Cancer Causes Control (2014) 25:425–435 DOI 10.1007/s10552-014-0345-y
ORIGINAL PAPER
Evaluation of epidemiological factors in survival of patients with de novo myelodysplastic syndromes Kplola Y. Elhor Gbito • Guillermo Garcia-Manero Sara S. Strom
•
Received: 25 April 2013 / Accepted: 15 January 2014 / Published online: 25 January 2014 Ó Springer International Publishing Switzerland 2014
Abstract Myelodysplastic syndromes (MDS) prognosis is currently based solely on clinical parameters. The identification of additional factors associated with MDS outcome could be used to further improve the current scoring system such as the International Prognostic Scoring System (IPSS). The present study evaluates the role of epidemiological markers as predictors of survival for 365 adult de novo MDS patients. Multivariable Cox regression analysis was used to estimate overall survival. Median follow-up time was 22 months. At the time of last follow-up, 271 patients (74.3 %) had died. For all MDS patients, medium–high lifetime occupational agrochemical exposure (HR 1.85, CI 1.19–2.89) remained as an independent predictor of MDS survival. Stratified analysis by gender showed that C25 pack-years smoked (HR 1.44, CI 1.001–2.09) and medium– high lifetime occupational agrochemical exposure (HR 1.84, CI 1.15–2.97) were independent predictors of MDS survival in men, but not in women. For MDS patients stratified by IPSS categories, C25 pack-years smoked (HR 1.75, CI 1.005–3.06) was an independent predictor for intermediate 1 IPSS risk group only, and medium–high lifetime occupational agrochemical exposure was associated with increased mortality (HR 4.36, CI 1.20–15.8) in the K. Y. Elhor Gbito Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA G. Garcia-Manero Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA S. S. Strom (&) Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Unit 1340, Houston, TX 77030, USA e-mail: [email protected]
high IPSS risk group. Smoking and lifetime occupational agrochemical exposure may play a role in MDS survival. Incorporating relevant epidemiological markers with known clinical predictors of outcome may help physician stratify patients and customize treatment strategies to improve the outcome of MDS. Keywords Myelodysplastic syndromes Epidemiologic prognostic factors Smoking Obesity Agrochemicals
Introduction Myelodysplastic syndromes (MDS) are a heterogeneous group of stem cell malignancies, which have a higher risk transforming into acute myeloid leukemia (AML) [1]. Most of the cases (60–70 %) have de novo MDS, meaning that the cause of the disease is unknown. In a small percentage, treatment-related MDS might be linked to cytotoxic chemotherapy (alkylating agents, topoisomerase inhibitors, or podophyllotoxins) or radiation therapy for other diseases [2]. Smoking, occupational exposure to solvents/agricultural chemicals, and a family history of hematopoietic cancer [3–7] have been suggested as risk factors for de novo MDS. However, little is known about the role that epidemiological factors have in MDS survival. Surveillance, epidemiology, and end result (SEER) data showed that individuals with MDS have a 3-year survival rate of 45 % and a 5-year survival rate of 29 % [8]. To date, MDS prognosis is based solely on clinical parameters. The International Prognostic Scoring System (IPSS), the most accepted risk scoring system used to predict outcome, includes four prognostic subgroups based on karyotype categorization, bone marrow blast percentages, and type of cytopenias [9]. However, limitations have been shown on
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the IPSS ability to predict prognosis for patients with low and intermediate 1 risk groups [10–12], and to differentiate between intermediate and long-term survivors [13]. Recently, a revised IPSS integrating demographic and new clinical factors has been developed in an attempt to refine the previous scoring system [14]. The identification of additional epidemiologic factors associated with MDS outcome could be used to further improve the current scoring system. The present study investigates the association between lifestyle, occupational exposure, and overall survival in conjunction with known clinical markers of prognosis. We evaluated the significance of various epidemiologic parameters [e.g., smoking, body mass index (BMI), and occupational exposure to agrochemicals and solvents] in MDS survival among a cohort of adult de novo MDS treated at the University of Texas MD Anderson Cancer Center (UTMDACC).
Materials and methods Study design and study population Details of the patient population and methods have been previously published [6]; however, newly enrolled patients have been added to the original population. Succinctly, our study population consisted of 365 adult patients registered at UTMDACC with a diagnosis of de novo MDS between 1999 and 2007, with no restrictions on gender or ethnicity. Cases were hematologically confirmed and classified initially according to the FAB classification system and reclassified using the 2001 WHO classification. Informed consent was obtained prior to the collection of epidemiologic data in accordance with institutional review board regulations. Clinical and outcome variables were obtained by chart review and from the leukemia department’s clinical database. Cases were identified by the clinical staff at the time of their first visit and were prospectively enrolled into the study. The response rate among de novo MDS cases was approximately 85 %. Reasons for nonenrollment included: patients living outside United States of America (USA) (4 %), refusals (8 %), or did not return the questionnaires despite several contacts (3 %).
Data analysis The exposure variables evaluated in this study included demographic (age, gender, and race), lifestyle exposure variables (obesity status, alcohol, smoking, exposure to solvents, and/or pesticides), educational level, and various clinical variables (bone marrow blasts, cytogenetics, and number of cytopenias).
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Patients were categorized as non-smokers, former smokers, or current smokers. Non-smokers were MDS patients who had never smoked more than 100 cigarettes in their lifetime; former smokers were those who had smoked previously but had quit more than 1 year prior to diagnosis; and current smokers were those who were still smoking at the time of diagnosis or quit \1 year prior to diagnosis. The number of pack-years smoked was calculated using the average number of packs smoked per day 9 the number of years smoked. Number of pack-years was categorized as none, \25 pack-years smoked, and C25 pack-years smoked based on the median of pack-years for former and current smokers. Alcohol drinkers were defined as patients who reported drinking an average of one drink or more per week of any type of alcohol for at least 1 year. Information collected on lifetime occupational history included job title, major duties, chemical and equipment used, work done by the company, and period of employment for each job. Exposure to agricultural chemicals (fertilizers, pesticides, and herbicides) and solvents was estimated based on each full-time occupation held for at least 1 year using a job-exposure matrix (JEM) developed by the National Cancer Institute [15]. The JEM was used to assign an intensity level of exposure to each job title. The intensity level of exposure has four levels: 0 = none, 1 = low, 2 = moderate, and 3 = high. We combined none–low and medium–high levels of exposure for our analyses. An exposure index for each job was calculated based on the level of exposure 9 number of years of exposure for each substance. A lifetime cumulative exposure index for a specific substance was also calculated to reflect the lifetime exposure history for that particular substance. The lifetime cumulative exposure index was calculated by summing the exposure indexes for all jobs held by each MDS patient. It was computed to reflect the lifetime exposure history for the agrochemicals because levels of exposure varied in some participants over their lifetimes. It can be expressed in level-year as unit. One level-year is equal to being exposed to low intensity level (level = 1) of agrochemicals for 1 year or medium intensity level (level = 2) for 1/2 of a year, or high intensity level (level = 3) for 1/3 of a year. Education was evaluated as categorical variable [\Bachelor and CBachelor]. BMI was calculated from self-reported weight and height at the time of diagnosis. BMI was evaluated as categorical variable [underweight/normal (\25 kg/m2, overweight (25 B BMI \ 30 kg/m2), and obese (BMI C30 kg/m2)]. Clinical variables included percent bone marrow blast, cytogenetics, hemoglobin, platelet cytopenia, treatment received, and IPSS score. Patients were categorized into three cytogenetic categories, good [del(5q), del(20q), -Y and normal cytogenetic], intermediate (all other abnormalities), and poor ([3 abnormalities or chromosome 7
Cancer Causes Control (2014) 25:425–435 Table 1 Demographic and lifestyle characteristics of MDS patients
427
Overall (n = 365)
Dead (n = 271)
Alive (n = 94)
Age at diagnosis (year) \60
103 (28.2 %)
64 (23.6 %)
39 (41.5 %)
C60
262 (71.8 %)
207 (76.4 %)
55 (58.5 %)
Male
237 (64.9 %)
184 (67.9 %)
53 (56.4 %)
Female
128 (35.1 %)
87 (32.1 %)
41 (43.6 %)
Gender
Race/ethnicity White
345 (94.6 %)
256 (94.5 %)
89 (94.7 %)
Hispanic
10 (2.7 %)
7 (2.6 %)
3 (3.2 %)
Other
10 (2.7 %)
8 (2.9 %)
2 (2.1 %)
\Bachelor
216 (59.2 %)
166 (61.2 %)
50 (53.2 %)
CBachelor
149 (40.8 %)
105 (38.8 %)
44 (46.8 %)
22.0 (11.5–46.1) 36.5 (1–212.3)
23.4 (8.4–63.8) 35.3 (1–212.3)
17.5 (13–44.3) 39.7 (1–167.8)
Education
Period of follow-up (months) Median (interquartile range) Mean (range) Body mass index groups \25 kg/m2
122 (33.8 %)
86 (32.1 %)
36 (38.7 %)
C25 and \30 kg/m2
160 (44.3 %)
121 (45.1 %)
39 (41.9 %)
79 (21.9 %)
61 (22.8 %)
18 (19.4 %)
C30 kg/m2 Smoking status Never
119 (32.6 %)
86 (31.7 %)
33 (35.1 %)
Ever
246 (67.4 %)
185 (68.3 %)
61 (64.9 %)
Former
193 (52.9 %)
142 (52.4 %)
51 (54.3 %)
Current
53 (14.5 %)
43 (15.9 %)
10 (10.6 %) 33 (35.1 %)
Pack-years smoked Never smoked
119 (32.6 %)
86 (31.7 %)
\25 Pack-years
107 (29.3 %)
71 (26.2 %)
36 (38.3 %)
C25 Pack-years
139 (38.1 %)
114 (42.1 %)
25 (26.6 %)
127 (35.8 %) 228 (64.2 %)
91 (34.6 %) 172 (65.4 %)
36 (39.1 %) 56 (60.9 %)
294 (80.6 %)
220 (81.2 %)
74 (78.7 %)
71 (19.4 %)
51 (18.8 %)
Alcohol Never Ever Solvent exposurea Total did not add up to 100 % for some variables due to missing values a
Lifetime occupational exposure
b
Exposure index (levelyears) = intensity level of exposure x years of exposure
None–low Medium–high Exposure index [mean (range)]b
15.0 (0–189)
14.9 (0–189)
20 (21.3 %) 15.4 (0–138)
Agricultural chemical exposurea None–low
336 (92.0 %)
246 (90.8 %)
Medium–high
29 (8.0 %)
25 (9.2 %)
Exposure index [mean (range)]b
5.5 (0–153)
6.6 (0–153)
anomalies) [9]. The IPSS score was divided into 4 categories, low risk, intermediate 1, intermediate 2, and high risk [9]. Treatment was classified as chemotherapy, supportive care (transfusion and/or growth factor), experimental agents, and observation. Observation meant patients were clinically followed but did not receive any clinical intervention. The outcome variable in this study was overall survival. Survival time was defined as the time from date of diagnosis to
90 (95.8 %) 4 (5.1 %) 2.5 (0–78)
date of death or last follow-up. The latest date of follow-up was 31 September 2011. The Kaplan–Meier method was used to compare differences in survival for all variables, and the log-rank test was used to test for significant differences between groups. Univariate Cox regression analysis was conducted to evaluate crude hazard ratios (HR) for each of the clinical and exposure variables. Multivariable Cox regression analysis was used to estimate the independent effects of the different prognostic factors. Independent models were run for
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Cancer Causes Control (2014) 25:425–435
Table 2 Clinical characteristics of MDS patients Overall (n = 365)
Dead (n = 271)
Alive (n = 94)
Normal
172 (47.8 %)
127 (47.4 %)
45 (48.9 %)
Abnormal
188 (52.2 %)
141 (52.6 %)
47 (51.1 %)
Karyotype
IPSS cytogenetic categoriesa Good
212 (58.9 %)
150 (55.9 %)
62 (67.4 %)
Intermediate
76 (21.1 %)
55 (20.5 %)
21 (22.8 %)
Poor % Marrow blast
72 (20.0 %)
63 (23.5 %)
9 (9.8 %)
\5
172 (47.1 %)
104 (38.4 %)
68 (71.3 %)
C5
194 (52.9 %)
167 (61.6 %)
26 (28.7 %)
Hemoglobin (g/dl) C10
118 (32.3 %)
77 (28.4 %)
41 (43.6 %)
\10
247 (67.7 %)
194 (71.6 %)
53 (56.4 %)
Platelet (109/L) C100
135 (37.0 %)
83 (30.6 %)
52 (55.3 %)
\100
230 (63.0 %)
188 (69.4 %)
42 (47.7 %) 25 (27.8 %)
Treatment modalities Chemotherapy
177 (49.2 %)
152 (56.3 %)
Experimental therapy
97 (27.0 %)
73 (27.0 %)
24 (26.7 %)
Supportive care
70 (19.4 %)
40 (14.8 %)
30 (33.3 %)
Observation
16 (4.4 %)
5 (1.9 %)
11 (12.2 %)
IPSS categories Low risk
70 (19.5 %)
36 (13.5 %)
34 (36.6 %)
Intermediate risk 1
124 (34.5 %)
92 (34.6 %)
32 (34.4 %)
Intermediate risk 2
112 (31.2 %)
93 (35.0 %)
19 (20.4 %)
53 (14.8 %)
45 (16.9 %)
8 (8.6 %)
High risk
Cytopenia defined as hemoglobin \10 and platelets \100 k Total did not add up to 100 % for some variables due to missing values IPSS International Prognostic Scoring System a
Cytogenetic subtype: good, normal karyotype, -Y, del(5q), and del(20q); intermediate, other abnormalities; poor, complex (C3 abnormalities), or chromosome 7 abnormalities
all MDS patients combined, each gender, and each IPSS category. Tests for linear trends were performed on ordinal variables by including them in regression models as continuous variables. A p value\0.05 was considered to be statistically significant. All statistical analyses were conducted using Stata 10 for Windows.
Results Table 1 shows the demographic and lifestyle characteristics of the study population. At the time of last follow-up, of the 365 patients, 271 (74.3 %) had died. Median followup time was 22 months. The mean age and the median at
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diagnosis were 64.5 and 66.0 years, respectively. The majority of the patients were male (64.9 %) and Caucasians (94.6 %). Table 2 includes clinical features of the patients by vital status. Normal karyotype was found in 47.8 % of our population. Chemotherapy was the predominant treatment received (49.2 %). Nine patients received stem cell transplantation. These patients were included in the analysis because their exclusion did not significantly affect the estimates. Univariate survival analysis (Table 3) showed, as expected, that most clinical markers analyzed were significantly associated with MDS prognosis. Patients with C5 % bone marrow blast were at a higher risk of death compared to those patients with \5 % blasts (HR 1.88, 95 % CI 1.47–2.41). Patients with hemoglobin and platelet cytopenia at admission to UTMDACC were also at a higher risk of death (HR 1.56, 95 % CI 1.20–2.04 and HR 1.92, 95 % CI 1.48–2.50, respectively). Patients with poor cytogenetics had the highest risk of dying compared to patients with good cytogenetics (HR 3.62, 95 % CI 2.65–4.94). We also found significant difference when categorizing subjects by IPSS categories. Patients in the intermediate 1 risk group (HR 2.14, 95 % CI 1.44–3.19), intermediate 2 risk group (HR 2.67, 95 % CI 1.79–3.94), and high risk (HR 3.72, 95 % CI 2.37–5.84) had worse prognosis than patients in the low-risk group. Education level was also a significant factor [HR 0.77, 95 % CI 0.60–0.98] for high education (CBachelor) compared to low education (\Bachelor). We observed a marginally significant increased risk of death associated with BMI C30 kg/m2 (HR 1.34, 95 % CI 0.96–1.86) and C25 pack-years smoked (HR 1.28, 95 % CI 0.96–1.69). Patients with medium or high lifetime occupational agricultural chemical exposure had worse overall prognosis (HR 1.87, 95 % CI 1.23–2.83), compared to none or low lifetime occupational agrochemical exposure. The reported occupations with agricultural chemicals were farming/ranching related jobs (80 %), lawn maintenance (10 %), US Army (6 %), and florist (4 %). No significant difference was found by gender, alcohol consumption, and occupational solvent exposure. Multivariable analysis results listed in Table 4 include two different sets of multivariable models, overall and gender specific models. The overall model showed that only medium–high occupational agricultural chemical exposure (HR 1.85, 95 % CI 1.19–2.89) was significantly associated with MDS mortality. No significant difference was found for smoking and obesity status in the overall multivariable model. In the multivariable gender specific models (Table 4), pack-year smoked was a statistically significant prognostic factor among men with C25 pack-years of smoking (HR 1.44, 95 % CI 1.001–2.09) with a significant dose response effect (p trend = 0.04), but not among women with C25 pack-years of smoking (HR 0.87, 95 % CI 0.49–1.56). In
Cancer Causes Control (2014) 25:425–435 Table 3 Univariate hazard ratios for prognostic factors for all MDS patients
Characteristics
429
Overall (n = 365)
Median survival (months)
Univariate analysis Hazard ratio (95 %)
p value
Age at diagnosis (year) \60 years
103 (28.2 %)
42.1
1
C60 years
262 (71.8 %)
25.7
1.83 (1.37–2.45)
Male
237 (65.0 %)
26.3
1
Female
128 (35.0 %)
30.1
0.83 (0.64–1.08)
172 (47.1 %)
46.6
1
193 (52.9 %)
22.9
1.88 (1.47–2.41)
C0
118 (32.3 %)
39.6
1
\10
247 (67.7 %)
25.7
1.56 (1.20–2.04)
\0.0001
Gender
% Marrow blast \5 C5
0.2
\0.0001
Hemoglobin (g/dl) 0.001
Platelet (109/L) C100
135 (37.0 %)
50.2
1
\100
230 (63.0 %)
23.9
1.92 (1.48–2.50)
Good
212 (58.9 %)
19.6
1
Intermediate
76 (21.1 %)
20.4
1.10 (0.81–1.50)
0.5
Poor
72 (20.0 %)
7.9
3.62 (2.65–4.94)
\0.001
Low risk
70 (19.5 %)
75.0
1
Intermediate risk 1 Intermediate risk 2
124 (34.5 %) 112 (31.2 %)
31.7 22.4
2.14 (1.44–3.19) 2.67 (1.79–3.94)
\0.0001 \0.0001
High risk
53 (14.8 %)
18.1
3.72 (2.37–5.84)
\0.0001
\0.0001
Cytogenetic categories
IPSS categories
Education \Bachelor
216 (59.2 %)
26.2
1
CBachelor
149 (40.8 %)
32.5
0.77 (0.60–0.98)
\25 kg/m2
122 (33.8 %)
32.9
1
C25 and \30 kg/m2
160 (44.3 %)
28.3
1.19 (0.90–1.58)
C30 kg/m2
79 (21.9 %)
23.9
1.34 (0.96–1.86)
0.04
BMI groups
p trend
0.2 0.08 0.07
Pack-years smoked Never smoked
119 (32.6 %)
27.8
1
\25 Pack-years
107 (29.3 %)
31.9
0.95 (0.69–1.30)
0.7
C25 Pack-years
139 (38.1 %)
25.7
1.28 (0.96–1.69)
0.09
p trend
Cytopenia defined as hemoglobin \10 and platelets \100 k IPSS International Prognostic Scoring System, BMI Body Mass Index a
Lifetime occupational exposure
0.08
Alcohol Never
127 (35.8 %)
27.1
1
Ever
228 (64.2 %)
27.7
0.86 (0.67–1.11)
None–low
294 (80.6 %)
28.2
1
Medium–high
71 (19.4 %)
26.3
1.06 (0.78–1.45)
0.3
Solvent exposure 0.7
Agricultural chemical exposurea None–low
336 (92.0 %)
29.3
1
Medium–high
29 (8.0 %)
18.6
1.87 (1.23–2.83)
0.003
123
123
139
C25 Pack-years p trend
160
79
C25 and \30 kg/m2
C30 kg/m2
29
Medium–high
382.8
61
1,037.4
223.2
466.5
405.3
378.0
337.6
27.8
18.6
29.3
23.9
28.3
32.9
25.7
31.9
1
1.85 (1.19–2.89)
1
0.1
1.31 (0.93–1.85)
1.16 (0.87–1.56)
1
1.29 (0.96–1.74) 0.08
0.94 (0.68–1.30)
71
25
212
55
107
73
101
65
48.1
670.8
167.1
323.3
226.0
284.8
218.8
215.3
Time at risk (person-years)
18.6
27.8
22.7
30.6
27.1
21.8
31.9
26.0
Median survival (months)
1.84 (1.15–2.97)
1
0.7
1.09 (0.71–1.66)
0.99 (0.69–1.41)
1
1.44 (1.001–2.09) 0.04
0.88 (0.58–1.33)
1
Hazard ratio (95 % CI)
24
53
49
38
42
48
4
124
n
12.9
366.6
56.1
143.2
179.3
93.2
118.8
167.5
Time at risk (person-years)
Female
a
Lifetime occupational exposure
BMI Body Mass Index
Hazard ratio adjusted for all variables in the table, age, gender, education, International Prognostic Scoring System (IPSS) categories, and treatment modalities
336
None–low
Agricultural chemical exposurea
p trend
122
\25 kg/m2
BMI groups
119
107
None
Hazard ratio (95 % CI)
n
Median survival (months)
n
Time at risk (person-years)
Male
Overall
\25 Pack-years
Pack-years smoked
Factor
Table 4 Multivariable analysis for prognostic factors for all MDS patients and by gender
13.6
30.1
24.8
27.9
37.9
30.2
33.0
29.3
Median survival (months)
1.94 (0.45–8.39)
1
0.07
1.76 (0.96–3.20)
1.36 (0.82–2.26)
1
0.87 (0.49–1.56) 0.7
1.08 (0.63–1.84)
1
Hazard ratio (95 % CI)
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Cancer Causes Control (2014) 25:425–435
further analysis evaluating the prognosis effect of packyears smoked by smoking status, among men, the mortality risk was higher among current male smokers with C25 pack-years as compared to never smokers [(HR 1.78, 95 % CI 1.08–2.91), data not shown]. For lifetime occupational agricultural exposures, medium or high lifetime occupational agricultural exposure remained a statistically significant factor among men only (HR 1.84, 95 % CI 1.15–2.97). The joint effect of chemical exposure by packyears smoked for male patients was also assessed. The worst overall prognosis was found among smokers with C25 pack-years who were exposed to medium or high lifetime occupational exposure as compared to never smokers with none–low occupational chemical exposure [(HR 2.62, 95 % CI 1.21–5.67), data not shown]. The HR for obesity status were 1.09 and 1.76 in men and women, respectively, but the difference was not statistically significant. We also compared the role of potential outcome factors (obesity, smoking, and lifetime occupational agricultural exposure) by IPSS categories. Table 5 shows demographic and lifestyle characteristics and median survival by IPSS categories. We found that, in the low-risk group, obesity was associated with statistically significant shorter survival time (median survival 94.3 vs. 64.8 months for BMI \25 and C30 kg/m2, respectively). Lifetime occupational agricultural exposure was also associated with shorter survival time in the low-risk group (86.6 vs. 23.7 months for none– low and medium–high occupational agricultural exposure, respectively). In high-risk group, lifetime occupational agricultural exposure was significantly associated with MDS survival (median survival 20.3 vs. 11.2 months for none–low and medium–high occupational agricultural exposure, respectively). After adjusting for relevant clinical factors, epidemiologic prognostic factors differed in multivariable stratified analysis by IPSS categories (Table 6). Smoking C25 pack-years was associated with increased mortality in the intermediate 1 risk group (HR 1.75, 95 % CI 1.005–3.06) with a significant trend (p trend = 0.01). We also found a similar excess mortality risk associated with smoking C25 pack-years in the high-risk group (HR 1.72, 95 % CI 0.74–3.96), but the difference was not statistically significant. Obesity was associated with an excess risk of death in the low, intermediate 2, and high-risk groups; however, due to limited sample size, these differences should be cautiously interpreted. Lifetime exposure to agrochemicals was a statistically significant prognosis factor in the high IPSS risk group (HR 4.36, 95 % CI 1.20–15.8) and marginally significant in the low IPSS risk group (HR 4.03, 95 % CI 0.92–17.6). When the analysis was stratified by the type or MDS treatment modalities, none of the factors studied was statistically significantly associated with risk of death (data not shown).
431
Discussion The evaluation of epidemiological factors that could impact MDS prognosis has not been adequately addressed. Most previous studies have mainly included clinical markers. We believe that our study at MD Anderson Cancer Center is the first to evaluate the combined effects of clinical, occupational, and lifestyle exposure data in MDS survival. Our results showed that smoking has a detrimental effect on MDS prognosis significantly lowering overall survival. Higher pack-years (C25 pack-years) smoked was associated with poorer MDS prognosis. This association between smoking and MDS outcome differed by gender and IPSS categories. Our findings agree with previous studies that have evaluated the relationship between smoking and MDS prognosis [16–18]. In contrast to previous reports, in the present study, we were able to quantify the lifetime smoking exposure by analyzing the number of pack-years of cigarettes smoked. Our results also suggest that the intensity and duration of smoking are relevant for MDS outcome. To the best of our knowledge, this is the first study to show a significant dose response relationship between smoking and MDS outcome. The exact mechanism by which smoking affects MDS outcome has not been elucidated. Tobacco has been shown to contain many mutagenic and carcinogenic chemicals [19]. These chemicals have multiple effects on the immune system such as decreased natural killer cell numbers and activity, weakened T cell defense abilities, leading to immunosuppression [20, 21]. As a result, cigarette smoking could lead to an elevated susceptibility to infection that might shorten MDS survival. It has been reported that infection is the most frequent MDS-related cause of death among lower risk MDS group [22]. The impact of smoking on MDS survival may also occur through other smoking-related comorbidities such as chronic obstructive pulmonary disease (COPD) or cardiovascular disease [23]. Further investigations are essential to clarify the role of specific smoking chemicals in MDS prognosis. Our results suggest an association between agrochemical occupational exposure and MDS prognosis in all patients combined and in men. The strength of the association among women was similar to that of men but not statistically significant, possibly due to the limited number of females who reported exposure to agrochemicals. There were more men exposed to medium–high agrochemical occupational exposure than women (11 vs. 3 %, respectively). Previous studies have found comparable effect of combined solvent and agrochemicals occupational exposure in MDS survival [7, 24] but none have evaluated the effect of solvents and agrochemicals independently and in the context of other relevant demographic, epidemiologic, and
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Lifetime occupational exposure a
Total did not add up to 100 % for some variables due to missing values
20.3
11.2 6.1
96.2 46
7 22.4
22.1 279.5
18.7 7
105 33.0
18.1 21.3
356.6 115
9 23.7
86.6 300.4
9.8 4
66
Agricultural chemical exposurea
Medium–high
52.5 103.1 26 64.8 11 C30 kg/m2
37.1
33.1
29.4 128.3
144.7 46
51 67.3
94.3
35 C25 and \30 kg/m2
112.6 23 \25 kg/m2
160.5
26.0 139.1 51 71.7 129.0 27 C25 Pack-years
Body mass index groups
43.8
28.2 127.1
111.7 34
39 86.6
75.8
105.4 25
18
Never smoked
\25 Pack-years
90.9
Median survival (months) Time at risk (person-years)
Pack-years smoked
None–low
21.8
20.3
12.6 23.7 14 15.9 55.0 25
44.7
33.2 15
23 23.6
21.9 110.0
132.3 50
36
16.3
20.3 39.1 21 15.1 63.6 36
34.5
28.7 15
17 25.7
23.4 121.6 40
113.0
Time at risk (person-years) Time at risk (person-years) n n
Median survival (months)
n
36
Time at risk (person-years) n Median survival (months)
High (n = 53) Intermediate 2 (n = 112) Intermediate 1 (n = 124) Low (n = 70)
IPSS categories
Table 5 Characteristics and median survival by IPSS categories
123
13.8
Cancer Causes Control (2014) 25:425–435 Median survival (months)
432
clinical markers of the patient population. We found only an association with agrochemical occupational exposure. The mechanism behind the association between MDS survival and agrochemical occupational exposure is biologically plausible, but no studies have identified a specific agent as probable culprit. In a study of 55 MDS patients, Rigolin et al. found a poorer survival among MDS patients who had shorter telomeres potentially associated with occupational exposure to pesticides and organic solvents [24]. Shorter telomeres have been associated with a higher mortality from age-related diseases [25]; however, the correlation with occupational chemical exposure still need to be further studied. Our findings suggest that obesity might be an important prognostic factor only among female MDS patients. The negative impact of BMI on prognosis has been reported in relation to other hematopoietic cancers such as leukemia overall [26, 27] and AML [28, 29]. However, AML studies did not analyzed the data by gender. Obesity probably affects MDS outcome through multiple biological mechanisms such as inflammation [30, 31], immune function perturbations [32], and cell proliferation [33]. Further studies are needed to confirm the possible differences by gender and investigate plausible underlying mechanisms such as obesity-related hormonal disturbances [34]. In addition, obesity-related comorbidities such as diabetes and cardiovascular diseases which have been shown to increase toxicity to treatment or treatment-related deaths in cancer patients [35] should to be included in future research. Our study has important strengths. It assesses the combined effects of clinical, occupational, and lifestyle exposure data in MDS survival in a relatively large cohort of de novo MDS cases. Detailed information on lifestyle characteristics such as smoking, lifetime agrochemical occupational exposure, and obesity was available, thus making it possible to assess their MDS prognostic implication. The patients were prospectively identified at the time of registration enabling data collection from the patients themselves, and avoiding a need for proxy interviews, thus minimizing the potential for recall bias. Since all patients in this study were recruited from UTMDACC, data on every patient were derived from standardized and validated clinical and epidemiological data collection instruments reducing errors in data collection. To obtain exposure information, we collected a lifetime job history with information on the exposures, job performed, and industry. It has been shown that self-reported occupational exposure is useful in epidemiological studies when documented occupational exposure is not available [36]. There are also some limitations in our study. The patient population of the UTMDACC is subject to the vagaries of referral patterns. Our patient population was younger
a
2.19 (0.79–6.1)
C30 kg/m2
3.83 (1.11–13.14)
Medium–high
4.03 (0.92–17.60)
1
0.3
2.54 (0.82–7.88)
0.84 (0.34–2.11)
1
0.6
1.24 (0.53–2.87)
0.65 (0.24–1.76)
1
1.55 (0.73–3.29)
1
0.4
0.66 (0.36–1.20)
1.51 (0.96–2.42)
1
0.2
1.38 (0.84–2.27)
1.01 (0.56–1.81)
1
1.66 (0.66–3.99)
1
0.6
0.72 (0.38–1.36)
1.27 (0.77–2.12)
1
0.01
1.75 (1.005–3.06)
0.85 (0.45–1.59)
1
1.25 (0.54–2.89)
1
0.1
1.55 (0.87–2.77)
1.25 (0.76–2.08)
1
0.2
1.43 (0.87–2.35)
0.84 (0.51–1.39)
1
Univariate Hazard ratio (95 % CI)
1.45 (0.58–3.65)
1
0.2
1.62 (0.83–3.14)
1.23 (0.70–2.17)
1
0.3
1.38 (0.78–2.47)
0.93 (0.53–1.64)
1
Multivariate Hazard ratio (95 % CI)
Intermediate 2 (n = 112)
a
Lifetime occupational exposure
Hazard ratio adjusted for all variables in the table, age, gender, education, bone marrow count, platelet count, and treatment modalities
1
None–low
Agrochemical exposure
0.2
1.07 (0.50–2.28)
C25 and \30 kg/m2
p trend
1
\25 kg/m2
0.93 (0.43–2.04)
C25 Pack-years
0.9
0.81 (0.33–2.00)
p trend Body mass index
1
Never smoked
\25 Pack-years
Pack-years smoked
Multivariate Hazard ratio (95 % CI)
Univariate Hazard ratio (95 % CI)
Univariate Hazard ratio (95 % CI)
Multivariate Hazard ratio (95 % CI)
Intermediate 1 (n = 124)
Low (n = 70)
IPSS categories
Table 6 MDS mortality by IPSS score categories
2.48 (1.02–6.04)
1
0.3
1.60 (0.73–3.49)
1.007 (0.49–2.05)
1
0.4
1.34 (0.65–2.74)
1.06 (0.47–2.41)
1
Univariate Hazard ratio (95 % CI)
High (n = 53)
4.36 (1.20–15.8)
1
0.1
2.07 (0.88–4.84)
0.94 (0.44–2.04)
1
0.2
1.72 (0.74–3.96)
0.71 (0.23–2.12)
1
Multivariate Hazard ratio (95 % CI)
Cancer Causes Control (2014) 25:425–435 433
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Cancer Causes Control (2014) 25:425–435
compared to other reports (median age in our study population = 66 years and mean age = 64.5 years, range: 18–90). Therefore, a cohort from UTMDACC does not necessarily represent the national MDS population [37, 38]. This bias may lead to an inability to generalize the findings, thus our results need to be interpreted cautiously. Although our analyses adjusted for age at diagnosis, sex, IPSS risk categorization, and clinical characteristics in the multivariable models, there may also be residual confounding from other factors such as comorbidities not assessed in these analyses. We were only able to compute overall survival in this study; however, future studies should analyze disease-specific survival as well.
Conclusion Our study suggests that smoking and lifetime agrochemical occupational exposure may have an impact on MDS prognosis with difference by gender and IPSS categories. Incorporating lifestyle characteristics to the established clinical factors may help improve current prognostic models such as the IPSS and allow physicians to improve personalized treatment strategies for a better MDS outcome. Additional research is greatly needed to confirm and expand our findings. Acknowledgments This work was supported by National Cancer Institute (NCI) Grants CA100632 and CA115180 and NIEHS ES007784. We are grateful to Suraj Sander whose thesis for Master in Public Health has inspired this project. Finally, we give special thanks to our data collection team and our participants. Conflict of interest
The authors declare no conflict of interest.
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ORIGINAL PAPER
Evaluation of epidemiological factors in survival of patients with de novo myelodysplastic syndromes Kplola Y. Elhor Gbito • Guillermo Garcia-Manero Sara S. Strom
•
Received: 25 April 2013 / Accepted: 15 January 2014 / Published online: 25 January 2014 Ó Springer International Publishing Switzerland 2014
Abstract Myelodysplastic syndromes (MDS) prognosis is currently based solely on clinical parameters. The identification of additional factors associated with MDS outcome could be used to further improve the current scoring system such as the International Prognostic Scoring System (IPSS). The present study evaluates the role of epidemiological markers as predictors of survival for 365 adult de novo MDS patients. Multivariable Cox regression analysis was used to estimate overall survival. Median follow-up time was 22 months. At the time of last follow-up, 271 patients (74.3 %) had died. For all MDS patients, medium–high lifetime occupational agrochemical exposure (HR 1.85, CI 1.19–2.89) remained as an independent predictor of MDS survival. Stratified analysis by gender showed that C25 pack-years smoked (HR 1.44, CI 1.001–2.09) and medium– high lifetime occupational agrochemical exposure (HR 1.84, CI 1.15–2.97) were independent predictors of MDS survival in men, but not in women. For MDS patients stratified by IPSS categories, C25 pack-years smoked (HR 1.75, CI 1.005–3.06) was an independent predictor for intermediate 1 IPSS risk group only, and medium–high lifetime occupational agrochemical exposure was associated with increased mortality (HR 4.36, CI 1.20–15.8) in the K. Y. Elhor Gbito Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA G. Garcia-Manero Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA S. S. Strom (&) Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 1155 Pressler Street, Unit 1340, Houston, TX 77030, USA e-mail: [email protected]
high IPSS risk group. Smoking and lifetime occupational agrochemical exposure may play a role in MDS survival. Incorporating relevant epidemiological markers with known clinical predictors of outcome may help physician stratify patients and customize treatment strategies to improve the outcome of MDS. Keywords Myelodysplastic syndromes Epidemiologic prognostic factors Smoking Obesity Agrochemicals
Introduction Myelodysplastic syndromes (MDS) are a heterogeneous group of stem cell malignancies, which have a higher risk transforming into acute myeloid leukemia (AML) [1]. Most of the cases (60–70 %) have de novo MDS, meaning that the cause of the disease is unknown. In a small percentage, treatment-related MDS might be linked to cytotoxic chemotherapy (alkylating agents, topoisomerase inhibitors, or podophyllotoxins) or radiation therapy for other diseases [2]. Smoking, occupational exposure to solvents/agricultural chemicals, and a family history of hematopoietic cancer [3–7] have been suggested as risk factors for de novo MDS. However, little is known about the role that epidemiological factors have in MDS survival. Surveillance, epidemiology, and end result (SEER) data showed that individuals with MDS have a 3-year survival rate of 45 % and a 5-year survival rate of 29 % [8]. To date, MDS prognosis is based solely on clinical parameters. The International Prognostic Scoring System (IPSS), the most accepted risk scoring system used to predict outcome, includes four prognostic subgroups based on karyotype categorization, bone marrow blast percentages, and type of cytopenias [9]. However, limitations have been shown on
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the IPSS ability to predict prognosis for patients with low and intermediate 1 risk groups [10–12], and to differentiate between intermediate and long-term survivors [13]. Recently, a revised IPSS integrating demographic and new clinical factors has been developed in an attempt to refine the previous scoring system [14]. The identification of additional epidemiologic factors associated with MDS outcome could be used to further improve the current scoring system. The present study investigates the association between lifestyle, occupational exposure, and overall survival in conjunction with known clinical markers of prognosis. We evaluated the significance of various epidemiologic parameters [e.g., smoking, body mass index (BMI), and occupational exposure to agrochemicals and solvents] in MDS survival among a cohort of adult de novo MDS treated at the University of Texas MD Anderson Cancer Center (UTMDACC).
Materials and methods Study design and study population Details of the patient population and methods have been previously published [6]; however, newly enrolled patients have been added to the original population. Succinctly, our study population consisted of 365 adult patients registered at UTMDACC with a diagnosis of de novo MDS between 1999 and 2007, with no restrictions on gender or ethnicity. Cases were hematologically confirmed and classified initially according to the FAB classification system and reclassified using the 2001 WHO classification. Informed consent was obtained prior to the collection of epidemiologic data in accordance with institutional review board regulations. Clinical and outcome variables were obtained by chart review and from the leukemia department’s clinical database. Cases were identified by the clinical staff at the time of their first visit and were prospectively enrolled into the study. The response rate among de novo MDS cases was approximately 85 %. Reasons for nonenrollment included: patients living outside United States of America (USA) (4 %), refusals (8 %), or did not return the questionnaires despite several contacts (3 %).
Data analysis The exposure variables evaluated in this study included demographic (age, gender, and race), lifestyle exposure variables (obesity status, alcohol, smoking, exposure to solvents, and/or pesticides), educational level, and various clinical variables (bone marrow blasts, cytogenetics, and number of cytopenias).
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Cancer Causes Control (2014) 25:425–435
Patients were categorized as non-smokers, former smokers, or current smokers. Non-smokers were MDS patients who had never smoked more than 100 cigarettes in their lifetime; former smokers were those who had smoked previously but had quit more than 1 year prior to diagnosis; and current smokers were those who were still smoking at the time of diagnosis or quit \1 year prior to diagnosis. The number of pack-years smoked was calculated using the average number of packs smoked per day 9 the number of years smoked. Number of pack-years was categorized as none, \25 pack-years smoked, and C25 pack-years smoked based on the median of pack-years for former and current smokers. Alcohol drinkers were defined as patients who reported drinking an average of one drink or more per week of any type of alcohol for at least 1 year. Information collected on lifetime occupational history included job title, major duties, chemical and equipment used, work done by the company, and period of employment for each job. Exposure to agricultural chemicals (fertilizers, pesticides, and herbicides) and solvents was estimated based on each full-time occupation held for at least 1 year using a job-exposure matrix (JEM) developed by the National Cancer Institute [15]. The JEM was used to assign an intensity level of exposure to each job title. The intensity level of exposure has four levels: 0 = none, 1 = low, 2 = moderate, and 3 = high. We combined none–low and medium–high levels of exposure for our analyses. An exposure index for each job was calculated based on the level of exposure 9 number of years of exposure for each substance. A lifetime cumulative exposure index for a specific substance was also calculated to reflect the lifetime exposure history for that particular substance. The lifetime cumulative exposure index was calculated by summing the exposure indexes for all jobs held by each MDS patient. It was computed to reflect the lifetime exposure history for the agrochemicals because levels of exposure varied in some participants over their lifetimes. It can be expressed in level-year as unit. One level-year is equal to being exposed to low intensity level (level = 1) of agrochemicals for 1 year or medium intensity level (level = 2) for 1/2 of a year, or high intensity level (level = 3) for 1/3 of a year. Education was evaluated as categorical variable [\Bachelor and CBachelor]. BMI was calculated from self-reported weight and height at the time of diagnosis. BMI was evaluated as categorical variable [underweight/normal (\25 kg/m2, overweight (25 B BMI \ 30 kg/m2), and obese (BMI C30 kg/m2)]. Clinical variables included percent bone marrow blast, cytogenetics, hemoglobin, platelet cytopenia, treatment received, and IPSS score. Patients were categorized into three cytogenetic categories, good [del(5q), del(20q), -Y and normal cytogenetic], intermediate (all other abnormalities), and poor ([3 abnormalities or chromosome 7
Cancer Causes Control (2014) 25:425–435 Table 1 Demographic and lifestyle characteristics of MDS patients
427
Overall (n = 365)
Dead (n = 271)
Alive (n = 94)
Age at diagnosis (year) \60
103 (28.2 %)
64 (23.6 %)
39 (41.5 %)
C60
262 (71.8 %)
207 (76.4 %)
55 (58.5 %)
Male
237 (64.9 %)
184 (67.9 %)
53 (56.4 %)
Female
128 (35.1 %)
87 (32.1 %)
41 (43.6 %)
Gender
Race/ethnicity White
345 (94.6 %)
256 (94.5 %)
89 (94.7 %)
Hispanic
10 (2.7 %)
7 (2.6 %)
3 (3.2 %)
Other
10 (2.7 %)
8 (2.9 %)
2 (2.1 %)
\Bachelor
216 (59.2 %)
166 (61.2 %)
50 (53.2 %)
CBachelor
149 (40.8 %)
105 (38.8 %)
44 (46.8 %)
22.0 (11.5–46.1) 36.5 (1–212.3)
23.4 (8.4–63.8) 35.3 (1–212.3)
17.5 (13–44.3) 39.7 (1–167.8)
Education
Period of follow-up (months) Median (interquartile range) Mean (range) Body mass index groups \25 kg/m2
122 (33.8 %)
86 (32.1 %)
36 (38.7 %)
C25 and \30 kg/m2
160 (44.3 %)
121 (45.1 %)
39 (41.9 %)
79 (21.9 %)
61 (22.8 %)
18 (19.4 %)
C30 kg/m2 Smoking status Never
119 (32.6 %)
86 (31.7 %)
33 (35.1 %)
Ever
246 (67.4 %)
185 (68.3 %)
61 (64.9 %)
Former
193 (52.9 %)
142 (52.4 %)
51 (54.3 %)
Current
53 (14.5 %)
43 (15.9 %)
10 (10.6 %) 33 (35.1 %)
Pack-years smoked Never smoked
119 (32.6 %)
86 (31.7 %)
\25 Pack-years
107 (29.3 %)
71 (26.2 %)
36 (38.3 %)
C25 Pack-years
139 (38.1 %)
114 (42.1 %)
25 (26.6 %)
127 (35.8 %) 228 (64.2 %)
91 (34.6 %) 172 (65.4 %)
36 (39.1 %) 56 (60.9 %)
294 (80.6 %)
220 (81.2 %)
74 (78.7 %)
71 (19.4 %)
51 (18.8 %)
Alcohol Never Ever Solvent exposurea Total did not add up to 100 % for some variables due to missing values a
Lifetime occupational exposure
b
Exposure index (levelyears) = intensity level of exposure x years of exposure
None–low Medium–high Exposure index [mean (range)]b
15.0 (0–189)
14.9 (0–189)
20 (21.3 %) 15.4 (0–138)
Agricultural chemical exposurea None–low
336 (92.0 %)
246 (90.8 %)
Medium–high
29 (8.0 %)
25 (9.2 %)
Exposure index [mean (range)]b
5.5 (0–153)
6.6 (0–153)
anomalies) [9]. The IPSS score was divided into 4 categories, low risk, intermediate 1, intermediate 2, and high risk [9]. Treatment was classified as chemotherapy, supportive care (transfusion and/or growth factor), experimental agents, and observation. Observation meant patients were clinically followed but did not receive any clinical intervention. The outcome variable in this study was overall survival. Survival time was defined as the time from date of diagnosis to
90 (95.8 %) 4 (5.1 %) 2.5 (0–78)
date of death or last follow-up. The latest date of follow-up was 31 September 2011. The Kaplan–Meier method was used to compare differences in survival for all variables, and the log-rank test was used to test for significant differences between groups. Univariate Cox regression analysis was conducted to evaluate crude hazard ratios (HR) for each of the clinical and exposure variables. Multivariable Cox regression analysis was used to estimate the independent effects of the different prognostic factors. Independent models were run for
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Table 2 Clinical characteristics of MDS patients Overall (n = 365)
Dead (n = 271)
Alive (n = 94)
Normal
172 (47.8 %)
127 (47.4 %)
45 (48.9 %)
Abnormal
188 (52.2 %)
141 (52.6 %)
47 (51.1 %)
Karyotype
IPSS cytogenetic categoriesa Good
212 (58.9 %)
150 (55.9 %)
62 (67.4 %)
Intermediate
76 (21.1 %)
55 (20.5 %)
21 (22.8 %)
Poor % Marrow blast
72 (20.0 %)
63 (23.5 %)
9 (9.8 %)
\5
172 (47.1 %)
104 (38.4 %)
68 (71.3 %)
C5
194 (52.9 %)
167 (61.6 %)
26 (28.7 %)
Hemoglobin (g/dl) C10
118 (32.3 %)
77 (28.4 %)
41 (43.6 %)
\10
247 (67.7 %)
194 (71.6 %)
53 (56.4 %)
Platelet (109/L) C100
135 (37.0 %)
83 (30.6 %)
52 (55.3 %)
\100
230 (63.0 %)
188 (69.4 %)
42 (47.7 %) 25 (27.8 %)
Treatment modalities Chemotherapy
177 (49.2 %)
152 (56.3 %)
Experimental therapy
97 (27.0 %)
73 (27.0 %)
24 (26.7 %)
Supportive care
70 (19.4 %)
40 (14.8 %)
30 (33.3 %)
Observation
16 (4.4 %)
5 (1.9 %)
11 (12.2 %)
IPSS categories Low risk
70 (19.5 %)
36 (13.5 %)
34 (36.6 %)
Intermediate risk 1
124 (34.5 %)
92 (34.6 %)
32 (34.4 %)
Intermediate risk 2
112 (31.2 %)
93 (35.0 %)
19 (20.4 %)
53 (14.8 %)
45 (16.9 %)
8 (8.6 %)
High risk
Cytopenia defined as hemoglobin \10 and platelets \100 k Total did not add up to 100 % for some variables due to missing values IPSS International Prognostic Scoring System a
Cytogenetic subtype: good, normal karyotype, -Y, del(5q), and del(20q); intermediate, other abnormalities; poor, complex (C3 abnormalities), or chromosome 7 abnormalities
all MDS patients combined, each gender, and each IPSS category. Tests for linear trends were performed on ordinal variables by including them in regression models as continuous variables. A p value\0.05 was considered to be statistically significant. All statistical analyses were conducted using Stata 10 for Windows.
Results Table 1 shows the demographic and lifestyle characteristics of the study population. At the time of last follow-up, of the 365 patients, 271 (74.3 %) had died. Median followup time was 22 months. The mean age and the median at
123
diagnosis were 64.5 and 66.0 years, respectively. The majority of the patients were male (64.9 %) and Caucasians (94.6 %). Table 2 includes clinical features of the patients by vital status. Normal karyotype was found in 47.8 % of our population. Chemotherapy was the predominant treatment received (49.2 %). Nine patients received stem cell transplantation. These patients were included in the analysis because their exclusion did not significantly affect the estimates. Univariate survival analysis (Table 3) showed, as expected, that most clinical markers analyzed were significantly associated with MDS prognosis. Patients with C5 % bone marrow blast were at a higher risk of death compared to those patients with \5 % blasts (HR 1.88, 95 % CI 1.47–2.41). Patients with hemoglobin and platelet cytopenia at admission to UTMDACC were also at a higher risk of death (HR 1.56, 95 % CI 1.20–2.04 and HR 1.92, 95 % CI 1.48–2.50, respectively). Patients with poor cytogenetics had the highest risk of dying compared to patients with good cytogenetics (HR 3.62, 95 % CI 2.65–4.94). We also found significant difference when categorizing subjects by IPSS categories. Patients in the intermediate 1 risk group (HR 2.14, 95 % CI 1.44–3.19), intermediate 2 risk group (HR 2.67, 95 % CI 1.79–3.94), and high risk (HR 3.72, 95 % CI 2.37–5.84) had worse prognosis than patients in the low-risk group. Education level was also a significant factor [HR 0.77, 95 % CI 0.60–0.98] for high education (CBachelor) compared to low education (\Bachelor). We observed a marginally significant increased risk of death associated with BMI C30 kg/m2 (HR 1.34, 95 % CI 0.96–1.86) and C25 pack-years smoked (HR 1.28, 95 % CI 0.96–1.69). Patients with medium or high lifetime occupational agricultural chemical exposure had worse overall prognosis (HR 1.87, 95 % CI 1.23–2.83), compared to none or low lifetime occupational agrochemical exposure. The reported occupations with agricultural chemicals were farming/ranching related jobs (80 %), lawn maintenance (10 %), US Army (6 %), and florist (4 %). No significant difference was found by gender, alcohol consumption, and occupational solvent exposure. Multivariable analysis results listed in Table 4 include two different sets of multivariable models, overall and gender specific models. The overall model showed that only medium–high occupational agricultural chemical exposure (HR 1.85, 95 % CI 1.19–2.89) was significantly associated with MDS mortality. No significant difference was found for smoking and obesity status in the overall multivariable model. In the multivariable gender specific models (Table 4), pack-year smoked was a statistically significant prognostic factor among men with C25 pack-years of smoking (HR 1.44, 95 % CI 1.001–2.09) with a significant dose response effect (p trend = 0.04), but not among women with C25 pack-years of smoking (HR 0.87, 95 % CI 0.49–1.56). In
Cancer Causes Control (2014) 25:425–435 Table 3 Univariate hazard ratios for prognostic factors for all MDS patients
Characteristics
429
Overall (n = 365)
Median survival (months)
Univariate analysis Hazard ratio (95 %)
p value
Age at diagnosis (year) \60 years
103 (28.2 %)
42.1
1
C60 years
262 (71.8 %)
25.7
1.83 (1.37–2.45)
Male
237 (65.0 %)
26.3
1
Female
128 (35.0 %)
30.1
0.83 (0.64–1.08)
172 (47.1 %)
46.6
1
193 (52.9 %)
22.9
1.88 (1.47–2.41)
C0
118 (32.3 %)
39.6
1
\10
247 (67.7 %)
25.7
1.56 (1.20–2.04)
\0.0001
Gender
% Marrow blast \5 C5
0.2
\0.0001
Hemoglobin (g/dl) 0.001
Platelet (109/L) C100
135 (37.0 %)
50.2
1
\100
230 (63.0 %)
23.9
1.92 (1.48–2.50)
Good
212 (58.9 %)
19.6
1
Intermediate
76 (21.1 %)
20.4
1.10 (0.81–1.50)
0.5
Poor
72 (20.0 %)
7.9
3.62 (2.65–4.94)
\0.001
Low risk
70 (19.5 %)
75.0
1
Intermediate risk 1 Intermediate risk 2
124 (34.5 %) 112 (31.2 %)
31.7 22.4
2.14 (1.44–3.19) 2.67 (1.79–3.94)
\0.0001 \0.0001
High risk
53 (14.8 %)
18.1
3.72 (2.37–5.84)
\0.0001
\0.0001
Cytogenetic categories
IPSS categories
Education \Bachelor
216 (59.2 %)
26.2
1
CBachelor
149 (40.8 %)
32.5
0.77 (0.60–0.98)
\25 kg/m2
122 (33.8 %)
32.9
1
C25 and \30 kg/m2
160 (44.3 %)
28.3
1.19 (0.90–1.58)
C30 kg/m2
79 (21.9 %)
23.9
1.34 (0.96–1.86)
0.04
BMI groups
p trend
0.2 0.08 0.07
Pack-years smoked Never smoked
119 (32.6 %)
27.8
1
\25 Pack-years
107 (29.3 %)
31.9
0.95 (0.69–1.30)
0.7
C25 Pack-years
139 (38.1 %)
25.7
1.28 (0.96–1.69)
0.09
p trend
Cytopenia defined as hemoglobin \10 and platelets \100 k IPSS International Prognostic Scoring System, BMI Body Mass Index a
Lifetime occupational exposure
0.08
Alcohol Never
127 (35.8 %)
27.1
1
Ever
228 (64.2 %)
27.7
0.86 (0.67–1.11)
None–low
294 (80.6 %)
28.2
1
Medium–high
71 (19.4 %)
26.3
1.06 (0.78–1.45)
0.3
Solvent exposure 0.7
Agricultural chemical exposurea None–low
336 (92.0 %)
29.3
1
Medium–high
29 (8.0 %)
18.6
1.87 (1.23–2.83)
0.003
123
123
139
C25 Pack-years p trend
160
79
C25 and \30 kg/m2
C30 kg/m2
29
Medium–high
382.8
61
1,037.4
223.2
466.5
405.3
378.0
337.6
27.8
18.6
29.3
23.9
28.3
32.9
25.7
31.9
1
1.85 (1.19–2.89)
1
0.1
1.31 (0.93–1.85)
1.16 (0.87–1.56)
1
1.29 (0.96–1.74) 0.08
0.94 (0.68–1.30)
71
25
212
55
107
73
101
65
48.1
670.8
167.1
323.3
226.0
284.8
218.8
215.3
Time at risk (person-years)
18.6
27.8
22.7
30.6
27.1
21.8
31.9
26.0
Median survival (months)
1.84 (1.15–2.97)
1
0.7
1.09 (0.71–1.66)
0.99 (0.69–1.41)
1
1.44 (1.001–2.09) 0.04
0.88 (0.58–1.33)
1
Hazard ratio (95 % CI)
24
53
49
38
42
48
4
124
n
12.9
366.6
56.1
143.2
179.3
93.2
118.8
167.5
Time at risk (person-years)
Female
a
Lifetime occupational exposure
BMI Body Mass Index
Hazard ratio adjusted for all variables in the table, age, gender, education, International Prognostic Scoring System (IPSS) categories, and treatment modalities
336
None–low
Agricultural chemical exposurea
p trend
122
\25 kg/m2
BMI groups
119
107
None
Hazard ratio (95 % CI)
n
Median survival (months)
n
Time at risk (person-years)
Male
Overall
\25 Pack-years
Pack-years smoked
Factor
Table 4 Multivariable analysis for prognostic factors for all MDS patients and by gender
13.6
30.1
24.8
27.9
37.9
30.2
33.0
29.3
Median survival (months)
1.94 (0.45–8.39)
1
0.07
1.76 (0.96–3.20)
1.36 (0.82–2.26)
1
0.87 (0.49–1.56) 0.7
1.08 (0.63–1.84)
1
Hazard ratio (95 % CI)
430 Cancer Causes Control (2014) 25:425–435
Cancer Causes Control (2014) 25:425–435
further analysis evaluating the prognosis effect of packyears smoked by smoking status, among men, the mortality risk was higher among current male smokers with C25 pack-years as compared to never smokers [(HR 1.78, 95 % CI 1.08–2.91), data not shown]. For lifetime occupational agricultural exposures, medium or high lifetime occupational agricultural exposure remained a statistically significant factor among men only (HR 1.84, 95 % CI 1.15–2.97). The joint effect of chemical exposure by packyears smoked for male patients was also assessed. The worst overall prognosis was found among smokers with C25 pack-years who were exposed to medium or high lifetime occupational exposure as compared to never smokers with none–low occupational chemical exposure [(HR 2.62, 95 % CI 1.21–5.67), data not shown]. The HR for obesity status were 1.09 and 1.76 in men and women, respectively, but the difference was not statistically significant. We also compared the role of potential outcome factors (obesity, smoking, and lifetime occupational agricultural exposure) by IPSS categories. Table 5 shows demographic and lifestyle characteristics and median survival by IPSS categories. We found that, in the low-risk group, obesity was associated with statistically significant shorter survival time (median survival 94.3 vs. 64.8 months for BMI \25 and C30 kg/m2, respectively). Lifetime occupational agricultural exposure was also associated with shorter survival time in the low-risk group (86.6 vs. 23.7 months for none– low and medium–high occupational agricultural exposure, respectively). In high-risk group, lifetime occupational agricultural exposure was significantly associated with MDS survival (median survival 20.3 vs. 11.2 months for none–low and medium–high occupational agricultural exposure, respectively). After adjusting for relevant clinical factors, epidemiologic prognostic factors differed in multivariable stratified analysis by IPSS categories (Table 6). Smoking C25 pack-years was associated with increased mortality in the intermediate 1 risk group (HR 1.75, 95 % CI 1.005–3.06) with a significant trend (p trend = 0.01). We also found a similar excess mortality risk associated with smoking C25 pack-years in the high-risk group (HR 1.72, 95 % CI 0.74–3.96), but the difference was not statistically significant. Obesity was associated with an excess risk of death in the low, intermediate 2, and high-risk groups; however, due to limited sample size, these differences should be cautiously interpreted. Lifetime exposure to agrochemicals was a statistically significant prognosis factor in the high IPSS risk group (HR 4.36, 95 % CI 1.20–15.8) and marginally significant in the low IPSS risk group (HR 4.03, 95 % CI 0.92–17.6). When the analysis was stratified by the type or MDS treatment modalities, none of the factors studied was statistically significantly associated with risk of death (data not shown).
431
Discussion The evaluation of epidemiological factors that could impact MDS prognosis has not been adequately addressed. Most previous studies have mainly included clinical markers. We believe that our study at MD Anderson Cancer Center is the first to evaluate the combined effects of clinical, occupational, and lifestyle exposure data in MDS survival. Our results showed that smoking has a detrimental effect on MDS prognosis significantly lowering overall survival. Higher pack-years (C25 pack-years) smoked was associated with poorer MDS prognosis. This association between smoking and MDS outcome differed by gender and IPSS categories. Our findings agree with previous studies that have evaluated the relationship between smoking and MDS prognosis [16–18]. In contrast to previous reports, in the present study, we were able to quantify the lifetime smoking exposure by analyzing the number of pack-years of cigarettes smoked. Our results also suggest that the intensity and duration of smoking are relevant for MDS outcome. To the best of our knowledge, this is the first study to show a significant dose response relationship between smoking and MDS outcome. The exact mechanism by which smoking affects MDS outcome has not been elucidated. Tobacco has been shown to contain many mutagenic and carcinogenic chemicals [19]. These chemicals have multiple effects on the immune system such as decreased natural killer cell numbers and activity, weakened T cell defense abilities, leading to immunosuppression [20, 21]. As a result, cigarette smoking could lead to an elevated susceptibility to infection that might shorten MDS survival. It has been reported that infection is the most frequent MDS-related cause of death among lower risk MDS group [22]. The impact of smoking on MDS survival may also occur through other smoking-related comorbidities such as chronic obstructive pulmonary disease (COPD) or cardiovascular disease [23]. Further investigations are essential to clarify the role of specific smoking chemicals in MDS prognosis. Our results suggest an association between agrochemical occupational exposure and MDS prognosis in all patients combined and in men. The strength of the association among women was similar to that of men but not statistically significant, possibly due to the limited number of females who reported exposure to agrochemicals. There were more men exposed to medium–high agrochemical occupational exposure than women (11 vs. 3 %, respectively). Previous studies have found comparable effect of combined solvent and agrochemicals occupational exposure in MDS survival [7, 24] but none have evaluated the effect of solvents and agrochemicals independently and in the context of other relevant demographic, epidemiologic, and
123
Lifetime occupational exposure a
Total did not add up to 100 % for some variables due to missing values
20.3
11.2 6.1
96.2 46
7 22.4
22.1 279.5
18.7 7
105 33.0
18.1 21.3
356.6 115
9 23.7
86.6 300.4
9.8 4
66
Agricultural chemical exposurea
Medium–high
52.5 103.1 26 64.8 11 C30 kg/m2
37.1
33.1
29.4 128.3
144.7 46
51 67.3
94.3
35 C25 and \30 kg/m2
112.6 23 \25 kg/m2
160.5
26.0 139.1 51 71.7 129.0 27 C25 Pack-years
Body mass index groups
43.8
28.2 127.1
111.7 34
39 86.6
75.8
105.4 25
18
Never smoked
\25 Pack-years
90.9
Median survival (months) Time at risk (person-years)
Pack-years smoked
None–low
21.8
20.3
12.6 23.7 14 15.9 55.0 25
44.7
33.2 15
23 23.6
21.9 110.0
132.3 50
36
16.3
20.3 39.1 21 15.1 63.6 36
34.5
28.7 15
17 25.7
23.4 121.6 40
113.0
Time at risk (person-years) Time at risk (person-years) n n
Median survival (months)
n
36
Time at risk (person-years) n Median survival (months)
High (n = 53) Intermediate 2 (n = 112) Intermediate 1 (n = 124) Low (n = 70)
IPSS categories
Table 5 Characteristics and median survival by IPSS categories
123
13.8
Cancer Causes Control (2014) 25:425–435 Median survival (months)
432
clinical markers of the patient population. We found only an association with agrochemical occupational exposure. The mechanism behind the association between MDS survival and agrochemical occupational exposure is biologically plausible, but no studies have identified a specific agent as probable culprit. In a study of 55 MDS patients, Rigolin et al. found a poorer survival among MDS patients who had shorter telomeres potentially associated with occupational exposure to pesticides and organic solvents [24]. Shorter telomeres have been associated with a higher mortality from age-related diseases [25]; however, the correlation with occupational chemical exposure still need to be further studied. Our findings suggest that obesity might be an important prognostic factor only among female MDS patients. The negative impact of BMI on prognosis has been reported in relation to other hematopoietic cancers such as leukemia overall [26, 27] and AML [28, 29]. However, AML studies did not analyzed the data by gender. Obesity probably affects MDS outcome through multiple biological mechanisms such as inflammation [30, 31], immune function perturbations [32], and cell proliferation [33]. Further studies are needed to confirm the possible differences by gender and investigate plausible underlying mechanisms such as obesity-related hormonal disturbances [34]. In addition, obesity-related comorbidities such as diabetes and cardiovascular diseases which have been shown to increase toxicity to treatment or treatment-related deaths in cancer patients [35] should to be included in future research. Our study has important strengths. It assesses the combined effects of clinical, occupational, and lifestyle exposure data in MDS survival in a relatively large cohort of de novo MDS cases. Detailed information on lifestyle characteristics such as smoking, lifetime agrochemical occupational exposure, and obesity was available, thus making it possible to assess their MDS prognostic implication. The patients were prospectively identified at the time of registration enabling data collection from the patients themselves, and avoiding a need for proxy interviews, thus minimizing the potential for recall bias. Since all patients in this study were recruited from UTMDACC, data on every patient were derived from standardized and validated clinical and epidemiological data collection instruments reducing errors in data collection. To obtain exposure information, we collected a lifetime job history with information on the exposures, job performed, and industry. It has been shown that self-reported occupational exposure is useful in epidemiological studies when documented occupational exposure is not available [36]. There are also some limitations in our study. The patient population of the UTMDACC is subject to the vagaries of referral patterns. Our patient population was younger
a
2.19 (0.79–6.1)
C30 kg/m2
3.83 (1.11–13.14)
Medium–high
4.03 (0.92–17.60)
1
0.3
2.54 (0.82–7.88)
0.84 (0.34–2.11)
1
0.6
1.24 (0.53–2.87)
0.65 (0.24–1.76)
1
1.55 (0.73–3.29)
1
0.4
0.66 (0.36–1.20)
1.51 (0.96–2.42)
1
0.2
1.38 (0.84–2.27)
1.01 (0.56–1.81)
1
1.66 (0.66–3.99)
1
0.6
0.72 (0.38–1.36)
1.27 (0.77–2.12)
1
0.01
1.75 (1.005–3.06)
0.85 (0.45–1.59)
1
1.25 (0.54–2.89)
1
0.1
1.55 (0.87–2.77)
1.25 (0.76–2.08)
1
0.2
1.43 (0.87–2.35)
0.84 (0.51–1.39)
1
Univariate Hazard ratio (95 % CI)
1.45 (0.58–3.65)
1
0.2
1.62 (0.83–3.14)
1.23 (0.70–2.17)
1
0.3
1.38 (0.78–2.47)
0.93 (0.53–1.64)
1
Multivariate Hazard ratio (95 % CI)
Intermediate 2 (n = 112)
a
Lifetime occupational exposure
Hazard ratio adjusted for all variables in the table, age, gender, education, bone marrow count, platelet count, and treatment modalities
1
None–low
Agrochemical exposure
0.2
1.07 (0.50–2.28)
C25 and \30 kg/m2
p trend
1
\25 kg/m2
0.93 (0.43–2.04)
C25 Pack-years
0.9
0.81 (0.33–2.00)
p trend Body mass index
1
Never smoked
\25 Pack-years
Pack-years smoked
Multivariate Hazard ratio (95 % CI)
Univariate Hazard ratio (95 % CI)
Univariate Hazard ratio (95 % CI)
Multivariate Hazard ratio (95 % CI)
Intermediate 1 (n = 124)
Low (n = 70)
IPSS categories
Table 6 MDS mortality by IPSS score categories
2.48 (1.02–6.04)
1
0.3
1.60 (0.73–3.49)
1.007 (0.49–2.05)
1
0.4
1.34 (0.65–2.74)
1.06 (0.47–2.41)
1
Univariate Hazard ratio (95 % CI)
High (n = 53)
4.36 (1.20–15.8)
1
0.1
2.07 (0.88–4.84)
0.94 (0.44–2.04)
1
0.2
1.72 (0.74–3.96)
0.71 (0.23–2.12)
1
Multivariate Hazard ratio (95 % CI)
Cancer Causes Control (2014) 25:425–435 433
123
434
Cancer Causes Control (2014) 25:425–435
compared to other reports (median age in our study population = 66 years and mean age = 64.5 years, range: 18–90). Therefore, a cohort from UTMDACC does not necessarily represent the national MDS population [37, 38]. This bias may lead to an inability to generalize the findings, thus our results need to be interpreted cautiously. Although our analyses adjusted for age at diagnosis, sex, IPSS risk categorization, and clinical characteristics in the multivariable models, there may also be residual confounding from other factors such as comorbidities not assessed in these analyses. We were only able to compute overall survival in this study; however, future studies should analyze disease-specific survival as well.
Conclusion Our study suggests that smoking and lifetime agrochemical occupational exposure may have an impact on MDS prognosis with difference by gender and IPSS categories. Incorporating lifestyle characteristics to the established clinical factors may help improve current prognostic models such as the IPSS and allow physicians to improve personalized treatment strategies for a better MDS outcome. Additional research is greatly needed to confirm and expand our findings. Acknowledgments This work was supported by National Cancer Institute (NCI) Grants CA100632 and CA115180 and NIEHS ES007784. We are grateful to Suraj Sander whose thesis for Master in Public Health has inspired this project. Finally, we give special thanks to our data collection team and our participants. Conflict of interest
The authors declare no conflict of interest.
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