Novel therapies for multiple myeloma (MM) have improved patient survival, but their high costs strain healthcare budgets. End-of-life phases of treatment are generally the most expensive, however, these high costs may be less justifiable in the context of a less pronounced clinical benefit. To manage drug expenses effectively, detailed information on end-of-life drug administration and costs are crucial. In this retrospective study, we analysed treatment sequences and drug costs from 96 MM patients in the Netherlands who died between January 2017 and July 2019. Patients received up to 16 lines of therapy (median overall survival: 56.5 months), with average lifetime costs of (sic)209 871 ((sic)3111/month; range: (sic)3942-(sic)776 185) for anti-MM drugs. About 85% of patients received anti-MM treatment in the last 3 months before death, incurring costs of (sic)20 761 (range: (sic)70-(sic)50 122; 10% of total). Half of the patients received anti-MM treatment in the last 14 days, mainly fully oral regimens (66%). End-of-life treatment costs are substantial despite limited survival benefits. The use of expensive treatment options is expected to increase costs further. These data serve as a reference point for future cost studies, and further research is needed to identify factors predicting the efficacy and clinical benefit of continuing end-of-life therapy.

Introduction: The development of treatment options for multiple myeloma (MM) has improved survival, but also come with increased treatment costs, which can pose a burden on health care funding and limit access to drugs. Therefore, costs and cost-effectiveness analyses are increasingly important in medical decision making. Such analyses are often based on average health outcomes and costs over a lifetime perspective. However, specifically the end of life (EOL) phase is accompanied by high health care costs, while the contribution of active drug treatment to survival time and quality of life might be minimal. Therefore, we investigated the drug costs from diagnosis to death in detail, in a real-world cohort of MM patients. Methods: We analysed health care records from all MM patients who received (a part of) their treatment in Amsterdam University Medical Centre, and who died between January 1st 2017 and July 1st 2019. We extracted all anti-MM treatments from diagnosis to death, including dose adjustments and start- and stop dates. We calculated drug costs using the Dutch Z-index (indicating drug costs), of August 2020. Results: 96 patients were eligible for analysis and received a median of 5 (range: 1-16) lines of therapy; 61 (63.5%) received a stem cell transplantation (SCT). Time to next treatment or death from 1st to 2nd line of therapy was longer in patients who received an SCT (median 23.9 vs 13.0 months, p=0.002) and was progressively shorter at later lines of therapy, without significant differences between patients who did or did not receive an SCT. Mean total drug costs of MM treatments (from diagnosis to death) were €211,563 (range: €3,942 - €776,185; €3,139 per month [range: €54 – €8,309]). Eighty-two patients (85.4%) received anti-MM treatment in the last 3 months before death and the mean drug costs in this period were €20,361 (range: €70 - €50,466; 9.6% of total). Forty-nine (51.0%) patients received anti-MM treatment in the last 14 days before death and 33 (34.4%) in the last seven days. Mean drug costs per month were approximately twice as high as compared to costs per month over the myeloma life time. (approximately €6,700 versus €3,139 per month), Table 1. Conclusion: The majority of patients received anti-MM therapy during the 14 days preceding death. Associated drug costs were considerable, especially in light of limited survival benefit. Moreover, this is expected to increase as the novel drugs that are currently available also been given at later lines are more expensive. In view of the increasing budget impact of anti-myeloma treatment, hampering access to novel drugs, and the possible negative impact of EOL treatment on QoL, the identification of factors predicting efficacy and clinical benefit of continuing EOL therapy, warrant further investigation. Table 1 - Numbers of patients and (relative) costs of treatment per period. Period Patients receiving treatment (% of total) Mean costs of treatment (range; % of total costs) Mean relative costs per month (range)* Diagnosis to death 96 (100) €211,563 (€3,942 - €776,185; 100) €3,139** (€54 – €8,309) - With SCT - 61 (63.5) - €210,863 (€17,317 - €621,756) - €2,921** (€87 - €8,309) - Without SCT - 35 (36.5) - €212,784 (€3,942 - €776,185) - €3,607** (€54 - €8,072) Last 3 months 82 (85.4) €20,361 (€70 - €50,466; 9.6) €6,787 (€23 - €16,822) Last 30 days 66 (68.8) €6,188 (€10 - €17,444; 2.9) €6,271 (€10 - €17,677) Last 14 days 49 (51.0) €3,001 (€9 - €9,512; 1.4) €6,516 (€20 - €20,655) Last 7 days 33 (34.4) €1,662 (€3 - €4,757; 0.8) €7,218 (€13 - €20,659) (*) 30.4 days(**) mean total costs divided by mean OS per groupAbbreviation: SCT = Stem cell transplantation

Introduction: The clinical outcome of older patients with multiple myeloma (MM) is heterogeneous, largely depending on frailty level. To assess frailty, clinical scores such as the International Myeloma Working Group frailty index (IMWG-FI) are used to distinguish between ‘fit’, ‘intermediate-fit’ and ‘frail’ patients. However, as the discriminative power of these scores is still insufficient to guide treatment choices, there is an unmet need for novel biomarkers that reflect biological age (‘frailty’) – rather than chronological age. The cell cycle regulator and tumor suppressor p16INK4a has been found to be a robust biomarker of cellular senescence and aging. Previous studies showed that p16INK4a markedly increases with age and p16INK4a inactivation partially reverses age-related phenotypes. p16INK4a positive cells in skin biopsies have also been found to be reflective of a person’s biological age. We therefore investigated whether p16INK4a expression in the skin is associated with frailty and could predict treatment outcomes in older patients with Newly Diagnosed Multiple Myeloma (NDMM). Methods: We evaluated p16INK4a expression in skin biopsies taken at baseline from newly diagnosed MM patients who were included in two prospective trials: the HOVON 123 and HOVON 143 study, including intermediate-fit and frail patients based on the IMWG-FI. Before start of treatment, biopsies were obtained from non-sun exposed skin (area above the bone marrow aspirate site at the posterior iliac crest). p16INK4a expression was defined as two variables: the number of p16INK4a positive cells 1) in the basal membrane, normalized to length of the basal membrane (mm) and 2) in the entire epidermis, normalized to the surface of the epidermis (mm2). Because p16INK4a positivity was not normally distributed, p16INK4a positivity was classified in tertiles (low < 0.93/mm, intermediate 0.93 – 3.03/mm, high > 3.03/mm). Statistical analysis was performed using the Wilcoxon signed-rank test and univariate Cox regression. Results: Prior to treatment initiation skin biopsies were obtained from 305 NDMM patients with a median age of 78 years (range 65 – 92). Median number of p16INK4a positive cells in the basal membrane was 1.75 per mm (range 0 – 30.8/mm) and 41.5/mm2 (range 0 – 850.5/mm2) in the epidermis. We found no significant difference in p16INK4a positivity in the basal membrane (1.55/mm vs 1.96/mm; p=0.26) or in the epidermis (38.0/mm2 vs 48.5/mm2; p=0.20) between the intermediate-fit and frail patients. Moreover, p16INK4a expression was not associated with progression free survival or overall survival (Figure 1). Conclusion: p16INK4a expression in the skin is not associated with frailty level or treatment outcome in intermediate fit and frail NDMM patients. Recent literature suggests that p16INK4a mRNA expression in peripheral T cells could be a more accurate marker for cellular senescence. Based on our results, we discourage the evaluation of p16INK4a protein levels in skin biopsies to improve frailty assessment in older patients with newly diagnosed MM.

Introduction: Non-transplant eligible newly diagnosed multiple myeloma (NTE-NDMM) patients have a heterogeneous clinical outcome, depending of frailty level. The aim of this study was to prospectively investigate the efficacy and tolerability of Ixazomib-Daratumumab-low dose dexamethasone (IDd) in intermediate-fit NTE-NDMM patients. Methods: In this phase II multicenter HOVON-143 study, IWMG-frailty index based intermediate-fit patients were treated with nine induction cycles of IDd, followed by maintenance with IDd for a maximum of two years. Health related quality of life (HRQoL) was investigated at baseline, after 3 and 9 induction cycles and after 6, 12 and 24 months of maintenance treatment. Results: Sixty-five patients were included. The overall response rate during induction was 71% (95% confidence interval (CI) 63-73%). After a median follow-up of 41 months (range 28.9-53.8), median PFS was 18.2 months. Median PFS2 and OS were not reached, PFS2 at 2 years was 80% (95% CI 68-88%), OS at 3 years was 83% (95% CI 71-90%). (Figure 1) Thirty-five patients (54%) completed induction treatment and started maintenance therapy. During maintenance, 12/35 (34%) patients had an improvement of response. Reasons for discontinuation of induction treatment were progressive disease (PD) (19/30; 63%), toxicity (4/30; 13%), incompliance (3/30; 10%), sudden death (1/30; 3%) and other (3/30; 10%). Of the 35 patients who started maintenance therapy, 15 (43%) patients completed the protocol and 20 patients discontinued treatment due to PD (13/20; 65%), refusal (2/20; 10%), toxicity (2/20; 10%), death (1/20; 5%) or other reasons (2/20; 10%). Hematologic adverse events (AE) grade ≥3 during induction occurred in 12% of patients, of which neutropenia was most commonly reported (6%). During maintenance only 1 patients (1/35; 3%) experienced a grade 3 hematologic AE (thrombocytopenia). Non-hematologic AEs grade ≥3 during induction occurred in 51% of patients, of which most commonly gastro-intestinal AEs (14%) and central nervous system AEs (14%). All grade polyneuropathy (PNP) occurred in 42% of patients, including 5% grade 3 PNP. During the maintenance phase non-hematologic AEs grade ≥3 occurred in 46% of patients, which were most commonly gastro-intestinal AEs (11%) and infections (9%). There was no new onset of grade ≥3 PNP. Dose modifications of ixazomib occurred in 24/65 (37%) patients during induction treatment and in 19/35 (54%) patients during maintenance. Eight/35 (23%) patients discontinued ixazomib treatment during the maintenance phase, while continuing with daratumumab once every eight weeks. The global health status/quality of life improvement significantly during treatment and was clinically significant from the 9th induction cycle onwards. Of the patients who experienced PD, second line treatment was started in 40 out of 42 patients (95%). The remaining 23 patients were still free of progression (18) or died before the occurrence of PD (5). Second line therapy was most commonly lenalidomide based (35/40; 88%). Conclusion: IDd treatment in intermediate-fit patients with NDMM is safe and improves global quality of life. However, PFS is limited, partly explained by limited efficacy due to frequent dose modifications of ixazomib, mainly due to neurotoxicity. This underscores the need for more efficacious and tolerable regimens improving the outcome in non-fit patients.

Background: Frailty in non-transplant eligible (NTE) newly diagnosed multiple myeloma (NDMM) patients is associated with toxicity which can negatively affect physical functioning and quality of life (QoL). Older patients may prefer QoL and physical independence over length of life, highlighting the importance of taking health-related (HR) QoL assessment into account for treatment guidance. Methods: The HOVON123 study (NTR4244) was a phase II trial in which 238 NTE-NDMM patients ≥75 years were treated with 9 dose-adjusted cycles MPV. Nine (3 functional; 6 symptom) subscales of two HRQoL instruments (EORTC QLQ-C30 and MY20) were obtained at baseline (T0), after 3 (T1) and 9 (T2) cycles of therapy, and 6 (T3) and 12 (T4) months after discontinuation of therapy in patients without progression. The presence of “tingling hands/feet” was used as a proxy for neuropathy. Differences in baseline HRQoL were analysed with independent t-tests and changes over time with linear mixed models. HRQoL changes and/or differences were reported only when both statistically significant (p<0.005, adjusted for multiple testing) and clinically relevant (>MID). Results: A total of 137 frail and 71 intermediate-fit patients were included in the HRQoL analysis, after exclusion of fit patients and patients whose frailty status or baseline HRQoL questionnaire was missing. Compliance was not materially different in both groups. Frail patients had an inferior HRQoL at baseline in the subscales global health status, physical functioning, fatigue and pain, compared with intermediate-fit patients. Both groups reported improvements in global health status and future perspective. In contrast to intermediate fit patients, frail patients improved in physical functioning, fatigue and pain over time. The improvements in global health status were reached earlier in frail patients (T1) compared with intermediate fit patients (T2), Figure 1. In both intermediate fit and frail patients there was an increase in neuropathy. All other subscales remained within MID ranges and/or were not statistically significant different from baseline. The improvement in global health status sustained after treatment completion (T3-T4) both for frail and intermediate fit patients. This also accounted for future perspective at T3, however, at T4 for intermediate fit patients only. In contrast, the improvement in all other HRQoL domains during treatment, lost clinical relevance and/or statistical significant difference during the TFI. The deterioration in neuropathy remained until T4 in frail patients, but not for intermediate fit patients, reversing at T4, Figure 1. Conclusion: HRQoL in frail patients is inferior as compared to intermediate fit patients at diagnosis. Importantly, treatment improved HRQoL, irrespective of frailty level, being more pronounced and occurring even faster in frail patients. Therefore, physicians should not withhold therapy in these patients because of their frailty status only.

Background: In the HOVON 126/NMSG 21.13 trial non-transplant eligible newly diagnosed multiple myeloma (NTE-NDMM) patients were treated with 9 induction cycles of ixazomib, thalidomide and dexamethasone (ITd), followed by randomization between either ixazomib or placebo until progression or unacceptable toxicity. The overall response rate and PFS data have been previously published. Aims: We here present the long-term PFS2 and overall survival data. Methods: Patients were treated with 9 induction cycles (28 days) of ixazomib (4mg on day 1, 8 and 15), thalidomide (100mg on day 1-28) and dexamethasone (40mg on day 1, 8, 15 and 22), followed by maintenance with either ixazomib or placebo (4mg, both on day 1, 8 and 15, every 28 days). Patients were classified as fit, intermediate fit or frail, based on a modified IMWG frailty index which incorporated age, the Charlson Comorbidity Index (CCI) and the WHO performance as a proxy for (instrumental) Activities of Daily Living (iADL) (scoring WHO 0 as 0 points, WHO 1 as 1 point, and WHO 2-3 as 2 points). Results:From registration: 143 eligible patients were included in the study. After a median follow-up (FU) of 67.4 months (m), the median PFS was 14.3m (95% CI 11.5-16.8), median PFS2 was 34.6m (30.7-41.5) and median OS was 58.3m (50.5-65.0). There was no difference in PFS between frailty subgroups. In contrast, median PFS2 and OS were longer in fit patients (PFS2: 49.1m (34.6-74.1), OS: NR (66.6-NR)) versus intermediate-fit (30.1m (25.1-39.0); 51.2m (32.3-63.9) resp.) and frail patients (30.9m (24.0-42.3); 50.5m (32.9-59.4) resp.). From randomization: 78 (55%) patients were randomized, 39 patients in each arm. After a median FU of 60 months from randomization, there was no difference in PFS between the ixazomib-arm (median 9.5m; 95% CI 5.5-14.8) and the placebo-arm (8.4m; 3.0-13.8). Median PFS2 was 39.8m (28.8-60.0) for patients on ixazomib, as compared to 28.7m (22.8-43.2) for patients in the placebo arm, although this difference was not statistically significant. Median OS was not reached for the ixazomib arm and was 50.7m (41.3-58.1) for the placebo arm (HR 0.39; 95% CI 0.19-0.78, p=0.008). In both arms 32 (82%) patients received 2nd line treatment. With the caveat of low numbers and heterogeneous treatment regimens, more patients in the ixazomib arm received daratumumab-lenalidomide-dexamethasone (4 patients, 13%) and panobinostat-bortezomib-dexamethasone (6 patients, 19%), compared to the placebo arm (2 patients (6%) and 3 patients (9%) respectively). In order to explain the difference in OS, subsequent lines of therapy are currently being investigated. Image:Summary/Conclusion: With longer FU, we here confirm that ixazomib maintenance therapy did not improve PFS, compared to placebo. However, PFS2 tends to be longer and OS was superior in patients treated with ITd followed by maintenance with ixazomib versus placebo.

Background: Frail patients with newly diagnosed multiple myeloma (NDMM) have an inferior PFS and OS, a higher treatment discontinuation rate and more grade ≥3 non-hematologic toxicity, compared to fit patients. In order to improve their outcome we investigated a three-drug regimen with a presumed low-toxicity profile; ixazomib, daratumumab and low-dose-dexamethasone (Ixa-Dara-dex). This trial is registered at www.trialregister.nl as NTR6297. Aims: To present the long term follow up outcome, with an emphasis on the maintenance phase. Methods: NDDM patients, who were frail according to the IMWG-Frailty Index, were included. Patients defined frail based on age only (frail-age) were compared to frail patients based on other reasons (impairments (i)ADL and/or CCI≥2; frail-other) and patients frail based on age as well as other reasons (frail-both). After nine cycles of Ixazomib (4mg; days 1, 8, 15), daratumumab (16mg/kg iv; cycles 1-2: days 1, 8, 15, 22; cycles 3-6: days 1, 15; cycles 7-9: day 1) and low dose dexamethasone (on the days daratumumab was administered; cycle 1-2: 20mg; subsequent cycles 10mg), patients without progressive disease or excessive toxicity continued with maintenance treatment, consisting of 8-week cycles with ixazomib (4mg orally on days 1, 8, 15, 29, 36, 43), daratumumab (16mg/kg iv or 1800mg subcutaneously on day 1) with dexamethasone (10mg intravenously on day 1), until progression, for a maximum of two years. Results: Sixty-five frail patients were included. After a median follow-up of 39 months, the median progression free survival was 13.8m (95%CI: 9.2-17.7). The median PFS2 for all patients was 30.7m (22.2-39.1), 39.1 months in patients classified as frail-age, 24.5 months in frail-other patients and 26.6 months in frail-both patient (log-rank p=0.30). Median OS for all patients was 34.0m (24.0-41.2), not reached (frail age), 28.1m (frail-other) and 30.7 months (frail-both) (log-rank p=0.29) (Table 1). Thirty-two patients (49%) proceeded to the maintenance phase. During maintenance treatment, 6 patients (19%) had improvement of response: 1 SD to MR, 3 PR to VGPR, 1 VGPR to CR and 1 VGPR to sCR. The rate of VGPR or better improved from 41% to 50% during maintenance. During maintenance, 21/32 (66%) patients discontinued therapy, because of progressive disease (14/21; 67%), toxicity (2/21; 10%; infection and intracranial hemorrhage), non-compliance (1/21; 5%), intercurrent death (1/21; 5%) and other reasons (3/21; 14%; physician’s choice, dementia and patient condition). Hematologic ≥3 grade adverse events (AEs) during maintenance were limited: neutropenia 0%, anemia 3% and thrombocytopenia 9%. Non-hematologic ≥3 grade AEs occurred in 18 (56%) patients. Most common AEs were infections (9%), nervous system disorders (9%; cognitive disturbance, stroke and syncope) and gastro-intestinal complaints (6%). There were 2 (6%) second primary malignancies and one patient (3%) experienced grade 3 neuropathy. Image:Summary/Conclusion: Ixa-dara-dex maintenance treatment in frail patients was safe, and resulted in an improvement in response rate in 19% of patients. Patients frail based on age, had higher PFS, PFS2 and OS, as compared to other frail subgroups.

Aims: We aimed to describe differences in the prevalence of intermediate hyperglycaemia (IH) between six ethnic groups. Moreover, to investigate differences in the association of the classifications of IH with the incidence of T2DM between ethnic groups. Methods: We included 3759 Dutch, 2826 African Surinamese, 1646 Ghanaian, 2571 Turkish, 2691 Moroccan and 1970 South Asian Surinamese origin participants of the HELIUS study. IH was measured by fasting plasma glucose (FPG) and HbA1c. We calculated age-, BMI and physical-activity-adjusted prevalence of IH by sex, and calculated age and sex-adjusted hazard ratios (HR)for the association between IH and T2DM in each ethnic group. Results: The prevalence of IH was higher among ethnic minority groups (68.6-41.7%) than the Dutch majority (34.9%). The prevalence of IH categories varied across subgroups. Combined increased FPG and HbA1c was most prevalent in South-Asian Surinamese men (27.6%, 95 %CI: 24.5-30.9%), and in Dutch women (4.2%, 95 %CI: 3.4-5.1%). The HRs for T2DM for each IH-classification did not differ significantly between ethnic groups. HRs were highest for the combined classification, e.g., HR = 8.1, 95 %CI: 2.5-26.6 in the Dutch. Conclusion: We found a higher prevalence of IH in ethnic minority versus majority groups, but did not find evidence for a differential association of IH with incident T2DM.

Several risk factors are associated with gallstone disease after bariatric surgery, but the underlying pathophysiological mechanisms of gallstone formation are unclear. We hypothesize that gallstone formation after bariatric surgery is induced by different pathways compared with gallstone formation in the general population, since postoperative formation occurs rapidly in patients who did not develop gallstones in preceding years. To identify both pathophysiological and potentially protective mechanisms against postoperative gallstone formation, we compared the preoperative fasting metabolome, fecal microbiome, and liver and adipose tissue transcriptome obtained before or during bariatric surgery of obese patients with and without postoperative gallstones. In total, 88 patients were selected from the BARIA longitudinal cohort study. Within this group, 32 patients had postoperative gallstones within 2 years. Gut microbiota metagenomic analyses showed group differences in abundance of 41 bacterial species, particularly abundance of Lactobacillaceae and Enterobacteriaceae in patients without gallstones. Subcutaneous adipose tissue transcriptomic analyses revealed four genes that were suppressed in gallstone patients compared with patients without gallstones. These baseline gene expression and gut microbiota composition differences might relate to protective mechanisms against gallstone formation after bariatric surgery. Moreover, baseline fasting blood samples of patients with postoperative gallstones showed increased levels of several bile acids. Overall, we revealed different genes and bacteria associated with gallstones than those previously reported in the general population, supporting the hypothesis that gallstone formation after bariatric surgery follows a different trajectory. Further research is necessary to confirm the involvement of the bile acids, adipose tissue activity, and microbial species observed here.

Background and Aims: Cardiovascular diseases (CVD) are one of the largest causes of death worldwide. The risk for atherosclerosis, the most common cause for CVD is increased specifically in patients with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD). In this pilot study plasma shotgun proteomics was performed to find biomarkers and changes in metabolic proteins in a control group versus a T2DM and NAFLD group prior to bariatric surgery.

Introduction The gut microbiome may contribute to the development of obesity. So far, the extent of microbiome variation in people with obesity has not been determined in large cohorts and for a wide range of body mass index (BMI). Here, we aimed to investigate whether the faecal microbial metagenome can explain the variance in several clinical phenotypes associated with morbid obesity. Methods Caucasian subjects were recruited at our hospital. Blood pressure and anthropometric measurements were taken. Dietary intake was determined using questionnaires. Shotgun metagenomic sequencing was performed on faecal samples from 177 subjects. Results Subjects without obesity (n = 82, BMI 24.7 +/- 2.9 kg m(-2)) and subjects with obesity (n = 95, BMI 38.6 +/- 5.1 kg m(-2)) could be clearly distinguished based on microbial composition and microbial metabolic pathways. A total number of 52 bacterial species differed significantly in people with and without obesity. Independent of dietary intake, we found that microbial pathways involved in biosynthesis of amino acids were enriched in subjects with obesity, whereas pathways involved in the degradation of amino acids were depleted. Machine learning models showed that more than half of the variance in body fat composition followed by BMI could be explained by the gut microbiome composition and microbial metabolic pathways, compared to 6% of variation explained in triglycerides and 9% in HDL. Conclusion Based on the faecal microbiota composition, we were able to separate subjects with and without obesity. In addition, we found strong associations between gut microbial amino acid metabolism and specific microbial species in relation to clinical features of obesity.

Introduction Prevalence of obesity and associated diseases, including type 2 diabetes mellitus, dyslipidaemia and non-alcoholic fatty liver disease (NAFLD), are increasing. Underlying mechanisms, especially in humans, are unclear. Bariatric surgery provides the unique opportunity to obtain biopsies and portal vein blood-samples. Methods The BARIA Study aims to assess how microbiota and their metabolites affect transcription in key tissues and clinical outcome in obese subjects and how baseline anthropometric and metabolic characteristics determine weight loss and glucose homeostasis after bariatric surgery. We phenotype patients undergoing bariatric surgery (predominantly laparoscopic Roux-en-Y gastric bypass), before weight loss, with biometrics, dietary and psychological questionnaires, mixed meal test (MMT) and collect fecal-samples and intra-operative biopsies from liver, adipose tissues and jejunum. We aim to include 1500 patients. A subset (approximately 25%) will undergo intra-operative portal vein blood-sampling. Fecal-samples are analyzed with shotgun metagenomics and targeted metabolomics, fasted and postprandial plasma-samples are subjected to metabolomics, and RNA is extracted from the tissues for RNAseq-analyses. Data will be integrated using state-of-the-art neuronal networks and metabolic modeling. Patient follow-up will be ten years. Results Preoperative MMT of 170 patients were analysed and clear differences were observed in glucose homeostasis between individuals. Repeated MMT in 10 patients showed satisfactory intra-individual reproducibility, with differences in plasma glucose, insulin and triglycerides within 20% of the mean difference. Conclusion The BARIA study can add more understanding in how gut-microbiota affect metabolism, especially with regard to obesity, glucose metabolism and NAFLD. Identification of key factors may provide diagnostic and therapeutic leads to control the obesity-associated disease epidemic.

Although the prognosis of multiple myeloma (MM) patients has dramatically improved during recent years, virtually all patients eventually develop relapsed refractory disease. Several new therapeutics have been developed in the last few years, including carfilzomib, a second-generation proteasome inhibitor (PI) that has been approved by the US Food and Drug Administration (FDA) in the setting of relapsed and/or refractory MM, as a single agent with or without dexamethasone, and in combination with lenalidomide in 2012 and 2015, respectively. Other promising combinations with carfilzomib are being investigated. Carfilzomib has shown superiority over the first-generation PI bortezomib on both efficacy and toxicity. In particular, profoundly lower incidence in polyneuropathy compared to bortezomib has been described. However, carfilzomib has a different toxicity profile, with more cardiovascular adverse events. Therefore, caution should be taken with the use of carfilzomib for elderly and cardiovascularly compromised patients. The once-weekly administration of carfilzomib, recently approved by the FDA in combination with dexamethasone, will lead to a lower burden for the patient and caregivers compared to the twice-weekly schemes that were routinely used until recently. This review has a focus on clinical trial data that has led to drug approval, as well as new promising combination studies, and provides advice for treating physicians who are now prescribing this drug to patients.

Dietary plant sterols and stanols as present in our diet and in functional foods are well-known for their inhibitory effects on intestinal cholesterol absorption, which translates into lower low-density lipoprotein cholesterol concentrations. However, emerging evidence suggests that plant sterols and stanols have numerous additional health effects, which are largely unnoticed in the current scientific literature. Therefore, in this review we pose the intriguing question “What would have occurred if plant sterols and stanols had been discovered and embraced by disciplines such as immunology, hepatology, pulmonology or gastroenterology before being positioned as cholesterol-lowering molecules?” What would then have been the main benefits and fields of application of plant sterols and stanols today? We here discuss potential effects ranging from its presence and function intrauterine and in breast milk towards a potential role in the development of non-alcoholic steatohepatitis (NASH), cardiovascular disease (CVD), inflammatory bowel diseases (IBD) and allergic asthma. Interestingly, effects clearly depend on the route of entrance as observed in intestinal-failure associated liver disease (IFALD) during parenteral nutrition regimens. It is only until recently that effects beyond lowering of cholesterol concentrations are being explored systematically. Thus, there is a clear need to understand the full health effects of plant sterols and stanols.

Abstract Background Currently used models to predict cardiovascular event risk have limited value. It has been shown repetitively that the addition of single biomarkers has modest impact. Recently we observed that a model consisting of a larger array of plasma proteins performed very well in predicting the presence of vulnerable plaques in primary prevention patients. However, the validation of this protein panel in predicting cardiovascular outcomes remains to be established. Purpose This study investigated the ability of a 384 preselected protein biomarkers to predict acute myocardial infarction, using state-of-the-art machine learning techniques. Secondly, we compared the performance of this multi-protein risk model to traditional risk engines. Methods We selected 822 subjects from the EPIC-Norfolk prospective cohort study, of whom 411 suffered a myocardial infarction during follow-up (median 15 years) compared to 411 controls who remained event-free (median follow-up 20 years). The 384 proteins were measured using proximity extension assay technology. Machine learning algorithms (random forests) were used for the prediction of acute myocardial infarction (ICD code I21–22). Performance of the model was tested against and on top of traditional risk factors for cardiovascular disease (refit Framingham). All performance measurements were averaged over several stability selection routines. Results Prediction of myocardial infarction using a machine-learning model consisting of 50 plasma proteins resulted in a ROC AUC of 0.74±0.14, in comparison to 0.69±0.17 using traditional risk factors (refit Framingham. Combining the proteins and refit Framingham resulted in a ROC AUC of 0.74±0.15. Focussing on events occurring within 3 years after baseline blood withdrawal, the ROC AUC increased to 0.80±0.09 using 50 plasma proteins, as opposed to 0.67±0.22 using refit Framingham (figure). Combining the protein model with refit Framingham resulted in a ROC AUC of 0.82±0.11 for these events. Diagnostic performance events <3yrs Conclusion High-throughput proteomics outperforms traditional risk factors in prediction of acute myocardial infarction. Prediction of myocardial infarction occurring within 3 years after inclusion showed highest performance. Availability of affordable proteomic approaches and developed machine learning pave the path for clinical implementation of these models in cardiovascular risk prediction. Acknowledgement/Funding This study was funded by an ERA-CVD grant (JTC2017) and EU Horizon 2020 grant (REPROGRAM, 667837)

BACKGROUND:Gut microbiota-derived short-chain fatty acids (SCFAs) have been associated with beneficial metabolic effects. However, the direct effect of oral butyrate on metabolic parameters in humans has never been studied. In this first in men pilot study, we thus treated both lean and metabolic syndrome male subjects with oral sodium butyrate and investigated the effect on metabolism. METHODS:Healthy lean males (n = 9) and metabolic syndrome males (n = 10) were treated with oral 4 g of sodium butyrate daily for 4 weeks. Before and after treatment, insulin sensitivity was determined by a two-step hyperinsulinemic euglycemic clamp using [6,6-2H2]-glucose. Brown adipose tissue (BAT) uptake of glucose was visualized using 18F-FDG PET-CT. Fecal SCFA and bile acid concentrations as well as microbiota composition were determined before and after treatment. RESULTS:Oral butyrate had no effect on plasma and fecal butyrate levels after treatment, but did alter other SCFAs in both plasma and feces. Moreover, only in healthy lean subjects a significant improvement was observed in both peripheral (median Rd: from 71 to 82 µmol/kg min, p < 0.05) and hepatic insulin sensitivity (EGP suppression from 75 to 82% p < 0.05). Although BAT activity was significantly higher at baseline in lean (SUVmax: 12.4 ± 1.8) compared with metabolic syndrome subjects (SUVmax: 0.3 ± 0.8, p < 0.01), no significant effect following butyrate treatment on BAT was observed in either group (SUVmax lean to 13.3 ± 2.4 versus metabolic syndrome subjects to 1.2 ± 4.1). CONCLUSIONS:Oral butyrate treatment beneficially affects glucose metabolism in lean but not metabolic syndrome subjects, presumably due to an altered SCFA handling in insulin-resistant subjects. Although preliminary, these first in men findings argue against oral butyrate supplementation as treatment for glucose regulation in human subjects with type 2 diabetes mellitus.

Aim: Pharmacological stimulation of the bile acid-activated nuclear receptor FXR has been demonstrated to improve non-alcoholic steatohepatitis. However, the effects of FXR activation on other aspects of the metabolic syndrome, including hyperlipidemia, are incompletely understood. Therefore, we assessed the effects of FXR stimulation in a mouse model with humanized lipoprotein metabolism.

Aim: We previously showed that brown fat activation accelerates the clearance of cholesterol-enriched remnants by the liver, thus reducing hypercholesterolemia and improving HDL functionality, consequently protecting against atherosclerosis development (Nat Commun 2015; Nat Commun 2017). Liver cholesterol is the main substrate for the synthesis of bile acids (BAs) that are secreted into the intestine and recycled via the enterohepatic circulation. Since the impact of brown fat activation on BA metabolism is still obscure, we now aimed to evaluate the effects of brown fat activation on cholesterol and BA metabolism without and with blocking the enterohepatic BA circulation.

A hallmark of the metabolic syndrome is low HDL-cholesterol coupled with high plasma triglycerides (TG), but it is unclear what drives this close association. Plasma triglycerides and HDL cholesterol are thought to communicate through two distinct mechanisms. Firstly, excess surface lipids from VLDL released during lipolysis are transferred to HDL, thereby contributing to HDL directly but also indirectly through providing substrate for LCAT. Secondly, high plasma TG increases clearance of HDL through core-lipid exchange between VLDL and HDL via CETP and subsequent hydrolysis of the TG in HDL, resulting in smaller HDL and thus increased clearance rates. To test our understanding of how high plasma TG induces low HDL-cholesterol, making use of established knowledge, we developed a comprehensive agent-based model of lipoprotein metabolism which was validated using monogenic disorders of lipoprotein metabolism. By perturbing plasma TG in the model, we tested whether the current theoretical framework reproduces experimental findings. Interestingly, while increasing plasma TG through simulating decreased lipolysis of VLDL resulted in the expected decrease in HDL cholesterol, perturbing plasma TG through simulating increased VLDL production rates did not result in the expected HDL-TG relation at physiological lipid fluxes. However, model perturbations and experimental findings can be reconciled if we assume a pathway removing excess surface-lipid from VLDL that does not contribute to HDL cholesterol ester production through LCAT. In conclusion, our model simulations suggest that excess surface lipid from VLDL is cleared in part independently from HDL. Author summary While it has long been known that high plasma triglycerides are associated with low HDL cholesterol, the reason for this association has remained unclear. One of the proposed mechanisms is that during catabolism of VLDL, lipoproteins rich in triglyceride, the excess surface of these particles become a source for the production of HDL cholesterol, and that therefore decreased catabolism of VLDL will lead to both higher plasma triglyceride and low HDL cholesterol. Another proposed mechanism is that during increased production of VLDL, there will be increased exchange of core lipids between VLDL and HDL, with subsequent hydrolysis of the triglyceride in HDL, leading to smaller HDL that is cleared more rapidly. To investigate these mechanisms further we developed a computational model based on established knowledge concerning lipoprotein metabolism and validated the model with known findings in monogenetic disorders. Upon perturbing the plasma triglycerides within the model by increasing the VLDL production rate, we unexpectedly found an increase in both triglyceride and HDL cholesterol. However, upon assuming that less excess surface lipid is available to HDL, HDL decreases in response to increased VLDL production. We therefore propose that there must be a pathway removing excess surface lipids that is independent from HDL. Abbreviations PR (production rate) FCR (fractional catabolic rate) ppd (pool per day) SRB1 (scavenger receptor B1) EL (endothelial lipase) HL (hepatic lipase) PLTP (phospholipid transfer protein) CETP (cholesteryl ester transfer protein) FC (free cholesterol) CE (cholesterol ester) PL (phospholipid) LpX (lipoprotein X).