1. Introduction
Whole-body magnetic resonance imaging (MRI) is increasingly utilized not only in population-based research [ 1 , 2 , 3 ], but also in clinical care such as screenings of oncological patients with prostate cancer or multiple myeloma [ 4 , 5 , 6 ], for instance, as well as in direct-to-consumer preventive health screenings [ 7 , 8 , 9 , 10 ]. Advantages of MRI in these settings include the lack of ionizing radiation, the high anatomic coverage, and technical developments providing decreased examination times and a higher soft-tissue resolution [ 11 ]. Whole-body MRI leads to a substantial number of incidental findings (IF), which are unexpected discoveries unrelated to the objectives of the examination but with potential health consequences for the study participant [ 12 ]. The range of detected IF varies dependent on the imaging protocol and type of cohort. In population-based whole-body MRI, IF were found in up to 35% of participants [ 9 , 13 ].
However, especially in population-based imaging research, a substantial number of IF turn out to be without clinical consequences or even false positive due to the lower pretest probability of pathological findings in contrast to patient cohorts [ 13 , 14 , 15 , 16 ]. For instance, only about 1/5 of potentially serious incidental findings were associated with serious final diagnoses in the population-based UK Biobank Imaging Study [ 17 ]. This is especially problematic as waiting for or receiving an IF report can cause substantial distress for participants [ 18 , 19 , 20 ]. Furthermore, disclosure of IF may have medical, financial and psychosocial consequences [ 15 , 16 ].
Nevertheless, research participants mainly wish for disclosure of IF, as gaining information about their own health is frequently the main motivation for participation in a research study besides the contribution to a scientific purpose [ 18 , 19 , 20 , 21 ]. Disclosing IF has the potential to provide information about previously unknown serious diagnoses such as malignancies to participants and clinical caregivers, which might improve the course of the disease [ 8 , 17 ].
Consequently, research investigators and clinical caregivers are confronted with ethical problems when addressing a balance between overreporting of IF and withholding information from participants [ 21 , 22 , 23 , 24 ]. Furthermore, a high number of reported IF might also have a crucial financial impact on the health care system due to further examinations such as biopsies and follow-up imaging [ 15 , 16 , 25 ].
In a previous study based upon a whole-body MRI-study within the population-based Cooperative Health Research in the Region of Augsburg (KORA), adverse short-term psychosocial consequences of IF disclosures showed to be limited as there was no significant increase in depression assessed by PHQ-9 (Patient Health Questionnaire) six months after the MRI examination [ 18 ]. Studies addressing the psychosocial impact of IF, however, are rare, especially with respect to long-term implications. Thus, we now aimed to analyze long-term psychosocial effects of MRI participation and IF disclosure within a population-based cohort.
2. Materials and Methods
2.1. Study Design, Sample and Endpoint Assessment
The KORA-MRI sample comprises 400 participants, who underwent whole-body MRI during the second follow-up examination (KORA-FF4, N = 2278) of the original KORA-S4 study in 2013/2014 (exam 2) [ 1 ]. The KORA-MRI study was designed to investigate subclinical disease burden in individuals with prediabetes and diabetes [ 1 ]. Exclusion criteria for MRI participation were age > 73 years, prior cardiovascular disease, or any contraindications to the MRI procedure, such as metal parts inside the body, claustrophobia, or allergy to the contrast agent [ 1 ]. Imaging revealed 113 clinically relevant incidental findings (IF) in 89 participants by reading of board-certified radiologists with unclear liver lesion, silent myocardial infarction and complex renal cyst being the most frequently reported IF [ 18 ]. The standardized IF reporting process has been described previously [ 18 ].
Examinations of the KORA-FF4 study sample (i.e., of both the KORA-MRI participants and those who did not participate in KORA-MRI) were also performed six years prior to MRI (KORA F4, exam 1) and four years after MRI (KORA FIT, exam 3). Study design, sampling methods and data compilation are specified in previous publications [ 1 , 18 , 26 , 27 ]. Our analysis thus includes three time points. For the analysis sample, we included all individuals who participated in all three exams and had values of PHQ-9 and DEEX (Depression and Exhaustion Scale) available at least at exam 2 and 3 (compare Figure 1 ). We denote those individuals who did not undergo the MRI examination as “control group”.
All KORA studies are approved by the ethics committee of the Bavarian Medical Association in Munich, Germany. The KORA-MRI sub-study was additionally approved by the institutional review board of the medical faculty of Ludwig-Maximilians-University Munich, Germany. Investigations were carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all individuals in this study.
All interviews, questionnaires and physical examinations were conducted according to highly standardized protocols by trained stuff and underwent rigorous quality control in line with the German guidelines for Good Scientific Practice [ 28 ].
Psychosocial and health assessments included the following standardized self-rated questionnaires: The Depression and Exhaustion Scale (DEEX) comprises eight items representing nervousness and anxiety, tiredness, fatigue, and lack of concentration with a scoring range of 0 to 24 [ 29 ]. The Somatization Scale was obtained as a continuous score with a range from 0–27 and a response scale from 0 (“not at all“) to 3 (“severe“) based on nine symptoms of somatization such as tachycardia and heavy sweating [ 30 , 31 ]. We also assessed depression by applying the Patient Health Questionnaire (PHQ-9), a nine-symptom checklist scoring each depressive symptom from 0 (“not at all“) to 3 (“nearly every day“) leading to a range of 0–27 [ 32 , 33 ]. The Perceived Stress Scale (PSS-10) measures to which extent life has been experienced as uncontrollable during the past month using a response scale ranging from 0 (“never”) to 4 (”very often”) with higher overall scores representing higher levels of perceived stress [ 34 , 35 ]. To investigate quality of life, we used the Short-Form-Health-Survey-12 (SF-12), which assesses the participant’s perception of physical ability, bodily pain, and vitality as well as emotional, social, and mental health creating two subscales, the physical component summary score (PCS) and the mental component summary score (MCS), with higher scores indicating a better quality of life [ 36 , 37 ].
DEEX, PHQ-9 and Somatization Scale were assessed at all three exams by the same questionnaires. SF-12 was only assessed at exam 1 and exam 2, again by the same questionnaires, and PSS-10 was merely assessed at exam 3. As the main focus of the present analysis was the psychosocial development after the MRI exam, exclusion criteria were missing values of PHQ-9 and/or DEEX at exam 2 or 3.
Furthermore, at all three exams, we investigated participants’ demographic characteristics such as age, sex, and body mass index (BMI). Family status, smoking status and physician-diagnosed diabetes mellitus were assessed by self-report. Physical activity was defined as >1h of self-reported regular physical activity per week. Antidepressant medication included Anatomical Therapeutic Chemical (ATC) codes N06A [A, B, F, G, X]. Hypertension was defined as blood pressure ≥ 140/90 or intake of antihypertensive medication.
2.2. Statistical Analysis
Demographics and psychosocial outcomes are reported as arithmetic mean and standard deviation for continuous variables, and as counts and percentages for categorical variables. Differences between MRI participants and the control group and between MRI participants with and without IF were analyzed by unpaired t-test, Mann–Whitney U test, or χ 2 -test, where appropriate. Furthermore, we compared demographics of included and excluded individuals. p -values for longitudinal trends of outcomes in PHQ-9, DEEX and the Somatization Scale were generated using repeated measures analysis of variance (ANOVA) for MRI participants, participants with/without IF, and the control group. To assess the association of MRI participation and IF reporting with trajectories of psychosocial outcomes, linear mixed models with random intercept per individual were calculated and adjusted for age, sex, BMI, hypertension, smoking status, diabetes mellitus, intake of antidepressants, physical activity, and family status. Choice of adjustment variables was guided by prior knowledge; no selection by pre-testing on the current data was done. A p -value < 0.05 indicated statistical significance. SPSS Version 26 and R Version 4.0.5 were used for analyses.
3. Results
3.1. Study Population and Demographic Characteristics
Our final analytical sample contained 212 MRI participants of whom 50 had at least one reported IF. The control group comprised 643 individuals who did not participate in the MRI examination.
Exclusion of individuals was mainly due to missing data in exam 3. In exam 2, excluded MRI participants were on average 4.8 years younger than included subjects (53.4 ± 11.5 years, n = 188, and 58.2 ± 5.6 years, n = 212, respectively) ( p < 0.001) and were less commonly hypertensive compared to included MRI participants (28%, n = 52, vs. 40%, n = 84) ( p = 0.02). Individuals who were excluded from the control group were on average 4.9 years older than included individuals in exam 2 (62.9 ± 14.9 years, n = 1236, and 58 ± 5.5, n = 643, respectively) ( p < 0.001), less commonly stated physical activity (53%, n = 653, vs. 64%, n = 414) ( p < 0.001) and were more frequently suffering from diabetes mellitus (18%, n = 222, vs. 8%, n = 52) ( p < 0.001) and hypertension (44%, n = 545, vs. 33%, n = 211) ( p < 0.001). Furthermore, the percentage of men in exam 2 was higher in the group of excluded participants than in the control group (49%, n = 600, vs. 42%, n = 271) ( p =0.01). Further demographic characteristics of excluded participants are shown in Appendix A Table A1 .
In exam 2, the mean age of MRI participants was 58.2 ± 5.6 years and the mean BMI was 28.2 ± 4.7 kg/m 2 , while the mean age of individuals in the control group was 58 ± 5.5 years and the mean BMI was 27.6 ± 5.3 kg/m 2 . The percentage of individuals with diabetes mellitus in exam 2 was higher in MRI participants (14%, n = 29) than in the control group (8%, n = 52), as well as the percentage of men (55%, n = 117, vs. 42%, n = 271).
The mean age of MRI participants with reported IF in exam 2 was 59.6 ± 5 years, while the mean age of MRI participants without reported IF in exam 2 was 57.7 ± 5.7 years. 50% (n = 25) of MRI participants with reported IF and 57% (n = 92) of participants without IF were male and the mean BMI was 28.3 ± 4.1 kg/m 2 and 28.2 ± 4.9 kg/m 2 , respectively. Further demographic characteristics of the study population are provided in Table 1 .
3.2. Psychosocial and Health Outcomes: PHQ-9, DEEX, SF-12, PSS-10, and Somatization Scale
Figure 2 shows the trajectories of all psychosocial outcomes in MRI participants, with and without IF, as well as in the control group without MRI participation. The PHQ-9, DEEX and Somatization Scale increased from exam 1 to 3 in all groups. Statistically significant longitudinal changes were observed for PHQ-9 in MRI participants (2.5 to 3.1 points), for DEEX in the control group (7.9 to 8.6 points) and for Somatization Scale in the MRI sample (4.2 to 4.8 points), MRI participants without IF (4.1 to 4.7 points) and the control group (4.7 to 5.9 points). There were no significant differences in psychosocial outcomes at the three timepoints between MRI participants with and without IF.
Non-MRI participants had the highest psychosocial health burden. Compared to MRI participants, they had significantly higher values of PHQ 9 in exam 1 (3.3 ± 3.3 vs. 2.5 ± 2.3) and of DEEX (8.6 ± 4.7 vs. 7.7 ± 4.4), Somatization Scale (5.9 ± 4.3 vs. 4.8 ± 3.8) and PSS-10 (14.7 ± 5.7 vs. 13.7 ± 5.3) in exam 3. Furthermore, the control group had a significantly lower value of MCS obtained by SF-12 than the MRI sample in exam 1, indicating reduced mental health related quality of life (50.6 ± 9.3 vs. 52.4 ± 7.6). MCS and PCS did not differ between groups in exam 2. Further psychosocial outcomes of our study population are shown in Table 2 .
3.3. Multivariate Analysis of MRI Participation in Association with Psychosocial Outcomes
In adjusted linear mixed models, MRI participation without IF reporting was associated with significantly lower scores of PHQ-9, DEEX and Somatization Scale compared to the control group without MRI participation. The estimated changes of <1 points on the respective scales ( Table 3 ) do not represent clinically actionable differences. MRI participation with IF reporting was also associated with lower scores of all three outcomes; however, the association was not statistically significant nor clinically actionable ( Table 3 ).
Regarding the adjustment variables, there were significant associations of age with higher scores of DEEX and Somatization Scale, of female sex and an intake of antidepressant medication with higher values in all three outcomes, and of BMI and hypertension with higher scores of Somatization Scale. Physical activity was significantly associated with lower values in all three outcomes and living with a partner with lower scores of PHQ-9 and DEEX ( Appendix A Table A2 ).
4. Discussion
In this longitudinal, population-based analysis, we investigated long-term associations of MRI participation and IF reporting with psychosocial outcomes in 643 non-MRI participants and 212 MRI participants, of whom 50 had IF.
Non-MRI participants had the highest psychosocial burden. MRI participation without IF reporting was significantly associated with lower values of DEEX, PHQ-9 and Somatization Scale after adjustment for potential confounders. Importantly, there were no significant differences in psychosocial outcomes at the three exams between MRI participants with IF and those without.
We note that men are overrepresented in the MRI group, which is due to the original study design. Since the main aim of the KORA-MRI was to study subclinical disease burden in individuals with prediabetes and diabetes [ 1 ], this led to an increased recruitment of men.
Considering the rarity of studies with a comparable design and objective, these results are mainly supported by findings of Schmidt et al., who found no significant adverse long-term psychosocial impact of MRI participation and IF reporting compared to a control group in the Study of Health in Pomerania (SHIP) after 2–3 years of follow-up [ 38 ]. In contrast to our study, they assessed intervention effects per year on values of SF-12 and PHQ-9 in a larger sample of 2011 MRI participants and 1735 control subjects in the long-term follow-up survey [ 38 ], but did not include data derived prior to MRI. Furthermore, the percentage of disclosed IF was higher in the SHIP cohort (31.5%) [ 2 ] than in our MRI sample (22%) [ 18 ].
Our mean PHQ-9 values are below the recommended cut-off score of 10 for detection of major depression [ 32 , 39 ] and also lower than reported values of 3.7 ± 3.5 from the German National Cohort [ 40 ] and 3.8 ± 3.5 from the SHIP cohort at MRI baseline [ 38 ]. The mean values of PCS and MCS we assessed in exam 1 and 2 are also in accordance with the mean PCS and MCS values obtained by Schmidt et al. (48.2 ± 8.1 and 52.8 ± 8.3, respectively) [ 38 ] as well as with long-standing values published by Gandek et al. for the German population in 1998 (49.6 ± 8.7 and 52.3 ± 8, respectively) [ 37 ]. Furthermore, our outcome values of DEEX are consistent with Ladwig et al., who published a mean DEEX of 8.23 ± 4.76 for the population-based cohort within the Monitoring Trends and Determinants in Cardiovascular Disease (MONICA)/KORA study when they first described the DEEX as a validated screening tool to assess depressive mood in 2004 [ 29 ]. The mean PSS-10 values found in our study are slightly elevated for both MRI participants (13.7 ± 5.3) and the control group (14.7 ± 5.7) in comparison to the mean PSS-10 of 11.94 ± 6.14 for individuals aged 60 or older published by Klein et al. based on a representative German community sample [ 35 ]. The Somatization Scale is a shortened version of the von Zerssen symptom checklist and has, to our knowledge, not yet been applied in other studies for assessment of somatization symptoms in contrast to a larger, modified version of the von Zerssen symptom checklist, which comprises 24 items [ 31 ]. Therefore, the comparability with literature is limited for the Somatization Scale.
Our results showed lower levels of perceived stress, somatization, and depressive mood and exhaustion in our MRI sample compared to the control group in exam 3, i.e., four years after the MRI procedure. This might be explained by a potential reduction of health concerns due to the whole-body MRI scan. This is confirmed not only by our previous finding during the short-term follow-up, in which 88% of MRI participants chose “knowing whether I’m healthy” retrospectively as their main motivation for MRI participation [ 18 ], but also by the participants′ common wish for IF reporting in population-based research as a potential expression of interest in their own health and autonomy [ 18 , 20 ]. However, our data showed that MRI participants had less psychosocial burden compared to non-MRI participants already years before the MRI examination. Therefore, our results are in line with the well-known finding that individuals who decide to participate in population-based health examinations are more health-conscious and often healthier than non-participants [ 41 ]. We thus cannot conclude that the MRI exam itself represented a beneficial intervention causing improvement in mental health scores. It also needs to be considered that preventive health screenings offered for the general asymptomatic population have been found to be without benefit for participants in terms of total mortality [ 10 ] and might cause a high rate of clinically relevant, unexpected findings which require further examinations or surveillance, but frequently turn out to be false positive [ 7 , 9 ].
Nevertheless, our results support the possibility of implementing whole-body MRI in population-based research without overall adverse long-term psychosocial effects even in case of IF disclosure. This finding might have different reasons: First, reported IFs could have mainly turned out to be false positive, although scans had been evaluated by board-certified radiologists [ 18 ], due to the high sensitivity of MRI and the low pretest probability of pathological findings in a general population cohort. Second, it is not entirely known if participants followed the recommendations given in IF reports for further examinations as pre- and post-scan survey data were available for only 243 MRI participants in the previously published short-term follow-up [ 18 ]. Third, the high quality of our consent form and standardized IF management including IF reports could have positively affected our study results. It is important to provide detailed information in an easily understandable wording to participants to reduce uncertainty in case of IF disclosure and to minimize false expectations regarding the potential disclosure and impact of Ifs, as they might lead to subsequent examinations and could have financial, social and emotional effects [ 17 , 19 , 22 , 23 , 24 , 25 ]. However, single cases of clinically highly relevant IF with potentially negative long-term psychosocial impacts are not ruled out by our study results as we focused on mean effects. [ 18 ]. In this population-based sample, mean values of psychosocial health burden were far below the threshold for clinical disorders. Furthermore, neither IF disclosure nor MRI participation was associated with clinically actionable changes in mental health outcomes.
Our models were adjusted for multiple variables that could possibly confound the association between MRI participation or IF reporting with psychosocial outcomes. In our analysis, female sex was associated with higher values of PHQ-9, DEEX, and somatization. The higher prevalence of depressive and mood disorders in women has been known for a long time [ 42 ], which might be due to different etiological factors such as coping mechanisms, response to pharmacological treatment and neurobiology [ 43 ]. BMI was associated with increased somatization, but not with depression or exhaustion. Findings on the relation of BMI with mental health burden are inconsistent, but a previous large study from the U.S. showed that unfavorable effects of BMI on depression were only visible in the severe obesity or underweight range [ 44 ]. Hypertension was associated with increased somatization. A previous report found that particularly isolated systolic hypertension is associated with somatization [ 45 ]. The association between hypertension and depression is still controversial [ 46 , 47 ]. In our sample, smoking was not significantly associated with any mental health parameter. Indeed, previous findings on this association are inconsistent, with both positive and Null findings in prior studies [ 48 ]. In the same vein, we did not detect significant associations of diabetes with mental health burden, although such associations have been reported previously [ 49 ]. Intake of antidepressant medication was associated with increased mental health burden, which is in line with the interpretation that it served as a proxy to identify affected individuals. Physical activity was associated with decreased mental health burden, which is supported by previous findings [ 50 ]. Cohabitation with a partner was associated with decreased mental health burden. This is in line with other studies explaining the protective effect by increased emotional support, intimacy and social network provided by cohabiting partnership [ 51 ].
Nevertheless, there are other potential confounders that were not considered in the current analysis. For example, education level, and socioeconomic status on both the individual and community level might influence mental health outcomes [ 52 , 53 ], as well as the willingness to participate in a health study. Future studies should take these factors into account to further characterize the influence of economic status on the association of IF disclosure and psychosocial outcomes.
Limitations of our study include the small sample size with only 50 participants with IF. Further efforts in larger studies, such as the German National Cohort or UK Biobank, are needed as the range of detected IF can vary according to imaging protocol and cohort. Moreover, our data stem from a single-center study, and included only participants without cardiovascular disease and with white ethnicity. Generalizability to other populations, such as high-risk patients, and other ethnicities still needs to be evaluated. Furthermore, not all psychosocial outcomes were available at every exam. Several individuals with missing outcome data had to be excluded, which was due to missing or incomplete assessment of the questionnaires.
Among the strengths of our study is the longitudinal, population-based study design including data before, at, and after the MRI examination. Moreover, we were able to include a control group without MRI participation. Another strength is the application of a panel of complementary scores based on standardized, self-rated questionnaires to assess psychosocial outcomes including depression, exhaustion, perceived stress, and health-related quality of life, which makes our study unique in the context of population-based MRI research.
Our results suggest that whole-body MRI in population-based research is feasible without adverse long-term psychosocial consequences, even in case of IF reporting, and that individuals who voluntarily participate have better mental health even years before MRI. We thus cannot conclude that the MRI examination had a causal beneficial effect on mental health scores. A high quality not only of informed consent procedures and IF reports, but also of a standardized and balanced IF management in imaging research appears to be of rising importance in this context. Standardized reporting of IF might also be beneficial in clinical care, but the transfer of our results to patient cohorts is clearly restricted. Further research regarding long-term psychosocial consequences of MRI participation and IF reporting in larger cohorts and specific subgroups, as well as medical courses after IF disclosures, is needed.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author Contributions
Conceptualization, D.K.-B., C.L.S. and S.R.; data curation, D.K.-B. and S.R.; formal analysis, D.K.-B. and S.R.; investigation, D.K.-B., F.B., A.P., B.L., K.-H.L., L.S., C.L.S. and S.R.; methodology, D.K.-B., C.L.S. and S.R.; resources, F.B., A.P., K.-H.L. and L.S.; supervision, C.L.S. and S.R.; visualization, D.K.-B., C.L.S. and S.R.; writing—original draft, D.K.-B.; writing—review and editing, F.B., A.P., B.L., K.-H.L., L.S., S.W., C.L.S. and S.R. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki. The KORA studies were approved by the ethics committee of the Bavarian Chamber of Physicians, Munich, Germany (KORA-F4 06068, KORA-FF4 06068, KORA-Fit 17040, KORA-MRI LMU 498-12). The KORA-MRI sub-study was additionally approved by the institutional review board of the Ludwig-Maximilians-University Munich, Germany.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Restrictions apply to the availability of these data. Data was obtained from the Helmholtz Zentrum München—German Research Center for Environmental Health and are available from the Helmholtz Zentrum München by submission of a research proposal application.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Appendix A
Table A1
Demographic Characteristics of Excluded Subjects according to Participation in Whole-body MRI (MRI) and Presence of Incidental Findings (IF).
Table A1 shows unadjusted, descriptive data; BMI denotes body mass index; physical activity was defined as self-reported regular physical activity exceeding 1h per week; * denotes at least 1 reported IF.
| No MRI | MRI | No IF | With IF * | |
| n = 2155 | n = 188 | n = 149 | n = 39 | |
| Age (years) | ||||
| Exam 1 | 58.7 ± 14.7 (n = 2073) | 46.9 ± 11.6 (n = 177) | 45.5 ± 11 (n = 139) | 52 ± 12.5 (n = 38) |
| Exam 2 | 62.9 ± 14.9 (n = 1236) | 53.4 ± 11.5 (n = 188) | 52 ± 10.9 (n = 149) | 58.5 ± 12.4 (n = 39) |
| Exam 3 | 62.2 ± 6.3 (n = 74) | 65.5 ± 5.7 (n = 6) | 63.3 ± 5.3 (n = 4) | 70 ± 4.2 (n = 2) |
| Sex (men) | ||||
| Exam 1 | 998 (48%) (n = 2073) | 111 (63%) (n = 177) | 89 (64%) (n = 139) | 22 (58%) (n = 38) |
| Exam 2 | 600 (49%) (n = 1236) | 114 (61%) (n = 188) | 91 (61%) (n = 149) | 23 (59%) (n = 39) |
| Exam 3 | 32 (43%) (n = 74) | 5 (83%) (n = 6) | 3 (75%) (n = 4) | 2 (100%) (n = 2) |
| BMI (kg/m 2 ) | ||||
| Exam 1 | 27.8 ± 4.9 (n = 2058) | 27.2 ± 4.3 (n = 177) | 27.2 ± 4.4 (n = 139) | 27.3 ± 4 (n = 38) |
| Exam 2 | 27.8 ± 4.9 (n = 1234) | 28 ± 5.2 (n = 188) | 27.9 ± 5.4 (n = 149) | 28.4 ± 4.2 (n = 39) |
| Exam 3 | 28.1 ± 5.4 (n = 74) | 30.1 ± 3.2 (n = 6) | 28.7 ± 2.9 (n = 4) | 32.8 ± 1.7 (n = 2) |
| Physical activity | ||||
| Exam 1 | 1044 (50%) (n = 2068) | 91 (51%) (n = 177) | 69 (50%) (n = 139) | 22 (58%) (n = 38) |
| Exam 2 | 653 (53%) (n = 1236) | 102 (54%) (n = 188) | 79 (53%) (n = 149) | 23 (59%) (n = 39) |
| Exam 3 | 50 (69%) (n = 73) | 1 (17%) (n = 6) | 1 (25%) (n = 4) | 0 (0%) (n = 2) |
| Family status (Living together with a partner) | ||||
| Exam 1 | 1499 (72%) (n = 2070) | 140 (79%) (n = 177) | 109 (78%) (n = 139) | 31 (82%) (n = 38) |
| Exam 2 | 886 (72%) (n = 1236) | 143 (76%) (n = 188) | 113 (76%) (n = 149) | 30 (77%) (n = 39) |
| Exam 3 | 53 (72%) (n = 74) | 4 (67%) (n = 6) | 2 (50%) (n = 4) | 2 (100%) (n = 2) |
| Diabetes mellitus | ||||
| Exam 1 | 295 (14%) (n = 2073) | 11 (6%) (n = 177) | 8 (6%) (n = 139) | 3 (8%) (n = 38) |
| Exam 2 | 222 (18%) (n = 1236) | 24 (13%) (n = 188) | 17 (11%) (n = 149) | 7 (18%) (n = 39) |
| Exam 3 | 8 (11%) (n = 73) | 0 (0%) (n = 6) | 0 (0%) (n = 4) | 0 (0%) (n = 2) |
| Hypertension | ||||
| Exam 1 | 913 (44%) (n = 2065) | 41 (23%) (n = 177) | 29 (21%) (n = 139) | 12 (32%) (n = 38) |
| Exam 2 | 545 (44%) (n = 1232) | 52 (28%) (n = 188) | 38 (26%) (n = 149) | 14 (36%) (n = 39) |
| Exam 3 | 38 (52%) (n = 73) | 1 (17%) (n = 6) | 0 (0%) (n = 4) | 1 (50%) (n = 2) |
| Intake of antidepressants | ||||
| Exam 1 | 96 (5%) (n = 2073) | 1 (1%) (n = 177) | 1 (1%) (n = 139) | 0 (0%) (n = 38) |
| Exam 2 | 78 (6%) (n = 1233) | 5 (3%) (n = 188) | 5 (3%) (n = 149) | 0 (0%) (n = 39) |
| Exam 3 | 5 (7%) (n = 73) | 0 (0%) (n = 6) | 0 (0%) (n = 4) | 0 (0%) (n = 2) |
| Smoking status | ||||
| Exam 1 | n = 2067 | n = 177 | n = 139 | n = 38 |
| Smoker | 353 (17%) | 36 (20%) | 30 (22%) | 6 (16%) |
| Ex-smoker | 823 (40%) | 73 (41%) | 53 (38%) | 20 (53%) |
| Never-smoker | 891 (43%) | 68 (38%) | 56 (40%) | 12 (32%) |
| Exam 2 | n = 1156 | n = 177 | n = 139 | n = 38 |
| Smoker | 150 (13%) | 31 (18%) | 25 (18%) | 6 (16%) |
| Ex-smoker | 490 (42%) | 78 (44%) | 58 (42%) | 20 (53%) |
| Never-smoker | 516 (45%) | 68 (38%) | 56 (40%) | 12 (32%) |
| Exam 3 | n = 72 | n = 4 | n = 2 | n = 2 |
| Smoker | 16 (22%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Ex-smoker | 26 (36%) | 1 (25%) | 1 (50%) | 0 (0%) |
| Never-smoker | 30 (42%) | 3 (75%) | 1 (50%) | 2 (100%) |
Table A2
Association of MRI Participation and IF Reporting with Trajectories of Psychosocial Outcomes. Results are from linear mixed regression models with random intercept using data from exams 1–3.
| Outcome PHQ-9 | Outcome DEEX | Outcome Somatization | |||||||
| β | 95%-CI | p -Value | β | 95%-CI | p -Value | β | 95%-CI | p -Value | |
| Control group (no MRI participation) | ref. | ref. | ref. | ref. | ref. | ref. | ref. | ref. | ref. |
| MRI participation, no IF | −0.46 | [−0.85, −0.04] | 0.036 | −0.82 | [−1.44, −0.14] | 0.016 | −0.88 | [−1.42, −0.31] | 0.004 |
| MRI participation, with IF | −0.69 | [−1.41, 0.05] | 0.064 | −0.56 | [−1.74, 0.61] | 0.362 | −0.73 | [−1.74, 0.27] | 0.172 |
| Age, years | 0.00 | [−0.01, 0.02] | 0.812 | 0.04 | [0.01, 0.06] | 0.002 | 0.06 | [0.05, 0.08] | <0.001 |
| Female sex | 0.94 | [0.61, 1.26] | <0.001 | 1.59 | [1.05, 2.11] | <0.001 | 1.65 | [1.18, 2.09] | <0.001 |
| BMI, kg/m 2 | 0.02 | [−0.01, 0.05] | 0.166 | 0.02 | [−0.03, 0.07] | 0.534 | 0.14 | [0.09, 0.18] | <0.001 |
| Hypertension | 0.06 | [−0.2, 0.33] | 0.654 | 0.18 | [−0.2, 0.56] | 0.33 | 0.37 | [0.05, 0.7] | 0.032 |
| Ex-smoker | 0.20 | [−0.12, 0.53] | 0.248 | 0.06 | [−0.44, 0.55] | 0.846 | 0.05 | [−0.38, 0.48] | 0.836 |
| Current smoker | 0.23 | [−0.19, 0.66] | 0.272 | 0.18 | [−0.48, 0.81] | 0.614 | 0.25 | [−0.31, 0.79] | 0.384 |
| Diabetes | 0.24 | [−0.27, 0.72] | 0.33 | 0.10 | [−0.66, 0.8] | 0.798 | 0.35 | [−0.3, 0.95] | 0.266 |
| Intake of antidepressants | 1.71 | [1.18, 2.23] | <0.001 | 1.71 | [0.99, 2.46] | <0.001 | 0.76 | [0.14, 1.4] | 0.022 |
| Physically active | −0.36 | [−0.59, −0.12] | 0.002 | −0.81 | [−1.14, −0.47] | <0.001 | −0.57 | [−0.86, −0.28] | <0.001 |
| Living with partner | −0.85 | [−1.18, −0.53] | <0.001 | −0.56 | [−1.02, −0.1] | 0.016 | −0.37 | [−0.77, 0.02] | 0.058 |
References
1. Bamberg F. Hetterich H. Rospleszcz S. Lorbeer R. Auweter S.D. Schlett C.L. Schafnitzel A. Bayerl C. Schindler A. Saam T. Subclinical Disease Burden as Assessed by Whole-Body MRI in Subjects with Prediabetes, Subjects with Diabetes, and Normal Control Subjects from the General Population: The KORA-MRI Study Diabetes 2017 66 158 169 10.2337/db16-0630 27999110
2. Hegenscheid K. Seipel R. Schmidt C.O. Völzke H. Kühn J.-P. Biffar R. Kroemer H.K. Hosten N. Puls R. Potentially relevant incidental findings on research whole-body MRI in the general adult population: Frequencies and management Eur. Radiol. 2013 23 816 826 10.1007/s00330-012-2636-6 22911290
3. Bamberg F. Kauczor H.U. Weckbach S. Schlett C.L. Forsting M. Ladd S.C. Greiser K.H. Weber M. Schulz-Menger J. Niendorf T. Whole-Body MR Imaging in the German National Cohort: Rationale, Design, and Technical Background Radiology 2015 277 206 220 10.1148/radiol.2015142272 25989618
4. Padhani A.R. Lecouvet F.E. Tunariu N. Koh D. de Keyzer F. Collins D.J. Sala E. Schlemmer H.P. Petralia G. Vargas H.A. METastasis Reporting and Data System for Prostate Cancer: Practical Guidelines for Acquisition, Interpretation, and Reporting of Whole-body Magnetic Resonance Imaging-based Evaluations of Multiorgan Involvement in Advanced Prostate Cancer Eur. Urol. 2017 71 81 92 10.1016/j.eururo.2016.05.033 27317091 PMC5176005
5. Messiou C. Hillengass J. Delorme S. Lecouvet F.E. Moulopoulos L. Collins D. Blackledge M.D. Abildgaard N. Østergaard B. Schlemmer H.-P. Guidelines for Acquisition, Interpretation, and Reporting of Whole-Body MRI in Myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS) Radiology 2019 291 5 13 10.1148/radiol.2019181949 30806604
6. Morone M. Bali M.A. Tunariu N. Messiou C. Blackledge M. Grazioli L. Koh D.-M. Whole-Body MRI: Current Applications in Oncology AJR Am. J. Roentgenol. 2017 209 W336 W349 10.2214/AJR.17.17984 28981354
7. Zugni F. Padhani A.R. Koh D.M. Summers P.E. Bellomi M. Petralia G. Whole-body magnetic resonance imaging (WB-MRI) for cancer screening in asymptomatic subjects of the general population: Review and recommendations Cancer Imaging 2020 20 34 10.1186/s40644-020-00315-0 32393345 PMC7216394
8. Hommes D. Klatte D. Otten W. Beltman M. Klass G. Zand A. Sprangers R. Health outcomes and experiences of direct-to-consumer high-intensity screening using both whole-body magnetic resonance imaging and cardiological examination PLoS ONE 2020 15 e0242066 10.1371/journal.pone.0242066 33216779 PMC7678982
9. Kwee R.M. Kwee T.C. Whole-body MRI for preventive health screening: A systematic review of the literature J. Magn. Reson. Imaging 2019 50 1489 1503 10.1002/jmri.26736 30932247 PMC6850647
10. Krogsbøll L.T. Jørgensen K.J. Gøtzsche P.C. General health checks in adults for reducing morbidity and mortality from disease Cochrane Database Syst. Rev. 2019 1 CD009009 10.1002/14651858.CD009009.pub3 30699470 PMC6353639
11. Summers P. Saia G. Colombo A. Pricolo P. Zugni F. Alessi S. Marvaso G. Jereczek-Fossa B.A. Bellomi M. Petralia G. Whole-body magnetic resonance imaging: Technique, guidelines and key applications Ecancermedicalscience 2021 15 1164 10.3332/ecancer.2021.1164 33680078 PMC7929776
12. Wolf S.M. Lawrenz F.P. Nelson C.A. Kahn J.P. Cho M.K. Clayton E.W. Fletcher J.G. Georgieff M.K. Hammerschmidt D. Hudson K. Managing Incidental Findings in Human Subjects Research: Analysis and Recommendations J. Law Med. Ethics 2008 36 219 248 10.1111/j.1748-720X.2008.00266.x 18547191 PMC2575242
13. Preez H.D. Lasker I. Rajakulasingam R. Saifuddin A. Whole-body magnetic resonance imaging: Incidental findings in paediatric and adult populations Eur. J. Radiol. 2020 130 109156 10.1016/j.ejrad.2020.109156 32645680
14. Bos D. Poels M.M.F. Adams H.H.H. Akoudad S. Cremers L.G.M. Zonneveld H.I. Hoogendam Y.Y. Verhaaren B. Verlinden V.J.A. Verbruggen J.G.J. Prevalence, Clinical Management, and Natural Course of Incidental Findings on Brain MR Images: The Population-based Rotterdam Scan Study Radiology 2016 281 507 515 10.1148/radiol.2016160218 27337027
15. Gibson L.M. Littlejohns T.J. Adamska L. Garratt S. Doherty N. UK Biobank Imaging Working Group Wardlaw J.M. Maskell G. Parker M. Brownsword R. Impact of detecting potentially serious incidental findings during multi-modal imaging Wellcome Open Res. 2017 2 114 10.12688/wellcomeopenres.13181.1 30009267 PMC6024231
16. Richter A. Sierocinski E. Singer S. Bülow R. Hackmann C. Chenot J.-F. Schmidt C.O. The effects of incidental findings from whole-body MRI on the frequency of biopsies and detected malignancies or benign conditions in a general population cohort study Eur. J. Epidemiol. 2020 35 925 935 10.1007/s10654-020-00679-4 32860149 PMC7524843
17. Gibson L.M. Nolan J. Littlejohns T.J. Mathieu E. Garratt S. Doherty N. Petersen S. Harvey N.C.W. Sellors J. Allen N.E. Factors associated with potentially serious incidental findings and with serious final diagnoses on multi-modal imaging in the UK Biobank Imaging Study: A prospective cohort study PLoS ONE 2019 14 e0218267 10.1371/journal.pone.0218267 31206530 PMC6576786
18. Schlett C.L. Rospleszcz S. Korbmacher D. Lorbeer R. Auweter S. Hetterich H. Selder S. Heier M. Linkohr B. Weckbach S. Incidental findings in whole-body MR imaging of a population-based cohort study: Frequency, management and psychosocial consequences Eur. J. Radiol. 2021 134 109451 10.1016/j.ejrad.2020.109451 33279799
19. Schmidt C.O. Hegenscheid K. Erdmann P. Kohlmann T. Langanke M. Völzke H. Puls R. Assel H. Biffar R. Grabe H.J. Psychosocial consequences and severity of disclosed incidental findings from whole-body MRI in a general population study Eur. Radiol. 2013 23 1343 1351 10.1007/s00330-012-2723-8 23239059
20. Hegedus P. von Stackelberg O. Neumann C. Selder S. Werner N. Erdmann P. Granitza A. Völzke H. Bamberg F. Kaaks R. How to report incidental findings from population whole-body MRI: View of participants of the German National Cohort Eur. Radiol. 2019 29 5873 5878 10.1007/s00330-019-06077-z 30915558
21. Bunnik E.M. van Bodegom L. Pinxten W. de Beaufort I.D. Vernooij M.W. Ethical framework for the detection, management and communication of incidental findings in imaging studies, building on an interview study of researchers’ practices and perspectives BMC Med. Ethics 2017 18 10 10.1186/s12910-017-0168-y 28166795 PMC5294804
22. Cole C. Petree L.E. Phillips J.P. Shoemaker J.M. Holdsworth M. Helitzer D.L. ‘Ethical responsibility’ or ‘a whole can of worms’: Differences in opinion on incidental finding review and disclosure in neuroimaging research from focus group discussions with participants, parents, IRB members, investigators, physicians and community members J. Med. Ethics 2015 41 841 847 26063579 10.1136/medethics-2014-102552
23. Wardlaw J.M. Davies H. Booth T.C. Laurie G. Compston A. Freeman C. Leach M. Waldman A.D. Lomas D. Kessler K. Acting on incidental findings in research imaging BMJ 2015 351 h5190 10.1136/bmj.h5190 26556813
24. Bunnik E.M. Vernooij M.W. Incidental findings in population imaging revisited Eur. J. Epidemiol. 2016 31 1 4 10.1007/s10654-016-0123-0 PMC4756040 26861155
25. Håberg A.K. Hammer T.A. Kvistad K.A. Rydland J. Müller T.B. Eikenes L. Gårseth M. Stovner L.J. Incidental Intracranial Findings and Their Clinical Impact; The HUNT MRI Study in a General Population of 1006 Participants between 50–66 Years PLoS ONE 2016 11 e0151080 26950220 10.1371/journal.pone.0151080 PMC4780781
26. Laxy M. Knoll G. Schunk M. Meisinger C. Huth C. Holle R. Quality of Diabetes Care in Germany Improved from 2000 to 2007 to 2014, but Improvements Diminished since 2007. Evidence from the Population-Based KORA Studies PLoS ONE 2016 11 e0164704 10.1371/journal.pone.0164704 27749939 PMC5066975
27. Löwel H. Döring A. Schneider A. Heier M. Thorand B. Meisinger C. The MONICA Augsburg surveys--basis for prospective cohort studies Gesundheitswesen 2005 67 (Suppl. S1) S13 S18 10.1055/s-2005-858234 16032512
28. Holle R. Happich M. Löwel H. Wichmann H.E. MONICA/KORA Study Group KORA—A research platform for population based health research Gesundheitswesen 2005 67 (Suppl. S1) S19 S25 10.1055/s-2005-858235 16032513
29. Ladwig K.H. Marten-Mittag B. Baumert J. Löwel H. Döring A. KORA Investigators Case-finding for depressive and exhausted mood in the general population: Reliability and validity of a symptom-driven diagnostic scale. Results from the prospective MONICA/KORA Augsburg Study Ann. Epidemiol. 2004 14 332 338 10.1016/j.annepidem.2003.09.003 15177272
30. von Zerssen D. The Symptom-checklist. Clinical Self-Rating Questionnaires from the Munich Psychiatric Information System Psycho Manual, Main Part Beltz-Verlag Weinheim, Germany 1976
31. Ladwig K.H. Marten-Mittag B. Erazo N. Gündel H. Identifying somatization disorder in a population-based health examination survey: Psychosocial burden and gender differences Psychosomatics 2001 42 511 518 10.1176/appi.psy.42.6.511 11815687
32. Kroenke K. Spitzer R.L. Williams J.B. The PHQ-9: Validity of a brief depression severity measure J. Gen. Intern. Med. 2001 16 606 613 10.1046/j.1525-1497.2001.016009606.x 11556941 PMC1495268
33. Martin A. Rief W. Klaiberg A. Braehler E. Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the general population Gen. Hosp. Psychiatry 2006 28 71 77 10.1016/j.genhosppsych.2005.07.003 16377369
34. Cohen S. Kamarck T. Mermelstein R. A global measure of perceived stress J. Health Soc. Behav. 1983 24 385 396 10.2307/2136404 6668417
35. Klein E.M. Brähler E. Dreier M. Reinecke L. Müller K.W. Schmutzer G. Wölfling K. Beutel M.E. The German version of the Perceived Stress Scale—Psychometric characteristics in a representative German community sample BMC Psychiatry 2016 16 159 10.1186/s12888-016-0875-9 27216151 PMC4877813
36. Ware J. Kosinski M. Keller S.A. 12-Item Short-Form Health Survey Med. Care 1996 34 220 233 10.1097/00005650-199603000-00003 8628042
37. Gandek B. Ware J.E. Aaronson N.K. Apolone G. Bjorner J.B. Brazier J.E. Bullinger M. Kaasa S. Leplege A. Prieto L. Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries: Results from the IQOLA Project. International Quality of Life Assessment J. Clin. Epidemiol. 1998 51 1171 1178 10.1016/S0895-4356(98)00109-7 9817135
38. Schmidt C.O. Sierocinski E. Hegenscheid K. Baumeister S.E. Grabe H.J. Völzke H. Impact of whole-body MRI in a general population study Eur. J. Epidemiol. 2016 31 31 39 10.1007/s10654-015-0101-y 26597793
39. Levis B. Benedetti A. Thombs B.D. DEPRESsion Screening Data (DEPRESSD) Collaboration Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: Individual participant data meta-analysis BMJ 2019 365 l1476 Erratum in BMJ 2019 , 365 , l1781 10.1136/bmj.l1476 30967483 PMC6454318
40. Peters A. Rospleszcz S. Greiser K.H. Dallavalle M. Berger K. The Impact of the COVID-19 Pandemic on Self-Reported Health Dtsch. Arztebl. Int. 2020 117 861 867 10.3238/arztebl.2020.0861 33295275 PMC8025933
41. Tolonen H. Dobson A. Kulathinal S. WHO MONICA Project Effect on Trend Estimates of the Difference between Survey Respondents and Non-respondents: Results from 27 Populations in the WHO MONICA Project Eur. J. Epidemiol. 2005 20 887 898 10.1007/s10654-005-2672-5 16284866
42. Kessler R.C. McGonagle K.A. Swartz M. Blazer D.G. Nelson C.B. Sex and depression in the National Comorbidity Survey. I: Lifetime prevalence, chronicity and recurrence J. Affect Disord. 1993 29 85 96 10.1016/0165-0327(93)90026-G 8300981
43. Rubinow D.R. Schmidt P.J. Sex differences and the neurobiology of affective disorders Neuropsychopharmacology 2019 44 111 128 10.1038/s41386-018-0148-z 30061743 PMC6235863
44. Zhao G. Ford E.S. Dhingra S. Li C. Strine T.W. Mokdad A.H. Depression and anxiety among US adults: Associations with body mass index Int. J. Obes. 2009 33 257 266 10.1038/ijo.2008.268 19125163
45. Rafanelli C. Offidani E. Gostoli S. Roncuzzi R. Psychological correlates in patients with different levels of hypertension Psychiatry Res. 2012 198 154 160 10.1016/j.psychres.2011.09.014 22386218
46. Hildrum B. Mykletun A. Stordal E. Bjelland I. Dahl A.A. Holmen J. Association of low blood pressure with anxiety and depression: The Nord-Trøndelag Health Study J. Epidemiol. Community Health 2007 61 53 58 10.1136/jech.2005.044966 17183016 PMC2465598
47. Meng L. Chen D. Yang Y. Zheng Y. Hui R. Depression increases the risk of hypertension incidence: A meta-analysis of prospective cohort studies J. Hypertens 2012 30 842 851 10.1097/HJH.0b013e32835080b7 22343537
48. Fluharty M. Taylor A.E. Grabski M. Munafò M.R. The Association of Cigarette Smoking with Depression and Anxiety: A Systematic Review Nicotine Tob. Res. 2017 19 3 13 10.1093/ntr/ntw140 27199385 PMC5157710
49. Diabetes Canada Clinical Practice Guidelines Expert Committee Robinson D.J. Coons M. Haensel H. Vallis M. Yale J.F. Diabetes and Mental Health Can. J. Diabetes 2018 42 (Suppl. S1) S130 S141 10.1016/j.jcjd.2017.10.031 29650085
50. Goodwin R.D. Association between physical activity and mental disorders among adults in the United States Prev. Med. 2003 36 698 703 10.1016/S0091-7435(03)00042-2 12744913
51. Giannelis A. Palmos A. Hagenaars S.P. Breen G. Lewis C.M. Mutz J. Examining the association between family status and depression in the UK Biobank J. Affect. Disord 2021 279 585 598 10.1016/j.jad.2020.10.017 33189065 PMC7780845
52. Fryers T. Melzer D. Jenkins R. Social inequalities and the common mental disorders: A systematic review of the evidence Soc. Psychiatry Psychiatr. Epidemiol. 2003 38 229 237 10.1007/s00127-003-0627-2 12719837
53. Sareen J. Afifi T.O. McMillan K.A. Asmundson G.J. Relationship between household income and mental disorders: Findings from a population-based longitudinal study Arch. Gen. Psychiatry 2011 68 419 427 10.1001/archgenpsychiatry.2011.15 21464366