Placebo effect on progression and regression in NASH:... : Hepatology (original) (raw)
Ng CH, Xiao J, Lim WH, Chin YH, Yong JN, Tan DJH, et al. Placebo effect on progression and regression in NASH: Evidence from a meta‐analysis. Hepatology. 2022;75:1647–1661. https://doi.org/10.1002/hep.32315NgCH, XiaoJ, LimWH, ChinYH, YongJN, TanDJH, et al. Placebo effect on progression and regression in NASH: Evidence from a meta‐analysis. Hepatology. 2022;75:1647–1661. https://doi.org/10.1002/hep.32315
Cheng Han Ng, Jieling Xiao, and Wen Hui Lim contributed equally to this work and share first authorship.
Mazen Noureddin and Mark D. Muthiah equal supervision.
INTRODUCTION
An estimated 25%–33% of the global population are affected by NAFLD, and the disease contributes a significant burden to the individual, society, and economy.[1,2] A subset of NAFLD known as NASH is characterized by the presence of inflammation, steatosis, and ballooning.[3] In the absence of an efficacious treatment for NASH,[4,5] liver transplantation (LT) remains the only curative therapy for end‐stage complications of NASH, and NASH is also the current leading cause of LT.[6] Although patients with NASH are known to suffer from both liver‐related and extrahepatic complications, there is yet to be a standardized consensus on the regularity in the screening and monitoring for these complications.[7] Current understanding of the disease has largely been based on cross‐sectional associative studies based on data collected at a single time point in conjunction with histology data. Although animal models are used to supplement our current understanding of NASH, these animal models are unable to fully recapitulate the clinical spectrum of overnutrition and disease progression in the liver with concomitant extrahepatic involvement that is seen in humans.[8] These limitations have contributed to the limited understanding of disease progression with time.
A liver biopsy remains the reference standard test in the diagnosis of NASH.[9] While noninvasive tests have been thought to be an effective assessment of fibrosis or provide a dichotomy for patient selection,[10] a liver biopsy (LB) can reveal core information about the state of inflammation, ballooning, and fibrosis in the liver, which are thought to be integral in the severity and prognostication of the disease. However, LB is inherently invasive[9] and repeated LB in regular intervals is rarely conducted in the clinical setting. Although NASH was traditionally thought to be a unidirectional pathology, recent evidence has alluded to NASH as a disease that has the potential to progress or regress over time.[11,12]
In theory, serial liver histology over time would be ideal to interrogate the natural history of the disease and understand the implications and long‐term outcomes in patients who progress or regress rapidly over time. This information can be accessed from the analysis of patients with placebo in randomized controlled trials (RCTs) that offer an advantage with stringent follow‐up with paired biopsies. Previous meta‐analysis by Han et al. have shown that 25% of patients demonstrated a two‐point improvement in NAFLD activity score (NAS) on interval biopsy despite being in the placebo arm[13] without endpoints required by the Food and Drug Administration. Yet, the effect of placebo on the biochemical, anthropometric, and other histological outcomes used as regulatory endpoints in the disease has yet to be examined.[13] With the recent addition of several well‐conducted trials incorporating histology data in the placebo arm, we sought to examine the placebo effect in NASH RCTs, including effects on the body mass index, lipids, liver enzymes, and histological outcomes, to derive more insights into the natural history of NASH.
METHODS
Search strategy
This was a post hoc analysis of a previous data set examining the outcomes of NASH in clinical trials.[14] This meta‐analysis was conducted with reference to the Preferred Reporting Items for Systematic Reviews and Meta‐analyses statement.[15,16] A comprehensive search was conducted on Medline, EMBASE, and the Cochrane Central Register of Controlled Trials with assistance from a medical librarian on NASH RCTs since inception on October 1, 2021. The full search strategy included is clinical adj3 trial).tw. or (singl$ OR doubl$ OR trebl$ OR tripl$).tw. and (mask$ OR blind$).tw. or placebo$.tw. or random$.tw. or exp randomized controlled trials/ or exp random allocation/ or exp double‐blind method/ or exp single‐blind method/ or exp placebos/ or research design/ and (nash or ([nonalcoholic or non*alcoholic or non alcoholic) adj3 steatohepatit*]).tw.. All references were imported into Endnote X9 for duplicate removal. The references of the included articles were also manually screened to maintain a comprehensive search.
Eligibility and selection criteria
Four authors (CHN, JX, WHL, and YHC) independently conducted the screening of abstracts and evaluation of full text for inclusion. Discrepancies were resolved by consensus and in consultation with a senior author (MDM). The eligibility criteria for this meta‐analysis included (1) RCT by study design, (2) studies that evaluated patients with NASH randomized to placebo treatment, and (3) those that reported sufficient data on outcomes of interest including but not limited to the resolution of NASH without worsening of fibrosis, two‐point reduction in NAS score without worsening of fibrosis, and at least one‐point reduction in fibrosis. Systematic reviews, meta‐analyses, conference abstracts, case series, correspondence, and editorials were excluded. The focus of this meta‐analysis was primarily on the adult population, and pediatric studies were excluded. Additionally, duplicate studies inferring results from the same databases were removed. Transformation of values was carried out using pre‐existing formulas, in which mean and SDs were estimated from median and range using the widely adopted formula by Wan et al.[17]
Outcomes of interest
The primary outcomes were (1) the resolution of NASH without worsening of fibrosis, (2) two‐point reduction in NAS without worsening of fibrosis, and (3) at least one‐point reduction in fibrosis. Secondary outcomes of interest included other histological endpoints from liver biopsy (i.e., at least a one‐point reduction and progression in steatosis, ballooning, and inflammation) and change in mean NAS score. Additionally, other outcomes included changes in biochemical parameters (i.e., of aspartate aminotransferase [AST] and alanine transaminase [ALT]) and metabolic parameters (i.e., body mass index [BMI], HDL, LDL, and triglycerides [TG]). Units of analysis for lipids were millimoles per liter (mmol/l), while liver enzymes were reported in international units (IU/L).
Risk of bias assessment
Four reviewers (CHN, JX, WHL, and YHC) independently assessed risk of bias of the included RCTs using the Cochrane Risk of Bias 2.0.[18] Briefly, included studies were examined on seven domains including random sequence generation, allocation concealment, masking of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective outcome reporting, and other sources of bias. Disagreements were resolved by consensus or appeal to a fifth author (MDM).
Statistical analysis
Statistical analysis was conducted in RStudio (R version 4.0.3). Generalized linear mix model with Clopper Pearson interval was used to estimate the pooled proportion and mean differences of the primary and secondary outcomes before and after receiving the placebo intervention.[19,20] Statistical heterogeneity was assessed via _I_2 and Cochran’s Q test values, where an _I_2 value of 40% or a Cochran’s Q test with p value ≤ 0.10 was considered heterogeneous.[21,22] Currently, there is a lack of an appropriate heterogeneity tool in prevalence meta‐analysis,[23] and _I_2 may prove to be an inadequate tool in the assessment of heterogeneity for proportional‐based meta‐analysis, evidenced by previous analyses involving large cohorts that often report heterogeneity estimates exceeding 90%.[24,25] The random effects model thus was used in all analyses regardless of heterogeneity scores, as recent evidence suggests that it provides more robust outcome measures compared with the alternative fixed‐effects models.[26] Subgroup analysis was subsequently conducted for both primary and secondary outcomes in patients with NASH across different geographical regions (i.e., North America, Europe, Asia, or multinational), differing duration of intervention (i.e., less than 6 months, between 6 and 12 months, or more than 12 months), single‐center versus multicenter trials, route of administration (i.e., oral or intravenous), frequency of oral dosing (i.e., once, twice, or thrice per day), and risk of bias (i.e., low, moderate, or high). Associated risk factors for primary and secondary outcomes were analyzed using mixed‐model meta‐regression to account for study‐level predictors (i.e., age, BMI change, gender, race, history of hyperlipidemia, hypertension, or type 2 diabetes mellitus) with the Hartung‐Knapp estimator to stabilize the variance.[27] Briefly, a negative regression coefficient describes the inverse relationship between the outcome variable and a unit increase of the independent variable. Statistical significance was considered for outcomes with p value < 0.05. Publication bias was not examined in the absence of a suitable assessment tool for single‐arm meta‐analysis.[28]
RESULTS
Summary of included articles
A total of 1435 articles were retrieved from the initial search strategy. After duplicate removal, 1201 references remained. A total of 165 full texts were reviewed after screening for title and abstract, of which 103 articles were excluded. Forty‐three RCTs consisting of 2649 placebo participants were included in this meta‐analysis and systematic review (Figure 1). Of these, 20 trials were conducted in North America,[29–48] five in Europe,[4953] four in Asia, [54–57] and one in Brazil and Turkey, respectively,[58,59] while 12 trials were multinational studies.[60–71] Of the 43 included studies, 19 were single‐center trials, and 24 were multicenter trials. Fourteen placebo groups reported histological data for resolution of NASH (n = 1066); 22 placebo groups reported two‐point improvement in NAS (n = 1287); and 29 placebo groups reported one‐point improvement in fibrosis score (n = 1522). Most studies had a study duration lasting between 6 and 12 months (n = 30) or exceeding 12 months (n = 10), with the exception of three studies that lasted less than 6 months. Most RCTs were found to have low (n = 16) to moderate risk of bias (n = 25) in at least half of the domains assessed (Supporting Information S1).
Preferred Reporting Items for Systematic Reviews and Meta‐analyses flow diagram
Primary outcomes
Resolution of NASH and two‐point reduction in NAS without worsening of fibrosis
A total of 1066 patients were assessed with 11.65% (CI: 7.98%–16.71%; Figure 2) achieving an event of NASH resolution without worsening of fibrosis (Table 1). Several factors including the geographical region, duration of study intervention, and route of administration did not significantly affect the resolution of NASH (Table 2). However, an older age was significantly associated with a decrease in the resolution in NASH (β = −0.0926; CI: −0.169 to −0.0163; p = 0.0174) (Supporting Information S2). Participants of African American ethnicity were found to have trend toward a decreased resolution of NASH (β = −0.408; CI: −0.839 to 0.0224; p = 0.0632) (Supporting Information S2). In the assessment of 1287 patients for a two‐point reduction in NAS without worsening of fibrosis, 21.11% (CI: 17.24%–25.57%) met the endpoint. Subgroup analysis shows that studies at low risk of bias had significantly higher rates of NAS reduction (26.45%, CI: 20.54%–33.35% vs. 17.24%, CI: 13.96%–21.10%; p = 0.010). Meta regression analysis did not find any significant factor affecting a two‐point reduction in NAS without worsening of fibrosis.
Resolution of NASH without worsening of fibrosis
TABLE 1 - Overall analysis of primary and secondary outcomes
| | No. of studies | Sample size | Effect size | 95% CI | I 2 | | | -------------------------------------------------------- | ----------- | ----------- | ------- | ----------------- | ----- | | Primary outcomes | | | | | | | Resolution of NASH without worsening of fibrosis | 14 | 1066 | 11.650 | 7.976 to 16.709 | 67.4% | | Two‐point reduction in NAS without worsening of fibrosis | 22 | 1287 | 21.108 | 17.242 to 25.574 | 62.9% | | At least one‐point reduction in fibrosis | 29 | 1522 | 18.824 | 15.650 to 22.470 | 53.5% | | At least one‐point advancement in fibrosis | 14 | 965 | 22.735 | 19.632 to 26.168 | 17.4% | | Secondary outcomes | | | | | | | Histological outcomes | | | | | | | At least one‐point reduction in steatosis | 20 | 1037 | 29.272 | 23.728 to 35.509 | 64.0% | | At least one‐point reduction in ballooning | 20 | 1075 | 25.519 | 21.418 to 30.104 | 47.7% | | At least one‐point reduction in inflammation | 20 | 1069 | 31.939 | 26.981 to 37.342 | 54.5% | | At least one‐point advancement in steatosis | 8 | 339 | 9.845 | 5.454 to 17.133 | 68.7% | | At least one‐point advancement in ballooning | 6 | 291 | 16.144 | 10.464 to 24.077 | 58.9% | | At least one‐point advancement in inflammation | 7 | 321 | 17.757 | 13.954 to 22.328 | 0.00% | | Mean NAS reduction | 26 | 1031 | −0.334 | −0.630 to 0.039 | 92.7% | | Other outcomes | | | | | | | Change in AST | 35 | 1986 | −6.333 | −8.108 to −4.559 | 81.9% | | Change in ALT | 38 | 2055 | −10.595 | −13.327 to −7.864 | 80.9% | | Change in HDL | 22 | 1547 | −0.009 | −0.032 to 0.014 | 89.1% | | Change in LDL | 26 | 1635 | −0.182 | −0.278 to −0.085 | 95.1% | | Change in TG | 25 | 1553 | −0.115 | −0.208 to −0.021 | 90.6% | | Change in BMI | 22 | 794 | −0.212 | −0.414 to −0.009 | 62.7% |
Abbreviation: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; NAS, NASH activity score; and TG, triglyceride.
TABLE 2 - Subgroup analysis of resolution of NASH without worsening of fibrosis and two‐point reduction in NAS without worsening of fibrosis
| | n | Sample size | Effect size | 95% CI | I 2 | Subgroup difference | n | Sample size | Effect size | 95% CI | I 2 | Subgroup difference | | | ---------------------------------------------------- | ------------------------------------------------------------ | ----------- | ------ | ---------------- | ------------------- | ----- | ----------- | ----------- | ------ | ---------------- | ------------------- | ---------- | | Resolution of NASH without worsening of fibrosis | Two‐point reduction in NAS without worsening of fibrosis | | | | | | | | | | | | | Region | | | | | | | | | | | | | | Multinational | 7 | 844 | 10.078 | 5.094 to 18.964 | 80.80% | 0.404 | 8 | 743 | 22.243 | 15.144 to 31.436 | 82.80% | 0.72 | | North America | 5 | 164 | 17.073 | 12.055 to 23.619 | 0.00% | | 12 | 485 | 21.443 | 18.016 to 25.321 | 0.00% | | | Europe | 1 | 22 | 9.091 | 2.284 to 29.964 | | | — | — | — | — | — | | | Asia | 1 | 36 | 11.111 | 4.234 to 26.115 | | | 2 | 59 | 16.976 | 9.146 to 29.344 | 53.00% | | | Center | | | | | | | | | | | | | | Multicenter | 10 | 934 | 10.255 | 6.421 to 15.987 | 71.40% | 0.054 | 14 | 293 | 20.654 | 14.757 to 28.129 | 70.20% | 0.882 | | Single center | 4 | 132 | 18.182 | 12.495 to 25.697 | 0.00% | | 8 | 994 | 21.295 | 16.546 to 26.968 | 44.60% | | | Duration | | | | | | | | | | | | | | ≤6 months | 4 | 76 | 17.153 | 7.464 to 34.700 | 58.40% | 0.343 | 6 | 108 | 18.13 | 10.511 to 29.454 | 39.40% | 0.737 | | 6–12 months | 6 | 496 | 8.322 | 4.116 to 16.106 | 70.30% | | 8 | 370 | 22.555 | 18.159 to 27.655 | 17.70% | | | ≥12 months | 4 | 494 | 13.592 | 8.622 to 20.775 | 77.40% | | 8 | 809 | 23.857 | 21.045 to 26.916 | 82.00% | | | Route of administration | | | | | | | | | | | | | | Oral | 10 | 959 | 11.289 | 7.018 to 17.665 | 75.10% | 0.52 | 18 | 1119 | 22.159 | 17.876 to 27.131 | 64.60% | 0.182 | | Intravenous | 4 | 107 | 14.019 | 8.631 to 21.960 | 7.40% | | 4 | 168 | 16.467 | 10.993 to 23.935 | 36.30% | | | Dosing frequency | | | | | | | | | | | | | | 1 | 6 | 431 | 10.087 | 3.648 to 24.951 | 79.70% | 0.21 | 11 | 768 | 19.691 | 16.032 to 23.947 | 26.30% | 0.37 | | 2 | 2 | 125 | 19.2 | 13.214 to 27.052 | 17.60% | | 2 | 125 | 33.68 | 15.883 to 57.732 | 0.00% | | | 3 | — | — | — | — | — | | 3 | 118 | 21.186 | 14.738 to 29.480 | 87.40% | | | Risk of bias | | | | | | | | | | | | | | Low | 8 | 894 | 11.302 | 7.463 to 16.758 | 76.00% | 0.892 | 9 | 768 | 26.452 | 20.537 to 33.355 | 71.80% | 0.010a | | Moderate | 6 | 172 | 12.094 | 4.765 to 27.445 | 31.00% | | 13 | 519 | 17.238 | 13.960 to 21.098 | 19.90% | |
aBolded p value < 0.05 denotes statistical significance.
At least one‐point reduction and progression in fibrosis
Of the 1522 patients, 18.82% (CI: 15.65%–22.47%) of patients achieved a one‐point reduction in fibrosis. Similarly, most factors from a subgroup analysis and meta regression did not influence the rate of fibrosis reduction (Figure 3). However, participants of African American ethnicity were found to have a trend toward decreased rate of one‐point reduction in fibrosis (β = −0.262; CI: −0.536 to 0.0131; p = 0.062) (Supporting Information S3). A total of 22.74% (CI: 19.63–26.17) in placebo experienced at least a one‐point progression of fibrosis. There were no statistically significant factors affecting one‐point progression of fibrosis, except studies with moderate risk of bias had higher progression rates compared with low risk of bias (Table 3).
Placebo effects on at least one‐point reduction and progression in fibrosis at ≤6 months, 6–12 months, and ≥12 months
TABLE 3 - Subgroup analysis of one‐point reduction and progression in fibrosis
| | n | Sample size | Effect size | 95% CI | I 2 | Subgroup difference | n | Sample size | Effect size | 95% CI | I 2 | Subgroup difference | | | ----------------------------------- | ------------------------------------- | ----------- | ------ | ---------------- | ------------------- | ----- | ----------- | ----------- | ------ | ---------------- | ------------------- | ---------- | | One‐point reduction in fibrosis | One‐point progression in fibrosis | | | | | | | | | | | | | Region | | | | | | | | | | | | | | Multinational | 9 | 872 | 16.58 | 13.077 to 20.796 | 56.90% | 0.596 | 6 | 736 | 21.197 | 17.801 to 25.045 | 36.00% | 0.093 | | North America | 13 | 459 | 21.548 | 15.516 to 29.117 | 30.80% | | 4 | 115 | 29.565 | 21.950 to 38.519 | 0.00% | | | Europe | 5 | 132 | 18.939 | 13.132 to 26.531 | 0.00% | | 3 | 78 | 19.231 | 11.939 to 29.484 | 0.00% | | | Asia | 2 | 59 | 14.281 | 3.849 to 40.945 | 81.80% | | 1 | 36 | 33.333 | 20.003 to 49.995 | — | | | Center | | | | | | | | | | | | | | Multicenter | 20 | 1229 | 18.791 | 14.726 to 23.667 | 67.10% | 0.681 | 9 | 767 | 21.392 | 18.247 to 24.914 | 17.00% | 0.090 | | Single center | 9 | 293 | 20.137 | 15.932 to 25.119 | 0.00% | | 5 | 198 | 27.273 | 21.525 to 33.893 | 0.00% | | | Duration | | | | | | | | | | | | | | ≤6 months | 5 | 92 | 17.32 | 7.944 to 33.711 | 43.00% | 0.981 | 1 | 36 | 33.333 | 20.003 to 49.995 | — | 0.325 | | 6–12 months | 14 | 570 | 18.627 | 13.539 to 25.071 | 53.20% | | 7 | 335 | 22.206 | 16.671 to 28.941 | 33.00% | | | ≥12 months | 10 | 860 | 18.698 | 14.837 to23.289 | 56.90% | | 6 | 594 | 22.559 | 19.376 to 26.096 | 0.00% | | | Route of administration | | | | | | | | | | | | | | Oral | 25 | 1351 | 19.709 | 16.325 to 23.596 | 54.60% | 0.215 | 13 | 896 | 22.867 | 19.512 to 26.609 | 23.00% | 0.833 | | Intravenous | 4 | 171 | 13.266 | 6.980 to 23.767 | 50.50% | | 1 | 69 | 21.739 | 13.552 to 32.984 | — | | | Dosing frequency | | | | | | | | | | | | | | 1 | 15 | 999 | 19.892 | 15.412 to 25.284 | 70.50% | 0.287 | 9 | 748 | 22.876 | 18.974 to 27.310 | 40.00% | 0.374 | | 2 | 5 | 194 | 22.165 | 16.870 to 28.551 | 0.00% | | 2 | 124 | 20.161 | 14.004 to 28.140 | 0.00% | | | 3 | 3 | 50 | 12 | 5.492 to 24.242 | 0.00% | | 2 | 24 | 33.333 | 17.627 to 53.881 | 0.00% | | | Risk of bias | | | | | | | | | | | | | | Low | 12 | 988 | 21.181 | 16.643 to 26.563 | 72.20% | 0.179 | 6 | 643 | 20.571 | 17.411 to 24.135 | 24.00% | 0.048a | | Moderate | 17 | 534 | 16.463 | 12.297 to 21.692 | 22.80% | | 8 | 322 | 26.398 | 21.871 to 31.483 | 0.00% | |
aBolded p value < 0.05 denotes statistical significance.
Secondary outcomes
At least one‐point reduction in steatosis, ballooning, and lobar inflammation
The one‐point reduction of steatosis, ballooning, and lobar inflammation was found in 29.28% (CI: 23.73%–35.51%), 25.52% (CI: 21.42%–30.10%), and 31.94% (CI: 26.98%–37.34%). Similar to the primary outcomes, most factors including but not limited to the study duration, route of administration, and baseline characteristics did not influence the rate of one‐point reduction (Supporting Information S4; Figure 4). The summary of results of a meta regression analysis is detailed in Supporting Information S5. In the analysis of steatosis, however, articles with a lower risk of bias resulted in a higher rate of one‐point reduction in steatosis compared with articles that were of moderate risk of bias (35.60%, CI: 28.30%–43.63% vs. 23.16%; CI: 17.30%–30.30%, respectively; p = 0.017). Patients with hypertension were also less likely to achieve a one‐point reduction of steatosis (β = −0.738; CI: −1.20 to −0.280; p = 0.002), while Caucasians were associated with an increased rate of reduced steatosis (β = 0.827; CI: 0.468–1.19; p < 0.001). In the assessment of ballooning, studies conducted in North America were associated with the highest percentage of one‐point reduction in ballooning (31.18%; CI: 25.74%–37.19%). In the assessment of lobular inflammation, multicenter studies were associated with a larger percentage of one‐point reduction compared with single‐center studies (35.43%, CI: 31.27%–39.82% vs. 21.08%, CI: 13.83%–30.77%, respectively; p = 0.009). Ethnicity did not influence the rate of reduction in ballooning and lobar inflammation (Supporting Information S5).
Placebo effects on at least one‐point progression in fibrosis, ballooning, steatosis, and lobar inflammation at ≤6 months, 6–12 months, and ≥12 months
At least one‐point progression in steatosis, ballooning, and lobar inflammation
The rate of one‐point progression in steatosis, ballooning, and lobar inflammation was 9.85% (CI: 5.45%–17.13%), 16.14% (CI: 10.46%–24.08%), and 17.76% (CI: 13.95%–22.33%), respectively.
Change in NAS
The pool mean reduction in NAS was −0.334 (CI: −0.630 to 0.039) in 1031 patients. Interestingly, duration of study affects the mean change in NAS (Supporting Information S6; p = 0.043). The average reduction of NAS in studies ≤6 months was −0.126 (CI: −0.318 to 0.067), −0.630 (CI: −0.988 to −0.272) in studies 6–12 months, and increased in studies with ≥12 months (0.012; CI: −0.781 to 0.804). In meta regression analysis, an older age resulted in an increase in mean NAS (Supporting Information S7; β = 0.101;, CI: 0.033–0.170; p = 0.005).
Change in BMI and liver enzymes
The average reduction of BMI was a mean difference (MD) of −0.21 (CI: −0.41 to −0.01), and the mean reduction in AST and ALT was a MD of −6.33 (CI: −8.12 to −4.56) and a MD of −10.60 (CI: −13.33 to −7.86) with placebo. Supporting Informations S8 and S9 details the subgroup analysis and meta regression for BMI and liver enzymes. Geographical region influenced the average reduction in BMI, with the largest reduction found in Asia (MD: −0.84; CI: −1.06 to −0.62) and the smallest reduction in North America (MD: −0.07; CI: 0.16–0.03; subgroup difference: p < 0.001). Studies with study duration of less than 6 months were associated with the greatest reduction in BMI (MD: −0.58; CI: −0.92 to −0.23) compared with studies between 6 to 12 months and those exceeding 12 months (p = 0.02) (Figure 5). Male patients with NASH were also associated with an increased rate of reduction in BMI (β = 0.211; CI: 0.064–0.359; p = 0.005), while a history of hypertension was associated with a decreased odd of BMI reduction (β = −0.428; CI: 0.643–0.214; p < 0.001). The reduction in AST was associated with dosing frequency in which a dosing strategy of three times daily resulted in the largest reduction in AST (MD: −11.54; CI: −14.60 to −8.49). In ALT, age was associated with an increased rate of ALT reduction (β = 1.02; CI: 0.530–1.15; p < 0.001), whereas diabetes was associated with a decreased odd of reduction in ALT (β = −2.72; CI: −5.31 to −0.139; p = 0.039).
Effect of placebo on liver enzymes and lipids profile at ≤6 months, 6–12 months, and ≥12 months
Change in lipids
Pooled mean reduction of LDL, HDL, and TG was MD of −0.18 (CI: −0.28 to −0.09), MD of −0.009 (CI: −0.032 to 0.01), and MD of −0.11 (CI: −0.21 to −0.02). A visual representation of the subgroup analysis by duration of study can be found in Figure 5. Study duration affects the reduction in LDL (e.g., studies ≥12 months had the largest reduction in LDL) (Supporting Information S10). The region of study and dosing frequency also affect the magnitude of LDL reduction (Supporting Information S10). Subgroup analysis, however, did not find any significant factors affecting HDL and TG. A summary of results from the meta regression can be found in Supporting Information S11.
DISCUSSION
The meta‐analysis provides the most comprehensive up‐to‐date literature on the histological outcomes in the placebo arms of NASH RCTs. Although previous analyses have examined the change in histology, AST and ALT,[13,72] the current analysis builds on previous studies and examines the natural history of the disease in terms of clinical histological endpoints, anthropometric, lipids, and liver enzymes. Significantly, an estimated one‐tenth of participants (11.65%) had NASH resolution without worsening of fibrosis, while one‐fifth had a two‐point reduction in NAS without worsening of fibrosis or one‐point reduction in fibrosis that were independent of reduction in BMI. However, similar to the rates of reduction in fibrosis, 22.74% (CI: 19.63–26.17) of patients with NASH had an advancement in fibrosis, and 9.85% (CI: 5.45%–17.13%) had progression in steatosis. Additionally, our meta‐analysis found that patients in studies conducted for ≤6 months had the largest reduction in BMI when compared with studies conducted for >12 months (MD: −0.578, CI: −0.922 to −0.234 vs. MD: −0.157, CI: −0.449 to 0.136; p = 0.02). The converse was true for LDL reduction, in which a longer duration of study resulted in the largest reduction of LDL. The findings mirror the current understanding in weight‐loss intervention sustainability, in which patients who lose weight with lifestyle interventions have been found to regain the weight eventually.[73] However, the larger reduction in BMI in shorter follow‐up could be a potential explanation in the lack of translatability between phase 2 and 3 trials, in which follow‐up durations are generally longer in the latter.[74–76]
Recent evidence has emerged to suggest differences in gender and the prognosis NASH.[77–79] In our analysis, gender did not appear to play a significant role in the attenuation of response, aside from the decrease in BMI. Although NASH is less prevalent in African Americans patients,[80,81] African Americans tend to have lower rates of resolution in NASH and one‐point reduction in fibrosis, and other ethnicities were not examined due to the sparsity in reporting. The differences in outcomes could be the result of differences in metabolic risk factors such as diabetes and obesity, which are significantly more common in African Americans[82,83] and may contribute to poorer disease prognosis. Additionally, while some disparities are driven by nonmodifiable genetic differences,[84] the downstream effects of potential racial and socioeconomic inequities may have significant implications on NASH outcomes among Black minority patients, and therefore warrants further investigation.
The resolution and progression of fibrosis in NASH affected an estimated one‐fifth of the population, and duration of follow‐up was a significant factor in the predictor of mean NAS change. The results could allude to the possibility that NASH is a dynamic disease that progresses and regresses with time, and the confluence of changes in the metabolic milieu can affect the state of the disease. As learned from studies conducted in viral hepatitis, liver fibrosis, which was traditionally thought to be irreversible, can be reversed once the underlying trigger of the disease has been removed.[85] Further studies have proposed that the liver is in a constant state[86] between “pro‐injury” and “anti‐injury,” and we hypothesize that the same might be observed in NASH, where a significant proportion of patients can have regression of disease. However, the interpretation of the results is subject to confounding from pathological interpretation, and the approximately similar rates of apparent fibrosis progression and fibrosis regression may truly just represent a variability in histologic assessment. The modest change in BMI in the study is also unlikely to explain a full reversal of disease, and the more studies are required to examine the exact pathogenesis of NASH.
Fundamentally, however, NASH is a metabolic disease, and alterations in diet and exercise can significantly influence the course of disease.[7,87] It is also possible that patients selected for RCTs fair better by virtue of trial participation due to the Hawthorne effect, in which the awareness of being observed and social desirability considerations may lead to a positive change in behavior.[88,89] Additionally, it is also possible that patients who receive the intensive follow‐up in clinical trials are likely to be more adherent to the adjunct therapy of NASH, including exercise and dietary changes. If the latter is true, there are implications on the current design of RCTs.[90] Current trials are often conducted in conjunction with lifestyle advice for both treatment arms. Adherence to these factors can attenuate the response and should therefore be measured objectively. Exercise is a known factor that affects hepatic steatosis and reduces systemic inflammation,[91] and diet modification can modulate liver fat with reduction in transaminitis.[92] Future studies can consider a standardized guide on dietary patterns, and physical activity to be used for standardization across all trials.[93]
Limitations
The current study details the most comprehensive review of placebo effect in NASH. However, there are several limitations. We were unable to assess progression of NASH, development of clinical endpoints, decompensation, and mortality in the absence of reporting in primary articles. Despite including only RCTs, there can also be variabilities in the interpretation of liver biopsy and definitions between studies,[94–96] which remains a challenge in NASH. Additionally, this study seeks to examine the nature history of NASH using the placebo‐controlled group from RCTs. Liver biopsy remains the most accurate measure of liver fibrosis, and we did not assess the variability of histology with other noninvasive tests, as this was beyond the objective of the study. There was also a moderate degree of statistical heterogeneity in most outcomes. Additionally, there were insufficient studies to examine the factors causing the progression of histology due to the smaller numbers involved. Finally, there was inherent variability in patient counseling, which may contribute to heterogeneity between articles.
Conclusions
This meta‐analysis summarizes the current literature on the natural history of patients with NASH not on any pharmacological treatments. Significantly, resolution of NASH with no worsening of fibrosis can occur in more than a tenth of patients, whereas both one‐point fibrosis reduction and two‐point NAS reduction with no worsening of fibrosis occurred in approximately a fifth of patients despite no pharmacological interventions. While the resolution of NASH can be the result of several factors, the meta‐analysis highlights the possibility that NASH might be dynamic with the potential to regress and progress with time. Future studies should assess the impact of ethnicity and racial disparities on variability in response, which may potentially affect the care of patients with NASH of various ethnicities.
CONFLICT OF INTEREST
Dr. Huang consults for Eisai. Dr. Noureddin advises and received grants from Gilead. He received grants and owns stock in Viking. He advises 89Bio, Intercept, Pfizer, Novo Nordisk, Blade, Echosens, Fractyl, Terns, Siemens, and Roche. He received grants from Allergan, Bristol‐Myers Squibb, Galmed, Galectin, Genfit, Conatus, Enanta, Madrigal, Novartis, Shire, and Zydus. He owns stock in Anaetos and Rivus. Dr. Sanyal consults and received grants from Conatus, Gilead, Mallinckrodt, Immuron, Boehringer Ingelheim, Novartis, Bristol‐Myers Squibb, Merck, Eli Lilly, Novo Nordisk, Fractyl, Siemens, Madrigal, Inventiva, and Covance. He consults and owns stock in Genfit. He consults for Intercept, Pfizer, Salix, Galectin, Hemoshear, Terns, Albireo, Sanofi, Janssen, Takeda, Northsea, AMRA, Perspectum, Poxel, 89 Bio, AstraZeneca, NGM, Amgen, Regeneron, Genentech, Roche, Prosciento, Histoindex, Path AI, and Biocellvia. He received grants from Echosens‐Sandhill, Owl, and Second Genome. He owns stock in Exhalenz, Sanyal Bio, Durect, Indalo, Tiziana, and Rivus. He received royalties from Elsevier and UptoDate. Dr. Loomba consults and received grants from AstraZeneca, Bristol‐Myers Squibb, Eli Lilly, Galmed, Gilead, Intercept, Inventiva, Janssen, Madrigal, Merck, NGM, and Pfizer. He consults for Aardvark, Altimmune, Alnylam/Regeneron, Amgen, Arrowhead, CohBar, Glympse, High Tide, Inipharm, Ionis, Metacrine, Novartis, Novo Nordisk, Sagimet, Theratechnologies, 89 Bio, and Viking. He received grants from Allergan, Boehringer Ingelheim, Galectin, Genfit, and Sonic Incytes.
AUTHOR CONTRIBUTIONS
Study concept: Nobuharu Tamaki, Mohammad Shadab Siddiqui, Arun J Sanyal, Rohit Loomba, Mark Dhinesh Muthiah, Mazen Noureddin, and Cheng Han Ng. Data curation: Cheng Han Ng, Jieling Xiao, Wen Hui Lim, Yip Han Chin, Jie Ning Yong, Darren Jun Hao Tan, and Phoebe Tay. Formal analysis: Cheng Han Ng, Jieling Xiao, Wen Hui Lim, and Yip Han Chin. Study supervision: Nicholas Syn, Roger Foo, Mark Chan, Nicholas Chew, Eunice XX Tan, Daniel Q Huang, Yock Young Dan, Nobuharu Tamaki, Mohammad Shadab Siddiqui, Arun J Sanyal, Rohit Loomba, Mark Dhinesh Muthiah, and Mazen Noureddin. Validation: Jie Ning Yong, Darren Jun Hao Tan, and Phoebe Tay. Manuscript draft: Cheng Han Ng, Jieling Xiao, Wen Hui Lim, and Yip Han Chin. Manuscript writing, review, and editing: Cheng Han Ng, Jieling Xiao, Wen Hui Lim, Yip Han Chin, Jie Ning Yong, Darren Jun Hao Tan, Phoebe Tay, Nicholas Syn, Roger Foo, Mark Chan, Nicholas Chew, Eunice XX Tan, Daniel Q Huang, Yock Young Dan, Nobuharu Tamaki, Mohammad Shadab Siddiqui, Arun J Sanyal, Rohit Loomba, Mark Dhinesh Muthiah, and Mazen Noureddin. All authors have read and approved the final version of the manuscript for submission.
REGISTRATION AND PROTOCOL
Study was not registered.
DATA AVAILABILITY STATEMENT
All articles in this manuscript are available from Medline and Embase.
ACKNOWLEDGMENT
All authors have made substantial contributions to the following: (1) the conception and design of the study, acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content; (3) final approval of the submitted version. No writing assistance was obtained in the preparation of the manuscript. The manuscript, including related data, figures and tables, has not been previously published, and is not under consideration elsewhere.
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