Atrial fibrillation (AF) is a common arrhythmia with increasing prevalence with respect to age and comorbidities. AF may influence the prognosis in patients hospitalized with Coronavirus disease 2019 (COVID-19). We aimed to assess the prevalence of AF among patients hospitalized due to COVID-19 and the association of AF and in-hospital anticoagulation treatment with prognosis.de novo). Compared to those without AF, patients with AF were older and had more cardiovascular disorders. In adjusted analysis, AF was independently associated with an increased risk of short-term {p = 0.019, Hazard Ratio [(HR)] 1.236; 95% CI: 1.035–1.476} and long-term mortality (Log-rank p < 0.001) as compared to patients without AF. The use of novel oral anticoagulants (NOAC) in AF patients was associated with reduced short-term mortality . Moreover, in AF patients, NOAC use was associated with a lower probability of MACEs  without increase of RBCs transfusion.We assessed the prevalence of AF among patients hospitalized due to COVID-19 and the association of AF and in-hospital anticoagulation treatment with prognosis. Data of all COVID-19 patients hospitalized in the University Hospital in Krakow, Poland, between March 2020 and April 2021, were analyzed. The following outcomes: short-term  and long-term  mortality, major cardiovascular events (MACEs), pulmonary embolism, and need for red blood cells (RBCs) transfusion, as a surrogate for major bleeding events during hospital stay were assessed. Out of 4,998 hospitalized patients, 609 had AF (535 pre-existing and 74 AF increases short- and long-term risk of death in patients hospitalized due to COVID-19. However, the use of NOACs in this group may profoundly improve prognosis. Since the beginning of the global pandemic, more than 620 million people worldwide have developed coronavirus disease 19 (COVID-19) and there were almost more than 6 million fatal cases globally  events among patients with AF that were hospitalized due to COVID-19.We retrospectively analyzed the medical records of all consecutive patients who were admitted due to a SARS-CoV2 infection to the University Hospital in Krakow between March 6, 2020 and April 30, 2021. Patients were diagnosed with COVID-19 according to the World Health Organization (WHO) and Polish guidelines with the use of RT-PCR method  –29. The de novo AF was defined as an electrocardiogram (ECG) finding of AF during the hospital stay in a patient without a prior history of AF. In our study the term “AF” refers to both pre-existing and newly diagnosed, except where it has been clearly emphasized that we are discussing the pre-existing or newly diagnosed subgroups in which case we use the term pre-existing AF or de novo AF, respectively. The CHA2DS2-VASc score was calculated according to the current ESC guidelines  variant of SARS-CoV2 was the predominant strain of COVID-19 in Poland at the time we carried our study . Detailsidelines . The estidelines .The primary outcome in our study was the incidence of death from any cause on short-term  and long-term  period. The last was assessed using data obtained from the National Electronic Population Registration System in Poland.The secondary outcomes analyzed during hospitalization period were: major adverse cardiovascular events (MACEs) , pulmonary embolism , the need for red blood cells (RBCs) transfusion (as a surrogate for major bleeding events).The study was conducted in accordance with the guidelines of the Declaration of Helsinki and was approved by the Bioethics Committee of Jagiellonian University, decision number 1072.6120.278.2020.t-test depending on the equality of variances for normally distributed variables. The Mann–Whitney U-test was used for non-normally distributed continuous variables. Cox-proportional hazards models were fit to determine the adjusted associations between AF and primary and secondary outcomes. Factors influencing short-term mortality were identified in logistic regression model .Categorical variables were presented as numbers and percentages. Continuous variables were expressed as mean and standard deviation (SD) or median and interquartile range (IQR). Normality was assessed by the Shapiro–Wilk test. We divided the study population into two groups according to presence of AF (patients with AF and patients without AF). Differences between groups were analyzed using the Student's or Welch's on model . Becauseon model , these vde novo AF (1.5%) and 535 (10.7%) with a history of pre-existing AF. Demographic and clinical characteristics of the patients stratified by the presence of AF are shown in Overall, 4,998 patients (median [IQR] age 64.0 [52.0–74.0] years) were admitted to the University Hospital in Krakow due to COVID-19 and completed their hospital course . Our study population included 2,247 (45.0%) women and 2,751 (55.0%) men. There were a total of 609 (12.2%) patients with AF including 74 patients with Patients with AF, in comparison to the rest of the cohort were older and were more likely to have the following comorbidities: arterial hypertension, diabetes, hypercholesterolemia, coronary artery disease, heart failure, chronic kidney disease, COPD, previous history of stroke and previous history of myocardial infarction (MI) .Clinical characteristics and drug therapy among patients with AF and without AF are presented in p < 0.001, de novo in comparison to those with preexisting AF , the median CHA2DS2-VASc score was 2.0 [IQR: 1.0–3.0]. For further analysis we excluded from this group patients with clinical conditions which could have an impact on the selection of anticoagulation strategy, as described above. Consequently, 421 AF patients (median [IQR] age 76.0 [70.0–83.0] years) remained in this sub-analysis. 2DS2-VASc categories in this analysis. In the subgroup of patients who were eligible for anticoagulation treatment  a higher number of deaths at 30 days follow up  was observed in patients without any anticoagulation treatment in comparison to those treated with any anticoagulation  including 419 patients with AF . Kaplan-endpoint .de novo AF at hospitalization have higher risk of long-term mortality after 180-days of follow up period after hospital-discharge as compared to patients without AF.Our findings confirm that AF is an important prognostic factor for short-term mortality outcome in patients hospitalized due to COVID-19. Moreover, the use of NOACs in patients with AF was associated with reduced short-term mortality in the 30-day observational period. We also showed that COVID-19 patients with either pre-existing or de novo cases of AF during hospitalization in patients with COVID-19 indicated that the prevalence of de novo AF varied from 5% to 15% depending on age, clinical severity of COVID-19 and the number of co-morbidities  , COVID-19 severity and cardiovascular comorbidities increased the risk of in-hospital mortality (HR: 1.73) in AF patients, after adjustment for clinical confounders. This HR is slightly higher than in or study which may be explained by the older age in the Italian cohort.The impact of AF on short-term mortality in patients with COVID-19 was described in previous studies. Peltzer et al. found that the presence of AF or atrial flutter was independently associated with 30-day mortality (Odds Ratio: 1.93) . In the In our registry, the use of anticoagulation treatment in patients with AF was lower than could be expected according to current ESC recommendations for the management of AF 2018) . The ESC18 . The An increased risk for MACEs and thrombotic events is a typical complication for patients with AF , which ede novo vs. pre-existing AF regarding the rate of MACE requires a separate comment. In our study, we found that although there were no significant differences in the rate of individual events evaluated separately, , the composite end-point occurred significantly more often in the subgroup of patients with de novo AF compared to patients with pre-existing AF. Similar trend was observed by us for the frequency rate of pulmonary embolism. The above indicates that a recorded first-time episode of AF triggered by pulmonary infection cannot be viewed as benign, transient, or insignificant. The results of other investigators also indicate that patients with de novo AF discovered during a pulmonary infection, especially in the absence of previously effective anticoagulation, have an increased risk of adverse events and require special attention (Our comparative results of the subgroup of patients with ttention .de novo) comorbidities and thromboembolic complications among patients with infection giving clinical manifestation of pneumonia regardless of the causative agent   leading to pneumonia, sepsis or septic shock. Based on an analysis of the available literature, it should be noted that the relationship between AF and pneumonia had been well described in pre-COVID-19 era , 50, howon, etc) , 4, 52.de novo during pneumonia have increased risk of bleeding events (depletion of coagulation factors and platelets as a result of inflammatory response during infection)  . Based ofection) , 53, 54.n = 154, 3.5%) (p = 0.036). However, reduced mortality in response to anticoagulation was not significant in this study in time to event analysis  . Denas e4, 3.5%)  found inanalysis . In our de novo AF. The lack of difference in survival probability among patients with de novo and preexisting AF further confirms that de novo AF in patients with COVID-19 should not be considered as a transient phenomenon caused by COVID-19, but should be considered as condition that indicates a poorer prognosis in follow up. Our findings confirm that COVID-19 patients with AF require closer monitoring and optimal treatment after hospital discharge. The optimization of treatment strategies and development of additional prognostic tools may help reduce the risk of mortality. For example, the association between the CHA2DS2-VASc score and mortality has already been demonstrated in patients with COVID-19  which may have influenced some selection bias in this subgroup analysis. Another limitation is lack of information about anticoagulation treatment adherence in patients with AF after hospital discharge. We used the need for RBCs transfusion as a surrogate for bleeding outcomes which should be interpreted cautiously due to possible variations in transfusion strategies and the possibility that transfusions were given irrespective of a bleeding event.We acknowledge that there are several potential limitations to this study. Firstly, not all patients admitted to the hospital were monitored using continuous electrocardiographic monitoring and the diagnosis of AF was mainly carried out using ECG tracings. As a result, the occurrence of paroxysmal AF during hospitalization may have been underdiagnosed. It is also entirely possible that some interpreted as de novo AF is associated with increased mortality in short- and long-term follow-up. The use of NOACs exhibited a profound beneficial effect on outcomes including reduced mortality and MACE.In conclusion our findings expand previous evidence that among patients with chronic NCDs those with AF hospitalized due to COVID-19 have a poor prognosis. Both pre-existing and


To investigate how body height and trajectories of height from infancy through childhood and adolescence were associated with spinal pain in pre- and late adolescence.This prospective study included 43,765 individuals born into The Danish National Birth Cohort (DNBC) from 1996 to 2003. DNBC-data were linked with health and social data identified from Statistics Denmark registers. Spinal pain was self-reported in both the 11-year- and 18-year follow-up of DNBC and classified according to severity. Body height was measured from birth and onwards and further modelled as distinct developmental height trajectories by using latent growth curve modelling. Associations were estimated by using multinomial logistic regression models.Taller body height in childhood and adolescence was associated with approximately 20% increased likelihood of spinal pain in pre- and late adolescence among girls compared to their peers in the normal height group. For boys, taller body height was associated with spinal pain by late adolescence only. Spinal pain in pre-adolescence almost doubled the likelihood of spinal pain in late adolescence regardless of body height at age 18. Height trajectories confirmed the relationship for girls with the tall individuals being most likely to have spinal pain in both pre- and late adolescence.Tall body height during childhood and adolescence predisposes to spinal pain among girls in both pre-and late adolescence, and among boys in late adolescence. Body height is a contributing factor to the pathogenesis of spinal pain in adolescence; however, the mechanisms may be related to growth velocity, but for now uncertain.The online version contains supplementary material available at 10.1186/s12891-023-07077-3. Spinal disorders constitute an enormous global burden with adverse societal and personal consequences , and lowBody height is hardly modifiable and mainly influenced by environmental factors and genetic predispositions –17. DespTherefore, the overall aim of this study was to investigate how body height from birth up to adolescence were associated with spinal pain in pre-adolescence and late adolescence. Specifically, we aimed to first investigate body height at different ages from birth through childhood to adolescence in relation to spinal pain in pre-adolescence (11–12 years of age) and subsequently to spinal pain in late adolescence (18 years of age). Furthermore, we investigated developmental patterns of height and related these distinct height trajectories to spinal pain in 11-12-year-olds and 18-year-olds, respectively.www.dnbc.dk) [This longitudinal study applied data from The Danish National Birth Cohort (DNBC), nested within the nationwide population of Denmark. In brief, DNBC is a population-based cohort including 98,825 children born between 1996 and 2003, followed from birth and through childhood and young adulthood. DNBC is described in detail elsewhere (dnbc.dk) . The oveDNBC-data was linked to Danish national registers through the unique personal identification number assigned to all persons with a permanent residence in Denmark . All regLength at birth was retrieved from the Danish Medical Birth Registry . The remZanthro package in STATA with standardization according to the WHO-reference and included age in months [For latent growth curve modeling (LGCM), we modelled height z-scores to enable comparisons across age and sex. Z-scores were calculated using the n months .no pain, moderate pain, or severe pain  designed and validated to measure neck, mid back and low back pain in 9–11 year-olds . Childrelsewhere , supportPotential confounders were selected a priori, and identified using the methods of causal diagrams  and corresponding 95% confidence intervals (CI)  to account for potential selection bias as to handle potential bias arising from exclusion of missing data , 33. PreLGCM was used to estimate developmental height patterns from childbirth through childhood to adolescence and their relation to spinal pain in adolescence. The analysis was conducted as a two-step process, first by estimating height trajectories with height included as z-scores as a function of age . A priorThe tallest individuals differed from their shorter peers by being more likely to be born to term and from families of higher socioeconomic status Table . On averLoss-to-follow-up analyses revealed that the study participants were more often girls, from families of higher socioeconomic status, had non-smoking mothers during pregnancy, and from urban areas compared to those lost to follow-up. This was slightly more significant among those with further follow-up in late adolescence  the likelihood of severe spinal pain in late adolescence independently of height at age 18 for both sexes .Additional file 6: Supplementary File 6. Prevalence of spinal pain in pre-adolescence and late adolescence.Additional file 7: Supplementary File 7. Characteristics of the 90,978 participants invited to participate in DNBC-11 and included in the baseline cohort according to follow-up status at age 11 and follow-up status at age 18.Additional file 8: Supplementary File 8. Adjusted relative risk ratio(RRR) of spinal pain at age 11-12 according to body height at age 7 and 11, respectively, as continuous variables in 5 cm intervals .Additional file 9: Supplementary File 9. Adjusted relative risk ratio(RRR) of spinal pain at age 11-12 according to body height at age 11, additionally adjusted for puberty maturation at age 11 .Additional file 10: Supplementary File 10. Adjusted relative risk ratio(RRR) of spinal pain at age 18 according to body height at age 18 taking spinal pain status in pre-adolescents into account .


A negative multiparametric magnetic resonance imaging (mpMRI)-guided prostate biopsy in patients with suspected prostate cancer (PC) results in clinical uncertainty, as the biopsy can be false negative. The clinical challenge is to determine the optimal follow-up and to select patients who will benefit from repeat biopsy. In this study, we evaluated the rate of significant PC  and PC detection in patients who received a follow-up mpMRI/ultrasound-guided biopsy for persistent PC suspicion after a negative mpMRI/ultrasound-guided biopsy. We identified 58 patients at our institution that underwent repeat targeted biopsy in case of PI-RADS lesions and systematic saturation biopsy between 2014 and 2022. At the initial biopsy, the median age was 59 years, and the median prostate specific antigen level was 6.7 ng/mL. Repeat biopsy after a median of 18 months detected sPC in 3/58 (5%) patients and Gleason score 6 PC in 11/58 (19%). Among 19 patients with a downgraded PI-RADS score at the follow-up mpMRI, none had sPC. In conclusion, men with an initial negative mpMRI/ultrasound-guided biopsy had a high likelihood of not harboring sPC at repeat biopsy (95%). Due to the small size of the study, further research is recommended. Prostate cancer (PC) is the second most frequently diagnosed malignancy in men, representing 27% of all new cancer diagnoses in this group . Early dMultiparametric magnetic resonance imaging (mpMRI) and fusion biopsy have emerged as promising techniques for PC detection, as they enhance the accuracy of PC diagnosis ,14,15,16The Prostate Imaging Reporting and Data System (PI-RADS) classification, proposed by the American College of Radiologists and European Society of Urogenital Radiology, has been suggested for standardized MRI interpretation ,19. The When a patient has a PI-RADS 4/5 lesion on initial mpMRI, a negative mpMRI/TRUS-guided biopsy presents a clinical challenge for future patient management . A negatThis study evaluates the rate of sPC and PC detection in repeat mpMRI/TRUS-guided biopsy after an initial negative mpMRI/TRUS-guided biopsy. At the same time, we aimed to identify subgroups of patients with a negative initial prostate biopsy that have a negligible risk of harboring sPC and who could therefore potentially avoid a further biopsy. This would provide clinicians with useful information on how to follow-up patients with an initial negative mpMRI/TRUS-guided fusion biopsy without missing men at higher risk of sPC.We conducted a retrospective analysis of 2051 cases in which patients underwent mpMRI and combined targeted (in the case of a suspicious PI-RADS lesion) and systematic prostate biopsy at our institutions between September 2014 and September 2022. Of these cases, 58 men underwent two mpMRI/TRUS-guided biopsies at the University Hospital in Heidelberg, Germany, because of a persistent suspicion of PC after a negative initial biopsy . At the 2. Dynamic contrast-enhanced MRI was performed with a T1-weighted transverse time-resolved MR sequence  during the bolus injection of a standard weight-adapted dose of 0.1 mmol/kg bodyweight of intravenous Gadobutrol  at an injection rate of 3 mL/s, followed by a 50 cc sodium chloride flush. MpMRI reporting was accomplished as part of clinical routine by board-certified radiologists (each with more than three years of experience in prostate imaging), according to PI-RADS guidelines. All exams were reviewed in a multidisciplinary conference prior to biopsy for quality assurance, including a review of all lesion targets stated in the clinical reports. The exams in the study cohort period were assessed under the supervision and guidance of the same specialized uro-radiologist with longstanding experience in prostate imaging (DB). In addition, all radiologists retrospectively reviewed the mpMRI reports and biopsy results on a regular basis.Prebiopsy mpMRI without the use of an endorectal coil was performed in all patients. MRI was performed with a 3-T scanner . All mpMRI scans followed the PI-RADS recommendations and were acquired with a standard multichannel body coil and an integrated spinal phased array coil, according to European Society of Urogenital Radiology guidelines ,30. The All patients underwent a transperineal prostate biopsy while under general anesthesia. The biopsies were performed using either the MedCom BiopSee platform  or UroNav , which utilize software registration. Prostate biopsy cores were taken by an 18 G × 250 mm biopsy needle with the assistance of a bk medical flex focus 1202 ultrasound device and a 12–4 MHz ultrasound frequency range. The biopsy procedure involved TB in case of suspicious lesions identified on the MRI, followed by a systematic saturation biopsy (SB) in accordance with the Ginsburg protocol, as described in previous literature ,11,31. AThe targeted and systematic core samples were securely fixed in paraffin and subjected to separate analysis under the guidance of a specialized uro-pathologist (AS), adhering to the standards set by the International Society of Urological Pathology (ISUP) . The diat-tests for continuous variables and chi-squared tests/Fisher’s exact tests for categorical variables. All statistical analyses were performed at a 5% significance level.Cohort characteristics were summarized using descriptive statistics, including median and interquartile range (IQR) for continuous variables and frequencies for categorical variables. Patient characteristics were compared between those who had PC detected by repeat biopsy and those who did not, using p = 0.03), but the number of PI-RADS 3–5 lesions on the initial MRI did not differ between the two groups (p = 0.11).The study cohort consisted of 58 patients who received a subsequent MRI/TRUS-guided prostate biopsy after an initial negative MRI/TRUS-guided biopsy. At the initial biopsy, the median age was 59 (IQR: 54–64) years and the median initial PSA was 6.7 (IQR: 4.8–8.9) ng/mL. The median prostate volume was 53 (IQR: 41–70) ml and the median number of sampled biopsy cores was 31 (IQR: 27–33). Overall, a PI-RADS score of 1–2 was present in 7 patients, PI-RADS 3 in 19 patients, PI-RADS 4 in 26 patients, and PI-RADS 5 in 6 patients. We identified a group of 14 patients (24%) in whom PC was detected on the repeat biopsy, and a group of 44 patients (76%) who showed no malignancy on the repeat biopsy. The two groups did not differ in clinical parameters such as age or PSA at the time of the initial biopsy, as shown in The median time between the initial and repeat biopsy was 18 (IQR: 10–24) months . sPC detThe axial images of two patients in whom sPC was detected on the repeat MRI/TRUS-guided fusion prostate biopsy are shown in Patient II is a 65-year-old patient presenting with an initial PSA value of 4.9 ng/mL . The iniPatient IV is a 55-year-old patient with an initial PSA value of 12 ng/mL . MpMRI rThe management of patients whose initial prostate biopsies are negative for PC is a common issue for urologists. MRI/TRUS-guided biopsies that do not result in a PC diagnosis may provide more reassurance than traditional TRUS-guided biopsies . The effOur analysis revealed that sPC was detected in 5% of men (3/58) on repeat MRI/TRUS-guided biopsy. This represents a significant improvement in an initial biopsy setting over the former standard 12-core TRUS-guided biopsy, which has a considerably lower diagnostic safety for the patient. In systematic TRUS-guided prostate biopsy, sPC is not an uncommon finding on repeat biopsy and the false negative rate for PC diagnosis is estimated to be 20–40% . Thus, oComparing our results with these previous studies, it is noteworthy that our repeat biopsy sPC detection rate of 5% (3/58) is lower than those reported in previous literature. This might be because our initial MRI/TRUS-guided biopsy approach offers high diagnostic security, since a high number of prostate biopsy cores (median: 31 cores) are taken, and biopsies are performed in a high-volume hospital with many years of experience. An extensive sampling during prostate biopsy is particularly relevant in larger prostate volumes, since increased prostatic volume significantly reduces the chance of detecting PC . MeanwhiIn our study, there was no significant association between clinical parameters, PI-RADS lesions, or PI-RADS lesion downgrading on repeat MRI and the risk of PC detection at repeat biopsy that could be used to decide for or against repeat biopsy. The lack of statistical significance might be due to the small number of patients. We observed that none of the 19 patients whose PI-RADS score was downgraded on follow-up mpMRI had sPC detected on repeat biopsy. The data may indicate that there might be an association between mpMRI downgrading and sPC detection on repeat biopsy; however, the number of patients with sPC detection is very limited in our study. Comparable to our study design, Meng et al.  performeFurthermore, as approximately 20% of primary PC is not visible on mpMRI , suspiciThe available literature with mostly small study sizes demonstrates the need for future prospective, multi-center trials to better manage patients with negative prostate biopsy results in the era of initial MRI/TRUS-guided biopsy. Future work should concentrate on defining a surveillance and intervention strategy for men with an initial negative MRI/TRUS-guided prostate biopsy, such as repeat mpMRI in case of suspicious PI-RADS lesions. A recent review suggested patient stratification according to the overall PI-RADS lesion on initial mpMRI for the follow-up of patients with an initial negative prostate biopsy : For PI-GSTP1, RASSF1, APC, CCND2, and PITX2. Their 5-mC levels have shown high sensitivity and specificity in distinguishing PC from benign tissue [GSTP1, RASSF1, and APC analysis showed better performance than histological evaluation of biopsy samples, identifying PC in 62% and 68% of histologically negative biopsies in two separate large studies  [Over the next few decades, clinical and genetic markers will have the potential to improve the ability to determine the necessity for repeat biopsy . Epigenon tissue ,50,51,52ctively) ,49. Therctively) . In addictively) .Our study has several limitations. First, our results were obtained in a single tertiary care center with extensive experience in mpMRI and prostate fusion biopsy. The use of mpMRI in PC detection requires a learning curve for the entire team; thus, the generalizability of our results to a real-world setting must be made with caution ,55,56.In addition, our study is subject to all the potential biases that are expected in a retrospective study design. We acknowledge that not all patients with an initial negative biopsy and clinical suspicion of PC have received a planned re-biopsy and that the number of men who underwent a repeat biopsy is limited. In addition, we did not have access to the information on whether patients have had a repeat mpMRI or prostate biopsy at another institution. Due to the small sample size, differences between the groups might well not have been detected. Nevertheless, our study corresponds to the real-life setting, since there are currently no international recommendations on who and when to re-biopsy after a negative MRI/TRUS-guided biopsy. These circumstances are consistent with the situation described in a recent review by Grivas et al., in which the authors were confronted with the small number of studies on repeat biopsy after an initial negative MRI/TRUS-guided prostate biopsy. After study protocol revision, nine studies were included in the review, seven of which were conducted with <50 study patients .Furthermore, it should be noted that in addition to lesions missed on the initial biopsy due to misregistration, the sPC detection rate of repeat targeted biopsy can be affected by newly detected lesions due to disease progression. Newly developed PC or PC newly visible on MRI should be especially considered if there is a long time interval between the initial and repeat biopsy.Our study distinguishes itself, as our approach of TB and SB at initial and repeat biopsy provides a very high diagnostic security of the presence of PC, having been correlated to RP histopathology . HoweverSeveral studies have shown that some sPC is missed in fusion TB, and that the combination of SB and TB detects more sPC than TB alone . It is uFinally, our reference standard for PC detection is SB and TB instead of RP, as expected in the setting of PC detection in an at-risk population. However, the method of combined MRI-TB and SB has previously been validated at our institution by correlation with RP specimen .The patients in our study cohort with an initial negative mpMRI/TRUS-guided prostate biopsy had a high likelihood of not having sPC (95%) at repeat biopsy after a median of 18 months. None of the patients with sPC detected on the repeat biopsy had PI-RADS score downgrading on the follow-up mpMRI, which may indicate that there is an association. Due to the small cohort size, further research is recommended.


Adult-onset diabetes mellitus (here: aDM) is not a uniform disease entity. In European populations, five diabetes subgroups have been identified by cluster analysis using simple clinical variables; these may elucidate diabetes aetiology and disease prognosis. We aimed at reproducing these subgroups among Ghanaians with aDM, and establishing their importance for diabetic complications in different health system contexts. We used data of 541 Ghanaians with aDM  from the multi-center, cross-sectional Research on Obesity and Diabetes among African Migrants (RODAM) Study. Adult-onset DM was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, documented use of glucose-lowering medication or self-reported diabetes, and age of onset ≥ 18 years. We derived subgroups by cluster analysis using (i) a previously published set of variables: age at diabetes onset, HbA1c, body mass index, HOMA-beta, HOMA-IR, positivity of glutamic acid decarboxylase autoantibodies (GAD65Ab), and (ii) Ghana-specific variables: age at onset, waist circumference, FPG, and fasting insulin. For each subgroup, we calculated the clinical, treatment-related and morphometric characteristics, and the proportions of objectively measured and self-reported diabetic complications. We reproduced the five subgroups: cluster 1  and cluster 5  with no dominant diabetic complication patterns; cluster 2  characterized by the highest proportions of coronary artery disease  and stroke (13%); cluster 3  showing the highest proportions of kidney dysfunction (40%) and peripheral artery disease ; and cluster 4  characterized by the highest proportion of retinopathy (14%). The second approach yielded four subgroups: obesity- and age-related (68%) characterized by the highest proportion of CAD (9%); body fat-related and insulin-resistant (18%) showing the highest proportions of PAD (6%) and stroke (5%); malnutrition-related (8%) exhibiting the lowest mean waist circumference and the highest proportion of retinopathy (20%); and ketosis-prone (6%) with the highest proportion of kidney dysfunction (30%) and urinary ketones (6%). With the same set of clinical variables, the previously published aDM subgroups can largely be reproduced by cluster analysis in this Ghanaian population. This method may generate in-depth understanding of the aetiology and prognosis of aDM, particularly when choosing variables that are clinically relevant for the target population. This comprises mainly type 2 diabetes, Latent Autoimmune Diabetes in Adults (LADA), and type 1 diabetes, and translates to 75% of all diabetes cases worldwide2. Among adults in Ghana and Ghanaian migrants in Europe, aDM is highly prevalent, ranging between 4 and 15%3. Importantly, there are ethnic differences in the risk of diabetic complications. A recent systematic literature review demonstrates that individuals with diabetes mellitus of African ancestry are at significantly higher risk of diabetic complications than European populations, such as end-stage renal disease4. At the same time, geographic context plays an important role for disease management and the prevalence of diabetic complications. Diabetes awareness, appropriate treatment, and good glycemic control are lower in non-migrants compared to Ghanaian migrants5. Non-migrant Ghanaians show higher prevalence of diabetic complications than Ghanaian migrants in Europe6. As a consequence, productivity and quality of life decrease among non-migrant Ghanaians diagnosed with aDM, translating into increased healthcare expenditure and elevated risk for premature mortality7.The worldwide upsurge of diabetes mellitus is mainly attributed to increases of adult-onset diabetes mellitus (aDM) in low- and middle-income countries9. Ahlqvist et al.9 have performed a hierarchical cluster analysis with six common clinical variables and have identified five subgroups among Scandinavian adults with newly diagnosed diabetes mellitus. These subgroups are characterized by distinct clinical and anthropometric features and show marked differences in the development of diabetic complications. Since then, several research groups have replicated this approach. In 2020, Sarria-Santamera et al.10 have synthesized the findings of 14 studies that identified subgroups by unsupervised learning methods. Five of them have applied hierarchical clustering13, and five subgroups are common10: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MORD), and mild age-related diabetes (MARD). Regarding complications, SIRD shows increased risk for kidney disease in some13, but not all studies15. SIDD and partly SAID show increased risks for retinopathy. The results are conflicting for proportions of macrovascular complications10. In summary, these studies suggest that cluster analysis can be useful for identifying aDM subgroups early in the course of the disease, and thereby, contributing to understanding disease aetiology. Still, the verification of this approach is pending in more diverse populations and with variables that have established clinical relevance for them. For instance, anthropometric measures reflecting fat distribution might be more suitable than BMI in sub-Saharan African populations16. So far, studies in African-descent populations are scarce17, and virtually absent for populations from sub-Saharan Africa.The classification into aDM subgroups based on clinical and anthropometric characteristics is a promising approach to understanding the disease aetiology and possibly progression9, (ii) to generate subgroups using Ghana-specific clinical variables, and (iii) to determine the proportions of diabetic complications among the identified subgroups.Therefore, the aim of this study was to establish the reproducibility of aDM subgroups by means of cluster analysis in a Ghanaian study population. The specific objectives were (i) to apply the previously published method by Ahlqvist et al. for the identification of aDM subgroups3, with a crude prevalence of aDM of 9%18. The primary objective of the RODAM study was to disentangle the relative contributions of (epi)genetic and non-genetic risk factors for obesity and diabetes mellitus. The study protocol and the procedures of the RODAM study have previously been published19. Medical history, lifestyle and socio-economic factors were recorded either by ethnically matched personnel in questionnaire-based interviews or were self-administered. Specifically, we included questions about the age of disease onset, previous diagnosis of diabetes mellitus, the start of medication prescription, and the type of glucose-lowering medications.The Research on Obesity and Diabetes among African Migrants (RODAM) study is a multi-center cross-sectional study that was conducted between July 2012 and September 2015. It involved Ghanaian adults (25–70 years) living in rural and urban Ghana (Ashanti region), and three European cities  (N = 6385)Adult-onset diabetes mellitus was defined according to WHO guidelines as fasting plasma glucose ≥ 7.0 mmol/L, documented use of glucose-lowering medication or self-reported diabetes. We excluded individuals with age of disease onset before 18 years. Fasting venous blood samples and urine samples were collected by trained personnel according to standard operating procedures; blood samples were centrifuged. Urine, serum and plasma samples were ultimately stored at − 80 °C. All biochemical analyses were performed using an ABX Pentra 400 chemistry analyser .20; using fasting plasma glucose and fasting insulin in the assessments. Serum creatinine concentration was determined by a kinetic colorimetric spectrophotometric isotope dilution mass spectrometry calibration method (Roche Diagnostics). Estimated glomerular filtration rate (eGFR) was calculated using the 2009 CKD-EPI (CKD Epidemiology Collaboration) creatinine equation and the severity of kidney disease categorized according to the 2012 KDIGO guidelines21. Urinary albumin concentration (in mg/L) was measured by an immunochemical turbidimetric method (Roche Diagnostics). Urinary creatinine concentration (in μmol/L) was measured by a kinetic spectrophotometric method (Roche Diagnostics). Urinary albumin–creatinine ratio  was calculated by taking the ratio between urinary albumin and urinary creatinine and stratified according to the 2012 KDIGO classifications: A1, < 3 mg/mmol ; A2, 3 to 30 mg/mmol (moderately increased); and A3, > 30 mg/mmol (severely increased).The biomarker profile of this study comprised HbA1c, serum insulin, blood lipids , inflammatory markers (C-reactive protein), and liver enzymes (aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyl transferase (GGT)). We calculated the Homeostatic Model Assessment for insulin resistance (HOMA-IR) and for beta-cell capacity (HOMA-beta) according to Matthews et al.22.Antibodies against glutamic acid decarboxylase (GAD65Ab) were determined by radioligand binding assay (RBA) using established cut-offs , as previously described by Hampe et al.2). Blood pressure (BP) was measured three times using a validated semiautomated device , with appropriately sized cuffs after at least 5 min rest while seated. The mean of the last two BP measurements was used for the analyses. Hypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg, and/or being on antihypertensive medication treatment. Ankle brachial index (ABI), the ratio of the resting systolic blood pressure at the ankle to the resting systolic brachial pressure at the arm, was obtained from two blood pressure measurements on the left side (leg and arm) and two on the right side (leg and arm) using the Microlife Watch BP Office ABI with appropriately sized cuffs, after at least 10 min of supine rest. The cuffs for measuring the ankle and brachial pressures were placed just above the ankle and at the upper arm, respectively.Trained study personnel performed the physical examinations. Anthropometric measurements were taken in light clothes and without shoes, including weight (kg) by a person scale, height (cm) by a stadiometer, and waist circumference (cm) using a measuring tape . We calculated Body Mass Index (BMI) as weight over squared height  < 0.924. CAD was assessed using the WHO Rose angina questionnaire25. We used self-reported data for retinopathy and stroke. Retinopathy was assessed by a positive response to the question ‘Have you ever been told by a doctor that you have eye disease or eye damage as a result of diabetes?’. Stroke was assessed by a positive reply to the question ‘Have you ever had a stroke?’.Validated methods were used to detect kidney dysfunction, peripheral artery disease (PAD) and coronary artery disease (CAD). Albuminuria was defined as urinary albumin ≥ 3 mg/mmol, and impaired eGFR was based on CKD-EPI eGFR criteria, comprising G3 to G5. Nephropathy was defined as albuminuria or microalbuminuria (stages 2–4) according to the report from Joint Committee on Diabetic NephropathyAll analyses were conducted using the software SAS, version 9.3 .Among the 541 participants with aDM, there were 140 individuals with missing or implausible values (< 2nd and ≥ 98th percentile) in any of the variables of interest. These variables comprised socio-economic data (n = 140), anthropometric measures (n = 52), biochemical data (n = 40), and lifestyle information (n = 24). To avoid selection bias and to improve statistical power, we applied multiple imputation using the discriminant fully conditional specification (FCS) method and derived 10 imputed datasets. FCS is also known as multiple imputation by chained equations (MICE) and uses separate conditional univariate imputation models specified for each incomplete variable, with other variables as predictors. Multiple imputation was applied under the assumption that the propensity of missing data can be explained by the observed data . This assumption was supported by the good imputation efficiency (98.2–99.9%).9 using the same six clinical and anthropometric variables (first approach). These have been chosen because they are readily available in routine clinical practice: age at onset of diabetes mellitus, body mass index (BMI), HbA1c, HOMA-beta, HOMA-IR, and the presence of glutamate decarboxylase antibodies (GAD65Ab). We centered all variables to a mean value of 0 and a standard deviation (SD) of 1. Then, a two-step cluster analysis was applied, in which the first step extracted a predefined number of clusters on the basis of silhouette width (n = 5). This step used log-likelihood as a distance measure and Schwarz’s Bayesian criterion for clustering. The second step involved k-means clustering to confirm the stability of clusters. In the k-means clustering, GAD65Ab presence was not included, because this analysis can only accommodate continuous variables. Cluster labels were assigned according to previously published subgroups by Ahlqvist et al.9.Two approaches were applied to derive aDM subgroups using exploratory cluster analysis. First, we re-constructed the clusters by Ahlqvist et al.27, and thus, waist circumference was preferred over BMI. Also, we included FPG as a readily available biomarker in resource-poor settings. Calculations of HOMA-beta and HOMA-IR based on C-peptide do not reflect the important role of serum insulin in aDM. While C-peptide only describes pancreatic insulin release, fasting insulin also reflects impaired hepatic insulin clearance, which likely contributes to insulin resistance28. Therefore, we preferred insulin values over C-peptide-based calculations of HOMA. In the second approach, we did not use GAD65Ab, because our previous work in the RODAM Study suggests that GAD65Ab may poorly discriminate for diabetes mellitus22 and may not be readily available in sub-Saharan Africa. Age at onset of aDM was also included in this second approach. The same two-step cluster analysis was applied to the data as described above, with the modification that 2 to 5 cluster solutions were generated in step 1. The optimal number of clusters to be extracted was identified based on cluster size (> 5% of the population) and the dendrogram of explained variance (R2 > 0.25). Cluster labels were assigned by examining the phenotypic characteristics.Second, we repeated the cluster analysis using variables that have established clinical relevance for aDM among Ghanaian populations (second approach). More specifically, we have previously seen that anthropometric measures of abdominal obesity have the best discriminative ability for diabetes mellitus among Ghanaian adultsTwo sensitivity analyses were performed. First, we repeated the cluster analysis after exclusion of individuals on insulin treatment to assess the stability of the subgroups. Second, we derived clusters stratified by study location (Ghana and Europe).2-test.For each cluster description, we calculated means and SD for normally distributed variables, medians and interquartile ranges (IQR) for skewed variables, and proportions for categorical variables. The proportions of diabetic complications were calculated for each cluster and were compared by χThe RODAM Study was conducted according to the guidelines laid down in the 1964 Declaration of Helsinki and its later amendments. All procedures involving human subjects were reviewed and approved by the respective ethics committees in Ghana , the Netherlands , the UK , and Germany . Written informed consent was obtained from all participants.The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript and in the decision to submit the paper for publication..2%). The proportion of individuals with GAD65Ab-positivity was similar between participants with diabetes mellitus and individuals without the disease (5% versus 6%). More than two-thirds of individuals with aDM were overweight or obese (BMI ≥ 25.0 kg/m2) and 56% had abdominal obesity (waist circumference > 102 cm for men and > 88 cm for women).Supplementary Table Figure 9: cluster 1 , cluster 2 , cluster 3 , cluster 4 , and cluster 5 . The obesity-related subgroup was characterized by late age of disease onset. The age-related subgroup also showed late age of disease onset and high frequency of female cases. The autoimmune-related subgroup (GAD65Ab-positive) had a younger age at disease onset and the least frequent family history of diabetes. The insulin-deficient subgroup was characterized by early age of disease onset, and the lowest serum insulin levels. For the insulin-resistant subgroup, the majority of cases were women with the highest serum insulin levels, HOMA-IR and HOMA-beta. The stability of subgroups according to k-means clustering was robust, except for the discrimination between autoimmune and insulin-deficient subgroups. These were extracted as one subgroup by k-means analysis , cluster 2 , cluster 3 , and cluster 4 . Despite the fact that we omitted GAD65Ab from this cluster analysis, 23 out of the 29 individuals with GAD65Ab clustered in the ketosis-prone subgroup. The malnutrition-related subgroup had the lowest serum insulin concentration and HOMA-beta, and showed the lowest BMI, waist circumference and fat mass. The body fat-related and insulin-resistant subgroup was characterized by female preponderance, high fasting glucose concentration, and high serum insulin levels. This was also seen for the ketosis-prone subgroup, who also showed high BMI and waist circumference; 6% of them had urinary ketone bodies. Again, the stability of subgroups was confirmed through k-means clustering, with some overlap between the obesity- and age-related subgroup and the body fat-related and insulin-resistant subgroup Table .Overlaps between the two approaches are presented in Supplementary Figure We describe the distributions of medications across subgroups in Table Among the subgroups derived from the first approach, the following diabetic complications were observed Fig. A,C. The The four subgroups derived from the second approach were characterized as follows Fig. B,D. The We performed two sensitivity analyses. First, we repeated the second cluster analysis approach to account for the influence of insulin treatment on serum insulin concentration. For this analysis, 87 out of 541 individuals who were on insulin medication were excluded. Supplementary Table Also, we derived aDM subgroups using the Ghana-specific set of variables and stratified by location (Ghana versus Europe) to account for contextual differences. The proportions of subgroups in the two locations are shown in Supplementary Figure 9: obesity-related (73%), age-related (10%), autoimmune-related (5%), insulin-deficient (7%), and insulin-resistant (5%). In comparison to European and Asian populations10, there were marginal differences in the characteristics of subgroups, their proportions, and the distributions of diabetic complications across clusters. In a second approach, we derived subgroups by employing variables with established clinical relevance for Ghanaian populations. This approach yielded four subgroups that had only some overlap with the five initial clusters regarding clinical, treatment-related and complication profiles: obesity- and age-related (68%), malnutrition-related (18%), body fat-related and insulin-resistant (8%), and ketosis-prone (6%).Here, we reproduced, for the first time among sub-Saharan African adults, subgroups of aDM using data-driven cluster analysis with six simple clinical variables. We extracted the same five clusters as in the original study9. However, the RODAM Study population showed differences in the occurrence and some characteristics of these clusters. A recent systematic review summarizes data of 14 studies that have employed unsupervised learning methods to derive subgroups of adult-onset diabetes in populations from Asia, Europe, the USA, and Australia10. In five of them, the same five subgroups were extracted. While the obesity-related subgroup is also highly prevalent in previous studies (20–34%), this cluster amounted to 73% among the present Ghanaian sample. Further, the age-related subgroup constitutes the most common cluster in non-Ghanaian populations (34–45%). Yet, only 10% of individuals with aDM in the RODAM Study exhibited an age-related phenotype. Another difference is discernible for the insulin-resistant subgroup. This yielded 7–24% in previous studies but only 5% in the present population. These findings might be explained by the poor discriminative ability of BMI for diabetes mellitus among Ghanaians27 and the suggested large proportion of metabolically healthy obesity among African populations29. In fact, all subgroups in this study, except for the age-related cluster in the first approach, showed high BMI values and low HOMA-IR . This subgroup resembled the obesity-related subgroup identified in the first approach, particularly mean BMI and mean HbA1c were high, while insulin resistance was moderate. The cluster characteristics largely overlapped with the body fat-related and insulin-resistant subgroup (18%), except for the high values of mean HOMA-IR in the latter. Therefore, the body fat-related and insulin-resistant subgroup is similar in proportion and features to SIRD derived in other populations10.In fact, the second approach appears to separate these so-called atypical forms of diabetes mellitus from the conventional types. Many features of the ketosis-prone subgroup (6%) from the second approach accord with previously published features of this atypical phenotype: Age at onset was in the fourth decade, family history of diabetes was common, BMI, and body fat percentage were high, 6% of these patients presented with urinary ketones, half of the participants received insulin immediately at diagnosis, and beta-cell reserve was moderate Table . In addi30 have suggested therapeutic strategies for the observed subgroups along a continuum of insulin demand and secretory capacity for the prevention of diabetic complications. For insulin-deficient subgroups, including the autoimmune-related and the insulin-deficient clusters, insulin therapy appears to be the treatment of choice, possibly in combination with oral insulin secretagogues, to stabilize long-term blood glucose (HbA1c) and to prevent the development of retinopathy—the most common diabetic complication in these subgroups12. This is corroborated by our findings in the RODAM Study for both analysis approaches. Yet, we also noticed some discrepancies between our results and the previously reported treatment and complication profiles of those clusters characterized by insulin deficiency. Among Ghanaians, kidney dysfunction and PAD were also common in those subgroups characterized by high proportions of GAD65Ab; they were mainly prescribed oral glucose-lowering medication. This difference may stem from our cross-sectional study design and the inclusion of prevalent aDM cases with existing diabetic complications. Indeed, disease duration affects the cluster allocation, as individuals potentially transition from one subgroup to another over time13. This remains to be investigated when follow-up data of the RODAM study population will be available. For subgroups characterized by older age of disease onset, metformin and dipeptidyl peptidase-4 inhibitors (DPP4i) have been proposed to prevent the observed risks of coronary events31. In the RODAM Study population, similar treatment profiles and diabetic complications  were discernible in the age-related subgroup (first approach) and in the obesity- and age-related subgroup (second approach). Yet, lifestyle modifications were also frequently prescribed for the obesity- and age-related subgroup derived in the second approach, possibly due to the large proportion of individuals with insulin resistance and obesity. In other studies, this subgroup emerged as the obesity-related cluster with high BMI and frequent occurrence of liver dysfunction. For diseases management, literature suggests to emphasize on weight control with diet and physical exercise plus metformin, and other oral glucose-lowering medication31. This strategy is promoted even stronger for insulin-resistant subgroups, and comprise a combination of weight-loss through lifestyle and surgery, as well as insulin-sensitizing drugs for the prevention of microvascular complications, mainly kidney dysfunction31. Our results from the first approach confirm these treatment and complication profiles, whereas the body fat-related and insulin-resistant subgroup from the second approach was dominated by liver dysfunction and cardiovascular events.Recently, Tanabe et al.32, which allows participants to represent in more than one subgroup—an approach that might have less clinical relevance but could facilitate the study of disease aetiology. Also, future work might expand to classify various states of dysglycemia, including impaired glucose tolerance and pre-diabetes. Reassuringly, the clusters in our study remained stable when individuals on insulin treatment were excluded from the analysis (Ghana-specific approach). Additional variables can aid the derivation of aDM subgroups, including metabolomics and genomics10. Yet, we refrained from these approaches as they are unlikely to be incorporated into clinical practice. Also, we acknowledge that HOMA-beta and HOMA-IR were based on fasting insulin, not on fasting C-peptide, which contrasts the methodology by Ahlqvist and colleagues. For HOMA-IR, insulin-based values may overestimate insulin resistance among individuals on insulin medication. Still, sensitivity analysis excluding participants on insulin treatment (16%) produced similar subgroups. For HOMA-beta, insulin-based calculations might underestimate beta-cell function due to hepatic insulin storages, but they reflect impaired hepatic insulin clearance, which likely contributes to insulin resistance28. In this cross-sectional study, we cannot comment on the stability of these clusters, as individuals can migrate over time and clinical features overlap between the identified subgroups. The RODAM Study population has been thoroughly phenotyped regarding biomarkers of glucose metabolism, anthropometric measurements, and several diabetic complications. Yet, we acknowledge that retinopathy, stroke and CAD were assessed by self-report only. Just as a large proportion of individuals with aDM from sub-Saharan Africa remains undiagnosed3, so do their diabetic complications. Therefore, self-reported complications might have underestimated the true proportions in our study population, particularly for microvascular complications, and thus, contributed to type II error. Similarly, the various oral glucose-lowering medications were not documented in this study; this limits the interpretation of treatment-related characteristics across subgroups.This analysis uniquely adds to understanding the aetiology of aDM, and thereby, contributes to improved clinician counseling and pharmacological interventions. We provide evidence that subgroups of diabetes mellitus may be reproduced in sub-Saharan African populations, when using the previously published clinical variables and methodological approach. Clearly, our results need to be interpreted with caution, as we studied prevalent aDM with different disease durations. These factors might have influenced cluster allocation, proportions, and complication profiles. As a research prospect, we aim at verifying our results in the follow-up data, 5 years after baseline. Here, we have applied hierarchical clustering followed by k-means clustering, which are more suitable when aiming to separate subgroups. Other colleagues have used soft-clustering as an alternative9) provided subgroups that separated well between diabetic complications, but differentiated less sharp between phenotypes. This was the opposite for the second approach (Ghana-specific variables): It produced well-known diabetic phenotypes among African populations, which however, did not separate well for diabetic complications. Pending verification in prospective studies with incident aDM among sub-Saharan African populations, our findings suggest that cluster analysis based on routinely collected clinical data can contribute to the understanding of diabetes aetiology and possibly disease progression.In this Ghanaian study population, the first approach (replication from Ahlqvist et al.Supplementary Information.


Post-acute sequelae SARS-CoV-2 (PASC), also known as Long COVID, is a complex and widely recognized illness with estimates ranging from 5 to 30% of all COVID-19 cases. We performed a retrospective chart review of patients who presented to a dedicated Post-COVID-19 clinic between June 2021 and May 2022. The median patient age was 44.5 years, 63.5% patients were female, and patients presented at a median of 10.4 months from acute COVD-19 infection. 78% self-identified their race as white, and 21% identified as Latino ethnicity. During the acute COVID-19 infection, 50% of patients experienced moderate disease severity and 10.5% were hospitalized. The top three co-morbid conditions prior to SARS-CoV-2 infection included mental health conditions, hypertension and asthma. Patients reported a median of 18 new symptoms following COVID-19 illness, the most common were fatigue (89%), forgetfulness or “brain fog” (89%), and difficulty concentrating (77%). MoCA  assessment demonstrated that 46% had mild cognitive dysfunction. PHQ-9  testing revealed 42% had moderate to severe depression, and 38% had moderate to severe anxiety on the GAD-7  assessment. Symptom burden was similar across gender, age, and initial disease severity. PASC patients presenting to an academic Post-COVID-19 clinic experienced numerous multisystem symptoms and functional impairment, independent of the initial COVID-19 disease severity. The World Health Organization (WHO) defines PASC as post-COVID-19 conditions “usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months”7, although there is no consensus definition, diagnostic criteria, or reliable test for PASC. Over 203 symptoms in 10 organ systems have been identified in PASC8, with most studies identifying the most common symptoms as fatigue, post-exertional malaise, anosmia/ageusia and neurocognitive dysfunction9.Post-acute sequelae SARS-CoV-2 (PASC) is a complex and now widely recognized illness with estimates ranging from 5 to 30% of all COVID-19 cases10, although the exact risk of PASC with ongoing variants is unknown. Vaccination has consistently been shown to reduce the risk of developing PASC12, although recently a large study found that vaccinations primarily reduce the risk by 15%13. It is estimated that 4 million people are out of the work force due to PASC and generate an addition $544 billion in healthcare costs each year14. Therefore, it is anticipated that PASC diagnoses and demand for specialized clinical services will continue to grow as variants continue to circulate.Recent data suggest that the risk of developing PASC may be decreasing with later SARS-CoV-2 variants, with one study demonstrating that 11% of Delta cases resulted in PASC compared to 4.5% of Omicron cases15. These clinics vary widely in the provided services, ranging from individual physical therapy practices to academic medical centers with multispecialty interprofessional teams16.Post-COVID-19 clinics have been created in most states across the United States to meet the demands of the growing PASC patient population, and Survivor Corps, a patient advocacy and support organization, has compiled a registry of more than 245 Post COVID Care Centers that have been opened in the United States18. In this study, we present a cohort of 252 patients who underwent evaluation at an academic tertiary center at Dell Medical School at the University of Texas at Austin, providing a comprehensive analysis of patient demographic factors, PASC symptomatology, co-morbid conditions, and clinical evaluations. Through this investigation, we aim to characterize the population of PASC patients seeking specialized medical care, which may inform the development of tailored PASC services and enhance the capacity of dedicated clinics to meet this escalating need.Most studies have described PASC in large retrospective studies from electronic medical records (EMRs), databases, or cohort studies through surveys relying on patient responses. Few studies have been published investigating the characteristics of patients seeking medical care at dedicated outpatient PASC clinicsIn our multidisciplinary clinic, patients are initially evaluated by a general internist or advanced care provider to develop a comprehensive rehabilitation plan. Internal referrals are available for mental health counseling, neuroimmunology and rheumatology. For further subspecialty care that is not directly part of the multidisciplinary team, patients are referred to a network of providers with an interest and expertise in PASC, including physical and occupational therapy, pulmonology, cardiology, and autonomic neurology among other specialties.The median age of our cohort was 44.5 years (IQR (interquartile range) 22.3), with a median BMI of 27.4 (IQR 9.85). 94% of patients had a COVID-19 diagnosis determined by lab criteria (confirmed by either SARS-COV-2 PCR or nucleocapsid antibody testing) or by the clinical evaluation of a healthcare provider, with the remaining 6% reporting that their initial COVID-19 infection was self-diagnosed. Patients presented at a median of 10.4 (IQR 10.25) months from acute SARS-CoV-2 infection, and 26/248 (10.5%) were hospitalized during their acute infection. Table Prior to their referral to the Post-COVID-19 Program, 91% of patients reported undergoing evaluation by a primary care physician, with cardiology, neurology, and pulmonology emerging as the top three specialties seen. Table p-value < 0.001). The spearman correlation coefficient between PHQ-9 and MoCA scores was -0.2 without controlling for age . For social determinants of health, 19% of patients had an unmet health related social need, most frequently being unable to get “medicine or health care” when needed. In addition, 12% of patients endorsed housing insecurity.Evaluation of health-related quality of life using the Patient-Reported Outcomes Measurement Information System (PROMIS) Global 10 v1.2 assessment demonstrated a mean T score of 37.9 and 37.5 for mental and physical health, respectively. The proportion of patients with “fair” or “poor” mental scores was 65%, and for physical scores it was 73% Table . MoCA scp.adj = 0.0168).Symptom prevalence in 10 body systems  was estimated through a total of 46 surveyed symptoms. More than three fourths of all participants experienced constitutional and neurological symptoms while respiratory, psychiatric, cardiovascular, and rheumatologic symptoms were prevalent in greater than 50% of our cohort. The top 5 symptoms were fatigue , forgetfulness or ‘brain fog’ , trouble concentrating , dizziness or lightheadedness  and pain or fatigue after exercising . Due to COVID-19 infection, participants experienced a median of 18 new symptoms (IQR 14) and a median of 1 resolved symptom (IQR 4) at the time of the initial clinic visit. 43% of patients reported less than 16 symptoms, 45% reported 16–30 symptoms, and 12% reported greater than 30 symptoms. Symptom prevalence was similar independent of the initial disease severity  of our patients were never hospitalized, which was similar to a study 21 where 82% of the cohort had mild or moderate initial illness. In part, this finding is reflective of the epidemiology of COVID-19, as the vast majority of patients do not require hospitalization. Given the young age of our cohort and that 29% of patients had no pre-existing medical conditions, research into identifying individual risk factors for the development of PASC is crucial. Based on these findings, an important conclusion from our study is that structuring PASC clinical services around a post-ICU outpatient model may only be appropriate for a minority of patients seeking PASC care.As seen in previous PASC studies, our patient population had a higher BMI, was middle aged, non-Hispanic white and consisted of a predominantly female cohort22, indicating a clear impact of PASC on the financial well-being of patients. Limited information is currently available regarding which specific symptoms of PASC may be contributing to the observed effect in employment. Possible explanations may include cognitive dysfunction, fatigue and pain experienced as evident in poor performances on MoCA and PROMIS assessments of our patient population, which may impede participation in the workforce.Not surprisingly, we observed that patients’ ability to work was significantly impacted by PASC with an overall reduction in full time employment, an increase in part time employment, and a doubling of the unemployment rate. This finding has been supported by several other studies outlined in a recent review23. More research is needed to further elucidate SARS-CoV-2 vaccine effects in PASC patients and identify biomarkers to determine which PASC patients may or may not have a therapeutic benefit from vaccination.In our study, we did not observe reliable improvement in PASC symptoms following vaccination. In a recent review evaluating 16 PASC studies, most patients with PASC reported symptomatic improvement following SARS-CoV-2 vaccination without a significant difference between the various types of SARS-CoV-2 vaccines, though the strength of these findings is limited by a small proportion of participants reporting either worsening of symptoms or no change in the symptoms24. The diagnostic criteria for ME/CFS include severe fatigue, post-exertional malaise, and unrefreshing sleep, in addition to either cognitive impairment or orthostatic intolerance25. Although we did not directly screen for ME/CFS, based on survey responses, we estimate that at least 60% of our cohort would meet the diagnostic criteria. This estimate is derived from patients endorsing concurrent ROS symptoms of fatigue, pain or fatigue after exercising, sleep disturbance, along with one additional symptom such as brain fog, trouble concentrating, dizziness/lightheadedness, or palpitations. This finding reinforces the need for dedicated screening and additional research on the intersection between PASC and ME/CFS.The symptoms of fatigue, “brain fog” and post-exertional malaise were reported by our patients at a very high frequency, aligning with the observed similarities between Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and PASC26. We observed a similar finding in our study, that MoCA scores were lower in patients who had severe or critical illness requiring hospitalization, compared to the group that was not hospitalized. In a systematic review and meta- analysis of the cognitive effects of COVID-19, impairment was found in executive function, attention and memory collectively referred to as “brain fog”28. Similarly, in our study, most patients endorsed forgetfulness or ‘brain fog’ (89%), and deficits in concentration and attention (77%).Despite the almost universal reporting of subjective neurocognitive complaints, we found that about half of our patient cohort had a score within the normal range on the MoCA, and the other half was noted to have mild or worse cognitive dysfunction. In other studies, the odds of having an abnormal MoCA score compared to normal was elevated (5.84) with high premorbid vulnerability NHS score and more severe pulmonary disease29. Interestingly, we also observed that the spearman correlation coefficient between PHQ-9 and MoCA scores was -0.2 without controlling for age. It is important to note that the PHQ-9 screens for both psychological and physiologic symptoms of depression which includes deficits in concentration, changes in sleep, and poor energy. Therefore, differentiating depression symptoms from the neurocognitive effects of PASC is difficult, and the overlap may account for the inverse relationship between MoCA and PHQ-9 scores. A prior study has found that the course of depression and anxiety was more severe in COVID-19 survivors in those with prior psychiatric history than those without30. Further research is necessary to determine if interventions targeted to the affective symptoms of PASC could alleviate the cognitive deficits, given the high co-occurrence of depression and anxiety in our cohort.In a recent study using MoCA screening in patients with PASC, depression was shown to be the strongest predictor of persistent cognitive complaints31. Studies have suggested autoimmunity as a key factor in PASC, with reports of persistently positive anti-nuclear antibodies (ANAs) associated with ongoing symptoms and inflammation in COVID-19 survivors32. Additionally, antibodies targeting angiotensin-converting enzyme 2 (ACE2) have been linked to a pro-inflammatory state in PASC33. In our study, we observed a multitude of constitutional symptoms, and the proposed immune dysregulation might explain the basis of these diverse manifestations in PASC patients. Other hypotheses propose that neurological symptoms might be due to direct virus-related CNS damage and/or indirect damage due to ongoing autoimmune inflammation, or sequelae of hypoxia34. Further research is warranted to identify precise biomarkers for PASC toward developing tailored therapeutic approaches.The pathophysiology of PASC remains poorly understood, though a key hypothesis posits that it is caused by immune dysregulation, and PASC appears to share similarities with autoimmune conditions19. In our study, 66% of patients reported that they were neither current nor former smokers and 57% reported that they had 0 alcohol drinks per week. The disparity in our findings compared to prior studies could be attributed to differences in our sample population, potentially comprising more health-conscious individuals. Moreover, our reliance on self-reporting of substance use might lead to underestimation in self-reports, which could also contribute to the variations observed.Lifestyle factors, such as smoking and alcohol abuse have been previously implicated in slower recovery of PASC symptoms in other studiesRegarding healthcare utilization, most patients initially sought care from their primary care providers, and many were referred to subspecialists before visiting our clinic. The high rate of subspecialty referrals likely stems from the unclear pathophysiology of PASC, the complex nature of patients' clinical presentation, and the challenges associated with diagnosing and treating PASC. Consequently, there is a clear need for consolidated care within multidisciplinary Post-COVID-19 clinics, capable of addressing the intricate diagnostics and treatment of patients while facilitating research evaluations to gain a more comprehensive understanding of PASC pathophysiology.35, and demonstrates the feasibility of integrating comprehensive research evaluations in PASC clinical care. Moreover, our assessments were conducted at a median of 10.5 months from acute infection, providing valuable insights into the persistence of PASC compared to studies with assessments at shorter durations37. We also specifically focus on patients who sought care for PASC regardless of their hospitalization history.The strengths of our study included a large cohort with a detailed description of clinical PASC symptoms through standardized clinical assessments and evaluation of patient demographic factors. Notably, our response rate was 100%, which is greater than other PASC clinic studies39, other instruments such as the Hospital Anxiety and Depression Scale (HADS) and Impact of Events Scale 6 (IES-6) may be potentially more effective at isolating affective symptoms directly related to COVID-19 illness. Additionally, our patient population consisted primarily of White female, non-Latino Americans, with Latinos representing the second-largest group. The overrepresentation of patients with private insurance and college education likely reflects the healthcare-seeking behavior and higher socioeconomic capacity of this group to obtain care, rather than the prevalence of PASC symptoms in the general population. Our findings underscore the importance that dedicated PASC clinics take proactive measures to enroll underserved patients and establish the capacity to provide inclusive services for those currently lacking access to specialized care.There were also several limitations in our study. First, this study was conducted at a single clinic at a tertiary academic center that drew from a single geographic region of Central Texas and consisted of patients who actively sought specialized PASC care without a control group, which limits the generalizability of the study findings. Survey responses relied on patient recall and answers may be less reliable particularly in those with cognitive complaints, introducing recall bias. In addition, none of the questions in the physician-designed standard of care questionnaire were mandatory, leading to missing data. We primarily used PHQ-9, GAD-7, and PC-PTSD-5 to compare our results with those of the general population, as these tools were already a standard part of care at our institution. While these general screening tools are utilized widelyThe University of Texas at Austin Institutional Review Board (IRB) reviewed the study protocol for this retrospective chart review and determined that this protocol met the criteria for exemption from IRB review under 45 CFR 46.104 (4) secondary research on data or specimens. The IRB approved the request to collect and use protected health information (PHI) for research purposes. In addition, the IRB waived the requirement to obtain subject authorization for use and disclosure of PHI and waived the requirement of informed consent to obtain prior authorization of the individuals affected under 45 CFR 164.512. All methods were carried out in accordance with relevant guidelines and regulations of the University of Texas at Austin IRB.The study population comprised of patients who presented to the Post-COVID-19 Program between June 2021 and May 2022, with an initial COVID-19 illness onset between November 2019 and January 2022. Patients were required to be 18 years or older to be included in the study. To become a patient at the clinic, individuals were required to have reached a minimum of 12 weeks from the onset of their illness. Documentation of a positive COVID-19 test was not required to receive services, although all patients were screened by a dedicated nurse to validate that their clinical history was consistent with a prior COVID-19 infection, and the symptoms are likely associated with PASC and not an obvious alternative etiology.40. The review-of-symptoms questionnaire list was adapted from the World Health Organizations Global COVID-19 Clinical Platform Case Report Form for Post COVID Condition41. For symptom response choices, we interpreted “Yes, the symptom is still present” and “Yes, the symptom comes and goes” as a positive for having the symptom, a negative answer for "Yes, but not present anymore," "Unknown," and un-answered responses, as indicated in the ROS instructions.Prior to their initial appointment at the Post-COVID-19 Program, patients completed a physician-designed standard of care questionnaire via Research Electronic Data Capture (REDCap). Categorization of the severity of the acute SARS-CoV-2 was determined using the National Institute of Health (NIH) definitions42. The PROMIS measures were interpreted using T-score cutoffs recommended for the general adult reference population43.The questionnaire included the PROMIS Global 10 v 1.2 score, which was used to collect patients' perceptions of their health. This item is useful in determining overall well-being and has been shown to reliably predict healthcare utilization and subsequent mortality27 during patients’ initial visits. Additionally, the PHQ-9, a self-administered diagnostic instrument, was used to screen for depression44. Anxiety symptoms were assessed using the GAD-7, a self-reported questionnaire for screening and severity of anxiety45. We screened for PTSD using the PC-PTSD-5 screening tool; a 5-item screening questionnaire, designed for use in primary care settings, with a cut-off score of 3 being optimally sensitive to suggest probable PTSD46. All these assessments were completed on paper or tablets and subsequently uploaded into REDCap. These assessments were encouraged but optional to patients, some declined or were not able to complete these assessments which resulted in missing data.We also administered MoCA scores to assess cognitive dysfunctionp values were corrected for multiple hypothesis testing using the Benjamini and Hochberg method and the double FDR adjustment when appropriate. All graphing and analyses were conducted using R 4.1.0. and Microsoft Excel Version 2019. Symptom prevalence was investigated by identifying the presence or absence of 46 PASC symptoms as listed in the Participant demographics were reported as mean, median, or frequency (percentage). Those with missing data were excluded from the analysis of the variable of interest. Comparisons between groups were done using Wilcoxon signed-rank test or chi-square tests when appropriate. The Fisher’s Exact Test was used to assess the association between symptom burden and patient characteristics. The calculated The majority of PASC patients in our cohort had mild to moderate acute COVID-19 disease and did not require hospitalization. Most patients experienced ongoing symptoms for the past 10 months, with significant functional impairment, indicating that PASC is a long-term debilitating illness for affected patients. Notably, there was significant symptom overlap between patients, suggesting likely overlapping pathophysiology across PASC. Given the complexity of diagnosis and treatment of PASC, multispecialty clinics are required to meet the needs of the PASC patient population with integration of research studies toward better understanding long-term health consequences of PASC and developing evidence based PASC treatments.Supplementary Information.


Comprehensive studies investigating sustained hypercoagulability, endothelial function, and/or inflammation in relation to post-COVID-19 (PCC) symptoms with a prolonged follow-up are currently lacking. Therefore, the aim of this single-centre cohort study was to investigate serum biomarkers of coagulation activation, microvascular dysfunction, and inflammation in relation to persisting symptoms two years after acute COVID-19.Patients diagnosed with acute SARS-CoV-2 infection between February and June 2020 were recruited. Outcome measures included the CORona Follow-Up (CORFU) questionnaire, which is based on an internationally developed and partially validated basic questionnaire on persistent PCC symptoms. Additionally, plasma biomarkers reflecting coagulation activation, endothelial dysfunction and systemic inflammation were measured.167 individuals were approached of which 148 (89%) completed the CORFU questionnaire. At 24 months after acute infection, fatigue was the most prevalent PCC symptom (84.5%). Over 50% of the patients experienced symptoms related to breathing, cognition, sleep or mobility; 30.3% still experienced at least one severe or extreme  PCC symptom. Multiple correlations were found between several PCC symptoms and markers of endothelial dysfunction (endothelin-1 and von Willebrand factor) and systemic inflammation (Interleukin-1 Receptor antagonist). No positive correlations were found between PCC symptoms and coagulation complexes.In conclusion, this study shows that at 24 months after acute COVID-19 infection patients experience a high prevalence of PCC symptoms which correlate with inflammatory cytokine IL-1Ra and markers of endothelial dysfunction, especially endothelin-1. Our data may provide a rationale for the selection of treatment strategies for further clinical studies.https://clinicaltrials.gov/ct2/show/ NCT05240742).This study was performed in collaboration with the CORona Follow-Up (CORFU) study (NCT05240742,  Post COVID-19 condition occurs in individuals with a history of probable or confirmed SARS CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms and that last for at least 2 months and cannot be explained by an alternative diagnosis’ (The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in substantial mortality and morbidity worldwide . A systeagnosis’ .In contrast to PCC symptoms, biomarkers associated with PCC have been scarcely investigated and the pathogenesis of PCC is largely unknown . PreviouThis cohort study was initiated at Radboud University Medical Center  in collaboration with the National CORona Follow-Up (CORFU) study (NCT05240742) in the Netherlands and conducted at Bernhoven Hospital  from November 2021 until July 2022. It was approved by the regional ethics committee Arnhem-Nijmegen (NL74101.091.20) and local approval was granted by the local directory boards. Additionally, for the CORFU study, ethics approval was obtained from the medical research ethics committee (MREC) of Maastricht University Medical Center+ and Maastricht University (METC2021-2990). Determinations and data handling were performed in agreement with the guidelines of The National Institutes of Health and in accordance with the declaration of Helsinki and its later amendments.st and June 1st were given written information about the study. Patients were contacted for participation in the original study if they indicated they wanted to be approached. For the current study, blood was drawn 18-24 months after acute SARS-CoV-2 infection for measurement of biomarkers in plasma and participants were asked to fill in a questionnaire regarding PCC symptoms. Additionally, ten healthy volunteers were included as controls. Healthy volunteers were included in autumn 2020 if they had neither experienced any symptoms of COVID-19 infection nor had been in recent contact with anyone who was infected with COVID-19. Written informed consent was obtained from the participants before the start of any study-related procedures.In this study, patients who previously participated in a cohort study were contacted , includiAll participants were approached 24 months after COVID-19 infection to respond to the CORFU questionnaire on long COVID outcomes and determinants that was developed by the EuroQol Research Foundation  according to the manufacturer’s protocol. Levels of activated coagulation factors in complex with their natural inhibitors  and von Willebrand factor antigen (VWF : Ag) were quantified using in-house developed enzyme-linked immunosorbent assays (ELISAs) (A whole blood sample was obtained by venipuncture in Lithium-Heparin (Vacuette) tubes 10 mL) during the study visit at 18-24 months after SARS-CoV-2 infection. Blood samples were centrifugated at 2500 g for 10 minutes at room temperature, followed by a second centrifugation step at 2500 g for 20 minutes at room temperature. Subsequently, the plasma was stored at -80 °C until further analysis. Plasma concentrations of Endothelin-1 (ET-1), interleukin-18 (IL-18), interleukin-6 (IL-6), and interleukin-1 receptor antagonist (IL-1Ra) were determined batchwise using R&D Systems Quantikine mL durinNormal ranges of in-house developed ELISA methods were defined as above normal mean ± 1 standard deviation (SD), based on previous validation studies , 10. Add® Proteomics AB . The Target 96 Inflammation Panel was run where 96 inflammatory proteins were measured. Statistical testing was performed by using the Wilcoxon matched-pairs signed rank test. For volcano plots, data were analysed by Mann-Whitney test and the Benjamini-Hochberg procedure was employed to correct for multiple testing errors. False discovery rate (FDR)-adjusted p-values smaller than 0.05 were considered statistically significant. Statistical analysis and data visualization were performed with R/Bioconductor (https://www.R-project.org/).Circulating plasma protein expression for 23 PCC subjects , with sr was calculated to examine correlations between PCC complaints, coagulation factors, markers of endothelial dysfunction and inflammatory cytokines. Linear regression with backward elimination of variables was performed to adjust for initial disease severity, comorbidities and baseline characteristics. Total symptoms score was calculated for each patient as the sum of PCC symptoms with at least moderate severity. Differences in PCC symptoms and biomarkers between patients who recovered at home, were hospitalised, or were transferred to the intensive care unit were determined by ANOVA. For plasma cytokine levels, also the proportion of patients with concentrations above normal range was reported. Figures were made using Graphpad Prism version 9.4.1 and statistical analyses were performed using IBM SPSS Statistics 27. P-values below 0.05 were considered statistically significant.Values were reported as mean ± SD, median (IQR) or n (%). Changes in biomarkers between 18-24 months and 3 months [as previously measured ] were teTwo hundred three patients who visited Bernhoven hospital for the COVAS trial at approximately 3 months after COVID-19 infection were contacted for the study visit. 36 participants did not participate in this study due to loss to follow-up, withdrawal of consent, or death. This resulted in a total of 167 participants who were approached to fill in the questionnaire at 24 months after COVID-19 infection. 148 participants (89%) completed the survey  have been identified since the onset of the pandemic. The first variants of concern were described in December 2020 . SARS-CoThis study revealed correlations between PCC symptoms and ET-1 plasma concentrations as a marker of microvascular dysfunction, which is a potent vasoconstrictor and pro-inflammatory peptide. Under healthy conditions, production of ET-1 is small and bioavailability of nitric oxide (NO) is preserved, inducing vasorelaxation. Altered ET-1 can play a pathogenic role in vascular dysfunction and the development of cardiovascular disease by NO modulation. As a result, ET receptor antagonists could provide therapeutic benefits because they may induce increased NO bioavailability and decreased concentrations of reactive oxygen species which could in turn improve endothelial function . In the Furthermore, this study revealed positive correlations between a large proportion of PCC symptoms at 24 months after acute COVID-19 infection and inflammation-associated cytokine IL-1Ra. Serum IL-1Ra concentrations have been positively associated with risk of cardiovascular disease (CVD) . In the Given the correlations between IL-1Ra and PCC symptoms and the presence of elevated protein levels in plasma samples PCC18-24 months after infection, anti-inflammatory therapies may also be beneficial for people with (severe) PCC symptoms. Filgueira et al. suggested that supervised controlled exercise could balance the inflammatory peripheral components, mitigating the inflammatory process associated with PCC and its consequences . In patiWe did not show any association between markers of coagulation activity and symptoms, except for one distinct feature, swollen ankles, negatively linked to activation of factors XI and IX; the latter may suggest an indirect involvement of the contact system, with biased signalling of factor XIIa in the direction of inflammation through the kinin/kallikrein pathway in favour of reduced activation of factor XI and downstream intrinsic coagulation . The oveTo the best of our knowledge, this is the first report presenting PCC symptoms 24 months after PCR-confirmed COVID-19 infection in relation to inflammatory cytokines, endothelial dysfunction and coagulation activation. Two out of the five hypothesized mechanisms of long COVID pathogenesis as described by Davis et al.  were invThis study has also some limitations. First, the normal ranges for the majority of the biomarkers are arbitrary and not strongly supported by robust data in literature. Second, robust data on the prevalence of core symptoms included in the questionnaire of individuals in the era before COVID-19 is lacking. Therefore, it remains challenging to attribute symptoms to PCC compared to comorbidities. Furthermore, the inclusion method and the drop-out of participants between the first measurement and the completion of the questionnaire may have increased the chance of selection bias. Individuals who are interested in the concept of PCC or who suffer from PCC symptoms may have been more likely to participate in this study. Lastly, a major limitation is that Olink analysis was performed in a relatively small subgroup of 23 participants of whom plasma was also collected during the acute and early-recovery phase of the COVID-19 infection.In summary, this study shows that at 24 months after acute COVID-19 infection, patients still experience a high prevalence of PCC symptoms which correlate with inflammatory cytokine IL-1Ra and markers of endothelial dysfunction, especially ET-1. Our data may provide a rationale for the selection of treatment strategies for further clinical studies.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Regional ethics committee Arnhem-Nijmegen (NL74101.091.20) and medical research ethics committee (MREC) of Maastricht University Medical Center+ and Maastricht University (METC2021-2990). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.LW, SK, CG-D, and MCW contributed to the concept of the study. LJ, MW, LW, LG, MaN, SK, CG-D, AP and MCW contributed to the trial design. LW, MW, LJ, MaN, and AP contributed to the data collection. LJ, LW, LG, HC, and MCW contributed to the data analysis. All authors contributed to the interpretation of the data. LJ and MCW wrote the first draft of the manuscript. All authors read, revised and approved the final manuscript.