CLUSTERING%20OF%20PRIMARY%20SCLEROSING%20CHOLANGITIS%20NEAR%20TOXIC%20WASTE%20SITES

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CLUSTERING OF PRIMARY SCLEROSING CHOLANGITIS
NEAR TOXIC WASTE SITES
Presented by Joseph A Odin, M.D., Ph.D. Assistant
Professor of Medicine Mount Sinai School of
Medicine

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I have nothing to disclose and my presentation
will not include discussion of off-label/investiga
tive use or application of a product or device
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Pathogenesis of Primary Sclerosing Cholangitis
Animal
Immune
Environmental
Genetic
Primary Sclerosing Cholangitis
Susceptibility
Factors
Dysregulation
Models
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PSC is prevalence poorly studied

• Boberg et al, 2001 report the prevalence of PSC
  is increased in Scandinavia, but the cause is
  unclear.
• Very difficult to know if prevalence differences
  with regard to race, familial incidence,
  geography are environmental, societal, or genetic
  in origin.
• In animal models, toxin exposure may induce
  PSC-like peri-biliary inflammation and fibrosis.

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PSC-like Animal Models

• Direct toxin-mediated biliary injury or murine
  GVHD models result in PSC-like lesions.
• Mdr2(-/-) knockout mice- leakage of bile through
  disrupted tight junction leads to a
  pro-inflammatory/fibrotic cascade. No known
  mutations of the human homologue of Mdr2 (MDR3).
• Dextran sulfate sodium (DSS) treated CFTR(-/-) KO
  mice develop colitis and biliary inflammation.
• Murine bacterial overgrowth or LPS in
  immuno-deficient mice may cause peribiliary
  inflammation.

Vierling, JM, 2003. Liver Immunology. Review.
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Autoimmune diseases have been associated with
toxins or xenobiotics
Reprinted from Selmi et al, 2004, Epidemiology
and pathogenesis of primary biliary cirrhosis
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Studies of disease prevalence have shown
geographic variability in PBC
Author(s), year Geo. clustering cases Site Factor
Triger, 1980 Yes 34 Sheffield, U.K. water?
Hamlyn et al, 1983 No (seasonal?) 117 northeast England (sunlight)
Triger et al, 1984 Yes 552 Western Europe none
Borda et al, 1989 Yes 50 Navarra, Spain none
Danielsson et al,1990 Yes 111 northern Sweden none
Myszor et al, 1990 No 347 northeast England
Witt-Sullivan et al, 1990 No 225 Ontario, Canada
Prince et al, 2001 Yes 770 northeast England urban
Studies of immigrants suggest environment affects
the prevalence of PBC (Watson et al, 1995
Anand, 1996)
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Pathogenesis of Cholestatic Liver Disease

• Certain toxins are known to cause secondary
  sclerosing cholangitis in individuals, but
  naturally history may be different than PSC.
• Question Do unrecognized environmental toxins
  trigger PSC or PBC in genetically susceptible
  individuals and/or promote disease progression?

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Possible Approaches to Identifying Pathologic
Toxins

• Epidemiological studies
• Questionnaires regarding exposures
• Prevalence/Cluster analysis and geographic
  information systems (GIS) technology
• Further studies in animals exposed to toxins
• Tissue analysis
• Accumulated toxins may be detected in adipose
  tissue
• exposure upregulates expression of certain genes
  (e.g. MDR, cytochrome p450 genes).

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What is GIS?

• Geographic Information Systems (GIS)- a
  structural approach to collecting, archiving,
  analyzing, manipulating, and displaying data
  having one or more spatial components, using a
  combination of personnel, equipment, computer
  software, and organizational procedures.
  (National Research Council)
• Layers of spatial data are combined on the same
  projection or map. Often unsuspected patterns
  that escape detection in tabular reports are
  identified by projection. (e.g. higher cervical
  cancer rates in mountainous rural areas and
  observation of increased lung tumors in port
  cities helped establish asbestos exposure in
  shipyards as a risk factor).
• For health studies the combined layers may
  include a digital photograph of the area under
  study, administrative boundaries (e.g. postal
  codes), environmental layers (e.g. contaminated
  areas), facility locations (e.g. hospitals),
  subject locations (e.g. patient residences).
• Each of these spatial layers can be linked by a
  unique identifier to attribute data (i.e.
  clinical data or demographic data). This linkage
  gives GIS its analytical power.

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GIS STEPS

1. Organize a team of individuals committed to the
   study including clinicians, epidemiliogists, data
   entry specialists and spacial statistician.
2. Identify study subjects and environmental points
   of interest and collect address locations.
3. Geocoding of address information-conversion to
   longitude and latitude. Best if done immediately
   when address supplied since administrative
   boundaries change over time. Collecting all
   lifetime addresses preferable.
4. Mapping of prevalence rates in different areas
   (e.g. postal codes)- adjust rates to control for
   potential confounders such as differences in age,
   gender, and race distribution among different
   areas. Confidence levels for each rate should be
   included.
5. Pattern analysis-spatial statistical methods may
   be helpful in providing a quantitative answer if
   not obvious from mapping.

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Step 1- A team approach

• Interested clinicians- Aftab Ala and Nancy Bach
• Spatial statistician- Sylvan Wallenstein
• Epidemiologist/data entry specialist- Carmen
  Stanca
• Substitute- Joseph Odin

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Step 2. Recommended guidelines for identifying
subjects

• stringent case inclusion criteria
• definition of date of disease onset
• well-defined study period, area and population
• multiple case finding methods
• rigorous tracing of all possible cases.
• Metcalf, J. James, O., Semin Liver Dis, 1997

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Step 3. Address geocoding-converting addresses
into map locations

• Software loaded with georeferenced files can
  automatically convert appropriately formatted
  street addresses if available into specific
  longitudes and latitudes. Confidentiality must be
  maintained however.
• Data Quality Issues
• Who- best if patient provides address as opposed
  to insurance data.
• What- street mail address of residence preferred
  since offers the smallest point of reference.
• When- address at diagnosis usually best for most
  studies.
• An important point is to be consistent and note
  each factor along with the address data. Spot
  checks of data accuracy essential.

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Available U.S. Liver Disease Clinical Databases

• Individual medical center patient databases.
• Independent laboratory records.
• Veterans hospitals records.
• OPTN (Organ Procurement and Transfer Network)
  data on patients listed for liver
  transplantation.
• Nascent international multi-center PSC and PBC
  registries.

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OPTN database

• OPTN data limits referral bias as compared to
  individual medical centers.
• Demographic information restricted to time of
  listing and is not updated.
• Limited subset of patients are listed based on
  clinical and non-clinical status.
• Accuracy of data is uncertain.

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OPTN database

• 102 individuals residing in the NY metropolitan
  area with a diagnosis of PSC and 127 individuals
  with PBC were listed in the OPTN database between
  1995 and 2003.

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Long Natural History May Distort Analysis of
Subject Spatial Data
Carcinoma/Death
Progression (diagnosis)
10-30 yrs?
Cirrhosis (listing)
Normal liver (disease onset)
Transplantation
Figure adapted from SL Friedman, MSSM
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Step 4. Prevalence Data Adjustment

• Demographic data available for all listed PSC and
  PBC patients living in New York State and US
  census data for individual zip codes was used to
  standardize expected prevalence rates for each
  New York City zip (postal) code.
• Observed/stdexpected prevalence rates were mapped
  for each zip code with darker colors indicating
  higher rates.
• There was no difference in confidence levels for
  each value.

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Calculating and Standardizing Prevalence Ratios
by Zip Code

• The expected prevalence of patients listed for
  transplant with PBC or PSC was based on overall
  data for UNOS region 9 (New York State and
  Vermont) from 2000 to 2004.
• Using the 2000 US census data for each zip code,
  we were thus able to correct for differences
  between zip codes with regard to population, age,
  gender, and race and standardize the expected
  prevalence rates (stdE).

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Step 4. PBC and PSC cluster differently
PBC PSC PBC PSC
Brooklyn Queens
Manhattan/Bronx Staten Island
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Identified Toxic Sites in New York City
This NY website also provides address information
for each of these sites, which is more accurate
than the locations shown on the map.
Courtesy of the NY DEC
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Superfund site locations matched best with high
disease prevalence areas
Westchester
Superfund Sites
Bronx
Boroughs/Counties
New Jersey
Manhattan
Queens
Nassau
BIGGER TRIANGLES
Brooklyn
Staten Is.
Adapted from the New York Department of
Environment 2004
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Superfund Toxic Waste Sites (SFS)

• SFS are sites of public health hazards that have
  been designated for immediate remedial action.
• A recent NYC Department of Health study showed an
  increased cancer incidence (e.g. breast and lung
  cancer) in neighborhoods surrounding NYC SFS.
• Vine et al, 2000, demonstrated deleterious
  effects on the immune system of those living near
  a SFS.

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Predominant SFS Toxins

• Heavy Metals e.g. mercury
• Methyl chloride
• Lacquer
• Solvents
• Trichloroethane
• Xylene

• Halogenated solvents
• PCE 1,1,2,2-tetrachlorethylene
• (dry cleaning industry, household detergents)
• PCB
• Polychlorinated biphenyls
• (formely used in hydraulic systems, plasticizer,
  textile, transformers)
• Tetrachloroethane

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Hypothesis

• The prevalence of PSC and PBC patients listed
  for transplantation are increased near Superfund
  sites.

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Step 5. Statistical Analysis

• (i) Comparison of prevalence between
  grouped zip codes with and without SFS
• (ii) Comparison of prevalence among boroughs
  and SFS density in each borough
• (iii) Validated computer analysis (SaTScan)
  
• a) global clustering of patients
• b) focused clustering of patients near SFS

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Grouped zip code comparison
Each zip code and its adjacent zip codes were
considered a group or cluster
SFS cluster
SFS
Non-SFS cluster
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The median std prevalence ratio of PBC is
significantly higher in SFS clusters
CLUSTERS WITHOUT SFS CLUSTERS WITH SFS P
PBC 0.51 0.94 0.001
PSC 0.28 0.28 0.572
p significance values for Mann-Whitney U test,
2-tailed Std prev ratio observed/std expected
prevalence
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Staten Island has the highest prevalence of PBC
the highest density of SFS
NYC Borough Std Prev Ratio (rank) SFS/100,000/sq mile (rank)
Manhattan 0.66 (5) 0.0211 (5)
Brooklyn 0.91 (3) 0.0411 (4)
Queens 0.88 (4) 0.0561 (2)
Bronx 1.02 (2) 0.0415 (3)
Staten Island 1.54 (1) 0.3150 (1)
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SaTScanTM METHOD

• Spatial analysis of the case distribution was
  conducted using a cluster detection spatial scan
  statistic, SaTScan v5.0 adjusting for the
  underlying background population.
• Longitudes and latitudes are used
• Global analysis-enter only patient lat long
  (zip code center)
• Focused analysis-enter patient and SFS long lat
• Bernoulli and Poisson ModelsKulldorff M. A
  spatial scan statistic. Communications in
  Statistics Theory and Methods,261481-1496,1997

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PSC clusters

• Global and focused cluster analysis of PSC-OLT
  patients revealed statistically significant
  clusters
• one that encompassed all of Staten Island
  (p0.050)
• a cluster in Nassau County, N.Y. that is near a
  known SFS (not shown).
• a cluster in Chicago, Illinois. We have started
  investigating Chicago given higher numbers of
  Scandinavians in that city.
• The cluster encompassing Staten Island encircles
  too many SFS to identify any specific toxin. The
  toxin at the Nassau County SFS included only
  tetrachloroethylene (PCE). However, as in Staten
  Island, a large county-wide active garbage
  disposal site is also present in this zip code.

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Two global clusters identified by SatScan overlap
with Mount Sinai PBC patient clusters
Bronx
Active Solid Waste Site
Superfund Sites
Global Clusters
Manhattan
Queens
New Jersey
BIGGER TRIANGLES
Nassau
Brooklyn
Staten Is.
10 km
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PBC clusters identified by global analysis were
not statistically significant

• SFS were present within 5 out of 6 clusters, but
  the clusters were not statistically significant.

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PBC SFS-focused analysis identified two
statistically significant clusters

• Statistically significant SFS-focused clusters
• 1. Westchester (10595, 10532) r2.67 km, plt0.05
• 2. Staten Is (10312, 10308) r3.69 km, p0.05

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Staten Island SFS Toxins

• Number of organic compounds including
• polychloroethane (PCE), predominately
• Heavy metals e.g. mercury
• Solvents
• Lacquer
• Exposure to toxins by aerosol distribution more
  likely since groundwater not used in New York
  City. Toxins may attach to particulate matter for
  wider distribution.

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Fresh Kills - Staten IslandThe largest land fill
in the world
Courtesy of the NYC DEC 2003
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Related Animal Studies

• We have begun to study hepatic changes in mice
  exposed to particulate matter air pollutants 5
  hours per day/ 5 days per week for variable
  periods.
• Early results indicate that exposure for 2
  months significantly increases hepatic
  inflammation with a trend towards increased
  fibrosis, but cholestatic changes have not been
  observed.
• Different mice strains may yield different
  results.

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Summary

• The overall prevalence of only PBC patients
  listed for transplant is increased in zip codes
  near NYC SFS.
• The increased prevalence of PSC and PBC patients
  listed for transplantation in Staten Island
  corresponds to its high density of SFS
• Statistically significant clustering of both PSC
  and PBC patients listed for transplantation
  occurs near SFS.

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Limitations

• Study population
• -Cases that never progress beyond early stage
  disease are excluded.
• -Economics may affect both SFS location and
  who is transplanted.
• -Relatively small number of cases limited
  the studys power.
• -The accuracy of OPTN database is unknown.
• Using zip codes
• -Exact addresses are not available from
  OPTN.
• -Only the zip code at the time of listing
  for transplant is saved.
• Migration
• -Length of time residing in given zip code
  prior to listing is unknown.
• -In NYC about 50 were living in the same house
  in 2000 as in 1995.

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Conclusion

• Exposure to toxic wastes may be one of the
  environmental factors that plays a role in the
  pathogenesis of PSC and PBC

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ACKNOWLEDGEMENTS
ARTZT FAMILY PBC FOUNDATION
This work was supported in part by Health
Resources and Services Administration contract
231-00-0115. The content is the responsibility of
the authors alone and does not necessarily
reflect the views or policies of the Department
of Health and Human Services, nor does mention of
trade names, commercial products, or
organizations imply endorsement by the U.S.
Government
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GLOBAL SaTScanTM ANALYSIS

• Most likely PBC clusters
• SI (10312, 10308, 10309) r3.44 km, p0.17

FOCUSED SaTScanTM ANALYSIS

• Most likely PBC clusters
• SI (10312, 10308, 10309) r3.69 km, p0.05

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PBC-Staten Island
MOST LIKELY PBC CLUSTER
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Prevalence May Vary Locally Due to Genetic or
Environmental Factors

• 1) Genetic predisposition
• - Family and twin studies support a link
• 2) Environmental factors
• - Differing water supplies has been linked
  to local variability of PBC.
• (Triger et al. Br Med J. 1980)
• - Cross-reactivity of PBC autoantibodies with
  microbial protein epitopes and with autoantigen
  modified by environmental chemicals (xenobiotics)

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Xenobiotics

• Xenobiotics are foreign chemicals that may alter
  defined self-proteins, inducing a change in the
  molecular structure of the native protein
  sufficient to induce an immune response
• Association of autoimmune diseases with
  xenobiotics.
• Carpenter D.O et al. Incidence of endocrine
  disease among residents of New York areas of
  concern. Environ Health Perspect 2001.
• Many xenobiotics are metabolized in the liver

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PBC in NYC

• An increased number of PBC cases noted in
  Staten Island. Referral bias?

Manhattan
Brooklyn
Queens
Staten Island
Bronx
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Staten Island is home to a huge garbage dump and
numerous other toxic sites
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Potential geoclustering of Mount Sinai patients
(212) with AMA PBC
10 km