The effect of birth-weight with genetic susceptibility on depressive symptoms in childhood and adolescence

Low birth-weight has been associated with depression and related outcomes in adults, and with problem behaviours in children. This study aimed to examine the association between low birth-weight for gestation and depressive symptoms in children and adolescents and to examine whether the relationship is moderated by genetic risk for depression. An epidemiological, genetically sensitive design was used including 2,046 twins aged 8–17 years (1,023 families). Data were obtained by parental report and analysed using regression analysis. A small but significant association between birth-weight for gestation and early depressive symptoms was observed. The unit increase in depressive symptoms per unit decrease in birth-weight for gestation was greater for individuals at genetic or familial risk for depression. For low birth-weight children, genetic risk for depression moderated the influence of birth-weight for gestation in predicting early depressive symptoms. Birth-weight for gestation is moderated by genetic and familial risk for depression in influencing early depression symptoms. These observations have clinical implications in that the impact of being small for gestational age on depressive symptoms is greater in children at familial/genetic risk although the association between birth weight and depression does not imply causality.
Introduction
Several cohort studies have shown associations between indexes of foetal nutrition such as pre-natal famine [6] and low birth-weight (LBW) with depression and related phenomena in adults [3, 7, 49]. However, the association between birth-weight for gestation and depressive symptoms in childhood has not been well examined. Instead, studies of the association between birth-weight and difficulties in childhood have focused on total behavioural problems or externalising problems [20, 52, 56]. Follow-up studies of children who were LBW at birth have reported that these children show a number of difficulties, in particular disruptive behavioural problems and reduced IQ in comparison to children who were born with normal weight at term [4, 20]. Although there have been relatively few studies that have examined the prevalence of depression in LBW children, there are now a growing number of reports illustrating that LBW children also show elevated rates of depressive symptoms [5, 17]. In addition, one recent nested case–control study of adolescent depression found increased rates of depressive disorder in adolescents who were either premature or LBW [32].
Preliminary evidence suggests that early adversity may modify the link between birth weight and depression in adults, with one study finding a stronger association in those adults exposed to more adverse socio-economic conditions during childhood [15]. One other factor that may potentially modify the relationship between birth weight and depressive symptoms is familial or genetic risk for depression. Genetic factors are known to be important in the aetiology of depression [29, 37]. To our knowledge, no study has examined the impact of genetic risk for depression on the association between birth-weight and depression in early life. We used a population-based twin register to first examine the association between birth-weight and depressive symptoms and then to test whether this effect was moderated by familial and genetic risk for depression.
Methods
Participants
A total of 1,581 families, a sub-sample from a systematically ascertained, population-based register of all twin births between 1980 and 1991, the Cardiff Study of All-Wales and North-west England Twins (CASTANET) were invited to participate. Twins on this register were identified through child health databases and the characteristics of the sample have been described in detail elsewhere [38, 47] but are representative of the local population in terms of social class and ethnicity. The percentages of families from social class groups I, II, III, IV and V were 11.4%, 29.3%, 30.1%, 4.9% and 1.4%, respectively, with the remainder classified as at-home or unemployed. These figures are in keeping with those expected from a population from Greater Manchester [36]. The present wave of data collection took place in 2000 including twins from the register aged less than 17 years (range 8–17) and in full-time education born in Greater Manchester. The mean age of participating twins was 11.17 (standard error=0.068). Completed questionnaires were received from 1,023 families, giving an overall response rate of 65% (1,023/1,581). Data on birth-weight, antenatal and perinatal factors and social class had been collected 3 years previously. There were 934/1,545 twin pairs who took part at both assessment points (60%). (Note: There were 934 pairs who took part in both waves of the study but missing computed scores for some variables meant that they were not included in regression analyses). There were no significant differences between those individuals who had complete data available and those who did not take part at Time 2 in terms of birth weight (t=0.545, SE=0.15, P=0.586) or those who did not take part at time 1 in terms of depressive symptoms (t=0.730, SE=0.06, P=0.465). In addition, information on emotional symptoms as assessed by parental report on the Rutter questionnaire (40) at Time 1 did not reveal any significant differences between those with complete data and those who did not participate at Time 2 (t=1.114, SE=0.06, P=0.465). Multi-centre ethical approval was obtained. The study was described to participants and informed consent was obtained following return of the completed questionnaire package. Complete data on birth weight, gestation and depressive symptoms were available for 370 monozygotic (MZ) and 495 dizygotic (DZ) pairs. Zygosity was assigned according to parent responses to the twin similarity questionnaire, which has been found to be over 90% accurate in distinguishing MZ and DZ twins. An algorithm based on previous work was used [8, 47].
Measures
Mothers of the twins reported information on antenatal and obstetric factors using a questionnaire adapted from Lewis & Murray [27], which included the twins’ birth-weights, number of weeks of gestation at birth, maternal age at birth and maternal smoking during pregnancy (yes/no). Mother reports of birth weight and gestation have been found to be very accurate in comparison to antenatal records with correlations of over 0.90 on average [16, 51]. Social class information was also collected and classified according to Standard Occupational Classification [31]. Depression in the children and mothers was assessed 3 years later in the year 2000. Child depressive symptoms were assessed using the Mood and Feelings Questionnaire (MFQ) [11]. The MFQ is a 34-item scale based on DSM-III-R symptoms of depression that is a reliable quantitative measure of depression and a useful screening questionnaire for clinical depression in children in the community [11, 48] as well as in clinical populations [57]. Parent reports of children’s depression symptoms were used as child reports are only reliably obtained for those over the age of 11 years. The mean depression score for the sample was 9.15 (range 0–60). Boys’ scores (mean=8.63; range 0–43) were lower than those of girls (mean=9.59; range=0–60) although this difference was not significant (t=−1.45, SE=0.05, P=0.15). Given that there was no significant gender difference in mean symptom counts for the sample, and no evidence for gender differences in the magnitude of the genetic influence on parent-rated depressive symptoms in a previous analysis of this sample [38], the primary analyses were conducted using the combined sample rather than separately for each gender. Parents also completed the depression sub-scale of the Hospital Anxiety and Depression Scale (HADS) [59] about their own depression symptoms in the past week. The mean depression score for parents was 4.46 (range 0–21). The majority of analyses were based on total symptom scores given that there are strong arguments for regarding depression as a continuous dimension of risk rather than as a categorical disorder (affected/unaffected) both in adults and in children. Firstly, studies of adults and children show that depressive symptoms behave as a continuum with no evidence of increased morbidity once clinical diagnostic criteria are met [23, 51]. For example, Pickles & colleagues [33] found that number of depressive symptoms but not functional impairment predicted future depressive episodes. Second, sub-clinical depressive symptoms are associated with impairment [2, 26]—one of the main criteria for fulfilment of disorder. Third, sub-clinical symptoms are associated with service use and significantly increase the risk for depressive disorder in adulthood [21, 34]. Moreover, it appears that a continuous measure of depression is more indicative of genetic risk in children and adolescents. Several studies have found that the aetiology of very high levels of depressive symptoms is less influenced by genetic factors than depressive symptoms across the normal range [13, 14, 38]. Thus, at least in children, symptoms of depression may give a better indicator of genetic risk than categorical diagnoses. Nonetheless, because of the suggestion of aetiological heterogeneity, depressive symptoms were analysed both as a continuous and a categorical measure in this report. Birth weight was examined both as a dimensional measure and as a category since both birth weight across the continuum and LBW have been reported to be associated with depression in adults [7, 15, 32].
Statistical analysis
Data were analysed using STATA [45] and SPSS [46]. We adopted two approaches for testing the effect of birth-weight for gestation and genetic/familial risk. First, genetic regression analysis [22, 56] was used to test for genetic effects and for interaction of genetic risk and birth-weight. We made use of the twin design that relies on the observation that identical (monozygotic; MZ) twins share all of their genes in common (coefficient of relationship=1), while on average, non-identical (dizygotic; DZ twins) share only half of their genes in common (coefficient of relationship=0.5). Genetic regression analysis is undertaken using standard forced entry linear regression analysis with one twin’s depression symptom scores being the dependent variable. The twin’s depression score was predicted using the formula T=B0+B1 C+B2 R+B3 I+B4 M+B5 G where T=the twin depression score, B0=the regression constant, C=the co-twin depression score, R=a coefficient of genetic relatedness (0.5 for DZ twins; 1 for MZ twins), I is the interaction term (C*R*birth-weight corrected for gestation), M=the main effect of birth-weight (with the length of gestation regressed out) and G=twin gender. One twin’s depression score was entered as the dependent variable, with co-twin depression, genetic relatedness and the interaction term as independent variables. Regression analyses were carried out using both continuous and categorical measures of birth-weight corrected for gestational age. Low birth-weight for gestation was classified as ≤10th centile since standard published cut points were not available for birth weights of twins.
Next, linear regression analysis was used to test for a main effect of birth-weight on depressive symptoms. The survey commands in STATA were used to take account of clustering within twin pairs by likening the twin data to a two-stage cluster design with the twin pairs as the primary sampling unit. A robust variance matrix calculation is utilised that relaxes the assumption of independence within groups. Birth-weight was corrected for number of weeks gestation at birth by regressing out the effect of length of gestation and using the unstandardized residuals for further analysis. As boys tended to be heavier at birth than girls, and the association between birth-weight and depressive symptoms may differ by gender. The effect of sex was controlled for by including sex as a predictor variable in the regression model. A number of factors have been reported to be associated with LBW [12]. Information on three potential influences was available—maternal smoking during pregnancy, social class and maternal age at birth. Social class was not associated with birth weight for gestation (b=−4.20, SE=3.32, P=0.206) or depressive symptoms (b=0.147, SE=0.107, P=0.169) in the present sample. Maternal pre-natal smoking (yes/no) was inversely associated birth weight for gestation (b=−141.62, SE=18.32, P=0.001) and positively associated with depressive symptoms in the offspring (b=2.16, SE=0.618, P=0.001). Maternal age at birth of child was negatively associated with child depressive symptoms (b=−.143, SE=0.053, P=0.007) and positively associated with twin birth weight (b=6.68, SE=1.65, P=0.001). These three variables were entered as predictor variables in regression models in addition to child gender.
The second approach to test the effects of birth-weight and genetic/familial risk was to use standard regression analysis but this time defining (1) genetic and (2) familial risk according to cut points on the depression questionnaires for (1) the co-twin and (2) the mother. Regression analysis was used to estimate the unit increase in depressive symptoms per unit decrease in birth weight for those individuals at genetic risk or family risk of depression and those not at risk. Both familial risk and genetic risk for depression was used for this analysis because to estimate the unit increase in symptoms by genetic risk meant restricting the analysis to MZ twins and thus a substantial reduction in sample size. Using familial risk allowed results to be replicated using the much larger full sample. Furthermore, using mother’s depression to index familial risk rather than the DZ twins allowed the entire sample to be double entered i.e. each twin to be entered as an individual in the analysis. This is because maternal depressive symptoms is a variable that is obligatory shared for each member of a twin pair. Appropriate correction for the non-independence of twins was undertaken using the survey commands in STATA as described above. For genetic risk, birth-weight and gestation were entered as independent variables to predict MZ twin depressive symptoms for MZ twins individuals without an affected co-twin (MZ co-twin depressive symptoms <90th centile) and those with an affected co-twin (MZ co-twin depressive symptoms ≥90th centile). This analysis was then repeated for the whole sample using parent depression symptoms as the cut point (parental depression score ≥90th centile) as a measure of familial risk. Dummy coded groups [9] were then analysed to assess whether there was an interaction between genetic or familial risk for depression and birth weight in predicting early depressive symptoms. These dummy variables allow the difference between the beta coefficients for the low-risk and high-risk groups to be tested.
Results
Descriptives
Parent-rated MZ mean depression symptoms were lower than those of DZ twins but this was not significant (MZ mean=8.97, standard deviation=9.49; DZ mean=9.14, standard deviation=8.92; t=0.980, df=967, P=0.327). There were no significant differences on the ante-natal and labour complications assessed according to zygosity (admission to hospital for high blood pressure χ2=0.328, P=0.609, vaginal bleeding χ2=0.088, P=0.800, labour less than 3 h χ2=1.035, P=0.345, emergency Caesarean, χ2=2.270, P=0.151, maternal smoking during pregnancy, χ2=0.192, P=0.697, forceps or ventouse delivery? χ2=1.398, P=0.237).
Descriptive statistics for the birth-weight and weeks gestation of twins are shown in Table 1. It can be seen that second-born twins were lighter at birth than first-born twins. MZ twins were lighter than DZ twins although they also tended to be born earlier in pregnancy. The average birth-weight for the entire sample was 2469.81 g (standard deviation=553.38) and the average length of gestation was 36.36 weeks (standard deviation=2.87).
Table 1Birth-weight in grams and gestation in weeks by zygosity, gender and birth order for twins with complete data pointsNMeanStandard deviationFirst-born twin birth-weight8892484.55544.03Second-born twin birth-weight8892457.62562.17MZ first-born birth-weight3812404.32531.55MZ second-born birth-weight3812392.41545.44DZ first-born birth-weight5082544.65546.01DZ second-born birth-weight5082506.10570.11Girl first-born birth-weight4892445.43544.59Girl second-born birth-weight4892426.00560.75Boy first-born birth-weight4002532.18540.06Boy second-born birth-weight4002494.76562.45MZ duration of gestation (weeks)37136.443.00DZ duration of gestation (weeks)49735.992.75Girls weeks gestation48336.312.87Boys weeks gestation38536.182.87
Main effect of birth-weight for gestation—standard regression analysis
Linear regression showed that birth-weight for gestation significantly predicted parent-rated child depression symptoms when controlling for child gender (b=−0.001, SE=0.001, P=0.003, 95% CI −0.003, −0.0006) (n=1,740). This remained significant when child gender, maternal age at birth, pre-natal smoking and social class were also included in the regression model (b=−0.001, SE=0.001, P=0.015, 95% CI=−0.003, −0.0003). This illustrates that children who were small for gestation had higher depressive symptoms in childhood and adolescence.
Genetic regression analysis
The results of genetic regression analyses with depression as a continuous variable are shown in Table 2. It can be seen from Table 2 that birth-weight for gestation, genetic risk and maternal smoking during pregnancy showed main effects on depressive symptoms. When birth-weight for gestation was included as a continuous variable there was no birth-weight by genetic risk interaction effect although there was a slight trend (β=−0.048, P=0.096) (n=861). Data were also analysed using LBW as a categorical variable. Significant main effects of birth-weight for gestation, genetic risk, pre-natal smoking and a significant genetic risk by LBW interaction were found (β=0.394, P=0.001) (n=861). This relationship is illustrated graphically in Fig. 1 and shows a stronger relationship between genetic risk (C*R) and depressive symtpoms for those individuals who were small for gestational age at birth (≤10th centile). This suggests that it is LBW for gestational age that interacts with genetic risk for depression when depression is assessed as a continuous variable.
Table 2Genetic risk for depression and birth-weightBeta (birth-weight as a continuous variable)P valueBeta (low birth-weight for gestation as a categorical variable)P valueInteraction between birth-weight corrected for gestation & genetic risk−0.048a0.0960.394*0.001 Birth-weight for gestation−0.081*0.006−0.075*0.032Co-twin depressive symptoms0.541*0.001 0.212*0.002Twin gender0.044a0.1300.049a0.080Zygosity0.0270.349−0.096*0.010Maternal pre-natal smoking (yes/no)0.063*0.0330.067*0.021Social class0.0110.7050.0110.700Maternal age at birth−0.0240.411−0.0180.529aP<0.1*P<0.05Fig. 1Genetic risk and depressive symptoms by birth-weight for gestation
Analyses of depression as a categorical variable—standard regression analysis
When twins were classified at genetic risk of depression if they had an affected MZ co-twin, the coefficients derived showed that for those twins at genetic risk for depression (N=32), for each 1 kg decrease in birth weight corrected for gestation, gender, social class, maternal age at birth and pre-natal smoking there was an increase of 2 depressive symptoms. As birth weight was measured in grams, this was calculated by the regression coefficent multiplied by 1000 (−0.002×1,000=−2). For those identical twins (N=338) not at genetic risk, the increase in depressive symptoms for each 1 kg decrease in birth weight was 1 symptom (−0.001×1,000=−1). Thus, there was a two fold increase in the number of depressive symptoms for each 1 kg decrease in birth-weight for twins at genetic risk (2 vs. 1 symptom) when controlling for covariates. Similarly, for each decrease in birth-weight of 1 kg there was an increase of 7 depressive symptoms for those at familial risk of depression (N=196) (−0.007×1,000=−7) and an increase of 1 depressive symptoms for those not at familial risk of depression (N=1478) (−0.001×1,000=−1) representing a seven-fold increase in number of symptoms.
Regression with dummy coded variables showed that the effect of birth-weight on depressive symptoms was not significantly different for those at high genetic risk of depression compared to those not at genetic risk of depression (for interaction term, β=0.009, P=0.853). It should be noted that regression with dummy coded variables for genetic risk of depression required restriction to MZ twins only and thus a substantial reduction in sample size. There was, however, a significant interaction according to familial risk (β=−0.086, t=−2.381, P=0.017).
Discussion
Depressive symptoms in young people show strong similarities with depression in adulthood [18, 19] and are associated with a number of deleterious health outcomes including increased risk for depressive disorder in adulthood; impaired educational and social functioning; increased service use and deliberate self-harm [2, 18, 21, 26, 34]. We find that genetic risk interacts with LBW for gestation in influencing depression. Standard regression analysis treating birth-weight for gestation as a continuous variable also showed that familial risk but not genetic risk interacts with birth-weight. These results suggest that being at genetic or familial risk for depression increases the influence that being small for gestational age has on depression. This result suggests that there may be public health implications for the future mental health of infants who are small for gestational age if there is also a family history of depression.
Results from genetic and standard regression analysis showed broadly similar results. However, results from genetic regression analysis suggested that LBW for gestation has the strongest effect on depressive symptoms in the context of genetic risk, rather than birth-weight for gestation across the continuum. It is possible that defining genetic risk according to cut points on the depression questionnaire may not be as robust an approach as examining depression as a continuum. Many studies of childhood depressive symptoms have reported estimates of genetic aetiology substantially lower for high depression scores than for scores within the normal range [13, 14, 38]. The results of studies reviewed earlier which suggest that depression may be viewed as a continuum [2, 21, 22, 26, 33, 34, 51] suggest that in the present study, results from analyses where depression is included as a continuous variable may therefore be more robust.
It is unclear whether birth-weight is a ‘causal’ risk factor for depression in children and adolescents, although some studies suggest that LBW is an important environmental risk factor for behavioural and cognitive problems [25, 52, 56]. However, the purpose of this paper was not to assess causality, but rather, to examine the association between birth-weight and depression and whether it was modified by genetic and familial risk for depression. Birth-weight has several properties that make it a useful risk factor for examining potential gene-environment interaction effects [25, 42]. First, birth-weight is primarily influenced by environmental rather than genetic factors. For example, in this sample, examination of the MZ and DZ correlations for birth-weight showed that birth-weight was influenced primarily by shared environmental rather than genetic factors as the MZ and DZ correlation coefficients are similar (rMZ=0.781; rDZ=0.697) (full model fitting results available from first author). Moreover, there was little evidence that there were common genes influencing both depressive symptoms and LBW as the cross-twin cross-trait correlations in this sample were near zero for both MZ and DZ twins. This is not true of other risk variables, indeed, there is consistent evidence that many important environmental risk factors (e.g. smoking in pregnancy) are not independent of genetic predisposition [42]. As many risk factors or indicators are influenced by genetic factors this can cause difficulty in detecting and interpreting gene by environment interactions [41, 44]. Second, LBW for gestation does not appear to be associated with maternal depression during pregnancy in developed countries [1, 35, 43, 58]. Indeed, there was no association between current maternal depressive symptoms and birth-weight corrected for gestation in this sample (rtwin 1 birth-weight=0.021, P=0.540; rtwin 2 birth-weight=0.001, P=0.975). This observation reduces the possibility that the influence of birth-weight on depressive symptoms observed in the present study is due to an association with maternal depression although it does not rule out the possibility. Birth weight is influenced by a number of psychosocial and maternal health characteristics including social class, pre-natal smoking, maternal height, weight, age and parity. There were three such factors that have previously been found to influence birth weight, for which data were available in this study (social class, pre-natal smoking, maternal age at birth of child). These were included as predictor variables in regression models in addition to child gender, thus effects for birth weight adjusted for gestation in the present study are independent of these variables although we were not able to control for all variables that have previously been associated with birth weight (e.g. maternal weight at birth).
In the present study, depression was assessed according to current symptoms. It is likely that this will underestimate the genetic risk for depression as life time ever rates of depression are significantly higher than point prevalence estimates [10, 24, 39]. We also relied on symptoms rather than diagnoses of depression. However, there is compelling evidence that depression appears as a continuum. Moreover, in particular for tests of interaction, statistical power is low [9, 42] and thus power is substantially increased with the inclusion of continuous rather than categorical measures. The reliance upon parental reports of birth-weight and antenatal risk factors in the present study is not ideal—information from antenatal records would have been preferable. However, the retrospective recollection of birth-weight of children by parents has been found to be accurate in comparison to medical records with correlations of over 0.90 between the two data sources reported [16, 54]. In addition, retrospective recall of pregnancy related events has been shown to be reliable [50]. The reliance on parental reports of the twins’ emotional state may have influenced results. Maternal reports of child psychopathology have been reported to be influenced by the mother’s own mental state. For the most part, it has been found that this results in inflated levels of emotional problems in children being reported by mothers who have emotional difficulties themselves. This is consistent with evidence that depression runs in families, but also raises the possibility that familial risk for children may be partly attributable to rater effects. However, there is some evidence that depressed mothers can be more sensitive to depression in their children [55]. In addition, maternal reports of anxiety and depression are predictive of outcome in young adulthood, which lends some predictive validity to maternal reports of emotional problems in children [53]. Assortative mating has been reported for depression, though reported spousal correlations for depression have been relatively small [28]. Information on the father’s mental state was not available in this study. Assortative mating for depression would result in an inflated DZ twin correlation and thus reduced evidence for genetic influences [30] and we have previously found a genetic influence for depressive symptoms in this sample [38]. One final potential criticism is that birth-weight in twins may not mean the same as in singletons. However, the twin design was necessary to allow for the effects of genetic influences and until susceptibility genes for depression are identified, molecular genetic indicators of risk for depression are not feasible.
In summary, the present results suggest that LBW for gestation and genetic/familial factors co-act and interact in influencing early parent-rated depressive symptoms. These observations suggest that it may be useful to monitor children in cases where there is a family history of depression and the child was small for gestational age.

gene
depression
child
twin
birth weight
interaction


Tight blood glucose control: a recommendation applicable to any critically ill patient?

The issue of tight glucose control with intensive insulin therapy in critically ill patients remains controversial. Although compelling evidence supports this strategy in postoperative patients who have undergone cardiac surgery, the use of tight glucose control has been challenged in other situations, including in medical critically ill patients and in those who have undergone non-cardiac surgery. Similarly, the mechanisms that underlie the effects of high-dose insulin are not fully elucidated. These arguments emphasize the need to study the effects of tight glucose control in a large heterogeneous cohort of intensive care unit patients.
Until the end of the past millenium, relatively little attention was given to control of blood sugar levels. In critically ill patients, hyperglycaemia was considered to be physiological because it results from the metabolic and hormonal changes that accompany the stress response to injury. In most intensive care units (ICUs), blood sugar was checked every 4–6 hours and hyperglycaemia (defined as blood sugar levels >10–12 mmol/l [180–216 mg/dl]) was corrected by subcutaneous or intravenous insulin. The presence of pre-existing diabetes mellitus or post-neurosurgical status often prompted more intense control of hyperglycaemia. Furthermore, the issue of glucose control was discussed in few sessions or satellite symposia during intensive care meetings.
The deleterious effects of hyperglycaemia during critical illness have been characterized over the past few years, and include an increased susceptibility to infections and thromboses, macrovascular and microvascular changes, and delayed wound healing, among other effects (for review [1]). Renewed interest in control of hyperglycaemia in critically ill patients (Fig. 1) followed the publication of a study conducted by Van den Berghe and coworkers in 2001 [2]. Those investigators reported a 43% decrease in relative intensive care mortality as well as consistent decreases in several surrogate markers of disease severity in patients randomly assigned to tight glucose control by intensive intravenous insulin therapy. A post hoc multivariate logistic regression analysis of these data suggested that control of hyperglycaemia played a more important role than did the amount of insulin administered [3]. Interestingly enough, at least two recent retrospective, large-scale studies [4,5] confirmed that outcome was improved in patients whose average blood glucose was maintained below 8 mmol/l (144 mg/dl; Table 1).
Although the findings reported by Van den Berghe and coworkers are impressive, some concern arose regarding the applicability of these results to other types of patients. Of the patients studied, 63% were admitted for follow up after cardiac surgery; this high proportion was felt to be consistent with a particular benefit from tight glucose control with intensive insulin in these patients, but there is uncertainty regarding whether tight glucose control is beneficial in patients who have not undergone cardiac surgery. Fear of life-threatening hypoglycaemia and increased workload and costs probably underlie the reluctance of many intensivists to launch systematic protocols of tight glucose control. Indeed, many intensivists still use a high glucose threshold (10 mmol/l [180 mg/dl]) [6]. In a European survey (unpublished data) we found considerable variation in the glycaemic thresholds employed in ICUs, which ranged from 6 to 11.1 mmol/l (108–200 mg/dl).
Some arguments against generalized use of tight glucose control are reported in the present issue of Critical Care by Vriesendorp and coworkers [7]. In a retrospective study performed at one centre in Amsterdam, those authors found that, after oesophageal surgery in patients without significant cardiovascular compromise (ASA class I–II), postoperative hyperglycaemia was not a risk factor for infectious complications. Only by univariate analysis were they able to find an improvement in patients with blood glucose levels below 9.3 mmol/l (167 mg/dl) in terms of length of ICU stay. These findings differ strikingly from those of other studies [2,4,5]. Although the report by Vriesendorp and coworkers challenges the concept of tight glucose control, it can hardly be considered a major piece of evidence against it. Indeed, blood glucose concentrations were presented as means of values recorded only over 48 hours, whereas the ICU stay extended up to 71 days, with a median of 3 days. Insulin was administered to only 9% of the patients during the 48-hour period of observation. In addition, patients received a mean of only 22.5 g glucose/day, and were fed early after surgery with an enteral solution of 'immunonutrients' – a potential confounding factor with respect to infectious morbidity. However, despite these limitations, as well as others that are acknowledged by the authors, the findings of the study support the hypothesis that tight glucose control could be of greater benefit to patients with cardiovascular disease than to those without.
In conclusion, as recently suggested by Van den Berghe [8], further studies are needed to confirm the benefits of tight blood glucose control with intensive insulin therapy in a heterogeneous population of ICU patients. Hence, a large randomized prospective multicentre trial is warranted. Such study will also help in determining the physiological importance of the effects of insulin and, more importantly, will provide intensive care workers with key information for guiding the management of blood glucose in critically ill patients.
Abbreviation
ICU = intensive care unit.
Competing interests
The author(s) declare that they have no competing interests.

critically ill
insulin
cardiac surgery
metabolism
hyperglycemia


Crosstalk between the Protein Surface and Hydrophobic Core in a Core-swapped Fibronectin Type III Domain

Two homologous fibronectin type III (fnIII) domains, FNfn10 (the 10th fnIII domain of human fibronectin) and TNfn3 (the third fnIII domain of human tenascin), have essentially the same backbone structure, although they share only ∼ 24% sequence identity. While they share a similar folding mechanism with a common core of key residues in the folding transition state, they differ in many other physical properties. We use a chimeric protein, FNoTNc, to investigate the molecular basis for these differences. FNoTNc is a core-swapped protein, containing the “outside” (surface and loops) of FNfn10 and the hydrophobic core of TNfn3. Remarkably, FNoTNc retains the structure of the parent proteins despite the extent of redesign, allowing us to gain insight into which components of each parent protein are responsible for different aspects of its behaviour. Naively, one would expect properties that appear to depend principally on the core to be similar to TNfn3, for example, the response to mutations, folding kinetics and side-chain dynamics, while properties apparently determined by differences in the surface and loops, such as backbone dynamics, would be more like FNfn10. While this is broadly true, it is clear that there are also unexpected crosstalk effects between the core and the surface. For example, the anomalous response of FNfn10 to mutation is not solely a property of the core as we had previously suggested.
Introduction
Studies of the folding of structurally related proteins have been a
powerful tool for investigating conservation of folding pathways,1 identifying structurally important residues,2 and examining the role of highly conserved
residues.3 One of the most common folds in the SCOP database4 is the immunoglobulin (Ig)-like fold. Over 40,000 Ig-like
domains have been identified in the current PFam database5 and the fold is found in a number of different superfamilies,
where there is no apparent sequence identity between superfamilies.4 In this study, rather than seeking to understand what related
proteins have in common, we ask a different question—can differences between
closely related proteins be explained?
Many members of the Ig-like fold have been well characterised, leading to:
identification of the key residues essential for formation of the Greek key
structure,6,7 the observation of a correlation between stability and folding
rate,8–11 the identification of a common folding pathway,12–16 and the identification of the role of conserved proline
residues3 and the conserved tyrosine corner motif.2 However, when two Ig-like domains from the fibronectin type III
(fnIII) superfamily, the 10th fnIII domain of human fibronectin (FNfn10) and the
third fnIII domain of human tenascin (TNfn3), were studied in detail and
compared, they were found to differ markedly in several respects. FNfn10 and
TNfn3 are essentially structurally identical (backbone RMSD is 1.2 Å), but have
low sequence identity (24%). They differ in their stabilities17,18 and response to mutation19; they differ in their folding kinetics17,18,20 (although they share a common folding mechanism12,16); they display different backbone and side-chain
dynamics21–23`; and, finally, they differ in their response to mechanical
force.24–26 Why do proteins that are structurally almost identical behave
so differently? Which components of a protein are responsible for the various
aspects of its behaviour? How independent are the properties of the surface and
hydrophobic core? We have addressed these questions by making a chimera of these
two fnIII domains.
The chimera, FNoTNc, was created with the “outside” (surface and loops) of
FNfn10 and the core of TNfn3.26 Fifteen mutations were made in the core of FNfn10 so that all
buried residues (with < 10% solvent-accessible surface
area) are identical with those residues in the core of TNfn3 (total number of
core residues = 27) (Fig. 1). Thus, we
can test how transferable the properties of the core and surface of the
respective parents are when combined in this way. FNoTNc is a stable, folded
protein that is structurally almost identical with the parent proteins
(Supplementary Fig. 1). FNoTNc has
also retained the cell-adhesion activity of the FNfn10 parent protein mediated
by specific integrin binding.26 We have previously used this chimera to demonstrate that
resistance to mechanical force is a core property: FNoTNc has mechanical
unfolding properties indistinguishable from TNfn3 and very different from
FNfn10.26 Here we investigate the stability, folding and dynamics of the
new, chimeric protein.
Most protein engineering analyses concentrate on the hydrophobic core of
proteins, since it was shown in 1959 that the hydrophobic interaction is the
major factor involved in protein folding27 and surface mutations rarely affect protein stability by more
than ∼ 1 kcal mol− 1. Our
results suggest, however, that the surface and loops can play a key (and
sometimes unexpected) role in determining the biophysical properties of a
protein.
Results
Thermodynamic stability
Wild-type FNoTNc
FNoTNc has a free energy of unfolding
(ΔGD–N) of 7.5 kcal
mol− 1, intermediate between
the stabilities of FNfn10 and TNfn3 (9.4 and 6.7 kcal
mol− 1, respectively) at pH
5.0. We infer that the surface interactions and loop and turn regions of
FNfn10 make a significant contribution to the overall stability of
FNoTNc. As was shown for FNfn10, the
ΔGD–N of FNoTNc is
independent of pH between pH 5.0 and pH 7.0, whereas TNfn3 is less
stable at pH 7 (5.7 kcal mol− 1). The
dependence of the stability of TNfn3 on pH has been shown to be the
result of the presence of patches of acidic residues on the surface of
TNfn3, which are, of course, not present in FNoTNc.17
Anomalous response of certain peripheral mutations
A number of core residues were mutated in FNoTNc to investigate the
response of the protein to mutation. These were positions that had
previously been investigated in the parent proteins FNfn10 and TNfn3.
The thermodynamic stability of the mutant proteins was determined by
chemical denaturation in guanidinium chloride (GdmCl). The mutations can
clearly be divided into two categories. A few peripheral mutations
(mutations in the A and G strands and the B–C loop) have little effect
on stability as was previously observed in FNfn10 (Fig. 2a),
whereas most other mutations were more typically destabilising. In the
latter case, the ΔΔGD–N was
similar to (but in general slightly lower than) what had been observed
previously in TNfn3 (Fig. 2b).
The residues in each category are mapped onto a backbone ribbon
representation of FNoTNc in Fig.
2c. ΔΔGD–N values
for all mutations are compared to those of FNfn10 and TNfn3 in
Supplementary Table
1.
Equilibrium hydrogen exchange
The rates of hydrogen–deuterium exchange were measured at pD 7.0.
Measured rate constants for exchange,
kex, ranged between 9.3 × 10− 2 and 1.6 × 10− 4 min− 1. Values of
kex and the apparent free
energies of exchange,
ΔGexapp,
are listed in Supplementary Table
2. The
ΔGexapp of
FNoTNc, FNfn10 and TNfn3 are compared in Fig.
3. The hydrogen
exchange behaviour of FNoTNc clearly resembles that of FNfn10: both have
many residues that exchange in the experimental dead time, in particular
in the edge strands (A, C′, E and G strands) that are more protected in
TNfn3. Note that this does not result from differences in the intrinsic
stabilities of the domains—TNfn3 is significantly less stable than
either FNoTNc or FNfn10 under these experimental conditions.
Folding kinetics
Wild-type FNoTNc
The rate constants of folding and unfolding were determined using
stopped-flow measurements monitored by changes in fluorescence
> 320 nm. The logarithm of the folding and
unfolding rate constants was plotted against concentration of denaturant
(Fig. 4). Both unfolding and refolding arms show a linear
dependence on denaturant concentration. There is an additional refolding
phase, which we attribute to proline isomerisation and do not consider
further. (Both FNfn10 and TNfn3 have proline-isomerisation limited
phases, and FNoTNc has eight Pro residues.) To compare the kinetics of
FNoTNc with those of FNfn10 and TNfn3 (which have been studied using
different denaturants due to large differences in stability), the
logarithm of the observed rate constants is plotted against stability in
Fig. 4. The free energy of
unfolding calculated from the ratio of the folding and unfolding rate
constants extrapolated to 0 M denaturant (7.7  kcal mol− 1) and the kinetic m
value (2.1 kcal mol− 1
M− 1) are the same as the
equilibrium ΔGD–N (7.5 kcal
mol− 1) and
m value (2.1 kcal mol− 1 M− 1) within
error—consistent with the folding being a 2-state process, with no
stable intermediates being populated. The relative compactness of the
transition state can be determined from the folding and unfolding
m values. FNoTNc has a βT
value of 0.6, similar to those of TNfn3 and FNfn10.
Φ-value analysis
The folding kinetics of 18 variants of FNoTNc with nondisruptive
deletion mutations were measured as for the wild type. Few of these
mutants were in the A, B and G strands due to very small changes in
stability with mutation in general. In a few of the mutants, the
unfolding arm of the chevron plots shows negative curvature at high
denaturant concentrations, which we attribute to the Hammond effect: the
simplest model that fits the data.28–30 All chevrons were fitted therefore to a two-state
equation with a quadratic term in the unfolding arm. (All the chevron
plots are shown in Supplementary Fig.
2.) Note that the model chosen to fit the kinetic data
does not affect our results because equilibrium values of
ΔΔGD–N were used to
determine Φ. In Table
1, the Φ values for
each mutant are compared to those in TNfn3 and FNfn10. The Φ values are
generally low, closely resembling those of TNfn3.
Dynamics
Backbone dynamics
15N
T1 and
T2 relaxation time constants
and 1H15N nuclear
Overhauser enhancement parameters were measured using 1H–15N correlation spectroscopy
(Supplementary Table 3).
The generalised order parameter,
S2, and a conformational
exchange broadening parameter,
Rex, were determined for each
backbone amide (Fig.
5 and Supplementary Table 4). The
S2 values are similar to
those of both the parents. However, the high values of
Rex that were seen in the
A/B region of FNfn10, have decreased in FNoTNc.
Side-chain dynamics
The relaxation of the operator terms IzCzDz, IzCzDy and IzCz was measured to give deuterium relaxation time constants
T1(D) and
T2(D)31 (Supplementary Table
5). Order parameters,
S2, were determined for each
methyl group using the standard model-free formalism as previously
described22 (Supplementary Table
6). The dynamics data for those methyl groups that had
overlapping peaks were treated with caution, as the contribution from
the overlapped peaks cannot be separated. Nevertheless, we have some
confidence in these results due to the agreement of the order parameters
for residues that have both overlapped and well-resolved methyl groups.
S2 ranges from zero to
unity, with higher values of S2
indicating greater conformational restriction. The methyl
S2 values are shown
projected onto the protein structure and compared to the parent proteins
in Fig. 6. Within the core of FNoTNc is a cluster of deeply
buried residues, which have unusually low order parameters, as has
previously been observed in TNfn3.
Discussion
The core of FNoTNc is similar to that of TNfn3, but apparently less closely
packed.
Evidence from mutations
FNoTNc is close in structure to both the parent domains (backbone RMSD,
excluding the mobile C–C′ and F–G loops, is 0.95 and 0.89 Å compared to
FNfn10 and TNfn3, respectively). From the crystal structure we would deduce
that the core of FNoTNc is essentially the same in terms of core packing and
number of core contacts as TNfn3, the parent protein that donated the core
residues (Fig. 7a). However, the calculated free volume in the interior of
FNoTNc is larger than for TNfn3 (128.4 Å3
versus 118.1 Å3, respectively). When
the response of FNoTNc to mutation is compared to TNfn3, it is clear that
the ΔΔGD–N values of FNoTNc are
slightly lower, on average ∼ 80% of those in TNfn3
(Fig. 7b). Since the loss of
free energy on mutation is strongly correlated with the number of side-chain
contacts deleted,32,33 we infer that the less tight packing of the core of FNoTNc
accounts for the difference in
ΔΔGD–N compared to TNfn3.
Evidence from side-chain dynamics
The same conclusion can be drawn from the analysis of the side-chain
dynamics. The core of TNfn3 has been shown to be exceptionally mobile, with
several of the most buried residues falling several standard deviations
below the expected order parameter for their residue type.22,23 FNfn10 has a core that is much more conformationally
restricted, with order parameters within the usual range for buried
residues. Our previous analysis of the side-chain dynamics of FNfn10 and
TNfn3 led us to suggest that the differences in core dynamics between FNfn10
and TNfn3 could, at least in part, be ascribed to the slightly lower core
packing in TNfn3; the residues in TNfn3 with unusually low order parameters
are also found to have packing volumes that are larger than expected. The
buried side chains of FNoTNc were found to have, on average, even lower
order parameters than the same side chains in TNfn3 (Fig. 6d).
Behaviour of peripheral regions of the protein is modulated by
the surface and loops
Evidence from mutation
A number of sites in FNfn10 were identified where upon mutation the
ΔΔGD–N is significantly
lower than for the same (or an equivalent) mutation in
TNfn3.19 These sites are at the periphery of the protein and
include residues Pro5 and Leu8 (A strand), Pro25 (B–C loop) and residues
Ser85 and Phe92 (G strand). Unexpectedly, in FNoTNc we see the same
anomalous response to mutation for Pro5, Pro25 and Ser85, although Ile8A
and Phe92A now have similar
ΔΔGD–N values to TNfn3
(Fig. 2a). Pro5, Pro25 and
Ser85 are all found in the same region of the molecule (Fig. 2c). The anomalous response to
mutation in this peripheral region of the core of FNoTNc and FNfn10 is
intriguing: apparently the “plastic” response of the protein to mutation
of these three buried residues is modulated by the surface of the
protein and is not determined by the core alone. The additional
plasticity at this end of the hydrophobic core may be due to its
proximity to the longer FG loop in FNfn10 and FNoTNc, which restricts
motion less than the corresponding loop in TNfn3.
Evidence from hydrogen exchange
FNoTNc, FNfn10 and TNfn3 have very similar structures and hydrogen
bonding patterns. Further evidence for “plasticity” of FNfn10 compared
to TNfn3 came from the observation that the peripheral A, C′, E and G
strands were significantly less well protected against amide exchange in
FNfn10 than in TNfn3, despite the fact that FNfn10 is considerably more
stable than TNfn3.19 Under the experimental conditions, hydrogen exchange is
in the EX2 regime,34,35 meaning that exchange reflects local stability; that
is, these peripheral regions of FNfn10 have lower local stabilities than
TNfn3. Our experiments clearly show that the exchange behaviour of
FNoTNc is similar to FNfn10—with low protection of residues in the same
peripheral strands (Fig. 3).
Again surprisingly, local instability appears to be a function of the
surface of the protein and not a property of the core; however, the
plasticity inferred from response to mutation and local instability
still appear to be related, as was previously inferred. This is not,
however, related to slow exchange motions of the backbone, as we had
previously suggested, since the millisecond time scale motions observed
in the A/B region of FNfn10 are not found in FNoTNc.
The stability of FNoTNc is modulated by both the core and the
surface
In summary, despite the evidence for FNoTNc having a less well packed
core than TNfn3, with the mutation of buried side chains having less effect
on the overall stability (Fig.
7b), FNoTNc is significantly more stable than TNfn3,
suggesting again that the surface and loops of FNfn10 contribute
significantly to the stability of FNoTNc. This is not a new observation; it
has been previously demonstrated that surface interactions can play a
significant role in the stabilisation of proteins.36 Interestingly, however, the “local stability” of the
peripheral regions of FNoTNc is lower than the local stability of TNfn3, as
manifested in the hydrogen exchange experiments. The surface residues of
FNfn10 are modulating the stability of these peripheral regions.
A concurrent study by Siggers et
al.37 investigated the stability and dynamics of a series of
loop-swap mutants of FNfn10 and TNfn3: essentially these were mutants where
the C–C′ and F–G loops were exchanged. Interestingly, both TNfn3 domains had
lower thermal stabilities than the wild-type protein, whereas the thermal
stability of FNfn10 was unaffected by the exchange. We infer that in our
chimeric protein, interactions of the surface residues are most important in
the increase in stabilisation of FNoTNc beyond that of TNfn3, and not the
new C–C′ and F–G loops.
The TNfn3 core governs the folding kinetics
Wild-type kinetics
In order to compare the kinetics of the three proteins directly,
the logarithm of the rate constant has been plotted against stability
(Fig. 4). This unusual
scale is used as each protein was characterised using a different
denaturant. FNoTNc folds at a rate intermediate between FNfn10 and
TNfn3. In broad terms, the chevron of FNoTNc resembles that of TNfn3. It
has previously been pointed out that the folding rates of Ig-like
domains in water do not correlate with contact order (the average
sequence separation of native contacts).8,38 For the present three proteins, which have almost
identical contact order (absolute contact order 15–16%, relative contact
order 16–17%), the rate constants at the folding midpoint span 2.5
orders of magnitude. Thus, the variation in the folding rate cannot be
explained only by differences in contact order—protein stability plays a
key role.8,39
FNfn10 shows clear rollover in both the folding and unfolding arms.
The rollover in the folding arm has previously been ascribed to the
presence of a populated folding intermediate.18 Neither FNoTNc nor TNfn3 show any evidence for
population of a folding intermediate—the presence of a stable folding
intermediate in FNfn10 appears to be the result of core interactions.
Curvature in the unfolding arm of a chevron plot has been ascribed
to the presence of a high-energy intermediate, to Hammond effects, or to
population of an unfolding intermediate.30,40,41 This has not been investigated for any of these fnIII
domains; indeed, it is often not possible to distinguish between the
first two cases.42 Both FNfn10 and some mutants of FNoTNc display curvature
in the unfolding arm that is not seen in wild-type TNfn3. It should be
noted, however, that unfolding curvature has been seen in a less stable
form of TNfn3 (missing the final two C-terminal residues) and may simply
not be observed in this case because the unfolding arm in the stable
form of TNfn3 used here is relatively short. In summary, FNoTNc has
similar folding characteristics to TNfn3. This suggests that the core of
these proteins plays the major role in determining the folding
behaviour.
However, FNoTNc is more stable than TNfn3. When we compare the
folding and unfolding rate constants in water we find that FNoTNc folds
some 10 times faster than TNfn3
(kfH2O = 60 and 6 s− 1, respectively) but unfolds at approximately the same
rate as TNfn3
(kuH2O = 2 × 10− 4 and 5 × 10− 4 s− 1, respectively).
Thus, the stabilising surface interactions apparently stabilise the
transition state of FNoTNc as much as the native state, while still not
causing a folding intermediate to be populated. This, perhaps, allows us
to pinpoint further which surface interactions are responsible for the
added stability of FNoTNc (over TNfn3). At the transition state, the
loops and the peripheral A and G strands are largely unstructured, but
there is structure in the B, C, C′, E and F strands, particularly
towards the centre of the core (see Φ value section below). Interactions
between surface residues in these strands are therefore the most likely
candidates for providing additional stability to the FNoTNc protein
(both native and transition states), above those interactions between
residues that are packing in the core.
In contrast, FNoTNc folds some three times more slowly than FNfn10
and the unfolding rate constant of FNfn10 in water is approximately an
order of magnitude lower (∼ 2 × 10− 5
s− 1).
Φ-value analysis
The Φ-value analysis reveals the extent of formation of structure
in the transition state.43 The pattern of Φ values in FNfn10 and TNfn3 are similar,
suggesting that they have a common folding mechanism. There are
differences, however. The Φ values of TNfn3 tend to be much lower than
those of FNfn10: TNfn3 has five Φ values greater than 0.4,12 while FNfn10 has eight16; in FNfn10 the folding nucleus appears to be more
extensive than in TNfn3, with more than one residue in the central C, E
and F strands having high Φ values. FNoTNc has even lower average Φ
values than TNfn3 with only two Φ values greater than 0.4 (Table 1) Both FNoTNc and TNfn3 have less
extensive formation of structure in the transition state than FNfn10.
However, Φ-value analysis is at its most powerful when patterns of
Φ values are compared in different proteins rather than by direct
comparison of Φ values. Previous analysis of TNfn3 has identified a ring
of interacting residues in the B, C, E and F strands as the folding
nucleus, with residues in the C′ strand packing onto these.12 The Φ values of the peripheral A and G strands are all
close to 0, suggesting that they are unformed at the transition state. A
similar pattern of Φ values was seen in FNfn10, although this Φ-value
analysis was less complete.16 The Φ values for FNoTNc were divided into three
categories of low (Φ < 0.25),
medium (0.25 < Φ < 0.35) and high (Φ > 0.35) (Fig. 8)
and compared to the pattern of Φ values for the identical residues in
TNfn3. The pattern of Φ values is very similar to that of TNfn3,
although there are slight qualitative differences. Again, the highest Φ
values are found in the B, C, C′, E and Φ strands and the Φ values in
the A and G strands are ∼ 0. The folding mechanism of
FNoTNc is unperturbed and the same as both the parents, although from
the magnitude and extent of the residues with higher Φ values the
transition-state structure appears to be closer to that of TNfn3 than
FNfn10.
Dynamics from NMR is determined by local interactions
Side-chain dynamics
The side-chain dynamics appear to reflect core packing, as was
discussed above, and so FNoTNc resembles TNfn3 more closely than
FNfn10.
Backbone dynamics
The S2 values of FNoTNc are
generally very similar to those of both the parents (Fig. 5). It is interesting to compare
our study with that of the loop swap mutants of FNfn10 and TNfn3 from
Palmer and coworkers,37 where the F–G loop of FNfn10 was grafted into TNfn3. In
that case, the S2 values in the
loop region were more similar to those in FNfn10, than those in the
equivalent positions in TNfn3. A similar result was seen in the protein
where the C–C′ loop was grafted in. It is difficult to compare our
results directly to those of Siggers
et al.37: their method of analysis leads to greater differences
between the S2 values of FNfn10
and TNfn3, resulting in more visible effects on the
S2 values when the loops are
swapped than we see in FNoTNc compared to the parent proteins. However,
there is a general agreement that the behaviour of the loops is a local
property, rather than being a direct effect of either core or surface
interactions.
Conclusion
We have grafted the core of one fnIII domain (TNfn3) into the homologue
FNfn10, creating a chimera, FNoTNc, which has retained the structure of the
parent proteins. Using several different probes, we have shown that FNoTNc does
not behave like either one of the parent proteins alone. Instead, it has
retained a number of properties of each. We find that each property investigated
clearly resembles the behaviour of one of the parents, enabling us to separate
the contribution of the core and the surface of the protein in determining the
behaviour of the domain. Some of these are unsurprising, such as the pH
dependence of stability, the core side-chain dynamics and the dependence of
folding on the composition of the core. However, the surface of the protein
confers significant stability not only on the native state, but also on the
transition state for folding. Others properties are less predictable. The
surface of the protein confers “plasticity” in peripheral regions of the
proteins as detected by the anomalous response of some regions of the core to
mutation and hydrogen exchange protection patterns.
This suggests that the surface of a domain may have a more significant
coupling with the core than we had previously considered. Since most biophysical
studies tend to focus on the core of a protein, this coupling is a relatively
unexplored area of research.
Materials and Methods
Chemicals
GdmCl was purchased from MP Biomedicals Inc., guanidine isothiocyanate
from Gibco-BRL and urea from BDH Laboratory Supplies.
Protein expression and purification
Site-directed mutagenesis reactions were performed with the QuickChange
kit from Stratagene using the FNoTNc plasmid. The identity of the mutants
was confirmed by DNA sequencing. The mutants studied in this work are listed
in Table 1 and Supplementary Table 1. The nature of the
mutation is indicated with the wild-type residue first (single-letter code),
the position of the mutation second and the mutant residue third. Expression
and purification of FNoTNc and mutants was performed as described earlier
for TNfn3.20
Measurements of protein stability
All biophysical measurements were performed in 50 mM sodium acetate
buffer, pH 5.0 at 25 °C unless otherwise stated. The stability of FNoTNc and
FNoTNc mutants were determined by equilibrium denaturation experiments using
GdmCl and by standard methods using 1 μM protein.20
Kinetic measurements
Kinetics were measured using fluorescence stopped-flow measurements in
50 mM sodium acetate buffer, pH 5.0, at 25 °C and were monitored by changes
in fluorescence above 320 nm. Refolding measurements for FNoTNc were made in
0 M denaturant by stopped-flow fluorescence using pH jumps from pH 12.4 to
pH 5.0 as previously described.12 (FNoTNc is acid stable.) NaOH (25 mM, pH 12.4) unfolds
FNoTNc completely (data not shown).
Φ-value analysis
Φ values were determined from refolding data at 1.0 M GdmCl, to avoid
the errors associated with long extrapolation, from Eq. (1):43whereand
kfWT and
kfmut are the
rate constants for folding of wild-type and mutant proteins,
respectively.
NMR sample preparation
FNoTNc was expressed and purified by affinity chromatography as
previously described.17 Uniformly 15N labelled and
13C and 15N
labelled samples were expressed in M9 minimal media containing 15NH4Cl and [U-13C]6-glucose as the sole nitrogen and carbon sources. Samples for
side-chain dynamics were expressed as previously described.22
Chemical shift assignments
Backbone assignment experiments were carried out on a double-labelled
(13C, 15N) sample
of FNoTNc, at an approximate concentration of 1–2 mM, in 50 mM imidazole
buffer at pH 7.0 in 10% D2O. Sodium azide (0.05%) was added
to prevent microbial growth. The sample was centrifuged to remove insoluble
protein and degassed. Spectra were acquired at 298 K on a Bruker DRX500
spectrometer with an inverse triple-resonance cryogenic probe. Backbone
assignments were based on HNCACB, HNCO and CBCA(CO)NH experiments together
with the 1H–15N
heteronuclear single quantum coherence (HSQC) spectrum. Side-chain 1H and 13C resonance
assignments were obtained from 3-D HCCH–total correlated spectroscopy
(TOCSY) H(CCCO)NH and (H)CC(CO)NH preceding TOCSY spectra. The spectra were
processed and analysed using NMRpipe and Sparky.44,45 The 1H–15N HSQC has excellent resolution, although resonances from 12
residues in loop regions cannot be detected at either pH 5 or pH 7
(Supplementary Fig. 3 and
Supplementary Table 7).
All samples for side-chain methyl assignment were prepared in 50 mM
sodium acetate buffer at pH 5.0 in 10% D2O, at a
concentration of 1–2 mM. Sodium azide was added to prevent microbial growth.
The sample was centrifuged to remove insoluble protein and degassed. All
experiments were carried out as previously described.22,46,47
Chemical shift assignments of the side chains were made using standard
triple-resonance experiments. Many of the signals in the 1H–13C HSQC are overlapped, meaning
that 5 of the 64 methyl groups in the 1H–13C HSQC could not be assigned with confidence.
Assignment of the leucine and valine methyl groups was made
stereospecifically, based upon the phase of peaks in a 1H–13C HSQC acquired for a sample
with 10% 13C enrichment (Supplementary Fig. 4 and Supplementary Table 7).
Hydrogen exchange
Hydrogen exchange experiments were carried out under EX2 conditions on
a 15N-labelled sample of FNoTNc, at an
approximate concentration of 1–2 mM, in 50 mM imidazole buffer at pD 7.0 in
10% D2O. Sodium azide (0.05%) was added to prevent
microbial growth. The exchange of amide protons was followed by the decay of
intensity of peaks in HSQC spectra.48
The apparent free energy of exchange,
ΔGexapp, was
determined from the rate constant of exchange,
kex, and the intrinsic rate
constant, kint, determined from
peptide data to take account of the primary sequence of the protein and
exchange conditions49,50 using Eq. (2) and
intrinsic rate constants determined using the software Sphere†.51
Backbone 15N relaxation
measurements
Backbone dynamics were determined from 15N
T1 and
T2 relaxation times and the
steady-state heteronuclear 1H15N nuclear Overhauser enhancement at 500 MHz as previously
described.22 The data were analysed using standard protocols for backbone
dynamics with the program TENSOR2.
Side-chain methyl 2H relaxation
measurements
Side-chain deuterium relaxation times
T1(D) and
T1ρ(D) were determined by
measuring the relaxation of the two- and three-spin operator terms,
IzCz,
IzCzDz
and
IzCzDy
and analysed as previously described.22,31

fnfn10, 10th fniii domain of human fibronectin

tnfn3, third fniii domain of human tenascin

fnotnc, a core-swapped protein with the “outside” (surface and loops) of fnfn10 and the core of tnfn3

hsqc, heteronuclear single quantum coherence


3.0 T cardiovascular magnetic resonance in patients treated with coronary stenting for myocardial infarction: evaluation of short term safety and image quality

Purpose To evaluate safety and image quality of cardiovascular magnetic resonance (CMR) at 3.0 T in patients with coronary stents after myocardial infarction (MI), in comparison to the clinical standard at 1.5 T. Methods Twenty-five patients (21 men; 55 ± 9 years) with first MI treated with primary stenting, underwent 18 scans at 3.0 T and 18 scans at 1.5 T. Twenty-four scans were performed 4 ± 2 days and 12 scans 125 ± 23 days after MI. Cine (steady-state free precession) and late gadolinium-enhanced (LGE, segmented inversion-recovery gradient echo) images were acquired. Patient safety and image artifacts were evaluated, and in 16 patients stent position was assessed during repeat catheterization. Additionally, image quality was scored from 1 (poor quality) to 4 (excellent quality). Results There were no clinical events within 30 days of CMR at 3.0 T or 1.5 T, and no stent migration occurred. At 3.0 T, image quality of cine studies was clinically useful in all, but not sufficient for quantitative analysis in 44% of the scans, due to stent (6/18 scans), flow (7/18 scans) and/or dark band artifacts (8/18 scans). Image quality of LGE images at 3.0 T was not sufficient for quantitative analysis in 53%, and not clinically useful in 12%. At 1.5 T, all cine and LGE images were quantitatively analyzable. Conclusion 3.0 T is safe in the acute and chronic phase after MI treated with primary stenting. Although cine imaging at 3.0 T is suitable for clinical use, quantitative analysis and LGE imaging is less reliable than at 1.5 T. Further optimization of pulse sequences at 3.0 T is essential.
Introduction
The combination of functional cardiovascular magnetic resonance (CMR) and late gadolinium enhancement (LGE) is evolving as an important diagnostic [1, 2] and prognostic [3–5] modality in patients with ischemic heart disease. Because the availability of high field MR systems is increasing, the need arises to evaluate the performance and clinical value of these systems in cardiovascular disease. Earlier reports already suggested that 3.0 T MR systems offer higher temporal and spatial resolution, due to an increased signal-to-noise ratio, which would be especially advantageous in CMR scanning [6–8]. However, these studies were all assessed in healthy volunteers and in patients with non-ischemic heart disease or suspected coronary artery disease, and without the presence of coronary stents [6–10]. There is limited to no data about safety and image quality at 3.0 T CMR scanning in the acute or chronic phase after myocardial infarction (MI), in patients treated with percutaneous coronary intervention and primary stenting. In these patients B0 inhomogeneities (induced by the heart-lung interface or from coronary stents), flow artifacts, inhomogeneity of normal myocardial suppression in LGE (induced by B1 inhomogeneity) and poor cardiac triggering may interfere with the gain offered by the higher magnetic field.
The aim of this study was to test whether CMR scanning at 3.0 T is safe and feasible in patients with coronary stents in the acute and chronic phase after MI, and to prospectively compare image quality at 3.0 T with the current clinical standard at 1.5 T. Furthermore, the presence and significance of different image artifacts are considered.
Methods
Patient population
Patients were eligible for the study if they had been admitted with a first ST-elevation acute MI, according to standard electrocardiographic and enzymatic criteria [11], and had undergone successful primary PCI with stent implantation. Exclusion criteria were electrocardiographic evidence of reinfarction, haemodynamic or other clinical instability or (relative) contraindications for CMR such as claustrophobia, pacemakers, intracerebral aneurysm clips or very irregular heart rhythm. Patients were treated with aspirin, heparin, abciximab, clopidogrel, statins, beta-blockade and ACE-inhibitors, according to ACC/AHA practice guidelines [12]. The study was approved by the local ethics committee and all patients gave written informed consent.
CMR parameters
Eighteen CMR scans were performed with a 3.0 T MR system (Intera, Philips, Best, The Netherlands), with a gradient performance of 30 mT/m and slew rate of 150 T/m/s, using a six element cardiac phased array surface coil. Another 18 CMR scans were acquired with a 1.5 T MR system (Magnetom Sonata, Siemens, Erlangen, Germany), with a gradient performance of 40 mT/m and slew rate of 200 T/m/s, using an eight element cardiac phased array surface coil.
Cine imaging was performed at both field strengths using a SSFP pulse sequence, without parallel imaging. Long axis views, as well as short axis views covering the entire left ventricle were acquired during repeated breath-holds in expiration. At 3.0 T, cine SSFP sequence parameters were a temporal resolution between 25 ms and 50 ms, excitation angle of 45°, receiver bandwidth 868 Hz/pixel, TR/TE of 3.8/1.9 ms, matrix 192 × 155 and voxel size of 1.5 × 1.8 × 6.0 mm3. At 1.5 T, the temporal resolution was between 35 ms and 50 ms with an excitation angle of 60°, receiver bandwidth 930 Hz/pixel, TR/TE of 3.2/1.6 ms, matrix 256 × 156 and voxel size of 1.4 × 1.9 × 6.0 mm3.
LGE images were acquired in mid-diastole using a 2D segmented inversion-recovery gradient-echo pulse sequence, 10–15 min after intravenous injection of 0.2 mmol/kg of a gadolinium chelate (Dotarem, Guerbet, Roissy, France). The LGE images were obtained in exactly the same orientation as the cine images. Sequence parameters at 3.0 T were an excitation angle of 25°, receiver bandwidth 434 Hz/pixel, TR/TE of 3.5/1.3 ms, matrix 192 × 119, voxel size of 1.7 × 2.1 × 6.0 mm3, triggering every other heart beat, and an inversion time between 250 ms and 350 ms to null remote myocardium. At 1.5 T, sequence parameters were an excitation angle of 25°, receiver bandwidth 130 Hz/pixel, TR/TE of 9.6/4.4 ms, matrix 256 × 166, voxel size of 1.4 × 1.7 × 6.0 mm3, triggering every other heart beat, and an inversion time between 220 ms and 300 ms to null remote myocardium.
Safety and quality analysis
To evaluate patient safety, a physician was present at the CMR scanner throughout the scan. Heart rhythm was monitored continuously. The patient was asked to report any discomfort and symptoms during the scan procedure. Additionally, a repeat catheterization was performed in 16 patients who underwent CMR examination in the acute phase after MI (9 at 3.0 T, 7 at 1.5 T). Stent position and patency after CMR scanning were visually assessed on the repeat catheterization and compared to the primary PCI. Occurrence of repeat intervention or hospitalization within 30 days of the CMR examination was recorded.
Different types of artifacts were reviewed and marked as being clinically relevant if the artifact interfered with visualisation of the myocardium. Flow related artifacts in SSFP imaging [13], and artifacts due to static field inhomogeneities such as the heart-lung interface were scored on cine images. Separately, we looked at artifacts caused by the coronary stent on the cine images, and measured the maximum artifact diameter, perpendicular to the length of the stent, using an appropriate cardiac view. In addition, the potential effect of B1 inhomogeneity on the homogeneous suppression of viable myocardium was visually assessed on LGE images.
Furthermore, image quality of the cine and LGE images were scored on a separate workstation (Centricity Radiology v6.1, GE Medical Systems, Zeist, the Netherlands) by four independent observers, who were blinded for MR system and clinical history. To distinguish between image quality that is satisfactory for clinical use or high quality images for research purposes, images were scored on a scale from 1 to 4: 1 not clinically useful; 2 clinically useful, but of insufficient quality for quantitative analysis; 3 clinically useful and of sufficient quality for quantitative analysis; 4 excellent quality. The following definitions were used for evaluation of the cine images: 1 poor quality, extensive artifacts, completely obscuring endocardial borders; 2 moderate quality, assessment of global function is possible, partly using assumptions, but regional wall thickening is not possible in all segments, due to interfering artifacts; 3 good quality, assessment of global and regional function is possible, despite some small artifacts; 4 excellent quality, functional analysis is possible and there is no interference of artifacts. And for the evaluation of LGE images: 1 poor quality, extensive artifacts, infarcted myocardium is not visible; 2 moderate quality, infarcted myocardium is visible, but delineation is not possible in all segments; 3 good quality, infarcted myocardium is easy to distinguish from viable myocardium, despite some small artifacts; 4 excellent quality, no artifacts. Two scores were given for each scan: 1 for the set of cine images and 1 for the set of LGE images. After completing the independent image quality assessment, all 4 observers exchanged their scores for each case and agreed on a consensus score.
Statistical analysis
Continuous variables with normal distribution are expressed as mean ± SD, or as median (25th–75th percentile). Comparison of the baseline characteristics and stent artifact diameter was done by using an unpaired Student’s t-test. The consensus score of image quality of both field strengths was assessed for statistical difference by a Mann-Whitney U test. All statistical tests were two-sided with a significance level of P < 0.05. SPSS 12.0.1 for Windows (SPSS Inc., Chicago, USA) was used for analysis.
Results
Twenty-five consecutive patients with 36 CMR studies were included in the study. One patient was studied on both MR systems in the acute phase, 9 patients (5 on 3.0 T, 4 on 1.5 T) were studied both in the acute phase and in the chronic phase, and 1 patient (3.0 T) was studied twice in the chronic phase after MI. The baseline characteristics of all 25 patients are listed in Table 1. On each system, 12 CMR scans were performed in the acute phase after MI, at 4 ± 2 days after primary PCI, and 6 CMR studies in the chronic phase, at 125 ± 23 days after primary PCI. One patient refused contrast injection during scanning in the chronic phase.
Table 1Patient characteristicsNumber of patients25Age (years)55 ± 9Men21 (84)BMI (kg/m2)25.5 ± 1.9Risk factorsDiabetes mellitus0 (0)Hyperlipidaemia3 (12)Hypertension6 (24)Smoking16 (64)Family history of CAD8 (32)Maximum peak CK-MB (U/L)308 (197–464)Infarct-related arteryLAD18 (72)RCx3 (12)RCA4 (16)Values are presented as number (ratio in %), mean ± standard deviation or median (25th–75th percentile)
ECG electrodes were positioned and displaced until an optimal ECG signal was acquired. Although it took more time to obtain a stable ECG waveform with a clearly delineated R wave at 3.0 T than at 1.5 T (10–15 min vs. <2 min respectively), it did not affect image quality, since scans were repeated when trigger problems occurred.
Stent safety and artifacts at 3.0 T
No patient reported any discomfort or symptoms during the CMR scan procedure. In addition, there were no clinical events during, or shortly after scanning at 3.0 T, and none of the patients underwent a repeat intervention or hospitalization for any reason within 30 days.
The mean number of stents implanted per patient per scan at 3.0 T was 1.3 ± 0.7, with a mean stent length of 19 ± 5 mm and diameter of 3.2 ± 0.4 mm. Further specifications of the implanted coronary stents are listed in Table 2. Repeat catheterization was performed 2 (1–4) days after scanning in 9 of the 12 patients who underwent 3.0 T CMR scanning in the acute phase after MI. Angiographic evaluation revealed no differences in stent position and patency compared to the initial result after primary PCI.
Table 2Type and number of coronary stents used per CMR examinationField strength and timingNumber of patientsNumber and type of coronary stents present during CMRa3.0 T acute phase91× Multi-Link Vision12× Multi-Link Vision21× Driver3.0 T chronic phase31× Multi-Link Vision21× Driver/2x TAXUS Libertéb11× Driver1.5 T acute phase41× Prokinetic12× Prokinetic21× Lekton Motion11× Lekton Motion/2× Multi-Link Vision11× Multi-Link Vision11× AVE11× Multi-Link Zeta11× CYPHERb1.5 T chronic phase21× Lekton Motion12× Lekton Motion11× AVE11× Multi-Link Zeta11× CYPHERbaStent material and manufacturer—Multi-Link Vision: cobalt chromium alloy, Abbott Vascular; Driver, cobalt chromium alloy, Medtronic; TAXUS Liberté, 316L stainless steel, Boston Scientific; Prokinetic, cobalt chromium alloy, Biotronik; Lekton Motion: 316L stainless steel, Biotronik; Multi-Link Zeta, 316L stainless steel, Abbott Vascular; AVE: 316L stainless steel, Medtronic; CYPHER: 316L stainless steel, CordisbDrug-eluting stent
During cine imaging, the coronary stent was visible in 14 of the 18 scans (78%), with a susceptibility related signal loss of 12.8 ± 4.3 mm in diameter. Due to severe interference of the coronary stent in 6 scans (33%), analysis of surrounding myocardium was impossible (Fig. 1A). Through-plane flow artifacts were present in 14 scans (from the aorta in 9 scans, from the pulmonary trunk in 7 scans, from the left ventricle in 3 scans), and hindered visualization of the myocardium in 7 scans (39%). In-plane flow artifacts were present in 4 scans, but were not clinically relevant. In the majority of the scans (94%) dark band artifacts appeared at the transition between myocardium and lung, with a predilection for the anterolateral wall, which resulted in severe image distortion in 8 scans (44%). Overall, artifacts were responsible for clinically relevant image deformation in 12 scans (67%), of which half was caused by the coronary stent (in 6 scans).
Fig. 1Short axis cine SSFP images in different patients, demonstrating a signal void from a coronary stent with myocardial interference at 3.0 T (A, white arrow head), and a smaller signal void without interference at 1.5 T (B, white arrow head)
No effect could be observed of B1 inhomogeneity on the suppression of viable myocardium on LGE images at 3.0 T.
Stent safety and artifacts at 1.5 T
Also during scanning at 1.5 T, no patient reported any discomfort or symptoms, and there were no clinical events during, or shortly after scanning. None of the patients underwent a repeat intervention or hospitalization for any reason within 30 days after CMR scanning.
The mean number of implanted stents per patient per scan at 1.5 T was 1.2 ± 0.5 (see also Table 2). The mean stent length was 19 ± 5 mm with a mean diameter of 3.2 ± 0.4 mm, which was comparable with the stent length and diameter scanned at 3.0 T (p = ns). In 7 of the 12 patients who underwent CMR scanning at 1.5 T in the acute phase, repeat catheterization at 2 (2–4) days after scanning revealed no differences in stent position and patency compared to post-PCI.
During cine imaging, the coronary stent was visible in 6 of the 18 scans (33%), with a susceptibility related signal loss of 5.5 ± 0.3 mm in diameter, which was significantly smaller than at 3.0 T (P < 0.01). Furthermore, visible stents did not cause clinically important image deformation (Fig. 1B). Besides the small signal void from coronary stents, no other artifacts were observed at 1.5 T.
Also at 1.5 T there was no effect of B1 inhomogeneity on the suppression of viable myocardium on LGE images.
Evaluation of image quality
With scanning at 3.0 T, localized shimming was compulsory in every scan to obtain better image quality (Fig. 2). At 1.5 T, there was no need for adjustments in sequence parameters or the use of local shimming to optimize image quality. At 3.0 T, in 44% of the cases, image quality was not sufficient for quantitative analysis and assessment of regional function of the left ventricle (Table 3). For clinical purposes and global assessment of left ventricular function, image quality at 3.0 T and 1.5 T were comparable. Image quality of LGE images was not sufficient for quantitative analysis in 53% of the cases at 3.0 T, and 12% of the images were not useful for clinical purposes. Both image quality of cine and LGE imaging at 3.0 T were significantly lower compared to 1.5 T (P < 0.001).
Fig. 2Four chamber cine SSFP images at 3.0 T in end-diastole (A & C) and end-systole (B & D), at the same slice position in one patient. Upper panels are without and lower panels with localized shimming. Dark band artifacts (apex) and flow artifacts (around the atrioventricular valves) are reduced with localized shimmingTable 3Consensus score of image quality of cine and late gadolinium-enhanced images for 1.5 T and 3.0 TCine SSFP imagesLate gadolinium-enhanced images1.5 T3.0 T1.5 T3.0 T1. Not clinically useful0 (0%)0 (0%)0 (0%)2 (12%)2. Clinically useful, no quantitative analysis0 (0%)8 (44%)0 (0%)7 (41%)3. Clinically useful, quantitatively analyzable0 (0%)4 (22%)1 (6%)8 (47%)4. Excellent quality18 (100%)6 (33%)17 (94%)0 (0%)Values are presented as absolute numbers (percentage)
Discussion
In this paper we report on our initial experience with CMR scanning at 3.0 T in a clinical situation. We found that it is safe and feasible to perform CMR scanning at 3.0 T in the acute and chronic phase after MI in patients treated with primary stenting. Image quality of cine imaging at 3.0 T is of sufficient quality for global assessment of left ventricular function, however, quantitative analysis is not possible in almost half of the patients, due to dark band, flow and stent artifacts. Image quality of LGE studies were significantly better at 1.5 T. Therefore further optimization of pulse sequences at 3.0 T is essential.
MR imaging is considered to be contraindicated in patients with ferromagnetic implants, primarily because of the potential risks associated with migration, the induction of an electrical current and heating of the implant [14, 15]. According to earlier reports and the American Society for Testing and Materials International, the overall magnetic field interaction and heating for coronary stents is limited or absent at 1.5 T and 3.0 T, as tested in vitro [16–18]. In vivo studies concerning stent safety at 1.5 T demonstrated that CMR scanning is safe in patients early after coronary artery stent placement [19–21]. In this in vivo study using both 1.5 T and 3.0 T, no clinical signs were observed by the attending physician, and no substantial side effects or clinical events occurred during or within 30 days of CMR scanning. In addition, in the patients who underwent a repeat catheterization there was no angiographic evidence of stent migration, confirming the in vitro data.
As discussed by Schär et al. [7], dark band and flow artifacts in SSFP cine imaging can be solved using optimized sequence parameters, localized shimming and correct water resonance frequency adjustment. In the clinical setting of the present study, it was not always possible to solve these artifact problems despite sequence optimization and localized shimming, especially in the presence of stent artifacts. The larger and diagnostically interfering artifacts of coronary stents at 3.0 T as opposed to the smaller stent artifacts at 1.5 T are an important issue in patients after MI, since primary PCI with stent implantation is the method of choice to re-establish coronary flow [22]. For clinical application, it may be more advantageous to return to spoiled gradient-echo cine imaging at 3.0 T, which includes longer acquisition times and lower contrast between blood and myocardium, but is less sensitive for off-resonance artifacts and equally accurate as SSFP cine imaging (Fig. 3) [9, 23].
Fig. 3Short axis cine SSFP image (A) and cine spoiled gradient-echo image (B), at the same slice position in one patient at 3.0 T. The coronary stent artifact on a SSFP image is larger than on a spoiled gradient-echo image (white arrows heads). Flow artifacts (A, asterisk) are less visible with spoiled gradient-echo imaging at 3.0 T
We initially intended to evaluate differences in signal- and contrast-to-noise ratios (SNR and CNR) of both pulse sequences as well. At 1.5 T good noise estimates could be made using the technique described by Constantinides et al. [24]. However, this method did not work at 3.0 T due to a different reconstruction algorithm, even without parallel imaging, and by switching off clear and image enhancement filters. Subtraction methods with two consecutive scans to estimate noise are inaccurate in a mobile tissue as the heart [25]. Other methods were beyond the scope of this initial study. Despite the fact that SNR’s and CNR’s were therefore omitted from this study, it is interesting to mention that an important reason for inferior quality of LGE images at 3.0 T was that infarcted myocardium was sometimes difficult to delineate from the left ventricular cavity (Fig. 4).
Fig. 4Short axis LGE images 15 min after injection of contrast at 3.0 T (A) and 1.5 T (B), in different patients. Visual assessment of infarct extent and location was more difficult at 3.0 T than at 1.5 T, since it was sometimes difficult to delineate infarcted myocardium from the left ventricular cavity at 3.0 T
We used the same contrast agent and dose at both field strengths. The contrast dose has been optimized for 1.5 T in the past [26, 27], but might be different at 3.0 T as T1 relaxation rates are in general field strength dependent. However, a recent study of Sharma and colleagues showed that there were only minor differences in post contrast myocardial T1 relaxation times between 1.5 T and 3.0 T, using a contrast dose of 0.2 mmol/kg [28]. As long as inversion times are set appropriately at each field strength as done in this study, it is not to be expected that significant differences in LGE image quality are caused by contrast dose effects.
In conclusion, our study demonstrates that it is safe and feasible to perform CMR scanning at 3.0 T in the acute and chronic phase after MI in patients treated with primary stenting. Although cine imaging at 3.0 T is of sufficient quality for clinical use, quantitative assessment is less reliable compared to 1.5 T, mainly due to dark band, flow and stent artifacts. Further optimization of pulse sequences at 3.0 T is essential to make 3.0 T CMR scanning suitable for clinical cardiology.
Limitations
The use of different MR systems, from different vendors, with different coils and sequence parameters, of course introduces confounding factors for a comparison. However, the sequences were optimized for their field strength to evaluate safety, feasibility and image quality rather than technical differences. A second limitation is that patients did not undergo a CMR examination at both 1.5 T and 3.0 T. Because the MR systems were on two different locations, it was not feasible to study the same patient twice in the acute phase after MI.

3.0 t
cardiovascular magnetic resonance
safety
late gadolinium enhancement
acute myocardial infarction