The full set of directed two-node motifs is represented for symmetry reasons.

It is tempting to compare brain modes with brain motifs (particularly for those depicted above involving two brain regions) and search for common features that could substantiate the former as being the same as the latter.

In the 'unicity' mode, an independent (non-interacting) node A should be the unique contributor to a brain function Fn.

In the 'equivalence' mode, both nodes A and B should contribute to a brain function Fn without redundancy, for instance, by A contributing to Fn serially through B. In the 'association' mode, both nodes A and B should contribute in parallel to brain function Fn.

Finally, in the 'summation' mode, both nodes should also contribute in parallel to a brain function Fn.

Additionally, however, they should interact, redundantly or synergistically.

In any case, the mapping between these two notions, brain modes and brain motifs, is not always straightforward and some of the brain motifs presented in this figure and the many that could be theoretically conceived with more than two nodes, cannot be unequivocally characterized in terms of a single 'brain mode', especially if one aims to take into account the number and type of functional interactions between the nodes.

A possibility to reveal the rich set of functional interactions of some brain motifs consist in removing connections between nodes.

In particular, this is possible in computationally implemented 'ground-truth' models, by systematically exploring the parametric space of possibilities, and 'virtually' damaging nodes and links in all possible ways to then use such evidence to infer the network structure from associated changes in brain function.

However, it is more difficult to infer all these different 'motifs' directly from changes in brain function after perturbing different nodes and/or the links between them.

( B) Algorithmic summary of brain motifs and modes.

Summary of the minimal circuit interactions of two brain nodes A and B potentially contributing to a brain function Fn.


The equation formalizes the strength of the interactions and functional contributions of the nodes, and appropriate settings of the path coefficients might capture all brain modes and motifs displayed in (B).

Specifically, setting all paths except k AF to zero corresponds to unicity; setting k AB and k BA to zero correspond to association; while leaving all coefficients non-zero can yield, with the right parameter settings, equivalence, summation or masked summation.

modes while helping to describe new typologies (see Table 1 showing applications of a multivariate method to explore brain-behaviour relationships and Box 2 for critical issues concerning formalization of lesion parameters in multivariate modelling) (Bzdok et al., 2017; Price et al., 2017; Xu et al., 2018).

Moreover, the use of brain modes as sets of elementary types of associations on brain-func- tion relationships would constrain multivariate approaches increasing their ability to deliver reliable outcomes.

However, promoting a rigorous use of multivariate meth- ods for such endeavours requires raising awareness of some critical issues impacting on their practical implementation.


First, defining the 'hypothesis space' is an essential step for making accurate inferences and predictions of brain-be- haviour relationships in the context of complex models such as those introduced by multivariate methods.

Recently published attempts have used hypothesis-based approaches pre-selecting a few sets of regions, composing a coalition of contributors, which can then be tested to- gether as part of the same model (Toba et al., 2017).

Nonetheless, more advanced methodological avenues use bias-free identification strategies, that can test sets of re- gional contributors, grouped as part of specific coalitions, without a priori assumptions (Zavaglia et al., 2019).


Considering the large spectrum of multivariate methods, Bayesian approaches based on a priori defined hypotheses have been useful to narrow down the number of relevant brain sites included in multivariate models (Chen and Herskovits, 2010; Duering et al., 2013, 2014; Arnoux et al, 2018).

Nonetheless, hypothesis-free identification meth- ods based on machine learning algorithms play a major role in formal hypothesis and model generation (Xu et al., 2018).

For example, in the equivalence brain mode, when multiple lesion sites cause the same impairment, ma- chine learning tools can generate predictive multivariate models, incorporating simultaneously information from multiple voxels without the need of a priori hypotheses (Hope et al., 2013; Smith et al., 2013; Price et al., 2017).

Interestingly, this same approach can be used to identify key variables and generate predictive models, con- tributing to the identification and classification of new brain modes.


Moreover, by integrating structural/functional neuroimaging, neurophysiological recordings and non-inva- sive brain stimulation, multivariate machine learning tools will reach full potential and drive a better understanding of functional anatomy (Price et al., 2017).

Nonetheless, these approaches are also costly in terms of computational time and need of independent training datasets.

Moreover, ma- chine learning algorithms operating as 'black boxes' often run into serious risks of inferring irrelevant relations due to overfitting.

Last but not least, to date, even the most sophisticated multivariate machine learning lesion analysis methods are unable to disambiguate the independent effects of different lesion sites in absence of patient datasets show- ing such dissociations.


Second, artificial ground-truth models (Mah et al., 2014) may help overcome some of the former limitations, and may become the 'gold standard' method to characterize and validate past and future brain mode typologies.

Indeed, the original conceptualization of brain modes was tested via simulated populations of patients, and later con- firmed in datasets from real-life stroke patients with motor disabilities.


Different ground truth models able to manipu- late the variability of anatomical patterns have been pro- posed (Inoue et al., 2014; Mah et al., 2014; Zhang et al., 2014; Sperber and Karnath, 2017; Pustina et al., 2018).

In particular, computationally implemented ground-truth syn- thetic models have the potential to reveal complex func- tional interactions between brain regions and explore the parametric space of possibilities by removing, with graded levels of severity in all possible ways, 'virtual' nodes and/or connections between sets of chosen regions of interest.


Such evidence can then be used to infer network structure from associated changes in brain function.

Last but not least, these same methods can prove useful for lesion-deficit ana- tomical mapping and ultimately for clinical predictions.


Ponder carefully the risk of characterizing regions of interest as being either totally lesioned or completely spared by using fixed binarized damage thresholds.

Employ a conservative lesion parametrization strategy, particu- larly when operating at the voxel level; this is a particularly sensitive issue for perilesional areas in the absence of func- tional MRI data informing on tissue viability.

Implement whenever possible a transnosological approach, which compares complementary lesion datasets from distinct neurological conditions characterized by different symptoms.

Prioritize the use of continuous over discrete or binarized variables when estimating the magnitude of behavioural per- formance or quantifying the severity of clinical scores.

Consider two potential scenarios with regards to the localiza- tion of


Two decades after its first theorization in 1998, the concept of brain modes remains a guiding framework in which to better characterize brain-behaviour associations and study their implementation in plausible structural and functional networks.


Significant conceptual and methodological ad- vances in multivariate analysis place the systems neurosci- ence community in an excellent position to reframe the inventory of the brain modes and critically evaluate the exhaustiveness of these relationships to define a compre- hensive set of brain-behaviour associations.


In particular, current brain mode typologies could be refined by compar- ing predictions made by computational lesion studies im- plementing a theoretical model (based on simulations of reliable structural and functional interactions among brain regions) with empirical results from brain lesion datasets.


Moreover, as we look to the future, we can anticipate the need to apply brain modes to personalized clinical rehabili- tation programmes (Nachev et al., 2018).


More specifically, data on brain modes hold the potential to inform compu- tational modelling and web-based platforms able to simu- late large-scale networks using biologically plausible structural and functional connectivities (Sanz Leon et al., 2013).


This type of environment (for example in The Virtual Brain; thevirtualbrain.org) allows the construction of personalized configurations of structural and functional systems, and the study of brain dynamics under the influ- ence of focal and network perturbations or disorders, simu- lating, for example, epilepsy or the cognitive consequences of stroke (Falcon et al., 2015).


They may provide predictive tools to investigate the impact of brain structural alter- ations on neural dynamics (Alstott et al., 2009; Aerts et al., 2016), study the remapping of function-specific brain modes and contribute to the planning of better strategies for cognitive rehabilitation or neurosurgical interventions.


We look forward to the realization of the potential of the brain modes approach over the next 20 years.


For over 150 years, neuroscientists have evaluated the behavioural effects of brain damage to understand the anatomical representation of cognitive functions in the human brain.

Early localizationist theories assumed lesion-behaviour relations in which essentially one region (e.g. primary visual cortex) codes for one behaviour (vision).

Meanwhile, the view prevails that many cognitive functions are organized in anatomically distributed networks and that brain areas inside such networks can interact in different ways.

Godefroy et al. ( 1998) systematically categorized classes of associations between brain structures inside a network and cognitive functions.

They suggested four possible interregional interaction modes.

The recent paper of Toba et al. ( 2019) in Brain added a fifth possible relationship, termed ‘mutual inhibition’.

Paired brain regions that behave according to this latter mode inhibit each other.

Therefore, if one of the brain regions is inactivated because of damage, the other area becomes disinhibited, which induces a cognitive deficit.

The striking aspect about this interaction mode is that a cognitive deficit might recede back to non-pathological functioning if the brain is affected by even more damage.


More exactly, if the second, disinhibited region also becomes damaged, normal cognitive functioning is supposed to be restored.

Toba et al. ( 2019) based this mutual inhibition mode on findings from the animal literature as well as on more recent findings in neurological patients with brain damage.

However, caution is advised with respect to the latter, i.e. the ‘evidence’ mentioned for mutual inhibition in humans.

Because of the brain’s vascular system, the anatomy of brain lesions is highly systematic; accordingly, the damage status between brain areas is dependent [3].

Because of this dependence, post-stroke symptoms are to some degree associated with most or even all areas in the brain.

These associations can both be positive or negative.

This is even the case if only damage to a small, confined area with only a non-paradoxical positive lesion-deficit relation is the actual cause of a symptom.

Only true ‘causal inference’ of lesion-brain relations could provide entirely unbiased lesion-brain inference [8].

However, common inference methods in lesion behaviour mapping—both mass-univariate and multivariate methods—are blind to the causality behind the data.


Therefore, these methods might also, to a small extent, associate a deficit with regions where damage is not causally related to the deficit; they might wrongfully identify paradoxical lesion effects.

A simple and efficient way to illustrate this issue is by using simulation studies.

Simulations allow one to validate lesion-behaviour inference by a priori defining in which way brain damage shall induce a symptom.

For example, one might decide to only include non-paradoxical positive lesion-brain relations.

We performed such a simulation and intended to simulate cognitive functions following the four classical brain modes according to Godefroy et al. ( 1998).

Importantly, all brain-deficit relations were non-paradoxical.

Yet, surprisingly, we found a considerable amount of paradoxical, negative signal in the data in all analyses using multivariate lesion-behaviour mapping (Fig. 1).

The multivariate analyses in the simulation examples identified simple paradoxical brain-deficit relations, but it did only consider direct lesion-behaviour relations, not the relations between interregional interactions and a deficit.

Could artefacts also arise on this more complex level? It seems so.

Assuming a systematic anatomy of lesions, situations are imaginable where a putative inhibitory inter-regional effect can be found in the data, while in fact such effect does not exist.


Figure 2 illustrates such an example, where only a simple single-region brain-deficit relation underlies a deficit.

However, the data can also be perfectly explained by assuming interregional inhibition between two areas.

To conclude, hypothesis-free interpretation of lesion-behaviour data can be misleading, and it might wrongfully identify paradoxical lesion effects.

Still, as Toba et al. ( 2019) pointed out, multivariate lesion-behaviour modelling can be a powerful method to identify paradoxical lesion effects.

However, this requires careful fine-tuning.

This can be achieved by simulation-based evaluation of lesion-brain inference methods, which can also test a method’s sensitivity and specificity in identifying paradoxical lesion effects.

Toba et al. ( 2019) highlight the benefits of multi-perturbation Shapley value analysis (MSA) in identifying the five different brain modes, including paradoxical facilitation in ‘mutual inhibition’ between areas.

However, as this method still remains to be validated for this use, to date, paradoxical findings should be interpreted with caution, and only in combination with a strong theoretical background [8].


In contrast to its use in anatomo-functional mapping, multivariate methods—including MSA—provide an undisputedly powerful tool in prediction.

When predicting long-term post-stroke outcome to guide rehabilitation and patient care, the primary aim is to maximize prediction performance.

For this aim, we do not need to care if multivariate algorithms identify the actual anatomical cause of post-stroke behavioural changes.

If we want to predict weather, the saying ‘When swallows fly high, the weather will be dry’ can be helpful, and from a purely use-oriented perspective there is no need to ask if it is the birds that cause rain.

Likewise, we might accept that multivariate algorithms to some extent use features that do not represent the true neural correlates of a deficit, as long as the association of feature and predicted variable is found across the whole population, and therefore predictive of the outcome.

A powerful advantage of MSA may now arise if there are indeed interregional interactions corresponding to brain modes to be found in lesion-behaviour data.

If such brain modes are predictive of stroke outcome, MSA might outperform any competing multivariate algorithms that are incapable of representing brain modes.

Going further, one might ask if the signal captured by MSA qualitatively differs from the signal captured by other prediction algorithms [7][9][5].

If this is the case, qualitatively complementary algorithms can be combined to surpass the performance of each individual prediction algorithm alone [6].

However, to investigate such purposes, publicly available MSA software is required.

Moreover, predictions might benefit from a focus on interregional interactions beyond only two brain regions [1][8], up to full multi-region networks that might underlie some cognitive functions.


Sir,


We thank Sperber and Karnath (2020) for their stimulating comments in response to our recent article (Toba et al., 2019) in which we reappraised and extended the notion of ‘brain modes’ proposed by Godefroy et al. ( 1998) two decades ago.

The authors ask two fundamental and important questions: do inhibitory interactions in the human brain really exist, and can such interactions be reliably revealed with the help of multivariate lesion inferences?

Here, we answer both questions strongly in the affirmative.


First, do inhibitory interactions exist in the human brain?

Strong evidence for such interactions comes from so-called ‘paradoxical’ lesion effects (Kapur, 1996, 2011), which are classically perceived of as unexpected behavioural improvements caused by focal lesions.

However, such ‘paradoxical’ effects, while initially appearing surprising and counter-intuitive, are in fact the predictable result of inhibitory interactions (single or multiple) in the brain, such as those driven by the fibres of the corpus callosum or the midbrain’s intercollicular commissure, or by multiple inhibitory interactions among basal ganglia regions (see Valero-Cabre et al., 2020, for review).


Thus, such interactions are an essential aspect of the fifth brain mode (Toba et al., 2019), and may reveal inhibitory contributions of a single brain region or several regions in the emergence of a brain function, evaluated via behavioural performance measures (Toba et al., 2017, 2020).

While classical studies conducted in the cat particularly argue in favour of inhibitory interactions driven by the midbrain’s collicular commissure (Sprague, 1966; Payne and Rushmore, 2004), single case studies (Vuilleumier et al., 1996; Weddell, 2004) and experimental neuromodulation experiments (Nyffeler et al., 2019, to cite a recent example) also attest in favour of the biological plausibility of inhibitory interactions in human brain systems, particularly those linking homotopic regions of both hemispheres via transcallosal commissural projections (for a review see Valero-Cabre et al., 2020).


Indeed, interhemispheric interactions have thus far provided the most solid examples of the fifth brain mode of ‘mutual inhibition’ included in our review (Toba et al., 2019).

Hence, mutually inhibitory transcallosal interactions appear to be wired into the human connectome and appear to be of functional relevance (see indirect evidence in Lunven et al., 2015; Nyffeler et al., 2019; Bartolomeo, 2019).

However, the extent to which this brain mode substantiates other inhibitory or competitive interactions, particularly within the same lobe or the same hemisphere, remains to be illustrated via clinical cases, which to date, remain rare (for evidence from experimental perturbation approaches see Walsh et al., 1998; Maniglia et al., 2019).


Second, can inhibitory interactions be reliably revealed by lesion inferences?

In this context, Sperber and Karnath raise two issues and illustrate them with ad hoc simulations.


In their first example, they suggest that inhibitory interactions may be misinferred because of lesion pattern correlations even when inhibitory interactions are not a priori present in a ground truth model (Fig. 1 in Sperber and Karnath, 2020).

For this demonstration, they implemented the four brain modes of Toba et al. ( 2019) that do not explicitly involve inhibitory interactions (namely ‘unicity’, ‘association’, ‘summation’ and ‘equivalence’), choosing an implementation that differs from the one suggested by the definitions in Toba et al. ( 2019).


We reanalysed their example with the help of the estimated Multiperturbation Shapley value Analysis (MSA; Keinan et al., 2004, 2006), a multivariate lesion inference approach based on coalitional game theory, and find that the analysis substantially reduces the misinference of negative contributions found by Sperber and Karnath (Supplementary Fig. 1A).

Moreover, in their simulations, Sperber and Karnath added a significant amount of noise to the actual signal (70% signal and 30% noise).

We find that this added noise is responsible for some part of the negative contributions they report and, when removed, MSA infers the actual positive contributions built into the model largely correctly (Supplementary Fig. 1B).


We conclude that misinferences of negative contributions in clinical lesion data can be largely avoided when using a reliable multivariate inference approach in conjunction with lesion data that are processed to be as free from noise as possible.

A more systematic quantitative investigation of these factors in the context of comparing different lesion inference approaches should be the subject of upcoming studies.


In their second example, Sperber and Karnath suggest that inhibitory interactions may be deduced even though positive functional contributions of brain regions may provide a more parsimonious explanation.

They set up a two-way scenario where a hypothetical brain function X may be affected either by lesion of a region x, or by disinhibition of a second region (Fig. 2 in Sperber and Karnath, 2020).


Indeed, our reanalysis of the example with MSA delivers results that are consistent with either scenario (Fig. 1).

Particularly, region x makes a large positive contribution to the putative function, such that a lesion of x would induce functional deficit.

Conversely, region 2 makes a negative contribution to the function, such that releasing this region from inhibition would increase a functional deficit.


Moreover, the analysis reveals that regions x and 2 have a ‘synergistic’ interaction, specifically indicating functional competition, as a contribution of region x may suppress the contribution of region 2 and vice versa.

Thus, even with the limited information given in the example of Sperber and Karnath, the analysis correctly infers the contributions and interactions consistent with the two scenarios, including the potential presence of negative functional contributions.


Naturally, however, the analysis cannot decide which scenario the authors intended to be the actual causal explanation, as both are equally possible.


This finding highlights the fact that lesion inferences operate in the space of behavioural data and therefore cannot directly reveal physiological-anatomical interactions without further context (Zavaglia and Hilgetag, 2016).

Thus, we agree with Sperber and Karnath that a hypothesis-free interpretation of paradoxical lesion-behaviour relations captured by MSA or other inference approaches can be misleading and may pose a risk of spurious findings.

Consequently, the accurate identification of relationships between brain function and specific neuroarchitectures (i.e. ‘brain modes’) must rely on a priori hypotheses (Sperber, 2020).

In this context, more realistic computational models of brain function and systematic ground truth simulations of functional perturbations will be a powerful strategy for elucidating the validity of lesion inference approaches and revealing the fundamental brain modes underlying brain function.


Non- alcoholic steatohepatitis (NASH), a progressive form of non- alcoholic fatty liver disease (NAFLD), is a critical health problem worldwide as it affects approximately 25% of the global population.


NASH is closely associated with obesity, type 2 diabetes mellitus, and metabolic syndrome; it is characterised by the accumulation of fat in the liver, which can lead to inflammation and fibrosis.

NASH can progress to severe fatal diseases, such as hepatocellular carci- noma and liver cirrhosis if left untreated.

1- 4 Moreover, the number of patients with NASH is likely to increase, considering that the number of patients with NAFLD is expected to grow.

5 In contrast to medical needs, no effective pharmacological treatment option has been ap- proved for NASH.

6 Previous studies have demonstrated a positive correlation between prolonged exposure to active glucocorticoids (cortisol in humans) and the development of metabolic syndrome.

7- 10 Al- though cortisol is predominantly produced in the adrenal cortex, it can also be produced locally in other tissues through the action of 11β- hydroxysteroid dehydrogenase type 1 (11β- HSD1), which is an enzyme expressed in metabolically active tissues, such as liver and adipose tissue, and facilitates the conversion of cortisone into corti- sol.

11,12 In fact, a previous study that measured [ 13 C 4 ] cortisol after dosing 5 mg [ 13 C 4 ] cortisone demonstrated that the oral administra- tion of MK- 0916, an 11β- HSD1 inhibitor, effectively reduced [ 13 C 4 ] cortisol levels in plasma.

13 Therefore, 11β- HSD1 has been consid- ered a key target for treating metabolic syndrome.


Recent findings suggest that 11β- HSD1 inhibitors may be a promising therapeutic option for patients with NASH.

A preclini- cal study on rats showed that the inhibition of 11β- HSD1 may im- prove NAFLD by reducing inflammation, restoring lipid metabolic homeostasis, and alleviating hepatic steatosis.

14 Moreover, ran- domised, placebo- controlled trials have repeatedly demonstrated that compared to placebo, 11β- HSD1 inhibitors successfully re- duce liver fat content in patients with NAFLD, NASH, and type 2 diabetes mellitus.

15,16 J2H- 1702 (also known as SKI2852) is a novel (the half maximal in- hibitory concentration = 2.9 nM or 1.58 ng/mL) 11β- HSD1 inhibitor.

17 J2H- 1702 significantly reduced hepatic lipid accumulation and im- proved liver fibrosis in diet- and toxicity- induced NASH mouse mod- els.

18 Considering that fibrosis is the most significant factor affecting the survival of patients with NASH, 19,20 we believe that these results provide evidence for the potential of J2H- 1702 as a therapeutic op- tion for patients with NASH.

Furthermore, preclinical studies have demonstrated that J2H- 1702 successfully inhibits hepatic glucone- ogenesis and improves lipid profiles and metabolic parameters (e.g. cholesterol, triglycerides, free fatty acids, postprandial glucose, blood haemoglobinA1c, body weight) in mice.

17,21 Therefore, J2H- 1702 may provide additional therapeutic benefits for NASH patients.


Therefore, we performed a phase 1 study to assess the phar- macokinetics (PKs), pharmacodynamics (PDs), safety, and tolerability of J2H- 1702 after a single- dose oral administration in healthy male Korean participants.


Healthy Korean men aged 19- 45 years with a body mass index of 18- 27.0 kg/m 2 participated in this study.

Eligible participants were evaluated based on their medical history, physical examination, vital signs (blood pressure, pulse rate, and body temperature), 12- lead electrocardiogram (ECG), clinical laboratory tests, serology (hepati- tis B, hepatitis C, human immunodeficiency virus and syphilis) and urinary drug screening.

Participants were excluded if they had (i) any clinically significant history of gastrointestinal disease or surgery that may affect drug absorption; (ii) history or a positive screening result for drug abuse; (iii) use of any drugs, including prescribed or herbal medicine, within 14 days and non- prescribed drugs within 7 days before the first drug administration; (iv) consumption of caf- feine- or grapefruit- containing products within 3 days before admin- istration and (v) use of drugs that induce or inhibit drug- metabolising enzymes, such as barbiturates, within 1 month before the first drug administration.


This study was approved by the Institutional Review Board of Ha- nyang University Seoul Hospital (IRB no. HYUH- 2020- 05- 012- 004) and was performed in accordance with the principles of the Decla- ration of Helsinki and Korean Good Clinical Practice (www.cris.nih.

go.kr, registration number: KCT0005295).

All the participants pro- vided written informed consent prior to the study.


This was a dose block- randomised, double- blind, placebo- controlled, dose- escalation phase I study of a single oral admin- istration of 25, 50, 100, 200, or 300 mg J2H- 1702 in healthy participants.

Eligible participants were admitted to the Clinical Trial Centre in Hanyang University Seoul Hospital 2 days before dosing (Day 2) and discharged 2 days after dosing (Day 3) when protocol- specified procedures up to 48 h post- dose were completed.

Base- line serum cortisol and 24- h urinary- free cortisol (UFC) levels were measured 1 day before dosing (Day 1).

Each dose group included 10 participants who were block- randomised to J2H- 1702 or pla- cebo at a ratio of 8:2 by the data manager.

The allocation code was concealed from investigators, clinical research assistants, field workers, care providers, and participants until the final data analysis was completed.

All participants received an assigned sin- gle dose of J2H- 1702 after overnight fasting (≥10 h) with 200 mL water, and food intake was prohibited for up to 4 h after adminis- tration.


The lowest no- observed adverse effect level of J2H- 1702 determined in a preclinical toxicity study was 40 mg/kg in dogs.

This can be translated into a human equivalent dose of 21.6 mg/ kg, which is 1296 mg, assuming a 60 kg adult.

To ensure the safety of the starting dose, we applied a safety factor of 50, which was calculated as 25.9 mg.

Therefore, the starting dose in the present study was set to 25 mg, which is considered adequately safe.

The 13652036, 2023, 11-12, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/apt.17726 by Stanford University, Wiley Online Library on [03/10/2025].

See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License administered dose was increased in an ascending order.

The inves- tigators decided whether to escalate the dose based on the safety data collected up to 48 h post- dose, which was reviewed by an independent safety monitoring committee.


Participants who completed the PD sampling as planned without any major protocol deviation were included in the analysis.


To evaluate the PD, we indirectly assessed the 11β- HSD1 activ- ity by measuring urinary metabolites of cortisol (tetrahydrocortisol [THF] and 5α- reduced tetrahydrocortisol [5α- THF]) and cortisone (tetrahydrocortisone [THE]).

The 11β- HSD1 activity was calculated as (THF + 5α- THF)/THE.

In order to measure THF, 5α- THF, and THE, urine samples were obtained at predetermined time intervals (i.e. 0- 6, 6- 12, 12- 24, and 24- 48 h post- dose).


We also monitored the serum cortisol concentration to evaluate the PD.

Because serum cortisol levels are subject to diurnal varia- tion, serum samples were obtained at baseline (Day 1), 0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10 and 12 h and at 0 (pre- dose), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12 and 24 h post- dose to calculate the time- matched change in serum cortisol from baseline.

PD parameters such as the maximum time- matched change from baseline in serum cortisol (ΔE max ), the area under the time- matched change from baseline in serum cortisol- time curve from 0 to the last measurable time point (ΔAUEC last ), AUEC from 0 to 4 h (ΔAUEC 0- 4 h ) and time to reach ΔE max were calculated.

ΔAUEC 0- 4 h was included in the PD analysis because a previous 13 C 4 isotope study using an 11β- HSD1 inhibitor showed that hepatic 11β- HSD1 inhibition could be observed within 4 h post- dose.

13 Additionally, change from baseline in 24 h- UFC (Δ24 h- UFC = 24 h- UFC at Day 1- 24 h- UFC at baseline) was also included in the PD analysis.


These parameters were calculated using the Phoenix WinNonlin software (version 8.1; Certara USA, Inc.).


Plasma and urine concentrations of J2H- 1702 were determined using a validated liquid chromatography- tandem mass spectrom- etry (LC- MS/MS) method developed by Global Clinical Central Lab Co., Ltd. Solifenacin succinate was used as an internal stand- ard.

Approximately 50 μL of sample plasma was thoroughly mixed with 50 μL of solifenacin (100 ng/mL in 50% methanol) and 1 mL of acetonitrile, and centrifuged at 17,532 g for 5 min.

Then, 100 μL of supernatant was collected and diluted in 200 μL of water and mixed properly using a vortex mixer for 1 min.

One microliter of the residual material was injected into the LC- MS/MS system.

Next, 100 μL of sample urine was thoroughly mixed with 10 μL of solifenacin (500 ng/mL in 50% methanol), 50 μL of 1 N- Sodium hydroxide standard solution, and 700 μL of tert- Butyl methyl ether.

The material was frozen (<−70°C) after centrifugation at 17,532 g for 5 min.

The supernatant was collected and subjected to N 2 evaporation at 40°C.

The residual material was then recon- stituted with 800 μL of 50% acetonitrile and injected into the LC- MS/MS system.


A mobile phase of 5 mM ammonium acetate in water (0.1% formic acid) and acetonitrile was used at a flow rate of 0.3 mL/min through a Poroshell 120 EC- C18 (3 × 50 mm, 2.7 μm; Agilent Technologies).

The MS/MS system was operated with an electrospray ioniser in positive ionisation mode.

J2H- 1702 was evaluated by multiple reaction monitoring using Triple Quad 6500 (AB Sciex).

The mass transitions for J2H- 1702 and solifenacin were m/z 544.0 → 258.2 (394.2 for urine) and 363.2 → 110.2, respec- tively.

The lower limit of quantification for J2H- 1702 was 5 ng/ mL, with linear calibration curves in the concentration range of 5- 5000 ng/mL (r > 0.99).

The accuracy and precision were 100.31%- 108.15% and 0.77%- 4.88% for plasma and 98.98%- 106.21% and 1.48%- 5.47% for urine, respectively.

The human plasma and urine samples measured in the present study were accurate and reliable.


PKs were assessed using plasma and urine concentrations of J2H- 1702.

Serial blood samples were collected in sodium heparin tubes at 0 (pre- dose), 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10, 12, 24, 36 and 48 h post- dose.

Approximately 6 mL of blood was collected in sodium heparin tubes at each point and then centrifuged (1880 g and 4°C for 10 min) to separate three plasma aliquots (1 mL each), which were stored at <−70°C until analysis.

In addition, urine samples were col- lected for up to 48 h post- dose.

Two urine aliquots (1 mL each) per time interval (0- 6, 6- 12, 12- 24, and 24- 48 h post- dose) were stored at <−70°C until analysis.


The PK parameters of J2H- 1702 were determined by a non- compartmental method using the Phoenix WinNonlin software (version 8.1; Certara USA, Inc.).

The maximum plasma concentra- tion (C max ) and time to reach C max (T max ) were determined based on observed concentrations and actual times.

The area under the plasma concentration- time curve (AUC) from time zero to the last measurable time point (AUC last ) was determined using the linear- up/log- down trapezoidal rule.

The AUC from time zero to infinity (AUC inf ) was calculated as AUC last + C t /λ

z , where C t is the last measurable concentration and λ z the terminal elimination rate constant estimated from linear regression of the log- linear por- tion of the plasma concentration- time curve.

AUC extrapolated (AUC extrap ) was defined as C t /λ

z divided by AUC inf .

The terminal half- life (t 1/2 ) was calculated as the natural logarithm of 2 divided by λ z .

The apparent clearance was calculated as the dose divided by the AUC inf .

For urinary PK parameters, the amount of unchanged J2H- 1702 excreted in urine (A e ), a fraction of unchanged J2H- 1702 excreted in urine (f e ) and renal clearance (CL R = A e /AUC inf ) were evaluated.


We observed a time delay between the PKs and PDs of J2H- 1702 in that the maximum inhibition of 11β- HSD1 activity was observed much later than the T max of J2H- 1702 (Table 1, Figure 1 and Fig- ure S1).

We found no correlation between the PK (i.e. C max and AU- C last ) and PD (i.e. 11β- HSD1 activity, ΔE max , ΔAUEC last , ΔAUEC 0- 4 h , and Δ24 h- UFC) parameters (Table 3).


In this study, genetic variants were observed in six genes (UGT1A1, CYP3A5, CYP2C19, CYP2C9, CYP2B6, and CYP2D6).

The genetic vari- ants were categorised into four phenotypes, which were normal me- tabolizer, intermediate metabolizer, poor metabolizer and unknown phenotype.

For all six genes that were analysed, no significant differ- ence was found in dose- normalised AUC inf among the phenotypes (Figure S4).


Safety and tolerability were evaluated by monitoring vital signs, physical examinations, 12- lead ECG (QT correction was performed using the Fridericia formula), clinical laboratory tests, and TEAEs.

Ad- ditionally, to evaluate the possible activation of the cortisol- negative feedback loop at the hypothalamic- pituitary- adrenal axis (HPA), adrenocorticotropic hormone (ACTH), aldosterone and testosterone levels were measured on Day 1 (pre- dose) and Day 3 (48 h post- dose).

The TEAEs were classified and recorded using the Medical Diction- ary for Regulatory Activities (version 23.0).

All safety and tolerabil- ity results were reviewed by the investigators and comprehensively judged for their relationship with J2H- 1702 and clinical significance.


Precise statistical sample size estimation was not required be- cause of the exploratory nature of this study.

Demographic, PK, and PD characteristics are described as arithmetic means and standard deviations.

Safety and tolerability profiles were analysed using frequencies and percentages.

Comparison of PD parameters with placebo was performed using a one- way analysis of variance with Dunnett's test.

The paired t- test and Wilcoxon signed- rank test were used for within- treatment group comparisons (i.e., Day 1 vs. Day 3; Day −1 vs. Day 1, respectively).


To evaluate dose pro- portionality, we conducted linear regression analysis with a power model using log- transformed C max and AUCs.

If the 95% confi- dence interval (CIs) of the gradient of the regression line was 1, the PK parameter was considered to be dose- proportional.

The relationship between PK and PD was assessed using Pearson's correlation coefficient.

The effect of PGx on PK was assessed using non- parametric analysis (i.e. Kruskal- Wallis test or Mann- Whitney test; the mean ranks of each phenotype were compared with those of normal metabolizers).


All statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc.).

Statistical significance was considered at p < 0.05.


A total of 50 individuals were enrolled in the study between 4 Au- gust 2020 and 8 November 2020.

The final visit of the last partici- pant was on 11 December 2020.

One participant in the 200 mg dose group withdrew consent and dropped out after receiving the study drug.

As a result, 49 participants completed the study.

The dropout participant was included in the PD and safety analysis because he had received the study drug and had completed the PD sampling.

However, he was excluded from the PK and PK- PD analyses because he did not complete PK sampling.

The mean ± standard deviations of age, weight, height, and body mass index of the participants were 26.5 ± 5.8 years, 71.2 ± 9.8 kg, 174.4 ± 6.4 cm, and 23.3 ± 2.4 kg/m 2 , respectively.

Demographic characteristics were comparable among the treatment groups (p > 0.05).


TA B L E 1 Summary of pharmacodynamic parameters of J2H- 1702 after a single oral administration in healthy participants.


In the J2H- 1702 group, we observed an initial decrease, which lasted up to 1.5- 4 h post- dose, in serum cortisol levels compared to baseline (Day 1), whereas it remained similar in the placebo group (Figure 2).

Correspondingly, AUEC 0- 4 h was significantly lower on Day 1 than that at baseline (Day 1) after J2H- 1702 treatment, whereas AUEC 0- 4 h remained comparable after placebo treatment (Figure S2).

However, among the baseline- corrected PD parameters (i.e. ΔE max , ΔAUEC last , ΔAUEC 0- 4 h , and Δ24 h- UFC), only ΔE max in 50 mg dose group and ΔAUEC 0- 4 h in 25, 50, and 300 mg dose groups were sig- nificantly lower compared to that in placebo.


After a single oral administration, the peak plasma concentration of J2H- 1702 was observed at 2- 2.9 h. Semi- log concentration- time curve of J2H- 1702 showed typical first- order elimination kinetics, with a mean elimination half- life of 9.8- 14.7 h. The fraction of un- changed J2H- 1702 excreted into urine was negligible (<0.05%) in all dose groups, indicating that J2H- 1702 was predominantly elimi- nated through non- renal pathways (Table 2, Figure 3).

Systemic exposure to J2H- 1702 (i.e. C max , AUC last and AUC inf ) increased in a dose- dependent manner, as the 95% CIs of the regression slope for log- transformed C max , AUC last and AUC inf were 0.63- 1.04, 0.67- 1.12 and 0.70- 1.12, respectively (Table S1).


Overall, J2H- 1702 was well tolerated after a single oral administra- tion of up to 300 mg.

A total of 11 TEAEs were observed in seven participants (14%; 25 mg, two participants; 50 mg, one participant; 100 mg, three participants; 200 mg, one participant; 300 mg, none; placebo, none), all of which were mild in severity and resolved spon- taneously.

The most common TEAE was diarrhoea (four partici- pants, 8%), followed by dizziness (two participants, 4%).

Regarding the upregulation of the HPA axis, a significant increase in ACTH and aldosterone (approximately twofold of ULN) was observed in one participant (R14003) in J2H- 1702 200 mg group on Day 3 (2 days after dosing) without any other clinical signs or symptoms.

In this participant, ACTH and aldosterone levels returned to normal lev- els within 4 days without any treatment.

However, testosterone levels were not affected at any dose.

No other clinically relevant abnormalities or changes were observed in the ECG parameters (in- cluding QT prolongation), vital signs, physical examination, or clinical laboratory tests.


F I G U R E 2 Time course of mean serum cortisol before and after a single oral administration of J2H- 1702 (or placebo) up to 24 h post- dose (insets: up to 4 h post- dose).

Error bars denote the standard deviations (○ = baseline, Day 1; ● = Day 1).


This is the first human study to comprehensively evaluate the PDs, PKs, safety, and tolerability of J2H- 1702 after a single oral admin- istration (25- 300 mg) in healthy male participants.

Intensive blood and urine sampling allowed us to successfully assess the PD and PK characteristics of J2H- 1702.


Although all TEAEs were more frequently reported in the J2H- 1702- treated groups than those in the placebo group, there was no dose- dependent increase in the frequency or intensity of TA B L E 2 Summary of pharmacokinetic parameters of J2H- 1702 after a single oral administration in healthy participants.

To evaluate the PD of J2H- 1702, we indirectly assessed the he- patic 11β- HSD1 activity by measuring the urinary (THF + 5α- THF)/ THE ratio.

27 Our results demonstrated that J2H- 1702 successfully inhibited the 11β- HSD1 activity in the liver and the effect was long- lasting.

Peak inhibition of 11β- HSD1 activity occurred between 12 and 24 h post- dose, and the inhibitory effect was sustained even during 24- 48 h post- dose (Figure S1).

This indicates that for J2H- 1702, the half- life of the inhibitory effect may be longer than the t 1/2 .

Consistent with our findings, a prior clinical study with ASP3662, an 11β- HSD1 inhibitor, showed that a return to baseline of 11β- HSD1 activity was not achieved even after 16 days of discontinuing the study drug.

28 Additionally, the discrepancies observed between T max (2- 2.9 h post- dose) and time to reach the maximal inhibitory effect of 11β- HSD1 activity (12- 24 h post- dose) could be attributed to the time required for the drug distribution from the systemic circulation to the site of action in the liver.


In contrast to the marked decrease in 11β- HSD1 activity, the vari- ations in the PD parameters with regard to serum and urinary cortisol levels were not as considerable after J2H- 1702 administration.

This could be attributed to the negative feedback control of the HPA axis by cortisol, as mentioned previously.

The attenuated activity of 11β- HSD1 could be translated as an increase in cortisol clearance.

There- fore, increased clearance may trigger an increase in ACTH secretion, ultimately elevating the levels of circulating cortisol.

This hypothesis is supported by the previously mentioned increase in ACTH and al- dosterone levels in the J2H- 1702 treated groups, in contrast to the the circulating cortisol levels.


After a single oral administration, mean t 1/2 of J2H- 1702 was shorter or similar compared with other 11b HSD1 inhibitors (ASP3662: 14.81- 37.74 h, ABT- 384: 10.4- 15.2 h, AMG- 221: 6.9- 9.3 h, BI- 187004: 13.4- 33.5 h), 28,30- 32 and the systemic exposure to J2H- 1702 was relatively dose- proportional.

However, to accu- rately assess dose proportionality, further evaluation is warranted in a multiple- dose study, as the PKs of J2H- 1702 were not dose- proportional after multiple dosing in mice and dogs.

17 Interestingly, the CL/F and V d /F ratios were relatively high in J2H- 1702 (50 mg).

This may be explained by the fact that one participant (R12007) hav- ing markedly low exposure to J2H- 1702 showed high CL/F and V d /F (C max : 23.67 ng/mL, AUC last : 173.49 h·ng/mL, AUC inf : 252.46 h·ng/ mL, CL/F: 198.05 L/h, V d /F: 1804.63 L) in the 50 mg dose group.

When excluding this participant from the PK analysis, the CL/F and V d /F at J2H- 1702 50 mg were comparable to those of other dose groups (mean ± SD for CL/F and V d /F at J2H- 1702 50 mg were 8.7 ± 1.8 L/h and 132 ± 57.2 L, respectively, for J2H- 1702 50 mg).


No specific abnormalities were reported during the dosing process and the reasons for the participant having a higher CL/F and V d /F ratios are unclear.

However, the absorption of J2H- 1702 may have been affected because the participant had diarrhoea on the day of administration.


All six genetic variants observed in this study (UGT1A1, CYP3A5, CYP2C19, CYP2C9, CYP2B6, and CYP2D6) were not associated with the systemic exposure (dose- normalised AUC inf ) of J2H- 1702.

This result is in line with the preclinical finding that the major metabolic pathway of J2H- 1702 is through CYP3A4.

Therefore, we believe J2H- 1702 can be conveniently used regardless of the patient's CYP2C19 genotype.

This contrasts with the findings of BMS- 823778, an 11β- HSD1 inhibitor candidate for type 2 diabetes treatment, which is significantly affected by CYP2C19 polymorphism.

33 This study has a few limitations.


First, because the primary PK parameters were observed in the plasma, it is unclear whether J2H- 1702 was well distributed in the liver, which is the target tis- sue.

However, a preclinical study with mice showed that J2H- 1702 is well distributed in the liver (tissue- to- plasma concentration ratio in the liver = 31.0).

17 Given the available information, we can infer that J2H- 1702 was well distributed in the human liver as well.

Sec- ond, because this study was conducted on healthy participants, it can be argued that the results may not be applicable to patients with NASH.

However, 11β- HSD1 inhibitors consistently have been shown to lower 11β- HSD1 activity in patients with metabolic dis- eases, including NASH.

15,32,34 Based on these results, we believe that the inhibitory effect of J2H- 1702 would likely be reproduced in patients with NASH.


As previously mentioned, a phase 2 study of J2H- 1702 is currently being conducted with patients with NASH in three dose groups (50, 100, and 200 mg).

As our study has shown that J2H- 1702 successfully decreased 11β- HSD1 activity in all dose groups and that J2H- 1702 was well tolerated up to 300 mg, we be- lieve that the dose levels set for the phase 2 study are sufficient to explore the efficacy of J2H- 1702 in NASH patients.


Third, as the study included only Korean men, it was not feasible to assess the effects of race and sex on the PKs, PDs, safety, and tolerability of J2H- 1702.

Therefore, further clinical studies involving diverse races and sexes are required to comprehensively understand the clinical pharmacology of J2H- 1702.


In conclusion, our study demonstrated that J2H- 1702 was safe and well- tolerated and provided preliminary evidence of its ability to in- hibit the activity of 11β- HSD1.

These findings provide a basis for future clinical development of J2H- 1702 as a novel therapeutic op- tion for the treatment of NASH.

However, further investigations are required to validate this hypothesis.


Yun Kim: Formal analysis (equal); investigation (supporting); meth- odology (equal); writing - original draft (equal); writing - review and editing (supporting).

Shi- Ra Lee: Data curation (lead); investigation (supporting); project administration (equal); writing - original draft (supporting); writing - review and editing (supporting).

Sang Won Lee: Conceptualization (lead); formal analysis (equal); funding ac- quisition (lead); investigation (lead); project administration (equal); software (lead); visualization (equal); writing - original draft (equal); writing - review and editing (lead).


This study was funded by J2H Biotech, Suwon- si, Gyeonggi- do, Republic of Korea, the manufacturer of J2H- 1702.

This work was supported by the research fund of Hanyang University (HY- 202300000001159).


All authors have nothing to declare.


With a global prevalence of 30% [1], metabolic dysfunction-associated steatotic liver disease (MASLD) is a worldwide epidemic closely associated with the epidemics of metabolic syndrome, obesity and type 2 diabetes [1].

MASLD is set to become a major socio-economic burden as its most severe form, metabolic dysfunction-associated steatohepatitis (MASH), is associated with significant morbidity/mortality such as major adverse cardiovascular events, major adverse liver outcomes, hepatocellular carcinoma, liver transplant and death.

To date, the management of patients with MASH remains challenging because of the absence of approved therapy.


Excessive levels of glucocorticoids have been associated with an increased risk of MASLD.

These hormones can mediate intrahepatic fat disposition through several mechanisms such as stimulation of de novo lipogenesis and lipolysis, increased food intake, increased glucogenesis, and inhibition of β-oxidation [2].

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is highly expressed in the liver and adipose tissue, and converts the inactive glucocorticoid hormone, cortisone, to the active cortisol.

As a consequence, 11β-HSD1 has been considered a potential target for MASH for over a decade.

It has been hypothesized that inhibition of 11β-HSD1 selectively lowers cortisol within the tissues without affecting the normal variations of plasma cortisol during stress response.

Several 11β-HSD1 inhibitors have been evaluated in different therapeutic indications such as type 2 diabetes, insulin resistance and obesity [3][4].

These trials have reported many beneficial effects such as weight loss [5][6], decrease in liver chemistry tests [5], and improvement in lipid and glycaemic profiles [6][7] suggesting potential overall metabolic benefit of this therapeutic class.

However, none of them has been approved to date, mostly due to limited efficacy results and potential safety concerns related to activation of the hypothalamic–pituitary–adrenal axis (HPA).

Previous studies have reported a mild activation of the HPA, returning to normal after stopping study drug [5].

Further long-term studies with close monitoring of the HPA are warranted to fully assess the potential impact of 11β-HSD1 inhibitors.


Kim et al. [ 8] reported the results of a human study of J2H-1702, a novel 11β-HSD1 inhibitor, which appeared to be well tolerated.

Previous animal studies suggested the potential of this drug candidate for the treatment of MASH and liver fibrosis [9].

Despite evidence of target engagement, previous early phase 2 studies, assessing the efficacy of other 11β-HSD1 inhibitors, reported either non-significant or mild efficacy on liver fat content reduction [5][7].

Longer duration of 11β-HSD1 inhibition might be needed to achieve a clinically meaningful benefit, and the potential effect on fibrosis regression of J2H-1702 vindicates further development.

Several questions remain such as the potential role of 11β-HSD1 genetic polymorphisms in the therapeutic response [10].

Nevertheless, the human study by Kim et al. [ 8], together with the ongoing phase 2 trial assessing the efficacy of J2H-1702 in patients with MASH, provide a good opportunity to further assess this therapeutic target in MASH as their potential benefit is still not well understood.

If successful, this class of drugs could be positioned as an add-on therapy in the future armamentarium of MASH treatment.


We appreciate the opportunity to respond to the editorial highlighting hypercortisolism as a potential target of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) [1].

The complex pathophysiology of MAFLD, involving insulin resistance, lipotoxicity, and genetic, environmental, and social factors, results in heterogeneous and variable disease progression [2].

Considering the complex and heterogeneous nature of MASLD, monotherapy may not be optimal for all patients.

For some patients, combination therapy may be a more promising approach.

The logical next step in development may be the development of safe, effective combination therapies based on therapeutic candidates developed to date.

As Drs.

Harrison and Dubourg stated, there has been consideration about the impact of glucocorticoids on the onset of this condition and the potential of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) inhibitors as a therapeutic approach for MASLD [4].


J2H-1702, a novel 11β-HSD1 inhibitor, has shown reduction of hepatic lipid accumulation and improvement in liver fibrosis in preclinical mouse model studies [5].

In particular, J2H-1702 controlled the production of cortisol in hepatic stellate cells to diminish TGF-β1-mediated fibrogenic morphology and signalling in a multicellular hepatic spheroid model that mimics fibrotic microenvironment [5].

Moreover, in a diet- and toxicity-induced mouse model, J2H-1702 reduced fibrosis along with steatosis and inflammation [5].

Since fibrosis has a strong association with long-term outcomes, J2H-1702 demonstrates the potential to be a part of the treatment for this condition [6].

Furthermore, our study showed that a single administration of J2H-1702 successfully reduced 11β-HSD1 activity in humans [7].

Therefore, J2H-1702 can be considered promising in treating MASH.


However, we agree that there is a need to assess the long-term effect of 11β-HSD1 inhibition.

A phase IIa clinical trial aims to evaluate safety and efficacy of J2H-1702 after repeated administration for 12 weeks in patients with non-alcoholic steatohepatitis (NASH) is ongoing (Registration number: KCT0007904) [8].

Clinical benefits will be assessed by analysing the changes from baseline in liver fat content (%), liver fibrosis (kPa), NAFLD fibrosis score, ALT/AST/lipid profiles and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR).

Results of this clinical trial might answer some questions on its efficacy.


Moreover, concurrent development of biomarkers enabling appropriate treatment selection should be accompanied regarding the complex and heterogenous pathophysiology of MASLD.


Type 2 diabetes is characterized by a progressive decline in b-cell function and increased insulin resistance that contribute to chronic hyperglycemia and subsequent diabetic complica- tions 1 .

Therefore, antidiabetes strategies so far have mainly focused on improving b-cell function and insulin resistance.

Among them, glucagon-like petide-1 receptor (GLP-1R) ago- nists, especially the so-called long-acting GLP-1R agonists, such as liraglutide, have been gaining attention because they enhance insulin secretion glucose-dependently and concurrently amelio- rate insulin resistance through bodyweight reduction 2,3 .


Previ- ous studies showed that the glucose-lowering effects of the long-acting GLP-1R agonist, liraglutide, rely on remaining b-cell function, as evaluated by postprandial levels of C-peptide immunoreactivity (CPR) 4 , the fasting C-peptide index (CPI) or the CPR/plasma glucose ratio 5-8 and glucagon-stimulated increments of CPR 7 .

These findings suggest that the earlier initiation of long-acting GLP-1R agonists results in greater glucose-lowering effects in patients with type 2 diabetes.

A meta-analysis of clinical trials on GLP-1R agonists and basal insulin combination therapy showed that they improve gly- cated hemoglobin (HbA1c) similarly to basal-bolus insulin therapy with a reduced risk of hypoglycemia and bodyweight gain 9 .

Therefore, it is conceivable that long-acting GLP-1R agonists are effective in type 2 diabetes patients with reduced remaining b-cell function when used with basal insulin.

How- ever, this has not been tested in clinical trials or in clinical settings, and requires further investigation.

The current retro- spective study was designed to evaluate: (i) the HbA1c-lower- ing effects of liraglutide and basal insulin combination therapy in type 2 diabetes patients who had previously received insulin therapy, both multiple-dose insulin (MDI) and basal insulin-supported oral therapy (BOT); and (ii) the possible association of remaining b-cell function with the HbA1c-lowering effects of the therapy.


A total of 72 type 2 diabetes patients who received a prescrip- tion change from insulin therapy, MDI or BOT to liraglutide and basal insulin combination therapy at Kansai Electric Power Hospital (Osaka, Japan) between September 2014 and February 2016 were retrospectively analyzed.

Patients with type 1 dia- betes, pregnancy, pancreatic disease, liver disease, renal disease, malignancy or receiving diabetogenic medication were excluded.

Patients with severe renal failure (creatinine clearance rate < 30 mL/min) and/or those undergoing dialysis were also excluded, as serum CPR levels are modified in these conditions.

Physical and laboratory data including HbA1c were acquired before and 12 - 3, 24 - 3, 36 - 3 and 54 - 3 weeks after the initiation of liraglutide and basal insulin combination therapy.

A schematic diagram of the study participants is summarized in Figure 1.

Of the 72 patients, 15 discontinued liraglutide as a result of hyperglycemia (1 patient), gastrointestinal side-effects (4 patients) and other reasons (10 patients); 12 patients contin- ued liraglutide therapy while receiving additional oral antidia- betic drugs (OAD) or insulin during 54 weeks, and the remaining 45 patients continued liraglutide therapy without additional OADs or bolus insulin.

There were 37 patients whose HbA1c values at week 54 were available.

No hypo- glycemia requiring hospitalization occurred during the study period.


HbA1c was measured using high-performance liquid chro- matography with cation-exchange resins that separate the stable form of b-N1-mono-deoxyfructosyl Hb.

The results are shown as National Glycohemoglobin Standardization Program values as recommended by the Japan Diabetes Society 10 .

The CPI, which is closely correlated with other indices of b-cell function, such as homeostatic model assessment of b-cell function and insulinogenic index 11-13 , and has been widely used as a practi- cal index to estimate b-cell function in clinical settings 14 , was calculated as follows: 100 9 fasting CPR / fasting plasma glu- cose.

The patients continued pre-existing insulin therapy up until the night before the measurement of fasting serum CPR in the morning to avoid hyperglycemia.

CPR was measured using Lumipulse Presto C-peptide (Fujirebio Inc., Tokyo, Japan).

Other laboratory measurements including plasma glu- cose were measured by standard assays.


The patient characteristics and results are reported as the mean - standard error of the mean (SEM).

The statistical anal- ysis was carried out using SPSS Statistics 22 software (IBM Corp., Armonk, NY, USA).

Repeated measures were analyzed using a mixed-effects model.

Clinical parameters were com- pared between the two groups at single time-points using the Mann-Whitney U-test.

P-values < 0.05 were considered statisti- cally significant.

Receiver operating characteristic curves were constructed for the CPI.

The sensitivity, specificity, cut-off point and area under the receiver operating characteristic curve were also calculated.


Of the 57 patients who initiated and continued liraglutide and basal insulin combination therapy for 54 weeks, we ana- lyzed 37 patients who did not take additional OADs or bolus insulin (Figure 1).

After the initiation of liraglutide and basal insulin combination therapy, HbA1c slightly improved (baseline 8.5 - 0.3% and at 54 weeks 8.0 - 0.2%; P = 0.138), with significant reductions in bodyweight (baseline 73.3 - 2.8 kg and at 54 weeks 68.6 - 2.4 kg; P < 0.01), insulin dose (baseline 25.9 - 3.1 units/day and at 54 weeks 16.2 - 1.4 units/day; P < 0.01) and insulin dose per bodyweight (baseline 0.34 - 0.04 units/kg bodyweight/day and at 54 weeks 823 O R I G I N A L A R T I C L E http://onlinelibrary.wiley.com/journal/jdi Liraglutide and b-cell function 0.23 - 0.02 units/kg bodyweight/day; P < 0.01; Figure 2a-d, black lines).


We then addressed whether remaining b-cell function affects the achievement of HbA1c < 7.0% at 54 weeks after the initia- tion of liraglutide and basal insulin combination therapy.

Among the 37 patients, the patients who achieved HbA1c < 7.0% showed higher HbA1c values and bodyweight throughout the observation period, although it did not reach statistical significance.

These patients had a total insulin dose similar to patients who did not achieve HbA1c < 7.0%.

Impor- tantly, the baseline values of the b-cell function index, CPI, were significantly higher in patients who achieved HbA1c < 7.0% than in those who did not achieve HbA1c < 7.0% (1.50 - 0.22 vs 0.98 - 0.10; P = 0.04; Fig- ure 2e).

The receiver operating characteristic analysis showed that the cut-off CPI value for the achievement of HbA1c < 7.0% was 1.103 ng/mL (area under the curve 0.752; 95% confidence interval 0.548-0.957; sensitivity 86% and speci- ficity 67%; Figure 2f).


We also carried out a subgroup analysis that showed that the changes in HbA1c from baseline to 54 weeks were affected only by baseline HbA1c, and did not differ by the baseline val- ues of age, body mass index (BMI) and type 2 diabetes duration, the insulin dose or the type of pre-existing insulin therapy (MDI vs BOT; Figure 3).

Although the changes in bodyweight and insulin dose were significantly affected by baseline BMI, insulin dose and insulin therapy type, the bodyweight and insulin dose at 54 weeks were not affected by baseline insulin therapy type (Figure 3).

Importantly, the dose of liraglutide did not differ by the baseline age (≥65 years, 0.86 - 0.03 mg and <65 years, 0.81 - 0.03 mg; P = 0.421), the medians of baseline HbA1c (≥8.2%, 0.82 - 0.03 mg and <8.2%, 0.85 - 0.03 mg; P = 0.538), baseline BMI (≥26.36 kg/m 2 , 0.87 - 0.02 mg and <26.36 kg/m 2 , 0.81 - 0.03 mg; P = 0.298), type 2 diabetes duration (≥16 years, 0.82 - 0.03 mg and <16 years, 0.85 - 0.03 mg; P = 0.620), baseline insulin dose (≥0.4 units/kg bodyweight/day, 0.85 - 0.03 mg and <0.4 units/kg bodyweight/day, 0.82 - 0.03 mg; P = 0.538) or the type of pre-existing insulin therapy (MDI 0.85 - 0.02 mg and BOT 0.79 - 0.05 mg; P = 0.332).

The ratio of basal insulin and liraglutide (units of basal insulin per mg of liraglutide) did not differ by baseline age (≥65 years, 20.74 - 2.47 and <65 years, 18.13 - 2.34; P = 0.354), the medians of baseline HbA1c (≥8.2%, 21.93 - 2.75 and <8.2%, 17.16 - 1.92; P = 0.159), type 2 diabetes duration (≥16 years, 21.88 - 2.28 and <16 years, 17.22 - 2.53; P = 0.258) or the type of pre-existing insulin therapy (MDI 21.24 - 2.06 and BOT, 15.76 - 2.92; P = 0.204), but it differed by the medians of baseline BMI (≥26.36 kg/m 2 , 23.22 - 2.70 and <26.36 kg/m 2 , 15.80


The current study showed that the achievement of HbA1c < 7.0% by liraglutide and basal insulin combination therapy was affected by remaining b-cell function, and that the cut-off value of the b-cell function index, CPI, for this target was 1.103, suggesting that the therapy requires additional OAD or bolus insulin in patients with CPI < 1.103 to achieve HbA1c < 7.0% 1 year after initiation.

The present study also suggested that liraglutide and basal insulin combination therapy improves HbA1c and bodyweight regardless of baseline charac- teristics, such as age, BMI, type 2 diabetes duration, insulin therapy type and the insulin dose.


Liraglutide exerts glucose-lowering effects by enhancing basal and postprandial insulin secretion from pancreatic b-cells 2,15 .

Therefore, it is conceivable that remaining b-cell function is one of the critical determinants in the glucose-lowering effects of liraglutide.

Our groups and others previously reported that liraglutide exerts greater HbA1c-lowering effects among non- insulin-treated type 2 diabetes patients with ample b-cell func- tion 5,7,16 .

However, whether these HbA1c-lowering effects depend on remaining b-cell function in patients receiving basal insulin is unknown.

As clearly shown in the current study, the HbA1c-lowering effects depend on remaining b-cell func- tion, even in patients receiving liraglutide with basal insulin.


Interestingly, the cut-off CPI value for the achievement of HbA1c < 7.0% by liraglutide and basal insulin combination therapy (i.e., 1.103) is lower than that of liraglutide - sulfony- lurea therapy (i.e., 1.86) 7 , suggesting that basal insulin can compensate for the b-cell function required for liraglutide to enhance basal insulin secretion.

However, as suggested by the current study, liraglutide and basal insulin combination therapy might require additional bolus insulin to fully compensate insu- lin insufficiency in individuals with reduced b-cell function.

Therefore, it would be interesting to evaluate the b-cell function requirement for the achievement of HbA1c < 7.0% in individu- als receiving both basal and bolus insulin.


More than 70% of the 37 patients examined in the current study stopped receiving pre-existing OADs and/or bolus insulin, and no washout peri- ods were implemented before the initiation of the basal insulin and liraglutide combination therapy, making it difficult to eval- uate patients' responses based on changes in HbA1c from base- line to 54 weeks.

In addition to the achievement of HbA1c < 7.0% at 54 weeks, we also evaluated CPI in HbA1c tertiles at 54 weeks, and found that the lowest HbA1c tertile had the highest CPI (Figure S2).


Insulin therapy, especially MDI, has been the most powerful tool for glycemic control, but the risk of hypoglycemia and increased bodyweight have been problems in the management of type 2 diabetes.

GLP-1R agonist and basal insulin combination therapy has been gaining attention, because it carries lower risks for hypoglycemia and bodyweight gain, and improves quality of life because of less frequent injections and exerts HbA1c-lower- ing effects similarly to MDI according to randomized controlled trials 17 .


Consistent with the results of randomized controlled tri- als, liraglutide and basal insulin combination therapy improved HbA1c, and decreased bodyweight and the insulin dose when switched from MDI or BOT in the current study (Figure 2) and in recent clinical trials in Japanese type 2 diabetes patients 18,19 .

Liraglutide and basal insulin combination therapy is effective regardless of baseline age, BMI, disease duration, insulin dose and the type of insulin injections (Figure 3).

In addition, the ratio of basal insulin and liraglutide was not affected by baseline values of age, HbA1c, type 2 diabetes duration or the type of pre-existing insulin therapy, suggesting a broad indication for a fixed-ratio combination of liraglutide and basal insulin in Japa- nese type 2 diabetes patients similar to type 2 diabetes patients of other ethnicities 20,21 .


The ratio of basal insulin and liraglutide significantly differ between individuals with baseline BMI ≥ 26.36 kg/m 2 and those < 26.36 kg/m 2 ; and between individuals with baseline insulin dose ≥ 0.4 unit/kg/day and those with < 0.4 unit/kg/day, suggesting that the ratio should be determined in light of obesity and subsequent insulin resistance.

However, it is important to note that liraglutide up to 0.9 mg q.d., unlike up to 1.8 mg q.d.

in other countries, is currently approved in Japan and that the results need to be interpreted accordingly.


There were several limitations to the current study.

First, the sample size of the current study was limited, as liraglutide and basal insulin combination therapy had only been approved in Japan for <3 years.


Second, just 19% of the patients achieved HbA1c < 7.0% at 54 weeks, partly due to the new guideline of HbA1c targets for the elderly announced by the Japan Diabetes Society in May 2016 (e.g., HbA1c 6.5-7.5% for individuals aged ≥65-years and individuals aged<75 years with normal cognitive function and activities of daily living, and with the use of insu- lin, sulfonylurea and glinide) 22 .


Third, because of the retrospec- tive nature of the study, there is no pre-fixed rule for dose adjustments of basal insulin by the physician-in-charge.

There- fore, it is difficult to exclude the possibility that dose adjust- ments of basal insulin affected the overall results, including the CPI cut-off value, although the basal insulin dose did not differ regardless of HbA1c < 7.0% achievement (Figure 2c,d).

In addi- tion, there is no pre-fixed rule for prescription changes.


The inclusion of the 12 patients originally excluded as a result of additional OADs or bolus insulin showed that baseline CPI was higher in the group achieving HbA1c < 7.0%, although it did not reach statistical significance (Figure S1).

Five out of the 12 excluded patients had baseline CPI values (1.71 - 0.13) that were higher than the mean CPI value of the group achieving HbA1c < 7.0% (Table 1).

These five patients had higher base- line insulin dose per bodyweight (0.47 - 0.08 units/kg body- weight/day) and younger baseline age (52.2 - 1.6 years), despite a similar baseline HbA1c (8.1 - 0.4%) compared with the group achieving HbA1c < 7.0% (Table 1), suggesting that additional factors (e.g., overeating and insulin resistance) might also con- tribute to achievement of HbA1c < 7.0% by liraglutide and basal insulin combination therapy.


Fourth, linear associations of CPI with b-cell function at higher fasting glucose ranges were not investigated.

However, fasting glucose levels in the current study participants were similar to or lower than those described in previous studies evaluating remaining b-cell function to avoid insulin injections and those investigating associations of CPI with other indices of b-cell function 12,13,23 .


Despite these limita- tions, the current findings strongly suggest that liraglutide requires some remaining b-cell function to exert glucose-lower- ing effects, even when used with basal insulin, and suggest that liraglutide and basal insulin therapy requires additional OAD or bolus insulin to achieve ideal HbA1c target.


In conclusion, the current study found that the patients who achieved HbA1c < 7.0% by liraglutide and basal insulin combi- nation therapy had more remaining b-cell function, and sug- gests that the earlier introduction of liraglutide allows better glycemic control outcomes, even when used with basal insulin.


We would like to comment on the recent article of Usui et al. and highlight findings of our study.

Usui et al. showed that the glucose-lowering effect of liraglutide as add-on therapy to insulin relies on the remaining b-cell function in type 2 diabetes [1].

Counterintuitively, this effect was not dependent on disease duration.


We retrospectively analyzed data from our cohort of type 2 diabetes patients (n = 69) at Slingeland Hospital, Doetinchem, the Netherlands, who instantaneously switched from insulin to liraglutide between 2010 and 2016.

Inclusion criteria for switch were body mass index ≥35 kg/m2 and glycated hemoglobin (HbA1c) >53 mmol/mol (>7.0%).

Response was defined as a decrease in HbA1c; non-responding necessitates discontinuing liraglutide due to insufficient glycemic control or side-effects.

Effects on weight and HbA1c were analyzed with a mixed model, and response by a multivariate Cox survival analysis.


The mean age of the patients was 62 years (standard deviation 7.9 years), and 54% were men.

The median disease duration was 13 years (interquartile range 25–75, range 6–17.7 years), and the mean HbA1c was 68.8 mmol/mol (8.4%) during insulin therapy at a median daily dose of 96 units (range 54–129 units).


At 3, 6, 12 and 24 months, the response rates were 65, 63, 51 and 23% for the total cohort, and 84, 84, 76 and 41% for patients with disease duration <10 years (n = 25).

Responders’ median weight reduction at 3, 6 and 12 months was 1 kg (range 0–4.5 kg), 3 kg (range 0–6.3 kg) and 5 kg (range 1–11 kg), respectively.

Responders’ median HbA1c reduction at 3, 6 and 12 months was 4 mmol/mol (range 0–12.2 mmol/mol), 8.5 mmol/mol (range 2–26.5 mmol/mol) and 7 mmol/mol (range 1–18 mmol/mol), respectively, and 2.5% (range 0–3%), 2.9% (range 2.3–4.6%) and 2.8% (range 2.2–3.8%).

Insufficient glycemic control was the main reason for non-responding (89%).


Longitudinal analysis adjusted for age and sex showed a significant positive association of disease duration with HbA1c (0.33, 95% confidence interval 0.07–0.58).

Survival analysis, adjusted for age, sex and body mass index, showed a significant association between disease duration and non-responding to liraglutide, with a hazard ratio of 2.39 (95% confidence interval 1.20–4.76) when disease duration was dichotomized in <10 years or ≥10 years (Figure 1).


Intriguingly, the present results seem to conflict with the finding of Usui et al. not establishing a relationship between disease duration and successful therapy of liraglutide in combination with insulin.

On the contrary, our results fit with their finding that liraglutide is most effective in patients with the highest remaining b-cell function, as it is well known that b-cell function declines with disease duration.

We conclude that instantaneously switching from insulin to liraglutide is an option for severely obese patients with type 2 diabetes with a short disease duration.


We would like to thank Wilbrink et al. [ 1] for their interest and comments on our recent article regarding the glycated hemoglobin (HbA1c)-lowering effect of glucagon-like peptide-1 receptor agonist liraglutide with basal insulin among Japanese individuals with type 2 diabetes.

We have reported that the HbA1c-lowering effects of liraglutide/basal insulin combination rely on remaining b-cell function, and that the cut-off value of the C-peptide immunoreactivity index, a b-cell function-related index frequently used in Japanese clinical settings, is 1.10 for the achievement of HbA1c < 7.0% at 54 weeks after initiating the liraglutide/basal insulin combination [2].

In our study, we found that changes in HbA1c were not affected by type 2 diabetes duration, unlike the Wilbrink et al. study (Figure 1b).

This discrepancy might be due to several reasons.

First, we studied patients receiving liraglutide/basal insulin combination in replacement of multiple daily injection insulin therapy or basal insulin-supported oral therapy, whereas Wilbrink et al. studied those receiving liraglutide in replacement of insulin therapy.

We previously showed that discontinuation of liraglutide as a result of hyperglycemia after switching from insulin is affected by remaining b-cell function and type 2 diabetes duration [3].

In addition, we also reported that the HbA1c-lowering effects of liraglutide monotherapy and sulfonylurea combination rely on remaining b-cell function and type 2 diabetes duration (Figure 1a) in a study in which 74% of the study patients had been taking insulin before initiating liraglutide [4].

Importantly, the C-peptide immunoreactivity index cut-off value for HbA1c < 7.0% achievement by liraglutide monotherapy and sulfonylurea combination was higher than that of liraglutide/basal combination (1.86 and 1.10, respectively) [2, 4].

It is widely accepted that b-cell function progressively declines over time in type 2 diabetes patients, making it difficult to obtain appropriate glycemic control without insulin use [5, 6].

It is possible that basal insulin co-administration compensated for the decline in b-cell function associated with longer type 2 diabetes duration in our study [2].

Indeed, it was shown that the addition of basal insulin significantly improved HbA1c in individuals inadequately controlled by liraglutide [7].


Second, the discrepancy between our study and the Wilbrink et al. study might be due to ethnic differences in type 2 diabetes pathophysiology.

Type 2 diabetes in East Asian patients is characterized primarily by non-obesity and b-cell dysfunction, unlike type 2 diabetes in Caucasian patients, which is characterized by obesity and insulin resistance [8].

As impaired b-cell function is observed even in the early stage of type 2 diabetes in East Asian patients, type 2 diabetes duration might have less significance in predicting the HbA1c-lowering effects of liraglutide.


Third, the discrepancy might be due to limited sample size (the Usui study on liraglutide/basal insulin, n = 38; the Usui study on liraglutide monotherapy or sulfonylurea combination, n = 88; and the Wilbrink et al. study, n = 69).

Dependence of HbA1c-lowering effects of liraglutide/basal insulin combination on type 2 diabetes duration awaits further investigation by studies with larger sample sizes.

Nevertheless, it is conceivable that liraglutide exerts greater HbA1c-lowering effects in the early stage of type 2 diabetes when ample b-cell function remains, and that addition of basal insulin or other antidiabetic drugs is required when b-cell function becomes substantially reduced.


Infertility is a prevalent feature of polycystic ovary syndrome (PCOS).

In 75% of these women, it occurs due to oligo-ovulation or anovulation [1-3].

PCOS is often associated with an increased prevalence of metabolic syndrome, cardiovascular diseases, and type 2 diabetes [4].

A common sign of PCOS, which is not included in the diagnostic criteria, is insulin resistance (IR): women with PCOS have increased visceral and subcutaneous body fat due to higher androgen levels, so obesity plays a significant role in explaining the metabolic characteristics of PCOS [3].

These patients present elevated levels of low- density lipoprotein (LDL), triglycerides, and cholesterol and reduced levels of high-density lipoprotein (HDL).


The maintenance of reproductive function is related to an optimal body weight in women: underweight as well as overweight and obesity are associated with an increased risk of anovulatory infertility.

During ovarian stimulation, the FSH requirement is increased, Nutrients 2023, 15, 4444.

https://doi.org/10.3390/nu15204444 https://www.mdpi.com/journal/nutrients fewer oocytes are collected, serum estradiol concentrations are decreased, frequent cycles are cancelled, and low pregnancy rates have been observed.

The treatment of infertility caused by PCOS includes lifestyle changes, pharmacological and surgical therapies, and in vitro fertilization (IVF) [5-9].

It has also been reported that obese women are at an increased risk of miscarriage after IVF or ICSI.


The reduced fertility in these patients may also be related to other factors, such as reduced oocyte and/or embryo quality, defects in endometrial development, and implanta- tion abnormalities.

Furthermore, PCOS may have significant implications for pregnancy outcomes and the long-term health of PCOS-affected women and their offspring [10].


IVF has become a viable alternative for women with PCOS when ovulation induction treatment has failed or there are coexisting tubal or male infertility factors.


Given the harmful effect of obesity, weight loss has been strongly promoted as one of the most effective means of increasing fertility in infertile overweight or obese women and improving the outcomes of assisted reproductive treatment among obese women.


Ovarian stimulation in PCOS patients is a crucial and challenging process since the ovaries of such patients generally exhibit an excessive and uncontrolled response to hor- monal stimulation.

OHSS is the main risk for PCOS patients undergoing IVF; in a review, an odds ratio of 6.8 was calculated for the development of OHSS in association with polycystic ovarian morphology [11,12].


Although OHSS is relatively rare, women with this iatrogenic disease should be moni- tored carefully to avoid life-threatening complications.

The evaluation of risk factors, such as PCOS and a high antral follicle count, is important [13].

Therefore, to reduce the risks, it is important to improve pregravid, prenatal, and intrapartum care.


Approximately 60% of PCOS patients are overweight/obese (classification ranges are described in Table 1), and weight gain often precedes the onset of PCOS' clinical manifestations.

This suggests that obesity (especially the abdominal type) interferes with the pathophysiology and the clinical expression of the syndrome; the mechanisms underlying this association are complex and not completely understood.

It has been observed that women with central obesity take longer to become pregnant and have more impaired ovulation, qualitative and quantitative defects in oocyte maturation, altered fertilization, disrupted meiosis, and mitochondrial dynamics abnormalities, leading to abnormal embryo preimplantation and lower birth rates [8,10,14-16].


There is a great deal of evidence that PCOS women with central obesity have higher levels of IR, dyslipidemia, and hyperandrogenism [7,17].

The temporary development of dyslipidemia and IR is a normal physiological response during pregnancy.

Obese/overweight PCOS patients may face an additional risk of developing gestational complications related to these metabolic disorders [10].

For example, dyslipidemia has a negative impact on IVF clinical outcomes.


A high pre-pregnancy weight is associated with an increased risk of pregnancy-induced hypertension, gestational diabetes, urinary infection, macrosomia, increased hospitalization, and Caesarean section and thus increased complexity of surgery (prolonged operative time and an increased risk of bleeding, wound infection, and wound dehiscence), increased difficulty of loco-regional anesthesia, and the increased use of general anesthesia, resulting in increased intraoperative mortality rates.

Thus, a BMI > 24.9 kg/m 2 (especially when associated with PCOS) significantly reduces IVF success rates, increases risks during hormonal stimulation for IVF (OHSS, incongruous response to stimulation, and increased thrombotic risk), and increases the risk of obstetric and neonatal complications (abortion, malformative disorders, preterm delivery, placental abruption, intrauterine death, the premature rupture of membranes, fetal macrosomia, increased risk of operative vaginal delivery and caesarean section, and neonatal hypoglycemia) [1,21,22].


In addition to the aforementioned factors, obesity alone is a risk factor in pregnancy, predisposing individuals to arterial hypertension, pulmonary hypertension, left ventricular dysfunction, hypoxia, hypercapnia, hypercoagulability, the regurgitation and pulmonary aspiration of gastric contents, and hepatic steatosis [10,22].

Specifically, obese/overweight PCOS patients have an all-or-none response to hormonal stimulation aimed at IVF: hyperstimulation or a nonresponse.

Moreover, such patients show low fertilization rates, poor embryo quality, and low pregnancy rates [8,15,23].


Weight loss is an effective treatment for PCOS; in fact, losing 5% or more of body weight reduces its symptoms and associated risks [9].

Several therapeutic strategies aim to reduce weight and body mass index (BMI), simultaneously improving reproductive functionality.

Weight loss has been shown to improve reproductive outcomes by enhancing fertility, regularizing menstrual cycles, and increasing the likelihood of spontaneous ovulation/conception in anovulatory overweight/obese women [16].

This is probably due to the effects of a reduc- tion in fat and/or lean mass, changes in some endocrinological and metabolic parameters, and a decrease in inflammatory status [8,24].


PCOS patients show increased inflammatory markers, C-reactive protein (CRP), interleukin 18 (IL-18), tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), white blood cell count (WBC), monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein-1α (MIP-1α).

In fact, the adipose tissue (especially visceral) is considered an endocrine organ, producing and releasing several adipokines and cytokines (leptin, adiponectin, resistin, visfatin, TNF-α, and IL-6) [25].

These proinflammatory molecules result in the development of metabolic diseases.

A con- dition of obesity/overweight determines endocrine mechanisms that alter inflammatory responses, interfering with both ovarian and endometrial function.

Thus, the loss of fat mass (which is an endocrine organ) could cause a decrease in inflammatory status (by decreasing IL-6 and thus reducing CRP synthesis), resulting in improved reproductive function [24,26].


Pharmacological strategies are insulin-sensitizing drugs (metformin or D-chiro-inositol) and anti-androgens [7].

Metformin is efficient but has gastroenteric side effects and is considered a US Food and Drug Administration (FDA) pregnancy category B drug [7,27].

Lifestyle modifications (dietary intervention and exercise) should be the first-line treatment in overweight/obese PCOS patients because of the low potential costs, absence of adverse effects, and potential for long-term management [21,28].

There is no or limited evidence that one specific dietary composition is better than another in PCOS, but it seems that only low-carbohydrate diets can lead to a significant decrease in IR and in circulating markers of inflammation in overweight/obese PCOS patients [4,29].

The aim of this study was to assess the effects of different diet regimens, namely the very-low-calorie ketogenic diet (VLCKD) and Mediterranean diet (MD), on PCOS patients prior to IVF cycles, specifically regarding anthropometric, metabolic, and hormonal parameters and the safety of IVF techniques (rate of OHSS).


The study was a nonrandomized controlled study in which 84 obese/overweight PCOS patients were recruited to evaluate a possible effect of two dietary therapies, VLKCD and Mediterranean, which they underwent before access to IVF techniques.

Patients were evaluated at baseline and at 90 and 120 days since the beginning of both diets.

The out- come measures were anthropometric measures (body mass index (BMI), waist and hip circumferences, waist-to-hip ratio (WHR), and abdominal circumference); hormone levels (luteinizing hormone (LH), follicle-stimulating hormone (FSH), FSH/LH ratio, estradiol, anti-Müllerian hormone (AMH), 17-alpha-hydroxy-progesterone, androstenedione, testos- terone); metabolic parameters (cholesterol, high-density lipoprotein (HDL), triglycerides); Homeostasis Model Assessment (HOMA index); and reproductive parameters (menstrual regularity, antral follicle count (AFC), AMH, and risk of developing OHSS).


For FSH, LH, estradiol, 17-alpha-hydroxy-progesterone, androstenedione, and testos- terone measurements, chemiluminescence immunoassay (CLIA) was performed, while AMH was analyzed by electrochemiluminescence immunoassay (ECLIA).

A blood sample was taken within 5 days of the start of the menstrual cycle (early follicular phase).

In patients with amenorrhea or oligomenorrhea, sampling was done randomly, or rather on any day of the menstrual cycle.

During the evaluation of the hormone profile, the patients had not undergone IVF-related treatment with exogenous hormones.


Oligomenorrhea is a condition involving a menstrual interval of 35 days or more, secondary amenorrhea comprises a menstrual interval of 90 days or more, and polymen- orrhea comprises a menstrual interval of less than 25 days [30].

The menstrual regularity of patients in an IVF center can be objectively assessed by transvaginal ultrasonography (echography ultrasound WS80A, Samsung; ultrasound probe V5-9, Samsung) or the study of the ovaries and subsequent endometrial changes.

Notably, the objectivity of this exami- nation was enhanced by the fact that it was performed by the same surgeon, a specialist in gynecology and obstetrics experienced in IVF.


AFC was assessed by transvaginal ultrasound (echography ultrasound WS80A, Sam- sung; ultrasound probe V5-9, Samsung, Suwon, South Korea) within 5 days of the start of the menstrual cycle (early follicular phase) or randomly in patients with amenorrhea.

In the case of the patients recruited for the study, the ultrasound examination was always performed by the same surgeon previously mentioned, to avoid operator-dependent bias.


Body weight was measured to the nearest 0.1 kg using an electronic scale (INNOFIT INN-112) and height to the nearest 1 cm using a stadiometer (ADE MZ10042 Portable Stadiometer).

BMI was calculated in kg/m 2 .


All circumferences were assessed with a measuring tape.

Waist circumference (WC) (cm) was measured in a standing position, keeping the abdomen relaxed, with the arms at the sides.

The narrowest circumference was measured between the thighs and the iliac crest (generally a few centimeters above the navel).

This is one of the indicators of abdominal body fat.

The hip circumference (cm) was measured while standing.

The operator stood sideways to take the maximum gluteal circumference.

WHR was obtained by dividing the circumference of the waist by the circumference of the hips; this is another indicator of abdominal body fat.

The thigh circumference (cm) was measured by having the subject stand, carrying their weight mainly on the limb that was not measured; the circumference was taken below the gluteal crease (higher circumference).

The abdomen circumference was measured 2 cm below the navel.

The patient was standing up and had to keep their abdomen relaxed.

In order to limit measurement error, the measurements were performed three times, and the average value was considered.


In total, 84 patients seeking infertility treatment at the Complex Organizational Unit (UOC) of Reproductive Pathophysiology and Andrology, which carries out its activities within the Sandro Pertini Hospital (Rome, Italy), were recruited.

Before starting the study (year 2022), all patients signed an informed consent form after being informed in detail about the study process.

The study was approved by the local ethics committee "Lazio 2", protocol 0137603/2022 (5 July 2022), and respected the Helsinki declaration.

The patients participating in the study had the following characteristics: (i) age between 18 and 39 years; (ii) condition of overweight or obesity (BMI > 24.9 kg/m 2 ) at the beginning of the treatment; (iii) diagnosis of polycystic ovary syndrome according to the criteria established in 2003 by the Rotterdam European Society of Human Reproduction and Embryology (ESHRE); (iv) nonuse of contraceptive methods during the experimental period; (v) motivation to lose weight; (vi) hyperinsulinemia.


During patient selection, exclusion criteria were based on all those concomitant dis- eases and conditions that could constitute a bias: pregnancy, lactation, liver, kidney and heart disease, episodes of gout or hyperuricemia, congenital adrenal hyperplasia, androgen- secreting tumors, Cushing's syndrome, decompensated thyroidopathies, hyperprolactine- mia, diabetes mellitus 1 and 2, endometriosis, and the use of ongoing insulin-sensitizing drugs.

An additional criterion for exclusion from the study was a lack of motivation to follow the proposed diet plan.


In addition, although the VLCKD represents a possible therapeutic strategy for li- pedema, it should be noted that the presence of this condition was absent in all patients recruited [31,32].

Since there are no known blood or urinary biomarkers or specific diagnos- tic tests for the diagnosis of lipedema, the assessment was performed through the patients' history, visual inspection, and physical examination.

The following criteria were evaluated by the nutritional biologist and gynecological surgeon: the excessive, bilateral, and sym- metrical presence of subcutaneous adipose tissue between the iliac crest and malleoli and on the arms; the abrupt cessation of such enlargement of the lower and upper limbs at the wrists and ankles; the loss of the concave spaces on either side of the Achilles tendon; the presence of bruising; alterations in the appearance, temperature, and texture of the skin; abnormal gait and limited mobility; negative Stemmer's sign; and punctate edema and/or chronic venous insufficiency [33,34].


The 84 patients were divided into two study groups: 42 patients received VLCKD therapy treatment and 42 patients received Mediterranean diet therapy treatment.

After careful counseling, patients chose whether to follow the diet regimen or not and, if they did, they chose which one (VLCKD or Mediterranean).

During counseling, the patient was informed that a BMI value > 24.9 kg/m 2 (especially when associated with PCOS) significantly reduces IVF success rates, increases risks during hormonal stimulation aimed at IVF, and increases the risk of obstetric and neonatal complications.

In addition, patients were thoroughly informed about all the diet's features.

However, under the internal regulation of the UOC, hormone stimulation aimed at IVF cannot be carried out in patients with BMI > 30 kg/m 2 .

Only the female component of infertility was considered, while the male infertility factor was neglected.

Therefore, embryo quality, implantation rates, and pregnancy outcomes were not evaluated.


All data were analyzed at baseline before the beginning of both diets and are sum- marized in Table 2.

Statistical analysis (Student's t test) found no statistically significant difference between the two groups.

In this regard, the groups appeared to be homogeneous at baseline.


For all patients, the protocol induced follicular growth by gonadotropin stimulation (mild stimulation with starting dose of 150 IU of recombinant gonadotropin), adding a GnRH antagonist at day 5 of stimulation.

Each patient was monitored from day 5 of stimulation on alternate days by dosing estradiol and progesterone and via ultrasound monitoring for possible dosage adjustments during therapy.

When the ultrasound and hormone parameters were found to be likely compatible with oocyte maturity (follicular diameter 18-20 mm and 200 pg/mL of E2 per follicle, presumably containing mature oocytes in MII stage), the trigger was set and oocyte pick-up was performed 32-36 h after trigger administration.

The usual trigger drug is recombinant hCG (250 mcg).

In case of OHSS risk, the drug administered is GnRH agonist (0.2 mg/mL).

The route of administration of all the aforementioned drugs was subcutaneous and the patients could perform it independently.


The objectives of the study were multiple: (i) the evaluation of the speed with which the adopted diet leads to the improvement of anthropometric parameters; (ii) the evaluation of the effects induced by different dietary therapies on metabolic and hormonal parameters affecting PCOS patient fitness and reproductive outcomes; and (iii) the evaluation of the incidence of the risk of developing ovarian hyperstimulation syndrome (OHSS) during hormone stimulation aimed at intracytoplasmic sperm injection (ICSI) in obese/overweight patients undergoing diet therapy.


The VLCKD group initially followed the "intensive phase" protocol for 60 days; during this period, patients used substitute foods, such as protein and carbohydrate sources already balanced specifically for this regimen, to which fresh non-starchy vegetables and some accompanying foods (roast chicken and turkey, bresaola, ham, salmon, tuna or swordfish) were added at each main meal.

The substitute foods used for the VLCKD contained plant-based and animal proteins.

Whey protein appears to substantially contribute to the preservation of muscle performance [35].

The sources of plant protein were peas and soybeans.

Regarding the possible harmful effects of soy, all patients accessing the IVF center are screened (breast ultrasound, mammogram, and Papanicolaou test) to diagnose pre-cancerous or cancerous lesions in estrogen-sensitive tissues.

This is because patients who access our center to undergo IVF will undergo hormone therapy (gonadotropins) with significantly increased levels of 17-beta estradiol.

Our study patients, therefore, had no contraindications to soy intake.

Vegetables were divided into unlimited and limited consumption, as described in Table S1.

During the first 3 days, we added medium-chain triglyceride (MCT) oil, which promotes and accelerates entry into ketosis under conditions of glucose restriction and consequently limits the effects of keto-adaptation [36].

The use of MCT oil has been discontinued because ketosis must be generated entirely by the oxidation of the endogenous lipids (excess adipose tissue).

MCT oil was optional according to the clinician's opinion and had the function of speeding up the patients' entry into ketosis.

Evaluation of the entry into ketosis was assessed by test strips for the measurement of ketone bodies in urine.

The consumption of fruit juices, sugary drinks, and alcohol was forbidden.

A maximum of 2 tablespoons of EVO per day was allowed.

The use of vinegar, pepper, lemon, and spices was possible.

The consumption of at least 2 L of water per day was strongly recommended.