FormalPara Key Summary Points

Why carry out this study?

Non-alcoholic fatty liver disease (NAFLD) represents a disease spectrum ranging from simple steatosis to steatohepatitis (NASH), fibrosis, and cirrhosis and is frequently associated to metabolic syndrome (MS) and type 2 diabetes mellitus (T2DM).

Neo-Policaptil Gel Retard (PGR), a natural phytocomplex, has been demonstrated to be effective in reducing factors associated with MS and T2DM, such as blood sugar, cholesterol, triglycerides, and abdominal fat distribution, all factors involved in MS and T2DM and strongly linked to fat accumulation in the liver.

Some non-invasive and validated indicators of steatosis and liver fibrosis, such as Fatty Liver Index (FLI), Fibrosis (FIB)-4 Score (FIB-4), and NAFLD Fibrosis Score (NFS), have long been used to identify and quantify their extent.

What was learned from the study?

This article documents for the first time how the use of PGR in subjects with fatty liver, MS, and T2DM is able to significantly reduce the number of subjects with and the entity of liver steatosis and fibrosis and to reduce the level of transaminases.

The use of PGR represents a new possible therapeutic option in subjects with MS and T2DM complicated by high at liver, as in metabolic NAFLD (MAFLD).

Introduction

Fatty liver includes several conditions ranging from non-alcoholic liver disease (NAFLD), steatohepatitis (NASH), liver fibrosis of different degrees up to liver cirrhosis, frequently associated with metabolic syndrome (MS) and/or over type 2 diabetes mellitus (T2DM), and in such a case defined as metabolic NAFLD (MAFLD) [1].

To define NAFLD, at least 5% of liver cells must be filled with fat (definition achieved by histological or imaging diagnosis) and independent of other toxic, infectious, or metabolic causes or excessive alcohol consumption by the patient, according to the current literature [1,2,3,4]. MAFLD is characterized by the presence of T2DM and various comorbidities such as dyslipidemia, hypertension, overweight or obesity, and others, all potent cardiovascular risk factors, as previously extensively described [1, 2, 5, 6].

Both NAFLD and MAFLD can initiate a pathway leading to necro-inflammation and fibrosis, indicators of disease progression (NASH) [7, 8] in about 40% of cases [3, 16], and are strongly related to excess triglyceride stores in the liver. They are often present in subjects with metabolic syndrome (MS) and T2DM, once again burdening patients with high cardiovascular risk [9, 10].

The histological diagnosis of NASH is characterized not only by high liver fat content but also by hepatocyte ballooning and lobular inflammation [17], which involve the risk of progression to advanced fibrosis and HCC [12, 13]. NASH is also associated with a high risk of advanced kidney disease [21], cardiovascular disease [10], and T2DM [4, 5, 9].

MAFLD variably includes a series of conditions not always present simultaneously but all clinically relevant, such as insulin resistance, visceral obesity, dyslipidemia, hypertension, overeating, sedentary lifestyle, genetic predisposition [4, 15], and microbiota alterations [16].

The progression of fibrosis is a powerful indicator of disease severity and risk of death [14, 17,18,19], and its severity is conventionally defined by the abbreviations from F0 (absent) to F4 (cirrhosis) [20], as confirmed by epidemiological studies and meta-analyses [21,22,23,24,25].

Liver biopsy remains the gold standard for NAFLD/NASH diagnostics. However, its invasive and time-consuming nature makes such a method unsuitable for large patient cohorts and even less for screening purposes [26]. On the other hand, parameters derived from ultrasound (US) scans and fibroscans may help stage NAFLD/NASH. Nevertheless, their ability to quantify liver damage is still debated [27, 28], so some easily available, inexpensive biomarkers have been validated for this purpose based on common anthropometric and laboratory parameters, including the Fatty Liver Index (FLI), Fibrosis (FIB)-4 Score (FIB-4), and the NAFLD Fibrosis Score (NFS; 21, 29,30,31). They are routinely used for many outpatients for their high predictive value quoad vitam [26].

Despite being largely used in the past, drugs and plant extracts cannot be considered effective against NAFLD and still await confirmation in the long run because of the complex pathogenetic mechanisms involved and clinical conditions associated with a high liver fat content. Indeed, especially in people with overweight/obesity and metabolic syndrome (MS), fatty liver depends on factors already shown to be positively affected by Neo-Policaptil Gel Retard® (PGR; 34, 35). The latter is a macromolecule phytocomplex covered by a European patent (no. 1679009) and based on polysaccharides endowed with the ability to reduce carbohydrate and fat absorption rates. In particular, PGR comes from high-fiber raw materials as previously described [34] and was successfully used in adolescents and adults with MS and T2DM [34, 35] at reducing circulating levels of insulin, lipids, and post-prandial triglycerides together with insulin resistance and body fat distribution. In detail, the positive effects of PGR on glucose control, insulin resistance, and body weight in adolescents and adults were not less than those of metformin, while body fat redistribution—with special reference to visceral fat—and blood lipid profile—in terms of total and high-density lipoprotein (HDL) cholesterol and triglycerides—were not affected by metformin and improved significantly in the PGR group only [34, 35]. Such findings were of particular interest considering that both treatment groups were on a hypocaloric diet (20% fewer calories than before the trial) [34, 35].

Therefore, with such premises, the present study aimed to assess PGR's ability to improve indirect signs of liver steatosis and fibrosis (primary outcome) besides confirming the reported positive PGR effects on anthropometric parameters, lipid levels, and circulating cytolysis liver markers, including transaminases and gamma-glutamyl transferase (γ-GT), in patients with MS/T2DM and MAFLD/NASH (secondary outcome).

Methods

Two hundred fifty subjects with MS and T2DM were consecutively enrolled in this longitudinal, single-center, single-blind study and randomized to active treatment (treatment group; TG), taking PGR as two sachets of 2.5 g of soluble granules dissolved in water consumed before the two main mails, and control group (CG) taking placebo, similar in appearance to TG, after signing informed consent (Fig. 1). The follow-up duration was 6 months.

Fig. 1
figure 1

Flow chart of the study. FIB-4 Fibrosis (FIB)-4 Score, FLI Fatty Liver Index, NFS NAFLD (non-alcoholic fatty liver disease) Fibrosis Score

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Ethical Approval

The Ethics Committee of the University Hospital of Campania "Luigi Vanvitelli", Naples, Italy, approved the present study (protocol no. 33478/2022, November 11, 2022). All participants signed informed consent, and data were processed anonymously according to good clinical practice guidelines. This study was conducted in conformance with good clinical practice standards. The study was carried out in accordance with the Declaration of Helsinki 1975, and subsequent amendments. All followed procedures were in accordance with the ethical standards of the responsible committee on human experimentation (both institutional and national) and in accordance with usual clinical practice. Written informed consent was obtained from all participants before enrollment.

Inclusion Criteria

  • Liver fat content > 5% (detected by ultrasonography).

  • Aspartate transferase (AST)/alanine transferase (ALT) no higher than 1.5–2 times the upper limit of normal.

  • Absence of viral, autoimmune, or toxic hepatitis.

  • Moderate or no alcohol habit.

  • Metabolic syndrome (defined according to the consensus document 2009; 9).

  • Age > 18 years for both sexes.

  • Body mass index (BMI) > 30 kg/m2.

  • Fasting glycemia (FPG) ≥ 126 mg/dl and hemoglobin A1C (HbA1c) ≥ 42 mmol/mol, or overt T2DM known for no more than 1 year (± 0.5) (American Diabetes Association [ADA] criteria 2022; 35).

  • Total cholesterol ≥ 200 mg/dl or LDL cholesterol ≥ 100 mg/dl.

  • Reliability (visiting the clinic steadily).

  • Normal estimated glomerular filtration rate (eGFR; 60–90 ml/min/1.73 m2).

  • No micro- or macro-albuminuria.

  • No previous utilization of lipid-lowering drugs against metabolic disorders.

  • Acceptance of informed consent.

Exclusion Criteria

  • Insulin treatment.

  • Previous bariatric surgery interventions.

  • Pregnancy or breastfeeding.

  • Disabling conditions, serious heart, liver, kidney, or neoplastic diseases, dementia, or inability to regularly comply with prescriptions.

  • History of drug allergy or known allergic disease.

  • Irritable bowel disease, dyspepsia, and celiac disease.

  • Any clinical condition incompatible with the study in the opinion of the investigators.

Safe limits of alcohol consumption in MAFLD were defined as alcohol consumption < 210 g per week for men and 140 g per week for women [36,37,38].

During the study, both groups of patients followed a personalized low-calorie diet ensuring 80% of current requirements (10–20% protein, 50–60% carbohydrates, and 20–30% fat) and received weekly phone calls to get motivated to continue with the diet and increase their aerobic physical activity up to at least 3 metabolic equivalents of task (METS) per week.

Similar methodologies, with the inclusion and exclusion criteria, have been described previously [32].

The general characteristics of the involved patients are shown in Table 1.

Table 1 Starting general features of the two treatment groups
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Parameters under study included: (1) body mass index (BMI), waist circumference (WC), and visceral fat percentage (% VF); (2) HbA1c and homeostatic model assessment for insulin resistance (HOMA-IR), insulin, and C-peptide; triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, aspartate transferase (AST), alanine transferase (ALT), gamma-glutamyl transferase (γ-GT), alkaline phosphatase (ALP), creatinine levels, and a complete blood count (automatic biochemistry analyzer with integrated system Selecta Pro XI, [Elithech, USA] and commercially available ELISA kit, as appropriate); (3) treatment adherence; (4) side effects presumably due to treatment; (5) any other adverse effects: clinical history plus circulating AST, ALT, γ-GT, alkaline phosphatase, and blood creatinine levels. Normal values were 5–55 U/l for AST, 8–48 U/l for ALT, 5–40 for γ-GT, and 40–174 IU/l for alkaline phosphatase. Venous blood samples were obtained in the morning after a 12-h overnight fast and immediately analyzed at the central laboratory of the hospital.

History of prescribed medications and their adverse reactions, anthropometry, physical examination results, and biochemical measurements were recorded for each participant at baseline and week 24. As previously described, the homeostasis HOMA-IR model was calculated to evaluate insulin resistance and islet beta-cell function, as previously described [32].

Visceral fat was measured with Body Metrix BX2000, Intela-Metrix, Inc. (Brentwood CA, US), a validated instrument based on ultrasound technology, allowing direct and immediate subcutaneous fat thickness measurement to avoid annoying and operator-dependent manual application maneuvers [39]. All parameters were measured at baseline (T-0) and after 3 (T-12) and 6 months (T-24), respectively. The indirect tests to define the presence and severity of steatosis and fibrosis for NAFLD/NASH diagnosis were:

Fatty Liver Index

FLI was calculated by the following formula, as previously defined [29]:

$$\begin{gathered} {\text{FLI}}\, = \,(e^{{0.{953} \times {\text{loge }}\left( {{\text{triglycerides}}} \right) \, + \, 0.{139} \times {\text{BMI }} + \, 0.{718} \times {\text{loge }}(\gamma {\text{GT}}) \, + \, 0.0{53} \times {\text{waist\, circumference}} - {15}.{745}}} ) \hfill \\ /({1}\, + \,e^{{0.{953} \times {\text{loge }}\left( {{\text{triglycerides}}} \right) \, + \, 0.{139} \times {\text{BMI }} + \, 0.{718} \times {\text{loge }}(\gamma {\text{GT}}) \, + \, 0.0{53} \times {\text{waist\, circumference}} - {15}.{745}}} )\, \times \,{1}00. \hfill \\ \end{gathered}$$

A FLI ≥ 60 indicates hepatic steatosis (SP = 86%; LR+ = 4.3) and < 30 rules it out (SN = 87%; LR– = 0.2), where SN = sensitivity, SP = specificity, LR+ = positive likelihood ratio, and LR– = negative likelihood ratio.

An FLI 31–59 is considered inconclusive.

FIB-4 was obtained as previously described [20, 21], with the following formula:

$${\mathbf{FIB}} - {\mathbf{4}}: \, ({\text{Age}} \times {\text{AST}}) \, / \, ({\text{Platelets}} \times \surd {\text{ALT}})$$

where a score < 2.0 = lower risk for advanced fibrosis (F3/F4) in subjects > 65 years of age and a score > 2.67 = high risk for advanced fibrosis for all ages, where F0 = no fibrosis; F1 = portal fibrosis without intralobular septa; F2 = portal fibrosis with few intralobular septa; F3 = numerous septa without cirrhosis; F4 = cirrhosis.

In NASH (non-alcoholic steatohepatitis):

  • Fibrosis F0-F1 (FIB-4 < 1.30)

  • Moderate fibrosis F2 (1.30 to 2.67)

  • Cirrhosis F3-F4 (FIB-4 >2.67)

An excellent correlation among FIB-4, Fibrotest, and elastography with histological fibrosis was demonstrated [22, 23].

NAFLD Fibrosis Score

NFS can be calculated using the following formula [21, 23]:

NAFLD fibrosis score = −1.675 + 0.037 × age (year) + 0.094 × BMI (kg/m2) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio − 0.013 × platelet count (×109 /L) − 0.66 × albumin (g/dL)

where IFG = impaired fasting glucose.

Interpretation

  • A score < −1.455 = lower risk of advanced fibrosis (F3/F4) in subjects > 65 years of age;

  • A score > 0.676 = high risk for advanced fibrosis (F3/F4) for all ages.

A close relationship between FIB-4 and NFS was described [24, 25].

Indeed, values of FIB-4 > 2.67 or NFS 0.676 identify people with a high probability of advanced liver fibrosis or advanced fibrosis (F3/F4), while people with FIB 0/1 or NFS < −1.455 are free of fibrosis, and those having FIB-4 Score F2 or NFS − 1.455 to 0.676 are undetermined [20, 21, respectively].

The cost for their calculation is negligible, and the result is immediate. Both scores were developed and validated for the identification of patients with NAFLD with a high probability of having bridging fibrosis (F3) or cirrhosis (F4) [24, 25, 30, 31].

Ultrasonography

Liver ultrasonography (US) was performed using the last generation Esaote MyLab™X75 equipped with SI2C41 convex ultrasound probe (Esaote S.p.A., Genoa, Italy), according to a validated procedure with a sensitivity of 100% and a specificity of 90% for the diagnosis of liver fat compared with liver biopsy in the event of ≥ 20% fatty liver (FL) content [27]. However, as precise FL quantification is still debated and awaiting validation on sufficiently large populations [27, 28], the US method served only as a screening tool at enrollment to assess the presence of FL, and a single US operator estimated FL content through FLI (with an inter-assay variability < 10%).

Treatment Adherence Assessment

At T-12 and T-24 visits, patients had to let the investigators analyze the drug boxes utilized during follow-up in search of any leftover sachets. Moreover, they all signed a questionnaire to record how often they inadvertently missed their expected dose. We considered “adherent” those taking at least 80% of the daily dosage [51]. To look for side effects, we also took advantage of a dedicated logbook consisting of ten dichotomic questions (yes/no answers) on gastrointestinal disorders.

Sample Size Calculation

We assumed that a 5.0% absolute difference in LF content between PGR and control groups would be the minimally appreciable and clinically relevant difference compared with baseline and the control group to have < 2.0% reduction in liver fat compared with the baseline, with a dropout rate of < 10%. With these assumptions, the sample size should be 100 per group to achieve a power of at least 90% with a p of 0.01. Therefore, we planned to randomize 125 participants per group to ensure adequate study power after any possible dropouts.

Statistical Procedures

Categorical data are presented as n (%). Continuous descriptive data were examined for normality, and normally distributed data were given as mean ± SD. Skewed data were presented as the median. To compare the two groups, the χ2 test or Fisher’s exact test were used for categorical variables, and the independent sample Student’s t test or Wilcoxon-Mann-Whitney U test were used to compare differences between continuous variables. The Pearson correlation coefficients (r) were determined by linear regression analysis. Univariate logistic regression was performed to screen for potential covariates associated with liver fat improvement. Covariates with p < 0.01 were entered into the multivariate model using the forward condition method. Multivariate linear regression analysis was conducted to estimate the effect of PGR on liver fat. Statistical analyses were performed by a biostatistician (MKS) using the SAS System version 9.4 (SAS Institute, Cary, NC, USA).

Results

Only two patients from the control group and three from the treatment group were excluded because of poor adherence to therapy. No one was excluded for side effects or adverse events, so 123 (CG) and 122 (TG) patients completed the study.

Table 1 displays general parameters, clearly showing superimposable values between groups. In particular, a typical MAFLD with high fatty liver content—as indicated by FLI—was observed in both the CG and the TG (94.31% vs. 95.08%, respectively), with AST and ALT γ-GT values higher than normal, as per enrollment criteria. Severe fibrosis was present in 88.25% vs. 83.60% of patients, respectively, as suggested by the FIB-3–4 Score, and medium fibrosis (i.e., F2) in 5.69% vs.7. 37%, respectively, with a normal condition or light fibrosis (F0–F1) in 9.75% vs. 9.01%, respectively. A similar trend was found at the NFS level for high and low fibrosis risk (i.e., 90.25% vs. 90.17% and 5.66% vs. 5.30%, respectively), with inconclusive results only observed at nominal rates (3.25% vs. 3.27%, respectively), with a 95.4% concordance rate between FIB and NFS scores. The chronic diabetes-related complications or comorbidities were similar, being hypertension 77.54% in the CG vs. 73.77% in the TG, cardio-vascular disease 62.82% vs. 61.56, and hypercholesterolemia 51.07% vs. 55.23%, respectively.

During the study, no participants had severe adverse effects, needed hospitalization, or died because of accidental causes or treatment. As reported in Table 2, gastrointestinal and general adverse effects were clinically irrelevant and comparable between groups. However, such symptoms caused no dropouts.

Table 2 Treatment side effects in the two groups
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Table 3 synthesizes the least squares mean T24-T0 between-group treatment differences (– D %) in general parameters. The CG displayed no significant changes in any studied parameters, ranging from the lowest one, observed in HDL cholesterol (– 0.1 ± 0.4%) to the highest one, observed in HbA1c (– 3.5 ± 0.4%), with only a slight decrease in AST, ALT, γ-GT, and ALP. Instead, at the end of the follow-up, the TG displayed a significant reduction in AST, ALT, and γ-GT levels, and also in ALP, although less markedly so. In greater detail, AST reduced to normal in 12% vs. 79% of participants from the CG vs. those from the TG (p < 0.001), ALT did so in 9% vs. 80% (p < 0.001), respectively, and γ-GT in 9% vs. 78% (p < 0.001), respectively (see Fig. 3).

Table 3 Least squares mean differences (T0–T24, %) in laboratory parameters and anthropometric measures between the two groups
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In greater detail, no significant differences were observed between groups during the time interval T0–T24 (Δ%) for albumin (± 0.2 ± 0.04% in the CG vs. ± 0.2 ± 0.04% int the TG), platelet count (– 0.9 ± 0.1% vs. – 0.8 ± 0.1%, respectively), and eGFR (– 0.4 ± 0.1% vs. – 0.6 ± 0.1%, respectively), while all other parameters improved significantly in the TG vs. the CG, including BMI (kg/m2) (– 1.2 ± 0. 01 vs. – 4.8 ± 0.4%, CI 2.201–6.466, p < 0.01), HbA1c (%) (– 3.5 ± 0. 4 vs. – 8.2 ± 1.0%, CI 0.686–3.318, p < 0.01), FPG (mg/dl) (– 1.6 ± 0.9 vs. – 7.7 ± 0.8%, CI 0.637–4.031, p < 0.01), PPG (postprandial glycemia; mg/dl) (– 2.5 ± 1.8 vs. – 8.6 ± 2.2%, CI 1.184–6.981, p < 0.001), total cholesterol (mg/dl) (– 2.5 ± 1.0 vs. – 9.9 ± 1.1%, CI 0.682–5.919, p < 0.001), LDL cholesterol (mg/dl) (– 2.4 ± 0.6 vs. – 9.9 ± 1.2%, CI 0.910–5.128, p < 0.01), and triglycerides (mg/dl) (– 2.5 ± 1.1 vs. – 9.8 ± 0.6%, CI 0.912–5.112, p < 0.01). A marked and significant variation between CG and TG during the T0–T24 period was observed in free insulin (μIU/ml) (– 1.3 ± 0.5% vs. – 38.6 ± 0.6%, respectively; CI 0.993–4.756, p < 0.0001), HOMA-IR (– 5.2 ± 0.4% vs. – 42.2 ± 0.6%, respectively; CI 0.789–4.235, p < 0.0001), and, less prominently, C-peptide (ng/ml) (– 0.4 ± 0.1% vs. – 7.2 ± 0.4%, respectively, CI 0.346–1.878, p < 0.05).

T0-T24 variations in body fat are presented in Fig. 2, including waist circumference (WC), trunk fat (TF), and visceral fat (VF). Both CG and TG displayed a significant decrease in all three parameters, despite an almost double effect being observed in the TG, with the highest variation occurring in VF (– 11.9 ± 4.3% vs. – 25.5.6%, CI 2.130–7.543, p < 0.001, respectively) (Table 4). Complication rates and comorbidities were similar between groups (data not reported in the Table 3).

Fig. 2
figure 2

Waist circumference (WC; cm), trunk fat (TF; %), and visceral fat (VF; %) changes from baseline (%) in the control and treatment groups at T-24

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Table 4 Changes from baseline (%) in waist circumference (WC; cm), trunk fat (TF; %), and visceral fat (VF; %) in the control and treatment groups at T-24
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Figure 3 displays FLI distribution in the two groups at baseline and end of follow-up. T0–T24 changes were virtually null in the CG (ranging from 0 to 3.69%; see Fig. 3). Contrarily, in the TG, the prevalence of subjects with severe steatosis (FLI > 60) significantly decreased from 95.08 to 47.53% (p < 0.001), with a concomitant significant increase in that of subjects with FLI < 30 from 1.63 to 49.3% (p < 0.001), with virtually no change in the prevalence of inconclusive results.

Fig. 3
figure 3

Comparison of the percentages of subjects with (FLI < 30), without (FLI > 60), and undetermined (FLI between 31 and 59) NAFLD at baseline and the end of follow-up: *p < 0.001 vs. baseline; #p < 0.001 vs. the other group. FLI Fatty Liver Index, NFS NAFLD (non-alcoholic fatty liver disease) Fibrosis Score

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When considering the FIB-4 (Fig. 4), at baseline medium-severe degrees (F3–F4) were similar in both groups (84.56% in the CG vs. 83.62% in the TG, p = ns). The same applied to low (F0–F1) (9.75% vs. 9.01, respectively, p = ns) and moderate degrees (5.69% vs. 7.37%, respectively, p = ns).

Fig. 4
figure 4

Comparison of FIB-4 in subjects without or with light fibrosis (FIB-4 < 1.30; F0-F1), severe fibrosis (FIB-4 > 2.67; F3-F4), and moderate diagnosis (FIB-4 between 1.31 and 2.66; F2) between baseline and end of follow-up. **p < 0.001 vs. baseline and CG. FIB-4 Fibrosis (FIB)-4 Score, CG control group

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T0-T24 variations after treatment were virtually null in the CG, while the prevalence of medium-severe cases (F3-F4) decreased significantly by 83.62% to 52.35% (p < 0.001) in the TG; that of low degrees (F0–F1) markedly increased by 9.01–42.15% (p < 0.001).

Figure 5 shows NFS results, which were virtually superimposable to this from FIB-4, with a maximal 95% concordance and a very low prevalence of inconclusive scores in both groups throughout the T0–T24 period. The prevalence of extreme scores stayed almost unchanged only in the CG, while improving significantly in the TG both within and between groups (p < 0.001): high probability scores (NFS > 0.6) decreased by 90.13% to 59.10%, p < 0.001), low probability scores (NFS < −1.455) increased by 6.65–37.80%, p < 0.001), while inconclusive cases were similar (3.27% vs 3.19%, respectively).

Fig. 5
figure 5

Comparison of percentages of subjects with (NFS > 0.676) and without (NFS < −1.455) advanced liver fibrosis, or inconclusive results (NFS −1.455 to 0.676), between baseline and the end of follow-up; *p < 0.001 vs. baseline; #p < 0.001 vs. the other group. NFS NAFLD (non-alcoholic fatty liver disease) Fibrosis Score

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Finally, Fig. 6 displays the significantly different changes occurring in ASL, ALT, and γ-GT in the PGR compared to the CG (p < 0.01).

Fig. 6
figure 6

Mean values and SDs of AST (IU/l), ALT (IU/l), and γ-GT (GGT; IU/l) in the two groups at baseline and end follow-up and significance of differences. *p < 0.01 and **p < 0.05 vs. control group: °p < 0.01 and °°p < 0.05 vs. baseline. ALT alanine transferase, AST aspartate transferase, γ-GT gamma-glutamyl transferase

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Discussion

The liver is the largest metabolic organ of the body and, as such, integrates numerous metabolic processes, including lipid and glucose metabolism and xenobiotic detoxification. Several hepatic metabolic pathways follow circadian patterns and are highly susceptible to nutrient metabolism challenges so that fatty liver results in disrupted circadian metabolic rhythms, insulin resistance, and consequent development of obesity and Type 2 diabetes, largely involved in the clinical phenotype of metabolic syndrome [35, 52,53,54].

NAFLD affects about 25% of the adult population [1]. Its prevalence increases in parallel to that of obesity and MS, with which it is intimately associated [1], even raising questions about its nosographic definition and changing it into MAFLD, where “M” stay for “metabolic,” indicating association with a long-standing, clinically evident diabetic condition [1, 2, 5, 6].

Indeed, NAFLD pathophysiology relies on factors contributing to the MS, including weight gain/obesity, insulin resistance, visceral obesity, dyslipidemia, hypertension, and prediabetes or clinically evident diabetes (in which case the name changes into MAFLD; 40, 41), besides physical inactivity, chronic inflammatory conditions, and altered gut microbiota. Clinicians have been looking for a single treatment tool against it for years without success. However, recent studies reported a series of metabolic benefits as a function of treatment with macromolecular complex based on polysaccharides and covered by a European patent (no. 1679009), slowing the rate of carbohydrate and fat absorption (Neo-Policaptil Gel Retard®). It is derived from high-fiber raw materials including glucomannan (Amorphophallus konjac), cellulose Opuntia pulp stem Opuntia ficus indica), chicory root (Cichorium intybus), freeze-dried mallow root mucilage (Althaea officinalis), freeze-dried flaxseed mucilage (Linum usitatissimum), and freeze-dried linden flower mucilage (Tilia platyphyllos Scop), as previously described [32].

PGR significantly reduces acanthosis nigricans expressions, HbA1c levels, and glucose metabolism abnormalities, including impaired glucose tolerance (IGT) and T2DM, in hyper-insulemic adolescents [32], because of blunted post-meal blood glucose and insulin peaks [33, 34, 42]. The attenuated insulin response to the PGR’s regulating and delaying activity on gastrointestinal glucose absorption [58, 59] decreases body weight, visceral fat, and cardiovascular risk [42,43,44].

Moreover, in the animal model, reduced carbohydrate and lipid availability to gut epithelium positively affects microbiota by favoring the growth of the Bacteroides phylum, endowed with anti-lipid absorption properties, against the previously overexpressed Firmicutes phylum, notoriously responsible for increased lipid gut absorption [56]. PGR also completely blocks the induction of genes involved in fat accumulation in the liver of mice treated with a high-fat diet (HFD) to reproduce a model of human MS during the night, thus positively modulating lipid metabolism, including liver cholesterol and fatty acid storage and triglyceride storage-associated protein (CD36) [56].

Our data provide support to the favorable effects of PGR plus a slightly hypocaloric diet on body weight and fat distribution, particularly evident in terms of visceral fat, as documented by the almost double VF reduction observed in the TG compared to the CG along with a significant decrease in plasma lipids and high tolerability.

However, the main result of our study was the 70.76% lower liver fat content attained by the TG compared to the only 2.5% change of the CG (p < 0.001), showing that only small hepatic changes can be obtained through diet, while PGR is much more effective.

The special feature of our study is the relevantly better fibrosis markers FIB-4 and NFS observed in treated subjects with a 95% concordance. PGR improved fibrosis extent significantly not only in those with high scores (FIB-4 F3–F4 and NFS > 0.626) but also in less severe cases (F0–F2 and < −1.455, respectively). Such results are quite new and difficult to explain without considering PGR’s ability to hinder glucotoxicity and lipotoxicity in adolescents and adults with MS [32, 33, 42, 44] and to improve the microbiota [42, 46, 47].

Based on animal studies where gut microbiota was manipulated and observational studies on human NAFLD, considerable evidence has emerged concerning the ability of altered microbiota to cause NAFLD through increased gut permeability to bacterial products and consequent liver inflammation and fibrosis triggered by harmful debris. Dysbiosis, combined with poor lifestyle and diet habits, also changes the luminal metabolism of food substrates through increased production of certain short-chain fatty acids and alcohol and depletion of choline. When such local environmental changes occur, patients experience gut inflammation, altered motility, and immune function, eventually leading to liver damage. However, gut microbiota analysis is expected to improve, thus allowing clinicians to identify appropriate treatment tools against NAFLD and associated disorders through a better understanding of subtle interactions among dysbiosis, diet, and environmental factors [42, 46, 47].

Finally, for the first time, we documented significantly improved AST, ALT, and γ-GT patterns in adults with MAFLD treated with PGR, opposite to those on a diet only. A possible explanation for this is the reduced liver fat content and consequently feer apoptotic changes dependent on excess fat, less relevant insulin resistance, and lower oxidative stress due to gluco-/lipo-toxicity and microbiota-related chronic low-grade inflammation.

Limitations

Despite several therapeutic attempts against NAFLD/NASH, long-term results were inconclusive in most cases [48]. In particular, a relatively short period spent on a Mediterranean diet associated with increased physical activity proved effective against NAFLD/NASH. However, long-term evidence of effectiveness is lacking in this case because of the well-known phenomenon of progressive adherence decay that all too often afflicts every lifestyle change attempt [49]. Our study has the intrinsic limitation of evaluating liver fat and fibrosis only through validated yet indirect indices, including FLI, FIB-4, and NFS. Therefore, further morphology-based studies, possibly associated with gut microbiota analysis, are needed to confirm our results. However, it has the merit of proposing for the first time a single, easy-to-handle treatment tool acting on different targets simultaneously, including metabolic and inexpensive liver morphology-related parameters that are readily available in large outpatient cohorts and thus allow our investigation to be easily repeated worldwide.

We did not use a comparator for the following reasons:

  • The study was not expected to assess the superiority or non-inferiority of PGR on another molecule.

  • Previous studies from other groups and ours have already shown that PGR significantly modifies the metabolic parameters involved in liver fat [34, 35, 57, 60, 71]; what had not been investigated and documented so far were the effects of PGR on the amount of stored liver fat and degree of fibrosis. Indeed, although we already showed the PGR's non-inferiority to metformin concerning glucose control and superiority in terms of lipids and body fat following a similar protocol [34], the aims of the present article were totally different; therefore, we considered duplicating a head-to-head comparison with the other molecule redundant.

Conclusions

NAFLD is widespread worldwide and is present in over half of T2DM subjects; it simultaneously increases the risk of T2DM in non-diabetic subjects, just as T2DM increases the risk of NAFLD [47]. Moreover, about one-third of people with NAFLD progress to NASH, thus running an increased risk of cirrhosis or even hepatocellular carcinoma. Nevertheless, the inner mechanisms behind NAFLD are still a matter of debate, and the same applies to standard NAFLD/NASH diagnosis and treatment criteria [46, 47].

In people with T2DM, NASH causes impaired hepatic function through the aggravation of pre-existing chronic low-grade inflammation [50,51,52] and the structural changes caused by increased fat stores. This phenomenon, in turn, reduces efficient and safe hypoglycemic treatment options for those patients, although some experimental studies look promising regarding some—still somewhat expensive—new entries in the therapeutic toolkit. Indeed, sodium-glucose cotransporter-2 inhibitors (SGLT-2is) [52] and the dipeptidyl peptidase-4 inhibitor (DPP-4i) sitagliptin [54] proved beneficial for NAFLD/NASH in mouse models and humans, respectively. In addition, previous preliminary experiences pointed to a possible positive impact of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on lipid oxidation and hepatic signaling alterations induced by a high-fat diet in non-alcoholic steatohepatitis [55, 56]. In greater detail, several old and new pharmacologic approaches approved for T2DM treatment were used in people with T2DM and NAFLD, including metformin, thiazolidinediones, insulin sensitizers, GLP-1Ras (exenatide, liraglutide, dulaglutide, and semaglutide), and DPP-4is approved, while SGLT-2is, nuclear receptor modulators, PPAR agonists, thyroid hormone receptor agonists (THRβAs), inhibitors of de novo lipogenesis (DNL), ketohexokinase inhibitors, and fibroblast growth factor regulators were tested in different experimental conditions [57, 58]. However, the efficacy and durability of all such attempts are still debated and await further investigation.

In addition, we found it very interesting that LFI, FIB-4, and NFS, i.e., easy, non-invasive, and low-cost tests validated in different populations and extensively used in the literature, especially for screening purposes, may represent a valuable diagnostic tool for any clinicians interested in the field [21, 31, 59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77].

Finally, our data show for the first time that PGR, in addition to lifestyle changes including a low-calorie diet and intensified physical activity in adults affected by metabolic syndrome and type 2 diabetes with NAFLD, nominally MAFLD, improves lipid and glucose metabolism-related parameters, including insulin resistance, and significantly reduces not only visceral fat but also liver fat content and related liver fibrosis severity. We also emphasize that, although we already found PGR to have a positive effect on metabolic parameters, lipids, and fat redistribution [35] and a significantly improved individual 10-year cardiovascular risk, even more than metformin [44], through this study, we showed for the first time that PGR significantly improves liver lipid content and fibrous components.