High-Volume versus Low-Volume for Esophageal Resections for Cancer : The Essential Role of Case-Mix Adjustments based on Clinical Data Background Most studies addressing the volume -- outcome relationship in complex surgical procedures use hospital mortality as the sole outcome measure and are rarely based on detailed clinical data . The lack of reliable information about comorbidities and tumor stages makes the conclusions of these studies debatable . The purpose of this study was to compare outcomes for esophageal resections for cancer in low - versus high-volume hospitals , using an extensive set of variables concerning case-mix and outcome measures , including long-term survival . Since Luft published his study about the inverse relationship between surgical volume and hospital mortality in 1979 , a plethora of studies has demonstrated an improvement of clinical outcome with increased hospital volume .1 Most of these studies use hospital mortality as the sole outcome measure . Often , data are obtained from insurance company 's databases , and few studies use clinical data for risk-adjustment.2 The surgical treatment of esophageal cancer is often mentioned as one of the procedures for which concentration in high-volume centers might improve outcome .3,4 Nevertheless , a clear volume cut-off point at which a cancer center is justified to perform esophageal resections can hardly be defined .5,6 Also , the volume -- outcome literature for esophageal resections is limited to postoperative mortality as the sole determinant of outcome . Considering the growing evidence for this volume -- outcome relationship for esophageal cancer surgery , we decided to investigate the outcome of these procedures in our region from 1990 until 1999 . During this study period none of the 11 hospitals affiliated with the Comprehensive Cancer Center Leiden -LRB- CCCL -RRB- in the Netherlands performed more than seven esophageal resections a year ; all are considered low-volume hospitals -LRB- LVH -RRB- . In contrast to most volume -- outcome studies , we decided to use clinical data obtained from the original patients ' files . We retrieved information about comorbid diseases , tumor characteristics , treatment , and outcome . Next to hospital mortality , several determinants of outcome were examined , such as the number of tumor-free margins and complication rates . Assuming that survival is an essential indicator for quality in cancer surgery , we included a 5-year follow-up . To put our data in the right perspective , we compared these outcomes to the results of the topographically nearest high-volume referral center -LRB- HVH -RRB- . PATIENTS AND METHODS All surgically treated esophageal carcinomas in the period 1990 -- 1999 were retrospectively identified through the Leiden Cancer Registry -LRB- LCR -RRB- of the Comprehensive Cancer Center Leiden -LRB- CCCL -RRB- , in which all cancer patients treated in the midwestern part of the Netherlands are registered -LRB- 1.7 million inhabitants -RRB- . All 11 hospitals gave consent to participate in this audit and were visited by two investigators to retrieve the original patient files . Patient demographics , pathological notes , data on the surgical and -LRB- neo -RRB- adjuvant treatments , comorbidity as well as postoperative morbidity , mortality , length of stay , radicality of the resection , and long-term survival could all be retrieved from the patient 's files . All tumors were staged according to the UICC TNM classification of 1997 . This was done by two independent researchers . The obtained pTNM stages were checked with the pTNM stages registered in the LCR . Any discrepancies were discussed between the researchers and a trained data manager from the CCCL . If consensus could not be reached , the pTNM stage was registered as `` unknown . '' To make a comparison with the outcomes of the nearest high-volume center , data were categorized according to the database of this center . In this hospital , data of patients operated on for an esophageal carcinoma are prospectively collected by a trained data manager . Differences in patient , tumor , and treatment characteristics as well as outcome measurements were assessed using the Kruskal -- Wallis test for continuous variables and the chi-square test for categorical variables . Logistic regression was used to determine prognostic factors of in-hospital mortality . Variables were entered in the multivariate model as a prognostic factor when P values < .10 . Survival was calculated as the difference between date of surgery and either the date of death or the date of last patient follow-up . For both groups , follow-up of the patients was completed until December 31 , , 2005 . Observed survival rates were estimated by using the Kaplan -- Meier method . The log-rank test was used to assess differences in survival between patients who were operated in LVHs and the HVH . All analyses were conducted using SPSS software -LRB- version 12.0 ; SPSS Inc. , Chicago . IL -RRB- . RESULTS Hospital Volume In 1990 -- 1999 the evaluation and treatment of patients with an esophageal carcinoma were performed in 11 hospitals in the region of the CCCL -LRB- one university hospital , five teaching hospitals , and five general hospitals -RRB- . In 342 patients the tumor was resected with curative intent . Figures 1A and B illustrate the distribution of surgical procedures within the studied time period and between the different hospitals . None of the CCCL hospitals performed more than seven esophageal resections a year , which makes them low-volume hospitals -LRB- LVHs -RRB- .7 In the same period , 561 esophageal resections were performed in the nearest high-volume referral center -LRB- HVH -RRB- ; a mean volume of 56 resections a year . FIG. 1 . -LRB- A -RRB- Number of esophageal resections per year in HVH versus LVH group -LRB- 1990 -- 1999 -RRB- . -LRB- B -RRB- Total number of esophageal resections per hospital for HVH and LVHs -LRB- 1990 -- 1999 -RRB- . Patient , Tumor , and Treatment Characteristics Table 1 shows the patient , tumor , and treatment characteristics of both groups . More patients from the HVH had a squamous cell carcinoma and an advanced stage of the disease . Operative strategy as well as adjuvant or neoadjuvant treatment varied widely between the groups . The vast majority of resections in the HVH was performed according to the transhiatal technique , with a gastric tube reconstruction and anastomosis to the cervical remnant esophagus . In the LVH group a substantial number of anastomoses were located in the thoracic cavity , after a -LRB- partial -RRB- gastroesophagectomy with either a gastric tube reconstruction or esophagojejunostomy . In the pathology , clear surgical margins -LRB- R0 -RRB- were reported in 72 % and 67 % , respectively for the LVHs and the HVH group . TABLE 1 . Patient , tumor , and treatment characteristics of esophageal resections in LVHa and HVHbCharacteristicsLVHHVHP valueNo . of patients % No . of patients % Age -LRB- years -RRB- 6564.240 Range -LRB- years -RRB- 33 -- 8731 -- 83Gender .072 Male2497343878 Female932712322Comorbidity .078 No1424227349 1 organ system1113217932 2 organ systems51158014 ≥ 3 organ systems113275 Unknown27820Histology .039 Adenocarcinoma2386934762 Squamous962819334 Barrett 's dysplasia4161 Other21143 Unknown2110Tumor localization .740 Cervical esophagus72143 Mid esophagus53158615 Distal esophagus1143320436 Gastroesophageal junction1664925145 Unknown2161Stage -LRB- pTNM -RRB- < .001 0 and I43126111 II1624721438 III1073118633 IV2169417 Unknown9361 -LRB- Neo -RRB- - adjuvant treatment < .001 None3169246483 Chemotherapy1759317 Radiotherapy0020 Chemoradiation4100 Unknown5210Surgical approach < .001 Abdomino-cervical1504446683 Thoraco-abdominal97286011 Abdomino-thoraco-cervical4313173 Abdominal5215183Anastomoses < .001 Cervical1955754196 Thoracic912782 Abdominal561682 Unknown0040Total No . of patients342561a LVH , low-volume hospitals.b HVH , high-volume hospital . Morbidity and Mortality A significantly higher postoperative morbidity rate was found in the LVH group , which probably is also reflected by the longer hospital stay -LRB- Table 2 -RRB- . The clinical anastomotic leakage rate differed between both groups : LVHs 17 % versus HVH 5 % . The mortality rate was almost three times higher for patients treated in the LVHs than those who had their operation in the HVH : 13 % vs 5 % , respectively -LRB- P < .001 -RRB- . None of the LVHs had a mortality rate lower than the 5 % of the HVH -LRB- Table 3 -RRB- . Univariate analysis showed that hospital volume , age , and comorbidity are prognostic factors for mortality -LRB- Table 4 -RRB- . The mortality risk increased with higher age and the number of organ systems affected . Especially cardiac -LRB- OR 3.22 , CI 1.91 -- 5.44 -RRB- , vascular -LRB- OR 2.49 , CI 1.45 -- 4.27 -RRB- , and respiratory -LRB- OR 1.90 CI 1.09 -- 3.33 -RRB- comorbidity were risk factors for postoperative mortality . TABLE 2 . Outcome after resection of esophagus for cancer in LVHa and HVHbOutcomeLVHHVHP valueNo . of patients % No . of patients % Margins .93 R02487237767 R1551616128 R23511214 Unknown4121Complications Surgical complications1444220737 .01 General complications1915620737 < .001 No complications892624744 < .001 Hospital stayMedian -LRB- days -RRB- 2114 < .001 In-hospital Mortality4513285 < .001 Survival Median -LRB- months -RRB- 2122.90 Range -LRB- months -RRB- -LRB- 1 -- 171 -RRB- -LRB- 1 -- 158 -RRB- Total No . of patients342561a LVH , low-volume hospitals.b HVH , high-volume hospital.TABLE 3 . Mortality after resection of esophagus for cancer in LVHa and HVHbHospitalsmIn-hospital mortalityNo . of patientsNo . of deaths % HVH561285 .0 LVH 116212.5 LVH 219210.5 LVH 32827.1 LVH 425312.0 LVH 51417.1 LVH 62827.1 LVH 73425.9 LVH 8641218.7 LVH 9441022.7 LVH 106233.3 LVH 1164710.9 Total No . of patients903738a LVH , low-volume hospitals.b HVH , high-volume hospital.TABLE 4 . Univariate analysis of in-hospital mortalityUnivariate analysisOR95 % CIP valueRegion < .001 HVH1 .00 Refa LVH2 .881.76 -- 4.72 Age -LRB- years -RRB- .01 < 500.190.04 -- 0.79 50 -- 590.510.25 -- 1.04 60 -- 691.00 Refa > 701.200.70 -- 2.04 Gender .20 Male1 .00 Refa Female0 .670.36 -- 1.24 Comorbidity < .001 No1 .00 Refa 1 organ system2 .021.06 -- 3.86 2 organ systems4 .512.30 -- 8.85 ≥ 3 organ systems4 .971.92 -- 12.83 Histology .97 Adenocarcinoma1 .00 Refa Squamous0 .990.60 -- 1.65 Stage .24 I1 .00 Refa II0 .500.24 -- 1.04 III0 .800.39 -- 1.63 IV0 .650.26 -- 1.61 Tumor localization .33 Cervical/mid esophagus1 .00 Refa Distal esophagus/gastroesophageal junction1 .410.71 -- 2.80 Neoadjuvant treatment .14 No1 .00 Refa Yes0 .490.20 -- 1.25 Surgical approach .31 Transhiatal1 .00 Refa Transthoracic1 .510.90 -- 2.54.12 Anastomosis .46 Cervical1 .00 Refa Thoracic1 .520.77 -- 3.01 Abdominal1 .260.52 -- 3.04 a Ref , reference category . Multivariate analysis showed that both hospital volume and comorbidity were independent prognostic factors for hospital mortality -LRB- Table 5 -RRB- . TABLE 5 . Multivariate analysis of in-hospital mortalityMultivariate analysisOR95 % CIP valueRegion < .001 HVH1 .00 Refa LVHs3 .051.82 -- 5.11 Age -LRB- years -RRB- .10 < 500.220.05 -- 0.96 50 -- 590.600.29 -- 1.25 60 -- 691.00 Refa > 701.070.61 -- 1.88 Comorbidity .004 No1 .00 Refa Yes2 .341.30 -- 4.19 a Ref , Reference category . Survival Figure 2 shows the crude 10-year overall survival rate of all patients , in which an esophageal resection for cancer was performed . Survival rates for patients treated in the HVH are significantly better -LRB- P = .01 -RRB- . This survival benefit loses its statistical significance , after exclusion of patients who died postoperatively of complications of the surgical procedure -LRB- Fig. 3 -RRB- . Only , when we select patients with stage I and II disease do we see a better survival in the HVH -LRB- Fig. 4 -RRB- , meaning that its overall results are worsened by the poor survival in the higher stages of the disease , stage III and IV . This can be explained by the unfavorable tumor mix , with significantly more stage IV disease treated in the HVH , than in the LVHs -LRB- 16.7 vs 6.1 % -RRB- . FIG. 2 . Overall survival after esophagus resection for cancer : LVHs vs HVH -LRB- in-hospital mortality included -RRB- . FIG. 3 . Overall survival after esophagus resection for cancer : LVHs vs HVH -LRB- in-hospital mortality excluded -RRB- . FIG. 4 . Overall survival after esophagus resection for stage I and II carcinoma : LVHs vs HVH -LRB- in-hospital mortality excluded -RRB- . DISCUSSION Currently , there is extensive interest in comparing outcome of complex surgical procedures between high - and low-volume providers . Most of the studies are registry-based or relatively small . Our series offers additional proof to the volume -- outcome relationship , because it is based on clinical data , retrieved from the original patient files . This allows us to make reliable comparisons for comorbidities and tumor stage , which proved to be important prognostic factors for in-hospital mortality and survival . A review of the evidence for a volume -- outcome relationship was published by Dudley in 20008 and Halm in 2002.2 In the latter publication 135 studies were reviewed , of which only five were not from the United States or Canada . The majority of reports were based on state or national hospital discharge databases , where only a few studies used clinical data for risk adjustment . The outcome measure was `` death '' in 79 % of the studies , without analyzing other dimensions of `` outcome , '' such as morbidity , length of hospital stay , reoperations , et cetera . For cancer-related procedures , long-term survival was not mentioned . Higher-level methodological issues were rarely addressed . Only five studies concerning cancer treatment adjusted for -LRB- neo -RRB- - adjuvant therapies or the type of surgical resection , but without any adjustment for tumor stage . Since 2002 , more extensive studies on hospital or surgeon volume appeared in the international literature . Birkmeyer reported a total number of 2.5 million operations concerning 14 different surgical procedures derived from the MEDICARE database .9 Mortality was the only outcome measure . Even after risk adjustment , which decreased the outcome differences between high - and low-volume hospitals , the differences in results for esophageal and pancreatic resections were highly significant , favoring surgery in a high-volume center . Two more recently published reviews of the volume -- outcome relationship for esophagectomies came up with 12 papers addressing this subject .4,5 Only two of these studies were based on clinical data . Although both showed a decrease in mortality , they failed to show a statistically significant relationship of operative mortality with hospital volume .10,11 In our own review of the literature we identified another study from the United Kingdom using clinical data , in which hospital case volume independently predicted operative mortality2 -LRB- Table 6 -RRB- . TABLE 6 . Volume -- outcome articles for in-hospital mortality after esophagectomy 1998 -- 2006AuthorJournal/YearDataVolume `` cut-off '' ConclusionDimick et al. 32Ann Thorac Surg 2005Adm < 6 > SUrbach and Baxter33BMJ 2004Adm < 9 > NSMcCulloch et al. 12BMJ 2003Clin < 10 -- 20 > SChristian et al. 34Ann Surg 2003Adm < 22 > SFinlayson et al. 35Arch Surg 2003Adm < 4 -- 9 > SUrbach et al. 36CMAJ 2003AdmaSDimick et al. 37Surgery 2003Adm < 7 > SBirkmeyer et al. 9N Engl J Med 2002Adm < 2 -- 4 -- 7 -- 19 > SGillison et al. 11Br J Surg 2002Clin < 19 > NSBachmann et al. 10Br J Surg 2002ClinaNSDimick et al. 38Ann Thorac Surg 2001Adm < 4 -- 15 > Svan Lanschot et al. 7Cancer 2001Adm < 10 -- 20 > SKuo et al. 39Ann Thorac Surg 2001Adm < 6 > SSwisher et al. 40J Thorac Cardiovasc Surg 2000Adm < 5 > SGordon et al. 41J Am Coll Surg 1999Adm < 10 -- 20 -- 50 > SBegg et al. 42JAMA 1998Mixed < 5 -- 10 > SPatti et al. 43J Gastrointest Surg 1998Adm < 1 -- 2 -- 4 -- 6 > Sa Urbach and Bachmann used equally sized groups and reported only median volumes of these groups.Adm denotes administrative data ; Clin denotes clinical data ; S denotes significant ; NS denotes not significant . In the present study , independent data managers collected data retrospectively from the patient files . Not only the -LRB- in-hospital -RRB- mortality rate was obtained , but also a range of other outcome data , such as complication rates , resection margins , length of stay , and long-term survival . In our opinion the latter is an important performance indicator in surgical oncology , surprisingly sporadically mentioned in the volume -- outcome literature . The results of patients treated in 11 low-volume hospitals were compared with the results of patients treated in the nearest high-volume referral center . Significant differences in outcome could be revealed . In-hospital mortality was significantly higher in the low-volume hospitals . The retrieved information about comorbidity and stage of the disease made an extensive preoperative risk and tumor load comparison possible . Risk adjustment is an important issue in outcome research , because patients with severe comorbidity may be unequally distributed between -LRB- groups of -RRB- hospitals . Especially , when only administrative data are used to assess hospital performances , a selection-bias could lead to inadvertently penalizing those surgeons who provide excellent care to patients with more severe comorbid disease .7,13 Administrative data sets were never designed to predict risk and should probably not be used as such .14 Therefore , the validity of studies that fail to make case-mix adjustments based on clinical data , has to be questioned . Nevertheless , a multivariate analysis of our data shows hospital volume to be an independent prognostic factor for in-hospital mortality . Although differences in surgical technique could be detected , with more transthoracic esophagectomies and intrathoracic anastomoses in the low-volume group , these factors are not significantly related to mortality . These findings are confirmed by earlier reports .15 -- 18 Also , there is little evidence for a beneficial role of neoadjuvant therapies .19 -- 22 However , above all , choices made concerning diagnostic strategy , neoadjuvant treatments , and surgical technique are related to the knowledge , experience , and judgment of the -LRB- team of -RRB- specialists . After exclusion of in-hospital mortality , the survival of patients in the HVH was equal to those treated in the LVHs . However , the results of the HVH were negatively influenced by its case-mix . More patients with stage IV disease were treated in the HVH , corresponding with its status as a tertiary referral center . The very poor survival in this group of patients influences the overall results significantly . Only when we are informed about differences in tumor stage , we are able to detect real differences in survival between patients treated in different hospitals . Although in this study , all pathology reports were reviewed , and the number of lymph nodes resected was equal for both groups , we still have to be cautious suggesting a survival benefit for high-volume surgery . Only when a uniform pathologic evaluation is guaranteed , can we be sure that observed differences in tumor stages are truly characteristic for patient groups . This could be the reason that few studies have attempted to examine the influence of hospital volume on long-term survival in cancer surgery , only one of them concerning esophagectomies .23 -- 27 A recent study from the Netherlands failed to show a survival benefit in high-volume hospitals -LRB- > 20 resections a year -RRB- , but did show an improved survival for esophagectomies performed in university compared to non-university hospitals .28 On the other hand , for pancreatectomies and hepatectomies registered in the MEDICARE-database , Fong showed a significantly better survival for procedures performed in high-volume centers .25 In his study , administrative data about age , gender , comorbidity , and extent of the resection were included in a univariate and multivariate analysis , but stages of the disease , radicality , and intent of the resection -LRB- palliative or curative -RRB- were not reported . In conclusion , our study shows that hospital volume is an important determinant of perioperative morbidity and mortality in esophageal cancer surgery . Nevertheless , volume in itself is no guarantee for high quality of surgical care in a specific institution . Selecting -LRB- only -RRB- favorable patients can be the basis of superior results . Therefore , case-mix adjustments are essential in the assessment of surgical performance of different institutions .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

esophageal cancer–esophagectomy
surgical outcomes
high-volume hospitals
case-mix


Does self-monitoring of blood glucose improve outcome in type 2 diabetes ? The Fremantle Diabetes Study Aims/hypothesis To assess whether self-monitoring of blood glucose -LRB- SMBG -RRB- is an independent predictor of improved outcome in a community-based cohort of type 2 diabetic patients . Introduction Intensive glycaemic control is a cost-effective way of reducing the complications associated with type 2 diabetes -LSB- 1 -RSB- . Whether self-monitoring of blood glucose -LRB- SMBG -RRB- can improve glycaemia is subject to debate -LSB- 2 , 3 -RSB- . The Retrolective Study ` Self-monitoring of Blood Glucose and Outcome in Patients with Type 2 Diabetes ' -LRB- ROSSO -RRB- investigators recently reported that SMBG was associated with decreased diabetes-related morbidity and all-cause mortality in type 2 diabetes , despite the SMBG group having a higher mean baseline fasting plasma glucose than the non-SMBG group -LSB- 4 -RSB- . In an Australian community-based cohort of type 2 patients we found that SMBG was not a determinant of glycaemic control -LSB- 5 -RSB- , and now investigate whether , as in ROSSO , those who performed SMBG had better outcomes than those who did not . Subjects and methods Subjects The Fremantle Diabetes Study -LRB- FDS -RRB- was a longitudinal observational study in a community of 120,097 people in the state of Western Australia . We identified 2,258 subjects between 1993 and 1996 , using all available sources , and recruited 1,426 -LRB- 63 % -RRB- to attend annual assessments , of whom 1,294 -LRB- 91 % -RRB- had type 2 diabetes -LSB- 6 -RSB- . The FDS protocol was approved by the Fremantle Hospital Human Rights Committee and all subjects gave informed consent . The present study included -LRB- 1 -RRB- 1,280 type 2 patients -LRB- mean age 64.1 ± 11.3 years , 48.8 % men -RRB- with complete diabetes treatment and mortality data who reported SMBG status at FDS entry , and -LRB- 2 -RRB- a subset of 531 patients -LRB- mean age 62.4 ± 9.4 years , 54.2 % men -RRB- who attended six or more consecutive annual assessments . Clinical assessment At baseline and annual reviews , a comprehensive history was taken and a physical examination was performed . Complications were identified using standard criteria -LSB- 5 , 7 -RSB- . Microalbuminuria was defined as an urinary albumin : creatinine ratio -LRB- ACR -RRB- ≥ 3.0 mg/mmol on a first morning sample , neuropathy as a score > 2/8 on the clinical portion of the Michigan Neuropathy Screening Instrument , and retinopathy as any grade in one/both eyes on direct and/or indirect ophthalmoscopy and/or detailed specialist assessment . Self-report and hospitalisations were used to identify cerebrovascular disease -LRB- CVD ; stroke , transient ischaemic attack -RRB- and CHD -LRB- myocardial infarction , angina , coronary revascularisation -RRB- . Peripheral arterial disease -LRB- PAD -RRB- was defined as an ankle : brachial index ≤ 0.9 or diabetes-related amputation . Blindness in one or both eyes , foot amputation and end-stage renal disease were also defined to allow comparison with the ROSSO findings -LSB- 4 -RSB- . Hospital morbidity and mortality Western Australian government registers record details of all deaths and hospital admissions , and make up part of the Western Australian Data Linkage System -LRB- WADLS -RRB- -LSB- 8 -RSB- , which provided endpoint data to the end of June 2006 . The Confidentiality of Health Information Committee approved linkage with the FDS database . All hospitalisations for endpoints were identified using the International Classification of Diseases -LRB- ICD -RRB- -9-CM and ICD-10-AM diagnosis/procedure codes . Causes of death were classified independently by two authors -LRB- D.G. Bruce , T.M.E. Davis -RRB- as ` cardiac ' or ` other ' -LSB- 7 -RSB- . Statistical analysis Data were analysed using SPSS for Windows -LRB- version 11.5 -RRB- and are presented as proportions or means ± SD . Comparison of two independent proportions was by Fisher 's exact test . Freedom from non-fatal endpoints , all-cause mortality or cardiac death was analysed by baseline SMBG status using the Kaplan -- Meier method and compared with the log-rank test . For outcomes with sufficient events , Cox proportional hazards modelling -LRB- forward conditional variable entry and removal with p < 0.05 and p > 0.05 , respectively -RRB- , was used to determine : -LRB- 1 -RRB- independent predictors of first ever occurrence of endpoints , with all clinically plausible univariate variables with a p value of less than 0.20 considered for entry ; and -LRB- 2 -RRB- whether , after adjusting for these variables , either SMBG at baseline or the time-dependent covariate SMBG was independently associated with outcome . A p value of less than 0.05 was considered significant . Results At baseline , 898 type 2 patients -LRB- 70.2 % -RRB- performed SMBG . During 12,491 patient-years of follow-up -LRB- mean 9.8 ± 3.5 years -RRB- , 486 -LRB- 38.0 % -RRB- died , of which 196 -LRB- 15.3 % -RRB- deaths were from cardiac causes . SMBG was significantly less prevalent in those who died during follow-up than in those who were alive at the end of June 2006 -LRB- 65.4 vs 73.0 % , p = 0.005 -RRB- . SMBG and mortality In unadjusted survival analysis -LRB- Model 1 -RRB- , SMBG was associated with a significant 24 % reduction in all-cause mortality -LRB- Table 1 -RRB- . After adjusting for age , sex and diabetes duration -LRB- Model 2 -RRB- , this became a non-significant 11 % increased risk . Additional adjustment for independent risk factors for all-cause mortality -LRB- Model 3 -RRB- did not alter this finding . In patients on diet ± oral hypoglycaemic agents -LRB- OHAs -RRB- and in those on insulin , baseline SMBG was associated with significant unadjusted 24 % and 54 % reductions in the risk of death , respectively , which became non-significant after full adjustment . Table 1Hazard ratios for all-cause and cardiac mortality in 1,280 type 2 diabetes patients by SMBG status for : -LRB- 1 -RRB- all diabetes treatment types ; -LRB- 2 -RRB- those who were treated by diet with or without OHAs ; and -LRB- 3 -RRB- those taking insulin with or without OHAs No SMBGSMBG Number of patientsNumber of eventsAbsolute riskaNumber of patientsNumber of eventsAbsolute riskaLog-rank p valueHR -LRB- 95 % CI -RRB- All patients All-cause mortality Model 1 -LRB- unadjusted -RRB- 38216846.789831835.80.0040.76 -LRB- 0.63 -- 0.92 -RRB- Model 2 -LRB- adjusted -RRB- b1 .11 -LRB- 0.92 -- 1.35 -RRB- Model 3 -LRB- adjusted -RRB- c1 .15 -LRB- 0.93 -- 1.44 -RRB- Cardiac mortality Model 1 -LRB- unadjusted -RRB- 3826117.089813515.20.460.89 -LRB- 0.66 -- 1.21 -RRB- Model 2 -LRB- adjusted -RRB- b1 .35 -LRB- 0.99 -- 1.86 -RRB- Model 3 -LRB- adjusted -RRB- d1 .55 -LRB- 1.07 -- 2.24 -RRB- Diet ± OHAs All-cause mortality Model 1 -LRB- unadjusted -RRB- 35414542.677325232.50.0080.76 -LRB- 0.62 -- 0.93 -RRB- Model 2 -LRB- adjusted -RRB- b1 .15 -LRB- 0.93 -- 1.43 -RRB- Model 3 -LRB- adjusted -RRB- e1 .20 -LRB- 0.94 -- 1.52 -RRB- Cardiac mortality Model 1 -LRB- unadjusted -RRB- 3545115.077310813.90.650.93 -LRB- 0.66 -- 1.29 -RRB- Model 2 -LRB- adjusted -RRB- b1 .51 -LRB- 1.06 -- 2.14 -RRB- Model 3 -LRB- adjusted -RRB- f1 .79 -LRB- 1.19 -- 2.69 -RRB- Insulin ± OHAs All-cause mortality Model 1 -LRB- unadjusted -RRB- 2823119.31256658.40.0010.46 -LRB- 0.29 -- 0.75 -RRB- Model 2 -LRB- adjusted -RRB- b0 .68 -LRB- 0.41 -- 1.14 -RRB- Model 3 -LRB- adjusted -RRB- g0 .73 -LRB- 0.43 -- 1.26 -RRB- Cardiac mortality Model 1 -LRB- unadjusted -RRB- 281051.91252723.90.0260.45 -LRB- 0.22 -- 0.93 -RRB- Model 2 -LRB- adjusted -RRB- b0 .58 -LRB- 0.27 -- 1.27 -RRB- Model 3 -LRB- adjusted -RRB- h0 .52 -LRB- 0.22 -- 1.19 -RRB- aEvents per 1,000 person -- yearsbModel 2 includes age , sex and duration of diabetes in each case . Model 3 includes all variables in Model 2 , plus : cprior CHD , CVD , PAD , neuropathy , retinopathy , ln -LSB- ACR -RSB- , any exercise in past 2 weeks -LRB- negative -RRB- , abdominal obesity -LRB- negative -RRB- , on lipid-lowering medications -LRB- negative -RRB- , Australian Aboriginal , Asian -LRB- negative -RRB- , current smokerdprior CHD , CVD , PAD , neuropathy , retinopathy , ln -LSB- ACR -RSB- , systolic BP -LRB- negative -RRB- , total serum cholesterol , Australian Aboriginal , current smokereprior CHD , CVD , PAD , neuropathy , retinopathy , ln -LSB- ACR -RSB- , abdominal obesity -LRB- negative -RRB- , on lipid-lowering medication -LRB- negative -RRB- , Australian Aboriginal , current smokerfprior CHD , PAD , neuropathy , retinopathy , ln -LSB- ACR -RSB- , systolic BP -LRB- negative -RRB- , total serum cholesterol , current smokergprior CHD , diabetes education -LRB- ever ; negative -RRB- , HbA1c , Australian Aboriginalhprior CHD , retinopathy , HbA1c , Australian AboriginalIn unadjusted models for cardiac death , there was a significant 55 % risk reduction in insulin-treated patients -LRB- Table 1 -RRB- ; after full adjustment , this became non-significant . SMBG was associated with a 55 % increased risk of cardiac death in Model 3 as a result of a significant 79 % increased risk in patients not treated with insulin . Five-year cohort Consistent with intensification of diabetes therapy , and as reported previously -LSB- 5 -RSB- , the proportion of the 5-year longitudinal cohort using SMBG increased over a mean of 5.4 ± 0.5 years , from 75.2 % at entry to 85.5 % at third review , with little change thereafter.We ascertained the incidence of first ever occurrence of micro - and macrovascular complications by baseline SMBG status -LRB- Table 2 -RRB- . Unadjusted survival analysis showed no significant benefit of baseline SMBG -LRB- p ≥ 0.11 -RRB- for any endpoint , single or combination . Neither adjustment for age , sex and diabetes duration -LRB- Model 2 -RRB- , nor relevant additional variables independently associated with the outcomes in Cox proportional hazards modelling -LRB- Model 3 -RRB- , improved this result . Table 2Hazard ratios of first-ever non-fatal complications in the 531 type 2 diabetes patients in the longitudinal arm for single and combined micro - and macrovascular events , and the ROSSO Study non-fatal endpoint , by SMBG status at : -LRB- 1 -RRB- baseline ; and -LRB- 2 -RRB- each annual assessment -LRB- time-dependent covariate -RRB- No SMBG at baselineSMBG at baseline Time-dependent SMBG useNo . of patientsNo . of eventsAbsolute riskaNo . of patientsNo . of eventsAbsolute riskaLog-rank p valueHR -LRB- 95 % CI -RRB- HR -LRB- 95 % CI -RRB- RetinopathyModel 1 -LRB- unadjusted -RRB- 1164790.034911268.40.110.76 -LRB- 0.54 -- 1.07 -RRB- 0.51 -LRB- 0.36 -- 0.72 -RRB- Model 2 -LRB- adjusted -RRB- b0 .80 -LRB- 0.57 -- 1.13 -RRB- 0.52 -LRB- 0.37 -- 0.74 -RRB- Model 3 -LRB- adjusted -RRB- c0 .82 -LRB- 0.58 -- 1.16 -RRB- 0.52 -LRB- 0.37 -- 0.73 -RRB- NeuropathyModel 1 -LRB- unadjusted -RRB- 8462199.3256186203.00.761.05 -LRB- 0.78 -- 1.40 -RRB- 0.77 -LRB- 0.57 -- 1.03 -RRB- Model 2 -LRB- adjusted -RRB- b1 .16 -LRB- 0.87 -- 1.55 -RRB- 0.79 -LRB- 0.59 -- 1.07 -RRB- Model 3 -LRB- adjusted -RRB- d1 .30 -LRB- 0.97 -- 1.74 -RRB- 0.89 -LRB- 0.66 -- 1.20 -RRB- Microalbuminuria Model 1 -LRB- unadjusted -RRB- 8237104.125810390.60.380.85 -LRB- 0.58 -- 1.23 -RRB- 0.77 -LRB- 0.52 -- 1.14 -RRB- Model 2 -LRB- adjusted -RRB- b0 .88 -LRB- 0.60 -- 1.28 -RRB- 0.78 -LRB- 0.53 -- 1.15 -RRB- Model 3 -LRB- adjusted -RRB- e0 .91 -LRB- 0.62 -- 1.33 -RRB- 0.74 -LRB- 0.50 -- 1.10 -RRB- Any microangiopathy Model 1 -LRB- unadjusted -RRB- 5850280.5172153302.60.591.09 -LRB- 0.79 -- 1.51 -RRB- 0.99 -LRB- 0.71 -- 1.38 -RRB- Model 2 -LRB- adjusted -RRB- b1 .22 -LRB- 0.89 -- 1.68 -RRB- 0.96 -LRB- 0.69 -- 1.33 -RRB- Model 3 -LRB- adjusted -RRB- f1 .34 -LRB- 0.97 -- 1.86 -RRB- 0.98 -LRB- 0.71 -- 1.37 -RRB- Myocardial infarction Model 1 -LRB- unadjusted -RRB- 13168.6386188.80.971.02 -LRB- 0.41 -- 2.57 -RRB- 0.58 -LRB- 0.24 -- 1.41 -RRB- Model 2 -LRB- adjusted -RRB- b1 .13 -LRB- 0.44 -- 2.89 -RRB- 0.63 -LRB- 0.26 -- 1.54 -RRB- Stroke Model 1 -LRB- unadjusted -RRB- 13322.739820.90.280.35 -LRB- 0.05 -- 2.52 -RRB- 0.19 -LRB- 0.03 -- 1.36 -RRB- Model 2 -LRB- adjusted -RRB- b0 .82 -LRB- 0.10 -- 6.60 -RRB- 0.30 -LRB- 0.04 -- 2.28 -RRB- Peripheral arterial diseaseModel 1 -LRB- unadjusted -RRB- 10151119.330713499.30.290.84 -LRB- 0.61 -- 1.16 -RRB- 0.74 -LRB- 0.53 -- 1.04 -RRB- Model 2 -LRB- adjusted -RRB- b0 .97 -LRB- 0.70 -- 1.36 -RRB- 0.80 -LRB- 0.57 -- 1.13 -RRB- Model 3 -LRB- adjusted -RRB- g1 .13 -LRB- 0.79 -- 1.62 -RRB- 0.89 -LRB- 0.62 -- 1.28 -RRB- Any macroangiopathyModel 1 -LRB- unadjusted -RRB- 9954133.7297133103.80.110.77 -LRB- 0.56 -- 1.06 -RRB- 0.67 -LRB- 0.48 -- 0.93 -RRB- Model 2 -LRB- adjusted -RRB- b0 .88 -LRB- 0.63 -- 1.21 -RRB- 0.71 -LRB- 0.51 -- 0.99 -RRB- Model 3 -LRB- adjusted -RRB- h0 .88 -LRB- 0.63 -- 1.22 -RRB- 0.74 -LRB- 0.52 -- 1.04 -RRB- All vascular diseaseModel 1 -LRB- unadjusted -RRB- 4441222.0141128230.10.950.99 -LRB- 0.69 -- 1.41 -RRB- 1.38 -LRB- 0.89 -- 2.15 -RRB- Model 2 -LRB- adjusted -RRB- b1 .15 -LRB- 0.80 -- 1.64 -RRB- 1.43 -LRB- 0.91 -- 2.23 -RRB- Model 3 -LRB- adjusted -RRB- i -- -- ROSSO endpointModel 1 -LRB- unadjusted -RRB- 110813.83282615.30.761.13 -LRB- 0.51 -- 2.50 -RRB- 0.54 -LRB- 0.26 -- 1.14 -RRB- Model 2 -LRB- adjusted -RRB- b1 .29 -LRB- 0.58 -- 2.87 -RRB- 0.58 -LRB- 0.27 -- 1.23 -RRB- aEvents per 1,000 person -- yearsbModel 2 includes age , sex and duration of diabetes in each case . Model 3 includes all variables in Model 2 , plus : cfasting plasma glucosedAsian -LRB- negative -RRB- , other European , mixed/other ethnicity -LRB- negative -RRB- eBMI , ln -LSB- ACR -RSB- , aspirin-use , any exercise in past 2 weeks -LRB- negative -RRB- , married -LRB- negative -RRB- fln -LSB- ACR -RSB- gsystolic BP , aspirin-use , diabetes education -LRB- ever ; negative -RRB- , smoker , √ daily alcohol consumptionhsystolic BP , aspirin-use , smoker , √ daily alcohol consumptioninone after adjustment forbTime-dependent Cox models were included to allow for commencement -LRB- or discontinuation -RRB- of SMBG during follow-up . In both unadjusted and adjusted models , SMBG during follow-up was associated with a reduction in the risk of retinopathy of approximately 50 % . In unadjusted analysis , the risk of the development of any macrovascular disease was reduced by 33 % in patients who used SMBG , but this became non-significant after full adjustment.SMBG was not associated with the first occurrence of the composite ROSSO endpoint -LRB- which occurred infrequently -RRB- , either at baseline or as a time-dependent covariate . Discussion The present study represents the second observational assessment of the relationship between SMBG and outcome in type 2 diabetes . With regard to all-cause mortality in the full FDS sample , our unadjusted data showed that baseline SMBG was associated with a significant risk reduction , a relationship observed in both non-insulin-treated and insulin-treated subgroups . After adjustment for other potential explanatory variables , this apparent benefit was lost in both subgroups . For both combined micro - and macroangiopathy outcomes , SMBG was not associated with a first ever event in the 5-year longitudinal cohort in either unadjusted or adjusted models , with or without time-dependent analyses . These findings are inconsistent with those of the ROSSO study -LSB- 4 -RSB- , in which all-cause mortality and diabetes-related morbidity were reduced by 32 and 51 % , respectively , in patients employing SMBG . What is the explanation for these discrepancies ? ROSSO had a larger number of patients than the FDS -LRB- 3,268 vs 1,280 -RRB- , followed for a similar period . However , while the FDS involved a prevalent , prospectively-studied , community-based cohort and all patients had access to subsidised SMBG -LSB- 5 -RSB- , ROSSO was ` retrolective ' -- newly-diagnosed patients aged ≥ 45 years were selected -- and only those receiving insulin were reimbursed for SMBG -LSB- 4 -RSB- . Whereas there was a low rate of migration of the FDS sample out of Western Australia -LSB- 9 -RSB- -LRB- 96 % had been captured on the WADLS by the end of June 2006 -LSB- 10 -RSB- -RRB- , the retention rate of the ROSSO primary care sample is unknown . The range of potential explanatory variables -LRB- including diabetes education , marital status , education , alcohol consumption , exercise and urinary ACR -RRB- and the completeness of data collection -LRB- e.g. HbA1c and serum HDL-cholesterol results from a single laboratory were available for ≥ 98.8 % of FDS patients compared with only 45.5 and 30.2 % , respectively , from multiple laboratories in ROSSO -LSB- 4 -RSB- -RRB- were both much greater in the FDS . The present analyses are therefore likely to have assessed the independent contribution of SMBG to diabetes-associated morbidity and mortality with greater validity . A healthy survivor effect may also have confounded the relationship between SMBG and non-fatal outcome in ROSSO , while these analyses were undertaken in FDS participants alive at the end of 5 years of follow-up . After adjustment , SMBG was associated with an increased risk of cardiac death in patients not treated with insulin . Although the ROSSO investigators suggest that , based on apparent benefits , SMBG may be a surrogate for greater patient empowerment and physician interest , with consequently improved compliance -LSB- 4 -RSB- , it is equally plausible that SMBG may represent a belated attempt to improve glycaemic control by mostly non-insulin-treated patients who become aware of symptoms of coronary insufficiency and heart failure . Retinopathy was the only non-fatal complication with which SMBG was independently associated , with a significantly lower relative risk in our 5-year cohort in both unadjusted and adjusted time-dependent models . This could be a chance finding , but could also reflect the effect of other variables not measured in the FDS . It is not due to better glycaemic control in patients who perform SMBG , since SMBG use did not improve glycaemia -LSB- 5 -RSB- . The ROSSO study report does not contain a similar disaggregated analysis -LSB- 4 -RSB- . The results of the present study do not support a relationship between SMBG and improved survival in a well-characterised community-based sample of type 2 diabetic patients . We found evidence that SMBG was associated with an increased risk of cardiac death and a reduced risk of retinopathy . These conflicting results might reflect complex interactions between patient , physician and disease factors in particular circumstances , but may also represent the effects of confounding , incomplete covariate adjustment or chance .

O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

cohort study
epidemiology
self-monitoring of blood glucose


Transforming growth factor beta-regulated gene expression in a mouse mammary gland epithelial cell line Background Transforming growth factor beta -LRB- TGF-β -RRB- plays an essential role in a wide array of cellular processes . The most well studied TGF-β response in normal epithelial cells is growth inhibition . In some cell types , TGF-β induces an epithelial to mesenchymal transition -LRB- EMT -RRB- . NMuMG is a nontransformed mouse mammary gland epithelial cell line that exhibits both a growth inhibitory response and an EMT response to TGF-β , rendering NMuMG cells a good model system for studying these TGF-β effects . Introduction Transforming growth factor beta -LRB- TGF-β -RRB- is the prototype of a large family of signaling molecules with more than 40 members . TGF-β signals through the type I receptor -LRB- TβRI -RRB- and the type II receptor -LRB- TβRII -RRB- , and the Smad transcriptional regulators , as well as many other signaling pathways -LSB- 1 -RSB- . TGF-β plays an essential role in a wide array of cellular processes , including early embryonic development , cell growth , differentiation , motility , and apoptosis . In nontransformed epithelial cells , the most studied TGF-β response is growth inhibition . In some cell types , TGF-β induces an epithelial to mesenchymal transition -LRB- EMT -RRB- . TGF-β plays an important , albeit complex , role in tumorigenesis . In many tumors of epithelial origin , cells become resistant to TGF-β-mediated growth inhibition . In parallel , TGF-β induces EMT in tumor cells , and increases tumor invasion and metastasis -LSB- 2 -RSB- . NMuMG is a nontransformed mouse mammary epithelial cell line that exhibits both a growth inhibitory and EMT response to TGF-β -LSB- 3 -RSB- . These properties render NMuMG cells a suitable model system for studying these TGF-β actions . Previous gene expression profiling studies of TGF-β action on cell lines utilized cDNA microarrays to characterize extracellular matrix-related genes in human dermal fibroblasts . The upregulation of these genes by TGF-β was Smad mediated -LSB- 4 -RSB- . Another study examining the genetic programs of epithelial cell plasticity directed by TGF-β utilized the human keratinocyte HaCaT cell line -LSB- 5 -RSB- . In this study , HaCaT cells were treated for up to 4 hours , and microarray gene expression profiling was performed using an array of 16,580 human cDNAs . The expression of 728 known genes -LRB- 10 % of those analyzed -RRB- was regulated by TGF-β within 4 hours of treatment . In the present study , we examined TGF-β regulation of gene expression in the NMuMG epithelial cell line . NMuMG cells were chosen as a model system because the results are more relevant to our studies employing transgenic mouse mammary tumor models , and because NMuMG cells are a well-established model system for studying both growth inhibition and EMT induced by TGF-β -LSB- 3 -RSB- . EMT represents a complex physiological process that includes dissolution of adherens junctions , a change to spindle-like cell morphology , cytoskeletal reorganization , increased cell motility , loss of epithelial markers , and induction of mesenchymal markers . EMT thus includes several cellular responses that may occur through different mechanisms and at different time points after TGF-β treatment . We consequently examined changes in gene expression at 1 , 6 , and 24 hours after TGF-β treatment . In agreement with previous studies with HaCaT cells -LSB- 5 -RSB- , approximately 10 % of the genes examined were regulated by TGF-β in our microarray studies that queried more than 15,000 mouse cDNAs . Our study demonstrates that by 1 hour , TGF-β suppresses the expression of multiple genes that play key roles in regulating cell cycle progression . At 6 and 24 hours , TGF-β enhances the expression of multiple genes involved in regulating cell shape and cell adhesion , and therefore may mediate TGF-β-induced EMT . Additionally , several novel TGF-β-regulated genes were identified that may play important roles in TGF-β responses . Materials and methods Cell culture and treatment NMuMG cells were cultured in Dulbecco 's modified Eagle 's medium containing 10 % serum and 10 μg/ml insulin , until reaching 70 -- 80 % confluency . The cells were treated with 4 ng/ml TGF-β1 for 1 , 6 or 24 hours . Untreated cells were used as reference samples at each time point , and were referred to as controls in the analyses . Four independent replicates of each experiment were performed . Cell morphology was examined under phase contrast using an Olympus CK40 microscope -LRB- Melville , NY , USA -RRB- . Growth inhibition assay 3H-thymidine incorporation assays were performed using cells that had been treated for 24 hours with TGF-β . The cells were pulsed with 3H-thymidine for the final 2 hours of the treatment period . 3H-thymidine incorporation was quantitated using a LS 6500 Multi-Purpose Scintillation Counter -LRB- Beckman -RRB- . Four replicates of each experiment were performed , and each 3H-thymidine incorporation experiment was performed in triplicate . RNA sample preparation NMuMG cells were lysed and homogenized in Trizol reagent -LRB- Invitrogen , Carlsbad , CA , USA -RRB- , and total RNA was prepared according to the manufacturer 's instructions . Two rounds of extraction were performed to isolate pure , high-quality total RNA . Thirty micrograms of total RNA from each sample were used for reverse transcription and cyanine -LRB- Cy -RRB- - dye incorporation . cDNA microarray hybridization The National Institutes of Aging -LRB- NIA -RRB- 15,000 cDNA microarray was printed on three contiguous slides by the Vanderbilt Microarray Shared Resource . The mouse 15,000 NIA microarray contains 15,247 genes of known and unknown function , developed at the NIA . Detailed descriptions of the gene array list , microarray hardware and procedures are available online . The reference RNA samples from nontreated NMuMG cells were labeled with Cy5 , and the test RNA samples from NMuMG cells treated with 4 ng/ml TGF-β for 1 , 6 , and 24 hours were labeled with Cy3 . The Cy3-labeled and Cy5-labeled samples were hybridized simultaneously to the same array . Four independent replicates of TGF-β treatment , RNA isolation and labeling , and microarray hybridization were performed . To estimate ` system noise ' that may be due to differences in Cy-dye labeling between samples , we performed a ` self-to-self ' hybridization , where the same control sample was labeled separately with Cy3 and Cy5 , and then hybridized . Oligo dT for reverse transcription was synthesized by Invitrogen . SuperScript II reverse transcriptase -LRB- Invitrogen -RRB- was used . A Qiagen PCR purification kit was used for purification of probes -LRB- Qiagen , Valencia , CA , USA -RRB- . Slides were prehybridized to eliminate nonspecific interactions in prehybridization solution : 1 % BSA , 5 × sodium chloride/sodium citrate buffer -LRB- SSC -RRB- , 0.1 % SDS at 65 °C for 45 min . Hybridizations were performed at 65 °C for 14 -- 16 hours in a humidified hybridization chamber -LRB- Corning , Acton , MA , USA -RRB- . After hybridization , the slides were washed once each in solutions 1 -- 3 -LRB- wash solution 1 , 2 × SSC , 0.1 % SDS ; wash solution 2 , 1 × SSC ; wash solution 3 , 0.1 × SSC -RRB- for 5 min at 55 °C with gentle stirring/agitation . Washed slides were centrifuged in a conical tube for 5 min at 1600 rpm to dry them . Microarray slides were scanned using a Genepix 4000 B scanner -LRB- Axon Instruments , Union City , CA , USA -RRB- at a resolution of 10 μm , and the original data files were generated by GenePixPro software -LRB- version 4.0 ; Axon Instruments -RRB- . Data analysis A merged 15,000 data file was constructed by combining the three original 5000 data files generated from the three contiguous slides on which the total 15,000 gene clones were printed . The GenBank accession number and the full annotation gene name for each gene were incorporated . Data analyses were performed using the GeneSpring 4.1.5 software package -LRB- Silicon Genetics , Redwood City , CA , USA -RRB- . The normalization for the two-color cDNA microarray data was designed as follows . The net signal intensity in each channel -LRB- Cy3 or Cy5 -RRB- was determined by subtracting the local background from signal intensity values . Each gene 's measured intensity was divided by its control channel value -LRB- the reference RNA sample channel of Cy5 -RRB- . A Cy3/Cy5 ratio represents the relative abundance of a target transcript in TGF-β-treated and nontreated samples respectively . When the control channel value was below 1000.0 , the data point was considered too weak and was discarded . Each sample was normalized to the 50th percentile of all measurements . The bottom 10th percentile was considered background , and was subtracted from all the other values . Each experimental result was interpreted as the average of the four replicates of each time point . Time-course gene expression profiles were required to be present in two of the four datasets , and to be present in at least two of the three time points . A signal to control channel ratio > 2.5 and a probability t test P < 0.05 was defined as upregulation by TGF-β . A signal to control channel ratio < 0.4 and a probability t test P < 0.05 was defined as downregulation . The annotated gene lists were constructed from all genes represented in the normalized dataset . The differentially expressed genes were classified into functional subgroups using Simplified Ontology in GeneSpring . For each pathway , pathway images were saved as GIF files and loaded into GeneSpring . Each pathway element was identified in the normalized data . Northern hybridization All cDNAs of the NIA mouse 15,000 microarray were cloned into the NotI/SalI sites of the ampicillin-resistant pSPORT1 vector -LRB- Invitrogen -RRB- . The insert sizes ranged from 0.5 to 3 kb , with an average insert size of 1.5 kb . The Escherichia coli host strain used was DH10B . Plasmid DNA was isolated using the High Pure Plasmid Isolation Kit -LRB- Roche , Indianapolis , IN , USA -RRB- . The inserts were verified for their correct annotation by DNA sequencing . The reverse pSPORT1 primer was used as the sequencing primer . To prepare cDNA probes , cDNA inserts were amplified using pSPORT1 primers : forward , GTTTTCCCAGTCACGACGTTG ; reverse , TGA-GCGGATAACAATTTCACACAG . Plasmid DNA was used as the template for PCR amplification under the following conditions : initial denaturation at 95 °C for 1 min ; then 40 cycles of denaturation at 94 °C for 30 s , annealing at 54 °C for 30 s , and extension at 72 °C for 3.5 min . A final extension was performed at 72 °C for 7 min . To examine the quality and quantity of PCR products , they were separated on 1.2 % agarose gels . Specific cDNA bands were recovered from the gels using QIAEX II Gel Extraction Kits -LRB- Qiagen -RRB- . Seventy-five nanograms of cDNA was labeled with 32P using a Random Primed DNA Labeling Kit -LRB- Roche -RRB- . For northern blot analysis , 10 μg total RNA was separated by electrophoresis on a 1 % agarose , 0.66 M formaldehyde gel and transferred to Hybrid Nylon transfer membranes -LRB- Amersham Biosciences , Piscataway , NJ , USA -RRB- . After transfer , the membrane was UV cross-linked -LRB- Stratalinker ; Stratagene , La Jolla , CA , USA -RRB- . All cDNA probes were hybridized overnight at 42 °C in ULTRAhyb solution -LRB- Ambion , Austin , TX , USA -RRB- . Each filter was washed twice for 5 min in 2 × SSC and 0.1 % SDS at 42 °C , followed by two 15 min washes each in 0.5 × SSC and 0.1 % SDS at 42 °C , followed by two 15 min washes in 0.1 × SSC , 0.1 % SDS at 42 °C . Equal loading of gel lanes was confirmed by hybridization with the house keeping gene 1B15 . Results were visualized and quantitated using a FUJIFILM-FLA-5000 Phosphoimager -LRB- Fuji Photo Film Co. Ltd , Stamford , CT , USA -RRB- . Results Biological and gene expression responses of NMuMG cells to TGF-β NMuMG cells react to TGF-β treatment with both an EMT response and a growth inhibitory response -LRB- Fig. 1 -RRB- . These characteristics render NMuMG cells an appropriate model system to study the signaling pathways mediating these TGF-β effects . To examine TGF-β regulation of genes involved in EMT and cell cycle control , we treated NMuMG cells with TGF-β for 1 , 6 , or 24 hours , isolated the total RNA , and performed gene expression profiling by cDNA microarray . The relative transcript abundance was expressed as Cy3/Cy5 ratios of signal intensities after background subtraction in each channel . Data analysis and quality control procedures are described in detail in Materials and methods . TGF-β treatment resulted in the upregulation or downregulation of 939 genes out of the 15,247 genes examined . Subtracting redundancy or signals too weak to score , this represents approximately 10 % of the genes analyzed . The time course of TGF-β1-dependent changes in gene expression demonstrates a progressively greater alteration of gene expression over time -LRB- Fig. 2 -RRB- . In general , TGF-β1 regulation of cell cycle-related genes occurred with an onset of less than 1 hour , while TGF-β1 regulation of EMT-related genes occurred with a later onset , with changes first becoming apparent at 6 or 24 hours -LRB- see later -RRB- . Figure 3 and Table 1 present the distribution of downregulated genes and of upregulated genes , and the sum of upregulated and downregulated genes at each time point . The total number of differentially expressed genes at each time point does not differ significantly . The 6 and 24 hour time points shared more TGF-β1-regulated genes than were shared between the 1 and 6 hour time points , or the 1 and 24 hour time points . Full lists of TGF-β-regulated genes at each time point can be accessed on our website . Functional class distribution of the novel and known TGF-β1-regulated genes Of the 939 genes regulated by TGF-β1 , approximately one-third -LRB- 350 genes -RRB- can be classified into functional groups . The classified genes were further separated into two tables of genes previously reported or not previously reported to be regulated by TGF-β . This was determined by performing a Medline search using the gene name and TGF-β as keywords . Tables of the complete dataset with each gene in its functional category are presented on our website . Tables 1 and 2 present some selected examples of known or unknown TGF-β-regulated genes , respectively . The genes involved in cell cycle regulation and cell adhesion -LRB- see later -RRB- are included . Northern analysis verification of microarray data To validate the microarray results , we subjected nine of the novel TGF-β-regulated genes to northern analysis . Of the nine genes examined , seven exhibited TGF-β1-induced changes in gene expression that matched those observed in the microarray experiments . The seven novel TGF-β1-regulated genes verified by northern analysis include Akt , mCalpain , RhoB , PR53 , actinin 3 , Ikki -LRB- Fig. 4 -RRB- and the IQ motif-containing GTPase-activating protein 1 -LRB- IQGAP1 -RRB- . IQGAP1 showed upregulation in NMuMG cells after 1 , 6 and 24 hours of treatment with TGF-β , supporting the microarray data . As reported by other investigators -LSB- 6 -RSB- , we detected multiple IQGAP1 transcripts . The IQGAP1 northern blot is thus not shown in Fig. 4 . Akt and RhoB were reported previously to be regulated by TGF-β at the translational level -LSB- 7,8 -RSB- , but not at the transcriptional level as we show here . Some of the TGF-β1-induced changes in gene expression were also verified by real-time RT-PCR -LRB- data not shown -RRB- . In further support of the reliability of the microarray experiments , genes that were represented multiple times in the array as different clones exhibited identical expression profiles -LRB- examples can be found in the supplementary tables at our website : . In addition , our expression profiling results are consistent with previously reported expression patterns of TGF-β1-responsive genes -LSB- 1,5 -RSB- . Examination of cell cycle control and cell adhesion-related genes by microarray There are multiple ways to explore the biological significance of results obtained in microarray experiments . One may organize the genes into functional groups or signaling pathways to examine the regulation of clusters of genes . We were interested in identifying TGF-β-regulated genes involved in the induction of EMT and cell cycle arrest . In our study in NMuMG cells , the expression of many cell cycle regulators was altered by TGF-β . One hour after TGF-β treatment , cyclin D2 , cdk4 , E2F5 , cyclin A , and c-myc were downregulated . Six hours after TGF-β treatment , cyclin D2 , p107 , E2F5 , c-myc , and Id2 were downregulated . Twenty-four hours after TGF-β treatment , cyclin D2 , p107 , E2F5 , cyclin A , cyclin B , cyclin H , and Id2 were downregulated -LRB- Fig. 5 -RRB- . These data are consistent with these genes serving as mediators of TGF-β-induced growth arrest . The microarray results also revealed upregulation of a number of genes that play a role in cell -- cell and cell -- matrix adhesion and EMT . At 1 hour , c-Jun , β-integrin , and IQGAP were upregulated . At 6 hours , actin , calpain , β-integrin , fibronectin , collagen , myosin light chain , α-actinin , β-catenin and IQGAP were upregulated . At 24 hours , c-Jun , actin , calpain , α-integrin , β-integrin , fibronectin , collagen , α-actinin , β-catenin and IQGAP were upregulated . The only gene in this category observed to be downregulated was E-cadherin at 6 hours -LRB- Fig. 6 -RRB- . The results are consistent with these genes playing a role in TGF-β1-mediated changes in cell -- cell and cell -- substratum adhesion . Our results are consistent with previous studies , but we identified several novel TGF-β-regulated genes that could play important roles in both EMT and cell cycle regulation , including RhoB , mCalpain , actinin 3 , IQGAP1 , Ikki and protein phosphatase 2A -LRB- PP2A -RRB- - PR53 . Discussion The mechanisms by which TGF-β signals are not fully understood . In this regard , gene expression profiling has been used successfully by a variety of investigators to explore genetic events involved in TGF-β signaling . Zavadil and colleagues detected genetic programs of epithelial cell plasticity directed by TGF-β -LSB- 5 -RSB- . Verrecchia and colleagues identified novel TGF-β/Smad gene targets in dermal fibroblasts -LSB- 4 -RSB- . Chen and colleagues found defective repression of c-myc in breast cancer cells , suggesting that this event is at the core of the TGF-β growth arrest program -LSB- 9 -RSB- . Shen and colleagues discovered that the activity of the guanine exchange factor NET1 is essential for TGF-β-mediated stress fiber formation -LSB- 10 -RSB- . In the present study , we have selected the NMuMG nontransformed mouse mammary gland epithelial cell line as a model system to identify TGF-β-regulated target genes involved in the control of growth inhibition and EMT . To identify TGF-β-regulated genes , we employed a cDNA microarray that represents 15,247 genes of known and unknown function . We found that the expression of 939 of the genes was altered by TGF-β regulation . This represents about 10 % of the genes examined . Many of the genes identified were previously determined to mediate key biological responses to TGF-β . In addition , several novel TGF-β-regulated genes were identified . The present report provides a comprehensive view of the coordinated regulation of genetic programs induced by TGF-β during the processes of cell cycle arrest and EMT . A remarkably large number of genes undergo rapid changes in expression after TGF-β treatment . This report adds to the database of hundreds of genes that are regulated by TGF-β -LSB- 5 -RSB- . In addition , we have identified several novel TGF-β-regulated genes , and have verified these results in northern blotting experiments . Table 1 presents previously identified TGF-β-regulated genes , many of which are known to play important roles in TGF-β induction of EMT and cell cycle arrest . Briefly , in the cellular communication category , platelet-derived growth factor was upregulated by TGF-β . Multiple genes involved in cell adhesion were upregulated by TGF-β , including integrins -LRB- integrin α3A , integrin β , integrin β5 -RRB- , integrin-linked kinase , and the integrin ligand fibronectin . These results are consistent with TGF-β activating integrin-dependent cell adhesion . TGF-β upregulation of α-actinin , β-tropomyosin 2 , and p120 catenin may play a role in the induction of EMT . RhoB was upregulated at 6 and 24 hours . RhoB is an endosomal small GTPase , and it functions as a negative regulator of integrin and growth factor signals -LSB- 11 -RSB- . Our previous results -LSB- 8 -RSB- and those of other workers -LSB- 12 -RSB- suggest a novel mechanism of tumor suppressive regulation by TGF-β , and implicate RhoB as a negative regulator of TGF-β signal transduction . TGF-β downregulation of the helix -- loop -- helix DNA binding protein Id2 and of the inhibitor of DNA binding Idb3 were previously identified as important events in TGF-β-regulated signaling . These proteins belong to a family of helix -- loop -- helix proteins that , in general , function as positive regulators of cell growth and as negative regulators of cell differentiation in many cell types -LSB- 13 -RSB- . TGF-β also regulates many genes involved in the progression of cancer . The proto-oncogene Akt is upregulated at 1 hour at the mRNA level . The Gro1 proto-oncogene is downregulated by TGF-β at all the time points examined . The roles that these gene products play downstream of TGF-β during tumorigenesis have not been extensively studied -LSB- 14,15 -RSB- . Well-established TGF-β responses such as downregulation of c-myc and upregulation of the Jun oncogene were also observed . TGF-β is a potent growth inhibitor of epithelial cells . TGF-β-mediated growth inhibition has been associated with effects on G1 phase cyclins , cyclin-dependent kinases -LRB- Cdks -RRB- , Cdk-activating kinase , Cdc25A , and Cdk inhibitors . TGF-β potently inhibits the activities of both the cyclin D -- Cdk4/Cdk6 and cyclin E -- Cdk2 complexes , resulting in the hypophosphorylation of pRbs and in the decreased transcriptional activity of E2Fs . TGF-β does not affect the expression levels of Cdk4 , Cdk6 , or Cdk2 in exponentially proliferating cells . TGF-β decreases the expression levels of cyclin A , which is probably the result of cell cycle arrest in the late G1 phase . TGF-β has been associated with upregulation of Cdk inhibitors p15Ink4B , p21Cip1 , and p27Kip1 . The c-myc proto-oncogene has been implicated in TGF-β-mediated growth inhibition . TGF-β rapidly inhibits c-myc expression in a wide variety of cell types -LSB- 1 -RSB- . c-Myc downregulation is a key event in the TGF-β program of growth inhibition -LSB- 9 -RSB- . Additional mechanisms mediating TGF-β-induced cell cycle arrest continue to be uncovered . In our study , examination of cell cycle genes demonstrated TGF-β1-induced downregulation of cyclin D2 , c-myc , Id2 , p107 , E2F5 , cyclin A , cyclin B , and cyclin H . These data are consistent with these genes serving as mediators of TGF-β-induced growth arrest . Of the genes that could be placed into functional categories , the expression of the majority of the genes was not previously reported to be regulated by TGF-β . Table 2 presents some examples of genes that we have identified in this study to be regulated by TGF-β that are involved in cell signaling . Gadd45g was upregulated . Gadd45g specifically interacts with the Cdk1-cyclin complex , B1 resulting in inhibition of kinase activity -LSB- 16 -RSB- . The IQGAP1 gene is a target molecule of Cdc42 and Rac1 . IQGAP negatively regulates the E-cadherin -- catenin complex-based cell -- cell adhesion by dissociating α-catenin and/or β-catenin , a key molecule that links the E-cadherin-catenin complex to the actin cytoskeleton . These data suggest that TGF-β upregulation of IQGAP may be one of the mechanisms by which TGF-β regulates cell -- cell adhesion -LSB- 17 -RSB- . Calpains are a family of Ca2 + - dependent intracellular cysteine proteases , including the ubiquitously expressed micro-calpains and m-calpains . Calpain activity may be regulated by targeting to specific adhesion-related substrates -LSB- 18 -RSB- . Our results also demonstrate that TGF-β regulates multiple genes involved in regulation of the actin cytoskeleton . Some of these components include actinin α3 , the Arp2 -- Arp3 complex , and myosin light chain 2 . Four different isoforms of tropomyosin were upregulated . Actinin α3 is a calcium-dependent cytoskeletal protein with an actin-binding domain . Actinin α3 is associated with adherens junctions and desmosomes , together with E-cadherin , α-catenin , β-catenin and γ-catenin , vinculin , α-actinin , and polymerized actin . The genes α-actinin , myosin light chain 1/myosin light chain 2 , and tropomyosin are components of myofilaments involved in cell contractility and motility . During our studies we identified several genes not previously reported to be regulated by TGF-β that are regulated by TGF-β in NMuMG cells . These novel TGF-β-regulated genes include IQGAP1 , mCalpain and actinin α3 , which were discussed earlier , and also Ikki and PP2A-PR53 . For the first time , we show in the present study that Ikki is downregulated by TGF-β within 1 hour of treatment , and that downregulation persisted for at least 24 hours . NF-κB activation depends on the phosphorylation and degradation of its inhibitor protein , IkB . The phosphorylation of IkBα is initiated by an IkB kinase complex that includes a catalytic heterodimer composed of IkB kinase 1 -LRB- IKK-1 -RRB- and IkB kinase 2 -LRB- IKK-2 -RRB- -LSB- 19 -RSB- . A novel inducible IkB kinase -LRB- IKKi -RRB- has recently been described . IKKi is functionally and structurally distinct from the constitutively expressed IKK-1 and IKK-2 . In contrast to TGF-β , tumor necrosis factor alpha stimulated IKKi expression , an effect that persisted for at least 24 hours -LSB- 20 -RSB- . The biological significance of TGF-β downregulation of IKKi is currently under investigation . PP2A is a multifunctional serine/threonine phosphatase that is critical to many cellular processes , including cell cycle regulation and signal transduction . PR53 belongs to one of the families of PP2A regulatory subunits . The significance of TGF-β regulation of PR53 expression is unknown -LSB- 21 -RSB- . Altogether , our results are consistent with the important role of TGF-β in regulating focal adhesions , integrin-based adhesion , actin cytoskeletal architecture , and cell motility . TGF-β plays a profound role in the dedifferentiation of epithelial cells , causing depolarization , disruption of epithelial interactions , altered expression of extracellular matrix proteins , rearrangement of the cytoskeleton , and formation of actin stress fibers . These TGF-β responses provide cells with increased metastatic potential and with increased motility . Over the past few years , tremendous progress has been made in identifying signal transduction pathways activated by TGF-β family members . In the present study , we have combined microarray analysis with the examination of specific signaling pathways . Because of the important role that TGF-β plays in controlling cell proliferation and cell adhesion , we used the signaling elements involved in these processes as a template for the analysis of our microarray results . Many of the genes that we identified as being regulated by TGF-β were previously reported to be downstream targets of TGF-β . We also discovered several novel TGF-β target genes known to play roles in regulating cell adhesion , and thus EMT , and also cell cycle regulation . Examining the biological functions of these novel TGF-β target genes will increase our knowledge of the mechanisms by which TGF-β mediates its cellular effects . Furthermore , our results indicate several possible points of convergence between TGF-β signaling and other major intracellular and intercellular signaling systems . Conclusion A 15,247 cDNA microarray was used to examine TGF-β-regulated gene expression in mouse mammary NMuMG cells . Expression of 10 % of the genes examined -LRB- 939 -RRB- was altered by TGF-β treatment . We have reported a comprehensive analysis of the coordinated regulation of genetic programs induced by TGF-β in mammary epithelial cells during the processes of cell cycle arrest and EMT . In addition , several genes previously not known to be regulated by TGF-β at the transcriptional level were identified , including Akt , RhoB , IQGAP1 , mCalpain , actinin α3 , Ikki and PP2A-PR53 . Competing interests None declared . Abbreviations BSA = bovine serum albumin ; Cdk = cyclin-dependent kinase ; Cy = cyanine ; EMT = epithelial to mesenchymal transition ; IKKi = inducible IkB kinase ; IQGAP1 = IQ motif-containing the GTPase-activating protein 1 ; NF = nuclear factor ; NIA = National Institutes of Aging ; PCR = polymerase chain reaction ; PP2A = protein phosphatase 2A ; RT = reverse transcriptase ; SSC = sodium chloride/sodium citrate -LRB- buffer -RRB- ; TGF-β = transforming growth factor beta .

O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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adhesion
cdna microarray
cell cycle control
epithelial
mammary gland