Vadadustat is a hypoxia‐inducible factor prolyl‐hydroxylase inhibitor being developed for the treatment of anemia in patients with chronic kidney disease. Sequelae of chronic kidney disease include hyperphosphatemia and anemia, which are frequently treated with phosphate binders and iron supplements, respectively. Two studies evaluating the pharmacokinetics, safety, and tolerability of a single oral dose of vadadustat coadministered with a phosphate binder or iron supplement were conducted in healthy adult participants. In study 1, 54 healthy women and men were administered vadadustat (300 mg) alone and 1 hour before, concurrently with, or 2 hours after a phosphate binder . In study 2, 10 healthy men were administered vadadustat (450 mg) alone and concomitantly with the oral iron supplement ferrous sulfate . Vadadustat exposure was reduced by coadministration with sevelamer carbonate, calcium acetate, ferric citrate, or ferrous sulfate. Geometric least squares mean ratios for area under the concentration‐time curve from time 0 to infinity were reduced 37% to 55% by phosphate binders and 46% by ferrous sulfate. However, when vadadustat was administered 1 hour before phosphate binders, 90% confidence intervals for vadadustat exposure were within the no‐effect boundaries of +50% to –33%, indicating that drug‐drug interactions can be reduced by administering vadadustat 1 hour before phosphate binders. Vadadustat was well tolerated when administered in conjunction with phosphate binders or an iron supplement. Chronic kidney disease (CKD) is a major health burden, with a global prevalence of 697.5 million (9.1%) in 2017.Hyperphosphatemia and anemia are common complications of CKD that become increasingly prevalent as disease severity increases.Management of hyperphosphatemia generally involves using an oral phosphate binder to reduce phosphate absorption in combination with dietary interventions to decrease phosphorus intake, thereby lowering serum phosphate levels toward the normal range.Anemia is another common complication of CKD that contributes to symptom burden and may arise because of iron or erythropoietin deficiency, blood loss, or increased erythrocyte turnover.Vadadustat, an orally bioavailable small‐molecule inhibitor of hypoxia‐inducible factor prolyl‐hydroxylases (HIF‐PHIs), is in development for the treatment of anemia in both dialysis‐dependent and non–dialysis‐dependent patients with CKD. HIF‐PHIs are a possible alternative to the current standard of care for patients with CKD and anemia since ESA use is limited by an increased risk of cardiovascular events.½) of vadadustat is 4.7 hours in healthy adults but ranges from 7.9 to 9.1 hours in patients with non–dialysis‐ and dialysis‐dependent CKD, respectively.Vadadustat demonstrates dose‐proportional pharmacokinetics (PK), with maximum plasma concentrations reached ≈1 to 4 hours after oral administration. Vadadustat undergoes extensive metabolism, primarily via O‐glucuronidation to the inactive metabolite vadadustat‐O‐glucuronide before being eliminated in the urine and feces. The terminal elimination half‐life . In particular, by forming a chelation complex with these agents, vadadustat absorption from the gastrointestinal tract may be altered.Two phase 1 studies were conducted to determine whether a DDI exists between vadadustat and phosphate binders and ferrous sulfate. The first study assessed the effect of a single oral dose of a polymer‐based (sevelamer carbonate [Renvela]), calcium‐based , or iron‐based (branded ferric citrate [Auryxia]) phosphate binder on the plasma PK of vadadustat. A second study assessed the effect of concurrent administration of a single dose of ferrous sulfate on the plasma PK of vadadustat and the inactive primary metabolite vadadustat‐O‐glucuronide. Both studies also aimed to assess the safety and tolerability of vadadustat coadministered with phosphate binders or supplemental iron.Two open‐label, phase 1 clinical pharmacology studies were conducted in healthy volunteers in accordance with the International Conference on Harmonisation Good Clinical Practice guidelines, US Food and Drug Administration regulations, and the principles of the Declaration of Helsinki. Study protocols were approved by institutional review boards for each study site at Advarra Institutional Review Board  and IntegReview . All participants provided written informed consent before enrollment. Both studies were conducted at a single site; study 1 was conducted at Syneos Health Clinique Inc. , and study 2 was conducted at Jasper Clinic . Each study was prospectively registered on ClinicalTrials.gov  before enrolling its first participant.Study 1 was an open‐label, 3‐part, fixed‐sequence study to evaluate the effect of a single oral dose of a phosphate binder on the PK of a single dose of vadadustat. In each part, a single dose of vadadustat  was administered alone and in combination with a single dose of 1 of 3 phosphate binders: sevelamer carbonate  in part 1; calcium acetate  in part 2; or ferric citrate  in part 3. To investigate the effect of timing of administration of each study drug, each phosphate binder was administered in combination with vadadustat on 3 separate occasions, with a 1‐day washout period following the day of administration.On day 1, vadadustat was administered immediately after a normocaloric breakfast totaling 665 kilocalories that included 1 individual cereal box, white bread, cheese, butter, sugar packet, milk with 2% milk fat, and 1 box of grape juice. On day 3, vadadustat and the phosphate binder were administered simultaneously immediately following breakfast. On day 5, vadadustat was administered after an overnight fast, and the phosphate binder was administered 1 hour later, followed immediately by breakfast. On day 7, the phosphate binder was administered immediately after breakfast, and vadadustat was administered 2 hours later. Participants were discharged from the study site on day 9, following collection of the final PK blood sample.2 and a minimum body weight of 45 kg for women and 50 kg for men. Participants were excluded if they had a current or past history of cardiovascular, respiratory, gastrointestinal, hematologic, renal, hepatic, immunologic, metabolic, urologic, neurologic, dermatologic, psychiatric, or other major disease, or clinically significant history of hypercalcemia, iron overload, liver disease, hyperphosphatemia, ulcerative colitis, or gastrointestinal bleeding. Other key exclusion criteria included the use of any prescription medicine, over‐the‐counter multivitamin supplement, or nonprescription product (excluding acetaminophen ≤2 g/d) within 14 days before day –1; use of any cytochrome P450 or P‐glycoprotein inducers or inhibitors within 14 days prior to day –1; daily use of nicotine‐containing products within 6 months of screening; or consumption of any food or drink containing grapefruit juice, apple or orange juice, Seville or blood oranges, or vegetables from the mustard‐green family within 7 days before study drug administration. See Table Study 1 enrolled healthy women and men aged 18 to 55 years, with a body mass index of 18.0 to 30.0 kg/mStudy 2 was an open‐label, randomized, single‐dose study to evaluate the effect of ferrous sulfate on vadadustat PK. Participants were admitted to the study site ≥20 hours before study drug administration. Participants received a single dose of vadadustat  alone and in combination with a single dose of ferrous sulfate  in a randomized crossover fashion. The study used a 2‐period randomized crossover design in which each participant served as their own control. Study drugs were administered with water (8 oz [≈240 mL]) following an overnight fast (10 hours). Fasting continued for 4 hours after dosing, and water was restricted for 1 hour before and after dosing. Participants were seated during study drug administration and for 2 hours after dosing. There was a washout period of 4 days between each administration of the study drug, and participants remained at the study site until collection of the final PK blood sample.2 who were able to and agreed to discontinue all iron preparations for 14 days before study drug administration. Participants were excluded if they had a current or past history of cardiovascular, cerebrovascular, pulmonary, renal, or liver disease; a positive test result for hepatitis B or C, or HIV infection; or a current or past history of gastric or duodenal ulcers or other gastrointestinal disease that could interfere with study drug absorption. Other key exclusion criteria included use of chronic daily medication, any prescription medication or over‐the‐counter multivitamin supplements within 14 days, or use of any herbal supplements within 28 days before admission to the study site; or a known history of smoking and/or use of nicotine or nicotine‐containing products within 6 months of screening. Participants were also required to abstain from consumption of alcohol within 48 hours of admission to the study site.Enrolled participants were healthy adult men aged 18 to 55 years with a body mass index of 18.0 to 30.0 kg/m1Safety and tolerability were evaluated in both studies through adverse event (AE) monitoring , 12‐lead electrocardiogram (ECG) recordings, vital signs, physical examinations, and laboratory tests. Telephone follow‐up with participants was conducted by 14 days after the last dose. The safety population included all participants who received at least 1 dose of vadadustat.In both studies, blood samples for PK analysis of vadadustat were taken before dosing and at 0.5, 1, 1.5, 2, 3, 4, 6, 12, 16, and 24 hours after vadadustat dosing. Additional samples were taken at 9 and 48 hours after dosing in study 1 and 8 hours after dosing in study 2. Plasma concentrations of vadadustat and its metabolite vadadustat‐O‐glucuronide were determined using validated liquid chromatography with tandem mass spectrometry; additional details have been published previously0‐last), AUC from time 0 to infinity (AUC0‐∞), maximum observed plasma concentration (Cmax), time to maximum observed plasma concentration (tmax), apparent terminal elimination half‐life (t½), fraction of AUC extrapolated from time 0 to infinity, and apparent total body clearance (CL/F). PK parameters were also calculated for vadadustat‐O‐glucuronide in study 2.PK parameters included vadadustat area under the plasma concentration–time curve (AUC) from time 0 to the last quantifiable concentration (AUCmax of 17.7%). To assess the effect of coadministration of phosphate binder or ferrous sulfate on vadadustat PK, an analysis of variance (ANOVA) was conducted on ln‐transformed AUC0‐last, AUC0‐∞, and Cmax. This was done at an alpha level of 0.05, including treatment as a fixed effect and participant as a random effect in study 1, and sequence, participant, period, and treatment as factors in study 2. The ratio of geometric means and corresponding 90%CIs were calculated for vadadustat with and without a phosphate binder using least squares (LS) means from the ANOVA of ln‐transformed AUC0‐last, AUC0‐∞, and Cmax. Plasma PK parameters were estimated using Phoenix WinNonlin software  or SAS software version 9.2 .In study 1, a sample size of 18 participants for each part of the study was selected to provide 90% power to detect a +50% or –33% difference in vadadustat PK parameters. These parameters were based on a review of vadadustat PK variability finding a coefficient of variation of 30% for vadadustat and a true geometric mean ratio of 1, and allowing for 10% dropout. In study 2, a sample size of 10 participants was selected to provide ≈80% power for confirming that the 90% confidence interval (CI) falls within 80% to 125% based on a review of vadadustat PK variability  were enrolled in study 1 (18 participants in each part), and 10 male participants were enrolled in study 2. All participants completed each study per protocol and were included in the PK and safety analyses. Study 1 was conducted from May 19, 2020, to August 2, 2020. Study 2 was conducted from December 2, 2014, to December 20, 2014. The demographics and baseline characteristics of participants were well balanced between treatment groups within each study  compared to vadadustat alone. When vadadustat was administered concurrently with calcium acetate, the geometric LS mean AUC0‐∞ was relatively lower at 72.44 μg · h/mL, with a geometric LS mean ratio of 45.00%  compared to vadadustat alone. Likewise, the geometric LS mean AUC0‐∞ for concurrent vadadustat and ferric citrate was 80.90 μg · h/mL, with a geometric LS mean ratio of 47.93%  compared to vadadustat alone.Vadadustat plasma PK parameter findings are summarized in Table % Figure . Specifi0‐last and AUC0‐∞ by 78% and Cmax by 66%  compared to vadadustat alone. However, when vadadustat was administered 1 hour before ferric citrate, we observed a smaller 18% reduction in AUC0‐last and AUC0‐∞ and an 11% increase in Cmax. Specifically, the geometric LS mean AUC0‐∞ for vadadustat 1 hour before ferric citrate was 138.00 μg · h/mL, with a geometric LS mean ratio of 81.76%  compared to vadadustat alone.Administration of vadadustat 2 hours after ferric citrate resulted in a greater reduction in vadadustat exposure compared with concomitant administration, which was not observed with the other phosphate binders. Specifically, ferric citrate reduced vadadustat AUC% Figure . The geo0‐last and AUC0‐∞ reduced by 54% and Cmax by 51%  compared to vadadustat alone. Median tmax was extended to 4 hours when vadadustat was coadministered with ferrous sulfate compared with 3 hours when vadadustat was administered alone, whereas the t½ remained similar at ≈5 hours. Mean plasma vadadustat concentrations exhibited a monoexponential decline after Cmax was reached  were recorded for 13 (72%) participants, of which 24 were considered to be related to the study drug. Seven treatment‐related TEAEs were recorded for 5 (28%) participants administered vadadustat alone vs 17 treatment‐related TEAEs among 6 (33%) participants administered vadadustat and sevelamer carbonate.In the calcium acetate cohort, a total of 36 TEAEs were reported in 14 (78%) participants, including 23 treatment‐related TEAEs. Nine treatment‐related TEAEs were recorded for 7 (39%) participants administered vadadustat alone vs 14 treatment‐related TEAEs among 8 (44%) participants administered vadadustat and calcium acetate.In the ferric citrate cohort, 37 TEAEs were reported in 9 (50%) participants overall, including 29 treatment‐related TEAEs. Nine treatment‐related TEAEs were recorded for 5 (28%) participants administered vadadustat alone vs 20 treatment‐related TEAEs among 7 (39%) participants administered vadadustat and ferric citrate.The most common vadadustat‐related TEAEs were headache, nausea, constipation, and hot flush, whereas the most frequent phosphate binder–related TEAEs were headache, nausea, and hot flush. However, all TEAEs were mild in severity; no moderate or severe AEs were reported in >2 participants in any treatment group. Furthermore, no deaths, serious AEs, or TEAEs leading to discontinuation of the study drug were reported, and there were no clinically significant findings relating to laboratory tests, vital signs, or ECGs in either study.Hyperphosphatemia and anemia are common complications of CKD. Phosphate binders and iron supplements may be administered concurrently to patients receiving vadadustat. Notably, phosphate binders and iron supplements have been shown to interfere with the absorption of numerous drugs, including immunosuppressants, antiplatelet agents, and antibiotics,½ of vadadustat remained unchanged by phosphate binders, irrespective of class of phosphate binder or the relative staggering of vadadustat and phosphate‐binder dosing. This implies that vadadustat absorption, rather than elimination, was affected.In both studies, a potential DDI was observed when vadadustat was coadministered with the phosphate binders sevelamer carbonate, calcium acetate, ferric citrate, or ferrous sulfate. DDI with non–iron phosphate binders can be overcome by administering vadadustat immediately upon waking, before breakfast, or 2 hours after administering a phosphate binder . DDI with ferric citrate can be reduced by administering vadadustat under fasting conditions before an iron‐containing phosphate binder. The median tThe observations in participants administered ferric citrate and vadadustat may reflect the unique properties of the branded ferric citrate used in the current study. The branded ferric citrate has a larger surface area than nonpharmaceutical, commercial‐grade ferric citrate,max remained comparable, indicating that vadadustat metabolism was not affected by coadministration.The bioavailability of vadadustat was also significantly reduced when it was administered concurrently with ferrous sulfate. Furthermore, PK parameters for the metabolite vadadustat‐O‐glucuronide were similarly reduced by concurrent administration, while metabolite‐to‐parent drug ratios for AUC and CThe potential for DDIs between HIF‐PHIs and phosphate binders has been previously reported. In healthy individuals, roxadustat exposure was markedly reduced by 67% and 46% when administered concomitantly with sevelamer carbonate or calcium acetate, respectively.Overall, vadadustat was well tolerated when coadministered with phosphate binders. There were no deaths, serious TEAEs, or TEAEs leading to discontinuation, and no clinically significant changes in laboratory tests, vital signs, or ECGs were observed. Furthermore, all TEAEs were mild in severity, and vadadustat‐related AEs were consistent with those reported previously.The current studies evaluated only a single dose of vadadustat, phosphate binder, or ferrous sulfate over a short duration of treatment and in healthy participants only. Therefore, the influence of repeat dosing on vadadustat PK, safety, and tolerability was not determined.Coadministration of a single oral dose of vadadustat with a phosphate binder or iron supplement reduces vadadustat exposure; however, the impact of these DDIs can be reduced by administering vadadustat 1 hour before phosphate binders or oral ferrous sulfate. Furthermore, vadadustat was well tolerated when administered in conjunction with phosphate binders or oral ferrous sulfate.This study was funded by Akebia Therapeutics, Inc. Medical writing assistance was provided by Cadent Medical Communications, LLC, a Syneos Health group company, and was supported by Akebia Therapeutics, Inc.S.K.P., J.M., R.S., S.K.B., and A.C. are employees of Akebia Therapeutics, Inc.All authors provided substantial contributions to the conception of the work. All authors substantially contributed to the acquisition, analysis, or interpretation of data for the manuscript. All authors participated in drafting, revising, and critically reviewing the manuscript for important intellectual content. All authors approved the final version of this manuscript to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.medicalinfo@akebia.comand will be reviewed by an independent review board, with final approval by Akebia. All approved proposals are subject to a research agreement, and data will be made available in a third‐party vendor software.Data sharing requests can be submitted to SUPPLEMENTARY INFORMATIONClick here for additional data file.SUPPLEMENTARY INFORMATIONClick here for additional data file.SUPPLEMENTARY INFORMATIONClick here for additional data file.


Gut‐brain axis is widely implicated in the pathophysiology of Parkinson's disease (PD). We take an integrated approach to considering the gut as a target for disease‐modifying intervention, using continuous measurements of disease facets irrespective of diagnostic divide.We characterised 77 participants with diagnosed‐PD, 113 without, by dietary/exogenous substance intake, faecal metabolome, intestinal inflammation, serum cytokines/chemokines, clinical phenotype including colonic transit time. Complete‐linkage hierarchical cluster analysis of metabolites discriminant for PD‐status was performed.p = .001). Faecal calprotectin was 44%  greater in PD , with 16% of PD‐probands exceeding a cut‐point for clinically significant inflammation compatible with inflammatory bowel disease. Higher maltose intake was associated with exceeding this calprotectin cut‐point.Longer colonic transit was linked to deficits in faecal short‐chain‐fatty acids outside PD, to a ‘tryptophan‐containing metabolite cluster’ overall. Phenotypic cluster analysis aggregated colonic transit with brady/hypokinesia, tremor, sleep disorder and dysosmia, each individually associated with tryptophan‐cluster deficit. Overall, a faster pulse was associated with deficits in a metabolite cluster including benzoic acid and an imidazole‐ring compound  and vitamin B3 (anti‐inflammatory) and with higher serum CCL20 . The faster pulse in PD was irrespective of postural hypotension. The benzoic acid‐cluster deficit was linked to (well‐recognised) lower caffeine and alcohol intakes, tryptophan‐cluster deficit to higher maltose intake. Free‐sugar intake was increased in PD, maltose intake being 63% higher (Emerging picture is of (i) clinical phenotype being described by deficits in microbial metabolites essential to gut health; (ii) intestinal inflammation; (iii) a systemic inflammatory response syndrome. A marker of intestinal inflammation was 44% greater in Parkinson's disease, exceeding a cut‐point for inflammatory bowel disease in 16%. It was associated with a higher maltose and sucrose intake. The emerging picture is of systemic inflammatory response consequent on intestinal inflammation, clinical phenotype being described by deficits in faecal metabolites essential to gut health. However, absence of overall delay in colonic transit and of transverse colon segmental delay, irrespective of laxative use, had 90% specificity for excluding PD.We have reported decreased frequency of defecation for up to three decades before neurological diagnosis, with worsening after diagnosis.Colonic Lewy body pathology is present 2 to 5 years before PD‐diagnosis.forme fruste of systemic inflammatory response syndrome (SIRS).Parkinson's disease could fit with the concept of premature or accelerated ‘inflammaging’ .1H‐nuclear magnetic resonance (NMR) and measurement of intestinal inflammation by faecal calprotectin. We integrate nutrient and medication intake, systemic immunome and clinical phenotype including colonic transit.This is a hypothesis building study of the pathophysiology of PD, central to which is a non‐targeted exploration of faecal metabolome using 22.1This observational study was set in a National gut‐brain axis research clinic with a focus on quantifying disease facets to define temporal and interventional change. People with diagnosed‐PD were invited to volunteer. Inclusion was according to UK Brain Bank Criteria with at least three supportive criteria,2.22.2.127 motor subscore 3.13 [normal (0) to severe (4)], based on the worst posture observed standing and walking, and ability to correct posture.Cardinal motor features of PD were assessed as follows: Brady‐ and hypokinesia was measured by free walking speed and mean stride length.Psychometric assessment was by (i) questionnaires: mini‐mental state examination (out of 30), depression and anxiety inventories (each out of 63) and rapid‐eye‐movement sleep behaviour disorder Orthostatic cardiovascular responses were summarised as mean arterial pressure, lying and standing (immediate and after 1 and 3 min), and corresponding pulse rates.Bowel function was defined by (i) questionnaires: Rome III functional constipation for its presence/absence,Current consumption of proton‐pump inhibitors (PPIs) and non‐steroidal anti‐inflammatory drugs was recorded, as was number of antimicrobial courses, in the last 3 years, and time since the last course.2.2.2Intake was measured using a standard consecutive 5‐day unweighted food, drink and supplement diary. Participants were provided with detailed verbal and written instructions on how to complete the diaries. It was explained how to estimate portion size using household measures . Validated coloured photographs were provided as examples of how to estimate portions of composite dishes. Participants were asked to maintain their usual intake for the duration of the diary. Participants with PD who had difficulty completing the food diaries were aided by their partner or carer. Participants were telephoned, initially to remind them of the importance of adequate completion, and, on completion of the diary, for clarification where necessary.Dietary data were entered onto an online nutritional analysis software . The database contains over 125 000 foods, with reference to the UK Composition of Foods database. Data were entered by participant number and visit‐day code, separately by two analysts.2.3g. Serum was stored in 250 μl aliquots. All samples were frozen at –80°C directly after processing.Faecal samples were collected by participants prior to arrival, or on‐site, using a standardised kit, previously posted with instructions stressing that delivery must be within 4 h of evacuation (time noted). Participants unable to comply with instructions that day did so in the subsequent few days. Faecal aqueous extracts2.42O, containing 0.05% w/w 3‐(trimethylsilyl)propionic‐2,2,3,3‐D4 acid (TSP) and 2% sodium azide, and buffered to pH 7.4 with 100 mM phosphate. 1H NMR spectra were obtained on a Bruker AVANCE 700 spectrometer , at proton frequency 700 MHz, using a Carr‐Purcell‐Meiboom‐Gill pulse sequence. Acquisition in automation used TopSpin v.4.0 . Pre‐processing entailed Fourier transformation, phase adjustment, baseline correction and calibration by reference to peak TSP resonance at δ = 0 ppm.Each thawed faecal aqueous extract (500 μl) was mixed with 100 μl of heavy water, DSpectral data were imported to MATLAB R2018b  using a custom workflow toolbox . Residual water peaks were removed (δ = 4.63–5.20 ppm), as were any polyethylene glycol (PEG) signals (δ = 3.53–3.85), attributable to macrogol laxatives. Samples (14) with gross spectral distortion due to PEG or (2) unusual NMR peaks were eliminated from exploration in the first 135 samples (44/119 were from PD‐probands). Peak alignment by fast Fourier transformhttps://hmdb.ca/); Human Faecal Metabolome ; Biological Magnetic Resonance Bank (https://bmrb.io); and Spectral Database for Organic Compounds (https://sdbs.db.aist.go.jp). These were supplemented by published literature . Statistical spectroscopic methods, Statistical Total Correlation Spectroscopy (https://pubs.acs.org/doi/10.1021/pr050399w) and Small Molecule Enhancement Spectroscopy (https://doi.org/10.1039/D0SC01421D) were applied to full spectra to suppress any macromolecular background and facilitate metabolite identification.Databases, using 1D and 2D NMR (see below), for assignment of endogenous faecal metabolites were Complex Mixture Analysis by NMR ;2O containing 0.05% w/w of TSP and 2% of sodium azide, and pooled to give three (600 μl) five‐sample clusters. Spectra were acquired over 72 h. A range of 2D NMR spectroscopy experiments were performed: J‐Resolved Spectroscopy; Total Correlation Spectroscopy ; and Heteronuclear Single Quantum Coherence . Where attribution was putative, 1D spectra were recorded before and after spiking samples with chemical standards  to confirm assignment . Aliquots (500 μl) of 15 aqueous extracts, containing higher amounts of signals of interest, were dehydrated (Eppendorf vacufuge concentrator) for 18 h, reconstituted in 120 μl Dsee also .2.5Multiplex‐assays  were used for 22/23 serum immune analytes (see Supporting Information), associated in literature with PD/co‐morbidities.2.640 for presence of histological activity and 188 μg/g41 for ‘clinically significant inflammation’ comparable with that found in IBD were selected, recognising both the diversity of recommendations and the importance of continuous numerical data in addressing inflammaging. Optimal standard curve fit for the enzyme‐linked immunosorbent assays (ELISA) was log concentration against log optical density. Intraclass correlation (95% CI) was excellent: 0.98 . We included a second observation, 1 year after the first, to indicate trajectory.Faecal calprotectin concentration was measured as a marker of colonic inflammation . Cut‐points of >50 μg/g2.7Two steps towards constructing a pathophysiological model for PD were made: (i) Appropriate dimension reduction by identifying redundant components within each functional data set and (ii) Assessing strength of association of remaining components with participant group‐status and with phenotypic facets measured across the diagnostic threshold. Appropriate generalised linear regression models were used, age, sex, weight and body mass index being considered as potential confounders. Disease‐status interaction terms were included in regression models: while significant interactions are noted, we recognise that non‐significance may be explained by insufficient power. Variables exhibiting a positively skewed distribution were transformed to approximate normality using natural logarithms. Models were fitted within Stata 15.1 .q‐value cut‐point <.05)]. ‘Concentration’ is taken as directly proportional to area under corresponding NMR peak. There was good agreement with output of an independent analyst. (ii) Complete‐linkage hierarchical cluster analysis (CL‐HCA), based on the L2 dissimilarity measure (i.e. Euclidian distance for metabolites), was visualised in dendrogram format. Optimal number of clusters was determined by greatest Calinski and Harabasz pseudo‐F index (i.e. ratio of between‐cluster to within‐cluster variance).Regarding metabolite profile: (i) Ratio of mean ‘concentration’ in those with PD to that in remainder provided a summary comparison between groups across all 98 metabolites documented by one analyst: 36 of these were associated with PD . Being prescribed levodopa or a monoamineoxidase‐B inhibitor was associated with an increase in unknown‐25 of 22%  or 28% , respectively (p = .03 and .004 after adjustment). These drug‐related metabolites were excluded from heatmapping.Table 3.3Figure p = .01) between PD‐status and total colonic transit on butyric acid concentration. The lower the butyric acid concentration, the longer was the colonic transit outside PD , but not within PD  towards a similar interaction was seen for propionic acid.Area‐1 (within Cluster‐1) showed strong negative associations of short‐chain fatty acids (SCFAs) with transit time through entire colon and its transverse segment. There was an interaction  the motor and non‐motor facets of Area‐2; (ii) the pulse series; (iii) the mean arterial pressure series; and (iv) objectively measured flexor rigidity. Rigidity was strikingly dissimilar from the others. Minor dissimilarity was seen between total number of markers retained in colon  and the other Area‐2 facets .Area‐2 associates seemingly diverse motor and non‐motor phenotypic facets with deficits in three of the four Cluster‐2 metabolites: tryptophan, trimethylamine and 3‐hydroxyphenylacetic acid Figure . Area‐2 p = .04 in either case). This was over and above the effect of PD on concentration . The single largest metabolite effect was of an imidazole‐ring‐containing compound, with a lower concentration by –10.8%  per 10 beats higher pulse, beside the PD‐effect. Pulse was faster within PD than outside by 5  beats per minute lying, 6  immediate standing, 4  after 1 min, and 4  after 3 min, respectively . The faster pulse of PD was irrespective of any postural fall in pressure  and of the free sugars, sucrose (p = .02) and, in particular, maltose (p = .001) contributed to this. Total free‐sugar intake was higher by a median (95% CI) of 6.4  g/day in PD (p = .04) (Table p = .001). Caffeine, alcohol and water intakes were lower in PD  , sucrose having a negative effect on the metabolite concentration outside PD , with no apparent effect within PD. Further analysis showed that maltose intake had a small additional decremental effect on concentrations of Cluster‐2 metabolites , over and above that of PD [–21.0%  and –22.8% ]. Indeed, these additional effects appeared embedded outside PD [–7%  and –5%  per g] not within, but the disease‐status interaction did not reach statistical significance. However, Figure p ≤ .05 in each case). Water intake also correlated positively with one Cluster‐4 metabolite, glutaric acid (p = .005).In contrast, caffeine and alcohol intakes each correlated positively with (7/10) Cluster‐3 metabolites Figure . That is3.5p = .03 at 3 min standing)  and CCL20 respectively: adjusted R2 12%]. There was a positive correlation between CCL20 and total colonic transit time (p < .05), but this did not complement the effect of butyric acid or tryptophan on transit. Faecal calprotectin did not provide insights into the CCL20 associations.In this exploratory analysis, none of the 8 serum immune analytes compliant with quality control criteria, discriminated for PD‐status. However, CCL20 had a positive correlation with serial pulse readings  Figure . So, pul3.6p = .009). Age‐at‐diagnosis of PD had no effect. No within‐participant temporal effects were detected for calprotectin over a mean of 672 (SD 304) days (29 participants with 2 observations). Sex and body mass index had no significant effect. Regarding drug consumption . More participants with PD (21%) than without (9%) were taking PPIs . Non‐steroidal anti‐inflammatory drug consumption had no effect on calprotectin. Antimicrobial history was associated: overall, calprotectin was higher  with any antimicrobial courses in the last 3 years, two courses having a large effect . The higher calprotectin was accounted for by exposure within the year preceding the faecal sample. Number of antimicrobial courses was greater with PD than without (p = .006), time since the last course similar.Age, in isolation, appeared to have a significant cross‐sectional effect on faecal calprotectin concentration (mean (95% CI) increase 24.5%  per decade, on Table , with a p = .003, Table p = .001). Figure p = .06). Indeed, 16% of PD‐probands exceeded the (188 μg/g)p = .004). However, the likelihood of a second calprotectin result concurring with first in PD was slight with respect to the lower cut‐point [Kappa (SE) 0.04 (0.18)], nil with respect to higher. Calprotectin tended to be higher with greater severity of PD . Being a proband's partner, rather than a control‐proper, had no significant effect on either the odds of exceeding the lower cut‐point [mean (95% CI) 1.36 ], or on the calprotectin concentration itself [5% ] using all measurements and adjusting for PPI prescription.Taking all available measurements, calprotectin was significantly higher in PD by 61%  (3.7p = .02), irrespective of PD‐status. Overall, calprotectin tended (p = .07) to be higher by 12%  per gram of daily maltose intake, after PPI adjustment. There was some evidence for a predisposition or sensitisation to maltose‐related intestinal inflammation in PD : a 23%  increase in calprotectin per gram of daily maltose within PD , no increase outside.Of the dietary components discriminant for PD Figure , only map = .07), the median (interquartile range) intake being 220  mg/day. Where the lower or higher calprotectin cut‐points were exceeded, cholesterol intake was higher by 64  or 135  mg/day, respectively (p = .01 and .001). A significantly higher protein intake was associated with exceeding the higher cut‐point, but it did not contribute to a model explaining increase in calprotectin in terms of maltose plus cholesterol intakes . After PPI adjustment, the size of these effects was 15% /g and 16% /100 mg.Cholesterol and protein intakes related to calprotectin, but not to PD Figure . Over al44.1We are the first to use an untargeted approach to explore the complete NMR faecal metabolic profile, according to PD‐status, and then cross‐examine discriminant metabolites against measures of disease facets across the diagnostic divide. The approach was rewarding in that the deficits in 3 of the 4 faecal metabolite clusters had clear disease‐facet profiles, and the nature of the metabolites gave clues to potential underlying mechanisms. Metabolite clustering corresponded to well‐recognised dietary characteristics of PD, lower caffeine, alcohol and water intakes and a new finding of increased free‐sugar intake, especially of maltose. Effects of diet on metabolome might be via reshaping the microbiota, rather than direct. The excess intake of free sugars was also linked to the intestinal inflammation of PD, as measured by calprotectin.Drivers of the pathophysiology may operate before and/or beyond the diagnostic threshold of this pleomorphic disease. Defining windows of opportunity for disease modification is the goal. Although a marked deficit in faecal SCFAs was seen in PD, the expected (from rodent models)4.2Lachnospiraceae family, notable SCFAs producers, is reported in PD,Slow colonic transit may encourage a bacterial microbiota deficient in taxa whose metabolites are essential to gut health and motility, a vicious cycle being set‐up. Instigation ;Far from the concept of cold neurodegeneration, the parkinsonian syndrome may be a Dietary intake of vitamin B3 was not lower in PD, but its deficiency has been reported with consumption of levodopa combined with decarboxylase inhibitors.Pulse was also faster with a higher serum CCL20 .4.4The increase in serum IL6 in PD, over and above the ageing effect,Follow‐up suggests a relapsing/remitting pattern of intestinal inflammation, even where initial calprotectin concentration was at a level comparable with that of IBD. Extensive longer‐term follow‐up, mindful of PPI consumption, may reveal heterogeneous patterns fitting with those of PD (e.g. ‘unremitting progression’ and ‘burn‐out’).4.5Reducing the gross excess in maltose intake in PD might ameliorate clinically significant intestinal inflammation. Any predisposition to maltose‐related intestinal inflammation in PD might be explained by reduced abundance of bacterial taxa which catabolise free sugars.An alternative source of inflammation to colitis is small intestine bacterial overgrowth (SIBO). However, faecal calprotectin is not a reliable marker of small intestinal inflammation: patients with ileal CD may not have markedly elevated concentrations.4.6Our systematic review4.7Regarding limitations, this is just a step on the way to a multidimensional fully quantifiable pathophysiological model for PD and overlap diseases, to describe where an individual is now and the trajectories with time and interventions. Candidate sections of the ‘drivers and mediators of PD’ jigsaw are assembled in a gastroenterological framework. The purpose is to lay a foundation for classification modelling for disease‐status, description of transitions in disease spectrum and development of time‐series models to understand evolution‐related factors. The next step is incorporation of faecal microbial taxonomy and functionality and the human genetics of immunoinflammatory response and PD‐risk. An adaptive approach allows expansion, in terms of cohorts of particular interest and datasets [e.g. reprioritise our reference list of serum immune analytes (see Supporting Information), initially selected on a 3‐study criterion as discriminant for PD5In conclusion, it is essential for comprehensive identification of disease modification targets to consider the phenotypic facets as having different drivers and mediators, and to measure facets irrespective of diagnostic threshold. We assemble two potential pathophysiological pathways  and give indicative evidence for a third (a pro‐inflammatory microbiota). Scope for simultaneous or sequential interplay between pathways is recognised. Disease‐modifying interventions, with reference to optimal timing in the natural history (e.g. premature/accelerated inflammaging or SIRS), site  and target , are needed. Simple good‐practice measures are potentially disease‐modifying: (i) reducing candidate provocateurs of intestinal inflammation, dietary  and, where appropriate, medicinal , and (ii) rigorously treating slow colonic transit with hydration and maintenance non‐irritant laxatives.The authors are not aware of financial conflicts with the subject matter or material discussed in this article with any of the authors or their academic institutions or employers.Metabolite annotation; Serum immune analytesClick here for additional data file.


Analyses were performed with regard to micro-state segmentation, ERP source localization, and correlation with the patients’ clinical characteristics. Topographical assessments of EEG configurations were associated with the pathological condition. The source analysis revealed altered trigeminal-sensory response patterns in the precuneus, temporal pole, and cerebellum for both migraine groups during the interictal phase. The estimated current source density was positively correlated with migraine disease duration, indicating brain functional and structural changes as a consequence of the disease. Hyperactivity of the cerebellar posterior lobe was observed as a specific trigeminal response of migraine patients with aura. In conclusion, our results suggest the presence of brain changes accompanying the advancement of migraine as an expression of dysfunctional central pain processing. Hence, we identified EEG patterns in response to mechano-/chemosensory stimuli that can serve as biomarkers of migraine.In migraine, the trigeminal nerve is intimately involved in the pathophysiology of the disease. We hypothesized that alterations in the sensory trigeminal activation in migraine would be reflected by EEG-derived event-related potentials (ERP). We aimed to investigate differences in the temporal and spatial processing of trigeminal stimuli between interictal migraine patients and healthy subjects. ERP to trigeminal stimuli were recorded at 128-channels to allow localization of their cortical sources with high temporal resolution. Seventeen patients with episodic migraine without aura, 17 subjects with episodic migraine with aura, and 17 healthy subjects participated in the study. The first branch of the trigeminal nerve was stimulated using intranasal chemical (COThe online version contains supplementary material available at 10.1007/s10548-022-00924-x. The trigeminal nerve is intimately involved in the pathophysiology of migraine. While the ophthalmic and maxillary branches of the nerve are purely sensory, the mandibular branch has sensory and motor functions. A variety of sensory inputs from the scalp, face, nose, mouth, and meninges are processed by the trigeminal pathway providing tactile, proprioceptive, and nociceptive as well as chemosensory information. Nociceptive afferents in the nasal cavity have free nerve endings located within the lining of the nasal vestibule and nasal chambers. When stimulated by chemical substances, they lead to sensations like stinging, burning, warmth, or cold  and without aura (PM) and healthy subjects (N) expressed as different brain response patterns. Specifically, responses were studied during the interictal period with regard to three types of stimuli: (a) intranasal chemical nociceptive stimulation with gaseous CO2, (b) cutaneous electrical stimulation, and (c) cutaneous mechanical stimulation using air puffs. To determine whether there were differences in trigeminal pain-processing at different brain levels, we used the recording of ERPs obtained with 128 channel EEG. This allowed us to examine the spatial distribution of the ERP sources inside the cortices.Fifty-one right-handed subjects participated in the study, including 17 PM patients (mean age ± sd = 40.1 ± 13.1 years), 17 PMA patients (mean age ± sd = 30.1 ± 10.3 years) and 17 N subjects (mean age ± sd = 37.8 ± 11.2 years). Patients were diagnosed according to the International Headache Society (IHS) criteria of the International Classification of Headache Disorders   was recorded during the second visit. The first branch of the trigeminal nerve was stimulated in three different ways; described below. Electrodes were mounted on a standard 10/10 System head cap (BioSemi cap) and connected to the EEG amplifier (BioSemi Active Two AD-box). The outputs of all the AD converters were digitally multiplexed and sent via a single optical fiber to a USB2 Receiver (BioSemi) connected to a PC, where the data were registered and stored through acquisition software (BioSemi ActiveView 605). The sampling frequency of the signal was set at 512 Hz. An electrode gel  was applied to each electrode tip to maximize the conductivity and reduce the impedance. Four pairs of flat-type, active electrodes were used to record the vertical electrooculogram (EOG), the horizontal EOG, both earlobes, and both mastoids. The olfactometer trigger was registered synchronously to the EEG signal. The whole acquisition lasted 120 min, with a break in between. The order of the conditions was randomized across the subjects. Acoustic white noise was played through headphones to cover the background noise. Participants were asked to rate intensity  and pleasantness  on a visual analog scale during the experiment.The three stimuli, intranasal nociceptive chemical, cutaneous electrical, and cutaneous mechanical (air-puff), were delivered to areas innervated by the first trigeminal branch. For chemical nociceptive stimulation, gaseous CO2 was chosen because it does not produce a concomitant odorous sensation which might interfere with pain sensation. CO2 was presented to the right nostril, while both electrical and mechanical stimuli were presented to the right forehead.The chemical stimulation was presented using a computer-controlled air-dilution olfactometer  with a constant flow of odorless, humidified air of controlled temperature (7.2 l/min with an 80% relative humidity and 36 °C). The electrical stimulus  was delivered through Ag–AgCl electrodes  using a constant current stimulator . The electrode was attached to the skin on the right eyebrow, innervated by the trigeminal nerve’s ophthalmic branch, with a ground electrode on the left forearm. However, due to the presence of large EEG artifacts, the electrical condition was excluded from the EEG analysis. The mechanical stimulus was an air puff of 250 ms duration, generated by the olfactometer. The opening of the Teflon tubing, through which the puff was delivered, had a diameter of 2 mm; the stimulator outlet was positioned approximately at 5 mm distance from the skin surface pointing towards the right forehead, airflow was 7.2 l/min. This stimulus was irritating but not painful.The setup of the stimulus condition was based on a pilot study aiming to match the stimuli in intensity. For this, 15 individuals were exposed to chemical, electrical, and mechanical conditions. They were asked to evaluate the conditions on a scale from 0 (not perceived) to 100 (extremely strong) in intensity. Initially, the chemical condition were split into two levels; CO2_low (44% v/v CO2) and CO2_high (56% v/v CO2). As regards the electrical stimulation, we first set the electrical threshold , then increased the relative experimental condition by 20% of this value. The mean ratings and standard deviation for the group in each condition are reported in supplemental digital content- (SDC-)Table 2 = 20.3, p < 0.001]. Higher median (Md) intensity values were observed for CO2_high (Md = 52) compared to CO2_low (Md = 31), electrical condition (Md = 32), and mechanical condition (Md = 33). A posthoc test (Wilcoxon) with Bonferroni correction showed no significant differences between CO2_low vs. electrical condition nor for CO2_low vs. mechanical or electrical condition vs. Puff (p > 0.16).A Friedman test revealed a statistically significant difference across the 4 intensity ratings , and age  with a relative mean age ± s.d. N = 38 ± 11, PM = 40 ± 13, PMA = 30 ± 10.The three groups were homogenous in gender . BDI post-hoc analyses (Tukey) showed significant differences between N- and PMA- group  = 0.53 (medium), mean difference (N-PMA) = − 12.41, CI(95%) =  Trigeminal lateralization test post-hoc analyses (Tukey) revealed significant differences between N- and PMA- groups . Data are illustrated in the boxplot in Fig. The data are illustrated in Fig. N group rated intensity  and pleasantness  differently among the three stimulus conditions. The post-hoc analysis revealed that N rated the intensity of the CO2 higher than the intensity of electric (mean difference intensity (C-E) = 16.95, CI(95%) = , p < 0.01, effect size (r) = 0.429) and less pleasant than both electric and the air-puff (mean difference hedonics (C-E) = − 3.59, CI(95%) = , p < 0.05 effect size (r) = 0.375; mean difference hedonics (C-M) = − 3.92 CI(95%) = , p < 0.01, effect size (r) = 0.449).Statistical analysis revealed that the PM group presented with significantly different ratings between the three conditions both in intensity F{2, 47} = 38.604, p = 0.000 and hedonics F{2, 47} = 18.176, p = 0.000, where post-hoc analysis revealed that PM rated the CO2 and the air puff higher than electrical in intensity  and CO2 pleasantness was lower than electrical and air puff .The PMA-group presented significantly different ratings of intensity F{2,44} = 9.485, p = 0.003  and the dependent variables of electrical threshold stimulus acquisition, mean intensity , mean hedonics , and mean performance  was performed using as co.-variate the preventive medications. Results with a medium effect size revealed a significant increase of the Chemosensory stimulation perception for PM versus both N and PMA groups, while the PMA group significantly prefer in comparison with PM the chemosensory stimulation. Finally, the PMA group was a better performer of the N group in all three conditions, in addition performed also better that PM during the CO2 conditions. A detailed account of all the statistics results is reported in Table A grand average (GM) for each group and condition was obtained on an epoched time range of 1.2 s; the relative figure is reported in the Supplementary Information .The GM for each condition/group is also illustrated as red (positive)—blue (negative) voltage topography in Fig. The segmentation, obtained from the 128-channel grand averages of the three groups, identified a total of 18 microstates, explaining 81.9% of the global variance ; right cerebellum: r = − 0.37, p = 0.05, CI(95%) = . Since other patient reported measures as Midas did not show any correlation, we view these results with caution, also due to subjectivity and inter-individual variability in a rather small group of patients.Details of the correlations are reported in Fig. Segmentation analysis and the fitting back to the single subjects of the variable defining the microstates have indicated map 7 and the strength and duration of map 9 as specific topographies for the migraine condition with aura. To confirm these results, we have computed the results of the inverse solution and the related eCSD in the sources for each group. A Kruskal–Wallis test revealed a statistically significant difference in eCSD signal extracted from the ROI with maximum brain activity sources of map 9 (right cerebellar cortex) across the three different groups, N , PM , and PMA  = , H = 8.496, p = 0.0143, effect size (r) = 0.47. This confirms an increase in the strength of the eCSD signal in the right cerebellar cortex during the tf1 for the PMA group.Data are illustrated in the box plot of Fig. Our study revealed characteristic EEG topographies for migraine patients with or without aura and healthy controls. Map c, map f, in the chemosensory condition Figs. , 6 and mEvidence also suggested that modulation in strength and duration of the signal for map 9 in tf1 and the relative eCSD in the left posterior lobe of the cerebellum (area VIII) is specific to migraine with aura condition.Regarding the clinical and psychophysical data, there were no differences between the study groups in olfactory identification ability. Trigeminal sensitivity however, was significantly higher in migraine patients with and without aura than controls. The BDI score was significantly higher in the PMA groups , indicating more depression-relevant symptoms in migraine patients than controls. Anxiety and depressive disorder are known comorbidities in migraines resulting in an additional disease burden  and is involved in pain sensitivity  participates in pain processing by mediating affective responses to painful stimuli and is a multisensory convergence zone responsible for processing visual, olfactory, and auditory stimuli Below is the link to the electronic supplementary material.


Fifteen out of 16 ‘Non-DR dermatologists’ (94%) did not apply DR due to lack of scientific evidence. In conclusion, DR of biologics for psoriasis is part of clinical practice in psoriasis experts globally. Barriers for applying DR included lack of evidence or guidelines, and uncertainty on DR effects and risks. Although growing evidence shows DR feasibility, future studies are needed to accumulate and broaden evidence, along with development of (inter)national guidelines on DR strategies.Dose reduction (DR) of biologics, where possible, seems promising for more efficient use of expensive biologics. For implementation of DR strategies, it is essential to get insight in factors that influence implementation. The objective of this study was to evaluate the attitudes and behaviour regarding dose reduction of biologic therapies for psoriasis among psoriasis expert dermatologists worldwide. A 27-question e-survey was sent through the International Psoriasis Council (IPC) to its 114 dermatologist councilors worldwide. The survey assessed demographics, general and DR prescription behaviour, and motivations for and barriers against application of DR. Of 57 respondents, 53 respondents who prescribed biologics were included for analysis. Thirty-seven (69.8%) applied DR , and 16 (30.2%) did not . DR strategies varied among respondents. Regarding criteria for starting DR, differences were reported in required treatment duration, and interpretation and duration of stable low disease activity. In addition, the prolongation of intervals between injections varied between respondents. For most ‘DR dermatologists’ (The online version contains supplementary material available at 10.1007/s00403-021-02273-4. Biologics have expanded treatment options for psoriasis in the last decades. These drugs reduce skin symptoms and improve quality of life in psoriasis patients . BesidesDose reduction (DR) of biologics , seems, therefore, a promising way for more efficient and safer use of biologics. By striving for the lowest effective dose, overtreatment can be prevented and healthcare costs can be reduced. To date, studies on biologic DR report different strategies, but DR seems feasible and safe in a substantial part of patients with low disease activity , 20, 21.For further implementation of DR strategies worldwide, it is essential to get more insight in factors which influence implementation . Possibln = 114). To maximize response rates, a reminder was sent after 10 weeks. The online survey was closed on 30 October 2020. All participant responses were anonymously collected using unique respondent identification numbers.A questionnaire was developed based on a previous questionnaire sent to Dutch dermatologists . All queThis study was reviewed by the ethics committee of the region of Arnhem-Nijmegen and Radboud University Medical Center and was deemed to not fall within the remit of the Medical Research Involving Human Subjects Act (2021-13093), as we did not collect any personal data. Therefore, informed consent from participants was not mandatory. Consent to participate was assumed in case of completion of the e-survey. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.Dose reduction was defined in the survey as ‘the application of injection interval prolongation’ and/or’decreasing the absolute dose in number of milligrams per administration’. The survey addressed demographics (country and place of work), prescription behaviour of biologics for psoriasis in general , application of DR . At last, respondents were asked for barriers which might prevent them from application of DR. Both open answers and predefined answers were used. In case of predefined answers, there was an option to add comments. Questions regarding DR were only displayed to respondents who indicated that they applied DR. For the complete questionnaire, see Supplement S1.Only completed surveys were included for analysis. Descriptive statistics were calculated to describe survey responses. As the number of respondents exposed to each question differed, results are presented as absolute numbers with percentages of respondents that were exposed to the question. All analyses were conducted in SPSS Statistics 25 .n = 5], Germany [n = 2], Italy [n = 3], Portugal [n = 2], Sweden [n = 1], Switzerland [n = 2], The Netherlands [n = 1], United Kingdom [n = 3]), 24.5% from South America , 17.0% from Asia , 15.0% from North America , 5.7% from Africa (Egypt [n = 3]), and 1.9% from Australia . The majority  was employed in an academic hospital. The majority of respondents  estimated the total number of patients treated with biologics at their departments between 100 and 500. Ten respondents (18.9%) estimated this number as < 100, whereas 15 respondents (28.3%) estimated that this number was > 500, and 1 respondent did not know. Biosimilars were prescribed by 66.0% (n = 35). Dose reduction was applied by 37 dermatologists (69.8%) , and 16 dermatologists (30.2%) did not apply DR (‘Non-DR dermatologists’).A total of 57/114 surveys were completed, indicating a response rate of 50%. Four respondents were excluded from analyses as they did not prescribe biologics or biosimilars for psoriasis. Among 53 dermatologists (46.5%) that prescribed biologics or biosimilars for psoriasis, 35.8% were from Europe , whereas brodalumab and tildrakizumab were prescribed least often . Tools that were used for measurement of disease activity were PASI, Body Surface Area (BSA) and Physician Global Assessment (PGA) by, respectively, n = 46 (86.8%), n = 42 (79.2%), and n = 28 (52.8%) of the respondents (multiple answers possible). One dermatologist (1.9%) did not use a disease activity score in daily practice. Six respondents replied ‘other’, and described using the DLQI (n = 2), Visual Analogue Scale (VAS) itch (n = 1), photo documentation (n = 1), subjective impact (n = 1), or a ‘VAS score of the patient’ (n = 1).Ustekinumab and secukinumab were prescribed by the highest absolute number of respondents (n = 1 (2.7%) in PASI ≤ 5, n = 2 (5.4%) in BSA ≤ 3, and n = 1 (2.7%) in BSA ≤ 5%. DR criteria were based solely on disease activity by 23 respondents (43.4%), whereas 14 respondents (37.8%) used a combination of disease activity measures, side effects and/or patient preferences. The majority of DR dermatologists  would consider DR after at least 1 year of treatment duration. Fifteen respondents (40.5%) considered stable low disease activity for the duration of at least 1 year prior to initiation of DR to be necessary. Detailed responses to questions on DR eligibility criteria are presented in Supplementary Tables S1 and S2.Criteria for applying DR are presented in Table n = 53), and the proportion of respondents that applied DR for each specific biologic. Dose reduction was applied by the largest number of ‘DR dermatologists’ for adalimumab , secukinumab , ustekinumab , and etanercept . Figure Figure n = 29, 78.3%) estimated that their patients were frequently  or ‘always’ ) willing to start DR. There was a large variability in the evaluation of DR success, ranging from estimated success rates of < 20%  to rates of > 80% .Table n = 9 (24.3%) on a combination of disease activity and patients’ request, n = 1 (2.7%) decided to re-increase the dose solely on patients’ request, and n = 1 (2.7%) based this decision on ‘nothing particular’. Among ‘DR dermatologists’ that used disease activity scores , n = 14 out of 26 (53.8%) would re-increase the dose in case of PASI or BSA ≥ 3. The maximum BSA at which a respondent would re-initiate treatment was BSA > 10% . One respondent  would re-increase the dose if total remission was lost (BSA > 0% or PGA > 0). Another respondent  determined drug levels before re-increasing doses. Besides using disease activity measures, n = 13 respondents (35.1%) made a general estimation of disease activity, and would re-increase the dose in case of ‘moderate disease activity’. Detailed responses to the question on re-treatment criteria are provided in Supplementary Table S3.In addition, ‘DR dermatologists’ were inquired about their criteria to stop DR, and/or re-increase the biologic dose. Twenty-six (70.3%) respondents based this decision on disease activity parameters, n = 32 out of n = 37 ‘DR dermatologists’, 86.5%). Fixed answer options ‘safety/less side effects’ and ‘at patients’ request’ were selected by n = 16 (43.2%) and n = 15 (40.5%) respondents, respectively (multiple answers possible). Two ‘DR dermatologists’ (5.4%) commented that patients should not be treated with more drugs than necessary. Twenty one (56.8%) ‘DR dermatologists’ would like to apply DR more often. Regarding reasons not to apply DR as much as they would like, n = 6 respondents felt that they were hampered by limited experience with DR, and n = 5 felt uncomfortable applying DR with biologics of the newer generations (IL-17/IL-23 inhibitors). Not having enough time (n = 3) or staff for support (n = 1), thinking the patient would not be interested (n = 1), and fearing financial consequences (n = 1) were other reasons not to apply DR more frequently. In addition, the risk of reduced effectiveness (n = 1) and lack of guidelines/scientific evidence (n = 3) were added as comments by respondents. To the question ‘Did you apply dose reduction more often during the COVID19 pandemic?’, n = 9 ‘DR dermatologists’ (24.3%) responded that they applied DR more often.Cost savings were one of the main reasons to apply DR (n = 10 (62.5%) ‘Non-DR dermatologists’ indicated that they would consider DR if scientific evidence was available, and n = 6 (37.5%) would ‘maybe’ consider DR in that case. Apart from the lack of scientific evidence, frequently reported reasons not to apply DR were potential risk of psoriasis exacerbation , fear of antibody formation , loss of effectiveness , and having the opinion that the costs of biologicals should be decreased instead of physicians applying DR  (multiple answers possible).Fifteen out of the 16 ‘Non-DR dermatologists’ (94%) did not apply DR due to the lack of scientific evidence on safety and efficacy of DR. However, n = 4 actually applied DR themselves. Thirteen respondents (24.5%) described their attitude towards DR as neutral. Respondents were asked if they felt the necessity for a guideline on DR of biologics. In total, n = 33 (62.3%) indicated that they felt the necessity for a guideline on biologic DR, of which n = 32 indicated that scientific background information should be covered in such a guideline. Nine respondents (17.0%) selected the answer option ‘other’, of which n = 6 stated that more clinical trials on DR should be conducted prior to the development of a guideline. Ten respondents (18.9%) did not feel the necessity for a guideline.Thirty-five respondents (66.0%) reported a positive attitude towards DR of biologics for psoriasis. Five respondents (9.4%) had a negative attitude towards DR, of which Results of this survey among dermatologist councilors of the International Psoriasis Council showed that DR is applied in the treatment of psoriasis patients. Of 53 respondents, the majority (70%) applied DR, most frequently for the biologics adalimumab, etanercept, ustekinumab and secukinumab. Main reasons for application of DR were cost savings, safety, and patients’ request. Barriers against DR in dermatologists that already applied DR were limited experience with DR, limited experience with the newer biologics in general, not having enough time or support, risk of reduced effectiveness, and lack of guidelines or scientific evidence. Among dermatologists who did not apply DR, barriers were the lack of scientific evidence, potential risk of flares, fear for antibody formation, loss of efficacy, and the opinion that costs should be decreased by the pharmaceutical companies.The used approaches differed among respondents. Globally, a more conservative approach was used in biologics with long injection intervals, leading to smaller DR steps, and, therefore, relatively less reduction of the original dose, as opposed to biologics with shorter injection intervals. The criteria for starting DR also varied among respondents. Most respondents required the patient to have stable low disease activity for at least 1 year or 6 months, but the definition of low disease activity varied among respondents. Almost half of the respondents (45.9%) would only consider DR if patients were free from psoriasis, whereas 6 respondents would still consider DR in case of PASI or BSA ≥ 3. This might be due to international differences in defined treatment goals and used outcomes , 14. In Among barriers against DR were lack of guidelines and scientific evidence, and fear of disease flare. Currently, the option of biologic DR is only mentioned in a few guidelines , 17. HowA strength of this study is the inclusion of dermatologists worldwide. To our knowledge this study is a first evaluation of attitudes in an international group of experts, specifically regarding biologic DR in psoriasis. Similar to the results of this survey, a national survey among Dutch dermatologists showed that DR was already applied in daily practice and also DR strategies differed . MotivatThe main limitation of this study is the small sample size. For further validation of our results and for identifying global differences, replication in larger cohorts is needed. In addition, more structured methods that allow for consensus would be of value as well in future studies. By sending the survey through the International Psoriasis Council, there is a potential selection bias towards dermatologists with an interest in biological treatment, limiting the generalizability of our results.In conclusion, the results of this worldwide survey among dermatologists show that 70% of responding psoriasis experts apply DR of biologics for psoriasis in clinical practice. However, respondents reported a large variety in used strategies regarding initiation and execution of DR. Dose reduction was applied less often in the more recently introduced biologics. Main motivations for applying DR were cost savings and improving safety. Among barriers against DR were the paucity of evidence or guidelines, and uncertainty on DR effect and risk of disease flares. Although growing evidence shows DR feasibility, future studies are needed for the development of local, tailored DR strategies and (inter)national guidelines.Supplementary file 1 (PDF 409 KB)Below is the link to the electronic supplementary material.


We also examined associations between menopausal progression and skeletal muscle fiber characteristics, as well as adipose tissue‐derived adipokines. Relative increases of 2%–14% were observed in regional and total body adiposity measures, with a pronounced fat mass increase in the android area (4% and 14% during short‐ and long‐term follow‐ups). Muscle fiber oxidative and glycolytic capacities and intracellular adiposity were not affected by menopause, but were differentially correlated with total and regional body adiposity at different menopausal stages. Menopausal progression and regional adipose tissue masses were positively associated with serum adiponectin and leptin, and negatively associated with resistin levels. Higher diet quality and physical activity level were also inversely associated with several body adiposity measures. Therefore, healthy lifestyle habits before and during menopause might delay the onset of severe metabolic conditions in women.For women, menopausal transition is a time of significant hormonal changes, which may contribute to altered body composition and regional adipose tissue accumulation. Excess adiposity, and especially adipose tissue accumulation in the central body region, increases women's risk of cardiovascular and metabolic conditions and affects physical functioning. We investigated the associations between menopausal progression and total and regional body adiposity measured with dual‐energy X‐ray absorptiometry and computed tomography in two longitudinal cohort studies of women aged 47–55 ( Menopause is associated with increases in total and regional body adiposity, with a pronounced increase in the android area. Adipose tissue‐derived adipokines, adiponectin, leptin and resistin, were uniquely associated with menopausal transition. Lifestyle habits, such as higher diet quality and physical activity level, but not external hormone use, were associated with lower adiposity in several body regions. ACC‐PAAccelerometer‐measured physical activityBMIBody mass indexCTComputed tomographyDHEASSulfated dehydroepiandrosteroneDQSDiet quality scoreDXADual‐energy X‐ray absorptiometryERMAEstrogenic Regulation of Muscle Apoptosis ‐ studyEsmiRsEstrogen, MicroRNAs and the Risk of Metabolic Dysfunction ‐ studyE2EstradiolFMFat massFSHFollicle‐stimulating hormoneGPDα‐glycerophosphate dehydrogenaseHUHounsfield unitsHTHormone therapyIMCLIntramyocellular lipid dropletIUDIntra‐uterine deviceLPLLipoprotein lipaseMETMetabolic equivalent of a taskMVPAModerate‐to‐vigorous physical activitySDHSuccinate dehydrogenaseSHBGSex‐hormone binding globulinSR‐PASelf‐reported physical activity1Adipose tissue has an important role in energy storage and hormonal supply  have an important role in energy storage during physical activity. Both IMCL concentration and the metabolic capacity of different muscle fiber types are associated with obesity. For example, increased lipid accumulation in muscle fibers, lower oxidative capacity, and a decreased proportion of type I fibers have all been found to positively correlate with overall adiposity and insulin resistance . The short‐term follow‐up sample included 230 perimenopausal women who were followed until early postmenopause (mean follow‐up time 1.3 ± 0.7 years). The long‐term follow‐up sample included 148 women who were pre‐ or perimenopausal at ERMA baseline and postmenopausal at the time of the EsmiRs laboratory measurement (mean follow‐up time 3.9 ± 0.2 years). This sample also included a subgroup of women (n = 62) who were measured at both the short‐ and long‐term follow‐up points.The data used were from the Estrogenic Regulation of Muscle Apoptosis (ERMA) . Of the lifestyle habits, accelerometer‐measured moderate‐to‐vigorous physical activity (ACC‐PA) decreased in the long‐term follow‐up and the use of external hormones increased, shifting from contraceptive use toward HT. In both follow‐ups, serum leptin and adiponectin increased and serum resistin concentrations decreased.Short‐ and long‐term follow‐up group characteristics are presented in Table p < 0.01 for all) and 7%–14% in the long‐term follow‐ups . Computed tomography (CT) was used to assess mid‐thigh adipose tissue area. Subcutaneous adipose tissue area increased by 2% during the short‐term follow‐up and 7% in the long‐term follow‐up (p < 0.01 for both). Muscle compartment adipose tissue area increased by an average of 16% (p < 0.05) and muscle density decreased by 7% (p < 0.05) during the long‐term but not the short‐term follow‐up.All total and regional fat variables measured with dual‐energy X‐ray absorptiometry (DXA) increased during the follow‐ups. The mean relative increments were 2%–4% in the short‐term . In this subpopulation, the mean follow‐up time between baseline (B) and the first follow‐up point (F1) was 1.4 (0.4–3.6) years, and between F1 and the second follow‐up point (F2) 2.5 (0.7–3.7) years. Median annual changes in adipose tissue variables were calculated for both follow‐ups to compare the rate of change between the transition from perimenopause to early postmenopause  and progression through postmenopause thereafter . Only the rate of change in muscle density differed significantly between FU1 and FU2 . Changes in sex hormones, body mass, BMI, physical activity, and adipokines were similar to the respective follow‐ups in the full sample (data not shown).Figure 2.4p < 0.001 for all). Higher diet quality score was associated with lower adiposity for all other variables  except for android‐to‐gynoid ratio. Higher physical activity was also associated with lower adiposity . Of the mid‐thigh fat variables, time was a significant predictor of subcutaneous adipose tissue area (p < 0.001). Higher physical activity was associated with lower subcutaneous adipose tissue area and higher muscle density (p < 0.05 for both).Linear mixed‐effect models were constructed to study associations between menopausal progression and total, regional and mid‐thigh fat variables, and to assess whether lifestyle habits  modulated these associations Table . Self‐re2.5p < 0.001 for all). Of the FM variables, adiponectin was positively associated with gynoid FM (p < 0.05). Leptin was positively associated with menopause in the total, gynoid, and gluteofemoral FM‐adjusted models . Combined estrogen and progestogen use and the total and regional FMs of android, gynoid, and gluteofemoral area were positively associated with leptin levels . Resistin concentration was negatively associated with menopause in all adiposity models  but was not associated with any FM variables.To study the associations between adipokines and selected adiposity variables, linear mixed‐effect models were constructed Figure . Adipone2.6). Samples from 10 participants were analyzed at baseline, eight at F1, and seven at F2. None of the participants used HT. At all time points, type I fibers had higher oxidative capacity than type II fibers, whereas glycolytic capacity was higher in type II than in type I fibers . Secondly, single lipid droplet area and lipid accumulation index were studied separately among fiber types at all three time points . No difference was observed at other time points. At all time points, lipid accumulation index was the largest in type I fibers .Muscle fiber oxidative capacity was investigated with succinate dehydrogenase and glycolytic capacity with α‐glycerophosphate dehydrogenase staining. Lipid accumulation was quantified with lipid droplet staining. First, oxidative and glycolytic capacities were studied between fiber types and separately at the B, F1, and F2 time points Figure . Samples Figure . At basen = 8 for the short‐term and n = 7 for the long‐term follow‐up) . In type I fibers, lipid accumulation index correlated positively with all studied FM variables in at least two time points . In type IIA fibers, lipid accumulation index correlated positively with gynoid and leg FM at baseline and F2 . For oxidative and glycolytic capacities, correlations were only found at single time points (results not shown). At F1, leg FM correlated positively with the glycolytic capacity of type II cells  and total tissue glycolytic capacity . At F2, oxidative capacity in type II cells and at the total tissue level correlated negatively with lipid accumulation index in type IIA cells .We also investigated associations between myofiber lipid and body adiposity variables at all measurement points Figure . In type3In this longitudinal study over the menopausal transition, changes were observed in multiple adiposity measures, from circulating adipokines to regional body fat. Our results show that menopause contributes to body fat accumulation and is uniquely associated with systemic leptin, adiponectin, and resistin levels. We observed fat accumulation at multiple adipose depots from the total body to the limb level. The increase was detectable already during the short‐term follow‐up, which focused on the time between perimenopause and early postmenopause, and was more noticeable when the follow‐up time was extended to cover the transition from premenopause to further postmenopause. The increase in android FM was relatively large compared with total body or other regional depots. Leptin and adiponectin levels increased during both follow‐ups, while resistin levels decreased. At the cellular level, we observed muscle fiber type‐specific oxidative and glycolytic capacities and lipid accumulation index, but no changes during menopausal transition except for an increment in type II fiber glycolytic capacity. Lipid droplet area and lipid accumulation index, especially in type I fibers, were positively correlated with total and regional body adiposity measurements during menopause.During the reproductive years, women typically have higher total body FM than men, especially in the gluteofemoral region due to hormonal and enzymatic actions Wells, . IncreasPhysical activity is a widely studied approach to manage body weight and adiposity in all age groups, including middle‐aged women  and EsmiRs  studies Figure . In totalong‐term follow‐up.”Four years after the ERMA baseline, 206 women took part in the EsmiRs laboratory measurements, where the baseline measurements were repeated. Each participant's menopausal status was again determined based on FSH level, menstrual bleeding data, and additional information from the ERMA longitudinal study for those who participated in it. Participants categorized as postmenopausal were included in the “Baseline ERMA data were collected during 2015–2016 and the ERMA follow‐up measurements were performed between August 2015 and early 2019. EsmiRs data collection started in January 2019 and finished in March 2020.The study followed the Declaration of Helsinki. All participants provided written informed consent, and the study was approved by the ethical committee of the Central Finland Health Care District (ERMA 8 U/2014 and EsmiRs 9 U/2018).4.2Fasting serum samples were taken from the antecubital vein between 7 and 10 AM. At baseline, women with a regular menstrual cycle were asked to visit the laboratory between cycle days 1 to 5. Serum E2, FSH, sulfated dehydroepiandrosterone (DHEAS), and sex‐hormone‐binding globulin (SHBG) were measured with IMMULITE 2000 XPi . Serum leptin, adiponectin, and resistin levels were measured with a Quansys Multiplex‐kit .After baseline measurements, participants started the follow‐ups. During the short‐term follow‐up, elevated FSH levels were verified with control blood samples. A participant was only categorized as early postmenopausal after two elevated FSH samples accompanied by at least six months without menstruation. The participant was then invited to the final short‐term follow‐up visit for physiological measurements, during which the E2 and FSH levels were again measured. For the final short‐term follow‐up analysis, the E2 and FSH values from the last regular follow‐up visit and final follow‐up visits were averaged to minimize the effect of daily fluctuation. Hormone levels from the participants who started using hormone therapy during the short follow‐up were only collected from the final follow‐up visit. For participants in the long‐term follow‐up, baseline hormone measurements were repeated during the EsmiRs laboratory visit. Serum leptin, adiponectin, and resistin levels were also measured at baseline.4.3Measurements were done after overnight fasting. Total, trunk, gynoid, android, and right leg FM and total fat‐% were analyzed from the DXA scans . Android‐to‐gynoid ratio was calculated by dividing android FM (kg) by gynoid FM (kg). Gluteofemoral area was outlined manually using the iliac crest line as the upper limit and the knee joint as the lower limit of a rectangle . Subcutaneous and muscle compartment (area inside muscle fascia) adipose tissue area was measured using appropriate thresholds in Python Software (version 3.6). From the cross‐sectional image, the muscle portion including the femur was first separated using a U‐net machine learning algorithm or manually if needed. Adipose tissue area was separated from muscle and bone tissues using Hounsfield Unit (HU) limits, and mean muscle density was calculated. Images were analyzed using ImageJ Software  and Python.4.4m. vastus lateralis using suction‐modified Bergström needle biopsies under local anesthesia. All visible connective and adipose tissues were removed, and the sample was divided into three parts. Two parts were assigned to biochemical and molecular biology analyses and were snap‐frozen in liquid nitrogen. The third part assigned for histology was embedded transversely on a cork with TissueTek and frozen in isopentane cooled in liquid nitrogen. All samples were stored at −150°C until analysis.Muscle biopsies were collected from the middle of 4.5Transverse, 10‐µm sections were cut with a cryostat at −21°C. Slides were air‐dried and fixed in 4% paraformaldehyde, incubated with 100 mM glycine, and blocked in 5% goat serum (GS). Primary and secondary antibodies were added to 5% GS. Myosin heavy chain distribution was analyzed with antibodies against type I , University of Iowa, IA, USA) and type IIX . Anti‐laminin  was used to detect cell borders. Primary antibodies were incubated at +4°C o/n, and the attachment was visualized with fluorescent secondary antibodies Alexa Fluor 405 goat anti‐mouse , Alexa Fluor 647 goat anti‐mouse , and Alexa Fluor 488 goat anti‐rabbit . LD540  was incubated in sections for 20 min RT to stain neutral lipids. After rinsing with PBS, sections were mounted with Mowiol‐Dabco and imaged with confocal microscopy . Laminin signal was enhanced, and broken cells were excluded in ImageJ with the Trainable Weka Segmentation plugin and later by human inspection. Cell segmentation and the measurement of lipid droplet size, number, and area fraction were achieved as previously reported , and 9.3 mM alfa‐glycerophosphate  in 0.05 M Tris‐Buffer, pH 7.4. Sections were incubated in prewarmed solution for 40 min at 37.2°C, washed with H2O, and cleared with acetone. Lastly, sections were rinsed with H2O and mounted with Mowiol. Samples were imaged with Olympus BX50 (10×/0.30) . In ImageJ (v. 1.53c), images were transformed into 8‐bit grayscale format. At least 55 cells per sample were manually cropped, and mean gray value was calculated, where 0 indicated black and 255 indicated white. To describe staining intensity more intuitively, mean cell gray values were subtracted from the maximum gray value of 255 and the resulting values were used for further analysis. Enzyme staining was paired with myosin fiber type staining in serial sections. 12‐µm sections were air‐dried and blocked in 10% GS. Primary and secondary antibodies were added to 10% GS. Fiber types were analyzed with antibodies against type I  and type II  fibers. Anti‐laminin  was used to detect cell borders. Sections were incubated in primary antibodies at RT for 1 h, washed, and incubated with secondary antibodies Alexa Fluor 546 goat anti‐mouse  and Alexa Fluor 488 goat anti‐rabbit  at RT for 1 h. Sections were mounted with Mowiol‐Dabco and imaged with confocal microscopy. Images were analyzed with ImageJ. Corresponding fibers were localized manually between histological and immunohistological images.Serial 12‐µm transverse sections were cut at −21°C. Enzyme activities of succinate dehydrogenase (SDH) and α‐glycerophosphate dehydrogenase (GPD) were measured with histological staining. SDH incubation medium consisted of 1 mg/ml NBT  and 27 mg/ml sodium succinate  in 0.2 M phosphate buffer, pH 7.4. Sections were incubated in prewarmed solution for 90 min at 37.2°C, washed with H4.7Physical activity was evaluated with a structured questionnaire  and new thyroid medication users (n = 7) for analysis was investigated, but since sensitivity analysis revealed no major difference in the results, they were included in the analysis.Information about medical conditions and prescription medicine that may have affected adiposity was collected from the questionnaires throughout the follow‐ups. Participants diagnosed with cancer during the study were excluded. The suitability of non‐insulin‐treated type 2 diabetics , oral (tablets), and intra‐uterine preparations were included, but local intravaginal estrogen therapy was not. The use of estrogen and progestogen preparations during menopause may affect blood FSH levels due to a negative feedback loop mechanism. We conducted sensitivity analyses (data not shown) between menopausal stages and external hormone use, and found that pre‐ and perimenopausal progestogen users had lower FSH levels compared with non‐users. At postmenopause, estrogen use was associated with higher E2 levels and decreased FSH levels when compared to non‐estrogen users. Yet, the difference was not consistent in all comparisons. At postmenopause, progestogen use was not associated with altered hormone levels.4.102). Data about smoking (current/quitter/never smoked), alcohol consumption (portions per week), and level of education  was determined with a questionnaire. Baseline missing information for education was n = 14 . These data were completed using information from the follow‐up questionnaires, except for one completely missing questionnaire in the perimenopausal group.Anthropometric measurements were done after overnight fasting. Body mass was measured with a digital scale and height with a stadiometer. Body mass index (BMI) was calculated as body mass divided by height squared  to examine associations between cell and selected adipose tissue variables.All variables were evaluated for normality and parametric tests were used whenever possible. Independent samples n = 3), SR‐PA (n = 5), ACC‐PA (n = 3), and adipokines (n = 3), participants were removed from the analyses. Multicollinearity between covariates was assessed with variance inflation factor analyses. The model assumptions were tested using Q–Q plots of residuals. Statistical data analysis was carried out using IBM SPSS Statistics Software version 26 , and a p‐value < 0.05 was considered statistically significant. Due to the observational nature of the study with predetermined associations of interest, the results are presented without multiple comparison corrections. With the large number of statistical comparisons in the study, this may increase the risk of type I error.Linear mixed‐effect models were created to examine associations between the adiposity measurements and covariates during the follow‐ups. Since the models included information from both the short‐ and long‐term follow‐up and because menopausal status at baseline and four‐year follow‐up was not uniform, duration of follow‐up in years was selected to represent time. To investigate associations between time and each of the adipose tissue mass variables, models were controlled for education, baseline mean‐centered age, mean‐centered physical activity, mean‐centered diet quality score, the use of external hormones, and the interaction between time and physical activity (fixed effects). The models were constructed using unstructured longitudinal correlation matrix, and intercept and time were used as random effects. Longitudinal associations between adipokines and adiposity variables were investigated with similar linear mixed‐effect models, but without the interaction between time and physical activity. The possible effect of smoking was tested, but because we did not see associations in any of the variables, it was left out of the final models. All variables and covariates were evaluated for outliers. A value was considered to be an outlier if it was above 3rd quartile +3*interquartile range or 1st quartile −3*interquartile range. Based on the extreme values in body adiposity (5For graphical abstract, BioRender's Academic License has been granted.The authors have no conflict of interest to declare.VK, EKL, and SS designed the ERMA study and UK, and PA contributed to the planning. EKL designed the EsmiRs study, and VK and SS substantially contributed to the planning. UMK and PA also supported planning of the study. HKJ was responsible for the majority of the laboratory measurements and analysis including adipokine analysis, tissue staining, microscopy imaging, DXA, and CT analyses, while EKL supervised them. HKJ performed statistical analysis with the guidance of MH. SS performed the CT scanning and supervised the analyses. VF was responsible for preparing a script for confocal microscopy image analysis and performed the analysis. NC prepared and analyzed CT scans. PA and UMK offered clinical knowledge and guidance to data analysis. JEK prepared the diet quality score. HS was the clinician responsible for performing muscle biopsies. VK and EKL provided funding for the study. SK assisted with laboratory assays. HKJ prepared the first version of the manuscript. SS, VF, MH, NC, PA, JEK, HS, UMK, VK, SK, and EKL have participated in the interpretation of the results and critically commented on the manuscript during the writing process. All the authors have read and approved the final manuscript.Fig S1Click here for additional data file.Table S1‐S2Click here for additional data file.