A prospective study of patients diagnosed with sarcoidosis: factors - environmental exposure, health assessment, and genetic outlooks

Abstract

This original research is a directional study that determined the habits of individuals using four analyses to find statistical significance in the data collected from the surveys of 801 qualified of 1,340 individuals who agreed to participate. Results from the self-reported diagnosis of individuals affected by sarcoidosis produced seven statistically significant indicators of future research needed. The demographics revealed a significantly greater number of women and African-Americans participants than other minorities in the United States and suggested a sense of urgency to find a cure. Most important are the seven statistically significant findings that also gave credence to the researchers’ four subdiagnostic classifications. They are acute sarcoidosis (AS) and chronic sarcoidosis with limited dissemination (CSLD), while more severe cases include those with chronic sarcoidosis with full dissemination including cutaneous involvement (CSFDIC) and chronic sarcoidosis with neurosarcoidosis (CSN). The most severe sarcoidosis cases (CSN) were on the “most likely” side of every statistically significant category except drinking alcohol, and the “least likely” to participate in physical activities. Conversely, the least severe case of sarcoidosis (AS) was the opposite. The complete list of statistically significant areas was related to alcohol use, tobacco use, ciprofloxacin use, environmental exposure to metals (copper, iron), infectious diseases (candidiasis), genetics, and physical exercise. Statistically, the most crucial study needed; emerged from the Rh blood grouping of the participants.

Introduction

The modern history of sarcoidosis, an enigmatic multisystem disease, goes back to 1899 when the pioneering Norwegian dermatologist Caesar Boeck coined the term “sarcoid” to describe skin nodules characterized by compact, sharply defined foci of “epithelioid cells with large pale nuclei and a few giant cells (1). Sarcoidosis is a systemic granulomatous disease of unknown cause that primarily affects the lungs; however, the abnormal inflammatory disease process may affect any organ and tissue of the body. The lungs, the lymph nodes of the thorax and the neck, skin, and the liver are the most often involved. The hallmark histological feature of the disease is epithelioid cell granuloma derived from activated T cells and macrophages triggered by unknown immune stimuli such as bacterial protein or beryllium metal (2). There are over 200,000 living individuals with sarcoidosis in the United States. It is hard to say how many worldwide suffer from this disease (2). Globally; the disease is more common in people of Scandinavian, German, Irish, and Puerto Rico descent (3). No one knows why this occurs. Granulomas of the lachrymal (concerned with the secretion of tears) and salivary glands are found in one-third of patients upon presentation. Since sarcoidosis mimics many other diseases, the number of people who have sarcoidosis is thought to be much higher than the number reported. The cause remains unclear; however, environmental, genetic, ethnic, and familial factors probably modify the expression of the disease. For example, African-Americans are at a greater risk of mortality and morbidity than are white Americans, and more often have a family history of sarcoidosis (4). More females are reported to have sarcoidosis than men. In fact, it is a two to one ratio over African American men. Current reported cases over the last several years show that the gender gap is narrowing. Often most patients that present with acute sarcoidosis (Löfgren syndrome) recover spontaneously, but some develop the chronic debilitating disease. Moreover, sarcoidosis is a multifactor disease of an abnormal immune response to unknown stimuli that produce granulomas in the organs and tissues, also referred to as Boeck’s Sarcoid. The proposed study was conducted to identify possible significant factors related to patients diagnosed with sarcoidosis.

Methods

This study used a prospective survey that included nutrition, health history, environmental exposures, lifestyle, genetics, and demographic information. The survey further collected data on both external and internal risk factors that might predispose one to sarcoidosis. The final survey contained 121 questions and took most respondents between thirty and sixty minutes to complete. From the data, statistics were run exceeding the more salient ones included.

Before the start period of the grant, substantial work was done to construct the survey questions to learn more about the causes and effects of sarcoidosis. The first obstacle was to identify commonalities to complete this directional study. Identified were acute, and chronic, with two further distinctions of recurrent or progressive sarcoidosis. In general, published researchers suggested the differentiation of identified cases based on clinical phenotypes of sarcoidosis. Others, not only suggested identifiable subclassifications, they thought them necessary for the treatment of sarcoidosis patients (5). From these documents, four subclassifications seem to capture the manifestations of sarcoidosis that were explicable to participants, with an increase in severity as suggested by the debilitating conditions in the subdiagnostic names given. Subdiagnostic refers to the reliance on the participants to accurately self-report their diagnosis. The first subdiagnostic classification of acute sarcoidosis (AS), though called subdiagnostic is recognized, is also called Löfgren syndrome (6). Moreover, the last three are in the recognized chronic category, but not the new subdivision (5). Consequently, the order of increasing severity is the somewhat defined chronic sarcoidosis with limited dissemination (CSLD) (7), and chronic sarcoidosis with full dissemination including cutaneous involvement (CSFDIC) (8), and the more defined, chronic sarcoidosis with neurosarcoidosis (CSN) (9). Prior classification of sarcoidosis was based on chest radiographic features of the disease but didn’t fully capture the chronic aspects of the disease (2). Subdiagnostic categories in this paper do not imply lack of lung involvement. The subdiagnostic categories were a way to organize the participants based on the severity of the condition. During this same period of classification, the investigators submitted an application to conduct research using human subjects to the Texas State University Institutional Review Board (IRB) for approval. The application was approved by the Texas State University IRB (IRB application number 2015Z8977) on June 18, 2015.

Participants for the study were recruited by posting a short web-based survey on 31 Sarcoidosis Support Groups on the social network Facebook. The criteria for inclusion in the study was a diagnosis of sarcoidosis as made by a licensed physician or healthcare provider. The short web-based survey explained the purpose of the study and requested that individuals with a diagnosis of sarcoidosis participate in the study. Those who agreed were asked to provide their names and email addresses. Using this technique, 1,340 individuals agreed to participate. Potential participants were later contacted using a custom email with an automatic login link to the research survey. Therefore, no identifying information was needed from this point forward. A sample size of 1000 was targeted. Numerous reminders were provided to participants, and ultimately, 801 individuals responded.

The Principal Investigator of the study developed a bank of possible research questions. From the bank of questions, the research team refined the questions until everyone agreed on a pilot version. This version was field tested with six individuals diagnosed with sarcoidosis, three health professionals, and one faculty member in health professions. Based on their feedback, the survey was finalized (see Appendix A for a copy of the survey).

The recruitment and main surveys were administered electronically using Snap Surveys© software, which can be used to create and manage web-based surveys. The university subscribes to an Education Enterprise License for this robust software package that includes technical support, software updates, and other application maintenance as needed. Further, this software package allowed the researchers to build a customized data collection instrument while managing and controlling user access. Specifically, when constructing instruments, Snap Survey© software allows for custom design throughout the online instrument, the ability to utilize conditional logic and data entry validations (i.e., question routing), and the creation of distinct participant groupings (e.g., specific classes). Snap Survey© software is installed on a university server, rather than hosted by a third-party cloud storage company. For this specific project, this feature allowed the online version of this survey to be hosted on the Texas State University data center, which brought with it all the security and protection features associated with the university’s IT infrastructure. Participants had the option to select a “choose not to answer” response to each question. Participants could also skip a question, resulting in a noted response of missing. Respondents were from all parts of the United States.

Results

Eight hundred and one participants responded to the survey. Demographic information, as well as self-reported subdiagnostic categories, are reported in Tables 1 and 2. Of the 801 respondents, 617 provided valid zip codes as seen in Table 1 along with the state in which respondents live. Representation came from all but two of the 50 states of the US, those being Montana and Nebraska. One respondent lives in the District of Columbia (1, 0.2%). The top five states with the most respondents in the study are Texas (49, 7.9%), New York (48, 7.8%), Michigan (25, 25%), Florida (37, 6.0%) and California (30, 5.0%). Hawaii, New Mexico, South Dakota, and Wyoming, each had one responded (Table 1). Of the 801 respondents, 30 live in the UK, 11 in Canada, seven (7) in Australia, five (5) in Scotland, four (4) in Ireland, one (1) each in Brazil, Netherlands, Slovenia, and New Zealand. Three respondents serve in the military living in the Middle East. Women constituted most of the 801 respondents. Notably, even the least common subdiagnostic classification chronic sarcoidosis with full dissemination, including cutaneous involvement (CSFDIC) was still well-represented among participants, comprising 15.9% of all responses. Chronic sarcoidosis with limited dissemination (CSID) was the largest group which represented 28.5% of all responses. Acute sarcoidosis represented the next largest at 20.7% (Table 2).

Table 1.

Frequencies and percentages of states in which respondents live

State	N	%
Alabama	8	1.3
Alaska	3	0.5
Arizona	7	1.1
Arkansas	8	1.3
California	31	5.0
Colorado	9	1.5
Connecticut	8	1.3
Delaware	3	0.5
District of Columbia	1	0.2
Florida	37	6.0
Georgia	23	3.7
Hawaii	1	0.2
Idaho	3	0.5
Illinois	18	2.9
Indiana	14	2.3
Iowa	5	0.8
Kansas	7	1.1
Kentucky	13	2.1
Louisiana	9	1.5
Maine	4	0.6
Maryland	15	2.4
Massachusetts	17	2.8
Michigan	25	4.1
Minnesota	15	2.4
Missouri	18	2.9
Nevada	5	0.8
New Hampshire	5	0.8
New Jersey	14	2.3
New Mexico	1	0.2
New York	48	7.8
North Carolina	28	4.5
North Dakota	3	0.5
Ohio	27	4.4
Oklahoma	8	1.3
Oregon	10	1.6
Pennsylvania	20	3.2
Rhode Island	3	0.5
South Carolina	20	3.2
South Dakota	1	0.2
Tennessee	12	1.9
Texas	49	7.9
Utah	6	1.0
Vermont	2	0.3
Virginia	14	2.3
Washington	12	1.9
West Virginia	7	1.1
Wisconsin	13	2.1
Wyoming	1	0.2
Total	617

Table 2.

Sample of self-reported demographic characteristics and subdiagnostic categories

Variable	N	%
Subdiagnostic Category
Acute sarcoidosis	166	20.7
Chronic sarcoidosis with limited dissemination	228	28.5
Chronic sarcoidosis with full dissemination, including cutaneous involvement	127	15.9
Chronic sarcoidosis with neurosarcoidosis	142	17.7
Choose not to answer	116	14.5
Missing	22	2.7
Gender
Female	654	81.6
Male	143	17.9
Self-Identified	2	0.2
Missing	2	0.2
Hispanic
No	744	92.9
Yes	40	5
Missing	17	2.1
Race
American Indian/Eskimo/Aleut Alaskan Native	8	1
Asian/Pacific Islander	9	1.1
Black/African American	103	12.9
White	603	75.3
Two or More	28	3.5
Other	23	2.9
Missing	27	3.4

Table 3 summarizes responses related to eating habits. Participants questions about their eating habits were prospecting for its role in causing sarcoidosis in addition to its known ability to exacerbate conditions of sarcoidosis (10, 11). About half of the respondents ate breakfast (48.6%) with only 8.2% reporting never eating breakfast. Slightly more than half ate lunch (50.6%) with 2.4% reporting never eating lunch. The largest meal eaten by respondents daily was dinner (78.7%) (12).

Table 3.

Sample of self-reported eating habits

Variable	N	%
How often do you eat breakfast
1-2 days a week	185	23.1
3-5 days a week	159	19.9
6-7 days a week	389	48.6
Never	66	8.2
Missing	2	0.2
How often do you eat lunch
1-2 days a week	110	13.7
3-5 days a week	258	32.2
6-7 days a week	405	50.6
Never	19	2.4
Missing	9	1.1
How often do you eat dinner
1-2 days a week	30	3.7
3-5 days a week	131	16.4
6-7 days a week	630	78.7
Never	2	0.2
Missing	8	1

Table 4 summarizes responses related to participants smoking habits and alcohol use. Of the 801 subjects in the study, 700 (87.4%) reported not to be a smoker. Of the 801 subjects in the study, 433 (54.1%) reported alcohol use, whereas 367 (45.8%) report no use. Medical marijuana was used by 47 (5.9%); the majority did not report using it (753, 94%).

Table 4.

Key Variables related to substance use

Variable	N	%
Use of tobacco
No	700	87.4
Yes	100	12.5
Missing	1	0.1
Use of medicinal marijuana
No	753	94
Yes	47	5.9
Missing	1	0.1
Use of alcohol
No	433	54.1
Yes	367	45.8
Missing	1	0.1

Table 5 summarizes responses related to exposure to heavy metals. Exposure was defined to participants with the statement, “exposure means that one work or have worked in an industry where this metal was used, in an environmental contamination exposure situation, or that one had any contact inhalation or skin contact with the metal.”

Table 5.

Sample of self-reported exposure to metals

Variable	N	%
Have you ever been exposed to…
Arsenic	20	2.50
Barium	19	2.40
Beryllium	21	2.60
Cadmium	16	2.00
Chromium	17	2.10
Cobalt	16	2.00
Copper	59	7.40
Iron	64	8.00
Lead	95	11.90
Mercury	57	7.10
Nickel	31	3.90
Platinum	14	1.70
Selenium	6	0.70
Thallium	5	0.60
Tungsten	12	1.50
Uranium	5	0.60
Zinc	34	4.20

Table 6 begins the genetic look at 438 self-reported blood phenotypes matched to their subdiagnosis. Another 84 self-reported their phenotypes but not their subdiagnosis. Associations between subdiagnostic classification and responses to the other questions in the survey were explored for the 663 participants who reported a subdiagnostic classification. Only those associations that were statistically significant are reported. Table 7 report crosstabulations of subdiagnosis with the response to those questions that were statistically significant at the .05 level. Neither gender nor race/ethnicity variables were significantly associated with the subdiagnostic category.

Table 6.

Sample of self-reported blood groups and subdiagnostic categories

Cases of sarcoidosis	Blood Type								Total
	A Positive	A Negative	B Positive	B Negative	AB Positive	AB Negative	O Positive	O Negative
AS	21	5	16	2	7	2	47	14	114
CSLD	46	6	19	2	6	2	53	21	155
CSFDC	21	4	15	2	1	6	26	10	85
CSN	12	5	13	3	4	1	34	12	84
Total	100	20	63	9	18	11	160	57	438

Table 7.

Cross tabulations and chi-square of subdiagnostic classification from exposure, usage, or infection

Cross Tabulations and Chi-Square Analyses (Table 7): The following are the results from the statistical analysis of the 663 participants who reported a subdiagnostic classification. Ciprofloxacin exposure was relatively common across all groups, with 47.8% of the total sample having reported some exposure. The past or present use of ciprofloxacin was statistically significantly associated with the subdiagnostic classification, χ²(3) = 8.49, p = .037. Table 7 shows this association. Comparison of observed cell counts with cell counts expected under the null hypothesis of statistical independence reveals that those with acute sarcoidosis (AS) reported the least exposure, followed by those with limited dissemination (CSLD). Those with a diagnosis of chronic sarcoidosis with full dissemination, including cutaneous involvement (CSFDIC) or chronic sarcoidosis with neurosarcoidosis (CSN) reported the greatest exposure.

Receiving a diagnosis of candidiasis was also associated with the subdiagnostic classification, χ²(3) = 9.34, p = .025. This association is shown in Table 7. As was the case with ciprofloxacin usage, the more severe the subdiagnostic classification, the more likely diagnosis of candidiasis was to be reported.

The association of subdiagnostic classification with tobacco usage (Table 7) was statistically significant, χ²(3) = 7.89, p = .048. Those with diagnoses of AS, CSLD, and CSFDIC reported the roughly equal incidence of tobacco usage, around 10%. Those with a diagnosis of CSN reported a higher incidence of usage at 19.0%.

The association of alcohol use with subdiagnostic classification was statistically significant, χ²(3) = 10.87, p = .012. Table 7 shows this association. Comparison of observed cell counts with cell counts expected under the null hypothesis of statistical independence reveals that those with either acute sarcoidosis (AS) or chronic sarcoidosis with limited dissemination (CSLD) are more likely to report the use of alcohol than would be expected by chance. Whereas those with chronic sarcoidosis with full dissemination, including cutaneous involvement (CSFDIC) or chronic sarcoidosis with neurosarcoidosis (CSN) are less likely to use alcohol.

The association of copper exposure with subdiagnostic classification was statistically significant, χ²(3) = 9.81, p = .020. As shown in Table 7, those with AS and CSLD reported between four and five percent incidence of exposure, whereas those with CSFDIC and CSN reported the considerably higher incidence of exposure at 11.8% and 9.9%, respectively.

Iron exposure was also associated with subdiagnostic classification, χ²(3) = 10.51, p = .015. As shown in Table 7, the pattern of reported exposure by classification was highly similar to the pattern observed for copper exposure. Those with AS and CSLD reported the relatively low incidence of exposure at 4.8% and 6.1%, respectively. In contrast, those with CSFDIC and CSN reported the noticeably higher incidence of exposure at 12.6% and 12.7%, respectively.

Cross Tabulations and Odds Ratio Analyses (Table 8): The statistically significant Chi-Squares in the above six subdiagnostic classifications warranted additional analyses. Specifically, we were interested in analyzing if there were differences based on the severity of sarcoidosis and if the person had an acute versus the chronic case of sarcoidosis. This study was a case-control because we started with the outcome and looked back to see which individuals in the two groupings were exposed or used alcohol, tobacco, and so on. The first grouping divided the 663 participants into artificial categories of “Less” and “More” severe cases of sarcoidosis. Less severe cases included those with acute sarcoidosis (AS) and chronic sarcoidosis with limited dissemination (CSLD), while more severe cases include those with chronic sarcoidosis with full dissemination including cutaneous involvement (CSFDIC) and chronic sarcoidosis with neurosarcoidosis (CSN). The second grouping divided the participants into two groups of acute sarcoidosis, which included only individuals in the AS classification versus chronic sarcoidosis, which included all other participants in the CSLD, CSFDIC, and CSN classifications. Table 8 report crosstabulations, the odds ratios, and Chi-Square based on severity and acute versus chronic. Alpha was set at the .05 level.

Table 8.

Cross tabulations, odds ratio, and chi-square of severity and disease onset from subdiagnostic classification of exposure, usage, or infection

Table 8 presents the association of ciprofloxacin use with the severity of sarcoidosis. The result was statistically significant, with an Odds Ratio (OR) of 0.66 and confidence interval (CI) 0.49 to 0.91. This result indicates that the odds an individual with a less severe case of sarcoidosis is 0.66, or there is a 34% less chance of them reporting the use of ciprofloxacin than an individual with a more severe case of sarcoidosis.

Table 8 presents the association of ciprofloxacin use with the acute versus chronic sarcoidosis was also statistically significant, with an OR of 0.65 (CI 0.45 to 0.92). This result indicates that the odds an individual with an acute case of sarcoidosis is 0.65, or there is a 35% less chance of them reporting the use of ciprofloxacin than an individual with a chronic case of sarcoidosis.

Table 8 presents the association of candidiasis with the severity of sarcoidosis. The result was statistically significant, with an OR 0.54 (CI 0.34 to 0.84). This result indicates that the odds an individual with a less severe case of sarcoidosis is 0.54, or there is a 46% less chance of them reporting having candidiasis than an individual with a more severe case of sarcoidosis.

Table 8 presents the association of candidiasis with the acute versus chronic sarcoidosis with an OR 0.67 (CI 0.39 to 1.18). Since the confidence interval (CI) crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between acute and chronic candidiasis at 95% confidence.

Table 8 presents the association of tobacco use with the severity of sarcoidosis with an OR of 1.46 (CI 0.92 to 2.33). Since the CI crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between the severity of sarcoidosis for tobacco use at 95% confidence.

Likewise, Table 8 presents the association of tobacco use with acute versus chronic sarcoidosis with an OR 1.04 (CI 0.61 to 1.78). Since the CI crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between the acute versus chronic sarcoidosis for tobacco use at 95% confidence.

Table 8 presents the association of alcohol use with the severity of sarcoidosis. The result was statistically significant, with an OR 1.6 (CI of 1.2 to 2.2). These results indicate that the odds an individual with a less severe case of sarcoidosis use of alcohol is 1.6 times that of the odds an individual with a more severe case of sarcoidosis. In other words, those with less severe cases are more likely to report the use of alcohol than those with more severe cases.

However, Table 8 presents the association of alcohol use with the acute versus chronic sarcoidosis with an OR 1.1 (CI 0.8 to1.6). Since the CI crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between the acute versus chronic sarcoidosis for alcohol use at 95% confidence.

Table 8 presents the association of copper exposure with the severity of sarcoidosis. The result was statistically significant, with an OR 0.40 (CI 0.22 to 0.73). This result indicates that the odds an individual with a less severe case of sarcoidosis is 0.40, or there is a 60% less chance of them reporting exposure to copper than those with a more severe case of sarcoidosis.

Table 8 presents the association of copper exposure with the acute versus chronic sarcoidosis with an OR 0.50 (CI 0.22 to 1.15). Since the confidence interval (CI) crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between acute and chronic association with copper exposure at 95% confidence.

Table 8 presents the association of iron exposure with the severity of sarcoidosis. The result was statistically significant, with an OR of 0.41 (CI 0.22 to 0.73). This result indicates that the odds an individual with a less severe case of sarcoidosis is 0.41, or there is a 59% less chance of them reporting exposure to iron than those with a more severe case of sarcoidosis.

Table 8 presents the association of iron exposure with the acute versus chronic sarcoidosis with an OR 0.47 (CI 0.22 to 1.02). Since the confidence interval (CI) crosses the line of no effect (1), it is not statistically significant. In other words, no statistical difference exists between acute and chronic association with iron exposure at 95% confidence.

Goodness of Fit Chi-Square Analyses (Table 10): We also ran the goodness of fit, Chi-Square analysis comparing the frequency of blood types in the sarcoidosis cases to that of the United States (US) general population. An initial check was statistically significant for the self-identified subdiagnostic classifications matched with their self-reported blood grouping; χ²(3) = 30.511, p <0.01 at α = .05 (table not shown) but did not reveal the root cause. Then a statistical analysis compared the ABO and Rh blood grouping of sarcoidosis participants to that in the United States (US) according to the American Red Cross (ARC) frequency distribution and Stanford School of Medicine (SSM) at α = .05 (13, 14). The ARC gave the frequency distribution of eight phenotypes for four ethnic groups (Caucasian, African-American, American-Latino, and Asian), with slightly different percentages than SSM that had the same eight phenotypes, but an unknown percentage compilation.

Table 10.

Rh blood grouping observed vs. expected significance

Matching the combined ABO and Rh blood types with self-identified subdiagnostic classifications were possible for 438 of the 663 participants (Table 6) and were statistically significant. ARC, χ²(7) = 146.448, p <0.01, SSM, χ²(7) = 95.852, p <0.01; at α = .05. However, attempts to understand the phenomena resulted in the separation of the phenotypes and analyzing the assumed more important ABO phenotype, resulted in conflicting results. The ARC frequencies were NOT statistically significant for the 438 participants, χ²(3) = 7.033, p = 0. 071, but was for the SSM, χ²(3) = 50.975, p <0.01; at α = .05.

Efforts to explain the apparent contradiction resulted in the comparison of the 438 participants Rh blood grouping alone and was statistically significant. ARC, χ²(1) = 101.764, p <0.001 (Table 10), which was greater than SSM, χ²(1) = 17.543, p <0.01; at α = .05. The significant finding of the Rh blood grouping compelled the final comparison of all 522 self-reported Rh blood grouping (Table 9), without regards to the subclassifications, that included 84 unknown subdiagnosis, resulted in the greatest statistical significance. ARC, χ²(1) = 126.128, p <0.001 (Table 10), again, greater than SSM, χ²(1) = 22.503, p <0.01; at α = .05.

Table 9.

Subdiagnostic sarcoidosis vs. us population

	Sarcoid Observed Frequency	US Expected Frequency	Residual	ABO & Rh % <or> Expected	ABO % <or> Expected
A Positive	127	176.8	-49.8	-28%	-42%
A Negative	27	31.2	-4.2	-13%
B Positive	70	42.1	27.9	66%	115%
B Negative	11	7.4	3.6	49%
AB Positive	21	16.8	4.2	25%	-38%
AB Negative	11	29.7	-18.7	-63%
O Positive	187	185.2	1.8	1%	109%
O Negative	68	32.7	35.3	108%
Total	522

Subclassification Association with Exercise: The Godin Leisure-Time Exercise Questionnaire (1985) was used to allow participants to self-report their level of physical activity (12). Of the 663 participants who reported a subdiagnostic classification, 659 provided sufficient information for the calculation of total physical activity (PA) score using Godin and Shephard’s (1985) formula, m = 19.03, sd = 21.59. Inspection of a histogram of PA scores revealed a continuously distributed variable with many zeros (Figure 1). This pattern suggested that an important distinction among these participants was whether they reported any physical activity at all. In other words, the PA scores observed for these n = 659 participants could be parsimoniously conceptualized as a mixture of two distributions. One distribution with only the value of zero (n = 218), and separate, continuous distribution of PA scores for those participants who actually engaged in physical activity (n = 441, m = 28.44, sd = 20.71).

Fig. 1.

Self-reported physical activity and activity frequency

A dichotomous variable was thus created to indicate whether (1) or not (0) each participant with a non-missing PA score had a PA score that was greater than zero. The association of this dichotomous variable with subdiagnostic classification was statistically significant, χ²(3) = 15.48, p = .001. Inspection of means of this dichotomous variable (i.e., the proportion of individuals reporting greater than zero physical activity) for each subdiagnostic classification showed that AS (n = 165, m = .72), CSLD (n = 227, m = .71), and CSFDIC (n = 126, m = .69) were similar in the proportion of participants reporting some physical activity. In contrast, participants in the CSN subdiagnostic classification were notably less likely to report engaging in any activity (n = 141, m = .53).

Perhaps unsurprisingly then, while a one-way ANOVA did not show statistically significant variation of mean PA score across subdiagnostic classifications [F (3, 655) = 1.93, p = .12], means of these PA scores showed a similar descriptive pattern. Those participants with AS (m = 20.75, sd = 23.53), CSLD (m = 19.60, sd = 19.83), and CSFDIC (m = 20.00, sd = 22.13) had similar mean PA scores. Participants in the subdiagnostic classification of CSN had lower PA scores on average (m = 15.26, sd = 21.24).

Finally, this one-way ANOVA was performed again after excluding those participants with PA scores of zero. There was no statistically significant variation of mean PA score across subdiagnostic classification [F(3, 437) = .13, p = .94], and mean PA scores across subdiagnostic classifications were descriptively homogeneous (AS, n = 118, m = 29.01, sd = 23.11; CSLD, n = 161, m = 27.63, sd = 18.22; CSFDIC, n = 87, m = 28.97, sd = 21.20; and CSN, n = 75, m = 28.68, sd = 21.53).

Discussion

More than three years are vested in this directional study of sarcoidosis using a prospective survey that covered demographic information, nutrition, environmental exposures, lifestyle, genetics, and health history. The investigators believe it is the single largest directional survey conducted of this population to date. Sarcoidosis is a systemic granulomatous disease of unknown cause that primarily affects the lungs and an abnormal inflammatory disease process that may affect any organ and tissue of the body. The lungs, the lymph nodes of the thorax and the neck, skin, and the liver are the most often involved. Although the hallmark histological feature of the disease is epithelioid cell granuloma derived from activated T cells and macrophages, the triggering stimuli are unknown but have indicators such as bacterial protein or beryllium metal (2).

Researchers of this study identified four subclassifications: acute sarcoidosis (AS) and chronic sarcoidosis with limited dissemination (CSLD), while more severe cases include those with chronic sarcoidosis with full dissemination including cutaneous involvement (CSFDIC) and chronic sarcoidosis with neurosarcoidosis (CSN). The subdiagnostic classification allowed further analysis of the data based on severity and, hopefully, can offer future insight into differential diagnoses, management, and additional research. The authors did not compare pulmonary function data to the four subcategories. That has not proven to be useful with past classifications; however, is an area of interest for future studies based on the results of this study (2). This directional study determined the habits of individuals and utilized four analyses to find statistical significance in the data collected from the surveys of 801 participants. Based on our analysis, seven statistically significant results emerged as indicators of future research needed. Namely, the “Cross Tabulations and the Chi-Square Analyses,” the “Cross Tabulations and Odds Ratio Analyses,” the “Goodness of Fit and Chi-Square Analyses,” and the “Godin and Shephard’s (1985)” formula were most valuable. These four statistics resulted in the seven statistically significant findings that gave credence to the four subdiagnostic classifications. These seven statistically significant findings are summarized in Figure 2 that reveals statistics that separates the four subcategories into a dichotomy of most likely and least likely occurrences. Six of the seven relates the frequency percentages, while the Rh D blood type reveals a dichotomy based on opposing (72 vs. -71) residual of observed versus expectancy that proved statistically significant. This study did not attempt to ascertain causality of the subcategories; rather, it focused on determining if these categories were statistically feasible. Moreover, the strength of this directional survey was not robust enough to speculate on causality but serves as a Pilot Study for more focused investigations.

Fig. 2.

Seven Statistically Significant Sarcoidosis Linked Dichotomies

*Boeck’s Sarcoid – printed by permission of John Lee Sang, M.D., Central Texas Medical Center, San Marcos, Texas

Demographic diversity, including ethnicity, presented two points of interest. Considering this study was of those requesting it, the more than 80% of females in our results exceeds recent and robust studies of around 50% and may indicate a greater felt urgency for resolve in that populous (15, 16). While the demographics of those with sarcoidosis mimic that of the 2010 census for Whites (75.2, 72.4) and African-Americans (12.9, 12.6), it is more striking that African-American representation is two to three times that of other closed groups such as Mexican Americans and Native Americans (17). It highlights studies demonstrating the disconcerting prevalence among African-American females, and may also reflect their desperation to find a cure (4).

The eating habits of the participants seem like those in the United States, with a greater percentage never missing the last meal of the day and did not reveal any statistical relationship to sarcoidosis. Likewise, 87.4% of the 700 reported nonsmokers is an expected response considering over 85% of people with sarcoidosis have the disease in the lungs. Nevertheless, the subdiagnostic classification with tobacco usage was statistically significant. However, tobacco is the only category that was statistically significant as a subcategory (Table 7) but not at levels of severity, and the acute or chronic onset of the disease (Table 7). Similarly, Gupta et al did not find an effect of tobacco smoke on disease severity in sarcoidosis (19). Perhaps a study with a larger number of participants can shed light on any association between tobacco use and sarcoidosis severity. The link between smoking and chronic obstructive lung disease has been well established. However, there is a dearth of research looking specifically at the impact tobacco use has on sarcoidosis and lung function across acute and chronic categories. We cannot support the notion that tobacco use is protective against sarcoidosis either. It is known that tobacco affects the immune system among other pathophysiological effects of nicotine toxins and increased serum angiotensin-converting enzyme activity (ACE) tested for in clinical laboratories (20). Whether it is because of the symptoms which mimic COPD or the tendency not to smoke because of the risk of cancer, is not clear. Along with that, the low (5.9%) use of medical marijuana may reflect the same, although some evidence exists for the use of medical marijuana to alleviate the discomfort associated with chronic noncancer pain (21).

On the other hand, alcohol use did not seem to cause an adverse effect. In fact, a null hypothesis of statistical independence revealed that those with AS and CSLD are more likely to use alcohol than would be expected by chance and those with the more severe conditions (CSDIC, CSN) were less likely to use alcohol. Using the same groupings (AS, CSLD versus CSDIC, CSN) provided means of conducting an odds ratio that associated alcohol use with the severity of sarcoidosis. Results indicated that those with less severe cases are more likely to report the use of alcohol than those with more severe cases.

In contrast, environmental exposure presented copper and iron metals with similar statistical significance in the same areas of classification as alcohol, but unfavorable since past research has shown that environmental factors may contribute to sarcoidosis risk. Moreover, researchers associated the risk with individuals who work in occupations with potential metal exposures (22), and association exposure to inorganic particles (16). Workers exposed to beryllium dust or fumes have developed an immune response known as sensitization, a slowly progressive respiratory disease characterized by the formation of lung lesions called granulomas that resemble those found in sarcoidosis patients. These granulomas and accompanying fibrosis impair the lung’s ability to expand fully and interfere with the normal gas exchange. Berylliosis has been suggested as a cause of pulmonary sarcoidosis and classified as a type of pneumoconiosis, a systemic granulomatous disease that mainly affects the lungs (17). Exposure to the minerals with statistical significance, copper, and iron, are known to work in tandem in the liver to produce hemochromatosis a condition that causes an iron overload that would affect other organs and tissue (20, 23). Also, high intakes of copper are known to cause gastrointestinal pain, erosion of epithelial cell lining, hemolytic anemia, kidney damage, and death, and therefore cannot be ruled out as contributing factors to sarcoidosis (24). Also, work environments that produce high levels of aerosolized inorganic particles, e.g., wood stoves, fireplaces, talc, human-made mineral fiber, silica dust, intense agricultural activities, continue to be places of higher risk for development of sarcoidosis (25).

Ciprofloxacin, like alcohol, copper, and iron, had similar statistical significance in the same areas of the null hypothesis of statistical independence and those with the more severe conditions (CSDIC, CSN). Unlike those mentioned, ciprofloxacin also has a significant statistical difference in subclassifications of acute and chronic sarcoidosis that revealed increased use of the drug with severity, regardless of the division. Notably, ciprofloxacin is the only category that resulted in a statistically significant difference in all categories. That is, it was statistically significant in subcategorization, levels of severity, and the acute or chronic onset of the disease. Admittedly, when considering the duration of the disease and speculations from the participants that their treatments resulted in other health issues, the relationship between the severity of the disease could be from other drug treatments, requiring additional research. However, ciprofloxacin is a widely used antimicrobial agent to treat bacterial infections of the respiratory and urinary tracts and implicated in cases of induced acute interstitial pneumonitis and hypersensitivity vasculitis (24, 26, 27). Therefore, we surveyed the cohort group to ascertain if ciprofloxacin might have an association with inducing sarcoidosis, and there is a statistically significant relationship between the drug and the severity of the disease.

Furthermore, since the cutaneous effect of candidiasis coincided with this study’s greater number of women, where the infection is common, but also the greater number with sarcoidosis, and given its statistical significance (like alcohol, copper, and iron) warrants further investigation (24). In other words, is the drug a stimulus for sarcoidosis and candidiasis? Ciprofloxacin is known to cause an allergic response that results in acute interstitial nephritis (AIN) of the kidney (24). Paone et al. found that kidney granulomas cause interstitial nephritis with an elevated serum angiotensin-converting enzyme (ACE) activity, and with a bronchoalveolar lavage fluid [BALF] lymphocytes, and a BALF CD4/CD8 ratio could potentially screen for sarcoidosis (28). Also, the fact that increased blood pressure and water retention indirectly results from the ACE activity suggests metabolic influence, and diabetes complication lends to other reasons for investigating the ciprofloxacin sarcoidosis axis.

Genetically, the greatest statistical significance is the Rh blood grouping of all 522 self-reports with an ARC, χ²(1) = 126.128, p <0.001. This finding suggests the need to investigate the Rh blood group genotypes as the triggering stimuli for sarcoidosis. Specifically, the significantly increased Rh negative grouping suggests that the significantly decreased Rh-positive may have a commonality. To understand the commonality requires a look at the Wiener and Fisher-Race genotype (29). Since the Rh-negative with the greater frequency is “r” or “dce” haplotypes in the respective system, the closest match of Rh-positive with the greater frequency is “R⁰” and “Dce” haplotypes respectively in the Wiener and Fisher-Race classification. Therefore, the genotypes of interest are R⁰r or Dce/dce, for the Rh-positive participants and the Rh negative is rr or dce/dce respectively for the Wiener and Fisher-Race genotypes.

Concerning physical activity, overall, a statistically significant difference exists between inactivity and activity for those with sarcoidosis. This significance did not carry over to differences between the subcategories. Given that a participant reports engaging in some activity (i.e., the participant’s PA score is greater than zero), the four subdiagnostic categories are approximately equal in the mean level of activity reported. Nevertheless, there was a decrease in activity with increased severity, with the first three categories exhibiting the least difference. Strikingly, the analyses together reveal that chronic sarcoidosis with neurosarcoidosis (CSN) participants are notably more likely than other participants to report engaging in no physical activity at all.

Because of the four subcategorical classifications (AS, CSLD, CSFDIC, CNS) by the researchers, through four statistical analysis, seven statically significant areas are of interest for future studies. Namely, the association with exercise using the Godin and Shephard’s (1985) formula that indicated that those with the most severe cases, CSN, were less likely to engage in physical exercise. Although the other three were roughly the same, they exhibited a decrease in activity with the designated increase in severity. Second; drug use, specifically, Ciprofloxacin association with, third, Candidiasis, and their association with sarcoidosis. Fourth, the association of the seemingly positive effect of alcohol, and fifth, the statistical significance of tobacco use. Sixth, environmental exposure to copper and iron as causative stimuli for sarcoidosis. Seventh, and most significant, the association of sarcoidosis with increased Rh-negative grouping and the significantly decreased Rh-positive and what they have in common.

These seven findings reveal the benefit, if not the need, to standardize the classifications of sarcoidosis. It also indicates that the subclassification in this study is as valid and perhaps simpler than any identified and maintained throughout any single study. A simpler standard classification like the one presented, or the same seems more likely to advance researchers’ ability to compare participants within and without the subcategories. Conceivably, such a standard subclassification could lead to other categories within them and assist in definitively proving or disproving causative, the exacerbating, or disabling agents, such as the seven (Figure 2) suggested (especially Rh genetics) having statistical significance in this study.

Appendix A. Sarcoidosis Study Project Pilot Survey

The purpose of this survey is to find out more about the causes and effects of Sarcoidosis. Your responses will be held in strictest confidence and no identifying information will be collected. This survey, designed to identify factors that may have contributed to your diagnosis of Sarcoidosis, will take about 15 to 30 minutes to complete. If you need to take a break, close your browser. When you are ready to begin again click the link in your email and you will be taken to the next question in the survey. You may choose not to answer any question by skipping the question or by selecting the: Choose not to answer option. You may stop participating at any time during the study by closing the browser.

In the future, we may share these study data with other researchers outside of this study team. If we share data or write a report for publication in professional journals about this study, we will do so in such a way that you cannot be identified. Risks from participating in this study are minimal. You will not have any costs for being in this study, nor will you be paid for being in this study. Taking part in this research study is completely voluntary. If you choose to participate in the study your responses will be reported in aggregate form only; no individual will be identified. If you have questions about this study, please contact the Principal Investigator Dr. Louis Caruana at loucaruana@txstate.edu <mailto:loucaruana@txstate.edu>.

This project (IRB application number 2015Z8977) was approved by the Texas State IRB on June 18, 2015. Pertinent questions or concerns about the research, research participants’ rights, and/or research-related injuries to participants should be directed to the IRB chair, Dr.

Jon Lasser (512-245-3413 - lasser@txstate.edu) and to Becky Northcut, Director, Research Integrity & Compliance (512-245-2314 - bnorthcut@txstate.edu).

By clicking next, you consent to participate in this survey.

1. What is your current age?

2. What was your age at the onset of symptoms of sarcoidosis?

3. What year were you first diagnosed as having sarcoidosis?

4. What is you sub diagnostic classification?

   1. Acute sarcoidosis
   2. Chronic sarcoidosis with limited dissemination
   3. Chronic sarcoidosis with full dissemination, including cutaneous involvement of the disease
   4. Chronic sarcoidosis with neurosarcoidosis
   5. Choose not to Answer

5. Did you seek a second opinion on this diagnosis?

   Yes

   No

   Choose not to Answer

6. Is your Sarcoidosis in remission?

   Yes

   No

   Choose not to Answer

7. What is your blood type?

   A Positive

   A Negative

   B Positive

   B Negative

   AB Positive

   AB Negative

   O Positive

   O Negative

   Not Sure

   The next set of questions ask about your health habits.

8. Do you use tobacco?

   Yes

   No

9. Which of the following best describes your status:

   I have never used tobacco.

   I used tobacco at one time but I quit.

10. What types of tobacco do you use (check all that apply)? Cigarettes

    Cigars

    Pipes

    Chew Snuff Others

11. How many packs do you currently smoke per day?

12. How many years have you smoked?

13. Were you exposed to second hand smoke?

    Yes

    No

14. How many years were you exposed to second hand smoke?

15. How many cigars do you smoke in a week?

16. How many years have you smoked cigars?

17. How many days a week do you typically smoke your pipe?

18. How many years have you smoked a pipe?

19. How many days a week do you typically use chew?

20. How many years have you used chew?

21. How many days a week do you typically use snuff?

22. How many years have you used snuff?

23. Do you drink alcohol?

    Yes

    No

24. How many 8 oz. drinks do you typically consume daily?

25. Do you use medical marijuana to elevate pain or symptoms associated with your sarcoidosis?

    Yes

    No

26. I use medical marijuana for pain/symptoms:

    Less than once a month

    Monthly

    Weekly Daily

27. During a typical week, how often do you eat the following meals?

    	Never	1-2 days a week	3-5 days a week	6-7 days a week
    Breakfast
    Lunch
    Dinner

28. During a typical week, how often do you eat the following types of foods?

    	Never	1-2 days a week	3-5 days a week	6-7 days a week	Every meal
    Meat, Poultry, Fish, Dry beans, Eggs & Nuts
    Milk, Yogurt & Cheese
    Fruit
    Vegetables
    Bread, Cereal, Rice, & Pasta

29. How often do you eat meals that contain at least one food that contains antioxidants (e.g., bananas, tomatoes, sweet peppers, broccoli, carrots, cabbage, cauliflower, leeks, onions, apricots, berries, carrots, spinach, apples, pumpkin, mangoes, pink grapefruit, watermelons, whole grains, nuts, and green tea)?

    Never

    1-2 days a week

    3-5 days a week

    6-7 days a week

    Every meal

    Choose not to Answer

30. How often do you eat meals that contain at least one food that contain magnesium (e.g., avocados, potatoes, corn, oats, soy, brown rice, dark leafy greens, nuts, seeds, fish, beans, whole grains, avocados, yogurt, bananas, dried fruit, and dark chocolate)?

    Never

    1-2 days a week

    3-5 days a week

    6-7 days a week

    Every meal

    Choose not to Answer

31. Approximately how much water do you drink daily? Enter the approximate number of cups you drink per day (e.g., 0, 2, 4.5).

32. How often do you consume foods or drinks that contain the following?

    	Monthly	Weekly	Daily
    MSG (Monosodium glutamate, also known as sodium glutamate)
    Artificial sweeteners, Aspartame know as Equal and NutraSweet
    High fructose corn syrup
    Trans fats

33. Do you take vitamins, herbals or dietary supplements?

    Yes

    No

34. How often do you take vitamins, herbals, or dietary supplements?

    Never

    1-2 days a week

    3-5 days a week

    6-7 days a week

35. Specifically do you take vitamin D supplements?

    Yes

    No

36. How many milligrams do you take daily?

37. How much sunlight exposure do you typically get daily?

    Less than or at least 15 minutes

    More than 15 minutes but less than 30 minutes

    30 minutes or more

38. Do you use protein powders, creatine or other supplements that claim to increase muscles?

    Yes

    No

39. Do you drink coffee?

    Yes

    No

40. Approximately how many 8 ounce cups of coffee do you typically consume daily? List any other food/drink that contains caffeine that you consume on a daily basis.

41. Do you use any pills or teas to lose weight?

    Yes

    No

42. How often do you use pills or teas to lose weight?

    Monthly Weekly Daily

43. Do you have a diagnosis of sleep apnea?

    Yes

    No

44. When were you diagnosed with sleep apnea?

    Before sarcoidosis diagnosis

    After sarcoidosis diagnosis

45. During a typical week, how many times on average do you do the following kinds of exercise?

    Strenuous (Hard) exercise (heart beats rapidly). Examples: running, jogging, football, soccer, basketball, martial arts, aerobics.

    --Click Here--

    0

    1

    2

    3

    4

    5

    6

    7

    Choose not to Answer

46. Average time spent at each session

    --Click Here--

    0 min

    15 min

    30 min

    45 min

    1 hr

    1 hr 15 min

    1 hr 30 min

    1 hr 45 min

    2 hr

    2+ hr

    Choose not to Answer

47. Moderate exercise (not exhausting). Examples: fast walking, tennis, easy bicycling, easy swimming.

    --Click Here--

    0

    1

    2

    3

    4

    5

    6

    7

    Choose not to Answer

48. Average time spent at each session

    --Click Here--

    0 min

    15 min

    30 min

    45 min

    1 hr

    1 hr 15 min

    1 hr 30 min

    1 hr 45 min

    2 hr

    2+ hr

    Choose not to Answer

49. Mild exercise (easy effort). Examples, fishing, bowling, easy walking.

    --Click Here--

    0

    1

    2

    3

    4

    5

    6

    Choose not to Answer

50. Average time spent at each session

    --Click Here--

    0 min

    15 min

    30 min

    45 min

    1 hr

    1 hr 15 min

    1 hr 30 min

    1 hr 45 min

    2 hr

    2+ hr

    Choose not to Answer

51. What types of exercise you engage in (check all that apply)?

    Endurance or Aerobic (e.g., cycling, walking, hiking, running, playing tennis) Strength or Anaerobic (e.g., weight training, resistance training)

    Balance exercises (e.g., stand on one foot, Tai Chi)

    Flexibility (e.g., yoga)

    The next set of questions ask about antibiotics, infectious agents, fungus infections, bacterial infections, viruses, or metals you may have ingested or been exposed to during your lifetime.

52. Have you ever taken the following types of antibiotics (check all that apply)?

    Ciprofloxacin

    Chloramphenicol

    Sulfonamides (sometimes called sulfa drugs or sulpha drugs)

53. List the major clinical symptoms you had as a result of taking Ciprofloxacin.

54. List the major clinical symptoms you had as a result of taking Chloramphenicol.

55. List the major clinical symptoms you had as a result of taking Sulfonamides.

56. Have you ever been diagnosed with any of the following infectious agents (check all that apply)?

57. Primary pulmonary tuberculosis (e.g., Mycobacterium tuberculosis) Chlamydophila pneumonia

58. Mycoplasma pneumonia (also known as walking pneumonia) Chlamydophila psittaci

59. Did the TB disease process spread to the following (check all that apply)?

    Liver

    Gastrointestinal tract

    Kidney(s)

    Bones

60. List the major clinical symptoms you experienced with tuberculosis

61. List the major clinical symptoms you experienced with Chlamydophila pneumonia.

62. List the major clinical symptoms you experienced with Mycoplasma pneumonia.

63. List the major clinical symptoms you experienced with Chlamydophila psittaci.

64. Have you ever been diagnosed as having any of the following systemic or deep seated fungus infections (check all that apply)?

    Coccidioides immitis (the cause of Coccidioidomycosis or Valley Fever) Cryptococcus neoformans

    Histoplasma capsulatum (Histoplasmosis)

    Blastomyces dermatitidis or B. resiliencies (Blastomycosis), Candida albicans (Candidiasis), Aspergillus fumigatus or A. flavus (Aspergillosis),

    Sporothrix schenckii (Sporotrichosis (often referred to as "rose handler’s disease")

65. List the major clinical symptoms you experienced with Coccidioides immitis

66. List the major clinical symptoms you experienced with Cryptococcus neoformans

67. List the major clinical symptoms you experienced with Histoplasma capsulatum

68. List the major clinical symptoms you experienced with Blastomyces dermatitidis

69. List the major clinical symptoms you experienced with Candida albicans

70. List the major clinical symptoms you experienced with Aspergillus fumigatus

71. List the major clinical symptoms you experienced with Sporothrix schenckii

72. Have you ever been infected by the following bacterial organisms (check all that apply)?

    Propionibacterium acnes (linked to the skin condition of acne) Lyme Disease Helicobacter pylori (major cause of gastric (stomach) cancer) Methicillin-resistant Staphylococcus aureus (MRSA) Legionella pneumophila (the cause of Legionnaires disease) Toxoplasma gondii (the cause of toxoplasmosis)

73. List the major clinical symptoms you experienced with Propionibacterium acnes

74. List the major clinical symptoms you experienced with Lyme Disease

75. List the major clinical symptoms you experienced with Helicobacter pylori

76. List the major clinical symptoms you experienced with MRSA

77. List the major clinical symptoms you experienced with Legionella pneumophila

78. List the major clinical symptoms you experienced with Toxoplasma gondii

79. Have you ever been diagnosed with the following viruses (check all that apply)?

    Hepatitis B

    Hepatitis C

    Human herpesvirus 8

    Herpes simplex virus 1

    Human papillomavirus

    Epstein-Barr virus

    Kaposi’s sarcoma herpes virus

    Human T-cell leukemia virus-1

    Human respiratory syncytial virus

    Rubella virus Cytomegalovirus Parvovirus B19

80. List the major clinical symptoms you experienced with hepatitis B.

81. List the major clinical symptoms you experienced with hepatitis C.

82. List the major clinical symptoms you experienced with human herpesvirus 8.

83. List the major clinical symptoms you experienced with herpes simplex virus 1.

84. List the major clinical symptoms you experienced with Legionella human papillomavirus.

85. List the major clinical symptoms you experienced with Epstein-Barr virus.

86. List the major clinical symptoms you experienced with Kaposi’s sarcoma herpes virus.

87. List the major clinical symptoms you experienced with human T-cell leukemia virus-1.

88. List the major clinical symptoms you experienced with human respiratory syncytial virus.

89. List the major clinical symptoms you experienced with rubella virus.

90. List the major clinical symptoms you experienced with cytomegalovirus.

91. List the major clinical symptoms you experienced with parvovirus B19.

92. To your knowledge have you ever been exposed to the following metals? Exposure means that you work or have worked in an industry where this metal was used, in an environmental contamination exposure situation, or that you had any contact inhalation or skin contact with the metal.

    (Check all that apply)?

    Lead Mercury Arsenic Beryllium Cadmium Chromium Nickel Barium Cobalt Copper Iron Platinum Selenium Thallium Tungsten Uranium Zinc

93. Do you still have symptoms related to Lead exposure?

    Yes

    No

94. Do you still have symptoms related to Mercury exposure?

    Yes

    No

95. Do you still have symptoms related to Arsenic exposure?

    Yes

    No

96. Do you still have symptoms related to Beryllium exposure?

    Yes

    No

97. Do you still have symptoms related to Cadmium exposure?

    Yes

    No

98. Do you still have symptoms related to Chromium exposure?

    Yes

    No

99. Do you still have symptoms related to Nickel exposure?

    Yes

    No

100. Do you still have symptoms related to Barium exposure?

     Yes

     No

101. Do you still have symptoms related to Cobalt exposure?

     Yes

     No

102. Do you still have symptoms related to Copper exposure?

     Yes

     No

103. Do you still have symptoms related to Iron exposure?

     Yes

     No

104. Do you still have symptoms related to Platinum exposure?

     Yes

     No

105. Do you still have symptoms related to Selenium exposure?

     Yes

     No

106. Do you still have symptoms related to Thallium exposure?

     Yes

     No

107. Do you still have symptoms related to Tungsten exposure?

     Yes

     No

108. Do you still have symptoms related to Uranium exposure?

     Yes

     No

109. Do you still have symptoms related to Zinc exposure?

     Yes

     No

110. Have you been exposed to silica or coal?

     Yes

     No

111. Did you develop symptoms of upper respiratory illness?

     Yes

     No

112. Have you ever been diagnosed with lung cancer or mesothelioma?

     Yes

     No

113. Have you been exposed to asbestos?

     Yes

     No

114. How many years was the exposure?

115. Have you been exposed to other organic dust?

     Yes

     No

116. How many years was the exposure?

117. Did you develop symptoms of upper respiratory illness?

     Yes

     No

118. Are you allergic to latex?

     Yes

     No

119. Have you worked in an industrial or other setting where lactic acid was used?

     Yes

     No

     Demographic Information

120. Are you Latino or Hispanic?

     Yes

     No

121. What is you race?

     --Click Here--

     White

     Black/African American

     American Indian/Eskimo/Aleut Alaskan Native

     Asian/Pacific Islander

     Two or more

     Other

122. What is your gender?

     Male

     Female

     Self-Identified

123. Are you on birth control pills?

     Yes

     No

124. What is your current zip code?

     Please add any additional information or comments below