Joint effects of HLA , INS , PTPN22 and CTLA4 genes on the risk of type 1 diabetes Background/hypothesis HLA , INS , PTPN22 and CTLA4 are considered to be confirmed type 1 diabetes susceptibility genes . HLA , PTPN22 and CTLA4 are known to be involved in immune regulation . Few studies have systematically investigated the joint effect of multiple genetic variants . We evaluated joint effects of the four established genes on the risk of childhood-onset type 1 diabetes . Introduction The risk of complex diseases such as type 1 diabetes is generally thought to be influenced by multiple genetic and non-genetic factors , and it has been hypothesised that interactions between genes , or epistasis , are very common for such diseases -LSB- 1 -RSB- . The presence of interactions could be one of the reasons why searching for susceptibility loci for many diseases has been less successful than expected -LSB- 2 -RSB- . When moving from monogenic diseases to complex diseases , it seems reasonable to assess more than one locus at a time , although models become increasingly complex as the number of loci increases -LSB- 3 -RSB- . Whereas few or no common genetic variants have been firmly established for most common diseases -LSB- 4 -RSB- , there are now at least four genetic loci that are established as causally involved in the aetiology of type 1 diabetes . They give us a unique possibility to evaluate gene -- gene interactions among established susceptibility genes . Specific allelic combinations of DRB1 , DQA1 and DQB1 in the human leucocyte antigen -LRB- HLA -RRB- complex , variants in the insulin gene -LRB- INS -RRB- , the cytotoxic T lymphocyte antigen-4 gene -LRB- CTLA4 -RRB- and the protein tyrosine phosphatase , non-receptor type 22 gene -LRB- PTPN22 -RRB- have been repeatedly associated with type 1 diabetes susceptibility -LSB- 5 -- 8 -RSB- using different approaches . All established loci are thought to be involved somehow in immune regulation , but details of the mechanisms relating the polymorphisms to risk of type 1 diabetes are in most cases poorly understood . Evaluating the joint effects of genes contributes important information for risk prediction , and is also thought to provide information about biological interactions , although the latter is controversial and more complex than commonly thought -LSB- 2 , 3 , 9 -RSB- . Previous studies have assessed interaction between HLA and INS and reported divergent results -LSB- 10 -- 18 -RSB- . The reported results of the joint effect of HLA and INS are confusing not only because they have shown diverging results but also because the definitions and terminology of interactions are not consistent -LSB- 19 -RSB- . The interpretation of statistical interaction depends on the choice of scale used to measure the effects -LSB- 2 -RSB- . Although additivity of risks is often taken as independence -LSB- 20 -RSB- , multiplicativity of risk is sometimes also taken as independence -LSB- 21 -RSB- . The joint action of HLA and INS has variously been described as being multiplicative -LSB- 14 , 15 -RSB- , additive -LSB- 13 -RSB- , providing evidence of interaction -LSB- 15 -RSB- , and non-interacting -LSB- 13 , 14 , 17 -RSB- . Few studies have investigated the more recently established susceptibility loci PTPN22 and CTLA4 in the context of joint effects on the risk of type 1 diabetes . The studies that have been done have mainly concluded that there is a non-interaction -LSB- 22 , 23 -RSB- effect , but here also the results have diverged -LSB- 24 -- 27 -RSB- . The aim of our study was to assess the joint effects of the four established susceptibility loci HLA , INS , CTLA4 and PTPN22 in type 1 diabetes , using a consistent approach with both population-based case-control and family trio designs and with large sample sizes . Methods Participants We analysed two independent type 1 diabetes data sets . One nuclear family set consisted of 421 trios with mother , father and one child diagnosed in Norway with type 1 diabetes before age 15 years -LRB- 225 -LSB- 53.4 % -RSB- of the affected children were boys -RRB- . The families were collected between 1993 and 1997 . In families with more than one affected sibling , only the proband was included in the analyses . The case-control data set consisted of 1,331 type 1 diabetes patients -LRB- 51.9 % boys and 48.1 % girls -RRB- and 1,625 -LRB- 51.4 % boys and 48.6 % girls -RRB- control participants aged < 15 years . In analyses involving age of disease onset , we divided the data sets according to age of disease onset of the affected child into three groups -LRB- 0 -- 4.9 , 5 -- 9.9 and ≥ 10 years -RRB- . The controls were randomly selected from the official population registry among children born between 1985 and 1999 and recruited in 2001 , as previously described -LSB- 28 -RSB- . The patients in the case-control material were from the Norwegian Childhood Diabetes Registry consecutively recruited between 1997 and 2000 -LSB- 29 -RSB- and between 2002 and 2005 . The type 1 diabetes patients and their family members were recruited by the Norwegian Childhood Diabetes Study Group , including all paediatric departments in Norway . All type 1 diabetes patients were diagnosed according to EURODIAB criteria -LSB- 30 -RSB- . The study was approved by the local ethics committee , and informed consent was obtained from all participants or their parents . Genomic DNA extraction and genotyping In the majority of the type 1 diabetes case-control samples we used DNA extracted from buccal cells -LSB- 31 -RSB- . In all remaining samples , DNA was extracted from peripheral whole blood using a salting-out protocol . Genotyping of HLA-DRB1 , − DQA1 and − DQB1 was performed using PCR-SSOP -LRB- sequence-specific oligonucleotide probing -RRB- mainly following published methods -LSB- 32 -RSB- , or PCR-SSP -LRB- sequence-specific primer -RRB- -LSB- 33 , 34 -RSB- , or using time-resolved fluorescence technology in the Delfia assay -LRB- Perkin-Elmer Life Sciences , Turku , Finland -RRB- . HLA genotypes were grouped into four risk categories based on DQB1 , DQA1 and DRB1 genotypes , including DRB1 * 04 subtyping . The majority of DRB1 * 04-DQA1 * 0301-DQB1 * 0302 haplotypes in Norway are DRB1 * 0401 or − 0404 -LRB- constituting > 94 % of the haplotypes -RRB- -LSB- 35 -RSB- . In the present study the other rare subtypes are referred to as DRB1 * 04XX . Because of the almost complete linkage disequilibrium between INS-VNTR allele classes and the − 23 HphI polymorphism , we genotyped − 23 HphI -LRB- rs689 -RRB- as a marker for the INS-VNTR . The − 23 HphI A allele corresponds to the VNTR class I and the − 23 HphI T allele correspond to the VNTR class III . In PTPN22 we genotyped the single nucleotide polymorphism -LRB- SNP -RRB- Arg620Trp -LRB- rs2476601 -RRB- . The SNP JO27_1 -LRB- rs11571297 -RRB- was genotyped in CTLA4 . SNP genotyping was performed by TaqMan allelic discrimination assays on an ABI 7900HT DNA Analyzer -LRB- Applied Biosystems , Foster City , CA USA -RRB- . Primer and probe sequences are shown in Electronic Supplementary Material -LRB- ESM -RRB- Table 1 . The PCR conditions are available on request . Data analysis The HLA haplotypes were grouped as high risk , intermediate risk , neutral risk and low risk according to the following criteria : high-risk category , DRB1 * 0401/04XX-DQA 1 * 03-DQB1 * 0302/DRB1 * 03-DQA1 * 05-DQB1 * 0201 -LRB- DR4-DQ8 / DR3-DQ2 -RRB- ; low-risk category , all genotypes with at least one DQB1 * 0602 allele ; intermediate-risk category , DRB1 * 0404-DQ8 / DR3-DQ2 , DR3-DQ2 / DR3-DQ2 , DR4-DQ8 / DR4-DQ8 -LRB- with the exception of DRB1 0404-DQ8 homozygotes , which were grouped as neutral -RRB- , DRB1 * 0401 or 040XX-DQ8 / X -LRB- X ≠ DQB1 * 0602 or DR3-DQ2 -RRB- . The remaining haplotypes were grouped in the neutral-risk category . For assessment of two-locus joint effects , we pooled genotypes of INS , PTPN22 and CTLA4 as follows : INS class I/I genotypes were compared with I/III together with III/III genotypes . The PTPN22 TT and CT genotypes were compared with CC . CTLA4 -LRB- JO27_1 -RRB- TT genotypes were compared with TC and CC genotypes . Not all individuals were genotyped for non-HLA polymorphisms because of lack of DNA . We did not exclude individuals with some missing genotypes to prevent the loss of important information when looking at joint effects between polymorphisms . The available numbers of individuals for each analysis are seen in the tables . Data were presented using stratified 2 × 2 tables and analysed using logistic regression models including interaction -LRB- product -RRB- terms , in SPSS for Windows -LRB- version 14.0 ; SPSS , Chicago , IL , USA -RRB- . In addition to formal analyses treating HLA categories as categorical in the logistic regression , we also tested for interactions when treating HLA category as a continuous variable coded 1 , 2 , 3 , 4 , thus maximising the power under alternative models where the effect of a non-HLA locus -LRB- as measured by the odds ratio -LSB- OR -RSB- -RRB- was assumed to decrease -LRB- or increase -RRB- -LRB- logit -RRB- linearly over the four HLA risk categories -LRB- test for interaction with one degree of freedom -RRB- . Case-only analyses were used to estimate interaction parameters and to test for deviation from multiplicative effects using logistic regression -LSB- 36 -RSB- . In addition to the increased power obtained by utilising all cases -LRB- from case-control and trio materials -RRB- simultaneously , case-only analyses have increased power by making the implicit assumption that there is no association between the two loci in the population ; i.e. the OR for their association is 1.0 in the population . Thus , under reasonable assumptions the case-only analysis makes the most efficient use of data to assess deviation from multiplicative models . In the case-control analysis , likelihood ratio tests comparing nested logistic regression models were used as global tests for interaction . The transmission disequilibrium test -LSB- 37 -RSB- was performed using the UNPHASED application implemented in the UNPHASED software version 2.4 -LSB- 38 -RSB- . For the trio data , 95 % confidence intervals for the relative risk were estimated using conditional logistic regression in UNPHASED . Receiver operating curve -LRB- ROC -RRB- and confidence bounds for the area under the curve were estimated assuming a non-parametric distribution and analyses were done using SPSS version 14.0 . Genotypes were added sequentially in order of likelihood ratio -LRB- or equivalently by the absolute risk conferred by a given genotype combination estimated using Bayes ' formula -RRB- . Two four-locus genotype combinations were absent among cases in our material , and a very low value for the estimated absolute risk was imputed for these to allow inclusion in the ROC curve estimation with all four loci simultaneously . A p value < 0.05 was considered to be statistically significant . Results The single-locus main effects are shown in ESM Table 2 -LRB- case-control data -RRB- and ESM Table 3 -LRB- trios -RRB- . Compared with the neutral HLA risk category , the high-risk category showed a strong association with type 1 diabetes , with OR 20.6 ; for the intermediate-risk category the OR was 5.7 and for the low-risk category it was 0.09 . INS , PTPN22 and CTLA4 also showed an association with type 1 diabetes , as expected . The transmission of the risk allele in the nuclear families confirmed the associations in INS , PTPN22 and CTLA4 -LRB- JO27_1 -RRB- , although with borderline significance for JO27_1 -LRB- ESM Table 3 -RRB- . Joint effect of HLA and PTPN22 ORs for the effect of PTPN22 varied across the HLA risk categories and were significant in some of the subgroups . The ORs were smaller for the risk-conferring HLA genotypes , indicating negative deviation from a multiplicative model . A global test of interaction -LRB- with 3 df -RRB- between HLA and PTPN22 in the logistic regression model confirmed a significant interaction -LRB- p = 0.024 -RRB- . In the trio data , the relative risk conferred by the PTPN22 T allele was similar in the strata defined by HLA group , with no evidence for deviation from a multiplicative model -LRB- ESM Table 4 -RRB- . A case-only analysis among all cases from the case-control and family materials -LRB- ESM Table 5 -RRB- supported a significant negative deviation from multiplicative effects , with weaker ORs conferred by PTPN22 in the HLA risk categories -LRB- 3 df test for interaction ; p = 0.028 -RRB- . When treating HLA-encoded risk as a continuous variable in the analysis -LRB- 1 df -RRB- , the interaction was even more statistically significant -LRB- p = 0.003 -RRB- . There was no association between HLA and PTPN22 among the controls -LRB- 3 df test ; p = 0.19 -RRB- . We tried to fit the case-control data to an additive odds model using generalised linear models in STATA -LRB- version 9 -RRB- , as described by Skrondal -LSB- 39 -RSB- . However , convergence was not obtained , suggesting that the data did not fit well to an additive model . Joint effect of HLA and INS The 3 df test for interaction between INS and HLA was not statistically significant -LRB- p = 0.67 -RRB- . There was also no statistically significant deviation from a multiplicative model in the trio data -LRB- test for interaction , p = 0.5 -RRB- -LRB- ESM Table 4 -RRB- or in the case-only analysis -LRB- 3 df test ; p = 0.49 -RRB- -LRB- ESM Table 5 -RRB- ; even when treating HLA-encoded risk as a continuous variable in the analysis there was no significance -LRB- 1 df test , p = 0.12 -RRB- . There was also no association between INS and HLA among controls , as expected -LRB- 3 df test ; p = 0.41 -RRB- . Joint effect of HLA and CTLA4 The ORs for CTLA4 in the different HLA categories -LRB- Table 1 -RRB- indicated no deviation from a two-locus multiplicative model -LRB- 3 df test ; p = 0.53 -RRB- . This was also the case in the trio data set -LRB- ESM Table 4 -RRB- and was supported by the case-only analysis -LRB- ESM Table 5 ; 3 df test ; p = 0.57 -RRB- . Again , there was no association between the two loci among controls -LRB- 3 df test ; p = 0.21 -RRB- . Table 1Interaction between HLA-INS -LRB- -23 HphI -RRB- , HLA-PTPN22 -LRB- Arg620Trp -RRB- and HLA-CTLA4 -LRB- JO27_1 -RRB- in the case -- control data set using logistic regressionHLA categoryaNon-HLA genotypesCasesControlsOR95 % CITest for interaction -LRB- p value -RRB- n -LRB- % -RRB- n -LRB- % -RRB- INSb0 .67 High riskI-I218 -LRB- 25.2 -RRB- 20 -LRB- 2.8 -RRB- 1.560.76 -- 3.21 III +98 -LRB- 28.6 -RRB- 14 -LRB- 2.5 -RRB- 1Intermediate riskI-I412 -LRB- 47.6 -RRB- 122 -LRB- 17.1 -RRB- 2.101.52 -- 2.89 III +160 -LRB- 46.8 -RRB- 99 -LRB- 17.4 -RRB- 1Neutral riskI-I221 -LRB- 25.5 -RRB- 346 -LRB- 48.5 -RRB- 2.341.74 -- 3.15 III +81 -LRB- 23.7 -RRB- 299 -LRB- 52.6 -RRB- 1Low riskI-I14 -LRB- 1.6 -RRB- 225 -LRB- 31.6 -RRB- 3.260.92 -- 11.54 III +3 -LRB- 0.9 -RRB- 156 -LRB- 27.5 -RRB- 1Total12071281PTPN220 .024 High riskTT + TC89 -LRB- 23.2 -RRB- 8 -LRB- 3.1 -RRB- 1.260.55 -- 2.88 CC237 -LRB- 27.8 -RRB- 27 -LRB- 2.6 -RRB- 1Intermediate riskTT + TC177 -LRB- 46.1 -RRB- 55 -LRB- 21.2 -RRB- 1.310.91 -- 1.86 CC406 -LRB- 47.7 -RRB- 164 -LRB- 15.6 -RRB- 1Neutral riskTT + TC111 -LRB- 28.9 -RRB- 121 -LRB- 46.7 -RRB- 2.441.80 -- 3.31 CC199 -LRB- 23.4 -RRB- 536 -LRB- 51.0 -RRB- 1Low riskTT + TC7 -LRB- 1.8 -RRB- 75 -LRB- 29.0 -RRB- 3.271.18 -- 9.06 CC9 -LRB- 1.1 -RRB- 323 -LRB- 30.8 -RRB- 1Total12351309CTLA40 .53 High riskTT122 -LRB- 24.9 -RRB- 14 -LRB- 3.6 -RRB- 0.900.45 -- 1.84TC-CC210 -LRB- 27.9 -RRB- 22 -LRB- 2.5 -RRB- 1Intermediate riskTT241 -LRB- 49.2 -RRB- 65 -LRB- 16.5 -RRB- 1.591.14 -- 2.23TC-CC346 -LRB- 46.0 -RRB- 148 -LRB- 16.7 -RRB- 1Neutral riskTT121 -LRB- 24.7 -RRB- 212 -LRB- 53.8 -RRB- 1.341.01 -- 1.77TC-CC187 -LRB- 24.9 -RRB- 439 -LRB- 49.7 -RRB- 1Low riskTT6 -LRB- 1.2 -RRB- 103 -LRB- 26.1 -RRB- 1.800.63 -- 5.18TC-CC9 -LRB- 1.2 -RRB- 275 -LRB- 31.1 -RRB- 1Total12421278n , Number of cases/controlsaHLA risk categories : high risk , DQA1 * 03-DQB1 * 0302/DQA1 * 05-DQB1 * 0201 -LRB- DQ8/DQ2 -RRB- , where DRB1 ≠ 0404 ; low risk , at least one DQB1 * 0602 allele independent of genotype on the other allele ; intermediate risk , DRB1 * 0404-DQ8 / DR3-DQ2 , DR3-DQ2 / DR3-DQ2 , DR4-DQ8 / DR4-DQ8 -LRB- excluding homozygous DRB1 0404 -RRB- , DRB1 * 0401 or 040x-DQ8 / X -LRB- X ≠ DQB1 * 0602 or DR3 -RRB- . The remaining haplotypes were grouped in the neutral risk category -LRB- see Methods -RRB- bRepresents genotypes III/III and I/III Joint effects of non-HLA loci There was also no indication of deviation from multiplicative two-locus joint effects of PTPN22-INS , INS-CTLA4 or PTPN22-CTLA4 in the case-control data -LRB- Table 2 -RRB- or in the case-only analysis -LRB- ESM Table 5 -RRB- -LRB- all p > 0.39 -RRB- . For the trios , the test for interaction between PTPN22 and CTLA4 showed p = 0.046 -LRB- ESM Table 6 -RRB- . Taken together with the analysis of the case-control and the case-only data , this weighs against any deviation from a multiplicative two-locus joint effect also of CTLA4 and PTPN22 . Table 2Interaction between INS-PTPN22 , INS-CTLA4 and PTPN22-CTLA4 in the case-control data set using logistic regressionNon-HLA genotypesCasesControlsOR95 % CITest for interactiona -LRB- p value -RRB- n -LRB- % -RRB- n -LRB- % -RRB- INSPTPN 0.67I-ITT + TC274 -LRB- 72.9 -RRB- 156 -LRB- 58.4 -RRB- 1.741.38 -- 2.18I-ICC594 -LRB- 71.6 -RRB- 587 -LRB- 54.8 -RRB- 1 III+TT+TC 102 -LRB- 27.1 -RRB- 111 -LRB- 41.6 -RRB- 1.891.38 -- 2.58 III+CC 236 -LRB- 28.4 -RRB- 485 -LRB- 45.2 -RRB- 1 Total -LRB- n -RRB- 1,2061,339 INSCTLA4 0.42I-ITT354 -LRB- 73.1 -RRB- 222 -LRB- 53.9 -RRB- 1.511.23 -- 1.86I-ITC-CC520 -LRB- 71.0 -RRB- 492 -LRB- 54.8 -RRB- 1 III+TT 130 -LRB- 26.9 -RRB- 190 -LRB- 46.1 -RRB- 1.310.99 -- 1.73 III+TC - CC212 -LRB- 29.0 -RRB- 405 -LRB- 45.2 -RRB- 1 Total -LRB- n -RRB- 1,2161,309 PTPNCTLA4 0.78 TT+TCTT 143 -LRB- 29.7 -RRB- 75 -LRB- 17.7 -RRB- 1.461.04 -- 2.05 TT+TCTC - CC239 -LRB- 31.7 -RRB- 183 -LRB- 20.1 -RRB- 1 CCTT339 -LRB- 70.3 -RRB- 348 -LRB- 82.3 -RRB- 1.41.15 -- 1.67CCTC-CC514 -LRB- 68.3 -RRB- 729 -LRB- 79.9 -RRB- 1 Total -LRB- n -RRB- 1,2351,335 The low-risk genotypes were used as reference -LRB- CC , TC-CC , TC-CC -RRB- aLikelihood ratio tests of whether the OR conferred by one locus is significantly different over strata defined by genotypes in the other locusn , Number of cases/controls Joint effects of more than two susceptibility loci We also tested models with all three-way and four-way interactions involving the four susceptibility loci using logistic regression -LRB- categorising all loci in two groups : increased risk genotypes or not -RRB- , but none of the multi-way interactions were statistically significant -LRB- all p > 0.29 -RRB- . The simultaneous distribution of risk genotypes at all four loci among cases and controls is shown in ESM Table 7 . The results show that the more risk loci an individual carries , the higher the relative risk , but the presence or absence of HLA risk loci influences the relative risk much more than the other loci , as expected . For instance , carrying risk genotypes at all three non-HLA loci but not at HLA is associated with a much lower risk than HLA risk genotypes together with low-risk genotypes at all three other loci . The relative risk -LRB- OR -RRB- conferred by simultaneously carrying high - or moderate-risk HLA and risk genotypes at all the three other loci compared with non-risk-associated genotypes at all four loci was 61 . The expected relative risk under a strict multiplicative model involving all four loci was 123 -LRB- multiplying all four single-locus effects by each other -RRB- . The relatively small number of individuals simultaneously carrying all risk genotypes indicates that the observed negative deviation from a four-way multiplicative model was not statistically significant , in accordance with the formal test cited above . ROC curve Another way to assess the predictive utility of combinations of genetic risk markers is the ROC curve -LSB- 40 -RSB- . This utilises the genotypes of all included individuals and assesses the combination of sensitivity and specificity of different combinations of genotypes . ROC curves for HLA alone , pairwise combinations of HLA and non-HLA loci , and multiple genotypes -LRB- Fig. 1 -RRB- showed an area under the curve of 0.82 for HLA alone , which was only marginally increased by adding non-HLA loci . Fig. 1ROC curve for HLA genotypes in four categories and for combinations of genotypes defined by HLA and non-HLA susceptibility loci . The area under the curve -LRB- 95 % confidence interval -RRB- was 0.820 -LRB- 0.803 -- 0.836 -RRB- for HLA -LRB- dark blue line -RRB- , 0.828 -LRB- 0.811 -- 0.844 -RRB- for HLA+CTLA 4 -LRB- purple line -RRB- , 0.835 -LRB- 0.819 -- 0.851 -RRB- for HLA+PTPN 22 -LRB- grey line -RRB- , 0.840 -LRB- 0.824 -- 0.855 -RRB- for HLA+INS -LRB- green line -RRB- , 0.848 -LRB- 0.833 -- 0.863 -RRB- for HLA+INS+PTPN 22 -LRB- yellow line -RRB- and 0.852 -LRB- 0.837 -- 0.867 -RRB- for HLA+INS+PTPN 22 + CTLA4 -LRB- red line -RRB- . Turquoise dashed line , reference line Age of disease onset and sex We found no significant deviation from a multiplicative model concerning age -- locus and sex -- locus interaction for any of the genes . This was confirmed in the trio families and case-only analysis -LRB- ESM Tables 8 and 9 -RRB- . Discussion The present study is a comprehensive evaluation of joint effects of the four most well established type 1 diabetes susceptibility genes in both a large case-control series and family material . The relative risk conferred by PTPN22 was stronger in the lower-risk HLA categories than in the high-risk HLA category , while all other two-locus combinations -LRB- HLA-INS , HLA-CTLA4 , INS-CTLA4 , INS-PTPN22 and PTPN22-CTLA4 -RRB- were consistent with multiplicative models . Although model-free methods have been developed for gene -- gene interaction studies , such as multifactor dimensionality reduction -LRB- see -LSB- 1 -RSB- and references therein -RRB- , these methods are designed for the detection of novel susceptibility loci , which was not the goal of our investigation . Two of the three previous studies of the joint effect of PTPN22 and HLA were in accordance with our results -LSB- 25 , 27 -RSB- while the other study found no deviation from a multiplicative model -LSB- 22 -RSB- . It should be noted that the interaction between PTPN22 and HLA found in the case-control material and case-only analysis was not replicated in our trio data . One of the reasons for this could be lower statistical power in the trio data . Using the Quanto program -LRB- -LSB- 41 -RSB- ; http://hydra.usc.edu/gxe -RRB- , we found that we had more than 80 % power to detect significant two-way gene -- gene interaction if the true interaction parameter was 0.5 . For the trio design we would need as many trios as we had cases in the case-control study to obtain a similar power . Our number of trios was only about a third of the number of cases in the case-control study , with consequently lower power . The case-only design is known to be the most efficient to detect interaction under certain assumptions . For instance , we had > 99 % power to detect interaction if the true interaction parameter was 0.5 . The few studies concerning two-locus interaction effects between HLA and CTLA4 and among non-HLA genes -LSB- 22 -RSB- have generally indicated multiplicative effects , which is in accordance with our results . Some previous studies have found that the relative risk conferred by INS was similar in subgroups defined by HLA susceptibility genes -LSB- 11 , 12 -RSB- , while three studies have indicated that the effect was stronger in the low risk-HLA categories -LSB- 16 -- 18 -RSB- , a finding that was only partially supported by our data . On the other hand , one relatively small study has found that the effect of INS was confined to the high-risk HLA-DR4 group -LSB- 10 -RSB- . The reason for diverging results of the joint effect of established type 1 diabetes susceptibility genes in the literature could be that the studies have been performed with varying sample sizes and with different study designs . Linkage studies -LSB- 14 , 15 -RSB- , case-control association studies -LSB- 10 , 12 , 13 , 16 -RSB- and family trio designs -LSB- 16 , 18 , 22 , 25 -RSB- have all been used in these studies . Smaller studies might be inadequate to reveal significant interactions . Different criteria for categorising the HLA risk groups could potentially influence the result of the joint effect of HLA and other type 1 diabetes susceptibility genes . However , our conclusions were not affected by alternative classification of HLA risk groups , such as into DR4-DQ8 vs DR3-DQ2 carriers -LRB- data not shown -RRB- . Studies in different populations and ethnic groups have indicated some heterogeneity in HLA-associated risk of type 1 diabetes and it is also possible that gene -- gene interactions may vary across populations . However , despite the observed variations in population risk of type 1 diabetes and in HLA haplotype frequencies across populations , the relative predisposing effects of HLA haplotypes seem to be consistent across populations -LSB- 42 -RSB- . In our study all the patients were diagnosed before 15 years of age . The fact that the relative risks associated with both risk genotypes and low-risk genotypes seem to diminish with age above 15 years -LSB- 43 -RSB- raises the question whether gene -- gene interactions may also differ in different age-groups . Although no preventive intervention is available for type 1 diabetes today , prediction of disease is an important part of strategies for prevention , both for recruitment of participants for research studies and for identification of target populations for future preventive interventions . Understanding the interacting effect of the established type 1 diabetes susceptibility genes will increase this possibility . In a multiplicative model the relative risk -LRB- RR -RRB- for a person holding a high-risk genotype at both loci compared with a person with low risk at both loci will be RRlocus1 × RRlocus2 . In terms of absolute risk differences , a doubling or tripling of risk due to INS or PTPN22 would be greater for a person with a high-risk HLA genotype than it would for someone with a low-risk HLA genotype . The absolute risk for persons with a given genotype can be estimated by multiplying the average cumulative incidence in the population -LRB- 0.42 % cumulative risk up to age 15 years in Norway -LSB- 44 -RSB- -RRB- by the ratio of genotype frequency in patients and genotype frequency in controls -LSB- 18 -RSB- , or using Bayes ' formula . For instance , for a person with a low-risk HLA genotype , a high - or low-risk INS genotype will define whether the estimated absolute risk is approximately 0.01 % or 0.028 % -LRB- absolute risk difference , 0.027 % -RRB- , whereas for those with the high-risk HLA genotype INS will define an estimated risk of approximately 3.0 % or 4.7 % -LRB- absolute risk difference 1.7 % -RRB- . As discussed in the general setting by Janssens et al. -LSB- 40 -RSB- , increasing the number of susceptibility loci considered simultaneously generally increases the predictive value for disease . The downside is that the proportion of the population simultaneously carrying multiple risk alleles becomes minute even with a moderate number of susceptibility polymorphisms , and that even with relatively large data sets , as in our study , the absolute risk estimate becomes imprecise . The high-risk HLA genotype is carried by fewer than 3 % of population controls , but confers a very high risk of disease . Several practical and scientific aspects of prediction should be considered when evaluating the utility of different prediction regimes . The ROC curve analysis confirms that , despite the higher absolute risk for those few with combinations of several risk markers , adding non-HLA genetic markers only marginally increases the utility of the prediction over that of HLA alone . While up to six susceptibility loci in addition to those studied here have recently been established in type 1 diabetes -LSB- 45 -RSB- , the magnitude of the effect for each additional locus is very much smaller than that of HLA and even smaller than that of INS and PTPN22 , suggesting that they are likely to add only marginally to the prediction of disease in individuals . Furthermore , an informal assessment of the number needed to be genetically screened in order to obtain a cohort of high-risk individuals , which will give rise to a given number of cases of type 1 diabetes , and the costs connected to the genotyping also suggest limited cost-effectiveness in adding non-HLA genetic markers to the prediction regime -LRB- data not shown -RRB- . In conclusion , in this comprehensive study of interactions among established type 1 diabetes susceptibility genes , we found that the joint effect of HLA and PTPN22 was significantly less than multiplicative in the case-control material , while a multiplicative model could not be rejected for HLA-INS , HLA-CTLA , PTPN-INS , INS-CTLA4 and PTPN-CTLA4 . Despite near-multiplicative effects for most loci , and the fact that groups with very high relative risk of type 1 diabetes can be identified by testing for multiple susceptibility genes , only a small proportion of the population -LRB- and cases with type 1 diabetes -RRB- simultaneously carry HLA and multiple non-HLA susceptibility genotypes . Electronic supplementary material Below is the link to the electronic supplementary material . ESM Table 1 -LRB- PDF 14.3 kb -RRB- ESM Table 2 -LRB- PDF 110 kb -RRB- ESM Table 3 -LRB- PDF 69.2 kb -RRB- ESM Table 4 -LRB- PDF 51.2 kb -RRB- ESM Table 5 -LRB- PDF 31.9 kb -RRB- ESM Table 6 -LRB- PDF 48.8 kb -RRB- ESM Table 7 -LRB- PDF 25.9 kb -RRB- ESM Table 8 -LRB- PDF 68.0 kb -RRB- ESM Table 9 -LRB- PDF 25.1 kb -RRB-

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

ctla4
genes
hla
ins
interaction
ptpn22
type 1 diabetes


Reproducibility of onset and recovery oxygen uptake kinetics in moderately impaired patients with chronic heart failure Oxygen -LRB- O2 -RRB- kinetics reflect the ability to adapt to or recover from exercise that is indicative of daily life . In patients with chronic heart failure -LRB- CHF -RRB- , parameters of O2 kinetics have shown to be useful for clinical purposes like grading of functional impairment and assessment of prognosis . This study compared the goodness of fit and reproducibility of previously described methods to assess O2 kinetics in these patients . Nineteen CHF patients , New York Heart Association class II -- III , performed two constant-load tests on a cycle ergometer at 50 % of the maximum workload . Time constants of O2 onset - and recovery kinetics -LRB- τ -RRB- were calculated by mono-exponential modeling with four different sampling intervals -LRB- 5 and 10 s , 5 and 8 breaths -RRB- . The goodness of fit was expressed as the coefficient of determination -LRB- R2 -RRB- . Onset kinetics were also evaluated by the mean response time -LRB- MRT -RRB- . Considering O2 onset kinetics , τ showed a significant inverse correlation with peak - -LRB- R = − 0.88 , using 10 s sampling intervals -RRB- . The limits of agreement of both τ and MRT , however , were not clinically acceptable . O2 recovery kinetics yielded better reproducibility and goodness of fit . Using the most optimal sampling interval -LRB- 5 breaths -RRB- , a change of at least 13 s in τ is needed to exceed normal test-to-test variations . In conclusion , O2 recovery kinetics are more reproducible for clinical purposes than O2 onset kinetics in moderately impaired patients with CHF . It should be recognized that this observation can not be assumed to be generalizable to more severely impaired CHF patients . Introduction Oxygen -LRB- O2 -RRB- kinetics describe the rate change of oxygen uptake during onset or recovery of exercise and reflect changes in cardiac output and tissue oxygen extraction . Compared to healthy individuals , patients with chronic heart failure -LRB- CHF -RRB- have slower O2 onset and recovery kinetics , resulting in early fatigue and slow recovery after exertion due to a greater reliance on anaerobic metabolism -LRB- Wasserman et al. 1996 ; Koike et al. 1995 -RRB- . Although peak is widely accepted as a reliable indicator of maximal aerobic capacity in CHF patients -LRB- Weber et al. 1982 ; Janicki et al. 1990 -RRB- , O2 kinetics provide additional objective information on the ability to adapt to and recover from exercise that is indicative of daily life -LRB- Riley et al. 1994 ; Koike et al. 1995 -RRB- . Furthermore , O2 kinetics are potentially useful for risk stratification of CHF patients -LRB- Brunner-La Rocca et al. 1999 ; Schalcher et al. 2003 -RRB- and for measuring the effects of exercise training , which has already been demonstrated in healthy individuals -LRB- Carter et al. 2000 -RRB- and patients with chronic obstructive pulmonary disease -LRB- COPD -RRB- -LRB- Puente-Maestu et al. 2000 -RRB- . In order to use O2 kinetics for these clinical purposes it is necessary to know more about the applicability and reproducibility of these exercise parameters in this specific patient group . Until now there has been no uniformity in the assessment of O2 kinetics in patients with CHF -LRB- Arena et al. 2001 -RRB- . In addition , the reproducibility of O2 kinetics at submaximal exercise in CHF patients has not been studied extensively . Two studies that assessed O2 onset kinetics by different modeling techniques , suggest an acceptable reproducibility of nonlinear regression and an algebraic method -LRB- Belardinelli et al. 1998 ; Sietsema et al. 1994 -RRB- . In both studies , however , intra-class correlations and limits of agreement were not mentioned . The purpose of this study is to evaluate the goodness of fit and reproducibility of previously described clinically applicable methods to characterize O2 onset and recovery kinetics in moderately impaired patients with CHF . Furthermore , we aim to define interventional changes that are required to distinguish from the normal test-to-test variations . Methods Subjects Nineteen patients -LRB- 15 men , 4 women -RRB- with stable CHF -LRB- New York Heart Association class II -- III and echocardiographical ejection fraction ≤ 40 % -RRB- attributed to idiopathic dilated cardiomyopathy -LRB- n = 4 -RRB- or ischemic heart disease due to myocardial infarction -LRB- n = 15 -RRB- were selected at the cardiology outdoor clinic of the Máxima Medical Centre -LRB- Veldhoven , The Netherlands -RRB- . Fifteen patients were in NYHA functional class II and four in class III . Subject characteristics are listed in Table 1 . Patients with recent myocardial infarction -LRB- < 3 months -RRB- , angina pectoris at rest , atrial fibrillation , or atrial flutter were not included . All patients performed a pulmonary function test using a spirometer -LRB- Masterlab , Jaeger , Würzburg , Germany -RRB- including measurement of forced expiratory volume in 1 s -LRB- FEV1 -RRB- and forced vital capacity -LRB- FVC -RRB- during a maximal forced expiratory effort . Patients with chronic airways obstruction , defined as FEV1/FVC < 60 % were excluded . Table 1Characteristics of included patients with CHF -LRB- N = 19 -RRB- VariablesMean ± SDRangeAge -LRB- years -RRB- 62 ± 843 -- 78Height -LRB- cm -RRB- 172 ± 8155 -- 184Weight -LRB- kg -RRB- 85 ± 1054 -- 97Body mass index -LRB- kg/m2 -RRB- 29 ± 422 -- 37Fat mass -LRB- % -RRB- a30 ± 720 -- 43Time since diagnosis -LRB- months -RRB- 20 ± 246 -- 96Left ventricular ejection fraction -LRB- % -RRB- 33 ± 719 -- 40a Fat mass was assessed by skinfold measurements -LRB- biceps , triceps , subscapular , and suprailiacal -RRB- according to standard procedures -LRB- WHO 1995 -RRB- Fifteen patients used beta-blockers and angiotensin-converting enzyme inhibitors , three patients used an angiotensin-converting enzyme inhibitor only , and one patient used a beta-blocker only . Sixteen patients used diuretics . The average duration that patients were using beta-blockers was 34 ± 33 months -LRB- range 7 -- 112 months -RRB- and 32 ± 29 months for ACE inhibitors -LRB- range 7 -- 118 months -RRB- . Patients who did not use beta-blockers were not different from the other patients with respect to age , peak - or left ventricular ejection fraction . The research protocol was approved by the local Research Ethics Committee of the Máxima Medical Centre , and all patients provided written informed consent . Exercise testing Subjects performed a symptom-limited , incremental exercise test , and on a separate day -LRB- at least 3 days later -RRB- , a constant-load test at 50 % of the maximum workload achieved at the first test . This test was repeated at the same time on another day within 2 weeks -LRB- mean difference between tests 6.7 ± 3.9 days -RRB- . All subjects took their medication at the usual time and were instructed not to perform any extra physical activity on testing days . During the testing period all patients were on sinus rhythm . Furthermore , none of the patients reported changes in symptoms , functional status or medication use . Therefore , they could be considered to be in a stable physical condition during the study period . All exercise tests were performed in an upright seated position on an electromagnetically braked cycle ergometer -LRB- Corival , Lode , Groningen , The Netherlands -RRB- . Measurements of and respired fractions of and were obtained breath by breath -LRB- Oxycon α , Jaeger , Germany -RRB- . Volumes and gas analysers were calibrated before each test . The incremental exercise test was performed using an individualized ramp protocol with a total test duration of 8 -- 12 min -LRB- Working Group on Cardiac Rehabilitation & Excercise Physiology and Working Group on Heart Failure of the European Society of Cardiology 2001 -RRB- . During the tests all patients were instructed to maintain a pedaling frequency of 70 per minute . A 12-lead electrocardiogram was registered continuously , and blood pressure was measured every 2 min -LRB- Korotkoff -RRB- . The test was ended when the patient was not able to maintain the required pedaling frequency . Maximal workload was defined as the final registered workload , peak - as the average of the last 30 s of the test . Predicted value of peak - was calculated with use of the Wasserman equation , normalizing peak - for age , gender , weight and height -LRB- Hansen et al. 1984 -RRB- . The ventilatory threshold was determined by two independent observers using the V-slope method as described by Beaver et al. -LRB- 1986 -RRB- . The constant-load tests included 2 min of rest , 2 min of unloaded pedaling , 6 min at 50 % of the maximum workload and 5 min of rest . Data analysis Mono-exponential model Time constants -LRB- τ -RRB- were calculated by fitting the data of the constant-load tests to a first-order -LRB- mono-exponential -RRB- model using the non-linear least squares method -LRB- Whipp and Wasserman 1972 -RRB- . Calculations were performed with breath-by-breath data averaged into four different sampling intervals that were used in previous studies : 5 s -LRB- 5 s -RRB- -LRB- Matsumoto et al. 1999 -RRB- , 10 s -LRB- 10 s -RRB- -LRB- Arena et al. 2002 -RRB- , 5 breaths -LRB- 5b -RRB- -LRB- Koike et al. 1995 -RRB- and 8 breaths -LRB- 8b -RRB- -LRB- Pavia et al. 1999 -RRB- . The following formulas were used : A = B = Td = time delayτ = time constant -LRB- s -RRB- Baseline - was defined as the average of the last minute of the unloaded-cycling stage and steady-state - as the average of the last minute of exercise . The time delay -LRB- Td -RRB- is a parameter allowed to vary in order to optimize the fit , representing the time between onset of exercise and the start of the mono-exponential increase of One of the determinants of this time delay is the lag time between the computer signal to deliver the work rate and the actual response of the ergometer , which amounted to 2.2 ± 0.6 s in this study . Occasional errant breaths -LRB- e.g. , due to coughing , swallowing or talking -RRB- were deleted from the data set when exceeded three standard deviations of the mean , defined as the average of two following and two preceding sampling intervals -LRB- Lamarra et al. 1987 -RRB- . In total , about 1 % of the breaths had to be deleted . Algebraic method O2 onset kinetics were also evaluated by an algebraic method calculating mean response time -LRB- MRT -RRB- -LRB- Sietsema et al. 1994 -RRB- , using the following formulas : MRTO2 deficitThe expected amount of O2-uptake was calculated by multiplying the O2-amplitude with exercise duration -LRB- 6 min -RRB- . Oxygen deficit was calculated by subtracting the summed above the baseline-value from the expected Statistical analysis All data -LRB- presented as mean ± SD -RRB- were analyzed using a statistical software program -LRB- SPSS 11.0 -RRB- . The ` goodness of fit ' for mono-exponential modeling was evaluated by the coefficient of determination -LRB- R2 -RRB- . The fitting procedure was considered acceptable when R2 ≥ 0.85 , as previously described by de Groote et al. -LRB- 1996 -RRB- . Differences between calculation methods were evaluated by one-way ANOVA with repeated measures and Bonferroni post hoc analyses . In order to assess differences between kinetic parameters of the two tests the paired Student 's t test was used . Linear regression was used to define correlations between variables . Agreement between the kinetic parameters was assessed by intra-class correlation coefficients , limits of agreement -LRB- mean difference ± 1.96 × SD -RRB- -LRB- Bland and Altman 1986 -RRB- and coefficients of variation -LRB- SD of difference as a percentage of the mean value -RRB- . Probability values < 0.05 were considered statistically significant . Results Symptom-limited exercise tests All subjects completed the exercise tests . The maximum workload was 109 ± 32 W , peak - was 20.0 ± 4.0 ml min − 1 kg − 1 -LRB- 73 ± 9 % of predicted peak - -RRB- and the maximal respiratory exchange ratio was 1.13 ± 0.13 . The ventilatory threshold could not be determined in three patients -LRB- 16 % -RRB- because of excessive ventilatory oscillations . In the remaining 16 patients the independent observers agreed on the determination of the ventilatory threshold -LRB- mean : 16.4 ± 3.2 ml min − 1 kg − 1 , 60 ± 11 % of predicted peak - -RRB- . Constant-load exercise tests The mean value of during the second minute of unloaded pedaling was 655 ± 78 ml min − 1 -LRB- 30 ± 5 % of predicted peak - -RRB- , and the steady-state value at 50 % of the maximal work load was 1,185 ± 228 ml min − 1 -LRB- 53 ± 7 % of predicted peak - -RRB- . Figure 1 shows changes in during a constant-load test in a representative subject . Fig. 1-response to steady-state exercise at 50 % of the maximal workload -LRB- 50 W -RRB- in a representative subject . The solid line represents 10 s averages of The curved dashed line is the computer-derived representation of the best fit of the mono-exponential model to the - response . The first dashed vertical line indicates onset of exercise and the second vertical line the end of exercise In 16 subjects , in whom the ventilatory threshold could be determined reliably , steady-state - was below the ventilatory threshold . None of the other three subjects demonstrated a significant rise of defined as in increase from the third to the sixth minute of exercise of more than two times the SD of the mean in the fourth minute . This indicates that these three patients also exercised below the ventilatory threshold -LRB- Whipp 1994 -RRB- . Comparison of calculation methods Concerning the onset phase , there were no significant differences between mono-exponential modeling and the algebraic method -LRB- differences between τ and MRT − 6.6 -- 1 s , SD 11 -- 17 s -RRB- . In the recovery phase , the use of different sampling intervals yielded significantly different time constants -LRB- P < 0.001 -RRB- . Paired comparisons showed that in two cases the time constants were not significantly different -LRB- 5 s vs. 5 breaths , P = 0.14 and 10 s vs. 8 breaths , P = 0.31 -RRB- . When comparing the applicability of the mono-exponential model to the - response during onset and recovery of exercise , the results indicate a better ` goodness of fit ' during recovery -LRB- P < 0.001 -RRB- -LRB- Table 2 -RRB- . Parameters of O2 onset kinetics showed significant correlations with peak - when calculating τ with sampling intervals of 10 s , 5 breaths and 8 breaths -LRB- Table 3 -RRB- . However , mainly due to the limited number of patients , the differences between these correlation coefficients were not statistically significant . During recovery of exercise τ was only correlated with peak - when 5 breath sampling intervals were used . Table 2O2 Kinetics using an algebraic method and a mono-exponential model with different sampling intervals -LRB- n = 19 -RRB- Kinetic parametersMean ± SD -LRB- s -RRB- Goodness of fit -LRB- R2 -RRB- Mean ± SDUseful measurements -LRB- % -RRB- aO2 Onset kinetics MRT71 ± 19 -- -- τ-5 s67 ± 200.69 ± 0.1526 τ-10 s74 ± 22 0.81 ± 0.1242 τ-5b75 ± 300.87 ± 0.0879 τ-8b74 ± 220.91 ± 0.0784 O2 Recovery kinetics τ-5 s60 ± 130.89 ± 0.0684 τ-10 s66 ± 140.94 ± 0.04100 τ-5b61 ± 130.96 ± 0.03100 τ-8b64 ± 130.98 ± 0.02100 Average duration of 5 breath sampling interval 15.9 ± 3.6 s at baseline , 13.0 ± 3.0 s at steady-state . Average duration of 8 breath sampling interval 25.0 ± 5.2 s at baseline , 20.9 ± 4.7 s at steady stateMRT Mean response time , τ time constant calculated with a mono-exponential model , 5s 5 s , 10 s 10 s , 5b 5 breaths , 8b 8 breathsa The assessment of τ was considered useful when R2 ≥ 0.85 Table 3Correlation coefficients between averaged kinetic parameters and peak Kinetic parametersCorrelation with peak 95 % Confidence intervalO2 Onset kineticsMRT − 0.33 − 0.680.15 τ − 5 s -- τ-10 s − 0.88 * − 0.99 − 0.24τ-5b − 0.67 * − 0.90 − 0.16τ-8b − 0.57 * − 0.83 − 0.10 O2 Recovery kineticsτ-5 s − 0.43 − 0.760.08τ-10 s − 0.45 − 0.750.01τ-5b − 0.47 * − 0.76 − 0.02τ-8b − 0.39 − 0.720.08 Only data with R2 > 0.85 when using a mono-exponential model were included ; differences between correlation coefficients were not statistically significant * P < 0.05 MRT Mean response time , τ = time constant calculated with a mono-exponential model , 5s 5 s , 10s 10 s , 5b 5 breaths , 8b 8 breaths Reproducibility In the two constant-load tests there were no statistically significant differences between during unloaded pedaling -LRB- 655 ± 78 vs. 633 ± 98 ml min − 1 -RRB- , at steady-state exercise -LRB- 1,185 ± 228 vs. 1,202 ± 235 ml min − 1 -RRB- , and respiratory exchange ratios at steady-state exercise -LRB- 0.94 ± 0.04 vs. 0.94 ± 0.06 -RRB- . In addition , there were no statistically significant differences between the kinetic parameters of both tests . Considering limits of agreement , coefficients of variation and intra-class correlation coefficients , recovery kinetics show better reproducibility than onset kinetics -LRB- Table 4 -RRB- . Table 4Comparison of kinetic parameters in two consecutive constant-load testsKinetic parameterNDifference mean ± SDLimits of agreementCoefficient of variation -LRB- % -RRB- Intraclass correlation coefficientO2 Onset kinetics -LRB- ml min − 1 kg − 1 -RRB- 190.1 ± 1.2 − 2.32.58.50.97 MRT -LRB- s -RRB- 192.8 ± 12.9 − 232918.40.86 τ − 5 s -LRB- s -RRB- 0 -- -- -- -- τ-10 s -LRB- s -RRB- 69.3 ± 11.8 − 143317.10.79τ-5b -LRB- s -RRB- 125.9 ± 19.8 − 344628.40.63τ-8b -LRB- s -RRB- 165.6 ± 16.7 − 283923.00.77 O2 Recovery kineticsτ-5 s -LRB- s -RRB- 16 − 0.4 ± 7.2 − 151412.20.91τ-10 s -LRB- s -RRB- 19 − 0.9 ± 10.7 − 222116.30.86τ-5 b -LRB- s -RRB- 19 − 0.9 ± 6.4 − 141210.40.94τ-8 b -LRB- s -RRB- 19 − 1.4 ± 8.6 − 191613.40.91 Only data with R2 > 0.85 when using a mono-exponential model were included Oxygen uptake at steady-state exercise , MRT mean response time ; τ time constant calculated with a mono-exponential model , 5 s 5 s ; 10 s 10 s ; 5b 5 breaths , 8b 8 breaths Figure 2 shows Bland Altman plots of the kinetic parameters during onset and recovery of exercise with the highest intra-class correlation coefficients . Fig. 2Bland Altman plots showing the difference of O2 kinetics between two constant-load tests during onset -LRB- upper graph -RRB- and recovery of exercise -LRB- lower graph -RRB- , using an algebraic method and mono-exponential modeling with 5 breath sampling intervals , respectively . The solid lines represent the mean difference between the two tests , the dashed lines indicate the 95 % confidence intervals of the difference . MRT mean response time . τ - Rec = time constant of during recovery with 5-breath sampling intervals Discussion The principal finding of this study is that , using the applied exercise protocol , O2 recovery kinetics were more reproducible than O2 onset kinetics in moderately impaired patients with CHF . It should be recognized , however , that this observation can not be assumed to be generalizable to more severely impaired CHF patients . O2 onset kinetics Because of the lack of standardized protocols , previous authors used both time constants -LRB- τ , non-linear regression -RRB- -LRB- Belardinelli et al. 1998 ; Koike et al. 1995 ; Matsumoto et al. 1999 -RRB- and mean response times -LRB- MRT , algebraic method -RRB- -LRB- Sietsema et al. 1994 -RRB- to assess O2 onset kinetics in CHF patients . Considering non-linear regression , the use of 10 s sampling intervals yielded the best results in terms of reproducibility in this study . However , the goodness of fit , which was comparable with the study of Arena et al. -LRB- 2002 -RRB- -LRB- 0.81 vs. 0.78 , respectively -RRB- , was insufficient in 58 % of the patients . Moreover , the wide limits of agreement restrict its use for clinical applications . While the calculation of τ with larger sampling intervals resulted in a better goodness of fit , their reproducibility was even lower . To our knowledge , only one study previously addressed reproducibility of O2 onset kinetics using mono-exponential modeling in CHF patients . In that study five patients with a peak - that was comparable to our study performed three constant-load tests at a workload of 50 W , starting from unloaded cycling . Although the low mean difference between the tests -LRB- 2 s -RRB- suggests good reproducibility , the actual variability between the tests was not mentioned , making it impossible to compare these results to our study -LRB- Belardinelli et al. 1998 -RRB- . In spite of the fact that almost all of the onset procedures showed a significant correlation with peak - -LRB- Table 3 -RRB- , differences between these correlations were not statistically significant . Therefore , one could not suggest a preferential use of one of the procedures . Although showing a slightly higher intra-class correlation coefficient -LRB- Table 4 -RRB- , the wide limits of agreement of the MRT also indicate low reproducibility of this parameter . In addition , this parameter did not correlate significantly with peak Sietsema et al. -LRB- 1994 -RRB- previously addressed reproducibility of the MRT in 18 CHF patients , reporting small mean differences between two tests . Again these results can not be compared to our study , because intra-class correlations and limits of agreement were not mentioned . When comparing reproducibility of O2 onset kinetics in CHF patients with healthy subjects -LRB- Kilding et al. 2005 -RRB- and patients with COPD -LRB- Puente-Maestu et al. 2001 -RRB- we found a lower reproducibility of τ -LRB- coefficients of variation 17.1 , 6.2 and 8.7 % , respectively -RRB- . There are several physiological and methodological factors that may explain this discrepancy . One significant physiological factor that may cause the large variability of O2 onset kinetics in CHF patients is the influence of typical ventilatory oscillations , especially when small sampling intervals are used -LRB- Francis et al. 2002 -RRB- . Although we did not quantify the effect of oscillations , we did observe ventilatory oscillations more clearly in patients with low coefficients of determination . Furthermore , it is postulated that ventilatory oscillations in patients with CHF increase in the transition from rest to exercise -LRB- Kremser et al. 1987 -RRB- and diminish with increasing exercise -LRB- Francis et al. 2002 -RRB- . This suggests that these oscillations have a greater influence on O2 onset kinetics than O2 recovery kinetics . Based on our study , variations in τ during onset of at least 24 s are needed to exceed the limits of the 95 % confidence interval , whereas a change of only 13 s in τ during recovery is sufficient to exceed the normal test-to-test variations . This latter variability is also observed during the on-transient response in patients with COPD -LRB- Puente-Maestu et al. 2001 -RRB- , which supports our view that the relatively large variability of the on-kinetic parameters was mainly caused by ventilatory oscillations . Another explanation for the limited reproducibility of O2 onset kinetics in CHF patients is their low exercise capacity , which reduces the amplitude of O2 uptake , and consequently the reliability of the determination of the time constant -LRB- Lamarra et al. 1987 -RRB- . Furthermore , the period between the tests -LRB- 6.7 ± 3.9 days -RRB- could be a long enough period of time to induce changes in the cardio circulatory condition of the patients . However , all patients were in a stable clinical or functional condition during the study period . Considering methodological factors , the fact that all patients started the constant-load test with unloaded pedaling in stead of rest resulted in a relatively low - amplitude . This could be a major factor contributing to the relatively low reproducibility of the on-kinetic parameters found in this study . The reasons for the authors , however , to apply this exercise protocol were threefold . First , starting exercise from unloaded pedaling results in a reduction of the duration of the early rapid increase of oxygen uptake , representing an initial increase of pulmonary blood flow -LRB- cardiodynamic phase -RRB- . Since in this phase the change of is functionally distinct from the subsequent mono-exponential increase of -LRB- phase II -RRB- , reducing the relative contribution of this phase might result in a better fit of the mono-exponential model to the data . Second , starting exercise from unloaded cycling might reduce ventilatory oscillations at the onset of exercise because of a reduction in variation of pedaling frequency and upper limb muscle activity . Third , in daily life exercise will be frequently started from an active state . Given the results of this study , however , the authors feel that additional research is required to re-assess the reproducibility of O2 onset kinetics using an exercise protocol starting from rest in stead of unloaded cycling . Starting exercise from rest might result in a better reproducibility of O2 onset kinetics than observed in this study , which may in particular be of importance for more severely impaired CHF patients than used in this study . Another factor that may have influenced reproducibility of O2 onset kinetics is the choice of the workload -LRB- i.e. 50 % of the maximum workload -RRB- . The approach to maximize - amplitude by relating the workload to the ventilatory threshold -LRB- e.g. 90 % of ventilatory threshold -RRB- was not used in this study , because it was postulated that such a fixed threshold can not be determined reliably in a substantial number of CHF patients -LRB- Meyer et al. 1996 -RRB- . When looking at the patients of this study in whom determination of this threshold was successful , at steady state amounted to 86 % of at the ventilatory threshold , with none of the patients exercising above this threshold . This suggests that the applied constant-load exercise protocol was adequate , because the exercise intensity remained below the ventilatory threshold . O2 recovery kinetics O2 recovery kinetics were more reproducible in moderately impaired patients with CHF than O2 onset kinetics . This difference in reproducibility is at least partly explained by the larger - amplitude in recovery due to the fact that subjects were not cycling in the recovery phase . From the results of this study it is difficult to conclude to what extent this difference between onset and recovery kinetics is caused by a smaller influence of the cardiodynamic phase during recovery or a more stable breathing pattern during recovery causing less ventilatory oscillations . Nevertheless , these observations are in line with previous studies in healthy individuals , which also reported a better reproducibility of O2 recovery kinetics than O2 onset kinetics -LRB- Kilding et al. 2005 ; Ozyener et al. 2001 -RRB- . In terms of reproducibility , the most optimal method to characterize O2 recovery kinetics was mono-exponential modeling with sampling intervals of 5 breaths -LRB- intra-class correlation 0.94 -RRB- . Using this method a change of at least 13 s in τ is needed to exceed the normal test-to-test variations . In addition , this method yielded an excellent goodness of fit -LRB- R2 0.96 ± 0.03 -RRB- . To our knowledge , other data on the reproducibility of the recovery kinetics after submaximal exercise in CHF patients are not available . Cohen-Solal et al. -LRB- 1995 -RRB- studied reproducibility of O2 recovery kinetics after maximal exercise in 10 patients with CHF -LRB- NYHA II-III -RRB- using a mono-exponential model with sampling intervals of 7 breaths . They found a coefficient of variation comparable to our study with 5 breath sampling intervals -LRB- 12.3 vs. 10.4 % , respectively -RRB- . Conclusion This study shows that the reproducibility of O2 onset kinetics assessed by mono-exponential modeling is too low to warrant their use for measuring effects of therapeutic interventions in moderately impaired patients with CHF . This might be mainly attributable to physiological factors like ventilatory oscillations and the applied exercise protocol . Future studies should address the effect of different exercise protocols . The determination of time constants of O2 recovery kinetics has been shown to be feasible and reproducible when using mono-exponential modeling with 5 breath sampling intervals . Since this variable represents recovery from exercise that is indicative of daily life , it is potentially useful for clinical purposes like grading of functional impairment in patients with CHF and measuring effects of therapeutic interventions .

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

exercise testing
oxygen uptake kinetics
time constant
mean response time