Modifying muscle metabolic dysregulation in inclusion body myositis with pioglitazone: a single-arm trial

Domain: biology
Published: 2026-03-15
Editorial outcome: minor_revision

Abstract
This single-arm, open-label phase 1 trial evaluated the PPARγ agonist pioglitazone in patients with
inclusion body myositis (IBM). After a 16-week observation (lead-in) period, participants received
pioglitazone 45 mg daily for 32 weeks. The primary outcome was the change in PPARGC1A expression
and related metabolic pathways in muscle after 16 weeks of treatment compared with the lead-in period.
Of the 16 enrolled participants, 13 initiated pioglitazone and completed at least one on-treatment
assessment; the trial was terminated early due to the COVID-19 pandemic. At baseline, muscle
metabolomics revealed broad metabolic abnormalities compared with controls. Pioglitazone reversed
elements of this signature, increasing PPARGC1A expression (p=0.099) and modulating downstream
pathways in muscle, including enhanced oxidative phosphorylation. Clinical outcomes were unchanged
overall, but a subset with favorable metabolic responses showed slower decline in the IBM-Functional
Rating Score (IBM-FRS) and Modified Timed Up and Go (m-TUG). Reported adverse effects included
myalgia and heart failure exacerbation. As a phase 1 trial with a limited cohort, these findings provide
preliminary evidence that pioglitazone modulates muscle metabolism and warrants further investigation
in IBM. This study was supported by the Ira T. Discovery Fund and the Peter and Carmen Lucia Buck
Foundation Myositis Discovery Fund. Clinical Trials Registration: NCT03440034

Introduction
Sporadic inclusion body myositis (IBM) is the most common inflammatory myopathy in the
elderly 1. It differs from other types of myositis by its insidious onset with slowly progressive weakness
primarily affecting the quadriceps and finger flexors, and lack of significant response to
immunosuppression. Although the clinical course is highly variable, IBM leads to significant disability
with the median time from onset of symptoms to wheelchair use of about 10 years 2. A barrier to finding
effective treatment is that its pathophysiology remains incompletely understood. It is known to share
features of both inflammation (infiltration of CD8+ T cells, MHC class I/II, pro-inflammatory cytokine
upregulation, association with the anti-cN1a autoantibody) as well as degeneration (presence of rimmed
vacuoles, sarcoplasmic aggregation of proteins, mitochondrial dysfunction) 3. Multiple pharmacologic
treatments targeting both the immune system as well as various degenerative aspects of the disease have
failed to show clinical benefit 4, and there is a need to explore additional mechanisms and novel targets.

One of the key characteristics of IBM is the presence of abnormalities in skeletal muscle
mitochondria, which are hypothesized to play a role in the pathophysiology of this disease. Muscle
biopsies from patients with IBM often display ragged red fibers, deficiency in activity of the electron
transport protein cytochrome C oxidase, abnormal proliferation and subsarcolemmal accumulation of
mitochondria, and large scale mitochondrial deoxyribonucleic acid (mtDNA) deletions 5–7. In addition,
endurance exercise, which activates key mitochondrial regulators and stimulates mitochondrial
biogenesis, improves muscle strength and slows disease progression in IBM 8,9. AMP-Activated Protein
Kinase (AMPK) and Peroxisome Proliferator-Activated Receptor gamma coactivator 1-alpha (PGC-1α)
are key regulators of mitochondrial function that are activated by endurance exercise 10,11. In a rat model
of IBM, PGC-1α expression was significantly reduced as compared to normal rats, and expression of
PGC-1α and severity of the IBM phenotype was rescued by resistance exercise 12. This raises the
possibility that mitochondrial dysregulation and the upstream molecular pathways that impact
mitochondrial function and metabolism may be promising therapeutic targets for stabilizing the disease
and improving strength in IBM.

Pioglitazone, a thiazolidinedione medication used to treat type 2 diabetes, targets the nuclear
receptor peroxisome proliferator-activated receptor gamma (PPARγ) and increases expression of AMPK
and PGC-1α 13. Pioglitazone enhances mitochondrial function, exercise capacity, and mitochondrial
biogenesis in skeletal muscle 13–17, which prompted evaluation of whether it could modulate similar
pathways in IBM. We conducted a proof-of-concept, open-label clinical trial of pioglitazone in IBM to
test this hypothesis. Here, we report that pioglitazone treatment was associated with observable
transcriptomic and metabolic changes in IBM muscle. These findings suggest targeting mitochondrial
pathways warrant further investigation in IBM.

Results
We utilized a single-arm study design in which all patients were observed off therapy for 16 weeks to
assess the trajectory of their disease (lead-in period), and then crossed over to receive a total of 32 weeks
of pioglitazone therapy (intervention period). Muscle and serum samples were obtained along with
functional measures at the baseline visit, week 16 (end of the lead-in period), and week 32 (after 16 weeks
of pioglitazone treatment). At week 48, only serum and functional measures were obtained (see study
schema, Figure 1).

Study participants
Among the 20 patients who were screened for eligibility, 16 patients met inclusion criteria and were
enrolled. Of the 16 enrolled participants, 3 did not initiate pioglitazone. One participant withdrew due to
a fracture sustained prior to treatment initiation, one withdrew due to concerns related to the COVID-19
pandemic, and one was withdrawn due to noncompliance. As a result, 13 participants initiated
pioglitazone therapy and were included in the on-treatment analysis (Figure 1).

The mean age of the enrolled IBM participants was 66.8 ± 7.3 years, 50% were women, and 100% were
white. The average duration of IBM since symptom onset was 5.4 ± 4.4 years and 4/16 (25%) used an
assistive device. The mean IBM-FRS at baseline was 28.9 ± 3.9 and creatine kinase (CK) was 498 ±
384 U/L. The mean age of the 5 control muscle specimens was 60.6 ± 15.4 years and 4/5 (80%) were
female. The 10 control sera were slightly younger than the IBM sera (56.3 ± 5.7 years, p=0.001) and
7/10 (70%) were female. Supplementary Tables 1 and 2 include additional demographic and clinical
features of the IBM patients and controls.

IBM muscle exhibits a distinct metabolic signature
To determine the effect of pioglitazone on the muscle metabolome in IBM, we first identified the
metabolic signature of IBM by comparing metabolites in muscle from IBM patients at study baseline with
that of healthy controls. Of the 16 IBM study patients who underwent a baseline muscle biopsy, one
sample was excluded due to poor tissue quality. Untargeted metabolomics was performed on the 15
remaining samples and compared to five healthy control muscle specimens obtained from the Johns
Hopkins Neuromuscular Biobank. Using principal component analysis, we found that samples from
healthy control and IBM muscle showed distinct clustering patterns, with a more variable clustering of
IBM samples than healthy controls (F-Test on PNC1, p=0.018) (Figure 2A). Several key metabolites
involved in glycolysis, pyrimidine metabolism, tryptophan metabolism, and oxidation-reduction (redox)
reactions were significantly different in IBM muscle compared to healthy controls (Figure 2B and
Supplementary Table 3). Pathway analysis using KEGG Pathways identified 8 pathways that were
differentially expressed in IBM vs controls, which included pyrimidine metabolism, amino acid and
nucleotide sugar metabolism, tryptophan metabolism, and glutathione metabolism (Figure 2C, D).

Given prior implications of disturbed mitochondrial function and bioenergetics in IBM, we next
specifically analyzed metabolites associated with glycolysis, the TCA cycle, and oxidative stress. We first
focused on glycolysis and the TCA cycle in IBM muscle tissue and identified significant abnormalities in
key metabolites including lactate and glucose-1-phosphate, which were significantly decreased in IBM
muscle (Figure 3B, lactate: log2FC -1.89, FDR=0.037; glucose-1-phosphate: log2FC -2.78, FDR=0.009).
In the TCA cycle, the TCA intermediate cis-aconitate was significantly elevated (log2FC 4.93,
FDR=0.038), with citric acid and α-ketoglutarate also trending higher in IBM muscle. In contrast, distal
TCA metabolites such as succinate, fumarate, and malate trended lower in IBM muscle (Figure 3B,
Supplementary Table 4). Propionylcarnitine, a metabolite in equilibrium with propionyl-CoA which
feeds into the TCA cycle through conversion to succinyl-CoA, was significantly reduced (log2FC -6.93,
FDR=0.0006) in IBM (Figure 2B), and may exacerbate distal TCA cycle dysfunction.

We next examined additional metabolites that contribute to replenishing intermediates of the TCA
cycle, including pyrimidines 18. Key pyrimidine metabolites, including the nucleoside cytidine (log2FC -
3.06, FDR=0.037) and the nucleotides cytosine, CMP, UMP, and GMP, were among the most reduced
metabolites in IBM (Figure 2B, Supplementary Table 3). As for nucleotide metabolism, while most
purine and pyrimidine metabolites were significantly decreased in IBM muscle, we observed increased
uracil (log2FC 3.71, FDR=0.039) and adenosine (log2FC 3.09, FDR=0.037) as well as methylated
nucleotides such as 1-methylguanine (log2FC 1.68, FDR=0.022) and methylguanosine (log2FC 2.15,
FDR=0.037) 19.

Given abnormalities in mitochondrial function, we examined differences in redox homeostasis.
Compounds such as 3-indoxyl sulfate, which is known to promote the production of reactive oxygen
species (ROS) and thus disrupt redox homeostasis 20, were increased in IBM muscle (log2FC 2.16,
FDR=0.025), whereas metabolites that alleviate oxidative stress such as indole-3 acrylate were decreased
(log2FC -4.44, FDR=0.003). Overall, redox homeostasis was disrupted, as reflected by increased
intensities of metabolites that induce reactive oxygen species, and decreased metabolites that moderate
oxidative stress (Figure 3A, Supplementary Tables 3 and 4).

Metabolic dysregulation in IBM muscle and disease severity
We next sought to determine if metabolic dysregulation is associated with disease severity in IBM. The
IBM patients in our cohort exhibited varying degrees of muscle involvement, and we stratified muscle
disease severity by muscle ultrasound (US) to capture a muscle quality/structural assessment. We
evaluated for increased echointensity and atrophy in the flexor digitorum profundus, rectus femoris and
vastus lateralis muscles, which indicates chronicity and damage. Patients with an US sum score > 10 were
characterized as having severe disease (n=7) and patients with an US sum score ≤ 10 were characterized
as having mild-moderate disease (n=6)21,22 (Figure 4A, 4B). This stratification correlated significantly
with function and those with severe disease by US had lower IBM-FSRS scores at baseline (Spearman’s
p= -0.62, p=0.03) (Supplementary Table 5). We performed a correlation analysis of the log2FC values
of 211 muscle metabolites comparing the fold change between IBM vs. controls (x-axis) and severe vs
mild-moderate IBM (y-axis). There was a positive moderate correlation (Spearman’s p=0.44, p<0.0001),
suggesting that the metabolic signature of IBM in muscle is more accentuated in severe IBM (Figure 4C).
We did not observe a statistically significant difference in the metabolic signature of severe versus mild-
moderate IBM in sera (Spearman’s p= -0.13, p=0.06) (Figure 4D).

Metabolic signature in IBM muscle does not mirror serum
After identifying multiple differences in the metabolic signatures of affected IBM muscle versus normal
control muscle, we examined differences between the serum metabolic profile in patients with IBM and
healthy controls to determine if differences seen in muscle metabolism were reflected in serum. As
observed with muscle, the metabolic profile of serum from patients with IBM exhibited distinct
clustering compared to healthy controls (Figure 5A). Specifically, 25 metabolites (Figure 5B,
Supplementary Table 6) and 25 pathways (Figure 5C) were differentially expressed in IBM serum
relative to that from healthy controls. Metabolites that were decreased in IBM serum included carnosine
(log2FC -5.99, FDR=4.54E-14), propionylcarnitine (log2FC -5.66, FDR= 1.13E-07), indole-3-acrylic
acid (log2FC -7.11, FDR= 1.10E-07) and tryptophan (log2FC -5.72, FDR= 1.13E-07). Pathway
enrichment analysis identified differentially expressed pathways involving beta-alanine metabolism,
histidine, tryptophan metabolism and pentose phosphate pathways among others (Figure 5C, 5D).

To compare the global metabolic signatures of IBM between serum and muscle, we performed a
correlation analysis using the Log2 fold change (log2FC) values of the 124 shared metabolites. Despite
notable agreement for a few key metabolites, such as propionylcarnitine and tryptophan which were
both decreased in serum and muscle, the overall correlation between the metabolic profiles in muscle
and serum was not significant (Figure 5E, Spearman’s p=0.06, p= 0.5). These findings suggest that
IBM has distinct effects on metabolites in serum and muscle.

Pioglitazone induces favorable metabolic changes in IBM muscle
Of the 16 enrolled participants with IBM, 13 received pioglitazone therapy and underwent at least one
on-treatment clinical evaluation and muscle biopsy, following a 16-week lead-in period. We performed
RNA sequencing and untargeted metabolomics to identify transcriptomic and metabolomic changes
with pioglitazone for each patient compared to the 16-week lead-in period.

i. Primary outcomes: The primary outcome was the change in expression of PPARGC1A and
other oxidative phosphorylation and mitochondrial pathway genes after treatment with pioglitazone
compared to the lead-in period. We did not identify a significant change in PPARGC1A gene expression
during the treatment-phase compared to the lead-in phase using a paired t-test (p=0.099, mean
difference=1.19[-0.26,2.64]), although there was a trend toward increased expression after treatment.
Notably, PPARCC1B gene expression, which is not a known target of pioglitazone and thus served as a
control, did not change with pioglitazone (Figure 6A). As PGC-1α is a transcriptional coactivator,
changes in its activity may be better assessed through identification of alterations in its target gene
expression. We therefore utilized ingenuity pathway analysis to assess the change in PPARGC1A
regulated gene expression, which identified PPARGC1A as a significantly activated upstream regulator
in the treatment phase (Activation z-score = 5.183, padj. = 0.00014). In contrast, PPARGC1A was non-
significantly inhibited in the lead-in phase (Activation z-score = -3.028, padj. = 0.123).

Gene set enrichment analysis of KEGG pathways identified 56 differentially expressed pathways
during pioglitazone treatment (Supplementary Table 7B), compared to 16 differentially expressed
pathways during the lead-in period (Supplementary Table 7A). After 16 weeks of pioglitazone, genes
involved in the TCA cycle, OXPHOS, PPAR signaling, and AMPK signaling were significantly
upregulated. In contrast, during the 16-week lead-in period, these same pathways, including OXPHOS
and the TCA cycle, were downregulated (Figure 6B). Critically, of the pathways significantly
upregulated in the treatment phase were 8/10 PPARGC1A containing KEGG pathways. Two key
pathway clusters – metabolism and inflammation/immune dysregulation – were identified. Before
treatment, metabolic pathways were downregulated and inflammatory pathways, such as IL-17 and
c and
cytokine interactions, were upregulated. During the treatment phase, an opposite pattern was observed
in which metabolic pathways were upregulated and inflammatory activity appeared reduced, a pattern
contrasting with that of the lead-in phase (Figure 6B).

ii. Secondary outcomes: We next investigated the effect of pioglitazone on the overall metabolic
signature of IBM, which was one of the secondary outcomes. One patient was excluded from these
analyses because one of their longitudinal muscle samples was of poor quality. Initially, we compared
the metabolic signature of IBM to the change in metabolites during the lead-in period (baseline to week
16) and observed no significant changes (Spearman’s p=0.03, p=0.65), indicating the relative stability of
the metabolic signature over the 16-week lead-in period (Figure 7A). We then performed a similar
analysis of the treatment period, comparing the IBM metabolic signature (x-axis) to the change in
metabolites during treatment (y-axis). In contrast to the lead-in period, there was a measurable change in
the metabolic profile over the 16 weeks of pioglitazone treatment. The negative correlation (Spearman’s
p= -0.34, p<0.0001) indicates that pioglitazone was associated with modest changes in the muscle
metabolome, with patterns that trended toward the profile of healthy controls and away from the
metabolic signature of IBM (Figure 7B).

We then performed an integrated pathway analysis using the transcriptomic and metabolomic
results. These outcomes largely aligned with those observed in the transcriptomic pathway analysis
(Figure 6B), suggesting increasing inflammation during the lead-in phase which was reduced in the
treatment-phase, as well as a reversal of metabolic dysregulation in the treatment phase
(Supplementary Tables 8A, 8B). Finally, while we did not have sufficient non-diseased control
samples for transcriptome analyses, we sought to evaluate if the observed effects on the IBM metabolic
signature were recapitulated on another “omics” layer. We therefore utilized a publicly available data
set with control and IBM muscle transcriptome samples to generate an external IBM transcriptomic
signature with which to compare (GSE102138) 23. Notably, a similar correlation analysis of our
transcriptome data compared to this external IBM transcriptomic signature detected a significant shift in
transcriptome to becoming more “IBM-like” during the lead-in phase (Spearman’s p= 0.4, p<0.0001)
and a significant shift to becoming less “IBM-like” during the treatment phase (Spearman’s p= -0.54,
p<0.0001), providing valuable validation of our findings on another “omic” layer and with use of an
external IBM cohort (Supplementary Figure 1).

The effect of pioglitazone was heterogenous, with a positive metabolic response observed in only
a subset of patients (4 out of 12, or 33% of patients) (Figure 7C). In an exploratory analysis, metabolic
responses varied by disease severity as stratified by US, with less affected individuals showing larger
changes (treatment x severity, p=0.013) (Figure 4A, 4B, 7D), indicating that treatment-associated
metabolic shifts were more evident in participants with greater muscle reserve. Two of the patients in the
mild-moderate category had a strong metabolic response to pioglitazone, which resulted in a significant
shift in their metabolic profile opposite that of the IBM metabolic signature (Figure 7D) A sensitivity
analysis, however, suggested the significant disease severity-metabolic response relationship was strongly
influenced by one of these patients, with exclusion of them from the analysis resulting in a substantial
increase in p-value for the interaction (from p=0.013 to p=0.103), necessitating caution in the
interpretation of these results. Notable metabolites that increased with pioglitazone treatment in muscle
included UMP, CMP, propionylcarnitine, tryptophan and mannose-6-phosphate, all of which were
significantly reduced in IBM compared to controls and were further reduced in severe compared to mild-
moderate IBM. There was no significant correlation with pioglitazone treatment and metabolites in serum
over the same period (Spearman’s p= -0.01, p=0.84) (Supplementary Figure 2), indicating that the
metabolic changes observed with pioglitazone were confined to muscle tissue.

Safety and Clinical Outcomes with Pioglitazone in IBM
The safety profile of pioglitazone in this study was similar to what has previously been reported in other
disease settings 24,25. There was no significant effect of treatment on weight (Treatment [Pio] x Time, 1.3
[-2.16 – 4.76], p=0.451) or pro-BNP (Treatment [Pio] x Time, 117.78 [-24.35 -259.91], p=0.102). Of the
13 patients with IBM who initiated pioglitazone therapy, 2/13 patients experienced an adverse event that
was attributed to study therapy: one with myalgia and another with a heart failure exacerbation. Our
patient did not have any clinical signs or symptoms of heart failure at the time of enrollment but had this
in their remote history as occurring after a virus, which then resolved. Two weeks prior to termination of
the trial, the patient felt a sense of chest discomfort which gradually worsened with finding of left sided
pleural effusion. Pioglitazone was stopped and diuretics were given with resolution of symptoms.

Aside from the single patient who discontinued study therapy due to an adverse event, all other
patients (12/13, 92%) continued pioglitazone therapy until the final clinical evaluation at week 48, which
was to assess the impact of extended use of the medication. However, 6 out of 12 final clinical evaluations
were conducted virtually due to restrictions related to the COVID-19 pandemic. In-person functional
measures, all of which were secondary outcome measures, included knee extensor dynamometry, m-TUG,
6-minute walk test, hand-grip strength, IBM-FRS, serum CK level, composite Fi-2, and self-reported
number of falls. For patients who were unable to present in person for the final visit due to the COVID-
19 pandemic, only the IBM-FRS and number of falls were assessed virtually and serum CK levels were
measured.

We next examined the impact of pioglitazone on the clinical secondary outcome measures. There
was no significant change in any clinical outcome during the 16-week lead-in period (p>0.05 for all
comparisons) (Table 1). IBM is a slowly progressive disease, and these data suggest that the baseline rate
of change in patients was below the level of detection of the clinical outcome measures. We subsequently
assessed changes in clinical measures after administration of pioglitazone using a mixed effects model to
determine the effect of pioglitazone on these same outcome measures. We did not observe a significant
change in any outcome measure after administration of pioglitazone (Treatment[Pio] and Time x
Treatment[Pio], p>0.05 for all outcomes) (Table 1).

We next assessed the relationship between metabolic response in muscle and clinical outcomes in
an exploratory analysis. We calculated individual metabolic correlation scores (rho) for each patient
during the lead-in period (baseline to week 16) and during the intervention period (week 16 to week 32).
A more positive rho (red) indicates that the patient’s metabolite signature was shifted in the same direction
as IBM, while a more negative rho (blue) indicates a shift that opposes the IBM metabolic signature. We
generated a mixed effect model to determine the effect of metabolic response (rho) on each clinical
outcome measure. There was no significant rho x time effect for Fi-2, hand-grip strength, CK, 6-minute
walk, and knee extensor dynamometry outcome measures. However, there was a significant rho x time
effect for both the IBM-FRS and the m-TUG, suggesting that a metabolic shift away from the IBM
signature was associated with a less severe decline in the IBM-FRS over time and a slower increase in m-
TUG score over time (IBM-FRS: Time X rho estimate= -3.32, p = 0.034; m-TUG: Time X rho estimate=
1.95, p=0.042) (Figure 8A). Of note, the model predicts that a shift in rho from the highest value observed
in the study to the lowest from week 16 to week 32 would lead to an improved IBMFRS and m-TUG
(Figure 8B). This suggests that the reduction in rho by pioglitazone observed in patients with mild disease
may have improved outcomes in patients with high baseline rho values.

Methods
Ethics Statement: This single-arm trial was approved by the Johns Hopkins Institutional Review Board
(IRB00130996) and was conducted in accordance with the principles of the Declaration of Helsinki. All
study participants provided written informed consent and were provided modest financial compensation
for their time and travel.

Study population and design: The study was pre- registered on ClinicalTrials.gov (Identifier:
NCT03440034) on February 21, 2018. The trial was conducted between June 2018 through June 2020 at
the Johns Hopkins Myositis Center. The study protocol, along with a summary of changes to the protocol,
can be accessed in the supplemental information file. Eligible patients included adults 50 years and older
(to enrich for those with typical IBM features) with a diagnosis of clinico-pathologically defined or
clinically defined IBM by ENMC criteria 33, were able to ambulate at least 20 feet with or without the use
of an assistive device, and could rise from a standard chair (seat height 18 inches) on their own with the
use of armrests if needed. Control muscle specimens were obtained from the Johns Hopkins
Neuromuscular Biobank. These samples were collected from patients undergoing muscle biopsy for
clinical evaluation. We selected available biopsies from older individuals whose pathology reports were
interpreted as normal and who were ultimately not diagnosed with, nor suspected to have, a neuromuscular
disorder. Serum samples were obtained from healthy controls as part of a separate IRB-approved study
for healthy donors and excluded anyone who was pregnant or had a history of cancer, autoimmune disease,
tuberculosis, HIV, or hepatitis infection.

The study schema is depicted in Figure 1 and more details are included in supplementary
information. All participants who met inclusion criteria completed a baseline visit and then were followed
without any intervention until their second visit 16 weeks later (lead-in phase) to serve as a self-
comparator. Pioglitazone was started at week 16, and all patients self-administered 30 mg of pioglitazone
by mouth daily for 2 weeks and then increased the dose to 45 mg daily. The study drug was continued for
a total of 32 weeks (intervention phase), and patients were evaluated at week 32 and week 48. Patients
brought in their empty pill bottles to each study visit and were questioned about adherence. At the baseline
visit, week 16, and week 32, a needle muscle biopsy from the vastus lateralis was performed, functional
measures were obtained, and serum was collected. At week 48, only serum and functional measures were
obtained.

The primary, secondary, and exploratory outcomes were assessed at predefined timepoints. Primary and
secondary transcriptomic and metabolic outcomes were evaluated by comparing the 16-week lead-in
period (baseline to week 16) with the first 16 weeks of pioglitazone treatment (week 16 to week 32).
Clinical secondary outcomes were assessed longitudinally at baseline, week 16, week 32, and week 48
when available. The trial was terminated earlier than planned due to the COVID-19 pandemic, which
limited completion of later study visits and long-term outcome assessments.

Safety monitoring: Safety labs were drawn at each visit, including a fasting blood glucose, creatinine,
liver enzymes, and a pro-B-type natriuretic peptide (pro-BNP). Adverse events were monitored and
reported in a standardized manner using the Common Terminology Criteria of the National Cancer
Institute V4.03, with the lead investigators (BA, JA) determining their relatedness to the drug.

Outcome Measures
Primary Outcome: The primary outcome was the change in expression of PPARGC1A and other oxidative
phosphorylation and mitochondrial pathway genes, comparing the 16 week pioglitazone treatment phase
to the 16 week lead-in period.

Secondary Outcomes: Secondary outcomes included the change during the treatment phase compared to
the lead-in period for metabolites in key energy pathways and for several clinical outcome measures
described below. All clinical measures were assessed at each study visit using standardized procedures.
licensed physical therapists (AM, RP) and occupational therapists (MM) underwent training prior to trial
initiation to standardize assessments.

i. Manual muscle testing: the Kendall 0-10 scale was used to assess strength in the knee
extensors34. Scores were determined by the examiner’s assessment of resistance against
gravity and manual pressure, with higher scores indicating greater muscle strength. Strength
was measured in the bilateral knee extensors 3 times and then averaged.
ii. Quantitative muscle dynamometry: A handheld dynamometer (Hoggan Scientific
MicroFET®2) was used to assess quantitative muscle strength in the knee extensors.
Participants were seated with their knee at 90 degrees of flexion. The dynamometer was placed
on the distal part of the lower leg, just above the ankle, and manual resistance was applied
gradually until the dynamometer recorded a measurement in pounds. Strength was measured
in both lower extremities and then averaged.
iii. Hand-grip strength: Grip strength was measured using a Jamar hydraulic hand dynamometer
following standardized procedures 35. Participants were seated with the elbow flexed at 90
degrees and the forearm in a neutral position. The test was performed on the standard (second)
handle setting, and the best of three trials for each hand was recorded. Grip strength for each
side was then averaged.
iv. Modified Timed Up and Go Test (m-TUG): Participants were instructed to rise from a standard-
height chair (seat height 18 inches), walk a distance of 3 meters at a comfortable pace, turn
around, return to the chair, and sit down 36. The time required to complete the task was
measured. Participants were permitted to use armrests to assist with standing if needed. A
single trial was performed.
v. Six Minute Walk Test: Participants were instructed to walk as far as possible for six minutes
along a marked, flat indoor hallway. Standardized verbal encouragement was provided.
Participants were allowed to slow down, stop or rest as needed but were instructed to resume
walking as soon as they were able. Total distance walked was recorded in feet. Participants
were allowed to use assistive devices such as canes or walkers, and the use of these devices
was documented 37.
vi. IBM-Functional Rating Scale (IBM-FRS): This questionnaire was administered by the
physical therapist and assessed functional status across 10 domains relevant to daily living in
IBM. Each item is scored on a 0-4 scale, with high scores indicating better function. The
maximum total score is 40 38.
vii. Composite Functional Index-2: The FI-2 measures the number of repetitions that can be
performed over a 60-second period for each of seven muscle groups: shoulder flexion,
shoulder abduction, neck flexion, hip flexion, knee extension, ankle dorsiflexion, and heel lifts.
Participants were instructed to perform each movement as many times as possible within the
time limit 39. The percent of completed repetitions was recorded for each muscle group and
averaged to derive a composite Fi-2.
viii. Creatine kinase (CK) level: this was measured in a commercial laboratory.
ix. Number of Falls: the number of self-reported falls during the preceding 16 weeks was
recorded.

Muscle Ultrasound: A GE Logiqe or S8 machine with linear probe was utilized with standardized
settings as previously described 22. All patients had an ultrasound (US) of the vastus lateralis at the
screening visit to ensure that there was viable muscle for biopsy, which was an inclusion criteria for this
trial. Muscle US images of the flexor digitorum profundus (FDP), vastus lateralis (VL), and rectus femoris
(RF) muscles were acquired at entry into the study to assess muscle quality and stratify severity based on
prior studies showing them as discriminating muscles in IBM 21,22. Each muscle was assessed for atrophy
and echointensity using the Heckmatt scale (1-4) 40. The highest echointensity score for each muscle on
either side was taken for each patient and added to create a sum score. To have a clear distinction of a
more advanced phenotype, we defined severe muscle disease as a sum score > 10 for echointensity with
atrophy.

Muscle biopsies and biospecimens: Needle muscle biopsies of the vastus lateralis were performed at the
baseline visit, at week 16, and week 32, using the same side for each patient. Muscle biopsies were
performed as previously described using a Pro-Mag Ultra Automatic Biopsy instrument (Argon Medical
Devices) with site for biopsy located by ultrasound 41. Specimens were immediately frozen in liquid
nitrogen or stored in RNA later (Thermo Fisher). Control muscle specimens obtained from the Johns
Hopkins Neuromuscular Biobank were obtained by open surgical biopsy of the rectus femoris, biceps, or
deltoid for clinical indications and were immediately flash-frozen. Sample processing and metabolomic
analysis of control and IBM specimens occurred in parallel.

Metabolomics Extraction and Analysis: Untargeted metabolomics was performed on all serum and
muscle samples at Gigantest (Baltimore, MD). 20 muscle samples and 25 serum samples were analyzed.
Controls consisted of pooled samples and were dispersed throughout the run between every 10 samples
(total of 5 controls). Number of technical and/or biological replicates was 1. The metabolite extraction
of serum and muscle samples was done by adding HPLC-grade methanol to achieve a 80% (vol/vol)
final methanol concentration. Muscle samples were homogenized to ensure metabolite extraction. Both
types of samples were then subjected to speed vacuum and lyophilization to ensure removal of the
methanol-water mixture. A 50% (vol/vol) acetonitrile solution was then used to resuspend the
lyophilized metabolite samples for LC-MS data acquisition. The LC-MS system consisted of a Thermo
Scientific Q Exactive Plus Orbitrap Mass Spectrometer with a Vanquish UPLC. Metabolomics data
acquisition of the metabolite-extracted samples was performed as previously described 42. Briefly,
reverse phase chromatography was performed using a Sigma HSF5 column (15cm x 2.1mm, 3um) with
a 15-minute total run time. Mobile phase is 0.1% formic acid in water and mobile phase B is 0.1%
formic acid in acetonitrile. Full MS and Full MS/ddMS2 was acquired. The column was kept at 35oC
while the autosampler was kept at 4oC. Data annotation was performed using Thermo Scientific
Compound Discoverer®, XCalibur®, TraceFinder® software and was achieved through fragmentation
pattern matching with the mzCloud database and/or accurate m/z match within 5 ppm. Chromatography
peak integration was reviewed, and peaks were manually integrated if required. Final peak intensities
were normalized to sample protein concentration measured with a BCA-based assay. The raw intensities
of discovered metabolites were normalized according to the protein concentration of the samples.

RNA library preparation and sequencing: RNA was isolated from muscle tissue using the RNeasy Plus
Kit (Qiagen). Samples were checked for quality/quantity on Fragment Analyzer and Qubit prior to library
prep. NEBNext Poly(A) Magnetic Isolation Module (NEB #E7490) and NEBNext Ultra II RNA Library
Prep Kit for Illumina (Cat# E7775) were used to generate libraries per manufacture instructions. The
Fragment Analyzer was used for quality control to ensure adequate concentration and appropriate
fragment size. The resulting library insert size was 200bp-500bp with a median size around 300bp.
Libraries were barcoded using unique dual indexing (E6440S) and pooled for sequencing. Pooled libraries
were sequenced on an Illumina NovaSeq6000 instrument using standard protocols for SP 2x100 bp paired
end sequencing. 13 samples were multiplexed for an estimated 558109295 reads per sample.
Demultiplexed FASTQ files were generated using bcl-convert v3.7.5.

Statistical Analysis: Baseline demographic, clinical, and laboratory characteristics were compared
between IBM and control groups using descriptive statistics. All statistical tests were 2-sided when
applicable and a p-value < 0.05 was considered significant. Differences in continuous variables were
assessed with the Wilcoxon rank-sum test, and categorical variables were compared using Fischer’s exact
test. RNA sequencing data were analyzed with DESeq2 to determine the genes present in each patient,
their expression levels, and the differences between expression levels in the muscle pre- and post-
treatment. Gene set enrichment analysis using KEGG pathways were performed with the clusterprofiler
package (Version 4.6.2). Pathway network analysis was performed with the aPEAR package (Version
1.0.0), with minimum cluster size set to 2, and cluster method “hier”. Cluster labels were determined
using default ‘pagerank’ setting to find the most connected node, with limited adjustments for improved
consistency between analyses. Ingenuity pathway analysis was performed using the log2 fold change
values corresponding to lead-in and treatment-phase to perform “upstream regulator analysis” of
PPARGC1A target genes in each phase. Change in PPARGC1A normalized counts was compared between
the lead-in (baseline to week 16) vs the treatment (week 16 to 32) phase with a paired t-test using the
“t.test” function.

Metabolomics data were analyzed using MetaboAnalyst and R. Epinephrine was detected in
muscle samples due to use with anesthetic for biopsy and was therefore removed from analysis due to the
non-physiological nature of its abundance. Data were log transformed, and samples normalized to median.
PCA plots were generated using the prcomp function and plotted with the autoplot function of the
ggfortify package (Version 0.4.17). Differentially abundant metabolites were determined using lmFit and
eBayes function (Version 3.54.2) and plotted with ggplot2 (Version 3.5.1). Pathway analysis was done
using MetaboAnalyst and the KEGG pathways, with a custom background library based on all detected
compounds in the experiment.

To test the effect of changes in the IBM metabolic signature (evaluated as rho), Spearman’s rho
was calculated for each patient using their log2FC for each metabolite from baseline to week 16 and from
week 16 to 32 compared to the log2FC of all IBM patients compared to controls. Each patient’s rho for
baseline to week 16 was used for those two time points, while their rho for week 16 to 32 was taken as
representative for week 32 and 48. A linear mixed effects model, “outcome ~ 1+Treatment*Time +
(1+Time|ID)”, was used to calculate the effect of rho on outcomes. To define metabolic "responder"
status, we compared each participant’s rho during the lead-in and treatment phases using jackknife
estimates of the confidence interval, implemented via the SpearmanCI package. Participants were
classified as responders only if there was no overlap in the confidence intervals between the two phases,
indicating a significant within-patient change. The subgroup assessment of metabolic response by muscle
US severity was performed using a two-way mixed ANOVA with the “aov” function in R. A “leave-one-
out" sensitivity analysis was then performed, repeating the ANOVA with each individual left out of one
analysis to identify possible influential patients.

Integrated transcriptomic-metabolomic pathway analysis was performed using the “multiGSEA”
package with KEGG pathways.

To evaluate the effect of pioglitazone treatment on outcomes, data were analyzed using a linear mixed
effects model with the lmer function in the lme4 package (Version 1.1.35.1) in R (Version 4.2.0).
Model equations were “outcome ~ 1+Treatment*Time + (1+Time|ID)” for all outcomes besides hand
grip and knee dynamometry, which had the random slope for time removed to improve model fit.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors performed a small open-label study of pioglitazone in IBM. I have the following concerns:\n1. The authors mention that “Eligible subjects included adults 50 years and older with a diagnosis of clinico-pathologically\ndefined or clinically defined IBM by ENMC criteria (Rose 2013) or data-driven criteria (DDC) (Lloyd 2014).”\n1a) Why was the age cut-off set at 50 years when the threshold for the 2013 ENMC criteria is 45 years, and this was further\nreduced to 40 years in the 2024 ENMC criteria?\n1b) The category of clinically defined IBM (mentioned by the authors in eligibility criteria) does not exist in either Rose 2013\nor Lloyd 2014. There is inconsistency in the definition of the recruited population.\n2. I could not find a ClinicalTrials.gov registration number. Was the study not registered?\n3. The study protocol and statistical analysis plan (SAP) were not provided. Could these be made available if they exist?\nWere all the analyses presented pre-specified in the protocol and SAP? What are the versions and dates of the protocol and\nSAP, and when did the database lock occur?\n4. When was the first patient’s first visit, and when was the last patient’s last visit?\n5. The eligibility criterion states that participants “…could rise from a chair on their own.” Were the dimensions of the chair\nstandardized? Could the chair have armrests?\n6. The inclusion criterion “Must have viable quadriceps muscle suitable for biopsy as seen on ultrasound and interpreted by\nthe investigator” is repeated in the Supplemental Methods (inclusion criteria 5 and 8). One item can be removed.\n7. What was the upper limit of normal for CK?\n8. How was “Malnutrition or malabsorptive syndrome defined as less than 0.8 g protein/kg/day” evaluated or defined?\n9. Why is “bladder cancer” specifically listed as an exclusion criterion when “History of cancer within the last five years, other\nthan local basal or squamous cell carcinoma,” is already included?\n10. There are significant gender and racial differences between IBM muscle/sera and control muscle/sera. Could these\ndifferences have influenced the results? Why was matching not performed?\n11. Was any method of drug accountability used? This is not mentioned.\n12. The manuscript states, “At the baseline visit, week 16, and week 32, a needle muscle biopsy from the vastus lateralis\nwas performed.” Were the biopsies taken from the same side and area of the vastus lateralis? Given that IBM is a patchy and\nasymmetrical disease, repeated biopsies may be problematic. If subsequent biopsies were taken from the same area, results\ncould be affected by scar tissue. If taken from different locations, results could be influenced by varying levels of\ninflammation, degeneration, fat deposition, and mitochondrial changes, as these features are heterogeneously distributed in\nIBM muscle.\n13. Which methods and devices were used for assessing “knee extensor strength by quantitative dynamometry” and “hand-\ngrip strength using handheld dynamometry”? Which side of the body was assessed? If both sides were assessed, was the\naverage value used? This is not explained in the manuscript\n14. Regarding the “modified timed up and go” (m-TUG) test, how did the authors handle patients who were unable to perform\nthe test, mathematically/in the change analysis?\n15. The manuscript states, “Certified physical (AM, RP) and occupational therapists (MM)...” What does “certified” mean in\nthe context of this trial? Which entity provided the certification? Did this include specific training on trial outcome\nassessments?\n16. The primary outcome is defined as “the change in PPARGC1A gene transcription measured by RNA sequencing after\npioglitazone (week 32) compared to before treatment (week 16),” which corresponds to the intervention phase. However, the\nsecondary outcome measures are described as changes “from baseline,” with baseline defined as week 0 (16 weeks before\npioglitazone initiation, as per Figure 1). Why was a different time frame used for the secondary outcomes (week 0 to week\n32) than for the primary outcome (week 16 to week 32)? This inconsistency is problematic.\n17. The manuscript states, “Baseline demographic, clinical, and laboratory variables were evaluated with descriptive\nstatistics, and differences were evaluated using the Wilcoxon rank sum test for continuous variables and Fisher’s exact test\nfor categorical variables.” This sentence is unclear. What differences were being compared? It reads as though baseline\nvalues are being compared (e.g., between IBM and healthy subjects), in which case a paired test would not be appropriate.\n18. The manuscript states, “Genes with a ≥1.5 log2 fold change in expression were considered to be significantly\ndifferentially expressed (at P < 0.05).” Given the large number of genes assessed, why was a more stringent p-value cut-off\nnot applied? Why was a correction method such as Bonferroni not used?\n19. The manuscript states, “Epinephrine was detected in muscle samples.” Could this have influenced the expression of\nother metabolites?\n20. The analysis of changes in the IBM metabolic signature is difficult to interpret. How can a correlation between two pre-\ntreatment values (baseline and week 16) be compared to a correlation between week 16 and week 32? How does this\ninform the treatment effect? This approach is unconventional. Why were week 16 and week 32 results not compared directly,\nas was done for the primary endpoint?\n21. Does “average duration of IBM” refer to the time since symptom onset or since diagnosis?\n22. How was control muscle obtained? Was it taken from the vastus lateralis using the same technique as in IBM patients?\n23. The manuscript states, “Untargeted metabolomics was performed on the 15 remaining samples and compared to five\nhealthy control muscle specimens.” These sample sizes are extremely low for metabolomics studies, raising concerns about\nthe validity of the results and the potential influence of outliers. The same concern applies to the serum sample analyses.\n24. The manuscript states, “Patients with a US sum score ≥ 10 were characterized as having severe disease (n=7), and\npatients with a US sum score < 10 were characterized as having mild-moderate disease (n=6).” Has this cut-off ever been\nvalidated? What was the rationale for choosing it?\n25. The manuscript states, “Using a linear mixed-effects model, we observed increased PPARGC1A gene expression after\npioglitazone, but this did not reach statistical significance (p=0.07).” Please clearly state that there was no treatment effect of\npioglitazone on the primary endpoint rather than focusing on “trends.” The same comment applies to the discussion section.\n26. The lack of a placebo group is a major limitation of this study. This should be further emphasised. Why was a placebo\ngroup not evaluated?\n27. Are the observed metabolomic changes specific to IBM? Would similar changes be expected in other conditions or even\nin healthy individuals receiving pioglitazone?\n28. The statistical methods section is unclear. Some statistical tests (e.g., Two-way ANOVA with Tukey HSD, mentioned in\nFigure 7) are referenced in figures but not described in the methods section.\n29. The manuscript states, “These metabolic responders tended to have earlier and less severe disease as stratified by US\n(Figure 4A, 4B, 7D).” However, these data are not convincing. Figure 7D shows only five patients in the mild-moderate\ngroup; if one “red dot” patient is removed, the effect would disappear, just as it is absent in the advanced group.\n30. This is a negative study in IBM patients. Highlighting a single patient with subjective “clinical improvement” is\ninappropriate. For some outcome measures, the reported variation for this patient falls within the expected measurement\nerror. Furthermore, given that this patient was obese and had metabolic syndrome, any observed “improvement” may simply\nreflect better cardiovascular and metabolic health rather than an IBM-specific effect.\n31. The manuscript states, “In a subset of patients, mostly with mild-moderate disease, a demonstrable metabolic response\nwas seen in IBM muscle, which was associated with a slower decline in IBM-FRS and m-TUG scores.” Please tone down\nthis statement, as the data are not convincing (see previous comments).\n32. The statement, “These results provide initial evidence that pioglitazone can partially reverse the metabolic abnormalities\nof IBM muscle and that this reversal can translate into improved clinical outcomes for a subset of patients,” should be toned\ndown, as the data do not convincingly support this conclusion.\n33. Table S2 states that comparisons between baseline values of IBM patients and controls were made using the paired\nWilcoxon test. This is inappropriate because these are independent samples.\n34. Table S7 should be included in the main manuscript, as it clearly shows that the study was negative for all assessed\nphysical function, strength outcome measures, and CK. For each outcome measure, please present their units, if applicable.", "reviewer_2": "This study is an exploratory, single-arm clinical trial investigating the effects of pioglitazone in patients with inclusion body\nmyositis (IBM), with metabolomic analysis of biopsied muscle as the primary endpoint. Additionally, the study compares the\nmetabolomic and transcriptomic profiles of patients at baseline with those of healthy controls from a biobank, allowing for a\nclearer interpretation of treatment-induced changes in the context of disease-specific alterations.\nDespite the small sample size, the study is well-designed, incorporating a lead-in period during which needle muscle\nbiopsies were performed. This design adds significant value beyond a simple pre- and post-intervention metabolomic\ncomparison. The manuscript is well-written and provides valuable insights. Although the effects of pioglitazone were not\nclearly detected—partly due to the exploratory nature of the trial—the findings contribute meaningfully to the design of future\nclinical trials for this debilitating disease. My specific comments are as follows:\nMajor Points\n1. Figure 4 effectively illustrates the relationship between ultrasound (US) severity and muscle metabolome. The authors\nmay also wish to examine the associations between US findings and IBM-FRS, as well as between metabolome profiles\nand IBM-FRS.\n2. The study identifies a subset of responders based on muscle metabolic changes following pioglitazone treatment (Figure\n7). To further elucidate the clinical significance of these metabolic alterations, the authors should compare motor\nperformance and biomarker changes between responders and non-responders.\n3. To strengthen the multi-omics approach, the authors should directly compare treatment-induced transcriptomic changes\n(Figure 6) with metabolomic alterations (Figure 7).\n4. Similarly, a comparison between disease-related transcriptomic changes and treatment-induced transcriptomic alterations\nwould provide additional valuable insights.\nMinor Points\n1. Was the muscle biopsy site in the control group the same as in the patient group (i.e., vastus lateralis)?\n2. A more detailed discussion is needed regarding the possible causes of heart failure exacerbation in one patient.\n3. There is an ethical concern regarding the re-initiation of pioglitazone in one patient. How did the ethical review committee\njustify this treatment outside of the study protocol?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_5": "General Comments\nThis was a small study which aimed to evaluate the impacts of pioglitazone on various outcomes and assessed the\ndifferences between healthy muscle specimens and serum samples with those from IBM patients. PPARGC1A expression\nincreased at week 32 compared to week 16 (before treatment) P=0.07, indicating that pharmacologically targeting\nmitochondria with pioglitazone may improve metabolic deficits in this population.\nFor a single arm study, the “lead-in” phase was a helpful addition to assess the stability of the clinical outcomes assessed.\nThe PPARGC1A gene expression (primary outcome) increased compared to pre-treatment timepoints however it is difficult\nto assess the extent of this since little information is reported about this outcome’s results.\nSmall sample size and no controls does limit the study. More patients would have given the authors a better\nunderstanding of the effect pioglitazone. The lack of controls does mean that it is hard to assess a potential Hawthorne effect, however due to\nprimarily biological outcomes (which can still be affected by being in a trial), and nature of the small/exploratory study\ndesign I don’t think this is a major flaw with the trial.\nIt is unclear if the statistical analyses were pre-specified prior to screening.\nI agree with the authors' conclusions.\nMain Comments\n“Control muscle specimens were obtained from the Johns Hopkins Neuromuscular Biobank and were collected from\npatients with normal muscle tissue and no diagnosed neuromuscular disorders.” and\n“Serum samples were obtained from healthy controls as part of a separate IRB-approved study for healthy donors.”\nIt is not clear what defines “normal muscle tissue or a healthy control?” Were these just samples from individuals without\nInclusion body myositis or without a set of conditions? Were they randomly selected from the biobank/ healthy donors in the\nIRB-approved study or were they all samples the bank/study had at the time? Without this detail, it would be hard to\nreproduce the work.\n“Using a linear mixed effects model, we observed increased PPARGC1A gene expression after pioglitazone, but this did not\nreach statistical significance (p=0.07).”\nThis is missing an effect size estimate and confidence interval. Since this is the primary outcome, I would have expected this\nto be clearly written. I think the interpretation of the P-value could be potentially less conservative with their (particularly\nconsidering the small sample size). They did observe that there was an increased PPARGC1A gene expression after\npioglitazone. The plot for this (Figure 6A) also hides the confidence interval for the PPARGC1A Normalized Averages of\nCounts due to the black colour for the bars and the error bars.\nMinor comments\n4. “Model equations were “outcome ~ 1+Treatment*Time + (1+Time|ID)”\nDid the authors consider adjusting for the baseline demographics they collected (age, sex, ethnicity, disease duration,\nbiomarkers etc.)?\n5. “The mean age of control muscle specimens was 60.6 ± 15.4 years and 4/5 (80%) were female (n=5). The control sera\nwas slightly younger than the IBM sera (56.3 ± 5.7 years, p=0.001) and 7/10 (70%) were female (n=10).”\nThe “(n=5)” and “(n=10)” at the end of these sentences is slightly confusing as it is not clearly referring to something. I\npresume this is simply referring to the number of control sera and muscle specimens but this could be clearer.\n6. “Using principal component analysis, we found that samples from healthy control and IBM muscle showed distinct\nclustering patterns, with a more variable clustering of IBM samples than healthy controls (Figure 2A).”\nThough this is only 5 healthy controls so could be due to randomness/only having 5 controls.\n7. “This stratification correlated with function and those with severe disease by US had lower IBM-FRS scores (28.57 ± 3.73\nvs 33.33 ± 3.72).”\nAre these recorded anywhere in a table/figure? I can’t see them reported elsewhere.\n8. “We did not observe any difference in the metabolic signature of severe versus mild-moderate IBM in sera (Spearman’s p=\n-0.13, p=0.06) (Figure 4D).”\nIt is stated that the investigators did not observe “any” difference however there was a small difference observed. Whether\nthis was due to chance or if there is a true difference is a slightly different matter.\n9. “Despite notable agreement for a few key metabolites, such as propionylcarnitine and tryptophan which were both\ndecreased in serum and muscle, the overall correlation between the metabolic profiles in muscle and serum was not\nsignificant (Figure 5E, Spearman’s p=0.06).”\nAs with the statement regarding correlation between fold change between IBM vs. controls and severe vs mild IBM (and\nvarious other statements), it would be good for the P-value to be stated here also (P=0.501)\n10. “After completing the clinical trial, the patient discontinued pioglitazone and his CK increased back to his pre-treatment\nbaseline of 2055 units/L,”\nCK units have been referred to as U/L throughout apart from this sentence.\n11. “Although there was no statistically significant change in disease trajectory for the study population as a whole, one\npatient (subject 11) demonstrated a clinical improvement with pioglitazone. ”\nSome context around why this participant was followed up so closely after the end of the trial might be helpful since I can’t\nsee this mentioned in the methods (was this planned or just because of their promising CK levels during the trial). Were any\nother patients followed-up post 48 weeks? By chance you would expect someone to have a strong reduction in levels of one\nof these 7 outcome measures so inference from this patient’s data should be limited (as I think it fairly is).\n12. “despite missing the primary endpoint of increasing PPARγ gene expression”\nThe choice of words here is slightly misleading/confusing – the primary endpoint was not missed. There was not as strong\nevidence as was hoped for but there was still some evidence."}, "author_response": "Thank you to the Reviewers for their valuable and insightful feedback on our manuscript. We have\ncarefully considered each of the comments and have provided detailed, point-by-point responses. All\nchanges made to the manuscript are clearly tracked. We believe these revisions have strengthened the\nmanuscript, and we hope that the revised version meets the expectations of the Editor and Reviewers.\nReviewer #1 (Remarks to the Author):\nThe authors performed a small open-label study of pioglitazone in IBM. I have the following concerns:\n1. The authors mention that “Eligible subjects included adults 50 years and older with a diagnosis of clinico-\npathologically defined or clinically defined IBM by ENMC criteria (Rose 2013) or data-driven criteria (DDC)\n(Lloyd 2014).”\n1a) Why was the age cut-off set at 50 years when the threshold for the 2013 ENMC criteria is 45 years, and\nthis was further reduced to 40 years in the 2024 ENMC criteria?\nWe appreciate the Reviewer’s observation regarding the age cut-off. While the 2013 ENMC criteria set a\nthreshold of >45 years, and the 2024 revision reduced this to >40 years, we selected an age cut-off of >50\nyears. At the time we designed the study, our intention for selecting this cutoff was to enrich the study\ncohort with individuals more likely to represent the classic clinical and pathological features of IBM.\nPatients under 50 may have more heterogeneous or atypical presentations, which could introduce\ndiagnostic uncertainty or potentially confound interpretation of treatment response. To maintain a more\nhomogeneous cohort for the purposes of this study, we opted for a more conservative age threshold. We\nhave added a note in the manuscript to clarify this point.\n1b) The category of clinically defined IBM (mentioned by the authors in eligibility criteria) does not exist in\neither Rose 2013 or Lloyd 2014. There is inconsistency in the definition of the recruited population.\nRose 2013 outlines both “clinico-pathologically defined IBM” and “clinically defined IBM” as part of the\nENMC diagnostic criteria. For clarity, we have included Table 1 from Rose et al., 2013 below, which\ndelineates these categories. In our opinion, the use of this terminology and classification in our eligibility\ncriteria is consistent with this published framework.\n2. I could not find a ClinicalTrials.gov registration number. Was the study not registered?\nThis study is registered in ClinicalTrials.gov. The title is “Study of Pioglitazone in Sporadic Inclusion Body\nMyositis” and the registration number is NCT03440034. This registration number is on the title page and\nwe also added it to the Methods section.\n3. The study protocol and statistical analysis plan (SAP) were not provided. Could these be made available if\nthey exist? Were all the analyses presented pre-specified in the protocol and SAP? What are the versions\nand dates of the protocol and SAP, and when did the database lock occur?\nWe appreciate the reviewer’s request for additional detail regarding the protocol and analysis plan. We\nhave provided the study protocol which contains the statistical analysis plan as supplementary materials.\nIn the revised manuscript, we now more clearly and explicitly report the pre-defined primary, secondary,\nand exploratory outcomes sequentially, as specified in the study protocol. We also updated the primary\noutcome description to match the protocol-approved definition: \"Change in expression of PGC-1α and\nother genes in the oxidative phosphorylation and mitochondrial function pathways.\" Statistical analyses\nhave been aligned with the pre-specified plan, using paired t-tests comparing changes in the lead-in and\ntreatment periods. As specified in the protocol, pathway analysis was originally planned using Ingenuity\nPathway Analysis (IPA). In the initial submission, we used clusterprofiler with KEGG pathways due to\ntemporary loss of access to IPA. For the current revision, we obtained a new IPA license and reanalyzed the\ndata, confirming consistent results across both platforms, with IPA identifying significant activation of\nPPARGC1A-regulated pathways during treatment. We believe this strengthens the evidence supporting\ntarget engagement and alignment with the pre-defined primary endpoint.\nAs this was a Phase I exploratory trial, no formal database lock was performed; however, the dataset used\nfor analysis was cleaned and finalized on December 6, 2022.\n4. When was the patient’s first visit, and when was the last patient’s last visit?\nThis trial was conducted from June 2018 through June 2020. These dates were added to the manuscript in\nthe Methods section.\n5. The eligibility criterion states that participants “…could rise from a chair on their own.” Were the\ndimensions of the chair standardized? Could the chair have armrests?\nWe used the same chair for all participants throughout the trial. The chair was a standard chair with a\nseat heat of 18 inches and armrests, in case the participant needed to use them. These details have been\nadded to the manuscript.\n6. The inclusion criterion “Must have viable quadriceps muscle suitable for biopsy as seen on ultrasound\nand interpreted by the investigator” is repeated in the Supplemental Methods (inclusion criteria 5 and 8).\nOne item can be removed.\nThank you for catching this error. Inclusion criteria 8 has been deleted.\n7. What was the upper limit of normal for CK? The upper limit of normal for CK is 170 units/L. This has been\nadded to the legend for Table 1.\n8. How was “Malnutrition or malabsorptive syndrome defined as less than 0.8 g protein/kg/day” evaluated\nor defined?\nThank you for the opportunity to clarify this criterion. The exclusion criterion which specified \"malnutrition\nor malabsorptive syndrome defined as less than 0.8 g protein/kg/day,\" was adopted from other\nneuromuscular trials, although in practice this was difficult to assess and we relied on BMI and serum\nalbumin levels as a surrogate (BMI < 18.5 kg/m2 or serum albumin < 3.5 g/dL were considered to be\nindicative of malnutrition). These measures were selected as practical, clinically accessible indicators of\nnutritional status that could be uniformly applied across participants. We acknowledge the discrepancy\nbetween the written criterion and the measures used and appreciate the opportunity to address this.\n9. Why is “bladder cancer” specifically listed as an exclusion criterion when “History of cancer within the\nlast five years, other than local basal or squamous cell carcinoma,” is already included?\nThere is an association between pioglitazone and an increased risk of bladder cancer, as highlighted by the\nFDA’s recommendation to exercise caution when prescribing pioglitazone to individuals with a history of\nbladder cancer. To prioritize patient safety and adhere to these guidelines, we established a specific\nexclusion criterion to exclude individuals with any history of bladder cancer, regardless of the time since\ndiagnosis. This approach was taken to minimize any potential risks associated with pioglitazone.\n10. There are significant gender and racial differences between IBM muscle/sera and control muscle/sera.\nCould these differences have influenced the results? Why was matching not performed?\nWe appreciate the reviewer’s important comment. The muscle samples used in our study were obtained\nfrom archived specimens in the Johns Hopkins Neuromuscular Biobank, which limited our ability to select\nperfectly matched controls. Due to the rarity of age-appropriate, histologically normal muscle biopsies,\nwe\nprioritized control samples from older individuals to better approximate the age range of IBM patients.\nUnfortunately, due to limited availability, we were unable to match the control group to the IBM group by\nsex or race. We acknowledge that this could have influenced the metabolomic results and have now added\nthis point explicitly to the limitations section of the manuscript.\n11. Was any method of drug accountability used? This is not mentioned.\nThank you for pointing this out. All participants brought in their empty pill bottles to each study visit and\nwere questioned about adherence. We added this to the Methods section under “Study Population and\nDesign.”\n12. The manuscript states, “At the baseline visit, week 16, and week 32, a needle muscle biopsy from the\nvastus lateralis was performed.” Were the biopsies taken from the same side and area of the vastus\nlateralis? Given that IBM is a patchy and asymmetrical disease, repeated biopsies may be problematic.\nIf subsequent biopsies were taken from the same area, results could be affected by scar tissue. If taken from\ndifferent locations, results could be influenced by varying levels of inflammation, degeneration, fat\ndeposition, and mitochondrial changes, as these features are heterogeneously distributed in IBM muscle.\nThank you for providing us with the opportunity to clarify our methods. Needle muscle biopsies were\nperformed on the same side and muscle for each patient to avoid variability given asymmetry of the\ndisease. The site for each biopsy was localized first by muscle ultrasound to ensure viable muscle tissue\nand avoid scar or extensive fibrosis. This has been added to the Methods section.\n13. Which methods and devices were used for assessing “knee extensor strength by quantitative\ndynamometry” and “hand-grip strength using handheld dynamometry”? Which side of the body was\nassessed? If both sides were assessed, was the average value used? This is not explained in the manuscript\nA handheld dynamometer was used to assess quantitative muscle strength of the knee extensor.\nParticipants were seated with their knee at 90 degrees of flexion. The dynamometer was placed on the\ndistal part of the lower leg, just above the ankle, and manual resistance was applied gradually until the\ndynamometer recorded a measurement. Strength was measured in both lower extremities and then\naveraged.\nHandgrip strength was measured using a Jamar hydraulic hand dynamometer following standardized\nprocedures. Participants were seated with the elbow flexed at 90 degrees and the forearm in a neutral\nposition. The test was performed on the standard (second) handle setting, and the best of three trials for\neach hand was recorded. The grip strength for each side was then averaged.\nThese details have been added to the Supplemental Methods. We also added more detailed information\nfor the other clinical outcome measures as well.\n14. Regarding the “modified timed up and go” (m-TUG) test, how did the authors handle patients who were\nunable to perform the test, mathematically/in the change analysis?\nAll participants were able to perform the m-TUG. Being able to rise from a chair was an inclusion criterion\nto ensure that participants would be able to complete this test.\n15. The manuscript states, “Certified physical (AM, RP) and occupational therapists (MM)...” What does\n“certified” mean in the context of this trial? Which entity provided the certification? Did this include specific\ntraining on trial outcome assessments?\nThank you for the opportunity to clarify this point. The term \"certified\" refers to the fact that the physical\n(AM, RP) and occupational therapists (MM) involved in the trial hold professional licensure and are\ncertified to practice in their respective fields. Their certification is granted by state licensure boards and\nprofessional organizations that govern physical and occupational therapy practice. While the therapists\ndid not receive specific certification for clinical trial outcome assessments, we conducted a training session\nprior to the start of the trial to standardize the assessment process. This training ensured consistency in\nevaluating strength and applying assessment techniques, aligning with best practices for clinical care in\nIBM. This is now clarified in the manuscript.\n16. The primary outcome is defined as “the change in PPARGC1A gene transcription measured by RNA\nsequencing after pioglitazone (week 32) compared to before treatment (week 16),” which corresponds to\nthe intervention phase. However, the secondary outcome measures are described as changes “from\nbaseline,” with baseline defined as week 0 (16 weeks before pioglitazone initiation, as per Figure 1). Why\nwas a different time frame used for the secondary outcomes (week 0 to week 32) than for the primary\noutcome (week 16 to week 32)? This inconsistency is problematic.\nThank you for bringing to our attention this important inconsistency. We have made several modifications\nto the manuscript to ensure the time frame of comparison is consistent throughout primary and secondary\noutcomes and with the study protocol. One such critical adjustment was more closely aligning primary\noutcomes with those detailed in the study protocol and modifying the analysis of PPARGC1A expression to\nbe a direct comparison of change from lead-in (baseline to week 16) to treatment phase (week 16 to 32).\n17. The manuscript states, “Baseline demographic, clinical, and laboratory variables were evaluated with\ndescriptive statistics, and differences were evaluated using the Wilcoxon rank sum test for continuous\nvariables and Fisher’s exact test for categorical variables.” This sentence is unclear. What differences were\nbeing compared? It reads as though baseline values are being compared (e.g., between IBM and healthy\nsubjects), in which case a paired test would not be appropriate.\nThe authors understand the sentence was likely confusing and lacked specificity. We have therefore updated\nit in the manuscript to clarify comparisons are being made between IBM and healthy subjects at baseline.\nThe authors would also agree use of a paired test would not be appropriate, however in this case the\nWilcoxon rank sum and Fisher’s exact tests used were not paired tests but rather standard independent\nstatistical tests.\n18. The manuscript states, “Genes with a ≥1.5 log2 fold change in expression were considered to be\nsignificantly differentially expressed (at P < 0.05).” Given the large number of genes assessed, why was a\nmore stringent p-value cut-off not applied? Why was a correction method such as Bonferroni not used?\nThe authors apologize for the inclusion of this sentence; it was unfortunately left in from a previous version\nof the manuscript which included an analysis of individual differentially expressed genes. The current\nmanuscript utilizes Gene set enrichment analysis, a method in which cutoffs for individual gene significance\nare not used. We have therefore removed the sentence from the methods section as it is not applicable to\nthe analyses performed in the study.\n19. The manuscript states, “Epinephrine was detected in muscle samples.” Could this have influenced the\nexpression of other metabolites?\nThank you for raising this important point. We acknowledge that the use of epinephrine can potentially\nimpact the expression of other metabolites, however, we did not see any of these potential effects (ex.\nincreased lactate due to vasoconstriction and anaerobic metabolism). We also note that the biopsies were\nquickly frozen, minimizing any potential for changes in more stable metabolites such as amino acids and\nfatty acids. Furthermore, because the aim was to assess longitudinal changes within the IBM group from\npioglitazone, any potential influence of epinephrine would be consistent across all time points, minimizing\nits effect on the within-group comparisons.\nNevertheless, we recognize that the differences in biopsy procedures and anesthetic protocols between the\nIBM and healthy control groups may have contributed to differences in metabolite levels. We have included\nthis as a limitation in the manuscript to provide appropriate context for interpreting our findings.\n20. The analysis of changes in the IBM metabolic signature is difficult to interpret. How can a correlation\nbetween two pre-treatment values (baseline and week 16) be compared to a correlation between week 16\nand week 32? How does this inform the treatment effect? This approach is unconventional. Why were\nweek 16 and week 32 results not compared directly, as was done for the primary endpoint?\nWe appreciate the reviewers' concerns regarding the interpretations of the correlation analysis. As\ndiscussed further in reviewer comment 26 and our response, the lack of a placebo group acted as a\nlimitation to our study design. To contextualize the effects observed during the treatment period (week 16\nto week 32 comparison, figure 7B) on IBM metabolic signature we also included an analysis assessing the\nlead-in untreated phase (baseline to week 16 comparison, figure 7A). We believe this serves as a valuable\n“negative control” in the absence of a placebo group in that it critically demonstrates the observed effect\n(seen in treatment phase, 7B) was not present in the pre-treatment phase (7A) - providing some\nconfidence the observed effect is not completely spurious. To the second point regarding the direct\ncomparison of week 16 and week 32 for the primary endpoint, we have now updated this to be more in\nline with the original protocol and therefore more consistent with the correlation analyses focused on\ncomparing lead-in phase to treatment phase.\n21. Does “average duration of IBM” refer to the time since symptom onset or since diagnosis?\nThis refers to the time since symptom onset and has been clarified in the manuscript.\n22. How was control muscle obtained? Was it taken from the vastus lateralis using the same technique as in\nIBM patients?\nWe obtained the control muscle samples from the Neuromuscular Biobank at Johns Hopkins University.\nThey were collected from patients undergoing open surgical muscle biopsies for clinical indications, and\nwe selected patients whose muscle symptoms were ultimately attributed to unrelated, non-myopathic\nconditions such as radiculopathy, and whose muscle biopsies were histologically normal. We prioritized\nolder individuals to more closely approximate the age distribution of the IBM cohort. As muscle biopsies\noften come from varied muscle groups, some samples came from the rectus femoris, deltoid or biceps.\nWhile we acknowledge differences in fiber type composition across muscles, it is important to note that\ncore metabolic pathways – such as glycolysis, the TCA cycle, and oxidative phosphorylation – are generally\nconserved across skeletal muscle, supporting the validity of our approach.\nWe recognize the differences in the biopsy procedure may introduce variability. However, the focus of our\nstudy was to identify an \"IBM metabolic signature\" and assess its longitudinal changes in response to\npioglitazone, which were all conducted under consistent biopsy conditions for the IBM group.\nWe have added these details to the Methods section of the manuscript, and we acknowledge the different\nprocedures used for controls in the limitations section. Thank you for your valuable feedback.\n23. The manuscript states, “Untargeted metabolomics was performed on the 15 remaining samples and\ncompared to five healthy control muscle specimens.” These sample sizes are extremely low for\nmetabolomics studies, raising concerns about the validity of the results and the potential influence of\noutliers. The same concern applies to the serum sample analyses.\nWe fully acknowledge that our sample size is small, which is a limitation of this study, and we now further\nemphasize this point in the discussion. However, we note that this is to our knowledge one of the largest\nmetabolomics studies in IBM to date. To enhance the robustness of our findings, we not only identified an\n\"IBM metabolic signature\" through cross-sectional analysis (IBM vs. healthy controls) but also conducted a\nlongitudinal analysis examining metabolic changes within the same individuals over time. By leveraging a\nwithin-subject design, we were able to reduce the impact of inter-individual variability and improve\nstatistical power in detecting meaningful metabolic changes in response to pioglitazone. Had we relied\nsolely on cross-sectional comparisons, we agree that the study would have been significantly\nunderpowered.\nWe have clarified this in the manuscript to acknowledge the sample size limitation while highlighting the\nstrengths of our longitudinal approach. We appreciate the reviewer’s insightful feedback.\n24. The manuscript states, “Patients with a US sum score ≥ 10 were characterized as having severe disease\n(n=7), and patients with a US sum score 10 were characterized as having mild-moderate disease (n=6).”\nHas this cut-off ever been validated? What was the rationale for choosing it?\nThank you for this question. The rationale for this scoring system was the need to capture a muscle\nquality/structural assessment of the involved muscles in IBM as an objective measure of disease severity,\naugmenting the IBM-FRS which is subjective and can reflect functional compensation. Prior studies have\nshown that the most discriminating muscles are the FDP, rectus, and vastus lateralis which was why they\nwere chosen, and this has been added in the manuscript. The degree of involvement was ascertained using\nthe heckmatt 4 point scale (with 1 being normal and 4 having a marked increase in echointensity). Muscles\nscored in the range of 3 and 4 represent more severe and obvious involvement, so a cutoff of > 10 as a\ncomposite score for the FDP, VL and rectus (with atrophy in any muscle) was used as this gave a clear\ndistinction of a more advanced phenotype. While this US score is novel, we believe that a structural\nassessment of the muscle is very helpful to stratify disease and is currently being validated in a larger IBM\nstudy.\nWe caught an error in the manuscript which was corrected. In the methods, we indicated >10, but in\nresults this was erroneously typed as ≥ 10 and has been amended.\n25. The manuscript states, “Using a linear mixed-effects model, we observed increased PPARGC1A gene\nexpression after pioglitazone, but this did not reach statistical significance (p=0.07).” Please clearly state\nthat there was no treatment effect of pioglitazone on the primary endpoint rather than focusing on\n“trends.” The same comment applies to the discussion section.\nThis result is no longer in the manuscript due to the adjustment in the analysis to better fit the pre-\nspecified outcomes in the protocol. However, the Reviewer’s point is well-taken and we have updated the\nlanguage in the discussion so we do not over-interpret our findings.\n26. The lack of a placebo group is a major limitation of this study. This should be further emphasized. Why\nwas a placebo group not evaluated?\nWe agree that the lack of a placebo group is a major limitation of this study, and we have further\nemphasized this point in the discussion. A placebo-controlled trial in IBM would require a substantially\nlarger sample size due to the considerable variability among IBM patients. Given that this was an\nexploratory study, our approach was to compare patients to themselves over time, which allowed us to\ndetect metabolic changes with a much smaller sample size. This within-subject design helped mitigate\ninter-individual variability and improve statistical power despite the limited number of participants.\nImportantly, our findings provide preliminary data that support the feasibility and rationale for a larger,\nplacebo-controlled trial in IBM. We appreciate the reviewer’s feedback and have clarified this in the\nmanuscript.\n27. Are the observed metabolomic changes specific to IBM? Would similar changes be expected in other\nconditions or even in healthy individuals receiving pioglitazone?\nThis is an important question that, to our knowledge, has not been directly investigated in healthy muscle.\nPrior studies have reported a similar favorable metabolic shift in muscle from individuals with insulin\nresistance, suggesting that some of these changes may not be exclusive to IBM. We have added several\nlines to the discussion acknowledging this knowledge gap and the need for further research.\n28. The statistical methods section is unclear. Some statistical tests (e.g., Two-way ANOVA with Tukey HSD,\nmentioned in Figure 7) are referenced in figures but not described in the methods section.\nThe authors apologize for the lack of inclusion of details on the two-way ANOVA analysis in the methods\nsections. It has been updated to include these details such as the software and packages used to perform\nthe test.\n29. The manuscript states, “These metabolic responders tended to have earlier and less severe disease as\nstratified by US (Figure 4A, 4B, 7D).” However, these data are not convincing. Figure 7D shows only five\npatients in the mild-moderate group; if one “red dot” patient is removed, the effect would disappear, just\nas it is absent in the advanced group.\nThe authors understand the reviewers' concerns and agree the small sample size is a limitation to the\nstudy, which is likely made more concerning with subgroup analyses. To provide some measure of\nrobustness we therefore performed a “leave-one-out” analysis. As the reviewer suspected, we did observe\nloss of significance in the severityXTreatment effect with removal of one of the 12 subjects (from p=0.013\nto p=0.103). We have updated the manuscript to include mention of this additional analysis and further\nhighlight limitations of the small sample size.\n30. This is a negative study in IBM patients. Highlighting a single patient with subjective “clinical\nimprovement” is inappropriate. For some outcome measures, the reported variation for this patient falls\nwithin the expected measurement error. Furthermore, given that this patient was obese and had metabolic\nsyndrome, any observed “improvement” may simply reflect better cardiovascular and metabolic health\nrather than an IBM-specific effect.\nWe appreciate the reviewer’s feedback and agree that highlighting a single patient’s improvement is not\nappropriate in the context of a negative study. To address this concern, we have removed the description\nof this patient from the manuscript.\n31. The manuscript states, “In a subset of patients, mostly with mild-moderate disease, a demonstrable\nmetabolic response was seen in IBM muscle, which was associated with a slower decline in IBM-FRS and m-\nTUG scores.” Please tone down this statement, as the data are not convincing (see previous comments).\nWe appreciate this feedback and agree this statement should be toned down. It now reads:\n“A subset of patients had a metabolic response to pioglitazone in muscle, and this metabolic response was\nassociated with a slower decline in IBM-FRS and m-TUG scores.”\n32. The statement, “These results provide initial evidence that pioglitazone can partially reverse the\nmetabolic abnormalities of IBM muscle and that this reversal can translate into improved clinical outcomes\nfor a subset of patients,” should be toned down, as the data do not convincingly support this conclusion.\nWe have toned down this statement as well. It now reads: “These results provide initial evidence that\npioglitazone can partially reverse the metabolic abnormalities in IBM muscle, but larger trials are still\nneeded to determine if this translates into improved clinical outcomes for patients.”\n33. Table S2 states that comparisons between baseline values of IBM patients and controls were made\nusing the paired Wilcoxon test. This is inappropriate because these are independent samples.\nThe authors agree use of a paired test would be inappropriate for comparison of the IBM patients to\ncontrols, however the data in table S2 was analyzed using a Wilcoxon rank-sum test and Fischer’s exact\ntest, neither of which was performed with a paired design.\n34. Table S7 should be included in the main manuscript, as it clearly shows that the study was negative for\nall assessed physical function, strength outcome measures, and CK. For each outcome measure, please\npresent their units, if applicable.\nWe agree with the reviewer and have incorporated Table S7 into the main manuscript as Table 1. We have\nincluded units for each outcome measure.\nReviewer #2 (Remarks to the Author):\nThis study is an exploratory, single-arm clinical trial investigating the effects of pioglitazone in patients with\ninclusion body myositis (IBM), with metabolomic analysis of biopsied muscle as the primary endpoint.\nAdditionally, the study compares the metabolomic and transcriptomic profiles of patients at baseline with\nthose of healthy controls from a biobank, allowing for a clearer interpretation of treatment-induced\nchanges in the context of disease-specific alterations.\nDespite the small sample size, the study is well-designed, incorporating a lead-in period during which\nneedle muscle biopsies were performed. This design adds significant value beyond a simple pre- and post-\nintervention metabolomic comparison. The manuscript is well-written and provides valuable insights.\nAlthough the effects of pioglitazone were not clearly detected—partly due to the exploratory nature of the\ntrial—the findings contribute meaningfully to the design of future clinical trials for this debilitating disease.\nMy specific comments are as follows:\nMajor Points\n1. Figure 4 effectively illustrates the relationship between ultrasound (US) severity and muscle\nmetabolome. The authors may also wish to examine the associations between US findings and IBM-FRS, as\nwell as between metabolome profiles and IBM-FRS.\nThe authors appreciate the reviewers' suggestions. To assess the association between US findings and\nIBM-FRS we performed a correlation analysis between US scores and IBM-FRS at baseline revealing a\nsignificant negative correlation, suggesting more severe US scores (higher value) were correlated with\ndecreased function assessed by IBM-FRS (lower values) with a Spearmans rho= –0.62 at p = 0.03. We\nbelieve this provides valuable additional support for the manuscript, in demonstrating the relationship\nbetween US severity and clinical function and have added a sentence to the manuscript describing this\nrelationship. With regards to assessment of associations between metabolome profiles and IBM-FRS, the\nauthors believe the analysis as presented in figure 8 A and B demonstrates the relationship between\nchanges in metabolome signature and IBM-FRS.\n2. The study identifies a subset of responders based on muscle metabolic changes following pioglitazone\ntreatment (Figure 7). To further elucidate the clinical significance of these metabolic alterations, the\nauthors should compare motor performance and biomarker changes between responders and non-\nresponders.\nThe authors agree with the reviewer that assessing motor performance and biomarker changes in\nresponders vs non-responders would be of great interest. Unfortunately, due to the small sample size,\nsuch\nanalyses are greatly limited in power and reliability. In particular, based on the observed change in “ibm\nmetabolic signature” (Figure 7C) there are 4 subjects who we would consider responders (significant\ndifference in lead-in vs treatment phase, based on an additional analysis comparing jacknife estimates of\nCI and only considering those with no overlap between phases). We therefore believe our current method\nof evaluating the clinical relevance of the metabolic score itself provides a slightly larger sample size from\nwhich to determine if metabolic response is likely to correspond to clinical outcomes (figure 8). We have,\nhowever, performed an additional more direct analysis as the reviewer suggested modifying our original\nlinear-mixed effects models for clinical outcomes to now include an additional “responder” variable, and\nreport in the table below the model estimates for timeXTreatment[Pio]Xresponder[Responder]. While\nlargely demonstrating null findings, the results do indicate that the small subset of responders showed a\nsignificant improvement in IBM-FRS over time with treatment compared to non-responders who saw\ndecreases.\nOutcome estimate p-value\nIBM-FRS 5 0.002\nKendall Score 0.19 0.9\nFi2 -0.07 0.52\nHand-grip average 5.56 0.091\nCreatine kinase -283.61 0.303\n6-minute walk -24.64 0.86\nDynaamometry knee extensors 10.27 0.169\nM_TUG 0.47 0.704\n3. To strengthen the multi-omics approach, the authors should directly compare treatment-induced\ntranscriptomic changes (Figure 6) with metabolomic alterations (Figure 7).\nThe authors thank the reviewer for the suggestion. As suggested, we have performed an additional\nanalysis integrating gene set enrichment analysis results across both omics layers to reinforce our findings\nusing the multiGSEA package. These results are mentioned in the updated manuscript and the output is\nprovided as a supplementary table (Table S8).\n4. Similarly, a comparison between disease-related transcriptomic changes and treatment-induced\ntranscriptomic alterations would provide additional valuable insights.\nThe authors agree that a comparison between disease-related transcriptomic changes and treatment-\ninduced transcriptomic changes would be informative. Unfortunately, transcriptomic data from our\ncontrol samples is not available as budget constraints did not allow for these analyses at the time. To gain\nsome insight into this question, however, the authors have utilized a publicly available transcriptomic\ndataset (GSE102138) containing control and IBM skeletal muscle samples to act as a “transcriptomic\ndisease signature”. Intriguingly, the resulting correlations between lead in and treatment period with this\ndisease signature align with our metabolomic signature results and even suggest potentially increased\nsensitivity of the transcriptome to detect disease progression in the lead in phase. We report a Spearmans\nrho of lead-in vs IBM signature of 0.4 with p=0, indicating a more “IBM-like” transcriptome profile over the\nlead-in period, while there was a Spearmans rho of –0.54 at p = 0 during the treatment period suggesting\na moderate reversal of transcriptome signature. We believe these results provide valuable support for our\nfindings in the metabolome by demonstrating a similar pattern at another “omics layer” and providing\nsome indication of external validity/generalizability for IBM disease signatures. The authors thank the\nreviewer for the suggestion and have added these results as a supplemental figure (Figure S1).\nMinor Points\n1. Was the muscle biopsy site in the control group the same as in the patient group (i.e., vastus lateralis)?\nThank you for the question. As we addressed in reviewer #1’s question: We obtained these samples from\nthe Neuromuscular Biobank at Johns Hopkins University. They were collected from patients undergoing\nopen surgical muscle biopsies for clinical evaluation, and we selected patients whose muscle symptoms\nwere ultimately attributed to unrelated conditions such as radiculopathy, and whose muscle biopsies were\nhistologically normal. We prioritized older individuals to more closely match the IBM cohort. As muscle\nbiopsies often come from varied muscle groups, samples came from the rectus femoris, deltoid or biceps.\nWhile we acknowledge differences in fiber type composition across muscles, it is important to note that\ncore metabolic pathways – such as glycolysis, the TCA cycle, and oxidative phosphorylation – are generally\nconserved across skeletal muscle, supporting the validity of our approach. However, we have included this\nas a limitation in the discussion.\n2. A more detailed discussion is needed regarding the possible causes of heart failure exacerbation in one\npatient.\nThank you for this comment. Pioglitazone can cause or exacerbate CHF in some patients and is\ncontraindicated in those with class III or IV heart failure. This patient did not have any clinical signs or\nsymptoms of heart failure at the time of enrollment but had this in their remote history as occurring after\na virus, which then resolved. Two weeks prior to termination of the trial the patient felt a sense of chest\ndiscomfort which gradually worsened with finding of left sided pleural effusion. Pioglitazone was stopped\nand diuretics were given with resolution of symptoms. More detail on this patient was included in the\npaper.\n3. There is an ethical concern regarding the re-initiation of pioglitazone in one patient. How did the ethical\nreview committee justify this treatment outside of the study protocol?\nWe appreciate the reviewer’s concern regarding the ethical considerations of pioglitazone re-initiation in\none patient. In response to this and related feedback, we have removed the discussion of this individual’s\npost-trial pioglitazone use and subsequent treatment course from the manuscript.\nTo clarify, the patient was treated with pioglitazone strictly according to the approved study protocol\nduring the trial period. After completion of the trial and discontinuation of study treatment, the patient\nand their independent treating physician—outside the direction or involvement of the study team—elected\nto re-initiate pioglitazone use as an off-label clinical decision. This occurred outside the scope of the study\nand was not subject to oversight by the study’s ethics committee. The study team became aware of this\noff-protocol treatment retrospectively, when the patient shared this information during participation in a\nseparate, observational longitudinal study of IBM. We agree that inclusion of this anecdotal clinical case\nmay have introduced confusion regarding the scope and oversight of the clinical trial, and we have\ntherefore removed it from the revised manuscript\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nReviewer #5 (Remarks to the Author):\nGeneral Comments\nThis was a small study which aimed to evaluate the impacts of pioglitazone on various outcomes and\nassessed the differences between healthy muscle specimens and serum samples with those from IBM\npatients. PPARGC1A expression increased at week 32 compared to week 16 (before treatment) P=0.07,\nindicating that pharmacologically targeting mitochondria with pioglitazone may improve metabolic deficits\nin this population.\nFor a single arm study, the “lead-in” phase was a helpful addition to assess the stability of the clinical\noutcomes assessed. The PPARGC1A gene expression (primary outcome) increased compared to pre-\ntreatment timepoints however it is difficult to assess the extent of this since little information is reported\nabout this outcome’s results.\nSmall sample size and no controls does limit the study. More patients would have given the authors a better\nunderstanding of the effect pioglitazone. The lack of controls does mean that it is hard to assess a potential\nHawthorne effect, however due to primarily biological outcomes (which can still be affected by being in a\ntrial), and nature of the small/exploratory study design I don’t think this is a major flaw with the trial.\nIt is unclear if the statistical analyses were pre-specified prior to screening.\nI agree with the authors' conclusions.\nMain Comments\n“Control muscle specimens were obtained from the Johns Hopkins Neuromuscular Biobank and were\ncollected from patients with normal muscle tissue and no diagnosed neuromuscular disorders.” and\n“Serum samples were obtained from healthy controls as part of a separate IRB-approved study for healthy\ndonors.”\nIt is not clear what defines “normal muscle tissue or a healthy control?” Were these just samples from\nindividuals without Inclusion body myositis or without a set of conditions? Were they randomly selected\nfrom the biobank/ healthy donors in the IRB-approved study or were they all samples the bank/study had at\nthe time? Without this detail, it would be hard to reproduce the work.\nThank you for this comment. We agree that more information on the controls is needed to improve the\nreproducibility of this study.\nThe control muscle samples were obtained from archived specimens in the Johns Hopkins Neuromuscular\nBiobank. They were collected from patients undergoing open surgical muscle biopsies for clinical\nindications, and we selected specimens from patients whose muscle biopsies were reported as normal,\nand whose symptoms were ultimately attributed to unrelated, non-myopathic conditions (such as\nradiculopathy). Any sample where a primary muscle disease (such as mitochondrial myopathy) could not\nbe ruled out was excluded. We also prioritized biopsies from older individuals to more closely approximate\nthe age distribution of the IBM cohort. This information has been added to the Methods section of the\nmanuscript.\nHealthy control serum came from a separate protocol which obtained blood samples from healthy donors.\nExclusion criteria for this cohort included pregnancy or a history of autoimmune disease, cancer, or active\ntuberculosis, HIV, or hepatitis infection. This also has been added to the manuscript.\n“Using a linear mixed effects model, we observed increased PPARGC1A gene expression after pioglitazone,\nbut this did not reach statistical significance (p=0.07).”\nThis is missing an effect size estimate and confidence interval. Since this is the primary outcome, I would\nhave expected this to be clearly written. I think the interpretation of the P-value could be potentially less\nconservative with their (particularly considering the small sample size). They did observe that there was an\nincreased PPARGC1A gene expression after pioglitazone. The plot for this (Figure 6A) also hides the\nconfidence interval for the PPARGC1A Normalized Averages of\nCounts due to the black colour for the bars and the error bars.\nThe authors appreciate the suggestions. We have revised the description and presentation of the primary\noutcome to more closely align with the original study protocol. We now include the effect size and\nconfidence interval for the change in PPARGC1A gene expression. In addition, we have updated the figure\nto improve visualization.\nMinor comments\n4. “Model equations were “outcome ~ 1+Treatment*Time + (1+Time|ID)”\nDid the authors consider adjusting for the baseline demographics they collected (age, sex, ethnicity, disease\nduration, biomarkers etc.)?\nThe reviewer raises an important point regarding model adjustment. While ideally several demographic\nfactors could be adjusted for, given the extremely small cohort size such adjustments can be limited. In\nparticular, some analyses were attempted with inclusion of sex as a covariate and it was found that this\ngenerally produced suspected overfitting evidenced by higher AIC and BIC scores compared to unadjusted\nmodels. It was therefore decided to use unadjusted models. We do, however, agree this is a point worth\nraising and have added a sentence discussing it in our limitations.\n5. “The mean age of control muscle specimens was 60.6 ± 15.4 years and 4/5 (80%) were female (n=5). The\ncontrol sera was slightly younger than the IBM sera (56.3 ± 5.7 years, p=0.001) and 7/10 (70%) were female\n(n=10).”\nThe “(n=5)” and “(n=10)” at the end of these sentences is slightly confusing as it is not clearly referring to\nsomething. I presume this is simply referring to the number of control sera and muscle specimens but this\ncould be clearer.\nWe have made this clearer. It now reads as follows: “The mean age of the 5 control muscle specimens was\n60.6 ± 15.4 years and 4/5 (80%) were female. The 10 control sera were slightly younger than the IBM sera\n(56.3 ± 5.7 years, p=0.001) and 7/10 (70%) were female.”\n6. “Using principal component analysis, we found that samples from healthy control and IBM muscle\nshowed distinct clustering patterns, with a more variable clustering of IBM samples than healthy controls\n(Figure 2A).”\nThough this is only 5 healthy controls so could be due to randomness/only having 5 controls.\nThe authors understand the low sample size may cause concern. To more precisely describe potential\ndifferences in variance between groups we therefore conducted an F-test on PC1 values for controls vs\nIBM to assess more robustly if there are statistically significant differences in variance. The results indicate\na statistically significant difference in variability between the two groups with p = 0.018, providing some\nsupport to the robustness of these observations. We have added this to the manuscript.\n7. “This stratification correlated with function and those with severe disease by US had lower IBM-FRS\nscores (28.57 ± 3.73 vs 33.33 ± 3.72).”\nAre these recorded anywhere in a table/figure? I can’t see them reported elsewhere.\nWe have included a table of the ultrasound score and IBM-FRS for each patient at the baseline visit in\nsupplemental Table S5.\nPatient US score Atrophy present IBM-FRS\n1 11 Yes 32\n2 5 No 35\n5 12 Yes 28\n7 11 Yes 22\n8 12 Yes 26\n10 12 Yes 28\n11 9 No 30\n12 12 Yes 30\n13 10 No 32\n14 7 No 39\n16 11 Yes 33\n17 8 No 29\n18 8 No 32\n8. “We did not observe any difference in the metabolic signature of severe versus mild-moderate IBM in\nsera (Spearman’s p= -0.13, p=0.06) (Figure 4D).”\nIt is stated that the investigators did not observe “any” difference however there was a small difference\nobserved. Whether this was due to chance or if there is a true difference is a slightly different matter.\nWe have updated the language in the manuscript to be more precise, it now reads “...a significant\ndifference...”.\n9. “Despite notable agreement for a few key metabolites, such as propionylcarnitine and tryptophan which\nwere both decreased in serum and muscle, the overall correlation between the metabolic profiles in muscle\nand serum was not significant (Figure 5E, Spearman’s p=0.06).”\nAs with the statement regarding correlation between fold change between IBM vs. controls and severe vs\nmild IBM (and various other statements), it would be good for the P-value to be stated here also (P=0.501)\nWe have added the p-value to this segment of the manuscript.\n10. “After completing the clinical trial, the patient discontinued pioglitazone and his CK increased back to\nhis pre-treatment baseline of 2055 units/L,”\nCK units have been referred to as U/L throughout apart from this sentence.\nThank you for catching this error. We have deleted this paragraph per the suggestion of Reviewers 1 and\n2.\n11. “Although there was no statistically significant change in disease trajectory for the study population as a\nwhole, one patient (subject 11) demonstrated a clinical improvement with pioglitazone. ”\nSome context around why this participant was followed up so closely after the end of the trial might be\nhelpful since I can’t see this mentioned in the methods (was this planned or just because of their promising\nCK levels during the trial). Were any other patients followed-up post 48 weeks? By chance you would\nexpect someone to have a strong reduction in levels of one of these 7 outcome measures so inference from\nthis patient’s data should be limited (as I think it fairly is).\nTo clarify, all participants were prospectively followed only through the 48-week study period, with no\nplanned protocolized follow-up beyond that timepoint. Subject 11 was concurrently enrolled in a separate\nlongitudinal study of inclusion body myositis (IBM) being conducted at our institution. As part of that\nseparate study, additional clinical data were collected after completion of the trial. We agree with the\nreviewer that inference from a single patient’s post-trial clinical course should be limited, particularly\nbecause such post-treatment information was not prospectively collected as part of the present study. In\nresponse to this and other reviewer feedback, we have removed detailed discussion of this patient’s post-\ntrial course to avoid potential overinterpretation or confusion regarding the scope of the trial and its\nprespecified outcomes.\n12. “despite missing the primary endpoint of increasing PPARγ gene expression”\nThe choice of words here is slightly misleading/confusing – the primary endpoint was not missed. There was\nnot as strong evidence as was hoped for but there was still some evidence. We appreciate the feedback.\nDiscussion of the primary endpoint has been updated to more closely align it with the study protocol.\nWe thank the Editor and Reviewers for their thoughtful review of our manuscript, which has helped\nto further improve its quality. We have addressed the two minor comments from Reviewer #1 and\nReviewer #5, as detailed below, and have made the corresponding changes to the manuscript. In\naddition, we have added the DOI for the data and code, which are stored in the Vivli repository.\nReviewer #1 (Remarks to the Author):\nOne minor comment: The manuscript now states that “Eligible subjects included adults 50\nyears and older (to enrich for those with typical IBM features) with a diagnosis of clinico-\npathologically defined or clinically defined IBM by ENMC criteria (18) or data-driven criteria\n(DDC) (19)”. However, in the protocol only ENMC criteria are mentioned. This discrepancy still\nrequires clarification.\nWe apologize for this discrepancy. All of the participants met ENMC criteria. Some participants also\nmet the DDC criteria, but this additional criteria is not necessary and not required by the study\nprotocol so we have removed it from the manuscript.\nReviewer #5 (Remarks to the Author):\nThank you for your active responses to these comments.\nPoint 8 was:\n8. “We did not observe any di(cid:431)erence in the metabolic signature of severe versus mild-\nmoderate IBM in sera (Spearman’s p= -0.13, p=0.06) (Figure 4D).”\nIt is stated that the investigators did not observe “any” di(cid:431)erence however there was a small\ndi(cid:431)erence\nobserved. Whether this was due to chance or if there is a true di(cid:431)erence is a slightly di(cid:431)erent\nmatter.\nAuthor response:\nWe have updated the language in the manuscript to be more precise, it now reads “...a\nsignificant\ndi(cid:431)erence...”.\nI appreciate the e(cid:431)orts to correct this however, it would be inappropriate to say a significant\ndi(cid:431)erence. Instead a better interpretation is \"some di(cid:431)erence...\"\nWe apologize for being unclear with the changes made to the manuscript. The current sentence\nreads, “We did not observe a significant di(cid:431)erence in the metabolic signature of severe versus mild-\nmoderate IBM in sera.” To clarify that this was not significant, we added the word “statistically.” The\nsentence now reads: “We did not observe a statistically significant di(cid:431)erence in the metabolic\nsignature of severe versus mild-moderate IBM in sera.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the reviewers for their responses, particularly their efforts to moderate some of their initial statements and\nconclusions, and to expand on the study’s limitations. These revisions contribute to a more balanced and nuanced\nperspective of the work, which also now seems aligned with the protocol.\nOne minor comment: The manuscript now states that “Eligible subjects included adults 50 years and older (to enrich for\nthose with typical IBM features) with a diagnosis of clinico-pathologically defined or clinically defined IBM by ENMC criteria\n(18) or data-driven criteria (DDC) (19)”. However, in the protocol only ENMC criteria are mentioned. This discrepancy still\nrequires clarification.", "reviewer_2": "The authors addressed all the points I raised. I have no further concern.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_5": "Thank you for your active responses to these comments.\nPoint 8 was:\n8. “We did not observe any difference in the metabolic signature of severe versus mild-moderate IBM in\nsera (Spearman’s p= -0.13, p=0.06) (Figure 4D).”\nIt is stated that the investigators did not observe “any” difference however there was a small difference\nobserved. Whether this was due to chance or if there is a true difference is a slightly different matter."}, "author_response": "We thank the Editor and Reviewers for their thoughtful review of our manuscript, which has helped\nto further improve its quality. We have addressed the two minor comments from Reviewer #1 and\nReviewer #5, as detailed below, and have made the corresponding changes to the manuscript. In\naddition, we have added the DOI for the data and code, which are stored in the Vivli repository.\nReviewer #1 (Remarks to the Author):\nOne minor comment: The manuscript now states that “Eligible subjects included adults 50\nyears and older (to enrich for those with typical IBM features) with a diagnosis of clinico-\npathologically defined or clinically defined IBM by ENMC criteria (18) or data-driven criteria\n(DDC) (19)”. However, in the protocol only ENMC criteria are mentioned. This discrepancy still\nrequires clarification.\nWe apologize for this discrepancy. All of the participants met ENMC criteria. Some participants also\nmet the DDC criteria, but this additional criteria is not necessary and not required by the study\nprotocol so we have removed it from the manuscript.\nReviewer #5 (Remarks to the Author):\nThank you for your active responses to these comments.\nPoint 8 was:\n8. “We did not observe any di(cid:431)erence in the metabolic signature of severe versus mild-\nmoderate IBM in sera (Spearman’s p= -0.13, p=0.06) (Figure 4D).”\nIt is stated that the investigators did not observe “any” di(cid:431)erence however there was a small\ndi(cid:431)erence\nobserved. Whether this was due to chance or if there is a true di(cid:431)erence is a slightly di(cid:431)erent\nmatter.\nAuthor response:\nWe have updated the language in the manuscript to be more precise, it now reads “...a\nsignificant\ndi(cid:431)erence...”.\nI appreciate the e(cid:431)orts to correct this however, it would be inappropriate to say a significant\ndi(cid:431)erence. Instead a better interpretation is \"some di(cid:431)erence...\"\nWe apologize for being unclear with the changes made to the manuscript. The current sentence\nreads, “We did not observe a significant di(cid:431)erence in the metabolic signature of severe versus mild-\nmoderate IBM in sera.” To clarify that this was not significant, we added the word “statistically.” The\nsentence now reads: “We did not observe a statistically significant di(cid:431)erence in the metabolic\nsignature of severe versus mild-moderate IBM in sera.”"}]

Decision letter