute pancreatitis (RAP) with complete resolution and without irreversible damage to the ducts or parenchyma. However, this is a high- risk stage for progression to chronic pancreatitis,

creatitis that are irreversible but the gland has sufficient ductal and acinar cell function to sustain nutritional needs without the requirement of pancreatic enzyme replacement therapy (PERT). In addition, other disorders or diseases with features that overlap the characteristics of CP (differential diagnosis, below) should be excluded.

DM, and pancreatic ductal adenocarcinoma (PDAC).

Stage E. End- Stage CP. A patient has reached end- stage

Stage C. Early CP. This stage includes all patients with

both biomarkers of chronic pancreatitis and exclusion ofother disorders or diseases with features that overlap the characteristics of chronic pancreatitis. Early CP is

chronic pancreatitis when exocrine function is lost from a CP- associated fibroinflammatory process to the point that it requires PERT. Features of this stage may also include fibrosis, calcifications, and DM type 2 or type

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3c[12–14]. This stage is also important because it defines the end- stage disease features for individual patients since not all features are present in everyone.

Diagnosis

A diagnosis is the art or act of identifying a disease in a patient from its signs and symptoms. Diagnosing a disease or disorder involves meeting predetermined criteria.

Diagnostic Criteria

Diagnostic criteria are a list of disease features that must exist in a subject in order to diagnose the subject with a disease. These criteria are typically defined by expert opinions and established as consensus statements by various organizations or societies. They typically included signs and symptoms of the disease, biomarkers, and pathologic features. The criteria also include degrees of severity and features that distinguish the disease from other disorders or diseases with overlapping features.

Pathologic Diagnosis

A pathologic diagnosis is the diagnosis of a disease based on tissue analysis (including culturing of pathogenic organisms). Because there are overlapping features of CP and other disorders it is not possible to diagnosis CP with certainty from tissue samples. Pathology is not the gold standard for diagnosing CP[15]. A diagnosis also requires the presence of clinical and functional elements.

Syndromes

A syndrome is a descriptive group of clinical signs and symptoms that occur together and characterize a particular abnormality or condition. A disease can be defined as syndromes without knowledge of the etiologies, pathogenic components, or mechanisms. AP, RAP, and CP are clinical syndromes.

Differential Diagnosis

upstream of a duct- obstructing mass[10]. The traditional diagnosis of CP required the underlying disorder to advance to the point that it could be nothing else. But since this stage is irreversible, only supportive treatments are available (e.g., insulin and PERT). In precision medicine the genetic and genomic information provides insights into the mechanism of pathogenesis and can also provide insights into the probability of the differential diagnosis[16]. This approach supports clinical decisions as further work- up and treatments can be targeted to one diagnosis or another, based on the likelihood that the diagnosis is correct and the effect of the treatment (i.e., an inexpensive and benign treatment can be started if there remains some uncertainty, whereas a more expensive and radical treatment should be delayed until the diagnosis is confirmed with a high degree of confidence).

Etiology

Etiology defines the cause or origin of something. In inflammatory diseases of the pancreas this may include the etiology of mechanism dysfunction, a disorder, a disease, or a syndrome. The difference between a risk and an etiology is whether or not there are biomarkers of abnormal activity linked to the factor that defines a disorder or disease. The TIGAR- O list (List 1) summarizes known risk/etiologies for RAP and CP[17].

Biomarkers

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention[18,19]. For CP, most biomarkers are normal biological molecules that are in the wrong place or amount[20]. They also tend to be nonspecific, requiring panels of biomarkers to be considered. In some cases biomarkers are more pancreasas serum trypsin, amylase, lipase, or CA19- 9 for pancreatic cancer, although exceptions always exist.

specific, such

The differential diagnosis is a list of conditions and diseases that have overlapping signs and symptoms. The differential diagnoses for CP include autoimmune inflammation, inflammation and fibrosis arising from the islets related to long- standing diabetes mellitus, renal disease causing secondary effects on the pancreas, medications that alter the immune system (e.g., cyclosporin), agerelated atrophy or fibrosis, intraductal papillary mucinous neoplasm, acinar cell cystadenoma , the desmoplastic response to pancreatic neoplasm, and inflammation

Classification ofChronic Pancreatitis

Chronic pancreatitis can be classified by structural severity, etiology, or functional stage:

Cambridge Classification of duct imaging. The Cambridge Classification was originally developed to correlate endoscopic retrograde cholangiography (ERCP) images with pathologic progression [21]. The criteria was later expanded to CT images and MRCP

Classification ofChronic Pancreatitis 321

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Table38.1 TIGAR- O Version 2 risk/etiology classification– Short

form. The list summarizes common factors associated with RAP and CP. It is used as a “checklist” to systematically document all factors that contribute to pancreatic disorders and diseases within a patient, and therefore highlights targets for therapy. The long form is available as an open access manuscript[17].

Toxic- metabolic

Alcohol-

Smoking (if yes, record pack-

Hypercalcemia– (ionized calcium levels >12.0 3 mmol/L)

Hypertriglyceridemia

Medications (name)

Toxins, other

Metabolic, other

Idiopathic

Early onset (<35 years of age)

Late onset (>35 years of age)

Genetic

Suspected; No or limited genotyping available

Autosomal dominant (Mendelian inheritance– single gene syndrome)

Autosomal recessive (Mendelian inheritance– single gene syndrome)

Complex genetics– (non- Mendelian, complex genotypes +/– environment)

Modifier genes (list pathogenic genetic variants)

related (susceptibility and/or progression)

3–4 drinks/day

5 or more drinks/day

years)

Non-

smoker (<100cigarettes in lifetime)

Past smoker

Current smoker

Other, NOS

mg/dL or

Hypertriglyceridemic risk– (Fasting >300 mg/dL; non- fasting >500 mg/dL)

Hypertriglyceridemic acute pancreatitis, history of (>500

mg/dl in first 72 hours)

Chronic kidney disease (CKD)– (CKD Stage 5– end stage renal disease; ESRD)

Other, NOS

Diabetes mellitus (with the date of diagnosis if available)

Other, NOS

PRSS1 mutations (Hereditary pancreatitis)

CFTR, 2 severe variants in trans (cystic fibrosis)

CFTR, <2 severe variants in trans (CFTR- RD)

SPINK1, 2 pathogenic variants in trans (SPINK1-

associated familial pancreatitis)

PRSS1- PRSS1locus

CLDN2locus

Others:

Hypertriglyceridemia (list pathogenic genetic variants)

Other, NOS

Autoimmune pancreatitis (AIP) / Steroidpancreatitis

AIP Type 1– IgG4-

AIP Type 2

Recurrent acute pancreatitis (RAP) and severe acute pancreatitis (SAP)

Acute pancreatitis (single episode, including date of event if available)

AP etiology– Extrapancreatic (excluding alcoholic, HTG, hypercalcemia, genetic)

Biliary pancreatitis

Post ERCP

Traumatic

Undetermined or NOS

Recurrent acute pancreatitis (number of episodes, frequency, and dates of events if available)

Obstructive

Pancreas divisum

Ampullary stenosis

Main duct pancreatic stones

Widespread pancreatic calcifications.

Main pancreatic duct strictures

Localized mass causing duct obstruction

related disease

responsive

images[22–25]. The 1984 Cambridge Classification system does not correlate with pancreatic function[14] or pain[26] and is therefore falling out of use as the technology for other imaging modalities improve.

TIGAR- O Etiologic Classification. TIGAR- O is an acronym for Toxic (including alcohol, smoking, hypertriglyceridemia), Idiopathic, Genetic, Autoimmune, Recurrent/ Severe AP and Obstructive etiologies[17] (Table38.1). In general, autoimmune and obstructive do not fit the mechanistic classification of chronic pancreatitis but are important both for consideration in the differential diagnosis and for treatment[1].

Functional Stage Classification. Functional classification systems are even more arbitrary than structural ones. This is because the definition of EPI is based on vague clinical signs and symptoms until the late stages, and the threshold for pancreatic insufficiency is also dependent on the metabolic and nutritional needs, the diet and intestinal anatomy and health, and adaptability. Furthermore, the tools for measuring pancreatic function often do not correlate with each other and no adequate test exists [27]. Several approaches are in

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common use including the M- ANNHEIM classification[28] and PROCEED[29].

Classification ofPancreatitis Using Machine- Readable Codes

Medical care is progressing slowly as the complexity of complex conditions becomes evident and new techniques provide massive amounts of information that cannot be assimilated, understood, and utilized by humans in efficient and organized ways. The advances in computers allow data to be stored, organized, and transformed into something useable by humans. However, the premise that “smart computers” can take blocks of unorganized data and solve complex diseases has stalled as limits in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) have failed to bring clarity out of chaos. And one of the many challenges is that the definitions and organization of data is based on the old Western medicine concepts arising from the germ theory of disease and the scientific method of populationpoorly defined syndromes as classifiers.

Challenges ofMultiple Object Identifiers andClassification Systems

The goal of basic science research is to translate findings into clinical research, and from clinical research into clinical practice. The reverse is also important, translating observations in clinical practice back to clinical research to generate new hypotheses, and then test the hypotheses using basic research techniques. In the middle of these translations are the terms and descriptions of data tags to be translated. Future success requires better harmonization of data terms and discipline from physicians and scientist to use them consistently.

Progress in medicine is intimately linked to advances in digital technology. This means that there needs to be better synchronization between advancing science, medicine, and electronic tags to specific disorders, diseases, biomarkers, and complications. The synchronization must also be mechanism- and disorder- based to achieve advantages of precision medicine, and existing data that is in a usable form must have the describers from different systems harmonized so that there is seamless interoperability.

Digital taxonomy systems use object identifiers (OID) to identify objects such as a code system or a value set that represents a group of concepts. The current challenges are that there are multiple classification systems and there are major overlaps of objects with different concepts and codes for the same thing in different

based case- control studies using

systems. The problem is further confounded by the fact that most terms are from modern Western medicine concepts and definitions used by physicians and scientists are not always used appropriately and that outdated terms and concepts are not purged at the end of their natural life cycle. Furthermore, many codes are nonspecific, so that clarity on the subject’s disorder is obscured.

Codes, Code Systems, andValue Sets

Successful interoperability is dependent on either using common code systems for data capture or through mapping the unique codes to an interoperable code system [30]. Code systems are a collection of concepts (ideas) with unique identifiers that are logically structured. This may be highly hierarchical systems or more complex and nuanced systems (e.g., ICD- 10). The code system structure provides each concept with a codesystem- specific meaning, a concept identifier (a code), and a string description (the name, and a definition of the concept meaning). Specific code systems used in electronic health records and health information technology include ICD- 10- CM, CPT, SNOMED CT, RxNorm, and LOINC. Other systems used in biology include various GenBank, RefSeq etc for genetic information, the Gene Ontology knowledgebase (GO), and many more. These systems encode content and enable computer- based systems to find and utilize data without human supervision. It has been noted that the best approach for measure developers is to reduce the number of mapping steps required by focusing on content that measured entities can easily capture during clinical care, where metrics that are useful in the care of the patient match those used in quality assessment and decision support systems[30].

Some Limitations

One of the key code systems used in the United States is the International Classification of Disease (ICD) system. This illustrates the challenge of transitioning from traditional “disease” definitions to defining disease mechanisms. Consider ICD- 10 codes for chronic pancreatitis in the K86 class. The primary diagnoses are limited to K86.0 (Alcohol- induced CP), K86.1 (Other CP), K86.8 Other specified diseases of pancreas (which are not billable codes in the United States) and K86.9 (Other specified disease s of the pancreas). K86.1includes “Pancreatitis (inflammation of pancreas), chronic; exocrine pancreatic insufficiency (K86.81); Chronic pancreatitis NOS [not specified elsewhere]; Infectious chronic pancreatitis; Recurrent chronic pancreatitis; Relapsing chronic pancreatitis” without distinction between these very

Conclusions 323

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different entities. K86.9 is even worse as “synonyms” terms include “Aseptic necrosis of pancreas, Atrophy of pancreas, Baggenstoss change of pancreas[31], Calculus of pancreas, Exocrine pancreatic insufficiency (also coded as K86.81), Fat necrosis of pancreas, Fibrosis of pancreas, Hemorrhage of pancreas, Mass of pancreas, Necrosis of pancreas, Pancreas – baggenstoss change, Pancreatemphraxis, Pancreatic acinar fistula, Pancreatic aseptic necrosis, Pancreatic atrophy, Pancreatic calculus, Pancreatic cirrhosis, Pancreatic exocrine insufficiency (see K86.81), Pancreatic fistula, Pancreatic hemorrhage, Pancreatic infantilism, Pancreatic insufficiency, Pancreatic mass, Pancreatic necrosis.”

Another system is the SNOMED- CT. This is a general clinical reference system containing more that 357,000 healthcare concepts with unique meaning and logicbased definitions organized into hierarchies. In pancreatic disease, for example, there is a “parent/child” relationship type that includes parent concept “Disorder of the pancreas (3855007)” with child concepts “Atrophy of the pancreas (88281007); Baggenstoss change of the pancreas (61221008); Bard- Pic syndrome (45019006); Calculus of pancreas (15402006); Complication of transplanted pancreas (79369007); Congenital malformation of the pancreas (23497701); Congenital pancreatic enterokinase deficiency (10866001); Cyst of pancreas (31258000); and Cystic fibrosis of the pancreas (235978006).” This system does not include disorders of the pancreas from most of the etiologies in the TIGAR- O list[17], and partially overlaps diagnoses in ICD- 10.

One of the keys to precision medicine is genetics. Specific human genetic variants can be coded using RefSeq identifiers (rsID), but more complex genetic variants, including copy number variants and large deletions or rearrangements, are not captured by these codes. Furthermore, the interpretation of the effect of genetic variants is primarily traditional autosomal dominate or recessive disease with genetic variants within the coding region identified as pathogenic (disease- causing alone), likely pathogenic, uncertain, likely benign or benign[32]. There is no commonly accepted way to grade the effects of “risk” variants, which are only disease- associated under specific conditions and are often tied to regulatory elements of gene expression rather than changing the amino acid sequence of the gene product (i.e., for protein coding genes). ClinVar is a public archive of genetic variants with free access to reports on the relationships between human genetic variations and phenotypes, with supporting evidence. But even here, the strength of evidence is variable, the effects of variants may be tissue- / disease- specific and risk factors, which are critical in understanding complex diseases, are seldom included or noted to have effects since the sequence reports are largely from traditional gene testing companies. Genetic

data is not well integrated into the electronic health records in formats that are easily accessible.

A Way Forward

One issue that plagues ML applications comes from lack of clean, well- structured data. Inclusion of IT- minded folks in study design/data collection may solve many issues of data cleaning, harmonization, and interpretation that remain a major barrier.

A solution to the larger problem of interpreting complex, multi- code, multi- concept data is the use of “expert systems”[33]. An expert system is a computer program that uses available information, heuristics, and inference to suggest solutions to problems in a particular discipline following the recommendations of experts in the field. In brief, this approach takes extensive genetic data, patient demographics, health histories, biomarkers, images, etc. and integrates them using sequential framing, modeling and trajectory approaches that an expert would use. In the case of pancreatic disease, cell-

based models would be used based on an understanding of acinar cell, duct cell, islet cell, neurons and the nervous system, inflammatory cells biology as parallel process (see Fig. 38.1, stages D and E), with cross- system integration (e.g., loss of some acinar cell function with loss of islet function). This is now feasible and may be an early example of true personalized medicine.

Conclusions

Health information technology has already linked ML and AI to assist in areas of reviewing and interpreting images (e.g., CT, MRI, pathology, colonoscopy). However, there is a lag in the development of useful clinical decision support tools in early diagnosis and accurate, mechanistic classification of complex disorders, such as pancreatic disorders and subsequent diseases. The first barrier to progress in complex disorders includes the concepts upon which many of the coding systems have been built, translation between genetics, genomics, biomarkers, systems biology, and human disorders that lead to chronic diseases and all their nuances. The second barrier is the proper use of terms and codes, including genetics, in the clinical arena where patient care is more important that billing codes, and where billing codes are inherently inadequate tools for disease management. A third barrier is the medical literature, where the descriptions of concepts, patient context, and complete data are inadequately coded so that studies can be seamlessly harmonized and NLP can be more useful because the authors adhere to a new HIT- enabling writing structure.

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This chapter does not present the answer to these problems and barriers. It is intended to enhance recognition of these barriers by highlighting limitations of the current systems with concepts that must be integrated into new systems. In the meantime, progress can be

References

1 Whitcomb DC. Definition and etiology of chronic

pancreatitis. In: DomínguezPancreatology for Practising Gastroenterologists and Surgeons. 2nd edn. Chichester: Wiley, 2021.

2 Whitcomb DC. Primer on precision medicine for complex

chronic disorders. Clin Transl Gastroenterol 2019;10(7): e00067.

3 Whitcomb DC. What is personalized medicine and what

should it replace? Nat Rev Gastroenterol Hepatol 2012;9(7):418–424.

4 Lowe ME, Andersen DK, Caprioli RM etal. Precision

medicine in pancreatic diseaseresearch opportunities: summary of a National Institute of Diabetes and Digestive and Kidney Diseases workshop. Pancreas 2019;48(10):1250–1258.

5 Whitcomb DC. Barriers and research priorities for

implementing precision medicine. Pancreas 2019;48(10):1246–1249.

6 Moskowitz SM, Chmiel JF, Sternen DL, Cheng E, Cutting

GR. CFTR-

related disorders. In: Pagon RA, Adam MP, Ardinger HH etal., eds. GeneReviews. Seattle: University of Washington, 1993.

7 Bombieri C, Claustres M, De Boeck K etal.

Recommendations for the classification of diseases as CFTR- related disorders. J Cystic Fibros 2011;10(Suppl

2):S86–102.

8 Baldwin C, Zerofsky M, Sathe M, Troendle DM, Perito ER.

Acute recurrent and chronic pancreatitis as initial manifestations of cystic fibrosis and cystic fibrosis transmembrane conductance regulatorPancreas 2019;48(7):888–893.

9 Farrell PM, White TB, Ren CL etal. Diagnosis of cystic

fibrosis: consensus guidelines from the Cystic Fibrosis Foundation. J Pediatr 2017;181S:S4–S15.e1.

10 Whitcomb DC, Shimosegawa T, Chari ST etal.

International consensus statements on early chronic pancreatitis. Recommendations from the working group for the international consensus guidelines for chronic pancreatitis in collaboration with the International Association of Pancreatology, American Pancreatic Association, Japan Pancreas Society, PancreasFest Working Group and European Pancreatic Club. Pancreatology 2018;18(5):516–527.

11 Whitcomb DC, Frulloni L, Garg P etal. Chronic

pancreatitis: an international draft consensus proposal for a new mechanistic definition. Pancreatology 2016;16:218–224.

Muñoz JE, ed.. Clinical

knowledge gaps and

related disorders.

made in the old fashion way of using the nonlinear, experience- based, heuristic thinking of expert physicians and scientists to begin bridging the gaps between the present and the future.

12 Bellin MD, Whitcomb DC, Abberbock J etal. Patient and

disease characteristics associated with the presence of diabetes mellitus in adults with chronic pancreatitis in the United States. Am J Gastroenterol 2017;112(9):1457–1465.

13 Goodarzi MO, Nagpal T, Greer P etal. Genetic risk score

in diabetes associated with chronic pancreatitis versus type 2 diabetes mellitus. Clin Transl Gastroenterol 2019;10(7):e00057.

14 Zhan W, Akshintala V, Greer PJ etal. Low serum

trypsinogen levels in chronic pancreatitis: correlation with parenchymal loss, exocrine pancreatic insufficiency, and diabetes but not CTfibrosis. Pancreatology 2020;20(7):1368–1378.

15 Esposito I, Hruban RH, Verbeke C etal. Guidelines on the

histopathology of chronic pancreatitis. Recommendations from the working group for the international consensus guidelines for chronic pancreatitis in collaboration with the International Association of Pancreatology, the American Pancreatic Association, the Japan Pancreas Society, and the European Pancreatic Club. Pancreatology 2020;20(4):586–593.

16 Whitcomb DC. Peering into the "black box" of the

complex chronic pancreatitis syndrome. Pancreas 2016;45(10):1361–1364.

17 Whitcomb DC, North American Pancreatitis Study

Group. Pancreatitis: TIGARchecklist with topic reviews, updates, and use primers. Clin Transl Gastroenterol 2019;10(6):e00027.

18 Whitcomb DC. Better biomarkers for pancreatic diseases.

Pancreas 2015;44(8):1171–1173.

19 Atkinson AJJ, Colburn WA, DeGruttola VG etal.

Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69(3):89–95.

20 Cruz- Monserrate Z, Gumpper K, Pita V etal. Biomarkers

of chronic pancreatitis: a systematic literature review. Pancreatology 2021. PMID:33558189.

21 Sarner M, Cotton PB. Classification of pancreatitis. Gut

1984;25(7):756–759.

22 Madzak A, Olesen SS, Haldorsen IS, Drewes AM, Frokjaer

JB. Secretin- stimulated MRI characterization of pancreatic morphology and function in patients with chronic pancreatitis. Pancreatology 2017;17(2):228–236.

23 Frokjaer JB, Akisik F, Farooq A etal. Guidelines for the

diagnostic cross sectional imaging and severity scoring of chronic pancreatitis. Pancreatology 2018. PMID:30177434.

based Cambridge severity scores for

O Version 2 risk/etiology

References 325

https://t.me/medicina_free

24 Dasyam AK, Vipperla K, Slivka A etal. Computed

tomography based scoring system in a prospectively ascertained cohort of patients with chronic pancreatitis. Pancreatology 2019. PMID:31630919

25 Tirkes T, Shah ZK, Takahashi N etal. Reporting standards

for chronic pancreatitis by using CT, MRI, and MR cholangiopancreatography: the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer. Radiology 2019;290(1):207–215.

26 Wilcox CM, Yadav D, Tian Y etal. Chronic pancreatitis

pain pattern and severity are independent of abdominal imaging findings. Clin Gastroenterol Hepatol 2014;13(3):552–560.

27 Uc A, Andersen DK, Bellin MD etal. Chronic pancreatitis

in the 21st century—

research challenges and opportunities: summary of a National Institute of Diabetes and Digestive and Kidney Diseases workshop. Pancreas 2016;45(10):1365–1375.

28 Schneider A, Lohr JM, Singer MV. The M- ANNHEIM

classification of chronic pancreatitis: introduction of a unifying classification system based on a review of previous classifications of the disease. J Gastroenterol 2007;42(2):101–119.

29 Yadav D, Park WG, Fogel EL etal. PROspective Evaluation

of Chronic Pancreatitis for EpidEmiologic and Translational StuDies: rationale and study design for PROCEED from the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer. Pancreas 2018;47(10):1229–1238.

30 Anon. Codes, Code Systems, and Value Sets, Supplemental

Material. 2021. In: CMS Measures Management System Blueprint Version 170 [Internet]. CMS/gov: Centers for Medicare and Medicade Services, 1–32. Available from: https://www.cms.gov/files/document/blueprint- codescode- systems- value- sets.pdf

31 Baggenstoss AH. Chronic relapsing pancreatitis: a review

of the pathologic anatomy in cases in which disease of the biliary or gastro-

intestinal tract did not coexist. Proc Staff

Meet Mayo Clin 1947;22:542–547.

32 Richards S, Aziz N, Bale S etal. Standards and guidelines

for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17(5):405–424.

33 Shelton CA, Whitcomb DC. Precision medicine for

pancreatic diseases. Curr Opin Gastroenterol 2020;36(5):428–436.

326

https://t.me/medicina_free

Molecular Understanding ofChronic Pancreatitis

Bomi Lee1, Monique T. Barakat

Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

1,2

, and Sohail Z. Husain

Introduction

Chronic pancreatitis (CP) is a clinically described syndrome that is characterized by consistent cellular stress or repeated bouts of pancreatic injury that result in pancreatic atrophy, fibrosis and duct distortion, a chronic pain syndrome (suggesting neural injury and responses), and eventually loss of both exocrine and endocrine function. Depending on the proposed etiology of the disease, there can be other secondary characteristics including calcifications, duct centric inflammation with either lymphocytes or neutrophils, or primarily an obstructive phenotype. Focusing on the etiology of the injury and the subsequent pathologic response is important to direct our understanding of the molecular mechanisms of CP.

Risk Factors inChronic Pancreatitis

Although the diagnosis of CP is due to a convergence of common pathological features seen at the end- stage of the disease, there are distinct risk factors for CP (Fig.39.1). These risk factors have been described by the TIGARenvironmental factors, obstructive etiologies such as anatomical or traumatic features, and immune- mediated etiologies. The typical organ response to injury and cellular stress promotes recovery and regeneration of the pancreas following injury. However, with repeated injury or prolonged cellular stress, the organ response may become pathologic and drive cellular loss and fibrotic replacement of exocrine and endocrine cells. A proper

O classification [1]. They include genetic and

understanding of the molecular mechanisms of CP requires that we address both the specific etiology, with its associated genetic and epigenetic modifiers, as well as the common pathologic response mediated through both parenchymal cells of the pancreas and mesenchymal cells, including the immune cells and the pancreatic stellate cells[2].

The current attempts to define CP focus on a clinical description of the end histologic appearance of the pancreas, resulting from an immune-

mediated response to stress or injury coupled to a fibrogenic wound response. Although such a definition is useful clinically, it belies the complexity of the process leading to CP. Pancreatitis is an inflammatory disorder that involves a complex immunologic cascade of events. Environmental cues shape the initiation and progression of the immune system to accelerate or limit the immune response. The immunologic response can be modulated by epigenetic and genetic factors that alter the progression of the disease. The inflammatory response is modulated by cytokines and chemokines, produced by injured acinar cells, that act as recruiting molecules for an immune repertoire. There are likely multiple modifying factors and intervening steps that impact an individual patient’s clinical course but at some point, progression becomes similar because the end result of this process is a similar histological pattern. In this chapter, we will focus on the current understanding of these two distinct stages of disease development, specific etiologies and the immuneactivated fibrogenic response. The first stage of CP development relates to specific risk factors, and the second stage is the organ fibrogenic response to injury.

The Pancreas: An Integrated Textbook of Basic Science, Medicine, and Surgery, Fourth Edition. Edited by Hans G. Beger, Markus W. Büchler, RalphH. Hruban, Julia Mayerle, John P. Neoptolemos, Tooru Shimosegawa, Andrew L. Warshaw, David C. Whitcomb, and Yupei Zhao. © 2023 John Wiley & Sons Ltd. Published 2023 by John Wiley & Sons Ltd. Companion website: www.wiley.com/go/beger/thepancreas4e

Epigenetics asa Modifying Factor inChronic Pancreatitis 327

Etiology

SPINK1

https://t.me/medicina_free

Figure39.1 Several etiologies exist for

chronic pancreatitis. Specific etiologies affect unique cell types within the pancreatic parenchyma, leading to repetitive injury.

Genetic disease/anatomic Environmental exposure Cell stress Inflammatory condition

Sentinel Acute Pancreatic Event Model

A disease model hypothesis has been proposed as a framework for understanding how different risk factors including environmental, genetic, and toxin mediated injury could impact progression toward CP. The model is the sentinel acute pancreatitis event hypothesis (or SAPE hypothesis), and it uses the TIGAR- O risk classification system, referring to Toxic- metabolic, Idiopathic, Genetic, Autoimmune, Recurrent and severe acute pancreatitis, and Obstructive [1]. In this model, risk factors impact the pancreas through alterations in stress responses by the parenchymal cells (acinar and duct cells). There are also wound healing responses within the pancreas, including the activation of the inflammatory (neutrophils and lymphocytes) and matrix remodeling systems (macrophages and stellate cells) to allow regeneration to occur. Once the risk factors overcome the adaptive mechanisms within the pancreas, injury overtakes the protective regenerative mechanisms, and clinical acute pancreatitis ensues. Repeated bouts of acute pancreatitis can lead to an injury-

wound response cycle that over time causes loss of pancreatic cells and replacement with a fibrotic matrix that is promoted by macrophages and stellate cells. The progression of parenchymal tissue destruction and fibrosis is the pathological phenomenon of CP, graphically represented in Fig.39.2.

Epigenetics asa Modifying Factor inChronic Pancreatitis

Epigenetics is the control of gene expression that does not involve a change in the primary DNA sequence. In other words, epigenetics refers to modifications of DNA, for instance by methylation, or of chromatin structure

Acinar cells

Ductal cells

Stellate cells

Premature enzyme activation Metabolic stress/toxins

Ductal anatomic variants Bicarbonate secretion (CFTR)

Inflammatory/fibrogenic response Metabolic stress/toxins

PRSS1

CTRC CASR

through the posttranslational modification of histone proteins. The posttranslational modification of histones can alter chromatin structure or may alter the affinity transcription factors to promoter regions. The epigenome can have significant effects on human health as well as disease susceptibility, and epigenetic alterations can be induced through environmental and prenatal exposures to toxins or stressors. Epigenetic changes can be stable and allow “memory” of past stresses to impact future exposures. Epigenetic alterations may be the result of “subclinical” stresses on the pancreas that over time allow molecular remodeling of stress responses to other stimuli. These “subclinical” stresses may alter the epigenetic pattern of genes with little or no immediate effect on gene expression; however, the gene may develop an altered response in gene expression when exposed to a new specific environmental cue. In the case of CP, there are likely situations of epigenetic change that evade clinical notice until the time of exposure or triggering of risk factors, which then induce the typical destructive, fibrogenic response of the disease. In experimental models of pancreatitis, chronically stressed animals (such as with ethanol exposure [3], a fatty diet [4], or genetic mutation[5]) have an altered response to acute injury compared to animals that have no chronic stress. Ethanol has been shown to affect the action of several epigenetic proteins including the histone acetylases CREB and CBP[6] as well as the histone deacetylases [7]. An epigenetic acetylation mechanism was implicated for controlling acute pancreatitis [8]. Delayed recovery was demonstrated in mice treated with the antiepileptic drug and histone deacetylase inhibitor valproic acid (VPA). VPA is thought to be a definite cause of pancreatitis, especially in pediatric patients[9,10]; however, the mechanism by which VPA induces pancreatitis was unknown. In fact, chronically treating experimental animals with VPA

Molecular Understanding ofChronic Pancreatitis

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328

(a)

Normal pancreas

Stellate cells (inactive)

(b)

Neutrophils Lymphocyte Macrophage

CD4+ (Th1)

Proinflammatory cells and cytokines

(c)

CD4 , CD25 (T reg)

nec

(ETOH, *

stress)

Mφ

(antiinflam-

matory)

Stellate cells (active)

Alcohol

(Metabolic and

oxidative stress)

threshold

SAPE

Acute pancreatitis

(sentinel event)

Late acute pancreatitis

(recovery phase)

Anti-inflammatory cells and cytokines

(d)

Resident

macrophages

Healed (post-SAPE)

(e)

Injury

Scarring

Collagen, etc.

Th1

Early CP Late CP

Treg

Healing

Fibrosis

(Stellate cell

apoptosis)

Recovered

pancreas

Recurrent acute

pancreatitis

(or equivalent)

Alcohol

or RAP

Chronic

pancreatitis

Figure39.2 Sentinel acute pancreatitis event (SAPE) hypothesis model. (a) Normal pancreas. If the subject is a heavy alcohol user, the acinar

cells are under metabolic and oxidative stress (indicated by asterisks) but histology remains relatively normal. Alcohol increases the risk of crossing the acute pancreatitis (AP) threshold (bold line crossing the dashed line). (b) Acute pancreatitis with pancreatic injury and infiltration of proinflammatory cells. (c) Late acute pancreatitis is dominated by anti‐inflammatory cells that limit further injury by proinflammatory cells and products, and promote healing. This includes activation of stellate cells, which produce collagen etc. (d) Recurrent acute pancreatitis (RAP): acinar cell injury or other factors that activate an acute inflammatory response (Th1) are immediately countered by an anti‐ inflammatory counterresponse (Treg) which, among other things, drives fibrosis. This vicious cycle results in both continued injury (top) and further fibrosis (bottom) leading to (e) extensive acinar cell loss and sclerosis (right) characteristic of chronic pancreatitis (CP). Both genetic factors and environmental factors play a role in this process by increasing susceptibility to acute pancreatitis, altering the severity and duration of acute pancreatitis and altering the healing processes that drive fibrosis. Source: Adapted from Whitcomb 2004 [3].

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