Background MRI slices of 1.5 mm thickness have been used in both cross sectional and longitudinal studies of osteoarthritis, but is difficult to apply to large studies as most techniques used in measuring knee cartilage volumes require substantial post-image processing. The aim of this study was to determine the optimal sampling of 1.5 mm thick slices of MRI scans to estimate knee cartilage volume in males and females for cross-sectional and longitudinal studies.
Background
MRI slices of 1.5 mm thickness have been used in both cross sectional and longitudinal studies of osteoarthritis, but is difficult to apply to large studies as most techniques used in measuring knee cartilage volumes require substantial post-image processing. The aim of this study was to determine the optimal sampling of 1.5 mm thick slices of MRI scans to estimate knee cartilage volume in males and females for cross-sectional and longitudinal studies.Methods A total of 150 subjects had a sagittal T1-weighted fat-suppressed MRI scan of the right knee at a partition thickness of 1.5 mm to determine their cartilage volume. Fifty subjects had both baseline and 2-year follow up MRI scans. Lateral, medial tibial and patellar cartilage volumes were calculated with different samples from 1.5 mm thick slices by extracting one in two, one in three, and one in four to compare to cartilage volume and its rate of change. Agreement was assessed by means of intraclass correlation coefficient (ICC) and Bland & Altman plots.
Methods
A total of 150 subjects had a sagittal T1-weighted fat-suppressed MRI scan of the right knee at a partition thickness of 1.5 mm to determine their cartilage volume. Fifty subjects had both baseline and 2-year follow up MRI scans. Lateral, medial tibial and patellar cartilage volumes were calculated with different samples from 1.5 mm thick slices by extracting one in two, one in three, and one in four to compare to cartilage volume and its rate of change. Agreement was assessed by means of intraclass correlation coefficient (ICC) and Bland & Altman plots.Results Compared to the whole sample of 1.5 mm thick slices, measuring every second to fourth slice led to very little under or over estimation in cartilage volume and its annual change. At all sites and subgroups, measuring every second slice had less than 1% mean difference in cartilage volume and its annual rate of change with all ICCs ≥ 0.98.
Results
Compared to the whole sample of 1.5 mm thick slices, measuring every second to fourth slice led to very little under or over estimation in cartilage volume and its annual change. At all sites and subgroups, measuring every second slice had less than 1% mean difference in cartilage volume and its annual rate of change with all ICCs ≥ 0.98.Conclusion Sampling alternate 1.5 mm thick MRI slices is sufficient for knee cartilage volume measurement in cross-sectional and longitudinal epidemiological studies with little increase in measurement error. This approach will lead to a substantial decrease in post-scan processing time.
Conclusion
Sampling alternate 1.5 mm thick MRI slices is sufficient for knee cartilage volume measurement in cross-sectional and longitudinal epidemiological studies with little increase in measurement error. This approach will lead to a substantial decrease in post-scan processing time.

Background Osteoarthritis (OA) is the most common form of arthritis and a leading cause of musculoskeletal disability in most developed countries [ 1 ]. The knee is one of the most frequently affected joints with a prevalence of 30% in people older than 65 years [ 2 ] and high resultant disability [ 3 ]. Defects in cartilage are widely considered to be the initial problem in OA [ 4 , 5 ], although this viewpoint is not shared by all investigators [ 6 ]. Detection of cartilage morphological change is critical in the evaluation, diagnosis, and monitoring of OA. Conventional radiography is used in evaluating the progression of OA but is limited by its inability to directly visualise cartilage. Magnetic resonance imaging (MRI) offers the distinct advantage of detecting morphologic changes in articular cartilage and is a sensitive and accurate test for evaluating articular cartilage non-invasively [ 7 - 11 ]. The correlation coefficient is 0.99 between knee cartilage volumes measured by MRI and the true volumes by means of water displacement [ 9 ]. This method uses 1.5 mm thick MRI slices and has high reproducibility with coefficients of variation of 2–3% [ 12 ] and has been used in both cross sectional and longitudinal studies of OA [ 12 - 15 ]. However, the method is difficult to apply to large studies as most techniques used in measuring knee cartilage volumes require substantial post-image processing [ 12 ] and the process has not yet been automated. One possible solution is to select a sample from within the 1.5 mm thick slices to reduce the post-image processing time, as has been reported for the estimation of brain compartment volume [ 16 ] and fetal volume[ 17 ]. The aim of the study, therefore, was to determine the optimal sampling of 1.5 mm thick MRI slices required to estimate the volumes of and rate of change in lateral, medial tibial and patellar cartilage with minimal increase in measurement error.
Background
Osteoarthritis (OA) is the most common form of arthritis and a leading cause of musculoskeletal disability in most developed countries [ 1 ]. The knee is one of the most frequently affected joints with a prevalence of 30% in people older than 65 years [ 2 ] and high resultant disability [ 3 ]. Defects in cartilage are widely considered to be the initial problem in OA [ 4 , 5 ], although this viewpoint is not shared by all investigators [ 6 ]. Detection of cartilage morphological change is critical in the evaluation, diagnosis, and monitoring of OA. Conventional radiography is used in evaluating the progression of OA but is limited by its inability to directly visualise cartilage. Magnetic resonance imaging (MRI) offers the distinct advantage of detecting morphologic changes in articular cartilage and is a sensitive and accurate test for evaluating articular cartilage non-invasively [ 7 - 11 ]. The correlation coefficient is 0.99 between knee cartilage volumes measured by MRI and the true volumes by means of water displacement [ 9 ]. This method uses 1.5 mm thick MRI slices and has high reproducibility with coefficients of variation of 2–3% [ 12 ] and has been used in both cross sectional and longitudinal studies of OA [ 12 - 15 ]. However, the method is difficult to apply to large studies as most techniques used in measuring knee cartilage volumes require substantial post-image processing [ 12 ] and the process has not yet been automated. One possible solution is to select a sample from within the 1.5 mm thick slices to reduce the post-image processing time, as has been reported for the estimation of brain compartment volume [ 16 ] and fetal volume[ 17 ]. The aim of the study, therefore, was to determine the optimal sampling of 1.5 mm thick MRI slices required to estimate the volumes of and rate of change in lateral, medial tibial and patellar cartilage with minimal increase in measurement error.Methods Subjects The present study consisted of two datasets; one was part of the Tasmanian Older Adult Cohort Study (TASOAC), an ongoing prospective population-based study aimed at identifying the environmental, genetic, and biochemical factors associated with the development and progression of OA at multiple sites (hand, knee, hip, and spine), which commenced in 2002. Subjects aged between 50 and 79 years were selected randomly from the electoral roll of Southern Tasmania, with an equal number of males and females. Another dataset was a younger adult sample from the Knee Cartilage Volume study (KCV) as previously reported [ 15 ]. Both studies were approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee and all subjects provided informed written consent. MRI An MRI scan of the right knee was performed on all subjects. Knee cartilage volume was determined by means of image processing on an independent work station using the software program Osiris as previously described [ 12 , 15 ]. Two observers were utilised. Knees were imaged in the sagittal plane on a 1.5-T whole body magnetic resonance unit (Picker) with use of a commercial transmit-receive extremity coil. The same machine scanned all knees and Philips Quality Procedure (Philips ACR Support Program, XJR153-2922.03) was utilised for MRI slice thickness quality assurance. The following image sequence was used: a T1-weighted fat saturation 3D gradient recall acquisition in the steady state; flip angle 55 degrees; repetition time 58 msecs; echo time 12 msec; field of view 16 cm; 60 partitions; 512 × 512 matrix; acquisition time 11 min 56 sec; one acquisition. Sagittal images were obtained at a partition thickness of 1.5 mm and an in-plane resolution of 0.31 × 0.31 (512 × 512 pixels). The image data were transferred to the workstation. The volumes of individual cartilage plates (medial tibial, lateral tibial and patella) were isolated from the total volume by manually drawing disarticulation contours around the cartilage boundaries on a slice-by-slice basis. All individual slice areas for each cartilage site and each subject were subsequently transferred to and recorded on a spreadsheet. The total area of each individual cartilage was then multiplied by the slice thickness to produce a volume estimate. This "all slice" estimate of cartilage volume (based on slice thickness of 1.5 mm) was used as the gold standard for other comparisons. Then, the volumes of all individual cartilage plates were recalculated based on different sampling intervals from 1.5 mm thick slices by extracting one in two, one in three, and one in four slice areas from the individual data file. These were then summed and the total was multiplied by the corresponding slice distance. Femoral cartilage volume was not assessed in this study as it is strongly correlated with tibial cartilage volume and thus adds little extra information [ 18 ], tibial cartilage volume is the parameter that is most frequently examined in the literature [ 12 , 19 - 23 ], and femoral cartilage volume has worse reproducibility than tibial cartilage volume [ 11 ]. Other measurements Weight was measured to the nearest 0.1 kg (with shoes, socks and bulky clothing removed) using a single pair of electronic scales (Seca Delta Model 707) which were calibrated using a known weight at the beginning of each clinic. Height was measured to the nearest 0.1 cm (with shoes and socks removed) using a stadiometer. Body Mass Index (BMI) was calculated as weight (kg) / height (m 2 ). A standing AP semi-flexed view of the right knee was performed in all subjects. Radiographs were then assessed utilising the Altman atlas[ 24 ]. Each of the following was assessed: medial joint space narrowing (0–3), lateral joint space narrowing (0–3), medial osteophytes (femoral and tibial combined) (0–3) and lateral osteophytes (femoral and tibial combined) (0–3). Each score was arrived at by consensus with two readers simultaneously assessing the radiograph with immediate reference to the atlas. Any knee ROA was defined as total score ≥ 1. The total score could vary from 0–12. This method had high reproducibility in our hands with ICCs >0.98 [ 25 ]. Statistics Descriptive statistics of the characteristics of the study subjects were tabulated. The annual change in knee cartilage volume was calculated as percent change by means of dividing absolute volume change by baseline cartilage volume. Intraclass correlation coefficient was utilized to assess the measurement agreement. The difference in cartilage volume measured with different samples extracting one in two, one in three, and one in four 1.5 mm thick slices of MR image compared to that measured using 1.5 mm thickness was calculated and expressed as percent absolute difference. Desirable agreement was defined as an ICC ≥ 0.98 with ≤ 1% difference between two measurements. In addition, Bland & Altman plots [ 26 ] were also utilized. Desirable agreement was defined as the mean difference between two measurements close to zero with 95% of individual differences being within 2 SD. All analyses were performed using the SPSS statistical package (version 12.1, SPSS, Chicago, IL).
Methods
Subjects The present study consisted of two datasets; one was part of the Tasmanian Older Adult Cohort Study (TASOAC), an ongoing prospective population-based study aimed at identifying the environmental, genetic, and biochemical factors associated with the development and progression of OA at multiple sites (hand, knee, hip, and spine), which commenced in 2002. Subjects aged between 50 and 79 years were selected randomly from the electoral roll of Southern Tasmania, with an equal number of males and females. Another dataset was a younger adult sample from the Knee Cartilage Volume study (KCV) as previously reported [ 15 ]. Both studies were approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee and all subjects provided informed written consent.
Subjects
The present study consisted of two datasets; one was part of the Tasmanian Older Adult Cohort Study (TASOAC), an ongoing prospective population-based study aimed at identifying the environmental, genetic, and biochemical factors associated with the development and progression of OA at multiple sites (hand, knee, hip, and spine), which commenced in 2002. Subjects aged between 50 and 79 years were selected randomly from the electoral roll of Southern Tasmania, with an equal number of males and females. Another dataset was a younger adult sample from the Knee Cartilage Volume study (KCV) as previously reported [ 15 ]. Both studies were approved by the Southern Tasmanian Health and Medical Human Research Ethics Committee and all subjects provided informed written consent.MRI An MRI scan of the right knee was performed on all subjects. Knee cartilage volume was determined by means of image processing on an independent work station using the software program Osiris as previously described [ 12 , 15 ]. Two observers were utilised. Knees were imaged in the sagittal plane on a 1.5-T whole body magnetic resonance unit (Picker) with use of a commercial transmit-receive extremity coil. The same machine scanned all knees and Philips Quality Procedure (Philips ACR Support Program, XJR153-2922.03) was utilised for MRI slice thickness quality assurance. The following image sequence was used: a T1-weighted fat saturation 3D gradient recall acquisition in the steady state; flip angle 55 degrees; repetition time 58 msecs; echo time 12 msec; field of view 16 cm; 60 partitions; 512 × 512 matrix; acquisition time 11 min 56 sec; one acquisition. Sagittal images were obtained at a partition thickness of 1.5 mm and an in-plane resolution of 0.31 × 0.31 (512 × 512 pixels). The image data were transferred to the workstation. The volumes of individual cartilage plates (medial tibial, lateral tibial and patella) were isolated from the total volume by manually drawing disarticulation contours around the cartilage boundaries on a slice-by-slice basis. All individual slice areas for each cartilage site and each subject were subsequently transferred to and recorded on a spreadsheet. The total area of each individual cartilage was then multiplied by the slice thickness to produce a volume estimate. This "all slice" estimate of cartilage volume (based on slice thickness of 1.5 mm) was used as the gold standard for other comparisons. Then, the volumes of all individual cartilage plates were recalculated based on different sampling intervals from 1.5 mm thick slices by extracting one in two, one in three, and one in four slice areas from the individual data file. These were then summed and the total was multiplied by the corresponding slice distance. Femoral cartilage volume was not assessed in this study as it is strongly correlated with tibial cartilage volume and thus adds little extra information [ 18 ], tibial cartilage volume is the parameter that is most frequently examined in the literature [ 12 , 19 - 23 ], and femoral cartilage volume has worse reproducibility than tibial cartilage volume [ 11 ].
MRI
An MRI scan of the right knee was performed on all subjects. Knee cartilage volume was determined by means of image processing on an independent work station using the software program Osiris as previously described [ 12 , 15 ]. Two observers were utilised. Knees were imaged in the sagittal plane on a 1.5-T whole body magnetic resonance unit (Picker) with use of a commercial transmit-receive extremity coil. The same machine scanned all knees and Philips Quality Procedure (Philips ACR Support Program, XJR153-2922.03) was utilised for MRI slice thickness quality assurance. The following image sequence was used: a T1-weighted fat saturation 3D gradient recall acquisition in the steady state; flip angle 55 degrees; repetition time 58 msecs; echo time 12 msec; field of view 16 cm; 60 partitions; 512 × 512 matrix; acquisition time 11 min 56 sec; one acquisition. Sagittal images were obtained at a partition thickness of 1.5 mm and an in-plane resolution of 0.31 × 0.31 (512 × 512 pixels). The image data were transferred to the workstation. The volumes of individual cartilage plates (medial tibial, lateral tibial and patella) were isolated from the total volume by manually drawing disarticulation contours around the cartilage boundaries on a slice-by-slice basis. All individual slice areas for each cartilage site and each subject were subsequently transferred to and recorded on a spreadsheet. The total area of each individual cartilage was then multiplied by the slice thickness to produce a volume estimate. This "all slice" estimate of cartilage volume (based on slice thickness of 1.5 mm) was used as the gold standard for other comparisons.Then, the volumes of all individual cartilage plates were recalculated based on different sampling intervals from 1.5 mm thick slices by extracting one in two, one in three, and one in four slice areas from the individual data file. These were then summed and the total was multiplied by the corresponding slice distance.Femoral cartilage volume was not assessed in this study as it is strongly correlated with tibial cartilage volume and thus adds little extra information [ 18 ], tibial cartilage volume is the parameter that is most frequently examined in the literature [ 12 , 19 - 23 ], and femoral cartilage volume has worse reproducibility than tibial cartilage volume [ 11 ].Other measurements Weight was measured to the nearest 0.1 kg (with shoes, socks and bulky clothing removed) using a single pair of electronic scales (Seca Delta Model 707) which were calibrated using a known weight at the beginning of each clinic. Height was measured to the nearest 0.1 cm (with shoes and socks removed) using a stadiometer. Body Mass Index (BMI) was calculated as weight (kg) / height (m 2 ). A standing AP semi-flexed view of the right knee was performed in all subjects. Radiographs were then assessed utilising the Altman atlas[ 24 ]. Each of the following was assessed: medial joint space narrowing (0–3), lateral joint space narrowing (0–3), medial osteophytes (femoral and tibial combined) (0–3) and lateral osteophytes (femoral and tibial combined) (0–3). Each score was arrived at by consensus with two readers simultaneously assessing the radiograph with immediate reference to the atlas. Any knee ROA was defined as total score ≥ 1. The total score could vary from 0–12. This method had high reproducibility in our hands with ICCs >0.98 [ 25 ].
Other measurements
Weight was measured to the nearest 0.1 kg (with shoes, socks and bulky clothing removed) using a single pair of electronic scales (Seca Delta Model 707) which were calibrated using a known weight at the beginning of each clinic. Height was measured to the nearest 0.1 cm (with shoes and socks removed) using a stadiometer. Body Mass Index (BMI) was calculated as weight (kg) / height (m 2 ).A standing AP semi-flexed view of the right knee was performed in all subjects. Radiographs were then assessed utilising the Altman atlas[ 24 ]. Each of the following was assessed: medial joint space narrowing (0–3), lateral joint space narrowing (0–3), medial osteophytes (femoral and tibial combined) (0–3) and lateral osteophytes (femoral and tibial combined) (0–3). Each score was arrived at by consensus with two readers simultaneously assessing the radiograph with immediate reference to the atlas. Any knee ROA was defined as total score ≥ 1. The total score could vary from 0–12. This method had high reproducibility in our hands with ICCs >0.98 [ 25 ].Statistics Descriptive statistics of the characteristics of the study subjects were tabulated. The annual change in knee cartilage volume was calculated as percent change by means of dividing absolute volume change by baseline cartilage volume. Intraclass correlation coefficient was utilized to assess the measurement agreement. The difference in cartilage volume measured with different samples extracting one in two, one in three, and one in four 1.5 mm thick slices of MR image compared to that measured using 1.5 mm thickness was calculated and expressed as percent absolute difference. Desirable agreement was defined as an ICC ≥ 0.98 with ≤ 1% difference between two measurements. In addition, Bland & Altman plots [ 26 ] were also utilized. Desirable agreement was defined as the mean difference between two measurements close to zero with 95% of individual differences being within 2 SD. All analyses were performed using the SPSS statistical package (version 12.1, SPSS, Chicago, IL).
Statistics
Descriptive statistics of the characteristics of the study subjects were tabulated. The annual change in knee cartilage volume was calculated as percent change by means of dividing absolute volume change by baseline cartilage volume. Intraclass correlation coefficient was utilized to assess the measurement agreement. The difference in cartilage volume measured with different samples extracting one in two, one in three, and one in four 1.5 mm thick slices of MR image compared to that measured using 1.5 mm thickness was calculated and expressed as percent absolute difference. Desirable agreement was defined as an ICC ≥ 0.98 with ≤ 1% difference between two measurements. In addition, Bland & Altman plots [ 26 ] were also utilized. Desirable agreement was defined as the mean difference between two measurements close to zero with 95% of individual differences being within 2 SD. All analyses were performed using the SPSS statistical package (version 12.1, SPSS, Chicago, IL).Results A total of 150 subjects took part in this study: 100 subjects with cross-sectional data (female: 48, male: 52) were from the TASOAC study and 50 subjects with longitudinal data (female: 31, male: 19) were from the KCV study. Characteristics of the study sample are presented in Table  1 . Subjects from TASOAC were older, heavier and had a higher prevalence of ROA than those from KCV. Most of participants with ROA were mild with a total ROA score ≤ 3 out of 12. Lateral and medial tibial cartilage volumes were lower in subjects from KCV than those from TASOAC. Table 1 Characteristics of the study population* TASOAC dataset N = 100 KCV dataset N = 50 Age (year) 62.3(7.6) 42.8(6.1) Sex (female %)† 48 62 Height (cm) 167.4(8.7) 168.6(7.9) Weight (kg) 76.0(15.0) 73.9(13.7) BMI (kg/m 2 ) 27.1(4.3) 25.9(4.1) Any knee ROA (%)† 62 18 Knee ROA total score (0- 12 ) 1.3 (1.7) 0.2(0.7) Lateral tibial cartilage volume (ml)‡ 3.0(0.7) 2.6(0.5) Medial tibial cartilage volume (ml)‡ 2.7(0.5) 2.2(0.5) Patellar tibial cartilage volume (ml)‡ 3.5(1.0) 3.5(0.9) Lateral tibial cartilage volume change (%) per year‡ - -1.2(3.4) Medial tibial cartilage volume change (%) per year‡ - -2.9(3.9) Patellar cartilage volume change (%) per year‡ - -3.8(3.4) *Values are mean (SD) except for indicated. BMI: body mass index. ROA: radiographic osteoarthritis. † Percentage. ‡ Measured with the whole sample of 1.5 mm thick MRI slices. In cross-sectional analysis, compared to the cartilage volume measured using 1.5 mm thickness, decreasing the number of the slices by extracting one in two to one in four led to a very little underestimation in the magnitude of the average cartilage volume at lateral, medial tibial and patellar sites with ICCs of 0.98–1.00 (Table  2 ). The maximum underestimation was 3.3% at the medial tibial site with one in four slices (Table  2 ). Similar results were obtained when the analysis was done separately for people with and without ROA (Table  3 ) although the differences tended to be larger in the ROA group. The difference also tended to be larger for medial tibial cartilage in the TASOAC sample and lateral tibial cartilage for the KCV sample (Table  2 ). At all sites and subgroups, cartilage volume measured with one in two slices had less than 1% difference compared to that measured with all 1.5 mm slices with an ICC of 1.0 (Table  2  & 3 ). Bland & Altman plots showed that the mean difference was zero for lateral tibial cartilage and -0.01 ml for medial tibial and patellar cartilage with 95% of individual differences within ± 2SD. The variability was random and uniform throughout the range of cartilage volume (Figure  1 ). Table 2 Agreement analysis of knee cartilage volume measured with different samples of 1.5 mm thick MRI slices* Whole sample (n = 150) TASOAC sample (n = 100) KCV sample (n = 50) %Difference (SD) ICC† %Difference (SD) ICC† %Difference (SD) ICC† Lateral tibial cartilage The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ -0.04(1.5) 1.00 0.35(1.4) 1.00 -0.84(1.4) 1.00 1/3 whole sample‡ -0.61(2.3) 1.00 0.11(2.1) 1.00 -2.09(1.8) 1.00 1/4 whole sample‡ -1.12(3.4) 1.00 -0.11(3.0) 1.00 -3.18(3.3) 0.99 Medial tibial cartilage The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ -0.50(1.7) 1.00 -0.98(1.4) 1.00 0.46(1.7) 1.00 1/3 whole sample‡ -1.70(3.3) 0.99 -2.97(2.8) 0.99 0.83(2.9) 1.00 1/4 whole sample‡ -3.27(5.0) 0.98 -5.09(3.9) 0.97 0.38(4.9) 0.99 Patellar cartilage        The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ -0.36(1.2) 1.00 -0.40(1.2) 1.00 -0.29(1.3) 1.00 1/3 whole sample‡ -0.91(2.0) 1.00 -0.93(2.0) 1.00 -0.86(1.9) 1.00 1/4 whole sample‡ -2.24(3.0) 1.00 -2.12(2.9) 1.00 -2.50(3.3) 1.00 * SD: standard deviation. ICC: intraclass correlation coefficient. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four of the 1.5 mm thick MRI slices. Table 3 Agreement analysis of cartilage volume measured with different samples of 1.5 mm thick MRI slices in people with and without ROA* ROA absent (n = 76) ROA present (n = 68) Difference (SD) ICC† Difference (SD) ICC† Lateral tibial cartilage The whole sample Reference Reference Reference Reference 1/2 whole sample‡ -0.30(1.4) 1.00 0.24(1.6) 1.00 1/3 whole sample‡ -1.14(2.3) 1.00 -0.01(2.1) 1.00 1/4 whole sample‡ -1.85(3.4) 0.99 -0.29(3.4) 1.00 Medial tibial cartilage The whole sample Reference Reference Reference Reference 1/2 whole sample‡ -0.39(1.7) 1.00 -0.77(2.2) 1.00 1/3 whole sample‡ -1.20(3.3) 0.99 -2.13(3.4) 0.99 1/4 whole sample‡ -2.56(5.3) 0.98 -3.77(4.5) 0.98 Patellar cartilage        The whole sample Reference Reference Reference Reference 1/2 whole sample‡ -0.38(1.2) 1.00 -0.40(1.2) 1.00 1/3 whole sample‡ -0.87(1.9) 1.00 -1.10(2.0) 1.00 1/4 whole sample‡ -2.02(2.9) 1.00 -2.50(3.2) 1.00 *Six subjects had missing values for ROA. Difference in cartilage volume measured with different thick slices of MR images is expressed as percentage. ICC: intraclass correlation coefficient. ROA: radiographic osteoarthritis. SD: standard deviation. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four 1.5 mm thick MRI slices. Figure 1 Bland & Altman plots of cartilage volume measured by every second 1.5 mm thick MRI slice compared to that measured by the total sample at lateral (a), medial tibial (b), and patellar (c) sites. The x-axis represents average values of two measurements while the y-axis represents the individual difference between two measurements, and the three horizontal lines stand for mean individual difference ± 2 SD. Similarly, in longitudinal analysis, compared to the cartilage volume change using 1.5 mm thick slices, decreasing the number of the slices by extracting one in two to one in four slices led to very little over or under estimation of the mean changes in cartilage volume at lateral, medial tibial and patellar sites (Table  4 ). The mean difference ranged from -0.05% to 0.14% with the maximum difference at the patellar site. ICCs ranged from 0.85 to 0.99 (Table  4 ). The difference became larger but all were ≤ 1% in subjects with and without ROA (Table  4 ). At all sites, the annual change in cartilage volume measured with one in two slices had an ICC ≥ 0.98 with less than 0.3% difference compared to that measured using all the slices. Bland & Altman plots showed that 95% of the individual differences were within ± 2 SD and the variability was random and uniform throughout the range of cartilage volume (Figure  2 ). Table 4 Agreement analysis of the annual change in knee cartilage volume measured with different samples of 1.5 mm thick MRI slices* Whole sample (n = 50) ROA present (n = 9) ROA absent (n = 41) Difference (SD) ICC† Difference (SD) ICC† Difference (SD) ICC† Lateral tibial cartilage The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ 0.06(0.9) 0.99 0.23(1.1) 0.99 0.02(0.9) 0.98 1/3 whole sample‡ 0.05(1.5) 0.96 -0.65(1.4) 0.98 0.20(1.5) 0.95 1/4 whole sample‡ -0.03(2.2) 0.92 -0.04(2.4) 0.95 -0.02(2.2) 0.91 Medial tibial cartilage The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ -0.05(1.1) 0.98 -0.29(1.0) 0.99 0.00(1.1) 0.98 1/3 whole sample‡ -0.03(1.8) 0.95 0.24(1.8) 0.97 -0.10(1.8) 0.95 1/4 whole sample‡ 0.02(3.0) 0.85 -1.04(2.7) 0.92 0.25(3.1) 0.83 Patellar cartilage        The whole sample Reference Reference Reference Reference Reference Reference 1/2 whole sample‡ 0.10(0.8) 0.99 -0.07(0.7) 1.00 0.13(0.8) 0.99 1/3 whole sample‡ 0.10(1.5) 0.96 -0.18(1.4) 0.98 0.16(1.5) 0.95 1/4 whole sample‡ 0.14(1.8) 0.93 0.61(1.5) 0.97 0.03(1.9) 0.92 * Difference in the annual change in cartilage volume was expressed in percentage. SD: standard deviation. ROA: radiographic osteoarthritis. ICC: intraclass correlation coefficient. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four 1.5 mm thick MRI slices. Figure 2 Bland & Altman plots of the annual change in cartilage volume measured by every second 1.5 mm thick MRI slice compared to that measured by the total sample at lateral (a), medial tibial (b), and patellar (c) sites. The annual change in cartilage volume was expressed as a percentage. The x-axis represents average values of two measurements while the y-axis represents the individual difference between two measurements, and the three horizontal lines stand for mean individual difference ± 2 SD.
Results
A total of 150 subjects took part in this study: 100 subjects with cross-sectional data (female: 48, male: 52) were from the TASOAC study and 50 subjects with longitudinal data (female: 31, male: 19) were from the KCV study. Characteristics of the study sample are presented in Table  1 . Subjects from TASOAC were older, heavier and had a higher prevalence of ROA than those from KCV. Most of participants with ROA were mild with a total ROA score ≤ 3 out of 12. Lateral and medial tibial cartilage volumes were lower in subjects from KCV than those from TASOAC.Characteristics of the study population*12*Values are mean (SD) except for indicated. BMI: body mass index. ROA: radiographic osteoarthritis. † Percentage. ‡ Measured with the whole sample of 1.5 mm thick MRI slices.In cross-sectional analysis, compared to the cartilage volume measured using 1.5 mm thickness, decreasing the number of the slices by extracting one in two to one in four led to a very little underestimation in the magnitude of the average cartilage volume at lateral, medial tibial and patellar sites with ICCs of 0.98–1.00 (Table  2 ). The maximum underestimation was 3.3% at the medial tibial site with one in four slices (Table  2 ). Similar results were obtained when the analysis was done separately for people with and without ROA (Table  3 ) although the differences tended to be larger in the ROA group. The difference also tended to be larger for medial tibial cartilage in the TASOAC sample and lateral tibial cartilage for the KCV sample (Table  2 ). At all sites and subgroups, cartilage volume measured with one in two slices had less than 1% difference compared to that measured with all 1.5 mm slices with an ICC of 1.0 (Table  2  & 3 ). Bland & Altman plots showed that the mean difference was zero for lateral tibial cartilage and -0.01 ml for medial tibial and patellar cartilage with 95% of individual differences within ± 2SD. The variability was random and uniform throughout the range of cartilage volume (Figure  1 ).Agreement analysis of knee cartilage volume measured with different samples of 1.5 mm thick MRI slices*The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡* SD: standard deviation. ICC: intraclass correlation coefficient. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four of the 1.5 mm thick MRI slices.Agreement analysis of cartilage volume measured with different samples of 1.5 mm thick MRI slices in people with and without ROA*The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡*Six subjects had missing values for ROA. Difference in cartilage volume measured with different thick slices of MR images is expressed as percentage. ICC: intraclass correlation coefficient. ROA: radiographic osteoarthritis. SD: standard deviation. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four 1.5 mm thick MRI slices.Similarly, in longitudinal analysis, compared to the cartilage volume change using 1.5 mm thick slices, decreasing the number of the slices by extracting one in two to one in four slices led to very little over or under estimation of the mean changes in cartilage volume at lateral, medial tibial and patellar sites (Table  4 ). The mean difference ranged from -0.05% to 0.14% with the maximum difference at the patellar site. ICCs ranged from 0.85 to 0.99 (Table  4 ). The difference became larger but all were ≤ 1% in subjects with and without ROA (Table  4 ). At all sites, the annual change in cartilage volume measured with one in two slices had an ICC ≥ 0.98 with less than 0.3% difference compared to that measured using all the slices. Bland & Altman plots showed that 95% of the individual differences were within ± 2 SD and the variability was random and uniform throughout the range of cartilage volume (Figure  2 ).Agreement analysis of the annual change in knee cartilage volume measured with different samples of 1.5 mm thick MRI slices*The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡The whole sample1/2 whole sample‡1/3 whole sample‡1/4 whole sample‡* Difference in the annual change in cartilage volume was expressed in percentage. SD: standard deviation. ROA: radiographic osteoarthritis. ICC: intraclass correlation coefficient. † All P < 0.001. ‡ Derived by extracting one in two, one in three, or one in four 1.5 mm thick MRI slices.Discussion This study suggests that lateral, medial tibial and patellar cartilage volumes measured from up to one in four 1.5 mm thick slices are quite comparable to those obtained from 1.5 mm thick slices. If the agreement is defined at high levels expected to lead to minimal measurement error, then knee cartilage volume can be measured sufficiently and accurately with one in two slices both cross-sectionally and longitudinally regardless of ROA status and./or reader. This approach will lead to a substantial decrease in post-scan processing time and make large-scale studies of knee cartilage volume more feasible. Currently, there is no reported information on the number of the slices of MRI scans to measure cartilage volume apart from a recent paper from our own group which had similar findings to this study with different readers and geographic location [ 27 ]. In a study estimating fetal volume by MRI, Roberts et al reported that using the same thickness of MRI slices (10 mm), volume measured from the low sampling intensity (the distance between scan section midplanes T = 4.5 cm) was virtually identical to those obtained with the high sampling intensity (T = 1.5 cm) with a coefficient of error (CE) < 5% [ 17 ]. In the study estimating brain compartment volume from MR Cavalieri slices [ 16 ], irrespective of slice thickness, a minimum of 3, 5, and 10 slices provided estimates of the true total volume of grey matter and white matter in the cerebrum with coefficients of error (CEs) of 10, 5, and 3%. For a given number of slices CE decreases rapidly when the slices are thicker than the gaps between them; when the slices are thinner than the gaps, then CE is similar to that in the situation when the slice thickness is zero. The current study demonstrates similar results for knee cartilage. Decreasing the number of slices by extracting up to one in four 1.5 mm slices resulted in a very little underestimation in average volume of lateral, medial tibial and patellar cartilage. The maximum mean difference in cartilage volume obtained from one in four slices to that obtained from all slices was 3.3%, which is substantially smaller than the difference of 9% between cartilage volume obtained from 1.5 mm thick slices of MR image and that measured by means of water displacement [ 9 , 19 , 28 , 29 ]. The difference increased slightly when we analysed the data separately for people with and without ROA, but the results were similar for both groups, suggesting ROA within the range we report has very limited effect on the cartilage volume measured with subsamples of MRI slices. If we arbitrarily define an ICC ≥ 0.98 with ≤ 1% difference as optimal as it is expected to minimise the measurement error and only slightly increase the variance, then cartilage volume and its rate of change can be measured accurately with one in two 1.5 mm thick slices for lateral, medial tibial and patellar cartilage. Bland & Altman plots confirmed this with a random scatter about zero as would be expected if there is no difference between two measurements and uniform variability throughout the range of measurements. Of note, for longitudinal data even decreasing the number of slices by extracting up to one in four resulted in a maximum difference of 0.14% in mean annual change in cartilage volume which is very small when compared to the 5% cartilage loss annually we have reported in patients with OA [ 30 ]. Thus, a subsample of MRI slices could also be utilised with marked decreases in processing time allowing greater numbers of subjects to be studied offsetting the accompanying increase in measurement error. Ideally, the more slices used, the more accurate the estimation of the object's volume, as they may contain more information. However, for a completely regular structure, such as a cylinder, the area of a single slice with length gives an exact volume. It is therefore reassuring but not surprising that the current study demonstrates a minimum reduction in the knee cartilage volume and volume change over time as tibial and patellar cartilages have a relatively regular structure. A different interpretation may apply to femoral cartilage and we do not have data on this imaging site. The current study simply examined the effect of decreasing the number of slices on the estimation of knee cartilage volume and volume change while all other variables were kept constant. We did not re-scan the study subjects but simply estimated the cartilage volume by using one in two, one in three, or one in four slices. This has an advantage of allowing us to examine the single effect of sampling intensity in the situation where all other variables such as re-positioning the subject and measurement were kept constant. The effect of these errors on measurement have been well-documented [ 9 , 31 ]. For longitudinal analysis, all the MR images were processed by a single observer. For cross sectional analysis, two observers processed the MR images, one for TASOAC data, and another for the KCV study. However, the difference was even smaller in the whole sample than in the two separate samples providing reassurance that our results may be generalisable to different observers as documented with different readers and machines in Melbourne [ 27 ]. The current study has a number of potential limitations. Firstly, which sampling intensity should be used in the MRI scan of knee cartilage depends on the purpose of the measurement. Our results cannot be applied to individual cartilage volume, but only for mean cartilage volume in groups as the individual difference in cartilage volume increases with decreasing sampling intensity. Secondly, decreasing sampling intensity will increase measurement error as the remaining slices focus on different portions of the irregularly shaped cartilage. Depending on what particular surfaces remain, however, the overall volume may be increased or decreased. If this is random, then the mean will remain the same as demonstrated in the current study. Thirdly, the ICC can be influenced by traits in the sample in which it is assessed. Age, sex and BMI have been reported to be associated with knee cartilage volume [ 32 ]. These may result in a higher ICC in the current study, as between-subject variance would become larger. However, subgroup analyses by sex, BMI (< 25, >= 25), and age (<50, >= 50 yr) did not change the results (data not shown). Further analysis using the Bland & Altman method confirmed the good agreement and interchangability between thick and thin slices, indicating that the result of the current study should be applicable to other populations regardless of the demographic factors related to cartilage volume. Fourthly, the participants in the study had only mild ROA, and these conclusions may not apply to subjects with more advanced OA. Lastly, the annual change in cartilage volume in our sample can not be generalized to other populations as half of our longitudinal study sample had a higher genetic susceptibility to OA [ 23 , 33 ].
Discussion
This study suggests that lateral, medial tibial and patellar cartilage volumes measured from up to one in four 1.5 mm thick slices are quite comparable to those obtained from 1.5 mm thick slices. If the agreement is defined at high levels expected to lead to minimal measurement error, then knee cartilage volume can be measured sufficiently and accurately with one in two slices both cross-sectionally and longitudinally regardless of ROA status and./or reader. This approach will lead to a substantial decrease in post-scan processing time and make large-scale studies of knee cartilage volume more feasible.Currently, there is no reported information on the number of the slices of MRI scans to measure cartilage volume apart from a recent paper from our own group which had similar findings to this study with different readers and geographic location [ 27 ]. In a study estimating fetal volume by MRI, Roberts et al reported that using the same thickness of MRI slices (10 mm), volume measured from the low sampling intensity (the distance between scan section midplanes T = 4.5 cm) was virtually identical to those obtained with the high sampling intensity (T = 1.5 cm) with a coefficient of error (CE) < 5% [ 17 ]. In the study estimating brain compartment volume from MR Cavalieri slices [ 16 ], irrespective of slice thickness, a minimum of 3, 5, and 10 slices provided estimates of the true total volume of grey matter and white matter in the cerebrum with coefficients of error (CEs) of 10, 5, and 3%. For a given number of slices CE decreases rapidly when the slices are thicker than the gaps between them; when the slices are thinner than the gaps, then CE is similar to that in the situation when the slice thickness is zero. The current study demonstrates similar results for knee cartilage. Decreasing the number of slices by extracting up to one in four 1.5 mm slices resulted in a very little underestimation in average volume of lateral, medial tibial and patellar cartilage. The maximum mean difference in cartilage volume obtained from one in four slices to that obtained from all slices was 3.3%, which is substantially smaller than the difference of 9% between cartilage volume obtained from 1.5 mm thick slices of MR image and that measured by means of water displacement [ 9 , 19 , 28 , 29 ]. The difference increased slightly when we analysed the data separately for people with and without ROA, but the results were similar for both groups, suggesting ROA within the range we report has very limited effect on the cartilage volume measured with subsamples of MRI slices. If we arbitrarily define an ICC ≥ 0.98 with ≤ 1% difference as optimal as it is expected to minimise the measurement error and only slightly increase the variance, then cartilage volume and its rate of change can be measured accurately with one in two 1.5 mm thick slices for lateral, medial tibial and patellar cartilage. Bland & Altman plots confirmed this with a random scatter about zero as would be expected if there is no difference between two measurements and uniform variability throughout the range of measurements. Of note, for longitudinal data even decreasing the number of slices by extracting up to one in four resulted in a maximum difference of 0.14% in mean annual change in cartilage volume which is very small when compared to the 5% cartilage loss annually we have reported in patients with OA [ 30 ]. Thus, a subsample of MRI slices could also be utilised with marked decreases in processing time allowing greater numbers of subjects to be studied offsetting the accompanying increase in measurement error.Ideally, the more slices used, the more accurate the estimation of the object's volume, as they may contain more information. However, for a completely regular structure, such as a cylinder, the area of a single slice with length gives an exact volume. It is therefore reassuring but not surprising that the current study demonstrates a minimum reduction in the knee cartilage volume and volume change over time as tibial and patellar cartilages have a relatively regular structure. A different interpretation may apply to femoral cartilage and we do not have data on this imaging site.The current study simply examined the effect of decreasing the number of slices on the estimation of knee cartilage volume and volume change while all other variables were kept constant. We did not re-scan the study subjects but simply estimated the cartilage volume by using one in two, one in three, or one in four slices. This has an advantage of allowing us to examine the single effect of sampling intensity in the situation where all other variables such as re-positioning the subject and measurement were kept constant. The effect of these errors on measurement have been well-documented [ 9 , 31 ]. For longitudinal analysis, all the MR images were processed by a single observer. For cross sectional analysis, two observers processed the MR images, one for TASOAC data, and another for the KCV study. However, the difference was even smaller in the whole sample than in the two separate samples providing reassurance that our results may be generalisable to different observers as documented with different readers and machines in Melbourne [ 27 ].The current study has a number of potential limitations. Firstly, which sampling intensity should be used in the MRI scan of knee cartilage depends on the purpose of the measurement. Our results cannot be applied to individual cartilage volume, but only for mean cartilage volume in groups as the individual difference in cartilage volume increases with decreasing sampling intensity. Secondly, decreasing sampling intensity will increase measurement error as the remaining slices focus on different portions of the irregularly shaped cartilage. Depending on what particular surfaces remain, however, the overall volume may be increased or decreased. If this is random, then the mean will remain the same as demonstrated in the current study. Thirdly, the ICC can be influenced by traits in the sample in which it is assessed. Age, sex and BMI have been reported to be associated with knee cartilage volume [ 32 ]. These may result in a higher ICC in the current study, as between-subject variance would become larger. However, subgroup analyses by sex, BMI (< 25, >= 25), and age (<50, >= 50 yr) did not change the results (data not shown). Further analysis using the Bland & Altman method confirmed the good agreement and interchangability between thick and thin slices, indicating that the result of the current study should be applicable to other populations regardless of the demographic factors related to cartilage volume. Fourthly, the participants in the study had only mild ROA, and these conclusions may not apply to subjects with more advanced OA. Lastly, the annual change in cartilage volume in our sample can not be generalized to other populations as half of our longitudinal study sample had a higher genetic susceptibility to OA [ 23 , 33 ].Conclusion Knee cartilage volume and its rate of change can be accurately measured with every second 1.5 mm thick MR slice. This approach will lead to a substantial decrease in post-scan processing time and make large-scale studies of knee cartilage volume more feasible.
Conclusion
Knee cartilage volume and its rate of change can be accurately measured with every second 1.5 mm thick MR slice. This approach will lead to a substantial decrease in post-scan processing time and make large-scale studies of knee cartilage volume more feasible.Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions GZ designed and carried out the study planning, data collection, analysis and interpretation of the analysis, and preparation of the manuscript. CD participated in data collection and critical revision of the manuscript. FC participated in the study planning and critical revision of the manuscript. GJ designed the study, participated in analysis and interpretation of the analysis, and critical revision of the manuscript. All authors read and approved the final manuscript.
Authors' contributions
GZ designed and carried out the study planning, data collection, analysis and interpretation of the analysis, and preparation of the manuscript. CD participated in data collection and critical revision of the manuscript. FC participated in the study planning and critical revision of the manuscript. GJ designed the study, participated in analysis and interpretation of the analysis, and critical revision of the manuscript. All authors read and approved the final manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Background Significant evidence suggests that a promoter polymorphism withinthe gene  SLC11A1  is involved in susceptibility to both autoimmune and infectious disorders. The aim of this study was to evaluate whether SLC11A1 has a role in the susceptibility to inflammatory bowel disease (IBD) by characterizing a promoter polymorphism within the gene and two short tandem repeat (STR) markers in genetic proximity to  SLC11A1 .
Background
Significant evidence suggests that a promoter polymorphism withinthe gene  SLC11A1  is involved in susceptibility to both autoimmune and infectious disorders. The aim of this study was to evaluate whether SLC11A1 has a role in the susceptibility to inflammatory bowel disease (IBD) by characterizing a promoter polymorphism within the gene and two short tandem repeat (STR) markers in genetic proximity to  SLC11A1 .SLC11A1SLC11A1Methods The studied population consisted of 484 Caucasians with IBD, 144 population controls, and 348 non-IBD-affected first-degree relatives of IBD patients. IBD subjects were re-categorized at the sub-disease phenotypic level to characterize possible  SLC11A1  genotype-phenotype correlations. Polymorphic markers were amplified from germline DNA and typed using gel electrophoresis. Genotype-phenotype correlations were defined using case-control, haplotype, and family-based association studies.
Methods
The studied population consisted of 484 Caucasians with IBD, 144 population controls, and 348 non-IBD-affected first-degree relatives of IBD patients. IBD subjects were re-categorized at the sub-disease phenotypic level to characterize possible  SLC11A1  genotype-phenotype correlations. Polymorphic markers were amplified from germline DNA and typed using gel electrophoresis. Genotype-phenotype correlations were defined using case-control, haplotype, and family-based association studies.SLC11A1Results This study did not provide compelling evidence for  SLC11A1  disease association; most significantly, there was no apparent evidence of  SLC11A1  promoter allele association in the studied Crohn's disease population.
Results
This study did not provide compelling evidence for  SLC11A1  disease association; most significantly, there was no apparent evidence of  SLC11A1  promoter allele association in the studied Crohn's disease population.SLC11A1SLC11A1Conclusion Our results therefore refute previous studies that have shown  SLC11A1  promoter polymorphisms are involved in susceptibility to this form of IBD.
Conclusion
Our results therefore refute previous studies that have shown  SLC11A1  promoter polymorphisms are involved in susceptibility to this form of IBD.SLC11A1

Background Inflammatory bowel disease (IBD) encompasses a number of disorders that are characterized by chronic inflammation of the gastrointestinal tract. The three specific forms of the disease known as Crohn disease (CD), ulcerative colitis (UC), or the less well-characterized indeterminate colitis (IC), are diagnosed based on the results of clinical observations, histology, radiology, and to a lesser extent, serology. Prominent clinical features include profuse diarrhea, abdominal pain, and increased colorectal cancer risk. IBD affects an estimated one million Americans [ 1 ], with UC being the more prevalent form. Current pathogenic models state that IBD develops in a genetically susceptible individual in response to environmental stimuli [ 2 ]. The aim of this study was to investigate the role of the gene  SLC11A1  in the susceptibility to IBD. SLC11A1 is a proton-coupled bivalent metal antiporter that is crucial in early macrophage activation [ 3 ].  SLC11A1  encodes a highly hydrophobic 550 amino acid membrane protein and is located on the long arm of chromosome 2 (2q35), a region that has not been identified as being within an IBD susceptibility locus. Genetic studies have shown that different alleles of a (GT) n  dinucleotide repeat polymorphism located within the promoter region of  SLC11A1  cause susceptibility to different human diseases. Allele 2 of this polymorphism is associated with susceptibility to intracellular pathogen infection [ 4 ], and allele 3 is associated with autoimmune disease susceptibility [ 3 ]. SLC11A1 has been implicated in susceptibility to IBD. Previous work from this laboratory [ 5 ] has shown that two tetranucleotide short tandem repeat (STR) polymorphisms genetically linked to  SLC11A1  are associated with CD [ 5 ]. The (GT) n  promoter polymorphism, however, was not examined in that study. A recent study by Kojima et al [ 6 ] indicated that promoter polymorphism allele 7 was associated with IBD in a Japanese population. In our current work, we used a case-control association study to define the role of the (GT) n  promoter polymorphism in susceptibility to IBD and to further characterize the STR markers previously described [ 5 ]. These markers (D2S434 and D2S1323) are 0.67 Mbp proximal and 3.44 Mbp distal of  SLC11A1 , respectively.
Background
Inflammatory bowel disease (IBD) encompasses a number of disorders that are characterized by chronic inflammation of the gastrointestinal tract. The three specific forms of the disease known as Crohn disease (CD), ulcerative colitis (UC), or the less well-characterized indeterminate colitis (IC), are diagnosed based on the results of clinical observations, histology, radiology, and to a lesser extent, serology. Prominent clinical features include profuse diarrhea, abdominal pain, and increased colorectal cancer risk. IBD affects an estimated one million Americans [ 1 ], with UC being the more prevalent form. Current pathogenic models state that IBD develops in a genetically susceptible individual in response to environmental stimuli [ 2 ].The aim of this study was to investigate the role of the gene  SLC11A1  in the susceptibility to IBD. SLC11A1 is a proton-coupled bivalent metal antiporter that is crucial in early macrophage activation [ 3 ].  SLC11A1  encodes a highly hydrophobic 550 amino acid membrane protein and is located on the long arm of chromosome 2 (2q35), a region that has not been identified as being within an IBD susceptibility locus. Genetic studies have shown that different alleles of a (GT) n  dinucleotide repeat polymorphism located within the promoter region of  SLC11A1  cause susceptibility to different human diseases. Allele 2 of this polymorphism is associated with susceptibility to intracellular pathogen infection [ 4 ], and allele 3 is associated with autoimmune disease susceptibility [ 3 ].SLC11A1SLC11A1SLC11A1SLC11A1 has been implicated in susceptibility to IBD. Previous work from this laboratory [ 5 ] has shown that two tetranucleotide short tandem repeat (STR) polymorphisms genetically linked to  SLC11A1  are associated with CD [ 5 ]. The (GT) n  promoter polymorphism, however, was not examined in that study. A recent study by Kojima et al [ 6 ] indicated that promoter polymorphism allele 7 was associated with IBD in a Japanese population.SLC11A1In our current work, we used a case-control association study to define the role of the (GT) n  promoter polymorphism in susceptibility to IBD and to further characterize the STR markers previously described [ 5 ]. These markers (D2S434 and D2S1323) are 0.67 Mbp proximal and 3.44 Mbp distal of  SLC11A1 , respectively.SLC11A1Methods Population This prospective study was approved by the University of Louisville Institutional Review Board. Written informed consent was obtained from all subjects. Patients were derived from a University-based colon and rectal surgery practice. Initial IBD diagnoses were determined through radiological, endoscopic, and/or histopathological studies. Histology was available in all cases. The study population consisted of a total of 628 Caucasians, including 254 unrelated individuals with CD (63% women), 165 with UC (53% women), 65 with IC (70% women), and 144 population controls (74% women). Demographic data for the IBD population is shown in Table  1 . For the purposes of more accurate phenotyping, the CD group was subdivided based on the Vienna classification [ 7 ]. This categorizes CD patients based on age of onset (A), location of disease (L), and disease behavior (B). With regard to age of onset, 175 of 254 (69%) CD patients were diagnosed at < 40 years of age (A1 group) and 47 of 254 (18%) had disease diagnosed at ≥40 years of age (A2 group). Information regarding age of onset was not available for 32 of 254 patients (13%). Regarding disease location, 70 of 254 (28%) had terminal ileal disease (L1 group), 152 of 254 (60%) had purely colonic and/or ileocolic (ileal and colonic) disease (L2/L3 groups), and 32 of 254 (13%) had disease located proximal to terminal ileum (L4 group). The L2 and L3 groups were combined for the purposes of this study, since colonic disease constitutes the primary focus of our group. Regarding disease behavior, 96 of 254 (38%) had uncomplicated inflammatory disease (B1 group), 28 of 254 (11%) had stricturing disease (B2 group), and 130 of 254 (51%) had penetrating disease (B3 group). A total of 348 non-affected first-degree relatives of IBD patients were available for the study. There were 63 CD families (29 triads, 30 discordant sibling pairs, 4 triad/discordant sibling pair combinations), 43 UC families (11 triads, 24 discordant sibling pairs, 8 triad/discordant sibling pair combinations), and 14 IC families (5 triads, 5 discordant sibling pairs, 4 triad/discordant sibling pair combinations). In addition, there were 17 CD families, 18 UC families, and 9 IC families who could not be classified as either a triad or discordant sibling pairs. Markers and genotyping STRs were amplified from genomic DNA extracted from peripheral leukocytes. Polymerase chain reaction (PCR) primers were custom-synthesized (Proligo, La Jolla, CA). PCR amplification of the  SLC11A1  promoter polymorphism was performed using 100 ng template DNA, 2.5 mM MgCl 2 , 50 mM KCl, 10 mM of tris/HCl, 0.2 μM of each dNTP, 200 pmol of each primer, and 1 unit of Taq polymerase (Promega, Madison, WI). Primer sequences were as follows: 5'GACATGAAGACTCGCATTAG3' & 5'TCAAGTCTCCACCAGCCTAGT3'. The product was amplified using the following conditions: 94°C for 10 min, followed by 25 cycles of 94°C for 30 sec, 55°C for 75 sec, and 72°C for 20 sec. A final extension step was run at 72°C for 6 min. Genotyping was performed using gel electrophoresis (Spreadex EL 300 S-100 gel [Elchrom Scientific, Lake Park, FL]). PCR amplification of the STR markers D2S434 and D2S1323 was performed as described previously [ 5 ]. Statistical analyses Case-control tests of association Case-control analyses were performed using STR genotype-allele frequencies in cohorts subdivided on the variables outlined in Table  1 . Correction for multiple testing was performed by using Storey's q-value method, where p-values were adjusted according to the experimental false discovery rate (FDR) [ 8 ]. The global null hypothesis of no difference in genotype-allele frequency between controls and any of the other groups was tested by using Fisher's method [ 9 ], which was used to combine all of the control group comparison global p-values obtained for each of the three markers. Q-values were calculated from these global p-values using the QVALUE program [ 10 ] with the tuning parameter λ = 0, which dictates the assumption π o  = 1, where π o  is the proportion of tests in which the null hypothesis is true. To limit the number of individual disease-control genotype-allele comparisons, further analyses were  only  performed in those disease groups possessing a global test q-value <0.05. Therefore, 2 × 2 contingency tables were  only  constructed for genotypes with a global test q-value meeting this condition. Disease  versus  control comparisons were performed using Fisher's exact test and corrected for multiple testing using the q-value method as described above. Since IBD affecting the colon is the primary focus of our research, the L2 colonic CD and L3 ileocolonic CD groups were combined, enabling maximization of statistical power. We argue that this approach will not impair the validity of statistical analyses given that the Vienna Classification is somewhat arbitrary and that group genetic homogeneity may be increased by combining all those cases with colonic IBD. Furthermore, owing to the relatively low numbers of individuals with CD proximal to the terminal ileum (L4 group, n = 32), this disease subgroup was excluded from case-control analyses. Family-based tests of association Family-based tests used included the pedigree disequilibrium test (PDT) [ 11 , 12 ] and a likelihood ratio test implemented in the computer program TRANSMIT [ 13 ]. PDT analysis has distinct advantages over other family-based tests of association. PDT allows for analysis of data from related nuclear families and discordant sibling pairs from extended pedigrees. The likelihood ratio test, as implemented in the computer program TRANSMIT [ 13 ], can account for missing parental genetic data by inferring parental genotypes based on the genotypes of their offspring. To maximize power, family-based analyses were not performed at the sub-phenotypic level.
Methods
Population This prospective study was approved by the University of Louisville Institutional Review Board. Written informed consent was obtained from all subjects. Patients were derived from a University-based colon and rectal surgery practice. Initial IBD diagnoses were determined through radiological, endoscopic, and/or histopathological studies. Histology was available in all cases. The study population consisted of a total of 628 Caucasians, including 254 unrelated individuals with CD (63% women), 165 with UC (53% women), 65 with IC (70% women), and 144 population controls (74% women). Demographic data for the IBD population is shown in Table  1 . For the purposes of more accurate phenotyping, the CD group was subdivided based on the Vienna classification [ 7 ]. This categorizes CD patients based on age of onset (A), location of disease (L), and disease behavior (B). With regard to age of onset, 175 of 254 (69%) CD patients were diagnosed at < 40 years of age (A1 group) and 47 of 254 (18%) had disease diagnosed at ≥40 years of age (A2 group). Information regarding age of onset was not available for 32 of 254 patients (13%). Regarding disease location, 70 of 254 (28%) had terminal ileal disease (L1 group), 152 of 254 (60%) had purely colonic and/or ileocolic (ileal and colonic) disease (L2/L3 groups), and 32 of 254 (13%) had disease located proximal to terminal ileum (L4 group). The L2 and L3 groups were combined for the purposes of this study, since colonic disease constitutes the primary focus of our group. Regarding disease behavior, 96 of 254 (38%) had uncomplicated inflammatory disease (B1 group), 28 of 254 (11%) had stricturing disease (B2 group), and 130 of 254 (51%) had penetrating disease (B3 group). A total of 348 non-affected first-degree relatives of IBD patients were available for the study. There were 63 CD families (29 triads, 30 discordant sibling pairs, 4 triad/discordant sibling pair combinations), 43 UC families (11 triads, 24 discordant sibling pairs, 8 triad/discordant sibling pair combinations), and 14 IC families (5 triads, 5 discordant sibling pairs, 4 triad/discordant sibling pair combinations). In addition, there were 17 CD families, 18 UC families, and 9 IC families who could not be classified as either a triad or discordant sibling pairs.
Population
This prospective study was approved by the University of Louisville Institutional Review Board. Written informed consent was obtained from all subjects. Patients were derived from a University-based colon and rectal surgery practice. Initial IBD diagnoses were determined through radiological, endoscopic, and/or histopathological studies. Histology was available in all cases.The study population consisted of a total of 628 Caucasians, including 254 unrelated individuals with CD (63% women), 165 with UC (53% women), 65 with IC (70% women), and 144 population controls (74% women). Demographic data for the IBD population is shown in Table  1 . For the purposes of more accurate phenotyping, the CD group was subdivided based on the Vienna classification [ 7 ]. This categorizes CD patients based on age of onset (A), location of disease (L), and disease behavior (B). With regard to age of onset, 175 of 254 (69%) CD patients were diagnosed at < 40 years of age (A1 group) and 47 of 254 (18%) had disease diagnosed at ≥40 years of age (A2 group). Information regarding age of onset was not available for 32 of 254 patients (13%). Regarding disease location, 70 of 254 (28%) had terminal ileal disease (L1 group), 152 of 254 (60%) had purely colonic and/or ileocolic (ileal and colonic) disease (L2/L3 groups), and 32 of 254 (13%) had disease located proximal to terminal ileum (L4 group). The L2 and L3 groups were combined for the purposes of this study, since colonic disease constitutes the primary focus of our group. Regarding disease behavior, 96 of 254 (38%) had uncomplicated inflammatory disease (B1 group), 28 of 254 (11%) had stricturing disease (B2 group), and 130 of 254 (51%) had penetrating disease (B3 group).A total of 348 non-affected first-degree relatives of IBD patients were available for the study. There were 63 CD families (29 triads, 30 discordant sibling pairs, 4 triad/discordant sibling pair combinations), 43 UC families (11 triads, 24 discordant sibling pairs, 8 triad/discordant sibling pair combinations), and 14 IC families (5 triads, 5 discordant sibling pairs, 4 triad/discordant sibling pair combinations). In addition, there were 17 CD families, 18 UC families, and 9 IC families who could not be classified as either a triad or discordant sibling pairs.Markers and genotyping STRs were amplified from genomic DNA extracted from peripheral leukocytes. Polymerase chain reaction (PCR) primers were custom-synthesized (Proligo, La Jolla, CA). PCR amplification of the  SLC11A1  promoter polymorphism was performed using 100 ng template DNA, 2.5 mM MgCl 2 , 50 mM KCl, 10 mM of tris/HCl, 0.2 μM of each dNTP, 200 pmol of each primer, and 1 unit of Taq polymerase (Promega, Madison, WI). Primer sequences were as follows: 5'GACATGAAGACTCGCATTAG3' & 5'TCAAGTCTCCACCAGCCTAGT3'. The product was amplified using the following conditions: 94°C for 10 min, followed by 25 cycles of 94°C for 30 sec, 55°C for 75 sec, and 72°C for 20 sec. A final extension step was run at 72°C for 6 min. Genotyping was performed using gel electrophoresis (Spreadex EL 300 S-100 gel [Elchrom Scientific, Lake Park, FL]). PCR amplification of the STR markers D2S434 and D2S1323 was performed as described previously [ 5 ].
Markers and genotyping
STRs were amplified from genomic DNA extracted from peripheral leukocytes. Polymerase chain reaction (PCR) primers were custom-synthesized (Proligo, La Jolla, CA). PCR amplification of the  SLC11A1  promoter polymorphism was performed using 100 ng template DNA, 2.5 mM MgCl 2 , 50 mM KCl, 10 mM of tris/HCl, 0.2 μM of each dNTP, 200 pmol of each primer, and 1 unit of Taq polymerase (Promega, Madison, WI). Primer sequences were as follows: 5'GACATGAAGACTCGCATTAG3' & 5'TCAAGTCTCCACCAGCCTAGT3'. The product was amplified using the following conditions: 94°C for 10 min, followed by 25 cycles of 94°C for 30 sec, 55°C for 75 sec, and 72°C for 20 sec. A final extension step was run at 72°C for 6 min. Genotyping was performed using gel electrophoresis (Spreadex EL 300 S-100 gel [Elchrom Scientific, Lake Park, FL]). PCR amplification of the STR markers D2S434 and D2S1323 was performed as described previously [ 5 ].SLC11A1Statistical analyses Case-control tests of association Case-control analyses were performed using STR genotype-allele frequencies in cohorts subdivided on the variables outlined in Table  1 . Correction for multiple testing was performed by using Storey's q-value method, where p-values were adjusted according to the experimental false discovery rate (FDR) [ 8 ]. The global null hypothesis of no difference in genotype-allele frequency between controls and any of the other groups was tested by using Fisher's method [ 9 ], which was used to combine all of the control group comparison global p-values obtained for each of the three markers. Q-values were calculated from these global p-values using the QVALUE program [ 10 ] with the tuning parameter λ = 0, which dictates the assumption π o  = 1, where π o  is the proportion of tests in which the null hypothesis is true. To limit the number of individual disease-control genotype-allele comparisons, further analyses were  only  performed in those disease groups possessing a global test q-value <0.05. Therefore, 2 × 2 contingency tables were  only  constructed for genotypes with a global test q-value meeting this condition. Disease  versus  control comparisons were performed using Fisher's exact test and corrected for multiple testing using the q-value method as described above. Since IBD affecting the colon is the primary focus of our research, the L2 colonic CD and L3 ileocolonic CD groups were combined, enabling maximization of statistical power. We argue that this approach will not impair the validity of statistical analyses given that the Vienna Classification is somewhat arbitrary and that group genetic homogeneity may be increased by combining all those cases with colonic IBD. Furthermore, owing to the relatively low numbers of individuals with CD proximal to the terminal ileum (L4 group, n = 32), this disease subgroup was excluded from case-control analyses. Family-based tests of association Family-based tests used included the pedigree disequilibrium test (PDT) [ 11 , 12 ] and a likelihood ratio test implemented in the computer program TRANSMIT [ 13 ]. PDT analysis has distinct advantages over other family-based tests of association. PDT allows for analysis of data from related nuclear families and discordant sibling pairs from extended pedigrees. The likelihood ratio test, as implemented in the computer program TRANSMIT [ 13 ], can account for missing parental genetic data by inferring parental genotypes based on the genotypes of their offspring. To maximize power, family-based analyses were not performed at the sub-phenotypic level.
Statistical analyses
Case-control tests of association Case-control analyses were performed using STR genotype-allele frequencies in cohorts subdivided on the variables outlined in Table  1 . Correction for multiple testing was performed by using Storey's q-value method, where p-values were adjusted according to the experimental false discovery rate (FDR) [ 8 ]. The global null hypothesis of no difference in genotype-allele frequency between controls and any of the other groups was tested by using Fisher's method [ 9 ], which was used to combine all of the control group comparison global p-values obtained for each of the three markers. Q-values were calculated from these global p-values using the QVALUE program [ 10 ] with the tuning parameter λ = 0, which dictates the assumption π o  = 1, where π o  is the proportion of tests in which the null hypothesis is true. To limit the number of individual disease-control genotype-allele comparisons, further analyses were  only  performed in those disease groups possessing a global test q-value <0.05. Therefore, 2 × 2 contingency tables were  only  constructed for genotypes with a global test q-value meeting this condition. Disease  versus  control comparisons were performed using Fisher's exact test and corrected for multiple testing using the q-value method as described above. Since IBD affecting the colon is the primary focus of our research, the L2 colonic CD and L3 ileocolonic CD groups were combined, enabling maximization of statistical power. We argue that this approach will not impair the validity of statistical analyses given that the Vienna Classification is somewhat arbitrary and that group genetic homogeneity may be increased by combining all those cases with colonic IBD. Furthermore, owing to the relatively low numbers of individuals with CD proximal to the terminal ileum (L4 group, n = 32), this disease subgroup was excluded from case-control analyses.
Case-control tests of association
Case-control analyses were performed using STR genotype-allele frequencies in cohorts subdivided on the variables outlined in Table  1 . Correction for multiple testing was performed by using Storey's q-value method, where p-values were adjusted according to the experimental false discovery rate (FDR) [ 8 ]. The global null hypothesis of no difference in genotype-allele frequency between controls and any of the other groups was tested by using Fisher's method [ 9 ], which was used to combine all of the control group comparison global p-values obtained for each of the three markers. Q-values were calculated from these global p-values using the QVALUE program [ 10 ] with the tuning parameter λ = 0, which dictates the assumption π o  = 1, where π o  is the proportion of tests in which the null hypothesis is true.To limit the number of individual disease-control genotype-allele comparisons, further analyses were  only  performed in those disease groups possessing a global test q-value <0.05. Therefore, 2 × 2 contingency tables were  only  constructed for genotypes with a global test q-value meeting this condition. Disease  versus  control comparisons were performed using Fisher's exact test and corrected for multiple testing using the q-value method as described above.versusSince IBD affecting the colon is the primary focus of our research, the L2 colonic CD and L3 ileocolonic CD groups were combined, enabling maximization of statistical power. We argue that this approach will not impair the validity of statistical analyses given that the Vienna Classification is somewhat arbitrary and that group genetic homogeneity may be increased by combining all those cases with colonic IBD. Furthermore, owing to the relatively low numbers of individuals with CD proximal to the terminal ileum (L4 group, n = 32), this disease subgroup was excluded from case-control analyses.Family-based tests of association Family-based tests used included the pedigree disequilibrium test (PDT) [ 11 , 12 ] and a likelihood ratio test implemented in the computer program TRANSMIT [ 13 ]. PDT analysis has distinct advantages over other family-based tests of association. PDT allows for analysis of data from related nuclear families and discordant sibling pairs from extended pedigrees. The likelihood ratio test, as implemented in the computer program TRANSMIT [ 13 ], can account for missing parental genetic data by inferring parental genotypes based on the genotypes of their offspring. To maximize power, family-based analyses were not performed at the sub-phenotypic level.
Family-based tests of association
Family-based tests used included the pedigree disequilibrium test (PDT) [ 11 , 12 ] and a likelihood ratio test implemented in the computer program TRANSMIT [ 13 ]. PDT analysis has distinct advantages over other family-based tests of association. PDT allows for analysis of data from related nuclear families and discordant sibling pairs from extended pedigrees. The likelihood ratio test, as implemented in the computer program TRANSMIT [ 13 ], can account for missing parental genetic data by inferring parental genotypes based on the genotypes of their offspring. To maximize power, family-based analyses were not performed at the sub-phenotypic level.Results Allele and genotype frequencies Alleles for the  SLC11A1  promoter polymorphism STR D2S434 and STR D2S1323 were named numerically according to molecular weight, with "1" representing a larger allele than "2" and so on. Allele distributions are shown in Table  2 . DNA sequence analysis of the three  SLC11A1  promoter polymorphism alleles identified in the study population revealed the following sequences: Allele 1: T(GT) 5 AC(GT) 5 AC(GT) 11 GGCAGA(G) 6 Allele 2: T(GT) 5 AC(GT) 5 AC(GT) 10 GGCAGA(G) 6 Allele 3: T(GT) 5 AC(GT) 5 AC(GT) 9 GGCAGA(G) 6 These sequences corresponded to alleles 1, 2, and 3 as described by Searle and Blackwell [ 14 ]. Alleles 4–7 were not identified in this population. Case-control association studies Results from global disease  versus  control comparisons of the studied markers are shown in Table  3 . Analysis of genotype and allele frequencies for the (GT) n  promoter polymorphism did not reveal any evidence of association. Additionally, we studied STR markers in genetic proximity to  SLC11A1 . The STR D2S1323 exhibited evidence of association with the test of the global null hypothesis of homogeneity of all groups exhibiting statistical significance for allele frequencies (q = 0.050, p = 0.008). To limit multiple testing, individual pair-wise disease  versus  control comparisons were only performed for D2S1323 alleles. These analyses showed over-representation of allele 1 in the UC group (q = 0.022, p = 0.005; UC frequency 78 of 100 alleles [78%]  versus  control frequency 54 of 92 alleles [59%]). The STR D2S434 did not reveal any evidence of association. Further analyses of genotype and allele data for all markers did not show a correlation with either age of onset or disease behavior. Family-based association studies Family-based tests were performed for each of the three markers. Neither the likelihood ratio test nor the PDT revealed significant evidence of association for any of these markers.
Results
Allele and genotype frequencies Alleles for the  SLC11A1  promoter polymorphism STR D2S434 and STR D2S1323 were named numerically according to molecular weight, with "1" representing a larger allele than "2" and so on. Allele distributions are shown in Table  2 . DNA sequence analysis of the three  SLC11A1  promoter polymorphism alleles identified in the study population revealed the following sequences: Allele 1: T(GT) 5 AC(GT) 5 AC(GT) 11 GGCAGA(G) 6 Allele 2: T(GT) 5 AC(GT) 5 AC(GT) 10 GGCAGA(G) 6 Allele 3: T(GT) 5 AC(GT) 5 AC(GT) 9 GGCAGA(G) 6 These sequences corresponded to alleles 1, 2, and 3 as described by Searle and Blackwell [ 14 ]. Alleles 4–7 were not identified in this population.
Allele and genotype frequencies
Alleles for the  SLC11A1  promoter polymorphism STR D2S434 and STR D2S1323 were named numerically according to molecular weight, with "1" representing a larger allele than "2" and so on. Allele distributions are shown in Table  2 .SLC11A1DNA sequence analysis of the three  SLC11A1  promoter polymorphism alleles identified in the study population revealed the following sequences:SLC11A1Allele 1: T(GT) 5 AC(GT) 5 AC(GT) 11 GGCAGA(G) 6Allele 2: T(GT) 5 AC(GT) 5 AC(GT) 10 GGCAGA(G) 6Allele 3: T(GT) 5 AC(GT) 5 AC(GT) 9 GGCAGA(G) 6These sequences corresponded to alleles 1, 2, and 3 as described by Searle and Blackwell [ 14 ]. Alleles 4–7 were not identified in this population.Case-control association studies Results from global disease  versus  control comparisons of the studied markers are shown in Table  3 . Analysis of genotype and allele frequencies for the (GT) n  promoter polymorphism did not reveal any evidence of association. Additionally, we studied STR markers in genetic proximity to  SLC11A1 . The STR D2S1323 exhibited evidence of association with the test of the global null hypothesis of homogeneity of all groups exhibiting statistical significance for allele frequencies (q = 0.050, p = 0.008). To limit multiple testing, individual pair-wise disease  versus  control comparisons were only performed for D2S1323 alleles. These analyses showed over-representation of allele 1 in the UC group (q = 0.022, p = 0.005; UC frequency 78 of 100 alleles [78%]  versus  control frequency 54 of 92 alleles [59%]). The STR D2S434 did not reveal any evidence of association. Further analyses of genotype and allele data for all markers did not show a correlation with either age of onset or disease behavior.
Case-control association studies
Results from global disease  versus  control comparisons of the studied markers are shown in Table  3 . Analysis of genotype and allele frequencies for the (GT) n  promoter polymorphism did not reveal any evidence of association. Additionally, we studied STR markers in genetic proximity to  SLC11A1 . The STR D2S1323 exhibited evidence of association with the test of the global null hypothesis of homogeneity of all groups exhibiting statistical significance for allele frequencies (q = 0.050, p = 0.008). To limit multiple testing, individual pair-wise disease  versus  control comparisons were only performed for D2S1323 alleles. These analyses showed over-representation of allele 1 in the UC group (q = 0.022, p = 0.005; UC frequency 78 of 100 alleles [78%]  versus  control frequency 54 of 92 alleles [59%]). The STR D2S434 did not reveal any evidence of association. Further analyses of genotype and allele data for all markers did not show a correlation with either age of onset or disease behavior.versusSLC11A1versusversusFamily-based association studies Family-based tests were performed for each of the three markers. Neither the likelihood ratio test nor the PDT revealed significant evidence of association for any of these markers.
Family-based association studies
Family-based tests were performed for each of the three markers. Neither the likelihood ratio test nor the PDT revealed significant evidence of association for any of these markers.Discussion No association was observed between any  SLC11A1  promoter polymorphism alleles and any IBD sub-phenotypic group in this population, which unexpectedly refutes the significant evidence of others [ 6 ]. This result was somewhat surprising, given that many studies have shown  SLC11A1  promoter polymorphisms to be involved in susceptibility to autoimmune disorders and disorders that are characterized by a high degree of immunological dysregulation, including IBD [ 3 ]. Searle and Blackwell [ 14 ] investigated the enhancer properties of each of the four identified promoter polymorphism alleles. Allele 3 was found to have endogenous enhancer activity, whereas alleles 1, 2, and 4 were poor promoters in the absence of exogenous stimuli. Stimulation of macrophages with bacterial lipopolysaccharide (LPS) had minimal effects on  SLC11A1  expression driven by alleles 1, 2, and 4; however, allele 3-driven  SLC11A1  expression was enhanced. It has long been recognized that altered expression of  SLC11A1  in response to LPS is a key event in the activation of macrophages upon contact with a variety of pathogens [ 15 ]. Based on those observations, we hypothesized that over-representation of promoter allele 3 in CD patients could lead to hyperactivation of bowel wall macrophages that are chronically exposed to high levels of LPS. This could subsequently cause the autoimmune-like phenotype characteristic of IBD. This hypothesis must be rejected in this cohort due to the absence of association with promoter allele 3 in any IBD subgroup. Analysis of intragenic single nucleotide polymorphisms are required to conclusively exclude  SLC11A1  as an IBD candidate gene. D2S1323 was associated with UC in this population. Indeed, this was the only evidence for association in any of the studied STR markers in proximity to  SLC11A1 . This association was not, however, confirmed upon family-based statistical analysis. The current finding was particularly surprising to us, given that our previous study showed this STR to be associated with CD rather than UC [ 5 ]. The significance of this finding is thus uncertain, and the divergent outcomes from the current and earlier studies could be considered somewhat concerning. We do, however, believe that a number of prominent features of the current study render our results of higher validity than those of our previous study. An important feature of our current work is that all histological specimens from our subjects have been reviewed by a single gastrointestinal pathologist. This was not the case in the earlier study, and diagnostic misclassification (i.e., UC and/or IC incorrectly diagnosed as CD and  vice versa ) may have been a confounding variable that may have in part led to what we now consider the false-positive association observed in the CD population. It should also be noted that the current study contains over two-fold the number of subjects as compared with the previous work, and thus statistical power has been significantly increased. Furthermore, our current statistical methodology is now far superior, and we argue that our stringent means of correcting for the effects of multiple testing effectively minimizes the likelihood of detecting a false-positive association. This is a vast improvement over our earlier study in which compounding of a type 1 error was not taken into account. Finally, genotyping in the earlier study was performed on specimens with a rather heterogeneous nature and consisted of genetic data derived from DNA extracted from both surgical specimens (i.e., somatic DNA) and leukocytes (i.e., germline DNA). In our current study, genetic material has only been isolated from leukocytes, thus reducing the possibility of detecting variations within somatic DNA.
Discussion
No association was observed between any  SLC11A1  promoter polymorphism alleles and any IBD sub-phenotypic group in this population, which unexpectedly refutes the significant evidence of others [ 6 ]. This result was somewhat surprising, given that many studies have shown  SLC11A1  promoter polymorphisms to be involved in susceptibility to autoimmune disorders and disorders that are characterized by a high degree of immunological dysregulation, including IBD [ 3 ]. Searle and Blackwell [ 14 ] investigated the enhancer properties of each of the four identified promoter polymorphism alleles. Allele 3 was found to have endogenous enhancer activity, whereas alleles 1, 2, and 4 were poor promoters in the absence of exogenous stimuli. Stimulation of macrophages with bacterial lipopolysaccharide (LPS) had minimal effects on  SLC11A1  expression driven by alleles 1, 2, and 4; however, allele 3-driven  SLC11A1  expression was enhanced. It has long been recognized that altered expression of  SLC11A1  in response to LPS is a key event in the activation of macrophages upon contact with a variety of pathogens [ 15 ].SLC11A1SLC11A1SLC11A1SLC11A1SLC11A1Based on those observations, we hypothesized that over-representation of promoter allele 3 in CD patients could lead to hyperactivation of bowel wall macrophages that are chronically exposed to high levels of LPS. This could subsequently cause the autoimmune-like phenotype characteristic of IBD. This hypothesis must be rejected in this cohort due to the absence of association with promoter allele 3 in any IBD subgroup. Analysis of intragenic single nucleotide polymorphisms are required to conclusively exclude  SLC11A1  as an IBD candidate gene.SLC11A1D2S1323 was associated with UC in this population. Indeed, this was the only evidence for association in any of the studied STR markers in proximity to  SLC11A1 . This association was not, however, confirmed upon family-based statistical analysis. The current finding was particularly surprising to us, given that our previous study showed this STR to be associated with CD rather than UC [ 5 ]. The significance of this finding is thus uncertain, and the divergent outcomes from the current and earlier studies could be considered somewhat concerning. We do, however, believe that a number of prominent features of the current study render our results of higher validity than those of our previous study. An important feature of our current work is that all histological specimens from our subjects have been reviewed by a single gastrointestinal pathologist. This was not the case in the earlier study, and diagnostic misclassification (i.e., UC and/or IC incorrectly diagnosed as CD and  vice versa ) may have been a confounding variable that may have in part led to what we now consider the false-positive association observed in the CD population. It should also be noted that the current study contains over two-fold the number of subjects as compared with the previous work, and thus statistical power has been significantly increased. Furthermore, our current statistical methodology is now far superior, and we argue that our stringent means of correcting for the effects of multiple testing effectively minimizes the likelihood of detecting a false-positive association. This is a vast improvement over our earlier study in which compounding of a type 1 error was not taken into account. Finally, genotyping in the earlier study was performed on specimens with a rather heterogeneous nature and consisted of genetic data derived from DNA extracted from both surgical specimens (i.e., somatic DNA) and leukocytes (i.e., germline DNA). In our current study, genetic material has only been isolated from leukocytes, thus reducing the possibility of detecting variations within somatic DNA.SLC11A1vice versaConclusion In summary, we have characterized the  SLC11A1  (GT) n  promoter polymorphism in IBD sub-phenotypic groups and accept the null hypothesis as confirmed by family-based testing. We did, however, identify an association between an allele of the STR D2S1323 and UC upon case-control analysis. This association was not confirmed on family-based and haplotype testing, and its actual significance in IBD remains to be seen.
Conclusion
In summary, we have characterized the  SLC11A1  (GT) n  promoter polymorphism in IBD sub-phenotypic groups and accept the null hypothesis as confirmed by family-based testing. We did, however, identify an association between an allele of the STR D2S1323 and UC upon case-control analysis. This association was not confirmed on family-based and haplotype testing, and its actual significance in IBD remains to be seen.SLC11A1Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions NPSC performed genotyping, PDT analyses, and drafted the manuscript; MRE performed genotyping and was responsible for coordination of laboratory work; authors DWC and RKL performed genotyping; GAC performed statistical analyses; REP performed re-review of colonic IBD histology; SG collected all patient samples, participated in study design, coordination, and manuscript preparation and obtained funding for this work. All authors read and approved the final manuscript.
Authors' contributions
NPSC performed genotyping, PDT analyses, and drafted the manuscript; MRE performed genotyping and was responsible for coordination of laboratory work; authors DWC and RKL performed genotyping; GAC performed statistical analyses; REP performed re-review of colonic IBD histology; SG collected all patient samples, participated in study design, coordination, and manuscript preparation and obtained funding for this work. All authors read and approved the final manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here: